A Research Project on An Empirical Analysis of Relationship between Stock Index Returns and Inflation. Submitted in partial fulfillment of the requirement of the MBA degree Bangalore University Submitted by MEGHA.N.BAIS Register number 04XQCM6054 Under the guidance of Dr. Nagesh Malavalli M.P.Birla Institute of Management, Bangalore. M.P.Birla Institute of Management, Associate Bharatiya Vidya Bhavan, Bangalore 560001
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A Research Project on
An Empirical Analysis of Relationship between Stock Index
Returns and Inflation.
Submitted in partial fulfillment of the requirement of the MBA degree
Bangalore University
Submitted by MEGHA.N.BAIS
Register number
04XQCM6054
Under the guidance of
Dr. Nagesh Malavalli
M.P.Birla Institute of Management,
Bangalore.
M.P.Birla Institute of Management,
Associate Bharatiya Vidya Bhavan,
Bangalore 560001
DECLARATION
I hereby declare that the research work embodied in the dissertation
entitled “An Empirical Analysis of Relationship between Stock Index Returns and
Inflation”” is the result of research work carried out by me, under the
guidance and supervision of Dr. Nagesh Malavalli, Principal, M. P. Birla
Institute of Management, Associate Bharatiya Vidya Bhavan, Bangalore.
I also declare that the dissertation has not been submitted to any
University/Institution for the award of any Degree/Diploma.
3 Research Methodology - Problem Statement - Objectives of the study - Purpose of the study - Hypothesis - Sample Design - statistical Methods - Limitations of the study
19 – 31
4 Empirical Results
32 – 48
5 Conclusions
49 – 51
Bibliography
52 – 53
Annexure
54 - 67
LIST OF TABLES
Table No.
Particulars Page No.
1 List of S&P CNX Nifty Companies
22
2 List of CNX Bank Index Companies
24
3 List of BSE Sensex Companies
24
4 Results of Wholesale Price Index ADF Unit Root Test
32
5 Results of S&P CNX Nifty ADF Unit Root Test
34
6 Results of CNX Bank Index ADF Unit Root Test
36
7 Results of BSE sensex ADF Unit Root Test
38
8 Results of S&P CNX Nifty and WPI Regression
40
9 Results of S&P CNX Nifty and WPI Granger Co-integration Test
40
10 Results of CNX Bank Index and WPI Regression
42
11 Results of CNX Bank Index and WPI Granger Co-integration Test
42
12 Results of BSE Sensex and WPI Regression
44
13 Results of BSE sensex and WPI Granger Co-integration Test
44
14 Results of S&P CNX Nifty and WPI Grangers Causality Test
46
15 Results of CNX Bank Index and WPI Grangers Causality Test
47
16 Results of BSE Sensex and WPI Grangers Causality Test
48
LIST OF GRAPHS
Graph No.
Particulars Page No.
1 S&P CNX Nifty Stationarity Graph
33
2 CNX Bank Index Stationarity Graph
35
3 BSE Sensex Stationarity Graph
37
Introduction Background
Globalization and financial sector reforms in India have ushered in a sea change
in the financial architecture of the economy. In the contemporary scenario, the activities
in the financial markets and their relationships with the real sector have assumed
significant importance. Since the inception of the financial sector reforms in the
beginning of 1990’s, the implementation of various reform measures including a number
of structural and institutional changes in the different segments of the financial markets,
particularly since 1997, have brought about a dramatic change in the financial
architecture of the economy. Altogether, the whole gamut of institutional reforms,
introduction of new instruments, change in procedures, widening of network of
participants call for a reexamination of the relationship between the financial sector and
the real sector in India. Correspondingly, researches are also being conducted to
understand the current working of the economic and the financial system in the new
scenario. Interesting results are emerging particularly for the developing countries where
the markets are experiencing new relationships which are not perceived earlier. The
analysis on stock markets has come to the fore since this is the most sensitive segment of
the economy. It analyses the relationship between stock prices and macroeconomic
variable inflation with implications on efficiency of stock prices.
Relationship between stock returns and inflation
There are several empirical explanations for the negative correlation between
stock returns & inflation. Some attribute this inconsistency between the theory &
empirical findings to market inefficiency. Others attribute it to the negative correlation
between real economic activity (fiscal & monetary) and inflation known as proxy effect
hypothesis. In an increasingly complex scenario of the financial world, it is of paramount
importance for the researchers, practitioners, market players and policy makers to
understand the working of the economic and financial system and assimilate the mutual
interlink ages between the stock and economic variables in forming their expectations
about the future policy and financial variables
The informational efficiency of major stock markets has been extensively
examined through the study of causal relations between stock price indices and inflation.
The findings of these studies are important since informational inefficiency in stock
market implies on the one hand, that market participants are able to develop profitable
trading rules and thereby can consistently earn more than average market returns, and on
the other hand, that the stock market is not likely to play an effective role in channeling
financial resources to the most productive sectors of the economy.
The Efficient Markets Hypothesis (EMH) assumes that everyone has perfect
knowledge of all information available in the market. Therefore, the current price of an
individual stock (and the market as a whole) portrays all information available at time t.
accordingly, if real economic activity affects stock prices, then an efficient stock market
instantaneously digests and incorporates all available information about economic
variables. The rational behavior of market participants ensures that past and current
information is fully reflected in current stock prices. As such, investors are not able to
develop trading rules and, thus may not consistently earn higher than normal returns.
Therefore, it can be concluded that, in an information ally efficient market, past (current)
levels of economic activity are not useful in predicting current (future) stock prices.
While finding causality from lagged values of stock prices to an economic
aggregate does not violate informational efficiency, this finding is equivalent to the
existence of causality from current values of stock prices to future levels of the economic
variable. This would suggest that stock prices lead the economic variable and that the
stock market makes rational forecasts of the real sector.
If, however, lagged changes in some economic variables cause variations in stock
prices and past fluctuations in stock prices cause variations in the economic variable, then
bi-directional causality is implied between the two series. This behavior indicates stock
market inefficiency. In contrast, if changes in the economic variable neither influence nor
are influenced by stock price fluctuations, then the two series are independent of each
other and the market is information ally efficient.
The inflation rate is an important element in determining stock returns due to the
fact that during the times of high inflation, people recognize that the market is in a state
of economic difficulty. People are laid off work, which could cause production to
decrease. When people are laid off, they tend to buy only the essential items. Thus
production is cut even further. This eats into corporate profits, which in turn makes
dividends diminish. When dividends decrease, the expected return of stocks decrease,
causing stocks to depreciate in value.
Inflation clearly affects different companies in different ways. Inflation is
essentially a disequilibrium phenomenon involving continuing distortions among relative
prices. These distortions result partly from time lags. A given impulse of money and
credit inflation pushes up different prices and wages at different rates and by varying
magnitudes. Moreover, when countries inflate at different rates, relative exchange rates
are distorted, and this in turn feeds back into the domestic price structure, altering relative
prices even further. There are also institutional factors which affect relative prices, for
example import controls, unions, and regulation and deregulation of such things as oil
and transport. Thus relative prices will shift during a period of fluctuating inflation rates,
affecting the growth and stability of earnings.
Common stock represents an ownership claim on the prospective after-tax
earnings of the company. Thus, an unexpected increase in the inflation rate would tend to
depress the after tax earnings on capital, thereby depressing the value of corporate assets
to potential owners. Accordingly, real stock prices tend to fall.
It has only been in periods of accelerating inflation and tight monetary policy that
the market has really been poor. The depressing effect of accelerating inflation on the
stock market resulted from the perceived risk by investors that the monetary authorities
would tighten policy in order to control inflation, and that this would work by depressing
the economy and the real earnings of the corporate sector.
The overall level of the stock market will be affected by cyclical movements of
the economy. Prices will rise at times of easy money and low interest rates, which
provide a stimulus to economic growth. As interest rates and money tightens, so the
business environment will worsen, costs will rise, demand will fall, profits will be
squeezed from both sides, and stock prices will become depressed.
Theoretical Framework The Indian Financial System
The Indian financial system consists of many institutions, instruments and
markets. Financial instruments range from the common coins, currency notes and
cheques, to the more exotic futures swaps of high finance.
Financial Markets
Generally speaking, there is no specific place or location to indicate financial
markets. Wherever a financial transaction takes place, it is deemed to have taken place in
the financial market. Hence financial markets are pervasive in nature since financial
transactions are themselves very pervasive throughout the economic system.
However, financial markets can be referred to as those centers and arrangements
which facilitate buying and selling of financial assets, claims and services. Sometimes,
we do find the existence of a specific place or location for a financial market as in the
case of stock exchange.
Classification of financial markets
Financial markets can be classified as
i) Unorganized Markets
In these markets there a number of money lenders, indigenous bankers, traders
etc. who lend money to the public.
ii) Organized Market
In organized markets, there are standardized rules and regulations governing their
financial dealings. There is also a high degree of institutionalization and
instrumentalization. These markets are subject to strict supervision and control by the
RBI or other regulatory bodies.
Organized markets can be further divided into capital market and Money market.
Capital market
Capital market is a market for financial assets which have a long or definite
maturity. Which can be further divided into:
• Industrial Securities Market
• Government Securities Market
• Long Term Loans Market
Industrial Securities Market
It is a market where industrial concerns raise their capital or debt by issuing
appropriate Instruments. It can be subdivided into two. They are:
• Primary Market or New Issues Market
Primary market is a market for new issues or new financial claims. Hence, it is
also called as New Issues Market. The primary market deals with those securities which
are issued to the public for the first time.
• Secondary Market or Stock Exchange
Secondary market is a market for secondary sale of securities. In other words,
securities which have already passed through the new issues market are traded in this
market. Such securities are listed in stock exchange and it provides a continuous and
regular market for buying and selling of securities. This market consists of all stock
exchanges recognized by the government of India.
Importance of Capital Market
Absence of capital market serves as a deterrent factor to capital formation and
economic growth. Resources would remain idle if finances are not funneled through
capital market.
• It serves as an important source for the productive use of the economy’s savings.
• It provides incentives to saving and facilitates capital formation by offering
suitable rates of interest as the price of the capital
• It provides avenue for investors to invest in financial assets.
• It facilitates increase in production and productivity in the economy and thus
enhances the economic welfare of the society.
• A healthy market consisting of expert intermediaries promotes stability in the
value of securities representing capital funds.
• It serves as an important source for technological up gradation in the industrial
sector by utilizing the funds invested by the public.
Inflation
Definition
Inflation is an increase in the amount of currency in circulation, resulting in a
relatively sharp and sudden fall in its value and rise in prices: it may be caused by an
increase in the volume of paper money issued or of gold mined, or a relative increase in
expenditures as when the supply of goods fails to meet the demand.
This definition includes some of the basic economics of inflation and would seem
to indicate that inflation is not defined as the increase in prices but as the increase in the
supply of money that causes the increase in prices i.e. inflation is a cause rather than an
effect.
On the other hand in this definition, inflation would appear to be the consequence
or result (rising prices) rather than the cause -- A persistent increase in the level of
consumer prices or a persistent decline in the purchasing power of money, caused by an
increase in available currency and credit beyond the proportion of available goods and
services.
High and Low inflation
It would seem obvious that low inflation is good for consumers, because costs are
not rising faster than their paychecks.
During the high inflation it is believed that "High Inflation introduces
uncertainty". This is not quite true either. The truth is that steady inflation, if it can be
relied upon to remain steady, does not introduce uncertainty. Changing (fluctuating)
inflation rates is what introduces uncertainty. The best stock market performance takes
place in years of price stability when nothing is happening on the inflation & deflation
front.
Causes
There may be difference of opinion on the causes and consequences of inflation
and the measures to be taken to deal with the problem. What needs to be done is not so
much a general statement of anti inflationary policies as the formulation, in great detail,
of remedial measures, short term as well as long term.
It is the result of economic forces at work, rather than the conspiracy of merchants
and manufacturers, or the faulty functioning of the market mechanism, these can only be
short period.
Inflation represents an imbalance between the flow of incomes to people and the
spending power available with them on the one hand and the availability of goods and
services on the other.
Inflation can occur with unchanged availability of goods and a marked increase in
incomes in the hands of the people and desire to spend from past savings. There are also
situations were production remains sluggish and even declines while incomes in the
hands of the public rises on account of high levels of government and non government,
financed by borrowing from the banking system in a big way.
If inflation continues for a long period, it causes a lot of disturbance to the
economy which gets distorted. The price rise in the country is on account of factors
operating both on the demand & supply sides:
• Expenditure by government larger than its receipts from revenue and loans from the
public, which is known as deficit financing.
Where the deficit financing is of large dimensions, naturally the flow of
incomes is proceeding at a much faster pace than the capacity of the economy to
generate a corresponding larger supply of goods and services.
• Larger credit given by banks to the commercial sector not supported by productive
activities.
The RBI regulates the latter by imposing restrictions on credit in order to
bring about some equilibrium. The former can be controlled by government
imposing greater self discipline and keeping its expenditure within the limits of its
resources.
• Demand generated by unaccounted money.
• Delay in monsoon
• Increase in the purchasing power of the house holds.
• Liberal govt. policies on taxation, excise, customs etc.
• Expansion of currency.
Inflation can be avoided
Price stability can be accomplished, provided there is a will on the part of the
govt. and public in this direction. The fight against inflation calls for a proper formulation
of economic plans and determined implementation of the plans. Fiscal & monetary action
constitutes important elements of the anti inflationary strategy. Various types of action
should be taken to raise the standards of productivity in the farm, factory & the office.
The immediate action should be to arrest forthwith any further rise in commodity prices,
by drastic action on the fiscal & monetary fronts. Measures may be:
• Role of fiscal policy
Reduction of budgetary deficits: Control over expenditure & maximizing tax
income and rise in the rates of interest rates. Ceiling should be put on purchase of
govt securities by the RBI and the commercial banks & the extension of ways &
advances to govt by the Reserve Bank.
• Role of monetary policy
The primary task of monetary policy is to restrain money circulation in the
economy. The RBI has a variety of instruments of credit control. It si the
responsibility of individual commercial banks to ensure that credit allocation to
the various sectors of the economy is done in a manner that fulfills broadly the
official objective of larger credit flows to the neglected & priority sectors while at
the same time keeping down the aggregate extension of credit to the limits
dictated by the overall economic situation. This can be done through selective
Infosys technologies ltd. Computers - software Infosystch Indian petrochemicals corporation ltd. Petrochemicals Ipcl I t c ltd. Cigarettes Itc Jet airways (india) ltd. Travel & transport Jetairways Larsen & toubro ltd. Engineering Lt Mahindra & mahindra ltd. Automobiles - 4 wheelers M&m Maruti udyog ltd. Automobiles - 4 wheelers Maruti Mahanagar telephone nigam ltd. Telecommunication - services Mtnl National aluminium co. Ltd. Aluminium Nationalum Oil & natural gas corporation ltd. Oil exploration/production Ongc Oriental bank of commerce Banks Orientbank Punjab national bank Banks Pnb Ranbaxy laboratories ltd. Pharmaceuticals Ranbaxy Reliance energy ltd. Power Rel Reliance industries ltd. Refineries Reliance Steel authority of india ltd. Steel and steel products Sail Satyam computer services ltd. Computers - software Satyamcomp State bank of india Banks Sbin Shipping corporation of india ltd. Shipping Sci Sun pharmaceutical industries ltd. Pharmaceuticals Sunpharma Tata chemicals ltd. Chemicals - inorganic Tatachem Tata motors ltd. Automobiles - 4 wheelers Tatamotors Tata power co. Ltd. Power Tatapower Tata steel ltd. Steel and steel products Tatasteel Tata tea ltd. Tea and coffee Tatatea Tata consultancy services ltd. Computers - software Tcs Videsh sanchar nigam ltd. Telecommunication - services Vsnl Wipro ltd. Computers - software Wipro Zee telefilms ltd. Media & entertainment Zeetele
CNX Bank Index
The Indian banking Industry has been undergoing major changes, reflecting a
number of underlying developments. Advancement in communication and information
technology has facilitated growth in internet-banking, ATM Network, Electronic transfer
of funds and quick dissemination of information. In order to have a good benchmark of
the Indian banking sector, India Index Service and Product Limited (IISL) has developed
the CNX Bank Index.
CNX Bank Index is an index comprised of the most liquid and large capitalized
Indian Banking stocks. It provides investors and market intermediaries with a benchmark
that captures the capital market performance of Indian Banks. The index will have 12
stocks from the banking sector which trade on the National Stock Exchange.
Table No.2 CNX Bank Index Companies
Company name Symbol Andhra bank Andhrabank Bank of baroda Bankbaroda Bank of india Bankindia Canara bank Canbk Corporation bank Corpbank Hdfc bank ltd. Hdfcbank Icici bank ltd. Icicibank Oriental bank of commerce Orientbank Punjab national bank ltd. Pnb State bank of india Sbin Syndicate bank Syndibank Union bank of india Unionbank
BSE Sensex Of the 23 stock exchanges in the India, Mumbai's (earlier known as Bombay),
Bombay Stock Exchange is the largest, with over 6,000 stocks listed. The BSE accounts
for over two thirds of the total trading volume in the country. Established in 1875, the
exchange is also the oldest in Asia. Among the twenty-two Stock Exchanges recognized
by the Government of India under the Securities Contracts (Regulation) Act, 1956, it was
the first one to be recognized.
SENSEX is a basket of 30 constituent stocks representing a sample of large,
liquid and representative companies. The base year of SENSEX is 1978-79 and the base
value is 100. The index is widely reported in both domestic and international markets
through print as well as electronic media.
Table No.3 BSE Sensex Companies
Company name Industry Reliance industries ltd. Refineries Infosys technologies ltd. Computer software Icici bank ltd. bank Itc ltd. Cigarettes Housing development finan Finance housing Larsen & toubro ltd. Engineering Hindustan lever ltd. Diversified Bharti tele-ventures ltd. Telecommunication - services Oil & natural gas corpora Oil exploration/production Tata steel ltd. Steel and steel products State bank of india bank Satyam computer services Computer software Hdfc bank ltd. bank Bajaj auto ltd. Automobiles - 2 and 3 wheelers Tata motors ltd. Automobiles - 4 wheelers Tata consultancy services Computer software Bharat heavy electricals Electrical equipment Hindalco industries ltd. Aluminium Ntpc ltd Grasim industries ltd. Cement and cement products Wipro ltd. Computer software Gujarat ambuja cements lt Cement and cement products Associated cement compani Cement and cement products Ranbaxy laboratories ltd. Pharmaceuticals Cipla ltd. Pharmaceuticals Maruti udyog ltd. Automobiles - 4 wheelers Hero honda motors ltd. Automobiles - 2 and 3 wheelers Dr. Reddy's laboratories Pharmaceuticals Tata power company ltd. Power Reliance energy ltd. Power
Limitations of the study
• The results may not give accurate picture as there could be many other macro
factors other than inflation which affects the stock returns at the same period.
The above graph of BSE Sensex is moving upward from January 1996 to march,
2005, indicating the non stationarity of the series.
Grangers Co integration Test: Co integration between CNX Nifty and Whole sale price index • An ordinary least square (OLS) regression is done on the data. First x is regressed
on y then y on x.
X on Y -- Dependent variable(X) is CNX Nifty and Independent variable(Y) is WPI.
Y on X -- Dependent variable(X) is WPI and Independent variable(Y) is CNX Nifty.
NIFTYt = a + bWPI t + e t ………………………………….. (1)
WPIt = a + bNIFTY t + e t ………………………………….. (2)
Table No.8 Nifty and WPI Regression Result
Parameter (1) (2)
Coefficient -3.027 -0.0174
(* Reference table 17)
• Residuals e=y-y^ Results
The residuals of both the regression equations are stationary.
Table No.9 Nifty and WPI Co-integration Test:
Constraints
(log 0)
ADF values Mackinnon
Critical values
None ( level)
[X on Y]
-12.08444*
1% (-2.530)
5% (-1.9426)
10% (-1.6171)
None ( level)
[Y on X]
-11.13941*
1% (-2.530)
5% (-1.9426)
10% (-1.6171)
(* indicates rejection of null hypothesis)
(* Reference no.18 & 19)
(*for reference 17,18 & 19 -see annexure)
Interpretation:
Unit root test for stationarity of residuals from the co integration equation shows
that the null hypothesis is rejected at 1%,5% and 10% level of significance implying
CNX Nifty and WPI are co integrated, but as the coefficients are statistically significant
with a negative sign. This indicates the negative relationship between CNX Nifty and
WPI.
Co integration between Bank Index and Whole sale price index • An ordinary least square (OLS) regression is done on the data. First x is regressed on
y then y on x.
X on Y -- Dependent variable(X) is Bank Index and Independent variable(Y) is WPI.
Y on X -- Dependent variable(X) is WPI and Independent variable(Y) is bank Index.
BANKt = a + bWPI t + e t ………………………………….. (1)
WPIt = a + bBANK t + e t ………………………………….. (2)
Table No.10 Bank Index and WPI Regression Results
Parameter (1) (2)
Coefficient -3.643 -0.0151
(* Reference 20)
• Residuals e=y-y^ Results
The residuals of both the regression equations are stationary.
Table No.11 Bank Index and WPI co-integration Test
Constraints
( log 0)
ADF values Mackinnon
Critical values
None ( level)
[X on Y]
-8.611257*
1% (-2.6000)
5% (-1.9457)
10% (-1.6185)
None ( level)
[Y on X]
-7.523411*
1% (-2.6000)
5% (-1.9457)
10% (-1.6185)
(* indicates rejection of null hypothesis)
(* Reference no.21 & 22)
(* for reference no.20,21 & 22 – see annexure)
Interpretation:
The Bank Index and WPI are correlated as Unit root test for stationarity of
residuals from the co integration equation shows that the null hypothesis is rejected at
1%,5% and 10% level of significance, implying Bank Index and WPI are co integrated,
but as the coefficients are statistically significant with a negative sign. This indicates the
negative relationship between CNX Nifty and WPI.
Co integration between BSE Sensex and Whole sale price index • An ordinary least square (OLS) regression is done on the data. First x is regressed on
y then y on x.
X on Y -- Dependent variable(X) is BSE Sensex and Independent variable(Y) is WPI.
Y on X -- Dependent variable(X) is WPI and Independent variable(Y) is BSE Sensex.
BSEt = a + bWPI t + e t ………………………………….. (1)
WPIt = a + bBSE t + e t ………………………………….. (2)
Table No.12 BSE Sensex and WPI Regression Results
Parameter (1) (2)
Coefficient -3.146 -0.0169
(* Reference no.23)
• Residuals e=y-y^ Results
The residuals of both the regression equations are stationary.
Table No.13 BSE Sensex and WPI co-integration Test
Constraints ADF values Critical values
None ( level)
[X on Y]
-10.56767*
1% (-2.5856)
5% (-1.9431)
10% (-1.6173)
None ( level)
[Y on X]
-10.34426*
1% (-2.6000)
5% (-1.9431)
10% (-1.6173)
(* indicates rejection of hull hypothesis)
(* Reference no.24 & 25)
(* for reference no.23,24, & 25 – see annexure)
Interpretation:
The BSE Sensex and WPI are co integrated but they have a negative relationship as
coefficients are negative. Unit root test for stationarity of residuals from the co
integration equation shows that the null hypothesis is rejected at 1%,5% and 10% level of
significance.
Grangers Causality Test:
CNX Nifty and Whole sale price index The causality results for the two variables are:
Table No.14 Nifty and WPI causality Test
Lags Hypothesis No of
observations F statistics ProbabilityWPI does not causes Nifty 3.33728 0.03910
2 Nifty does not causes WPI 117 3.37426 0.03776WPI does not causes Nifty 3.64799 0.00797
4 Nifty does not causes WPI 115 2.00270 0.09940WPI does not causes Nifty 2.81231 0.01461
6 Nifty does not causes WPI 113 1.51338 0.18144WPI does not causes Nifty 2.70617 0.00400
12 Nifty does not causes WPI 107 1.54497 0.12503WPI does not causes Nifty 2.18444 0.01131
24 Nifty does not causes WPI 95 1.00842 0.47574 (* Reference no.26)
H0 = NIFTY does not causes WPI
H1 = NIFTY causes WPI
H0 =WPI does not causes NIFTY
H1 = WPI causes NIFTY
Interpretation
The calculated F values from lag 2 to 24 are greater than the F statistics, which
rejects the null hypothesis. And the P value is also close to zero. Thus there is
bidirectional causality at every lag between CNX Nifty and WPI.
( * for reference no.26 see annexure)
Bank Index and Whole sale price index The causality results for the two variables are:
Table No.15 Bank Index and WPI causality Test
Lags Hypothesis No of
observations F statistics ProbabilityWPI does not causes Bank 3.22490 0.04739
2 Bank does not causes WPI 60 6.43431 0.00308WPI does not causes Bank 3.03205 0.02599
4 Bank does not causes WPI 58 3.43213 0.01498WPI does not causes Bank 2.47260 0.03830
6 Bank does not causes WPI 56 2.37257 0.04554WPI does not causes Bank 1.82921 0.09817
12 Bank does not causes WPI 50 1.18496 0.34507 (* Reference 27)
H0 = BANK does not causes WPI
H1 = BANK does WPI
H0 =WPI does not causes BANK
H1 =WPI causes BANK
Interpretation
As the P value is close to zero and the calculated F values from lag 2 to 12 are
greater than the F statistics, the null hypothesis is rejected. Thus there is bidirectional
causality between bank indexes to WPI.
(* for reference no.27 – see annexure)
BSE Sensex and Whole sale price index The causality results for the two variables are:
Table No.16 BSE Sensex and WPI causality Test
Lags Hypothesis No of
observations F statistics ProbabilityWPI does not causes BSE 5.38766 0.00603
2 BSE does not causes WPI 103 2.93531 0.05780WPI does not causes BSE 3.11784 0.01874
4 BSE does not causes WPI 101 1.44428 0.22575WPI does not causes BSE 3.44288 0.00428
6 BSE does not causes WPI 99 1.48854 0.19167WPI does not causes BSE 3.00089 0.00203
12 BSE does not causes WPI 93 1.19113 0.30734WPI does not causes BSE 1.75291 0.06878
24 BSE does not causes WPI 81 1.22307 0.29359 (* Reference no.28)
H0 = BSE does not causes WPI
H1 = BSE causes WPI
H0 =WPI does not causes BSE
H1 = WPI causes BSE
Interpretation
The calculated F values from lag 2 to 24 are greater than the F statistics, which
rejects the null hypothesis. The P values are also close to zero. Thus there is bidirectional
causality between bank indexes to WPI.
(* for reference no.28 – see annexure)
Conclusions
The Fisher hypothesis states that the real rates of returns on common stock and
expected inflation rates are independent and the nominal stock returns vary in one to one
correspondence with expected inflation. The expected nominal returns contain market
assessments of expected inflation rates. Thus, if the market is efficient processor of the
information available, it will set the prices so that the expected nominal return is the sum
of the expected real return and the best possible assessment of the expected inflation.
As the index is nothing but weighted average of the share prices of various
companies from different sectors, the sensex has been considered to see the impact of
inflation on it. Sensex, Nifty and Bank index are considered to see where they move in
the same direction or not.
After analyzing the data using the various Grangers test, it has been found that
there is no positive relationship between stock returns and inflation. The results of the
three indices are:
• CNX Nifty:
The nifty is considered for a period of 10 years. The series is stationary at I(1), but
it is negatively related to inflation as the coefficients statistically significant with negative
sign (-3.027) & (-0.01742) And there exists a bidirectional causal relationship between
nifty and inflation.
• Bank Index:
The bank index is considered for 5 years. The series is stationary at I(1). And its
coefficient is also statistically significant with a negative sign (-3.643) & (-0.01515).
Thus showing the negative relation between the two. Its causal relationship is in both
directions.
• BSE Sensex:
The results of BSE are also same as nifty and bank. Study is done for a period of 9
years. It is stationary at I (1) and its coefficients are (-3.146) & (-0.01692) showing the
negative relation. And the causality runs from the both direction.
Thus, the relationship between stock returns and inflation does not change with
indices.
It is evident from the overall results that the causality runs from inflation to stock
returns and also in the reverse order with a negative sign in both directions. The
coefficients are statistically significant with a negative sign.
The negative relationship can be interpreted several ways: for example the
unexpected inflation is generally considered to be positively correlated with inflation
uncertainty and high level of inflation uncertainty discourages investments in risky assets
and results in reduced nominal returns. Another interpreted is that the negative relation is
due to the fact that changes in expected inflation are most likely to be positively
correlated with unexpected inflation.
The market informational efficiency hypothesis can be rejected, as there exists a
bidirectional relationship between stock returns and inflation. The market is
informationally inefficient with respect to the rate of inflation. The market participants
can develop profitable trading rules and thereby can consistently earn more than average
market returns, as future inflation can be predicted.
It can be concluded that the stocks are a perverse inflation hedge. This does not
mean that equities are hazardous to investors’ health. Stocks are priced today to yield
very lucrative returns. The prospective returns have to be good; however, to compensate
stockholders for the risk they bear because equities are a perverse inflation hedge. When
the rate of inflation unexpected increases, real stock prices will fall. Conversely, when the
rate of inflation unexpectedly drops, real stock prices will raise.
So if one does not mind bearing some risk especially the risk that the inflation rate
may be higher than stocks are a good investment. If one seeks an inflation hedge, stocks
are generally poor investments. My conclusion rests on the observation that rising
inflation rates tend to depress corporate earnings and thereby stock prices, which has
been proved by the Grangers co integration test.
Thus, if one wants to cover the stock price risks, he can go for derivative market.
If an investor is having underlying asset and wants to protect himself from unexpected
inflation movements, he can enter the future market by entering into long and short
contracts based on future predictions.
Bibliography
TEXT BOOKS:
• Basic Econometrics
- Damodar N.Gujarati, (fourth edition)
• Macroeconomics
- Mishra and Puri
• Multinational Financial Management
- Alan C. Shapiro ( seventh edition)
REFERENCE BOOKS
• Inflation in India – Indian Institute of Management, Bangalore
• Market Models -- Indian Institute of Management, Bangalore.
WEB SITES:
• www.nseindia.com
• www.financeyahoo.com
• www.inflationdata.com
• www.google.com
• www.investorpedia.com
• www.bseindia.com
• www.rbi.org.in
ARTICLES:
• Stock market and macro economic behaviour in India
-- Sangeeta Chakravarty, Institute of Economic growth, University
Enclave, Delhi.
• An overview of the impact of inflation on the stock market
R-squared 0.006599 Mean dependent var 0.589916 Adjusted R-squared 0.006599 S.D. dependent var 0.837105 S.E. of regression 0.834339 Akaike info criterion 2.484013 Sum squared resid 82.14227 Schwarz criterion 2.507367 Log likelihood -146.7988 Durbin-Watson stat 1.530646
Reference No. 2 -- ADF Unit Root Test on WPI [intercept]
ADF Test Statistic 0.893221 1% Critical Value* -3.4861 5% Critical Value -2.8857 10% Critical Value -2.5795
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(WPI) Method: Least Squares Date: 06/11/06 Time: 20:46 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints
R-squared 0.254591 Mean dependent var -0.001695 Adjusted R-squared 0.254591 S.D. dependent var 1.034613 S.E. of regression 0.893255 Akaike info criterion 2.620549 Sum squared resid 93.35481 Schwarz criterion 2.644029 Log likelihood -153.6124 Durbin-Watson stat 2.123953
Reference No. 5 -- ADF Unit Root Test on Nifty [none]
ADF Test Statistic 0.985726 1% Critical Value* -2.5830
5% Critical Value -1.9426 10% Critical Value -1.6171
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(NIFTY) Method: Least Squares Date: 06/11/06 Time: 20:55 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints
R-squared -0.002772 Mean dependent var 9.191765 Adjusted R-squared -0.002772 S.D. dependent var 87.89297 S.E. of regression 88.01472 Akaike info criterion 11.80125 Sum squared resid 914097.7 Schwarz criterion 11.82461 Log likelihood -701.1746 Durbin-Watson stat 1.982355
Reference No. 6-- ADF Unit Root Test on Nifty [intercept]
ADF Test Statistic -0.497179 1% Critical Value* -3.4861 5% Critical Value -2.8857 10% Critical Value -2.5795
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(NIFTY) Method: Least Squares Date: 06/11/06 Time: 20:56 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints
R-squared 0.489975 Mean dependent var -1.043814 Adjusted R-squared 0.489975 S.D. dependent var 124.0451 S.E. of regression 88.58815 Akaike info criterion 11.81431 Sum squared resid 918199.7 Schwarz criterion 11.83779 Log likelihood -696.0444 Durbin-Watson stat 1.994521
Reference No. 9-- ADF Unit Root Test on Bank Index [none]
ADF Test Statistic 1.909361 1% Critical Value* -2.6000
5% Critical Value -1.9457 10% Critical Value -1.6185
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(BANK) Method: Least Squares Date: 06/11/06 Time: 21:01 Sample(adjusted): 2000:02 2005:03 Included observations: 62 after adjusting endpoints
R-squared 0.010723 Mean dependent var 38.51210 Adjusted R-squared 0.010723 S.D. dependent var 176.4821 S.E. of regression 175.5333 Akaike info criterion 13.18953 Sum squared resid 1879528. Schwarz criterion 13.22384 Log likelihood -407.8755 Durbin-Watson stat 2.131805
Reference No. 10 -- ADF Unit Root Test on Bank Index [intercept]
ADF Test Statistic 0.807602 1% Critical Value* -3.5380 5% Critical Value -2.9084 10% Critical Value -2.5915
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(BANK) Method: Least Squares Date: 06/11/06 Time: 21:06 Sample(adjusted): 2000:02 2005:03 Included observations: 62 after adjusting endpoints
R-squared 0.492085 Mean dependent var -1.083934 Adjusted R-squared 0.492085 S.D. dependent var 255.2891 S.E. of regression 181.9398 Akaike info criterion 13.26149 Sum squared resid 1986125. Schwarz criterion 13.29609 Log likelihood -403.4753 Durbin-Watson stat 1.987823
Reference No. 13-- ADF Unit Root Test on BSE Sensex [none]
ADF Test Statistic 0.847576 1% Critical Value* -2.5856
5% Critical Value -1.9431 10% Critical Value -1.6173
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(BSE) Method: Least Squares Date: 06/11/06 Time: 21:10 Sample(adjusted): 1996:07 2005:02 Included observations: 104 after adjusting endpoints
R-squared -0.001734 Mean dependent var 27.89750 Adjusted R-squared -0.001734 S.D. dependent var 300.1898 S.E. of regression 300.4499 Akaike info criterion 14.25801 Sum squared resid 9297827. Schwarz criterion 14.28344 Log likelihood -740.4164 Durbin-Watson stat 1.968782
Reference No. 14-- ADF Unit Root Test on BSE Sensex [intercept]
ADF Test Statistic -0.340429 1% Critical Value* -3.4940 5% Critical Value -2.8892 10% Critical Value -2.5813
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(BSE) Method: Least Squares Date: 06/11/06 Time: 21:12 Sample(adjusted): 1996:07 2005:02 Included observations: 104 after adjusting endpoints
R-squared 0.489775 Mean dependent var 4.208738 Adjusted R-squared 0.489775 S.D. dependent var 422.3126 S.E. of regression 301.6581 Akaike info criterion 14.26613 Sum squared resid 9281755. Schwarz criterion 14.29171 Log likelihood -733.7055 Durbin-Watson stat 2.005614
Reference no.17 Regression of X on Y and Y on X [NIFTY AND WPI]
Coefficients
UnstandardizedCoefficients
Standardized Coefficients
t Sig.
Model B Std. Error Beta 1 (Constant) 4.048 1.186 3.414 .001
R-squared 0.553050 Mean dependent var -0.000955 Adjusted R-squared 0.553050 S.D. dependent var 0.105286 S.E. of regression 0.070388 Akaike info criterion -2.461212 Sum squared resid 0.584632 Schwarz criterion -2.437858 Log likelihood 147.4421 Durbin-Watson stat 1.940615
Reference no.19 Residual based Co-integration Test on Y on X
[WPI & Nifty]
ADF Test Statistic -11.13941 1% Critical Value* -2.5830 5% Critical Value -1.9426 10% Critical Value -1.6171
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(E) Method: Least Squares Date: 06/11/06 Time: 21:51 Sample(adjusted): 1995:05 2005:03 Included observations: 119 after adjusting endpoints
R-squared 0.512522 Mean dependent var -0.001063 Adjusted R-squared 0.512522 S.D. dependent var 0.106185 S.E. of regression 0.074138 Akaike info criterion -2.357417 Sum squared resid 0.648575 Schwarz criterion -2.334063 Log likelihood 141.2663 Durbin-Watson stat 1.973553
Reference no.20Regression of X on Y and Y on X [BANK AND WPI]
R-squared 0.548661 Mean dependent var -0.000223 Adjusted R-squared 0.548661 S.D. dependent var 0.124962 S.E. of regression 0.083952 Akaike info criterion -2.101146 Sum squared resid 0.429924 Schwarz criterion -2.066837 Log likelihood 66.13552 Durbin-Watson stat 1.951314
Reference no.22 Residual based Co-integration Test on Y on X [WPI & Bank]
ADF Test Statistic -7.523411 1% Critical Value* -2.6000
5% Critical Value -1.9457 10% Critical Value -1.6185
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(E) Method: Least Squares Date: 06/11/06 Time: 22:04 Sample(adjusted): 2000:02 2005:03 Included observations: 62 after adjusting endpoints
R-squared 0.481284 Mean dependent var -0.000683 Adjusted R-squared 0.481284 S.D. dependent var 0.124620 S.E. of regression 0.089753 Akaike info criterion -1.967502 Sum squared resid 0.491397 Schwarz criterion -1.933193 Log likelihood 61.99256 Durbin-Watson stat 1.965120
Reference no.23 Regression of X on Y and Y on X [BSE AND WPI]
R-squared 0.520206 Mean dependent var 3.79E-05 Adjusted R-squared 0.520206 S.D. dependent var 0.106673 S.E. of regression 0.073889 Akaike info criterion -2.362935 Sum squared resid 0.562338 Schwarz criterion -2.337508 Log likelihood 123.8726 Durbin-Watson stat 1.983658
Reference no.25 Residual based Co-integration Test on Y on X [BSE & WPI]
ADF Test Statistic -10.34426 1% Critical Value* -2.5856
5% Critical Value -1.9431 10% Critical Value -1.6173
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(E) Method: Least Squares Date: 06/11/06 Time: 22:11 Sample(adjusted): 1996:07 2005:02 Included observations: 104 after adjusting endpoints
R-squared 0.509532 Mean dependent var -0.000109 Adjusted R-squared 0.509532 S.D. dependent var 0.110501 S.E. of regression 0.077388 Akaike info criterion -2.270405 Sum squared resid 0.616855 Schwarz criterion -2.244978 Log likelihood 119.0610 Durbin-Watson stat 1.983072
Reference no.26 Granger’s causality Test [NIFTY AND WPI]
F critical values:
No. of observations for different lags Level of significance 117