Relationship between Macroeconomic Variables and Stock
Market Development: Evidences from the Indian Economy
THESIS
Submitted in partial fulfilment
Of the requirements of the degree of
DOCTOR OF PHILOSOPHY
By
Pooja Joshi
2011PHXF412P
Under the Supervision of
Prof. A. K. Giri
BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI
2015
BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI
CERTIFICATE
This is to certify that the thesis entitled “Relationship between Macroeconomic Variables and
Stock Market Development: Evidences from the Indian Economy” submitted by Pooja Joshi
ID No. 2011PHXF412P for award of Ph.D. degree of the Institute, embodies original work
done by her under my supervision.
Signature of the supervisor
Name in capital letters : Prof. A. K. GIRI
Designation : Associate Professor
Date:
To my parents and my loving daughter
i
ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to my supervisor, Prof. A. K. Giri, for his
valuable support, encouragement and direction throughout the entire duration of the
preparation of this thesis. He was always available when I need his advice, and I am grateful
for his patience, wisdom and immense knowledge.
I wish to acknowledge Prof. N V M Rao, Professor and Convenor DRC for his valuable
suggestions and guidance. I am grateful to Dr. Geetilaxmi Mohapatra, Prof. A K Vaish and
Dr. M Krishna Assistant Professor in the Department of Economics and Finance at Birla
Institute of Science and Technology, Pilani for their valuable comments and suggestions.
I want to acknowledge my sincere gratitude to all the faculty members of Economics and
Finance Department for their help and interest in my work. I also thank the supporting staff
of the department of Economics and Finance, BITS-Pilani for their cooperation and help. I
would like to extend my gratitude to the BITS-Pilani library support for their excellent
service.
Many of my friends offered their assistance in different ways; I want to acknowledge all my
friends. I thank my family, my parents: Mr. Pawan and Mrs. Shyama for their love and
affection, inspiration and constant support throughout my academic life. Special thanks to my
husband Jyotirmoy who contributed in numerous ways to the success of this project. Without
his assistance and encouragement my success would have been hampered. I would like to
thank my daughter Adwitiya who provided me support, patience and understanding during
my study. Last, but not the least, I would also like to thank my father-in-law and mother-in
law: Mr. Ashok and Mrs. Premlata for their continuous support.
I again thank all the people who are directly and indirectly involved in helping me to write
this thesis.
Pooja Joshi
ii
ABSTRACT
Stock market performance is considered as the reflector of financial and economic conditions
of a country. The dynamic linkage between macroeconomic variables and stock prices has
fetched increasing amount of attention from economists, financial analysts, investors and
policy makers, since 1980s. There are number of domestic and international macroeconomic
factors that potentially can affect the stock returns of the companies (Fama, 1981, Chen et al.,
1986). According to Fama (1981), there is a comprehensive group of macroeconomic
variables that influences the stock prices in the share market of any country. It is believed
that, if a country’s economy is performing well and expected to grow at a vigorous pace, the
market is frequently anticipated to reflect the same.
The relationship between macroeconomic variables and stock prices has been the focus of an
immense body of theoretical and empirical research since the 19th century. The debate over
the decades has been whether the movement in stock prices leads to the change in economic
activity or it is one of the causes of change. However, the literature suggests some
contradictory findings regarding which precise events or economic factors are likely to
influence the stock prices and the degree of influencing power of the economic factors.
Having generated strong controversy, the debate concerning the relationship between stock
market development and macroeconomic variables is still difficult to solve and causality hard
to pin down. Arguments both theoretical and empirical have been diverse. Some group of
studies advocates that the stock prices do respond to the changes in macroeconomic
fundamentals, but the sign and causal relationship might not hold equal for all the studies,
given different set for similar macroeconomic variables and also the methodologies used for
the study in this area are different (Fama (1981, 1990), Geske and Roll (1983), and Chen,
Roll, and Ross (1986)). Further, existing Financial and Economic literature advocates the
relationship between the stock market and macroeconomic variables, but, they do not specify
the type or the number of macroeconomic factors that should be included. Besides this, the
main key conclusion drawn from literature review is, that, so far, no study has been done on
the relationship between sectoral stock indices and respective sectoral GDP, which provides
the investors a new insight to track the changes in a particular sector of the stock market by
analyzing the movement of sectoral GDP of that particular sector. Thus, this study is the
initiative taken in this area.
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By now, it is a well knowned fact that the stock market development plays an important role
in economic prosperity, fostering capital formation and sustaining the economic growth of
the economy. Stock prices can be considered as an indicator of a country’s economic status
and social mood and are seen as a leading indicator of the real economic activity. New
theoretical and empirical research works provides support to the growing assertion that the
financial development is treated as a precondition for economic growth of a country.
Neverthless a vast majority of these studies have concentrated only on developed economies.
However, research on the relationship between real economic activity and the stock market in
developing countries, such as Latin American, Eastern Europe, Middle Eastern, and South
Asian countries, is still ongoing. Further, in respect to the Indian economy, few studies have
been conducted on the dynamic relationships between the stock market and macroeconomic
variables. Therefore, the primary purpose of this study is to empirically examine the
relationship between macroeconomic variables and stock market in India, following which,
the research is extended to cover the effect of deficits on stock market development in India
and the investigation of empirical relationship between sectoral Contribution of GDP and its
impact on respective sectoral indices. In particular, the study tries to examine the long-run
and short-run dynamic relationship along with the direction of causality between stock
market in India with different sets of domestic and international macroeconomic variables.
Towards this effort different models has been formulated, using the secondary data for
different time span and frequency, according to the need of the study. The empirical analysis
of the study began with testing the stationarity properties of the variables by applying Ng-
Perron unit root test. To study the long-run and short-run cointegrating relationship among
the variables ARDL bounds testing approach is used. The error correction term ECMt-1
identifies the speed of adjustment towards the equilibrium. VECM based Granger causality
test has been applied in the study to determine the direction of causality between
macroeconomic variables and Indian stock market. Additionally, CUSUM and CUSUMQ
have been employed to test the stability of the variables. Finally, to predict the long run and
short run shocks Variance Decomposition and Impulse Response Function techniques are
used in the study.
The study first undergoes for the empirical estimation of macroeconomic determinants of the
stock market development in India, using data for different time periods. The study is divided
into three parts as per the frequency and availablity of data. The first part of the study deals
with the estimation and discussion on the relationship between BSE Sensex and economic
iv
growth, along with some controlled macroeconomic variables. The macroeconomic variables
used for the study include GDP, crude oil prices, inflation (CPI), real effective exchange rate,
FDI and real interest rate, for the period from the year 1979 to 2014. The estimation results of
ARDL test confirms significant and positive influence of economic growth, exchange rate
and inflation on stock price movements in India. The results are consistent for both long run
and short run. The error correction model of ARDL approach reveals that the adjustment
process from the short-run deviation is quite high. Moreover, the VECM based Granger
causality test showed that there exists a short run unidirectional causality running from GDP
to BSE in India. Further, the result indicates the presence of long run causality for the
equation with the stock price as the dependent variable. The results of the VDC analysis show
that a major percentage of stock price change is its own innovative shocks.
Next part of the study deals with quarterly data and to know the relationship between stock
market development and macroeconomic variables. The variables used are Market
capitalization, Real Gross Domestic Product (GDP), Foreign Direct Investment, Foreign
Institutional Investment and Trade openness. The data employed covering the period from
1996: Q1 to 2014: Q3. The test results suggest that economic growth, FIIs and Trade
openness in India influence market capitalization positively. Consistent results are found for
FII and trade openness in short run also. The error correction model of ARDL approach
reveals that the adjustment process from the short-run deviation is low. The results of VECM
based Granger causality indicates the presence of long run causality for the equation with
Stock Market Capitalization (LMCAP) as the dependent variable whereas, in short-run the
change in trade openness causes a change in Stock Market Capitalization, whereas a change
in stock market capitalization will cause a change in FII. The results of VDC analysis shows
that out of the all of exogenous variables used for the study, trade openness is having
maximum shock on stock market capitalization.
Moreover, the study also focuses on short-run frequency data, to observe the relationship
between macroeconomic variables and the stock prices, by incorporating data for monthly
frequency variables. Further, the monthly study has been divided into two sections, which
constitutes two models in relation with different set of macroeconomic variables and stock
prices. The first section of the study highlights the relationship between fundamental
macroeconomic variables and Sensitivity Index of Bombay Stock Exchange (Sensex), using
the monthly time series data from the April 2004 to July 2014. The variables used for the
study are Sensex, Index of Industrial Production, Consumer Price Index, Real Effective
v
Exchange Rate, Call Money Rates and Gold price. The long-run estimates of ARDL test
showed a significant and positive influence of economic growth, Exchange Rate and Inflation
on stock prices. Further, the study confirms negative and significant relationship between
gold prices and stock prices in India. The results for IIP, Inflation and Gold prices are
consistent in short-run also. The error correction model of ARDL approach reveals that the
adjustment process from the short-run deviation is slow. The results of VECM indicates the
presence of long run causality for the equation with the stock price as the dependent variable.
The results of VDC analysis shows that out of the all of exogenous variables used for the
study, Gold price is having maximum shock on stock prices.
The second section of the monthly study focuses on the relationship between fundamental
macroeconomic variables and Index of National stock exchange (CNX nifty), using the
monthly time series data from the April 2004 to July 2015. The variables used for the study
are CNX Nifty, Index of Industrial Production, Foreign Institutional Investment, Gold price,
Treasury bills rate, Wholesale Price Index, International Crude Oil price and Real Effective
Exchange Rate. The long-run estimates of ARDL test showed a negative and significant
effect of crude oil prices, Inflation on stock prices. The results of the influence of both the
variables on stock prices are consistent in the short run as well. Further, for short-run the
study confirms positive and significant relationship for Gold, T-bill rates and Real Effective
Exchange Rate. The error correction model of ARDL approach reveals that the adjustment
process from the short-run deviation is high. The VECM based Granger causality test found
short run causality running from Inflation and crude oil price to National Stock Exchange in
India. It is also observed that bidirectional causality is running between Inflation and CNX
nifty index. Further, the result indicates the presence of long run causality for the equation
with CNX nifty index as the dependent variable. The results of VDC analysis shows that the
inflation and crude oil are having maximum shock on stock prices. The results of IRF shows
that in its response to the shocks of IIP it is observed that there is a negative relationship in
the long run.
The study next endeavors to focuse on the relationship between the deficit situation and stock
market development in India, by incorporating data for annual frequency variables. The study
for the relationship between deficits and stock market development has been divided into
two sections, the first section of which encomposes the estimation results of the relationship
between BSE Sensex and fiscal deficit, along with some controlled macroeconomic variables.
The macroeconomic variables used for the study include fiscal deficit, money supply (M3),
vi
inflation (CPI) and real interest rate, for the period from the year 1988 to 2014. The ARDL
results suggest a long run negative and significant relationship exists between budget deficit
and stock prices. However, the relationship does not show any significant results in the short
run. The error correction term is negative and significant and full convergence process
between stock prices and macroeconomic variables takes about two years in India. The
results of VECM based Granger causality test suggests that there exists a short run causality
running from fiscal deficit to stock price. Further, the result indicates the presence of long run
causality for the equation with the stock price as the dependent variable. The results of VDC
analysis show that the fiscal deficit plays an important role in explaining the variation in
stock prices in India.
The second part of the study on the relationship between deficits and stock market
development in India, discusses the estimation results of the relationship between stock
market development (MCAP) and twin deficit, along with some controlled macroeconomic
variables. The macroeconomic variables used for the study include current account deficit
(CAD), fiscal deficit, GDP, crude oil prices, trade openness and real effective exchange rate,
for the period from the year 1979 to 2014. The long-run estimates of ARDL test showed that
negative and significant relationship exists between the current account deficit (CAD) and
stock market capitalization. The results are consistent in short run also. The error correction
model of ARDL approach reveals that the adjustment process from the short-run deviation is
high. The results of VECM based Granger causality found short run causality running from
CAD to market capitalization in India. Further, the result indicates the presence of long run
causality for the equation with a market capitalization as the dependent variable. The results
of VDC analysis shows that crude oil price and CAD plays an important role in explaining
the variation in stock market development in India. The results of IRF shows that in its
response to the shocks of current account deficit, it is observed that there is a positive
relationship in the long run and reverse is observed in the case for the shocks of fiscal
deficits, throughout the period.
Finally, the study contributes to a new aspect of the relationship between macroeconomic
variables and stock prices by undergoing estimating the effect of sectoral GDP and controlled
macroeconomic variables on respective sectoral indices in India by employing quarterly data
covering the period from 2003:Q4 to 2014:Q4. The main variables used for the study include
Manufacturing sector index, electricity, gas and water supply sector index, service sector
vii
index, contribution of GDP in manufacturing sector, contribution of GDP in electricity, gas
and water supply sector, contribution of GDP in service sector, and the controlled
macroeconomic variables used for the study are Crude Oil Price, Real Effective Exchange
Rate, T-bill rates, Trade openness and WPI, a proxy for inflation. Principle component
analysis is used in this study to construct the composite index of manufacturing index;
electricity, gas and water supply index; and service index. For the purpose of study, three
models has been framed, in which each of the sectoral stock price indices is placed as
dependent variable; and Crude Oil Price, REER, T-bill rates, Trade openness and WPI along
with respective sectoral GDP worked as independent variables. The long-run and short-run
estimates of ARDL test for the model I (Manufacturing sector index and share of
manufacturing sector in GDP) showed that positive and significant relationship exists
between the manufacturing sector share in GDP with the manufacturing index. For model II
(Electricity, Gas and Water supply sector index and share of Electricity, Gas and Water
supply sector in GDP), the results show that the electricity, gas and water supply sector share
in GDP and inflation has a positive effect on electricity, gas and water supply index, unlike
short-run. For model III (Service sector index and share of Service sector in GDP), results
show that the service sector share in GDP and T-bills rate has a positive effect on service
sector index in the long-run and in short-run as well along with crude oil price. The error
correction model of ARDL approach reveals that the adjustment process from the short-run
deviation is high. The results of VECM based Granger causality test suggests a unidirectional
short-run causality running from sectoral GDP to respective sectoral stock indices in India.
Further, the result indicates the presence of long-run causality for the equation with
manufacturing index and electricity, gas and water supply index as the dependent variable,
but, except for the service sector index. To predict the long-run and short-run shocks variance
decomposition is used for the study, the result of VDC analysis, for all three models, show
that a major percentage of sectoral indices are its own innovative shocks.
To sum up, it can be concluded that economic growth plays an important role in the
development of stock market therefore,it can be said that the stock market acts as a predictor
of GDP. Additionally, the the positive influence of exchange rate on stock price movements
is also observed and the influence of inflation also comes out to be positive which proves
Fisher (1911) hypothesis. Thus, the estimated results of the study indicate that the Indian
stock market is sensitive to changes in macroeconomic fundamentals in the long run.
However, in the short run also few of the macroeconomic variables affect stock prices.
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Further, the stock prices are relatively exogenous in relation to most of the macroeconomic
variables selected for the study, as major percentage of the variation in the forecast of the
Indian stock prices is attributable to its own shocks. The results of the study suggest a
positive impact of macroeconomic variables on the stock market development in India.
Therefore, in order to facilitate stock marketdevelopment and economic growth,
macroeconomic development is solely desirable in developing countries like India. Moreover,
it is also true that the informed and sensible investor in India can attain super normal profit,
by tracking the historical data of stock market and the change in macroeconomic variables.
This may help the investors to formulate a profitable strategy to for trading and making
profitable decisions. The implications of the present study are multifaceted and the findings
of the study implies that, the stock markets can be flourished with economic growth of the
nation, because it plays a significant positive role in the developments of capital markets of
India. In a country, when the real GDP will raise it will help stock prices to increase and
boost up the investor’s confidence, with the growing economy.
Key words: Stock price, Stock market development, Economic Growth, Twin deficit, crude oil
price, inflation, Real Effective Exchange Rate, India, Auto Rergressive Distributed Lag
approach (ARDL), Vector Error Correction Method (VECM), Variance Decomposition Test
(VDC), Impulse Response Function (IRF).
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TABLE OF CONTENTS
Chapter1: Introductory background, Need and Objectives of the study Page No.
1.1. Introduction ...... 1
1.2. Need for the Study and Research Questions ..... 3
1.3. Objectives of the study ...... 7
1.4. Significance of the study ...... 8
1.5. Organization of the study ...... 9
Chapter 2: An overview of the developments in Indian stock market
2.1. Introduction .... 12
2.2. Historical Development of the Indian Stock Market .... 13
2.3. India as an emerging market economy .... 16
2.4. Reforms in the financial sector .... 19
2.5. Trends of the Indian Stock Market .... 23
2.6. Summary .... 27
Chapter 3: Theoretical underpinnings of the relationship between macroeconomic variables
and Stock market development indicators
3.1.Introduction ..... 28
3.2.Theoretical Background ..... 28
3.2.1. Random Walk and The Theory of Efficient Market Hypothesis (EMH) ..... 28
3.2.2. Arbitrage Price Theory (APT) ..... 31
3.3. Stock Market Development Indicators ..... 32
3.3.1. Stock Market Size ..... 33
3.3.2. Stock Market Liquidity ..... 33
3.3.2.1.Total value of shares traded ratio ..... 34
3.3.2.2.Turnover ratio ..... 34
3.3.3. Volatility ..... 35
3.3.4. Stock market index ..... 35
3.4. Macroeconomic Variables ..... 35
3.4.1. Money Supply ..... 35
3.4.2. Economic Growth ..... 38
3.4.3. Trade Openness ..... 41
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3.4.4. International Financial Flows ..... 44
3.4.4.1.Foreign Direct Investment (FDI) ..... 45
3.4.4.2.Foreign Institutional Investment (FII) ..... 46
3.4.5. Interest Rate ..... 47
3.4.6. Inflation ..... 49
3.4.7. Crude Oil Prices ..... 51
3.4.8. Exchange Rate ..... 54
3.4.9. Gold Prices ..... 57
3.4.10. Budget Deficit ..... 58
3.4.11. Current Account Deficit ..... 59
Chapter 4: Methodology and Data Issues
4.1. Introduction ..... 61
4.2. Methodology ..... 62
4.2.1. Ng-perron unit root test ..... 62
4.2.2. ARDL co-integration ..... 63
4.2.3. VECM based Granger Causality …. 65
4.2.4. Stability tests …. 66
4.2.4.1. CUSUM Test ..... 66
4.2.4.2. CUSUM of Squares Test ..... 67
4.2.5. Impulse Response Functions ..... 67
4.2.6. Variance Decomposition Technique ..... 68
4.2.7. Principal Component Analysis ..... 70
4.3. Data Issues ….. 71
4.3.1. Stock market ..... 71
4.3.1.1. Stock prices ..... 71
4.3.1.2. Stock market development ..... 71
4.3.2. Economic Growth ..... 72
4.3.2.1. Real Gross Domestic Product ..... 72
4.3.2.2. Index of Industrial Production (IIP) ..... 72
4.3.3. Real Effective Exchange Rate (REER) ..... 73
4.3.4. International crude oil price ..... 73
4.3.5. Foreign Direct Investment ..... 73
4.3.6. Foreign Institutional Investment ..... 73
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4.3.7. Inflation ..... 74
4.3.7.1. Consumer Price Index (CPI) ..... 74
4.3.7.2. Wholesale Price Index (WPI) ..... 74
4.3.8. Real Interest Rate ..... 75
4.3.9. Short term interest rates ..... 75
4.3.9.1. Treasury bill rates ..... 75
4.3.9.2. Call Money Rate (CMR) ..... 75
4.3.10. Fiscal Deficit ..... 75
4.3.11. Current Account Deficit ..... 76
4.3.12. Money supply (M3) ..... 76
4.3.13. Trade openness ….. 76
4.3.14. Gold Prices ..... 77
Chapter 5: Macroeconomic Determinants of the Stock Market Development in India
5.1.Introduction ..... 78
5.2.Review of Literature ..... 79
5.2.1. Studies of overall economies other than India ..... 79
5.2.2. Studies related to Indian economy ... 110
5.2.3. Summary of Literature review ... 124
5.3.Estimation results of the study using annual frequency data ... 126
5.3.1. Model specification … 126
5.3.2. Stationarity test and Lag length selection before co-integration ... 126
5.3.3. ARDL Bounds Test ... 127
5.3.4. VECM based causality ... 130
5.3.5. Variance Decomposition (VDC) Analysis ... 131
5.4. Estimation results of the study using quarterly frequency data ... 133
6.3.1. Model specification … 133
6.3.2. Stationarity test and Lag length selection before co-integration ... 133
6.3.3. ARDL Bounds Test ... 134
6.3.4. VECM based causality ... 137
6.3.5. Variance Decomposition (VDC) Analysis ... 138
5.5. Estimation results of the study using monthly frequency data ... 139
5.5.1. Relationship between macroeconomic variables and Indian stock price ... 139
5.5.1.1. Model specification … 139
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5.5.1.2. Stationarity test and Lag length selection before co-integration ... 139
5.5.1.3. ARDL Bounds Test ... 141
5.5.1.4. VECM based causality ... 144
5.5.1.5. Variance Decomposition (VDC) Analysis ... 145
5.4.2. Relationship between Fundamental Macroeconomic Variables and CNX nifty
... 147
5.4.2.1. Model specification ... 147
5.4.2.2. Stationarity test and Lag length selection before co-integration ... 147
5.4.2.3. ARDL Bounds Test ... 148
5.4.2.4. VECM based causality ... 151
5.4.2.5. Variance Decomposition (VDC) Analysis ... 152
5.4.2.6. Impulse Response Function (IRF) ... 154
6.5. Summary … 156
Chapter 6: Fiscal policy variables and Stock Market Development in India
6.1. Introduction ... 158
6.2. Review of Literature ... 159
6.2.1. Studies of overall economies other than India ... 159
6.2.2. Studies related to Indian economy ... 163
6.3. Relationship between Fiscal Deficits and Stock Prices in India ... 165
6.3.1. Model specification … 165
6.3.2. Stationarity test and Lag length selection before co-integration ... 165
6.3.3. ARDL Bounds Test ... 166
6.3.4. VECM based causality ... 169
6.3.5. Variance Decomposition (VDC) Analysis ... 170
6.4. Relationship between Twin Deficit and Stock Market Development in India … 172
6.3.1. Model specification … 172
6.3.2. Stationarity test and Lag length selection before co-integration ... 172
6.3.3. ARDL Bounds Test ... 173
6.3.4. VECM based causality ... 176
6.3.5. Variance Decomposition (VDC) Analysis ... 177
6.3.6. Impulse Response Function (IRF) ... 178
6.4. Summary … 180
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Chapter 7: Macroeconomic Determinants of Sectoral Stock Market Development in India
7.1. Introduction … 182
7.2. Review of Literature ... 183
7.2.1. Studies of overall economies other than India … 183
7.2.2. Studies related to Indian economy … 187
7.3. Model specification and data validation … 188
7.4. Stationarity test and Lag length selection before co-integration ... 189
7.5. ARDL Bounds Test ... 190
7.6. VECM based causality ... 193
7.7. Variance Decomposition (VDC) Analysis ... 195
7.8. Summary ... 195
Chapter 8: Summary and Policy Implications of the study
8.1.Summary and Conclusion ... 197
8.2.Policy Implications of the study ... 206
8.3.Contribution of the study ... 209
8.4.Limitations of the study ... 212
8.5.Scope for further studies ... 212
References ... 214
Appendixes … 239
A. Lag length selection criteria … 240
A.1. Akaike’s information criterion … 240
A.2. Schwarz’s information criterion … 241
A.3. Hannan-Quinn’s information criterion … 241
A.4. Final Prediction Error … 242
Publications form the Thesis ... 244
Brief Biography of the Candidate ... 245
Brief Biography of the Supervisor ... 246
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LIST OF TABLES
Table 2.1: Key indicators of Indian stock market activity ..... 24
Table 5.3.1: Unit root test: Ng-Perron Test ... 127
Table 5.3.2: Lag Order Selection Criterion ... 127
Table 5.3.3: ARDL Bounds test ... 128
Table 5.3.4: Estimated Long Run Coefficients using ARDL Approach (Dependent variable:
LBSE) ... 129
Table 5.3.5: Estimated Short Run Coefficients using ARDL Approach (Dependent variable:
LBSE) ... 130
Table 5.3.6: Results of Vector Error Correction Model ... 130
Table 5.3.7: Variance Decomposition (VDC) Analysis ... 132
Table 5.4.1: Unit root test: Ng-Perron Test ... 133
Table 5.4.2: Lag Order Selection Criterion ... 134
Table 5.4.3: ARDL bounds test results ... 134
Table 5.4.4: Estimated Long-run Coefficients using ARDL Approach (Dependent variable:
LMCAP) ... 135
Table 5.4.5: Estimated Short-run Coefficients using ARDL Approach (Dependent variable:
LMCAP) ... 136
Table 5.4.6: Results of Vector Error Correction Model ... 137
Table 5.4.7: Variance Decomposition (VDC) Analysis ... 138
Table 5.5.1.1: Unit root test: Ng-Perron Test ... 140
Table 5.5.1.2: Lag Order Selection Criterion ... 141
Table 5.5.1.3: ARDL bounds test results ... 142
Table 5.5.1.4: Estimated Long Run Coefficients using ARDL Approach (Dependent variable:
LBSE) ... 143
Table 5.5.1.5: Estimated Short Run Coefficients using ARDL Approach (Dependent variable:
LBSE) ... 144
Table 5.5.1.6: Results of Vector Error Correction Model ... 144
Table 5.5.1.7: Variance Decomposition (VDC) Analysis ... 146
Table 5.5.2.1: Unit root test: Ng-Perron Test … 148
Table 5.5.2.2: Lag Order Selection Criterion … 148
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Table 5.5.2.3: ARDL bounds test results ... 149
Table 5.5.2.4: Estimated Long-run Coefficients using ARDL Approach (Dependent variable:
LNSE) ... 150
Table 5.5.2.5: Estimated Short-run Coefficients using ARDL Approach (Dependent variable:
LNSE) ... 151
Table 5.5.2.6: Results of Vector Error Correction Model ... 152
Table 5.5.2.7: Variance Decomposition (VDC) Analysis ... 153
Table 5.5.2.8: Impulse Response Function (IRF) ... 154
Table 6.3.1: Unit root test: Ng-Perron Test ... 165
Table 6.3.2: Lag Order Selection Criterion ... 166
Table 6.3.3: ARDL Bounds test ... 166
Table 6.3.4: Estimated Long Run Coefficients using ARDL Approach (Dependent variable:
LBSE) ... 168
Table 6.3.5: Estimated Short Run Coefficients using ARDL Approach (Dependent variable:
LBSE) ... 168
Table 6.3.6: Results of Vector Error Correction Model ... 169
Table 6.3.7: Variance Decomposition (VDC) Analysis ... 171
Table 6.4.1: Unit root test: Ng-Perron Test ... 173
Table 6.4.2: Lag Order Selection Criterion ... 173
Table 6.4.3: ARDL bounds test results ... 174
Table 6.4.4: Estimated Long Run Coefficients using ARDL Approach (Dependent variable:
LMCAP) ... 175
Table 6.4.5: Estimated Short Run Coefficients using ARDL Approach (Dependent variable:
LMCAP) ... 176
Table 6.4.6: Results of Vector Error Correction Model ... 176
Table 6.4.7: Variance Decomposition (VDC) Analysis ... 178
Table 6.4.8: Impulse Response Function (IRF) ... 179
Table 7.1: Unit root test: Ng-Perron Test ... 190
Table 7.2: Lag Order Selection Criterion ... 190
Table 7.3: ARDL Bounds test ... 191
Table 7.4: Estimated Long-run Coefficients using ARDL Approach (Dependent variable:
LMANI, LEGWI, LSERI) ... 192
xvi
Table 7.5: Estimated Short-run Coefficients using ARDL Approach (Dependent variable:
LMANI, LEGWI, LSERI) ... 193
Table 7.6: Results of Vector Error Correction Model ... 194
Table 7.7: Variance Decomposition (VDC) Analysis ... 195
xvii
LIST OF FIGURES
Figure 2.1: Indian Stock Market Trends ..... 25
Figure 5.3.1: Plots of Stability Test ... 131
Figure 5.4.1: Plots of Stability Test ... 137
Figure 5.5.1.1: Plots of Stability Test ... 145
Figure 5.5.2.1: Plots of Stability Test ... 152
Figure 5.5.2.2: VDC analysis combined graph ... 153
Figure: 5.5.2.3 Impulse Response Function combined graph ... 155
Figure 6.3.1: Plots of Stability Test ... 170
Figure 6.4.1: Plots of Stability Test ... 177
Figure 6.4.2.: VDC analysis combined graph ... 178
Figure: 6.4.3. Impulse Response Function combined graph ... 179
xviii
LIST OF ABBREVIATIONS
∆ : First difference of the variables
AIC : Akaike’s information criterion
APT : Arbiterage Pricing Theory
ARCH : Autoregressive conditional heteroskedasticity proposed by Engle (1982)
ARDL : Auto Regressive Distributed Lag
BSE : Sensitivity index of Bombay Stock Exchange (Sensex)
CAD : Current account deficit as a percentage of GDP
CMR : Call Money Rate
CO : International crude oil price
CPI : Consumer Price Index
CSO : Central Statistics Office
DIPP : Department Of Industrial Policy & Promotion
ECM : Error correction model
ECT : Error-correction term
EG : Engle and Granger cointegration
EGWI : Electricity, Gas and Water Index
EMH : Efficient market hypothesis
FD : Fiscal Deficit as a percentage of GDP
FDI : Foreign Direct Investment
FII : Foreign Institutional Investors
FPE : Final Prediction Error
GARCH : Generalized Autoregressive conditional heteroskedasticity model proposed by
Bollerslev (1986)
GEGW : Electricity, Gas and Water supply sector share in GDP
GDP : Real Gross Domestic Product
GDR : Global Depository Receipts
xix
GOR : Gold Prices
GMAN : Manufacturing sector share in GDP
GRT : Granger’s representation theorem
GSER : Service sector share in GDP
HQ : Hannan-Quinn’s information criterion
IIP : Index of Industrial Production
IRF : Impulse Response Function
JJ : Johansen and Juselius cointegration
L : Natural logarithm of the variables
LR : log likelihood ratio
M3 : Money Supply (broad money)
MANI : Manufacturing Sector Index
MCAP : Market Capitalization as a percentage of GDP used as a proxy for stock
market development
MNC : Multi National Corporation
NSCC : National Securities Clearing Corporation
NSDL : National Securities Depository Limited
NSE : National Stock Exchange represented by CNX nifty index
OLS : Ordinary Least Square Techniques
OTC : Over the Counter Exchange of India
PCA : Principal Component Analysis
RBI : Reserve Bank of India
REER : Real Effective Exchange Rate
RIR : Real Interest rate
SEBI : Securities Exchange Board of India
SERI :Service Sector Index
SIC : Schwarz’s information criterion
xx
TBR : Treasury bill rates (T-bill rates)
TO : Trade Openness (export+import/GDP)
VAR : Vector Auto Regression
VDC : Variance Decomposition
VECM : Vector Error Correction Method
WPI : Wholesale price Index used as a proxy for inflation
1
CHAPTER 1
Introductory background, Need and Objectives of the study
1.1. Introduction
The history has shown that the price of shares and other financial assets are an
important aspect of the dynamics of economic activity, performing a crucial role in the
economy of any nation. Further, many researchers have proved that the stock market plays an
important role in economic prosperity, fostering capital formation and sustaining the
economic growth of the economy (Charles and Adjasi, 2008; Essaied, Hamrita et al., 2009;
Pilinkus, 2015; Quayes, 2010). The stock market is one of the most vital components of a
free-market economy, as it helps to arrange capital for the companies from shareholders in
exchange for shares in ownership to the investors. Stock exchange provides businesses with
the facility to raise capital by selling shares to the investor (Black and Gilson, 1998). Stock
prices can be considered as an indicator of a country’s economic status and social mood and
are seen as a leading indicator of the real economic activity. Share prices also affect the
wealth of households and their consumption; savings and investment decisions. Thus, it can
be said that, the stock market is an integral part of the financial system of any economy, as it
plays a significant role in channelizing funds, connecting savers and investors, which led to
economic growth of the economy. Further, it is believed that there exist many factors to
which the stock market reacts, factors like the economic, political and socio-cultural behavior
of any country. Hence, investors carefully watch the performance of the stock markets by
observing the composite market index, before investing funds. The market index acts as the
yardstick to compare the performance of individual portfolios and also provides investors for
forecasting future trends in the market. Especially the stock markets of emerging economies
are likely to be sensitive to fundamental changes in macroeconomic structure and policies,
which plays an important role in achieving financial stability. Being one of the most
important pillars of the country's economy, the stock market is carefully observed by
governmental bodies, companies and investors (Nazir et al., 2010). Therefore, economic
policy makers and researchers keep an eye on the behavior of the stock market, as it’s smooth
and risk free operation is essential for economic and financial stability.
The dynamic linkage between macroeconomic variables and stock prices has fetched
increasing amount of attention from economists, financial analysts, investors, practitioners
and policy makers (Kwon and Shin, 1999). The claim that macroeconomic variables affect
2
stock market is a well-established theory in the literature and has been an area of intense
interest among academics, investors and stock market regulators since 1980s. In the past
three decades, there has been growing efforts made by researchers to estimate this
relationship since the attempt made by Fama (1981). Following his study, a number of
empirical studies explored this topic to understand the fundamentals of this association in one
country or in a selected group of countries. (Chen et al. (1986), Poon and Taylor (1992),
Fama (1991), Pearce & Roley (1988)) modeled the relation between asset prices and real
economic activities in terms of production rates, productivity, and growth rate of gross
national product, unemployment, yield spread, interest rates, inflation, dividend yields, and so
forth. In the last two decades, because of the globalization trend, a number of researchers –
such as Fama (1990), Geske and Roll (1983), Chen, Roll, and Ross (1986), Canova and de
Nicolo (1995) and Nasseh and Strauss (2000) also investigated the international effects of
macroeconomic indicators on stock prices. Theoretical work shows the significant positive
effect of stock market development on economic growth of specific economies (Levine and
Zervos (1998); Modigliani (1971) and Kunt and Levine (1996)). At the same time, the
development of the stock market is the outcome of many macroeconomic variables like
foreign direct investment, foreign institutional investment, exchange rate and economic
reforms (Gay (2008)), whereas, economic growth also plays an important role in the stock
market development in developing or developed economies. Duca (2007) argues that
countries doing well in terms of economic growth have better stock market performance.
These studies are different in terms of their hypotheses and the methodologies used. Other
previous studies have examined the short and the long run relationship between stock prices
or returns and some macroeconomic and financial variables such as inflation, interest rate,
output, etc. Within this group of studies, some studies seek to examine local and international
economic factors that affect stock prices or returns, while others examine factors that
determine stock return volatility (Semmler, 2006). Some other explores the role of monetary
policy in responding to or altering the stock market (Sellin, 2001). More recently, an
increasing amount of empirical studies has been focusing attention to relate the stock prices
and macroeconomic factors for both developed and emerging economies (Mukherjee and
Naka (1995), Maysami et al. (2004), Ratanapakorn and Sharma (2007), Rahman et al.
(2009)). These studies concluded that stock prices do respond to the changes in
macroeconomic fundamentals, but the sign and causal relationship might not hold equal for
all the studies. Based on the existing literature, it has been concluded that extensive research
has been conducted for developed economies. However, research on the relationship between
3
real economic activity and the stock market in developing countries, such as Latin American,
Eastern Europe, Middle Eastern, and South Asian countries, is still ongoing. Further, in
respect to the Indian economy, few studies have been conducted on the dynamic relationships
between the stock market and macroeconomic variables.
1.2. Need for the study and Research Questions
The last two decades have witnessed a dramatic change in world financial markets,
particularly the stock markets, and the fundamental causes of these changes were probably
the end of fixed exchange rates in the early 1970s and the progressive removal of
international financial flows. These changes resulted in a significant increase in the volatility
of prices and trade volumes and also lead to noticeable contradictions between market
sentiments and economic growth, due to irrational behavior of investors. The practical
consequences of these changes sometimes have discouraging and humiliating challenges for
policy makers and forecasters, and the investors have to bear greater risk and uncertainty
regarding their investment decisions. Therefore, to predict the possible changes in the stock
market those fundamental factors should be studied, who works as the triggers of changes
and drives the market. According to Fama (1981), there is a comprehensive group of
macroeconomic variables that influences the stock prices in the share market of any country.
If a country’s economy is performing well and expected to grow at a vigorous pace, the
market is frequently anticipated to reflect the same.
Indian stock market has developed in terms of number of stock exchanges and other
intermediaries, the number of listed stocks, market capitalization, trading volumes, turnover
of the stock exchanges, investor population and the price indices. The process of reforms has
led to a pace of growth almost unparalleled in the history of any country. The shape and
structure of the market have undergone remarkable changes in the recent past. The stock
market of emerging economics like India carries huge expectations of the investors. The
Indian stock market has also undergone tremendous changes since 1991, when the
government has adopted liberalization and globalization policies. As a result, there is a
growing importance of the stock market from the aggregate economic point of view.
Nowadays, the stock market has become a key driver of the modern market based economy
and is one of the major sources of raising resources for Indian corporate, thereby enabling
financial development and economic growth. In fact, Indian stock market is one of the
emerging markets in the world. The smoothing development process in Indian stock markets
continues to be breathtaking. From 3,739.69 points on March 31st, 1999, within nine years;
4
Bombay Stock Exchange (BSE) Sensitivity Index (SENSEX) had reached to 21,000 level
points in January, 2008. But this impact doesn’t last long as it was affected by the recent
global financial crisis of 2008-09; emerging euro-crisis; and the recent slowdown of the
Chinese economy. Now SENSEX is hovering around 25,500 points after breaching 30,000
points in march 2015, its all time high (BSE India); and similarly after the establishment of
nifty in 1994, it goes to its all time high breaching 9,000 points (NSE India). In the context of
this effect in Indian Stock Market, the critical question is whether the decades old
development or recent degradation in the markets are in any way influenced by the domestic
and international macroeconomic fundamentals. There are several studies concluding
contradictory results, based on different methodologies, regarding the interaction of share
market returns and the macroeconomic variables, viz-a-viz, Agrawalla (2006) stated that
rising indices in the stock markets cannot be taken to be a leading indicator of the revival of
the economy in India and vice-versa. However, Shah and Thomas (1997) supported the idea
that stock prices are a minor which reflect the real economy. Similarly, Kanakaraj et al.
(2008) examined the trend of stock prices and various macroeconomic variables between the
time periods 1997-2007 and tried to explore upon if the rise in the stock market can be
explained in the terms of macroeconomic fundamentals and concluded by recommending a
strong relationship between the two. Despite the growth of Indian stock market, it is suffering
from various typical weaknesses of an emerging market. First, speculation practices cause
high market volatility, which makes the market highly unpredictable. Second, it is widely
known that one of the biggest problems facing by investors is the lack of transparency.
Reporting requirements for listed companies are not well defined, and significantly less
comprehensive than those in the developed stock markets. Third, information disclosed to the
public is not clear and transparent, thus, not reliable. Due to all these problems, investors
become irrational and may base their actions on the decisions of others who are well
informed about market developments, by following the market consensus. In other words, the
herding behavior may exist in the Indian stock market. Therefore, from the point of view of
policy makers, investors and research practitioners, it is important to study the effect of both
domestic and international macroeconomic variables on the performance of the stock market
because both investors and policy makers mostly concern if the current market prices reflect
all publicly available information, such as information on inflation, economic growth, money
supply, exchange rates, interest rates, foreign inflows, gold prices, etc. Hence, this study tries
to investigate whether or not it is possible for market participants to make consistently
superior returns just by analyzing the movement in fundamental macroeconomic factors of
5
the country. In other words, the focus of this study is to find out the relationship between
stock prices and macroeconomic variables in India.
More recently, the renewed interest on the relationship between fiscal deficits and stock
prices has been partly informed by the sudden occurrence of the global financial turmoil, its
severity and potentially long-lasting impact and, in particular, the apprehension that such
large budget deficits could lead to stock market crash (Roley and Schall, 1988). In contrast,
however, other analysts claim that budget deficits have little effect on stock prices. Friedman
(1987), for example, characterized much of the links between fiscal deficits and stock market
crashes (via a collapse in asset valuation) as reflecting reliance on economic fallacies, as
witnessed throughout the 1980s when stock prices surged despite mounting fiscal deficits.
Whereas, there are cases, however, where current account deficits have been associated with
a strong and thriving economy. In the context of foreign capital-led growth, capital inflows
help to lift savings and investment constraint on growth. In such cases, national savings are
not sufficient to all new profitable investment projects. It is sometimes necessary for a
country to run current account deficit and rely on foreign savings to finance the savings
investment gap. Over time, the goods produced by the new capital will lead to increased
export earnings that will eventually generate trade and current account surpluses necessary to
repay foreign debt and the interest on it. Hence, current account deficits and foreign debt
accumulation generated by an investment boom might actually increase the rate of a
country’s economic growth where domestic savings are not sufficient. Further, an
unsustainable budget deficit indicates either future inflation rate or future tax rate increases.
Sargent and Wallace (1981) said that the unsustainable budget deficit will eventually have to
be managed because large budget deficit will increase inflation. Greenspan and Allen (1995)
investigated that a decrease in the budget deficit will reduce inflationary expectations.
Inflationary expectations may have reverse effects on equity prices. Budget deficits also
affect stock prices through anticipated future taxes, particularly if tax rates are below their
revenue-maximizing levels. Hall and Taylor (1993) and Ball and Mankiw (1995) claimed that
increase in budget deficit forecast future tax increases, which may decrease current
consumption by households and harm stock prices and vice versa. In contrast, however,
Friedman (1987) claims that budget deficits have little effect on stock prices. Since, the
research on the concepts of the deficit situation of the nations and their relationship with
stock market development are very scant, this proved to be a motivation to investigate the
relationship in Indian context.
6
Further, since no research has been done considering the impact of sectoral contribution
of GDP on the sectoral indices, as it provides a better understanding to investors and policy
makers to judge the market sentiments or a better approach to track the changes of a
particular segment of the industry, which are reflected by the respective sectors of the market.
This phenomenon worked as the motivation to study the relationship between sectoral
contribution of GDP and sectoral stock indices.
The empirical investigations of the present study are carried out on an annual time
series data, quarterly time series data and monthly time series data, with different time
periods. The choice of sample period is suggested by the availability of data and the
requirement of a good number of observations which is essential for empirical investigation.
Monthly data are used to grasp the short run dynamics of the stock prices and
macroeconomic variables. The study also focuses on the relationship between deficits and the
stock market development in India. Further, the study is also conducted on the relationship
between sectoral indices and sectoral contribution of GDP, respectively, which seems more
relevant to track the changes in the stock market, while predicting the changes in any
particular sector index, due to the change in the GDP of that sector only. The variables used
in empirical experimentation, their definitions, sources of time series data and the method by
which some of the time series are constructed are discussed and presented in the subsequent
chapters of the study
There have been divergent views in the literature with respect to the
choice/identification of measures of these variables. Further, in the context of frequency of
the variables used and availability of data from the same base period, the selection of
determinants become more complex. As mentioned earlier, Indian economy has undergone
tremendous changes after adopting liberalization and globalization policies, therefore, besides
the domestic variables some international macroeconomic factors like foreign capital inflows
(FII, FDI), trade openness, international crude oil prices, and the exchange rate (Real
effective exchange rate) has also been included in the study to know their relationship with
the stock market. Stock prices (Sensex and CNX Nifty) are incorporated in the study to
empirically observe the relationship of Indian stock prices with fundamental macroeconomic
variables. Accordingly, a measure of stock market development, i.e., market capitalization, is
also included in one of the models in the study, to capture its relationship with
macroeconomic variables of quarterly frequency.
For the purpose of the empirical study, Autoregressive Distributed Lag (ARDL) bounds
test is employed to determine the cointegration among the variables. Before going for co-
7
integration test, Ng-Perron unit root test is employed to check the stationarity of the variables.
After employing bounds test, the long run and short run dynamic relationship are estimated.
Further, to know the direction of causality, Granger causality based on Vector error
Correction Model is used for the variables. The stability of the variables has been examined
by using CUSUM and CUSUM Square tests. Additionally, Variance Decomposition (VDC)
and Impulse Response Function (IRF) are used to used to predict long run exogenous shocks
of the variables.
Hence, the primary motive of the present work is to answer the following research
questions:
Q.1. Do the key macroeocnomic variables included in this study have long-run
cointegrating relationship with Indian stock market proxied by BSE Sensex, CNX Nifty, and
market capitalization?
Q.2. Do these key macroeconomic variables have causal relationships during the
sample period? If so, what is the direction of the causality between BSE, NSE, market
capitalization and each of these variables in long-run and short-run?
Q.3. How does the stock market development indicators respond to an external shock
from any of these variables?
Q.4. To what extent can innovation in each of the key macroeconomic variables explain
the movements in stock market variables?
Q.5. How does the sectoral stock market indices being influenced by the set of sectoral
real activity in the Indian economy?
1.3. Objectives of the study
The present work is designed to address the linkage between macroeconomic variables
and stock market development in the present context for Indian economy. Accordingly the
objectives of the present study are set as follows:
1. The first objective is to examine the role of macroeconomic variables on the stock
market development in the context of financial innovation, liberalization,
globalization and asset market changes in India.
2. The second objective is to examine the dynamic relationship between fiscal policy
variables and the stock market development in India.
8
3. The third objective is to explore the relationship between sectoral stock market
indices and sectoral macroeconomic parameters.
4. The fourth objective is to evaluate the implications of evidence for framing
appropriate economic policies for improving stock market efficiency.
1.4. Significance of the study
The present study is expected to add several primary contributions to the existing
literature. First, it will add to the present literature by examining the relationship of the stock
market with a set of macroeconomic variables in emerging markets like India, in the present
context. Second, the study will apply different modern econometric methods like Auto
regressive Distributed Lag (ARDL), Vector Error Correction Model (VECM), Variance
Decomposition (VDC) and Impulse Response function (IRF), which may provide insight for
the existing literature if the analysis is sensitive to the methods employed. To the best of my
knowledge, this will be the first study to estimate sectoral contribution of GDP and its impact
on respective sectoral indices of stock market using data on the Indian economy. The
importance of the sectoral analysis is that, if any sector performs extremely well than it will
help policy makers and investors especially, to predict the changes in the prices of the stocks
of that particular sector. This study is expected to offer some insights for Indian
policymakers, investors, researchers and portfolio managers. Investors may be able to make
informed decisions based on macroeconomic dynamics and it is possible for them to decide
the ideal time to buy and sell the stocks. The study will be advantageous to know the
relationship of prices and economic activity; the direction of the outcome of the relationship
may enhance the predictive ability of policy makers; thus, both contractions and expansion of
the Indian economy may be forecasted and predicted with some degree of certainty.
Policymakers are mainly interested in exploring the determinants of the stock market, and
how stock market reflects the changes in domestic and international macroeconomic
variables of the economy, thus, the study will provide them a background to determine the
variables, which are expected to influence the stock market. Moreover, economic theory
suggests that stock prices should reflect expectations about future corporate performance, and
the corporate profits generally reflect the level of economic activities. If stock prices
accurately reflect the underlying fundamentals, then the stock prices should be considered as
the leading indicators of future economic activities, and not the other way around. Therefore,
the study of the causal relations and dynamic interactions between macroeconomic variables
9
and stock prices will help in the formulation of the nation’s macroeconomic policy. Further,
the study will provide an insight to researchers for future research in the area.
1.5. Organization of the study
The rest of the study is organized in seven chapters; Chapter 2 focuses on the overview
of Indian stock market, which aims to present a historical review of the development stages
of the Indian stock market since its establishment in 1875. The chapter has been organized as
follows. Section 2.1 presents an introduction to the chapter; Section 2.2 provides highlights
the historical development of the Indian stock market; section 2.3 discusses the case for India
as an emerging market economy. In Section 2.4, some of the major changes in the financial
sector of the Indian economy are briefly described; the section 2.5 consists of the description
of the trends of the Indian Stock Market; and the last section 2.6 outlines the summary of the
chapter.
Chapter 3 discusses the Theoretical Underpinnings of the study, establishing the
theoretical relationship of the macroeconomic variables with stock market development
indicators. This chapter passes through four sections. The section 3.1 of the study presents the
introduction to the chapter. Section 3.2 provides the theoretical background of the study,
which is again sub-divided into two parts; the first part of the section 3.2 talks about random
walk theory and the Theory of Efficient Market Hypothesis (EMH), which is considered as
the reason for the genesis of the concept of efficient capital markets; and the second part,
explains the theory of asset pricing or the Arbitrage Pricing Theory (APT). Section 3.3
discusses about the stock market development indicators, which encompasses stock market
size represented by market capitalization ratio, stock price, stock market liquidity and market
volatility. Section 3.4 discusses about the theoretical relationship of various macroeconomic
variables and stock market.
Chapter 4 addresses the econometric methods that are employed for the study. The
chapter encompasses two sections, the methodology and the data definition. The first section
comprises seven sub-sections. The first sub-section will give a brief introduction of the
chapter. Sub-section 4.2.1 presents the empirical methods for the unit root test (Ng-Perron)
used for the study, to test the stationarity of the variables. Sub-section 4.2.2 consists of the
description of the Autoregression Distributed Lag (ARDL) approach and bounds testing
approach, used to analyze short run and long run results of the study. In sub-section 4.2.3,
description of Vector Error Correction Model (VECM) based granger causality is provided.
Sub-section 4.2.4 presents an explanation of stability tests, for which CUSUM and CUSUMQ
10
are employed. Sub-section 4.2.5 composed of describing about Impulse Response Function
(IRF) which helps to examine the current and future behavior of a variable that following a
shock to another variable within the system. Sub-section 4.2.6 discusses Variance
Decomposition (VDC) analysis, which is used to determine the relative importance of each
innovation to the variables in the system; and sub-section 4.2.7 presents the methodology for
Principal Component Analysis that computes new variables called principal components
(PCs) as linear combinations of the original variables. And in section 4.3, all the variables
which have been used for the study are defined along with the sources of data collection.
Chapter 5 concentrates on the discussion of empirical results to study the
macroeconomic determinants of the Stock market development in India, using different
econometric techniques. The chapter is segmented into six sections; The first section 5.1,
gives a brief introduction of the chapter and econometric techniques used. Section 5.2
documents the established empirical relationship between the stock market and
macroeconomic variables Section 5.3 consist of yearly studies, incorporating empirical
results using yearly frequency data; Section 5.4 is composed of the quarterly study for the
estimation of the relationship between macroeconomic variables and stock market
development, based on the empirical finding using quarterly frequency of data; in the section
5.5, the results of the studies having monthly frequency data are discussed, showing the
empirical relationship between macroeconomic variables and stock prices; and the section 5.6
is composed of the summary of the findings of all the empirical studies performed in the
present chapter.
Chapter 6 demonstrates and discusses the empirical results to study the relationship
between deficits and the Stock market development in India, using different econometric
techniques. The chapter is segmented into three sections; The first section 6.1, gives a brief
introduction of the chapter and econometric techniques used. Section 6.2 documents the
established empirical relationship between the stock market development and fiscal policy
variables. Section 6.3 consists of the study of the relationship between fiscal deficit and the
stock prices in India, using yearly frequency data; Section 6.4 is composed of the study of the
relationship between twin deficit and the stock market development in India, using quarterly
frequency of data; and the section 6.5 is composed of the summary of the findings of all the
empirical studies performed in the present chapter.
Chapter 7 discusses the empirical results of the study of macroeconomic determinants
and the sectoral stock market development in India. The chapter starts with the introduction
followed by model specification, and by going through the empirical estimation techniques,
11
used for the study, the chapter ends with the summary of the findings of all the empirical
studies performed in the present chapter.
The last Chapter (Chapter 8) of the thesis presents the summary of the study and a brief
discussion of the implications and the major findings of the study. The first section 8.1 of the
chapter discusses summary and conclusion of the study. Section 8.2 of the chapter composed
of the policy implications of the study. Section 8.3 of the study shows the specific
contributions of the study. Section 9.5 of the chapter consists of the limitations of the study;
and in the last section 8.5, scope for further studies are mentioned.
12
CHAPTER 2
An overview of the developments in Indian stock market
2.1. Introduction
During the last two decades, Indian stock market faced various ups and downs.
Moreover, it is forced to severe corrections which were initiated by the SEBI and the
government of India. The important events, news and views which were published in
National dailies and various Magazines were considered to provide an idea about the trends
in the Indian Capital Market. Well-developed securities markets are the backbone of any
financial system. Apart from providing the medium for channelizing funds for investment
purposes, securities markets aid in pricing of assets and serve as a barometer of the financial
health of the economy. The Indian securities markets have witnessed extensive reforms in the
post-liberalization era in terms of market design, technological developments, settlement
practices and the introduction of new instruments. The markets have achieved tremendous
stability and as a result, have attracted huge investments by foreign investors. There still is
tremendous scope for improvement in both the equity market and the government securities
market. Prior to the early 1990s, most of the financial markets in India faced controls of
pricing, entry barriers, transaction restrictions, high transaction costs and low liquidity. A
series of reforms were undertaken since the early 1990s, so as to develop the various
segments of financial markets by phasing out administered pricing system, removing barrier
restrictions, introducing new instruments, establishing an institutional framework, upgrading
technological infrastructure and evolving efficient, safer and more transparent market
practices, which ultimately leads to the economic development of the nation. Since the study
is concerned with studying predictability in the Indian stock market, it is necessary as well as
logical to present the origin, any relevant details about the Indian stock market and its
important stock indices. To this end, we first present the history and origin of the Indian stock
market. Followed by historical overview, we will state some recent facts on the performance
of the Indian economy to get the knowledge of India’s current status as one of the most
important emerging market economies with huge growth potential and the role other
variables in its sustainability. Since we are concerned with the behavior of the stock market,
we then cite a few major structural, operational and regulatory reforms which were carried
out in the Indian stock market during the last one and-a-half decades since its reform process
started in the early nineties of the last century. This chapter has thus been organized as
follows. The next section gives highlights the historical development of the Indian stock
13
market, and section 2.3 discusses the case for India as an emerging market economy. In
Section 2.4, some of the major changes in the financial sector of the Indian economy are
briefly described; and the last section contains the summary of the chapter.
2.2. Historical Development of the Indian Stock Market
The history of Indian stock market is about 200 years old. Prior to this the hundis and
bills of exchange were in use, especially in the medieval period, which can be considered as a
form of virtual stock trading but it was certainly not an organized stock trading. The recorded
stock trading can be traced only after the arrival of the East India Company. The first
organized stock market that was governed by the rules and regulations came into the
existence in the form of The Native Share and Stock Broker’s Association in 1875. After
passing through numerous changes this association is today better as Bombay Stock
Exchange, which remains the premier stock exchange since its inception. The formation of
the native share and the stock broker’s Association at Bombay in 1875 was an important
early event in the development of the stock market. This was followed by the formation of
association/exchanges in Ahmedabad (1894), Calcutta (1908), and Madras (1937). In
addition, a large number of short-lived exchanges emerged mainly in rising periods to go
back into darkness during depressing times subsequently. Indian stock market marks to be
one of the oldest stock market in Asia. It dates back to the close of the 18th century when the
East India Company used to transact loan securities. In the 1830s, trading on corporate stocks
and shares in the Bank and Cotton presses took place in Bombay. However, the items in
which the trading took place increased tremendously by the end of 1839. Thereafter, the
concept of broker business was started which show momentum in the mid-18th century.
Though the trading was broad but the brokers were hardly a half dozen during 1840 and
1850. An informal group of 22 stockbrokers began trading under a banyan tree opposite the
Town Hall of Bombay from the mid-1850s, each investing a (then) princely amount of Rupee
one. This banyan tree still stands in the Horniman Circle Park, Mumbai. In 1860, the
exchange flourished and the number of brokers who are dealing in the trading of items goes
up to 60. Around 1860-61, there is no supply of cotton from America as the civil war took
place in America. Due to this, there is a concept of “Share Mania” that took place in India.
Further, the number of brokers increased from 60 to 250 in around 1862-1863. The informal
group of stockbrokers organized themselves as The Native Share and the Stock Brokers
Association, which, in 1875, was formally organized as the Bombay Stock Exchange (BSE).
In 1930, BSE was shifted to an old building near the Town Hall in Bombay. On 31 August
14
1957, the BSE became the first stock exchange to be recognized by the Indian Government
under the Securities Contracts Regulation Act. And finally in 1986, it developed the BSE
SENSEX index, giving the BSE a means to measure overall performance of the exchange.
Early 1960s was starting of bearish phase in the stock exchange as the Indo China war
took place. After the ban in forward trading and badla in 1969, the bearish trend became
worse. Badla in share trading means something in return. It is a system to carry-forward.
Badla is the charge, which the investor pays to carry forward his position. Using the Badla
tool or system, an investor can take a position in the scrip without actually taking delivery of
the stock. He can carry-forward his position on the payment of small margin. Financial
institutes helped to boost the sentiment by injecting liquidity in the market. In 1964, the first
Indian mutual fund came into market, named the Unit Trust of India.
The badla trading was resumed again in 1970s, under another form of hand delivery
contracts. But in 1974, 6th of July capital market had to face a bad news. The government
introduced the Dividend Restriction Ordinance (DRO); this rule restricting the companies for
the payment of dividend up to 12 per cent of the face value or one-third of the profits of the
companies can be distributed (Whichever was lower). With the news, the stock market
crashed again. Stocks went down by 20% and the market was closed for nearly a fortnight.
The stock market remained in a bearish trend until the optimism came to market with the
MNCs who forced to dilute majority stocks in their company in favor of Indian public. It was
the first time Indian public had the opportunity to invest in some of the finest MNCs. In 1977,
Mr. Dhirubhai Ambani entered in Indian stock exchange.
The period of 1980s, proved to be the growth period for Indian stock exchange. Indian
public discovered profitable opportunities in the stock exchange. It was the time when people
became aware of the stock exchange and started to get attracted and invest in the same. It was
the time when convertible debentures and public sector bonds were popular in the market.
New stock market entries like Reliance and LNT re-defined Indian stock market scenario.
Such factors enlarged volume in the stock exchange. 1980s can be characterized by the huge
increase in the number of listed companies in the stock market and increase in market
capitalization.
The 1990s can be described as the most decisive decade in the history of Indian stock
market. Everyone was talking about liberalization and globalization. The Capital Issue Act of
1947 was replaced in 1992. SEBI was emerging as a new regulator of the market. FII is
coming to India and re-rating India as one of the most attractive market in the world. Number
of new stock exchanges were rising in the county. Private sector mutual funds were welcome
15
in the market. Some very big scams of Indian scam history took place in 1990s. A major
scandal with market manipulation by a BSE member named Harshad Mehta took place in
1992. The impact of such incidence was very deep. Indian investors drove their money out of
the market for some years. With this BSE responded to calls for reform with intransigence.
The slow actions by the BSE helped radicalize the position of the government and opened
Indian government eyes, which encouraged the creation of the National Stock Exchange
(NSE), which created an electronic marketplace. New technology new systems were
introduced in Indian stock exchange. The Bombay stock exchange had two new competitors
in the market, the OTC (Over the Counter Exchange of India) and NSE established in 1992.
The national securities clearing corporation (NSCC) and National securities depository
Limited (NSDL) were established in 1995 and 1996 respectively. Option trading service was
started in 1995—1996. Rolling settlement was introduced in India in early 1998. 1990s are
known as era of Indian IT companies too. Wipro, Infosys, Satyam were some of the preferred
stocks. Telecom and Media sector also rising during the same time.
After Ketan Parekh scam in early 2000, Badla system was banned in Indian market and
rolling settlement was introduced in all scripts. Future trading and Internet trading started in
2000, these events changed picture of the old stock market. In 2001, The Unit Trust of India
(UTI) suspended the sale and repurchase of its popular US-64 scheme for six months. It
created panic among investors. One big incidence of VSNL (Videsh Sanchar Nigam Ltd.)
disinvestment took place in 2002. In 2003, the government took the decision to privatize PSU
banks and it again proved to be a market buster. In 2000s, FII money started coming in Indian
market in large volumes. NSE turnover exceeded the BSE. BSE rapidly automated, but it
never caught up with NSE spot market turnover. Global recession hits Indian market in late
2007 and throughout 2008. Big Satyam Scam was exposed in 2008 again and it hit investor’s
sentiments badly. Since the second quarter of 2009, we have been watching upward moving
trend in the markets again.
It has been a long journey for the Indian capital market to become organized, mature,
fairly valued, nicely regulated, liberal and more global. The Indian market is one of the most
attractive and developing markets today, which gives a stable and a high rate of return
compared to other countries.
16
2.3. India as an emerging market economy
The Indian economy has shown a remarkable performance over the last two decades.
From the year 1985 onwards, the problem of balance of payments was getting started in
India. In the middle of 1991, exchange rate of India was subjected to a severe correction. The
corrections were started with the decline in the value of the rupee leading up to mid-1991.
The action against this event was taken by the authorities of the Reserve Bank of India, as
they expended international reserves in order to slow down the decline in value of the rupee.
The reserves were near to depletion and the exchange rate was devalued sharply in the first
week of July, against major foreign currencies. By the end of 1990, the country was in a
serious economic crisis. After the occurrence of this major economic crisis, the Government
of India had taken some major reforms, in terms of globalization and liberalization, and thus,
the economy started experiencing a rapid economic growth with the inflow of increasing
foreign investment. According to International Monetary Fund, the Indian Economy is the
seventh-largest economy in the world in terms of nominal GDP and the third-largest by
purchasing power parity (PPP). The Central Intelligence Agency (the Fact Book Indian
Economy) stated that, India is also classified as Newly Industrialized Country, one of the G-
20 major economies, a member of BRICS and a developing economy with approximately 7%
average growth rate for the last two decades. India's economy became the world's fastest
growing major economy from the last quarter of 2014, replacing China's and India’s gross
domestic product (GDP) grew at 7.5% during the January-March of 2014-15 period, faster
than China’s 7% in the same period, mainly on account of improvement in services and
manufacturing sectors. (DNA, Indian economy overtaken china growth rate). As per the
report of Economic Survey 2007-08, show, the Indian economy registered a growth rate of
10.2 percent during the year 2007, the highest ever and after that, the GDP comes down to
3.7 in 2008-09, due to the Global financial crisis in that year so-called sub-prime housing
mortgage crisis. Because there was fiscal and monetary space, timely stimulus allowed the
economy to recover fairly quickly to a growth of 8.4 percent in 2009-10 and 2010-11. Since
then, however, the fragile global economic recovery and a number of domestic factors have
led to a slowdown once again (as per the Ministry of Finance). Therefore, the GDP in 2011-
12 also moderated to 6.5 percent from 9.8 percent in the 2010-11, as per the former finance
minister Pranab Mukherjee “the negative growth in the mining sector along with a slowdown
in the construction sector has also contributed to the decline in GDP growth”. The GDP
growth in 2012-13 was worse than expected, according to the Ministry of Finance, the
slowing growth rate in 2012-13 can be explained in terms of both global factors and domestic
17
factors. The slowdown in growth in advanced economies and near recessionary conditions
prevailing in Europe resulted not only in lower growth of international trade but also lower
capital flows. The growth rate of India’s exports declined. At the same time, however, the
international price of crude oil remained high. Hence, India’s trade and current account
deficits widened. Turning to domestic factors, rainfall in the monsoon season of 2012-13 has
been below normal, particularly in the key months of June and July. This affected sowing and
resulted in a lower growth rate of agriculture and allied sectors. The Gross Domestic Product
(GDP) growth rate for 2013-14 has revised upwards to 6.9 percent following adoption of the
new series with base year 2011-12. The GDP growth rate in the year 2014-15 is 7.4 percent
as per the statistics of the Central Statistics Office (CSO). The overall economic situation in
the country is looking better and the basic parameters of the Indian economy are moving in
the right direction, according to Union Finance Minister Arun Jaitley. As per the Indian
Economic Survey 2014-15, the Indian economy in 2014-15 has emerged as one of the largest
economies with a promising economic outlook on the back of controlled inflation, rise in
domestic demand, increase in investments, decline in oil prices and reforms among others.
Apart from India’s success in terms of high growth rates, there exist other important
factors necessary for the sustainability of the growing economy, which came into the picture
after the 1991 reforms, i.e., after the adoption of globalization and liberalization policies, the
role of international financial inflows and the degree of openness to trade has been increased,
accelerating the growth of the overall economy. Capital formation is an important element of
any economy. International financial inflows play a complementary role in the overall capital
formation of an economy, by filling the gap between domestic savings and investment.
International Financial inflows can be broadly categorized in two components, the FDI
(Foreign Direct Investment) and the FII (Foreign Institutional Investment). Foreign Direct
investment plays an important role in the growth of an economy. It is a direct investment by
the company situated in a country, into the production or business running in another country,
either by buying a company in the target country or by expanding operations of an existing
business in that country. Inflows of FDI have increased substantially after adopting
globalization and liberalization policies relating to FDI in 1991 by the government of India,
increased FDI from US$ 2696 million in 1996 to US$ 4,029 million in 2000-01 and in 2005-
06 inflow of FDI becomes more than doubled to exceed US$ 8,961 million in 2005-06. But
after 2005-06, the reported statistics show a steep increase in inflows: from US$ 22,826
million in 2006 to nearly US$ 37,758 million in 2014-15, as reported by the DIPP
(Department of Industrial Policy and Promotion). Thus, the trend analysis of the FDI data
18
from 1995-96 to 2014-15 shows that there is always a positive average trend of FDI in India
but if we deeply analyze the data, FDI flow in India. Further, the FIIs has emerged as
noteworthy players in the Indian stock market and their growing contribution adds an
important feature to the development of stock markets in India. To facilitate foreign capital
flows, developing countries have been advised to strengthen their stock markets. FII inflows
into Indian equities have been steady ever since the markets were opened up for it in 1993. It
owns a dominant 16% of Indian equities (worth US$147bn) and account for 10-15% of the
equity volumes. It injected US$ 23 billion in the Indian equity markets during the third
quarter of 2012, which is very negligible when compared to second quarter in the same year
due to lack of confidence among investors and the prevailing economic downturn. But in the
year 2014-15 FIIs have invested a net of US$ 43.5 billion, the highest investment in any
fiscal year. This huge investment is because of expectations of the investors for an economic
recovery, falling interest rates and improving earnings outlook. Although there are some
debate over the inherent weaknesses with FII flows and their destabilizing effects on equity
and foreign exchange markets, it cannot be ignored that India is increasingly becoming an
attractive destination for the global investors.
In India, the average percentage growth rate of inflation was 12.56 in the year 1988–
1989 which rose up to 19.30 in the year 1991–1992 and in the 2000–2001 the average
percentage annual growth rate of inflation was negative (−0.33). It turned positive in the
consecutive year, and in the last few years, the percentage annual growth rate of inflation
increased rapidly. Significantly, in 2008–2009 it crossed the level of double digits. Money
supply in India has increased considerably over the past decades, from Rs. 44.77 billion in
1988–1989 to Rs. 53.92 billion in the year 2000–2001 and to Rs. 76.33 billion in the year
2012–2013, showing an upward trend and becomes Rs. 108.519 billion in 2015. Given
India’s long history of running huge fiscal deficits, the sharp increase in its fiscal deficit over
the last few years is a major concern for both academicians and policy makers in India (Rao,
2009; Rangarajan, 2009). The fiscal deficit of India stood at 7.08 % of GDP in 1988. There
was a clear improvement till 1990. After 1991 it started to fall with minor fluctuations till
2007–2008, when it was at its minimum of 2.54% of GDP in the year 2007–2008. After
falling to 2.54 % in 2007–2008, the fiscal deficit to GDP ratio started rising again and was
around 5.7% in 2012 and the fiscal deficit for 2014-15 is 4% of gross domestic product (ENS
Economic Bureau, May 2015). In the year 2011-12 and 2012-13, current account balance for
India reached deficit levels of 4.6 % (US$ 78 billion) and 4.8 % of GDP (US$ 87 billion).
India’s current account deficit (CAD) for the January-March period narrowed sharply to $1.2
19
billion (0.2 per cent of GDP) from $18.1 billion (3.6 per cent of GDP) in the same period last
year, which was also lower than $4.2 billion (0.9 per cent of GDP) in the October-December
quarter of 2013-14. “The lower CAD was primarily on account of a decline in the trade
deficit as decline in imports was sharper than that in exports,” the Reserve Bank of India
(RBI). Further, the Current account deficit is estimated to come down to 1.3 per cent
of GDP in the fiscal ending March, due to moderation in petroleum and gold imports, the
Reserve Bank of India (RBI).
With a stockpile of 25000 tonnes and accounting for around a quarter of the world
demand, gold imports in India increased from US$ 33 billion (in 2009-10) to US$ 57 billion
(2011-12) and US$ 53 billion (2012-13). Share of gold in imports has also increased
considerably from 7.6% (2005-06) to 12.6% (2011-12). The main reason for this
phenomenon is said to have been the increase in global prices of gold in the post financial
crisis scenario, as the world’s savers looked for ‘safe havens’ to park their savings. Since
2005, gold price has doubled in terms of US$ and tripled in terms of Rupee. Surpassing
returns on other investment, between 2007 and 2012, gold gave annual average returns of
23.7% as compared to 7.3% by Nifty (National Stock Exchange in India) and 8.2% by
Savings Deposits (Sehgal et al. 2012, cited in Economic Survey 2012-13). In addition, it acts
as a good hedge against inflation, which reduces real return on investments (inflation in India
stood at 9.6% and 8.9% for the years 2010-11 and 2011-12 respectively). Hence, the recent
spurt in gold demand and import by India is less about its historical affinity for consumption
as jewelry and more about investment dynamics. (Center for Budget and Governance
Accountability (CBGA), India). This brief presentation of statistical data on India’s economic
performance thus establishes that India is now one of the fastest growing economies in the
world with huge growth potential.
2.4. Reforms in the financial sector
In 1990s, India was undergoing extremely fragile financial condition arising out of
exceptionally severe balance of payment crisis, sluggish growth, and political instability,
therefore, India’s economic reforms began in 1991 when the newly elected congress
government, wanted to get rid of the pertaining economic and financial problems of the
country, thus, major policy changes and reform programs were initiated in most of the sectors
of the economy including, of course, the financial sector, to achieve short term stabilization
combined with a longer term program of comprehensive structural reforms. The reforms
initiated in 1991 were formulated by keeping in view the need for a system change, involving
20
liberalization of government controls, a larger role for the private sector and greater
integration with the world economy. Consequently, some fundamental changes have taken
place in the Indian economy as a whole, and in particular, in the financial sector. Parallel with
the reforms in the banking sector, an action has been taken for the reforms of the capital
market, as it was an important part of the agenda of financial sector reforms. In 1991 India’s
capital market did not have any statutory regulatory framework. The process of reform of the
capital market was initiated in 1992 as per the guidelines recommended by the Narasimham
Committee. It aimed at removing direct government control and replacing it by a regulatory
framework based on transparency and disclosure supervised by an independent regulator.
Therefore, in the context of Indian capital market, which is the focus of the study, the first
step was taken in 1992 when the Securities and Exchange Board of India (SEBI), which was
originally established as a non-statutory body in 1988, was formed to a full-fledged capital
market regulator with statutory powers in 1992. The requirement of prior government
permission for accessing capital markets and for prior approval of issue pricing was abolished
and companies were allowed to access markets and price issues freely, subject only to
disclosure norms laid down by SEBI. Consequently, the stock exchanges, which were earlier
dominated by brokers and lacked effective supervision, are now much better governed.
Another important policy initiative in 1993 was taken; the economy was opened to
portfolio investment in two ways. The opening of the capital market to foreign institutional
investors (FIIs) meeting certain minimum standards were allowed to invest in Indian equity
and later also in debt instruments through secondary market purchases in the stock market. At
present 528 FIIs are registered with the Securities and Exchange Board of India (SEBI) and
around 150 are active investors. A second window for portfolio investment was provided by
allowing Indian companies to issue fresh equity abroad through the mechanism of Global
Depository Receipts (GDRs). This enabled Indian companies to raise resources from passive
investors in world markets instead of seeking active investors as is the case with joint venture
partners. Portfolio investment has expanded rapidly in the post-reform period (Montek S.
Ahluwalia, 1999).
An important reform measure had been taken place in the form of modernization of
technology of trading with the formation of a new stock exchange, called the National Stock
Exchange (NSE). The NSE was set up in 1994 as an automated electronic exchange as a
competitor to the oldest stock exchange of India viz., the Bombay Stock Exchange (BSE). It
enabled brokers in 220 cities all over the country to link up with the NSE computers via
VSATs and trade in a unified exchange with automatic matching of buy and sell orders with
21
price time priority, thus ensuring maximum transparency for investors. The introduction of
electronic trading by the NSE generated competitive pressure which forced the BSE to also
introduce electronic trading in 1995. This was the first step towards paperless trading, which
allowed dematerialization of share certificates with settlement by electronic transfer of
ownership from one account to another within a depository and dematerialization of shares.
Another major development concerning the secondary segment of the Indian capital market
was the introduction of Futures trading in 1999. A well-functioning market in index futures
would help in risk management and provide greater liquidity to the market.
Another measure was taken by the government of India in July 1991, by devaluing the
rupee by 24% as part of the initial stabilization program, and a dual exchange rate was
introduced in March 1992. Shortly after March 1993, the dual exchange rate was unified and
the unified rate was allowed to float. The cumulative effect of these changes was that
between June 1991 and March 1993 the exchange rate depreciated from $1= Rs. 20 to $1=Rs.
31, a depreciation of 35% in the dollar value of the rupee and a real depreciation (adjusting
for price changes) of around 27% viz.-a-viz., India’s major trading partners. This adjustment
in the exchange rate clearly helped the Indian industry to meet the import competition
resulting from trade liberalization. Exchange rate management has avoided the danger of
excessive rigidity and also the opposite dangers of overshooting with associated loss of
confidence (Montek S. Ahluwalia, 1999). As part of the process of transiting to an open
economy, the rupee was made convertible on the current account in 1993. The Indian
currency is set to be made fully convertible in phases over the five years ending 2010-2011.In
June 2008, the rupee appreciated to a ten-year high of US$ 39.29. The stability of the Indian
economy attracted substantial foreign direct investment, while high interest rates in the
country led the companies to borrow funds from abroad. The global financial crisis of 2007-
08, exerted pressure on crude oil prices, which gradually plunged to below $50 a barrel. Due
to this, inflow of dollar declined, with oil companies and investors purchasing more and more
dollars. Persistent outflow of foreign funds increased the pressure on the rupee, causing it to
decline. On March 5, 2009, the Indian currency depreciated to a record low of US$ 52.06
(Economy Watch, 2010).
India has 23 stock exchanges across the country, 20 of them being regional ones with
allocated areas of operation. The major stock exchanges of India are the Bombay Stock
Exchange and the National Stock Exchange. The Securities and Exchange Board of India
(SEBI) works as a regulatory authority, which regulates all the stock exchanges of the
country. The Bombay Stock Exchange (BSE) which was established in 1875, is the primary
22
stock exchange of India. More than 5500 companies are listed on BSE, making it world's No.
1 exchange in terms of listed members. The companies listed on BSE command a total
market capitalization of USD 1.68 Trillion as of March 2015. It is also one of the world's
leading exchanges (5th largest in March 2015) for Index options trading (World Federation of
Exchanges). BSE is the first exchange in India and second in the world to obtain an ISO
9001:2000 certification. It is also the first Exchange in the country and second in the world to
receive Information Security Management System Standard BS 7799-2-2002 certification for
its On-Line trading System (BOLT). The growth in both investment and number of listed
companies in various stock exchanges in India, and also changes in trading rules and in the
volume of capital raised from private investors has helped in achieving globalization and
international competitiveness of Indian capital market and sparking a boom in its stock
prices.
BSE's popular equity index, the S&P BSE SENSEX, is India's most widely tracked
stock market benchmark index. The S&P BSE SENSEX (S&P Bombay Stock Exchange
Sensitive Index), also-called the BSE 30 or simply the SENSEX, is a free-float market-
weighted stock market index of 30 well-established and financially sound companies listed
on Bombay Stock Exchange. The 30 component companies which are some of the largest and
most actively traded stocks, are representative of various industrial sectors of the Indian
economy. The S&P BSE SENSEX is regarded as the pulse of the domestic stock markets in
India. Many newly important sectors like finance, pharmaceutical, healthcare are also well
represented in this index. As the oldest index in the country, it provides the time series data
over a fairly long period of time. With base at 1978-79 = 100, this index has been serving the
purpose of quantifying the equity price movements and it also reflects the sensitivity of the
Indian capital market in an effective manner. The growth of the equity market in India has
been phenomenal from last few years. Right from the early nineties the stock market
witnessed heightened activities in terms of various bull and bear runs. The BSE SENSEX has
captured all these events in the most judicial manner. The BSE SENSEX was initially a full-
market-capitalization weighted index. But since September 1, 2003, it follows the free-float
market capitalization methodology of index construction which is now considered to be the
widely followed index construction methodology on which majority of global equity
benchmarks such as MSCI, FTSE, STOXX, S&P 500, and Dow Jones are based. As of 21
April 2011, the market capitalization of S&P BSE SENSEX was about US$ 464 billion
(47.68% of the market capitalization of BSE), while its free-float market capitalization was
US$ 245 billion. During 2008-12, Sensex 30 Index share of BSE market capitalization fell
23
from 49% to 25% (SEBI Report, 2015) due to the rise of the sectoral indices like BSE PSU,
Bankex, BSE-Teck, etc. On 19 February 2013, the SENSEX becomes S&P SENSEX as BSE
ties up with Standard and Poor's to use the S&P brand for Sensex and other indices. On 4
March 2015, The Sensex breaches 30000 mark following steps taken by the Reserve Bank Of
India in cutting the repo rates.
2.5.Trends of the Indian Stock Market
The Indian stock exchanges hold a place of prominence not only in Asia but also at the
global stage. As mentioned above, the Bombay Stock Exchange (BSE) is one of the oldest
exchanges across the world, while the National Stock Exchange (NSE) is among the best in
terms of sophistication and advancement of technology. The Indian stock market scene really
picked up after the opening up of the economy in the early nineties. The whole of nineties
were used to experiment and fine tune an efficient and effective system, and right from the
era of globalization, the stock market started to work efficiently and showed its new heights,
at different phases of its development. There are times when the Indian stock market achieves
new heights, breaching its previous records and there are time also when stock market
plunges upto its extreme. Theses ups and downs of the stock market were definitely due to
some unavoidable circumstances created in the economy, because the stock market index is
also an important part of the economic cycle. The present section of the chapter gives a brief
overview of the development of the Indian stock market.
24
Table 2.1: Key indicators of Indian stock market activity
Year
BSE
Sensex % Changei
MCAP
ratioiv CNX Nifty % Changei
1990-91 1049.53 NA 09.06 NA NA
1991-92ii 1879.51 79.08 11.81 NA NA
1992-93 2895.67 54.06 17.35 NA NA
1993-94 2898.69 00.10 22.19 NA NA
1994-95iii 3974.91 37.12 34.48 1203.06 NA
1995-96 3288.68 -17.26 38.43 962.63 -19.98
1996-97 3469.24 05.49 34.69 1006.59 04.56
1997-98 3812.86 09.90 30.66 1087.50 08.03
1998-99 3294.78 -13.58 30.35 955.39 -12.14
1999-00 4658.63 41.39 24.53 1368.62 43.25
2000-01 4269.69 -08.34 39.54 1334.76 -02.47
2001-02 3331.95 -21.96 31.06 1077.03 -19.31
2002-03 3206.29 -03.77 22.34 1037.23 -03.69
2003-04 4492.19 40.10 25.00 1427.50 37.62
2004-05 5740.99 27.79 45.13 1805.26 26.46
2005-06 8278.55 44.20 53.74 2515.48 39.34
2006-07 12277.3 48.30 66.29 3572.44 42.01
2007-08 16568.9 34.95 86.27 4896.60 37.06
2008-09 12365.6 -25.36 146.85 3731.03 -23.80
2009-10 15585.2 26.03 52.73 4657.77 24.83
2010-11 18605.2 19.37 86.36 5583.54 19.87
2011-12 17422.9 -06.35 94.58 5242.74 -06.10
2012-13 18202.1 04.47 55.30 5520.34 05.29
2013-14 20120.1 10.53 68.96 6009.51 08.86
2014-15 26556.5 31.98 70.00 7969.74 32.61 i Annual percentage changes calculated by author ii The year of adoption of liberalization and globalization policies iii NSE was introduced in the capital markets iv MCAP has been taken as a percentage of GDP, which is popularly known as Buffet Valuation Indicator
The table 2.1 represents the growth statistics for the key indicators of Indian stock
market activity. The Sensex has increased by over twenty five times from June 1990 to the
present. Using information from April 1979 onwards, the long-run rate of return on the S&P
BSE SENSEX works out to be 18.6% per annum (BSE india). In the mid of 1990, the
SENSEX touched the four-digit figure for the first time and closed at 1,001 due to good
monsoon in the country and excellent corporate results. The Sensex crossed the 2,000 points
with the start of the year 1992, i.e. january, because of the liberal economic policy initiatives
undertaken by the then finance minister and Former Prime Minister of India Dr Manmohan
Singh. And right in the next month of the year 1992, with the announcement of the market
friendly budget, the index surged to 3000 points. Further, in the consequtive month of 1992,
25
the sensex crossed the 4,000 points due to the expectations of a liberal export-import policy.
The The development process of stock market wass give a jerk in October 1992 when the
Sensex registered a fall of 570 points (12.77 per cent) to close at 3,870, following the
Harshad Mehta securities scam. In the year 1999, the Sensex crossed the 5,000 mark,
because of the political factors, as the Bharatiya Janata Party-led association won the majority
in the 13th Lok Sabha election. In February 2000, the information technology boom helped
the Sensex to cross the 6,000 points. This record would stand for nearly four years, until May
2004,when the Sensex faced another fall of 565 points, due to the political unstability in the
country.
The gradual development process of Indian stock market continued in the subsequent
years, due to the affect of various economic and political, domestic and international
circumstances of the nation. On 22 May 2006, the Sensex plunged by 1,100 points during
intra-day trading, leading to the suspension of trading for the first time since 17 May 2004.
The volatility of the SENSEX had caused investors to lose Rs 6 trillion (US$131 billion)
within seven trading sessions. When trading resumed after the reassurances of the Reserve
Bank of India and the Securities and Exchange Board of India (SEBI), the Sensex managed to
move up 700 points, but still finished the session 457 points in the red. The Sensex eventually
recovered from the volatility, and on 16 October 2006, the Sensex closed at an all-time high
of 12,928.18. This was a result of increased confidence in the economy and also because
India's manufacturing sector grew by 11.1% in August 2006. On 29 October 2007, the Sensex
crossed the 20,000 mark for the first time with a massive 734.5-point gain. The journey of the
0.00
20000.00
40000.00
60000.00
80000.00
100000.00
120000.00
140000.00
199
0
199
1
199
2
199
3
199
4
199
5
199
6
199
7
199
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Figure 2.1: Indian Stock Market Trends
BSE Sensex
(Base : 1978-79 =100)
MARKET CAPITALISATION - BSE
26
last 10,000 points was covered in just 483 sessions, compared to 7,297 sessions taken to
touch the 10,000 mark from its base value of 100 points. After a long spell of growth, the
Indian economy were experiencing a downturn, because the Industrial growth has been
faltering, inflation remains at double-digit levels, the current account deficit is widening,
foreign exchange reserves are depleting and the rupee is depreciating. Furthermore, the
breathtaking development process continued till the effects of the subprime crisis in the U.S.,
started to spread on Indian economy, and the investors panicked following weak global cues
accompanied by the fears of a recession in the US and consequently, in the third week of
January 2008, the Sensex experienced huge falls along with other markets around the world.
On 21 January 2008, the Sensex saw its highest ever loss of 1,408 points at the end of the
session. The most immediate effect of that crisis on India has been an outflow of foreign
institutional investment from the equity market. Foreign institutional investors, who need to
retrench assets in order to cover losses in their home countries and were seeking havens of
safety in an uncertain environment, have become major sellers in Indian markets. In 2007-08,
net FII inflows into India amounted to $20.3 billion. As compared with this, they pulled out
$11.1 billion during the first nine-and-a-half months of calendar year 2008, of which $8.3
billion occurred over the first six-and-a-half months of financial year 2008-09 (April 1 to
October 16). In addition, this withdrawal by the FIIs led to a sharp depreciation of the rupee.
Between January 1 and October 16, 2008, the RBI reference rate for the rupee fell by nearly
25 per cent, even relative to a weak currency like the dollar, from Rs 39.20 to the dollar to Rs
48.86 (Chart 2). This was despite the sale of dollars by the RBI, which was reflected in a
decline of $25.8 billion in its foreign currency assets between the end of March 2008 and
October 3, 2008. In March 2008, the Sensex dropped by 951.03 points on the global credit
crisis and distress, and the volatility in market continued till the mid of 2009. The Sensex
plunged by 869.65 points in July 2009, the day of Union Budget presentation in Parliament
on concerns over high fiscal deficit. Further, the Sensex closed at more than 21,000 points for
the first time, in November 2010. The Sensex crossed the historical mark of 30,000 after repo
rate cut announcement by RBI. But the volatility in the market still continues, due to the euro
crisis, Greece debt crisis and the emerging crisis of slow down of Chinese economy.
Thus, it can be concluded that the stock market movements are the effect of changes in
the various economic and political conditions of an economy. Furthermore, it is also observed
that various domestic and international macroeconomic factors works as the driving forces of
the Indian stock market.
27
2.6. Summary
The present chapter of the study tried to give a historical overview of the development
of stock markets in India and its major policy reforms. India is considered as the developing
country with many investment opportunities and higher growth potential. The Indian
Economy is the seventh-largest economy in the world in terms of nominal GDP and the third-
largest by purchasing power parity (PPP). The Central Intelligence Agency (the Fact Book
Indian Economy) stated that, India is a developing economy with approximately 7% average
growth rate for the last two decades. India's economy became the world's fastest growing
major economy from the last quarter of 2014, replacing China's and India’s gross domestic
product (GDP) grew at 7.5% during the January-March of 2014-15 period, faster than
China’s 7% in the same period, mainly on account of improvement in services and
manufacturing sectors. From the year 1991 onwards the economy of India has undergone
tremendous changes, after adopting globalization and liberalization policies, which enabled
the economy of India to prosper and grow with a great pace. The decade of 1990s proved to
be a decisive decade for Indian economy, as it introduced many major reforms in the year
like the formulation of Securities and Exchange Board of India (SEBI) in the year 1992 as
capital market regulator with statutory powers. The other important policy initiative was
taken in 1993 with the opening of the capital market to foreign institutional investors (FIIs)
and by allowing Indian companies to issue fresh equity abroad through the mechanism of
Global Depository Receipts (GDRs). Another important reform measure had been taken place
in the form of modernization of technology of trading with the formation of a new stock
exchange, called the National Stock Exchange (NSE). One more important measure was
taken by the government of India in July 1991, by devaluing the rupee by 24% as part of the
initial stabilization program. This adjustment in the exchange rate clearly helped the Indian
industry to meet the import competition resulting from trade liberalization. Thus, these
reforms introduced by the government of India, proved to be the milestones of Indian
economy, which changed the economic scenario of the country.
28
CHAPTER 3
Theoretical underpinnings of the relationship between macroeconomic variables and
Stock market development indicators
3.1. Introduction
The present chapter of the study contains a review of the theory and evidence on, the
relationship between macroeconomic variables and stock market development.
Macroeconomic variables (such as inflation, interest rates, gold prices, trade openness,
deficits, international financial inflows, crude oil prices, economic activity, the money supply
and exchange rates) are closely examined to understand their effects on stock market
development. The change in macroeconomic variables may affect future dividends and cash
flows by affecting profitability which ultimately reflects changes in stock prices. It is
believed that the effects of macroeconomic variables on the profitability of individual
industries or sectors vary depending on their sensitivity to these variables. For example,
capital-intensive industries (such as banking sector industries or other non-banking financial
firms) are likely to be more sensitive to interest rate changes. Similarly, the earnings of
sectors such as retail and tourism are more likely to be affected by a slowdown in economic
activity. However, the slowdown is less likely to affect sectors, such as consumer staples or
health industries that produce goods and services that are essential to consumers. Moreover, it
seems that, if we move our focus of study from the determination of individual stock prices to
study the determination of the impact on the aggregate stock market using market indices, a
macro model of stock prices is more appropriate (than a micro model based on financial
ratios). Thus, the information contained regarding the changes in macroeconomic data that
affects investors’ expectations for the state of the economy is useful to predict the movement
in stock prices. Hence, the present chapter deals with the review of the theory behind and the
evidence for the macroeconomic factors that influence stock prices.
3.2. Theoretical Background
3.2.1. Random Walk and The Theory of Efficient Market Hypothesis (EMH)
Before discussing the efficient market hypothesis, we have to talk about random walk
which is considered as the reason for the genesis of the concept of efficient capital markets
(Brealey & Myers, 2000). Bachelier (1900), a French mathematician, concluded that the
stock price movement was unpredictable and followed a Brownain motion by empirical
study. Afterwards Kendall (1953), a British statistician, proved the behavior of stock and
29
commodity prices appeared amble. Instead of obtaining regular price cycles, he found the
movement of stocks and commodity prices followed a random walk. That specified that the
stock price changes are independent from the past prices (Brealey & Myers, 2000). Brealey &
Myers (2000) stated that the investors barely can receive any clue about tomorrow’s expected
change according to today’s price change. To be precise, the prices of the stock cannot be
predicted and it is impossible for investors to earn excess profits for a long period by only
depending on past series of returns. Therefore, the random walk theory worked as the base
for academicians, investors and regulatory authorities. For academicians, the random walk
behavior of prices is the base and tool to study and understand the movement of stock prices.
For investors, when they design their trading strategies, they have to consider the stock prices
followed a random walk or have to trace a particular pattern followed by stock prices.
In Fama’s (1965) article, the concept of efficient market was introduced in the
securities markets. The definition of efficient market was “a market where there are large
numbers of rational, profit-maximizers actively competing, with each trying to predict future
market values of individual securities, and where important current information is almost
freely available to all participants”. Formally, the EMH can be explained using the following
equation:
𝛺𝑡∗=𝛺𝑡 (3.1)
The left side represents a set of relevant information available to the investors, at time
“t”. The right side is the set of information used to price assets, at time “t”. The equivalence
of these two sides implies that the EMH is true, and the market is efficient.
Although this definition provides us with a first idea of what an efficient market is, it
does not really explain what is meant by available information. This is why Fama (1970)
included some elaboration on this definition, making a distinction between three types of
efficient markets1, based upon the level of information used by the market.
Weak form efficient market (level of information = historical price information);
Semi-strong form efficient market (level of information = all publicly available
information);
Strong-form efficient market (level of information = all information, both public and
private).
Originally the idea of different forms of market efficiency comes from Roberts (1967),
but Fama (1970) was most successful introducing the concept to the general public. With the
1 Fama (1991) revised these three terms to be predictability, event studies, and inside information for the weak form, semi-
strong form, and strong form, respectively.
30
additional knowledge on what is meant by available information, it becomes perfectly clear
what efficient markets are.
The weak form of the EMH confines itself to just one subset of public information,
namely historical information about the share price itself, i.e., it employs that asset prices
today incorporate all relevant past information, i.e., past asset prices, security dividends, and
trading volume. Knowing the past behavior of stock prices provides no indication of future
stock prices. In other words, the EMH theory hypothesizes that asset prices evolve according
to a random walk. Thus, asset prices cannot be predicted, and investors cannot beat the
market.
The semi-strong form EMH also incorporates the weak-form hypothesis. It states that
current asset prices fully reflect all available public information. Public information includes
not only information about an asset’s past price, but includes all information related to the
company's performance, future prospects of the company, expectations regarding driving
forces of the economy, and many other factors like the policies of the central banks,
government policies and the economic trends.
The strong-form EMH implies that the market is efficient: it reflects all information,
both public and private, incorporating the weak-form EMH and the semi-strong form EMH.
Therefore, in addition to relevant past information and public information, the strong form of
the EMH requires that asset prices fully incorporate more than past and public information. In
particular, the strong form of the EMH declares that stock prices reflect all information
(public as well as private) no investor would be able to profit above the average investor,
even if he was given new information.
There are wider implications of EMH. From an investor’s perspective, whatever level
of information, participants in the stock market may possess, they should not be able to
generate an abnormal profit. As mentioned before, in the world of a perfect capital market,
investors cannot consistently beat the market. This is consistent with the financial idea that
the maximum price that investors are willing to pay is the current value of future cash flows.
The current value of a future cash flow is usually evaluated by a discount rate, which
represents the degree of uncertainty associated with the investment, considering all relevant
available information.
From an economic standpoint, an efficient stock market will assist with the efficient
allocation of available economic resources. For instance, if the shares of a financially poor
company are not priced correctly, i.e., the shares are overvalued or undervalued, new savings
will not be invested within that industry. In the world of the EMH, the level of asset price
31
fluctuations, or volatility, fairly reflects underlying economic fundamentals. Along these
lines, Levich (2001) argues that policymakers interventions may disrupt the market, and
cause it to be inefficient. In the literature, the three forms of the EMH are usually used as
guidelines rather than strict facts (Fama, 1991). Also, most empirical studies have examined
the EMH in its weak or semi-strong forms, partly because the strong form is difficult to
measure, and there is a high cost associated with acquiring private information (Timmermann
and Granger, 2004).
3.2.2. Arbitrage Price Theory (APT)
The theory of asset pricing is a pricing model that seeks to calculate the appropriate
price of an asset while taking into account systemic risks common across a class of assets.
The Arbitrage Price Theory (APT) suggested by Ross (1976) has been an influential form of
asset price theory. APT is often viewed as an alternative of Sharpe’s (1964) capital asset
pricing model (CAPM)2 since the APT has the potential to overcome CAPM weaknesses: it
requires less and more realistic assumptions to be generated by a simple arbitrage argument
and its explanatory power is potentially better since it is a multifactor model. Whereas the
CAPM formula requires the market's expected return, APT uses the risky asset's expected
return and the risk premium of a number of macro-economic factors. Mathematically APT
can be expressed as:
𝑹𝒊𝒕 = 𝒓𝒊𝒇+ 𝜷𝒊𝑿𝒕 + 𝜺𝒕 (3.2)
Where 𝑅𝑖𝑡 is the return of the stock 𝑖 at time 𝑡, 𝑟𝑖𝑓 is the risk free interest rate or the
expected return at time 𝑡. 𝑋𝑡 is a vector of the predetermined economic factors or the
systematic risks while 𝛽𝑖 measures the sensitivity of the stock to each economic factor
included in 𝑋𝑡. 휀𝑡 , the error term, represents unsystematic risk3 or the premium for risk
associated with assets that cannot be diversified where (휀𝑡|𝑋𝑡)=0, 𝐸(𝑋𝑡) =0, and
𝐸(휀𝑡휀𝑡′|𝑋𝑡)=𝛴.
Ross (1976) shows that there is an approximate relationship between the expected
returns and the estimated 𝛽 𝑖𝑘 in the first step provided that the no arbitrage condition is
2We restrict our analysis to the APT theory since empirical studies on the CAPM fail to support the assumptions theory
(Semmler, 2006). However, CAPM is a single linear equation that links the expected return of an asset or a portfolio to its
expected risk. Thus, the CAPM is a single factor model: expected return is determined by a single factor systematic risk or
beta; whereas the APT is a muli-factor model: expected return is determined by more than one single factor (Lumby, 1980). 3 Unsystematic risk, also known as “nonsystematic risk,” "specific risk," "diversifiable risk" or "residual risk," can be
reduced through diversification. It is the type of uncertainty that comes with the company or industry you invest in. For
example, news that is specific to a small number of stocks, such as a sudden strike by the employees of a company you have
shares in, is considered to be unsystematic risk.
32
satisfied, i.e., the expected return E(Ri) increases as investors accept more risk, assuming all
assets in the market are priced competitively. This relationship can be represented as a cross-
sectional equation where the estimated 𝛽 𝑖𝑘 are used as explanatory variables:
𝐸(��𝑖) = 𝜆0 + 𝜆1��𝑖 + 𝜆2��2𝑖 + ⋯+ 𝜆𝑛��𝑛𝑘 + 𝜇𝑖 (3.3)
Where ��𝑖 is the mean excess return for asset 𝑖 and the 𝛽′𝑠 represent the sensitivity of a
security’s return 𝑛 to the risk factor 𝑘. The 𝜆𝑛’s represents the reward for bearing the risk
associated with the economic factor fluctuations. Equation (3.3) simply says that the expected
return of an asset is a function of many factors and the sensitivity of the stock to these factors.
Interestingly, APT failed to specify the type or the number of macroeconomic factors
for researchers to include in their study. For example, although Ross, et al. (1986) examined
the effect of four factors, including inflation, gross national product (GNP), investor
confidence, and the shifts in the yield curve, they suggested that the APT should not be
limited to these factors. Therefore, there is a large body of empirical studies that have
included a large number of different macroeconomic factors, depending on the stock market
they studied. Even though analysts can predetermine some economic factors, their selection
must be based upon reasonable theory (Chen et al., 1986). Before going for the empirical
relationships, it is mandatory to explore the theoretical underpinnings part of the relationship
of different macroeconomic variables with the stock market.
3.3. Stock Market Development Indicators
It is a well-known fact that, well-functioning stock markets can play an important role
in economic development processes and a sound economy can boost up the performance of
the stock market. A stock market can mobilize capital, enhance liquidity, diversify risk and
can effect saving decisions, on the other hand the economic prosperity of the country leads to
boost up investor confidence and encourage them for further investments. It is difficult,
however, to construct accurate measures of these functions. Therefore, the study has used
indicators that suit the purpose of the concept of stock market development, by including
proxies for stock market development that are most commonly used by academics and
practitioners (Pagano, 1993; Demirguc-Kunt and Levine, 1996a; Levine and Zervos, 1998a,
b; and Beck et al., 1999a). These indicators are associated with the size, liquidity and
volatility of the stock market. Brief and schematic descriptions of such indicators are as
follows:
33
3.3.1. Stock Market Size
Market Capitalization Ratio (MCR) is an indicator to measure the stock market size.
The market capitalization ratio equals the value of listed shares divided by GDP and the ratio
has frequently been used as a measure of stock market size by the analysts. The assumption
behind this measure is that overall market size is positively correlated with the ability to
mobilize capital and diversify risk on an economy-wide basis. The market capitalization
refers to the total value of listed shares on the stock exchange. Capitalization of a company is
calculated by multiplying the number of shares outstanding of that company by its share
price. The size of the stock market is a measure of the availability of finance (Rajan and
Zingales, 1996; Demirguc-Kunt and Maksimovic, 1998; and Subrahamanyam and Titman,
1999) and the ability to mobilize capital, diversify the risk and resources allocation processes.
Bekaert and Harvey (1995b, 1997) also argue that the ratio of equity capitalization to GDP is
a useful tool in characterizing the time-series of market integration. A large market size
(market capitalization as a percentage of GDP) suggests that the country is more likely to be
integrated into world capital markets. Furthermore, in an important empirical study,
Demirguc-Kunt and Levine (1996a) find that large stock markets measured by equity
capitalization to GDP are more liquid, less volatile, more internationally integrated, stronger
with regard to information disclosure laws and international accounting standards, and have
unrestricted capital flows than smaller markets.
3.3.2. Stock Market Liquidity
Liquidity is the term used to describe how easy it is to convert assets to cash. Market
liquidity is a market's ability to facilitate the purchase or sale of an asset without causing
much change in the asset's price. The stock market is said to be liquid if the shares can be
rapidly sold and the act of selling has little impact on the stock's price. Liquidity is an
important indicator of stock market development because theoretically more liquid stock
markets improve the allocation of capital to their optimal use, influence long term investment
decisions and facilitate technological innovation, thereby enhancing long term growth.
Greater liquidity also has a direct impact on the stock market performance. First, with the
increase in market activity, the information content for the share prices also increases and
more investors show their attention towards the stock. Second, to control the corporate
activities, the effective use of stock market requires that the market should be liquid. The
must condition for takeovers is that it requires a liquid capital market where bidders access a
huge amount of capital at short notice. Thus, it can be said that the stock market liquidity also
34
works as a function of corporate control. Therefore, stock market liquidity may be a good
proxy for information acquisition as well as the control function of capital markets. It is
believed that liquidity positively impacts the stock market. When stock prices rise, it is said
to be due to a confluence of extraordinarily high levels of liquidity on household and business
balance sheets, combined with a simultaneous normalization of liquidity preferences. On the
margin, this drives a demand for equity investments (Kostohryz, J., 2013)). Increased stock
market liquidity can also reduce the cost of equity capital through a reduction in the expected
return that investors require when investing in equity to compensate them for the risks, i.e.,
risk premium (Ahimud and Mendelson, 1986; Ahimud et al., 1997; Henry, 2000a, b). The
measure of liquidity would quantify all the costs associated with trading, including the time-
costs and uncertainty of finding a counterpart and settling the trade. There are two methods to
measure the stock market liquidity as described below:
Measure of Liquidity
3.3.2.1.Total value of shares traded ratio
The total value of the shares traded ratio equals total value of shares traded on the stock
market exchange divided by GDP. This ratio measures the trading of domestic equities on
domestic exchanges relative to the size of the market. It is the organized trading of firm
equity as a share of national output and therefore should positively reflect liquidity on an
economy-wide basis. A higher value traded ratio reflects greater liquidity in the market and
greater attractiveness for investors. If trading in the market represents the buying and selling
actions of investors in order to attain their desired position, then the speed at which new
information is incorporated into prices is measured by the trading activity. This ratio also
complements the market capitalization ratio; although market capitalization may be large,
but, there may be little trading.
3.3.2.2.Turnover ratio
The second measure of market liquidity is the turnover ratio. This ratio is equal to the
value traded divided by market capitalization. It measures the size of equity transaction
relative to the size of the stock market. The high turnover ratio is often used as an indicator of
low transaction costs. A higher turnover ratio may represent greater liquidity and market
efficiency. Brennan and Subrahmanyam (1996) find that the number of analysts following a
stock is strongly positively related to the liquidity of the stocks and that low turnover stocks
are followed by fewer analysts and thus are slower to react to information than high turnover
stocks. However, an excessively high turnover ratio may represent inefficiency or excessive
35
speculative trading. Bencivenga et al., (1996) gave a model in which excessive liquidity and
turnover lower the economic growth rates. Further, turnover also complements the total value
traded/GDP. While the total value traded /GDP captures trading compared with the size of
the economy, turnover measures trading relative to the size of the stock market.
3.3.3. Volatility
Volatility refers to the amount of uncertainty or risk about the size of changes in a
security's value. A higher volatility means that a security's value can potentially be spread out
over a larger range of values. This means that the price of the security can change
dramatically over a short time period in either direction. A lower volatility means that a
security's value does not fluctuate dramatically, but changes in value at a steady pace over a
period of time.
3.3.4. Stock market index
A stock index or stock market index is a measurement of the value of a section of the
stock market. It is computed from the prices of selected stocks (typically a weighted average).
It is a tool used by investors and financial managers to describe the market, and to compare
the return on specific investments. Or one can define it as an aggregate value produced by
combining several stocks or other investment vehicles together and expressing their total
values against a base value from a specific date. Market indexes are intended to represent an
entire stock market and thus track the changes in the market over time.
3.4. Macroeconomic Variables
3.4.1. Money Supply
The amount of money in an economy is referred to as the money supply or it is the total
amount of monetary assets available in an economy at a specific time. There are several ways
to define "money," but standard measures usually include currency in circulation and demand
deposits. Money supply is one of the components of monetary policy that any central bank
uses to cause a desired level of change in real activities. These frequent changes in the
monetary policy component are believed to have a significant effect on the stock market.
Therefore, it is important to analyze the relationship between money supply and an important
determinant of the economy, the stock market. The price of a stock is determined by the
present value of the future cash flows. The present value of the future cash flows is calculated
by discounting the future cash flows at a discount rate. The money supply has a significant
relationship with the discount rate and, hence, with the present value of cash flows. There are
36
many competing theories on how money supply affects stock market prices and has been
widely discussed in the economic literature. Although a strong relationship between the
money supply and the stock market prices has been found, but the relationship is still the
issue of debate, that the changes in money supply can be either predicted or unexpected by
the people. In the early 1970’s a number of papers were published that propounded that past
money supply data could be used to predict future returns (Sprinkel (1964), Homa and Jaffee
(1971), Hamburger and Kochin (1972), Hashemzadeh and Taylor (1988)). But the studies are
contradictory to the theory of efficient markets developed by Fama (1970), which states that
all available information should be reflected in current stock prices.
Friedman and Schwartz (1963) introduced the modern quantity theory that suggests that
an exogenous shock that increases the money supply changes the equilibrium position of
money with respect to other assets included in the portfolio. As a result, asset holders adjust
the proportion of their portfolios taking the form of money balances. This adjustment alters
the demand for other assets that compete with money balances such as equity shares. An
increase in the money supply is expected to generate an excess supply of money balances
which leads to an excess demand for shares. In this case, share prices are expected to rise.
Sprinkel (1964) pioneered the exclusive study on the relationship between money supply and
the stock market, using the quantity theory of money and concluded that there is a strong
relationship between the stock market and money supply in the United States.
Homa and Jaffe (1971) estimated the relationship between the money supply and stock
price index, in search of a forecasting tool in the implementation of investment strategies.
Their findings indicated that the price of any common stock is determined by three variables:
the level and growth rate of dividends, the risk-free rate of interest, and the risk premium.
The risk-free rate of interest being a function of money supply, they concluded that the
average level of stock prices is positively related to the money supply. Hamburger and
Kochin (1972) started with the standard valuation model and added current price level and
the corporate bond rate to capture the direct and indirect impacts of money supply on the
stock market. They concluded that changes in monetary growth could have a number of
different effects on the stock market. Pesando (1974) found empirical and theoretical
problems in the models used by Hamburger-Kochin and Homa-Jaffe. He concluded that the
inability of these models to generate accurate forecasts of stock prices was evidence against a
structural and stable relationship between money supply and common stock prices.
Rozeff (1974) examined stock market efficiency with respect to money supply data by
testing, regression models of stock returns on monetary variables and trading rules based on
37
money supply data. The evidence indicates no meaningful lag in the effect of monetary
policy on the stock market and that no profitable security trading rules using past values of
the money supply exist. Cooper (1974) tried to provide a plausible framework for estimating
the relationship between the money supply and the stock market returns, and, therefore to
offer another test of the efficient markets hypothesis. Cooper stated that although the quantity
theory of money and the theory of efficient markets hypothesis appear to be contradictory,
but, the major finding of the study is that the two theories are in fact complementary. The
results of the study offer additional support for the concept of market efficiency, since stock
returns lead rather that lag money changers.
Rogalski and Vinso (1977) further improved Rozeff’s (1974) analysis by synchronizing
the data so that the money supply data were generated at intervals that were same as those for
the stock return data and also by taking proper account of the autocorrelation in the time
series. The Rozeff’s results proved to be robust to these technical improvements. The study
concluded that “causality does not appear to go from money supply to stock prices, but rather
from stock prices to the money supply. Gautam Kaul (1987) hypothesized that the relation
between stock returns and inflation is caused by the equilibrium process in the monetary
sector. More importantly, these relations vary over time in a systematic manner depending on
the influence of money demand and supply factors. Post-war evidence from the United
States, Canada, the United Kingdom and Germany indicates that the negative stock return-
inflation relations are caused by money demand and counter-cyclical money supply effects.
On the other hand, pro-cyclical movements in money, inflation, and stock prices during the
1930’s lead to relations which are either positive or insignificant.
Chan, Foresi and Lang (1996) developed and tested a Money based Capital Asset
Pricing Model (M-CAPM). Inside money was used as a proxy for consumption. This was
justified on the grounds that in money (as opposed to outside money) can be viewed as
endogenous and varies with the transaction requirements of the economy. The use of money
as a proxy for consumption results in a stochastic discount factor which captures the risk
associated with monetary factors in addition to those associated with real factors. The use of a
broad monetary aggregate instead of consumption data should result in a more volatile
discount factor and a lower estimated coefficient of relative risk aversion compared to
consumption based models. Thorbecke (1997) used ten sizes-ranked portfolio in addition of
using a stock market index. He concluded that monetary tightening has the strongest negative
effect on the equity prices of small firms. This evidence is consistent with the hypothesis that
38
an important channel of monetary policy is that it affects small firms’ ability to borrow
(Gertler and Gilchrist, 1993).
3.4.2. Economic Growth
The growing importance of stock markets in developing economies around the world
over the last few decades has shifted the focus of many researchers, academicians, policy
makers and economists, to explore the relationship between the stock market and economic
growth. The idea that financial markets may be related to economic activities is not new, but
the interpretation of this relationship has changed over time, with changing international and
domestic economic environment and growing econometric techniques. Explaining such a
relationship involves assessing the direction of causality and the type of influence (positive
and negative). Does the stock market affect GDP, or is the causality in the opposite direction,
such that GDP triggers fluctuations in the stock market? Academic literature on the
relationship between financial development and economic growth dates back to as early as
the early twentieth century (Schumpeter, 1911). He argued that technological innovation is
the force underlying long-run economic growth, and that the cause of innovation is the
financial sector’s ability to extend credit to the “entrepreneur” and focused on the services
provided by financial intermediaries and argues that these are essential for technological
innovation and economic development. A well-developed financial system promotes
investment by identifying and investing in profitable business opportunities, mobilizing
savings, allocating resources, diversifying risks and facilitating trade activities. Joan
Robinson (1952), on the other hand, maintained that economic growth creates a demand for
particular types of financial arrangements, and the financial system responds automatically to
these demands, so that “where enterprise leads finance follows”. He concluded that banks
respond passively to economic growth. Until 1970’s, due to scarce literature, there was lack
of evidences on the relationship between financial markets and real output. Further, studies
by Goldsmith (1969), Shaw (1973) and McKinnon (1973) found that the development of
financial markets was significantly correlated with the level of per capita income.
Gurley and Shaw (1955) were the first to study the relationship between financial
markets and real activity. They argued that one of the differences between developed and a
developing country is that the financial system is more developed in the developing countries.
The argument was that financial markets could extend a borrower’s financial capacity which
improves trade efficiency. With well-developed financial market investors can be provided
with the necessary funds for their projects. They concluded that financial markets contribute
39
to economic development through enhancing the physical capital accumulation. A distinctive
feature of the theory Gurley and Shaw offered was an emphasis on financial intermediation,
and particularly the role of intermediaries in the credit supply process as opposed to the
money supply process. Goldsmith (1969) established a link between financial structure and
economic development by using data of data showing a well-defined upward secular drift in
the ratio of financial institutions' assets to gross national product for both developed and less
developed countries for the year 1860 to 1963. The author stated that the financial
superstructure of an economy "accelerates economic growth and improves economic
performance to the extent that it facilitates the migration of funds to the best user, i.e., to the
place in the economic system where the funds will yield the highest social return". As he
notes, though, it is difficult to establish "with confidence the direction of the causal
mechanism, i.e., of deciding whether financial factors were responsible for the acceleration of
economic development or whether financial development reflected economic growth whose
mainsprings must be sought elsewhere". Tobin (1969) focused on the impact that share prices
have on the cost of capital, and is captured by a coefficient known as Tobin’s Q, which is the
ratio of the market value of the current capital to the cost of replacement capital. When share
prices are high, the value of the firm relative to the replacement cost of its stock of capital
(Tobin’s Q) is also high. Consequently, this leads to increased investment expenditure and
thus to higher aggregate economic output as firms find it easier to finance investment
expenditures. This occurs because the investment would be easier as it would require a lower
share offering in a situation of a high share price.
Greenwald and Stiglitz (1990) proposed a theoretical model to examine the impact of
financial market imperfections on the long-term productivity growth of firms. Their model
focused on the failures of firms in selling equity securities, which help firms by diversifying
the risk of real investment. They argued that failures in stock markets limit the abilities of
firms to diversify the risks of their operations and hence lead to a reduction in the level of
such operations as an alternative means of risk management. They show that since the
restriction of firms' operations will limit the extent of "on-the-job training" and other learning
effects, as well as direct investment in productivity improvements, the stock market
imperfection will adversely affect the rate of productivity growth. Greenwood and Jovanovic
(1990) developed a model with two assets: safe, low-yield technology, and a risky high-yield
one, where the return on the latter is affected by an aggregate and a project specific shock. In
their model they emphasized both the informational and risk sharing roles of financial
markets in improving capital mobilization to the optimal use and hence in increasing growth.
40
Financial markets are able to offer agents a higher return than they invested individually
because they collect information that enables them to decipher the aggregate productivity
shock and they can better diversify project-specific risk due to the large portfolios they hold.
Therefore, financial markets allocate capital more efficiently and the resulting higher
productivity of capital increases growth. It is worth noting that in this model higher growth
stimulates increased participation in financial markets, which leads to the expansion of
financial institutions. Thus, a two-way causality between financial development and growth
emerges in their model.
Levine (1991) constructed an endogenous growth model in which the stock market
emerges to allocate risk, and explores how the markets alter investment incentives in ways
that change steady-state growth rates. He demonstrated that stock markets accelerate growth
by facilitating the ability to trade ownership of firms without disrupting the production
process occurring within firms and by allowing agents to diversify portfolios. He further
explained the effect of tax policies on growth both directly by altering investment incentives
and indirectly by changing the incentives underlying financial contracts. Levine's model used
the Diamond and Dybvig (1983) structure of preference to create liquidity risk and also to
include productivity shocks that create production risk. Liquidity risk and the productivity
risk create incentives for the formation of stock markets. Productivity risk lowers welfare and
discourages agents from investing in firms. The stock market allows investors to invest in a
large number of firms and to diversify away from idiosyncratic productivity shocks. This
raises welfare, the fraction of resources invested in firms, and the economy's steady-state
growth rate. In Levine's model, the stock market raises the growth rate by increasing the
productivity of firms or by improving the allocation of resources. Thus, the emergence of
stock markets to manage productivity and liquidity risk, accelerates growth by attracting
resources to socially productive firms.
King and Levine (1994) proposed a model in which innovation activities serve as an
engine of growth. A higher rate of successful innovation results in a high growth rate of
productivity. Financial markets appear in two different forms in the model. The first is where
the intermediaries’ acts like venture capital firms. They evaluate, finance and monitor the
risky and costly innovations. The second form is like the stock market. The present value of
the innovation is revealed in the stock market and selling the equity shares on the market can
diversify the risk associated with innovation. Therefore, according to King and Levine, the
better development of the financial market can improve the possibility of successful
innovations. They point out that financial institutions play an active role in evaluating,
41
managing, and funding the entrepreneurial activity that leads to productivity growth. Singh
(1997) concentrates on the role of stock markets in the liberalization process in the
developing countries in the 1980's and 1990's. He argues that stock market development is
unlikely to help in achieving quicker industrialization and faster long-term economic growth
in most developing countries. He had cited three reasons for the same. First, the inherent
volatility and the arbitrariness of the stock market pricing process under developing country
conditions make a poor guide to efficient investment allocation. Second, the interactions
between the stock and currency markets in the wake of unfavorable economic shocks may
exacerbate macroeconomic instability and reduce long-term growth. Third, stock market
development is likely to undermine the existing group-banking systems in developing
countries, which, despite their many difficulties, have not been without merit in several
countries, not least in the highly successful East Asian economies.
The most commonly used proxies for economic growth is per capita GDP. GDP
represents economic growth and economic growth is the increase in the inflation-adjusted
market value of the goods and services produced by an economy over time. It is
conventionally measured as the percent rate of increase in real gross domestic product, or real
GDP. Gross domestic product (GDP) is regarded as one of the important determinants of
stock market performance and has often been used to measure the growth of real economic
activity. Growth is usually calculated in real terms, i.e., inflation-adjusted terms to eliminate
the distorting effect of inflation on the price of goods produced. Real per capita GDP is often
used as a way of communicating average income, though it can also be used as a measure of
the wealth of the population of a nation, particularly in comparison to other nations. Per
capita income is often used to measure a country's standard of living. It is usually expressed
in terms of a commonly used international currency such as the Euro or US dollar, and is
useful because it is widely known, easily calculated from readily-available GDP and
population estimates, and produces a useful statistic for comparison of wealth between
countries. This helps the country to know their development status.
3.4.3. Trade Openness
The trade-to-GDP-ratio is often called the 'trade openness ratio'. The trade-to-GDP-
ratio is the sum of exports and imports divided by GDP. This indicator measures a country’s
'openness' or 'integration' in the world economy. It represents the combined weight of total
trade in its economy, a measure of the degree of dependence of domestic producers on
foreign markets and their trade orientation (for exports) and the degree of reliance of
42
domestic demand on foreign supply of goods and services (for imports). The indicator
reflects the liberalization policies of the economy and provides an insight for the investment
opportunities in a particular economy. A low ratio for a country does not necessarily imply
high (tariff or non-tariff) obstacles to foreign trade, but may be due to the factors like size and
geographic remoteness from potential trading partners. For example, it is generally the case
that exports and imports play a smaller role in larger economies than they do in small
economies. Trade openness promotes the efficient allocation of resources through
specialization and comparative advantage; it stimulates competition in domestic and
international markets, and allows for easier transmission of knowledge and technology across
countries. Opening up of an economy for the cross border flows of goods and services creates
a high competitive environment, which will drive down the revenue of existing firms and
diminish their profits, requiring them to search for external sources of finance (Quy-Toan and
Levchenko, 2004). These sources of finance will be available to the present firms and their
rich, elite owners only if they support the compulsory institutional reforms to make the
financial system efficient and well-functioning and by solving the problem of asymmetric
information. These reforms will extend the size of the financial system and works as an
important ingredient that stimulates financial sector development. Trade openness promotes
financial development, not just because it expands opportunities, but because it increases
competition (Rajan and Zingales, 2003).
It is not possible that one country is specialized in the production of all types of goods
and can deliver every service because all the countries differ in the resource endowments.
Each country is specialized in the production of a particular type of commodities. Thus, to
procure needed goods and services a country has to opt for the policy of trade openness.
Hence, the concept of absolute and comparative cost advantage theories emphasized that like
domestic and inter-regional trade, the international trade is also beneficial for the trading
countries. Therefore, when a country enters into trade with another country, it can export
those commodities in which its production cost is less, and can import those commodities in
which its production cost is high. This results in greater output and consumer welfare in both
the trading countries, which in turn, will lead to higher employment and hence economic
growth. Flexible trade openness policies attract foreign investors to invest in the stock market
of the economy, which gives a boost to the stock market development. Thus, the classical
economists were in favor of free trade policy, as they assumed that free trade among different
nations maximizes the output and employment of all the participating countries (Salvatore,
2010). Edwards (1998) noted that countries that are more open to the rest of the world are
43
better placed in capturing the advanced technologies of leading nations. However, some
economists argued that free trade between developed and developing countries shifts the
gains from developing to developed nation because developing countries are largely
dependent on the production of primary goods, whereas the developed nations,mostly
depends upon manufacturing products (Prebish, 1959; Singer, 1950; Myrdal, 1957).
Newbery and Stiglitz (1984) argued that the trade openness affects financial
development because free trade will result in uncertainty and income inconsistency of agents,
which in turn raises the demand for insurance and other financial services and thereby
increases the size of the financial system. Furthermore, free trade among different nations
generally will increase the demand for external finance, as they produce more financial-
dependent goods. Thus, free trade increases the demand for external finance and thereby size
and quality of the financial system. Quy-Toan and Levchenko (2004) noted that if there is
free trade between rich and poor countries, in rich countries, more trade would be associated
with faster financial development as they are specialized in financial-dependent good.
Whereas more trade lead to deterioration in the size of the financial system in poor countries,
as they import financial-dependent goods rather than produce them domestically. While,
Rajan and Zingales (2003) postulated that trade openness is linked with financial market
development, especially when cross-border capital flows are free, and that changes in
openness are associated with changes in the size of financial markets.
Peter (2003) concluded that free trade helps to develop the domestic financial markets
and then the economic growth. Because free trade expands the size of the market for
domestic goods, which in turn encourages the domestic production and thus production of
more goods and services, more capital is required. Therefore, allowing foreign capital into
domestic financial markets by financial openness increases the availability of funds, which in
turn lowers the cost of borrowing and thereby increases the investment and economic growth.
Thus, trade openness and financial openness are not substitutes rather they are
complementary to each other as their coexistence will result in the domestic financial sector
development and hence higher economic growth. The results are similar to the hypothesis
given by Rajan and Zingales (2003).
The theoretical as well as empirical research has strongly argued the possible links
between financial development and trade openness, particularly in the case of developing
countries. These researches can be characterized in two groups: i) one investigating the role
of financial development on generating gains in terms of trade openness; ii) the other
discussing the possibility that trade openness can influence the development of financial
44
systems. Comparing the links between financial development and trade, and between
financial openness and trade, many recent empirical studies have begun to establish the
possible linkage between financial development, financial openness and trade openness
altogether (e.g. Rajan and Zingales (2003) and Baltagi et al. (2009)). Rajan and Zingales’s
(2003) analysis, suggests that the simultaneous opening of both trade and capital accounts
holds the key to successful financial development. Baltagi’s (2009) finding provides a partial
support to the Rajan and Zingales (2003) hypothesis, and suggests that trade and financial
openness is statistically significant determinants of financial sector development.
3.4.4. International Financial Flows
In recent years the global financial flows have been increasing in volume in both the
developed and the developing economies, creating new opportunities and challenges for
policymakers. It is a general myth that the phenomenon of financial flows is new to the
economy. Though, the global financial system, at that time, was, according to the rules of the
classical gold standard, there was a massive flow of private capital across borders before the
First World War (1914- 1918), in the form of bonds financing railways, roads and other
infrastructure projects. Thus, it can be said that the present era of globalization represents the
recurrence of finance capital on a global scale. Initially the process of economic growth was
initiated by the respective governments by planning, developing and implementing the
agricultural, manufacturing and the infrastructure facilities in the country. Gradually these
facilities became inadequate for the economy due to technological innovations as it didn't
boost the economic growth of the country with much pace, resulting in less saving for further
investment. Since these domestic savings were inadequate, countries had to depend on
external sources of finance like loans from different countries. This capital taken from other
countries helped the economies to grow; this phenomenon took the form of foreign financial
investments which came in the form of overseas loans. It fills the gap between domestic
savings and its required investment for growth. Foreign capital plays a significant role in the
development of any economy. For the developed countries, it is necessary to sustain the
process of development. For the developing countries, it is used to increase the rate of
investments and to boost up the entire development of the nation in productivity of the
labour, machinery etc. which leads to economic growth. For the transition countries, it is
useful to carry out the reforms and become open economies by liberalizing its trade policies
to create conditions for stable and continuous growth, as well as to integrate into the world
economy. International financial flows can be classified into two categories:
45
3.4.4.1.Foreign Direct Investment (FDI)
Foreign Direct Investment is the investment from one country into another (normally
by companies rather than governments) that involves establishing operations or acquiring
tangible assets, including stakes in other businesses (Financial Times). In the era of
globalization, FDI is a major source of capital in most of developing economies where it
bridges the gap of capital, technology, managerial skill, human capital formation and more
competitive business environment. To increase their share of FDI flows, most of the countries
ease restrictions on foreign direct investment, strengthened macro stability, privatization of
state-owned enterprises, domestic financial reforms, capital account liberalization, tax
incentives and subsidies have been instituted (World Bank, 1997a).
The long-term impact of FDI inflows on the development of the domestic capital
market and on the increase of investors’ participation in stock exchange was established
earlier by Errunza (1983), while Yartey (2008) stated that FDI promotes institutional and
regulatory reforms which encourage greater confidence in the domestic capital market, which
further increases the variety of investors and trading volume. Adam and Tweneboah (2008 a,
b) highlight an indirect, but strong relationship between stock markets and FDI inflows. FDI
inflows are a source of technological progress and increasing employment in most developing
countries, which increases the production of goods and services and, ultimately, increases
GDP. Economic growth, then has a positive effect on the development of stock markets and
the rise of share prices. Using the cointegration method, the authors found evidence of a long-
term positive relationship between FDI and stock market development in Ghana. Some
economist found that FDI contributes by fulfilling the gap of technology, capital formation,
human capital, managerial skill and provide a more competitive environment for domestic
producers (Helpman and Kruman (1985); Lucas (1988)). While some other researcher found
that more focus and dependence on FDI may discourage the domestic industry. Entry of the
foreign companies in the market may lead to a reduction in market share of domestic
producers. The possibilities of economies of scale also suffer to the domestic producers,
which affect the productivity negatively (Adams (2009)). Adam and Tweneboah (2009)
observed the triangular relationship between these FDI and stock market development: (1)
FDI stimulates economic growth (2) Economic growth exerts a positive impact on stock
market development and (3) implication is that FDI promotes stock market development.
46
3.4.4.2.Foreign Institutional Investment (FII)
As defined by the European Union Foreign Institutional Investment is an investment
in a foreign stock market by the specialized financial intermediaries managing savings
collectively on behalf of investors, especially small investors, towards specific objectives in
term of risk, return and maturity of claims. And SEBI’s definition of FIIs presently includes
foreign pension funds, mutual funds, charitable/endowment/university funds, asset
management companies and other money managers operating on their behalf in a foreign
stock market. Foreign institutional investment is a liquid nature investment, i.e., short-term
investment, which is motivated by international portfolio diversification benefits for
individuals and institutional investors in industrial country.
Foreign investment refers to the investments made by the residents of a country in the
financial assets and production process of another country. FII is a short term investment by
foreign institutions, in the financial markets of other countries. These institutions are
generally mutual funds, investment companies, pension funds and insurance houses. The
FIIis playing an important role in bringing in funds needed by the equity market.
Additionally, if the funds from multilateral finance institutions and FDI are insufficient, they
contribute to the foreign exchange inflow. According to Lalitha, S. (1992), the main reason
for opening stock market for FIIs was to attract foreign investments and stop countries from
raising more debts.
The waves of liberalization results in the appreciation of the stock price which is
followed by inflows from foreign investors (Bekaert and Harvey, 1998) and (Henry, 1997).
As the economy liberalizes the stock market shows more reaction to foreign investments. A
concern with the entry of FIIs is that they are positive feedback traders—traders who buy
when the market increases and sell when the market falls. This acts as a destabilizing factor,
because the sales by FIIs lead the stock market to fall further and their buys increase the stock
market as concluded by Radelet and Sachs (1998). Not only this, these trades push the stock-
prices away from the fundamentals as revealed by studies on the contemporaneous relation
between FIIs investments and equity returns based on monthly data (Bohn and Tesar, 1996,
Berko and Clark, 1997). The increasing role of institutional investors has brought both
qualitative and quantitative developments in the stock market viz., expansions of security
businesses, increased depth and breadth of the market, and above all, their dominant
investment philosophy of emphasizing the fundamentals has rendered efficient pricing of the
stocks (Khanna, 2002). Rangarajan (2000) suggested that foreign portfolio investments would
47
help the stock markets directly through the widening investor base and indirectly by
compelling local authorities to improve the trading system.
3.4.5. Interest Rate
The interest rate is one of the important ingredients of any economy, which is directly
related to economic growth. Generally, interest rate is considered as the cost of capital, means
the price paid for the use of money for a period of time. According to Kevin (2000), in an
organized financial sector of the economy the interest rates are guided through monetary
policy. However, for the unorganized financial sector, the rates are not controlled and may
fluctuate widely depending upon the demand and supply of funds in the market. An investor
has to evaluate the impact of the level and growth of interest rates, on the performance and
profitability of companies of different sectors of the economy. Further, from the point of view
of a borrower, the interest rate is the cost of borrowing money (borrowing rate). From a
lender’s point of view, the interest rate is the fee charged for lending money (lending rate). If
the rate of interest paid by banks to depositors increases, people will start to deposit their
capital from share market to the banks, resulting in the decreasing demand of shares, which
leads to decrease in share prices and vice versa. On the other way, when rate of interest paid
by banks to depositors increases, the lending interest rate also increases lead to decrease the
investments in the economy. Maysami et al. (2004) explains, when a substantial amount of
stocks is purchased with borrowed money, an increase in interest rate would make a stock
transaction more costly. Investors will expect a higher rate of return before investing, which
results the demand to fall and hence leads to price depreciation. The interest rate varies with
time, default risk, inflation rate, and productivity of capital, among others (Chandra 2004).
Aydemir and Demirhan (2009) stated that the relationship between stock market
capitalization rate and interest rate have preoccupied the minds of economists since they both
play important roles in influencing a country’s economic development. Theoretically, interest
rates have a negative impact on stock market performance. The logic behind the negative
relationship between interest rates and stock prices suggest that an upward trend in interest
rate enhances the opportunity cost of holding money and thus substitution between stocks and
interest bearing securities resulting declining stock prices. Thus, a change in nominal interest
rates should move asset prices in the opposite direction. According to French et al.(1987) an
increase in interest rates would avoid investors making high risk stock market investments
compared to low risk interest bearing security investments such as fixed deposits, savings
certificates, treasury bills etc. On the other hand, The Central Banks of the country use
48
interest rate is as a tool to control inflation. The change in interest rates by the Central Bank
would indirectly affect the stock market performance, and will lead to have a spillover effect
on overall economic development of the country. Thus, determination of ideal interest rate is
a very important policy decision that a country has to consider regularly (Pallegedara, 2012).
The role of interest rates in stock pricing models was not well established in the asset
pricing literature until Stone (1974) suggested a two-variable model formalizing a relation
between stocks, the market portfolio and yields in the debt market. Following Sharpe (1964),
Lintner (1965) and Mossin (1966), Stone showed that there were limitations in models such
as the CAPM due to the non-inclusion of interest rates as a unique factor. Stone verified that
interest rate changes are negatively related to stock returns and suggested that the interest rate
risk varied in a cross-section of stocks in homogenous groupings. This is confirmed by
Martin and Keown (1977) who verified this cross sectional variation in interest rate
sensitivity amongst certain classes of stocks. Lynge and Zumwalt (1980) enhanced Stone's
model with additional variables for short and long term interest rate impacts and
demonstrated significant differences in sensitivities in stock returns when the term structure
was accounted for. Following Stone’s (1974) work, Chance and Lane (1980) demonstrated
the higher sensitivities of bank and utility stocks to interest rate changes. Jensen et al. (1997)
found significant differences in interest rate sensitivity across industries to interest rate
changes. Utility firms exhibited very strong reactions to discount rate changes, as did
financial firms. They also found evidence of substantial positive effects following interest
rate decreases. Smirlock and Yawitz (1985) found that interest rate changes produce a very
rapid response in stock prices, but vary in their effects, depending on whether or not the
changes constitute new information.
According to Fama (1981) the expected inflation is often proxied by the short-term
interest rate and is negatively correlated with anticipated real activity, which in turn is
positively related to returns on the stock market. Therefore, stock market returns should be
negatively correlated with expected inflation. On the other hand, the influence of the long-
term interest rate on stock prices stems directly from the present value model through the
influence of the long-term interest rate on the discount rate. Rather than using either short-
term or long-term interest rates, Campbell (1987) analyzed the relationship between the yield
spread and the stock market returns. He argued that the same variables that have been used to
predict excess returns in the term structure also predict excess stock returns, deducing that a
simultaneous analysis of the returns on bills, bonds and stock should be beneficial. His results
support the effectiveness of the term structure of interest rates in predicting excess returns on
49
the US stock market. Kaul (1990) studied the relationship between expected inflation and the
stock market, which, according to the proxy hypothesis of Fama (1981) should be negatively
related since expected inflation is negatively correlated with anticipated real activity, which
in turn is positively related to returns on the stock market. Instead of using the short-term
interest rate as a proxy for expected inflation, Kaul (1990) explicitly models the relationship
between expected inflation and stock market returns. Smith (1990) found that stock prices
jump immediately after (and sometimes before) the Federal Reserve announces a cut in the
interest rate or discount rate, or Chase Manhattan announces a drop in its prime loan rate.
Zhou (1996) also studied the relationship between interest rates and stock prices using
regression analysis. He found that interest rates have an important impact on stock returns,
especially in long-term investment horizons, but the hypothesis that expected stock returns
move one-for-one with ex ante interest rates is rejected. In addition, his results showed that
long-term interest rate explains a major part of the variation in price dividend ratios. Besides,
he suggests that the high volatility of the stock market relates to the high volatility of long-
term bond yields and may be accounted for by changing forecasts of discount rates. Kunt
(1996) found that countries with lesser interest rates have a strong stock market as compared
to countries which have higher interest rate. The author also mentioned that developed
countries are usually having low interest rates due which their stock market’s performance is
extra-ordinary. Lee (1997) used a three-year rolling regressions to analyze the relationship
between the stock market and the short-term interest rate. He tried to forecast excess returns
(i.e. The differential between stock market returns and the risk-free short-run interest rate) on
the Standard and Poor 500 index with the short-term interest rate, but found that the
relationship is not stable over time. It gradually changes from a significantly negative to no
relationship, or even a positive although insignificant relationship. Jefferis and Okeahalam
(2000) worked on South Africa, Botswana and Zimbabwe stock market, where higher interest
rates are hypothesized to depress stock prices through the substitution effect (interest-bearing
assets become more attractive relative to shares), an increase in the discount rate (and hence a
reduced present value of future expected returns), or a depressing effect on investment and
hence on expected future profits.
3.4.6. Inflation
The effects of inflation on an economy are varied and can have either positive or
negative effects and is a subject of intense debate due to reported inconsistencies in the
effects and the complexity of the mechanisms. This debate is motivated partially by the
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theory that the stock market provides an effective hedge against inflation, (Bodie, 1976).
Theoretically, stock prices or returns should have a positive relation to inflation; this may be
due to the reasons that as firms are able to pass the extra costs to customers in the long-run
even if there are varying degrees pertaining to demand effects. However, in a high inflation
environment this pass-through policy may not be achieved effectively in the short run as it
also depends on the level of competition or market regulation that limits the individual firms
to raise prices to maintain or to increase their profitability. This shows that if inflation rises,
negative income effects, due to higher input costs, imply that stocks may decline in value in
the short-run. In the long-run, assuming firms are able to pass on rising costs and achieve the
desired profitability; stocks prove to be a good hedge against inflation. The argument that the
stock market serves as a hedge against inflation is based on the fundamental idea of Irving
Fisher (1930), and is known as the Fisher Effect. Fisher (1930) hypothesized that stock
market returns are independent of inflation expectations, but the two variables, namely
inflation and stock market returns are positively related. Fisher's conclusions and hypothesis
gave credence to the assertion that if inflation and stock market returns are positively related,
then, equities serve as a hedge against inflation. The Fischer hypothesis is of prime
significance in the field of global finance, because it sheds light on the expected nominal
stock market returns, which equates the sum of expected inflation and real rate of return. The
“Fisher Effect” postulates that expected nominal asset returns have a unitary effect on
expected inflation. Thus, the hypothesis predicts a direct positive relationship between
inflation and stock market return. Bodie (1976), Jaffe and Mandelker (1976), Nelson (1976),
Fama and Schwert (1977), Firth (1979) and Boudhouch and Richardson (1993) extended the
original concept of a Fisher Effect to examine the specific interrelationships between rates of
return on common stocks and the expected and unexpected rate of inflation. Bodie (1976)
also concluded that equities are a hedge against inflation as shares are a claim on real
underlying assets. If the underlying assets rise in value due to inflation, so should the price of
the share by a similar amount and therefore the real change should be unaffected.
However, Fama (1981), Gultekin (1983) and Kaul (1987) found that the Fisher
hypothesis do not hold even when income growth is controlled for (Balduzzi, 1995; Cochran
and Defina, 1993; Caporale and Jung, 1997). Further, Fama and Schwert (1977) reported an
“anomalous result” in that they found a negative relation between stock returns and inflation.
According to Fama (1981), the real activity is positively associated with the stock return, but
negatively associated with inflation through the money demand theory; therefore, stock return
will negatively influence by inflation. The negative relationship between inflation and stock
51
return can also be explained through the dividend discount model. Since, stock price can be
viewed as the discounted value of expected dividend, an increase in inflation may enhance
the nominal risk free rate and thus the discount rate leading to declining stock price. The
negative relationship between stock price and inflation support the proxy effect of Fama
(1981) which explains that higher inflation raise the production cost which adversely affects
the profitability and the level of real economic activity; since the real activity is positively
associated with stock return, an increase in inflation reduces the stock price. The study was
further done by Mandelker and Tandon (1985), and they found similar results. Chen et al.
(1986) explored a set of macroeconomic variables as a systematic influence on stock market
returns by modeling equity return as a function of macro variables and non-equity assets
returns for US. They empirically found that the macroeconomic variables such as industrial
production anticipated and unanticipated inflation, yield spread between the long and short
term government bond were significantly explained the stock returns. The authors showed
that the economic state variables systematically affect the stock return via their effect on
future dividends and discount rates.
Ram and Spencer (1983) found consistent evidence for a positive relation between real
activity and inflation and a negative relation between real activity and real stock returns.
Kaul's (1987) main hypothesis is that the equilibrium process in the monetary sector causes
the observed stock returns-inflation relation. Basically, money demand and counter-cyclical
money supply leads to negative relations between stock returns and expected-changes in
inflation, unexpected-changes in inflation, and changes in expected inflation since positive
shocks to output precipitate monetary tightening.
3.4.7. Crude Oil Prices
The relationship between oil prices and economic growth is believed to be an existing
fact. Chen et al., (1985) and Chen and Jordan, (1993) provide evidence that oil prices affect
stock returns. However, this relationship varies from country to country, according to the
dependency and consumption of each country on oil, and whether the country is an oil
importer or exporter. Increases in oil prices will be beneficial for those countries whose
export products are derived from crude oil or refined oil products. Thus, in theory as well as
empirical evidence suggests that, there should be a positive relationship between the oil price
and stock prices in those oil-exporting countries. But there should be a negative relationship
for oil importer countries the reason behind this is that the increases in oil price would lead to
increase the cost of production and, consequently, the expected cash flow would decrease or
52
one can say that with the exception of oil producing firms the relationship between changes
in oil prices and share returns is negative because an increase in oil prices should result in
higher costs and, hence, lower equity values. Several studies have explored the oil price-
macroeconomics causal relationship and among them are Hamilton (1983), Burbridge and
Harrison (1984), Gisser and Goodwin (1986), Mork (1989), Loungani (1986), Hooker (1996),
and Hamilton (2000). Since oil is an essential input cost for final products in today’s
economy, it is reasonable to anticipate that oil prices can affect stock prices both directly and
directly. Oil prices can directly affect stock prices through its effects on the expected cash
flows and indirectly through its effects on discount rates. That is, changes in the price of oil
may directly affect future cash flows via its effects on the cost of final products in the
economy, which would cause opposite changes in stock prices. Whereas, regarding the
discount rate, changes in the price of oil may affect stock prices via its effects on the expected
inflation rate and the expected real interest rate. For instance, a higher price of oil places
upward pressure on expected domestic inflation. In this case, a higher expected inflation rate
is positively related to the discount rate and is negatively related to stock prices. Also, a
higher price of oil could cause the real interest rate to rise. As a result, the rate of return
required by investors would increase, which would cause a decrease in stock prices (Huang et
al., 1996).
Hamilton (1983) made a major contribution in this context and argued that the most
recessions after World War II was preceded by increasing oil prices. Various explanations are
mentioned as the reason of the relationship between oil prices and economic activity.
Between these explanations, temporizing GDP growth and inflation due to higher oil prices
appears to be most preferred. According to the author, the data (real GNP, unemployment,
implicit price deflator for non farm, hourly compensation per worker, import prices, and M1)
indicated that economic recession preceded an oil price increase over 3-4 quarters, with
recovery starting after 6-7 quarters.
Huang, Masulis and Stoll (1996) describe the theoretical linkage between crude oil and
stock returns using economic linkages at a general level. The stock valuation of a company is
based on the discounted values of expected future cash flows. Movements in oil prices can
influence these parameters for many reasons. Oil is a basic ingredient, real resource and an
essential material which is used for the production of many goods, and can be considered as
an important variable like other variables viz., labor and capital. Higher oil prices cause
movements in expected costs and would depress stock market performance. Oil price
movements also influence stock market performance through the mechanism of discount rate.
53
The discount rate is used to evaluate the company’s intrinsic value from the expected
inflation rate and interest rate, which may depend on expected oil prices. For instance, for oil
importing country a rise in oil prices may influence the balance of payments negatively.
Therefore, a higher inflation rate is positively linked to the discount rate and consequently
negatively linked to the stock performance. Going one step further, since oil is a commodity,
expected oil prices can be used as a proxy for the expected inflation rate. The interest rate is
also closely related to the oil price. As mentioned before, oil is a major resource and therefore
higher oil prices compared to the general inflation level could drive the interest rate upwards.
A higher interest rate will make bonds more attractive and motivates investors to change their
portfolios by buying bonds and selling stock, and lead to falling stock prices. Mussa (2000)
presented a variety of channels through which higher oil prices can affect the global
economy. First, there will be some decrease in demand and therefore a swift of income from
energy consumers to energy producers. Second, there will be an increase in the cost of
production and a pressure on yield margins. Third, a higher oil price will influence the price
levels and the level of inflation. This will vary with the degree of monetary tightening. The
expected duration of the rise in price levels will create incentives for oil suppliers to expand
the production and investments. Furthermore, this all will have both direct and indirect
influences on the financial markets.
Kilian (2007) stated that higher oil prices may be transmitted to changes in stock prices
through increases in the cost of production and will cause a sudden change in the expected
future cash flows. This will depend on the level of the costs of oil. He also added another
view and argued that the oil prices affect the performance of firms through the change in
consumer expenditures and firm expenditures. In this view, there will be both a reduction in
demand from the consumers and firms as well. There will be a reduced demand for the
company’s output, because consumer spending will increase in response to increasing oil
prices, since this is an important energy resource for households. The negative effect of
higher oil prices on consumption, investments and stock prices is also documented by Lardic
and Mignon (2008). The authors argued in the same context, consumption is affected through
its relationship with the disposable income and the investments are influenced due to higher
costs of the company. Higher costs will cause a reduction in the profits and the discounted
sum of expected future dividends, which are key drivers of stock prices (Lescaroux and
Mignon, 2008). Filis (2010) mentioned that oil prices affect the overall stock market
performance in both direct and indirect ways. The direct negative influence can be justified
by the fact that oil price increases create uncertainty in financial markets, which in turns
54
decreases stock prices. The indirect negative effect can be explained due to the
aforementioned reasons, namely the increase in production level and the increase in inflation
rates, as a result of increasing oil prices.
However, the relationship between oil prices and the stock market is a complicated
theory and cannot be explained only by the concepts of higher costs or higher revenues and
the demand and supply curves. It is a well-documented fact that stock markets are very
sensitive to the changes in oil prices. As stated above, the changes in oil prices happen due to
various reasons and does not show the same type of effect every time. Considering a rise in
the demand for oil due to developing economies, there could be a positive linkage between
oil prices and stock returns. Another reason is speculation; the oil can be kept in hidden
reserves form, to generate the scarcity and because of the belief that the cost of production in
the future will become higher. Furthermore, the change in oil price can be due to natural
disasters and internal conflicts.
3.4.8. Exchange Rate
The Exchange rate is the value of a nation’s currency in terms of the value of another
country’s currency. An exchange rate, thus has two components, the domestic currency and a
foreign currency, and can be quoted either directly or indirectly. In a direct quotation, the
price of a unit of foreign currency is expressed in terms of the domestic currency. In indirect
quotation, the price of a unit of domestic currency is expressed in terms of the foreign
currency. An exchange rate that does not have the domestic currency as one of the two
currency components is known as a cross currency, or cross state. Exchange rates can
fluctuate for many reasons, including macroeconomic factors that affect the behavior of
market participants. Firms face a significant source of risk from exchange rate fluctuations,
because these fluctuations increase the volatility of their realized cash flows. There are three
types of exchange rate viz., nominal exchange rate, real exchange rate and real effective
exchange rate while Olisadebe (1991) identified two additional exchange rates namely
nominal effective exchange rate and equilibrium exchange rate. The exchange rate can be
floating or fixed. Floating exchange rates are those in which currency rates are determined by
market forces and are the norm for most major nations, some nations prefer to fix or peg their
domestic currencies to a widely accepted currency like the US dollar.
The nominal exchange rate is defined as the number of units of the domestic currency
that can purchase a unit of a given foreign currency. A decrease in this variable is termed
nominal appreciation of the currency and an increase in this variable is termed nominal
55
depreciation of the currency (Under the fixed exchange rate regime). The real exchange rate
is defined as the ratio of the price level abroad and the domestic price level, where the foreign
price level is converted into domestic currency units via the current nominal exchange rate.
An increase in real exchange rate is termed as an appreciation of the real exchange rate, and a
decrease is termed as depreciation. The real rate tells us how many times more goods and
services can be purchased abroad (after conversion into a foreign currency) than in the
domestic market for a given amount. Real effective exchange rate is the weighted average of
a country's currency relative to an index or basket of other major currencies adjusted for the
effects of inflation. The weights are determined by comparing the relative trade balances, in
terms of one country's currency, with each other country within the index. Nominal effective
exchange rate is the unadjusted weighted average value of a country's currency relative to all
major currencies being traded within an index or pool of currencies. The weights are
determined by the importance a home country places on all other currencies traded within the
pool, as measured by the balance of trade. Equilibrium exchange rate is the exchange rate at
which the supply for a currency meets the demand of the same currency. As foreign exchange
rates are affected by a number of factors, the equilibrium exchange rate in turn, are also
influenced by its supply and demand. Hence equilibrium is achieved when a currency's
demand is equal to its supply.
Stock prices are negatively affected by unfavorable changes. Exchange rate fluctuations
increase the business risk of domestic firms which are involved in imports and exports. If the
value of the domestic currency appreciates it makes domestic products and services more
expensive in foreign markets (the opposite is true for importers). Fluctuations in exchange
rates affect firms due to changes in their costs, revenues and incomes. Investors can
experience fluctuations in the value of their investments if these are held in foreign-
denominated assets, so that appreciation of the domestic currency results in lower returns,
and vice versa. Changes in the exchange rate can occur with changes in inflation and interest
rates. Depreciation of the domestic currency can lead to a rise in inflation and therefore affect
interest rates, thus impacting on stock prices. Franck and Young (1972) investigated the
impacts of exchange rates on the stock prices of multinationals across six currencies during
the period of the Bretton Woods agreement. Their study, using non-parametric tests, was
unable to report consistent effects.
The theoretical underpinning of the relationship between exchange rate and stock prices
could be traced to two main theories that relate these segments of financial markets. Firstly,
the traditional approach, which assumes that exchange rates leads stock prices. This theory
56
hypothesized that stock prices and exchange rates can interact was incorporated in “flow
oriented” models given by Dornbusch and Fisher (1980), which postulate that exchange rate
movements cause stock price movements. In the language of Granger Sims causality, this is
termed as “unidirectional” causality running from exchange rates to stock prices, or exchange
rates “Granger-cause” stock prices. This model is built on the macro view that as stock prices
represent the discounted present value of a firm’s expected future cash flows. Gavin (1989),
also postulated same and stated that the transmission channel is from exchange rate
fluctuations which affect firms‘ values via changes in competitiveness and changes in the
value of firms‘ assets and liabilities, dominated in foreign currency, thereby affecting firms‘
profits and therefore the value of equity. The second is “stock-oriented” economic theory
captured in the portfolio balance model which postulates a negative relationship between
stock prices and exchange rates (Branson et al. 1977). The crux of the theory is that a rise in
domestic stock prices would attract capital flows, which increase the demand for domestic
currency and cause the exchange rate to appreciate. In contrast to “flow oriented” models,
“stock-oriented” or “portfolio balance theory” postulate that movement in stock prices
Granger-cause movements in the exchange rate via capital account transactions. The degree
to which stock oriented models explain currency movements are a function of stock market
liquidity. Jorion (1991) concluded that appreciation of local currency reduces the profit for an
exporting firm and thereby affect its value of stock price negatively.Consequently, all firms
may react sooner or later to changes in the exchange rates. Even if a firm does not directly
involve in the export import business, Adler and Dumas (1984) show domestic firms that
have minimal international activities can still be affected by the exchange rate movements if
their input prices, output prices, or product demand depends on the fluctuation of exchange
rate. Depending on the moment in time when exchange rates change, a company might face:
(1) transaction exposure, that arises whenever the firm commits or is contractually bounded
to make or receive a payment at a future date denominated in a foreign currency; (2)
translation exposure, arising from the need to globally consolidate the financial reports of a
multinational company from affiliates‘ reports denominated in various currencies; and (3)
economic exposure, seen as the change in the firm‘s present value as a result of changes in
the value of the firm‘s expected future cash flows and cost of capital, induced by unexpected
exchange rate changes.
The importance of stock markets in an economy cannot be overlooked. This is because
the stock markets act as a means of channeling and diversification of domestic savings and
foreign capital for enhanced investments and capital formation.
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3.4.9. Gold Prices
Gold has been used in the market since 1971 as a commodity. The importance of gold
has been increased in the present world due to the financial crisis in the present economic
world. The investors are investing in the Gold. In the recent decade the gold prices and oil
prices rise day by day. Gold is treated as an alternative investment avenue. It is often stated
that gold is the best preserving purchasing power in the long run. Gold also provides high
liquidity; it can be exchanged for money anytime the holders want. Gold investment can also
be used as a hedge against inflation and currency depreciation. From an economic and
financial point of view, movements in the price of gold are both interesting and important. It
is often argued that investment in gold is historically associated with fears about rising
inflation and/ or political risk. However, financial markets do not currently show the classic
symptoms associated with such fears. Gold is a financial instrument that owns the
characteristics of both a commodity and currency. In the past it was used as money and as a
medium of exchange. Nowadays, it acts as a store of wealth and it is a known instrument for
investment uses. It has been highly demanded for many reasons such as scarcity, highly
mobile, liquidity and uniformity. The price of gold depends on the supply and demand for the
commodity and government auction policy. Throughout history, gold is also considered to
reduce risks and portfolio diversification (Ciner, 2001). Gold is also stored in central banks
for various reasons, such as diversification, economic security, physical security, confidence,
income and insurance (Tully and Lucey, 2007). Throughout the recent decade the demand for
gold has been expanding rapidly. The economic recession, high inflation rates and reduction
in world gold production may be reasons for that (Do, Mcaleer and Sriboonchitta, 2009).
Since gold is also used to hedge the risks, investors tend to replace their shares with gold,
which results in a lower demand for shares and volatility on stock markets. Therefore, getting
a better understanding of this linkage will help investors and firms to diversify their portfolios
and reduce their risks. Due to unstable world markets, there is an increasing interest in gold.
Some financial theories argue that gold could be considered as a safe investment when the
economic environment is uncertain. When other investments are decreasing, gold usually
increases. Gold is mostly considered as independent from other factors, and therefore it is
believed that it is low correlated with stock (Baur and Lucey, 2010). However, the theoretical
linkage between gold and stocks is unclear, and there is a lack of theoretical research. An
increase in gold prices attracts investors towards the commodity market, might decrease
investor preference towards the equity market. This indicates that a negative relationship is
expected between gold and silver, and stock market returns.
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3.4.10. Budget Deficit
The current and the future economic growth of the economy depend on country’s stock
market performance up to some extent and the stock market performance depends on the
country’s deficits. This is partly due to the notion that large budget deficit could affect current
and future economic growth of the nation through its effects on the stock markets. The budget
deficit is the amount by which a government, company, or individual's spending exceeds its
income over a particular period of time and the opposite phenomenon is called a budget
surplus. There can be many causes of the budget deficit, which includes a most elementary
phenomenon that when a government spends more than it earns revenue. Further, reducing
tax rates may also cause a deficit if spending isn't reduced to account for the decrease in
revenue. However, the world is more complex and a bit more than the basic analysis is
required. Essentially, large deficits entail additional risks to the economy which include a loss
in investor’s confidence (domestic and foreign) and adverse effects on the volume of
transactions. Specifically, a loss in investor’s confidence would cause a shift of portfolio
away from home currency assets into foreign currency assets which would limit the ability of
the country to finance its liabilities and increase the country’s exposure to exchange rate
fluctuations. This situation could weaken capital spending and ignite a drop in asset prices
which would further restrain real economic activity.
Theoretically, it is true that when the budget of the country is in deficit, it will depress
the stock prices and undermine the investor’s confidence. Hence, the firm’s ability to get
capital on favorable terms will be diminished to a large extent. Large budget deficits could
lead to stock market crash (Roley and Schall, 1988). According to Geske and Roll (1983), the
expected directional impact of budget deficits on stock return should be negative. This is
because a government budget deficit exerts upward pressure on the nominal interest rate
which, in turn, lowers expected returns on stocks. They argued further that, increases in risk
premia, due to fiscal deficits, expose investors to an uncertainty surrounding the reaction of
the Central Bank and thus further confound the equity market. Friedman (1987) discussed the
link between budget deficit and stock prices crush by reflecting it as “reliance on economic
fallacies”, moreover stock prices surged 1980s, despite of mounting deficits and perhaps
investor did not consider budget deficits a major problem. The economic news has impacted
on stock prices and cause variations in stock returns (Cutler, David et al. 1987). Hall and
Taylor (1993) claimed that increase in fiscal deficit forecast future tax increase, which may
cause a reduction in current consumption expenditures by households and harm stock prices.
This explanation supports the notion of Ricardian Equivalence hypothesis. Budget deficits
59
impose costs on the economy and have many effects, (Ball & Mankiw, 1995) investigated
that the effect of fiscal deficit followed by single initial effect: a deficit tends to reduce the
national savings, reduced investment, reduced exports and create the flow of assets overseas.
Greenspan and Allen (1995) investigated that a decrease in the budget deficit will reduce
inflationary expectations. Inflationary expectations may have reverse effects on equity prices.
For example, an increase in inflationary expectations may give benefit to equity instruments
by decreasing the real value of corporate debt, thus increasing the firm’s value. On the other
hand, a decrease in the future inflation rate may decrease equity values because the real value
of debt increases, reducing the firm’s value. Furthermore, a decrease in inflationary
expectations decreases nominal interest rates, which may cause stock prices to go up because
lower rates mean a higher present value of the future stream of corporate earnings. But lower
inflationary expectations may also lower the expected future stream of earnings, which could
lower stock prices. So the inflationary expectation effect on stock prices may be neutral or
indeterminate
3.4.11. Current Account Deficit
Due to the existence of large and continuous global current account imbalances in the
last two decades, economists, policymakers and researchers have paid attention to the issue of
current account deficits. The current account deficit is a measurement of a country’s trade in
which the imports exceeds the value of goods and services it exports. Determinants of current
account balances are of considerable interest in open economies. The behavior of the current
account balance contains important information about the economic performance of any
country, and also provides valuable macroeconomic policy recommendations. Less
theoretical literature has been done on the relationship between the current account deficit
and stock market. Sachs (1982) proposed the model for current account, named fundamental
equation of the current account. This model features a risk free bond as a unique fundamental
instrument. Therefore, this model is inappropriate to study the impact on the dynamics of
current account.
The current account deficit is the broadest measure of the flow of goods, services and
investment into and out of a country. Economists carefully watch the current-account deficit
because of its implications for the currency and domestic economic growth. Increasing the
deficit means that the economy must borrow abroad to finance its imports. When the current
account deficit is increasing, foreigners will lose faith that they will get their money back and
will worry about buying the country’s stocks, bonds and other assets. The implication could
60
be that the currency will depreciate, interest rates will rise and the economy will stall.
Thorbecke (1994), using the APT, in the US, demonstrated that the trade deficit was a source
of systematic risk and unexpected increases in the trade deficit reduced equity returns.
According to Thorbecke (1994) there are several reasons why the trade deficits might be a
source of systematic risk affecting asset returns. First, an increase in the trade deficit might
have implied a drop in demand for Australian goods and thus in the cash flow of Australian
companies. Second, a larger trade deficit might cause investors to expect protectionism, such
as restriction on imports or a high interest rate. Third, the trade deficit might ultimately raise
the price of foreign goods and cause inflation. Many have demonstrated that inflation affects
stock prices. Fourth, to finance these massive deficits foreigners had to hold more and more
Australian stocks and bonds. According to the principle of portfolio diversification they
would have become increasingly reluctant to allocate additional wealth into dollar assets.
Thus, because the trade deficit forced foreigners to hold more dollars, it might have raised the
risk premium on Australian assets. Fifth, news of higher trade deficits depreciates the dollar,
raises interest rates, and increases fear among Australian investors that foreign investors
would sell dollar-denominated stocks. For all these reasons news of large trade deficits could
have increased the perception of the systematic risk in holding Australian equities.
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CHAPTER 4
Methodology and Data Issues
4.1. Introduction
Many macroeconomic time series data theoretically have long-run relationship. It is
also widely claimed that these time series data evolve over time such that their mean and
variance are not constant. Relying such non-stationary time series data may lead
macroeconomists to wrongly conclude that two variables are related when in reality they are
not. This phenomenon is well knowned in the literature as spurious regression (Stock and
Watson, 2006).
The typical metod to analyze a non-stationary process is either to detrend or difference
the data depending on the type of trend. While these methods may provide stationary
variables for the regression, they can cause a loss of significant long-run information and
omitted variables bias.
Granger’s representation theorem (GRT) introduced an effective method to analyze the
non-stationary process without losing valuable long-run information as with differencing or
detrending techniques. This method is well known in literature by the contegration technique.
There are few methods available in literature to examine the long-run relationship
among variables based on the idea of GRT. One is the Engle and Granger (EG) cointegration
and the other is Johansen and Juselius (JJ) cointegration tests. However, the latest
cointegration technique proposed by Pesaran et al. (2001) as Auto Regressive Distributed Lag
(ARDL) provides some econometric ans estimation advantages over both EG and JJ
cointegration techniques. The following sections will provide the detailed discussion of these
techniques used in the study.
Further, the econometric literature confirms that when the macroeconomic series are
cointegrated, there will be causality in atleast one direction among the variables. According
to literature, VECM based granger causality is best suitable for multivariate data set.
The dynamic relationship among macroeconomic time series data are tested through the
VAR models. In the model, some lagged variables may have co-efficients which change
signs across the lags and this together with the interconnectivity of the equations, could
render it difficult to see what effect a given change in variable would have upon the future
values of the variable in the system. In order to alleviate this weakness, statistician normally
uses impulse response function (IRF) and variance decomposition (VDC) techniques. The
impulse response function traces out the responsiveness of the dependent variables in the
62
Vector Auto Regression (VAR) framework to shocks to each of the variables. Whereas, the
variance decomposition allows the proportion of the movements in the dependent variables
not only due to their own shocks, but also to the shocks in the other variables in the system.
The sixth section of this chapter describes the technique in detail.
Section 4.3 of the chapter addresses the data issues related to data definition,
transformation, nature of the data and the sources of data collection.
4.2. Methodology
4.2.1. Ng-perron unit root test
The ADF and PP unit root tests are known (from MC simulations) to suffer potentially
severe finite sample power and size problems. Firstly, the ADF and PP tests are known to
have low power against the alternative hypothesis that the series is stationary (or TS) with a
large autoregressive root (DeJong, et al, 1992). Secondly, the ADF and PP tests are known to
have severe size distortion (in the direction of over-rejecting the null) when the series has a
large negative moving average root.
Ng and Perron (Econometrica, 2001), building on some of their own work (Perron and
Ng, 1996) and work by Elliott, Rothenberg, and Stock (Econometrica, 1996), new tests to
deal with both of these problems. Their tests, in contrast to many of the other “new” unit root
tests that have been developed over the years, seem to have caught on as a preferred
alternative to the traditional ADF and PP tests. The family of Ng-Perron tests (which includes
among others, modified DF and PP test statistics) share the following features. First, the time
series is de-meaned or detrended by applying a GLS estimator. This step turns out to improve
the power of the tests when there is a large AR root and reduces size distortions when there is
a large negative MA root in the differenced series. The second feature of the Ng-Perron tests
is a modified lag selection (or truncation selection) criteria. It turns out that the standard lag
selection procedures used in specifying the ADF regression (or for calculating the long run
variance for the PP statistic) tends to underfit, i.e., choose too small a lag length, when there
is a large negative MA root. This creates additional size distortion in unit root tests. The Ng-
Perron modified lag selection criteria accounts for this tendency.
Ng and Perron (2001) construct four test statistics that are based upon the GLS
detrended data. These test statistics are modified forms of Phillips and Perron and statistics,
the Bhargava (1986) statistic, and the ERS Point Optimal statistic. First, define the term:
𝐾 = ∑ 𝑦𝑡−12 /𝑇2𝑇
𝑡=2 (4.2.1.1)
63
The modified statistics may then be written as,
Using the GLS detrended ỹ𝑡 data, the efficient modified PP tests are defined as
𝑀𝑍𝑡 =(𝑇−1ỹ𝑡
2−𝑓0)
2𝐾 (4.2.1.2)
𝑀𝑆𝐵 = (𝐾
𝑓𝑜)1/2
(4.2.1.3)
𝑀𝑍𝑡 = 𝑀𝑍𝛼 × 𝑀𝑆𝐵 (4.2.1.4)
𝑀𝑃𝑇 = {
(𝑐2𝐾−𝑐𝑇−1ỹ𝑡2)
𝑓0 , 𝑖𝑓 𝑥𝑡 = {1}
(��2𝐾 +1−𝑐)𝑇−1ỹ𝑡
2
𝑓0 , 𝑖𝑓 𝑥𝑡 = {1, 𝑡}
(4.2.1.5)
Where, 𝑓0 is an estimate of the residual spectral density at the zero frequency.
The statistics 𝑀𝑍𝛼 and 𝑀𝑍𝑡 are efficient versions of the PP Zα and Zt tests that have
much smaller size distortion in the presence of negative moving average errors. Again the
choice of the autoregressive truncation lag, p, is critical for correct calculation of f0. Here p is
chosen using the Modified Information Criteria (MIC(p)) of Ng and Perron (2001) as p =
pMIC = arg minpMIC(p) where:
𝜏𝜏(𝑝) = (��𝑃2)−1��2 ∑ ỹt−1
2𝑇𝑡=𝑝𝑚𝑎𝑥+1 (4.2.1.6)
��𝑝2 = (𝑇 − 𝑝𝑚𝑎𝑥)
−1 ∑ ut−12𝑇
𝑡=𝑝𝑚𝑎𝑥+1 (4.2.1.7)
4.2.2. ARDL co-integration
The study adopts an Auto-Regressive Distributed Lag (ARDL) bounds testing approach
developed by Pesaran et al (2001) to model the long run determinants. This approach has
some econometric advantages over the Engle-Granger (1987) and maximum likelihood-based
approach proposed by Johansen and Juselius (1990), and Johansen (1991) cointegration
techniques. First, the bounds test does not require pre-testing of the series to determine their
order of integration since the test can be conducted regardless of whether they are purely I(1),
purely I(0), or fractionally integrated. Second, endogeneity problems and inability to test
hypotheses on the estimated coefficients in the long-run associated with the Engle-Granger
(1987) method are avoided. According to Pesaran and Shin (1999), modeling the ARDL with
the appropriate lags will correct for both serial correlation and endogeneity problems. Jalil et
64
al (2008) argues that endogeneity is less of a problem if the estimated ARDL model is free of
serial correlation. In this approach, all the variables are assumed to be endogenous and the
long run and short run parameters of the model are estimated simultaneously (Khan et al,
2005). Third, as argued in Narayan (2004), the small sample properties of the bounds testing
approach are far superior to that of multivariate cointegration (Halicioglu, 2007). The
approach, therefore, modifies the Auto-Regressive Distributed Lag (ARDL) framework while
overcoming the inadequacies associated with the presence of a mixture of I(0) and I(1)
regressors in a Johansen-type framework. Fourth, the long and short-run parameters of the
model in question are estimated simultaneously. Lastly, The ARDL has superior small
sample properties compared to the Johansen and Juselius (1990) cointegration test (Pesaran
and Shin, 1999). The procedure will, however crash in the presence of I(2) series.
Following Pesaran et al. (2001) as summarized in Choong et al. (2005), we apply the
bounds test procedure by modelling the long-run equation as a general vector autoregressive
(VAR) model of order p, in t zt :
𝑍𝑡 = 𝑐0 + 𝛽𝑡 + ∑ ∅𝑖𝑍𝑡−𝑖 + 휀𝑡, 𝑡 = 1,2,3… , 𝑇𝑝𝑖=1 (4.2.2.1)
With 𝑐0 representing a (k+1)-vector of intercepts (drift), and β denoting a (k+1)-vector
of trend coefficients. Pesaran et al. (2001) further derived the following vector equilibrium
correction model (VECM) corresponding to (4.2.2.1):
∆𝑍𝑡 = 𝑐0 + 𝛽𝑡 + ∏𝑍𝑡−1∑ Γ𝑖∆𝑍𝑡−𝑖
𝑝𝑖=1 + 휀𝑡, 𝑡 = 1,2,3… , 𝑇 (4.2.2.2)
Where the (k+1)x(k+1)-matrices ∏ = 𝐼𝑘+1 + ∑ 𝛹𝑖𝑝𝑖=1 and 𝛤𝑖 = −∑ 𝛹𝑗 , 𝑖 =𝑝
𝑗=𝑖+1
1,2,3. . , 𝑝 − 1 contain the long-run multipliers and short-run dynamic coefficients of the
VECM. Zt is the vector of variables yt and xt respectively. yt is an I(1) dependent variable
defined as lnYt and xt=[yit, i=1,2,3..., T] is a vector matrix of ‘forcing’ I(0) and I(1) regressors
as already defined with a multivariate identically and independently distributed (i.i.d) zero
mean error vector 휀𝑡 = (휀1𝑡, 휀′2𝑡)
′, and a homoskedastic process. Further, assuming that a
unique long-run relationship exists among the variables, the conditional VECM (4.2.2.2) now
becomes
∆𝑌𝑡 = 𝑐𝑦0 + 𝛽𝑡 + 𝛿𝑦𝑦𝑦𝑡−1 + 𝛿𝑥𝑥𝑥𝑡−1 + ∑ 𝜆𝑖∆𝑦𝑡−𝑖 + ∑ 𝜉𝑖∆𝑥𝑡−1 + 휀𝑦𝑡, 𝑡 = 1,2,3, … , 𝑇𝑝−1𝑖=0
𝑝−1𝑖=1
(4.2.2.3)
Where 𝛿𝑖 are the long run multipliers, 𝑐0 is the drift, and 휀𝑡 are white noise errors.
65
Bounds Testing Procedure
The implementation of the ARDL approach involves two stages. First, the existence of
the long-run nexus (cointegration) between the variables under investigation is tested by
computing the F-statistics for analyzing the joint significance of the coefficients of the lagged
levels of the variables. Pesaran and shin, 1999 and Narayan, 2004 have provided two sets of
appropriate critical values for different numbers of regressors (variables). This model
contains an intercept or trend or both. One set assumes that all the variables in the ARDL
model are I(0), and another assumes that all the variables are I(1). If the F-statistic lies above
the upper-bound critical value for a given significance level, the conclusion is that there is a
non-spurious long-run level relationship with the dependent variable. If the F-statistic lies
below the lower bound critical value, the conclusion is that there is no long-run level
relationship with the dependent variable. If it lies between the lower and the upper limits, the
result is inconclusive. The approximate critical values for the F-test were obtained from
Pesaran and Pesaran (1997). The general form of the null and alternative hypotheses for the
F-statistic test is as follows:
𝐻0: 𝛿1 = 𝛿2 = 𝛿3 = 𝛿4 = 𝛿5 = 0; Against the alternative 𝐻1: 𝛿1 ≠ 𝛿2 ≠ 𝛿3 ≠ 𝛿4 ≠ 𝛿5 ≠ 0
Secondly, if the cointegration between variables is identified, then one can undertake
further analysis of long-run and short-run (error correction) relationship between the
variables.
4.2.3. VECM based Granger Causality
The Granger representation theorem suggests that there will be Granger causality in at
least one direction if there exists a cointegration relationship among the variables, providing
that they are integrated order of one. The direction of causality is investigated by applying
Vector Error Correction Model (VECM) granger causality approach only after confirming the
presence of co-integrating relationship among the variables in the study. Granger (1969)
argued that VECM is more appropriate to examine the causality between the series at I(1).
VECM is restricted form of unrestricted VAR and restriction is levied on the presence of the
long run relationship between the series. The system of error correction model (ECM) uses
all the series endogenously. This system allows the predicted values to explain itself both by
its own lags and lags of forcing variables as well as the lags of the error correction term and
by residual term. Engle and Granger (1987) caution that the Granger causality test, which is
conducted in the first differences variables by means of a vector autoregression (VAR), will
66
be misleading in the presence of cointegration. Therefore, an inclusion of an additional
variable to the VAR system, such as the error correction term would help us to capture the
long run relationship. To this end, an augmented form of the Granger causality test involving
the error correction term is formulated in a multivariate pth order vector error correction
model. The VECM equation is as follows:
(
∆𝑥1𝑡
∆𝑦1𝑡
∆𝑦2𝑡
∆𝑦3𝑡..∆𝑦𝑛𝑡)
=
(
𝐶1𝐶2𝐶3𝐶4..𝐶𝑛
)
+ ∑
[ 𝛽11𝑖 𝛽12𝑖 𝛽13𝑖 𝛽14𝑖 . . 𝛽1𝑛𝑖
𝛽21𝑖 𝛽22𝑖 𝛽23𝑖 𝛽24𝑖 . . 𝛽2𝑛𝑖
𝛽31𝑖 𝛽32𝑖 𝛽33𝑖 𝛽34𝑖 . . 𝛽3𝑛𝑖
𝛽41𝑖 𝛽42𝑖 𝛽43𝑖 𝛽44𝑖 . . 𝛽4𝑛𝑖
. . . . . . .
. . . . . . .𝛽𝑛1𝑖 𝛽𝑛2𝑖 𝛽𝑛3𝑖 𝛽𝑛4𝑖 . . 𝛽𝑛𝑛𝑖]
𝑝𝑖=1
(
∆𝑥1𝑡−𝑖
∆𝑦1𝑡−𝑖
∆𝑦2𝑡−𝑖∆𝑦3𝑡−𝑖..∆𝑦𝑛𝑡−𝑖)
+
(
𝛾1
𝛾2
𝛾3𝛾4
.
.𝛾𝑛)
𝐸𝐶𝑀𝑡−1 +
(
휀1𝑡
휀2𝑡
휀3𝑡
휀4𝑡
.
.휀𝑛𝑡)
(4.2.3.1)
The C’s, β’s and ’s are the parameters to be estimated. ECMt-1 represents the one
period lagged error-term derived from the co-integration vector and the ε’s are serially
independent with mean zero and finite covariance matrix. From the Equation (5.5.1) given
the use of a VAR structure, all variables are treated as endogenous variables. The F test is
applied here to examine the direction of any causal relationship between the variables. The
coefficients on the ECM represent how fast deviations from the long-run equilibrium are
eliminated. Another channel of causality can be studied by testing the significance of ECM’s.
This test is referred to as the long run causality test.
4.2.4. Stability tests
4.2.4.1. CUSUM Test
The CUSUM test (Brown, Durbin, and Evans, 1975) is based on the cumulative sum of
the recursive residuals. This option plots the cumulative sum together with the 5% critical
lines. The test finds parameter instability if the cumulative sum goes outside the area between
the two critical lines. The CUSUM test is based on the statistic:
𝑊𝑡 = ∑ 𝑤𝑟/𝑠𝑡𝑟=𝑘+1 , 𝑡 = 𝑘 + 1… . , 𝑇 (4.2.4.1)
Where w is the recursive residual defined above, and s is the standard error of the
regression fitted to all T sample points. If the b vector remains constant from period to period,
E[𝑊𝑡] = 0, but if 𝛽 changes, 𝑊𝑡 will tend to diverge from the zero mean value line. The
significance of any departure from the zero line is assessed by reference to a pair of 5%
significance lines, the distance between which increases with t. The 5% significance lines are
found by connecting the points.
67
[𝑘, ±0.948(𝑇 − 𝑘)1/2] and [𝑇, ±3 × 0.948(𝑇 − 𝑘)1/2]
Movement of 𝑊𝑡 outside the critical lines is suggestive of coefficient instability.
4.2.4.2. CUSUM of Squares Test
The CUSUM of squares test (Brown, Durbin, and Evans, 1975) is based on the test
statistic
𝑆𝑡 = ∑ 𝑤𝑟2/∑ 𝑤𝑟
2𝑇𝑟=𝑘+1
𝑡𝑟=𝑘+1 (4.2.4.2)
The expected value of S under the hypothesis of parameter constancy is
𝐸[𝑆𝑡] = (𝑡 − 𝑘)/(𝑇 − 𝑘) (4.2.4.3)
Which goes from zero at t=k to unity at t=T. The significance of the departure of S
from its expected value is assessed by reference to a pair of parallel straight lines around the
expected value. See Brown, Durbin, and Evans (1975) for a table of significance lines for the
CUSUM of squares test.
The CUSUM of squares test provides a plot of 𝑆𝑡 against and the pair of 5 percent
critical lines. As with the CUSUM test, movement outside the critical lines is suggestive of
parameter or variance instability.
4.2.5. Impulse Response Functions
The impulse response function (IRF) is one of the essential tools for interpreting VAR
model results. The IRF allows researchers to examine the current and future behavior of a
variable that following a shock to another variable within the system. The IRF is a useful tool
for determining the magnitude, direction, and the length of time that the variables in the
system are affected by a shock to another variable. To estimate IRFs, some practical issues
need to be considered. The VAR model needs to be transformed into the vector moving
average (VMA) representation. Enders (2010) advocate that this transformation is an
essential feature of Sims’s (1980) methodology since it allows for tracing out the effects of
various shocks on variables contained in the VAR system. In the case of a VAR model with
two variables included, the form of the IRFs can be written as shown in Enders (2004):
[𝑌𝑡
𝑍𝑡] = [��
��] + ∑
𝐴𝑖
1−𝑏12𝑏21[
1 −𝑏12
−𝑏21 1] [
휀𝑌𝑡−𝑖
휀𝑍𝑡−𝑖]∞
𝑖=0 (4.2.5.1)
68
[𝑌𝑡
𝑍𝑡] = [��
��] + ∑ [
𝜃11𝑖 𝜃12
𝑖
𝜃21𝑖 𝜃22
𝑖 ] [휀𝑌𝑡−𝑖
휀𝑍𝑡−𝑖]∞
𝑖=0 (4.2.5.2)
And;
𝑋𝑡 = 𝜇 + ∑ 𝜃𝑖휀𝑡−𝑖∞𝑖=0 (4.2.5.3)
Where 𝜃𝑖 is the IRFs of disturbances. Therefore, the IRF is found by reading off the
coefficients in the moving average representation of the process. If the innovations 휀𝑡−𝑖 are
contemporaneously uncorrelated, the interpretation of the impulse response is
straightforward. For example, the ith innovation of 휀𝑡 is simply a shock to the ith endogenous
variable in the system Enders (2004).
However, the residuals generated by the VAR models are usually contemporaneously
correlated. This is because in a VAR model only lagged endogenous variables are admitted
on the right-hand side of each equation (in addition to a constant term), and hence all the
contemporaneous shocks which impact on Xt are forced to feed through the residuals, uit
(Kuszczak and Murray, 1986). While this may not cause a problem in the estimation of the
VAR model, the impulse responses and variance decompositions derived from the initial
estimates of the VAR model could be affected such that any adjustment to the order in which
the variables are entered in the system could produce different results (Kuszczak and Murray,
1986). Thus, there is a need to impose some restrictions when estimating the VAR model to
identify the IRFs. In this regard, a common approach is the Cholesky decomposition, which
was originally applied by Sims (1980). The Cholesky decomposition overcomes the problem
of contemporaneous relationships among the innovations error terms within the estimated
VAR model by identifying the structural shocks such that the covariance matrix of the
estimated residuals is lower triangular. In fact, the Cholesky decomposition suggests that
there is no contemporaneous pass-through from 𝑌𝑡 to the other variable, 𝑧𝑡. More formally, in
the VAR, the matrix error structure becomes left triangular, [𝑒1𝑡
𝑒2𝑡] = [
1 −𝑏12
0 1] [
휀𝑌𝑡
휀𝑍𝑡]. In
practice, this means that the Cholesky decomposition attributes all the effect to the variable
that comes first to the target variable in the VAR system.
4.2.6. Variance Decomposition Technique
For any variable, short run variations are due to its own shocks, but over time other
shocks contribute to these changes as well. Forecast error variance decomposition (FEVD) is
a method available to examine this interesting phenomenon. In fact, while the IRFs analyze
69
the dynamic behavior of the target variables due to unanticipated shocks within a VAR
model, variance decompositions determine the relative importance of each innovation to the
variables in the system. That is, variance decompositions can be considered similar to 𝑅2
values associated with the dependent variables in different horizons of shocks. Enders (2010)
show how to write FEVD to conditionally calculate n-period forecast error 𝑋𝑡+𝑛 considering
the VMA representation of VAR presented in equation (4.2.6.1) as:
𝑋𝑡+𝑛 − 𝐸𝑡𝑋𝑡+𝑛 = 𝜇 + ∑ 𝜃𝑖휀𝑡+𝑛−1𝑛−1𝑖=0 (4.2.6.1)
Considering Yt, the first element of the Xt+n matrix in equation (4.2.6.2), the variance of
the n-step-ahead forecast error can be calculated as :
𝑌𝑡+𝑛 − 𝐸𝑡𝑌𝑡+𝑛 = 𝜃11(0)휀𝑌𝑡+𝑛 + 𝜃11(1)휀𝑌𝑡+𝑛−1 + ⋯+ 𝜃11(𝑛 − 1)휀𝑌𝑡+1 + 𝜃12(0)휀𝑍𝑡+𝑛 +
𝜃12(1)휀𝑍𝑡+𝑛−1 + ⋯+ 𝜃12(𝑛 − 1)휀𝑍𝑡+1 (4.2.6.2)
Or
𝜎𝑦(𝑛)2 = 𝜎𝑦2[𝜃11(0)2 + 𝜃11(1)2 + ⋯+ 𝜃11(𝑛 − 1)2] + 𝜎𝑍
2[𝜃12(0)2 + 𝜃12(1)2 + ⋯+
𝜃12(𝑛 − 1)2] (4.2.6.3)
Where 𝜎𝑦(𝑛)2 and 𝜎𝑍(𝑛)2 denote the n-step-ahead forecast error variance of 𝑌𝑡+𝑛 and
Z𝑡+𝑛, respectively. The first part of the equation (4.2.6.3) shows the proportion of variance
due to the variables own shock, 𝑌𝑡, while the second part of eqthe equation (42.6.3) shows the
proportion of variance due to the other variables shock, 𝑧𝑡. Theoretically, the first part
decreases over time while the second part of the variance increases. However, it is typical for
a variable to explain almost all of its forecast error variance at a short horizon and smaller
proportions at longer horizons (Enders, 2010). From this standpoint VDC is useful to assess
the Granger causal relationships among variables when the variance decomposition results
imply that one variable explains a high portion of the forecast error variance of another
variable. That is, when a shock 휀𝑧 explains none of the forecast error variance of the sequence
𝑌𝑡 at all forecast horizons, i.e., 𝜕𝜎𝑦2/𝜎𝑧
2 ≈ 0, we may say that 𝑌𝑡 evolves indecently of the 𝑍𝑡
shocks, 휀𝑧. Also, when a shock to the 𝑍𝑡 sequence, 휀𝑧, explains the entire forecast error
variance of the sequence the 𝑌𝑡 at all forecast horizons, i.e., 𝜕𝜎𝑦
2
𝜎𝑧2 ≈ 100%, may say that 𝑌𝑡
sequence is totally endogenous (Enders, 2010).
70
4.2.7. Principal Component Analysis
Principal component analysis is a multivariate technique that analyzes a data table in
which observations are described by several inter-correlated quantitative dependent variables.
The goals of PCA are to find and extract the most important information from the data and
compress the size while at the same time keeping the important information and simplify the
description of data, and then the structure of the observations and variables can be analyzed.
(Abdi and Williams 2010)
The PCA computes new variables called principal components (PCs) as linear
combinations of the original variables. The first principal component is required to have the
largest possible variance (in other words, inertia and therefore explains the largest part of the
inertia of the data table). The second has to be orthogonal to the first and have the second
largest possible inertia. The rest of the components are computed likewise. The values of
these new variables for the observations are called factor scores, which can be interpreted
geometrically as the projections of the observations onto the principal components (Abdi and
Williams 2010). To be able to find the principal components there is a need for both vectors
and matrixes.
The different principal components are acquired from the singular value decomposition
of the data table. Specifically with 𝑋 = 𝑃∆𝑄𝑇, the matrix of factor scores, denoted F is
obtained as, Equation (4.2.7.1):
𝐹 = 𝑃∆ (4.2.7.1)
Where P is the I×L matrix of left singular vectors, Q is the matrix of the right singular
vectors and ∆ is the diagonal matrix of singular vectors. The squared diagonal matrix (∆2) is
equal to ᴧ which is the diagonal matrix of the (non-zero) eigenvalues of XTXand XXT.
The inertia of a column is defined as the sum of the squared elements of this column
and is computed as, Equation (4.2.7.2):
𝛾𝑗2 = ∑ 𝑥𝑡,𝑗
2𝐼𝑖 (4.2.7.2)
The sum of all the 𝛾𝑗2 is denoted I and it is called the inertia of the data table or the total
inertia. Note that the total inertia is also equal to the sum of the squared singular values of the
data table.
The matrix Q gives the coefficients of the linear combinations used to compute the
factor scores. This matrix can also be interpreted as a projection matrix because multiplying
71
X by Q gives the values of the projections of the observations on the principal components,
Equation (4.2.7.3):
𝐹 = 𝑃∆= 𝑃∆𝑄𝑄𝑇 = 𝑋𝑄 (4.2.7.3)
The strengths of the principal component analysis are that a large amount of variables
can be used without adding much to the complexity of the model.
4.3. Data Issues
The present section of the study deals with data definition and validation. The different
set of variables and their proxies has been used for the purpose of estimating empirical results
of the study. Those variables and their proxies are defined in the present section along with
the sources of their procurements. All these variables are used in different combinations
according to the requirement and general model specification of the study.
4.3.1. Stock market
Stock market development is usually measured by stock market size, liquidity,
volatility, concentration and integration with world capital markets. The present study
incorporates two measures of stock market growth, which are as follows:
4.3.1.1. Stock prices
The stock market index is Sensex (or BSE 30), an index of 30 well established and
financially sound companies listed on the BSE. The Sensex is intended to represent an entire
stock market and thus track the market changes over time. Therefore, in this study, we have
taken the sensitivity index of BSE (Sensex) to track the changes in the market over time (with
respect to other macroeconomic variables) represented by LBSE. The data for BSE Sensex is
available both in annual and monthly frequency and the data has been taken from the official
website of Bombay Stock Exchange.
4.3.1.2. Stock market development
One of the measures of stock market development is market capitalization as a
proportion of GDP. This measure equals the value of listed shares divided by GDP. Market
Capitalization to GDP is a long-term valuation indicator that has become popular in recent
years, given by Warren Buffett (2001), hence, also known as the Buffett Valuation Indicator4.
According to him “it is probably the best single measure of where valuations stand at any
4 In 2001, Warren Buffet remarked in a Fortune Magazine interview that "it is probably the best single measure
of where valuations stand at any given moment.
72
given moment”. Hence, it is an important tool to gauge the overall attractiveness of the stock
market in any country. The assumption behind this measure is that overall market size is
positively correlated with the ability to mobilize capital and diversify risk on an economy-
wide basis. Therefore, in this study, we have taken Market Capitalization as a percentage of
GDP 5as a proxy for stock market development. Quarterly frequency data for MCAP as a
percentage of GDP is used for the study and the data has been taken from Handbook of
Statistics on Indian economy, RBI.
4.3.2. Economic Growth
Economic growth is one of the important macroeconomic variables, and its relation to
the stock market is an important aspect of the study. For the purpose of yearly and quarterly
estimation of the studies the data for GDP is available, but, for monthly study, the proxy for
GDP, i.e., IIP (Index of Industrial Production) is used. The present study incorporates two
measures of economic growth, which are as follows:
4.3.2.1. Real Gross Domestic Product
Real GDP represents economic growth and economic growth is the increase in the
inflation-adjusted market value of the goods and services produced by an economy over time.
It is conventionally measured as the percent rate of increase in real gross domestic product, or
real GDP6. Gross domestic product (GDP) is regarded as one of the important determinants
of stock market performance and has often been used to measure the growth of real economic
activity. Growth is usually calculated in real terms, i.e., inflation-adjusted terms to eliminate
the distorting effect of inflation on the price of goods produced. This helps the country to
know their actual development status. Therefore, real GDP has been included for the purpose
of study. The data for real GDP is available in both annual and quarterly frequency and the
data has been collected from Handbook of Statistics on Indian economy, RBI.
4.3.2.2. Index of Industrial Production (IIP)
The present study has taken Index of Industrial Production (LIIP) as the proxy for
economic growth. The IIP as a monthly indicator is widely used for assessing both the current
state and the short-term outlook for GDP (NBER`s Business Cycle Dating Committee,
Sédillot and Pain, 2003) one of the main reasons why the IIP was considered to be a good
5 Other indicators of stock market development that has been used in the literature include the number of listed
companies, changes in the stock market index etc. We focus on market capitalization as a percentage of GDP
because it is less arbitrary than the other measures. In addition, Demiguc-Kunt and Levine (1996) have shown
that different measures of stock market development are highly correlated. 6 Statistics on the Growth of the Global Gross Domestic Product (GDP) from 2003 to 2013, IMF, October 2012
73
proxy for GDP was that the value added by industrial production represented a substantial
share of GDP. The data for IIP has been obtained from the official website of the Ministry of
Statistics and Program Implementation, Government of India.
4.3.3. Real Effective Exchange Rate (REER)
The Real Effective Exchange Rate (REER) is the weighted average of a country’s
currency relative to an index or basket of other major currencies adjusted for the effects of
inflation. Or, conceptually, the REER, defined as a weighted average of nominal exchange
rates adjusted for relative price differential between the domestic and foreign countries,
relates to the purchasing power parity (PPP) hypothesis (RBI Bulletin). The Indian rupee’s
strength is calculated based on a basket of six major currencies and also against 36
currencies, both based on weights assigned as per bilateral trade. The currency basket is more
relevant as this represents a wider set of trading countries. Here, for the purpose of the study
REER based on 36 currency indices has been taken to know that over a trend how the change
in the exchange rate has an impact on stock prices. The data for the REER has been collected
from Handbook of Statistics on Indian economy, RBI.
4.3.4. International crude oil price
Changes in the international crude oil prices are often considered an important factor
for understanding fluctuations in stock prices. For the purpose of study, international crude
oil prices per 1000 barrels have been used. The data for international crude oil prices is
available in both yearly and quarterly frequency and the data has been collected from World
Bank database.
4.3.5. Foreign Direct Investment
FDI is increasingly being recognized as a major source of economic development. The
general belief is that FDI facilitates the transfer of technology, organizational and managerial
practices, skills and access to international market. Therefore, to access the impact of foreign
capital inflows, we have taken Foreign Direct Investment (FDI). The data for FDI has been
collected from Handbook of Statistics on Indian economy, RBI.
4.3.6. Foreign Institutional Investment
Foreign Institutional Investment refers to the investments made by the residents of a
country in the financial assets and production process of another country. FII is a short term
investment by foreign institutions, in the financial markets of other countries. The FII is
playing an important role in bringing in funds needed by the equity market. Additionally, if
74
the funds from multilateral finance institutions and FDI are insufficient, they contribute to the
foreign exchange inflow. According to Lalitha, S. (1992), the main reason for opening stock
market for FIIs was to attract foreign investments and stop countries from raising more debts.
The FIIs has emerged as noteworthy players in the Indian stock market and their growing
contribution adds as an important feature of the development of stock markets in India. The
data from FII has been taken from Handbook of Statistics on Indian economy, RBI.
4.3.7. Inflation
Inflation is a rise in prices of several items over a period of time. It is measured through
various indices and each provides specific information about the prices of items that it
represents. The index could be the Wholesale Price Index (WPI) or the Consumer Price Index
(CPI) for specified categories of people like agricultural workers or urban non-manual
employees. Each of the indices is created in a specific manner with a certain year as the base
year and they consider the price change over a year. Inflation represents one of the major
threats to stock investors. However, high inflation is not always bad and low inflation need
not always be good for the equity markets, as the impact will differ from companies and
sectors across different time horizons. Therefore, the present study incorporates two measures
of economic growth, which are as follows:
4.3.7.1. Consumer Price Index (CPI)
CPI and has been used as the proxy for inflation to identify its relationship with Indian
stock prices. Consumer price index (CPI) measures changes in the price level of a market
basket of consumer goods and services purchased by households. The CPI is a statistical
estimate constructed using the prices of a sample of representative items whose prices are
collected periodically. The data for CPI has been collected from Economic Survey,
Government of India.
4.3.7.2. Wholesale Price Index (WPI)
Wholesale Price Index (WPI) is taken as the proxy of inflation. This index is the most
widely used inflation indicators in India. WPI captures price movements in a most
comprehensive way. It is widely used by Government, banks, industry and business circles.
Important monetary and fiscal policy changes are linked to WPI movements. The data for
WPI is monitored and updated on a monthly basis, taking into account all the 679 items that
form the index. The data for WPI is taken from the Official website of Economic Adviser,
Ministry of Commerce and Industry.
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4.3.8. Real Interest Rate
Several studies have established the fact that the interest rate and stock prices are
closely related. The interest rate is one of the important macroeconomic variables, which is
directly related to economic growth. Generally, interest rate is considered as the cost of
capital, means the price paid for the use of money for a period of time. From the point of
view of a borrower, the interest rate is the cost of borrowing money (borrowing rate). From a
lender’s point of view, the interest rate is the fee charged for lending money (lending rate).
The impacts of interest rate on stock exchange provide important implications for monetary
policy, risk management practices, financial securities valuation and government policy
towards financial markets. A real interest rate is the interest rate that has been adjusted to
remove the effects of inflation to reflect the real cost of funds to the borrower, and the real
yield to the lender. Therefore, real interest rates have been taken for the study. The data for
real interest rate has been taken from World Bank database.
4.3.9. Short term interest rates
Short term interest rates are the interest rates on loan contracts or debt instruments such as
Treasury bills, bank certificates of deposit or commercial paper-having maturities of less than
one year. The present study includes two proxies of short term interest rate, which are as
follows:
4.3.9.1. Treasury bill rates
Short Term Treasury Bills Rate has been taken as the proxy of interest rate in the study
(TBR): Interest rate varies with default risk, time, and marginal productivity of capital
(Chandra 2004). Increasing or decreasing of interest, encourages substation between
speculative, market instrument, and stock market. The data for the T-bill rates is collected
from Handbook of Statistics on Indian economy, RBI.
4.3.9.2. Call Money Rate (CMR)
Call money rate is considered as a proxy of short term interest rates. To know the
impact of short term interest rates on stock prices, call money rates has been used for the
study. The data for call money rate has been retrieved from Handbook of Statistics on Indian
economy, RBI.
4.3.10. Fiscal Deficit
FD is the Fiscal Deficit as a percentage of GDP. Fiscal deficit/surplus is the difference
between the government’s expenditures and its revenues (excluding the borrowed money).
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The study includes the fiscal deficit as a percentage of total gross domestic products (GDP)
because it is an important metric as it shows how large the fiscal deficit is in relation to
overall output in the economy. Additionally, fiscal deficit is usually communicated as a
percentage of its gross domestic product (GDP). The data for fiscal deficit has been collected
from World Bank database.
4.3.11. Current Account Deficit
Essentially, large deficits entail additional risks to the economy which include a loss in
investor’s confidence and adverse effects on the volume of transactions. The study includes
the current account deficit as a percentage of total gross domestic product (GDP) because it is
an important metric as it shows how large the current account number is in relation to overall
output in the economy. The data for CAD as a percentage of GDP has been taken from
Handbook of Statistics on Indian economy, RBI.
4.3.12. Money supply (M3)
The amount of money in an economy is referred to as the money supply. In this study,
money supply has been measured through M3 (Broad money) and has increasingly been
recognized as a major source of financial development. Money supply is one of the most
basic parameters in an economy and measures the abundance or scarcity of money. Plenty of
money circulating in the economy, both makes more money available to invest in stocks and
also makes alternative investment instruments, such as bonds less attractive. Therefore, to
know the relationship between money supply and stock prices M3 has been used for the study
and the data for money supply has been taken from Handbook of Statistics on Indian
economy, RBI.
4.3.13. Trade openness
The trade-to-GDP-ratio is the sum of exports and imports divided by GDP. This
indicator measures a country’s 'openness' or 'integration' in the world economy. It represents
the combined weight of total trade in its economy, a measure of the degree of dependence of
domestic producers on foreign markets and their trade orientation (for exports) and the degree
of reliance of domestic demand on foreign supply of goods and services (for imports). The
indicator reflects the liberalization policies of the economy and provides an insight for the
investment opportunities in a particular economy. It is believed that openness of the economy
helps to attract foreign investment. This in turn increases the activities on the stock market as
firms would attempt to raise investment funds (capital) from the stock market and therefore
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we have taken TRADE is total of export and an import divided by the GDP of the country
and is a measure of openness. The data for trade openness has been taken from Handbook of
Statistics on Indian economy, RBI.
4.3.14. Gold Prices
Gold is a substitute investment avenue for Indian investors. The importance of gold has
been increased in the present world due to the financial crisis in the present economic world.
The investors are investing in the Gold. Gold is treated as an alternative investment avenue. It
is often stated that gold is the best preserving purchasing power in the long run. Gold
investment can also be used as a hedge against inflation and currency depreciation. From an
economic and financial point of view, movements in the price of gold are both interesting and
important. The measurement of gold taken for the study is USD/Oz. The data for gold has
been obtained taken from the official website of gold price (http://goldprice.org/).
All the variables are taken in their natural logarithm, during empirical estimation .
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CHAPTER 5
Macroeconomic Determinants of the Stock Market Development in India
5.1. Introduction
In the last three decades, numerous studies have examined the dynamic relationships
between macroeconomic variables and stock market, particularly for developed economies
such as the U.S., United Kingdom (UK), Germany, and Japan. The pioneering studies of the
field are carried out by Fama (1981, 1990), Geske and Roll (1983), and Chen, Roll, and Ross
(1986). However, the hypothesis and methodologies used for the studies in this area are
different. A large pool of studies investigated the predictability of stock returns for real
economic activity. Examples of such studies are Estrella and Hardouvelis (1991), Estrella and
Mishkin (1996), and Domain and Louton (1997). An extensive amount of research focuses on
the integration of stock markets across the economies. Examples of these studies are Jeon and
Chiang (1991), Kasa (1992), Arshanapalli and Doukas (1993), Becker, Finnerty and
Friedman (1995), and Longin and Solnik (1995). Another dimension in previous literature
examined the short and the long-run relationship between stock prices and domestic and
international macroeconomic variables such as inflation, exchange rate, FIIs, FDIs, money
supply, interest rate, output and many more. Within this group of studies, some studies
examined macroeconomic factors that affect stock prices, while others examined factors that
determine stock return volatility (Semmler, 2006).
This chapter of the study deals with the discussion of empirical results derived using
different econometric techniques, to know the relationship between different macroeconomic
variables and the Indian stock prices. The econometric methodologies used for estimating the
empirical results of the studies are, Ng-Perron unit root test is utilized to check the order of
integration of the variables. Lag-length selection criteria are used to determine the
appropriate lag length for the model. The long run relationship is examined by implementing
the ARDL bounds testing approach to co-integration. VECM method is used to test the short
and long run causality and variance Decomposition and Impulse Response Function are used
to predict long run exogenous shocks of the variables.
The chapter has been segmented into five sections; the first section presents a broad
literature review based on the relationship between macroeconomic variables and stock
prices; the second section encomposes the yearly studies, incorporating empirical results
using yearly frequency data; the third section is composed of the quarterly study for the
estimation of the relationship between macroeconomic variables and stock market
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development, based on the empirical finding using quarterly frequency of data; in the fourth
section, the results of the studies having monthly frequency data are discussed, showing the
empirical relationship between macroeconomic variables and stock prices; and the fifth
section is composed of summary of the findings.
5.2. Review of Literature
For this study, it is not viable to survey all the literature in every dimension. However,
the present study focuses on the causal relationship between macroeconomic factors and
stock prices. Therefore, in this section, we will discuss the studies showing the relationship
between macroeconomic variables and stock prices. The first section will discuss the relevant
studies from overall economies, the studies related to Indian economy will be provided in the
second section.
5.2.1. Studies conducted in Rest of the World
Asprem (1989) investigated the relationship between stock indices, asset portfolios and
macroeconomic variables in ten European countries. The study uses quarterly data from 1968
to 1984. Correlation and regression techniques were adopted for estimation. Variables used
for the study were changes in industrial production, real gross national product, gross capital
formation, employment, exports, exchange rate, consumption, interest rate, inflation and
money supply. Results showed that employment, imports, inflation and interest rates, are
negatively correlated with stock prices. Changes in imports may be viewed as an indicator for
changes in consumption. Thus, the relation between imports and stock prices is evidence in
support of the consumption capital asset pricing model.
Campbell and Hamao (1992) studied the predictability of monthly excess returns on
equity portfolios over the domestic short-term interest rate in the U.S. and Japan during the
period January 1971 to March 1989. A highly restricted model was estimated and tested for
the study, in which expected excess returns in Japan and the U.S. are driven by a common
unobserved variable, so that they are perfectly correlated. The paper found that similar
variables, including the dividend-price ratio and interest rate variables help to forecast excess
returns in each country. In addition, in the 1980's U.S. variables help to forecast excess
Japanese stock returns.
Pesaran and Timmermann (1995) examined the robustness of the evidence on the
predictability of U.S. stock returns, using recursive modeling approach. Monthly time series
data from January 1954 to December 1992. Variables used for the study include S&P 500
index, one month T-bill rate, producer price index (inflation), twelve month discount bond
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rate, rate of change in industrial production index. It is found from the study that the
predictive power of various economic factors over the stock returns changes through time and
tends to vary with the volatility of returns.
Canova and Nicolo (1995) analyzed the relationship between stock returns and real
activity from the point of view of a general equilibrium, multi country model of the business
cycle, using correlation and regression techniques. The data set consists of quarterly data on
real stock returns, dividend yields and real GNP, consumption and investment for the US, the
UK, France, Germany and Italy for the period 1970-1991. We found from the study that
when government expenditure shocks drive the international cycle the association between
real GNP growth and stock returns is primarily due to the strong positive effect these
disturbances have on dividend payments. And, when technology shocks drive the cycle, the
association is weaker because dividend yields are less correlated with GNP.
Mookerjee and Yu (1997) explored the nexus between Singapore stock returns and
macroeconomic variables, using cointegration and causality techniques. Monthly time series
data from October 1984 through April 1993 was used. Macroeconomic variables used for the
study were money supply, nominal exchange rates and aggregate foreign currency reserves
and all-share price index for the Singapore stock market. The results indicated that three of
the four macro variables are cointegrated with stock prices, suggesting potential inefficiencies
in the long run. The causality tests and forecasting equations provide conflicting evidence on
the informational efficiency of the stock market in the short run.
Cheung and Ng (1998) studied the empirical evidence of long run co-movements
between five national stock market indexes and measures of aggregate real activity, including
the real oil price, real consumption, real money supply and real output (GNP), using
cointegration and error correction mechanism (ECM). The quarterly stock index and
macroeconomic data from 1957:Q1 to 1992:Q2 for Canada, Germany, Italy, Japan, and the
U.S. was considered for the study. The findings showed that the real stock market indexes are
typically cointegrated with measures of the countries’ aggregate real activity such as the real
oil price, real consumption, real money stock, and real output. Based on the ECM, it was also
found that the real returns on stock indexes are generally related to deviations from the
empirical long run relationship and to changes in macro variables.
Garcia and Liu (1999) examined the macroeconomic determinants of stock market
development, particularly market capitalization for fifteen industrial and developing countries
from 1980 to 1995, using correlation and regression techniques. The study focused on the
determinants of stock market capitalization as a proxy for stock market development.
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Macroeconomic variables considered were real income and income growth rate, the savings
and investment and the financial intermediary development. The paper found that the
variables like real income, saving rates, financial intermediary development, and stock
market liquidity are important determinants of stock market capitalization; and stock market
development and financial intermediary development are complements instead of substitutes.
Kwon and Shin (1999) investigated that whether current economic activities in Korea
can explain stock market returns by using a cointegration test and a Granger causality test
from a vector error correction model by using monthly data from January 1980 to December
1992. The macroeconomic variables used for the study include the production index,
exchange rate, trade balance, money supply, Korea Composite Stock Price Index (KOSPI)
and Small-size Stock Price Index (SMLS). The results showed that stock market index and
macroeconomic variables are cointegrated. The study also found that the stock price indices
are not a leading indicator for economic variables.
Gjerde and Saettem (1999) investigated to what extent important results on relations
among stock returns and macroeconomic factors from major markets are valid in a small,
open economy by utilizing the multivariate vector autoregressive (VAR) approach on
Norwegian data. Monthly time series data from 1974 to 1994 was used for the study.
Variables used include stock returns, interest rates, inflation, industrial production,
consumption, OECD industrial production index, foreign exchange rate NOK/USD, and oil
prices. The results suggested that real interest rate changes affect both stock returns and
inflation, and the stock market responds accurately to oil price changes. On the other hand,
the stock market shows a delayed response to changes in domestic real activity.
Nasseh and Strauss (2000) explored the existence of a significant, long-run relationship
between stock prices and domestic and international economic activity in six European
economies, namely, France, Germany, Italy, Netherlands, Switzerland and the U.K, using a
vector error correction model (VECM). The data set consists of quarterly data from 1962:Q1
to 1995:Q4 for real industrial production indices and business surveys of manufacturing
orders (real domestic macroeconomic activity), money market or call interest rates (short-
term interest rates) and long-term government bond rates (long-term interest rates); and the
industrial (INSEE) share price index represents stock prices (SP) for France, the all share
price index is used for Germany, Netherlands and Switzerland, the MSE share price index for
Italy, and the FT 500 share price index for the U.K. The results showed that the stock price
levels are significantly related to industrial production, business surveys of manufacturing
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orders, short- and long-term interest rates as well as foreign stock prices, short-term interest
rates and production.
Canova and Nicolo (2000) analyzed the empirical interdependencies among asset
returns, real activity, and inflation from multicountry and international points of view, using
the VAR model. Monthly data from 1973:1 to 1995:12 was used for the study. Variables used
were - measure of nominal stock returns (SR), slope of the nominal term structure (TERM),
real activity growth (IP), and inflation (INF). The findings of the study suggested that
innovations in nominal stock returns are not significantly related to inflation or real activity,
that the U.S. term structure of interest rates predicts both domestic and foreign inflation rates
and domestic future real activity.
Maysami and Koh (2000) examined the long-term equilibrium relationships between
the Singapore stock index and selected macroeconomic variables, as well as among stock
indices of Singapore, Japan, and the United States by using month-end data for the period
from January 1988 to January 1995. Variables used for the study were weighted average
closing prices for all shares listed on the Stock Exchange of Singapore, exchange rate of the
Singapore SDRs (Special Drawing Rights), Money Supply (M2), Consumer Price Index,
Industrial Production Index, 3-month Interbank Offer Rate, yield on 5-year government
securities, stock-price index of the United States, stock price index of Japan, Total Domestic
Export from Singapore. The methodology adopted was Vector Error-Correction Models
(VECM) to examine the dynamic relations. The study concluded that changes in Singapore’s
stock market levels do form a co-integrating relationship with changes in price levels, money
supply, short- and long-term interest rates, and exchange rates. While changes in interest and
exchange rates contribute significantly to the co-integrating relationship, those in price levels
and money supply do not. This suggests that the Singapore stock market is interest and
exchanges rate sensitive. Additionally, the article also concluded that the Singapore stock
market is significantly and positively co-integrated with stock markets of Japan and the
United States.
Ibrahim and Yusoff (2001) analyzed dynamic interactions among macroeconomic
variables such as real output, price level, and money supply, exchange rate, and equity prices
for the Malaysia (Kuala Lumpur Composite Index (KLCI)), using cointegration and vector
auto regression techniques. Monthly time series data from January 1977 to August 1998 was
considered for the study. The findings showed that the money supply exerts a positive effect
on the stock prices in the short run. However, money supply and stock prices are negatively
associated in the long run.
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David E. Rapach (2001) examined the effects of money supply, aggregate spending,
and aggregate supply shocks on real US stock prices in a structural vector auto regression
framework. Macroeconomic variables used for the study include S&P 500 index deflated by
the implicit GDP deflator, 3 month T-bills rate and GDP. Quarterly time series data from the
period 1959: Q3–1999: Q1 was used for the study The empirical results indicated that each
macro shock has important effects on real stock prices.. The real stock price impulse
responses to the various macro shocks follow to the standard present-value equity valuation
model, and they shed considerable light on the well-known negative correlation between real
stock returns and inflation.
Wongbangpo and Sharma (2002) investigated the role of selected macroeconomic
variables, i.e., GNP, consumer price index, money supply, interest rate and the exchange rate
on the stock prices in five ASEAN countries, namely, Indonesia, Malaysia, Philippines,
Singapore and Thailand, using cointegration and Granger causality. The data set consists of
monthly data from 1985 to 1996 for Jakarta composite stock price index (JCSPI) for
Indonesia, Kuala Lumpur Stock Exchange Composite Index (KLSE) for Malaysia,
Philippine Stock Exchange Composite Index (PSE) for Philippine, Stock Exchange of
Singapore Index (SES) for Singapore and the Stock Exchange of Thailand Index (SET) for
Thailand. The study observed long and short term relationships between stock prices and the
macroeconomic variables; and the macroeconomic variables in these countries cause and are
caused by stock prices in the granger sense.
Ewing (2002) studied the response of the NASDAQ Financial 100 index to
macroeconomic news, by using generalized impulse response analysis. Monthly time series
data from January 1988 to September 2000 was used for the study. Macroeconomic variables
used for the study were the coincident index (real output), changes in the fed funds rate
(stance of monetary policy), spread between Baa and Aaa corporate bond rates (interest rate
spread), consumer price index. The results indicated that a monetary policy shock reduces
financial sector returns, having a significant initial impact effect that continues to affect
returns for around 2 months. Unexpected changes in economic growth have a positive initial
impact effect, but exhibit no persistence. An inflation shock is associated with a negative and
statistically significant initial impact effect which lasts for up to 1 month after the time of
shock. The financial sector responds immediately to an unanticipated rise in risk, but the
effect does not persist into the future.
Carlstrom T.C., Fuerst S.T., Ioannidou P.V. (2002) studied the relationship between
stock prices (S&P 500) and the GDP of US for quarterly data from 1961:Q1 to 2001:Q1. The
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simple correlation technique was used for the study and concluded that future GDP growth
affects current stock prices, and this change in stock prices affects future GDP growth. It was
also found that an upcoming decline in productivity will lower GDP tomorrow and cause the
stock market to drop today.
Flannery and Protopapadakis (2002) studied that whether future GDP growth affects
current stock prices, and this change in stock prices affects future GDP growth. GARCH
model was used to estimate the study. Seventeen macro-series announcements over the
period from 1980 to 1996 were taken into consideration. From the study it was found that six
of the seventeen macro variables are strong risk factor candidates. Of these, two inflation
measures (the CPI and the PPI) affect only the level of the market portfolio’s returns. Three
real factor candidates (balance of trade, employment/unemployment, and housing starts)
affect only the return’s conditional volatility. A monetary aggregate (generally M1) affects
both returns and conditional volatility.
Ibrahim and Aziz (2003) analyzed dynamic linkages between stock prices (month end
values of the Kuala Lumpur Composite Index (KLCI)) and four macroeconomic variables
viz-a-viz real output (industrial production index), price level (CPI), money supply (M2) and
exchange rate (bilateral Ringgit exchange rate) for the case of Malaysia using cointegration
and vector auto regression techniques. The data was of monthly frequency for the period
from January 1977 to August 1998. Empirical results suggested the presence of a long-run
relationship between these variables and the stock prices.
AL-Sharkas, Adel (2004) paper analyzed long-term equilibrium relationships between a
group of macroeconomic variables and the Amman Stock Exchange index (Jordan), by using
macroeconomic variables, namely, industrial production index, the consumer price index,
money supply (M2) and the Treasury bill rate. Quarterly data from 1980:Q1 to 2003:Q3 was
used along with the methodology of vector error correction model (VECM) was used for the
study. The results of the study showed that these macroeconomic variables are cointegrated
i.e., there exists a cointegrating relation among the variables.
Nishat, Shaheen and Hijazi (2004) analyzed long-term equilibrium relationships
between a group of macroeconomic variables and the index of Karachi Stock Exchange by
using Granger-causality and VAR techniques. Macroeconomic variables used for the study
were industrial production index, the consumer price index, Ml, and the value of an
investment earning the money market rate. Data of quarterly frequency from 1973:Q1 to
2002:Q4 was used for the study. The results showed that all the variables are cointegrated and
it was also indicated that the industrial production is the largest positive determinant of
85
Pakistani stock prices, while inflation is the largest negative determinant. The results also
confirmed that macroeconomic variables Granger-caused stock price movements and the
reverse causality were observed in the case of industrial production and stock prices.
Naceur, Ghazouani and Omran (2005) tried to identify the main macroeconomic
determinants of stock market development and examined the impact of financial intermediary
development on stock market capitalization. The study was conducted using an unbalanced
panel data from twelve MENA region countries, using yearly data from 1990 to 1999.
Macroeconomic variables used were Income, savings rate, investment rate, credit to the
private sector, M3, stock market liquidity and macroeconomic stability. The study found that
saving rate, financial intermediary (specially credit to private sector), stock market liquidity
(specially the ration of value traded to GDP) and the stabilization variable (inflation change)
are the important determinants of stock market development; and it was also found that
financial intermediaries and stock markets are complements rather than substitutes in the
growth process.
Menike (2006) investigated the effects of macroeconomic variables on stock prices in
emerging Sri Lankan stock market (Colombo Stock Exchange), using multivariate regression.
Monthly time series data from September 1991 to December 2002 was considered for the
study. The variables used for the study were money supply, exchange rate, inflation rate and
interest rate. Findings suggest that inflation rate and exchange rate react negatively to the
stock prices of the Colombo Stock Exchange (CSE) and the negative effect of Treasury bill
rate implies that whenever the interest rate on Treasury securities rise, investors tend to
switch out of stocks causing stock prices to fall.
Yusof and Majid (2007) explored both short- and long-run dynamics between the
macroeconomic variables and stock market behavior in Malaysia (Kuala Lumpur Composite
Index (KLCI)) during the post 1997 financial crisis, using Autoregressive Distributed Lag
(ARDL) model. Monthly frequency data from May 1999 to February 2006 was used for the
study. Macroeconomic variables used were industrial production index (IPI), federal funds
rate (FFR), real effective exchange rate and interest rate (T-bill rate). The study concluded
that changes in the FFR, seems to have a significant direct impact on the Malaysian stock
market behavior during the period of analysis. This implies that any changes in the US
monetary policy may affect the Malaysian stock market.
Humpe and Macmillan (2007) examined whether a number of macroeconomic
variables influence stock prices in the US and Japan, using cointegration analysis. Monthly
data from January 1965 to June 2005 was used for the study. Variables used for the study
86
include industrial production, the consumer price index, money supply, long term interest
rates and stock prices in the US and Japan. The results suggested that for the US, stock prices
are positively related to industrial production and negatively related to both the consumer
price index and a long term interest rate. However, for Japan, stock prices are influenced
positively by industrial production and negatively by the money supply.
Thomas Nitschka (2007) studied the international evidence for return predictability and
the implications for long-run covariation of the G7 stock markets, using VECM techniques.
Quarterly data from the period 1969:Q4 to 2005:Q1 was considered for the study. The
findings suggested that there exists a common temporary component in international stock
markets that is reflected in the predictive power of short-run variations in the U.S.
consumption-wealth ratio, cay, for excess returns on foreign stock markets at the business
cycle frequency. This common component is responsible for 15 to 60 percent of the
covariation between 3-year excess returns on the G7 stock markets.
Coleman and Tettey (2008) investigated the effects of macroeconomic indicators on the
performance of Ghana Stock Exchange (GSE), using Cointegration and the error correction
model techniques. Quarterly time series data from the period 1991:Q1 to 2005:Q4 were used.
Variables used for the study include GSE all-share-index (GSI), inflation, real exchange rate,
interest rates (91 day T bill rates) and Ashanti Goldfields Company (AGC) dummy. The
findings of the study revealed that lending rates from deposit money banks have an adverse
effect on stock market performance and particularly serve as a major hindrance to business
growth in Ghana. And it was also found that inflation rate has a negative effect on stock
market performance.
Hasan and Nasir (2008) examined the relationship between macroeconomic variables,
namely inflation, industrial production, oil prices, short term interest rate, exchange rates,
foreign portfolio investment, money supply and the stock prices of Pakistan (Karachi Stock
Index), by using monthly data from June 1998 to June 2008 by employing ARDL approach.
Results of ARDL long run coefficients reveal that industrial production, oil prices and
inflation are statistically insignificant in determining equity prices in the long run while
interest rates, exchange rates and money supply have a significant long run effect on equity
prices. The error correction model based upon ARDL approach captures the short term
dynamics of prices and it also confirms that changes in industrial production, oil prices and
inflation are not statistically significant in the short run while changes in interest rates,
exchange rates, and money supply have significant short term effect. However, foreign
87
portfolio investment has significant short term effect in the short term and no long term effect
in the long term.
Abdul Rashid (2008) investigated the dynamic interactions between four
macroeconomic variables and stock prices in Pakistan, using cointegration and Granger
causality tests. Variables used for the study were the general Share Price Index, consumer
price index, manufacturing output index (industrial production), nominal exchange rate and
the market rate of interest. Monthly frequency data from June 1994 to March 2005 were used
for the study. The results revealed that there is long run bidirectional causation between the
stock prices and all the said macroeconomic variables with the exception of consumer prices
that only lead to stock prices.
Abugri (2008) investigated whether dynamics in key macroeconomic indicators like
exchange rates, interest rates, industrial production and money supply in four Latin American
countries significantly explain market returns. The Morgan Stanley Capital International
(MSCI) world index and the U.S. 3-month T-bill yield was also included to proxy the effects
of global variables. Vector autoregressive (VAR) model was adopted for empirical
estimation, using monthly data from January 1986 to August 2001. The study found that the
global factors are consistently significant in explaining returns in all the markets. The country
variables are found to impact the markets at varying significance and magnitudes.
Sohail and Hussain (2009) examined the long-run and short-run relationships between
Lahore Stock Exchange and macroeconomic variables such as index of industrial production,
money supply (M1), interest rate and CPI in Pakistan. Quarterly data starting from 1973:1 to
2004:4 were used for the study. Cointegration test and VECM approach were used to
estimate the results of the study. The results revealed that there is a negative impact on
consumer price index on stock returns, while the industrial production index, real effective
exchange rate, money supply had a significant positive effect on the stock returns in the long-
run.
Adam and Tweneboah (2009) examined the impact of macroeconomic variables on
stock prices in Ghana, by using quarterly data from 1991-Q1 to 2007-Q4. The variables used
for the study were the Databank stock index, inward foreign direct investments, the Treasury
bill rate, the consumer price index, average crude oil prices, and the exchange rate as
macroeconomic variables. For the study co-integration test and vector error correction models
(VECM) were adopted to examine both long-run and short-run dynamic relationships. The
paper established that there exist long-run cointegration between macroeconomic variable
88
and Stock prices. The VECM analysis shows that the lagged values of interest rate and
inflation has a significant influence on the stock market.
Hussainey and Ngoc (2009) investigated the effects of macroeconomic indicators (the
interest rate and the industrial production) on Vietnamese stock prices. For the study monthly
time series data from January 2001 to April 2008 was used. The methodology introduced by
Nasseh and Strauss and Canova-de-Nicolo to investigate the linkage between stock prices and
macroeconomic indicators was used. It was found that there are statistically significant
associations among the domestic production sector, money markets, and stock prices in Viet
Nam. Another novel finding was that the US macroeconomic fundamentals significantly
affect Vietnamese stock prices. Finally, the results showed that the influence of the US real
sector is stronger than that of the money market.
Charles K. D. Adjasi (2009) analyzed the impact of macroeconomic uncertainty on
stock‐price volatility in Ghana. The method of analysis is in two stages. The first stage
estimates uni-variate volatility models for each macroeconomic variable; namely consumer
price index (proxy for inflation), exchange rate, money supply, interest rates, oil price, gold
price, and cocoa price using the exponential generalized autoregressive conditional
heteroskedasticity (EGARCH) model. In the second stage volatility effect of macroeconomic
variables on stock prices is estimated using the most recent squared residuals from the
mean‐conditional variance of macroeconomic variables as exogenous variables in the
conditional variance equation of the stock price. The results showed that higher volatility in
cocoa prices and interest rate increases volatility of the stock prices, whilst higher volatility in
gold prices, oil prices, and money supply reduces volatility of stock prices.
Keray Raymond (2009) focused on the interrelationships between stock prices and
monetary indicators by examining the dynamics between these variables for Jamaica using
monthly data from January 1990 to March 2009. Variables used for the study were Jamaica
stock exchange index, money supply, interest rate, inflation rate and the exchange rate. The
Johansen cointegration test was used to determine the long term relationship between stock
prices and monetary variables. It was found that the variables were co-integrated with
significant relationships in line with a priori expectations. Coefficients from the co-
integrating vector, normalized on the stock price, suggest that the JSE Main Index is
positively influenced by the inflation rate and M3 and negatively by the exchange rate,
interest rate and M2.
Rahman, Sidek and Tafri (2009) studied the interactions between selected
macroeconomic variables and stock prices for the case of Malaysia, using VECM. Monthly
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time series data from January 1986 to March 2008 was considered for the study. Variables
used for the study include Kuala Lumpur Composite Index (KLCI),industrial production
index (IPI), real exchange rate (RER), money supply (M2), reserves (RES) and interest rates
(TB). The study concluded that all six variables contribute significantly to the co-integrating
relationship. This proves that the Malaysian stock market is sensitive to changes in the
macroeconomic variables. Furthermore, based on the variance decomposition analysis, the
paper highlights that the Malaysian stock market has stronger dynamic interaction with
reserves and industrial production index.
Shiu-Sheng Chen (2009) investigated whether macroeconomic variables can predict
recessions in the stock market, i.e., bear markets of the US, using parametric and
nonparametric approaches. Monthly returns of the S&P 500 price index from February 1957
to December 2007 and macroeconomic variables used were interest rate spreads, inflation
rates, money-stocks, aggregate output, unemployment rates, federal funds rates, federal
government debt, and nominal exchange rates. Results suggested that among the
macroeconomic variables, yield curve spreads and inflation rates are the most useful
predictors of recessions in the US stock market.
Alam and Uddin (2009) studied the relationship between stock index and interest rate
for fifteen developed and developing countries- Australia, Bangladesh, Canada, Chile,
Colombia, Germany, Italy, Jamaica, Japan, Malaysia, Mexico, Philippine, S. Africa, Spain,
and Venezuela, using the random walk model. Monthly data from January 1988 to March
2003 was considered for the study. It was found that for all of the countries, interest rate has a
significant negative relationship with share price and for six countries it is found that changes
of the interest rate has a significant negative relationship with changes of share price. So, if
the interest rate is considerably controlled for these countries, it will be the great benefit of
these countries’ stock exchange through demand pull way of more investors in share market,
and supply push way of more extensional investment of companies.
Rjoub, Tursoy and Gunsel (2009) studied the effects of macroeconomic factors on
stock returns of Istanbul Stock Market, using monthly frequency data from January 2001 to
September 2005. The methodology employed was OLS techniques, using macroeconomic
variables, namely, the term structure of interest rate, unanticipated inflation, risk premium,
exchange rate and money supply. The results of the study indicated that there exist a
significant pricing relationship between the stock return and the tested macroeconomic
variables; namely, unanticipated inflation, term structure of interest rate, risk premium and
money supply have a significant effect in explaining the stock market returns in various
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portfolios. But these results showed a weak explanatory power based on the findings. This
means that there are other macroeconomic factors affecting stock market returns in ISE other
than the tested ones.
Pilinkus and Boguslauskas (2009) studied the short-run relationship between
Lithuanian stock market prices and macroeconomic variables by employing the Impulse
response function by using monthly data from January 2000 to June 2009. The
macroeconomic variables used in this paper were as follows: seasonally adjusted gross
domestic product (GDP) at previous year prices; harmonized consumer price index (HCPI),
the narrow money supply (M1), unemployment rate (UR); three months Vilnius inter-bank
offered rate. The result revealed that the GDP and money supply have a positive effect on
stock market prices while most of the time unemployment rate, exchange rate, and short-term
interest rates negatively influence stock market prices.
Dickinson (2010) explored the relationship between selected European stock markets
(France, Germany, UK and US) and macroeconomic fundamentals, using a vector error
correction model (VECM). Monthly data from January 1988 to December 1995 was
considered for the study. Variables used include a real share index (own currency), real share
index ($US terms), industrial production, real interest rate and real exchange rate. The study
found a long run relationship between the stock market index and the selected
macroeconomic variables.
Lijuan and Ye (2010) studied the relationship between macroeconomic factors and
stock prices in China (Shanghai composite Index), using monthly frequency data from
January 2008 to December 2009. The methodology employed for the study was multi-linear
regression model. Macroeconomic variables used for the study include exchange rate,
corporate goods price index, interest rate (base rate for RMB deposit), macroeconomic
prosperity index (the consistency index reflects the basic trend of the current economy,
synthesized from industrial production, employment, social demand and social income),
consumer confidence index, money supply, national foreign exchange reserve. The results of
the study showed that the change in stock price is mainly affected by the exchange rate,
interest rate, macroeconomic prosperity index, consumer confidence index and corporate
goods price index.
Cherif and Gazdar (2010) studied the influence of the macroeconomic environment and
institutional quality on stock market development, using data from 14 MENA countries over
the period of 1990-2007. Both panel data and instrumental variable techniques were used for
the empirical estimation. Variables used were; income level, savings, investment rate,
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financial intermediary development, stock market liquidity, and macroeconomic stability.
The study found that income level, saving rate, stock market liquidity, and interest rate
influence stock market development with the expected theoretical signs. The results also
showed that the banking sector and the stock market sectors, are complementary instead of
being substituted. It was also found that the institutional environment as captured by a
composite policy risk index does not appear to be a driving force for the stock market
capitalization in the region.
George Filis (2010) examined the relationship among the consumer price index,
industrial production, stock market and oil prices in Greece. Cointegration, VECM and the
multivariate VAR model used to study the data. Monthly data from January 1996 to June
2008 was used. Findings suggested that oil prices and the stock market exercise a positive
effect on the Greek CPI, in the long run. The cyclical components analysis suggested that oil
prices exercise significant negative influence on the stock market and oil prices are
negatively influencing CPI, at a significant level.
Nicholas M Odhiambo (2010) examined the relationship between banks and stock
market development in the South Africa by adopting ARDL-Bounds testing approach.
Annual time series data from the year 1969 to 2008 has been used for the study. The variables
used were, bank development (ratio of the domestic credit to the private sector to GDP), the
stock market development (the stock market capitalization to GDP), per capita real GDP, a
savings ratio to GDP and inflation. The empirical results show that there is a distinct positive
relationship between banks and stock markets in South Africa. The results apply irrespective
of whether the model is estimated in the short run or in the long run. Other results show that
in the short run, the stock market development in South Africa is positively determined by the
level of savings, but negatively affected by the rate of inflation and the lagged values of the
stock market development. However, in the long run, the stock market is positively
determined by real income and the inflation rate.
Victor and Kuwornu (2011) examined the relationship between macroeconomic
variables and stock market returns, using three multivariate APT models with the dependent
variables as Ghana All Share Index. Monthly time series data from January 1992 to
December 2008 was considered for the study. Macroeconomic variables used were consumer
price index, crude oil price, exchange rate and 91 day Treasury bill. The empirical results
revealed that there is a significant relationship between stock market returns and three
macroeconomic variables; consumer price index, exchange rate and the Treasury bill rate.
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Where, consumer price index had a positive significant effect, while exchange rate and the
Treasury bill rate had negative significant influence on stock market returns.
Adaramola, Anthony Olugbenga (2011) investigate the impact of macroeconomic
indicators on stock prices in Nigeria (study based on the individual firm’s level), using both
time series and cross-sectional data. Quarterly data from 1985:Q1 and 2009:Q4 were used for
the analysis. The macroeconomic variables used for the study were money supply (BRDM),
interest rate (INTR), exchange rate (ECHR), inflation rate (INF), oil price (OIL) and gross
domestic product (GDP). The empirical findings of the study revealed that macroeconomic
variables have varying significant impact on stock prices of individual firms in Nigeria. Apart
from inflation rate and money supply, all the other macroeconomic variables have significant
impacts on stock prices in Nigeria.
Hussain (2011) investigated the return and the volatility response of major European
and US equity indices to monetary policy surprises by utilizing extensive intra-day data on 5-
min price quotes (from September 1, 2000 through September 30, 2008) along with a
comprehensive dataset on monetary policy decisions and macroeconomic news
announcements. Return-generating model and Volatility response model were adopted for the
estimation. The results of the study indicated that the monetary policy decisions generally
exert immediate and significant influence on stock index returns and volatilities in both
European and the US markets. The findings also showed that press conferences held by the
European Central Bank (ECB) that follow monetary policy decisions on the same day have a
clear impact on European index return volatilities.
Yu Hsing (2011) examined the macroeconomic determinants of the U.S. stock market
index, using the GARCH model. Quarterly time series data from 1978:Q1 to 2010:Q1 was
used for the study. Macroeconomic variables used for the study were the stock market index
in U.S., real output in U.S., stock earnings, the government debt, the money supply, the real
short-term interest rate in the U.S., the real long-term interest rate in the U.S., the nominal
effective exchange rate (NEER), the expected inflation rate, the foreign stock market index,
and the foreign interest rate. The findings suggested that a higher real GDP, a higher stock
earning, a lower government debt/GDP ratio, a lower M2/GDP ratio, a lower real Treasury
bill rate, a lower real corporate bond yield, a higher nominal effective exchange rate (NEER),
a lower expected inflation rate, a higher U.K. stock index, or a lower U.K. Treasury bill rate
would cause the U.S. stock market index to rise.
Oseni and Nwosa (2011) examined the volatility in the stock market and
macroeconomic variables in Nigeria, and used lag-augmented vector auto regression (LA-
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VAR) Granger Causality test for annual data from 1986 to 2010. Variables used for the study
were real GDP growth, inflation rate, interest rate and stock returns. The results of the
findings revealed that there exists a bi-directional causal relationship between stock market
volatility and real GDP; and there is no causal relationship between stock market volatility
and the volatility in interest rate and inflation rate.
Olweny and Kimani (2011) investigated the causal relationship between stock market
performance and economic growth in Kenya, using cointegration and granger causality test.
Quarterly time series data for the period 2001:Q1-2010:Q4 was considered for the study.
Variables used for the study include NSE 20-share index (Nairobi stock exchange), GDP and
CPI. The variables were found to be cointegrated with at least one co-integrating vector. And
the results of the Granger causality test indicated that the causality between economic growth
and stock market runs unilaterally or entirely in one direction from the NSE 20-share index to
the GDP.
Ade. O. Adenuga (2011) examined the relationship between stock market development
and economic growth in Nigeria, using vector error-correction model (VECM) technique.
Quarterly data from 1990:Q1 to 2009:Q4 was employed for the study. Variables incorporated
in the study were economic growth (rate of change of real GDP), macroeconomic stability
(CPI), Investment Ratio (gross fixed capital formation divided by nominal GDP), Market
Capitalization Ratio (market capitalization as a ratio of GDP), Capital Flows (foreign direct
investment as a percentage of GDP), Banking Sector Development (domestic credit provided
by the banking system to the private sector relative to GDP). The study showed that the
model validates the hypothesis that stock market development promoted economic growth in
Nigeria during the period of analysis.
Hsing (2011) examined the relationship between Hungary’s stock market index and
relevant macroeconomic variables by employing the GARCH model. Monthly frequency data
from 2000:Q1 to 2010:Q2 was used for the study. Macroeconomic variables used for the
study real output, government debt, money supply, real interest rate in Hungary, nominal
effective exchange rate (NEER), expected inflation rate, foreign stock market index and
foreign interest rate. The study found that Hungary’s stock market index has a positive
relationship with real GDP, the ratio of the government debt to GDP, the nominal effective
exchange rate and the German stock market index, a negative relationship with the real
interest rate, the expected inflation rate and the government bond yield in the euro area, and a
quadratic relationship with real M2 money supply.
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Zivengwa, Mashika, Bokosi and Makova (2011) explore the causal link between stock
market development and economic growth in Zimbabwe using annual time series data for the
period 1980 to 2008. The stock market development was measured using two variables,
namely stock market capitalization as a ratio of GDP and value of stocks traded as a ratio of
stock market capitalization and the macroeconomic variables used were per capital real GDP
and investment. Vector Autoregressive (VAR) and Granger Causality tests were applied for
the estimations. The results showed a unidirectional causal link that runs from stock market
development to economic growth and there is evidence of an indirect transmission
mechanism through the effect of stock market development on investment.
Abu-Libdeh, H., and Harasheh, M. (2011) investigated the correlation and causality
relationships between stock prices in Palestine and some macroeconomic variables, namely
GDP, inflation, exchange rate, Libor rate and balance of trade, by employing quarterly data
from March 2000 to June 2010. The methodology used includes regression analysis and
Granger Causality Test. The results of the regression analysis indicated a significant
relationship between the macroeconomic variables used and stock prices. Further, the
causality analysis neglected any kind of causal relationships between each particular
macroeconomic variable and stock price.
Ali, M. B. (2011) investigated the impact of changes in selected microeconomic and
macroeconomic variables on stock returns at Dhaka Stock Exchange, using monthly
frequency data from July 2002 to December 2009. A Multivariate Regression Model
computed with Standard OLS Formula has been used to estimate the relationship. The
variables used for the study include DSE all share price index as dependent variable and
inflation (CPI), industrial production index and foreign remittance as macroeconomic
predictor variable and market price/earnings commonly known as Market P/E and monthly
average growth in market capitalization measured in percent as macroeconomic predictor
variables. The study found that inflation and foreign remittance have a negative influence and
industrial production index; market P/Es and monthly percent average growth in market
capitalization have a positive influence on stock returns.
Hsing (2011) examined the relationship between the Czech stock market index and
selected macroeconomic variables using quarterly data from 2002:Q1 to 2010:Q2. The
variables used for the study include real output, government borrowing, money supply,
domestic real interest rate, CZK/USD exchange rate, expected inflation rate, foreign stock
market index, and foreign interest rate. Regression techniques were used for the estimation of
the study. The study found that the Czech stock market index is positively associated with
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real GDP and the German and US stock market indexes, is negatively influenced by the ratio
of government borrowing to GDP, the domestic real interest rate, the CZK/USD exchange
rate, the expected inflation rate and the euro area government bond yield, and exhibits a
quadratic relationship with the ratio of M2 to GDP.
Barbic and Jurkic (2011) tested the presence of informational inefficiencies in stock
markets of selected CEE countries (Croatia, Czech Republic, Hungary, Poland and Slovenia)
analyzing the relationship between stock market indices and macroeconomic variables,
namely, including inflation rate, broad money supply, money market interest rate and foreign
currency reserves. Johansen cointegration method and Granger causality test were employed
for empirical estimation. Cointegration results showed a long run relationship between stock
market indices and macroeconomic variables, especially in the case of Poland and Czech
Republic. And the results of granger causality test revealed that there is no causal linkage
between any macroeconomic variable and stock market index in Croatia, Hungary and
Poland; money supply and foreign exchange lead stock index in Czech Republic, while
inflation rate and money market interest rate lead Slovene stock index; and stock market
index leads money market interest rate in Hungary and Czech Republic, foreign exchange
reserves in Slovenia and money supply in Poland.
Athapathu and Jayasinghe (2012) examined the causal relationship between stock
market performance and economic growth in Sri Lanka for annual data from the year 1997 to
2008. Econometric methods such as co-integration analysis, error-correction mechanism and
Granger causality tests were employed to investigate the relationship between market
capitalization of all share price index; and real and nominal GDP. Results revealed that a
unidirectional causal relationship is observed between stock market performance indicators
and GDP growth.
Muhammed Monjurul Quadir (2012) investigated the effects of macroeconomic
variables like Treasury bill, interest rate and industrial production on stock returns on the
Dhaka Stock Exchange for the period from January 2000 to February 2007 by using monthly
time series data and Autoregressive Integrated Moving Average (ARIMA) model was
adopted to determine the relationship between stock return and macroeconomic variables.
Though the ARIMA model founds a positive relationship between Treasury bill, interest rate
and industrial production with market stock returns, but the coefficients have turned out to be
statistically insignificant.
Tsai (2012) estimated the relationship between the stock price index and exchange rate
for six Asian countries, using ordinary least squares method. Monthly data for the stock and
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foreign exchange markets in Singapore, Thailand, Malaysia, the Philippines, South Korea,
and Taiwan from January 1992 to December 2009 were used. The results of the study showed
that the data in all six Asian countries have a similar pattern in the various coefficients
obtained from different quantile functions. The coefficients are more significantly negative
when the exchange rates are extremely high or low. The negative coefficients support the
portfolio balance effect in these two markets, which states that the increase (decrease) of the
returns of stock price index will decrease (increase) the exchange rate, which means the
domestic currency appreciates (depreciates).
Muthike and Sakwa (2012) studied weather macroeconomic indicators can be used as
predictors of the stock exchange index trends. Annual time series data from 1976 to 2008 was
used for the study. Variables used were money supply, inflation rate, T bill rate, gross
domestic product and the foreign exchange rate, and the Nairobi Stock Exchange (NSE) 20
share index. The findings of the study showed that the 91-Day Treasury Bills and the
Inflation rate were the only clear leading macroeconomic indicators on the NSE 20-Share
Index. The money supply and real exchange rates were both leading and lagging
macroeconomic indicators on the NSE 20-Share index; and the gross domestic product
showed the weakest relationship with the NSE 20 Share index.
Sajjad, Shafi, Jan, Saddat and Rehman (2012) examined the relationship between
Karachi stock exchange and macroeconomic variables, i.e. inflation rate, exchange rate,
treasury bills and interest rate by using monthly time series data from January 2005 to
December 2010. The co-integration test and granger casualty was applied to drive the short
and long-term investigation. The results found bi-directional granger causality between KSE
and exchange rate and unidirectional granger causality exists from interest rates to KSE.
Mehmet Gencturk, Ismail Celik and Omer Binici (2012) studied the causal relationship
between Istanbul Stock Exchange (ISE) stock prices, dollar rate, consumer price index,
interest rates and industrial production index by using monthly data from January 2005 to
July 2011. Methodology adopted was Johnsen-Juselius co-integration test, Vector Error
Correction Model (VECM). Research showed that the existence of a long-run relationship is
only between ISE and industrial production index. VECM showed a unidirectional causality
from stock prices to industrial production index.
Ochieng, D. E., & Adhiambo, E. O. (2012) investigated that whether the changes in
macroeconomic variables can be used to predict the future NSE All share index (NASI) for
Kenya. The three key macroeconomic variables were examined which includes lending
interest rate, inflation rate and 91 day Treasury bill (T-bill) rate from March 2008 to March
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2012. The data were analyzed using regression methods. It was concluded that that the 91 –
day T bill rate has a negative relationship with the NASI while inflation has a weak positive
relationship.
Javed and Akhtar (2012) investigated the risk-return relationship of three major
macroeconomic indicators, money supply, term structure, and interest rate with stock returns
of 50 firms listed at the Karachi stock exchange of Pakistan, for monthly data from July 1998
to December 2008. The study employed GARCH model to demonstrate the behavior of the
variance of macroeconomic variables in relation with stock returns. The results found a
significant relationship between the macroeconomic indicators and stock return, and showed
that macroeconomic indicators as risk factors influence the movement of returns. The money
supply risk positively effects, stock returns and exchange rate shock negatively effects stock
return.
Martin Sirucek (2012) focused on the effect, implication, impact and relationship
between selected macroeconomic variables and wider US indices S&P 500 and industrial
Dow Jones Industrial Average (DJIA). Macroeconomic variables used for the study were
inflation, interest rates, money supply, producer price index, industrial production index, oil
price and unemployment for annual data from 1999 to 2012 for USA. Correlation and
regression techniques, adopting the OLS method were used for the study. The results showed
that the producer price index, industrial production index, oil price and Dow Jones index are
having a stronger relationship than between these factors and S&P 500.
Douglas (2012) attempted to use the Arbitrage Pricing Theory framework to explain the
variations on the returns on the Ghana Stock Exchange, by employing Ordinary Least
Squares Regression, cointegration analysis and Granger causality tests. Monthly data from
1991 to 2009 was considered for the study. Macroeconomic variables used for the study
include inflation rate, Cedi-USD exchange rate, 91-Day T-Bill rate, broad money supply
(M2), World Cocoa and Gold prices and World Crude Oil prices. The results of the Ordinary
Least Squares regression analysis showed that four out of the seven macroeconomic variables
possess statistically significant power for stock returns on the Ghana Stock Exchange:
inflation rate, the treasury bill rate, money supply and world crude oil prices. Further, the
results of Granger cointegration test signal the existence of an overall long-run relationship
between stock returns and the observed variables on the GSE, the same could not be said of
the long-run relationship between individual macroeconomic variables and stock returns. On
the contrary, the Johansen and Juselius cointegration test shows the existence of at least two
cointegrating relationships between stock returns and the macroeconomic variables. Further,
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the Engle and Granger causality test points to unidirectional causality between stock returns
and the foreign exchange rate and the money supply
Hussin, Fidlizan, Abu and Awang (2012) focused on the relationship between the
development of Islamic stock market and macroeconomic variables in Malaysia. Vector Auto
Regression (VAR) method was applied for the estimation of the study. The variables
involved in this research are Kuala Lumpur Syariah Index (KLSI), Industrial Production
Index (IPI), Consumer Production Index (CPI), Aggregate Money Supply (M3), Islamic Inter
Bank Rate (IIR) and Exchange Rate of Malaysian Ringgit-United States Dollar (MYR) for
monthly data from April 1999 to October 2007. The results of the study showed that Islamic
stock prices are co-integrated with the selected macroeconomic variables, and the stock price
is related positively and significantly with IPI and CPI variables but related negatively and
significantly with M3 and MYR variables.
Zohaib Khan, Sangeen Khan and Lala Rukh (2012) studied the impact of interest rate,
exchange rate and inflation on stock returns of KSE 100 index for Pakistan. Monthly data
from July 2001 to June 2010 was used for the study. Multiple regression models were applied
for the estimation. The results showed that the exchange rate has significant impact on stock
returns of KSE 100 index.
Osisanwa and Atanda (2012) examined the determinants of the stock market returns in
Nigeria by employing the OLS techniques using annual data for the period between 1984 and
2010. Their variables were consumer price index, exchange rate, broad money, interest rate
and real per capital income. The findings showed that exchange rate, interest rate, money
supply and previous stock return levels are the primary determinants of stock returns in
Nigeria. Critical analysis of this study shows that the method used for the analysis is not
popular and widely used. In time series analysis, the ordinary least squares regression results
might provide a spurious regression if the time series are non-stationary. Again, consumer
price index is not an accurate index for inflation; this is because the index takes the price of
fixed representative basket and does not consider the price of investment.
Oriwo, E. A. (2012) investigates the relationship between macroeconomic variables on
NSE All share index of Kenya (Nairobi Securities Exchange), by using monthly frequency
data from March 2008 to March 2012. The three key macroeconomic variables used for the
study were Interest rate, Inflation Rate and 91 day T bill. The methodology applied include
ARDL approach. The findings of the study indicated that 91 –day T-bill has a negative
relationship with the NSE All share Index while Inflation do have a weak positive
relationship with the NASI index.
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Taiwo, M., Taiwo, A. and Olowookere, D. (2012) analyzed the impact of crude oil
price, stock price and some selected macroeconomics variables on the growth of the Nigerian
economy for annual data from 1980 – 2010. Co-integration and Error correction model were
used for estimation purpose. Macroeconomic variables used for the study were Growth rate
of Gross Domestic Product, Growth rate of stock price indexed by GDP, Growth rate of oil
price indexed by GDP, Interest rate and Real exchange rate. From the study it was found that
crude oil price, stock price and exchange rate have significant influence on the growth of the
Nigerian economy.
Ibrahim and Shah (2012) examine the interrelations between bank lending,
macroeconomic conditions and financial uncertainty for an emerging economy, Malaysia.
Macroeconomic variables used for the study include real bank loans, real GDP, nominal
lending rate, real stock prices and a measure of stock market volatility. Cointegration,
causality and vector auto regressions (VARs) techniques were used for the estimation for
quarterly data from the period 1991:1–2011:2. The study found a long run positive relation
between real output and both real bank credits and real stock prices.
Joseph Tagne Talla (2013) investigated the impact of changes in selected
macroeconomic variables on stock prices of the Stockholm Stock Exchange by applying
monthly data from January 1993 to December 2012. Variables used for the study were
Consumer Price Index (CPI) as a proxy for inflation rate, Exchange Rate (ER), Money
Supply (MS), Interest Rate (IR) and on the Stockholm Stock Exchange indices (OMXS30).
To estimate the relationship, unit root test, Multivariate Regression Model computed on
Standard Ordinary Linear Square (OLS) method and Granger causality test was used. Based
on estimated regression coefficients and t-statistics, it was found that inflation and currency
depreciation have a significant negative influence on stock prices. No unidirectional Granger
Causality was found between stock prices and all the predictor variables under study except
one unidirectional causal relation from stock prices to inflation.
Kalyanaraman and Tuwajri (2013) analyzed the long run relationship between five
macroeconomic variables viz-a-viz., consumer price index, industrial output, money supply,
exchange rate, oil prices along with the global stock prices proxy S&P 500 index and Saudi
all share stock index, using cointegration and VECM (vector error correction model).
Monthly data from January 1994 to June 2013 was considered for the study. It was found
from the study that all macroeconomic variables are found to impact stock prices, but
Standard and Poor’s 500 index does not affect Saudi stock prices. The results also showed the
presence of long run causality from the explanatory variables to the stock prices. Short run
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causality test finds a two-way causality between stock prices and oil prices. Impulse response
function showed that industrial production shocks pushes up stock prices while consumer
price index shocks pulls it down. Variance decompositions showed that historical stock prices
are the major driver of Saudi stock prices.
Babayemi Asare, Onwuka, Singh and James (2013) examined the panel data of seven
major African stock markets with a view to investigate the long run relationship between
these markets and some vital macroeconomic variables, using the Panel residual based test on
Pedroni and error correction based test of the Wasteland. The macroeconomic variables were
Stock Market index, External Debt, Money Supply and Foreign Direct Investment. The
African Stock Markets of the following countries used in the study were Botswana, Egypt,
Ghana, Kenya, Morroco, Nigeria, and South Africa for annual periodic data from 1988-2011.
The result showed that in the long run, Foreign Direct Investment (FDI) and External Debt
exert a positive impact on the African stock markets while negative impact will be recorded
for Money supply. However, the extent is much greater in FDI, as for every 1% increment in
its value brings about 2.01% change in market value.
Bellalah and Habiba (2013) investigated the long run relationship between
macroeconomic indicators, namely, trade, oil prices, rate of interest, money supply (M3),
index of industrial production and stock exchange price indices for USA, Japan and China.
Monthly time series data from January 2005 to May 2010 was considered for the study.
ARDL co-integration approach was used for empirical estimation. The results showed that
rate of interest, industrial production index and Money supply (M3) are positively related to
the stock prices in the long run and short run, for USA and China; and the rate of interest is
positive and highly significant in the long run for Japan.
Zhou, Zhao, Belinga and Gahe (2013) examined the macroeconomic factors that affect
the stock market development in Cameroon, using the Calderon-Rossell model, by applying
monthly data from January 2006 to December 2011. Variables used for the study include
Stock Market Capitalization, Domestic Credit to the Private Sector, Stock Market Value
Added Ratio, Gross Domestic Product per Capita, Gross Domestic Investment, Gross
Domestic Saving, Current Inflation Rate, Real Interest Rate, Foreign Direct Investment and
Net Capital Flows. The results of the study found that the stock market liquidity and financial
openness represented by foreign direct investment and private capital flows are important
determinants of stock exchange development in Cameroon.
Gupta and Modise (2013) examined both in-sample and out-of-sample predictability of
South African stock return using macroeconomic variables, using monthly data covering the
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sample period between January 1990 and December 1996, and the out-of sample period
commencing from January 1997 to June 2010. Variables used for the study were All share
index (real stock returns), relative long-term bond yield, relatively 90 days T bill rate, term
spread, the employment growth rate, inflation rate, real effective exchange rate, broad money
supply growth rate, narrow money supply growth rate, industrial production growth rate,
relatively money market rate, world oil production growth rate, and crude oil price growth
rate. For the in-sample test, the t-statistic corresponding to the slope coefficient of the
predictive regression model was used, and for the out-of-sample tests the MSE-F and the
ENC-NEW test statistics were employed. For the in-sample tests, the results showed that
different interest rate variables, world oil production growth, as well as, money supply have
some predictive power at certain short-horizons. For the out-of-sample forecasts, only interest
rates and money supply show short-horizon predictability. Further, the inflation rate shows
very strong out-of-sample predictive power from 6-month-ahead horizons.
Wang and Ajit (2013) investigated the impact of stock market development on
economic growth in China. To this end, the quarterly data from 1996 to 2011 was used and
the cointegration framework was adopted for empirical investigation. Variables used for the
study were market capitalization, real GDP, real government spending, and real money
supply (M1). It was found from the study that the stock market development generally does
not contribute positively to economic growth in developing countries if the stock market is
mainly an administratively-driven market.
Ikramullah, Ahmed, Kamel and Yaqoob (2013) study investigates the link between the
macroeconomic variables and equity returns in Pakistan, using monthly frequency data from
November 1991 to March 2013 of KSE-100 index, consumer price index (LCPI), the
industrial production (LIP), the exchange rate (LER), the money supply (LM2), and the
interest rate (LIR). The methodology used was the ARDL approach.The findings of the study
suggested that, in the short run, all the used macroeconomic variables affect stock returns.
While in the long run industrial production and money supply have a positive and
incremental impact on consumer price index. On the other hand, the interest rate has a
negative impact on stock returns.
Issahaku, Ustarz and Domanban (2013) examined the existence of causality between
macroeconomic variables and stock returns in Ghana (Ghana Stock Exchange), using
monthly time series data from the period January 1995 to December 2010. The methodology
employed was Vector Error Correction (VECM) model and Granger Causality tests.
Macroeconomic variables used include exchange rate (Cedi/United State dollar rate), the
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Consumer Price Index (to represent inflation), treasury-bill rate, money supply and FDI. The
findings of the study revealed that a significant long run relationship exists between stock
returns and inflation, money supply and Foreign Direct Investment (FDI). In the short-run, a
significant relationship exists between stock returns and macroeconomic variables such as
interest rate, inflation and money supply. Further, a causal relationship running from stock
returns to money supply, interest rate and FDI has also been revealed.
Naseri and Masih (2013) examined the long-term equilibrium relationships between
FTSE Bursa Malaysia Emas Shariah Index as a proxy for Islamic stock market and three
selected macroeconomic variables, namely, money supply, consumer price index and
exchange rate, using a vector error correction model (VECM). Monthly data from November
2006 to September 2013 was used for the study. The findings suggested that there is a
cointegration between Islamic stock market and chosen macroeconomic variables and
macroeconomic variables have had an influence on the Islamic stock market in Malaysia.
Abdelbaki (2013) investigated the relationship between macroeconomic variables and
Bahraini stock market development by using the Autoregressive Distributed Lag model.
Monthly frequency time series data from January 1990 to December 2007 was used. The
market capitalization as a percentage of GDP was used as a proxy of stock market
development. Macroeconomic variables used for the study include GDP, investment rate,
saving rate, credit to the private sector, per capita income, M2, FDI and GDP Deflators. The
findings of the study suggested that income level, domestic investment, banking system
development; private capital flows and stock market liquidity are important determinants of
Bahraini stock market development
Haroon and Jabeen (2013) examined the impact of macroeconomic variables, i.e. 3-
Months, 6-Month and 12 Month Treasury Bill Rate (Proxy of Interest Rate), Consumer Price
Index, Wholesale Price Index and Sensitive Price Index (Proxy for Inflation) with Karachi
Stock Exchange - KSE 100 Share index of Pakistan, using monthly frequency data from July
2001 to June 2010. Coefficient of correlation and regression analysis have been used to test
the hypothesis. The study examined the impact of inflation indices, interest rate (treasury
bills), on KSE movement. Further, the results showed that there was a significant relationship
between macroeconomic variables and KSE-100 Share index. The study further revealed a
significant impact of treasury bills on KSE-100 index.
El-Nader and Alraimony (2013) examines the causes of stock market development in
Jordan, using monthly frequency data from January 1990 to December 2011.The
methodology employed for the study includes co-integration and VECM techniques. The
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study used market capitalization as a percentage of GDP as a proxy for measurement of stock
market development and the macroeconomic variables used were nominal GDP, nominal
money supply (M2), total value traded ratio, growth rate of stock market liquidity, gross
capital formation, net remittances to Jordan, consumer price index and credit to the private
sector. The estimated findings demonstrated that the variables, namely, Money Supply, Total
Value Traded relative, Gross Capital Formation, Consumer Price Index (CPI), and Credit to
private Sector all have positive and considerable influences on stock market development. On
the other hand, nominal GDP and net remittances have a negative impact. The Johansen and
Juselius multivariate cointegration and variance decomposition analysis also confirmed the
presence of both a long-term and the short-term dynamic relationship between the Stock
market capitalization as a percentage of GDP and macroeconomic variables.
Mirza Vejzagic and Hashem Zarafat (2013) examined the long-term equilibrium
relationships between selected macroeconomic variables and the FTSE Bursa Malaysia
Hijrah Shariah Index. The methodology used for the study was VECM, VDC and IRF by
using monthly data from 2006 September to 2012 September. Macroeconomic variables used
for the study were, exchange rate, money supply, CPI and interest rate. The result shows that
there exists a cointegrating relationship, along with identification of the exogeneity and
endogeneity of the variables. It is depicted that FTSE Bursa Malaysia Hijrah Shariah Index
leads major macroeconomic variables which are interest rate, money supply, consumer price
index, and exchange rate.
Asma Rafique, Amara, Naseem and Sultana (2013) studied the impact of four
macroeconomic variables, i.e. GDP per capita, gross domestic savings, inflation and discount
rate on KSE index of Pakistan for annual data from 1991 to 2010. Statistical Package for
Social Sciences (SPSS) was used to test the multiple regression models. Results indicated that
GDP per capital and gross domestic savings have a significant and positive impact on KSE
Index. On the other hand, discount rate and inflation (being measured through CPI) possess a
significant but negative impact on KSE Index.
Sarwar, Aftab, Khan, and Qureshi (2014) examined the role of macroeconomic factors
like merchandize import, CPI, industry index, trade balance, exchange rate index, crude oil
prices, merchandize export, broad money supply and dollar price on stock return Karachi
Stock Exchange (KSE), Pakistan, using monthly data for the period from January 1997 to
December 2013. Multiple regression and correlation techniques were employed for the
empirical findings of the study. The findings showed that the trade balance and exchange rate
negatively affect the KSE 100 stock index, contrary to the merchandize import, CPI, industry
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index, crude oil price, merchandize export, broad money supply, and dollar price that affects
it positively.
Anigbogu and Nduka (2014) examined the long-run and causal relationship between
stock market performance and economic growth in Nigeria employing quarterly data for the
period from 1987:Q1 to 2012:Q4. The study used the Johansen Maximum Likelihood
cointegration technique, Vector Error Correction Model framework, Granger Causality,
Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). The
variables used for the study include real GDP, inflation, investment ratio, savings ratio,
turnover ratio, total value of shares traded ratio, market capitalization ratio, capital flows and
banking sector development. The results of the cointegration test confirmed that there exists a
long-run relationship between stock market performance and economic growth, while the
causality test results suggested that stock market performance causes economic growth with
feedback. Further, the Impulse Response Function (IRF) and Forecast Error Variance
Decomposition (FEVD) suggest that shocks from the stock market do not impede economic
growth.
Khodaparasti, R. B. (2014) studied the role and impact of macro variables on the
Iranian stock market, using monthly frequency data from 2007- 2011. Macroeconomic
variables used for the study include exchange rates, inflation, industrial index and money
supply (M1). The methodology employed was the Vector auto regression approach. The
results of the study showed that the exchange rate and industrial index have more effect on
the stock market than inflation and M1.
Ibrahim and Musah (2014) investigated the effects of macroeconomic variables on
stock market returns by employing the Johansen multivariate co-integration approach and
vector error correction model (VECM) by using monthly data from September, 2000 to
September, 2010. Variables used for the study were, inflation (INFL), exchange rate (EXR),
broad money supply (M2), interest rate (INTR), index of industrial production (IIP) and
Ghana Stock Exchange index (GSEI). Results of cointegration analysis showed the existence
of the long-run relationship between stock returns and macroeconomic fundamentals.
However, the Granger causality test could not establish causality from any direction between
macroeconomic variables and stock prices. Results from both the impulse response functions
and variance decomposition suggested that among the macroeconomic variables, shocks to
inflation, money supply and exchange rate do not only explain a significant proportion of the
variance error of stock returns but their effects persist over a long period.
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Pradhan, Arvin, Hall, & Bahmani (2014) examined the relationship between banking
sector development, stock market development, economic growth, and four other
macroeconomic variables in ASEAN countries, using principal component analysis for the
construction of the development indices and a panel vector auto-regressive model for testing
the Granger causalities. The data set consists of annual time series data from 1961 to 2012 for
banking sector development (BSD), stock market development (SMD), per capita economic
growth (GDP), and a set of four other macroeconomic variables (MED), namely foreign
direct investment (FDI), trade openness (OPE), inflation rate (INF), and government
consumption expenditure (GCE). The sample countries consists of the ten countries, among
the ARF-26 that are recognized as ARF-Member Countries (AMC), which includes Brunei,
Burma, Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand, and
Vietnam. The second broad sample consists of the nine countries, among the ARF-26 that are
recognized as ARF-Dialogue Partner Countries (ADC) which includes Australia, Canada,
China, India, Japan, New Zealand, the Korean Republic, the Russian Federation, and the
United States. The third broad sample consists of the six countries, among the ARF-26 that
are recognized as ARF-Observer Countries (AOC), which includes Papua New Guinea,
Mongolia, Pakistan, East Timor, Bangladesh, and Sri Lanka. The fourth sample consists of all
26 countries (ATC) that were included in the AMC, ADC, and AOC. The empirical study
founds the presence of both unidirectional and bidirectional causality links between these
variables.
Inyiama and Ekwe (2014) determined the relationship between All Share Index (the
proxy for capital market performance) and real gross domestic product, monetary policy rate,
inflationary rate and foreign exchange rate (the proxy for macroeconomic variables of the
study), applying annual frequency data from 1985 to 2013. The methodology adopted for the
study includes Granger Causality procedure, multiple regression models in the form of
Ordinary Least Square (OLS) method, correlation technique and Johansen cointegration
procedure. The findings of the study suggested that there is a unidirectional causality running
from log of All Share Index to foreign exchange rate. Johansen cointegration tests revealed a
long run relationship among the variables.
Hussain, Rasool, Baig, Fayyaz, & Mumtaz (2014) analyzed the long run and the causal
relationship between stock prices and selected macroeconomic variables for Islamabad Stock
Exchange using monthly data from January 2001 to December 2010. Methodology employed
was co-integration and Granger causality tests. The macroeconomic variables used for the
study include Industrial Production Index (IPI), Interest Rate of three month treasury bills
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(IR), Wholesale Price Index as inflation (WPI), Real Exchange Rate (ER), Exports (X),
Foreign Exchange Reserve (FER), Imports (M) and Money Supply (MS). The findings of the
study showed no causal relationship except exports while the long run relationship was
existed between stock prices and macroeconomic variables for ISE.
Samontaray, Nugali & Sasidhar (2014) studied the impact of different macro-economic
variables on the returns of the Saudi stock market (Tadawul All Stock Index (TASI)), using
monthly frequency data from December 2003 to December 2013. The methodology
employed for the study includes correlation and regression techniques. Macroeconomic
variables used for the study were Oil West Texas Intermediate (WTI), Saudi Export and PE
Ratio. The study confirmed that TASI is positively correlated with the three economic
variables considered, viz., Oil WTI, Saudi Exports and Price Earnings ratio. Since the three
independent variables are significantly correlated with the dependent variable, the step-wise
regression confirmed the significant importance each of these three variables have in
predicting the TASI. Further, it is observed that these three variables explain about 93% of
variation in TASI.
Mutuku& Ng’eny (2014) investigated the dynamic relationship between stock prices
and four macroeconomic variables in Kenya, using VAR and VECM framework, by
employing quarterly frequency data from 1997:Q1 to 2010:Q2. The variables used for the
study include Nairobi share prices (NSE), nominal gross domestic product-GDP, consumer
price index-CPI, Treasury bond rate and Nominal exchange rate-EXR. The study revealed
that positive relationships were found between the Nairobi share prices (NSE); the growth
rate (GDP), exchange rate (EXR) and T-bill rate (TBR). However, the study found a negative
relationship between NSE performance and consumer price index (CPI). The short term
analysis reveals that the relationship between the variables, adjust to equilibrium at a speed of
3.8% per quarter. The study nullifies the argument that the stock market can hedge inflation.
Kibria, Mehmood, Kamran, Arshad, Perveen and Sajid (2014) studied the impact of
macroeconomic variables on stock market returns in Pakistan (KSE 100 index of Pakistan)
using annual time series data from the year 1991 to 2013. Macroeconomic variables used for
the study include Inflation, GDP Per Capita, GDP savings, Money supply and Exchange rate.
The methodology employed for the study was Correlation Analysis, Granger Causality test
and Regression Analysis. The results of the Granger Causality test showed that there exists
unidirectional causality from GDP, savings and Exchange rate to Money supply. On the other
side, GDP savings also unidirectional Granger Cause the KSE. The results of Regression
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Analysis showed that the Inflation, Exchange rate, Money supply, GDP per capita and GDP
savings has a positive significant impact on the KSE 100 index.
Ouma and Muriu (2014) investigated the impact of the macroeconomic variables on
stock returns in Kenya (Nairobi Securities Exchange), using the Arbitrage Pricing Theory
(APT), Capital Asset Pricing Model (CAPM) framework and Ordinary Least Square (OLS)
technique. Monthly data from the period January 2003 to December 2013 was used.
Macroeconomic variables used for the study were money supply (M2), exchange rate,
inflation (CPI) and interest rate (91-T bill rates). The findings of the study suggested that
money supply, exchange rates and inflation affect the stock market returns in Kenya. The
exchange rate is, however, found to have a negative impact on stock returns, while interest
rates is not important in determining long rung run returns in the NSE.
Abdullah, Saiti & Masih (2014) investigated the lead-lag relationship between stock
market index and macroeconomic variables, using wavelet analysis, cointegration and
VECM. Monthly data from January 1996 to September 2013 was considered for the study.
Variables used include Kuala Lumpur Composite Index, exchange rate, inflation, government
bond yield, short-term interest rate and export. Findings suggested that the cointegration
relationship does exist between KLCI and selected macroeconomic variables. The results of
the error correction model, the generalized variance decompositions as well as the wavelet
cross-correlation analysis suggested that the short-term interest rate, KLCI and government
bond yields are exogenous variables; especially, the short-term interest rate is the most
leading variable.
Yu Hsing (2014) examined the relationship between the Romanian stock market index
and relevant macroeconomic variables by using quarterly data from 2001-Q4 to 2010-Q2.The
variables used for the study were Romanian stock market index, Industrial production index,
Government borrowing to GDP, M2 to GDP, domestic real interest rate, the nominal
effective exchange rate, expected inflation rate, and stock market index of U.S. For this study
GARCH model was adopted for empirical work. The study found that the Romanian stock
market index is positively affected by industrial production and the U.S. stock market index
and negatively associated with the ratio of government borrowing to GDP, the domestic real
interest rate, the expected inflation rate and the euro area government bond yield.
Barnor, C. (2014) examined the relationships between selected macroeconomic
variables and their effect on the stock market returns on the Ghana stock market, by
employing monthly frequency time series data from January 2000 to December 2013. The
methodology applied for the study, include multiple regression, VAR and VECM techniques.
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Macroeconomic variables used were inflation rate, exchange rate, interest rate, and money
supply. The findings revealed that interest rates and money supply had a significant negative
effect on stock market returns; however, exchange rates had a significant positive effect on
stock market returns. Moreover, inflation rate did not significantly affect stock market returns
in Ghana.
Pimentel and Choudhary (2014) analyzed the relationship of high inflation and interest
rates with stock returns of Brazil, using a tri-variate vector autoregressive (VAR) model.
Monthly time series data from May 1986 to May 2011 was considered for the study. The
findings suggested a bi-directional causal relationship between stock returns and inflation.
Pilinkus (2015) analyzes relationships between a group of macroeconomic variables
and the Lithuanian stock market index, i.e. OMX Vilnius index, using granger causality test.
Monthly time series data from December 1999 to March 2008 was considered for the study.
Variables used for the study were gross external debt (GED), gross domestic product (GDP),
gross domestic product deflator (GDPd), index of energy products (IEP), export volumes
(Ex), producer price index of industrial production (PPI), index of capital goods (ICG),
harmonised consumer price index (HCPI), import volumes (Im), index of durable consumer
goods (IDCG), granted permits for new residential buildings (GP), money supply in a narrow
sense (M1), money supply in a broader sense (M2), balance of payments (BP), investment in
tangible fixed assets (ITFA), retail trade index (RTI), unemployment rate (UR), final
consumption expenditure (FCE), changes in prices of industrial production (CPIP), index of
own-account construction work carried out within the country (IOCW), construction price
index (CPI), index of non-durable consumer goods (INCG), foreign direct investment (FDI),
index of intermediate goods (IIG), employment rate (ER), manufacturing index (MI),
exchange rate of the Litas against the US dollar (ExR), average number of hours actually
worked per employee per month (AHW), government final consumption expenditure
(GFCE), overnight Vilnius interbank offered rate (VILIBOR1N), one month Vilnius
interbank offered rate (VILIBOR1M), three months Vilnius interbank offered rate
(VILIBOR3M), six months Vilnius interbank offered rate (VILIBOR6M), one year Vilnius
inter-bank offered rate (VILIBOR1Y), the difference between one year and overnight Vilnius
interbank offered rates (VILIBOR1Y_1N), general government financial balance (GGFB),
general government revenue (GGR), general government expenditure (GGE), general
government debt (GGD), net export (NEx). The research revealed that some macroeconomic
variables (e.g., GDP deflator, net export, foreign direct investment, etc.) lead Lithuanian
stock market returns, some macroeconomic variables (e.g., GDP, material investment,
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construction volume index, etc.) are led by the OMXV index and, finally, some
macroeconomic indices (e.g., money supply, payment balance, etc.) and the stock market
returns Granger-cause each other.
Ahmad, Abdullah, Sulong and Abdullahi (2015) investigated the causal relationship
between stock market returns and macroeconomic variables in Nigeria using Autoregressive
Distributive Lag (ARDL) and a Vector Autoregressive Model (VAR). Annual time series
data of six variables, namely; broad money supply, nominal effective exchange rate, short
term T-bills rate, foreign direct investment, gross domestic per capita income, and gross
domestic saving from 1984-2013 were employed in the study. The Bounds test revealed that
the stock market returns and the macroeconomic variables were cointegrated and, thus, a
long-run equilibrium relationship exists between them. The results of Granger causality tests
showed that some of the macroeconomic variables were having bidirectional causality with
the stock market returns; while others have unidirectional causality. Furthermore, the impulse
response function indicated that the impact of shocks in broad money supply, nominal
effective exchange rate, gross domestic per capita income and short-term treasury bill rate on
the stock market returns in this study was consistent with other stock market empirical
results. The variance decomposition test indicated that the stock market returns can be
explained by gross domestic saving and nominal effective exchange rate.
Ilahi, I., Ali, M., & Jamil, R. A. (2015) investigated the linkage between
macroeconomic variables, namely inflation rate, exchange rate and interest rate on stock
market returns in Pakistan (Karachi stock exchange), by employing monthly frequency time
series data from January 2007 to December 2012. The methodology applied was Multiple
Linear Regression for the purpose of data analysis. The study found that there is a weak
connection between macroeconomic variables and stock market returns.
Nkechukwu, G., Onyeagba, J., and Okoh, J. (2015) studied the effect of
macroeconomic variables on stock market prices using annual time series data for Nigeria for
the period 1980-2013. OLS regression technique, Johansen cointegration and VECM based
on arbitrage pricing theory (APT) was applied for data analysis. The macroeconomic
variables utilized were gross domestic product (GDP) and broad money supply (M2). The
results of the findings indicated that the GDP has significant long-run negative effect on
Nigerian stock market prices and M2 has significant long-run positive effect on stock prices.
Further, there exists a unidirectional causal effect between GDP and stock prices with
direction running from stock prices to GDP.
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5.2.2. Studies related to Indian economy
Shah and Thomas (1997) argue that because of the enabling government policies stock
market in India is more efficient than the Indian banking system, both in terms of quality of
information processing and imposition of transaction cost. Their research supports the idea
that stock prices are a mirror which reflect the real economy, and are relatively insensitive to
factors internal to the financial system such as market mechanisms. However the arguments
require more explanation.
Naka, Mukherjee and Tufte (1998) analyzed relationships among selected
macroeconomic variables and the Indian stock market, using a vector error correction model.
Quarterly time series data from 1960:Q1 to 1995:Q4 was used. Macroeconomic variables
used for the study were real output (IPI), inflation (CPI), money stock (M1) and interest rate
(money market rate in the Bombay inter-bank market) and Sensex. The results suggested that
three long-term equilibrium relationships exist among the variables. It was also found that
domestic inflation is the most severe deterrent to Indian stock market performance, and
domestic output growth is its predominant driving force.
Pethe and Karnik (2000), using Indian data for April 1992 to December 1997, attempts
to find the way in which stock price indices are affected by and affect other crucial
macroeconomic variables in India. But this study runs causality tests in an error correction
framework on non cointegrated variables, which is inappropriate and not econometrically
sound and correct. The study, of course avers that in the absence of cointegration it is not
legitimate to test for causality between a pair of variables and it does so in view of the
importance attached to the relation between the state of the economy and stock markets. The
study reports weak causality running from IIP to share price index (Sensex and Nifty) but not
the other way round. In other words, it holds the view that the state of the economy affects
stock prices.
Muradoglu, Taskin And Bigan (2000) investigated the relationship between stock
returns and macroeconomic variables in emerging markets like Argentina, Brazil, Columbia,
and Mexico from South America; Portugal and Greece from Europe; Korea from the Pacific
rim; Jordan, Pakistan, and India from Asia; and Nigeria and Zimbabwe from Africa., using
cointegration and granger causality test. Monthly time series data from 1976 through 1997.
Variables used were, stock returns, exchange rates, and interest rates were assumed to be
linear in a set of local and global information variables, whereas inflation and industrial
production were assumed to be linear in a set of local information variables only. The global
information variable was the return on the S&P 500 index, which represents the world market
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portfolio, and controls for the degree of market liberalization. Local informational variables
were, return on country indices, exchange rates, interest rates, inflation, and industrial
production index, which is a measure of general economic activity and proxies for GDP. The
results of the study explored that the two-way interaction between stock returns and
macroeconomic variables is mainly due to the size of the stock markets, and their integration
with the world markets, through various measures of financial liberalization.
Mohtadi and Agarwal (2001) examined the relationship between stock market
development and economic growth for 21 emerging markets, using a dynamic panel method.
Annual data from 1977 to 1997 was used for the study. Stock Market Variables used were,
market capitalization ratio, total value of shares traded ratio, and turnover ratio; and
macroeconomic variables include growth, foreign direct investment, investment (real
investment divided by GDP) and Secondary School Enrollment. The results suggested a
positive relationship between several indicators of the stock market performance and
economic growth both directly, as well as indirectly by boosting private investment behavior.
Biswal and Kamaiah (2001) addressed the behavior of stock market development
indicators, namely, market size, liquidity, and volatility and examined whether these
indicators have exhibited any trend changes after India liberalized its financial policies.
Variables considered for the study were three stock market indicators, viz.,size, liquidity and
volatility, and two time series trend break techniques of Perron were applied on monthly data
of Bombay Stock Exchange. Data for market capitalization and turnover ratio range from
1991:1 through 1998:12 while that for the value traded spans from 1989:1 through 1998:12.
Required price data for constructing volatility series has been collected as the average
monthly value of the BSE Sensitive Index for the period 1983:12 through 1998:12. The study
suggested that the stock market has become larger and more liquid, in the post liberalization
period. In respect of volatility, however, the market does not exhibit any significant change.
Bilson, Brailsford & Hooper (2001) addressed the question of whether local
macroeconomic variables have explanatory power over stock returns in emerging markets,
incorporating six Latin American countries, eight Asian countries, three European countries,
one Middle Eastern country and two African countries, using correlation and regression.
Monthly data from January 1985 to December 1997 was used for the study. Macroeconomic
variables used were money supply (M1), consumer price index, industrial production index
and exchange rate. The results show that while emerging stock markets are segmented to a
degree, there is significant commonality in return variation across markets. Furthermore, little
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evidence of common sensitivities to the extracted factors was found when the markets are
considered in aggregate, but common sensitivity is found at the regional level.
Bhattacharya and Mukherjee (2002) studied the nature of the causal relationship
between stock prices and macroeconomic aggregates in India, by applying the techniques of
unit–root tests, cointegration and the long–run Granger non–causality test proposed by Toda
and Yamamoto (1995), Variables used for the study were the BSE Sensitive Index and the
five macroeconomic variables, viz., money supply, index of industrial production, national
income, interest rate and rate of inflation using monthly data from 1992-93 to 2000-01. The
study found that there is no causal linkage between stock prices and money supply, stock
prices and national income and stock prices and interest rate; index of industrial production
leads the stock price; and there exists a two-way causation between stock price and rate of
inflation.
Pretorius (2002) estimates cross-section and time-series models to determine the
fundamental factors that influence the correlation and evolvement of the correlation between
emerging stock markets, using Ordinary Least Square (OLS) methodology. Quarterly data
from 1995:Q1 to 2000:Q2 was considered for the study. Ten emerging stock markets
(according to the Emerging Market Database definition) with the highest market
capitalization was used in the study. Variables used were, inflation, exchange rate, trade and
industrial production. The results showed that only the extent of bilateral trade and the
industrial production growth differential were significant in explaining the correlation
between the two countries on a cross-sectional basis. In addition, countries in the same region
are more correlated than countries in different regions.
Chancharoenchai, Diboog Lu & Mathur (2005) investigated the relationship between
domestic macroeconomic variables and stock excess returns to evaluate the effects of
macroeconomic variables on excess returns and assess market efficiency in the six Southeast
Asian economies prior to the 1997 Asian crisis. Monthly data from January 1987 to
December 1996 was considered for the study. GARCH model was used for the empirical
estimation. Variables used were interest rate (risk-free rate of return), inflation and excess
stock returns. The results showed that some macroeconomic variables evidently had a certain
predictive power for excess returns and their volatility.
Srivyal Vuyyuri (2005) investigated the causal relationship between the financial and
the real sectors of the Indian economy using multivariate cointegration and Granger causality
tests. Monthly time series data from July 1992 to December 2002 was used for the study.
Financial variables included were interest rates, inflation rate, exchange rate, stock return,
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and real sector is proxied by industrial productivity. The results showed that there exist a long
run equilibrium relationship between the financial sector and the real sector and
unidirectional Granger causality was also found between the financial sector and the real
sector of the economy.
Nikkinen, Omran, Sahlström & Äijö (2006) investigated how global stock markets are
integrated with respect to the U.S. macroeconomic news, announcements, using data from the
period July 1995 to March 2002. Methodology adopted was GARCH volatilities around ten
important scheduled U.S. macroeconomic news announcements on 35 local stock markets
that are divided into six regions. These regions were the G7 countries, the European countries
other than G7 countries, developed Asian countries, emerging Asian countries, Latin
American countries and countries from Transition economies. The results showed that theG7
countries, the European countries other thanG7 countries, developed Asian countries and
emerging Asian countries are closely integrated with respect to the U.S. macroeconomic
news, while Latin America and Transition economies are not affected by U.S. news.
Yartey (2008) examined the institutional and macroeconomic determinants of stock
market development using a panel data of 42 emerging economies for the period 1990 to
2004. Macroeconomic variables used for the study were income level, banking sector
development, savings and investment, stock market liquidity, macroeconomic stability,
private capital flows and institutional quality. The study found that macroeconomic factors
such as income level, gross domestic investment, banking sector development, private capital
flows, and stock market liquidity are important determinants of stock market development in
emerging market countries.
Agrawalla and Tuteja (2008) examined the interaction between stock prices and a few
important macroeconomic variables for India using cointegration analysis and granger
causality. Monthly time series data for the period November 1965 to October 2000 was
considered. Macroeconomic variables used were share price index, industrial production,
money supply, credit to the private sector, exchange rate, wholesale price index, and money
market rate. The study reported unidirectional causality running from economic growth
proxied by industrial production to share price index and not the other way round.
Lekshmi R. Nair (2008) examined the macroeconomic determinants of stock market
development in India for monthly data from 1993-94 to 2006-07.Cointegration and error
correction modeling were used for the analysis. Macroeconomic variables used for the study
were turnover ratio (As an indicator of stock market development), inflation rate, real income
and its growth rate, financial intermediary development, foreign institutional investment,
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exchange rate and SBI Prime lending rate. The results showed that there exists a long run
relationship between all the macroeconomic variables used and stock market development.
Variables like real income and its growth rate, interest rate and financial intermediary
development significantly affect stock market development in the short run.
Gay, Jr. (2008) investigated the time-series relationship between stock market index
prices and the macroeconomic variables used were, exchange rate and oil price for Brazil,
Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. Monthly data from
March 1993 to June 2006 was used for the study. The study found no significant relationship
between respective exchange rate and oil price on the stock market index prices of either
BRIC country, this may be due to the influence other domestic and international
macroeconomic factors on stock market returns, warranting further research.
Shahid Ahmed (2008) investigated the nature of the causal relationships between Indian
stock prices, and the key macroeconomic variables for the period March, 1995 to March,
2007 using quarterly data. Variables used were index of industrial production, exports,
foreign direct investment, money supply, exchange rate, interest rate, NSE Nifty and BSE
Sensex. Johansen`s approach of co-integration and Toda and Yamamoto Granger causality
test were applied to explore the long-run relationships while BVAR modeling for variance
decomposition and impulse response functions was applied to examine short run
relationships. The study revealed that stock prices in India lead economic activity except
movement in interest rate as interest rate seem to lead the stock prices. The study concluded
that the movement of stock prices is not only the outcome of behavior of key macroeconomic
variables, but it is also one of the causes of movement in other macro dimension in the
economy.
Seetanah, Sannassee & Lamport (2008) examined simultaneously banking sector
development, stock market development, and economic growth in a unified framework for 27
developing countries, using rigorous panel VAR procedures. Annual time series data from
1991 to 2007 was considered for the study. The variables used were real per capita gross
domestic product, investment ratio, openness and secondary enrolment ratio. Results showed
that stock market development is an important ingredient of growth, but with a relatively
lower magnitude as compared to the other determinants of growth, particularly with banking
development. Interestingly, stock market development and banking development are seen to
be complement to each other and moreover there are important indirect effects through
‘investment channel’ to grow.
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Habibullah, Baharom and Fong (2009) examined the impact of inflation and output
growth on stock market returns and volatility in selected Asian countries, namely India,
Japan, Korea, Malaysia and Philippines, using monthly data from 1991 to 2004. GARCH (1,
1) model was employed for the estimations. Variables used include the Consumer Price Index
(CPI), major stock index or share prices and Index of Industrial Production (IIP). It is found
from the study that macroeconomic volatility, which is measured by movement in inflation
and output growth, has a weak predictive power for stock market returns and volatility in
these countries. The movements of the inflation rate have significant impact to the stock
market returns, either positive or negative depending on the inflation rates and their
fluctuation in that country. While output growth movements have a significant effect to stock
market volatility, countries with relatively higher output volatility is associated with higher
conditional volatility of stock returns, which is positive effect but is negative for countries
which have relatively lower output volatility.
Srivastava, A. (2010) attempted to establish relationship between change in
macroeconomic factors and stock market returns, using monthly time series for the period
April 1996 to January 2009. The methodology applied for the study was Johansen
multivariate cointegration and vector error correction model (VECM). Macroeconomic
variables used for the study include IPI, WPI, interest rate, exchange rate of Indian rupee with
US dollar and MSCI world index. The findings of the study concluded that emerging
economies like India in the long term are more affected by domestic macroeconomic factors
than global factors. The main domestic macroeconomic factors affecting the stock market in
the long run are industrial production; wholesale price index and interest rate.
Sharma and Mahendru (2010) analyzed the long-term relationship between BSE and
macroeconomic variables of India, using simple correlation and regression techniques.
Monthly time series data from January 2008 to January 2009 was taken. Macroeconomic
variables used for the study include change in exchange rate, foreign exchange reserves,
inflation rate and gold prices. The results revealed that exchange rate and gold prices
significantly affect stock prices, whereas the influence of foreign exchange reserves and
inflation on stock prices is negligible.
Dharmendra Singh (2010) explored the causal relationship between stock market index,
i.e. BSE Sensex and three key macroeconomic variables of the Indian economy by using
correlation and Granger causality tests. Monthly time series data have been used from April,
1995 to March, 2009 for all the variables, like, BSE Sensex, wholesale price index (WPI),
index of industrial production (IIP) and exchange rate (Rs/$). The granger causality test
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indicated that IIP is the only variable having bilateral causal relationship with BSE Sensex.
WPI is having a strong correlation with Sensex but it is having unilateral causality with BSE
Sensex.
Hosseini, Ahmad & Lai (2011) investigated the relationships between stock market
indices and four macroeconomics variables, namely crude oil price (COP), money supply
(M2), industrial production (IP) and the inflation rate (IR) in China and India, using
cointegration and VECM. Monthly data from January 1999 to January 2009 was used for the
study. The results of the study indicated that there are both long and short run linkages
between macroeconomic variable and stock market index in each of these two countries.
Al-Jafari, Salameh and Habbash (2011) examined the links between the
macroeconomic variables (real economic activity, inflation, interest rate, money supply and
exchange rate) and stock prices for sixteen developed and sixteen emerging markets by using
quarterly data from the period of January 2002 to December 2008. The methodology used for
the study includes Granger causality test and Pedroni panel cointegration tests. The results of
the study showed a significant causal relationship between macroeconomic variables, with
the exception of interest rate and money supply, and stock prices for the developed and
emerging markets. The study also found a significant causal relationship between stock prices
and macroeconomic variables for developed and emerging markets with the exception of the
exchange rate and money supply for developed markets. The findings also showed a positive
long-run relationship between real economic activity level and stock prices for developed
markets. Furthermore, the results found that the relationship between macroeconomic
variables and stock return in emerging markets is significantly more established than in
developed markets.
Haque and Hossain (2011) estimated the impact of stock market development on
economic growth in the SAARC region, namely, Bangladesh, India, Pakistan and Srilanka,
using two dynamic panel models for the period from 1980 to 2008. The macroeconomic
variables used for the study include per capita growth rate of GDP, Market Capitalization
ratio, value traded ratio, turnover ratio, domestic investment to GDP ratio, foreign direct
investment to GDP ratio, Secondary school enrollment as a percentage of the school
population, openness ratio and domestic credits to GDP ratio. The first model tries to assess
the stock market effect directly after controlling for other variables, whereas the second one
does it by having its influence through investment. The study found that none of the dynamic
model is effective to identify the stock market linkage to per capita growth rate in SAARC
region.
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Pal and Mittal (2011) examine the long-run relationship between the Indian capital
markets (BSE Sensex and S&P CNX Nifty) and key macroeconomic variables such as
interest rates, inflation rate, exchange rates and gross domestic savings (GDS) of Indian
economy, by using Quarterly time series data from January 1995 to December 2008. Error
correction mechanism (ECM) has been applied to derive the long run and short-term
statistical dynamics of the study. The ECM shows that the rate of inflation has a significant
impact on both the BSE Sensex and the S&P CNX Nifty. Interest rates on the other hand,
have a significant impact on S&P CNX Nifty only. However, in case of foreign exchange
rate, significant impact is seen only on BSE Sensex. The changing GDS is observed as
insignificantly associated with both the BSE Sensex and the S&P CNX Nifty.
Naliniprava Tripathy (2011) investigated the market efficiency and the causal
relationship between selected Macroeconomic variables and the Indian stock market during
the period January 2005 to February 2011 by using Ljung-Box Q test, Breusch-Godfrey LM
test, Unit Root test, Granger Causality test. The macroeconomic variables used for the study
were interest rate (91-days T-bill rate), inflation rate (WPI), exchange rate, international
market (S&P 500 index) and BSE Sensex. Weekly frequency data were used for the study.
The study confirmed the presence of autocorrelation in the Indian stock market and
macroeconomic variables. And the Granger-causality test showed evidence of the
bidirectional relationship between interest rate and stock market, exchange rate and stock
market, international stock market and BSE volume, exchange rate and BSE volume. The
study also reported unidirectional causality running from the international stock market to the
domestic stock market, interest rate, exchange rate and inflation rate.
Lairellakpam and Dash (2012) focused on identifying the factors affecting the volatility
in Indian stock markets (S&P CNX Nifty), while considering certain macroeconomic
variables, including exchange rates, crude oil prices, interest rates and gold prices. Monthly
frequency time series data from January 2000 to June 2011. The methodology employed for
the study include vector autoregressive (VAR) techniques and Granger causality tests. The
results of the study indicated that none of the macroeconomic factors Granger-caused
changes in Nifty returns, while changes in Nifty returns unidirectionally Granger-caused
changes in INR/USD exchange rates.
Pramod Kumar Naik and Puja Padhi (2012) investigates the relationships between the
Indian stock market index (BSE Sensex) and five macroeconomic variables, namely,
industrial production index, wholesale price index, money supply, treasury bill rates and
exchange rates over the period 1994:04–2011:06. Johansen’s co-integration and vector error
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correction model was applied to explore the long-run equilibrium relationship between stock
market index and macroeconomic variables. The analysis revealed that there exist a long-run
equilibrium relationship between macroeconomic variables and the stock market index. It is
also observed that the stock prices positively relate to the money supply and industrial
production but negatively relate to inflation. It is also found that there exist bidirectional
causality between industrial production and stock prices, whereas unidirectional causality
from money supply to stock price, stock price to inflation; and interest rates to stock prices.
Makan, Ahuja and Chauhan (2012) studied that whether some of the identified
macroeconomic factors can influence the Indian stock market in India, using simple
correlation, regression techniques and granger causality test. The variables used were,
industrial production index, consumer price index, interest rate (call money rate), exchange
rate, gold price, oil price, foreign institutional investment and BSE Sensex. Monthly time
series data from April 2005 – March 2012 was considered. The study concluded that three
(exchange rate, foreign institutional investment and call money rate) out of seven variables
are relatively more significant and likely to influence the Indian stock market. The study also
indicated a positive relation between FII; call money rate and the Sensex, whereas exchange
rate and Sensex showed a negative relation.
Narayan and Narayan (2012) examined the impact of US macroeconomic conditions—
namely, exchange rate and short-term interest rate (3 month T-bill rate) - on the stock markets
of seven Asian countries (China, India, the Philippines, Malaysia, Singapore, Thailand, and
South Korea), using daily data for the period 5 January 2000–25 January 2010. OLS and
GARCH techniques were used for the estimation. Sample data is divided into a pre-crisis
period (pre-August 2007) and a crisis period (post-August 2007). It was found that, in the
short-run, the interest rate has a statistically insignificant effect on returns for all countries,
except for the Philippines in the crisis period. On the other hand, except for China, regardless
of the crisis, depreciation has a statistically significant and negative effect on returns.
Basher, Haug and Sadorsky (2012) estimated a structural vector auto regression model
to investigate the dynamic relationship between oil prices, exchange rates and emerging stock
markets including India. Monthly time series data from January 1988 to December 2008 was
considered for the study. Variables were collected on global oil production, oil prices, global
real economic activity, exchange rates, emerging market stock prices and interest rates. The
findings suggested that positive shocks to oil prices tend to depress emerging market stock
prices and US dollar exchange rates in the short run; and a positive oil production shock
lowers oil prices while a positive shock to real economic activity increases oil prices.
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Yahyazadehfar and Babaie (2012) investigated the impact of macroeconomic variables
such as interest rate, house price and gold price on the stock price in capital market of Iran.
Monthly time series data from March 2001 to April2011 was used. The study was based upon
a vector auto regression (VAR) model and Johansen-Juselius Cointegration test. The study
found a positive relationship between stock price and house price, but the relationship
between nominal interest rate and the gold price with stock price were found to be negative.
Further, the results of Impulse Response Functions showed that stock price reaction to the
shocks is very fast. The variance decomposition analysis indicated that although most of the
fluctuation in stock price can be attributed to itself, but among the selected variables, the
house price has main role on stock price fluctuation.
Aurangzeb (2012) identified the factors affecting performance of the stock market in
three South Asian countries, namely, Pakistan, India and Sri Lanka, using data from 1997 to
2010. Correlation and regression were used as the methodology. The variables used for the
study include stock performance, interest rate, inflation, exchange rate and foreign direct
investment. The results of the study indicated that foreign direct investment and exchange
rate have significant positive impact on the performance of the stock market in South Asian
countries, while; interest rate has a negative and significant impact on the performance of the
stock market in South Asia.
Sarbapriya Ray (2012) explored the impact of different macroeconomic variables on
the stock prices in India, using annual data from 1990-91 to 2010-11. The variables used for
the study were BSE Sensex, balance of trade, call/notice money rate, CPI, FDI, foreign
exchange reserve, GDP, gross fixed capital formation, gold price, index of industrial
production, broad money supply, demand deposits of banks, demand deposits with RBI,
crude oil prices, exchange rate and the wholesale index of price. Multiple regression model
and Granger causality test were used for the estimations. The study revealed that there is no
causal association between stock price and interest rate, stock price and index of industrial
production, but unidirectional causality exist between stock price and inflation, stock price
and foreign direct investment, stock price and gross domestic product, stock price and
exchange rate, stock price and gross fixed capital formation. However, bi- directional
causality exist between stock price and foreign exchange reserve, stock price and money
supply, stock price and crude oil price and stock price and whole price index
Singh, Tripathi & Lalwani (2012) examined the primary factors responsible for
affecting Bombay Stock Exchange (BSE) in India, using monthly frequency data from
January 2007 to December 2012. Macroeconomic variables used for the study include foreign
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exchange rate and inflation, along with the linear regression techniques. The results of the
regression analysis suggest that both exchange rate and inflation significantly affects the
performance of BSE Sensex.
Malarvizhi, Thenmozhi and Jaya (2012) focused on analyzing the long term dynamic
relationship between the GDP and Nifty index of India, using quarterly data from June 2000
to March 2010. Cointegration test and granger causality were used to estimate the results of
the study. The results suggested that there is a bidirectional causal relationship between GDP
and Nifty.
Sharma and Chaitanya (2013) explored the influential relationship between the Sensex
of Bombay Stock Exchange (BSE) and selected macroeconomic variables of India by using
Stepwise Regression model. Quarterly frequency data from 2005:Q1 to 2011:Q2 was
considered for the study. Macroeconomic variables used for the study include GDP, IIP,
WPI, foreign exchange rate, gold rate and crude oil rate. The findings of the study revealed
that there is an influential relationship on SENSEX by Industrial Production and Foreign
Exchange Rate
Dey (2013) investigated the relationship between foreign exchange rates, foreign
exchange reserve and BSE Sensex return (India) using monthly frequency data from March
1992 to June 2012. The methodology applied includes correlation analysis, regression
analysis, Johansen co-integration test and granger causality. The results of regression analysis
found that there is a significant impact of returns of exchange rate, foreign exchange reserves
on the returns of BSE-Sensex return. Also, the findings of Johansen co-integration test
proved that, variables are not co-integrated and hence, have not long term relationship.
Further, the Granger causality test concludes that, foreign exchange rate causes the BSE-
Sensex return.
Vashishtha, S. D., Singh and Kumar (2013) examined the relationships between
economic growth rates and Indian capital market sensitivity, using monthly frequency data
from April 2006 to March 2011. Simple regression and correlation techniques were employed
for the study using variables, namely, IIP and WPI and S&P BSE Sensex. The result showed
that there exist an inverse relationship between the S&P BSE Sensex and IIP; and BSE
Sensex and WPI.
Sireesha (2013) investigated the impact of selected macroeconomic factors upon the
movements of the Indian stock market index (S&P CNX Nifty), using monthly frequency
data from January 1993 to December 2012.The macroeconomic variables used for the study
include CPI, Gross Domestic Product (GDP) growth rate, the Index of Industrial Production
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(IIP), Money Supply (M3), exchange rate returns of USD-INR, Foreign Institutional
Investors (FIIs), Domestic Institutional Investors (DIIs). Gold returns and Silver returns.
Nifty along with gold and silver prices by using linear regression techniques. The study
concluded that stock returns are significantly influenced by inflation, GDP, and exchange
rates, thus, stock returns can be used to hedge against these variables.
Md. Al-Mamun (2013) studied the effect of macroeconomic & market specific
dynamics on stock market development in 11 global growth generator countries (3G),
namely, Bangladesh, China, Egypt, India, Indonesia, Iraq, Mongolia, Nigeria, Philippines, Sri
Lanka and Vietnam, using the panel ARDL model for eight out of eleven 3G countries over a
period of 1980-2011. To measure stock market development, growth in market capitalization
of listed companies in respective countries was used as a proxy variable. And the dependent
variables included in the study were domestic credit provided by the banking sector, gross
domestic savings, gross domestic product, total value of stock market trading, stock market
turnover ratio, real interest rate, and foreign direct investment. The study confirmed that
several macroeconomics i.e. foreign direct investment, real interest rate and stock market
operating characteristics have a significant long run contribution to the development of stock
market and thereupon a sustained economic growth.
Parmar, C. (2013) studied the impact of macroeconomic variables, namely reverse repo
rate, CRR, SLR, Repo rate, inflation rate, CPI, Index of industrial production, gold rate, oil
rate and exchange rate on Indian stock market, by applying monthly frequency data from
January 2004 to December 2012. The methodology employed include regression and
correlation techniques. The results of the study concluded that in the long term the Indian
stock market is more driven by domestic macroeconomic factors rather than global factors.
Pathan and Masih (2013) studied, the direction of causality between the stock market in
India (BSE-Sensex) and macroeconomic variables, namely, interest rate, exchange rate, FII,
WPI, and money supply (M3), by applying monthly data from April 2004 to February
2013.Methodology adopted was Vector Error Correction Method (VECM). The findings of
the study provided evidence of a stable, long run equilibrium relationship between the stock
market and economic growth in India. The study reconfirmed the traditional belief that the
real economic variables continue to affect the stock market in the post-reform era in India and
also highlights the insignificance of certain variables with respect to the stock market.
Kumar Rakesh (2013) studied the effect of macroeconomic factors on Indian stock
market performance, using monthly frequency data from January 2001 to May 2013. The
data reduction technique of factor analysis was used to derive the factors which determine the
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performance of the stock market in India. Macroeconomic variables used for the study
include money supply (M3), CPI, gold prices, crude oil prices, foreign exchange reserves,
FDI, FII, call money rate, balance of trade, foreign exchange rate, repo rate and industrial
growth rate. The findings of the study suggested that the industrial growth rate performance
plays a significant role in influencing the stock market.
Subburayan and Srinivasan (2014) explored the effects of macroeconomic variables on
stock return of the CNX Bank index of Indian stock market, using monthly data from January
2004 to December 2013. The macroeconomic variables, namely exchange rate, interest rate
and inflation rate were considered for the study. The methodology employed for the study
includes regression, co-integration test and Granger causality test. The findings of the study
suggested that bank stock returns are having fixed long run relationship with selected
macroeconomic variables and the exchange rate and interest rate affect positively on bank
stock returns. Further, bank stock returns have a unidirectional causal relationship with the
exchange rate.
Kumar and Singh (2014) analyzed the impact of Macroeconomic Variables on Sensex
of India. The three Macroeconomic Variables, namely, Wholesale Price Index, Index of
Industrial Production and Exchange Rate were considered for the purpose of the study, along
with monthly data from January 2008 to December 2012. The study employed regression
analysis and correlation analysis as a part of the methodology. The study found a high
correlation among the variables, namely, WPI, IIP, Exchange Rate and Sensex and it was also
found that there is exists a significant relationship between macroeconomic variables and
Sensex.
Venkatraja, B. (2014) investigated the relationship between the Indian stock market
performance (BSE Sensex) and five macroeconomic variables, namely, index of industrial
production, wholesale price index, gold price, foreign institutional investment and real
effective exchange rate over the period April 2010- June 2014 using monthly data. The
multiple regression technique was employed for the purpose of study. The study revealed that
the Wholesale price index, index of industrial production, foreign institutional investment and
real effective exchange rate have a high degree of positive influence on the Sensex. It was
also found that Sensex is inversely influenced by changes in the gold price. Further, of the
five variables, the coefficients of all the variables except index of industrial production are
statistically significant.
Dasgupta (2014) studied the relationships between BSE Sensex and seven key
macroeconomic variables, both in the long-run and short-run, by using descriptive statistics,
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correlation test results, ADF tests, Johansen and Juselius’s cointegration test and Granger
causality test. Monthly data have been used from April 2007 to March 2012 for all the
variables, i.e., BSE Sensex, index of industrial production, wholesale price index, crude oil
prices, gold prices, money supply, exchange rate and foreign exchange reserve. Johansen and
Juselius’s cointegration test of the study pointed out at least one cointegration vector and long
run relationships between BSE Sensex with index of industrial production, gold prices,
money supply and foreign exchange reserve. Further, the Granger causality test found some
short-run unilateral or bilateral causal relationships between BSE Sensex with the
macroeconomic variables.
Kantesha Sanningammanavara, Kiran K. V., and Rakesh H. M. (2014) examined the
relationship between various economic indicators and the Indian stock market by using
simple correlation and regression techniques. Yearly data from From April 1998 to March
2014 was used for the study, which includes the variables like BSE Sensex, GDP Growth
rate, Inflation rate (WPI), Exchange rate (Rs/USD), Gross Domestic Savings as % of GDP,
Gross Capital Formation as of GDP, Real Interest Rate, and the Unemployment Rate. The
researchers found that the Depreciation in the Rupee against the Dollar has led to decrease in
the share prices. It has a negative impact on the stock prices and Increase in the Inflation rate
has led to decrease in the share prices.
Tripathi and Seth (2014) examined the causal relationships between the stock market
performance and selected macroeconomic variables in India, using monthly data from July
1997 to June 2011. The methodology employed for the study was Regression, ARCH model,
Granger causality and Johansen Co-integration test by using variables, namely, exchange
rate, the Index of Industrial Production (IIP), interest rate, money supply, oil prices and WPI;
and stock market indicators, namely BSE India Sensex, BSE India market capitalization and
BSE India market turnover. The study found a significant correlation between stock market
indicators and macroeconomic factors.Further, the overall explanatory power of the
regression model is 23.8%, 23.3% and 16.9% respectively for Sensex, Market capitalization
and Market Turnover. The causality test found that there exists unidirectional causality from
the stock market to the real economy.
Ray, H., & Sarkar, J. (2014) investigated the dynamic relation between the stock
market and the select macroeconomic variables in India, by employing monthly data for the
period from January 1991 to April 2008. The variables used for the study include an Index of
Industrial Production (IIP), Whole Sale Price Index (WPI), Money Supply (M3), Yields on
91-day Treasury Bills (YTB), Yields on Long-term (10-year) Government Bonds (YLGB),
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Competitiveness of Domestic Currency measured by the price of one US $ expressed in terms
of the Rupee (EX) and the BSE SENSEX 30(Index) to represent Stock Market Prices. The
methodology used for the study co-integration analysis and Granger casualty tests. The
findings of the study showed that the long-run stock market behavior is positively related to
output and exchange rate, and negatively related to short- and long-term interests, money
supply and inflation. Further, the results of the causality and innovation analysis suggested
that the stock market influences the economic activities, more specifically the industrial
activities and the market are expected to be more sensitive to the shocks of itself over the
projected period of the study.
Mohanamani and Sivagnanasithi (2014) investigated the impact of macroeconomic
variables on the behavior of Indian Stock market, using monthly frequency data from April
2006 to July 2013, employing variables, namely, BSE Sensex, Call Money rate, Exchange
rate between Indian Rupees and US dollar, Foreign Institutional Investment, Industrial
productivity, money supply and wholesale price index. The Methodology used includes
Granger Causality tests. The empirical analysis of the study revealed that Indian stock market
is positively related to wholesale price index, money supply and industrial productivity.
Further, the results of In the Granger Causality showed that the wholesale price index and
industrial productivity influence the stock market to a great extent.
Billah, Shah, Bhanja & Samantaraya (2014) estimated the relationship between stock
prices and exchange rates of eight Asian countries, using correlation and regression
techniques. Monthly data from February 1996 to September 2013 was considered for the
study. In accordance with the portfolio balance effect, it was observed that stock prices and
exchange rates are negatively correlated at all frequencies. In particular, the negative
correlation grows with higher time scales (lower frequency intervals). The findings from
quantile regression also suggested that the coefficients are more inclined to be negative when
exchange rates are extremely high.
5.2.3. Summary of Literature review
The objective of detailed literature review was to point out the contradictory views
regarding the effect of macroeconomic variables on the stock prices with reference to the
empirical analysis approach of cross-sectional and time series data. From this comprehensive
literature review, several key conclusions can be drawn. One of them states that, while the
existing theories hypothesize a link between macroeconomic variables and stock markets,
they do not specify the type or the number of macroeconomic factors that should be included.
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Thus, the existing empirical studies, reviewed in this chapter, have shown the use of a vast
range of macroeconomic variables to examine their influence on stock prices. A brief
summary of the literature review indicate that the macroeconomic variables that were mainly
used by the researchers are Index of Industrial production, real gross national product, gross
capital formation, employment, exports, exchange rate (Real Effective Exchange Rate,
Nominal Effective Exchange Rate), consumption, interest rate (T-bill rate, call money rate),
inflation (Producer Price Index, Consumer Price Index and Wholesale Price Index), aggregate
foreign currency reserves, Crude oil price, real consumption, consumption expenditures,
investment expenditure, federal funds rate, Foreign Direct Investment, Foreign Institutional
Investment, foreign portfolio investment, GDP deflator, trade balance, school enrollment,
trade openness, money supply (M1, M2, M3), unemployment rate, gold prices, foreign
exchange reserves, macroeconomic prosperity index, consumer confidence index, corporate
goods price index and gross fixed capital formation. And to study the impact of these
macroeconomic variables the dependent variables used for the study are, stock market
capitalization, stock market index, market liquidity and stock market turnover ratio. All the
researches are conducted by applying different methodologies, namely, correlation analysis,
regression analysis under Ordinary Least Square (OLS) method, generalized autoregressive
conditional heteroskedasticity (GARCH) model, cointegration tests using Vector Auto
Regression (VAR) framework, causality tests by employing Vector Error Correction Model
(VECM), and Auto Regressive Distributed Lag (ARDL) approach. These researches are
conducted using different sets of data periods starting with the frequency of daily data,
weekly data, monthly data, quarterly and annual data, further, all the studies use time series
data and the studies with multi country data uses the cross-sectional approach.
The other key conclusion drawn by the study indicates that, while previous studies have
significantly improved our understanding of the relationships between macroeconomic
variables and stock prices, the findings from the literature are mixed given that they were
sensitive to the choice of countries, variable selection, and the time period studied. It is
difficult to generalize the results because each market is unique in terms of its own rules,
regulations, and type of investors. Additionally, the VAR framework, cointegration tests,
Granger causality tests, and GARCH models were commonly used to examine the
relationships between stock prices and macroeconomic variables. However, there is no
definitive guideline for choosing an appropriate model. Further, the review of literature
clearly indicates that there exists a large pool of studies for developed economies regarding
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the investigation of the relationship between macroeconomic variables and stock prices, but
there is a shortage of literature concerning emerging stock markets.
5.3. Estimation results of the study using annual frequency data
The present section of the study includes the estimation results for the relationship
between macroeconomic variables and the stock prices, by incorporating data for yearly
frequency variables. The study empirically estimated the effect of fundamental
macroeconomic indicators7 on stock prices with the help of econometric techniques in India.
The study uses annual data covering the period from 19798 to 2014.
5.3.1. Model specification
The following general specification has been used in this study to empirically examine
the effect of economic growth and other fundamental macroeconomic factors on the stock
market.
𝐿𝐵𝑆𝐸 = 𝛼0 + 𝛼1𝐿𝐺𝐷𝑃 + 𝛼2𝐿𝐶𝑂 + 𝛼3𝐿𝐶𝑃𝐼 + 𝛼4𝐿𝑅𝐸𝐸𝑅 + 𝛼5𝐿𝐹𝐷𝐼 + 𝛼6𝐿𝑅𝐼𝑅 + 휀𝑡
(5.1)
5.3.2. Stationarity test and Lag length selection before co-integration
Before we conduct tests for co-integration, we have to make sure that the variables
under consideration are not integrated at an order higher than one. Thus, to test the
integration properties of the series, we have used Ng-Perron unit root test. The results of the
stationarity tests are presented in Table 5.3.1. The results show that all the variables are non-
stationary at levels. The next step is to difference the variables once in order to perform
stationary tests on differenced variables. The results show that after differencing the variables
once, all the other variables were confirmed to be stationary. It is, therefore, worth
concluding that all the variables used in this study are integrated of order one i.e. difference
stationary I(1). Therefore the study uses autoregressive distributed lag (ARDL) approach to
co-integration. In addition, it is also important to ascertain that the optimal lag order of the
model is chosen appropriately so that the error terms of the equations are not serially
correlated. Consequently, the lag order should be high enough so that the conditional ECM is
not subject to over parameterization problems (Narayan, 2005; Pesaran, 2001). The results of
these tests are presented in Table 5.3.2. The results of Table 5.3.2 suggest that the optimal lag
length is one based on both LR, FPE, SIC and HQ.
7 The study excludes the variable Money Supply (M3) because of the high correlation of M3 with inflation,
exchange rate and FDI. 8 The study limits to the starting period as 1979-80 due to the non-availability of data on BSE Sensex prior to
this period.
127
Table 5.3.1: Unit root test: Ng-Perron Test
Variables With constant and trend Stationarity
Status Mza MZt MSB MPT
LBSE 0.624 0.461 0.739 38.204 I (1)
ΔLBSE -16.386 -2.861 0.174 1.499
LGDP 2.210 2.215 1.002 86.222 I (1)
ΔLGDP -15.289 -2.717 0.177 1.780
LCO -2.858 -1.172 0.409 8.501 I (1)
ΔLCO -16.390 -2.820 0.172 1.651
LCPI -12.87 -2.492 0.193 2.073 I (1)
ΔLCPI -16.161 -2.841 0.173 1.518
LREER 0.142 0.093 0.652 28.471 I (1)
ΔLREER -14.298 -2.640 0.184 1.840
LFDI -0.365 -0.207 0.566 20.950 I (1)
ΔLFDI -16.359 -2.857 0.174 1.508
LRIR -7.083 -1.881 0.265 3.459 I (1)
ΔLRIR -14.593 -2.685 0.270 3.818 Source: Author’s own Calculation by using E-views 8.0
∆ denotes the first difference of the series. L implies that the variables have been transformed in natural logs.
Table 5.3.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ
0 -62.752 NA 1.01e-08 4.288 4.650 4.410
1 194.262 373.839* 9.36e-14* -7.409 -4.144* -6.311*
2 266.632 70.176 1.21e-13 -7.917* -1.749 -5.842 * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
5.3.3. ARDL Bounds Test
After determining the order of integration of all the variables in table 1 and lag length
selection in table 5.3.3, the next step is to employ an ARDL approach to co-integration in
order to determine the long run relationship among the variables. By applying, the procedure
in OLS regression for the first difference part of the equation (5.1) and then test for the joint
significance of the parameters of the lagged level variables when added to the first regression.
The F-Statistics tests the joint Null hypothesis that the coefficients of lagged level
variables in the equation (5.1) are zero. Table 5.3.3, reports the result of the calculated F-
Statistics & diagnostic tests of the estimated model. The result shows the calculated F-
statistics are 5.5113. Thus the calculated F-statistics turns out to be higher than the upper-
bound critical value at the 5 percent level. This suggests that there is a co-integrating
relationship among the variables included in the model, i.e. Sensex (LBSE), Crude Oil Prices
(LCO), Inflation (LCPI), Exchange Rate (LREER), Foreign Direct Investment (LFDI) and
Real Interest Rate (LRIR).
128
Table 5.3.3: ARDL Bounds test
Panel I: Bounds testing to co-integration:
Estimated Equation : LBSE = F (LGDP LCO LCPI LREER LFDI LRIR)
Indicators
Optimal lag 01
F – Statistics 5.5113
Panel II: Diagnostic Tests:
Diagnostic Tests Indicators
Normality J-B value 0.8901
Serial Correlation LM Test 1.5214
Heteroscedasticity Test (ARCH) 1.0145
Ramsey Reset Test 0.0724
The second step is to estimate the long- and short-run estimates of ARDL test. The long
run results are illustrated in Table 5.3.4. The results show that a rise in GDP has positive
effect on stock prices. The coefficient of GDP, Inflation (LCPI), and Exchange Rate
(LREER) are statistically significant at 1%. It is evident from the table that 1% in increase
GDP, Inflation and Exchange Rate leads to 2.311%, 0.390% and 1.126% respectively,
increase in Stock Prices (Sensex). The findings are consistent with Fama (1981, 1990) and
Chen et al. (1986) for GDP; Kessel (1956), Ioannidis et al. (2004) for Inflation; and
Mukherjee and Naka (1995) and, Nadeem and Zakir (2009) for Exchange Rate.
Whereas, the coefficient of crude oil price is negative and significant at 1%. Therefore,
crude oil prices have a significant negative relationship adversely affecting stock prices and
the findings are consistent with Miller and Ratti (2009) and Basher et al. (2012).
129
Table 5.3.4: Estimated Long Run Coefficients using ARDL Approach
(Dependent variable: LBSE) Regressors ARDL(1,0,0,0)
Coefficient t- values Prob. Values
LGDP 2.311*** 4.047 0.000
LCO -0.917*** -3.012 0.006
LCPI 0.390*** 2.060 0.050
LREER 1.126*** 3.372 0.002
LFDI -0.167 -1.356 0.187
LRIR 0.128 0.718 0.479
CONS -4.202 -2.936 0.007
Robustness Indicators
R2 0.987
Adjusted R2 0.984
F Statistics 243.364 [0.000]
D.W. Stat 2.131
Serial Correlation, F 0.537 [0.464]
Heteroskedasticity, F 0.424 [0.515]
Ramsey reset test, F 0.086 [0.769] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *** indicate significant at the 1 percent level of significance. Values in [#] are probability
values.
The short-run relationship of the macroeconomic variables on stock market index is
presented in Table 5.3.5. As can be seen from the table, GDP, Exchange Rate and Inflation
have a significant and positive impact on stock market index in the short run also and similar
to long-run is the situation for crude oil prices. The short run adjustment process is examined
from the ECM coefficient. The coefficient lies between 0 and -1, the equilibrium is
converging to the long run equilibrium path, is responsive to any external shocks. However,
if the value is positive, the equilibrium will be divergent from the reported values of ECM
test. The coefficient of the lagged error-correction term (-0.536) is significant at the 1% level
of significance. The coefficient implies that a deviation from the equilibrium level of stock
market index in the current period will be corrected by 53 percent in the next period to resort
the equilibrium.
130
Table 5.3.5: Estimated Short Run Coefficients using ARDL Approach
(Dependent variable: LBSE)
Regressors ARDL(1,0,0,0)
Coefficient T – Ratio Prob. Values
ΔLGDP 1.238*** 4.006 0.000
ΔLCO -0.491*** -3.277 0.003
ΔLCPI 0.209* 1.749 0.092
ΔLREER 0.604** 2.183 0. 038
ΔLFDI 0.049 0.804 0.429
ΔLRIR 0.069 0.719 0.478
ΔCONS -2.251 -2.056 0.050
ECM t-1 -0.536 -3.333 0.003
Robustness Indicators
R2 0.459
Adjusted R2 0.286
D.W. Stat 2.131
SE Regression 0.195
RSS 0.952
F Statistics 3.029[0.018] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
5.3.4. VECM based causality
The next step is to test for the causality between the variables, the short run and long
run granger causality test findings are reported in Table 5.3.6. The results of table 5.3.6
indicate that short run unidirectional causality running from LFDI, LGDP and LRIR to LBSE
in India. It is also observed that error correction term is statistically significant for
specification with LBSE as the dependent variable which indicate that there exist a long run
causal relationship among the variable with LBSE as the dependent variable. This result is
also confirmed by the ARDL test statistics.
Table 5.3.6: Results of Vector Error Correction Model
Dependent
variable
Sources of Causation
Short run independent variables Long run
ΔLBSE ΔLCO ΔLCPI ΔLREER ΔLFDI ΔLGDP ΔLRIR ECM(t-1)
ΔLBSE - 0.636 -1.283 -1.414 3.115**
* -2.239** 1.916* -3.906***
ΔLCO 0.198 - 0.174 -0.293 -0.407 0.389 -0.378 -0.849
ΔLCPI 0.183 -1.157 - -1.757* 0.911 0.823 0.135 0.691
ΔLREER 0.544 0.086 0.292 - -1.044 0.089 0.722 -0.402
ΔLFDI 1.590 1.792* -0.416 -0.056 - -0.396 -0.306 -0.149
ΔLGDP 0.433 0.433 -0.920 -1.651 1.632 - -0.379 -1.230
ΔLRIR -0.284 0.484 -0.579 0.694 0.655 0.242 - -1.066
*, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
The robustness of the short run result are investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
131
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of a long run and short run parameters. The cumulative sum
(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 5.3.1) are between
critical boundaries at 5% level of significance. This confirms the stability property of a long
run and short run parameters which have an impact on the market index in case of India. This
confirms that models seem to be steady and specified appropriately.
Figure 5.3.1: Plots of Stability Test
5.3.5. Variance Decomposition (VDC) Analysis:
It is pointed out by Pesaran and Shin (2001) that the variable decomposition method
shows the contribution in one variable due to innovation shocks stemming in the forcing
variables. The variance decomposition indicates the amount of information each variable
contributes to the other variables in the autoregression. It determines how much of the
forecast error variance of each of the variables can be explained by exogenous shocks to the
other variables. The main advantage of this approach as it is insensitive to the ordering of the
variables. The results of the VDC are presented in table 5.3.7. The empirical evidence
indicates that 78.33% of stock price change is contributed by its own innovative shocks.
Further, shock in crude oil price explains the stock price by 12.73%. Foreign Direct
Investment contributes to stock prices by 2.835% and consumer price contributes 2.01%.
From this analysis, it can be referred that the movement in stock prices can be predicted from
the crude oil prices. The share of other variables is very minimal.
132
Table 5.3.7: Variance Decomposition (VDC) Analysis
Period S.E. LBSE LCO LCPI LREER LFDI LGDP LRIR
1 0.223 100.000 0.000 0.000 0.000 0.000 0.000 0.000
2 0.303 91.256 2.442 2.718 0.151 2.108 0.814 0.065
3 0.344 88.673 4.758 2.145 0.367 2.499 0.717 0.183
4 0.366 86.802 6.364 1.962 0.397 2.624 0.651 0.365
5 0.379 85.334 7.528 1.916 0.372 2.720 0.609 0.541
6 0.386 84.115 8.473 1.864 0.384 2.803 0.596 0.678
7 0.391 83.047 9.289 1.816 0.442 2.861 0.621 0.765
8 0.395 82.091 10.001 1.802 0.524 2.889 0.652 0.809
9 0.398 81.242 10.613 1.827 0.604 2.895 0.758 0.827
10 0.401 80.510 11.131 1.875 0.671 2.888 0.833 0.830
11 0.404 79.897 11.568 1.926 0.720 2.876 0.895 0.828
12 0.406 79.393 11.935 1.968 0.755 2.863 0.941 0.824
13 0.409 78.978 12.245 1.995 0.777 2.852 0.974 0.820
14 0.411 78.632 12.510 2.010 0.792 2.842 0.997 0.817
15 0.413 78.337 12.737 2.014 0.800 2.835 1.013 0.816
Cholesky Ordering: LBSE LCO LCPI LREER LFDI LGDP LRIR
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5.4. Estimation results of the study using quarterly frequency data
The present section of the study includes the estimation results for the relationship
between macroeconomic variables and the stock market development, by incorporating data
for quarterly frequency variables. The study uses quarterly data on the above described
variables covering the period from 1996: Q1 to 2014: Q3.
5.4.1. Model specification
The following general specification has been used in this study to empirically examine
the effect of economic growth and other fundamental macroeconomic factors on the stock
market development.
𝐿𝑀𝐶𝐴𝑃 = 𝛼0 + 𝛼1𝐿𝐺𝐷𝑃 + 𝛼2𝐿𝐹𝐷𝐼 + 𝛼3𝐿𝐹𝐼𝐼 + 𝛼4𝐿𝑇𝑂 + 휀𝑡
(5.2)
5.4.2. Stationarity test and Lag length selection before co-integration
Before we proceed for ARDL estimation, we test for the stationarity of the variables
and to determine their order of integration. The test for unit root is to ensure that none of the
series in integrated at I(2). The present study uses newly developed Ng- Perron test
developed by Ng- Perron (2001). The test result is presented in Table 5.4.1. The analysis of
the unit root test results indicates that LFDI and LTO are I(0) and the remaining variables are
integrated order one (I(1)) and none of the variables are I(2) series.
Table 5.4.1: Unit root test: Ng-Perron Test
Variables With trend and intercept Stationarity
Status Mza Mzt MSB MPT
LMCAP -8.321 -2.723 0.354 10.678 I (1)
ΔLMCAP -30.512 -3.796 0.225 3.426
LFDI -29.568 -3.921 0.368 3.989 I (0)
ΔLFDI -1175.319 -24.654 0.521 0.189
LFII -14.221 -2.567 0.374 4.449 I (1)
ΔLFII -31.097 -3.786 0.448 3.981
LGDP -13.224 -2.450 0.390 8.985 I (1)
ΔLGDP -25.974 -3.994 0.166 1.964
LTO -25.372 -3.679 0.248 4.679 I (0)
ΔLTO -1.467 -0.902 0.579 46.210 Source: Author’s own Calculation by using E-views 8.0
∆ denotes the first difference of the series. L implies that the variables have been transformed in natural logs.
The next step involves the selection of optimal lag length of the model. The optimal lag
length was determined by different criterion suitable to the models (Table 5.4.2) using 5
maximum lags in the model. The results of table 5.4.2 suggest that the optimal lag length is 4
based on LR, FPE and HQ.
134
Table 5.4.2: Lag Order Selection Criterion
Lag LogL LR FPE AIC SIC HQ
0 -188.926 NA 0.000 6.267 7.067 6.215
1 -7.955 322.417 2.37e-06 1.468 2.218* 1.412
2 19.245 43.342 2.65e-06 1.392 3.354 2.128
3 55.890 51.812 1.91e-06 0.741 3.661 2.960
4* 97.267 52.416* 1.07e-06* 0.199 4.110 1.428*
5 122.780 27.225 1.21e-06 0.293* 4.813 2.133 * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
5.4.3 ARDL Bounds test
After determining the order of integration and lag length, the next step is to employ
bounds test to confirm the long-run relationship among the variables. The bounds test result
confirms the long-run relationship because the calculated F-statistics are 7.5673 which are
greater than the critical value of the upper level of bounds at the 1% level of significance
(Pesaran (2001) and Narayan (2005)). This evidence gives strong indication of the existence
of a long-run relationship among the variables included in the model. Further, the estimated
statistics show that the model specification seems to pass all diagnostic tests successfully.
Table 5.4.3: ARDL bounds test results
Panel I: Bound testing to co-integration:
Estimated Equation : LMCAP = F (LFDI LFII LGDP LTO)
Indicators
Optimal lag 04
F – Statistics 7.489
Panel II: Diagnostic Tests:
Diagnostic Tests Indicators
Normality J-B value 0.8901
Serial Correlation LM Test 1.5214
Heteroscedasticity Test (ARCH) 1.0145
Ramsey Reset Test 0.0724
Once we established that a long-run co-integrating relationship exists, the next step is to
estimate the long-run coefficient. The estimated long-run coefficients are reported in table
5.4.4. The estimated result shows that coefficient of FDI is positive, but not significant. This
implies that FDI has not been effective in influencing stock market development in India. The
findings are consistent with Raza (2013). However the study found that the stock market is
positively related to real GDP. The coefficient of real GDP has positive impact on the Stock
135
Market and it’s significant at the 5% level. The value of coefficient implies that 1% increase
in real GDP leads to increase in the stock market by 21% on an average. The result implies
that the GDP affects the stock market indirectly through its effect on inflation, and because
investors use it as a key indicator of economic activity and future economic
prospects. Therefore, any significant change in the GDP, either up or down, can have a
significant effect on the sentiments of the investors. If investors believe the economy is
improving (and corporate earnings along with it) they are likely to be willing to pay more for
any given stock. If there is a decline in GDP (or investors expect a decline) they would only
be willing to buy a given stock for less, leading to a decline in the stock market and the result
that there exist a positive nexus between the stock market and economic growth are
consistent with the studies of Randall et al. (2000), Rousseau and Wachtel (2000), Daferighe
and Aje (2009) and Hsing (2011).
Considering the impact of trade openness, it is found the variable is significant at 1%
and has a positive impact on stock market development. This finding supports the view that
trade openness of the economy helps to attract foreign investment. This in turn increases the
activities on the stock market as firms would attempt to raise investment funds (capital) from
the stock market (Nurudeen (2009)). The FIIs are significant at 1% and has a positive impact
on market capitalization and a 1% rise in FIIs increases market capitalization by 15%. The
findings are consistent with the findings of Loomba (2012), this implies that increase in FIIs
investments brings inflow of capital and the country can have access to foreign capital for
financial development.
Table 5.4.4: Estimated Long-run Coefficients using ARDL Approach
(Dependent variable: LMCAP)
Regressors ARDL(1,0,0,0)
Coefficient t- values Prob. Values
LFII 0.161** 2.586 [0.016]
LFDI 0.052 0.416 [0.731]
LGDP 0.221** 1.988 [0.055]
LTO 2.673*** 3.983 [0.000]
CONS 10.191 2.226 [0.029]
Robustness Indicators
R2 0.982
Adjusted R2 0.980
F Statistics 1069.10
D.W. Stat 1.912
Serial Correlation, F 8.356[0.671]
Heteroskedasticity, F 0.551[0.698]
Ramsey reset test, F 0.094[0.715] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) ** and *** indicate significant at 5 and 1 percent level of significance, respectively. Values in [#] are
probability values.
136
Next, the short-run dynamics can be achieved by constructing an ARDL-based Error
Correction Model (ECM). The results of short-run dynamics using the ECM version of
ARDL are reported in table 5.4.5. The short-run adjustment process is examined from the
ECM coefficient. The coefficient lies between 0 and -1, the equilibrium is converging to the
long-run equilibrium path, is responsive to any external shocks. From the reported values of
ECM test, we found that the ECMt-1 term is -0.15 and is significant at 3%, again confirming
the existence of co-integration that the derivation from long-run equilibrium path is corrected
15% per year.
Table 5.4.5: Estimated Short-run Coefficients using ARDL Approach
(Dependent variable: LMCAP)
Regressors ARDL(1,0,0,0)
Coefficient T – Ratio Prob. Values
ΔLFII 0.035*** 4.225 [0.000]
ΔLFDI 0.015 0.057 [0.597]
ΔLGDP 0.045 0.431 [0.689]
ΔLTO 0.326*** 3.534 [0.001]
ΔCONS 1.675 2.435 [0.028]
ECM t-1 -0.159 -3.248 [0.003]
Robustness Indicators
R2 0.501
Adjusted R2 0.418
D.W. Stat 1.926
SE Regression 0.225
RSS 0.008
F Statistics 8.618 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *** indicate significant at the 1 percent level of significance, respectively. Values in [#] are probability
values.
The comparison of long-run coefficients with that of short-run ECM coefficients
confirms that the directions of relationships are maintained. However, the economic growth
variable which is positive and significant at the 10% level in the long-run failed to explain the
variation in stock market growth significantly in the short-run. This may be due to the fact
that investor’s behavior in the stock market regulated by long-term growth rate of GDP and
may not bother about short-term fluctuations in it. Other variables, such as FII and TO are
significantly influencing the market capitalization both in the short-run as well as in the long-
run. Here also, the coefficient of FDI is positive and insignificant.
137
5.4.4. VECM based causality
The short-run and long-run granger causality test findings are reported in Table 5.4.6.
The results of table 5.4.6 indicate that short-run unidirectional causality running from LTO
variable to MCAP in India. It is also observed that error correction term is statistically
significant for specification with MCAP as the dependent variable which indicate that there
exist a long-run causal relationship among the variables with MCAP as the dependent
variable. This result is also confirmed by the ARDL test statistics.
Table 5.4.6: Results of Vector Error Correction Model
Dependent
variable
Sources of Causation
Short run independent variables Long run
∆LMCAP ∆LGDP ∆LTO ∆LFDI ∆LFII ECM(t-1)
∆LMCAP - 1.073 1.321 1.895 3.133*** -2.554**
∆LGDP 0.563 - 0.979 0.094 0.674 -0.381
∆LTO 2.226** 3.006*** - 1.535 3.035*** -0.986
∆LFDI 0.411 1.057 1.225 - 0.541 2.280
∆LFII 1.376 1.977 0.414 2.767* - 0.977 *** indicates 1% level of significance, ** indicates 5% level of significance
The robustness of the short-run result are investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of long-run and short-run parameters. This confirms the stability
property of long-run and short-run parameters. This confirms that models seem to be steady
and specified appropriate.
Figure 5.4.1: Plots of Stability Test
138
5.4.5. Variance Decomposition Analysis:
The Variance Decomposition analysis indicates the percentage of forecast error
variance in one variable that is due to errors in forecasting itself and each of the variables.
The results of Variance Decomposition are illustrated in table 5.4.7. The empirical results
show that the LMCAP explanatory has increased over the time through FDI growth variable
as the second year, 4.05% of market capitalization variable changes is explained by the
variance. However, Trade openness variable play the most important role, explaining 45%
variation in stock market capitalization in India.
Table 5.4.7: Variance Decomposition (VDC) Analysis
Period S.E. LMCAP LGDP LTO LFDI LFII
1 0.145 100.000 0.000 0.000 0.000 0.000
2 0.298 92.157 2.094 1.676 4.112 0.096
3 0.300 88.598 2.057 2.357 6.695 0.367
4 0.305 80.855 3.014 2.314 11.126 2.781
5 0.335 75.731 3.378 2.567 16.159 2.374
6 0.357 72.599 3.592 4.724 16.524 2.599
7 0.389 67.653 3.441 8.935 16.707 3.312
8 0.407 64.498 3.291 12.569 16.460 3.383
9 0.424 61.759 2.941 15.936 16.143 3.219
10 0.456 60.019 2.718 19.109 15.215 2.998
11 0.467 58.038 2.549 22.407 14.275 2.729
12 0.479 56.224 2.394 25.258 13.641 2.672
13 0.497 54.121 2.116 28.057 13.225 2.436
14 0.514 52.131 2.091 30.949 12.547 2.345
15 0.535 50.117 1.966 33.864 11.909 2.226
16 0.547 48.303 1.850 36.514 11.451 2.132
17 0.572 46.532 1.786 38.894 10.854 1.979
18 0.588 45.134 1.671 41.227 10.319 1.901
19 0.599 43.541 1.599 43.245 9.818 1.892
20 0.616 42.259 1.567 44.912 9.299 1.879
Cholesky Ordering: LMCAP LGDP LTO LFDI LFII
139
5.5. Estimation results of the study using monthly frequency data
The present section of the study includes the estimation results for the relationship
between macroeconomic variables and the stock prices, by incorporating data for monthly
frequency variables. Further, the study has been divided into two sub-sections, which
constitutes two models in relation with different set of macroeconomic variables and stock
prices. The first sub-section of the study shows the empirical relationship between
fundamental macroeconomic variables and Sensitivity Index of Bombay Stock Exchange
(Sensex), using the monthly time series data from the April 2004 to July 2014. The second
sub-section of the study exhibits the empirical relationship empirical relationship between
fundamental macroeconomic variables and Index of National stock exchange (CNX nifty),
using the monthly time series data from the April 2004 to July 2015. Each sub-section of the
study will include model specification and data validation.
5.5.1. Relationship between macroeconomic variables and Indian stock price
The study empirically estimated the effect of fundamental macroeconomic indicators
on stock prices in India, with the help of econometric techniques. The study uses monthly
data covering the period from April 2004 to July 20149. The selection of the monthly data set
is used to capture the short run fluctuation in the variables. Most of the study in Indian
context is carried on annual data; hence this study will provide valuable information on the
dynamic relationship of stock prices and macroeconomic variables. Based on the extensive
literature review the above macroeconomic variables are selected for the study, which are
expected to have some influence on stock market performance in the present context.
5.5.1.1. Model specification
The following general specification has been used in this study to empirically examine
the effect of economic growth and other fundamental macroeconomic factors on the stock
market.
𝐿𝐵𝑆𝐸 = 𝛼0 + 𝛼1𝐿𝐼𝐼𝑃 + 𝛼2𝐿𝐶𝑃𝐼 + 𝛼3𝐿𝑅𝐸𝐸𝑅 + 𝛼4𝐿𝐶𝑀𝑅 + 𝛼5𝐿𝐺𝑂𝑅 + 휀𝑡
(5.3)
5.5.1.2. Stationarity test and Lag length selection before co-integration
Before we conduct tests for co-integration, we have to make sure that the variables
under consideration are not integrated at an order higher than one. Thus, to test the
integration properties of the series, we have used Ng-Perron unit root test. The results of the
9 The study limits to the starting period as April 2004 to July 2014 due to the non-availability of data with
common base year on IIP and CPI prior to this period.
140
stationarity tests are presented in Table 5.5.1.1. The results show that all the variables are
non-stationary at levels. The next step is to difference the variables once in order to perform
stationary tests on differenced variables. The results show that after differencing the variables
once, all the other variables were confirmed to be stationary. It is, therefore, worth
concluding that all the variables used in this study are integrated of order one i.e. difference
stationary I(1). Therefore the study uses autoregressive distributed lag (ARDL) approach to
co-integration. In addition, it is also important to ascertain that the optimal lag order of the
model is chosen appropriately so that the error terms of the equations are not serially
correlated. Consequently, the lag order should be high enough so that the conditional ECM is
not subject to over parameterization problems (Narayan, 2005; Pesaran, 2001). The results of
these tests are presented in Table 5.5.1.2. The results of Table 5.5.1.2 suggest that the optimal
lag length is one based on both LR, FPE, SIC and HQ.
Table 5.5.1.1: Unit root test: Ng-Perron Test
Variables With trend and intercept Stationarity
Status Mza MZt MSB MPT
LBSE -90.810 -6.722 0.074 1.068 I (1)
ΔLBSE -19.954 -3.156 0.158 4.582
LCMR -20.416 -3.192 0.156 4.478 I (1)
ΔLCMR -42.693 -4.619 0.108 2.136
LCPI -22.071 -3.314 0.150 4.172 I (1)
ΔLCPI -9.894 -2.103 0.212 9.752
LGOR -2.784 -0.852 0.306 23.938 I (1)
ΔLGOR -39.29 -4.432 0.112 2.319
LIIP -26.410 -3.579 0.135 3.770 I (1)
ΔLIIP -7.641 -1.950 0.255 11.935
LREER -6.723 -1.833 0.272 13.552 I (1)
ΔLREER -38.759 -4.401 0.113 2.355 Source: Author’s own Calculation by using E-views 8.0
∆ denotes the first difference of the series. L implies that the variables have been transformed in
natural logs.
141
Table 5.5.1.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ
0 396.298 NA 1.59e-12 -10.137 -9.954 -10.064
1 969.597 1042.363 1.39e-18 -24.093 -22.815* -23.582
2 996.843 45.291 1.77e-18 -23.866 -21.491 -22.916
3 1026.728 45.021 2.17e-18 -23.707 -20.237 -22.319
4 1071.441 73.913* 1.11e-18 -24.605 -18.943 -22.340
5 1133.304 60.391 1.88e-18 -23.933 -19.367 -22.107
6 1163.409 31.277 1.62e-18 -24.452 -17.694 -21.749
7 1211.150 42.161 1.69e-18 -24.757 -16.903 -21.615
8 1272.728 44.784 1.47e-18 -25.421 -16.472 -21.841
9 1340.677 38.827 1.41e-18 -26.251 -16.206 -22.233
10 1446.572 44.008 7.98e-19 -28.066 -16.926 -23.610
11 1605.994 41.408 2.75e-19* -31.272* -19.036 -26.378* * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
After determining the order of integration of all the variables in table 5.5.1.1, the next
step is to employ an ARDL approach to co-integration in order to determine the long run
relationship among the variables. By applying, the procedure in OLS regression for the first
difference part of the equation (5.3) and then test for the joint significance of the parameters
of the lagged level variables when added to the first regression.
5.5.1.3. ARDL Bounds test
The F-Statistics tests the joint Null hypothesis that the coefficients of lagged level
variables in the equation (5.3) are zero. Table 5.5.1.3, reports the result of the calculated F-
Statistics and diagnostic tests of the estimated model. The result shows the calculated F-
statistics were 5.3790. Thus the calculated F-statistics turns out to be higher than the upper-
bound critical value at the 5 percent level. This suggests that there is a co-integrating
relationship among the variables included in the model, i.e. Sensex (LBSE), the Index of
Industrial Production (LIIP), Inflation (LCPI), Real Effective Exchange Rate (LREER), Call
Money Rate (LCMR) and Gold Prices (LGOR)
142
Table 5.5.1.3: ARDL Bounds test
Panel I: Bound testing to co-integration:
Estimated Equation : LBSE = F (LIIP LCPI LREER LCMR LGOR)
Indicators
Optimal lag 04
F – Statistics 5.379053
Panel II: Diagnostic Tests:
Diagnostic Tests Indicators
Normality J-B value 0.8901
Serial Correlation LM Test 1.5214
Heteroscedasticity Test (ARCH) 1.0145
Ramsey Reset Test 0.0724
The second step is to estimate the long-run and short-run estimates of ARDL test. The
long run results are illustrated in Table 5.5.1.4. The results show that the rise in IIP, Inflation
and Exchange Rate has a positive effect on stock prices. The coefficient of Index of Industrial
Production (LIIP), Inflation (LCPI) and Real Effective Exchange Rate (LREER) is
statistically significant and positive at 5%, 1% and 10% respectively. It is evident from the
table that 5% increase in IIP, a 1% increase in Inflation, and 10% increase in Exchange Rate
leads to 1.200%, 1.922%, and 1.211%, respectively, increase in Stock Prices (Sensex). The
findings are consistent with Chen et al. (1986), Maysami et al. (2004), Rahman et al. (2009),
and Ratanapakorn and Sharma, (2007) for IIP, Ioannidis et al. (2004) for Inflation and
Mukherjee and Naka (1995) for Exchange Rate. Whereas, the coefficient of Gold Price is
negative and significant at the 1% level in explaining the variation in stock prices. Therefore,
Gold Prices have a significant negative relationship adversely affecting stock prices and the
findings are consistent with Ray, S., (2012); Gupta and Reid (2013).
143
Table 5.5.1.4: Estimated Long Run Coefficients using ARDL Approach
(Dependent variable: LBSE) Regressors ARDL(1,0,0,0)
Coefficient t- values Prob. Values
LIIP 1.2003** 2.260 [0.027]
LCPI 1.9215*** 3.353 [0.001]
LREER 1.2119* 1.758 [0.083]
LCMR -0.090 -0.756 [0.452]
LGOR -0.866*** -2.953 [0.004]
CONS -3.271 -1.237 [0.220]
Robustness Indicators
R2 0.946
Adjusted R2 0.938
F Statistics 127.778[0.000]
D.W. Stat 1.899
Serial Correlation, F 9.632 [0.648]
Heteroskedasticity, F 7.867 [0.005]
Ramsey reset test, F 2.901 [0.089] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively. Values
in [#] are probability values.
The short-run relationship of the macroeconomic variables on stock market index is
presented in Table 5.5.1.5. As can be seen from the table, IIP and Inflation has a significant
and positive impact on stock market index in the short run also at 10% and 1% level,
respectively. Similar to long-run, gold prices is significantly negative at 1% in the short-run
also. The short run adjustment process is examined from the ECM coefficient. The
coefficient lies between 0 and -1, the equilibrium is converging to the long run equilibrium
path, is responsive to any external shocks. However, if the value is positive, the equilibrium
will be divergent from the reported values of ECM test. The coefficient of the lagged error-
correction term (-0.222) is significant at the 1% level of significance. The coefficient implies
that a deviation from the equilibrium level of stock market index in the current period will be
corrected by 22 percent in the next period to resort the equilibrium.
144
Table 5.5.1.5: Estimated Short Run Coefficients using ARDL Approach
(Dependent variable: LBSE)
Regressors ARDL(1,0,0,0)
Coefficient T – Ratio Prob. Values
ΔLIIP 0.267* 1.776 [0.080]
ΔLCPI 0.428*** 2.724 [0.008]
ΔLREER 0.270 1.625 [0.108]
ΔLCMR -0.019 -0.876 [0.383]
ΔLGOR -0.546*** -3.494 [0.001]
ΔCONS -0.729 -1.232 [0.222]
ECM t-1 -0.222 -3.238 [0.002]
Robustness Indicators
R2 0.426
Adjusted R2 0.348
D.W. Stat 1.899
SE Regression 0.053
RSS 0.202
F Statistics 6.033 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
Values in [#] are probability values.
5.5.1.4. VECM based causality
The results of table 5.5.1.6 indicate that there is no short run causality running from any
of the variable to LBSE in India. It is observed that error correction term is statistically
significant for specification with LBSE as the dependent variable which indicate that there
exist a long run causal relationship among the variable with LBSE as the dependent variable.
This result is also confirmed by the ARDL test statistics.
Table 5.5.1.6: Results of Vector Error Correction Model
Dependent
variable
Sources of Causation
Short run independent variables Long run
ΔLBSE ΔLIIP ΔLCPI ΔLREER ΔLCMR ΔLGOR ECM(t-1)
ΔLBSE - 0.535 0.667 0.870 0.689 0.703 -2.794***
ΔLIIP 5.490*** - 1.713 0.789 0.508 5.822*** -2.563***
ΔLCPI 2.331* 3.224** - 1.405 0.854 1.729 2.182
ΔLREER 0.679 0.280 1.367 - 0.203 0.332 0.145
ΔLCMR 1.543 4.677*** 1.363 0.212 - 0.509 4.848
ΔLGOR 1.136 0.702 1.241 1.026 0.311 - 0.785
*, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
The robustness of the short run result is investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of a long run and short run parameters. The cumulative sum
(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 5.5.1.1) are between
145
critical boundaries at 5% level of significance. This confirms the stability property of the long
run and short run parameters which have an impact on the market index in case of India. This
confirms that models seem to be steady and specified appropriate.
Figure 5.5.1.1: Plots of Stability Test
5.5.1.5. Variance Decomposition (VDC) Analysis:
It is pointed out by Pesaran and Shin (2001) that the variable decomposition method
shows the contribution in one variable due to innovation shocks stemming in the forcing
variables. The main advantage of this approach as it is insensitive to the ordering of the
variables. The results of the VDC are presented in table 5.5.1.7. The empirical evidence
indicates that 72.02% of stock price change is contributed by its own innovative shocks.
Further shock in Gold price explains the stock price by 12.92%. IIP contributes to stock
prices by 9.74% and inflation and exchange rate contributes 2.16% and 2.82% respectively.
The share of other call money rate is very minimal.
146
Table 5.5.1.7: Variance Decomposition (VDC) Analysis
Period S.E. LBSE LIIP LCPI LREER LCMR LGOR
1 0.059 100.00 0.000 0.000 0.000 0.000 0.000
2 0.100 97.545 1.917 0.295 0.068 0.020 0.151
3 0.125 95.908 2.976 0.506 0.279 0.196 0.131
4 0.146 94.879 3.246 0.555 0.714 0.305 0.298
5 0.170 94.009 3.639 0.706 0.920 0.346 0.376
6 0.191 92.822 4.951 0.823 0.727 0.373 0.301
7 0.207 91.810 6.032 0.741 0.701 0.408 0.306
8 0.220 91.021 6.730 0.659 0.830 0.426 0.331
9 0.232 89.664 7.622 0.615 1.147 0.416 0.533
10 0.243 87.496 8.429 0.679 1.668 0.384 1.341
11 0.253 85.134 9.061 0.883 2.104 0.355 2.460
12 0.262 82.626 9.562 1.209 2.439 0.337 3.825
13 0.272 79.997 9.826 1.563 2.689 0.335 5.586
14 0.280 77.609 9.988 1.841 2.830 0.342 7.388
15 0.288 75.629 10.041 2.051 2.900 0.350 9.026
16 0.296 74.114 9.973 2.177 2.912 0.357 10.464
17 0.302 73.068 9.895 2.211 2.889 0.354 11.581
18 0.309 72.374 9.823 2.199 2.860 0.344 12.397
19 0.315 72.020 9.737 2.164 2.821 0.332 12.923
20 0.320 71.929 9.676 2.109 2.774 0.320 13.190
Cholesky Ordering: LBSE LIIP LCPI LREER LCMR LGOR
147
5.5.2. Relationship between Fundamental Macroeconomic Variables and CNX nifty
The study empirically estimated the effect of fundamental macroeconomic variables on
stock prices (CNX Nifty) in India. The study uses monthly data covering the period from
April 2004 to July 2015.
5.5.2.1. Model Specification
The following general specification has been used in this study to empirically
examine the effect of fundamental macroeconomic factors on stock prices.
𝐿𝑁𝑆𝐸 = 𝛼0 + 𝛼1𝐿𝐼𝐼𝑃 + 𝛼2𝐿𝐹𝐼𝐼 + 𝛼3𝐿𝐺𝑂𝑅 + 𝛼4𝐿𝑇𝐵𝑅 + 𝛼5𝐿𝑊𝑃𝐼 + 𝛼5𝐿𝐶𝑂 + 𝛼6𝐿𝑅𝐸𝐸𝑅 + 휀𝑡
(5.4)
5.5.2.2. Stationarity test and Lag length selection before co-integration
Before we conduct tests for co-integration, we have to make sure that the variables
under consideration are not integrated at an order higher than one. Thus, to test the
integration properties of the series, we have used Ng-Perron unit root test. The results of the
stationarity tests are presented in Table 5.5.2.1. The results show that all the variables are
non-stationary at levels. The next step is to difference the variables once in order to perform
stationary tests on differenced variables. The results show that after differencing the variables
once, all the variables were confirmed to be stationary. It is, therefore, worth concluding that
all the variables used in this study are integrated of order one i.e. difference stationary I(1).
Therefore the study uses autoregressive distributed lag (ARDL) approach to co-integration. In
addition, it is also important to ascertain that the optimal lag order of the model is chosen
appropriately so that the error terms of the equations are not serially correlated.
Consequently, the lag order should be high enough so that the conditional ECM is not subject
to over parameterization problems (Narayan, 2005; Pesaran, 2001). The results of these tests
are presented in Table 5.5.2.2. The results of Table 5.5.2.2 suggest that the optimal lag length
is one based on both LR, FPE, SIC and HQ.
148
Table 5.5.2.1: Unit root test: Ng-Perron Test
Variables Without trend and intercept Stationarity
Status Mza MZt MSB MPT
LNSE 0.576 0.429 0.744 38.514 I (1)
ΔLNSE -6.556 -1.739 0.265 3.983
LFII 0.481 1.630 9.626 51.912 I (1)
ΔLFII -54.747 -5.231 0.095 0.447
LGOR 0.828 1.514 1.828 209.246 I (1)
ΔLGOR -15.656 -2.791 0.178 1.589
LIIP -3.459 -1.243 0.359 7.068 I (1)
ΔLIIP -57.168 -5.345 0.093 0.431
LREER 0.153 0.098 0.642 28.032 I (1)
ΔLREER -53.440 -5.129 0.095 0.557
LTBR 1.457 1.558 1.070 85.539 I (1)
ΔLTBR -16.494 -2.869 0.174 1.494
LWPI 0.143 0.093 0.652 28.471 I (1)
ΔLWPI -14.298 -2.640 0.185 1.840
LCO -2.340 -1.065 0.455 10.366 I (1)
ΔLCO -23.521 -3.323 0.141 1.402 Source: Author’s own Calculation by using E-views 8.0
∆ denotes the first difference of the series. L implies that the variables have been transformed in
natural logs.
Table 5.5.2.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ
0 -281.469 NA 1.27e-08 4.522 4.701 4.595
1 725.217 1871.807 5.11e-15* -10.206* -8.602* -9.554*
2 786.204 105.775* 5.41e-15 -10.159 -7.129 -8.928
3 837.308 82.245 6.81e-15 -9.957 -5.501 -8.147
4 880.668 64.362 9.93e-15 -9.635 -3.753 -7.245
5 931.249 68.758 1.34e-14 -9.425 -2.117 -6.456
6 982.161 62.844 1.90e-14 -9.221 -0.486 -5.672
7 1041.636 65.979 2.51e-14 -9.150 1.009 -5.022
8 1097.682 55.170 3.81e-14 -9.026 2.560 -4.318 * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
After determining the order of integration of all the variables in table 5.5.2.1, the next
step is to employ an ARDL approach to co-integration in order to determine the long run
relationship among the variables. By applying, the procedure in OLS regression for the first
difference part of the equation (5.4) and then test for the joint significance of the parameters
of the lagged level variables when added to the first regression.
5.5.2.3. ARDL Bounds test
The F-Statistics tests the joint Null hypothesis that the coefficients of lagged level
variables in the equation (5.4) are zero. Table 5.5.2.3, reports the result of the calculated F-
Statistics & diagnostic tests of the estimated model. The result shows the calculated F-
149
statistics were 5.25316. Thus the calculated F-statistics turns out to be higher than the upper-
bound critical value at the 5 percent level. This suggests that there is a cointegrating
relationship among the variables included in the model, i.e. CNX nifty (LNSE), the Index of
Industrial Production (LIIP), Financial Institutional Investment (LFII), Gold (LGOR), T-Bill
Rate (LTBR), Wholesale Price Index (LWPI), Crude oil price (LCO) and Real Effective
Exchange Rate (LREER).
Table 5.5.2.3: ARDL bounds test results
Panel I: Bound testing to co-integration:
Estimated Equation: LNSE = F (LIIP LFII LGOR LTBR LWPI LCO LREER)
Indicators
Optimal lag 02
F – Statistics 5.25316
Panel II: Diagnostic Tests:
Diagnostic Tests Indicators
Normality J-B value 0. 9011
Serial Correlation LM Test 1.4214
Heteroscedasticity Test (ARCH) 1.0215
Ramsey Reset Test 0.0694
The second step is to estimate the long and short-run estimates of ARDL test. The long
run results are illustrated in Table 5.5.2.4. The results show that the coefficient of Crude oil
prices (LCO) is statistically significant and negative at 5%. It is evident from the table that
5% increase in Crude oil price leads to 0.644% decrease in CNX nifty (LNSE). The findings
are consistent with Valadkhani et al. (2009), Hosseini et. al., (2011) (For India) and Kuwornu
(2012). The result found in this study implies that, since India is an oil importer country,
therefore, the increases in oil price would lead to increase the cost of production and,
consequently, the expected cash flow would decrease and it is also evident that the increase in
oil prices should result in higher costs and, hence, lower equity values.
Similarly, the coefficient of Inflation (LWPI) is negative and significant at 1%. It is
evident from the table that 1% increase in Inflation leads to -0.328%, decrease in CNX nifty
(LNSE). The findings of the study are consistent with Fama (1981), Mukherjee and Naka
(1995), and Maysami and Koh (2000), who have found a negative correlation between
inflation and stock prices. The negative relationship may be due to the reason that because
inflation causes the value of money to decrease and consequently the purchasing power of the
150
people decreases, which leads to a negative effect of saving and investment activities of the
stock exchange.
Table 5.5.2.4: Estimated Long Run Coefficients using ARDL Approach
(Dependent variable: LNSE) Regressors ARDL(1,0,0,0)
Coefficient t- values Prob. Values
LIIP 0 .082 0.948 [0.345]
LFII -0.010 -0.466 [0.642]
LGOR 0.293 2.309 [0.355]
LTBR 0.228 0.838 [0.403]
LWPI -0.328*** 2.919 [0.004]
LCO -0.644** 0.928 [0.023]
LREER 0.428 0.339 [0.735]
CONS -0.840 -0.112 [0.911]
Robustness Indicators
R2 0.988
Adjusted R2 0.987
F Statistics 877.934 [0.000]
D.W. Stat 1.845
Serial Correlation, F 1.374 [0.189]
Heteroskedasticity, F 2.899 [0.091]
Ramsey reset test, F 0.926 [0.338] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) ** and *** indicate significant at 5 and 1 percent level of significance, respectively. Values
in [#] are probability values.
The short-run relationship of the macroeconomic variables on the National Stock
Exchange is presented in Table 5.5.2.5. As can be seen from the table, Inflation (LWPI) and
Crude oil price (LCO) has a significant and negative impact on CNX nifty (LNSE) in the
short run at 1% level of significance. One can say that 1% increase in inflation and crude oil
price leads to 0.021% and 0.203%, decrease in CNX nifty. This may be due to the fact that
investors are more sensitive towards the movements in crude oil price and inflation in the
short run.
Whereas, Gold (LGOR), T-bill rates (LTBR) and Real Effective Exchange Rate
(LREER) are significantly positive at 10%, 10% and 1% level, respectively, in short-run. The
positive impact of T-bill rates on the CNX nifty Index is to some extent consistent with
Kuwornu (2012), implying that investors do not view Short Term T-bill rate with the
associated interest rates as option to investment opportunities. Therefore, increases in T-bill
rates lead to increased investment in stocks, causing stock returns to rise in India. The
appreciation of the Real Effective Exchange Rate in India would attract more investors to
invest in the stock market in the short run. The short run adjustment process is examined
from the ECM coefficient. The coefficient lies between 0 and -1, the equilibrium is
151
converging to the long run equilibrium path, is responsive to any external shocks. However,
if the value is positive, the equilibrium will be divergent from the reported values of ECM
test. The coefficient of the lagged error-correction term (-0.0746) is significant at the 1%
level of significance. The coefficient implies that a deviation from the equilibrium level of
National Stock Exchange in the current period will be corrected by 7 percent in the next
period to resort the equilibrium.
Table 5.5.2.5: Estimated Short Run Coefficients using ARDL Approach
(Dependent variable: LNSE)
Regressors ARDL(1,0,0,0)
Coefficient T – Ratio Prob. Values
ΔLIIP 0.006 0.880 [0.381]
ΔLFII -0.745E-3 -0.471 [0.638]
ΔLGOR 0.0479* 1.724 [0.087]
ΔLTBR 0.0669* 1.802 [0.074]
ΔLWPI -0.0217*** 3.144 [0.002]
ΔLCO -0.2036*** 3.913 [0.000]
ΔLREER 1.391*** 5.464 [0.000]
ΔCONS -0.0623 -0.111 [0.911]
ECM t-1 -0.0746 3.106 [0.002]
Robustness Indicators
R2 0.430
Adjusted R2 0.374
D.W. Stat 1.845
SE Regression 0.047
RSS 0.264
F Statistics 10.163 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) * and *** indicate significant at 10 and 1 percent level of significance, respectively. Values
in [#] are probability values.
5.5.2.4. VECM based causality
The results of table 5.5.2.6 indicate that there exists a short-run causality running from
inflation and crude oil price to stock prices in India. Furthermore, a unidirectional causality is
also running from stock prices to gold and inflation. Thus, it is clearly observed that
bidirectional causality is running between inflation and CNX nifty index. It is also observed
that error correction term is statistically significant for specification with LNSE as the
dependent variable which indicate that there exist a long-run causal relationship between the
variable with LNSE as the dependent variable. This result is also confirmed by the ARDL test
statistics.
152
Table 5.5.2.6: Results of Vector Error Correction Model
Dependent
variable
Sources of Causation
Short run independent variables Long run
ΔLNSE ΔLIIP ΔLFII ΔLGOR ΔLTBR ΔLWPI ΔLCO ΔLREER ECM(t-1)
ΔLNSE - 0.380 0.530 1.612 2.090 6.833** 5.613** 0.897 1.664**
ΔLIIP 3.656 - 0.567 2.673 1.094 1.729 2.793 0.714 -0.364
ΔLFII 0.799 0.389 - 0.148 0.380 3.116 0.411 1.352 0.723***
ΔLGOR 5.484** 1.504 1.577 - 1.187 1.336 0.282 0.078 -0.276
ΔLTBR 5.207* 0.860 2.689 1.492 - 1.921 1.493 0.257 -0.508
ΔLWPI 7.012** 0.024 3.813 0.037 3.690 - 6.250** 1.063 -1.817*
ΔLCO 1.200 0.204 1.779 0.738 3.265 0.321 - 0.182 -1.197
ΔLREER 2.696 7.242* 2.199 2.186 2.607 1.153 1.964 - -0.356
*, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
The robustness of the short run result are investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of a long run and short run parameters. The cumulative sum
(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 5.5.2.1) are between
critical boundaries at 5% level of significance. This confirms the stability property of a long
run and short run parameters which have an impact on the market index in case of India. This
confirms that models seem to be steady and specified appropriate.
Figure 5.5.2.1: Plots of Stability Test
5.5.2.5. Variance Decomposition (VDC) Analysis:
It is pointed out by Pesaran and Shin (2001) that the variable decomposition method
shows the contribution in one variable due to innovation shocks stemming in the forcing
variables. The variance decomposition indicates the amount of information each variable
contributes to the other variables in the autoregression. It determines how much of the
forecast error variance of each of the variables can be explained by exogenous shocks to the
other variables. The main advantage of this approach as it is insensitive to the ordering of the
variables. The results of the VDC are presented in table 5.5.2.7. The empirical evidence
153
indicates that 71.85% of CNX nifty index change is contributed by its own innovative shocks.
Further shock in inflation explains CNX nifty index by 15.67%. Crude oil price contributes to
the CNX nifty index by 9.24%, and the results are consistent with the results of VECM. Thus,
it can be said that the most important macroeconomic variables that influence CNX nifty
index in India are inflation and crude oil prices, though they are marginal at 15.67% and
9.24% respectively. From this analysis, it can be referred that the Indian Stock Market
Returns can be predicted from the inflation and crude oil prices. The share of other variables
is very minimal.
Table 5.5.2.7: Variance Decomposition (VDC) Analysis
Period S.E. LNSE LFII LGOR LREER LTBR LIIP LWPI LCO
1 0.054 100.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 0.085 97.815 0.252 0.352 0.148 1.012 0.320 0.086 0.010
3 0.107 97.020 0.532 0.395 0.146 1.019 0.297 0.575 0.010
4 0.123 95.720 0.668 0.371 0.168 1.016 0.456 1.565 0.032
5 0.135 94.387 0.681 0.326 0.181 0.913 0.502 2.816 0.189
6 0.145 92.809 0.657 0.286 0.176 0.809 0.500 4.188 0.570
7 0.153 90.911 0.628 0.258 0.162 0.727 0.483 5.631 1.195
8 0.160 88.714 0.600 0.250 0.149 0.675 0.462 7.111 2.035
9 0.165 86.301 0.574 0.263 0.150 0.647 0.442 8.583 3.035
10 0.171 83.774 0.549 0.299 0.178 0.639 0.422 10.005 4.130
11 0.176 81.222 0.526 0.359 0.241 0.640 0.403 11.347 5.258
12 0.180 78.714 0.504 0.441 0.349 0.644 0.389 12.589 6.368
13 0.184 76.298 0.485 0.544 0.508 0.646 0.379 13.725 7.419
14 0.188 74.006 0.468 0.669 0.719 0.643 0.351 14.752 8.383
15 0.192 71.854 0.453 0.815 0.981 0.634 0.345 15.672 9.244
Cholesky Ordering: LNSE LFII LGOR LREER LTBR LIIP LWPI LCO
Figure 5.5.2.2: VDC analysis combined graph
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Variance Decomposition of LNSE
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Variance Decomposition of LFII
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Var iance Decomposition of LGOLD
0
20
40
60
80
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Variance Decomposition of LREER
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Variance Decomposition of LTBR
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Variance Decomposition of LIIP
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Var iance Decomposition of LWPI
0
20
40
60
80
100
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Variance Decomposition of LCO
154
5.5.2.6. Impulse Response Function (IRF)
An impulse response refers to the reaction of any dynamic system in response to some
external change. It helps to trace out the responsiveness of the dependent variables in the
VAR to shocks to each of the variables. Table 5.5.2.8 presents the estimates from the impulse
response function of stock market index as against its “own shocks” and the shocks of
Foreign Institutional Investors, gold, Real Effective Exchange Rate, T-bill rates, the Index of
Industrial Production, Inflation and crude oil prices. The result shows that the CNX nifty
index has a negative relationship with its past on the long-run. In its response to the shocks of
Index of Industrial Production, it is observed that there is a negative relationship throughout
the period, whereas, a similar relationship is observed in the case of inflation and crude oil in
the long run, except for the first three periods, i.e. it shows a positive relationship in the short
run. Further, T-bill rates show a positive relationship in the long run, except for the second
period, the result is consistent with the result of short run ARDL estimation. In its response to
the shocks of Foreign Institutional Investors, it is also observed that there is a negative
relation in second to sixth period, i.e. in the short run and thereafter it shows a positive
relationship in the long run. Furthermore, in its response to the shocks of Real Effective
Exchange rate and Gold the negative relationship starts from seventh and eighth period,
respectively, but it shows a positive relationship in the short run. Also, in its response to the
shocks of explanatory variables, CNX nifty does not respond in the first period. The
evidences in favor of the explanations given in the table are also presented in graphical
format in figure 5.5.2.3.
Table 5.5.2.8: Impulse Response Function (IRF)
Period S.E. LNSE LFII LGOR LREER LTBR LIIP LWPI LCO
1 0.054 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 0.064 -0.004 -0.005 0.003 0.008 -0.004 -0.002 0.000 0.000
3 0.063 -0.006 -0.004 0.002 0.006 0.003 -0.007 0.000 0.000
4 0.058 -0.006 -0.003 0.002 0.006 0.005 -0.013 -0.001 -0.001
5 0.052 -0.004 -0.001 0.002 0.003 0.004 -0.016 -0.005 -0.005
6 0.047 -0.003 -0.000 0.001 0.001 0.003 -0.019 -0.009 -0.009
7 0.041 -0.002 0.000 0.000 -0.000 0.002 -0.020 -0.012 -0.012
8 0.036 -0.002 0.001 -0.000 -0.001 0.002 -0.022 -0.015 -0.015
9 0.032 -0.002 0.002 -0.001 -0.002 0.001 -0.023 -0.017 -0.017
10 0.028 -0.001 0.003 -0.003 -0.003 0.001 -0.023 -0.019 -0.019
11 0.024 -0.001 0.004 -0.004 -0.003 0.001 -0.024 -0.020 -0.020
12 0.021 -0.001 0.005 -0.006 -0.003 0.000 -0.024 -0.021 -0.021
13 0.019 -0.001 0.006 -0.007 -0.003 0.000 -0.024 -0.021 -0.021
14 0.016 -0.000 0.007 -0.009 -0.002 0.000 -0.023 -0.021 -0.021
15 0.014 -0.000 0.007 -0.010 -0.002 0.000 -0.023 -0.020 -0.020
Cholesky Ordering: LNSE LFII LGOR LREER LTBR LIIP LWPI LCO
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Figure: 5.5.2.3 Impulse Response Function combined graph
-.04
-.02
.00
.02
.04
.06
.08
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LNSE to Cholesky
One S.D. Innovations
-0.5
0.0
0.5
1.0
1.5
2.0
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LFII to Cholesky
One S.D. Innovations
-.02
-.01
.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LGOLD to Cholesky
One S.D. Innovations
-.010
-.005
.000
.005
.010
.015
.020
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LREER to Cholesky
One S.D. Innovations
-.04
.00
.04
.08
.12
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LTBR to Cholesky
One S.D. Innovations
-.2
.0
.2
.4
.6
.8
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LIIP to Cholesky
One S.D. Innovations
-.1
.0
.1
.2
.3
.4
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LWPI to Cholesky
One S.D. Innovations
-.02
.00
.02
.04
.06
.08
.10
2 4 6 8 10 12 14 16 18 20
LNSE LFII LGOLD
LREER LTBR LIIP
LWPI LCO
Response of LCO to Cholesky
One S.D. Innovations
156
5.6. Summary
In the present chapter of the study, with the help of modern econometric techniques, an
effort has been made to empirically investigate the relationship between stock prices or stock
market development with different sets of domestic and international macroeconomic
variables. Towards this effort different models has been formulated, using the data for
different time span and frequency, according to the need of the study. The study is
categorised into three major categories, viz.-a-viz., the first category is the empirical
estimation of the study using annual frequency data; the second category is the empirical
estimation of the study using quarterly frequency data; and the third category consist of the
study using monthly frequency data.
The first category, deals with the estimation and discussion on the relationship between
stock prices and macroeconomic variables by using data from the year 1979 to 2014. The
long-run estimates of ARDL test showed that positive and significant relationship exists
between economic growth and stock prices. It also confirms a significant and positive
influence of Exchange Rate and Inflation on stock price movements in India. However, there
exists a negative and significant relationship between crude oil price and stock prices. The
results of long run estimates of ARDL are consistent in the short run as well. The error
correction model of ARDL approach reveals that the adjustment process from the short-run
deviation is quite high. The result of VECM based granger causality shows that there exists a
short run unidirectional causality running from foreign direct investment, GDP and real
interest rate to BSE in India. Further, the result indicates the presence of long run causality
for the equation with the stock price as the dependent variable. The results of the VDC
analysis show that a major percentage of stock price change is its own innovative shocks.
The second category, i.e. the study with quarterly frequency data, empirically examined
the relationship between macroeconomic variables and stock market development (MCAP) in
India, data from the period 1996:Q1 to 2014:Q3. The long-run estimates of ARDL test
showed that economic growth, FIIs and Trade openness in India significantly influence
market capitalization positively. However, economic growth failed to explain the variation in
stock market growth significantly in the short-run. The results of VECM based granger
causality show that there exists long-run causality running from economic growth, trade
openness, FDI and FII in the long-run towards Stock Market Capitalization, whereas, in
short-run the change in trade openness causes a change in Stock Market Capitalization. The
result of the VDC analysis shows that trade openness is having maximum shock on stock
market capitalization.
157
The third category, i.e. the study with monthly frequency data, empirically examined
the relationship between stock prices and macroeconomic variables, using different time
period for the study and different set of macroeconomic variables, formulating different
models. The first part of the monthly study deals with the estimation and discussion on the
relationship between BSE Sensex and macroeconomic variables by using data from the
period April 2004 to July 2014. The long-run estimates of ARDL test showed that positive
and significant relationship exists between economic growth (IIP), Exchange Rate and
Inflation on stock price movements in India. Further, the study confirms negative and
significant relationship between gold prices and stock prices. The error correction model of
ARDL approach reveals that the adjustment process from the short-run deviation is slow. The
result of VECM based causality found no short run causality running from any of the
variables to BSE in India. Further, the result indicates the presence of long run causality for
the equation with the stock price as the dependent variable. The results of VDC show that a
major percentage of stock price change is its own innovative shocks.
The second part of the monthly study investigated the relationship between
fundamental macroeconomic variables and the index of National Stock Exchange (CNX
Nifty) in India, by using data from the period April 2004 to July 2015. The long-run estimates
of ARDL test showed that negative and significant relationship exists for the crude oil prices,
Inflation with stock prices. The results of the influence of both the variables on stock prices
are consistent in the short run as well. Further, for short-run the study confirms positive and
significant relationship for Gold, T-bill rates and Real Effective Exchange Rate. Furthermore,
for short-run the study confirms positive and significant relationship for Gold, T-bill rates and
Real Effective Exchange Rate. The error correction model of ARDL approach reveals that the
adjustment process from the short-run deviation is high. The result of VECM based causality
found short run causality running from Inflation and crude oil price to National Stock
Exchange in India. The results of VDC analysis and IRF show that a major percentage of
stock prices change is its own innovative shocks.
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CHAPTER 6
Fiscal policy variables and Stock Market Development in India
6.1. Introduction
It is well said that the current and the future economic growth of the economy depend
on country’s stock market performance and the stock market performance depends on the
country’s fiscal budget. This is partly due to the notion that a large budget deficit could affect
the current and future economic growth of the nation through its effects on the stock markets.
Theoretically, it is true that when the budget of the country is in deficit, it will depress the
stock prices and undermine the investor’s confidence. Falling current investment reduces
future competitiveness of an economy. Roley and Schall (1988) investigated the potential
effects of federal deficits on the stock market and concluded that the growing budget deficit
may influence the conditions of the economy. The empirical evidence from this study
suggested that increases in the structural deficits have historically led to increase in stock
prices and the structural deficit has typically risen during the recession and then decreased
early in the subsequent expansion. In terms of the stock market, a prolonged trade deficit
could have adverse effects. If a country has been importing more goods than it is exporting
for a sustained period of time, it is essentially going into debt (much like a household would).
Over time, investors will notice the decline in spending on domestically produced goods,
which will hurt domestic producers and their stock prices. Given enough time, investors will
realize fewer investment opportunities domestically and begin to invest in foreign stock
markets, as prospects in these markets will be much better. This will lower demand in the
domestic stock market and cause that market to decline.
Hence, this chapter of the study deals with the discussion of empirical results derived
using different econometric techniques, to know the relationship between fiscal policy
variables of India along with some macroeconomic variables and the Indian stock market
development. The econometric methodologies used for estimating the empirical results of the
studies are, Ng-Perron unit root test is utilized to check the order of integration of the
variables. Lag-length selection criteria are used to determine the appropriate lag length for the
model. The long run relationship is examined by implementing the ARDL bounds testing
approach to co-integration. VECM method is used to test the short and long run causality and
variance Decomposition and Impulse Response Function are used to predict long run
exogenous shocks of the variables.
159
The chapter has been segmented into four sections; the first section consists of the
extensive literature review based on the relationship between stock market development and
the fiscal and current account deficit, along with controlled macroeconomic variables; second
section encomposes the study of relationship between fiscal deficit and stock prices in India,
using yearly frequency data; the third section is composed of the studying the relationship
between twin deficit and stock market development in India, using yearly frequency data; and
the fourth section is composed of summary of the findings.
6.2. Review of Literature
For this study, it is not viable to survey all the literature in every dimension. However,
the present study focuses on the causal relationship between fiscal policy variables and stock
market development, along with some controlled macroeconomic variables. The first section
will discuss the relevant studies from overall economies, the studies related to Indian
economy will be provided in the second section.
6.2.1. Studies of overall economies other than India
Darrat (1990) employed Akaike’s final prediction error (FPE) criteria in conjunction
with multivariate Granger causality tests to examine whether changes in Canadian stock
returns are predicted by several economic variables including the money base, interest rates,
interest rate volatility, real income, inflation, exchange rates, and fiscal deficits. The
empirical study used monthly data from January 1972 to February 1987. Results indicated
that current stock prices in Canada fully incorporate all available information from monetary
policy instruments, and that stock returns are Granger-caused by lagged changes in fiscal
deficits. This conclusion held even when interest rates, interest rate volatility, real income,
inflation, monetary policy, and exchange rates are excluded from the estimation. Under the
assumption of constant expected stock returns, such findings appear inconsistent with the
stock market efficiency hypothesis.
Abdullah and Hayworth (1993) used seven macroeconomic variables to explain
fluctuations of monthly stock returns in the U.S. stock market using a vector Autoregressions,
Granger causality tests, and impulse response analysis. The macroeconomic variables were
M1, budget deficits, trade deficits, inflation, IP, short-term interest rates, and the S&P 500.
The results indicated that money growth, budget deficits, trade deficits, inflation, and both
short-term and long-term interest rates Granger-cause stock returns. Additionally, stock
returns were positively related to inflation and money growth, but, consistent with economic
160
theory, stock returns were negatively related to budget deficits, trade deficits, and both short-
term and long-term interest rates.
Abdullah (1998) employed Sims (1980) forecast error variance decompositions to
analyze the effects of six macroeconomic variable changes on UK stock returns, proxied by
the London share price index. The macroeconomic variables were M1, budget deficits and
surpluses, IP, the consumer price index (CPI), and a long term interest rate. The results
suggested that money growth variability accounts for 22.82% and 19.53% of the variance in
interests’ rates and stock returns, respectively. Therefore, money growth variability
contributed to the uncertainty associated with returns on investments in stocks and other
financial assets. The other variables included in the model were statistically significant in
explaining the variance of UK stock returns.
Adrangi and Allender (1998) provide empirical evidence regarding budget deficit and
stock prices in industrialized countries such as Japan, US, France and Germany by using
Monthly data from 1974-1995. Granger causality, VAR test results showed a negative
relationship between budget deficit and equity returns in the U.S. However, in Japan, France
and Germany change in deficits do not affect the equity market returns.
Hanousek and Filer (2000) examine the possibility that newly-emerging equity markets
in Central Europe exhibit semi-strong form efficiency such that no relationship exists
between lagged values of changes in macroeconomic variables (M1, M2, exports, imports,
trade balance, foreign capital inflow, budget deficit, government debt, CPI, PPI, exchange
rate, and industrial production) and changes in equity prices using Granger causality tests.
They find that while there are connections between real economy and equity market returns
in Poland and Hungary, these links occur with lags, suggesting the possibility of profitable
trading strategies based on public information and rejecting semi-strong efficiency
hypothesis. For Czech Republic and Slovakia, the situation is more complex. In the early
years of their existence, these markets may have possessed elements of semi-strong
efficiency, with both lagged and contemporaneous relationships between real variables and
equity markets. However, these links have disappeared over time. In other words, these stock
markets appear to have become increasingly divorced from reality.
Laopodis (2007) examined the dynamic linkages among the federal budget deficit,
monetary policy and the stock market of US, by using quarterly data from 1960:Q1 to
2004:Q4. The methodology employed was Granger causality, vector autoregressions and
cointegration techniques. The empirical results generally suggest that deficits matter for the
stock market and imply a violation of the Ricardian Equivalence Proposition. Further
161
analyses using taxes and government spending show a higher sensitivity of the stock market
to taxes relative to spending.
Emrah Ozbay (2009) addressed the causal relationship between stock prices and
macroeconomic factors such as interest rate (Overnight Interest Rate, Treasury Bill Interest
Rate), inflation (Producer Price Index, Consumer Price Index), exchange rates, Current
Deficit to Gross Domestic Production (CD/GDP), foreign transactions, money supply and the
real economy, applying monthly data covering the period of 1998:01 to 2008:12 from
Turkey. Granger causality model is employed to explore such relationships. The results of the
study indicated that the interest rate, inflation, CD/GDP, and foreign sale do Granger cause
stock returns, while stock returns do Granger cause money supply, exchange rate, interest rate
inflation (PPI), foreign transactions. Industrial production is indicated as neither the result
variable nor the cause variable of stock price movement. Furthermore, the analysis of the
results showed that interest rates (CPI and PPI) are the negative determinants of stock prices,
while foreign transactions are the positive determinants of stock prices in Turkey.
Asaolu and Ogunmuyiwa (2010) investigated the impact of macroeconomic variables
on Average Share Price (ASP) and goes further to determine whether changes in
macroeconomic variables explain movements in stock prices in Nigeria. Granger Causality
test, Co-integration and Error Correction Method (ECM) were employed on annual time
series data from 1986-2007. Macroeconomic variables used for the study were External Debt
(ED), Interest Rate (IR), Fiscal Deficit (FD), Exchange Rate (EX), Foreign Capital Inflow
(FCI), Investment (INV), Industrial Output (INDO) and Inflation Rate (INF). The results
revealed that a weak relationship exists between ASP and macroeconomic variables in
Nigeria. The findings further showed that ASP is not a leading indicator of macroeconomic
performance in Nigeria.
Hsing, Budden and Phillips (2011) examined the macroeconomic factors that are
expected to influence the Argentine stock market index, using quarterly time series data from
1998:Q1 to 2011:Q2. The exponential GARCH model was employed for the study. The
variables included were real GDP, monetary policy, fiscal policy, exchange rate, inflation rate
and the world stock market as represented by the U.S. stock market index. It was found from
the study that the Argentine stock market index is positively associated with real GDP, the
ratio of M2 money supply to GDP, the peso/USD exchange rate and the U.S. stock market
index. And it is negatively influenced by the money market rate, government spending as a
percent of GDP and the inflation rate.
162
Yu Hsing (2011) examined the effects of selected macroeconomic variables on the
stock market index in South Africa, using exponential GARCH (Nelson, 1991) model. The
quarterly time series data from 1980:Q2 to 2010:Q3 was used. Macroeconomic variables
used were real output, the government deficit, the money supply, domestic real interest rate,
the nominal effective exchange rate, the inflation rate, the world stock market index, and the
world interest rate. The study showed that the South Africa’s stock market Index is positively
influenced by the growth rate of real GDP, the ratio of the money supply to GDP and the U.S.
stock market index and negatively affected by the ratio of the government deficit to GDP, the
domestic real interest rate, the nominal effective exchange rate, the domestic inflation rate,
and the U.S. government bond yield.
Ahmet Ozcan (2012) examined whether macroeconomic variables have a significant
relationship with the ISE industry index by using monthly data for the period from 2003 to
2010. The selected macroeconomic variables for the study include interest rates, consumer
price index, money supply, exchange rate, gold prices, oil prices, current account deficit and
export volume. The Johansen’s cointegration test was adopted to determine the impact of
selected macroeconomic variables on the ISE industry index. The result suggested that
macroeconomic variables exhibit a long run equilibrium relationship with the ISE industry
index.
Osamwonyi and Osagie (2012) attempted to determine the relationship between
macroeconomic variables and the Nigerian capital market index. Macroeconomic variables
used for the study were interest rates, inflation rates, exchange rates, fiscal deficit, GDP and
money supply, from the year 1975 to 2005 with annual frequency. Vector Error Correction
Model (VECM) was used to study the short-run dynamics as well as the long-run relationship
between the stock market index and selected macroeconomic variables. From the study it was
found that the macroeconomic variables influence stock market index in Nigeria.
Şerife and Ergun (2012) identified the effects of selected macroeconomic variables,
including inflation rate, exchange rate, interest rate, current account deficit and the
unemployment rate on stock returns of 45 companies from 11 different sectors.
Autoregressive distributed lag method was employed for the monthly data spanning from
February, 2005 to May, 2012. Results suggested that the exchange rate and interest rate are
the most significant factors in the stock price fluctuations of the companies. Stock returns of
the companies in any industry are very sensitive to the changes in exchange rate and interest
rate.
163
Osahon and Dickson (2013) investigated the effects of fiscal deficits on stock prices in
Nigeria, utilizing vector auto-regression and error-correction mechanisms (ECM) techniques
with annual time series data spanning 1984-2010. The controlled variables used for the study
were interest rate, money supply, volume of transaction, and inflation. The results showed
that fiscal deficit is negatively related to stock prices.
Luqman Safdar (2014) examined the relationship of the twin deficit with the stock
market of Pakistan by using yearly data from 1992 to 2012. Variables used for the study were
Karachi stock exchange index, current account deficit as a percentage of GDP, and Budget
deficit as a percentage of GDP. ARDL approach was used to examine the long run
relationship among variables. The result confirmed that there exists a positive relationship of
twin deficit for Pakistan and for short-run also the result remains the same.
6.2.2. Studies related to Indian economy
Vuyyuri and Seshaiah (2004) studied the interaction of the budget deficit of India with
other macroeconomic variables such as Nominal effective exchange rate, GDP, CPI and
money supply (M3) giving special emphasis on the budget deficit-exchange rate relationship
using Cointegration approach and Variance Error Correction Models (VECM) for the period
1970-2002, using the annual frequency of data. The results revealed that the variables under
study are cointegrated and there is bidirectional causality between budget deficit and nominal
effective exchange rates. It was also observed that the GDP Granger causes budget deficit,
whereas budget deficit does not.
Saleem et al. (2012) studied that weather changes in deficits cause changes in stock
prices of Pakistan and India and if so, in what direction, using the Johansen Cointegration
technique and Granger Causality Test. Annual data from 1990-2010 was considered for the
study. Stock price indices under consideration were, KSE 100 index for Pakistan and BSE
200 index for India. This study suggested that high developmental expenditure in Pakistan is
the reason for long term positive causal relationship between budget deficit and stock prices
in case of Pakistan while in India a long term negative relationship is observed which is due
to high current expenditures.
Prantik and Vina (2012) studied the relationship between the real economic variables
and the capital market in Indian context, using VAR and Artificial Neural Network. The
paper considers the monthly time series data from 1994 to 2003. Macroeconomic variables
used for the study were national output, fiscal deficit, interest rate, inflation, exchange rate,
money supply, foreign institutional investment BSE Sensex. The finding showed that the
164
variables like interest rate, output, money supply, inflation rate and exchange rate has
considerable influence in the stock market movement.
Aggarwal, P. and Kumar, M. M. (2012) analyzed the relationship between stock prices
and macroeconomic variables in India and US, using monthly frequency data from January
1994 to December 2011. Nifty and S&P 500 were used to represent the stock prices of India
and US, respectively, and the macroeconomic variables used for the study include foreign
institutional investment (FII), exchange rate, gold price/(10 gm), fiscal deficit, industrial
production index (IIP), inflation (WPI), interest rate and gross domestic product (GDP).
Cointegration technique was adopted as the methodology. The results suggested that the
macroeconomic variables have a significant impact on stock prices.
Singh (2014) examined the relationship between macroeconomic variables and the
Indian stock market. The methodology employed was multivariate stepwise regression
analysis and Granger’s causality test, using monthly frequency data from January 2011 to
December 2012. The variables used for the study include the average monthly closing price
of BSE Sensex and S&P CNX Nifty and the explanatory variables were the Index of
Industrial Production (IIP), Wholesale Price Index (WPI), Money Supply (M3), Interest Rates
(IR), Trade Deficit (TD), Foreign Institutional Investment (FII), Exchange rate (ER), Crude
Oil Price (CP) and Gold Price (GP). The result showed significant impact of macroeconomic
variables on the Indian stock market. The gold prices have its negative impact on the stock
market. Further, the study proves that the Indian Stock market improves with the increase in
the inflow of foreign investment. Also, the exchange rate shows its adverse effect on the
stock market during the study period. The Granger causality test confirmed that there exists a
unidirectional causal relationship from the exchange rate to stock market. On the other hand
the causality is also running from the index to trade deficit and foreign institutional investors.
From the above review of literature it can be concluded that the studies, particularly, on
the relationship between fiscal policy variables (Fiscal Deficit and Current Account Deficit)
are very scant. Further, the study finds that there has been no study conducted while taking
into account the effects of the twin deficits, along with other controlled macroeconomic
variables on stock market development using the ARDL approach on the emerging economy
like India. This study attempts to fill this gap by exploring the effects of variations in twin
deficit and other macroeconomic variables towards stock market development in India with
the help of annual time series data.
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6.3. Relationship between Fiscal Deficits and Stock Prices in India
The Study empirically estimated the effect of fiscal deficit and controlled
macroeconomic variables on stock prices in India. The study uses annual data covering the
period from 1988 to 2014.
6.3.1. Model specification
The following general specification has been used in this study to empirically examine
the effect of fiscal deficit and other fundamental macroeconomic factors on the stock market.
𝐿𝐵𝑆𝐸𝑡 = α0 + α1𝐿𝐹𝐷𝑡 + α2𝐿𝑀3𝑡 + α3𝐿𝐶𝑃𝐼𝑡 + α4𝐿𝑅𝐼𝑅𝑡 + ε𝑡
(6.1)
6.3.2. Stationarity test and Lag length selection before co-integration
Before we proceed to ARDL testing, we test for unit root of the variables to determine
their order of integration. The test for unit root is to ensure that none of the series is
integrated at I(2). In the present study, we have used Ng-Perron unit root tests. The results of
the newly developed Ng-Perron (2001) test developed by Ng-Perron are presented in Table
6.3.1. The analysis of the unit root test results indicates that the variables are integrated order
one (I(1)) and none of the variables are I(2) series10.
Table 6.3.1: Unit root test: Ng-Perron Test
Variables With Trend and Intercept Stationarity
Status Mza Mzt MSB MPT
LBSE −0.504 −0.239 0.473 16.189 I (1)
ΔLBSE −11.187 −2.363 0.211 2.195
LFD −1.851 −1.981 0.252 3.120 I (1)
ΔLFD −10.896 −2.322 0.213 2.294
LM3 −0.267 −0.149 0.559 20.937 I (1)
ΔLM3 −10.130 −2.218 0.219 2.539
LCPI −2.918 −0.385 0.135 1.441 I (1)
ΔLCPI −17.659 −1.920 0.250 3.329
LRIR −2.488 −0.074 0.218 2.967 I (1)
ΔLRIR −8.930 −2.102 0.235 2.781
Source: Author’s own Calculation by using E-views 8.0. ∆ denotes the first difference of the
series. L implies that the variables have been transformed in natural logs.
The next step involves estimating the model and determining the rank, r to find the
number of co-integrating relations in our model. In the ARDL model specification, it has
been specified that the number of lags is the same for all the variables taken for the study
because all these variables are incorporated in a model as specified in Equation (6.3.1), where
LBSE is taken as dependent variable and other variables as independent. The optimal lag
10 ARDL technique is applicable irrespective of whether regressor in the model is I(0) or I(1).
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length was determined by different criterion suitable to the models (Table 6.3.2) using two
maximum lags in the model. The aim is to choose the number of parameters, which
minimizes the value of the information criteria.
Table 6.3.2: Lag Order Selection Criterion
Lag LogL LR FPE AIC SIC HQ
0 −8.382 NA 2.20 × 10−6 1.163 1.410 1.225
1* 116.236 184.218* 4.05 ×10−10 * −7.498* −6.017* −7.126*
2 139.492 24.266 6.78×10−10 −7.347 −4.631 −6.664
* Indicates lag order selected by the criterion;
LR: sequential modified LR test statistic (each test at the 5% level);
FPE: Final prediction error;
AIC: Akaike information criterion;
SIC: Schwarz information criterion;
HQ: Hannan-Quinn information criterion.
6.3.3. ARDL Bounds Test
The paper estimates the ARDL bounds test approach to co-integration. We used AIC,
LR, SIC, HQ and FPE for selecting a minimum lag order of 1 for conditional ARDL-VECM,
by applying the procedure in OLS regression for the first difference part of the Equation (6.1)
and then testing for the joint significance of the parameters of the lagged level variables when
added to the first regression. The F-Statistics test the joint Null hypothesis that the
coefficients of lagged level variables are zero. Table 6.3.3 reports the result of the calculated
F-Statistics which are more than UCB which is at 5% (Pesaran (2001) or Narayan (2005)).
Thus the Null Hypothesis of no co-integration is rejected, implying long run co-integrating
relationship amongst the stock market index and economic growth. The estimated statistics
show that the model specification seems to pass all diagnostic tests successfully.
Table 6.3.3: ARDL bounds test results
Panel I: Bound testing to co-integration:
Estimated Equation : LBSE = F (LFD LM3 LCPI LRIR)
Indicators
Optimal lag 01
F – Statistics 4.715
Panel II: Diagnostic Tests:
Diagnostic Tests Indicators
Normality J-B value 0.8801
Serial Correlation LM Test 1.5414
Heteroscedasticity Test (ARCH) 1.0245
Ramsey Reset Test 0.0714
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The results of long run estimation have been shown in Table 6.3.4, which shows that
the coefficient of FD is negative and significant at the 1% level. It means that FD has a
significant negative relationship with the stock market index. This implies that FD is
negatively affecting stock market index, which shows that with the increase in Fiscal deficit,
stock market index is decreasing. This is due to the fact that the extent of fiscal deficit and
means of financing it, influence the money supply and the interest rate in the economy. High
interest rates mean higher cost of capital for the industry, lower profits and hence lower stock
prices. The findings are consistent with Geske and Roll (1983), Laopodis (2006), Asaolu and
Ogunmuyiwa (2011), and Quayes (2010) Saleem and Yasir et al. (For India, 2012)); but the
contrast to the finding of Van Aarle, et al. (2003), Udegbunam and Oaikhenan (2012).
The coefficient of money supply has a positive impact on the Stock Market and it is
significant at the 1% level. The value of coefficient implies that a 1% increase in M3 leads to
an increase in stock market index with the fact that the increase in the money supply meaning
that money demands are increasing in anticipation of an increase in economic activity. Higher
economic activity implies higher expected profitability, which causes stock prices to rise11.
Considering the impact of inflation, it is significant at 1% and has a positive impact on a
market index. This finding supports the views of Kessel (1956) and Ioannidis et al. (2005).
The coefficient of real interest rate is positive, but not significant which shows there is no
significant relationship between LRIR and stock market index.
The results of short run dynamics using the ECM version of ARDL are reported in
Table 6.3.5. The short run adjustment process is examined from the ECM coefficient. The
coefficient of the error correction term is an adjustment coefficient capturing the proportion
of the disequilibrium in economic growth in one period which is corrected in the next period.
The coefficient generally represents the speed of adjustment towards equilibrium, that means
how quickly the equilibrium is established if the path is in disequilibrium. The larger the error
term, the earlier the economy’s return to the equilibrium rate of growth; following a shock.
The coefficient lies between 0 and −1, the equilibrium is converging to the long run
equilibrium path and is responsive to any external shocks.
11 Homa and Jaffe [52].
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Table 6.3.4: Estimated Long-run Coefficients using ARDL Approach
(Dependent variable: LBSE)
Regressors ARDL(1,0,0,0)
Coefficient t- values Prob. Values
LFD -1.597*** -4.201 [0.001]
LM3 2.840*** 7.226 [0.000]
LCPI 0.931*** 4.751 [0.000]
LRIR 0.005 0.040 [0.968]
CONS -1.930 -0.875 [0.394]
Robustness Indicators
R2 0.980
Adjusted R2 0.971
F Statistics 112.92
D.W. Stat 2.2876
Serial Correlation, F 1.162 [0.281]
Heteroskedasticity, F 2.471 [0.116]
Ramsey reset test, F 0.197 [0.657] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC);
(2)*** indicates significant at the 1 percent level of significance.
Table 6.3.5: Estimated Short-run Coefficients using ARDL Approach
(Dependent variable: LBSE)
Regressors ARDL(1,0,0,0)
Coefficient t-Ratio Prob. Values
ΔLFD -0.211 -1.330 [0.200]
ΔLM3 1.453*** 3.795 [0.001]
ΔLCPI 0.274* 2.811 [0.012]
ΔLRIR 0.002 0.040 [0.968]
ΔCONS -0.987 -0.815 [0.425]
ECM t-1 -0.511 -5.547 [0.000]
Robustness Indicators
R2 0.716
Adjusted R2 0.592
D.W. Stat 2.288
SE Regression 0.151
RSS 0.366
F Statistics 8.072 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC);
(2) *** and * indicate significant at the 1% and 10% level of significance, respectively.
The comparison of long run coefficients with that of short run ECM coefficients
confirms that the directions of relationships are maintained. However, the Fiscal Deficit was
negative and significant at the 1% level in the long run and failed to explain the variation in
the stock market index significantly in the short run. This may be due to the fact that
investor’s behavior in the stock market is regulated by long term fiscal deficit and may not
bother about short term fluctuations in it. Other variables, such as M3 (1%) and CPI (10%)
are significant and positively influencing the market index both in the short run as well as in
the long run. Here also, the coefficient of LRIR is positive, but not significant in both long
run as well as short run. Table 6.3.5 also shows that the coefficient of ECM(t−1)is significant
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at the 1% level, which indicates that the speed of adjustment for a short run to reach long run
is significant. Further, the error correction term is −0.51 with expected sign, suggesting that
when the stock price is above or below its equilibrium level, it adjusts by almost 51% per
year and the full convergence process takes about two years.
6.3.4. VECM based causality
The short run and long run Granger causality test findings are reported in Table 6.3.6.
In the above table the values mentioned under the heading ECM(t−1) are indicating long run
Granger causality, whereas, the rest of the values are the values of F-test. The results of Table
6.3.6 indicate short run unidirectional causality running from LFD to LBSE in India. It is also
observed that error correction term is statistically significant for specification with LBSE as
the dependent variable which indicates that there exists a long run causal relationship among
the variable with LBSE as the dependent variable. The result is also confirmed by the ARDL
test statistics.
Table 6.3.6: Results of Vector Error Correction Model
Dependent Variable
Sources of Causation
Short Run Independent Variables Long Run
Independent Variables
∆LBSE ∆LFD ∆LCPI ∆LM3 ∆LRIR ECM(t−1)
∆LBSE - 4.464* 0.458 0.361 0.000 −4.874*
∆LFD 0.145 - 0.132 0.667 1.403 −1.061
∆LCPI 0.063 0.062 - 0.115 0.017 −0.042
∆LM3 0.098 0.653 0.511 - 0.053 −0.505
∆LRIR 10.031* 9.702* 0.183 8.907* - −0.337
* Indicates 1% level of significance.
The robustness of the short run results are investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of a long run and short run parameters. The cumulative sum
(CUSUM) (Figure 6.3.1) and the cumulative sum of square (CUSUMSQ) plots is between
critical boundaries at 5% level of significance. This confirms the stability property of a long
run and short run parameters which have an impact on the market index in case of India. This
confirms that the models seem to be steady and are specified as appropriate.
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Figure 6.3.1: Plots of Stability Test
6.3.5. Variance Decomposition (VDC) Analysis
Brooks (2008) stated that variance decomposition accounts for the share of variations in
the endogenous variables resulting from the endogenous variables and the transmission to all
other variables in the system, because of the dynamic nature of the VAR. Hence, VDC gives
the proportion of the movements in the dependent variables that are due to their “own”
shocks, versus shocks to the other variables. It is pointed out by Pesaran and Shin (2001) that
the variable decomposition method shows the contribution in one variable due to innovation
shocks stemming in the forcing variables. The variance decomposition indicates the amount
of information each variable contributes to the other variables in the auto regression. It
determines how much of the forecast error variance of each of the variables can be explained
by exogenous shocks to the other variables. The main advantage of this approach is it is
insensitive to the ordering of the variables. The residuals generated by the VAR models are
usually contemporaneously correlated. This is because in a VAR model only lagged
endogenous variables are admitted on the right-hand side of each equation (in addition to a
constant term), and hence all the contemporaneous shocks which impact on LBSE are forced
to feed through the residuals (Kuszczak and Murray, 1986). While this may not cause a
problem in the estimation of the VAR model, the impulse responses and variance
decompositions derived from the initial estimates of the VAR model could be affected such
that any adjustment to the order in which the variables are entered in the system could
produce different results (Kuszczak and Murray, 1986). Thus, there is a need to impose some
restrictions when estimating the VAR model to identify the VDC. In this regard, a common
approach is the Cholesky decomposition, which was originally applied by Sims (1980), The
Cholesky decomposition overcomes the problem of contemporaneous relationships among
the innovations error terms within the estimated VAR model by identifying the structural
shocks such that the covariance matrix of the estimated residuals is lower triangular.
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The ordering of the variables was done after going through the iteration process and we
have tried various ordering of variables to check the consistency of the results. The main
principal of Cholesky ordering is that the first variable should be selected such that it is the
only one with potential immediate impact on other variables. The ordering of the variables
was based on the assumption that a shock in the real interest rate would be
contemporaneously transmitted to money supply, fiscal deficit, inflation and stock prices, and
a shock in money supply would be transmitted to the fiscal deficit, which would, in turn,
affect inflation. However, this shock in money supply will not affect the interest rate variable.
Similarly, the shock in fiscal deficit would contemporaneously affect inflation and stock
prices, but not to the money supply and interest rate.
The results of the VDC are presented in Table 6.3.7. The column SE is the forecast
error of the variable to be forecast at different lengths into the future. The empirical evidence
indicates that 57.86% of stock prices change is contributed by its own innovative shocks.
Further shock in fiscal deficit explains stock prices by 21.03% and the money supply
contributes to market capitalization by 16.08%. The share of other variables is very minimal.
Thus, the result indicates that the stock prices behave exogenously. During the initial period,
the variation in changes in stock prices is caused by the stock price itself.
As time passes, the change in LBSE is contributed by fiscal deficit. However, the impact
exerted by other macroeconomic variables on stock prices is very low. Therefore, it can be
said that over the horizon of 10 years, fiscal deficit plays the most important role, explaining
21% variation in stock market prices in India.
Table 6.3.7: Variance Decomposition (VDC) Analysis
Period S.E. LBSE LCPI LFD LM3 LRIR
1 0.193 100.000 0.000 0.000 0.000 0.000
2 0.283 84.980 0.009 12.600 0.048 2.361
3 0.333 76.863 0.313 19.045 1.508 2.269
4 0.362 70.667 1.028 21.937 4.441 1.925
5 0.380 65.830 1.737 22.716 7.835 1.880
6 0.391 62.444 2.180 22.449 10.843 2.081
7 0.398 60.313 2.346 21.888 13.100 2.350
8 0.403 59.051 2.352 21.427 14.606 2.562
9 0.406 58.306 2.320 21.165 15.531 2.676
10 0.408 57.861 2.321 21.037 16.068 2.711
Cholesky Ordering: LBSE LCPI LFD LM3 LRIR
Note: All the values of VDC are calculated using E-views 8.0.
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6.4. Relationship between Twin Deficit and Stock Market Development in India
Recently the role of twin deficit in the economy and its potential effects on stock prices
attracted some serious consideration from academics and policymakers in both developed and
developing economies. The linkage of twin deficits is largely observed due to its important
implication as large twin deficits could affect current and future economic growth and their
imbalance could impair economic activity, undermine wealth creation and can be risky for
the well-being of the nation. Twin deficit identity is used to refer to a nation’s current account
deficit and simultaneous fiscal deficit and it is a shorthand summary for two related economic
problems, the government budget deficit and the current account (international trade) deficit.
From a policy perspective, if the rising current account deficit is indeed due to the increasing
fiscal deficit, then the external balance cannot be remedied unless the policies that address to
government deficits are not first put in place. Further, it can be said that the twin deficits
(Fiscal deficit and Current account deficit) through their effects on macroeconomic variables,
can significantly influence stock market development i.e. market capitalization. Hence, the
Study empirically estimated the effect of twin deficit and controlled macroeconomic
variables on market capitalization in India. The study uses annual data covering the period
from 1979 to 2014.
6.4.1. Model specification
The following general specification has been used in this study to empirically examine
the effect of twin deficit and other controlled macroeconomic factors on market
capitalization.
𝐿𝑀𝐶𝐴𝑃 = 𝛼0 + 𝛼1𝐿𝐶𝐴𝐷 + 𝛼2𝐿𝐹𝐷 + 𝛼3𝐿𝐺𝐷𝑃 + 𝛼4𝐿𝐶𝑂 + 𝛼5𝐿𝑇𝑂 + 𝛼5𝐿𝑅𝐸𝐸𝑅 + 휀𝑡
(6.2)
6.4.2. Stationarity test and Lag length selection before co-integration
Before we conduct tests for co-integration, we have to make sure that the variables
under consideration are not integrated at an order higher than one. Thus, to test the
integration properties of the series, we have used Ng-Perron unit root test. The results of the
stationarity tests are presented in Table 6.4.1. The results show that all the variables are non-
stationary at levels. The next step is to difference the variables once in order to perform
stationary tests on differenced variables. The results show that after differencing the variables
once, all the variables were confirmed to be stationary. It is, therefore, worth concluding that
all the variables used in this study are integrated of order one i.e. difference stationary I(1).
Therefore the study uses autoregressive distributed lag (ARDL) approach to co-integration. In
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addition, it is also important to ascertain that the optimal lag order of the model is chosen
appropriately so that the error terms of the equations are not serially correlated.
Consequently, the lag order should be high enough so that the conditional ECM is not subject
to over parameterization problems (Narayan, 2005; Pesaran 2001). The results of these tests
are presented in Table 6.4.2. The results of Table 6.4.2 suggest that the optimal lag length is
one based on both LR, FPE, SIC and HQ.
Table 6.4.1: Unit root test: Ng-Perron Test
Variables Without trend and intercept Stationarity
Status Mza MZt MSB MPT
LMCAP 1.030 1.032 1.003 70.125 I (1)
ΔLMCAP -14.186 -2.663 0.188 1.727
LCAD -5.609 -1.636 0.292 4.478 I (1)
ΔLCAD -13.312 -2.453 0.184 2.317
LFD -10.530 -2.286 0.217 2.360 I (1)
ΔLFD -15.730 -2.780 0.177 1.647
LGDP 2.210 2.215 1.002 86.223 I (1)
ΔLGDP -15.290 -2.717 0.178 1.780
LCO -2.859 -1.172 0.410 8.501 I (1)
ΔLCO -16.391 -2.820 0.172 1.652
LTO 1.457 1.558 1.070 85.539 I (1)
ΔLTO -16.494 -2.869 0.174 1.494
LREER 0.143 0.093 0.652 28.471 I (1)
ΔLREER -14.298 -2.640 0.185 1.840 Source: Author’s own Calculation by using E-views 8.0
∆ denotes the first difference of the series. L implies that the variables have been transformed in
natural logs.
Table 6.4.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ
0 -26.42035 NA 1.79e-08 2.025476 2.342917 2.132285
1 190.3338 328.4154 7.36e-13 -8.141444 -5.601916* -7.286970
2 263.7382 80.07755* 2.59e-13* -9.620500* -4.858885 -8.018361* * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
6.4.3. ARDL Bounds test
After determining the order of integration of all the variables in table 6.4.1, the next
step is to employ an ARDL approach to co-integration in order to determine the long run
relationship among the variables. By applying, the procedure in OLS regression for the first
difference part of the equation (6.2) and then test for the joint significance of the parameters
of the lagged level variables when added to the first regression.
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The F-Statistics tests the joint Null hypothesis that the coefficients of lagged level
variables in the equation (6.2) are zero. Table 6.4.3, reports the result of the calculated F-
Statistics & diagnostic tests of the estimated model. The result shows the calculated F-
statistics were 5.6890. Thus the calculated F-statistics turns out to be higher than the upper-
bound critical value at the 5 percent level. This suggests that there is a cointegrating
relationship among the variables included in the model, i.e. Stock Market Capitalization
(LMCAP), Current Account Deficit (LCAD), Fiscal Deficit (LFD), Real GDP (LGDP),
Crude Oil (LCO), Trade Openness (LTO) and Real Effective Exchange Rate (LREER).
Table 6.4.3: ARDL bounds test results
Panel I: Bound testing to co-integration:
Estimated Equation: LMCAP = F (LCAD LFD LGDP LCO LTO LREER)
Indicators
Optimal lag 02
F – Statistics 5.689053
Panel II: Diagnostic Tests:
Diagnostic Tests Indicators
Normality J-B value 0.8901
Serial Correlation LM Test 1.5214
Heteroscedasticity Test (ARCH) 1.0145
Ramsey Reset Test 0.0724
The second step is to estimate the long and short-run estimates of ARDL test. The long
run results are illustrated in Table 6.4.4. The results show that the coefficient of Current
account Deficit and Crude oil are statistically significant and negative at 3% and 1%
respectively. It is evident from the table that 1% increase in CAD and 1% increase in crude
oil leads to 0.266% and 0.638%, decrease in Market Capitalization (LMCAP), respectively.
The findings for crude oil are consistent with Miller and Ratti (2009) and Basher et al.
(2012). The result found in this study implies that a prolonged trade deficit could have an
adverse effect on the stock market in the long-run. As investors notice the decline in spending
overtime on domestically produced goods which will hurt domestic producers and their stock
prices. Given enough time, investors will realize fewer investment opportunities domestically
and begun to invest in foreign stock markets, as prospects in these markets are better, this will
lower demand in the domestic market and cause stock market volume to decline.
Whereas, the coefficient of Real GDP (LGDP) and Real Effective Exchange Rate
(LREER) are positive and significant at 1%. It is evident from the table that 1% increase in
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Real GDP and exchange rate leads to 3.265% and 1.381%, respectively, increase in Market
Capitalization (LMCAP). Therefore, GDP and Exchange rate have a significant positive
relationship favourably affecting market capitalization. The findings are consistent with Fama
(1981, 1990), Chen et al. (1986) for GDP and Nadeem and Zakir (2009) for Exchange Rate.
Fiscal deficit does not show any impact on stock prices, and the finding is consistent with
Adrangi and Allender (1998)12.
Table 6.4.4: Estimated Long Run Coefficients using ARDL Approach
(Dependent variable: LMCAP)
Regressors ARDL(1,0,0,0)
Coefficient t- values Prob. Values
LCAD -0.266** -2.245 [0.033]
LFD -0.220 -0.634 [0.531]
LGDP 3.265*** 6.579 [0.000]
LCO -0.638*** -2.688 [0.012]
LTO -0.832 -1.224 [0.232]
LREER 1.381*** 4.644 [0.000]
CONS -9.994 -7.943 [0.000]
Robustness Indicators
R2 0.986
Adjusted R2 0.983
F Statistics 272.249 [0.000]
D.W. Stat 1.656
Serial Correlation, F 1.167 [0.280]
Heteroskedasticity, F 0.129 [0.719]
Ramsey reset test, F 2.116 [0.146] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) ** and *** indicate significant at 5 and 1 percent level of significance, respectively. Values
in [#] are probability values.
The short-run relationship of the macroeconomic variables on market capitalization is
presented in Table 6.4.5. As can be seen from the table, CAD and Trade Openness (LTO) has
a significant and negative impact on market capitalization in the short run at 1% and 10%
level, respectively. One can say that 1% increase in CAD and 1% increase in trade openness
leads to 0.293% and 1.243%, decrease in Market Capitalization (LMCAP), respectively.
Whereas, GDP and Exchange rate are significantly positive at 1% and 5% level,
respectively, in short-run. The short run adjustment process is examined from the ECM
coefficient. The coefficient lies between 0 and -1, the equilibrium is converging to the long
run equilibrium path, is responsive to any external shocks. However, if the value is positive,
the equilibrium will be divergent from the reported values of ECM test. The coefficient of the
12 Adrangi and Allender (1998) examine the evidence regarding budget deficit and stock prices in industrialized
countries such as Japan, US, France and Germany. Granger causality and VAR test shows the negative relation
of the budget deficit and stock prices for US; however, in other countries deficits do not affect stock prices.
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lagged error-correction term (-0.681) is significant at the 1% level of significance. The
coefficient implies that a deviation from the equilibrium level of market capitalization in the
current period will be corrected by 68 percent in the next period to resort the equilibrium.
Table 6.4.5: Estimated Short Run Coefficients using ARDL Approach
(Dependent variable: LMCAP)
Regressors ARDL(1,0,0,0)
Coefficient T – Ratio Prob. Values
ΔLCAD -0.293*** -2.504 [0.019]
ΔLFD -0.471 -1.254 [0.221]
ΔLGDP 2.765*** 4.737 [0.000]
ΔLCO -0.141 -0.500 [0.621]
ΔLTO -1.243* -1.739 [0.094]
ΔLREER 0.948** 2.338 [0.027]
ΔCONS -7.328 -3.442 [0.002]
ECM t-1 -0.681 -3.274 [0.003]
Robustness Indicators
R2 0.563
Adjusted R2 0.424
D.W. Stat 2.183
SE Regression 0.283
RSS 1.997
F Statistics 4.608 [0.002] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
Values in [#] are probability values.
6.4.4. VECM based causality
The results of table 6.4.6 indicate that there exists short-run causality running from
current account deficit, Real GDP, trade openness and crude oil to market capitalization in
India. In fact, trade openness is having a bi-directional causality with MCAP in short-run. It
is also observed that error correction term is statistically significant for specification with
LMCAP as the dependent variable which indicate that there exist a long-run causal
relationship between the variable with LMCAP as the dependent variable. This result is also
confirmed by the ARDL test statistics.
Table 6.4.6: Results of Vector Error Correction Model
Dependent
variable
Sources of Causation
Short run independent variables Long run
ΔLMCAP ΔLCAD ΔLFD ΔLGDP ΔLCO ΔLTO ΔLREER ECM(t-1)
ΔLMCAP - 2.437** -0.666 3.240*** -2.962** 2.390** 0.306 -7.790***
ΔLCAD -1.527 - -1.975** 0.005 2.012** 0.307 -1.192 1.921
ΔLFD -0.357 -1.866* - 1.819 0.936 -1.216 1.279 0.392
ΔLGDP -0.582 -1.123 -3.089*** - 1.418 -0.131 -1.601 0.370
ΔLCO -1.036 -2.316** -1.456 2.447** - -0.727 1.070 -0.590
ΔLTO 7.165*** 2.312** 4.390*** 3.226*** -3.521*** - 3.444*** -6.320***
ΔLREER 1.287 0.145 1.917* 0.364 -1.263 0.533 - 0.764
*, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
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The robustness of the short run result are investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of a long run and short run parameters. The cumulative sum
(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 6.4.1) are between
critical boundaries at 5% level of significance. This confirms the stability property of a long
run and short run parameters which have an impact on the market index in case of India. This
confirms that models seem to be steady and specified appropriate.
Figure 6.4.1: Plots of Stability Test
6.4.5. Variance Decomposition (VDC) Analysis:
It is pointed out by Pesaran and Shin (2001) that the variable decomposition method
shows the contribution in one variable due to innovation shocks stemming in the forcing
variables. The variance decomposition indicates the amount of information each variable
contributes to the other variables in the autoregression. It determines how much of the
forecast error variance of each of the variables can be explained by exogenous shocks to the
other variables. The main advantage of this approach as it is insensitive to the ordering of the
variables. The results of the VDC are presented in table 6.4.7. The empirical evidence
indicates that 64.83% of market capitalization change is contributed by its own innovative
shocks. Further shock in crude oil price explains market capitalization by 14.61%. CAD
contributes to market capitalization by 10.02%, fiscal deficit and exchange rate contributes
5.03% and 3.59% respectively. The share of other variables is very minimal.
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Table 6.4.7: Variance Decomposition (VDC) Analysis
Period S.E. LMCAP LCAD LFD LGDP LCO LTO LREER
1 0.313 100.000 0.000 0.000 0.000 0.000 0.000 0.000
2 0.385 77.696 6.303 3.053 1.582 10.505 0.560 0.297
3 0.427 74.314 7.135 3.986 1.618 11.795 0.564 0.585
4 0.457 71.578 7.789 4.573 1.626 12.928 0.571 0.932
5 0.480 70.031 8.155 4.857 1.590 13.527 0.553 1.284
6 0.499 68.911 8.449 4.996 1.555 13.933 0.529 1.624
7 0.516 68.074 8.702 5.052 1.525 14.200 0.502 1.941
8 0.530 67.409 8.931 5.066 1.503 14.379 0.477 2.232
9 0.544 66.863 9.141 5.059 1.488 14.498 0.455 2.494
10 0.556 66.402 9.333 5.044 1.481 14.572 0.435 2.729
11 0.567 66.005 9.507 5.030 1.481 14.616 0.417 2.940
12 0.578 65.659 9.663 5.020 1.487 14.637 0.402 3.129
13 0.588 65.352 9.800 5.018 1.499 14.641 0.389 3.299
14 0.597 65.078 9.919 5.024 1.515 14.633 0.377 3.452
15 0.606 64.830 10.022 5.039 1.534 14.615 0.366 3.591
Cholesky Ordering: LMCAP LCAD LFD LGDP LCO LTO LREER
Figure 6.4.2.: VDC analysis combined graph
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Variance Decomposition of LMCAP
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Variance Decomposition of LCAD
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Variance Decomposition of LFD
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Variance Decomposition of LGDP
0
20
40
60
80
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Variance Decomposition of LCR
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Var iance Decomposition of LTO
0
10
20
30
40
50
60
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Variance Decomposition of LEX
6.4.6. Impulse Response Function (IRF)
An impulse response refers to the reaction of any dynamic system in response to some
external changes. It helps to trace out the responsiveness of the dependent variables in the
VAR to shocks to each of the variables. Table 6.4.8 presents the estimates from the impulse
response function of stock market development as against its “own shocks” and the shocks of
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current account deficit, fiscal deficit, GDP, crude oil prices, trade openness and exchange rate
over the time period. The result shows that market capitalization has a positive relationship
with its past on the long-run. In its response to the shocks of current account deficit, GDP and
exchange rate, it is observed that there is a positive relationship in the long run and reverse is
observed in the case for the shocks of fiscal deficits and crude oil prices, i.e. there is a
negative relationship in the long run throughout the period for fiscal deficit and crude oil
prices. Also, in its response to the shocks of explanatory variables, market capitalization does
not respond in the first period. The evidences in favor of the explanations given in the table
are also presented in graphical format in figure 6.4.3.
Table 6.4.8: Impulse Response Function (IRF)
Figure: 6.4.3. Impulse Response Function combined graph
-.2
-.1
.0
.1
.2
.3
.4
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LMCAP to Cholesky
One S.D. Innovations
-.2
.0
.2
.4
.6
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LCAD to Cholesky
One S.D. Innovations
-.10
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LFD to Cholesky
One S.D. Innovations
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LGDP to Cholesky
One S.D. Innovations
-.10
-.05
.00
.05
.10
.15
.20
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LCR to Cholesky
One S.D. Innovations
-.06
-.04
-.02
.00
.02
.04
.06
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LTO to Cholesky
One S.D. Innovations
-.06
-.04
-.02
.00
.02
.04
1 2 3 4 5 6 7 8 9 10
LMCAP LCAD LFD
LGDP LCR LTO
LEX
Response of LEX to Cholesky
One S.D. Innovations
Period LMCAP LCAD LFD LGDP LCO LTO LREER
1 0.313 0.000 0.000 0.000 0.000 0.000 0.000
2 0.130 0.096 -0.067 0.048 -0.124 0.028 0.020
3 0.142 0.060 -0.052 0.024 -0.077 0.014 0.025
4 0.117 0.057 -0.047 0.021 -0.074 0.012 0.029
5 0.109 0.050 -0.040 0.016 -0.064 0.009 0.031
6 0.101 0.047 -0.035 0.014 -0.059 0.006 0.033
7 0.096 0.045 -0.031 0.013 -0.055 0.004 0.033
8 0.093 0.044 -0.028 0.013 -0.051 0.002 0.033
9 0.090 0.043 -0.026 0.013 -0.049 0.001 0.033
10 0.087 0.042 -0.025 0.013 -0.046 0.000 0.032
Cholesky Ordering: LMCAP LCAD LFD LGDP LCO LTO LREER
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6.5. Summary
In the present chapter of the study, with the help of modern econometric techniques, an
effort has been made to empirically investigate the relationship between stock prices or stock
market development with fiscal policy variables in India, along with different sets of
domestic and international macroeconomic variables. Towards this effort two different
models has been formulated, using the data for different time span and annual frequency,
according to the need of the study and availability of the data. The first model is formulated
for the empirical estimation of the study using annual frequency data to know the relationship
between stock prices and fiscal deficit; and the second model is formulated for the empirical
estimation of the study using annual frequency data to study the relationship between stock
market development in India and twin deficits.
The first part of the study discusses the estimation results of the relationship between
BSE Sensex and fiscal deficit by employing data from the period 1988 to 2014. The test
statistics of the unit root suggest that none of the variables included in the study are I(2). The
bounds test confirms that the estimated equation and the series are co-integrated. The ARDL
results suggest a long run negative and significant relationship exists between budget deficit
and stock prices. However, the relationship does not show any significant results in the short
run. Further, the money supply and inflation in India influence stock prices positively both in
the long run as well as in the short run. The result of VECM based causality shows that there
exist a short run Granger causality running from fiscal deficit to stock price. Further, the
result indicates the presence of long run Granger causality for the equation with the stock
price as the dependent variable. The results of the VDC analysis show that the fiscal deficit
plays an important role in explaining the variation in stock prices in India.
The second part of the study discusses the estimation results of the relationship between
stock market development (MCAP) and twin deficit by applying data from the year 1979 to
2014. The long-run estimates of ARDL test confirmed the negative and significant
relationship between the current account deficit (CAD) and crude oil with stock market
capitalization. It also confirms a significant and positive influence of Real GDP and
Exchange Rate on market capitalization in India both in long-run and short-run. Further, for
short-run the study confirms negative and significant relationship between CAD and trade
openness with stock market capitalizations in India. The error correction model of ARDL
approach reveals that the adjustment process from the short-run deviation is high. The result
of VECM found short run causality running from CAD, Real GDP and crude oil to market
capitalization in India. Additionally, trade openness is having a bi-directional causality with
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MCAP in short-run. Further, the result indicates the presence of long run causality for the
equation with a market capitalization as the dependent variable. The results of VDC analysis
and IRF show that a major percentage of market capitalization change is its own innovative
shocks.
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CHAPTER 7
Macroeconomic Determinants of Sectoral Stock Market Development in India
7.1. Introduction
It is a proved fact that aggregate GDP affects composite stock market indexes, but
sometimes a change in aggregate GDP, for example, an increase in aggregate GDP cause
composite index to increase, but an increase in composite index does not mean that all the
sectors of the composite index or all the sector indices are increasing, a few of the sectors
cannot perform well even if the GDP of the economy is increasing, while others can
outperform the market. Further, it should also be noticed that, with the change in the GDP of
a particular sector, it is not necessary that the stock market changes, but if any of the sector
performs extremely well and attains a significant change in GDP than it can give a boost to
the composite stock index. All these phenomena can be better understood with the help of
sector wise study. Therefore, an attempt has been taken to study the impact of sectoral
contribution of GDP in explaining the variation in the sectoral stock market index. Further,
apart from sectoral GDP, few other macroeconomic variables are expected to influence the
stock prices of a specific sector. Hence, the study also attains to identify the impact of
sectoral GDP, along with certain controlled variables, on respective sectoral indices. The
study uses three different sectors, viz-a-viz, manufacturing sector index, electricity, gas and
water sector index and service sector index of BSE and the respective sectors of GDP are; (1)
manufacturing sector share in GDP, (2) electricity, gas and water sector share in GDP and (3)
service sector share in GDP. The three sectors have been chosen for the study because these
three sectors are the fastest growing sectors in India.
The service sector contributes maximum to the India’s GDP with 57% share of GDP in
2013-14, up from 15% in 1950-51.Whereas, manufacturing sector contributes about 15.1% of
India’s GDP and 50% of the India’s export, which shows that they are playing a significant
role in Indian economy. While the electricity, gas and water supply sector is also an
important part of the Indian economy from an industrial point of view, as because this is the
basic necessity of any of the industry to develop. This sector constitutes a small portion of
India’s GDP with a 2.5% share of GDP, in 2013-14, up from 0.24% in 1950-51. The three
indices (manufacturing index; electricity, gas and water supply index; and service index) are
taken according the sectoral GDP. It is a general belief that all the indices should be
positively affected by the respective GDP, because the increase in the GDP of a particular
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sector gives confidence to investors which leads to increase in the index of that particular
sector.
Thus, this chapter of the study deals with the discussion of empirical results derived
using different econometric techniques, to examine the cointegration and long-run stability
between the sectoral BSE indices with sectoral contribution in GDP along with other
controlled variables. The econometric methodologies used for estimating the empirical results
of the studies are, Ng-Perron unit root test is utilized to check the order of integration of the
variables. Lag-length selection criteria are used to determine the appropriate lag length for the
model. The long run relationship is examined by implementing the ARDL bounds testing
approach to co-integration. VECM method is used to test the short and long run causality and
the Variance Decomposition (VDC) is also used to explore the degree of exogeneity of the
variables involved in this study. For the purpose of analysis quarterly data starting from the
year 2003:Q4 to 2014:Q4 are used.
7.2. Review of Literature
For this study, it is not viable to survey all the literature in every dimension. However,
the present study focuses on the causal relationship between different sectors of stock market
and macroeconomic factors. Therefore, in this section, we will discuss the studies showing
the relationship between macroeconomic variables and different sectors of stock market. The
first section will discuss the relevant studies from overall economies, the studies related to
Indian economy will be provided in the second section.
7.2.1. Studies of overall economies other than India
Ta and Teo (1985) studied Co-movement and cointegration among sectoral stock
market indices, and observed high correlation among six Singapore sector indices in the
period 1975 to 1984 and the overall SES market return (e.g. All-S Equities Industrial and
Commercial Index, SES All-S Equities Finance Index, SES All-S Equities Property Index,
SES All-S Hotel Index, SES All-S Plantation Index and SES All-S Mining Index). Using
daily data in examining the relationships, they had concluded that sector returns were highly
correlated to each other, although such correlations did not remain stable over time.
Sun and Brannman (1994) found a single long-run relationship among the SES All-S
Equities Industrial & Commercial Index, Finance Index, Hotel Index, and Property Index by
applying annual data from 1975 to 1992. The study employed Johansen’s (1988) VECM to
examine the long-run equilibrium relationship between selected macroeconomic variables
and stock market sector indices represented on the Stock Exchange of Singapore (recently
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demutualized and renamed the Singapore Exchange (SGX)): the Finance Index, the Property
Index, and the Hotel Index. The choice of macroeconomic variables and the hypothesized
relations with the sector indices are discussed next.
Maysami, Howe and Hamzah (2004) examined the long-term equilibrium relationships
between selected macroeconomic variables and the Singapore stock market index (STI), as
well as with various Singapore Exchange Sector indices—the finance index, the property
index, and the hotel index. Monthly time series data from January 1989 to December 2001
was considered. Macroeconomic variables used for the study were interest rate (short-run),
inflation (CPI), exchange rate, industrial production and money supply (M2). The study
concluded that the Singapore’s stock market and the property index form cointegration
relationship with changes in the short and long-term interest rates, industrial production, price
levels, exchange rate and money supply.
Maysami et al. (2005) studied the existence of long-run cointegrating relationship
between stocks listed dually in the US and Singapore stock markets. In addition, they used
Johansen’s (1988) VECM, to examine the co-movement between sectoral stock indices of the
U.S. and Singapore, through examining whether the S&P 500 Electronics (Semiconductor)
Price Index leads Stock Exchange of Singapore’s Electronics Price Index. While their results
confirmed the long term cointegrating sectoral relationships, there was evidence pointing to a
short-term disequilibria in the prices of dually listed stocks, leading to the conclusion that
short-run arbitrage opportunities may exist.
Gunsel et al. (2007) performed a sectoral study on the effect of macroeconomic factors,
as well as industry specific variables, risk premia, and sectoral unanticipated dividend yields
on London Stock Exchange returns. They found evidence that the variables indentified (term
structure of interest rates, unanticipated inflation, unanticipated sectoral industrial production,
risk premium, real exchange rate, money supply and sectoral unanticipated dividend yield) all
had a significant effect on investment returns. More conclusive findings were that different
industries sometimes had opposite results for the variables used. Unexpected inflation was
found to have a significant and negative effect on the food, beverage and tobacco sectors. The
effective exchange rate had a significant and positive effect on the chemical sector, but a
negative and significant effect on the building materials and merchants, and engineering
sectors. Money supply had a positive and significant effect on the building materials and
merchants, as well as the food, beverage & tobacco sectors while a negative relationship was
found with household goods and textiles. Onemonth-lagged-term structure of interest rate
was found to have a positive and significant relationship with the construction; food,
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beverage and tobacco; oil exploration and production; and electronic and electrical equipment
sectors. Unanticipated sectoral production was found to have a negative and significant effect
on the food, beverage and tobacco and engineering industries. The causes of these differences
was attributed to the differing nature of dividend yields within the industries analysed as well
as the various exposures present in each sector to global macroeconomic factors such as the
exchange rate‟s effect on import and export revenues, while interest rate yields will have a
stronger influence over domestic lending dependant sectors such as the financial sector.
Tursoy et al. (2008) empirically tested the Arbitrage Pricing Theory (APT) and tested
13 macroeconomic variables against 11 industry portfolios of Istanbul Stock Exchange to
observe the effects of those variables on stocks’ returns, by employing data from February
2001 to September 2005 on monthly base. Macroeconomic variables used for the study were
money supply (M2), industrial production, crude oil price, consumer price index (CPI),
import, export, gold price, exchange rate, interest rate, gross domestic product (GDP), foreign
reserve, unemployment rate and market pressure index (MPI) which is built by the authors.
The empirical estimation is carried out using regression analysis. Regression results indicate
that macroeconomic factors do not have significant effect on stock returns.
Hancocks (2010) determined the effect of selected macroeconomic variables on stock
market prices of the All-Share, Financial, Mining and Retail Indices of the Johannesburg
Stock Exchange in South Africa, by applying monthly data from July 1996 to December
2008. Acroeconomic variables used for the study include interest rates (91-Day Treasury
Bill), Consumer Price Inflation, exchange rates (nominal bilateral exchange rate), money
supply (M2) and long-term interest rates (the yield on long-dated government bonds).
Methodology employed consist of cointegration and VECM approaches. The results showed
that certain macroeconomic variables had differing influences on each sector of the stock
market. Impulse Response tests indicated that the selected macroeconomic variables caused a
shock to the sectoral indices in the short-run.
Chinzara (2011) analyzed how systematic risk emanating from the macro-economy is
transmitted into stock market volatility, using augmented autoregressive Generalised
Autoregressive Conditional Heteroscedastic (AR-GARCH) and vector autoregression (VAR)
models. Aggregate stock market index and the four main sectors (Financial, industrial,
mining and general retail) and macroeconomic variables were used for the study. The study
also examined is whether the relationship between the two is bidirectional. By imposing
dummies for the 1997-1998 Asian and the 2007-2009 sub-prime financial crises,It was found
186
from the study that volatility transmission between the stock market and most of the
macroeconomic variables and the stock market is bidirectional.
Saeed, S. (2012) examined the impact of macroeconomic variables on stock returns by
applying multifactor model within an APT framework. This study consists of five
macroeconomic variables Money Supply, Exchange Rate, Industrial Production, Short Term
Interest Rate and Oil prices. Nine sectors are selected for the study on the basis of data
availablefor the Karachi Stock Exchange 100 index. These sectors are Oil and Gas, Textile
Composite, Jute, Cement, Cable and electrical Goods, Automobile, Chemical and
Pharmaceutical, Leasing and Glass and Ceramics. The closing prices of each firm, of the
respective sector are obtained for the period of ten years starting from June 2000- June 2010.
The methodology includes Ordinary Least Square techniques to analyze the impact of
macroeconomic variables on the returns. The result reveals that macro-economic variables
have a significant impact on the returns of sectors, but their contribution to bring variation in
their returns is very small. Only Short Term Interest Rate has a significant impact on returns
of various sectors where as Exchange Rate and Oil prices have a significant impact on
specific sectors like and Oil and Gas sector, Automobile and Cable and Electronics.
Hasanzadeh and Kianvand (2012) examined the effects of selected macroeconomic
variables on the stock market index in Iran, Using cointegration and Vector Error Correction
Method (VECM). Quarterly data from 1996:Q1 to 2008:Q1 was considered for the study.
Variables used for the study include Tehran Stock Index (TSI) and five macroeconomic
variables which consist of gross domestic product, nominal effective exchange rate, money
supply, gold coin price and investment in housing sector. Findings suggested that that Iran’s
stock market index is positively influenced by the growth rate of the GDP, the money supply
and negatively affected by the gold prices, the private sector investment in housing sector and
the nominal effective exchange rate.
Sharabati (2013) investigated the relationship between independent variables: ASE
market sectors on dependent variable i.e. Real GDP. The sectoral indices used for the study
include Banks, Insurances, Services and Industry sectors. The data were of the annual
frequency from 1999 to 2012. The methodology used includes correlation, simple and
multiple regressions and stepwise regression techniques. The results of the study showed that
the four sectors of the ASE market are strongly related to each other and are strongly related
to ASE general indicator. Among the four ASE sector only industrial sector showed a strong
relationship with GDP. Further, simple regression test showed that there is no effect of ASE
general indicator on the GDP. While multiple regressions showed that there is a strong effect
187
of the ASE sectors together on GDP, but results did not show any significant effect of each
sector when considering the four sectors together on GDP. Furthermore, the first stepwise
regression model showed that there is a strong positive significant effect of industry sectors
on GDP, while the second model showed that there is a strong positive significant effect of
industry sectors on GDP and there is a negative significant effect of insurance sector on GDP.
7.2.2. Studies related to Indian economy
Sinha and Kohli (2013) studied the effect of exchange rate on three market indices;
BSE Sensex index, BSE IT sector index and BSE Oil & Gas sector index for monthly data
from January 2006 to March 2012. Simple regression techniques were used for the
estimation. The result revealed that no interrelation between the daily returns in the foreign
exchange and the stock market of India were found.
Tripathi, Parashar and Jaiswal (2014) examined the long term relationship between
selected external macroeconomic variables and different sectoral indices at National Stock
Exchange (NSE) India, using monthly frequency data from April 2005 to March 2013. The
methodology employed for the study was variables Multiple Regression equation model
(Galton, 1877) using SPSS-16. The macroeconomic variables, namely, Exchange Rate
(USD), Crude Oil prices, Foreign Institutional Investments, Current Account Balance and
Foreign Exchange Reserves have been used to magnify the impact of external
macroeconomic variables on different sectors of Indian economy represented by Sectoral
Indices at National Stock Exchange (NSE) viz. CNX Auto, CNX Bank, CNX Energy, CNX
FMCG and CNX IT. The results so obtained revealed a high correlation among the variables
and suggested that amongst all macroeconomic variables only except Foreign Institutional
Investment (FII) affects all sectoral indices, however, the rest of the macroeconomic variables
selectively affect different sectoral indices in India.
The main key conclusion drawn from literature review is, that, so far, no study has been
done on the relationship between sectoral stock indices and respective sectoral GDP, which
provides the investors a new insight to track the changes in a particular sector of the stock
market by analyzing the movement of sectoral GDP of that particular sector. Thus, this study
is the initiative taken in this area. Finally, after going through literature, it has been concluded
that, this study, to the best of my knowledge, will be among the first empirical studies in
India to consider the relationships between the Indian stock market and a set of
macroeconomic variables, using the ARDL and VECM approach for the analysis.
188
7.3. Model Specification and Data validation:
For the study, three models are framed, in which each of the sectoral stock price indices
is placed as dependent variable and Crude Oil Price, REER, T-bill rates, Trade openness and
WPI along with respective sectoral GDP worked as independent variables. The models are
defined as:
MANI = f (GMAN, CO, REER, TBR, TO, WPI)………….. Model I;
EGWI = f (GEGW, CO, REER, TBR, TO, WPI)…………. Model II;
SERI = f (GSER, CO, REER, TBR, TO, WPI)……………. Model III
Principle component analysis is used in this study to construct the composite index of
manufacturing index; electricity, gas and water supply index; and service index.
Manufacturing index has been formulated by incorporating automobile index, consumer
durables index, capital goods index, metal index and fast moving consumer goods index.
Electricity, gas and water supply index has been formulated by incorporating oil and gas
index and power sector index. Service index has been formulated by incorporating bank
index, health care index, IPO index, information technology index and Telecom, Media, and
Telecommunications index. All the three aggregate indexes were formulated following the
guidelines of BSE.
The following general specification has been used in this study to empirically examine
the effect of sectoral GDP and other controlled macroeconomic factors on respective sectoral
indices.
𝐿𝑥 = 𝛼0 + 𝛼1𝑦1 + 𝛼2𝑦2 + 𝛼3𝑦3 + 𝛼4𝑦4 + 𝛼5𝑦5 + 𝛼5𝑦6 + 휀𝑡
(7.1)
Here, x is considered as the dependent variable (LMANI, LEGWI, and LSERI) and y1
(LGMAN, LGEGW, LGSER), y2 (LCO), y3 (LREER), y4 (LTBR), y5 (LTO) and y6 (LWPI)
as independent variables.
Where:
LMANI= Manufacturing sector index,
LGMAN= manufacturing sector share in GDP,
LEGWI= Electricity, gas and water index,
LGEGW= electricity, gas and water supply sector share in GDP,
LSERI= Service sector index,
LGSER= service sector share in GDP,
189
All the indexes are listed on Bombay Stock Exchange (BSE)13 and are collected from
the official website of Bombay Stock Exchange. All the variables are taken in their natural
logarithm.
7.4. Stationarity test and Lag length selection before co-integration
Before we conduct tests for co-integration, we have to make sure that the variables
under consideration are not integrated at an order higher than one. Thus, to test the
integration properties of the series, we have used Ng-Perron unit root test. The results of the
stationarity tests are presented in Table 7.1. The results show that all the variables are non-
stationary at levels. The next step is to difference the variables once in order to perform
stationary tests on differenced variables. The results show that after differencing the variables
once, all the other variables were confirmed to be stationary. It is, therefore, worth
concluding that all the variables used in this study are integrated of order one, i.e. difference
stationary I(1), except for LMANI, LGMAN, LGSER and LWPI. Therefore the study uses
autoregressive distributed lag (ARDL) approach to co-integration. In addition, it is also
important to ascertain that the optimal lag order of the model is chosen appropriately so that
the error terms of the equations are not serially correlated. Consequently, the lag order should
be high enough so that the conditional ECM is not subject to over parameterization problems
(Narayan, 2005; Pesaran, 2001). The results of these tests are presented in Table 7.2. The
results of Table 7.2 suggest that the optimal lag length is one based on SIC.
13 National Stock Exchange (NSE) sectoral indices are not incorporated in the study due to unavailability of
sectoral data.
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Table 7.1: Unit root test: Ng-Perron Test Variables With constant and trend Stationarity
Status Mza MZt MSB MPT
LMANI 0.448 0.296 0.659 30.823 I (1)
ΔLMANI -19.566 -3.127 0.159 1.252
LEGWI -0.719 -0.436 0.606 21.241 I (1)
ΔLEGWI -20.365 -3.188 0.156 1.212
LSERI -0.215 -0.093 0.434 15.519 I (1)
ΔLSERI -19.607 -3.125 0.159 1.268
LGMAN 1.130 0.974 0.861 54.734 I (0)
ΔLGMAN -3.362 -1.280 0.380 7.274
LGEGW -1.168 -0.464 0.397 12.057 I (1)
ΔLGEGW -11.063 -2.339 0.211 2.261
LGSER 1.757 1.549 0.881 63.651 I (0)
ΔLGSER -1.128 -0.698 0.619 19.702
LCO -1.445 -0.780 0.540 15.364 I (1)
ΔLCO -57.648 -5.265 0.091 0.669
LREER -5.578 -1.616 0.289 4.546 I (1)
ΔLREER -21.008 -3.240 0.154 1.168
LTBR -2.450 -0.899 0.367 8.926 I (1)
ΔLTBR -20.297 -3.178 0.156 1.232
LTO -3.771 -1.172 0.310 6.591 I (1)
ΔLTO -21.423 -3.272 0.152 1.146
LWPI 0.353 0.198 0.560 23.773 I (0)
ΔLWPI -11.302 -2.374 0.210 2.179 Source: Author’s own Calculation by using E-views 8.0
∆ denotes the first difference of the series. L implies that the variables have been transformed in
natural logs.
Table 7.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ
Model I 4 802.817 58.391 5.33e-21* -29.259* -20.775 -26.169*
Model II 4 851.626 62.032 4.92e-22* -31.640* -23.156 -28.550*
Model III 4 839.183 80.389* 9.03e-22* -31.033* -22.549 -27.943* * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
After determining the order of integration of all the variables in table 7.1, the next step
is to employ an ARDL approach to co-integration in order to determine the long-run
relationship among the variables. By applying, the procedure in OLS regression for the first
difference part of the equation (7.1) and then test for the joint significance of the parameters
of the lagged level variables when added to the first regression.
7.5. ARDL Bounds test
The F-Statistics tests the joint Null hypothesis that the coefficients of lagged level
variables in the equation (7.1) are zero. Table 7.3, reports the result of the calculated F-
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Statistics & diagnostic tests of the estimated model. The result shows the calculated F-
statistics were 9.4890, 10.3724 and 8.2299 for the model I, model II and model III
respectively. Thus the calculated F-statistics turns out to be higher than the upper-bound
critical value at the 5 percent level. This suggests that there is a co-integrating relationship
among the variables included in the models.
Table 7.3: ARDL Bounds test
Panel I: Bound testing to co-integration:
Estimated Equation
Model I : LMANI = F (LGMAN LCO LREER LTBR LTO LWPI)
Model II : LEGWI= F (LGEGW LCO LREER LTBR LTO LWPI)
Model III : LSERI = F (LGSER LCO LREER LTBR LTO LWPI)
The second step is to estimate the long- and short-run estimates of ARDL test. The
long-run results are illustrated in Table 7.4. The results of the model I show that the rise in
LGMAN has a positive effect on LMANI. It is evident from the table that 1% increase
LGMAN leads to 0.345% increase in the LMANI. This is due to the fact that with the rise in
manufacturing sector share in GDP, the expectations of investors increases, which gives a
motivation to investors to invest in the shares of manufacturing sector. The investment leads
to rise in manufacturing index.
The results of the model II show that the rise in LGEGW and LWPI has a positive
effect on LEGWI. The coefficient of LGEGW and LWPI are statistically significant and
positive at 1% level. It is evident from the table that 1% increase in LGEGW and LWPI leads
to 1.043% and 0.771% respectively increase in LEGWI. The rationale behind this explains the
Fisher hypothesis (1911) for inflation. And the rise in the electricity, gas and water supply
sector share in GDP gives a boost to investors’ confidence to invest in the shares of
electricity, gas and water supply sector.
The results of the model III show that the rise in LGSER and LTBR has a positive
effect on service index. The coefficient of LGSER and LTBR are statistically significant and
positive at 1% and 10% respectively. It is evident from the table that 1% increase in LGSER
and 10% increase in LTBR leads to 0.5% and 0.065% respectively increase in the LSERI.
Indicators Model I Model II Model III
Optimal-lags 01 01 01
F – Statistics 9.4890 10.3724 8.2299
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The rationale behind this is the same as mentioned above for the rest two models for the
relation of service sector share in GDP and service index.
Table 7.4: Estimated Long-run Coefficients using ARDL Approach
(Dependent variable: LMANI, LEGWI, LSERI)
Regressors Model I Model II Model III
Coefficient t- values Coefficient t- values Coefficient t- values
LGMAN 0.345* 3.033 - - - -
LGEGW - - 1.043* 3.193 - -
LGSER - - - - 0.500** 2.164
LCO -0.032 -0.555 -0.027 -0.340 -0.117 -1.334
LREER 0.052 0.471 0.087 0.515 0.099 0.753
LTBR 0.031 1.042 0.052 0.896 0.065*** 1.713
LTO 0.116 1.606 0.052 0.603 0.134 1.504
LWPI -0.158 -1.609 0.771* 8.434 -0.431 -1.643
CONS -0.502 -0.560 3.411 3.538 -1.619 -0.876
Robustness Indicators
R2 0.972 0.995 0.974
Adjusted R2 0.966 0.993 0.9690
F Statistics 157.369 636.710 169.075
D.W. Stat 2.971 -0.802 2.297
Serial Correlation, F 6.120 [0.190] 9.201 [0.056] 6.067 [0.194]
Heteroskedasticity, F 0.240 [0.624] 0.008 [0.926] 0.018 [0.891]
Ramsey reset test, F 11.464 [0.001] 1.315 [0.251] 6.109 [0.013] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *, ** and *** indicate significant at 1, 5 and 10 percent level of significance, respectively. Values in [#] are
probability values.
The short-run relationship of the sectoral index with respective sectoral GDP along
with some controlled variables is presented in Table 7.5. As can be seen from the table, for
the model I LGMAN, LCO and LTO has a significant and positive impact on LMANI in the
short-run at 1%, 1% and 5% level, respectively.
For the model II, unlike the long-run result, LGEGW is not significant to LEGWI in the
short-run. But LCO and LREER has a significant and positive impact on the LEGWI in the
short-run at 1% level. Whereas, LWPI is negatively significant to LEGWI at 1% level.
For the model III, LGSER, LCO and LTBR has a significant and positive impact on
LSERI in the short-run at 1%, 1% and 10% level, respectively. Whereas, LWPI is negatively
significant to LSERI at 10% level in the short-run.
The short-run adjustment process is examined from the ECM coefficient. The
coefficient lies between 0 and -1, the equilibrium is converging to the long-run equilibrium
path, is responsive to any external shocks. However, if the value is positive, the equilibrium
will be divergent from the reported values of ECM test. The coefficient of the lagged error-
correction term (-0.333), (-0.318) and (-0.215) are significant at the 1 % level of significance
for the model I, model II and model III, respectively. The coefficient implies that a deviation
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from the equilibrium level of stock market index in the current period will be corrected by
33% for model I, 31% for model II and 21% for model III, in the next period to resort the
equilibrium.
Table 7.5: Estimated Short-run Coefficients using ARDL Approach
(Dependent variable: LMANI, LEGWI, LSERI)
Regressors Model I Model II Model III
Coefficient t- values Coefficient t- values Coefficient t- values
ΔLGMAN 0.115* 2.744 - - - -
ΔLGEGW - - -0.181 -0.708 - -
ΔLGSER - - - - 0.107* 2.801
ΔLCO 0.047* 3.520 0.082* 2.668 0.039* 3.455
ΔLREER 0.017 0.449 0.239* 2.640 0.021 0.731
ΔLTBR 0.010 1.012 0.016 1.040 0.014*** 1.737
ΔLTO 0.038** 1.943 0.016 0.639 0.028 1.618
ΔLWPI -0.052 -1.474 -1.354* -3.864 -0.092*** -1.863
CONS -0.167 -0.574 1.087 1.747 -0.348 -1.070
ECMt-1 -0.333 -2.860 -0.318 -2.373 -0.215 -2.313
Robustness Indicators
R2 0.647 0.606 0.665
Adjusted R2 0.566 0.470 0.588
D.W. Stat 1.431 2.109 1.455
SE Regression 0.011 0.015 0.008
RSS 0.004 0.007 0.002
F Statistics 9.186 [0.000] 7.039 [0.000] 9.944 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).
(2) *, ** and *** indicate significant at the 1, 5 and 10 percent level of significance, respectively. Values
in [#] are probability values.
7.6. VECM based Causality
The results of table 7.6 indicate that there is causality running from LGMAN to
LMANI in India, which shows that a change in manufacturing sector share in GDP causes a
change in manufacturing index. It is also observed that the error correction term is
statistically significant for specification with LMANI as the dependent variable which
indicate that there exist a long-run causal relationship among the variables with LMANI as
the dependent variable.
The results of table 7.6 (Model II) indicate that there is causality running from LGEGW
and LWPI to LEGWI in India, which shows that a change in electricity, gas and water supply
sector share in GDP and change in inflation causes a change in electricity, gas and water
index. It is also observed that the error correction term is statistically significant for
specification with LEGWI as the dependent variable which indicate that there exist a long-
run causal relationship among the variables with LEGWI as the dependent variable.
Estimation results show a unidirectional causality running from LEGWI to LTO.
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The results of table 7.6 (Model III) indicate that there is no causality running from any
of the variables to LSERI in India. It is also observed that the error correction term is also not
statistically significant for specification with LSERI as the dependent variable which indicate
that there exist no long-run causal relationship among the variables with LSERI as the
dependent variable.
Table 7.6: Results of Vector Error Correction Model Dependent
variable Sources of Causation
Short-run independent variables Long-run
Model I ΔLMANI ΔLGMAN ΔLCO ΔLREER ΔLTBR ΔLTO ΔLWPI ECM(t-1)
ΔLMANI - -2.200** 0.126 -0.300 -0.889 0.916 -1.375 -2.724*
ΔLGMAN -0.028 - -0.659 0.594 -1.211 -0.208 -0.458 0.310
ΔLCO -0.647 1.090 - -1.132 -0.938 -0.605 -3.148* -0.883
ΔLREER -0.132 1.756*** -0.714 - 0.423 -1.824*** 0.277 -0.832
ΔLTBR -0.787 2.010** 0.813 0.276 - -0.072 0.365 -3.025*
ΔLTO -0.136 0.407 2.357** 0.388 -1.310 - -1.382 0.550
ΔLWPI -0.210 -0.693 2.951* 0.113 -0.491 -1.327 - -0.471
Model II ΔLEGWI ΔLGEGW ΔLCO ΔLREER ΔLTBR ΔLTO ΔLWPI
ΔLEGWI - 1.704*** 0.492 0.289 0.441 1.074 -1.752*** -5.428*
ΔLGEGW -1.594 - -2.739* -2.187** -1.452 -1.470 -0.411 2.066
ΔLCO -1.177 -0.674 - -0.379 -0.373 0.031 -2.917* 0.170
ΔLREER 0.358 0.393 -0.645 - -0.133 -1.499 0.242 -1.013
ΔLTBR 0.914 -0.246 1.118 0.493 - 0.426 0.472 -1.827***
ΔLTO -1.893*** -0.179 2.330** 1.142 0.039 - -1.803*** 1.663
ΔLWPI -0.900 -0.420 3.013* 0.691 0.761 -0.361 - 2.147
Model III ΔLSERI ΔLGSER ΔLCO ΔLREER ΔLTBR ΔLTO ΔLWPI
ΔLSERI - -0.873 0.004 0.217 -1.296 0.659 -0.444 -0.425
ΔLGSER -0.119 - -0.378 -0.223 -1.585 -0.043 0.584 -1.943**
ΔLCO -0.439 -0.138 - -1.189 -0.928 0.044 -3.051 0.757
ΔLREER 0.678 0.884 -0.579 - 0.508 -1.671 0.388 -0.205
ΔLTBR 0.092 2.437** 0.198 0.646 - -0.423 -0.602 -3.343*
ΔLTO -0.187 -0.361 2.067** 0.107 -1.402 - -1.343 -0.032
ΔLWPI -0.588 -1.884** 3.237* 0.208 -0.174 -0.181 - -0.641
*, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.
The robustness of the short-run result are investigated with the help of diagnostic and
stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,
functional misspecification and non-normal error. The cumulative sum (CUSUM) and the
cumulative sum of square (CUSUMSQ) tests have been employed in the present study to
investigate the stability of long-run and short-run parameters. The cumulative sum (CUSUM)
and the cumulative sum of square (CUSUMSQ) plots are between critical boundaries at 5%
level of significance. This confirms the stability property of long-run and short-run
parameters which have an impact on the sectoral indices in case of India. This confirms that
models seem to be steady and specified appropriate.
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7.7. Variance Decomposition (VDC) Analysis:
It is pointed out by Pesaran and Shin (2001) that the variable decomposition method
shows the contribution in one variable due to innovation shocks stemming in the forcing
variables. The variance decomposition indicates the amount of information each variable
contributes to the other variables in the autoregression. It determines how much of the
forecast error variance of each of the variables can be explained by exogenous shocks to the
other variables. The main advantage of this approach as it is insensitive to the ordering of the
variables. The results of the VDC for all the models are presented in table 7.7. The empirical
evidence indicates that 39.63% of LMANI change is contributed by its own innovative
shocks. Further, shock in LGMANI explains manufacturing index by 26.22%. Shock in LCO
also explains LMANI by 23.48%, which shows that crude oil price also plays an important
role in explaining manufacturing index. The share of other variables is minimal.
The empirical evidence for model II, indicates that 35.22% of LEGWI change is
contributed by its own innovative shocks. Further, shock in LGEGW explains LEGWI by
5.21%. LCO contributes the maximum to LEGW by 43.32%.
The empirical evidence for model III, indicates that 34.45% of LSERI change is
contributed by its own innovative shocks. Further, shock in LGSER explains LSERI by
18.05%. LCO contributes the maximum to LSERI by 38.53%.
Table 7.7: Variance Decomposition (VDC) Analysis
Period S.E. LMANI LGMAN LCO LREER LTBR LTO LWPI
Model I
1 0.015 100.000 0.000 0.000 0.0000 0.000 0.000 0.000
5 0.032 54.845 19.741 22.374 0.008 0.152 2.768 0.109
10 0.037 42.114 26.777 24.579 0.661 1.754 2.831 1.280
15 0.038 39.632 26.223 23.481 1.852 3.000 2.899 2.909
Model II
LEGWI LGEGW LCO LREER LTBR LTO LWPI
1 0.013 100.000 0.000 0.000 0.000 0.000 0.000 0.000
5 0.034 47.809 7.994 34.810 2.143 1.822 5.132 0.287
10 0.043 36.389 5.477 43.123 3.235 3.626 7.956 0.191
15 0.045 35.229 5.211 43.321 3.283 3.974 8.746 0.233
Model III LSERI LGSER LCO LREER LTBR LTO LWPI
1 0.012 100.000 0.000 0.000 0.000 0.000 0.000 0.000
5 0.027 51.364 13.502 33.333 0.611 0.925 0.003 0.259
10 0.033 36.791 19.070 39.573 0.501 1.905 0.035 2.122
15 0.034 34.453 18.052 38.538 0.633 3.096 0.390 4.835
Cholesky Ordering: LSERI LGSER LCO LREER LTBR LTO LWPI
7.8. Summary
This study aims to examine the relationship between gross domestic product and stock
prices from a sectoral perspective. Precisely, an effort has been made in this study to
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investigate whether sectoral GDP, i.e. Manufacturing sector, electricity, gas and water supply
sector and service sector share in GDP affect respective sectoral stock indices in India or not.
Towards this effort, quarterly data from 2003:Q3 to 2014:Q4 for the all the variables included
in the estimation has been used. The bounds test used for the study, confirms that there exists
a long-run co-integrating the relationship between sectoral GDP and sectoral stock indices in
India. The long-run estimates of ARDL test for model I showed that positive and significant
relationship exists between the manufacturing sector share in GDP with the manufacturing
index. It also confirms that the manufacturing sector share in GDP, crude oil price and trade
openness have a significant and positive impact on the manufacturing index in the short-run.
For model II, the results show that the electricity, gas and water supply sector share in GDP
and inflation has a positive effect on electricity, gas and water supply index, unlike short-run.
Crude oil price and real effective exchange rate has a significant and positive impact on the
electricity, gas and water index in the short-run. For model III, results show that the service
sector share in GDP and T-bills rate has a positive effect on service sector index in the long-
run and in short-run as well along with crude oil price. The results suggest that sectoral
indices are affected by changes in sectoral GDP in the long-run, whereas, all the three indices
are sensitive to the change in crude oil price in the short-run. The error correction model of
ARDL approach reveals that the adjustment process from the short-run deviation is high.
the result of VECM based causality found unidirectional short-run causality running
from sectoral GDP, crude oil price, REER, T-bill rates, trade openness and WPI to respective
sectoral stock indices in India. Further, the result indicates the presence of long-run causality
for the equation with manufacturing index and electricity, gas and water supply index as the
dependent variable, but, except for the service sector index which shows no long-run
causality running from any of the independent variables. The result of VDC analysis, for all
three models, shows that a major percentage of sectoral indices are its own innovative shocks.
Other than the respective sectoral GDP, crude oil price is a common variable which is playing
a crucial role in explaining all three indices by contributing its maximum towards the shock,
hence, reflecting maximum information about the movement of the indices.
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CHAPTER 8
Summary and Policy Implications of the study
8.1. Summary and Conclusion
The relationship between macroeconomic variables and stock prices has been the focus
of both theoretical and empirical research since early nineteenth century. Since then, there
has been growing effort made by researchers to empirically estimate this relationship by
using sophisticated econometric methods (Fama, 1965; Ross, 1976; Friedman 1987;
Mishikin, 1988; Flannery and Protopapadakis (2002); and Semmler, 2006). The exisiting
empirical studies have shown the use of vast range of macroeconomic variables to examine
their influence of stock prices. The main macroeconomic variables identified by various
studies are Real Gross Domestic Product (GDP), Index of Industrial Product (IIP), Real
Effective Exchange Rate (REER), International crude oil prices, Foreign Direct Investment
(FDI), Foreign Institutional Investment (FII), Inflation (Consumer Price Index (CPI) and
Wholesale Price Index (WPI)), Real Interest Rate, Short term Interest Rate (T-bill rates and
Call Money Rates (CMR)), Money Supply (M3), Fiscal Deficit, Current Account Deficit
(CAD), Trade openness, and Gold prices. The proxy variables for stock market development
used in the study are stock market capitalization (MCAP), stock price index, market liquidity
and turnover ratio.
All the research are conducted by applying different methodologies, namely,
correlation analysis, regression analysis using OLS, ARCH and GARCH models,
cointegration techniques using EG, JJ and ARDL methods, the causality tests like bivariate
and multivariate granger causality. The studies are accomplished by using different frequency
of data viz. daily, weekly, monthly, Quarterly or annual data sets. All country specific studies
use time series data, whereas, studies with multi country uses panel data series.
Last two decades has witnessed a dramatic change in the world financial markets
particularly in the stock market due to globalization and financial sector reforms across the
world market. These changes in the macro environment influence the stock prices of a single
country.
Indian stock market has developed in terms of the number of stock exchanges, number
of listed stocks, market capitalization, trading volume, turnover of the stock exchanges,
investor’s population and price during these years. Since 1991, the Indian economy has
experienced many major reforms policy initiatives in the financial system. The opening of
capital market to foreign institutional investors, allowing Indian companies to issue equity
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abroad through Global Depository Receipts (GDRs), formation of new stock exchange NSE,
liberalization and decontrolling crude oil price are few initiatives which are expected to have
huge impact on the stock market volatility.
In the last two decades, numerous empirical studies have examined the dynamic
relationships between stock market behaviour and macroeconomic variables, particularly for
developed economies. However, research on the above relationship in developing countries,
such as Latin America, Eastern Europe, Middle East and South Asian countries are still at
infant stage. With regard to the Indian economy, little work has been done in the dynamic
relationship between stock market and macroeconomic variables. To the best of researcher’s
knowledge, there is no published work to link fiscal policy variables and stock market
development and explore the link in a sectoral stock market perspective. Further, exploring
the dynamic relationship between oil prices, gold price, FII, FDI along with other
macroeconomic variables in a multivariate setting is not explained with sophisticated
econometric techniques like Auto Regressive Distributed Lag (ARDL), Vector Error
Correction Model (VECM), Impulse Response Function (IRF) and Variance Decomposition
(VDC). Hence, the primary motive of the present work is to answer the following research
questions:
Q.1. Do the key macroeocnomic variables included in this study has long-run
cointegrating relationship with Indian stock market proxied by BSE Sensex, CNX Nifty, and
market capitalization?
Q.2. Do these key macroeconomic variables have causal relationships during the
sample period? If so, what is the direction of the causality between BSE, NSE, market
capitalization and each of these variables in long-run and short-run?
Q.3. How does the stock market development indicators respond to an external shock
from any of these variables?
Q.4. To what extent can innovation in each of the key macroeconomic variables explain
the movements in stock market variables?
Q.5. How does the sectoral stock market indices being influenced by the set of sectoral
real activity in the Indian economy?
In an effort to investigate the effect of macroeconomic variables on the stock price in
India, the study examines the role of some fundamental macroeconomic variables in
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explaining the long-run and short-run behaviour of the Indian stock market. In particular, the
study tries to examine the long-run and short-run dynamic relationship and the direction of
causality between stock market in India with different sets of domestic and international
macroeconomic variables. Towards this effort different models has been formulated, using
the data for different time span and frequency. The empirical analysis of the thesis began with
testing the stationarity properties of the variables by applying Ng-Perron unit root test. After
testing the stationarity properties of the variables, the lag-length selection criteria is
determining in order to ascertain that the optimal lag order of the model is chosen
appropriately so that the error terms of the equations are not serially correlated. To study the
long-run and short-run cointegrating relationship among the variables ARDL bounds testing
approach is used. The error correction term ECMt-1 identifies the speed of adjustment towards
the equilibrium. Once the co-integrating relationships among the variables are identified the
direction of causality being tested with the use of VECM based Granger causality test.
Additionally, CUSUM and CUSUMQ have been employed to test the stability of the
variables. Finally, Impulse Response Function (IRF) and Variance Decomposition (VDC)
analysis were used to predict the long run and short run shocks in the model.
Existing Financial and Economic literature, such as Efficient Market Hypothesis
(EMH) and Arbitrage Pricing Theory (APT) advocates the relationship between the stock
market and macroeconomic variables. However, these theories have been silent about
determining which precise events or economic factors are likely to influence asset prices. The
macroeconomic variables selected for the study are considered on the basis of existing
literature which examines the theoretical and empirical relationship between the two. Further,
the variables are selected on the basis of availability of data with respect to the frequency and
common base year.
The study first accomplishes the empirical estimation of macroeconomic determinants
of the stock market development in India, using data for different time periods. The study is
divided into three parts as per the frequency and availability of data to capture the dynamic
movement of the stock market. The first part of the study deals with the estimation and
discussion of the relationship between BSE Sensex and economic growth, along with some
selected macroeconomic variables. The macroeconomic variables used for the study include
GDP, crude oil prices, Consumer Price Index (CPI), real effective exchange rate, Foreign
Direct investment (FDI) and real interest rate, for the period from the year 1979 to 2014. The
empirical results of Ng-Perron unit root test shows that all the variables used for the study are
stationary at level. The estimation results of ARDL test confirms significant and positive
200
influence of economic growth, exchange rate and inflation on stock price movements in
India. However, there exists a negative and significant relationship between crude oil price
and stock prices. The results are consistent for both long run and short run. The error
correction model of ARDL approach reveals that the adjustment process from the short-run
deviation is quite high. More precisely, it is found that the ECMt-1 term is -0.536 (significant
at 1%), again confirming the existence of co-integration that the derivation from the long run
equilibrium path is corrected 53% per year. Moreover, it is found from VECM based Granger
causality test that there exists a short run unidirectional causality running from foreign direct
investment, GDP and real interest rate to BSE in India. Further, the result indicates the
presence of long run causality for the equation with the stock price as the dependent variable.
To predict the long-run and short-run shocks variance decomposition is used for the study,
the results of the VDC analysis show that a major percentage of stock price change is its own
innovative shocks. Further, the shock in crude oil prices explains stock prices by 12.73%,
hence, the movement of stock prices can be tracked by analysing the movement in crude oil
prices. Thus, it is concluded from the estimation that economic growth, exchange rate, and
inflation effects positively to the stock market and change in crude oil price effects
negatively.
The estimated results of the quarterly time series data are presented in the next section,
the stock market development is represented by market capitalization ratio (MCAP) and
macroeconomic variables quarterly time series data is used for the study. The variables used
are Market capitalization, Real Gross Domestic Product (GDP), Foreign Direct Investment
(FDI), Foreign Institutional Investment (FII) and Trade openness (TO). The data employed
covering the period from 1996: Q1 to 2014: Q3. The ARDL bounds test confirms that the
estimated equation and the series are co-integrated. The test results suggest that economic
growth, FIIs and Trade openness in India influence market capitalization positively.
Consistent results are found for FII and trade openness in short run also. The findings suggest
that openness of the economy helps to attract foreign investment. This in turn increases the
activities on the stock market as firms would attempt to raise investment funds (capital) from
the stock market, which will lead to increase in market capitalization. However, economic
growth failed to explain the variation in stock market growth significantly in the short-run.
This may be due to the fact that investor’s behaviour in the stock market regulated by long-
term growth rate of GDP and may not bother about short-term fluctuations in it. The error
correction model of ARDL approach reveals that the adjustment process from the short-run
deviation is low. More precisely, it is found that the ECMt-1 term is -0.159 (significant at 1%),
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again confirming the existence of cointegration that the derivation from the long run
equilibrium path is corrected 15% per quarter. The results of VECM based Granger causality
shows that there exists long-run causality running from four independent variables (GDP,
TO, FDI and FII) in the long-run towards Stock Market Capitalization (MCAP), whereas, in
short-run the change in trade openness causes a change in Stock Market Capitalization,
whereas a change in stock market capitalization will cause a change in Foreign Institutional
Investment. The results of VDC analysis shows that out of the all of exogenous variables
used for the study, trade openness is having maximum shock on stock market capitalization.
Moreover, the study also focuses on short-run dynamic relationship between
macroeconomic variables and the stock prices, by incorporating data for monthly frequency
of the variables. Further, the monthly study has been divided into two sections, which
constitutes two models in relation with different set of macroeconomic variables and stock
prices (BSE Sensex and CNX Nifty). The first section of the study highlights the relationship
between fundamental macroeconomic variables and Sensitivity Index of Bombay Stock
Exchange (BSE Sensex), using the monthly time series data from the April 2004 to July
2014. The independent variables used for the study are Real Effective Exchange Rate
(REER), Index of Industrial Production (IIP), Consumer Price Index (CPI), Call Money Rates
(CMR) and Gold price (GOR). The ARDL bounds test confirms the existence of long-run
cointegrating relationship between different macroeconomic variables and stock prices in
India. The long-run estimates of ARDL test showed a significant and positive influence of
economic growth (IIP), Exchange Rate and Inflation on stock prices. Further, the study
confirms negative and significant relationship between gold prices and stock prices in India
because gold is a substitute investment avenue for Indian investors. As the gold price rises,
Indian investors tend to invest less in stocks, causing stock prices to fall. The results for IIP,
Inflation and Gold prices are consistent in short-run also. The error correction model of
ARDL approach reveals that the adjustment process from the short-run deviation is slow.
More precisely, it is found that the ECMt-1 term is -0.222 (significant at 1%), again
confirming the existence of co-integration that the derivation from the long run equilibrium
path is corrected 22% per year. The VECM based granger causality result shows that there is
no short run causality running from any of the variables to BSE in India. Further, the result
indicates the presence of long run causality for the equation with the stock price as the
dependent variable. The results of VDC analysis shows that out of the all of exogenous
variables used for the study, Gold price is having maximum shock on stock prices.
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The second section of the empirical study focuses on the relationship between
fundamental macroeconomic variables and Index of National stock exchange (NSE), using
the monthly time series data from the April 2004 to July 2015. The variables used for the
study are CNX Nifty, Index of Industrial Production (IIP), Foreign Institutional Investment
(FII), Gold price (GOR), Treasury bills rate (TBR), Wholesale Price Index (WPI),
International Crude Oil price (CO) and Real Effective Exchange Rate (REER). The ARDL
bounds test confirms that there exists a long-run co-integrating relationship between different
macroeconomic variables and the stock prices in India. The long-run estimates of ARDL test
showed a negative and significant effect of crude oil prices, Inflation (WPI) on stock prices.
The results of the influence of both the variables on stock prices are consistent in the short
run as well. Further, for short-run the study confirms positive and significant relationship for
Gold, T-bill rates (TBR) and Real Effective Exchange Rate (REER). The error correction
model of ARDL approach reveals that the adjustment process from the short-run deviation is
high. More precisely, it is found that the ECMt-1 term is -0.0746 (1%), again confirming the
existence of cointegration that the derivation from the long run equilibrium path is corrected
7% per month. The VECM based Granger causality test found short run causality running
from Inflation and crude oil price to National Stock Exchange in India. Additionally, a
unidirectional causality is also running from national stock exchange to gold and inflation.
Hence, it is observed that bidirectional causality is running between Inflation and CNX nifty
index. Further, the result indicates the presence of long run causality for the equation with a
CNX nifty index as the dependent variable. To predict the long-run and short-run shocks
variance decomposition is used for the study, the results of VDC analysis shows that the
shock in inflation and crude oil explains stock prices by 15.67% and 9.244%, respectively.
The results of IRF show that in its response to the shocks of IIP it is observed that there is a
negative relationship in the long run.
The next empirical chapter presents the relationship between the fiscal policy variables
and stock market development in India, with the use of annual frequency data for the
variables. The study for the relationship between fiscal policy variables and stock market
development has been divided into two sections, the first section of which contains the
estimation results of the relationship between BSE Sensex and fiscal deficit, along with some
controlled macroeconomic variables. The macroeconomic variables used for the study
include money supply (M3), consumer price index (CPI) and real interest rate (RIR), for the
period from the year 1988 to 2014. The test statistics of the unit root suggest that none of the
variables included in the study are I(2). The ARDL results suggest a long run negative and
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significant relationship exists between budget deficit and stock prices. However, the
relationship does not show any significant results in the short run. The findings imply that, in
a country when the budget is in deficit, it will depress the stock prices and undermine the
investor’s confidence, so the firm’s ability to get capital on favorable terms will be
diminished in the long run. Further, as the deficit increases, future tax burden, interest rates,
and the dollar value increases, leading to decrease in corporate profits because of weak
domestic as well as export revenues. So, sales decrease which ultimately lowers net earnings,
thus decreasing equity prices. These findings are analogous with the work of Adrangi and
Allender (1998); Salem and Yasir et al. (2012). However, investors are indifferent to the
short run fluctuations in the fiscal deficits. The money supply and inflation in India influence
stock prices positively both in the long run as well as in the short run. The results of VECM
based Granger causality test suggests that there exists a short run causality running from
fiscal deficit to stock price. Further, the result indicates the presence of long run causality for
the equation with the stock price as the dependent variable. The results of VDC analysis show
that the fiscal deficit plays an important role in explaining the variation in stock prices in
India.
The second part of the study on the relationship between fiscal policy variables and
stock market development in India, discusses the estimation results of the relationship
between stock market development (MCAP) and twin deficit, along with other
macroeconomic variables. The study uses Current Account Deficit (CAD) and Fiscal Deficit
(FD) as the fiscal policy variables. The other macroeconomic variables used for the study
include Gross Domestic Product (GDP), crude oil prices (CO), trade openness (TO) and real
effective exchange rate (REER), for the period from the year 1979 to 2014. The long-run
estimates of ARDL test showed that negative and significant relationship exists between the
current account deficit (CAD) and crude oil with stock market capitalization. It also confirms
a significant and positive influence of Real GDP and Exchange Rate on market capitalization
in India both in long-run and short-run. Further, for short-run the study confirms negative and
significant relationship between CAD and trade openness with stock market capitalizations in
India. The error correction model of ARDL approach reveals that the adjustment process
from the short-run deviation is high. More precisely, it is found that the ECMt-1 term is -0.681
(significant at 1%), again confirming the existence of cointegration that the derivation from
the long run equilibrium path is corrected 68% per year. The results of VECM based Granger
causality found short run causality running from CAD, Real GDP, trade openness and crude
oil to market capitalization in India. In fact, trade openness is having a bi-directional causality
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with MCAP in short-run. Further, the result indicates the presence of long run causality for
the equation with a market capitalization as the dependent variable. To predict the long-run
and short-run shocks variance decomposition is used for the study, the results of VDC
analysis shows that the shock in crude oil price explains market capitalization by 14.61%,
CAD and fiscal deficit contributes to market capitalization by 10.02% and 5.03%,
respectively. The results of IRF shows that in its response to the shocks of current account
deficit, GDP and exchange rate, it is observed that there is a positive relationship in the long
run and reverse is observed in the case for the shocks of fiscal deficits and crude oil prices,
i.e. there is a negative relationship in the long run throughout the period.
The last empirical chapter contains the relationship between macroeconomic variables
and stock at sectoral level by employing quarterly data covering the period from 2003:Q4 to
2014:Q4. The main variables used for the study include Manufacturing sector index,
electricity, gas and water supply sector index, service sector index, contribution of GDP in
manufacturing sector, contribution of GDP in electricity, gas and water supply sector,
contribution of GDP in service sector, and the other macroeconomic variables used for the
study are Crude Oil Price (CO), Real Effective Exchange Rate (REER), T-bill rates (TBR),
Trade openness (TO) and Wholesale Price Index (WPI), a proxy for inflation. Principle
component analysis is used in this study to construct the composite index of manufacturing
index; electricity, gas and water supply index; and service index. For the purpose of study,
three models has been framed, in which each of the sectoral stock price indices is placed as
dependent variable; and Crude Oil Price, REER, T-bill rates, Trade openness and WPI along
with respective sectoral GDP worked as independent variables. The bounds test used for the
study, confirms that there exists a long-run co-integrating the relationship between sectoral
GDP and sectoral stock indices in India. The long-run estimates of ARDL test for the model I
(Manufacturing sector index and share of manufacturing sector in GDP) showed that positive
and significant relationship exists between the manufacturing sector share in GDP with the
manufacturing index. It also confirms that the manufacturing sector share in GDP, crude oil
price and trade openness have a significant and positive impact on the manufacturing index in
the short-run. For model II (Electricity, Gas and Water supply sector index and share of
Electricity, Gas and Water supply sector in GDP), the results show that the electricity, gas
and water supply sector share in GDP and inflation has a positive effect on electricity, gas
and water supply index, unlike short-run. Crude oil price and real effective exchange rate has
a significant and positive impact on the electricity, gas and water index in the short-run. For
model III (Service sector index and share of Service sector in GDP), results show that the
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service sector share in GDP and T-bills rate has a positive effect on service sector index in
the long-run and in short-run as well along with crude oil price. The results suggest that
sectoral indices are affected by changes in sectoral GDP in the long-run, whereas, all the
three indices are sensitive to the change in crude oil price in the short-run. The error
correction model of ARDL approach reveals that the adjustment process from the short-run
deviation is high. More precisely, it is found that the ECMt-1 term is (-0.333), (-0.318) and (-
0.215). These terms are significant at 1%, for all three models, again confirming the existence
of cointegration that the derivation from the long-run equilibrium path is corrected 33%, 31%
and 21%, respectively, per quarter. The results of VECM based Granger causality test
suggests a unidirectional short-run causality running from sectoral GDP, crude oil price,
REER, T-bill rates, trade openness and WPI to respective sectoral stock indices in India.
Further, the result indicates the presence of long-run causality for the equation with
manufacturing index and electricity, gas and water supply index as the dependent variable,
but, except for the service sector index which shows no long-run causality running from any
of the independent variables. To predict the long-run and short-run shocks variance
decomposition is used for the study, the result of VDC analysis, for all three models, show
that a major percentage of sectoral indices are its own innovative shocks. Other than the
respective sectoral GDP, crude oil price is a common variable which is playing a crucial role
in explaining all three indices by contributing its maximum towards the shock, hence,
reflecting maximum information about the movement of the indices.
From the above mentioned empirical results of the relationship between
macroeconomic variables and stock price development it has been concluded that the effect
of economic growth (GDP in yearly study and IIP in monthly study) in almost all the studies
is positive. From the next observation of the empirical results, we conclude that there exist a
positive relationship between real effective exchange rate and stock prices; the positive
influence of exchange rate on stock price movements is favorable for export based countries.
The influence of inflation also comes out to be positive which proves Fisher (1911)
hypothesis, according to him, shares, hedged against inflation in the sense that an increase in
expected inflation leads to a proportional change in nominal share returns. The findings of
our study are contradictory to the findings of Fama (1981). The findings seem to suggest that
investors in making better portfolio decisions should perhaps view shares as long-term
holdings against inflation’s loss of purchasing power. While coming to the fiscal policy
variables, it has been concluded from the empirical results that fiscal deficit and current
account defict are negatively affecting stock market development process in India. It has also
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been analysed that current account deficit and fiscal deficit along with the international crude
oil prices plays an important role in in explaining the variation in stock market development
in India.
All the international macroeconomic variables used in the study viz. trade openness,
Foreign Institutional Investors, Foreign Direct Investment, International gold prices and
International crude oil prices are significantly influencing the stock market development in
India. Variables like trade openness, FDI and FII are positively influencing the stock market
development in the long-run, whereas the variables like international crude oil prices and
gold prices are negatively influencing stock market development in the long run.
Further, considering the empirical results of sectoral study, it has been found that all the
sectoral indices are having a significant long-run relationship with the share of that particular
sector in GDP of the nation.
Thus, the estimated results of the study indicate that the Indian stock market is sensitive
to changes in macroeconomic fundamentals in the long run. However, in the short run also
few of the macroeconomic variables affect stock prices. Further, the stock prices are
relatively exogenous in relation to most of the macroeconomic variables selected for the
study, as major percentage of the variation in the forecast of the Indian stock prices is
attributable to its own shocks. This may be due to the fact that speculative trading continues
to dominate the Indian stock market. The results of the study suggest a positive impact of
macroeconomic variables on the stock market development in India. Therefore, in order to
facilitate economic growth, macroeconomic development is solely desirable in developing
countries like India. Moreover, it is also true that the informed and sensible investor in India
can attain super normal profit, by tracking the historical data of stock market and the change
in macroeconomic variables. This may help the investors to formulate a profitable strategy to
for trading and making profitable decisions.
8.2. Policy Implications of the study
The stock market plays an important role in the financial and economic development of
a country. Therefore, an open, disciplined, transparent and regulated securities market is
considered as the essential element for the economic development of a country. Hence, the
government must have to play a positive role in reinforcing the stock market operations. The
government should formulate some policies for protecting and safeguarding investor’s
interest against all possible insecurities related to investment, this measure will help in
building investor’s confidence. Such a policy for investor’s protection will not only attract
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domestic investors, but it will also help to increase foreign inflows. This section of the study
intends to suggest some policy recommendations for domestic as well as foreign investors,
stock market regulators, policy makers and stock market analysts. Investors and stock market
analysts could forecast stock prices and earn profits. Stock market regulators could take
initiatives for the accountability of companies to prevent manipulation of stock prices and to
educate layman investors for stock market and encourage them to invest in stocks. Policy
makers should be acquainted of these macroeconomic effects on stock market and help them
to take efficient and effective decisions.
The implications of the present study are multifaceted and the findings of the study
implies that, the relationship between economic growth and stock market development found
to be positive, this could have been due to various reasons including pure coincidence, the
working of the wealth effect, the stock market acting as a predictor of GDP or that the stock
market does not move of its own accord but rather remains in line with physical production
conditions. The stock prices and GDP are related because changes in information about the
future course of GDP cause prices to change in the stock market today (Carlstrom, et al.
(2002)). GDP is the most crucial economic indicator which tells us about the health of our
economy. Higher economic activity implies higher expected profitability, which causes stock
prices to rise. Therefore, the stock markets can be flourished with economic growth of the
nation, because it plays a significant positive role in the developments of capital markets of
India. In a country, when the real GDP will raise it will help stock prices to increase and
boost up the investor’s confidence, with the growing economy. It can help companies and
investors decide on, what investment strategies they should adopt. It also guides the policy
makers for taking decisions for formulating and implementing the effective policies. Steps
should be taken to develop export based businesses, to promote economic growth of the
country. Therefore, the authorities concerned should formulate such a policy, so as to support
stock market by promoting economic growth.
The relationship between real exchange rate and stock market development comes out
to be postitive and this relationship may be useful because devaluation of domestic currency
increase export, hence improve the cash flow and divide payoffs for firms that rely on exports
in India. This relationship may also be useful for portfolio managers interested in global asset
allocation or investors trying to hedge against foreign exchange risk. The positive impact of
real effective exchange rate on Indian stock market suggests that, in order to develop the
stock market in India, the exchange rate should be managed carefully by keeping in view the
elasticity of exports and imports that leads to stability in the stock market.
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World oil price is a powerful exogenous variable which influences the stock market and
the findings imply that increase in crude oil prices leads to decreased stock prices, creating an
unfavorable investment climate. But up to some extent that the negative impact of oil prices
can be mitigated, only if the uses of alternative energy resources are facilitated. Therefore,
the rising crude oil prices should serve as the reminder for policy makers to monitor and
control its effects on economic conditions.
The empirical data also suggests that, attracting foreign capital inflows (both Foreign
Direct Investment (FDI) and Foreign Institutional Investment (FII)) and promoting trade
openness can facilitate further investment and easier means of raising capital to support the
activities of the stock markets, which will lead to increased economic activity. Foreign capital
inflow is an important determinant is an important determinant of stock market development
in India. Hence, more liberalized policies in context of foreign capital inflows, must be
formulated so as to ensure more liquidity in the stock market in India, as a result the Indian
capital markets become more attractive for the foreign investors of major economies of the
world.
As per the empirical results the inflation is showing both positive (for yearly data
estimation) and negative impact (for monthly data estimation) on stock prices. Therefore,
keeping in view long run and short run frequency of data, appropriate policies should be
formulated for balancing the inflation in the country. The positive relationship may be due to
the reason that the stock market returns may provide an effective hedge against inflation in
India. This is explained by the significant and positive relationship between inflation and
stock prices as the Fisher (1930) hypothesis postulates. This also implies that investors in
making better portfolio decisions should perhaps view shares as long-term holdings against
inflation’s loss of purchasing power. The study also suggests that suitable policy measures
should be taken by the proficient authorities for the purpose of controlling inflation, which
ultimately leads to the control of volatility of the stock market. By implementing appropriate
monetary policies and setting appropriate fiscal measures, the Indian government will be in
the situation to control and regulate the rate of inflation, to promote a healthy growth of the
stock markets in India. Therefore, the study suggests that the financial regulators and
policymakers should consider the effect of these fundamental macroeconomic variables while
formulating fiscal and economic policies.
The result suggests a negative impact of fiscal deficit on the stock prices in India.
Hence, the government must adopt appropriate policies to improve budget deficit. A stable
government with stable policies can help in achieving confidence among foreign and
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domestic investors. If the government seriously targets these variables, the stock market will
develop resulting in the financial development of the country.
The findings imply that increase in current account deficit leads to decrease stock
prices; therefore, the rising deficits should serve as the reminder for policy makers to monitor
and control its effects on economic conditions. The policymakers should take corrective
measures to curb the deficits up to the level which is acceptable for current economic
conditions. The concerned authorities should promote bilateral trade and should formulate a
tax structure which benefits export businesses to reduce the gap of export and import thus
reducing the current account deficit.
The finding also implies that, the increase in gold prices, gives an alternative and
uncontroversial safe investment during the time of financial crisis as it allows its holder to
resell it without loss at any time especially in the financial markets collapse.
It is believed that the effects of macroeconomic variables on the profitability of
different sectors vary depending on their sensitivity to these variables or it can be said that
every sector is sensitive to the changes in particular macroeconomic variables. For example,
capital-intensive industries (such as banking sector industries or other non-banking financial
firms) are likely to be more sensitive to interest rate changes. Similarly, the earnings of
sectors such as retail and tourism are more likely to be affected by a slowdown in economic
activity. Another perspective of the sectoral study shows that some secoTrs are immune to
the changes in the aggregate macroeconomic variables. For example, the slowdown of the
economy is less likely to affect sectors, such as consumer staples or health industries that
produce goods and services that are essential to consumers. Therefore, the empirical results
of the sectoral study imply that the investors should follow the changes in the sectoral
contribution of GDP, to predict the movement of the shares of that particular sector.
8.3. Contribution of the study
The present study on the relationship between macroeconomic variables and stock
prices has been extensive for many developed economies. However, the study in the context
of emerging economies like India is limited and orthodox in nature. Findings in the present
thesis provide a broad understanding on the dynamic relationship between macroeconomic
variables and the Indian stock market. The study attempted to discuss theoretical hypothesis
on the relationship between macroeconomic variables and stock market development; and
compares it with empirical evidences from previous research works. The present study adds
several primary contributions to the exisiting literature in this field.
210
First, it extends the literature by examining the relationship between macroeconomic
variables and stock prices in the context of emerging Indian economy and intends to be a
basic coverage for further research; and the study helps in observing possible time series
correlation between the Indian stock market prices and domestic and international
macroeconomic factors to enhance investors portfolio understanding and evaluation in terms
of the sensitivity of respective stock market prices to the systematic effect of the selected
macroeconomic factors.
The study contributes to the literature with the addition of fiscal policy variables (Fiscal
Deficit and Current Account Deficit) and their relationship with the stock market
development in India. the study focuses on the relationship between fiscal deficit and stock
market development, which will help the policy makers and regulators to formulate the
appropriate policies in order to improve the conditions of fiscal deficit. Further, the study is
first to attempt the empirical relationship between twin deficit and stock market development
in India.
The study is first to attempt the empirical relationship between sectoral index and
sectoral contribution of GDP. This relationship will help investors, portfolio managers, policy
makers and financial regulators to track the movement in a particular sector index, due to the
changes in the share of GDP of that particular sector. This sectoral study will specifically
contribute in concentrating on the shares of a particular sector so as to get a better insight of
the performance of stocks of that particular sector. Thus, the sectoral analysis of stock market
provides better insight about the performance of the market to both the investors and the
regulators. Sectoral analysis is a better approach for both investors as well as regulators. In a
sectoral study the impact of macroeconomic factors is studied on various sectors. The
performance of different sectors in same economic conditions is different. This gives an idea
of risk diversification to investors and enables them to design well diversified portfolios. The
relationship of sectoral GDP with respective sectoral indices is a matter of interest to
investors, institutions, researchers and policy makers.
The study also contributes by including the techniques like Impulse Response Function
(IRF) and Variance Decomposition (VDC) in the study. These are one of the essential tools
for interpreting VAR model results. The IRF allows us to examine the current and future
behavior of a variable that following a shock to another variable within the system. Whereas,
the VDC determines the relative importance of each innovation to the variables in the system.
Both the techniques help in predictin the responsiveness of variables towards the shock in
other variables.
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The findings of the present study, helps the investors and portfolio managers to make
effective investment decisions, because the knowledge of this inter-relationship between
macroeconomic variables and stock prices provides a better understanding of portfolio
structure. Further, the study also provides an insight to investors and portfolio managers in
making an evaluation for the improvement of overall portfolio design, which ultimately leads
to a better risk diversification strategy and more return. Therefore, it can be said that this
study is significant for investors and portfolio managers. Similarly, the study is expected to
offer some insights to financial regulators and policy makers in terms of formulating
economic and financial policies. A specific precondition of this type of relationship, where
the change in a particular variable can influence the change in another variable, may help the
government agencies in designing economic policies so as to encourage more capital inflows
into the capital market, which leads to economic development of the country. Moreover, the
results provide an opportunity for risk diversification in Indian stock market. Since the stock
returns of different industries behave differently in similar economic conditions so investors
should analyze the nature of industry before making an investment decision. The results can
help investors and portfolio managers in extending their understanding of the risk return
relationship as well as pricing of macroeconomic risk.
Apart from identifying and relating the movement in stock market with the changes in
macroeconomic variables, the present study sheds some light by providing better
understanding on the depth of the stock market activities, especially in an emerging market
like India. Therefore, this study identifies the speed of adjustment towards the long-run
equilibrium by estimating the error correction term.
The study applies different modern econometric methods that may provide insight for
the exisiting literature about the sensitivity of the analysis to the methods employed. Further,
the study employs ARDL techniques to address the cointegration among variables in both
long-run and short-run, since the traditional econometric techniques does not provide enough
scope to capture both long-run and short-run cointegrating relationship among
macroeconomic variables and stock prices.
Thus, conducting such study is worth for the emerging economies like India, as the
study provides a better way of understanding the movement of stock prices through
identification and validation of the effects of macroeconomic variables on the stock market
performance, both on aggregate basis and on sectoral basis. Thus, more efficient risk
measurement and management models can be formulated with a greater confidence in the
decision making process for investments in the stock market.
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8.4. Limitations of the study
This research has attempted to address a number of issues regarding the relationship
between macroeconomic variables and stock prices in India, by framing different estimation
models as per the availability of the data. Although the thesis has made every attempt to
provide a comprehensive and detailed analysis of the relationship between two, some
limitations remain. These limitations are discussed in this section of the concluding chapter.
One of the limitations of the research is the use of Index of Industrial Production
(IIP) as a proxy for economic activity, due to unavailability of GDP in monthly frequency
data. Further, while conducting the study on the relationship between BSE Sensex and
macroeconomic variables on monthly data, CPI has been used as the proxy for inflation and
the data period taken for the study was starting from April 2004, due to unavailability of CPI
data in common base year prior to this period.
The study has not considered the effect of changes in the monetary policy and fiscal
policy on the movement of stock prices. Furthermore, the study has not incorporated the
effect of stock prices of other major economies on the Indian stock prices. The external
variables like changes in the federal rates which can influence the foreign inflows of the
country, which ultimately effects stock prices, has also not been taken into account.
8.5. Scope for further studies
The study suggests further scope for the research to increase the understanding about
the dynamic relationship between the macroeconomic variables and stock prices in India.
Further research may either eliminate some of the limitations or expand the scope of
relationship already done in the present thesis.
Future work might re-examined the issues addressed in this thesis using a relatively
more comprehensive data set (i) including more recent share price data; and (ii) the data of
major leading stock indices of developed economis can also be included. This research would
be particularly valuable as a more recent time period and inclusion of share prices of
developed economies will give a better insight to predict the movement of share prices by
tracking the changes in leading share markets of the world. Examining how the developed
markets of the UK and the US affect the emerging markets like India could be valuable.
The current thesis focuses exclusively on the time series data of Indian economy, but
the further studies can be done by considering panel data incorporating similar
macroeconomic variables for more countries of south asian region. This would help in
213
examining why various domestic and global factors are important in various countries of the
region by performing a research on panel data.
The current thesis focuses on the relationship between macroeconomic variables and
stock prices without considering the impact of any global financial crisis, which can give a
better understanding of the global economic scenario with respect to major events occurred in
the economy.
The research can be further extended by considering the impact of selected
macroeconomic variables along with other important economic determinants like
employment rate, education level, political conditions; which are not included in the analysis.
Moreover, the research can also be extended to analyse the stock market volatility with the
help of GARCH family model, by incorporating the set of macroeconomic variables used in
the present study.
The present research focused on sectoral index and its relationship with respective
sectoral GDP, but the research can further be extended by including some more sectors like
infrastructure sector and agriculture sector.
This thesis used the same set of macroeconomic variables to test for the relationships
on the Sector indices. It may be useful for future studies to include other economic variables
that might affect each sector specifically. It is also recommended to work out research that
compares results with other developing countries’ under similar assessment and
measurement.
Finally, the sectoral research can further be segmented to industry level, because the
research at industry level may help the investors to understand the response of the shares of
that industry due to the changes in external economic environment.
214
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Appendix
B. Lag length selection criteria
A standard problem in time series analysis is the choice of an appropriate model to
represent the data. This is a common problem when a statistical model contains many
variables. According to Parzen (1982), statistical data modelling is a field of statistical
reasoning that seeks to fit models to data without knowing what the “true” model is or might
be. Consequently, one seeks to learn the model and study the quality of the model by a
process which is called statistical model identification or evaluation. In recent years, in the
literature, the necessity of introducing the concept of model selection or model evaluation has
been recognized. Sclove (1994) describes model selection as the choice of selecting the best
model(s) from a set of models and the different type of models that one compares and selects
can be characterized according to the number of lags, the different number of explanatory
variables and other factors. Also, there is presently a great deal of interest in simple criteria
represented by the parsimony of parameters for choosing one of a set of competing models to
describe a given data set. As discussed in Stone (1981), parsimony can take into account a
variety of attributes of the selected model. One such attribute is the cost of measuring the
models that required implementing the model and a second attribute is the complexity of the
selected model. The general principle is that for a given level of accuracy, a simpler or a
more parsimonious model is preferable to a more complex one.
This study focuses on four well-known model selection criteria to determine the order
of the model and each of these criteria is discussed in the literature that follows. The four
criteria are Akaike’s information criterion, Schwarz’s information criterion, Hannan-Quinn’s
information criterion and Final Prediction Error. In this study, these criteria are used to
analyze simulated data from a theoretical cointegrated model. The criterion which identifies
the correct model most often is identified as the most appropriate criterion.
The four well-known information criteria that are used in this research follow a similar
format to the general information criterion (GIC) and the formula of the GIC is illustrated
below. The first term of the GIC measures the lack of fit of the model and the second term is
a penalty function for the number of parameters in the model. The lack of fit of the model
involves a measure of the lack of parsimony or complexity of the model. One of the issues
that lead to model complexity is the number of parameters incorporated in the model.
𝐺𝐼𝐶 = −2log (𝐿𝑘) + 𝑃𝑘 (a)
240
Where: Lk is the likelihood value of the k-th model
Pk is the penalty for the k-th model
A.1. Akaike’s information criterion
During the last three decades, Akaike’s information criterion (AIC) has had an
important impact on statistical model evaluation problems. AIC has been developed for the
identification of an optimal and parsimonious model in data analysis for a class of competing
models which take model complexity into account. The introduction of AIC furthered the
recognition of the importance of good modelling statistics. The model selection strategy of
AIC has the objective of selecting a model based on simply minimizing the Kullback-Leibler
discrepancy between the unknown (true) and the approximating data based models. The
finding of the true model can be very complex and may require a great amount of time, since
the model may incorporate an infinite number of parameters. Therefore, obtaining a true
model is not an ideal manner to represent the recorded data, but rather allow for the best
approximating model and that is what AIC does.
𝐴𝐼𝐶(𝑝) = 𝑙𝑛|∑| +2𝑘2𝑝
𝑇 (a.1)
Where:
k = the number of variables in the model
p = the number of lag terms in the model
T = the number of observations used
𝑙𝑛|∑| = the estimated covariance matrix of the fitted multivariate model taken from
Lutkepohl (1985) and Gonzalo and Pitarakis (1998) and it consists of two measurement
terms. The first term (i.e. 𝑙𝑛|∑|) measures the inaccuracy or poorness of fit of the model. The
second term (i.e. 2𝑘2𝑝
𝑇 ) measures the complexity or the penalty due to the increase of
unreliability in the first term which depends upon the number of parameters used to fit the
data.
Consequently, when there are several competing models the parameters within the
models are estimated by the method of maximum likelihood and the values of the AIC are
computed and compared to find a model with the minimum value of AIC. This approach is
called the minimum AIC procedure and the model with the minimum AIC value is called the
minimum AIC estimator and is chosen to be the best model. For us the best model is the one
with the least complexity, or equivalent, the highest information gain. In applying AIC, the
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emphasis is on comparing the goodness of fit of various models with an allowance made for
parsimony.
A.2. Schwarz’s information criterion
This model selection criterion is used when a true model exists and has a finite and
small dimension that does not increase with sample size. This criterion does not receive any
benefit from the theory of Kullback-Leibler discrepancy, but is derived based on a Bayesian
viewpoint. The best fitting true model is chosen from the list of candidate models as the one
that has the lowest Schwarz’s information criterion (SIC) value.
Lutkepohl (1985) performed a comparison of several information criteria used for
determining the order of a vector autoregressive process for different sample sizes. The result
indicated that the Schwarz’s information criterion estimated the order of an autoregressive
process correctly most often and estimated correctly more often when the sample size
increased. Lutkepohl suggested that the Schwarz’s information criterion and the Hannan-
Quinn’s criterion were the most parsimonious criteria as these two criteria produced the
smallest average squared forecasting error and estimated the order of an autoregressive
process correctly more often. The criterion developed by Schwarz is often referred to as SIC,
Bayesian information criterion (BIC) or even Schwarz Bayesian criterion (SBC).
𝑆𝐼𝐶(𝑝) = 𝑙𝑛|∑| +𝑘2𝑝ln(𝑇)
𝑇 (a.2)
Where:
k = the number of variables in the model
p = the number of lag terms in the model
T = the number of observations used
𝑙𝑛|∑| = the estimated covariance matrix of the fitted multivariate model
A.3. Hannan-Quinn’s information criterion
Hannan and Quinn (1979) provide a brief discussion on methods used for the
determining the order of an autoregressive model. They realized that a method such as
Shibata’s information criterion was inconsistent in the estimation of the order of the
autoregressive model. Hannan and Quinn (1979) claimed that the best-known rule for
estimating the true order of an autoregression was to make use of the method developed by
Akaike (1969). They followed a similar estimation procedure where the method was strongly
consistent for estimating the order of the autoregression. This model is called the Hannan-
242
Quinn’s information criterion (HQ) and it has been used in analysis by Lutkepohl (1985),
Quinn (1980) and Gonzalo and Pitarakis (1998).
Lutkepohl (1985) illustrated in his analysis that the method developed by Hannan and
Quinn was consistent in the estimation of the true order of an autoregressive process. This
was established when performing a comparison with other consistent criteria of various
sample sizes. Lutkepohl suggested that the Schwarz’s information criterion and Hannan-
Quinn’s information criterion were the best criteria when one was interested in forecasting
(minimizing the mean square forecasting error) or estimating the order of a finite order vector
autoregressive model. Quinn (1980) extended the procedure developed by HQ to the larger
dimension case. This larger dimension case was referred to as the multivariate autoregressive
process. This procedure was developed in such a way that it has been strongly consistent just
as in the situation of a univariate autoregression. During the same period, Hannan (1980)
extended the original work of HQ by determining the order of an autoregressive moving
average process.
𝐻𝑄(𝑝) = 𝑙𝑛|∑| +2𝑘2𝑝lnln𝑇
𝑇 (a.3)
Where:
k = the number of variables in the model
p = the number of lag terms in the model
T = the number of observations used
𝑙𝑛|∑| = the estimated covariance matrix of the fitted multivariate model
A.4. Final Prediction Error
Akaike (1969) provided a brief discussion on the practical use of the Final Prediction
Error (FPE) in determining the order of an autoregressive model. The practical application of
the FPE is to estimate the FPE of each autoregressive model within a prescribed sufficiently
wide range of possible orders and to select the one that gives the minimum of the estimates.
Akaike (1969) claimed that by seeking the minimum of FPE, we would be able to arrive at an
autoregressive model of an order that did not have a significant bias and simultaneously did
not have a large mean square prediction error.
In research published during 1969, Akaike performed a comparison of three types of
predictors that were used for model selection. These predictors were the original minimum
FPE, the modified version denoted by the minimizing (FPE)1/4 and the FPE proposed by
Anderson (1963) for the decision of the order of a Gaussian autoregressive process. These
243
three predictors were compared based on various simulated time series models, the predictor
that indicated the true model most often was the one selected. The results showed that for
practical applications, the original procedure, minimum FPE, was the best procedure to use
for model comparison. Lutkepohl (1985) also compared several types of information criteria
and found that the predictor FPE had a tendency to over-estimate the order of an
autoregressive process. In addition, the criteria FPE, AIC and Shibata all had a tendency to
obtain the same number of lag terms for large sample sizes.
𝐹𝑃𝐸(𝑝) = |∑| + (𝑇+𝑝𝑘2+1
𝑇−𝑝𝑘2−1)𝑘2
(a.4)
ln𝐹𝑃𝐸(𝑝) = ln|∑| + 𝑘2ln (𝑇+𝑝𝑘2+1
𝑇−𝑝𝑘2−1)𝑘2
(a.5)
Where:
k = the number of variables in the model
p = the number of lag terms in the model
T = the number of observations used
𝑙𝑛|∑| = the estimated covariance matrix of the fitted multivariate model
244
List of Publications and Presentations
Publications from the Ph. D. thesis
“Fiscal Deficits and Stock Prices in India: Empirical Evidence”, International Journal of
Financial Studies, 2015, Vol. 3, No.3, pp. 393-410.
"Cointegration and Causality between Macroeconomic variables and Stock Prices: Empirical
Analysis from Indian Economy", Business and Economic Research, 2015, Vol. 5, No. 2, pp.
327-245.
"Examining the Relationship between Sectoral Stock Market Indices and Sectoral Gross
Domestic Product: An Empirical Evidence from India”, Global Journal of Management and
Business Research, 2015, Vol. 15, No. 9, pp. 14-26.
“Dynamic Relations between macroeconomic variables and Indian Stock Price: An
application of ARDL bounds testing approach”, Asian Economic and Financial Review, Vol.
5, No. 10, pp. 1119-1133.
“Macroeconomic determinants of Stock market development: Empirical evidence from
India”, Business Perspectives, 2015, Vol. 14, No. 2, pp. 36-50.
“Macroeconomic Variables and Stock market development in India: An application of ARDL
bounds testing approach", Empirical Economics Letters, 2015, Vol. 14, No.7, pp. 705-718.
“Causal Relationship between Stock market Indices and macroeconomic Variables:
Empirical Evidences from India”, International Journal of Multidisciplinary Research, 2013,
Vol. 2, No. 3, pp.114-117.
“An Empirical Analysis of the Relationship between Stock Market Indices And
Macroeconomic Variables: Evidences from India”, International Academic Research Journal
of Economic and Finance, 2013, Vo. 2, No.1.
Conference Papers:
“Fiscal Deficits and Stock Prices in India: An Empirical Evidence”, Paper presented
in 4th International Conference on Applied Econometrics, IBS Hyderabad, 20-21 March
2014, Hyderabad, India
“Sensitivity of Stock Market Indices to Oil Prices, Exchange Rate and Economic Growth:
Evidence from Industrial Sub-sectors in India”, Paper presented in the National Seminar on
Econometric application in Management at Central University of Rajasthan during 20 – 21
November, 2013.
245
Brief Biography of the Candidate
Pooja Joshi is currently pursuing Ph. D. At the Department of Economics and Finance at
BITS, Pilani Campus in the area of Financial Economics. Her Ph. D. Thesis is entitled
“Relationship between Macroeconomic Variables and Stock Market Development: Evidences
from the Indian Economy”. She has received a first class Master’s degree in Management,
from Rajasthan Technical University in 2008. She has more than three years of teaching and
research experience. She is active in research and authored a number of research papers in
international and national journals. Her research interest include Financial Economics,
Macroeconomics, Business Economics, Capital Markets and Financial management
246
Brief Biography of the Supervisor
Prof. A.K. Giri is an Associate Professor of Macroeconomics and Monetry Economics, at
Department of Economics and Finance, Birla Institute of Technology and Science (BITS-
Pilani), Pilani. He is currently the Head of the Department of Economics and Finance. He has
received a first class Master’s and M. Phil. In Economics, from Department of Economics,
Central University, Hyderabad and a Doctorate in Macro-Monetry Economics from the same
University in 1998. His research interest include Macroeconomics, Monetry Economics,
Financial Economics, and Economics of Growth and Development. He has more than 16
years of experience in teaching and research in Economics at postgraduate level. He has
authored a number of research papers in international and national journals and conference
proceedings.