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THE DETERMINANTS OF STOCK MARKET DEVELOPMENT:
A PANEL STUDY OF DEVELOPED, EMERGING AND FRONTIER
MARKETS
By
GULBAZ MAHMOOD
(REG NO 110927)
DOCTOR OF PHILOSOPHY IN MANAGEMENT SCIENCES
(FINANCE)
AIR UNIVERSITY, SCHOOL OF MANAGEMENT, ISLAMABAD
APRIL 2018
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THE DETERMINANTS OF STOCK MARKET DEVELOPMENT:
A PANEL STUDY OF DEVELOPED, EMERGING AND FRONTIER
MARKETS
By
Gulbaz Mahmood
(Reg No 110927)
A research thesis submitted to the Air University School of Management (AUSOM),
Islamabad in partial fulfillment of the requirement for the degree of
DOCTOR OF PHILOSOPHY IN MANAGEMENT SCIENCES
(FINANCE)
AIR UNIVERSITY, SCHOOL OF MANAGEMENT, ISLAMABAD
APRIL 2018
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ACKNOWLEDGEMENT
First of all, I am grateful to Almighty Allah, who bestowed me with an opportunity and
knowledge to do this project successfully. By the Grace of God, this project has been
successfully completed with the assistance of numerous wonderful people. I would like to take
this opportunity to thank all those individuals who have contributed directly or indirectly in the
successful completion of this project.
I am whole-heartedly thankful to my great supervisors, Dr. Shahnaz A Rauf and
Dr. Eatzaz Ahmed, whose valuable guidance and professional support from the initial to the
final level enabled me to develop an understanding of the subject. They have left no stone
unturned during the supervision of this project. I am also thankful to my local and foreign
examiners for their valuable comments, which made this study as a more comprehensive. The
faculty members and Dean had been very supportive in all phases of the project. Moreover,
the untiring support of our dynamic coordinator, Mr Syed Farhan Shah, had been quite
instrumental in coordinating and compiling the project activities in an amicable manner.
Ordinary words of appreciation do not cover my family’s true love and their guidance
at every corner of my life. The genuine and well-motivated support of my spouse gave me
encouragement to complete the project in addition to my present job. My mother had also been
praying all the time for the success of my project. All of my family members including my
mother, wife and kids were supported me very well in every facets. Their keen interest, prayers
and encouragement have been a very strong support for me and enabled me to finish my project
in an effective and efficient manner.
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TABLE OF CONTENTS
Certificate of Approval ………………………………………………………...........iii
Thesis Evaluation Report………………………………………………….…...........iv
Thesis Completion Certificate…………………………………………….…...........v
Author’s Declaration …………………………………………………………..........vi
Plagiarism Undertaking……………………………………..…………….…..........vii
Acknowledgements…………….………………………………………….….........viii
Table of Contents…………………………………………………………...………ix
List of Tables ……..……………………………………………………………….xiii
List of Figures …….……………………………………………………………….xvii
List of Abbreviations …….……………………..…………………………………xxi
Abstract…………………………………………………………………………....xxii
Chapter 1 Introduction………………………………………………………. 1
1.1. Background of the study…………………………………………………... 1
1.2. Research Gap and Motivation ……………………………………………. 4
1.3. Significance of the Study ………………………………………………… 7
1.4. Research Objectives ……………………………………………………. 8
1.5. Research Questions ……………………………………………………… 9
1.6. Contribution to Knowledge………………………………………………. 10
Chapter 2 Review of Literature …………………………………………….. 11
2.1. Introduction……………………………………………………………….. 11
2.2. Literature on Stock Market Development………………………………… 11
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2.3. Literature on Governance Factors ………………………………………... 12
2.4. Literature on Empirical Methodology …………………………………….. 14
2.5. Stock Markets …………………………………………..………………… 17
2.6. Classification of Stock Markets ………………………………..………... 18
2.7. Concluding Remarks of Literature Review……………………………….. 18
Chapter 3 Empirical Model and Hypotheses Development………………. 20
3.1. Introduction ………………………………..…………………………….. 20
3.2. Theoretical Framework…………………………………………….…….. 20
3.2.1. Basic Caldron-Rossell Model……………………..……………………… 20
3.2.2. Augmented Caldron-Rossell Model………………………..…………...... 23
3.2.3. Diagrammatical Model of the Study……………………………………… 26
3.2.4. Empirical Models and Hypotheses Development………………………… 27
Chapter 4 Methodology……..……………………………………..………… 36
4.1. Introduction………………………………………………………...……… 36
4.2. Statistical Methodology….………………………………………….…….. 36
4.3. Econometric Methodology ……………………………………….............. 37
Chapter 5 Data and Variables………………………………………………. 40
5.1. Introduction………………………………………………………………... 40
5.2. Dependent Variable………………………………………………………... 40
5.3 Economic Factors………………………………………………………..… 41
5.4 Governance Factors……………………………………………………….. 43
5.5 Predicted Variable Signs…………………………………………………... 46
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5.6. Data Analysis Software…………………………………………………… 47
5.7 Data Period and Classification……………………………………………... 47
Chapter 6 Empirical Analysis..………………………………………………. 48
6.1. Introduction………………………………………………………………… 48
6.2. Statistical Analysis……………………………………................................. 48
6.2.1 Statistical Analysis of Developed Financial Markets……………………… 48
6.2.2 Statistical Analysis of Emerging Financial Markets………………………. 54
6.2.3 Statistical Analysis of Frontier Financial Markets………………………… 59
6.2.4 Statistical Analysis of World Financial Markets…………………………... 64
6.3. Econometric Analysis……………………………………………………… 70
6.3.1. Principal Component Analysis (PCA)……………………………………... 72
6.3.2. PCA of Developed Financial Markets……………………………………... 72
6.3.2 PCA of Emerging Financial Markets……………………………………… 82
6.3.3 PCA of Frontier Financial Markets………………………………………… 92
6.3.4 PCA of World Financial Markets…………………………………………...102
6.4. Panel GMM Estimation……………………………………………………..112
6.4.1. Results of Panel GMM Estimation for Model-1……………………….……114
6.4.2. Results of Panel GMM Estimation for Model-2……………………….……117
6.4.3. Results of Panel GMM Estimation for Model-3……………………….……120
6.4.4. Results of Panel GMM Estimation for Model-4……………………….……128
6.4.5. Results of Panel GMM Estimation for Model-5……………………….……131
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Chapter 7 Summary and Conclusions……………………..………………. 137
7.1. Combined Results…………………………………………….…………… 137
7.2. Overall Summary of the Results…………………………………………... 170
7.3 Conclusions………………………………………………………………... 173
7.4 Policy Recommendations…………………………………………………. 175
7.5 Contributions of the Study…………………………………………………. 175
7.6 Limitations of the Study…………………………………………………… 176
7.7 Future Research Avenues …………………………………………………. 177
Appendices……………………….…………………………………… 178
References…………………………………………………………….. 219
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List of Tables
Table No Description of the Table Page No
Table 3.1 Country Classification of World Equity Markets by FTSE as on
30 September 2015
19
Table 5.1
Summary of the Variables along with their Predicted Signs 46
Table 6.1 Preliminary Statistics of Economic Variable of Developed Equity
Markets
52
Table 6.2 Preliminary Statistics of Governance Variables of Developed
Equity Markets
52
Table 6.3 Correlation Matrix of Economic Variables of Developed Equity
Markets
53
Table 6.4 Correlation Matrix of Governance Variables of Developed Equity
Markets
54
Table 6.5 Preliminary Statistics of Economic Variable of Emerging Equity
Markets
57
Table 6.6 Preliminary Statistics of Governance Variables of Emerging
Equity Markets
58
Table 6.7 Correlation Matrix of Economic Variables of Emerging Equity
Markets
59
Table 6.8 Correlation Matrix of Governance Variables of Emerging Equity
Markets
59
Table 6.9 Preliminary Statistics of Economic Variable of Frontier Equity
Markets
62
Table 6.10 Preliminary Statistics of Governance Variables of World Equity
Markets
63
Table 6.11 Correlation Matrix of Economic Variables of World Equity
Markets
64
Table 6.12 Correlation Matrix of Governance Variables of Frontier Equity
Markets
64
Table 6.13 Preliminary Statistics of Economic Variable of World Equity
Markets
68
Table 6.14 Preliminary Statistics of Governance Variables of World Equity
Markets
68
Table 6.15 Correlation Matrix of Economic Variables of Frontier Equity
Markets
69
Table 6.16 Correlation Matrix of Governance Variables of Frontier Equity
Markets
70
Table 6.17 List of All Variables for Statistical and Econometric Analysis 72
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Table No Description of the Table Page No
Table 6.18 Principal Components Analysis for Economic Variables of
Developed Stock Markets (25 Countries)
74
Table 6.19 Principal Components Analysis for Governance Variables of
Developed Stock Markets (25 Countries)
77
Table 6.20 Principal Components Analysis for Economic Variables of
Emerging Stock Markets (21 Countries)
83
Table 6.21 Principal Components Analysis for Governance Variables of
Emerging Stock Markets (21 Countries)
87
Table 6.22 Principal Components Analysis for Economic Variables of
Frontier Stock Markets (24 Countries)
93
Table 6.23 Principal Components Analysis for Governance Variables of
Frontier Stock Markets (24 Countries)
97
Table 6.24 Principal Components Analysis for Economic Variables of World
Stock Markets (70 Countries)
103
Table 6.25 Principal Components Analysis for Governance Variables of
World Stock Markets (70 Countries)
106
Table 6.26 All Economic Variables of Developed Stock Markets (25
Countries) and Depended Variable : Market Capitalization as
%age of GDP
115
Table 6.27 All Economic Variables of Emerging Stock Markets (21
Countries) and Depended Variable : Market Capitalization as
%age of GDP
116
Table 6.28 All Economic Variables of Frontier Stock Markets (24 Countries)
and Depended Variable : Market Capitalization as %age of GDP
116
Table 6.29 All Economic Variables of World Stock Markets (70 Countries)
and Depended Variable of Market Capitalization as %age of GDP
117
Table 6.30 All Governance Variables of Developed Stock Markets (25
Countries) and Depended Variable : Market Capitalization as
%age of GDP
118
Table 6.31 All Governance Variables of Emerging Stock Markets (21
Countries) and Depended Variable : Market Capitalization as
%age of GDP
118
Table 6.32 All Governance Variables of Frontier Stock Markets (24
Countries) and Depended Variable : Market Capitalization as
%age of GDP
119
Table 6.33 All Governance Variables of World Stock Markets (70 Countries)
and Depended Variable of Market Capitalization as %age of GDP
119
Table 6.34 All Economic and Governance Variables of Developed Stock
Markets (25 Countries) and Depended Variable : Market
Capitalization as %age of GDP
121
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Table No Description of the Table Page No
Table 6.35 All Economic and Governance Variables of Emerging Stock
Markets (21 Countries) and Depended Variable : Market
Capitalization as %age of GDP
122
Table 6.36 All Economic and Governance Variables of Frontier Stock
Markets (24 Countries) and Depended Variable : Market
Capitalization as %age of GDP
122
Table 6.37 All Economic and Governance Variables of World Stock Markets
(70 Countries) and Depended Variable : Market Capitalization as
%age of GDP
123
Table 6.38 All Economic Variables and Composite Index of Governance
Variables of Developed Stock Markets (25 Countries) and
Depended Variable of Market Capitalization as %age of GDP
124
Table 6.39 All Economic Variables and Composite Index of Governance
Variables of Emerging Stock Markets (21Countries) and
Depended Variable of Market Capitalization as %age of GDP
124
Table 6.40 All Economic Variables and Composite Index of Governance
Variables of Frontier Stock Markets (24 Countries) and Depended
Variable of Market Capitalization %age of GDP
125
Table 6.41 All Economic Variables and Composite Index of Governance
Variables of World Stock Markets (70 Countries) and Depended
Variable of Market Capitalization as %age of GDP
125
Table 6.42 All Governance Variables and Composite Index of Economic
Variables of Developed Stock Markets (25 Countries) and
Depended Variable of Market Capitalization as %age of GDP
126
Table 6.43 All Governance Variables and Composite Index of Economic
Variables of Emerging Stock Markets (21 Countries) and
Depended Variable of Market Capitalization as %age of GDP
127
Table 6.44 All Governance Variables and Composite Index of Economic
Variables of Frontier Stock Markets (24 Countries) and Depended
Variable of Market Capitalization as %age of GDP
127
Table 6.45 All Governance Variables and Composite Index of Economic
Variables of World Stock Markets (70 Countries) and Depended
Variable of Market Capitalization as %age of GDP
128
Table 6.46 Composite Indices of Economic and Governance Variables of
Developed Stock Markets (25 Countries) and Depended Variable
of Market Capitalization as %age of GDP
129
Table 6.47 Composite Indices of Economic and Governance Variables of
Emerging Stock Markets (21 Countries) and Depended Variable
of Market Capitalization as %age of GDP
130
Table 6.48 Composite Indices of Economic and Governance Variables of
Frontier Stock Markets (24 Countries) and Depended Variable of
Market Capitalization as %age of GDP
130
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Table No Description of the Table Page No
Table 6.49 Composite Indices of Economic and Governance Variables of
World Stock Markets (70 Countries) and Depended Variable of
Market Capitalization as %age of GDP
130
Table 6.50 Composite Indices of Economic and Governance Variables with
indirect effect of Developed Stock Markets (25 Countries) and
Depended Variable of Market Capitalization as %age of GDP
132
Table 6.51 Composite Indices of Economic and Governance Variables with
indirect effect of Emerging Stock Markets (21 Countries) and
Depended Variable of Market Capitalization as %age of GDP
133
Table 6.52 Composite Indices of Economic and Governance Variables with
indirect effect of Frontier Stock Markets (24 Countries) and
Depended Variable of Market Capitalization as %age of GDP
133
Table 6.53 Composite Indices of Economic and Governance Variables with
indirect effect of World Stock Markets (70 Countries) and
Depended Variable of Market Capitalization as %age of GDP
134
Table 6.54 Stock Market Development and Composite Index of Governance
Variables of Developed Stock Markets (25 Countries) and
Depended Variable of Composite Economic factors (Peco)
135
Table 6.55 Stock Market Development and Composite Index of Governance
Variables of Emerging Stock Markets (21 Countries) and
Depended Variable of Composite Economic factors (Peco)
135
Table .6.56 Stock Market Development and Composite Index of Governance
Variables of Frontier Stock Markets (25 Countries) and Depended
Variable of Composite Economic factors (Peco)
136
Table 6.57 Stock Market Development and Composite Index of Governance
Variables of World Stock Markets (70 Countries) and Depended
Variable of Composite Economic factors (Peco)
136
Table 7.1 Determinants of Stock Market Development for Developed
Region (25 Countries) and Depended Variable : Market
Capitalization as %age of GDP
138
Table 7.2 Reverse Impacts on Composite Economic Factors for Developed
Region (25 Countries) and Depended Variable : Composite
Economic Factors
141
Table 7.3 Determinants of Stock Market Development for Emerging Region
(21 Countries) and Depended Variable : Market Capitalization as
%age of GDP
146
Table 7.4 Reverse Impacts on Composite Economic Factors for Emerging
Region (21 Countries) and Depended Variable : Composite
Economic Factors
149
Table 7.5 Determinants of Stock Market Development for Frontier Region
(24 Countries) and Depended Variable : Market Capitalization as
%age of GDP
154
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Table No Description of the Table Page No
Table 7.6 Reverse Impacts on Composite Economic Factors for Frontier
Region (24 Countries) and Depended Variable : Composite
Economic Factors
157
Table 7.7 Determinants of Stock Market Development for All World Equity
Markets (70 Countries) and Depended Variable : Market
Capitalization as %age of GDP
162
Table 7.8 Reverse Impacts on Composite Economic Factors for the All
World Equity Markets ( 70 Countries) and Depended Variable :
Composite Economic Factors
165
Table 7.9 Overall Summary of the Empirical Results 170
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List of Figures
Figure
No
Description of the Figure Page
No Figure 1.1 Diagrammatic view of relations among development of stock
market, economic and governance factors 3
Figure 1.2 Diagrammatic view of the world stock markets with all three
regions of developed, emerging and frontier markets 5
Figure 1.3 Relational Impact of governance factors on development of stock
market and economic factors 8
Figure 3.1 Diagrammatic view of the main model depicting relations among
variables of development of stock market, economic and
governance factors
26
Figure 6.1 No of Listed Companies of Developed Financial Markets
(Average No from 19996 to 2015) 49
Figure 6.2 Market Capitalization of Developed Financial Markets in USD
Billions (Average from 19996 to 2015) 50
Figure 6.3 Basic Statistics of Stock Market Development of Developed
Financial Markets 51
Figure 6.4 No of Listed Companies of Emerging Financial Markets (Average
No from 19996 to 2015) 55
Figure 6.5 Market Capitalization of Emerging Financial Markets in USD
Billions (Average from 19996 to 2015) 56
Figure 6.6 Basic Statistics of Stock Market Development of Emerging Equity
Markets 57
Figure 6.7 No of Listed Companies of Frontier Financial Markets (Average
No from 19996 to 2015) 60
Figure 6.8 Market Capitalization of Frontier Financial Markets in USD
Billions (Average from 1996 to 2015) 61
Figure 6.9 Basic Statistics of Stock Market Development of Frontier
Financial Markets 62
Figure 6.10 No of Listed Companies of World Financial Markets (Average No
from 19996 to 2015) 65
Figure 6.11 Market Capitalization of World Financial Markets in USD
Billions (Average from 1996 to 2015) 66
Figure 6.12 Basic Statistics of Stock Market Development of World Financial
Markets 67
Figure 6.13 Mean of Composite of Economic Variables of Developed Stock
Markets 75
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Figure
No
Description of the Figure Page
No Figure 6.14 Standard Deviation of Composite of Economic Variables of
Developed Stock Markets 76
Figure 6.15 Mean of Composite of Governance Variables of Developed Stock
Markets 78
Figure 6.16 Standard Deviation of Composite of Governance Variables of
Developed Stock Markets 79
Figure 6.17 Mean of Cross Composite Index of Economic and Governance
Variables of Developed Stock Markets 80
Figure 6.18 Standard Deviation of Cross Composite Index of Economic and
Governance Variables of Developed Stock Markets 81
Figure 6.19 Scatter plots of Composite of Indices of Economic and
Governance Indices of Developed Stock Markets 82
Figure 6.20 Mean of Composite Index of Economic Variables of Emerging
Stock Markets 85
Figure 6.21 Standard Deviation of Composite of Economic Variables of
Emerging Stock Markets 86
Figure 6.21 Mean of Composite of Governance Variables of Emerging Stock
Markets 88
Figure 6.22 Standard Deviation of Composite of Governance Variables of
Emerging Stock Markets 89
Figure 6.23 Mean of Cross Composite Index of Economic and Governance
Variables of Emerging Stock Markets 90
Figure 6.24 Standard Deviation of Cross Composite Index of Economic and
Governance Variables of Emerging Stock Markets 91
Figure 6.25 Scatter plots of Composite of Indices of Economic and
Governance Indices of Emerging Stock Markets 92
Figure 6.26 Mean of Composite Index of Economic Variables of Frontier
Stock Markets 95
Figure 6.27 Standard Deviation of Composite of Economic Variables of
Frontier Stock Markets 96
Figure 6.28 Mean of Composite of Governance Variables of Frontier Stock
Markets 98
Figure 6.29 Standard Deviation of Composite of Governance Variables of
Frontier Stock Markets 99
Figure 6.30 Mean of Cross Composite Index of Economic and Governance
Variables of Frontier Stock Markets 100
Figure 6.31 Standard Deviation of Cross Composite Index of Economic and
Governance Variables of Frontier Stock Markets 101
Figure 6.32 Scatter plots of Composite of Indices of Economic and
Governance Indices of Frontier Stock Markets 102
Figure 6.33 Mean of Composite Index of Economic Variables of World Stock
Markets 105
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Figure
No
Description of the Figure Page
No Figure 6.34 Standard Deviation of Composite Economic Variables of World
Stock Markets 106
Figure 6.35 Mean of Composite Governance Variables of World Stock
Markets 108
Figure 6.36 Standard Deviation of Composite of Governance Variables of
World Stock Markets 109
Figure 6.37 Mean of Cross Composite Index of Economic and Governance
Variables of World Stock Markets 110
Figure 6.38 Standard Deviation of Cross Composite Index of Economic and
Governance Variables of World Stock Markets 111
Figure 6.39 Scatter plots of Composite of Indices of Economic and
Governance Indices of World Stock Markets 112
Figure 7.1 Scatter Plot of Stock Market Development & Economic Variables
of Developed Markets 143
Figure 7.2 Scatter Plot of Stock Market Development & Governance
Variables of Developed Markets 144
Figure 7.3 Scatter Plot of Stock Market Development & Economic Variables
of Emerging Markets 151
Figure 7.4 Scatter Plot of Stock Market Development & Governance
Variables of Emerging Markets 152
Figure 7.5 Scatter Plot of Stock Market Development & Economic Variables
of Frontier Markets 159
Figure 7.6 Scatter Plot of Stock Market Development & Governance
Variables of Frontier Markets 160
Figure 7.7 Scatter Plot of Stock Market Development & Economic Variables
of World Markets 167
Figure 7.8 Scatter Plot of Stock Market Development & Governance
Variables of World Markets 168
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List of Abbreviations
CAPM Capital Asset Pricing Model
E1 GDP per capita growth (annual %)
E2 Inflation, consumer prices (annual %)
E3 Real interest rate (%)
E4 Domestic credit to private sector by banks (% of GDP)
E5 Gross domestic savings (% of GDP)
E6 Trade (% of GDP)
E7 Foreign direct investment, net inflows (% of GDP)
E8 Current account balance (% of GDP)
FTSE Financial Times Stock Exchanges
G1 Control of Corruption
G2 Government Effectiveness
G3 Political Stability and Absence of Violence/Terrorism
G4 Regulatory Quality
G5 Rule of Law
G6 Voice and Accountability
GDP Gross domestic product
G-Index Governance Index
GMM Generalized Method of Moments
IFC International Finance Corporation
IMF International Monetary Fund
ISE Islamabad Stock Exchange
ISS Institutional Shareholder Services
KSE Karachi Stock Exchange LSDV Least Squares Dummy Variable Model OECD Organization for Economic Co-operation and Development
PPP Purchasing Power Parity
RIV Residual Income Valuation Model
ROA Return on Assets
ROE Return on Equity
S3 Stocks traded, total value (% of GDP)
SBP State Bank of Pakistan
SECP Securities and Exchange Commission of Pakistan
SEO Securities and Exchange Ordinance
UAE United Arab Emirates
UK United Kingdom
UNCTAD United Nations Conference on Trade and Development
UNDP United Nations Development Program
USA United States of America
WDI World Development Indicators
WGI World Governance Indicators
Y Stock Market Development
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ABSTRACT
The objective of the present study is to find the determinants of equity market
development with a panel data of Developed, Emerging and Frontier Equity Markets. The
determinants of equity market include both the quantitative and qualitative factors, whereas the
former represents the macroeconomic variables, and the later embodies the governance
variables. The study incorporates the panel data set of world stock markets of 70 countries,
which are classified by international group of Financial Times Stock Exchanges (FTSE) in
three main regions of the world as Developed (25), Emerging (21) and Frontier (24) Equity
Markets and period of the study is 20 years starting from 1996 to 2015. Given the panel nature
of the data, the econometric methodologies of dynamic Generalized Methods of Moments
(GMM) has been incorporated to find the significant relationships on subject matter Moreover,
the study has incorporated multifaceted statistical methodologies in all three regions of the
world stock markets. Despite having different dynamics and resources, there are few
similarities but there are some of the stark differences, which lead them to identify their
uniqueness. The study finds that effects of economic and governance factors on stock market
development are peculiar in nature and quite unique as per the dynamics of that particular
region. For instance, the study finds that economic and governance factors are more influential
in developed region as compared to emerging and frontier regions which is mainly due to strong
institutional quality in the developed countries. The study has formed a composite index of
Economic and Governance factors through Principal Component Analysis by using factors for
each region of the world equity markets. Afterwards, cross-index of Economic and Governance
Factors is formed for exploring the joint effects of these variables. The study reveals that there
is strong correlation in these composite indices in the developed region, where as there is no
clear pattern in the developing countries. Moreover, there is quite dispersion in the composite
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data of the developing countries. So, there is no direct correlation in the composite factors of
economic and governance in the developing countries. The studies on exploring the direct
effects are quite in abundance but the literature on indirect and cross effects of governance and
economic factors on the development of stock market are quite scarce. The present study
explores the comprehensive direct and indirect effects of governance factors as well as vice
versa effects on the development of economy and equity market in all regions of the world
equity markets. After the estimation, the study finds that effects of economic and governance
factors on stock market development are not only unidirectional, but also bidirectional as well.
Particularly, the emerging markets have dual effects on economic and governance factors. The
indirect effects of governance through economic factors are significant in developed region and
cross effects of governance and economic factors are significant in emerging markets. The
vice versa effects of stock market development and economic growth suggests that economic
growth is also affected by the development of stock market and governance factors particularly
in frontier markets. The reverse impacts of stock market development on the economic growth
are quite captivating in which development of stock market also affects the growth of economy
in all three regions of the world. In the end, the study recommends that determinants of equity
market may not solely based on economic factors rather the significance of governance factors
may be taken into account while taking the complete picture of the subject. This study will
append the knowledge of prevailing institutional works on equity markets and economy as
well. Moreover, the formation of composite indices for governance and economic factors for
all the regions of developed, emerging and frontier markets may append the existing knowledge
database of financial markets.
JEL classification: C33, C36, C38, E6, G15, P52
Keywords: Stock Market Development; Economic Factors, Governance Factors,
Developed Stock Markets, Emerging Stock Markets, Frontier Stock Markets, Composite
Index, Dynamic GMM
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CHAPTER 1
INTRODUCTION
1.1. Background of the study
The economic performance of any country can be very well analyzed through the
development of its equity market, which plays a vital role in the economic development of any
country. For instance, Levine and Zervos (1996) stipulate that equity market development
shows an important role in foreseeing the future economic growth of particular country. During
the last few decades, the equity markets all over the world have increased extensively and this
magnitude trend of stock market development in the developing countries have been
unprecedented. The development of stock market is a complex phenomenon and its
determinants cannot be measured solely by the economic factors rather there are few other
factors that are affecting the growth of stock markets. Latest theoretical research depict that
development of stock market is affected by governance factors of a particular country apart
from its economic factors and empirical evidence provides support to this assertion. For
instance, Yartey (2008) analyzes the development of equity market by using a panel data of
forty two markets of emerging region. The result suggests that the governance factors are the
vital elements in the stock markets development in emerging markets.
According to Seeking Alpha(2008), the market size of stocks and derivatives was
estimated to US$828 trillion, which was measured as eleven times larger than the size of whole
economy of the world. Keeping in view the significance of equity markets, it is imperative to
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understand the multifaceted dynamics of this market. From the period starting from 1984 with
ending in 1995, world stock markets had a very fast growth and equity markets in emerging
region showed a more speedy growth by taking larger chunk of development of global stock
markets. In the same context, Mohtadi & Agarwal, 2001 find that the total capitalization in the
global stock markets jumped from the amounts of US$4.7 trillion to the amounts of US$15.2
trillion and the share of emerging markets surged from 4 to 13 percent during the decade after
1985. So, the significance for the development of equity market cannot be marginalized from
any regions of world equity markets.
Critics of equity market development, however, argue that equity market is grown by
its own dynamics which is quite complex in its nature. Sometimes, it is affected by the
performance of financial condition of the country and rarely in developed and often in
underdeveloped countries, the governance factors play a major role in development of stock
market. Critics claim that liquidity of stock market may inversely affect corporate governance
because it may inspire myopia of investors. In the same context, Bhide (1993) argue that, as
investors have easy access to sell their stocks, then highly liquid stock markets may deteriorate
commitment of investors to put forth corporate control. On the other side, Acemoglu, Johnson,
and Robinson (2005) highlight that there is a dichotomy over social choices and distribution of
political power. They further explains that political institutions assign political power
according to rightful entitlement, while greater economic groups retain superior existing
political power. Despite having critics for the complexity of stock market dynamics, still it is
necessary to make use of existing framework of quantitative and qualitative factors for
analyzing these subtleties of stock market development, where quantitative represents the
economic factors and qualitative denotes the governance factors.
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The objective of present study is to explore the determinants of equity market
development particularly joint and isolated effects by economic and governance factors. The
former factors represents the quantitative factors as macroeconomic variables, whereas the later
covers the governance factors as qualitative variables. El Wassal (2013) presents the
framework to explore the leading determinants for development of stock markets and he
presents 04 sets of factors that determine the development of stock market, that is, demand and
supply factors alongwith institutional and economic factors. The investment in equities
becomes progressively more conducive to invest as the element of governance is improved
over period of time Perotti and Van Oijen (2001). Hence, the improvement of high quality of
governance can enhance the investment size in equities, which in turn lead towards equity
market development. So, the development of stock market is mainly affected by macro
economic and governance factors as depicted below:-
Figure 1.1 Diagrammatic view of relations among development of stock market, economic
and governance factors
Macro Economic
Factors
Governance Factors
A Panel of Stock Markets Development
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Although several empirical studies identifying the relationship between equity returns
and economic variables are concerning the developed stock markets, yet very few empirical
studies are conducted on emerging and frontier stock markets such as the work highlighted by
Sirucek (2012), Wongbangpo and Sharma (2002) and Bekaert, Harvey, and Lundblad (2001).
However, the present study analyzes the dual impact of economic and governance factors on
equity market development of all regions because it extensively believed that the improvement
of both the factors could boost the confidence in the investment of stock markets. So, the
development of good governance and improvement of economic indicators can augment the
magnetism of equity investment, which resultantly enhances the stock market development.
1.2. Research Gap and Motivation
The studies for finding the effect of economic variables on of development of equity
market are in abundance but discovering the joint impact of governance and economic factors
on equity market development are quite scarce in numbers. Particularly, studies on indirect
and cross effects of governance and economic factors are quite scarce in the existing literature.
Despite the fact that there is a dichotomy on the true determinants of equity market
development, yet this study would augment the database of factual position on qualitative as
well as quantitative factors of equity market development. In isolation, the studies are available
in the existing literature regarding impact of governance factors on the stock market
development but further research is being demanded on the comprehensive study of economic
and governance factors according different regions of the world equity markets. Moreover,
there are no ranking of countries for quantifying the effects of governance and economic factors
on the equity markets and vice versa effect of economy on the stock market which identify the
reverse effects as well. Likewise, there are total of five indices for measuring country risk, out
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of which, four measures are from International Country Risk Guide (ICRG) and one from
Investors’ guide Erb, Harvey, and Viskanta (1996). Nevertheless, they have not covered all
political risk factors and combine effect of economic variable was ignored. Levine and Zervos
(1996) analyze the empirical evidence of equity market development and economic growth in
long-run. They find that equity market development is robustly and positively correlated with
long-term growth of economy but they have ignored the dimension of governance factors.
However, the present study encompasses wide-ranging analysis covering direct and indirect
effects including cross effects of both the economic and governance factors on the panel of
developed, emerging and frontier equity markets.
The development of equity markets is a complex phenomenon and it requires a
comprehensive and rigorous approach to explore its factors encompassing both qualitative and
quantitative nature. Yartey (2008) has conducted the study on emerging market only but the
author ignored analysis of developed and frontier markets. So there is a dire need of having a
more comprehensive study which should not only encompasses the economic and governance
factors but also covers all three regions of developed, emerging and frontier markets as per the
region classification by FTSE (accessed on 30 October, 2015) as follows .
World Stock Markets
(70)
Developed Stock
Markets (25)
Frontier Stock
Markets (24)
Emerging Stock
Markets (21)
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Figure 1.2 Diagrammatic view of the world stock markets with all three regions of
developed, emerging and frontier markets
Emerging and Frontier economies are more prone to factors of institutional quality as
compared to developed economies. This is reflected by the weak institutions and low quality
of governance in implementation of rules and regulations in emerging and frontier markets.
The aim of the dissertation is to identify and classify the stock markets and regions, which are
more affected by the governance factors than economic factors and finding a quantitative scale
for the ranking of countries as per the effects of political and economic factors. Moreover,
efforts would be made to quantify the regional differences in affecting the economic and
governance factors on the development of equity market. On the other side, analysis would be
carried out to find vice versa effects of development of equity market on the economic and
governance factors.
This study has been conducted on three dimensions in which first dimension covers
world equity markets into three regions. The second dimension encompasses impacts of
economic factors. The third dimension comprises the impacts of governance factors on the
stock market development. Finally, the sensitivity analysis would be conducted for the direct
and indirect including cross effects of economic and governance factors on the stock market
development. The comprehensive nature of the study and latest methodologies will definitely
append the knowledge of the worthy readers. This study would fill the research gap by making
an in-depth study of developed, emerging and frontier equity markets to find the most affected
determinants of stock market development.
Although, the focus of existing studies cover the quantitative impact on the
development of stock market, yet the other dimension of the present study is on the qualitative
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7
aspects. The Panel study incorporates stock markets from 70 countries, which includes 25
Developed, 21 Emerging and 24 Frontier Equity Markets and the motivation of this study is as
follows:-
(a) To analyze the impact of economic factors on the stock market development.
(b) To analyze the impact of governance factors on the stock market development.
(c) To form composite factors of economic and governance factors according to
regional markets of developed, emerging and frontier regions.
(d) To analyze the indirect and joint impacts of governance and economic factors
on the stock market development.
(e) To conduct the sensitivity analysis of quantitative and qualitative factors and
find the array of countries, where the development of stock markets are more
prone to qualitative than quantitative factors.
(f) To find vice versa effects of governance and economic factors
1.3. Significance of the Study.
This study is different from other empirical studies for couple of reasons that need to
be ponder upon. Firstly, it focuses more on the governance factors than economic factors.
Secondly, it is aimed at all regions of the world, namely, developed (25 markets), emerging
(21 markets) and frontier (24 markets) region. Thirdly, it is intended to find indirect and cross
impacts of governance factors on the equity markets as well as on economic factors. Fourthly,
identifying region, which are having reverse impact of stock market development on economic
indicators. If we focus on the reality on ground that the efficacy of economic factors is quite
fragile when the institutions controlling and affecting the economy are quite weak. So, the
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governance factors plays pivotal role in the development of economic factors as well as equity
market development. Whereas, several studies are ignoring the pivotal factors which are
responsible for the development of stock markets as well.
Figure 1.3 Relational Impact of governance factors on development of stock market and
economic factors
However, this study has been aimed to identify the effects of governance factors on the
development of stock market as well as economic factors. In return, the effects of economic
factors on the development of stock market is also be explored in the study. Finally, a
quantified portion of economic and governance factors have been identified according to
regional standing.
1.4. Research Objectives.
Keeping in view the motivation and significance of the study, the major research
objectives are enumerated below:
(a) To develop indices for the composite factors of economic and governance
factors along with their cross indices according to regional markets of
developed, emerging and frontier regions.
(b) To analyze the impact of economic factors on the equity market development.
Governance Factors
Stock Market Development
Economic Factors
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(c) To investigate the impact of governance factors on the stock market
development.
(d) To analyze the combined effects of governance and economic factors on the
development of equity markets.
(e) To investigate the indirect effects of governance factors through economic
factors.
(f) To examine the cross effects of governance and economic factors on the
development of stock market
(g) To explore the inter-dependence of governance and economic factors?
1.5. Research Questions.
In order to meet the study objectives, the research questions are as follows:-
(a) What are the composite factors for economic and governance variables along
with their cross composite factors according to regional markets of developed,
emerging and frontier regions?
(b) What are the impacts of Economic variables on the equity market development
of World Stock Markets?
(c) What are the impacts of Governance variables on the equity market
development of World Stock Markets?
(d) What are the combined impacts of Governance and Economic variables on the
development of stock market concerning World Equity Markets?
(e) What are the indirect effects of governance factors through economic factors on
the development of stock markets?
(f) What are the cross effects of governance and economic factors on the
development of stock market?
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(g) How much is the inter-dependence of Governance and Economic factors with
each other?
Abovementioned research questions will be tested for all segments of world stock
markets separately, that is, all world (70), developed(25), emerging(21), and frontier(24)
markets. The estimation of the study models is based on these questions according to region
wise.
1.6. Contribution to Knowledge.
There are number of studies conducted for analyzing the impact of economic variables
on development of stock market but the literature is quite limited on the comprehensive study
of both quantitative and qualitative variables along with their indirect and cross effects
according to region wise. Moreover, this study has been planned to analyze the dual role of
governance factors on the returns of equity markets as well as economic factors. As there is a
scarcity of the literature concerning qualitative institutional variables, so this study will append
the knowledge of prevailing institutional works. Moreover, the formation composite indices
of each region may enhance the knowledge database of financial markets.
The rest of the study is presented in seven chapters. In Chapter 2, the literature review
of the study is presented and it also describes the Stock Markets as classified by Financial
Times Stock Exchange (FTSE) Group. Chapter 3 represents empirical model and hypotheses
development and Chapter 4 describes the methodology of Dynamic GMM Model. Chapter 5
encompasses the data & variables and Chapter 6 presents and discusses the empirical results of
the study. Finally, the summary and conclusions of the study are drawn in seventh Chapter.
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CHAPTER 2
REVIEW OF LITERATURE
2.1. Introduction
The literature review of the study has been presented in three sections. Section 2.2
describes literature on stock market development. Section 2.3 covers the literature on
Governance Indicators and Section 2.4 describes empirical methodologies concerning present
study. Finally, concluding remarks of the literature review are presented in Section 2.5.
2.2. Literature on Stock Market Development
In this section, the literature review of main determinants for the development of equity
markets is presented. It is quite imperative to clarify the main terms of stock markets right at
the outset of the study. In the literature of stock markets, its nomenclature of stock market is
referred by the names of equity or financial market. However, in this study, the term of stock
market is mostly referred as equity market. Calderon-Rossell (1991) is considered as a pioneer
to present a partial equilibrium model for the growth of equity market. Uptill now, this model
symbolizes the most somber endeavor to make the foundational financial theory for the
development of equity market. In the recent times, El Wassal (2013) identifies the structure
for the major determinants for the development of equity market and he suggests four factors
as demand factors, supply factors, economic and institutional factors. On the other side, Yartey
(2008) analyzes the economic and institutional determinants of equity market development by
employing the panel dataset of forty two emerging economies. He finds that economic factors
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are important determinants of stock market development. His results furthermore show that
governance, bureaucratic quality and law & order are vital determinants of equity market
development.
There is sufficient proof exists amid the previous couple of decades that securities exchanges
have turned out to be more corresponded with each other in regard of universal exchange and
capital streams Forbes and Chinn (2004). It shows that the cross-fringe exchange and capital
streams have improved the probability for the transmission of stuns started in a monetarily and
fiscally vital nation to the worldwide market. Bekaert, Hodrick, and Zhang (2005) inspect the
level of territorial and worldwide combination utilizing securities exchange returns in twenty
two nations amid the period beginning from January 1980 and finishing off with December
1998. They discover that the level of incorporation of stock returns in these twenty two nations
is not as awesome as was for the most part thought at the time. A compelling examination by
King and Wadhwani (1990) inspects the distinction between the relationship coefficients of the
stock exchange returns of Japan, UK, and US for the periods previously, then after the fact the
share trading system crash in 1987. The examination finds that there had been a sensational
increment in the coefficients of connections after the crash. The investigation additionally
contends that the stock returns in these business sectors fell together after the rate of securities
exchange crash in light of the fact that the private data set contains both eccentric and orderly
segments. Bayraktar (2014) studied the measurement of relative development level of stock
markets in the panel of 104 developed and developing countries. The author finds that effective
financial system is one of the main determinants of equity market development.
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2.3. Literature on Governance Indicators
Governance factors plays an important role in the management of country’s economy
and output of the economy is depended on the quality of institutions controlling them. If the
institutions of any country are weak, then long term survival of the economy quite fragile. To
the degree that open dispositions impact an open authority's appraisal of the social stigma
joined to a corrupt demonstration, open discernments about the nature of administration may
affect the level of defilement. There are number of endeavors have been done by different
agencies to develop governance indicators and recent endeavors at the World Bank, depicted
by Kaufmann (2007), to develop an arrangement of total Worldwide Governance Indicators
(WGI), give a wellspring of high-quality information on these open observations. They have
developed governance indicators in six categories. One of the factors in WGI set is the Absence
of Violence and Political Stability, which measures "view of the probability that the
administration will be toppled by illegal or rough means, including abusive behavior at home
and psychological warfare" Kaufmann, Kraay, and Mastruzzi (2007) p.3.
Another factor from the WGI is the Voice and Accountability, which measures
discernments concerning "the degree to which a nation's natives can take an interest in choosing
their legislature, and additionally opportunity of articulation, flexibility of affiliation, and free
media" (Kaufmann et al., 2007 p. 3). If there is no accountability, then level of corruption is
increased in manifold. An open authority in a nation whose natives trust these flexibilities are
solid and all around ensured is probably going to feel that a degenerate demonstration will be
immediately found and rebuffed.
The Rule of Law factor of WGI measures open impression of "the degree to which
specialists have trust in and submit to the tenets of society, and specifically the nature of
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agreement implementation, the courts and the police and also the probability of wrongdoing
and brutality" Kaufmann et al. (2007) p.4. This factor demonstrate the strength of law
enforcement and establishment of the strong judiciary system. Since enhanced level of peace
expands the likelihood of recognizing and rebuffing unlawful lease apportionments, a
recognition that the control of law is solid brings down the motivating forces to act deceptively.
2.4. Literature on Methodology
This study looks at the governance and economic determinants for the development
stock market in the panel data of developed, emerging and frontier markets. As we probably
aware that both governance and macroeconomic components are imperative in securities
exchange improvement. Garcia and Liu (1999) demonstrated that economic factors, like,
savings rate, stock market liquidity, real income and financial intermediary development are
the vital determinants for the development of equity market. On the other side, Pagano (1993)
demonstrates that governance components may impact the effective working of securities
exchanges. For instance, required revelation of dependable data about firms may improve
financial specialist cooperation, and controls that ingrain speculator's trust in dealers ought to
empower venture and exchanging the stock exchange. La Porta et al (1996).
Naceur, Ghazouani, and Omran (2007) examine the role of equity markets in the growth
of an economy and ponder on economic determinants that influence the development of stock
markets. They find that stock market liquidity, financial intermediary, stabilization and saving
rate variable are the vital determinants of equity market development. Beck and Levine (2004)
using a panel data set examine the effect of equity markets and banking sector on economic
growth by applying GMM techniques developed for dynamic panels. They find that banking
sector and equity markets effect positively on the growth of economy. Kanetsi (2015)
investigates the relationship between economic growth and equity market development in
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seven countries of Sub-Saharan African region and the study finds that the stock markets are
partially and in some countries marginally responsible for output growth.
Apergis, Artikis, and Eleftheriou (2011) examine the dynamic relationship between
economic factors and excess returns for the region of emerging markets by applying the panel
GMM estimator methodology. Their findings indicate that several economic factors has a
significant part in explaining excess returns of emerging economies. However, in the case for
the implementations in statistical terms, the economic models carries few significant
disadvantages. For instance, Clare and Thomas (1994) discover that that the factor structure
changes over time and not a robust to the formation of portfolio criteria. The studies that have
applied APT model discover that the same kinds of variables used by Chen, Roll, and Ross
(1986) are more country-specific and priced as well.
Regarding, panel data estimation of financial markets, Arellano and Bond (1991)
suggest applying an estimator of dynamic panel data based on the methodology of General
Methods of Moments that optimally exploits the linear moment restrictions implied by the
growth model of dynamic panel. The dynamic estimator of GMM is an instrumental variable
that applies with current and lagged values of all strictly exogenous regressors and lagged
values of all endogenous regressors as instruments.
2.5. The Stock Markets
The stock markets are likely to boost economic growth by enhancing the domestic
savings and increasing the quantity and the quality of Investment. Stock markets are also
refereed as equity or financial markets where a common person can invest his or her earnest
money without having pressure. Moreover, it is a platform for buying or selling equity of large
companies with any magnitude of investment. But this market is driven by certain factors which
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creates trading movements in certain direction depending upon the dynamics of that particular
country. At the national level, the factors of economy and governance issues play a vital role
in determining its trend, which may also differ from, developed to developing region. The
stock market is not developed at its own, rather the development in this sector is dependent
upon external factors. So, the phenomena of stock market development is not quite simple,
rather intricate in comprehending the true determinants of this market. At present, majority of
the researchers in this field are making predictive analysis based on the past movements and
trends in the economic conditions but governance issues are not being fully analyzed. As it is
clear that dynamics of developed region are quite dissimilar than developing region, so the
investors in that particular region are also behave differently depending upon the regional
dynamics. There may be a role for governance in elucidating the difference in magnitude, as
slight change in the policy might have significant effect. Baumol (1965) explains the economic
efficiency of assets and stock market returns. The conjecture of the author is that, if
management does not increase value of the firm value, then another economic agent may take
control and manage the firm efficiently and resultantly reap the benefits of highly efficient
firm.
2.6. Classification of Stock Markets
The stock markets play a vital role in the economics of any country which are further
classified into Developed, Emerging or Frontier Markets according to their size and operations.
Financial Times Stock Exchange (FTSE) Group annually publishes the results of country
classification by a process in which stock markets are classified as either Developed, Emerging
or Frontier.
The developed equity markets contains the largest and highly industrialized economies.
The economic systems of developed countries are well established, they are politically stable
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and the rule of law is well deep-rooted. Developed markets are typically considered as the
safest investment terminuses, but their economic growth rates frequently follows those
countries, which are in an earlier development stage.
Emerging markets face swift development and often depict extremely high economic
growth. This high economic growth can occasionally translate into investment returns that are
higher to those available in developed markets. Nevertheless, emerging equity markets are
more riskier having political uncertainty with high fluctuations.
Frontier markets denote "the next wave" of investment terminuses. These equity
markets are usually either smaller than emerging markets, or having constraints on foreign
investment. Although frontier markets can be extremely risky and suffer from low liquidity,
yet they also offer above-average returns. Frontier equity markets are also not well correlated
with other markets.
The country classification method has been adopted for more than a decade, and with
the passage of time, it has developed into a transparent and unbiased mechanism of classifying
markets to meet the prerequisites of institutional investors. The country classification of world
equity markets duly classified by FTSE as on 30 September 2015 is shown in the table 2.1.
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Table 2.1
Country Classification of World Equity Markets by FTSE as on 30 September 2015
2.7. Concluding remarks of Literature Review
This section is concluded on the observation that there are number of studies for
economic and governance factors in isolation but there are very few empirical studies that
conduct comprehensive analysis of both economic and governance factors according to the
combined and isolated panels of developed, emerging and frontier markets. Moreover, there
is no quantification of regional variances in affecting the economic and governance factors on
development of equity market. As there is a scarcity of the literature concerning qualitative
institutional variables, so this study will append the knowledge of qualitative works.
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Furthermore, this study has been planned to analyze the dual role of governance factors on the
returns of equity markets as well as economic factors, which will fairly contribute the existing
literature.
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CHAPTER 3
EMPIRICAL MODEL AND HYPOTHESES DEVELOPMENT
3.1. INTRODUCTION
This section encompasses the Empirical Model and Hypotheses Development of the
study. In the former section, the proposed model has been linked up with existing literature
and further explanation of the model and its variable is presented in diagrammatically and
equation wise as well. However, in the later part of this section, the main and subsidiary
hypotheses of the study are presented in chronological order.
3.2. Theoretical Framework
Economic theory concerning the relationship between equity market development and
economic variables is quite intricate. Since 1980s, there have been several studies to identify
factors in the arbitrage pricing theory (APT) model with economic variables affecting returns
of assets and one of leading studies is conducted by Chen et al. (1986). Later on, Calderon-
Rossell (1991) presented a basic model which is still considered as the basis to analyze
determinants of equity market development.
3.2.1. Basic Caldron-Rossell Model
Calderon-Rossell (1991) analyzed a behavioral and structural approach to equity
market development in which equity market liquidity and economic growth are considered as
the vital determinants of equity market development. It is hypothesized in the model that equity
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market development is derived from the economic development, which is further calculated by
market liquidity and output growth. The Basic Calderon-Rossell model states that equity
market capitalization is a function of the number and value of listed companies. In this model,
liquidity of equity market and economic growth are referred as the vital determinants for the
development of equity market. Therefore, the market capitalization is shown as appended below:
Y = P*V (1)
Where:
P = Number of listed firms in the equity market;
Y = Market capitalization of stocks; and
V = Mean price of listed companies.
The model can be presented formally as follows:
Y = P*V = Y (G,T) (2)
V = V (G,P) (3)
P = P (T,V) (4)
The exogenous variable T denotes the turnover ratio and variable G denotes per capita Gross
National Product (GNP). The endogenous variables are P, V, and M. As it is evident that
Calderon-Rossell model signifies as a set of interrelated functions. So, the equation (3) and (4)
can expressed in terms of growth rates as follows:-
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Now by combining equations (5) and (6) with equation (2), the result would be as follow:-:
After factoring, the equation (7) becomes
The model specification in Equation (8) can be articulated as the reduced form behavioral
model:
Where,
)10).......(( 111
)11).......(( 222
Equation (8) illustrates the effects of stock market liquidity (T) and economic growth
(G) on equity market development (Y).
To validate this model, Calderon-Rossell incorporated data from the annual observations of
forty-two countries from the world’s major stock markets. The analysis depicts that equity
)5...(....................21 LogTLogGLogV
)6..(....................21 LogTLogGLogP
)7.....()( 2121 LogTLogGLogTLogGPVLogLogY
)8.(..........)()( 2211 LogTLogGLogY
)9..(..............................21 LogTLogGLogY
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economic growth and market liquidity are the vital determinants for the growth of stock
markets.
3.2.2. Augmented Caldron-Rossell Model
Calderon-Rossell (1991) developed a comprehensive partial equilibrium model which
served as a conceptual underpinning of financial theory for development of equity markets.
The study identified economic growth and stock market liquidity as the vital determinants for
the growth of equity markets. Garcia and Liu (1999) while analyzing the data of Asian and
Latin American markets have found that economic factors such as growth rate in financial
intermediary sector development, domestic investment and gross domestic product are vital
elements for the growth of equity market. Furthermore, La Porta, Lopez-de-Silanes, Shleifer,
and Vishny (1997) argued that legal origin have significant impact on the equity market
development.
In order to capture the role of qualitative variables (like governance, legal system and
accountability), an additional element of governance indicator is to be introduced into the Basic
model. Now the econometric model would be as follows:
Y = f ( Yt-1, E, G )……………………………………………………(12)
Where: Y –Equity market capitalization in %age of GDP;
Y t-1 –Lag dependent variable;
E –Vector of economic variables (inflation rate, income level and banking
sector development);
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G – Vector of Governance variables (which include: rule of law, govt
effectiveness etc).
The econometric model as mentioned above is then transformed to logarithmic model
as we are interested in elasticity (growth rate) of equity markets as shown below:
The Augmented model of Calderon-Rossell is based on undermentioned assumptions:
(a) Generally, Investors prefer doing business in a safe economy with strong
fundamentals.
(b) Investors, in general, tends to make their investments in a country with having
better political stability.
By applying logarithms on both sides of the equation, the abovementioned model would
become as follows:
Log Yt = Log A + δ LogYt -1 + β LogEt +ϖ LogGt + µt …………………(14)
Where µ ~ NID ( 0 , σ2 )
Now by assuming natural logarithms on both sides of equation (14), we will get
)13.(..........1 tt
tttt GEAYY
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LogY = LnY, Log A = α, LogYt−1 = Ln Yt-1, LogE = LnE, LogG = LnG
Hence, the general econometric model applied in the present study becomes as follows:
LnYt = α + δ LnYt-1 + β LnEt +ϖ LnGt + µt……………………………………(15)
The equation (15) represents an Augmented model of Calderon-Rossell, which
is the theoretical foundation of the present study. It is combination of basic determinants
of equity market development and governance factors. In particular, we will examine
the role of governance and economic factors in explaining stock market development
and further endeavors would be made to find the cross effects of governance and
economic factors. Due to the dynamic characteristics of the data, the undermentioned
regression would be estimated:-
Where, Yit is the matrix of equity market capitalization in proportion to GDP of
particular country i in year t , αi is the unobserved country wide fixed effect, and εit denotes
usual white noise. Eit is a vector of economic variables and G it is a vector of governance
factors. The study also incorporates dependent variable (Yit-1) in lag form as one of the right
hand side variables because it is believed that development of equity market is a dynamic
concept.
)16.(..........1 ititjitkitiit GEYY
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3.2.3. Diagrammatical Model of the Study
The determinants of stock market development include both the quantitative and
qualitative variables. The quantitative variables encompass the macroeconomic factors,
whereas the qualitative variables consist of governance factors and world governance
indicators. These variables can predict the behavior of the stock market which is shown in a
diagrammatic view as below.
Figure 3.1 Diagrammatic view of the main model depicting relations among variables of
development of stock market, economic and governance factors
Stock Market
Development
-Market capitalization as %
of GDP
Economic Factors
GDP per capita growth rate
Gross Dom Savings as %of GDP
Real Interest rate
Annual Inflation Rate
FDI as %age of GDP
Broad Money as % of GDP
Trade as %age of GDP
Credit by financial sec % of GDP
World Governace Factors
Voice and Accountability
Political Stability and Absence of Violence
Government Effectiveness
Regulatory Quality
Rule of Law
Control of Corruption
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3.2.4. Empirical Models and Hypothesis Development
Keeping in view the aforementioned theoretical and diagrammatical models, five
empirical models have been formulated that are to be tested for meeting our research objectives.
Each model is to encompass the diagrammatical model, mathematical equation and its related
Hypotheses. Then in the estimation phase, each model is to be tested for the panel data of
world and all of the three regions, developed, emerging and frontier markets. The first model
is as follows:
4.2.4.1. Empirical Model No 1: The impact of economic variables on stock market
development
The equation for testing the model-1 would be:
Economic FactorsStock Market Development
)17.(..........1 ititkitiit EYY
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Where, Yit is the matrix of stock market capitalization relative to GDP of
particular country i in year t , αi is the unobserved country specific fixed effect,
and εit is the usual white noise. Eit is a vector of macroeconomic variables
4.2.4.2. Hypothesis Set for Model-1
The Null Hypotheses for the Model-1 with exogenous variables as 08 Economic
Variables and one composite economic variable are appended below:-
H011: GDP growth rate do not affect the development of stock markets.
H012: Annual Inflation Rate do not affect the development of stock markets.
H013: Real Interest rate do not affect the development of stock markets.
H014: Domestic credit to private sector as %age of GDP do not affect the development
of stock markets.
H015: Gross Dom Savings as % GDP do not affect the development of stock markets.
H016: Trade as %age of GDP do not affect the development of stock markets.
H017: FDI as %age of GDP do not affect the development of stock markets.
H018: Current Account Balance % of GDP do not affect the development of stock
markets.
4.2.4.3. Empirical Model No 2: The impact of governance variables on stock market
development
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The equation for testing the model-2 would be:
Where, Yit is the matrix of financial market capitalization relative to GDP of particular
country i in year t , αi is the unobserved country specific fixed effect, and εit is the
usual white noise. G it is a vector of governance factors.
3.2.4.4. Hypothesis Set for Model-2
The Null Hypotheses for the Model-2 with exogenous variables as 06
Governance Variables are appended below:-
H021: Voice and Accountability do not affect the development of stock markets.
H022: Political Stability and Absence of Violence do not affect the development of
stock markets.
H023: Government Effectiveness do not affect the development of stock markets.
H024: Regulatory Quality do not affect the development of stock markets.
H025: Rule of Law do not affect the development of stock markets.
H026: Control of Corruption do not affect the development of stock markets.
Governance FactorsStock Market Development
)18.(..........1 ititjitiit GYY
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3.2.4.5. Empirical Model No 3: This model measures the joint impact of economic and
governance factors on the stock market development
The equation for testing the model-1 would be:
Where, in the above equation, Yit is the matrix of stock market capitalization relative
to GDP of particular country i in year t , αi is the unobserved country specific fixed
effect, and εit is the usual white noise. E it is a vector of macroeconomic variables and
G it is a vector of governance factors.
3.2.4.6. Hypothesis Set for Model-3
The Null Hypotheses for the Model-3 with exogenous variables as 08
Economic Variables and 06 Governance Variables with are appended below:-
Economic Factors
+
Governance Factor
Stock Market Development
)19.(..........1 ititjitkitiit GEYY
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31
H011: GDP growth rate do not affect the development of stock markets.
H012: Annual Inflation Rate do not affect the development of stock markets.
H013: Real Interest rate do not affect the development of stock markets.
H014: Domestic credit to private sector as %age of GDP do not affect the development
of stock markets.
H015: Gross Dom Savings as % GDP do not affect the development of stock markets.
H016: Trade as %age of GDP do not affect the development of stock markets.
H017: FDI as %age of GDP do not affect the development of stock markets.
H018: Current Account Balance % of GDP do not affect the development of stock
markets.
H021: Voice and Accountability do not affect the development of stock markets.
H022: Political Stability and Absence of Violence do not affect the development of
stock markets.
H023: Government Effectiveness do not affect the development of stock markets.
H024: Regulatory Quality do not affect the development of stock markets.
H025: Rule of Law do not affect the development of stock markets.
H026: Control of Corruption do not affect the development of stock markets.
3.2.4.7. Empirical Model No 4: This model measures the channel effects of governance
factors on stock market development and further the indirect effect governance variables
through economic variables.
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The set of equations for testing the model-4 would be:
Where, in the above equation, Yit is the matrix of stock market capitalization relative
to GDP of particular country i in year t , αi is the unobserved country specific fixed
effect, and εit is the usual white noise. Eco it is a matrix of composite macroeconomic
variables and Gov it is a matrix of composite of governance factors.
Now for estimating the direct effect of Governance(Gov) on Stock Markey Development(Y)
and Indirect effect of Governance(Gov) factors on the development of Stock Markey (Y)
through Economic Factors(Eco), the following methodology is adopted:-
Governance Factors
Economic Factors
Stock Market Development
)20...(*1 itititkitjitkitiit GovEcoGovEcoYY
)21...(ititktit GovEco
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33
First of all, above mentioned equations No 20 and 21 are estimated as a System and then
estimated values are placed in the equations and following steps are adopted:-
The expression is to be tested for Wald test for its significance
Where,
= Direct effect of Governance factors (Eco) on the development of stock market (Y)
= Indirect effect of Governance factors (Gov) on the development of stock market
(Y) through Economic Factors (Eco).
3.2.4.8. Hypothesis Set for Model-4
To test the significance of values in model-4, a set of hypotheses is formed. Therefore,
the Null Hypotheses for the Model-4 with exogenous variables as 08 Economic Variables and
06 Governance Variables are appended below:-
H011: A composite of Economic variables do not affect the development of stock
markets.
H012: A composite of Governance variables do not affect the development of stock
markets.
H013: There is no direct effect of composite Governance variables on the development
of stock markets.
)22(...........).().(
GovY
).(
.
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H014: There is no indirect effect of composite Governance variables through
Economic variables on the development of stock markets.
.
3.2.4.9. Empirical Model No 5: This model measures the cross effects of governance
factors on economic variables and stock market development viz-a-viz impact of economic
variables on stock market development.
The set of equation for testing the model-5 would be:
Where, in the above equation, Yit is the matrix of stock market capitalization relative
to GDP of particular country i in year t , αi is the unobserved country specific fixed
Governance Factors
Economic Factors
Stock Market Development
)23...(*1 itititkitjitkitiit GovEcoGovEcoYY
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35
effect, and εit is the usual white noise. Eco it is a composite of macroeconomic variables
and Gov it is a composite of governance factors.
= Direct effect of Economic factors(Eco) on the development of stock market (Y)
= Direct effect of Governance factors(Gov) on the development of stock market (Y)
= Cross effects of Governance factors(Gov) and Economic Factors (Eco).on the
development of stock market (Y)
3.2.4.10. Hypothesis Set for Model-5
The Null Hypotheses for the Model-5 with exogenous variables as 08
Economic Variables and 06 Governance Variables with are appended below:-
H011: A composite of Economic variables do not affect the development of stock
markets.
H012: A composite of Governance variables do not affect the development of stock
markets.
H013: A cross composite of Economic and Governance variables do not affect the
development of stock markets.
H014: A composite of Economic variables do not affect the composite of Governance
variables.
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36
CHAPTER 4
METHODOLOGY
4.1. Introduction
In order to combat the challenges of panel data analysis, the study has adopted two
pronged methodology to empirically analyze the panel data. In the first part, the statistical
analysis is carried out and later part covers the econometric analysis of panel observations.
4.2. Statistical Methodology
As the scope of our study is to analyze 70 countries of world stock markets, so it is
imperative to know their trends and results of basic statistical technique. The list of statistical
technique is appended below:-
(i) Basic Statistics of Stock Market Development (Dependent Variable)
(ii) Basic Statistics of Economic Factors (Predictor Variable)
(iii) Basic Statistics of Governance Factors (Predictor Variable)
(iv) Correlation among Economic Factors (Predictor Variable)
(v) Correlation among Governance Factors (Predictor Variable)
(vi) Scatter Plots of Stock Market Development and Economic Composite Variable
with their respective Distribution and Kernel Regressions.
(vii) Scatter Plots of Stock Market Development and Governance Composite
Variable with their respective Distribution and Kernel Regressions.
(viii) Scatter Plots of Economic and Governance Composite Variables with their
respective Distribution and Kernel Regressions.
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37
4.3. Econometric Methodology
The study incorporates panel dataset with time period of 20 years (t) alongwith four
country groups of 24 (developed), 21 (emerging), 24 (frontier) and 70 (world) equity markets.
Keeping in view, the complexities and comprehensive nature of data, several techniques have
been studied and appropriate econometric methodology has been adopted in this study. Given
the panel nature of our study, the estimation of proposed models in dynamic panel data can
have numerous problems due to following reasons:
(a) Inconsistent and biased estimators due to time invariant country fixed
characteristics.
(b) Heteroscedasticity and serial correlation may lead to the idiosyncratic
disturbances.
(c) Possibility of endogeneity of regressors would be there. Consequently, the
orthogonality condition between regressors and error term would not be true.
(d) Perfect instrumental variable does not exist that can satisfy the requirement of
strict exogeneity.
(e) Inclusion of lagged dependent variable as an explanatory variable for dynamic
process creates autocorrelation, which biases estimator upward by applying Ordinary
Least Squares.
To resolve abovementioned problems, there would be mainly one econometric
methodology, that is, Dynamic General Methods of Moments (GMM), and the same has been
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incorporated in this study to find the nature of relationship among the panel data sets of Equity
Market Development, Economic and Governance Factors. The details of this econometric
methodology is enumerated in the subsequent paragraphs.
4.3.1. Dynamic Panel Generalized Methods of Moments (GMM)
As the nature of our dataset is in panel form of different countries of three regions,
which is, developed, emerging and frontier across number of years, so this study incorporates
panel data methods for the estimation of regression models. Entire relationships to be
premeditated can be described by the joint endogeneity of included variables of Economic and
Governance factors. It means that most exogenous variables are either have a two-way causal
relationship or simultaneously determined with the dependent variable. The possibilities for
the unobserved country specific effects could be present.
The econometric technique of Dynamic General Methods of Moments (GMM) has been
incorporated in estimating the regression models of the present study. For estimating dynamic
panel models, there are two methods for estimating panel models, first one is the Arellano-
Bond dynamic panel, in which fixed or individual effects are taken by differencing the data.
The second method is the approach of Arellano-Bovver, which allows the fixed effects through
orthogonal deviations. The relationships to be studied can be depicted by the joint endogeneity
of most involved variables. Explicitly, the majority of exogenous variables in the model are
either have a two-way causal relationship with each other or simultaneously determined with
the dependent variable.
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39
Arellano and Bond (1991) suggest an estimator of dynamic panel data based on the
methodology of Generalized Method of Moments (GMM) that is implied by the dynamic
panel growth model and optimally exploits the linear moment restrictions. The dynamic
GMM estimator is an instrumental variable estimator, which uses lagged values of all
endogenous regressors with current and lagged values of all strictly exogenous regressors as
instruments. The dynamic GMM estimator is given as follows:−
YZZAXXZZAX NN
1
Where in the model
is the vector of endogenous and exogenous coefficient estimates
X and Y are the vectors of the first differences of all the exogenous variables,
Z is the vector of instruments as specified in GMM and
NA is a vector applied to weigh the instruments.
In the present study, the exogenous variables are economic and governance variables and stock
market development as dependent variable. The instruments are taken as the dynamic
regressors of GMM as presented by Arellano and Bond with lagged values of stock market
development alongwith lag values of the exogenous variables as specified in the relevant model
(Arellano and Bond (1991)) . The care has been taken for the over-identifying restrictions on
the instruments of GMM model through Sargan Test.
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CHAPTER 5
DATA AND VARIABLES
5.1. Introduction
The determinants of equity market development constitute both the quantitative and
qualitative variables. The former encompass the macroeconomic factors, whereas the later
consist of governance factors. In the literature, there are several Macroeconomic factors that
have been taken for the of Stock Market development but in our study, we are focusing on GDP
per capita, Savings, Macroeconomic Stability, Liquidity of Stock Market, and Private Capital
Flows. On the qualitative variable sides, we have taken Governance factors comprising
Political Stability & Absence of Violence/Terrorism, Voice & Accountability, Government
Effectiveness, Regulatory Quality, Control of Corruption and Rule of Law.
5.2. Dependent Variable : Stock Market Development
The dependent variable of our study is stock market development, which is measured
by trade and market value of listed share in the equity market. In our study, stock market
development has been measured by using market capitalization as a percentage of GDP, which
equals to the market value of listed shares divided by GDP. The postulation behind this
measure is that the size of market is correlated positively with the ability to mobilize capital
and diversify risk on an economy-wide basis. So, in the subsequent analytical part, the
development of stock market would be determined by the market capitalization as percentage
of GDP of that very country.
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5.3. The Economic Factors
Stock markets, like other financial institutions, intermediate savings to investment
projects. Usually, larger the savings leads towards higher volume of capital flows in the stock
market. Macroeconomic stability plays an important role for the development of stock markets
in all regions of the world. Thus, we expect from our study that economic factors to be
important determinants in the development of stock market. In our study, the following
components are used to produce the effects of Economic factors on stock market development:-
(a) Income Level: Income level is calculated by using the data of GDP per
capita in US dollars. As per the hypothesis of demand driven, the expansion in
economy would generate new demands for the financial activities. Resultantly,
the escalated demand will pressurize the establishment of larger and highly
sophisticated financial institutions to meet the fresh demands.
Income Level= GDP per capita growth
(b) Savings: Equity markets play an intermediary role converting savings to
investment projects. Generally, the higher savings produce the larger capital
flows through the equity market. The savings is considered as a vital
determinant of stock market development, which is determined by gross
domestic savings as %age of GDP.
Savings= Gross domestic savings as %age of GDP
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(c) Stock Market Liquidity: The stock market liquidity is measured by using
the Stock value traded as a %age of GDP. This ratio does not measure directly
as how effortlessly investors can trade their shares, rather it measures the value
of equity transactions in comparison with economy size. Nonetheless, it
definitely measures the degree of trading in comparison with economy size. So,
it depicts the liquidity of stock market in an economy as it is highlighted by
Levine and Zervos (1998).
Stock Market Liquidity = Stock value traded as a %age of GDP
(d) Macroeconomic Stability: The higher stability in macroeconomic factors
creates higher incentive to investors and companies for participating in the
equity market. Moreover, profitability of companies becomes quite volatile by
fluctuations in exchange rate, fiscal and monetary policies. So, developed
equity markets have stability in their macroeconomic factors. In this study, we
incorporate two measures for measuring the stability in macroeconomic factors,
that is, real interest rate and inflation rate because of their significance in the
literature as used by Garcia and Liu (1999).
Macroeconomic Stability = Inflation rate and Real interest rate
(e) Capital Flows. Errunza (1983) stipulates that inflows of foreign capital on the
equity market development creates broader impacts than initial flows and higher
investor participation. He further suggests that foreign investment is correlated
with governance factors and fair practices in trading of stocks. In this study,
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capital flows is determined by incorporating the indicator of foreign direct
investment as a %age of GDP.
Capital Flows = Foreign direct investment as a %age of GDP
(f) Trade Openness. As per the studies of (Fischer (2003)), trade openness is
referred as the monetary relationship among nations in exchange of goods and
services. In this process, the countries make certain financial transaction for
import and export of goods services to get the benefit of cross border trade. It
is determined by using the Trade in the country as a %age of GDP.
Trade Openness = Trade as a %age of GDP
Additionally, a composite variable of under studied economic factors is formed through
Principal Component Analysis (PCA) for analyzing the combined effect of economic factors.
Additionally, this variable is used to measure the cross effects and reverse impacts of stock
market development.
5.4. The Governance Factors
The current study proposes that another set of factors which may unfavorably influence
stock market development is governance risk (Diamonte, Liew, and Stevens (1996); Erb et al.
(1996); Perotti and Van Oijen (2001)). The idea of governance contains political unsteadiness,
as well as outer clash; defilement in government; military in legislative issues; lawfulness
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custom; racial and national pressures; political psychological oppression; common war chance;
nature of organization. All the more correctly, lawful and political instability about future
mediation of political strengths in the administration of monetary action through
nationalizations and seizures of private investment, is probably going to dampen the equity
markets.
The marvel of governance factors envelops administration, legitimate framework and
responsibility. Low governance exhibits the presence of higher quality foundations. Three
methods of institutional quality are utilized as a part of the writing. The first is the nature of
administration, including, defilement, political rights, open division productivity, and
administrative weights. The second is the legitimate assurance of private property and law
authorization. The third is responsibility and the points of confinement put on the official and
political pioneers (Edison, 2003).
The Worldwide Governance Indicators (WGI) cover more than two hundred nations
and domains, measuring six measurements of administration beginning in 1996: Political
Stability and Absence of Violence, Government Effectiveness, Voice and Accountability,
Regulatory Quality, Control of Corruption and Rule of Law. The total pointers depend on a
few hundred individual hidden factors, derived from a wide assortment of existing information
sources. The information mirror the perspectives on administration of review respondents and
open, private, and NGO segment specialists around the world. The WGI construct measures
of governance corresponding to six dimensions as appended below:
(i) Political Stability and Absence of Violence
(ii) Government Effectiveness
(iii) Voice and Accountability
(iv) Regulatory Quality
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45
(v) Control of Corruption
(vi) Rule of Law
Finally, a composite index of governance factors is formed through Principal
Component Analysis (PCA) for analyzing the combined effects of governance and economic
factors. Additionally, these variables are used to measure the cross effects and reverse impacts
of stock market development.
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46
5.5. Predicted Variable Signs.
The expected signs of the variables under studied are depicted below:-
Table 6.1
Summary of the Variables long with their Predicted Signs
Characteristics
Variables
Expected
Signs
Stock Market
Development
- Stock Market Size Y= Market capitalization of listed dom cos (% of
GDP) -
- Stock Market Liquidity S3= Stocks traded, total value (% of GDP) -
Economic Factors
- GDP per capita E1= GDP per capita growth (annual %) +ve
- Inflation rate E2= Inflation, consumer prices (annual %) -ve
- Real interest rate E3= Real interest rate (%) -ve
- Domestic credit to private
sector
E4= Domestic credit to private sector by banks (%
of GDP)
+ve /-ve
- Gross domestic savings E5= Gross domestic savings (% of GDP) +ve /-ve
- Trade E6= Trade (% of GDP) +ve
- Foreign direct investment, E7= Foreign direct investment, net inflows (% of
GDP) +ve
- Current account balance E8= Current account balance (% of GDP) +ve
Governance Factors
- Control of Corruption G1= Control of Corruption +ve
- Government Effectiveness G2= Government Effectiveness +ve
- Political Stability and
Absence of Violence
G3= Political Stability and Absence of
Violence/Terrorism
+ve
- Regulatory Quality G4= Regulatory Quality +ve
- Rule of Law G5= Rule of Law +ve
- Voice and Accountability G6= Voice and Accountability +ve
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47
5.6. Data Analysis Software.
The challenge of equating data would be managed by using database software like MS
Access and further analysis would be done by using MS Excel. The following software has
been used to analyze the data:-
(a) Econometric Views
(b) STATA
(c) SPSS
(d) MS Excel
(e) MS Access
5.7. Data Period and Classification.
The period of data under study is 20 years starting from 1996 to 2015. The study has
applied annual panel data of 70 countries as classified by Financial Times Stock Exchange
(FTSE). On an annual basis, FTSE Group publishes the results of country classification
through a refine process for classifying the world equity markets as Developed, Emerging and
Frontier Markets. So, our financial data would be grouped into three groups as developed,
emerging and frontier groups. Finally, the data of the variables have been collected from the
official sites of World Bank, IMF and FTSE group.
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CHAPTER 6
EMPIRICAL ANALYSIS
6.1. Introduction
The study is conducted on panel data of all three stock markets of the world, that is,
developed, emerging and frontier financial markets. In the previous chapters, the empirical
model and methodology alongwith data and variable is presented. In this chapter, the presented
data is going to be tested in all possible known directions for each equity market of the world.
To further elucidate, the empirical analysis would be segmented into Statistical and
Econometric Analysis.
6.2. Statistical Analysis
In this part, the preliminary statistics and correlation matrix of all three regional
financial markets is studied. That is, the developed(25), emerging(21) and frontier(24)
financial markets are tested separately and at the end a comparative analysis would be
presented.
6.2.1. Developed Financial Market
In the developed region, there are 25 countries as classified by FTSE and these will be
analyzed for basic statistics with correlation matrix.
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49
Figure 6.1
No of Listed Companies of Developed Financial Markets (Average No from 1996 to 2015)
As it is quite evident from the abovementioned graph that United States has the highest number
of listed companies followed by Canada in the category of developed financial markets.
Whereas, at the lower level, Ireland has the least number of listed companies in its group of
countries.
0 1,000 2,000 3,000 4,000 5,000 6,000
United StatesCanada
SpainJapan
United KingdomAustralia
Korea, Rep.Hong Kong SAR, China
FranceGermany
IsraelSingapore
ItalyGreece
SwedenSwitzerland
DenmarkNetherlands
BelgiumNorway
New ZealandFinlandAustria
PortugalIreland
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50
Figure 6.2
Market Capitalization of Developed Financial Markets in USD Billions (Average from
1996 to 2015)
This chart shows the average market capitalization of each financial market in the
developed region that depicts the market value of stocks in each country. According to the
above depicted graph, United States of America has the highest market capitalization of USD
16,400 Billion during the last twenty years.
Basic Statistics for the Stock Market Development as Market Capitalization %age of
GDP for the panel data of 25 developed countries from 1996 to 2015 are shown in the following
Figures 6.3
0.000
4,000.000
8,000.000
12,000.000
16,000.000
20,000.000
Austr
alia
Austr
ia
Belg
ium
Canada
Denm
ark
Fin
land
Fra
nce
Germ
an
y
Gre
ece
Hong K
ong S
AR
, C
hin
a
Irela
nd
Isra
el
Italy
Japan
Kore
a,
Rep.
Neth
erl
ands
New
Zeala
nd
Norw
ay
Port
uga
l
Sin
gapo
re
Spain
Sw
eden
Sw
itzerl
and
Unit
ed K
ingdom
Unit
ed S
tate
s
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51
Figure 6.3
Basic Statistics of Stock Market Development of Developed Financial Markets
0
10
20
30
40
50
60
70
80
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0
Series: LNYSample 1996 2015Observations 485
Mean 4.280667Median 4.276242Maximum 7.025276Minimum 1.584224Std. Dev. 0.760649Skewness 0.098865Kurtosis 4.022460
Jarque-Bera 21.91638Probability 0.000017
The figure shows that the preliminary statistics on the market capitalization as
percentage of GDP. The average capitalization during the whole study period comes to 4.2807
with the standard deviation of 0.76. The Jarque Bera test rejects normality of the stock market
development.
The preliminary statistics of Economic variables for the panel data of all 25 developed
countries from 1996 to 2015 is shown in the following table 6.1
Table 6.1
Preliminary Statistics of Economic Variable of Developed Equity Markets
Statistics E1 E2 E3 E4 E5 E6 E7 E8
Mean 1.633594 1.905921 4.227565 105.2107 26.37494 97.18841 5.805275 1.992301
Median 1.677344 1.921281 3.932860 101.3587 25.72429 69.83773 2.440012 1.467750
Maximum 24.66657 11.27662 13.34727 233.2110 54.28837 442.6200 87.44259 26.05861
Minimum -8.997955 -4.479938 -5.634759 29.53919 8.330869 18.34896 -5.670905 -14.47630
Std. Dev. 2.832779 1.611234 2.802013 38.33464 8.218851 83.93756 9.646420 6.533388
Skewness 0.757940 0.521129 0.334021 0.445454 0.973990 2.513723 3.552466 0.697199
Kurtosis 13.06886 6.983954 3.783508 2.856114 4.828405 8.972572 21.20060 3.991862
Jarque-Bera 2159.997 353.2957 20.09899 15.74544 148.7018 1269.726 7809.805 57.22070
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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52
The table shows the preliminary statistics of eight economic variables. The average
during the whole study period ranges from 0.10 to 0.51. The Jarque Bera test rejects normality
of the economic variables.
The preliminary statistics of governance variables are presented in the table 6.2
Table 6.2
Preliminary Statistics of Governance Variables of Developed Equity Markets
Statistics G1 G2 G3 G4 G5 G6
Mean 89.75072 90.98496 76.62031 90.14083 90.41419 86.74615
Median 93.17073 93.17073 80.84356 93.13725 93.26923 92.21296
Maximum 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
Minimum 51.44231 61.65049 7.109005 61.76471 60.28708 35.57692
Std. Dev. 10.41610 8.014274 20.37435 8.522897 8.927048 13.83109
Skewness -1.582314 -1.401515 -1.376391 -0.937198 -1.379488 -1.443880
Kurtosis 4.932210 4.692988 4.926033 3.010753 4.335455 4.536303
Jarque-Bera 286.4232 223.3998 235.1544 73.19736 195.7372 222.9039
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
The table shows the preliminary statistics of six governance variables of developed markets.
The average during the whole study period ranges from 76.62 to 90.98. The Jarque Bera test
rejects normality of the economic variables.
The Correlation Matrix of Economic variables for the panel data of all 25 developed
countries from 1996 to 2015 is shown in the following table 6.3
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53
Table 6.3
Correlation Matrix of Economic Variables of Developed Equity Markets
Correlation E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 0.010197 1.000000
E3 -0.021103 -0.029653 1.000000
E4 -0.194476 -0.122716 -0.137786 1.000000
E5 0.302341 -0.112108 -0.074511 0.123852 1.000000
E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000
E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000
E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000
The Correlation Matrix of Governance Factors for the panel data of all 25 developed
countries from 1996 to 2015 is shown in the following table 6.4
Table 6.4
Correlation Matrix of Governance Variables of Developed Equity Markets
Correlation G1 G2 G3 G4 G5 G6
G1 1.000000
G2 0.920087 1.000000
G3 0.595142 0.549431 1.000000
G4 0.847346 0.824036 0.493675 1.000000
G5 0.912385 0.900648 0.627760 0.769201 1.000000
G6 0.440133 0.364032 0.425705 0.295824 0.560548 1.000000
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54
6.2.2. Emerging Financial Markets
In the emerging financial markets, there are 21 countries as classified by FTSE Group
and these are to be analyzed for basic statistics with correlation matrix. First of all, the No of
Listed Companies alongwith their Market capitalization of Emerging Financial Markets is
appended below:
Figure 6.4
No of Listed Companies of Emerging Financial Markets (Average No from 1996 to 2015)
The abovementioned graph depicts that India has the highest number of listed companies
followed by China in the category of developed financial markets. Whereas, at the lower level,
Czech Republic has the least number of listed companies in its group of countries.
0 1,000 2,000 3,000 4,000 5,000 6,000
IndiaChina
MalaysiaPakistan
Egypt, Arab Rep.Thailand
South AfricaPolandBrazil
IndonesiaRussian Federation
TurkeyChile
PhilippinesPeru
MexicoColombia
United Arab EmiratesMoroccoHungary
Czech Republic
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55
Figure 6.5
Market Capitalization of Emerging Financial Markets in USD Billions (Average from
19996 to 2015)
Aforementioned chart depicts the average market capitalization of each financial
market in the developed region that depicts the market value of stocks in each country.
According to the graph, China has highest market capitalization and Czech Republic has the
lowest market capitalization in its category.
Basic Statistics of Stock Market Development for the panel data of all 21 emerging
countries from 1996 to 2015 is shown in the following Figure 6.6
0.0E+00
5.0E+11
1.0E+12
1.5E+12
2.0E+12
2.5E+12
3.0E+12
3.5E+12
Brazil
Chi
le
Chi
na
Col
ombia
Cze
ch R
epub
lic
Egypt
, Ara
b Rep
.
Hun
gary
India
Indo
nesia
Malay
sia
Mex
ico
Mor
occo
Pakista
nPer
u
Philip
pine
s
Polan
d
Rus
sian
Fed
erat
ion
South
Afri
ca
Thaila
nd
Turke
y
Uni
ted
Ara
b Em
irate
s
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56
Figure 6.6
Basic Statistics of Stock Market Development of Emerging Equity Markets
0
10
20
30
40
50
60
70
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
Series: LYSample 1996 2015Observations 411
Mean 3.696866Median 3.638601Maximum 5.718953Minimum 1.677979Std. Dev. 0.746575Skewness 0.161084Kurtosis 2.804618
Jarque-Bera 2.431179Probability 0.296535
The figure shows that the preliminary statistics on the market capitalization as
percentage of GDP. The average capitalization during the whole study period comes to 3.6968
with the standard deviation of 0.75. The Jarque Bera test rejects normality of the stock market
development.
The preliminary statistics of Economic variables for the panel data of all 21 emerging
countries from 1996 to 2015 is shown in the following Table 6.5
Table 6.5
Preliminary Statistics of Economic Variable of Emerging Equity Markets
Statistics E1 E2 E3 E4 E5 E6 E7 E8
Mean 1.633594 1.905921 4.227565 105.2107 26.37494 97.18841 5.805275 1.992301
Median 1.677344 1.921281 3.932860 101.3587 25.72429 69.83773 2.440012 1.467750
Maximum 24.66657 11.27662 13.34727 233.2110 54.28837 442.6200 87.44259 26.05861
Minimum -8.997955 -4.479938 -5.634759 29.53919 8.330869 18.34896 -5.670905 -14.47630
Std. Dev. 2.832779 1.611234 2.802013 38.33464 8.218851 83.93756 9.646420 6.533388
Skewness 0.757940 0.521129 0.334021 0.445454 0.973990 2.513723 3.552466 0.697199
Kurtosis 13.06886 6.983954 3.783508 2.856114 4.828405 8.972572 21.20060 3.991862
Jarque-Bera 2159.997 353.2957 20.09899 15.74544 148.7018 1269.726 7809.805 57.22070
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Page 80
57
The table shows the preliminary statistics of eight economic variables. The average
during the whole study period ranges from 0.10 to 0.51. The Jarque Bera test rejects normality
of the economic variables.
The preliminary statistics of governance variables are presented in the table 6.6
Table 6.6
Preliminary Statistics of Governance Variables of Emerging Equity Markets
Statistics G1 G2 G3 G4 G5 G6
Mean 89.75072 90.98496 76.62031 90.14083 90.41419 86.74615
Median 93.17073 93.17073 80.84356 93.13725 93.26923 92.21296
Maximum 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
Minimum 51.44231 61.65049 7.109005 61.76471 60.28708 35.57692
Std. Dev. 10.41610 8.014274 20.37435 8.522897 8.927048 13.83109
Skewness -1.582314 -1.401515 -1.376391 -0.937198 -1.379488 -1.443880
Kurtosis 4.932210 4.692988 4.926033 3.010753 4.335455 4.536303
Jarque-Bera 286.4232 223.3998 235.1544 73.19736 195.7372 222.9039
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
The table shows the preliminary statistics of six governance variables of emerging markets.
The average during the whole study period ranges from 76.62 to 90.98. The Jarque Bera test
rejects normality of the economic variables.
The Correlation Matrix of Economic variables for the panel data of all 21 emerging
countries from 1996 to 2015 is shown in the following table 6.7
Page 81
58
Table 6.7
Correlation Matrix of Economic Variables of Emerging Equity Markets
Correlation E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 0.010197 1.000000
E3 -0.021103 -0.029653 1.000000
E4 -0.194476 -0.122716 -0.137786 1.000000
E5 0.302341 -0.112108 -0.074511 0.123852 1.000000
E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000
E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000
E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000
The Correlation Matrix of Governance Factors for the panel data of all 21 emerging
countries from 1996 to 2015 is shown in the following table 6.8
Table 6.8
Correlation Matrix of Governance Variables of Emerging Equity Markets
Correlation G1 G2 G3 G4 G5 G6
G1 1.000000
G2 0.920087 1.000000
G3 0.595142 0.549431 1.000000
G4 0.847346 0.824036 0.493675 1.000000
G5 0.912385 0.900648 0.627760 0.769201 1.000000
G6 0.440133 0.364032 0.425705 0.295824 0.560548 1.000000
Page 82
59
6.2.3. Frontier Financial Markets
In the frontier region, there are 24 countries which are classified as frontier markets by
FTSE Group and these will be analyzed for basic statistics with correlation matrix. First of all,
the number of listed companies alongwith their market capitalization is analyzed as per
following details:
Figure 6.7
No of Listed Companies of Frontier Financial Markets (Average No from 1996 to 2015)
As it is quite evident from the abovementioned graph that Serbia has the highest number
of listed companies followed by Bangladesh in the category of frontier financial markets.
Whereas, at the lower level, Botswana has the least number of listed companies in its group of
countries.
0 50 100 150 200 250 300 350 400 450 500
SerbiaBangladesh
BulgariaVietnam
Sri LankaJordanNigeriaCroatia
LithuaniaOman
ArgentinaCyprus
SloveniaRomania
Slovak RepublicKenya
TunisiaQatar
BahrainCote d'Ivoire
GhanaEstonia
MaltaBotswana
Page 83
60
Figure 6.8
Market Capitalization of Frontier Financial Markets in USD Billions (Average from 1996
to 2015)
This chart shows the average market capitalization of each financial market in the
frontier region that depicts the market value of stocks in each country. According to the data,
Qatar has highest average market capitalization during the last twenty years, where Serbia had
the highest number of listed companies and now it is standing at nowhere near to the
comparison of market capitalization.
The Basic statistics for the Stock Market Development of Frontier region from 1996 to
2015 is shown in the following Figure 6.9
0
20
40
60
80
100
120
140
Arg
entina
Bahra
in
Bangla
desh
Bots
wana
Bulg
aria
Cote
d'Iv
oire
Cro
atia
Cyp
rus
Est
onia
Ghana
Jord
an
Kenya
Lithuania
Malta
Nig
eria
Om
an
Qata
r
Rom
ania
Serb
ia
Slo
vak
Republic
Slo
venia
Sri L
anka
Tunis
ia
Vie
tnam
Page 84
61
Figure 6.9
Basic Statistics of Stock Market Development of Frontier Financial Markets
0
10
20
30
40
50
60
-4 -3 -2 -1 0 1 2 3 4 5
Series: LYSample 1996 2015Observations 427
Mean 3.001197Median 3.025950Maximum 5.700869Minimum -3.917356Std. Dev. 1.106285Skewness -1.039623Kurtosis 8.515425
Jarque-Bera 618.1389Probability 0.000000
The figure shows that the preliminary statistics on the market capitalization as
percentage of GDP. The average capitalization during the whole study period comes to 3.0012
with the standard deviation of 1.10. The Jarque Bera test rejects normality of the stock market
development.
The preliminary statistics of Economic variables for the panel data of all 24 frontier
countries from 1996 to 2015 is shown in the following table 6.9
Table 6.9
Preliminary Statistics of Economic Variable of Frontier Equity Markets
Statistics E1 E2 E3 E4 E5 E6 E7 E8
Mean 4.243440 2.752889 9.760702 5.050776 47.20415 22.42467 100.8636 -1.806020
Median 4.489896 3.024745 4.483331 4.919558 36.75874 20.61065 92.75692 -2.356626
Maximum 33.73578 30.35658 1058.374 93.93745 253.4578 75.54961 327.0551 33.18472
Minimum -14.81416 -14.55986 -4.863278 -70.43220 0.185853 -6.725383 21.12435 -25.54857
Std. Dev. 4.271855 4.079186 50.03471 10.27559 39.35828 13.99407 49.07045 7.763565
Skewness 0.359854 -0.026442 19.62829 0.521925 2.492803 1.321858 1.511951 0.942567
Kurtosis 11.44854 8.389892 409.8423 25.16564 11.57764 5.709075 7.020425 6.191409
Jarque-Bera 1422.937 575.0217 3292508. 8822.263 1960.440 286.5663 506.1559 252.4508
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Page 85
62
The table shows the preliminary statistics of eight economic variables. The average
during the whole study period ranges from 0.10 to 0.51. The Jarque Bera test rejects normality
of the economic variables.
The preliminary statistics of governance variables are presented in the table 6.10
Table 6.10
Preliminary Statistics of Governance Variables of Frontier Equity Markets
Statistics G1 G2 G3 G4 G5 G6
Mean 53.78940 57.53469 49.77110 57.17460 53.15534 49.01110
Median 57.56098 61.46341 52.39027 60.29412 57.34663 53.12500
Maximum 93.17073 92.41706 99.51691 93.26923 92.82297 92.78846
Minimum 1.463415 7.317073 0.966184 8.333333 3.827751 4.807693
Std. Dev. 22.87330 20.54213 26.38134 22.28422 22.70401 24.10185
Skewness -0.582949 -0.666706 -0.169439 -0.390822 -0.459305 -0.013586
Kurtosis 2.393753 2.609144 1.962952 2.020576 2.330239 1.736069
Jarque-Bera 34.53707 38.61515 23.80613 31.40475 25.84850 31.96519
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
The table shows the preliminary statistics of six governance variables of Frontier markets.
The average during the whole study period ranges from 49.01 to 57.53. The Jarque Bera test
rejects normality of the economic variables.
The Correlation Matrix of Economic variables for the panel data of all 24 Frontier
countries from 1996 to 2015 is shown in the following table 6.11
Page 86
63
Table 6.11
Correlation Matrix of Economic Variables of World Equity Markets
Correlation E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 0.010197 1.000000
E3 -0.021103 -0.029653 1.000000
E4 -0.194476 -0.122716 -0.137786 1.000000
E5 0.302341 -0.112108 -0.074511 0.123852 1.000000
E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000
E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000
E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000
The Correlation Matrix of Governance Factors for the panel data of frontier markets
from 1996 to 2015 is shown in the following table 6.12
Table 6.12
Correlation Matrix of Governance Variables of Frontier Equity Markets
Correlation G1 G2 G3 G4 G5 G6
G1 1.000000
G2 0.920975 1.000000
G3 0.791937 0.805177 1.000000
G4 0.850098 0.866145 0.713883 1.000000
G5 0.936074 0.917416 0.789577 0.890990 1.000000
G6 0.547714 0.592083 0.578830 0.660876 0.578308 1.000000
Page 87
64
6.2.4. World Financial Markets
In the world equity markets, there are total of 70 countries as classified by FTSE Group
and these will be analyzed for basic statistics with correlation matrix. First of all, the number
of listed companies is to be analyzed and its average from 1996 to 2015 is appended below:
Figure 6.10
No of Listed Companies of World Financial Markets (Average No from 1996 to 2015)
As it is quite evident from the abovementioned graph that United States has the highest number
of listed companies followed by India and Canada in the category of developed financial
0 1,000 2,000 3,000 4,000 5,000 6,000
United StatesIndia
CanadaSpain
JapanUnited Kingdom
AustraliaChina
Korea, Rep.Hong Kong SAR, China
Malays iaFrance
GermanyPak is tan
Egypt, Arab Rep.Israel
ThailandSerbia
South AfricaSingapore
PolandBraz il
Indones iaRuss ian Federation
BangladeshItaly
GreeceBulgariaTurkeyVietnamSweden
Sri LankaSwitzerland
ChilePhilippines
DenmarkNetherlands
PeruJordanNigeriaBelgiumNorwayCroatia
LithuaniaMexico
OmanNew Zealand
FinlandArgentina
CyprusAustria
ColombiaSlovenia
United Arab EmiratesRomaniaPortugal
MoroccoIreland
Slovak RepublicKenya
Tunis iaHungary
QatarBahrain
Cote d'IvoireCzech Republic
GhanaEstonia
MaltaBotswana
Page 88
65
markets. Whereas, at the lower level, Botswana has the least number of listed companies in its
group of countries.
Figure 6.11
Market Capitalization of World Financial Markets in USD Billions (Average from 1996 to
2015)
This chart shows the average market capitalization of each financial market in the
developed region that depicts the market value of stocks in each country. According to the
data, United States has highest average market capitalization during the last twenty years,
where India had the second highest number of listed companies and now it is standing at quite
lower standing in comparison of market capitalization.
Basic Statistics of Stock Market Development for the panel data of all 70 countries from
1996 to 2015 is shown in the following Figures 6.12.
0
4,000
8,000
12,000
16,000
20,000
Arg
en
tina
Au
stra
liaA
ust
riaB
ah
rain
Ba
ng
lad
esh
Be
lgiu
mB
ots
wa
na
Bra
zil
Bu
lga
riaC
an
ad
aC
hile
Ch
ina
Co
lom
bia
Co
te d
'Ivo
ireC
roa
tiaC
ypru
sC
zech
Re
pu
blic
De
nm
ark
Eg
ypt,
Ara
b R
ep
.E
sto
nia
Fin
lan
dF
ran
ceG
erm
an
yG
ha
na
Gre
ece
Hon
g Ko
ng S
AR, C
hina
Hu
ng
ary
Ind
iaIn
do
ne
sia
Ire
lan
dIs
rae
lIt
aly
Jap
an
Jord
an
Ke
nya
Ko
rea
, R
ep
.L
ithu
an
iaM
ala
ysia
Ma
ltaM
exi
coM
oro
cco
Ne
the
rlan
ds
Ne
w Z
ea
lan
dN
ige
riaN
orw
ay
Om
an
Pa
kist
an
Pe
ruP
hilip
pin
es
Po
lan
dP
ort
ug
al
Qa
tar
Ro
ma
nia
Ru
ssia
n F
ed
era
tion
Se
rbia
Sin
ga
po
reS
lova
k R
ep
ub
licS
love
nia
So
uth
Afr
ica
Sp
ain
Sri
La
nka
Sw
ed
en
Sw
itze
rlan
dT
ha
ilan
dT
un
isia
Tu
rke
yU
nite
d A
rab
Em
irate
sU
nite
d K
ing
do
mU
nite
d S
tate
sV
ietn
am
Page 89
66
Figure 6.12
Basic Statistics of Stock Market Development of World Financial Markets
0
40
80
120
160
200
240
-3 -2 -1 0 1 2 3 4 5 6 7
Series: LYSample 1996 2015Observations 1072
Mean 3.787584Median 3.835368Maximum 7.134465Minimum -2.893285Std. Dev. 1.043240Skewness -0.596591Kurtosis 5.823311
Jarque-Bera 419.6330Probability 0.000000
The figure shows that the preliminary statistics on the market capitalization as
percentage of GDP. The average capitalization during the whole study period comes to 3.7875
with the standard deviation of 1.04. The Jarque Bera test rejects normality of the stock market
development.
The preliminary statistics of Economic variables for the panel data of all 70 countries
from 1996 to 2015 is shown in the following table 6.13
Table 6.13
Preliminary Statistics of Economic Variable of World Equity Markets
Statistics E1 E2 E3 E4 E5 E6 E7 E8
Mean 2.369998 6.202609 6.213175 67.82527 24.69457 91.07710 5.721348 -0.014181
Median 2.428009 3.037021 4.639557 55.66380 23.57400 75.21890 2.669790 -0.777464
Maximum 30.35658 1058.374 93.93745 253.4578 75.54961 442.6200 451.7155 33.18472
Minimum -14.78631 -4.863278 -70.43220 0.185853 -6.725383 15.63556 -43.46255 -25.54857
Std. Dev. 3.569523 30.13143 9.850004 45.61329 10.89235 63.77629 20.95003 6.686508
Skewness -0.094824 31.30399 2.399185 0.986900 0.990774 2.561513 14.94683 0.767272
Kurtosis 8.541588 1085.398 22.55318 3.708501 5.850949 11.65933 270.4033 5.536110
Jarque-Bera 1787.062 67248644 22277.44 249.5786 700.6647 5883.956 4196075. 479.6063
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Page 90
67
The table shows the preliminary statistics of eight economic variables. The average
during the whole study period ranges from 0.10 to 0.51. The Jarque Bera test rejects normality
of the economic variables.
The preliminary statistics of governance variables are presented in the table 6.14
Table 6.14
Preliminary Statistics of Governance Variables of World Equity Markets
Statistics G1 G2 G3 G4 G5 G6
Mean 65.75201 70.05147 55.18526 69.41799 65.77213 61.85943
Median 67.39059 72.90066 59.71564 73.05807 67.46411 64.66038
Maximum 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000
Minimum 1.463415 7.317073 0.473934 8.333333 3.827751 4.694836
Std. Dev. 25.60633 22.07683 29.24921 22.92404 25.41489 27.40518
Skewness -0.508788 -0.610941 -0.257793 -0.591285 -0.455644 -0.306721
Kurtosis 2.310306 2.636630 1.790822 2.384133 2.176094 1.860424
Jarque-Bera 88.14989 94.79358 100.7966 103.7028 88.04055 97.70498
Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
The table shows the preliminary statistics of six governance variables of world markets. The
average during the whole study period ranges from 55.18 to 70.05. The Jarque Bera test
rejects normality of the economic variables.
The Correlation Matrix of Economic variables for the panel data of all 70 countries
from 1996 to 2015 is shown in the following table 6.15
Page 91
68
Table 6.15
Correlation Matrix of Economic Variables of Frontier Equity Markets
Correlation E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 -0.022557 1.000000
E3 -0.067811 -0.126985 1.000000
E4 -0.195199 -0.132959 -0.186364 1.000000
E5 0.115561 -0.056593 -0.197085 0.237533 1.000000
E6 0.039468 -0.040500 -0.157520 0.341373 0.361747 1.000000
E7 0.030077 -0.016539 -0.047653 0.155165 0.019697 0.317627 1.000000
E8 -0.040251 -0.016718 -0.157900 0.153372 0.655817 0.313136 0.016081 1.00000
The Correlation Matrix of Governance Factors for the panel data of all 70 countries
from 1996 to 2015 is shown in the following table 6.16
Table 6.16
Correlation Matrix of Governance Variables of Frontier Equity Markets
Correlation G1 G2 G3 G4 G5 G6
G1 1.000000
G2 0.944768 1.000000
G3 0.822448 0.802101 1.000000
G4 0.915314 0.923145 0.766807 1.000000
G5 0.951607 0.944216 0.825524 0.912514 1.000000
G6 0.757272 0.756508 0.691295 0.785424 0.782673 1.000000
Page 92
69
6.3. Econometric Analysis
In order to achieve the distinct results of the study, the econometric techniques have
been applied to the panel data sets of 70 countries by dividing into different groups separately
as depicted below.
Developed markets (25)
Emerging markets (21)
Frontier market (24)
World Markets (70)
The panel data of 70 countries (25,21,24,70) is used for the period starting from 1996 to
2015 (T=20 years). The Econometric analysis of the study has been further divided into two
categories as Principal Component Analysis (PCA) and Panel GMM. In PCA, the composite
Indices of Governance, Economic and Cross factors have been formed and the study has
applied Panel GMM as suggested by Arellano and Bond (1991) for estimating panel datasets
to obtain the empirical results.
Before embarking to the analysis, it is imperative to know the description of variables used
in the study. Though, these variables have been have been amply highlighted in the chapter of
Data and Variables, yet the summary of these variables will further elucidate the estimation of
results. There are number of variables that have been denoted to certain variables and factors
in the equations and estimation of the models. The list all these variables used in the study are
as follows:
Page 93
70
Table 6.17
List of All Variables for Statistical and Econometric Analysis
Variable Description
Y = Market capitalization of listed domestic companies (% of GDP)
S3 = Stocks traded, total value (% of GDP)
E1 = GDP growth (annual %)
E2 = Inflation, consumer prices (annual %)
E3 = Real interest rate (%)
E4 = Domestic credit to private sector by banks (% of GDP)
E5 = Gross domestic savings (% of GDP)
E6 = Trade (% of GDP)
E7 = Foreign direct investment, net inflows (% of GDP)
E8 = Current account balance (% of GDP)
G1= Control of Corruption
G2= Government Effectiveness
G3= Political Stability and Absence of Violence/Terrorism
G4= Regulatory Quality
G5= Rule of Law
G6= Voice and Accountability
PECO= Composite Economic Factors of all Economic variables
PGOV= Composite Governance Factors of all Governance variables
PCROSS= Cross Factors of Composite Economic & Governance Factors (Eco*Gov)
Yit= The matrix of stock market capitalization relative to GDP of particular
country i in year t
Eit= Vector of Economic variables
Git= Vector of Governance variables
αi= The unobserved country specific fixed effect
εit= The usual white noise
Page 94
71
6.2. Results of Principal Component Analysis (PCA)
The technique of Principal Component Analysis (PCA) has been incorporated in this
study for the formation of Composite Indices of Economic, Governance and Cross factors.
These three indices are created for all three regions of the world stock markets. First question
of the study pertains to the formation of composite index for Governance and Economic factors
along with their cross factor index of both the composite variables, which is as follows:
Q No.1. What are the composite index factors for economic and governance variables along
with their cross composite index factors according to regional markets of developed, emerging
and frontier regions?
Abovementioned question encompasses three indices on three regions, so the
estimation of results are categories in the same way. Therefore, the classification would be
made region wise in which each indices will be analyzed.
6.2.1. Developed Markets.
The index for composition of economic and governance factors of developed market is
formed by using Principal Component Analysis (PCA). These PCAs are formed for three
categories, that is, composite indices of economic, governance and cross factors.
6.2.1.1. Composite Index of Economic Factors : First all, the composite index of
economic factors is formed by using PCA technique and same is placed in the appendices as
Appendix-1A. Their Eigen Values and Correlation matrix is appended below:
Page 95
72
Table 6.18
Principal Components Analysis for Economic Variables of Developed Stock Markets (25
Countries)
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 2.931890 1.669409 0.3665 2.931890 0.3665
2 1.262481 0.213879 0.1578 4.194371 0.5243
3 1.048603 0.005208 0.1311 5.242974 0.6554
4 1.043394 0.261846 0.1304 6.286368 0.7858
5 0.781548 0.250330 0.0977 7.067916 0.8835
6 0.531218 0.302141 0.0664 7.599135 0.9499
7 0.229077 0.057288 0.0286 7.828211 0.9785
8 0.171789 --- 0.0215 8.000000 1.0000
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8
E1 0.217698 0.598697 -0.181100 0.071286 -0.573299 0.446836 -0.138298 0.093967
E2 -0.128903 0.291779 0.137898 0.792956 0.437890 0.145397 0.066592 0.182020
E3 -0.049060 0.246085 0.830136 -0.380381 0.092192 0.227747 0.153212 0.139204
E4 0.157355 -0.697268 0.169655 0.252768 -0.258232 0.547230 0.080578 0.154093
E5 0.502280 0.085662 -0.133386 -0.068172 0.323232 0.276611 0.476680 -0.556526
E6 0.501374 -0.043280 0.292410 0.118898 0.123780 -0.104459 -0.744912 -0.256873
E7 0.408698 0.021674 0.264551 0.288484 -0.406964 -0.583465 0.405388 0.094311
E8 0.487985 -0.002290 -0.238736 -0.235692 0.344931 -0.003220 0.003893 0.728229
Sub table-3: Ordinary correlations:
Variable E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 0.010197 1.000000
E3 -0.021103 -0.029653 1.000000
E4 -0.194476 -0.122716 -0.137786 1.000000
E5 0.302341 -0.112108 -0.074511 0.123852 1.000000
E6 0.179809 -0.049874 0.085779 0.276886 0.643437 1.000000
E7 0.280987 -0.044748 -0.019691 0.215121 0.393389 0.636166 1.000000
E8 0.193857 -0.274164 -0.143161 0.071313 0.785996 0.615757 0.350946 1.000000
Page 96
73
Abovementioned table depicts the results of PCA for Economic variables of developed
stock markets constituting 25 countries. Sub table-3 shows the correlations among 08
economic variables and it is revealed that Gross Domestic Savings (E5) and Trade (E6) is
having the highest correlation of 0.64, whereas the lowest correlation of 0.01 is between GDP
growth (E1) and Interest Rates (E2). On the other side, there is negative correlation of 0.19
between GDP growth (E1) and private capital by banks (E4).
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of economic variables, which is depicted below:
Figure 6.13
Mean of Composite of Economic Variables of Developed Stock Markets
The Figure 6.13. depicts the mean values of composite economic index of 25 developed
countries and it shows that Singapore has the highest mean value of its economic composite
index, whereas Greece has lowest mean value of its economic composite. Moreover, the
distribution along the bottom axis of the figure shows that majority of the values of economic
composite index falls between 0 and 1and most of them are lying on the left side of the figure.
-3 -2 -1 0 1 2 3 4 5
AustraliaAustria
BelgiumCanada
DenmarkFinlandFrance
GermanyGreece
Hong Kong SAR, ChinaIrelandIsrael
ItalyJapan
Korea, Rep.Netherlands
New ZealandNorway
PortugalSingapore
SpainSweden
SwitzerlandUnited Kingdom
United States
Page 97
74
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
Figure 6.14
Standard Deviation of Composite of Economic Variables of Developed Stock Markets
Aforementioned figure depicts that there is quite high dispersion in the economic
composite index of Ireland which is quite evident when we see their economic indicators tells
that there is quite volatility in the variables of interest rates and trade. Moreover, the distribution
along the bottom axis of the figure shows that most of the values of economic composite index
falls around 0.4 and majority of them are lying on the right side of the figure.
6.2.1.2. Composite Index of Governance Factors: Secondly, the composite index of
governance factors is formed by using PCA technique and same is placed in the appendices as
Appendix-1B. Their Eigen Values and Correlation matrix are appended below:
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
AustraliaAustria
BelgiumCanada
DenmarkFinlandFrance
GermanyGreece
Hong Kong SAR, ChinaIrelandIsrael
ItalyJapan
Korea, Rep.Netherlands
New ZealandNorway
PortugalSingapore
SpainSweden
SwitzerlandUnited Kingdom
United States
Page 98
75
Table 6.19
Principal Components Analysis for Governance Variables of Developed Stock Markets (25
Countries)
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 4.280970 3.440560 0.7135 4.280970 0.7135
2 0.840410 0.315608 0.1401 5.121380 0.8536
3 0.524802 0.311264 0.0875 5.646182 0.9410
4 0.213538 0.141035 0.0356 5.859720 0.9766
5 0.072502 0.004724 0.0121 5.932222 0.9887
6 0.067778 --- 0.0113 6.000000 1.0000
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6
G1 0.462370 -0.166899 -0.097386 -0.138624 -0.775541 0.358041
G2 0.450035 -0.257490 -0.109327 -0.374628 0.612673 0.451111
G3 0.346197 0.297471 0.885335 0.067423 0.038725 0.042383
G4 0.417765 -0.353979 -0.104481 0.807431 0.126753 -0.145750
G5 0.463050 0.022405 -0.121081 -0.386378 -0.008317 -0.788077
G6 0.273656 0.831596 -0.411302 0.186166 0.074381 0.155568
Sub table-3: Ordinary correlations:
Variable G1 G2 G3 G4 G5 G6
G1 1.000000 G2 0.920087 1.000000 G3 0.595142 0.549431 1.000000 G4 0.847346 0.824036 0.493675 1.000000 G5 0.912385 0.900648 0.627760 0.769201 1.000000 G6 0.440133 0.364032 0.425705 0.295824 0.560548 1.000000
Aforesaid table shows the results of PCA for Governance variables of developed stock markets
constituting 25 countries. Sub table-3 shows the correlations among 06 governance variables
and it is revealed that G1 and G2 are having the highest correlation of 0.92, whereas the lowest
Page 99
76
correlation of 0.29 is between G4 and G6. On the other side, there is no negative correlation
among these variables.
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of governance variables, which is depicted below:
Figure 6.15
Mean of Composite of Governance Variables of Developed Stock Markets
The Figure 6.15 depicts the mean values of composite Index of governance variables
of 25 developed countries and it shows that Finland has the highest mean value of its economic
composite index Greece has lowest mean value of its governance composite. Moreover, the
distribution along the bottom axis of the figure shows that majority of the values of economic
composite index falls between 0.5 and 2.5 and most of them are lying on the right side of the
figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
-5 -4 -3 -2 -1 0 1 2 3
AustraliaAustria
BelgiumCanada
DenmarkFinlandFrance
GermanyGreece
Hong Kong SAR, ChinaIrelandIsrael
ItalyJapan
Korea, Rep.Netherlands
New ZealandNorway
PortugalSingapore
SpainSweden
SwitzerlandUnited Kingdom
United States
Page 100
77
Figure 6.16 Standard Deviation of Composite of Governance Variables of Developed Stock Markets
Aforementioned figure depicts that there is quite high dispersion the governance
composite index of Greece which is quite evident when we see their governance indicators.
Moreover, the distribution along the bottom axis of the figure shows that most the values of
economic composite index falls around 0.2 and majority of them are lying on the left side of
the figure.
6.2.1.3. Cross Composite Index of Economic and Governance Factors: The index
for cross composition of economic and governance factors of developed market is formed by
the interaction of these variables and same is placed in the appendices as Appendix-1C.
To analyze the trend of average values in the data, a graph has been generated as Means
of composites of economic governance variables, which is depicted below:
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4
AustraliaAustria
BelgiumCanada
DenmarkFinlandFrance
GermanyGreece
Hong Kong SAR, ChinaIrelandIsrael
ItalyJapan
Korea, Rep.Netherlands
New ZealandNorway
PortugalSingapore
SpainSweden
SwitzerlandUnited Kingdom
United States
Standard Deviation of PGOV by COUNTRY
Page 101
78
Figure 6.17 Mean of Cross Composite Index of Economic and Governance Variables of Developed Stock Markets
The Figure 6.17. depicts the mean values of cross composite Index of economic and
governance index of 25 developed countries and it shows that Greece has the highest mean
value of its cross composite index and New Zealand has lowest mean value of its cross
composite index. Moreover, the distribution along the bottom axis of the figure shows that
majority of the values of economic composite index falls between -1 and 3 and most of them
are lying on the left side of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
-2 -1 0 1 2 3 4 5 6 7 8 9 10 11
AustraliaAustria
BelgiumCanada
DenmarkFinlandFrance
GermanyGreece
Hong Kong SAR, ChinaIrelandIsrael
ItalyJapan
Korea, Rep.Netherlands
New ZealandNorway
PortugalSingapore
SpainSweden
SwitzerlandUnited Kingdom
United States
Page 102
79
Figure 6.18 Standard Deviation of Cross Composite Index of Economic and Governance Variables of Developed Stock Markets
Aforementioned figure depicts that the dispersion of governance composite index and
it shows that Greece has highest dispersion in the data which is quite evident when we see their
governance indicators tells that there is quite volatility in the variables interest rates and trade.
Moreover, the distribution along the bottom axis of the figure shows that most the values of
economic composite index falls between 0 and 2 and majority of them are lying on the left side
of the figure.
Now coming towards the analysis of cross effects of economic and governance
composite indices, the study has generated a scatter plot with their distributions and Kernel Fit
line of these two composite indices. The scatter plot of these variable is appended below:
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
AustraliaAustria
BelgiumCanada
DenmarkFinlandFrance
GermanyGreece
Hong Kong SAR, ChinaIrelandIsrael
ItalyJapan
Korea, Rep.Netherlands
New ZealandNorway
PortugalSingapore
SpainSweden
SwitzerlandUnited Kingdom
United States
Standard Deviation of PCROSS by COUNTRY
Page 103
80
Figure 6.19 Scatter plots of Composite of Indices of Economic and Governance Indices of Developed Stock Markets
-8
-6
-4
-2
0
2
4
Com
po
site I
nde
x o
f G
overn
an
ce V
ari
ab
les (
PG
OV
)
-4 -2 0 2 4 6 8
Composite Index of Economic Variables (PECO)
Aforementioned figure of scatter plots depicts that values of the observations have their
positive relations with majority of the observations are lying at one place. According to Kernel
Fit line, it is deduced that as governance factors moves and in the same direction economic
factors also move.
Page 104
81
6.2.2 Emerging Markets.
The index for composition of economic and governance factors of emerging market is
formed by using Principal Component Analysis (PCA), These PCAs are formed for three
categories, that is, composite index of economic, governance and cross factors.
6.2.2.1. Composite Index of Economic Factors : First all, the economic factors of
emerging markets are combined and formed a composite index by using Principal
Component Analysis (PCA) and same is placed in the appendices as Appendix-2A. Their
Eigen Values and Correlation matrix is appended below:
Table 6.20
Principal Components Analysis for Economic Variables of Emerging Stock Markets (21
Countries)
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 2.812725 1.546564 0.3516 2.812725 0.3516
2 1.266161 0.162971 0.1583 4.078886 0.5099
3 1.103190 0.127743 0.1379 5.182076 0.6478
4 0.975448 0.280409 0.1219 6.157524 0.7697
5 0.695038 0.208057 0.0869 6.852562 0.8566
6 0.486982 0.073323 0.0609 7.339543 0.9174
7 0.413659 0.166862 0.0517 7.753203 0.9692
8 0.246797 --- 0.0308 8.000000 1.0000
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8
E1 0.195626 -0.147776 -0.772373 0.362086 -0.096917 -0.166650 0.304227 0.287240
E2 -0.244977 0.517768 0.139943 0.510101 -0.435455 -0.283683 -0.302250 0.175112
E3 -0.359950 0.267929 0.104908 0.416226 0.622683 0.117199 0.456797 -0.065319
E4 0.469089 0.076691 0.136359 0.078516 0.539969 -0.128272 -0.406190 0.525648
E5 0.493083 0.190033 -0.140366 0.295175 0.105927 -0.023611 -0.204996 -0.748414
E6 0.413012 -0.008615 0.468335 0.000109 -0.169150 -0.492585 0.581852 0.014352
E7 0.108502 -0.564812 0.339394 0.573640 -0.176678 0.433119 -0.044945 0.064303
E8 0.355845 0.526509 -0.005240 -0.114413 -0.225029 0.656333 0.244625 0.204430
Page 105
82
Sub table-3: Ordinary correlations:
Variable E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 -0.144020 1.000000
E3 -0.189172 0.382387 1.000000
E4 0.115489 -0.285034 -0.260103 1.000000
E5 0.375543 -0.125489 -0.313327 0.649109 1.000000
E6 -0.044537 -0.170761 -0.358431 0.485944 0.439465 1.000000
E7 0.054455 -0.105178 -0.090552 0.105836 0.101140 0.213905 1.000000
E8 0.068512 -0.002082 -0.245719 0.371049 0.505460 0.333537 -0.169148 1.000000
Abovementioned table depicts the results of PCA for Economic variables of developed stock
markets constituting 25 countries. Sub table-3 shows the correlations among 08 economic
variables and it is revealed that Gross Domestic Savings (E5) and Domestic Credit (E4) is
having the highest correlation of 0.65, whereas the lowest correlation of 0.02 is between GDP
growth (E1) and Interest Rates (E2). On the other side, there is highest negative correlation of
0.36 between (E3) and (E6).
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of economic variables, which is depicted below:
Figure 6.20 Mean of Composite Index of Economic Variables of Emerging Stock Markets
-3 -2 -1 0 1 2 3 4 5
BrazilChile
ChinaColombia
Czech RepublicEgypt, Arab Rep.
HungaryIndia
IndonesiaMalaysia
MexicoMoroccoPakistan
PeruPhilippines
PolandRussian Federation
South AfricaThailand
TurkeyUnited Arab Emirates
Mean of PECO by COUNTRY
Page 106
83
The above Figure. depicts the mean values of composite economic index of 21
emerging countries and it shows that Malaysia has the highest mean value of its economic
composite index Turkey has lowest mean value of its economic composite. Moreover, the
distribution along the bottom axis of the figure shows that majority of the values of economic
composite index falls between -.2.5 and 1and most of them are lying on the left side of the
figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
Figure 6.21 Standard Deviation of Composite of Economic Variables of Emerging Stock Markets
Aforementioned figure depicts that there is quite high dispersion the economic
composite index of Turkey which is quite evident when we see their economic indicators tells
that there is quite volatility their economic variables. Moreover, the distribution along the
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
BrazilChile
ChinaColombia
Czech RepublicEgypt, Arab Rep.
HungaryIndia
IndonesiaMalaysia
MexicoMoroccoPakistan
PeruPhilippines
PolandRussian Federation
South AfricaThailand
TurkeyUnited Arab Emirates
Standard Deviation of PECO by COUNTRY
Page 107
84
bottom axis of the figure shows that most the values of economic composite index falls
between 0.3 and 0.7 and majority of them are lying on the left side of the figure.
6.2.2.2. Composite Index of Governance Factors: Secondly, the composite index of
governance factors is formed by using PCA technique and same is placed in the appendices
and same is placed in the appendices as Appendix-2B.
Table 6.21
Principal Components Analysis for Governance of Emerging Markets (21 Countries)
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 4.694591 4.112365 0.7824 4.694591 4.694591
2 0.582227 0.300882 0.0970 5.276818 0.582227
3 0.281345 0.081186 0.0469 5.558163 0.281345
4 0.200159 0.055847 0.0334 5.758321 0.200159
5 0.144312 0.046944 0.0241 5.902633 0.144312
6 0.097367 --- 0.0162 6.000000 0.097367
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6
G1 0.430023 -0.165428 -0.208272 -0.193172 -0.764404 0.350297
G2 0.425139 -0.248850 -0.325685 -0.173126 0.637768 0.463183
G3 0.408157 -0.223014 0.651213 0.580279 0.022346 0.149572
G4 0.423217 0.073346 -0.538409 0.475807 -0.002010 -0.547017
G5 0.425553 -0.146997 0.345434 -0.603204 0.075002 -0.555419
G6 0.327928 0.913227 0.131399 -0.075771 0.053010 0.180728
Sub table-3: Ordinary correlations:
Variable G1 G2 G3 G4 G5 G6
G1 1.000000 G2 0.853454 1.000000 G3 0.787502 0.775957 1.000000 G4 0.842034 0.842044 0.750062 1.000000 G5 0.849122 0.841745 0.819884 0.759013 1.000000 G6 0.569602 0.525796 0.527851 0.653774 0.589693 1.000000
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85
Aforesaid table shows the results of PCA for Governance variables of emerging stock markets
constitutes 21 countries. Sub table-3 shows the correlations among 06 governance variables
and it is revealed that G1 and G2 are having the highest correlation of 0.85, whereas the lowest
correlation of 0.52 is between G2 and G6. On the other side, there is no negative correlation
in the governance varaibles.
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of governance variables, which is depicted below:
Figure 6.21 Mean of Composite of Governance Variables of Emerging Stock Markets
The above Figure depicts the mean values of composite Index of governance variables
of 21 emerging countries and it shows that Chile has the highest mean value of its governance
composite index, while Pakistan has the lowest mean value of its governance composite.
-4 -3 -2 -1 0 1 2 3 4 5
BrazilChile
ChinaColombia
Czech RepublicEgypt, Arab Rep.
HungaryIndia
IndonesiaMalaysia
MexicoMoroccoPakistan
PeruPhilippines
PolandRussian Federation
South AfricaThailand
TurkeyUnited Arab Emirates
Mean of PGOV by COUNTRY
Page 109
86
Moreover, the distribution along the bottom axis of the figure shows that majority of the values
of economic composite index falls between -2 and 0 and most of them are lying on the left side
of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite economic variables, which is depicted below:
Figure 6.22 Standard Deviation of Governance Variables of Emerging Stock Markets
Aforementioned figure depicts that there is quite dispersion in the governance
composite index and Egypt dispersion is quite high, when we see their governance indicators
tells that there is quite volatility. Moreover, the distribution along the bottom axis of the figure
shows that most the values of economic composite index falls below 0.4 and majority of them
are lying on the left side of the figure.
6.2.2.3. Cross Composite Index of Economic and Governance Factors: The index
for cross composition of economic and governance factors of emerging market is formed by
the interaction of these variables and same is placed in the appendices as Appendix-2C.
.2 .3 .4 .5 .6 .7 .8 .9
BrazilChile
ChinaColombia
Czech RepublicEgypt, Arab Rep.
HungaryIndia
IndonesiaMalaysia
MexicoMoroccoPakistan
PeruPhilippines
PolandRussian Federation
South AfricaThailand
TurkeyUnited Arab Emirates
Standard Deviation of PGOV by COUNTRY
Page 110
87
To analyze the trend of average values in the data, a graph has been generated as Means
of composites of economic governance variables, which is depicted below:
Figure 6.23 Mean of Cross Composite Index of Economic and Governance Variables of Emerging Stock Markets
Above Figure depicts the mean values of cross composite Index of economic and
governance index of 21 emerging countries and it shows that Pakistan has the highest mean
value of its cross composite index and China has lowest mean value of its cross composite
index. Moreover, the distribution along the bottom axis of the figure shows that majority of
the values of economic composite index falls between -2 and 2 and most of them are lying on
the right side of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
-8 -6 -4 -2 0 2 4 6 8 10 12 14 16
BrazilChile
ChinaColombia
Czech RepublicEgypt, Arab Rep.
HungaryIndia
IndonesiaMalaysia
MexicoMoroccoPakistan
PeruPhilippines
PolandRussian Federation
South AfricaThailand
TurkeyUnited Arab Emirates
Mean of PCROSS by COUNTRY
Page 111
88
Figure 6.24 Standard Deviation of Cross Composite Index of Economic and Governance Variables of Emerging Stock Markets
Aforementioned figure depicts that the dispersion of governance composite index and
it shows that Greece has highest dispersion in the data which is quite evident when we see their
governance indicators tells that there is quite volatility in the variables interest rates and trade.
Moreover, the distribution along the bottom axis of the figure shows that most the values of
economic composite index falls between 0 and 2 and majority of them are lying on the left side
of the figure.
Now coming towards the analysis of cross effects of economic and governance
composite indices, the study has generated a scatter plot with their distributions and Kernel Fit
line of these two composite indices. The scatter plot of these variable is appended below:
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
BrazilChile
ChinaColombia
Czech RepublicEgypt, Arab Rep.
HungaryIndia
IndonesiaMalaysia
MexicoMoroccoPakistan
PeruPhilippines
PolandRussian Federation
South AfricaThailand
TurkeyUnited Arab Emirates
Standard Deviation of PCROSS by COUNTRY
Page 112
89
Figure 6.25 Scatter plots of Composite of Indices of Economic and Governance Indices of Emerging Stock Markets
-6
-4
-2
0
2
4
6
PG
OV
-6 -4 -2 0 2 4 6
PECO Aforementioned figure of scatter plots depicts that values of the observations have their
positive and negative relations with observations are lying at scattered places with no clear
pattern. According to Kernel Fit line, it is deduced that as governance factors do not move in
the same direction as economic factors move.
Page 113
90
6.2.3. Frontier Markets.
The index for composition of economic and governance factors of frontier market is
formed by using Principal Component Analysis (PCA). These PCAs are formed for three
categories, that is, composite index of economic, governance and cross factors.
6.2.3.1. Composite Index of Economic Factors : First all, the composite index of
economic factors is formed by using PCA technique and same is placed in the appendices as
Appendix-3A. The Eigen Values and Correlation matrix of composite index of economic
factors of frontier markets are appended below:
Table 6.22
Principal Components Analysis for Economic Variables of Frontier Stock Markets
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 1.854284 0.139213 0.2318 1.854284 0.2318
2 1.715071 0.392208 0.2144 3.569355 0.4462
3 1.322863 0.295184 0.1654 4.892218 0.6115
4 1.027680 0.267811 0.1285 5.919898 0.7400
5 0.759869 0.188467 0.0950 6.679767 0.8350
6 0.571402 0.146710 0.0714 7.251169 0.9064
7 0.424692 0.100554 0.0531 7.675861 0.9595
8 0.324139 --- 0.0405 8.000000 1.0000
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8
E1 -0.204530 0.027370 0.131593 0.889514 -0.214276 0.210393 0.242158 0.006768
E2 -0.141158 0.087436 -0.722512 -0.087506 0.075920 0.650924 0.084396 0.078486
E3 0.132969 -0.329535 0.590718 -0.165604 0.208358 0.669727 0.037581 0.063204
E4 0.551013 0.224081 0.013493 -0.168060 -0.428238 0.030327 0.640537 0.152153
E5 -0.271211 0.595563 0.243247 -0.092471 -0.026693 0.063981 -0.187144 0.681323
E6 0.467825 0.431892 0.039644 0.140286 -0.200690 0.249448 -0.574353 -0.375466
E7 0.414049 0.219347 -0.072929 0.276318 0.804551 -0.112111 0.155487 0.121379
E8 -0.391271 0.493419 0.213239 -0.202127 0.182291 0.060764 0.365316 -0.588847
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91
Sub table-3: Ordinary correlations:
Variable E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 -0.073383 1.000000
E3 -0.063859 -0.369855 1.000000
E4 -0.210158 -0.095001 0.005505 1.000000
E5 0.082915 -0.030995 -0.166418 -0.035422 1.000000
E6 -0.019237 -0.057128 -0.074816 0.515323 0.181223 1.000000
E7 -0.035058 -0.017251 -0.036450 0.242858 -0.039874 0.366296 1.000000
E8 0.037805 0.022002 -0.128427 -0.159360 0.628036 -0.028473 -0.084244 1.000000
Abovementioned table depicts the results of PCA for Economic variables of frontier stock
markets constituting 24 countries. Sub table-3 shows the correlations among 08 economic
variables and it is revealed that (E5) and (E8) are having the highest correlation of 0.63,
whereas the lowest correlation of 0.006 is between (E3) and (E4). On the other side, there is
highest negative correlation of -0.36 between (E2) and (E3).
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of economic variables, which is depicted below:
Figure 6.26 Mean of Composite Index of Economic Variables of Frontier Stock Markets
-3 -2 -1 0 1 2 3 4
ArgentinaBahrain
BangladeshBotswana
BulgariaCote d'Ivoire
CroatiaCyprusEstoniaGhanaJordanKenya
LithuaniaMalta
NigeriaOmanQatar
RomaniaSerbia
Slovak RepublicSlovenia
Sri LankaTunisia
Vietnam
Page 115
92
The above Figure. depicts the mean values of composite economic index of 24 frontier
countries and it shows that Malta has the highest mean value of its economic composite index
and Qatar has lowest mean value of its economic composite. Moreover, the distribution along
the bottom axis of the figure shows that majority of the values of economic composite index
falls between -.1 and 1and most of them are lying on the central part of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
Figure 6.27 Standard Deviation of Composite of Economic Variables of Frontier Stock Markets
Aforementioned figure depicts that there is quite high dispersion the economic
composite index of Malta which is quite evident when we see their economic indicators tells
that there is quite volatility their economic variables. Moreover, the distribution along the
bottom axis of the figure shows that most the values of economic composite index falls
between 0.2 and 0.8 and majority of them are lying on the left side of the figure.
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
ArgentinaBahrain
BangladeshBotswana
BulgariaCote d'Ivoire
CroatiaCyprusEstoniaGhanaJordanKenya
LithuaniaMalta
NigeriaOmanQatar
RomaniaSerbia
Slovak RepublicSlovenia
Sri LankaTunisia
Vietnam
Page 116
93
6.2.3.2. Composite Index of Governance Factors: Secondly, the composite index of
governance factors is formed by using PCA technique and same is placed in the appendices
and same is placed in the appendices as Appendix-3B.
Table 6.23
Principal Components Analysis for Governance Variables of Frontier Stock Markets (24
Countries)
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 4.852634 4.280553 0.8088 4.852634 4.852634
2 0.572081 0.264481 0.0953 5.424715 0.572081
3 0.307600 0.181244 0.0513 5.732315 0.307600
4 0.126356 0.041795 0.0211 5.858672 0.126356
5 0.084561 0.027794 0.0141 5.943233 0.084561
6 0.056767 --- 0.0095 6.000000 0.056767
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6
G1 0.429490 -0.271398 -0.095280 0.461163 0.357205 0.626528
G2 0.433319 -0.181334 -0.063980 0.300400 -0.815472 -0.141508
G3 0.394789 -0.062057 0.864448 -0.300662 0.039300 0.032849
G4 0.421527 0.032456 -0.455053 -0.743652 -0.062974 0.239173
G5 0.434646 -0.211928 -0.180065 0.075624 0.446267 -0.727236
G6 0.324518 0.918501 -0.007847 0.218983 0.052401 -0.016840
Sub table-3: Ordinary correlations:
Variable G1 G2 G3 G4 G5 G6
G1 1.000000 G2 0.920975 1.000000 G3 0.791937 0.805177 1.000000 G4 0.850098 0.866145 0.713883 1.000000 G5 0.936074 0.917416 0.789577 0.890990 1.000000 G6 0.547714 0.592083 0.578830 0.660876 0.578308 1.000000
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94
Aforesaid table shows the results of PCA for Governance variables of emerging stock markets
constitutes 21 countries. Sub table-3 shows the correlations among 06 governance variables
and it is revealed that G1 and G5 are having the highest correlation of 0.94, whereas the lowest
correlation of 0.55 is between G1 and G6. On the other side, there is no negative correlation
in the governance variables.
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of governance variables, which is depicted below:
Figure 6.28 Mean of Composite of Governance Variables of Frontier Stock Markets
The above Figure depicts the mean values of composite Index of governance variables
of 24 frontier countries and it shows that Malta has the highest mean value of its governance
composite index, while Nigeria has the lowest mean value of its governance composite.
Moreover, the distribution along the bottom axis of the figure shows that majority of the values
of economic composite index falls between -2 and 2 and most of them are lying on the central
part of the figure.
-5 -4 -3 -2 -1 0 1 2 3 4
ArgentinaBahrain
BangladeshBotswana
BulgariaCote d'Ivoire
CroatiaCyprusEstoniaGhanaJordanKenya
LithuaniaMalta
NigeriaOmanQatar
RomaniaSerbia
Slovak RepublicSlovenia
Sri LankaTunisia
Vietnam
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95
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite economic variables, which is depicted below:
Figure 6.29 Standard Deviation of Composite of Governance Variables of Frontier Stock Markets
Aforementioned figure depicts that there is quite dispersion in the governance
composite index and Serbia dispersion is quite high, when we see their governance indicators
tells that there is quite volatility. Moreover, the distribution along the bottom axis of the figure
shows that most the values of economic composite index falls below 0.4 and majority of them
are lying on the left side of the figure.
6.2.3.3. Cross Composite Index of Economic and Governance Factors: The index
for cross composition of economic and governance factors of emerging market is formed by
the interaction of these variables and same is placed in the appendices as Appendix-3C.
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
ArgentinaBahrain
BangladeshBotswana
BulgariaCote d'Ivoire
CroatiaCyprusEstoniaGhanaJordanKenya
LithuaniaMalta
NigeriaOmanQatar
RomaniaSerbia
Slovak RepublicSlovenia
Sri LankaTunisia
Vietnam
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96
To analyze the trend of average values in the data, a graph has been generated as Means
of composites of economic governance variables, which is depicted below:
Figure 6.30 Mean of Cross Composite Index of Economic and Governance Variables of Frontier Stock Markets
Above Figure depicts the mean values of cross composite Index of economic and
governance index of 21 emerging countries and it shows that Malta has the highest mean value
of its cross composite index and Qatar has lowest mean value of its cross composite index.
Moreover, the distribution along the bottom axis of the figure shows that majority of the values
of economic composite index falls between -1 and 2 and most of them are lying on the right
side of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
-6 -4 -2 0 2 4 6 8 10 12
ArgentinaBahrain
BangladeshBotswana
BulgariaCote d'Ivoire
CroatiaCyprusEstoniaGhanaJordanKenya
LithuaniaMalta
NigeriaOmanQatar
RomaniaSerbia
Slovak RepublicSlovenia
Sri LankaTunisia
Vietnam
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97
Figure 6.31 Standard Deviation of Cross Composite Index of Economic and Governance Variables of Frontier Stock Markets
Aforementioned figure depicts that the dispersion of governance composite index and
it shows that Malta has highest dispersion in the data which is quite evident when we see their
governance indicators tells that there is quite volatility in the variables interest rates and trade.
Moreover, the distribution along the bottom axis of the figure shows that most the values of
governance composite index falls between 0 and 1.5 and majority of them are lying on the left
side of the figure.
Now coming towards the analysis of cross effects of economic and governance
composite indices, the study has generated a scatter plot with their distributions and Kernel Fit
line of these two composite indices. The scatter plot of these variable is appended below:
0 1 2 3 4 5 6 7
ArgentinaBahrain
BangladeshBotswana
BulgariaCote d'Ivoire
CroatiaCyprusEstoniaGhanaJordanKenya
LithuaniaMalta
NigeriaOmanQatar
RomaniaSerbia
Slovak RepublicSlovenia
Sri LankaTunisia
Vietnam
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98
Figure 6.32 Scatter plots of Composite of Indices of Economic and Governance Indices of Frontier Stock Markets
-5
-4
-3
-2
-1
0
1
2
3
4
PG
OV
-6 -4 -2 0 2 4 6 8
PECO
Aforementioned figure of scatter plots depicts that values of the observations have their
positive relations with observations are lying at scattered in upward trend pattern. According
to Kernel Fit line, it is deduced that as governance factors move in the same direction as
economic factors move.
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99
6.2.4 World Markets.
Now coming towards the combination of all regional markets, that is, world equity
markets. The index for composition of economic and governance factors of world market is
formed by using Principal Component Analysis (PCA). These PCAs are composite indices of
economic and governance and further their cross factors are built to explore their cross effects
on the development of stock markets.
6.2.3.1. Composite Index of Economic Factors: First of all, the composite index of
economic factors is formed by using PCA technique. The Eigen Values and Correlation matrix
of composite index of economic factors of frontier markets are appended below:
Table 6.24
Principal Components Analysis for Economic Variables of World 70 Stock Markets
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 2.212070 0.964343 0.2765 2.212070 0.2765
2 1.247727 0.121100 0.1560 3.459796 0.4325
3 1.126627 0.044478 0.1408 4.586423 0.5733
4 1.082149 0.209703 0.1353 5.668572 0.7086
5 0.872446 0.244014 0.1091 6.541018 0.8176
6 0.628432 0.109065 0.0786 7.169450 0.8962
7 0.519367 0.208184 0.0649 7.688817 0.9611
8 0.311183 --- 0.0389 8.000000 1.0000
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100
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 PC 7 PC 8
E1 0.011069 -0.358771 0.549087 0.567424 -0.231242 0.336840 0.195366 0.206378
E2 -0.062586 -0.246821 0.432829 -0.647864 0.437150 0.334160 0.159140 -0.002274
E3 -0.270704 0.131682 -0.364086 0.414601 0.652132 0.389980 0.165759 0.009480
E4 0.370984 0.443297 -0.191346 -0.191698 -0.336560 0.502208 0.445833 0.168061
E5 0.531497 -0.341032 -0.127036 0.117156 0.100341 -0.017653 0.253924 -0.704619
E6 0.476363 0.246875 0.194931 0.101976 0.185707 0.319359 -0.725808 -0.021467
E7 0.186221 0.538051 0.476857 0.159586 0.319700 -0.454879 0.337400 -0.000874
E8 0.490879 -0.360223 -0.245889 -0.008232 0.264795 -0.247712 0.071767 0.657358
Sub table-3: Ordinary correlations:
Variable E1 E2 E3 E4 E5 E6 E7 E8
E1 1.000000
E2 -0.022557 1.000000
E3 -0.067811 -0.126985 1.000000
E4 -0.195199 -0.132959 -0.186364 1.000000
E5 0.115561 -0.056593 -0.197085 0.237533 1.000000
E6 0.039468 -0.040500 -0.157520 0.341373 0.361747 1.000000
E7 0.030077 -0.016539 -0.047653 0.155165 0.019697 0.317627 1.000000
E8 -0.040251 -0.016718 -0.157900 0.153372 0.655817 0.313136 -0.016081 1.000000
Abovementioned table depicts the results of PCA for Economic variables of world
stock markets constituting 70 countries. Sub table-3 shows the correlations among 08 economic
variables and it is revealed that (E5) and (E8) are having the highest correlation of 0.65,
whereas the lowest correlation of 0.019 is between (E5) and (E7). On the other side, there is
highest negative correlation of -0.19 between (E1) and (E4).
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of economic variables, which is depicted below:
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101
Figure 6.33 Mean of Composite Index of Economic Variables of World Stock Markets
The above Figure. depicts the mean values of composite economic index of 70 frontier
countries and it shows that Singapore has the highest mean value of its cross composite index
and Ghana has lowest mean value of its cross composite index. Moreover, the distribution
along the bottom axis of the figure shows that majority of the values of economic composite
index falls between -1 and 1 and most of them are lying on the right side of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted below:
-3 -2 -1 0 1 2 3 4 5 6
ArgentinaAustralia
AustriaBahrain
BangladeshBelgium
BotswanaBrazil
BulgariaCanada
ChileChina
ColombiaCote d'Ivoire
CroatiaCyprus
Czech RepublicDenmark
Egypt, Arab Rep.EstoniaFinlandFrance
GermanyGhana
GreeceHong Kong SAR, China
HungaryIndia
IndonesiaIrelandIsraelItaly
JapanJordanKenya
Korea, Rep.LithuaniaMalaysia
MaltaMexico
MoroccoNetherlands
New ZealandNigeria
NorwayOman
PakistanPeru
PhilippinesPoland
PortugalQatar
RomaniaRussian Federation
SerbiaSingapore
Slovak RepublicSlovenia
South AfricaSpain
Sri LankaSweden
SwitzerlandThailandTunisiaTurkey
United Arab EmiratesUnited Kingdom
United StatesVietnam
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102
Figure 6.34 Standard Deviation of Composite Economic Variables of World Stock Markets
Aforementioned figure depicts that Malta has highest dispersion in the data which is
quite evident when we see their governance indicators tells that there is quite volatility in the
variables interest rates and trade. Moreover, the distribution along the bottom axis of the figure
shows that most the values of governance composite index falls between 0.3 and 1.0 and
majority of them are lying on the left side of the figure.
6.2.3.2. Composite Index of Governance Factors: Secondly, the composite index of
governance factors is formed by using PCA technique and its Eigen values are appended below.
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
ArgentinaAustralia
AustriaBahrain
BangladeshBelgium
BotswanaBrazil
BulgariaCanada
ChileChina
ColombiaCote d'Ivoire
CroatiaCyprus
Czech RepublicDenmark
Egypt, Arab Rep.EstoniaFinlandFrance
GermanyGhana
GreeceHong Kong SAR, China
HungaryIndia
IndonesiaIrelandIsraelItaly
JapanJordanKenya
Korea, Rep.LithuaniaMalaysia
MaltaMexico
MoroccoNetherlands
New ZealandNigeria
NorwayOman
PakistanPeru
PhilippinesPoland
PortugalQatar
RomaniaRussian Federation
SerbiaSingapore
Slovak RepublicSlovenia
South AfricaSpain
Sri LankaSweden
SwitzerlandThailandTunisiaTurkey
United Arab EmiratesUnited Kingdom
United StatesVietnam
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103
Table 6.25
Principal Components Analysis for Governance Variables of World Stock Markets (70
Countries)
Sub table-1: Eigenvalues: (Sum = 8, Average = 1)
Cumulative Cumulative
Number Value Difference Proportion Value Proportion
1 5.207129 4.877016 0.8679 5.207129 5.207129
2 0.330113 0.060145 0.0550 5.537241 0.330113
3 0.269968 0.179274 0.0450 5.807209 0.269968
4 0.090693 0.035605 0.0151 5.897903 0.090693
5 0.055088 0.008078 0.0092 5.952990 0.055088
6 0.047010 --- 0.0078 6.000000 0.047010
Sub table-2: Eigenvectors (loadings):
Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6
G1 0.424090 -0.199345 -0.186390 -0.313092 -0.452207 0.665695
G2 0.422540 -0.173127 -0.274330 -0.142948 0.828406 0.097666
G3 0.384231 -0.337787 0.840653 0.172705 0.041727 -0.000982
G4 0.416930 0.014610 -0.344400 0.818406 -0.171882 -0.089510
G5 0.425882 -0.122138 -0.145793 -0.414130 -0.276291 -0.731171
G6 0.372515 0.895018 0.208416 -0.102078 0.040373 0.068472
Sub table-3: Ordinary correlations:
Variable G1 G2 G3 G4 G5 G6
G1 1.000000 G2 0.944768 1.000000 G3 0.822448 0.802101 1.000000 G4 0.915314 0.923145 0.766807 1.000000 G5 0.951607 0.944216 0.825524 0.912514 1.000000 G6 0.757272 0.756508 0.691295 0.785424 0.782673 1.000000
Aforesaid table shows the results of PCA for Governance variables of emerging stock
markets constitutes 21 countries. Sub table-3 shows the correlations among 06 governance
variables and it is revealed that G1 and G5 are having the highest correlation of 0.95, whereas
Page 127
104
the lowest correlation of 0.69 is between G1 and G6. On the other side, there is no negative
correlation in the governance variables.
To analyze the trend of average values in the data, a graph has been generated as Means
of composite of governance variables, which is depicted below:
Figure 6.35 Mean of Composite Governance Variables of World Stock Markets
-6 -5 -4 -3 -2 -1 0 1 2 3 4
Argentina
Austria
Bangladesh
Botswana
Bulgaria
Chile
Colombia
Croatia
Czech Republic
Egypt, Arab Rep.
Finland
Germany
Greece
Hungary
Indonesia
Israel
Japan
Kenya
Lithuania
Malta
Morocco
New Zealand
Norway
Pakistan
Philippines
Portugal
Romania
Serbia
Slovak Republic
South Africa
Sri Lanka
Switzerland
Tunisia
United Arab Emirates
United States
The above Figure shows the mean values of composite Index of governance variables
of 70 world countries and it shows that Malta has the highest mean value of its governance
composite index, while Nigeria has the lowest mean value of its governance composite.
Moreover, the distribution along the bottom axis of the figure shows that majority of the values
of economic composite index falls between -2 and 2 and most of them are lying on the right
central part of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite economic variables, which is depicted in the figure 6.36:
Page 128
105
Figure 6.36 Standard Deviation of Composite of Governance Variables of World Stock Markets
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5
Argentina
Bangladesh
Bulgaria
Colombia
Czech Republic
Finland
Greece
Indonesia
Japan
Lithuania
Morocco
Norway
Philippines
Romania
Slovak Republic
Sri Lanka
Tunisia
United States
Aforementioned figure depicts that there is quite dispersion in the governance
composite index and Slovak republic dispersion is quite high, when we see their governance
indicators tells that there is quite volatility. Moreover, the distribution along the bottom axis of
the figure shows that most the values of economic composite index falls below 0.4 and majority
of them are lying on the left side of the figure.
6.2.3.3. Cross Composite Index of Economic and Governance Factors: The index
for cross composition of economic and governance factors of emerging market is formed by
the interaction of these variables.
To analyze the trend of average values in the data, a graph has been generated as Means
of composites of economic governance variables, which is depicted below:
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106
Figure 6.37 Mean of Cross Composite Index of Economic and Governance Variables of World Stock Markets
Above Figure depicts the mean values of cross composite Index of economic and
governance index of 70 world countries and it shows that Singapore has the highest mean value
of its cross composite index. Moreover, the distribution along the bottom axis of the figure
shows that majority of the values of economic composite index falls between -2 and 2 and most
of them are lying on the left side of the figure.
In order to observe the dispersion in the data, a graph has been generated as standard
deviation of composite of economic variables, which is depicted in the figure 6.38
-6 -4 -2 0 2 4 6 8 10 12 14
ArgentinaAustralia
AustriaBahrain
BangladeshBelgium
BotswanaBrazil
BulgariaCanada
ChileChina
ColombiaCote d'Ivoire
CroatiaCyprus
Czech RepublicDenmark
Egypt, Arab Rep.EstoniaFinlandFrance
GermanyGhana
GreeceHong Kong SAR, China
HungaryIndia
IndonesiaIreland
IsraelItaly
JapanJordanKenya
Korea, Rep.LithuaniaMalaysia
MaltaMexico
MoroccoNetherlands
New ZealandNigeria
NorwayOman
PakistanPeru
PhilippinesPoland
PortugalQatar
RomaniaRussian Federation
SerbiaSingapore
Slovak RepublicSlovenia
South AfricaSpain
Sri LankaSweden
SwitzerlandThailand
Tunis iaTurkey
United Arab EmiratesUnited Kingdom
United StatesVietnam
Page 130
107
Figure 6.38 Standard Deviation of Cross Composite Index of Economic and Governance Variables of World Stock Markets
Aforementioned figure depicts that the dispersion of governance composite index and
it shows that Norway has highest dispersion in the data. Moreover, the distribution along the
bottom axis of the figure shows that most the values of governance composite index falls
between 0.25 and 1.25 and majority of them are lying on the left side of the figure.
Now coming towards the analysis of cross effects of economic and governance
composite indices, the study has generated a scatter plot with their distributions and Kernel Fit
line of these two composite indices. The scatter plot of these variable is appended below:
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5
Argentina
Austria
Bangladesh
Botswana
Bulgaria
Chile
Colombia
Croatia
Czech Republic
Egypt, Arab Rep.
Finland
Germany
Greece
Hungary
Indonesia
Israel
Japan
Kenya
Lithuania
Malta
Morocco
New Zealand
Norway
Pakistan
Philippines
Portugal
Romania
Serbia
Slovak Republic
South Africa
Sri Lanka
Switzerland
Tunis ia
United Arab Emirates
United States
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108
Figure 6.39 Scatter plots of Composite of Indices of Economic and Governance Indices of World Stock Markets
-6
-4
-2
0
2
4
Com
po
site I
nde
x o
f G
overn
an
ce V
ari
ab
les (
PG
OV
)
-4 -2 0 2 4 6 8
Composite Index of Economic Variables (PECO)
Aforementioned figure of scatter plots depicts that values of the observations have their
positive relations with observations are lying at scattered in upward trend pattern. According
to Kernel Fit line, it is deduced that as governance factors move in the same direction as
economic factors move.
Page 132
109
6.3. Results of Dynamic GMM for Panel data estimation
This study applies dynamic panel GMM and dynamic panel System GMM to tackle the
typical problems of Ordinary Least Squares (OLS) like endogeneity. The panel GMM has been
applied to all three regions of the world equity markets as developed, emerging and frontier
markets. Before applying panel GMM approach, the study has estimated the models for Fixed
Effects, Pooled and Random Effects. Their results suggest the suitability of GMM technique
and completed results are appended bellows:
Likelihood test for Redundant Fixed Effects Tests: The results of Likelihood test
for Redundant Fixed Effects Tests are depicted below:-
Effects Test Statistic d.f. Prob.
Cross-section F 1.358177 (13,953) 0.1735
Cross-section Chi-square 17.990384 13 0.1579
Lagrange Multiplier Tests for Pooled Tests: The results of Lagrange Multiplier
Tests for Pooled Tests are depicted below:-
Test Hypothesis
Cross-section Time Both
Breusch-Pagan 0.385031 3813.084 3813.469
(0.5349) (0.0000) (0.0000)
Honda 0.620509 61.75018 44.10273
(0.2675) (0.0000) (0.0000)
King-Wu 0.620509 61.75018 25.15605
(0.2675) (0.0000) (0.0000)
Standardized Honda 0.917347 67.50249 41.79053
(0.1795) (0.0000)
Standardized King-Wu 0.917347 67.50249 22.30559
(0.1795) (0.0000) (0.0000)
Gourierioux, et al.* -- -- 3813.469
(< 0.01)
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110
Hausman Test for Random Effects: The results of Hausman Test for Random
Effects are depicted below:-
Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 17.656298 13 0.1710
Above mentioned results of Hausman Test for Random Effects and Lagrange Multiplier
Tests for Pooled Tests shows that there are random effects in cross section, so the application
of Panel GMM is appropriate technique for our panel datasets.
Now, after it is decided that GMM is appropriate econometric technique for our panel
dataset, so the estimation of our study will be considered in the light of our panel dataset nature.
Question No 1 has been answered in the PCA results, now remaining questions are answered
by using GMM technique. The estimation of the GMM results is conducted as per research
questions of the study followed by its empirical model and concerned hypotheses, the details
of which are as follows:
6.2.2. Research Question No 2: This question of the study is related to the analysis of impact
of economic variables on stock market development, which is as follows:
Q No.2. What are the impacts of Economic variables on the equity market development of
World Stock Markets?
The Null Hypotheses for the Model-1 with exogenous variables as 08 Economic Variables
are appended below:-
H011: GDP growth rate do not affect the development of stock markets.
H012: Annual Inflation Rate do not affect the development of stock markets.
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111
H013: Real Interest rate do not affect the development of stock markets.
H014: Domestic credit to private sector as %age of GDP do not affect the development
of stock markets.
H015: Gross Dom Savings as % GDP do not affect the development of stock markets.
H016: Trade as %age of GDP do not affect the development of stock markets.
H017: FDI as %age of GDP do not affect the development of stock markets.
H018: Current Account Balance % of GDP do not affect the development of stock
markets.
H019: Composite variable for Governance factors do not affect the development of
stock markets.
The equation for testing the model-1 would be:
After estimating equation 1 in Dynamic GMM technique for the panel data set of developed
countries, the results are as follows:
Table 6.26
All Economic Variables of Developed Stock Markets (25 Countries) and Depended
Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.597964 0.017148 34.86990 0.0000
E1 3.467382 0.291562 11.89245 0.0000
E2 -2.842335 0.596598 -4.764235 0.0000
E3 -0.872048 0.500641 -1.741865 0.0824
E4 0.045208 0.192031 0.235423 0.8140
)1.(..........1 ititkitiit EYY
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112
E5 -5.479524 0.799343 -6.855038 0.0000
E6 0.756222 0.069139 10.93775 0.0000
E7 0.344247 0.191185 1.800598 0.0726
E8 1.333611 0.839905 1.587812 0.1133
Note: Complete estimated results of this table are referred at Appendix-4A
Table 6.27
All Economic Variables of Emerging Stock Markets (21 Countries) and Depended
Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.313267 0.131758 2.377600 0.0180
E1 1.189405 0.706579 1.683329 0.0932
E2 -0.562928 0.312187 -1.803174 0.0723
E3 0.060928 0.369616 0.164840 0.8692
E4 0.179470 0.293191 0.612128 0.5409
E5 0.594218 1.175760 0.505390 0.6136
E6 -0.257738 0.274936 -0.937448 0.3492
E7 0.361362 0.919325 0.393073 0.6945
E8 3.804343 0.862114 4.412807 0.0000
Note: Complete estimated results of this table are referred at Appendix-4B
Table 6.28
All Economic Variables of Frontier Stock Markets (24 Countries) and Depended Variable :
Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.538880 0.077532 6.950413 0.0000
E1 0.358943 0.154132 2.328806 0.0207
E2 -0.599268 0.511840 -1.170811 0.2428
E3 0.302298 0.301020 1.004244 0.3163
E4 -0.079352 0.222970 -0.355884 0.7222
E5 -0.281559 0.388632 -0.724487 0.4695
E6 0.518521 0.077727 6.671048 0.0000
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E7 -0.021566 0.065399 -0.329759 0.7419
E8 1.285982 0.513060 2.506496 0.0128
Note: Complete estimated results of this table are referred at Appendix-4C
Table 6.29
All Economic Variables of World Stock Markets (70 Countries) and Depended Variable of
Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.346661 0.001628 212.9583 0.0000
E1 1.133814 0.038646 29.33851 0.0000
E2 -1.124654 0.056726 -19.82595 0.0000
E3 -0.878688 0.039527 -22.23030 0.0000
E4 0.165796 0.011069 14.97878 0.0000
E5 -1.825737 0.081713 -22.34320 0.0000
E6 0.755894 0.012985 58.21152 0.0000
E7 0.155027 0.009660 16.04792 0.0000
E8 0.839415 0.045524 18.43899 0.0000
Note: Complete estimated results of this table are referred at Appendix-4D
6.2.3. Research Question No 3: The third question of the study pertains to the analysis of
impact of governance variables on stock market development, which is as follows:
Q No.3. What are the impacts of Governance variables on the equity market development of
World Stock Markets?
The Null Hypotheses for the Model-2 are appended below:-
H021: Voice and Accountability do not affect the development of stock markets.
H022: Political Stability and Absence of Violence do not affect the development of
stock markets.
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H023: Government Effectiveness do not affect the development of stock markets.
H024: Regulatory Quality do not affect the development of stock markets.
H025: Rule of Law do not affect the development of stock markets.
H026: Control of Corruption do not affect the development of stock markets.
The equation for testing the model-2 would be:
After estimating equation 2 in Dynamic GMM technique for the panel data set of
developed countries, the results are as follows:
Table 6.30
All Governance Variables of Developed Stock Markets (25 Countries) and Depended
Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.682435 0.025572 26.68715 0.0000
G1 -0.578566 1.102372 -0.524837 0.6000
G2 3.331904 0.750033 4.442345 0.0000
G3 -0.506273 0.134392 -3.767136 0.0002
G4 -3.274225 0.629735 -5.199368 0.0000
G5 3.235296 0.346588 9.334691 0.0000
G6 2.622147 0.264680 9.906852 0.0000
Note: Complete estimated results of this table are referred at Appendix-5A
Table 6.31
All Governance Variables of Emerging Stock Markets (21 Countries) and Depended
Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.065809 0.008060 8.165054 0.0000
G1 -0.056858 0.183259 -0.310258 0.7565
G2 0.897967 0.190954 4.702532 0.0000
)2.(..........1 ititjitiit GYY
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G3 0.183235 0.118612 1.544826 0.1233
G4 0.037555 0.272284 0.137927 0.8904
G5 -1.801663 0.379471 -4.747824 0.0000
G6 -0.246916 0.054973 -4.491621 0.0000
Note: Complete estimated results of this table are referred at Appendix-5B
Table 6.32
All Governance Variables of Frontier Stock Markets (24 Countries) and Depended
Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.526775 0.012996 40.53328 0.0000
G1 -0.497369 0.111855 -4.446557 0.0000
G2 0.023674 0.120949 0.195737 0.8449
G3 0.591086 0.049267 11.99752 0.0000
G4 -0.050505 0.083193 -0.607082 0.5442
G5 -0.038931 0.145167 -0.268181 0.7887
G6 -0.007288 0.079142 -0.092084 0.9267
Note: Complete estimated results of this table are referred at Appendix-5C
Table 6.33
All Governance Variables of World Stock Markets (70 Countries) and Depended Variable
of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.427911 0.000794 538.7451 0.0000
G1 -1.319646 0.043310 -30.46979 0.0000
G2 1.453818 0.056146 25.89362 0.0000
G3 -0.200078 0.021599 -9.263211 0.0000
G4 -0.246351 0.070530 -3.492870 0.0005
G5 2.153088 0.044617 48.25721 0.0000
G6 1.073616 0.017052 62.96247 0.0000
Note: Complete estimated results of this table are referred at Appendix-5D
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6.2.4. Research Question No 4: The fourth question of the study pertains to the analysis of
combined impacts of economic and governance variables on stock market development, which
is as follows:
Q No.4. What are the combined impacts of Governance and Economic variables on the
development of stock market concerning World Equity Markets?
This model measures the joint impact of economic variables, governance and governance
factors on the stock market development
The Null Hypotheses for this Model are appended below:-
H011: GDP growth rate do not affect the development of stock markets.
H012: Annual Inflation Rate do not affect the development of stock markets.
H013: Real Interest rate do not affect the development of stock markets.
H014: Domestic credit to private sector as %age of GDP do not affect the development
of stock markets.
H015: Gross Dom Savings as % GDP do not affect the development of stock markets.
H016: Trade as %age of GDP do not affect the development of stock markets.
H017: FDI as %age of GDP do not affect the development of stock markets.
H018: Current Account Balance % of GDP do not affect the development of stock
H021: Voice and Accountability do not affect the development of stock markets.
H022: Political Stability and Absence of Violence do not affect the development of
stock markets.
H023: Government Effectiveness do not affect the development of stock markets.
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H024: Regulatory Quality do not affect the development of stock markets.
H025: Rule of Law do not affect the development of stock markets.
H026: Control of Corruption do not affect the development of stock markets.
The equation for testing the model-3 would be:
After estimating equation 3 in Dynamic GMM technique for the panel data set of developed
countries, the results are as follows:
Table 6.34
All Economic and Governance Variables of Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.545137 0.059281 9.195741 0.0000
E1 3.104011 1.264182 2.455352 0.0146
E2 -2.613447 2.217913 -1.178336 0.2395
E3 0.145459 1.753669 0.082946 0.9339
E4 0.284822 0.401479 0.709433 0.4785
E5 -6.886241 2.074414 -3.319608 0.0010
E6 0.755269 0.306879 2.461132 0.0144
E7 0.381957 0.815504 0.468370 0.6398
E8 0.440446 1.831055 0.240542 0.8101
G1 1.502209 1.374600 1.092834 0.2752
G2 3.553311 1.757400 2.021913 0.0440
G3 -0.541886 0.760343 -0.712686 0.4765
G4 -2.890350 1.300561 -2.222387 0.0269
G5 -0.384385 1.685741 -0.228021 0.8198
G6 2.721788 0.994169 2.737752 0.0065
Note: Complete estimated results of this table are referred at Appendix-6A
)3.(..........1 ititjitkitiit GEYY
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Table 6.35
All Economic and Governance Variables of Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.284319 0.111553 2.548744 0.0113
E1 1.277739 0.579049 2.206615 0.0280
E2 -0.659128 0.263382 -2.502559 0.0128
E3 -0.078533 0.313548 -0.250466 0.8024
E4 0.302719 0.224006 1.351391 0.1775
E5 -0.166436 0.975932 -0.170541 0.8647
E6 0.177252 0.186361 0.951119 0.3422
E7 0.357971 0.233686 1.531842 0.1265
E8 3.259688 1.037422 3.142104 0.0018
G1 0.004531 0.333132 0.013600 0.9892
G2 0.335903 0.176556 1.902529 0.0580
G3 0.188202 0.173330 1.085805 0.2784
G4 0.335695 0.377145 0.890095 0.3741
G5 -0.713518 0.662969 -1.076246 0.2826
G6 -0.655727 0.441015 -1.486861 0.1380
Note: Complete estimated results of this table are referred at Appendix-6B
Table 6.36
All Economic and Governance Variables of Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.526180 0.049394 10.65260 0.0000
E1 0.337931 0.138198 2.445259 0.0152
E2 -0.091362 0.235204 -0.388438 0.6980
E3 -0.052941 0.083830 -0.631531 0.5283
E4 -0.347753 0.286500 -1.213795 0.2260
E5 -0.751510 0.183769 -4.089421 0.0001
E6 0.125278 0.151272 0.828164 0.4084
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E7 0.138549 0.048630 2.849012 0.0048
E8 -0.051486 0.244437 -0.210633 0.8333
G1 -0.266518 0.183906 -1.449205 0.1485
G2 -0.453460 0.393824 -1.151429 0.2507
G3 0.183899 0.144857 1.269523 0.2054
G4 -0.332209 0.240763 -1.379817 0.1689
G5 0.440128 0.476963 0.922773 0.3570
G6 0.031788 0.159366 0.199464 0.8421
Note: Complete estimated results of this table are referred at Appendix-6C
Table 6.37
All Economic and Governance Variables of World Stock Markets (70 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.327335 0.002463 132.9053 0.0000
E1 1.053651 0.062257 16.92414 0.0000
E2 -0.983563 0.104093 -9.448874 0.0000
E3 -0.750464 0.073542 -10.20456 0.0000
E4 0.139105 0.023038 6.038110 0.0000
E5 -1.708201 0.150669 -11.33745 0.0000
E6 0.732865 0.022961 31.91849 0.0000
E7 0.171974 0.022617 7.603729 0.0000
E8 0.646026 0.116716 5.535043 0.0000
G1 -0.581906 0.099173 -5.867587 0.0000
G2 1.360316 0.101266 13.43313 0.0000
G3 -0.471558 0.031663 -14.89320 0.0000
G4 0.082546 0.138736 0.594987 0.5520
G5 1.851517 0.107640 17.20099 0.0000
G6 0.530616 0.069041 7.685482 0.0000
Note: Complete estimated results of this table are referred at Appendix-6D
Another equation for testing the model-3 would be:
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After estimating equation 4 in Dynamic GMM technique for the panel data set of developed
countries, the results are as follows:
Table 6.38
All Economic Variables and Composite Index of Governance Variables of Developed Stock
Markets (25 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.550686 0.045371 12.13734 0.0000
E1 4.311606 1.026719 4.199402 0.0000
E2 -2.890759 1.137811 -2.540631 0.0115
E3 0.056528 0.892238 0.063355 0.9495
E4 0.534424 0.387282 1.379933 0.1685
E5 -7.159806 1.588324 -4.507775 0.0000
E6 0.415630 0.266821 1.557711 0.1202
E7 0.610334 0.402158 1.517647 0.1300
E8 2.490189 1.538691 1.618382 0.1065
PGOV 27.82690 10.81695 2.572528 0.0105
Note: Complete estimated results of this table are referred at Appendix-7A
Table 6.39
All Economic Variables and Composite Index of Governance Variables of Emerging Stock
Markets (21Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.337380 0.093790 3.597178 0.0004
E1 1.009993 0.413552 2.442238 0.0152
E2 -2.467357 1.030531 -2.394258 0.0172
E3 0.728884 0.235190 3.099128 0.0021
E4 0.340306 0.367234 0.926674 0.3548
E5 2.947501 2.750370 1.071674 0.2847
E6 -0.364526 0.438448 -0.831401 0.4064
)4.(..........1 ititjitkitiit GovEYY
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E7 1.034320 0.808566 1.279203 0.2018
E8 4.001587 0.627373 6.378326 0.0000
PGOV -11.39619 9.027543 -1.262380 0.2078
Note: Complete estimated results of this table are referred at Appendix-7B
Table 6.40
All Economic Variables and Composite Index of Governance Variables of Frontier Stock
Markets (24 Countries) and Depended Variable of Market Capitalization %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.513626 0.076773 6.690172 0.0000
E1 0.332205 0.092064 3.608407 0.0004
E2 0.052924 0.131461 0.402583 0.6875
E3 0.082910 0.044068 1.881400 0.0609
E4 -0.277189 0.145858 -1.900407 0.0583
E5 -0.602946 0.091526 -6.587695 0.0000
E6 0.184761 0.033869 5.455203 0.0000
E7 0.160200 0.058052 2.759618 0.0061
E8 0.375126 0.140506 2.669823 0.0080
PGOV 3.243283 2.430260 1.334542 0.1830
Note: Complete estimated results of this table are referred at Appendix-7C
Table 6.41
All Economic Variables and Composite Index of Governance Variables of World Stock
Markets (70 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.252702 0.003437 73.52188 0.0000
E1 2.046103 0.060612 33.75750 0.0000
E2 -3.686475 0.182334 -20.21826 0.0000
E3 -1.586586 0.055425 -28.62560 0.0000
E4 0.583136 0.015244 38.25313 0.0000
E5 -4.384335 0.109060 -40.20123 0.0000
E6 0.841175 0.031276 26.89554 0.0000
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E7 0.219161 0.031826 6.886194 0.0000
E8 1.538483 0.113689 13.53235 0.0000
PGOV 49.64352 1.424435 34.85137 0.0000
Note: Complete estimated results of this table are referred at Appendix-7D
Another equation for testing the model-3 would be:
After estimating equation 5 in Dynamic GMM technique for the panel data set of developed
countries, the results are as follows:
Table 6.42
All Governance Variables and Composite Index of Economic Variables of Developed Stock
Markets (25 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.632040 0.024249 26.06447 0.0000
G1 -0.052337 0.689560 -0.075899 0.9395
G2 4.427687 0.613857 7.212893 0.0000
G3 -1.076544 0.150147 -7.169923 0.0000
G4 -3.405756 0.831739 -4.094743 0.0001
G5 1.705086 0.266668 6.394030 0.0000
G6 2.760608 0.303206 9.104742 0.0000
PECO 11.65072 2.101860 5.543055 0.0000
Note: Complete estimated results of this table are referred at Appendix-8A
)5.(..........1 ititjitkitiit GEcoYY
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Table 6.43
All Governance Variables and Composite Index of Economic Variables of Emerging Stock
Markets (21 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.223543 0.032918 6.790925 0.0000
G1 0.401874 0.479664 0.837823 0.4027
G2 0.521704 0.719001 0.725595 0.4686
G3 0.480946 0.254370 1.890733 0.0595
G4 -0.176801 1.020584 -0.173235 0.8626
G5 -1.544353 1.064800 -1.450369 0.1479
G6 -0.474660 0.817542 -0.580594 0.5619
PECO 17.62040 2.314310 7.613676 0.0000
Note: Complete estimated results of this table are referred at Appendix-8B
Table 6.44.
All Governance Variables and Composite Index of Economic Variables of Frontier Stock
Markets (24 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.632040 0.024249 26.06447 0.0000
G1 -0.052337 0.689560 -0.075899 0.9395
G2 4.427687 0.613857 7.212893 0.0000
G3 -1.076544 0.150147 -7.169923 0.0000
G4 -3.405756 0.831739 -4.094743 0.0001
G5 1.705086 0.266668 6.394030 0.0000
G6 2.760608 0.303206 9.104742 0.0000
PECO 11.65072 2.101860 5.543055 0.0000
Note: Complete estimated results of this table are referred at Appendix-8C
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Table 6.45
All Governance Variables and Composite Index of Economic Variables of World Stock
Markets (70 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.412911 0.001438 287.2115 0.0000
G1 -1.003143 0.036920 -27.17038 0.0000
G2 1.476385 0.052826 27.94794 0.0000
G3 -0.462321 0.014483 -31.92139 0.0000
G4 -0.063720 0.036346 -1.753157 0.0800
G5 2.141841 0.036974 57.92854 0.0000
G6 0.972312 0.019071 50.98503 0.0000
PECO 14.09162 0.200610 70.24373 0.0000
Note: Complete estimated results of this table are referred at Appendix-8D
6.2.5. Research Question No 5: Fifth question of the study pertains to the analysis of cross
effects of economic and governance variables on stock market development, which is as
follows:
Q No.5. What are the cross effects of governance and economic factors on the development of
stock market?
This model measures the cross effects of governance factors and economic variables on stock
market development viz-a-viz impact of economic & governance variables on stock market
development.
The Null Hypotheses for the Model-4 with exogenous variables as Composite Economic and
Governance Factors are appended below:-
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125
H011: A composite of Economic variables do not affect the development of stock
markets.
H012: A composite of Governance variables do not affect the development of stock
markets.
H013: A cross composite of Economic and Governance variables do not affect the
development of stock markets.
The set of equation for testing the model-4 would be:
Where,
= Direct effect of Economic factors on the development of stock market (Y)
= Direct effect of Governance factors on the development of stock market (Y)
= Cross effects of Governance factors (Gov) and Economic Factors (Eco).on the
development of stock market (Y)
After estimating equation 6 in Dynamic GMM technique for the panel data set of developed
countries, the results are as follows:
Table 6.46
Composite Indices of Economic and Governance Variables of Developed Stock Markets (25
Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.662996 0.006466 102.5361 0.0000
PECO 13.43409 0.884676 15.18533 0.0000
PGOV 10.37992 1.365497 7.601572 0.0000
PECO*PGOV 2.477447 0.402805 6.150488 0.0000
Note: Complete estimated results of this table are referred at Appendix-9A
)6...(*1 itititkitjitkitiit GovEcoGovEcoYY
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Table 6.47
Composite Indices of Economic and Governance Variables of Emerging Stock Markets (21
Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.222044 0.007481 29.68213 0.0000
PECO 20.11261 0.772316 26.04195 0.0000
PGOV -4.352256 1.292038 -3.368520 0.0008
PECO*PGOV 0.959003 0.424660 2.258281 0.0246
Note: Complete estimated results of this table are referred at Appendix-9B
Table 6.48
Composite Indices of Economic and Governance Variables of Frontier Stock Markets (24
Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.603374 0.000783 770.2145 0.0000
PECO 1.266656 0.203741 6.216999 0.0000
PGOV 0.758107 0.088048 8.610106 0.0000
PECO*PGOV -0.568153 0.083030 -6.842738 0.0000
Note: Complete estimated results of this table are referred at Appendix-9C
Table 6.49
Composite Indices of Economic and Governance Variables of World Stock Markets (70
Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.386455 0.001405 275.0485 0.0000
PECO 18.22461 0.141548 128.7525 0.0000
PGOV 23.50915 0.582312 40.37210 0.0000
PECO*PGOV 7.614872 0.045824 166.1748 0.0000
Note: Complete estimated results of this table are referred at Appendix-9D
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6.2.6. Research Question No 6: Fifth question of the study pertains to the analysis of direct
and indirect effects governance variables on stock market development, which is as follows:
Q No.5. What are the indirect effects of governance factors through economic factors on the
development of stock markets?
This abovementioned question is to be answered by measuring the channel effects of
governance factors on stock market development and further the effect through economic
variables .
The Null Hypotheses for the Model-5 are appended below:-
H011: A composite of Economic variables do not affect the development of stock
markets.
H012: A composite of Governance variables do not affect the development of stock
markets.
H013: There is no direct effect of composite Governance variables on the development
of stock markets.
H014: There is no indirect effect of composite Governance variables through
Economic variables on the development of stock markets.
The set of equations for testing the model-5 would be:
)8...(*1 itititkitjitkitiit GovEcoGovEcoYY
)8...( aGovEco ititktit
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128
Now for estimating the direct effect of Governance(Gov) on Stock Market
development(Y) and indirect effects of Governance(Gov) factors on the development of Stock
Market (Y) through Economic Factors(Eco), the following methodology is adopted:-
First of all, above mentioned equations are estimated as a System and then estimated
values are placed in the equations and following steps are adopted:-
The expression is to to tested for Wald test for its significance
= Direct effect of Governance factors(Gov) on the development of stock market (Y)
= Indirect effect of Governance factors(Eco) on the development of stock market
(Y) through Economic Factors (Eco).
After estimating the set of equations in System option of Dynamic GMM technique for the
panel data set of developed countries, the results are as follows:
Table 6.50
Composite Indices of Economic and Governance Variables with indirect effect of Developed
Stock Markets (25 Countries) and Depended Variable of Market Capitalization as %age of
GDP
Variable Coefficient Std. Error t-Statistic Prob.
C(11) 0.646604 0.130420 4.957863 0.0000
C(12) -0.010593 0.014358 -0.737769 0.9395
C(13) 0.015863 0.010555 1.502991 0.0000
C(14) 0.851001 0.029454 28.89264 0.0000
C(21) -0.307497 0.214673 -1.432399 0.0001
C(22) 0.184279 0.089369 2.061990 0.0000
Note: Complete estimated results of this table are referred at Appendix-10A
)8(...........).().( bGovY
).(
.
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129
Table 6.51
Composite Indices of Economic and Governance Variables with indirect effect of Emerging
Stock Markets (21 Countries) and Depended Variable of Market Capitalization as %age of
GDP
Variable Coefficient Std. Error t-Statistic Prob.
C(11) 10.33597 3.095994 3.338499 0.0009
C(12) 0.816095 0.867415 0.940835 0.3471
C(13) 0.425459 0.504257 0.843734 0.3991
C(14) 0.793465 0.067957 11.67599 0.0000
C(21) -0.327752 0.260178 -1.259722 0.2082
C(22) 0.189012 0.094144 2.007693 0.0450
Note: Complete estimated results of this table are referred at Appendix-10B
Table 6.52
Composite Indices of Economic and Governance Variables with indirect effect of Frontier
Stock Markets (24 Countries) and Depended Variable of Market Capitalization as %age of
GDP
Variable Coefficient Std. Error t-Statistic Prob.
C(11) 6.056385 1.059980 5.713679 0.0000
C(12) 0.370360 0.628811 0.588984 0.5561
C(13) 0.557779 0.418102 1.334075 0.1826
C(14) 0.856991 0.032050 26.73908 0.0000
C(21) -0.009275 0.160960 -0.057625 0.9541
C(22) 0.301746 0.058461 5.161530 0.0000
Note: Complete estimated results of this table are referred at Appendix-10C
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130
Table 6.53
Composite Indices of Economic and Governance Variables with indirect effect of World
Stock Markets (70 Countries) and Depended Variable of Market Capitalization as %age of
GDP
Variable Coefficient Std. Error t-Statistic Prob.
C(11) 4.324358 1.645948 2.627275 0.0087
C(12) 3.618091 0.911072 3.971247 0.0001
C(13) -0.002804 0.289226 -0.009695 0.9923
C(14) 0.951191 0.023176 41.04266 0.0000
C(21) -0.273180 0.141945 -1.924544 0.0544
C(22) 0.210139 0.061519 3.415829 0.0007
Note: Complete estimated results of this table are referred at Appendix-10D
6.2.7. Research Question No 7: This question of the study pertains to the analysis of inter-
dependence of Governance and Economic factors with each other, which is as follows:
Q No.7. How much is the inter-dependence of Governance and Economic factors with each
other?
The Null Hypotheses for the Model-4 with exogenous variables as Composite Economic and
Governance Factors are appended below:-
H011: A composite of Economic variables do not affect the development of stock
markets.
H012: A composite of Governance variables do not affect the development of stock
markets.
H013: A cross composite of Economic and Governance variables do not affect the
development of stock markets.
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The set of equation for testing the model-4 would be:
Where,
= Direct effect of development of stock market (Y) on Composite Economic factors
(Eco)
= Direct effect of Governance factors on Composite Economic factors (Eco)
After estimating equation 9 in Dynamic GMM technique for the panel data set of
developed countries, the results are as follows:
Table 6.54
Stock Market Development and Composite Index of Governance Variables of Developed
Stock Markets (25 Countries) and Depended Variable of Composite Economic factors (Peco)
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.342913 0.009164 37.41869 0.0000
Y 0.001422 0.000354 4.021204 0.0001
PGOV -0.096181 0.013402 -7.176911 0.0000
Note: Complete estimated results of this table are referred at Appendix-11A
Table 6.55
Stock Market Development and Composite Index of Governance Variables of Emerging
Stock Markets (21 Countries) and Depended Variable of Composite Economic factors (Peco)
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.484705 0.025322 19.14169 0.0000
Y 0.004823 0.000131 36.88157 0.0000
PGOV 0.140191 0.053639 2.613622 0.0094
Note: Complete estimated results of this table are referred at Appendix-11B
)9...(1 ititjitkitiit GovYEcoEco
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Table 6.56
Stock Market Development and Composite Index of Governance Variables of Frontier
Stock Markets (25 Countries) and Depended Variable of Composite Economic factors
(Peco)
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.342913 0.009164 37.41869 0.0000
Y 0.001422 0.000354 4.021204 0.0001
PGOV -0.096181 0.013402 -7.176911 0.0000
Note: Complete estimated results of this table are referred at Appendix-11C
Table 6.57
Stock Market Development and Composite Index of Governance Variables of World Stock
Markets (70 Countries) and Depended Variable of Composite Economic factors (Peco)
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.057129 0.002154 26.52363 0.0000
Y 0.000865 4.95E-06 174.7373 0.0000
PGOV 0.029588 0.003113 9.504310 0.0000
Note: Complete estimated results of this table are referred at Appendix-11D
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CHAPTER 7
SUMMARY AND CONCLUSIONS
7.1. Combined Results
This study focuses on finding the Determinants of Stock Market Development in the
panel data of Developed, Emerging and Frontier Markets. The econometric technique of
Dynamic Panel GMM has been applied to the four regions of the global markets and there are
total of five models tested and each model has been tested in each region to explore the
following effects:
(i) Direct impact of Economic on the Development of Stock Markets
(ii) Direct and Indirect impact of Governance on the Development of Stock Markets
(iii) Combined Effect of Economic and Governance Factors on the Development of
Stock Market.
(iv) Cross Effects of Economic and Governance Factors on the Development of
Stock Market.
(v) Reverse impact of Development of Stock Markets on Economic Indicators
The study has summarized the results into four categories, that is, Developed,
Emerging, Frontier and World equity markets.
7.1.1. Determinants of Stock Market Development for Developed Region. First part
of the summarized result table pertains to determinants of stock market development for
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developed region (25 countries) with depended variable of market capitalization as %age of
GDP, which is as follows:
Table 7.1
Determinants of Stock Market Development for Developed Region (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variables Model-1 Model-2 Model-3 Model-4 Model-5
Lagged Depended 0.545137*** 0.5512*** 0.6320*** 0.6629*** 0.6428***
GDP growth (annual %) 3.104011*** 4.2395***
Inflation, CPI (annual %) -2.613447 -2.7855**
Real interest rate (%) 0.145459 -0.2114
Domestic Bank credit (% of GDP) 0.284822 0.5249
Gross domestic savings (% of GDP) -6.886241*** -6.6194***
Trade (% of GDP) 0.755269*** 0.3964
FDI (% of GDP) 0.381957 0.6911*
Current A/C balance (% of GDP) 0.440446
Control of Corruption 1.502209 -0.0523
Government Effectiveness 3.553311** 4.4276***
Political Stability -0.541886 -1.0765***
Regulatory Quality -2.890350 -3.4057***
Rule of Law -0.384385 1.7050***
Voice and Accountability 2.721788** 2.7606***
Composite Economic Factors 11.650*** 13.434*** 11.135***
Composite Governance Factors 2.4413** 10.379*** 21.278***
Cross Factors of Composite
Economic & Governance Factors
(Eco*Gov)
2.477***
No. of observations 352 352 352 352 352
Instruments Rank 25 25 25 25 25
J-statistics (Sargan test for over
identifying restrictions)
15.6295 18.0643 22.0245 23.3441 23.1675
Arellano-Bond serial correlation test
AR(1)
0.0021 0.0000 0.0000 0.0000 0.0000
Arellano-Bond serial correlation test
AR(2)
0.7325 0.8567 0.9143 0.6315 0.6471
Note: Table shows Panel GMM Estimation Results of Market Capitalization as % GDP regressed on Economic
and Governance Factors alongwith their composite factors. The annual data period is from 1996 to 2015 The
symbols of ***, ** and * indicate significance levels at 1%, 5% and 10% respectively. The results of shown
Models have been extracted from the estimated Models in Chapter-6.
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The estimation of the panel data of developed equity has been conducted by using of
Panel GMM regression, the estimated results are presented in table 7.1. In this regression,
Market Capitalization as %age of GDP is regressed on various explanatory variables. This table
depicts the results five Models (1, 2, 3, 4 & 5) of developed market.
In Model-1 of the Table 7.1, the results indicate that the coefficient on lag dependent
variable is positive indicating that past values of Stock Market Development (Y) positively
affects the current values of Y of developed equity markets. The values of Lag dependent
variable further reveal that the speed of adjustment is 0.54 indicating that companies in
developed markets make 54% adjustment towards their target market capitalization. Further,
it is evident from the results that GDP growth, Gross Domestic Savings and Trade have their
significant impact on the development of stock market of developed region. If GDP growth is
to grow by one unit, then Market Capitalization is affected by 3.1 times, so the economic
growth plays a vital role in the movements of stock market capitalization. In the Governance
indicators, the Government Effectiveness and Accountability have the significant impacts on
the market capitalization.
In Model-2, the market capitalization of developed equity market has been regressed
on all economic variables and one composite index of governance variables. The result of this
model depict that GDP growth, inflation and gross domestic savings are statistically significant.
GDP growth affects the market capitalization by 4.3 times and gross domestic savings is
inversely affecting the relation by 6.6 times. Additionally, Inflation is inversely affects the
market capitalization by 2.8 times. Lag dependent variable in this model shows that the speed
of adjustment is 0.55 indicating that companies in developed markets make 55% adjustment
towards their target market capitalization.
According to Model-3, the market capitalization of developed equity market has been
regressed on all governance variables and one composite index of economic variables. The
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result of this model depict that Government Effectiveness, Political Stability, Regulatory
Quality, Rule of Law, Voice & Accountability and composite index of Economic variables are
statistically significant. Whereas, the composite index of Economic variables affects the
market capitalization by 11.6 times, which is also a quite positive significant value. Lag
dependent variable in this model shows that the speed of adjustment is 0.63 indicating that
companies in developed markets make 63% adjustment towards their target market
capitalization.
The Model-4 regresses market capitalization of developed equity market on composite
indices of governance and economic variables alongwith their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite indices
of governance and economic variables affects the market capitalization by 13.4 and 10.4 times
respectively. Additionally, cross effect of composite indices of governance and economic
variables affects the market capitalization by 2.5 times. Lag dependent variable in this model
shows that the speed of adjustment is 0.66 indicating that companies in developed markets
make 66% adjustment towards their target market capitalization.
In Model-5, market capitalization of developed equity market regresses on composite
indices of governance and economic variables without their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite indices
of governance and economic variables affects the market capitalization by 11.1 and 21.3 times
respectively. Lag dependent variable in this model shows that the speed of adjustment is 0.64
indicating that companies in developed markets make 64% adjustment towards their target
market capitalization.
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7.1.2. Reverse Impacts on Composite Economic Factors for Developed Region. In
order to depict the reverse impacts on composite economic factors for developed region (25
countries), the summarized results are on the following table:
Table 7.2
Reverse Impacts on Composite Economic Factors for Developed Region (25 Countries)
and Depended Variable : Composite Economic Factors
Variables Model-1A Model-2A Model-3A
Lagged Depended 0.3429*** 0.3393*** 0.3888***
Market Capitalization as %age of GDP 0.0014*** 0.0015***
Composite Governance Factors -0.0961*** -0.024***
No. of observations 339 347 306
J-statistics (Sargan test for over identifying
restrictions)
22.0343 23.3526 23.4962
Arellano-Bond serial correlation test AR(1) 0.0000 0.0000 0.0000
Arellano-Bond serial correlation test AR(2) 0.8312 0.8567 0.7352
Note: Table shows Panel GMM Estimation Results of Composite Economic factors regressed on Stock Market
Capitalization as % GDP and Governance Factors. The annual data period is from 1996 to 2015. The
symbols of ***, ** and * indicate the significance levels at 1%, 5% and 10% respectively. The results of
shown Models have been extracted from the estimated Models in Chapter-6.
In Table 7.2, the Composite Index of Economic Variables is regressed with explanatory
variables of Composite Index of Governance Variables and Market Capitalization as %age of
GDP. This table depicts the results of three Models (1A, 2A & 3A) of developed market.
In Model-1A, composite index of economic variables regresses on composite indices
of governance variables and market capitalization of developed equity market. The result of
this model shows that the explanatory variables are statistically significant. The composite
indices of governance variables affects the composite index of economic variables by 0.14%
and stock market development inversely affects the composite index of economic variables by
9.6%. Lag dependent variable in this model shows that the speed of adjustment is 0.34
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indicating that companies in developed markets make 34% adjustment towards their target
market capitalization.
Furthermore, composite index of economic variables are regressed on composite
indices of governance variables and market capitalization of developed equity market
separately. In Model-2A, composite index of economic variables are regressed on composite
index of governance variables and the result of this model shows that the explanatory variables
are statistically significant. The composite index of governance variables negatively affects
the composite index of economic variables by 2.4%. In Model-3A, composite index of
economic variables are regressed on stock market development and the result of this model
shows that the explanatory variables are statistically significant. The composite indices of
governance variables negatively affects the composite index of economic variables by 0.15%.
Lag dependent variable in this model shows that the speed of adjustment is 0.38 indicating that
companies in developed markets make 38% adjustment towards their target market
capitalization.
7.1.3. Scatter Plots of Stock Market Development for Developed Markets. To further
elucidate the diversity of relationship among stock market development, economic and
governance variables, the scatter plots along with their statistical distribution and Kernel Fit
line has been generated in this study. These are shown separately in the pairs of scatter plots
of stock market development & economic variables and scatter plots of stock market
development & governance variables. Firstly, the scatter plots of stock market development &
economic variables are shown as follows:
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Figure 7.1
Scatter Plot of Stock Market Development & Economic Variables of Developed Markets
-4
-2
0
2
4
6
8
Com
posite I
nd
ex o
f E
co
no
mic
Facto
rs (
PE
CO
)
1 2 3 4 5 6 7 8
Stock Market Development (LY)
Figure 7.1 depicts that observed data of stock market development and economic
variables are lying across the graph and showing its trend upward. The Kernel Fit posit the
mix trend that in the very few initial values behave in opposite direction and afterwards as the
stock market development increases, so the increase in economic factors in the same direction
and vice versa if economic factors going down then it also affects development of stock
markets. On each axis, the distribution of their data is also displayed and it shows that the stock
market development data is mainly centrally located, whereas the data of economic variables
is heavily located in the first half of the distribution.
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140
Secondly, the scatter plots of stock market development & governance variables in the
developed markets are shown as follows:
Figure 7.2
Scatter Plot of Stock Market Development & Governance Variables of Developed Markets
-8
-6
-4
-2
0
2
4
Com
po
site I
nde
x o
f G
overn
an
ce F
acto
rs (
PG
OV
)
1 2 3 4 5 6 7 8
Stock Market Development (LY)
Figure 7.2 illustrates that observed data of stock market development and governance
variables are lying across the graph and showing its trend upward. Most of the values lie at the
middle top of the graph. The trend line drawn by using Kernel Fit posit quite mix trend that as
the stock market development increases, so the governance factors behave in different
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directions and vice versa if governance factors going down do not necessarily affects
development of stock markets. On each axis of the figure, the distribution of their data is also
displayed and it shows that the stock market development data is mainly centrally located,
whereas the data of governance variables is scattered across the distribution.
7.1.4. Determinants of Stock Market Development for Emerging Region. Second
part of the summarized results table pertains to determinants of stock market development for
emerging region (21 countries) with depended variable of market capitalization as %age of
GDP, which is as follows:
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Table 7.3
Determinants of Stock Market Development for Emerging Region (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variables Model-1 Model-2 Model-3 Model-4 Model-5
Lagged Depended 0.284319*** 0.33738*** 0.222044*** 0.6629*** 0.2095***
GDP growth (annual %) 1.277739** 1.00999***
Inflation, CPI (annual %) -0.659128*** -2.46735***
Real interest rate (%) -0.078533 0.728884***
Domestic Bank credit (% of GDP) 0.302719 0.340306
Gross domestic savings (% of GDP) -0.166436 2.947501
Trade (% of GDP) 0.177252 -0.364526
FDI (% of GDP) 0.357971 1.034320
Current A/C balance (% of GDP) 3.259688*** 4.00158***
Control of Corruption 0.004531 0.401874
Government Effectiveness 0.335903** 0.521704
Political Stability 0.188202 0.480946**
Regulatory Quality 0.335695 -0.176801
Rule of Law -0.713518 -1.544353
Voice and Accountability -0.655727 -0.474660
Composite Economic Factors 17.62040*** 20.1126*** 21.287***
Composite Governance Factors -11.39619 -4.3522*** -4.8667***
Cross Factors of Composite
Economic & Governance Factors
(Eco*Gov)
0.95900**
No. of observations 344 319 344 343 343
Instruments Rank 185 20 20 20 20
J-statistics (Sargan test for over
identifying restrictions)
318.06 8.0538 14.2326 23.3441 23.1675
Arellano-Bond serial correlation test
AR(1)
0.0021 0.0000 0.0000 0.0032 0.0021
Arellano-Bond serial correlation test
AR(2)
0.5050 0.9929 0.8511 0.52960 0.6061
Note: Table shows Panel GMM Estimation Results of Market Capitalization as % GDP regressed on Economic
and Governance Factors alongwith their composite factors. The annual data period is from 1996 to 2015 The
symbols of ***, ** and * indicate significance levels at 1%, 5% and 10% respectively. The results of shown
Models have been extracted from the estimated Models in Chapter-6.
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The estimation of the panel data of emerging equity has been done by incorporating the
econometric method of Panel GMM, the estimated results are presented in table 7.3. As per
this method, Market Capitalization as a %age of GDP is regressed on various explanatory
variables of economic and governance factors. This table depicts the results in five Models (1,
2, 3, 4 & 5) of emerging market.
In Model-1 of the Table 7.3, the results indicate that the coefficient on lag dependent
variable is positive indicating that past values positively affects current values of emerging
equity markets. Lag dependent variable further reveal that the speed of adjustment is 0.28
indicating that companies in developed markets make 28% adjustment towards their target
market capitalization. Furthermore, it is evident from the results that GDP growth, Inflation
and Current Account balance have their significant impact on the development of stock market
of emerging region. If GDP growth is to grow by one unit, then Market Capitalization is
affected by 1.27 times, so the economic growth plays a vital role in the movements of stock
market capitalization. In the Governance indicators, the Government Effectiveness has the
significant impacts on the market capitalization.
In Model-2 of the table 7.2 of the emerging equity markets, the market capitalization of
emerging equity market has been regressed on all economic variables and one composite index
of governance variables. The result of this model depict that GDP growth, inflation, interest
rates and current account balance are statistically significant. GDP growth affects the market
capitalization by 1.01 times and. Inflation is inversely affecting the relation by 2.4 times.
Additionally, Interest rate and current account balance affects the market capitalization by 0.7
and 4.0 times respectively. Lag dependent variable in this model shows that the speed of
adjustment is 0.34 indicating that companies in developed markets make 34% adjustment
towards their target market capitalization.
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According to Model-3, the market capitalization of emerging equity market has been
regressed on all governance variables and one composite index of economic variables. The
result of this model depict that Political Stability and composite index of Economic variables
are statistically significant. Whereas, the composite index of Economic variables affects the
market capitalization by 17.6 times, which is also a quite positive significant value. Lag
dependent variable in this model shows that the speed of adjustment is 0.22 indicating that
companies in emerging markets make 22% adjustment towards their target market
capitalization.
The Model-4 regresses market capitalization of emerging equity market on composite
indices of governance and economic variables along with their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite indices
of governance and economic variables affects the market capitalization by 20.1 and -4.3 times
respectively. Additionally, cross effects of composite indices of governance and economic
variables affects the market capitalization by 0.96 times. Lag dependent variable in this model
shows that the speed of adjustment is 0.66 indicating that companies in emerging markets make
66% adjustment towards their target market capitalization.
In Model-5, market capitalization of emerging equity market regresses on composite
indices of governance and economic variables without their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite indices
of governance and economic variables affects the market capitalization by 21.3 and -4.8 times
respectively. Lag dependent variable in this model shows that the speed of adjustment is 0.21
indicating that companies in emerging markets make 21% adjustment towards their target
market capitalization.
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7.1.5. Reverse Impacts on Composite Economic Factors for Emerging Region. In
order to depict the reverse impacts on composite economic factors for emerging region (21
countries), the summarized results are on the following table:
Table 7.4
Reverse Impacts on Composite Economic Factors for Emerging Region (21 Countries) and
Depended Variable : Composite Economic Factors
Variables Model-1A Model-2A Model-3A
Lagged Depended 0.484705*** 0.478638***
0.423222***
Market Capitalization as %age of GDP 0.004823***
0.00494***
Composite Governance Factors 0.140191*** 0.085739***
No. of observations 343 310 284
J-statistics (Sargan test for over identifying
restrictions)
17.8444 19.7793 13.654
Arellano-Bond serial correlation test AR(1) 0.0256 0.0025 0.0146
Arellano-Bond serial correlation test AR(2) 0.3512 0.3726 0.3226
Note: Table shows Panel GMM Estimation Results of Composite Economic factors regressed on Stock Market
Capitalization as % GDP and Governance Factors. The annual data period is from 1996 to 2015. The
symbols of ***, ** and * indicate the significance levels at 1%, 5% and 10% respectively. The results of
shown Models have been extracted from the estimated Models in Chapter-6.
In Table 7.4, the Composite Index of Economic Variables is regressed with explanatory
variables of Market Capitalization as %age of GDP and Composite Index of Governance
Variables. This table depicts the results in three Models (1A, 2A & 3A) of emerging markets.
In Model-1, composite index of economic variables regresses on composite indices of
governance variables and market capitalization of emerging equity market. The result of this
model shows that the explanatory variables are statistically significant. The composite indices
of governance variables affects the composite index of economic variables by 0.4% and stock
market development affects the composite index of economic variables by 14% in positive
direction. Lag dependent variable in this model shows that the speed of adjustment is 0.48
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indicating that companies in emerging markets make 48% adjustment towards their target
market capitalization.
Furthermore, composite index of economic variables are regressed on composite
indices of governance variables and market capitalization of emerging equity market
separately. In Model-2, composite index of economic variables are regressed on composite
indices of governance variables and the result of this model shows that the explanatory
variables are statistically significant. The composite indices of governance variables affects
the composite index of economic variables by 8.5% in positive direction. In Model-3,
composite index of economic variables are regressed on stock market development and the
result of this model shows that the explanatory variables are statistically significant. The
composite indices of governance variables negatively affects the composite index of economic
variables by 0.5%. Lag dependent variable in this model shows that the speed of adjustment is
0.42 indicating that companies in emerging markets make 42% adjustment towards their target
market capitalization.
7.1.6. Scatter Plots of Stock Market Development of Emerging Markets. To further
elucidate the diversity of relationship among stock market development, economic and
governance variables, scatter plots along with their distribution and Kernel Fit line has been
generated in this study. These are shown separately in the pairs of scatter plots of stock market
development & economic variables and scatter plots of stock market development &
governance variables. Firstly, the scatter plots of stock market development & economic
variables are shown as follows:
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147
Figure 7.3
Scatter Plot of Stock Market Development & Economic Variables of Emerging Markets
-6
-4
-2
0
2
4
6
Com
posite I
nd
ex o
f E
con
om
ic v
ara
ible
s (
PE
CO
)
1 2 3 4 5 6
Stock Market Development (LY)
Figure 7.3 depicts that observed data of stock market development and economic
variables of emerging markets that are lying across the graph and showing its trend upward.
The Kernel Fit line posits the positive trend that as the stock market development increases, so
the economic factors and vice versa if economic factors going down then it also affects
development of stock markets. On each axis, the distribution of their data is also displayed and
it shows that the stock market development data is mainly centrally located, whereas the data
of economic variables is heavily located in the first half of the distribution.
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Secondly, the scatter plots of stock market development & governance variables in the
emerging markets are shown as follows:
Figure 7.4
Scatter Plot of Stock Market Development & Governance Variables of Emerging Markets
-6
-4
-2
0
2
4
6
Com
po
site I
nde
x o
f G
ove
rna
nce
va
raib
les (
PG
OV
)
1 2 3 4 5 6
Stock Market Development (LY)
Figure 7.4 illustrates that observed data of stock market development and governance
variables are lying across the graph and not showing any linear trend. The Kernel Fit line posits
quite mix trend that as the stock market development increases, so the governance factors
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behave in different directions and vice versa if economic factors going down do not necessarily
affects development of stock markets. On each axis of the figure, the distribution of their data
is also displayed and it shows that the stock market development data in emerging markets is
mainly centrally located, whereas the data of governance variables is scattered across the
distribution.
7.1.7. Determinants of Stock Market Development for Frontier Region. Third part
of the summarized results table pertains to determinants of stock market development for
frontier region (24 countries) with depended variable of market capitalization as %age of GDP,
which is as follows:
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Table 7.5
Determinants of Stock Market Development for Frontier Region (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variables Model-1 Model-2 Model-3 Model-4 Model-5
Lagged Depended 0.526180*** 0.513626*** 0.61592*** 0.60337*** 0.6033***
GDP growth (annual %) 0.337931** 0.332205***
Inflation, CPI (annual %) -0.091362 0.052924
Real interest rate (%) -0.052941 0.082910**
Domestic Bank credit (% of GDP) -0.347753 -0.277189**
Gross domestic savings (% of GDP) -0.75151*** -0.60294***
Trade (% of GDP) 0.125278 0.184761***
FDI (% of GDP) 0.138549*** 0.160200***
Current A/C balance (% of GDP) -0.051486 0.375126***
Control of Corruption -0.266518 0.45851***
Government Effectiveness -0.453460 -0.24620
Political Stability 0.183899 0.43215***
Regulatory Quality -0.332209 -0.196686
Rule of Law 0.440128 0.242199
Voice and Accountability 0.031788 -0.265916
Composite Economic Factors 0.214651 1.266656*** 0.010182
Composite Governance Factors 3.243283 0.758107*** -0.11489**
Cross Factors of Composite
Economic & Governance Factors
(Eco*Gov)
-0.56815***
No. of observations 264 317 230 258 358
Instruments Rank 24 24 18 23 23
J-statistics (Sargan test for over
identifying restrictions)
12.8384 14.7391 12.6583 21.5611 23.1675
Arellano-Bond serial correlation test
AR(1)
0.0252 0.0000 0.0000 0.0000 0.0204
Arellano-Bond serial correlation test
AR(2)
0.8089 0.9994 0.9934 0.3182 0.7877
Note: Table shows Panel GMM Estimation Results of Market Capitalization as % GDP regressed on Economic
and Governance Factors alongwith their composite factors. The annual data period is from 1996 to 2015 The
symbols of ***, ** and * indicate significance levels at 1%, 5% and 10% respectively. The results of shown
Models have been extracted from the estimated Models in Chapter-6.
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In this section of, the estimation of the panel data of frontier region has been done by
incorporating the econometric method of Panel GMM regression, the estimated results are
presented in table 7.5. As per this regression, Market Capitalization as %age of GDP is
regressed on various explanatory variables economic and governance factors. This table depicts
the results in five Models (1, 2, 3, 4 & 5) of frontier equity markets.
In Model-1 of the Table 7.5, the results indicate that the coefficient on lag dependent
variable is positive indicating that past values positively affects current values of frontier equity
markets. The values of Lag dependent variable reveal that the speed of adjustment is 0.52
indicating that companies in frontier region make 52% adjustment towards their target market
capitalization. Furthermore, it is evident from the results that GDP growth, Gross Domestic
Savings and FDI have their significant impact on the development of stock market of frontier
region. If GDP growth is to grow by one unit, then Market Capitalization is affected by 0.34
times, so the economic growth plays a vital role in the movements of stock market
capitalization.
In Model-2 of the table 7.5 of the frontier equity markets, the market capitalization of
frontier equity markets has been regressed on all economic variables and one composite index
of governance variables. The result of this model depict that almost all of the economic
variables are statistically significant except annual inflation rate of frontier equity markets.
GDP growth affects the market capitalization by 0.33 times and. FDI and current account
balance affects the market capitalization by 0.16 and 0.37 times respectively. Lag dependent
variable in this model shows that the speed of adjustment is 0.51 indicating that companies in
frontier markets make 51% adjustment towards their target market capitalization.
According to Model-3, the market capitalization of frontier equity market has been
regressed on all governance variables and one composite index of economic variables. The
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result of this model depict that Political Stability and Control on corruption are statistically
significant. Lag dependent variable in this model shows that the speed of adjustment is 0.61
indicating that companies in frontier markets make 61% adjustment towards their target market
capitalization.
The Model-4 regresses market capitalization of frontier equity market on composite
indices of governance and economic variables alongwith their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite indices
of economic and governance variables affects the market capitalization by 1.26 and 0.75 times
respectively. Additionally, cross effect of composite indices of governance and economic
variables affects the market capitalization by -0.56 times. Lag dependent variable in this model
shows that the speed of adjustment is 0.60 indicating that companies in frontier markets make
60% adjustment towards their target market capitalization.
In Model-5, market capitalization of frontier equity market regresses on composite
indices of governance and economic variables without their cross effects. The result of this
model depict that all explanatory variables are not statistically significant, rather composite
index of governance variables have inverse relation with the development of stock market of
frontier region. The composite index of governance variables affects the market capitalization
4.8 times. Lag dependent variable in this model shows that the speed of adjustment is 0.61
indicating that companies in developed markets make 61% adjustment towards their target
market capitalization.
7.1.8. Reverse Impacts on Composite Economic Factors for Frontier Region. In
order to depict the reverse impacts on composite economic factors for frontier region (24
countries), the summarized results are produced on the following table:
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Table 7.6
Reverse Impacts on Composite Economic Factors for Frontier Region (24 Countries) and
Depended Variable : Composite Economic Factors
Variables Model-1A Model-2A Model-3A
Lagged Depended 0.3715*** 0.4026*** 0.3743***
Market Capitalization as %age of GDP 0.00216*** 0.0023***
Composite Governance Factors 0.34324*** 0.2742***
No. of observations 313 352 309
J-statistics (Sargan test for over identifying
restrictions)
21.3671 22.2252 21.1069
Arellano-Bond serial correlation test AR(1) 0.0347 0.0356 0.0452
Arellano-Bond serial correlation test AR(2) 0.8481 0.7071 0.7197
Note: Table shows Panel GMM Estimation Results of Composite Economic factors regressed on Stock Market
Capitalization as % GDP and Governance Factors. The annual data period is from 1996 to 2015. The
symbols of ***, ** and * indicate the significance levels at 1%, 5% and 10% respectively. The results of
shown Models have been extracted from the estimated Models in Chapter-6.
In Table 7.6, the Composite Index of Economic Variables is regressed with explanatory
variables of Market Capitalization as %age of GDP and Composite Index of Governance
Variables of frontier markets. This table depicts the results in three Models (1A, 2A & 3A) of
frontier markets.
In Model-1A, composite index of economic variables regresses on composite indices
of governance variables and market capitalization of frontier equity market. The result of this
model shows that all the explanatory variables are statistically significant. The composite
indices of governance variables affects the composite index of economic variables by 0.2% and
stock market development affects the composite index of economic variables by 34% in
positive direction. Lag dependent variable in this model shows that the speed of adjustment is
0.37 indicating that companies in frontier markets make 37% adjustment towards their target
market capitalization.
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Furthermore, composite index of economic variables are regressed on composite
indices of governance variables and market capitalization of frontier equity market separately.
In Model-2, composite index of economic variables are regressed on composite indices of
governance variables and the result of this model shows that the explanatory variables are
statistically significant. The composite indices of governance variables affects the composite
index of economic variables by 8.5% in positive direction. In Model-3, composite index of
economic variables are regressed on stock market development and the result of this model
shows that the explanatory variables are statistically significant. The composite index of
governance variables positively affects the composite index of economic variables by 27%.
Lag dependent variable in this model shows that the speed of adjustment is 0.40 indicating that
companies in frontier markets make 40% adjustment towards their target market capitalization.
7.1.9. Scatter Plot of Stock Market Development of Frontier Markets. To further
elucidate the diversity of relationship among stock market development, economic and
governance variables, scatter plots along with their distribution and Kernel Fit line has been
generated in this study. These are shown separately in the pairs of scatter plots of stock market
development & economic variables and scatter plots of stock market development &
governance variables. Firstly, the scatter plots of stock market development & economic
variables are shown as follows:
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Figure 7.5
Scatter Plot of Stock Market Development & Economic Variables of Frontier Markets
-6
-4
-2
0
2
4
6
8
Cio
mposit
e I
nd
ex o
f E
co
no
mic
Va
ria
ble
s (
PE
CO
)
-4 -2 0 2 4 6
Stock Market Devlopment (LY)
Figure 7.5 depicts that observed data of stock market development and economic
variables of frontier markets that are mainly lying at the right upper side of the graph except
few isolated observation lying in the left side of the graph and rest of the graph is showing its
upward trend. The Kernel Fit posits mainly the positive trend that as the stock market
development increases, so the economic factors and vice versa if economic factors going down
then it also affects development of stock markets. On each axis, the distribution of their data
is also displayed and it shows that the stock market development data is mainly rightly located,
whereas the data of economic variables is mainly located in the central part of the distribution.
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Secondly, the scatter plots of stock market development & governance variables in the
frontier markets are shown as follows:
Figure 7.6
Scatter Plot of Stock Market Development & Governance Variables of Frontier Markets
-5
-4
-3
-2
-1
0
1
2
3
4
Com
po
site I
nde
x o
f G
overn
an
ce V
ari
ab
les (
PG
OV
)
-4 -2 0 2 4 6
Stock Market Development (LY)
Figure 7.6 illustrates that observed data of stock market development and governance
variables are lying across the graph and not showing any linear trend. The Kernel Fit line posits
quite mix trend that as the stock market development increases, so the governance factors
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behave in different directions and vice versa if economic factors going down do not necessarily
affects development of stock markets. On each axis of the figure, the distribution of their data
is also displayed and it shows that the stock market development data in frontier markets is
located on the right side of the distribution, whereas the data of governance variables is
scattered across the distribution similar to the characteristics of other regions.
7.1.10. Determinants of Stock Market Development for World Markets. Third part
of the summarized results table pertains to determinants of stock market development for all
World Markets (70 countries) with depended variable of market capitalization as %age of GDP,
which is as follows:
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Table 7.7
Determinants of Stock Market Development for All World Equity Markets (70 Countries)
and Depended Variable : Market Capitalization as %age of GDP
Variables Model-1 Model-2 Model-3 Model-4 Model-5
Lagged Depended 0.327335*** 0.25270*** 0.4129*** 0.3864*** 0.4309***
GDP growth (annual %) 1.053651*** 2.04610***
Inflation, CPI (annual %) -0.98356*** -3.6864***
Real interest rate (%) -0.75046*** -1.5865***
Domestic Bank credit (% of GDP) 0.139105*** 0.58313***
Gross domestic savings (% of GDP) -1.70820*** -4.3843***
Trade (% of GDP) 0.73286*** 0.84117***
FDI (% of GDP) 0.17197*** 0.21916***
Current A/C balance (% of GDP) 0.64602*** 1.53848***
Control of Corruption -0.58190*** -1.0031***
Government Effectiveness 1.36031*** 1.47638***
Political Stability -0.47155 -0.4623***
Regulatory Quality 0.08254*** -0.06372*
Rule of Law 1.851517*** 2.1418***
Voice and Accountability 0.530616*** 0.9723***
Composite Economic Factors 14.091*** 18.224*** 16.281***
Composite Governance Factors 49.643*** 23.509*** 18.296***
Cross Factors of Composite
Economic & Governance Factors
(Eco*Gov)
7.6148***
No. of observations 824 809 824 824 824
Instruments Rank 67 67 67 67 67
J-statistics (Sargan test for over
identifying restrictions)
58.5999 54.2453 61.5439 61.9803 65.3912
Arellano-Bond serial correlation test
AR(1)
0.0000 0.0000 0.0000 0.0000 0.0000
Arellano-Bond serial correlation test
AR(2)
0.9999 0.9985 0.9990 0.9910 0.9568
Note: Table shows Panel GMM Estimation Results of Market Capitalization as % GDP regressed on Economic
and Governance Factors alongwith their composite factors. The annual data period is from 1996 to 2015 The
symbols of ***, ** and * indicate significance levels at 1%, 5% and 10% respectively. The results of shown
Models have been extracted from the estimated Models in Chapter-6.
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This is final section of estimation, in which, estimation of the panel data of whole world
equity markets has been done by incorporating the econometric method of Panel GMM
regression, the estimated results are presented in table 7.7. According to this regression, Market
Capitalization as %age of GDP is regressed on various explanatory variables of economic and
governance factors. This table portrays the results in five Models (1, 2, 3, 4 & 5) of world
equity markets.
In Model-1 of the Table 7.7, the results depict that the coefficient on lag dependent
variable is positive indicating that past values positively affects current values of world equity
markets. The values of Lag dependent variable reveal that the speed of adjustment is 0.32
indicating that companies in frontier region make 32% adjustment towards their target market
capitalization. Furthermore, it is evident from the results that all economic variables have their
significant impact on the development of stock market of world region. However, Inflation,
Real interest rate and Gross Domestic Savings have negative impact on the development of
stock markets. Furthermore, the results indicate that if GDP growth is to grow by one unit, then
Market Capitalization is affected by 1.05 times, so the economic growth plays a vital role in
the movements of stock market capitalization. Regarding, the results of governance variables,
it is revealed that all variables are statistically significant except political stability. These
results are quite in consonance with previous studies as conducted by Yartey (2008), Cherif
and Gazdar (2010) and Bayraktar (2014).
In Model-2 of the table 7.7 of the world equity markets, the market capitalization of
world equity markets has been regressed on all economic variables and one composite index
of governance variables. The result of this model depict that almost all of the economic
variables are statistically significant annual inflation rate of world equity markets. GDP growth
affects the market capitalization by 2.04 times and. FDI and current account balance affects
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the market capitalization by 0.21 and 1.53 times respectively. Lag dependent variable in this
model shows that the speed of adjustment is 0.25 indicating that companies in world markets
make 25% adjustment towards their target market capitalization.
According to Model-3, the market capitalization of world equity market has been
regressed on all governance variables and one composite index of economic variables. The
result of this model depict that all governance variables are statistically significant. Lag
dependent variable in this model shows that the speed of adjustment is 0.41 indicating that
companies in world markets make 41% adjustment towards their target market capitalization.
The Model-4 regresses market capitalization of world equity market on composite
indices of governance and economic variables alongwith their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite indices
of economic and governance variables affects the market capitalization by 18.2 and 23.5 times
respectively. Additionally, cross effect of composite indices of governance and economic
variables affects the market capitalization by 7.6 times. Lag dependent variable in this model
shows that the speed of adjustment is 0.38 indicating that companies in world markets make
38% adjustment towards their target market capitalization.
In Model-5, market capitalization of world equity market regresses on composite
indices of governance and economic variables without their cross effects. The result of this
model depict that all explanatory variables are statistically significant. The composite index of
economic and governance variables affects the market capitalization 16.28 and 18.29 times
respectively. Lag dependent variable in this model shows that the speed of adjustment is 0.43
indicating that companies in world markets make 43% adjustment towards their target market
capitalization.
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7.1.11. Reverse Impacts on Composite Economic Factors for all World Equity Markets.
In order to depict the reverse impacts on composite economic factors for all world
equity markets (70 countries), the summarized results are produced on the following table:
Table 7.8
Reverse Impacts on Composite Economic Factors for the All World Equity Markets
( 70 Countries) and Depended Variable : Composite Economic Factors
Variables Model-1A Model-2A Model-3A
Lagged Depended 0.05712*** 0.14356*** 0.05838***
Market Capitalization as %age of GDP 0.00086*** 0.00088***
Composite Governance Factors 0.02958*** 0.09980***
No. of observations 840 1084 840
J-statistics (Sargan test for over identifying
restrictions)
65.7542 68.8099 64.9826
Arellano-Bond serial correlation test AR(1) 0.0229 0.0211 0.0219
Arellano-Bond serial correlation test AR(2) 0.4657 0..4913 .4728
Note: Table shows Panel GMM Estimation Results of Composite Economic factors regressed on Stock Market
Capitalization as % GDP and Governance Factors. The annual data period is from 1996 to 2015. The
symbols of ***, ** and * indicate the significance levels at 1%, 5% and 10% respectively. The results of
shown Models have been extracted from the estimated Models in Chapter-6.
In Table 7.8, the Composite Index of Economic Variables is regressed with explanatory
variables of Market Capitalization as %age of GDP and Composite Index of Governance
Variables of world markets. This table depicts the results in three Models (1A, 2A & 3A) of
world markets.
In sub Model-1, composite index of economic variables regresses on composite indices
of governance variables and market capitalization of world equity market. The result of this
model shows that all the explanatory variables are statistically significant. The composite
indices of governance variables affects the composite index of economic variables by 2.9% and
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stock market development affects the composite index of economic variables by 0.1% in
positive direction. Lag dependent variable in this model shows that the speed of adjustment is
0.05 indicating that companies in world markets make 5% adjustment towards their target
market capitalization.
Furthermore, composite index of economic variables are regressed on composite
indices of governance variables and market capitalization of world equity market separately.
In Model-2, composite index of economic variables are regressed on composite indices of
governance variables and the result of this model shows that the explanatory variables are
statistically significant. The composite indices of governance variables affects the composite
index of economic variables by 9.9% in positive direction. In Model-3, composite index of
economic variables are regressed on stock market development and the result of this model
shows that the explanatory variables are statistically significant. The stock market
development positively affects the composite index of economic variables by 0.1%. Lag
dependent variable in this model shows that the speed of adjustment is 0.14 indicating that
companies in world markets make 14% adjustment towards their target market capitalization.
7.1.12. Scatter Plots of Stock Market Development of World Equity Markets. To
further elucidate the diversity of relationship among stock market development, economic and
governance variables, scatter plots along with their distribution and Kernel Fit line has been
generated for all world equity markets in this study. These are shown separately in the pairs of
scatter plots of stock market development & economic variables and scatter plots of stock
market development & governance variables. Firstly, the scatter plots of stock market
development & economic variables are shown as follows:
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Figure 7.7
Scatter Plot of Stock Market Development & Economic Variables of World Markets
-4
-2
0
2
4
6
8
Com
posite I
nd
ex o
f E
co
no
mic
Vari
able
s (
PE
CO
)
-4 -2 0 2 4 6 8
Stock Market Development (LY)
Figure 7.7 depicts that observed data of stock market development and economic
variables of emerging markets that are mainly lying at the lower right side of the graph except
few isolated observation lying in the left side of the graph and rest of the graph is showing its
upward trend. The Kernel Fit line of world equity markets posits mainly the positive trend that
as the stock market development increases, so the economic factors and vice versa if economic
factors going down then it also affects development of stock markets. On each axis, the
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distribution of their data is also displayed and it shows that the stock market development data
is mainly centrally located at the right side of the graph, whereas the data of economic variables
is mainly located in the lower central part of the distribution.
Secondly, the scatter plots of stock market development & governance variables in the
world markets are shown as follows:
Figure 7.8
Scatter Plot of Stock Market Development & Governance Variables of World Markets
-6
-4
-2
0
2
4
Com
po
site I
nde
x o
f G
ove
rna
nce
va
riab
les (
PG
OV
)
-4 -2 0 2 4 6 8
Stock Market Development (LY)
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Figure 7.8 portrays that observed data of stock market development and governance
variables are lying across the graph and not showing any linear trend. The Kernel Fit line posits
quite mix trend at beginning and further upward trend in the majority values of the distribution.
Therefore trend shows that as the stock market development increases, so the governance
factors behave in different directions in few values and majority of the observations follow
upward trend. On each axis of the figure, the distribution of their data is also displayed and it
shows that the stock market development data in world markets is almost normally distributed
on the right side of the distribution, whereas the data of governance variables is scattered across
the distribution.
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7.2. Overall Summary of the Empirical Results
The results of determinants of equity market development have been analyzed by
using statistical and econometric techniques on the panel data sets of world equity markets.
After thorough analysis, the overall summary of the results is appended below:
Table 7.9
Overall Summary of the Empirical Results
Models/Effects
Developed
Markets
(25)
Emerging
Markets
(21)
Frontier
Markets
(24)
World
Markets
(70)
Effects of Economic variables on the
equity market development
Positively
Significant
Positively
Significant
Positively
Significant
Positively
Significant
Effects of Governance variables on
the equity market development
Positively
Significant
Marginally
Significant
Marginally
Significant
Positively
Significant
Indirect Effects of Governance
variables through Economic
Variables on the equity market
development
Marginally
Significant
Not
Significant
Not
Significant
Positively
Significant
Combined Effects of Economic and
Governance variables on the equity
market development
Positively
Significant
Partially
Significant
Partially
Significant
Positively
Significant
Cross effects of Economic and
Governance variables on the equity
market development
Positively
Significant
Positively
Significant
Negatively
Significant
Positively
Significant
Reverse impacts of Stock market
development on Economic variables
Positively
Significant
Positively
Significant
Positively
Significant
Positively
Significant
Impacts of Governance variables on
Economic variables
Negatively
Significant
Positively
Significant
Positively
Significant
Positively
Significant
Table 7.9 depicts the model/effect wise summary of the results in four categories, that
is, Developed, Emerging, Frontier and World Equity Markets. The brief summaries of the
results according to different regions of the equity markets are as follows:
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7.2.1. Short Summary of the Results for Developed Equity Markets: In the developed
region, there are 25 stock markets and it is found that Economic and Governance variables
have their significant effects on the development of stock market. In Economic variables, the
GDP growth, Gross Domestic savings and Trade are the significant variables. In Governance
variables, Government Effectiveness and Accountability are the significant variables which are
in conformity with the results of Pagano (1993), Perotti and Van Oijen (2001) and El Wassal
(2013). While, the analysis of composite variables depict that both the composite variables of
Economic and Governance factors are having significant impact on the development of stock
markets. There is also significant indirect effect of Governance through Economic variables on
the development of equity markets. Moreover, the cross effects of Governance and Economic
factors are significant on the development of stock market. Both the indirect and cross effects
are the contributions of the study as well. On the other hand, the reverse analysis depicts that
development of stock market is also contributing the growth of economy which is in line with
study of Levine and Zervos (1996).
7.2.2. Short Summary of the Results for Emerging Equity Markets: In the
emerging region of the equity markets, there are 21 stock markets and it is found that mostly
Economic variables have their significant effects on the development of stock market except
few Governance variables have their significant effects. In Economic variables, the GDP
growth, Gross Domestic savings and Current Account Balance are the significant variables.
These results are in conformity with the studies as conducted by Yartey (2008) and Bayraktar
(2014). In Governance factors, only Government Effectiveness has significant impacts. The
analysis of composite variables depict that both the composite variables of Economic and
Governance factors are having significant impact on the development of stock markets but
Governance variables have their negative impacts. The cross effects of Governance and
Economic factors are significant on the development of stock market. On the other hand, the
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analysis for knowing contribution of stock markets in an economy of emerging markets, the
reverse analysis depicts that development of stock market is also contributing the growth of
economy that is in conformity with the results of Levine and Zervos (1996).
7.2.3. Short Summary of the Results for Frontier Equity Markets: In the frontier
region, there are 24 stock markets and it is found that Mainly Economic variables have their
significant effects on the development of stock market. In Economic variables, the GDP
growth, Gross Domestic savings and FDI are the significant variables. In Governance factors,
there are marginal significant impacts. The analysis of composite variables depict that both the
composite variables of Economic and Governance factors are having significant impact on the
development of stock markets but Governance variables have their negative impacts.
Moreover, the cross effects of Governance and Economic factors are significant on the
development of stock market. On the other hand, the analysis for knowing contribution of
stock markets in an economy of frontier markets, the reverse analysis depicts that development
of stock market is also contributing the growth of economy as per the study of Levine and
Zervos (1996).
7.2.4. Short Summary of the Results for World Equity Markets: As a whole of
world equity markets, there are 70 stock markets that have been identified by FTSE and it is
found that Economic and Governance variables have their significant effects on the
development of stock market. In Economic variables, mainly all are the significant variables
which are conformity with the results of Apergis et al. (2011). In Governance variables, all are
the significant variables except political stability and these results are in consonance with the
studies of El Wassal (2013) and Perotti and Van Oijen (2001). While, the analysis of
composite variables depict that both the composite variables of Economic and Governance
factors are having significant impact on the development of stock markets. There is also
positive significant effect of Governance through Economic variables on the development of
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equity markets. There is also significant indirect effect of Governance through Economic
variables on the development of world equity markets. In the same way, the cross effects of
Governance and Economic factors are significant on the development of world stock market.
Both the indirect and cross effects are the contributions of the study as well. On the other
hand, the reverse analysis depicts that development of world stock market is also contributing
the growth of their economy and these results are consonance with the studies of Levine and
Zervos (1996) and Kanetsi (2015).
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7.3. Conclusions
The study has explored multifaceted analysis of equity markets in all three regions of
the world stock markets. Despite having different dynamics and resources, there are few
similarities but there are some of the stark differences, which lead them to identify their
uniqueness. To narrow down the final words on this study, the conclusion is divided into two
segments, that is, micro and macro views.
First in the micro view, the study finds that effects of economic and governance factors
on stock market development of developed, emerging and frontier markets are peculiar in
nature and unique as per the dynamics of that particular region. For instance, this study finds
that economic and governance factors are more influential in developed region as compared to
their impacts in emerging and frontier region. In economic factors, it is found that GDP growth,
FDI, Trade and Stock market liquidity are found to be leading determinants of stock market
development. On the governance factors, it is revealed that Governance effectiveness and
Accountability are the main determinants in the development of stock markets.
Another dimension of micro view is conducted on the formation of composite indices
of Economic and Governance factors through Principal Component Analysis by using factors
for each region of the world equity markets. Afterwards, cross-index of Economic and
Governance Factors is formed for exploring the joint effects of these variables. The study
reveals that there is strong correlation in these composite variable in the developed region,
where is there is no clear pattern in the developing countries. The postulation behind this trend
is that institutions are strong in the developed countries and if there is any negative thing
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happens in the governance issues, the economy respond quickly. Whereas, in the developing
countries, the institutions are not fully strong and there are frequent governance issues as well.
Moreover, there is quite dispersion in the composite data of the developing countries. So, there
is no direct correlation in the composite factors of economic and governance in the developing
countries. The plausible postulation behind this phenomena could be the weak institutional
quality and frequent lower governance level in the developing countries.
Now coming towards macro view, the study finds that effects of economic and
governance factors on stock market development are not only unidirectional, but also
bidirectional as well. Particularly, the developed markets have dual effects on economic and
governance factors. The indirect effects of governance through economic variables revealed
the significant effects in developed markets and no indirect effects in emerging and frontier
markets. While analyzing the cross effects, it is revealed that it is quite significant in developed
markets and found quite trivial in emerging stock markets. The plausible reason for this
significance in developing market is due to the fact that developing countries, due to their
strong institutional quality, are more reactive to both the governance and economic issues. The
reverse impact of stock market development on the economic growth is also quite captivating
in which development of stock market also effect the growth of economy in all three regions
of the world.
At the outset of this study, it was postulated that governance factors might play a
significant role in emerging than developed markets, but the results show that markets in
emerging and frontier regions are effecting negatively by governance factors. There is no vivid
picture to identify its pattern except the weak institutional issues in developing countries.
Regarding the negative behavior of governance factors in emerging and frontier markets, the
plausible cause could be the governance issues in these countries. When the governance factors
are in positive side, the investor may not be necessarily investing in stock markets, rather they
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may invest in other sectors like banking, real estate or industry which are more safer and less
volatile than equity markets. On the other side, when the governance factors are not good, then
investor invests their money in stock market to get higher gain on their investment. The same
is validated by the negative significant values of gross domestic investment in emerging and
frontier markets. So, the inverse trend of investment augment the assertion of negative trend
in composite governance factors in developing countries.
While investigating the reverse impact of stock market development on the economic
factors, it is found that economic factors in developed and emerging markets are also prone to
the effects of stock market development. The postulation behind this phenomena is that stock
market is depicting the market performance of companies which are directly and indirectly
contributing to the growth of economy and in return the growth of economy is also effected by
the development of stock markets.
7.4. Policy Recommendations
Keeping in view the findings of the study on the economic and governance factors of
equity markets, the following policy recommendations are suggested:
1. The study recommends that determinants of equity market may not solely based
on economic factors rather the significance of governance factors may be taken into
account while taking the complete picture of the subject.
2. The spectrum for the application of governance factors may be enhanced
particularly in emerging and frontier regions of the world equity markets.
3. The linkage of economic and governance factors may be thoroughly researched
and common factors may be established for effective determinants of financial markets.
7.5. Contributions of the Study
The aim of the study is to find the important determinants of stock market development
by using the panel data of developed, emerging and frontier regions. During the empirical
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testing, it is found that economic factors and stock markets have their dual effects. The
significant contributions of the study are as follows:
1. First, the study has analyzed the isolated and combined effects of economic and
governance factors on the development of stock markets on all three stock markets of
the world. Moreover, the correlation effects of economic and governance factors for
each region are also analyzed to know the dynamics of factors in isolation and collective
as well.
2. Secondly, the cross effects of governance and economic variable on the
development of stock markets on all three equity markets of the world are explored to
know the joint impacts of these variables on the development of stock market.
3. Thirdly, the indirect effects of governance factors through economic variable
on the development of stock markets for all regional markets by estimating their
systems of equations.
4. Last but not the least, the formation of index for the isolated and cross composite
variable of economic and governance factors with classification of developed, emerging
and frontier regions.
7.6. Limitations of the Study
Apart from the limitations in this part of the world, the main limitations of the study are
appended below:
1. The scope of the study was limited to the stock market development, however,
after analyzing the data of economic and governance factors of all regions of the world,
it is found that there is lot more to do in the fields of economic and governance factors.
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The reverse impacts of governance and stock market development on the economy
poses multiple questions to investigate other side of the study as well.
2. Stock market data is quite in abundance on daily, monthly and yearly basis, but
there are number of challenges for economic and governance factors data. The data on
factors of economic and governance is compiled after end of the year, which may not
reflect the real-time picture of the economic condition and its governance at certain
time for a particular year.
3. There was quite dearth of qualitative data and availability of that data in this
part of the world, like Pakistan, is a big challenge to the researcher. The high quality
data is not available without huge funding which is far from the reach University
resources for one researcher. Moreover, there were number challenges in the existing
data of World Development Indicators (WDI), like, at number of places, either the data
was missing or wrongly placed in another year. Therefore, an interim solution was
evolved by extracting individual countries data from the respective state bank web sites
and at some places, the method of extrapolation was also incorporated to make the
meaningful data.
7.7. Future Research Avenues
The section comprises the areas to be focused for future research. This study has been
focused on the determinants of stock market development on the panel study of all regions of
the world. During the research, it was revealed that there are number of other directions which
require deliberations and further research. The salient areas for future research are as follows:-
1. The reverse impacts of governance factors and stock market development on
economic factors may be investigated. By which, we will be having more vivid picture
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of the fact that governance factors play vital role in its growth apart from traditional
factors particularly in developing countries.
2. Existing research has focused its attention in the direction of external factors
rather if the internal plus governance factors are resolved, then market is automatically
going to be improved as the investor may find safe investment inland than going abroad.
So, due focused may be placed in the future studies for tackling the issues of internal
and governance factors.
3. Keeping in view the dynamics of different regions of the world, economic
indicators may be revised according to the nature and realities of that particular region.
The production and types of goods and services produced in developed country is quite
different than developing country, then comparison of both the developed and
underdeveloped country at the same scale is not at a parity. This disparity may be
resolved by creating the economic indicators according to governance dynamics of each
country.
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APPENDICES
Appendix-1A
Composite Index of Economic Variables of Developing Stock Markets (25 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Australia -0.965754 0.206988 -0.235267 2.416603
Austria -0.020635 0.453560 1.086004 2.796837
Belgium 0.148951 0.550040 -0.392688 1.812621
Canada -0.329221 0.262239 -0.147106 2.274181
Denmark -0.219381 0.590056 0.755236 2.478274
Finland -0.213434 0.648560 -0.453611 1.816350
France -0.930771 0.188327 -0.506843 2.948668
Germany -0.260907 0.421799 -0.160253 1.666128
Greece -2.238874 0.537864 -0.593165 2.437103
Hong Kong 3.109351 0.955035 -1.240720 4.180725
Ireland 1.956902 1.921279 1.946727 6.309741
Israel -0.881353 0.610179 -0.672300 2.373559
Italy -1.175533 0.253756 -0.642280 2.613781
Japan -0.423302 0.349008 -0.106963 2.174554
Korea, Rep. 0.350447 0.413523 -0.137820 2.122725
Netherlands 1.377850 1.194165 1.071487 3.510925
New Zealand -0.962546 0.298254 -0.334904 2.824914
Norway 0.936153 0.662562 -0.440915 2.246504
Portugal -1.466352 0.432565 0.771124 2.381135
Singapore 4.723261 0.867499 0.170697 1.979008
Spain -0.895517 0.282663 0.858171 3.266056
Sweden 0.247483 0.396757 -0.577912 2.762711
Switzerland 1.256337 0.483430 0.111595 2.219408
United Kingdom -1.038344 0.231107 -0.725392 3.907891
United States -1.694127 0.232620 0.106421 2.044053
All -1.05E-16 1.714394 1.505926 5.431351
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Appendix-1B
Composite Index of Governance Variables of Developing Stock Markets (25 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Australia 1.089340 0.200782 -0.353185 1.716721
Austria 1.217786 0.272650 0.457234 2.977997
Belgium -0.036496 0.246959 0.214047 2.565366
Canada 1.297703 0.150367 -0.798390 4.663154
Denmark 2.087905 0.188092 -1.367705 4.187274
Finland 2.278833 0.206717 -0.812653 3.062307
France -0.614126 0.268572 0.110590 1.988135
Germany 0.686426 0.221552 -0.397381 1.894194
Greece -4.470563 1.312934 -0.596092 1.742103
Hong Kong -0.052191 0.947192 -0.668476 1.735310
Ireland 0.862161 0.226033 -0.507899 2.222526
Israel -3.039911 0.418212 -0.337373 2.559646
Italy -4.264235 1.035477 0.237419 1.319231
Japan -0.917697 0.656976 -0.345975 2.361420
Korea, Rep. -3.795460 0.914866 -0.602105 2.129513
Netherlands 1.726172 0.290358 0.260651 1.803974
New Zealand 1.887547 0.286199 0.147645 1.801809
Norway 1.662479 0.293047 -0.092778 1.885976
Portugal -1.289040 0.835635 0.067164 1.518722
Singapore 0.744807 0.238273 0.794563 2.613796
Spain -1.539454 1.015319 -0.086846 1.448040
Sweden 1.837521 0.229779 0.299867 2.163824
Switzerland 1.853912 0.166713 0.185566 2.330356
United Kingdom 0.728759 0.348729 0.080685 1.970068
United States 0.057821 0.446286 0.359859 1.772998
All 5.51E-16 2.071123 -1.139304 3.478826
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Appendix-1C
Composite Index of Cross Variables of Developing Stock Markets (25 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Australia -1.035869 0.233349 -0.396988 2.609203
Austria 0.061303 0.651933 1.412186 3.838495
Belgium -0.003365 0.114603 -0.471906 3.138398
Canada -0.420915 0.357186 -0.285545 2.192777
Denmark -0.481914 1.218928 0.695997 2.375537
Finland -0.472077 1.495062 -0.489665 1.870070
France 0.552501 0.267453 -0.068158 1.865060
Germany -0.189689 0.339007 -0.760367 2.452431
Greece 10.10881 4.494001 0.935223 2.577739
Hong Kong 0.897412 2.336990 -1.089663 3.368527
Ireland 1.478845 1.506424 1.859503 5.982037
Israel 2.564234 1.566611 0.254741 1.815734
Italy 5.267852 1.920199 0.471055 2.025572
Japan 0.281626 0.356990 0.921568 3.381340
Korea, Rep. -1.111330 1.635903 0.091179 2.898054
Netherlands 2.121435 1.647763 0.748343 2.650031
New Zealand -1.671158 0.563261 -0.006523 2.327884
Norway 1.378623 1.009041 -0.477658 2.694128
Portugal 2.048443 1.367827 0.017120 1.671340
Singapore 3.549761 1.367043 0.442224 1.802650
Spain 1.447292 0.938253 0.214146 1.639614
Sweden 0.422898 0.666499 -0.678151 2.794817
Switzerland 2.327054 0.918478 0.218764 2.266642
United Kingdom -0.697196 0.296238 -0.137171 2.389872
United States -0.062646 0.696179 -0.280529 1.727448
All 1.257152 2.886701 2.445255 12.97456
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Appendix-2A
Composite Index of Economic Variables of Emerging Stock Markets (21 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Brazil -1.942774 0.752322 -0.723954 2.425411
Chile 0.564227 0.639131 0.253769 2.344943
China 2.799715 0.669681 -0.276066 1.787414
Colombia -1.193455 0.536418 -0.760103 2.479304
Czech Republic 0.729795 0.506211 0.279079 2.422474
Egypt, Arab Rep. -0.913957 0.605611 0.001079 2.172584
Hungary 0.587482 0.839548 0.193664 1.679691
India 0.007839 0.550841 -0.389739 1.740199
Indonesia 0.028988 0.375987 -1.449514 5.872522
Malaysia 3.999529 0.809248 -0.104962 1.917183
Mexico -0.845606 0.317537 -0.289528 1.654079
Morocco -0.198296 0.387816 -0.215857 1.693290
Pakistan -1.938645 0.452450 0.065541 3.719674
Peru -1.046059 0.698570 -0.101534 1.834719
Philippines -0.273177 0.253658 0.038469 2.038193
Poland -0.603993 0.545307 0.385652 2.387534
Russian Federation 0.309999 1.311317 -1.316722 4.448749
South Africa -0.340938 0.224320 0.034326 2.356726
Thailand 2.141891 0.362671 0.634694 2.639749
Turkey -2.068850 1.395165 -0.357613 1.628124
All 5.48E-17 1.679278 0.662668 3.741399
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Appendix-2B
Composite Index of Governance Variables of Emerging Stock Markets (21 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Brazil 0.080476 0.345247 -0.370429 2.295428
Chile 4.147182 0.259894 -0.830821 3.442916
China -1.603256 0.208888 0.625533 4.272645
Colombia -1.364435 0.563606 -0.065333 1.476513
Czech Republic 3.144388 0.344200 -1.655000 5.171553
Egypt, Arab Rep. -1.959756 0.863976 -0.720025 2.373738
Hungary 3.147643 0.562921 -0.549158 1.820401
India -0.895317 0.248011 -0.413207 2.466593
Indonesia -2.443294 0.828108 0.027400 1.895751
Malaysia 1.608865 0.311800 -0.277997 2.953435
Mexico -0.361182 0.391752 0.150782 2.070351
Morocco -0.604099 0.528012 0.778163 2.544536
Pakistan -3.729705 0.289851 0.062054 2.249251
Peru -1.029763 0.308005 -0.187629 2.177875
Philippines -1.081058 0.649361 0.616961 2.473367
Poland 2.587971 0.460122 -0.536775 2.100241 Russian
Federation -3.049469 0.391748 -0.746559 3.169700
South Africa 1.327535 0.239012 -0.084030 1.915885
Thailand 0.143625 0.811489 0.230379 1.286876
Turkey -0.224816 0.521788 -0.726807 2.177821 United Arab
Emirates 2.158466 0.350571 0.689920 2.599022
All -6.60E-16 2.169285 0.222681 2.212145
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Appendix-2C
Composite Index of Cross Variables of Emerging Stock Markets (21 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Brazil -0.221073 0.699871 0.161105 2.561821
Chile 2.443935 2.710681 0.366230 2.222646
China -4.454534 1.109893 -0.027676 1.821227
Colombia 1.855126 1.439524 0.740998 2.047867
Czech Republic 2.344404 1.750691 0.452967 2.488879
Egypt, 2.038805 2.236131 1.230110 3.093461
Hungary 1.542016 2.355155 0.132725 1.917083
India 0.003645 0.464606 0.473914 1.805221
Indonesia 0.024910 1.150538 1.761255 6.430198
Malaysia 6.397100 1.642142 -0.057766 2.261986
Mexico 0.283860 0.383374 0.633501 3.719693
Morocco -0.082289 0.107279 0.102920 1.964007
Pakistan 7.258902 1.887531 0.032557 3.147607
Peru 1.011339 0.661966 0.034272 2.119076
Philippines 0.240050 0.332930 0.570219 2.530857
Poland -1.512753 1.485325 0.416524 2.541127
Russia -0.840029 4.271261 1.077596 4.196973
South Africa -0.444715 0.305019 -0.188755 2.668042
Thailand 0.353811 1.835152 0.417180 1.620842
Turkey 1.079118 1.825674 1.161474 3.138860
All 0.996084 3.035410 0.678259 4.005079
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Appendix-3A
Composite Index of Economic Variables of Frontier Stock Markets (24 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Argentina -1.220608 0.534112 0.245688 1.865731
Bahrain -0.337962 0.619410 0.022351 2.254788
Bangladesh -1.072808 0.104767 0.590534 2.139264
Botswana -1.295906 0.649235 0.697073 3.301188
Bulgaria 0.103046 1.302010 -2.345895 9.439417
Cote d'Ivoire -0.803793 0.165826 0.087099 1.792122
Croatia 0.090416 0.268334 0.357275 2.951517
Cyprus 2.334506 1.141779 1.231037 4.062338
Estonia 0.522680 0.459411 0.741343 3.413566
Ghana -0.104609 0.231534 -0.793476 2.365102
Jordan 1.121692 0.539673 0.479710 2.720841
Kenya -0.262951 0.391249 0.516459 2.651393
Lithuania 0.054069 0.420363 0.812936 2.672201
Malta 3.115624 1.698996 1.140209 3.290800
Nigeria -1.496189 0.882823 -0.153147 3.184306
Oman -1.225697 0.728499 0.989966 4.219315
Qatar -2.634517 1.144860 0.901449 2.536023
Romania -0.385378 0.304740 0.188634 2.528231
Serbia 0.742568 0.181413 0.204842 2.166360
Slovak Republic 0.349525 0.276375 -0.204150 3.356366
Slovenia -0.001228 0.504557 0.374271 1.844806
Sri Lanka -0.659922 0.276782 -0.920464 4.258084
Tunisia 0.483210 0.420043 -0.179652 1.349893
Vietnam 0.188577 0.661479 0.127017 1.542964
All 1.30E-16 1.363422 1.174093 7.545286
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Appendix-3B
Composite Index of Governance Variables of Frontier Stock Markets (24 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Argentina -1.113651 0.826943 0.767677 2.205729
Bahrain 0.401422 0.361682 0.682720 2.485337
Bangladesh -3.381544 0.425396 0.398719 3.105378
Botswana 1.965427 0.156058 1.580156 5.658519
Bulgaria 0.208842 0.510899 -1.831850 5.417083
Cote d'Ivoire -3.617081 1.126899 0.776771 2.357674
Croatia 0.568730 0.868217 -1.324704 3.235838
Cyprus 2.858672 0.169407 0.791708 3.419025
Estonia 2.706934 0.413493 -0.835690 2.747196
Ghana -0.344637 0.486763 -0.580833 2.002510
Jordan -0.052168 0.221243 -0.397478 2.202829
Kenya -2.788794 0.280456 0.000155 2.298052
Lithuania 1.833325 0.404564 0.141107 2.357675
Malta 3.360873 0.186914 -0.089567 1.992881
Nigeria -4.206050 0.215372 -0.791068 2.766568
Oman 0.856380 0.310863 -0.052554 2.071833
Qatar 1.412253 0.613843 -0.369490 2.878535
Romania -0.145912 0.518493 -0.331148 1.995397
Serbia -1.997467 1.605809 -0.341552 1.624412
Slovak Republic 1.894445 0.312563 -0.544374 1.680247
Slovenia 2.815675 0.285798 0.416034 2.327837
Sri Lanka -0.986006 0.261286 0.762919 2.806351
Tunisia -0.302383 0.358153 -0.761795 2.274586
Vietnam -1.947284 0.195525 0.626789 3.775588
All 3.40E-16 2.205168 -0.388274 2.258845
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Appendix-3C
Composite Index of Cross Variables of Frontier Stock Markets (24 Countries)
COUNTRY Mean Std. Dev. Skew. Kurt.
Argentina 1.422364 1.175117 -0.385277 1.753637
Bahrain -0.210974 0.300304 -0.460115 1.815708
Bangladesh 3.632448 0.638240 0.647455 2.243514
Botswana -2.549830 1.266309 0.670046 3.139963
Bulgaria 0.285888 0.959387 2.279196 11.15372
Cote d'Ivoire 3.450465 0.516051 -0.148253 2.951905
Croatia 0.165580 0.246594 0.767251 3.171441
Cyprus 6.734739 3.459554 1.252704 4.225037
Estonia 1.504852 1.354307 0.862683 3.421386
Ghana -0.028050 0.040179 -1.526562 4.372070
Jordan -0.071973 0.239277 -0.507492 2.778704
Kenya 0.682430 1.113058 -0.784243 2.838036
Lithuania 0.139802 0.759261 0.873835 2.886447
Malta 10.59778 6.107309 1.206577 3.545088
Nigeria 6.286088 3.799575 0.373531 3.743734
Oman -1.034918 0.694563 -0.008863 3.388001
Qatar -5.298165 2.375804 0.991799 2.398005
Romania 0.123137 0.289584 0.411769 2.565340
Serbia -0.491378 0.340537 -0.083587 2.167255
Slovak Republic 0.693511 0.586164 -0.040657 2.861024
Slovenia -0.069010 1.456953 0.284652 1.872703
Sri Lanka 0.662511 0.422823 1.316496 4.604055
Tunisia -0.384915 0.367760 -0.070954 1.178538
Vietnam -0.308949 1.272961 -0.108585 1.637664
All 1.416327 3.669467 2.527687 13.29536
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Appendix-4A
Model-1 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.597964 0.017148 34.86990 0.0000
E1 3.467382 0.291562 11.89245 0.0000
E2 -2.842335 0.596598 -4.764235 0.0000
E3 -0.872048 0.500641 -1.741865 0.0824
E4 0.045208 0.192031 0.235423 0.8140
E5 -5.479524 0.799343 -6.855038 0.0000
E6 0.756222 0.069139 10.93775 0.0000
E7 0.344247 0.191185 1.800598 0.0726
E8 1.333611 0.839905 1.587812 0.1133
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 2.586788 S.D. dependent var 50.50454
S.E. of regression 59.62322 Sum squared resid 1219340.
J-statistic 20.23588 Instrument rank 25
Prob(J-statistic) 0.209784
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Appendix-4B
Model-1 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.313267 0.131758 2.377600 0.0180
E0G 1.189405 0.706579 1.683329 0.0932
E2 -0.562928 0.312187 -1.803174 0.0723
E3 0.060928 0.369616 0.164840 0.8692
E4 0.179470 0.293191 0.612128 0.5409
E5 0.594218 1.175760 0.505390 0.6136
E6 -0.257738 0.274936 -0.937448 0.3492
E7 0.361362 0.919325 0.393073 0.6945
E8 3.804343 0.862114 4.412807 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.511905 S.D. dependent var 25.28359
S.E. of regression 29.29072 Sum squared resid 287412.1
J-statistic 11.85550 Instrument rank 20
Prob(J-statistic) 0.374603
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Appendix-4C
Model-1 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.538880 0.077532 6.950413 0.0000
E0G 0.358943 0.154132 2.328806 0.0207
E2 -0.599268 0.511840 -1.170811 0.2428
E3 0.302298 0.301020 1.004244 0.3163
E4 -0.079352 0.222970 -0.355884 0.7222
E5 -0.281559 0.388632 -0.724487 0.4695
E6 0.518521 0.077727 6.671048 0.0000
E7 -0.021566 0.065399 -0.329759 0.7419
E8 1.285982 0.513060 2.506496 0.0128
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.301700 S.D. dependent var 19.21250
S.E. of regression 51.24700 Sum squared resid 643432.4
J-statistic 5.981277 Instrument rank 18
Prob(J-statistic) 0.741791
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Appendix-4D
Model-1 Panel GMM Estimation Results for All Economic Variables of World Stock
Markets (70 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.346661 0.001628 212.9583 0.0000
E1 1.133814 0.038646 29.33851 0.0000
E2 -1.124654 0.056726 -19.82595 0.0000
E3 -0.878688 0.039527 -22.23030 0.0000
E4 0.165796 0.011069 14.97878 0.0000
E5 -1.825737 0.081713 -22.34320 0.0000
E6 0.755894 0.012985 58.21152 0.0000
E7 0.155027 0.009660 16.04792 0.0000
E8 0.839415 0.045524 18.43899 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.581042 S.D. dependent var 44.14761
S.E. of regression 53.92072 Sum squared resid 2369567.
J-statistic 55.20834 Instrument rank 67
Prob(J-statistic) 0.579760
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Appendix-5A
Model-2 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.682435 0.025572 26.68715 0.0000
G1 -0.578566 1.102372 -0.524837 0.6000
G2 3.331904 0.750033 4.442345 0.0000
G3 -0.506273 0.134392 -3.767136 0.0002
G4 -3.274225 0.629735 -5.199368 0.0000
G5 3.235296 0.346588 9.334691 0.0000
G6 2.622147 0.264680 9.906852 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 2.129092 S.D. dependent var 47.35929
S.E. of regression 58.92179 Sum squared resid 1485921.
J-statistic 20.87224 Instrument rank 25
Prob(J-statistic) 0.285908
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Appendix-5B
Model-2 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.065809 0.008060 8.165054 0.0000
G1 -0.056858 0.183259 -0.310258 0.7565
G2 0.897967 0.190954 4.702532 0.0000
G3 0.183235 0.118612 1.544826 0.1233
G4 0.037555 0.272284 0.137927 0.8904
G5 -1.801663 0.379471 -4.747824 0.0000
G6 -0.246916 0.054973 -4.491621 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.714972 S.D. dependent var 24.80010
S.E. of regression 26.22782 Sum squared resid 249707.1
J-statistic 17.68934 Instrument rank 21
Prob(J-statistic) 0.221298
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Appendix-5C
Model-2 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.526775 0.012996 40.53328 0.0000
G1 -0.497369 0.111855 -4.446557 0.0000
G2 0.023674 0.120949 0.195737 0.8449
G3 0.591086 0.049267 11.99752 0.0000
G4 -0.050505 0.083193 -0.607082 0.5442
G5 -0.038931 0.145167 -0.268181 0.7887
G6 -0.007288 0.079142 -0.092084 0.9267
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.712066 S.D. dependent var 24.38703
S.E. of regression 31.39310 Sum squared resid 378442.2
J-statistic 16.65426 Instrument rank 24
Prob(J-statistic) 0.478018
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Appendix-5D
Model-2 Panel GMM Estimation Results for All Governance Variables of World Stock
Markets (70 Countries) and Depended Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.427911 0.000794 538.7451 0.0000
G1 -1.319646 0.043310 -30.46979 0.0000
G2 1.453818 0.056146 25.89362 0.0000
G3 -0.200078 0.021599 -9.263211 0.0000
G4 -0.246351 0.070530 -3.492870 0.0005
G5 2.153088 0.044617 48.25721 0.0000
G6 1.073616 0.017052 62.96247 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.291315 S.D. dependent var 42.49977
S.E. of regression 52.82786 Sum squared resid 2550775.
J-statistic 67.18272 Instrument rank 70
Prob(J-statistic) 0.335856
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Appendix-6A
Model-3 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.545137 0.059281 9.195741 0.0000
E1 3.104011 1.264182 2.455352 0.0146
E2 -2.613447 2.217913 -1.178336 0.2395
E3 0.145459 1.753669 0.082946 0.9339
E4 0.284822 0.401479 0.709433 0.4785
E5 -6.886241 2.074414 -3.319608 0.0010
E6 0.755269 0.306879 2.461132 0.0144
E7 0.381957 0.815504 0.468370 0.6398
E8 0.440446 1.831055 0.240542 0.8101
G1 1.502209 1.374600 1.092834 0.2752
G2 3.553311 1.757400 2.021913 0.0440
G3 -0.541886 0.760343 -0.712686 0.4765
G4 -2.890350 1.300561 -2.222387 0.0269
G5 -0.384385 1.685741 -0.228021 0.8198
G6 2.721788 0.994169 2.737752 0.0065
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 2.586788 S.D. dependent var 50.50454
S.E. of regression 59.17278 Sum squared resid 1179978.
J-statistic 15.62948 Instrument rank 25
Prob(J-statistic) 0.110742
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194
Appendix-6B
Model-3 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.284319 0.111553 2.548744 0.0113
E1 1.277739 0.579049 2.206615 0.0280
E2 -0.659128 0.263382 -2.502559 0.0128
E3 -0.078533 0.313548 -0.250466 0.8024
E4 0.302719 0.224006 1.351391 0.1775
E5 -0.166436 0.975932 -0.170541 0.8647
E6 0.177252 0.186361 0.951119 0.3422
E7 0.357971 0.233686 1.531842 0.1265
E8 3.259688 1.037422 3.142104 0.0018
G1 0.004531 0.333132 0.013600 0.9892
G2 0.335903 0.176556 1.902529 0.0580
G3 0.188202 0.173330 1.085805 0.2784
G4 0.335695 0.377145 0.890095 0.3741
G5 -0.713518 0.662969 -1.076246 0.2826
G6 -0.655727 0.441015 -1.486861 0.1380 Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.511905 S.D. dependent var 25.28359
S.E. of regression 29.34822 Sum squared resid 283373.7
J-statistic 318.0683 Instrument rank 185
Prob(J-statistic) 0.000000
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195
Appendix-6C
Model-3 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.526180 0.049394 10.65260 0.0000
E0G 0.337931 0.138198 2.445259 0.0152
E2 -0.091362 0.235204 -0.388438 0.6980
E3 -0.052941 0.083830 -0.631531 0.5283
E4 -0.347753 0.286500 -1.213795 0.2260
E5 -0.751510 0.183769 -4.089421 0.0001
E6 0.125278 0.151272 0.828164 0.4084
E7 0.138549 0.048630 2.849012 0.0048
E8 -0.051486 0.244437 -0.210633 0.8333
G1 -0.266518 0.183906 -1.449205 0.1485
G2 -0.453460 0.393824 -1.151429 0.2507
G3 0.183899 0.144857 1.269523 0.2054
G4 -0.332209 0.240763 -1.379817 0.1689
G5 0.440128 0.476963 0.922773 0.3570
G6 0.031788 0.159366 0.199464 0.8421 Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.470057 S.D. dependent var 24.32506
S.E. of regression 30.10866 Sum squared resid 225726.3
J-statistic 12.83849 Instrument rank 24
Prob(J-statistic) 0.170052
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196
Appendix-6D
Model-3 Panel GMM Estimation Results for All Economic and Governance Variables of
World Stock Markets (70 Countries) and Depended Variable of Market Capitalization as
%age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.327335 0.002463 132.9053 0.0000
E1 1.053651 0.062257 16.92414 0.0000
E2 -0.983563 0.104093 -9.448874 0.0000
E3 -0.750464 0.073542 -10.20456 0.0000
E4 0.139105 0.023038 6.038110 0.0000
E5 -1.708201 0.150669 -11.33745 0.0000
E6 0.732865 0.022961 31.91849 0.0000
E7 0.171974 0.022617 7.603729 0.0000
E8 0.646026 0.116716 5.535043 0.0000
G1 -0.581906 0.099173 -5.867587 0.0000
G2 1.360316 0.101266 13.43313 0.0000
G3 -0.471558 0.031663 -14.89320 0.0000
G4 0.082546 0.138736 0.594987 0.5520
G5 1.851517 0.107640 17.20099 0.0000
G6 0.530616 0.069041 7.685482 0.0000 Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.581042 S.D. dependent var 44.14761
S.E. of regression 53.68673 Sum squared resid 2331752.
J-statistic 58.59993 Instrument rank 67
Prob(J-statistic) 0.246221
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197
Appendix-7A
Model-4 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.550686 0.045371 12.13734 0.0000
E0G 4.311606 1.026719 4.199402 0.0000
E2 -2.890759 1.137811 -2.540631 0.0115
E3 0.056528 0.892238 0.063355 0.9495
E4 0.534424 0.387282 1.379933 0.1685
E5 -7.159806 1.588324 -4.507775 0.0000
E6 0.415630 0.266821 1.557711 0.1202
E7 0.610334 0.402158 1.517647 0.1300
E8 2.490189 1.538691 1.618382 0.1065
PGOV 27.82690 10.81695 2.572528 0.0105
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 2.586788 S.D. dependent var 50.50454
S.E. of regression 59.22249 Sum squared resid 1199498.
J-statistic 17.49002 Instrument rank 25
Prob(J-statistic) 0.290424
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198
Appendix-7B
Model-4 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.337380 0.093790 3.597178 0.0004
E0G 1.009993 0.413552 2.442238 0.0152
E2 -2.467357 1.030531 -2.394258 0.0172
E3 0.728884 0.235190 3.099128 0.0021
E4 0.340306 0.367234 0.926674 0.3548
E5 2.947501 2.750370 1.071674 0.2847
E6 -0.364526 0.438448 -0.831401 0.4064
E7 1.034320 0.808566 1.279203 0.2018
E8 4.001587 0.627373 6.378326 0.0000
PGOV -11.39619 9.027543 -1.262380 0.2078
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.804668 S.D. dependent var 24.93885
S.E. of regression 33.02911 Sum squared resid 337094.8
J-statistic 8.053870 Instrument rank 20
Prob(J-statistic) 0.623575
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199
Appendix-7C
Model-4 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.513626 0.076773 6.690172 0.0000
E0G 0.332205 0.092064 3.608407 0.0004
E2 0.052924 0.131461 0.402583 0.6875
E3 0.082910 0.044068 1.881400 0.0609
E4 -0.277189 0.145858 -1.900407 0.0583
E5 -0.602946 0.091526 -6.587695 0.0000
E6 0.184761 0.033869 5.455203 0.0000
E7 0.160200 0.058052 2.759618 0.0061
E8 0.375126 0.140506 2.669823 0.0080
PGOV 3.243283 2.430260 1.334542 0.1830
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.289003 S.D. dependent var 22.67601
S.E. of regression 31.23227 Sum squared resid 299464.5
J-statistic 14.73914 Instrument rank 24
Prob(J-statistic) 0.396216
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200
Appendix-7D
Model-4 Panel GMM Estimation Results for All Economic Variables and Composite Index
of Governance Variables of World Stock Markets (70 Countries) and Depended Variable of
Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.252702 0.003437 73.52188 0.0000
E1 2.046103 0.060612 33.75750 0.0000
E2 -3.686475 0.182334 -20.21826 0.0000
E3 -1.586586 0.055425 -28.62560 0.0000
E4 0.583136 0.015244 38.25313 0.0000
E5 -4.384335 0.109060 -40.20123 0.0000
E6 0.841175 0.031276 26.89554 0.0000
E7 0.219161 0.031826 6.886194 0.0000
E8 1.538483 0.113689 13.53235 0.0000
PGOV 49.64352 1.424435 34.85137 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.367992 S.D. dependent var 44.09912
S.E. of regression 54.49751 Sum squared resid 2373013.
J-statistic 59.24529 Instrument rank 67
Prob(J-statistic) 0.393626
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201
Appendix-8A
Model-5 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.632040 0.024249 26.06447 0.0000
G1 -0.052337 0.689560 -0.075899 0.9395
G2 4.427687 0.613857 7.212893 0.0000
G3 -1.076544 0.150147 -7.169923 0.0000
G4 -3.405756 0.831739 -4.094743 0.0001
G5 1.705086 0.266668 6.394030 0.0000
G6 2.760608 0.303206 9.104742 0.0000
PECO 11.65072 2.101860 5.543055 0.0000 Effects Specification
Cross-section fixed (first differences)
Mean dependent var 2.586788 S.D. dependent var 50.50454
S.E. of regression 61.36038 Sum squared resid 1295193.
J-statistic 22.02451 Instrument rank 25
Prob(J-statistic) 0.183778
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202
Appendix-8B
Model-5 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.223543 0.032918 6.790925 0.0000
G1 0.401874 0.479664 0.837823 0.4027
G2 0.521704 0.719001 0.725595 0.4686
G3 0.480946 0.254370 1.890733 0.0595
G4 -0.176801 1.020584 -0.173235 0.8626
G5 -1.544353 1.064800 -1.450369 0.1479
G6 -0.474660 0.817542 -0.580594 0.5619
PECO 17.62040 2.314310 7.613676 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.511905 S.D. dependent var 25.28359
S.E. of regression 29.34838 Sum squared resid 289406.0
J-statistic 14.23265 Instrument rank 20
Prob(J-statistic) 0.286099
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203
Appendix-8C
Model-5 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.615926 0.017504 35.18684 0.0000
G1 0.458516 0.090900 5.044160 0.0000
G2 -0.246200 0.437428 -0.562835 0.5741
G3 0.432157 0.171076 2.526111 0.0122
G4 -0.196686 0.389381 -0.505125 0.6140
G5 0.242199 0.807948 0.299771 0.7646
G6 -0.265916 0.285089 -0.932746 0.3520
ECO -0.214651 0.168485 -1.274011 0.2040
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.740994 S.D. dependent var 18.38334
S.E. of regression 30.16184 Sum squared resid 201961.6
J-statistic 12.65830 Instrument rank 18
Prob(J-statistic) 0.243409
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Appendix-8D
Model-2 Panel GMM Estimation Results for All Governance Variables and Composite
Index of Economic Variables of World Stock Markets (70 Countries) and Depended
Variable of Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.412911 0.001438 287.2115 0.0000
G1 -1.003143 0.036920 -27.17038 0.0000
G2 1.476385 0.052826 27.94794 0.0000
G3 -0.462321 0.014483 -31.92139 0.0000
G4 -0.063720 0.036346 -1.753157 0.0800
G5 2.141841 0.036974 57.92854 0.0000
G6 0.972312 0.019071 50.98503 0.0000
PECO 14.09162 0.200610 70.24373 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.581042 S.D. dependent var 44.14761
S.E. of regression 55.02001 Sum squared resid 2470197.
J-statistic 61.54391 Instrument rank 67
Prob(J-statistic) 0.385103
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205
Appendix-9A
Model-6 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.662996 0.006466 102.5361 0.0000
PECO 13.43409 0.884676 15.18533 0.0000
PGOV 10.37992 1.365497 7.601572 0.0000
PECO*PGOV 2.477447 0.402805 6.150488 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 2.586788 S.D. dependent var 50.50454
S.E. of regression 62.14579 Sum squared resid 1344011.
J-statistic 23.34414 Instrument rank 25
Prob(J-statistic) 0.325907
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206
Appendix-9B
Model-6 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.222044 0.007481 29.68213 0.0000
PECO 20.11261 0.772316 26.04195 0.0000
PGOV -4.352256 1.292038 -3.368520 0.0008
PCROSS 0.959003 0.424660 2.258281 0.0246
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.564191 S.D. dependent var 25.31039
S.E. of regression 28.59882 Sum squared resid 277265.5
J-statistic 16.85068 Instrument rank 20
Prob(J-statistic) 0.395322
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207
Appendix-9C
Model-6 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.603374 0.000783 770.2145 0.0000
PECO 1.266656 0.203741 6.216999 0.0000
PGOV 0.758107 0.088048 8.610106 0.0000
PCROSS -0.568153 0.083030 -6.842738 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.334119 S.D. dependent var 24.56446
S.E. of regression 33.67913 Sum squared resid 288108.1
J-statistic 21.56110 Instrument rank 23
Prob(J-statistic) 0.306641
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208
Appendix-9D
Model-6 Panel GMM Estimation Results for Composite Indices of Economic and
Governance Variables of World Stock Markets (70 Countries) and Depended Variable of
Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
Y(-1) 0.386455 0.001405 275.0485 0.0000
PECO 18.22461 0.141548 128.7525 0.0000
PGOV 23.50915 0.582312 40.37210 0.0000
PCROSS 7.614872 0.045824 166.1748 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 1.581042 S.D. dependent var 44.14761
S.E. of regression 54.70721 Sum squared resid 2454160.
J-statistic 61.98026 Instrument rank 67
Prob(J-statistic) 0.512694
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209
Appendix-10A
Model-6 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Coefficient Std. Error t-Statistic Prob. C(11) 0.646604 0.130420 4.957863 0.0000
C(12) -0.010593 0.014358 -0.737769 0.4609
C(13) 0.015863 0.010555 1.502991 0.1333
C(14) 0.851001 0.029454 28.89264 0.0000
C(21) -0.307497 0.214673 -1.432399 0.1525
C(22) 0.184279 0.089369 2.061990 0.0396 Determinant residual covariance 0.445211
J-statistic 0.015135
Equation: LY = C(11) + C(12)*PECO + C(13)*PGOV +C(14)*LY(-1)
Instruments: LY(-1) PECO(-1) PGOV(-1) C
Observations: 364
R-squared 0.729094 Mean dependent var 4.348144
Adjusted R-squared 0.726837 S.D. dependent var 0.768536
S.E. of regression 0.401675 Sum squared resid 58.08353
Durbin-Watson stat 1.951553
Equation: PECO= C(21) + C(22)*PGOV
Instruments: LY(-1) PECO(-1) PGOV(-1) C
Observations: 368
R-squared 0.056794 Mean dependent var 0.034384
Adjusted R-squared 0.054217 S.D. dependent var 1.735346
S.E. of regression 1.687648 Sum squared resid 1042.424
Durbin-Watson stat 0.115050
Wald Test:
System: sys_peco_pgov Test Statistic Value df Probability Chi-square 0.165066 1 0.6845
Null Hypothesis: C(13)+C(12)*C(22)=0
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(13) + C(12)*C(22) 0.246344 0.606338
Delta method computed using analytic derivatives.
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210
Appendix-10B
Model-6 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Coefficient Std. Error t-Statistic Prob. C(11) 10.33597 3.095994 3.338499 0.0009
C(12) 0.816095 0.867415 0.940835 0.3471
C(13) 0.425459 0.504257 0.843734 0.3991
C(14) 0.793465 0.067957 11.67599 0.0000
C(21) -0.327752 0.260178 -1.259722 0.2082
C(22) 0.189012 0.094144 2.007693 0.0450 Determinant residual covariance 1730.114
J-statistic 0.007434
Equation: Y = C(11) + C(12)*PECO + C(13)*PGOV +C(14)*Y(-1)
Instruments: C Y(-1) PECO(-1) PGOV(-1)
Observations: 363
R-squared 0.687319 Mean dependent var 54.31321
Adjusted R-squared 0.684706 S.D. dependent var 45.09586
S.E. of regression 25.32180 Sum squared resid 230188.5
Durbin-Watson stat 2.388483
Equation: PECO= C(21) + C(22)*PGOV
Instruments: C Y(-1) PECO(-1) PGOV(-1)
Observations: 365
R-squared 0.016598 Mean dependent var 0.033715
Adjusted R-squared 0.013889 S.D. dependent var 1.671071
S.E. of regression 1.659426 Sum squared resid 999.5912
Durbin-Watson stat 0.070832
Wald Test:
System: sys_peco_pgov Test Statistic Value df Probability Chi-square 1.121494 1 0.2896
Null Hypothesis: C(12)+C(13)*C(22)=0
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(12) + C(13)*C(22) 0.896512 0.846560
Delta method computed using analytic derivatives.
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211
Appendix-10C
Model-6 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Coefficient Std. Error t-Statistic Prob. C(11) 6.056385 1.059980 5.713679 0.0000
C(12) 0.370360 0.628811 0.588984 0.5561
C(13) 0.557779 0.418102 1.334075 0.1826
C(14) 0.856991 0.032050 26.73908 0.0000
C(21) -0.009275 0.160960 -0.057625 0.9541
C(22) 0.301746 0.058461 5.161530 0.0000 Determinant residual covariance 928.8613
J-statistic 0.044667
Equation: Y = C(11) + C(12)*PECO + C(13)*PGOV +C(14)*Y(-1)
Instruments: C Y(-1) PECO(-1) PGOV(-1) PCROSS(-1)
Observations: 333 c(12)+c(13)*c(22)=0
c(12)+c(13)*c(22)=0
c(12)+c(13)*c(22)=0 c(12)+c(13)*c(22)=0
R-squared 0.663365 Mean dependent var 34.37020
Adjusted R-squared 0.660295 S.D. dependent var 43.03730
S.E. of regression 25.08395 Sum squared resid 207008.3
Durbin-Watson stat 2.091816
Equation: PECO= C(21) + C(22)*PGOV
Instruments: C Y(-1) PECO(-1) PGOV(-1) PCROSS(-1)
Observations: 345 c(12)+c(13)*c(22)=0
c(12)+c(13)*c(22)=0
c(12)+c(13)*c(22)=0 c(12)+c(13)*c(22)=0
R-squared 0.247466 Mean dependent var 0.029530
Adjusted R-squared 0.245272 S.D. dependent var 1.411240
S.E. of regression 1.226015 Sum squared resid 515.5679
Durbin-Watson stat 0.243991
Wald Test:
System: sys_peco_pgov Test Statistic Value df Probability Chi-square 0.860934 1 0.3535
Null Hypothesis: c(12)+c(13)*c(22)=0
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(12) + C(13)*C(22) 0.538667 0.580545
Delta method computed using analytic derivatives.
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Appendix-10D
Model-6 Panel GMM Estimation Results for Composite Indices of Economic and
Governance Variables with indirect effect of World Stock Markets (70 Countries) and
Depended Variable of Market Capitalization as %age of GDP
Coefficient Std. Error t-Statistic Prob. C(11) 4.324358 1.645948 2.627275 0.0087
C(12) 3.618091 0.911072 3.971247 0.0001
C(13) -0.002804 0.289226 -0.009695 0.9923
C(14) 0.951191 0.023176 41.04266 0.0000
C(21) -0.273180 0.141945 -1.924544 0.0544
C(22) 0.210139 0.061519 3.415829 0.0007 Determinant residual covariance 4087.955
J-statistic 0.009904
Equation: Y = C(11) + C(12)*PECO + C(13)*PGOV +C(14)*Y(-1)
Instruments: Y(-1) PECO(-1) PGOV(-1) C
Observations: 875 c(13)+c(12)*c(22)=0
c(13)+c(12)*c(22)=0
c(13)+c(12)*c(22)=0 c(13)+c(12)*c(22)=0
R-squared 0.872598 Mean dependent var 77.37965
Adjusted R-squared 0.872159 S.D. dependent var 121.9224
S.E. of regression 43.59319 Sum squared resid 1655219.
Durbin-Watson stat 2.799016
Equation: PECO= C(21) + C(22)*PGOV
Instruments: Y(-1) PECO(-1) PGOV(-1) C
Observations: 904 c(13)+c(12)*c(22)=0
c(13)+c(12)*c(22)=0
c(13)+c(12)*c(22)=0 c(13)+c(12)*c(22)=0
R-squared 0.121897 Mean dependent var 0.092187
Adjusted R-squared 0.120923 S.D. dependent var 1.569915
S.E. of regression 1.471938 Sum squared resid 1954.275
Durbin-Watson stat 0.097505
Wald Test:
System: %system Test Statistic Value df Probability Chi-square 4.367647 1 0.0366
Null Hypothesis: C(13)+C(12)*C(22)=0
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(13) + C(12)*C(22) 0.757499 0.362458
Delta method computed using analytic derivatives.
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Appendix-11A
Model-6 Panel GMM Estimation Results for Developed Stock Markets (25 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.342913 0.009164 37.41869 0.0000
Y 0.001422 0.000354 4.021204 0.0001
PGOV -0.096181 0.013402 -7.176911 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.009424 S.D. dependent var 0.472622
S.E. of regression 0.590882 Sum squared resid 117.3116
J-statistic 22.03431 Instrument rank 25
Prob(J-statistic) 0.457842
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Appendix-11B
Model-6 Panel GMM Estimation Results for Emerging Stock Markets (21 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.484705 0.025322 19.14169 0.0000
Y 0.004823 0.000131 36.88157 0.0000
PGOV 0.140191 0.053639 2.613622 0.0094
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.050247 S.D. dependent var 0.438429
S.E. of regression 0.511979 Sum squared resid 89.12165
J-statistic 17.84444 Instrument rank 20
Prob(J-statistic) 0.398718
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Appendix-11C
Model-6 Panel GMM Estimation Results for Frontier Stock Markets (24 Countries) and
Depended Variable : Market Capitalization as %age of GDP
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.345052 0.001972 175.0095 0.0000
Y 0.001642 0.000380 4.317262 0.0000
PGOV 0.642321 0.007697 83.45177 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.034628 S.D. dependent var 0.603575
S.E. of regression 0.743951 Sum squared resid 166.0390
J-statistic 18.30426 Instrument rank 21
Prob(J-statistic) 0.435785
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Appendix-11D
Model-6 Panel GMM Estimation Results for Stock Market Development and Composite
Index of Governance Variables of World Stock Markets (70 Countries) and Depended
Variable of Composite Economic factors (Peco)
Variable Coefficient Std. Error t-Statistic Prob.
PECO(-1) 0.057129 0.002154 26.52363 0.0000
Y 0.000865 4.95E-06 174.7373 0.0000
PGOV 0.029588 0.003113 9.504310 0.0000
Effects Specification
Cross-section fixed (first differences)
Mean dependent var 0.038139 S.D. dependent var 0.385402
S.E. of regression 0.467864 Sum squared resid 183.2164
J-statistic 65.75417 Instrument rank 66
Prob(J-statistic) 0.381670
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