THE DYNAMICS OF MACROECONOMIC VARIABLES, FINANCIAL PERFORMANCE INDICATORS AND ECONOMIC EVENTS IN INDIAN STOCK MARKET:

THE DYNAMICS OF MACROECONOMIC VARIABLES, FINANCIAL PERFORMANCE INDICATORS AND ECONOMIC EVENTS IN INDIAN STOCK MARKET:

AN EMPIRICAL STUDY

Thesis submitted to Goa University for the degree of

DOCTOR OF PHILOSOPHY

In

COMMERCE

BY

PARAB NARAYAN DATTARAM Research Scholar

Under the supervision of Prof. Y. V. REDDY Registrar, Goa University and

Professor (HAG) (on lien)

Department of Commerce Goa University, Taleigao Plateau Goa, India, 403206

January 2019

Dedicated to My Parents

Shri Dattaram Narayan Parab Smt. Smeeta Dattaram Parab

&

My Professor Prof. Y. V. Reddy

For their blessings, trust, motivation, and support in my endeavors

INDEX

SR. NO. TOPIC PAGE NO.

A Declaration II

B Certificate III

C Acknowledgment IV

D Table of Contents VI

E List of Tables XII

F List of Figures XV

G List of Abbreviations XVII

II

DECLARATION

I, Mr. Parab Narayan Dattaram, declare that the thesis entitled “The Dynamics of Macroeconomic Variables, Financial Performance Indicators and Economic Events in Indian Stock Market: An Empirical Study” submitted to Goa University, Goa for the award of degree of Doctor of Philosophy in Commerce is the outcome of original and independent work undertaken by me under the supervision and guidance of Prof. Y. V.

Reddy, Registrar, Goa University, and Professor, Department of Commerce (on lien), Goa University. This research work has not previously formed the basis for the award of any Degree/ Diploma/ Certificate/ Associateship/ Fellowship or any such similar title to the candidates of this university or any other universities. I have duly acknowledged all the sources used by me in the preparation of this thesis.

Date: Mr. Parab Narayan Dattaram Place: Goa University, Taleigao, Goa.

III

CERTIFICATE

This is to certify that the thesis titled “The Dynamics of Macroeconomic Variables, Financial Performance Indicators and Economic Events in Indian Stock Market: An Empirical Study” for the award of Ph.D. Degree in Commerce is the bonafide record of original work done by Mr. Parab Narayan Dattaram under my guidance and supervision. This thesis has not formed the basis for an award of any Degree/ Diploma/

Certificate/ Associateship/ Fellowship or any such similar title to the candidates of this university or any other universities.

Date: Prof. Y. V. Reddy

Place: Goa University, Taleigao, Goa. (Research Guide)

IV

ACKNOWLEDGMENT

The research has no end; however reporting of the findings is also very essential. The thesis in its present form would not have been in reality without the support and motivation of my research guide, Prof. Y. V. Reddy. I want to thank Prof. Reddy for providing constructive suggestions and guidance as a result of which quality publications emerged from the work of thesis.

I thank Prof. K. B. Subhash, Head, Department of Commerce and Dean, Faculty of Commerce and Management Studies, Goa University who sown seeds of research in my mind when I was a PG student. His approach towards research ethics and discipline in research had a significant positive impact on my thesis as well as on my life. I thank VC nominees and members of DRC, Dr. P. Sri Ram and Dr. Pournima Dhume for their innovative suggestions. I also thank Prof. B. Ramesh and Prof. Anjana Raju for their motivation and valuable inputs during DRC presentations. I am grateful to Dr. K. G.

Sankaranarayanan, Dr. Poornima B. G., and Dr. Pushpender Kumar for the support and valuable suggestions during research work.

I sincerely thank Vice Chancellor of Goa University, Prof. Varun Sahni for giving me an opportunity to join as faculty in Department of Commerce due to which I was able to devote more time for research work and utilize advanced research resources of Goa University. I acknowledge the support I received from the Librarian of Goa University, Dr.

Gopakumar V., Nonteaching staff of the Department of Commerce and Administration Block of Goa University. I also thank the entire staff of Narayan Zantye College of Commerce, Bicholim for their motivation and support.

I am grateful to the reviewers of Sage Publications, Australasian Accounting Business and Finance Journal, International Journal of Business and Society, Amity Journal of Finance,

V

IUP Journal of Accounting and Audit Practices, Key Note Speakers and Session Chairpersons of all the international conferences and seminars attended during study period for their critical reviews and suggestions which I incorporated in the thesis.

I express my great thanks to Vision Infosys for providing prompt and quality printing services. I thank my parents and my friends Ramashanti Naik, Priyanka Naik, Marlow Lawrence, Frazer Taylor, Adarsh Phadke, Mona Tilve and Arpan Warik for their unlimited support, trust, and blessings on me. I also thank my colleagues and students for their constant encouragement. I thank almighty God for providing me with good mental and physical health. May God bless all of them for unlimited support in my research work. I hope such support and motivation I will continue receiving from them in my future research endeavors.

Date: Mr. Parab Narayan Dattaram

Place: Goa University, Taleigao, Goa.

VI

TABLE OF CONTENTS

Sr. No. Contents Page No.

Chapter I Introduction 01-46

1.1 Introduction 01

1.2 Need for the Study 04

1.3 Scope of the Study 05

1.4 Objectives of the Study 07

1.5 Research Methodology 08

1.5.1 Examining Impact of Macroeconomic Variables on Stock Market Returns

09

1.5.1.1 Period of study 09

1.5.1.2 Sample Design 09

1.5.1.3 Data Variables and Data Sources 09

1.5.1.4 Statistical and Econometric Techniques 11

1.5.2 Examining Impact of Financial Performance Indicators on Stock Market Returns

18

1.5.2.1 Period of study 18

1.5.1.2 Sample Design 19

1.5.2.3 Data Variables and Data Sources 19

1.5.2.4 Statistical and Econometric Techniques 19

1.5.3 Examining Impact of Economic Events on Stock Market Returns

21

1.5.3.1 Period of study 21

1.5.3.2 Sample Design 23

1.5.3.3 Data Variables and Data Sources 24

1.5.3.4 Statistical and Econometric Techniques 24

1.6 Hypotheses Development 28

1.6.1 Hypotheses framed to support the analysis concerning objective I

28 1.6.1.1 Hypotheses to test the stationarity of stock markets returns

and macroeconomic variables

28 1.6.1.2 Hypotheses to examine the unidirectional causality from 29

VII

macroeconomic variables to stock market returns

1.6.1.3 Hypotheses to examine the unidirectional causality from stock market returns to macroeconomic variables

31 1.6.1.4 Hypotheses supporting regression results with structural

breaks between stock markets returns and macroeconomic variables

32

1.6.1.5 Hypotheses to test the presence of serial correlation while analyzing the impact of macroeconomic variables on stock market returns

34

1.6.1.6 Hypotheses to test the presence of Heteroscedasticity while analyzing the impact of macroeconomic variables on stock market returns

36

1.6.2 Hypotheses framed to support the analysis concerning objective II

38 1.6.2.1 Hypotheses to test the stationarity of stock returns, traditional

and modern performance measures

38 1.6.2.2 Hypothesis for model selection using Hausman Test 38 1.6.2.3 Hypotheses supporting FEM and REM results in analyzing

the impact of traditional and modern performance measures on stock returns

39

1.6.3 Hypotheses framed to support the analysis concerning objective III

39 1.6.3.1 Hypotheses supporting regression results in analyzing the

impact of economic events on stock market returns

39 1.6.3.2 Hypotheses to test the presence of serial correlation while

analyzing the impact of economic events on stock market returns

41

1.6.3.3 Hypotheses to test the presence of Heteroscedasticity while analyzing the impact of economic events on stock market returns

42

1.7 Significance of the Study 44

1.8 Limitations of the Study 45

1.9 Organization of Study 46

VIII

Chapter II Review of Literature 47-63

2.1 Literature Review of Macroeconomic Variables and Stock Market Returns

47 2.2 Literature Review of Financial Performance Indicators and

Stock Market Returns

54 2.3 Literature Review of Economic Events and Stock Market

Returns

58

2.4 Research Gap 62

Chapter III Theoretical Background of Study 64-81

3.1 Overview of Indian Stock Market 64

3.2 Overview of Macroeconomic Variables 66

3.2.1 Gross Domestic Product (GDP) 66

3.2.2 Gross Fixed Capital Formation 67

3.2.3 Final Consumption Expenditure 67

3.2.4 Current Account Balance 68

3.2.5 Gold Prices 68

3.2.6 Silver Prices 69

3.2.7 Crude Oil Prices 69

3.2.8 Export of Goods and Services 70

3.2.9 Import of Goods and Services 70

3.2.10 Inflation 70

3.2.11 Interest Rate 71

3.2.12 Foreign Direct Investment (FDI) 71

3.2.13 Foreign Portfolio Investment (FPI) 72

3.2.14 Foreign Exchange Reserves 72

3.2.15 Real Effective Exchange Rate (REER) 72

3.2.16 Broad Money and Narrow Money 73

3.2.17 Tax Revenue 73

3.2.18 Value added in Agriculture, Industry and Service Sector 74 3.3 Overview of Financial Performance Indicators 74

3.3.1 Return on Assets (ROA) 74

3.3.2 Return on Equity (ROE) 74

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3.3.3 Return on Invested Capital (ROIC) 75

3.3.4 Economic Value Added (EVA) 75

3.4 Overview of Economic Events 75

3.4.1 Demonetization 75

3.4.2 Brexit referendum 76

3.4.3 Chinese Stock Market Meltdown 76

3.4.4 Major Depreciation of Indian Rupee 77

3.4.5 Announcement of LTCG Tax 77

3.4.6 Bank Recapitalization 78

3.4.7 US Subprime Mortgage Crisis 79

3.4.8 Goods and Service Tax (GST) 79

3.4.9 Real Estate Regulatory Authority (RERA) 80

3.4.10 Bank Frauds 80

Chapter IV Macroeconomic Variables and Stock Market Returns 82-111

4.1 Theoretical Consideration 82

4.2 Results and Discussion 83

4.2.1 Graphical Analysis of Macroeconomic Variables and Nifty 50 Index

83

4.2.2 Summary Statistics 93

4.2.3 Stationarity Test 94

4.2.4 Relationship between Stock Market Returns and Macroeconomic Variables

96 4.2.5 Causation effect between Stock Market Returns and

Macroeconomic Variables

97 4.2.6 Regression Analysis with Structural Breaks 99

4.2.7 CUSUM Test 102

4.2.8 Test for examining Serial Correlation and Heteroscedasticity 110 Chapter V Financial Performance Indicators and Stock Market

Returns

112-126

5.1 Theoretical Considerations 112

5.2 Results and Discussion 113

5.2.1 Summary Statistics 113

X

5.2.2 Panel Unit Root Test 117

5.2.3 Correlation Analysis 120

5.2.4 Selection of Appropriate Model using Hausman Test 122

5.2.5 Fixed Effects Model (FEM) 123

5.2.6 Random Effects Model (REM) 125

Chapter VI Economic Events and Stock Market Returns 127-221

6.1 Theoretical Considerations 127

6.2 Results and Discussion 130

6.2.1 Impact of Demonetization on Stock Market Returns 130

6.2.1.1 Regression Analysis 130

6.2.1.2 Residual Diagnostics 133

6.2.1.3 Model Stability Diagnostics 134

6.2.2 Impact of Brexit Referendum on Stock Market Returns 136

6.2.2.1 Regression Analysis 136

6.2.2.2 Residual Diagnostics 138

6.2.2.3 Model Stability Diagnostics 139

6.2.3 Impact of Chinese Stock Market Meltdown on Stock Market Returns

141

6.2.3.1 Regression Analysis 141

6.2.3.2 Residual Diagnostics 144

6.2.3.3 Model Stability Diagnostics 145

6.2.4 Impact of Major Depreciation of Indian Rupee on Stock Market Returns

147

6.2.4.1 Regression Analysis 147

6.2.4.2 Residual Diagnostics 150

6.2.4.3 Model Stability Diagnostics 151

6.2.5 Impact of Announcement of LTCG Tax on Stock Market Returns

153

6.2.5.1 Regression Analysis 153

6.2.5.2 Residual Diagnostics 156

6.2.5.3 Model Stability Diagnostics 157

6.2.6 Impact of Bank Recapitalization announcement on Stock 159

XI

Market Returns

6.2.6.1 Regression Analysis 159

6.2.6.2 Residual Diagnostics 162

6.2.6.3 Model Stability Diagnostics 163

6.2.7 Impact of US Subprime Mortgage Crisis on Stock Market Returns

165

6.2.7.1 Regression Analysis 165

6.2.7.2 Residual Diagnostics 168

6.2.7.3 Model Stability Diagnostics 169

6.2.8 Impact of GST on Stock Market Returns 171

6.2.8.1 Regression Analysis 171

6.2.8.2 Residual Diagnostics 177

6.2.8.3 Model Stability Diagnostics 180

6.2.9 Impact of RERA on Stock Market Returns: 184

6.2.9.1 Regression Analysis 184

6.2.9.2 Residual Diagnostics 190

6.2.9.3 Model Stability Diagnostics 193

6.2.10 Impact of Bank Frauds on Stock Market Returns: 197

6.2.10.1 Regression Analysis - PNB Fraud 197

6.2.10.2 Residual Diagnostics- PNB Fraud 199

6.2.10.3 Model Stability Diagnostics- PNB Fraud 200

6.2.10.4 Regression Analysis - Andhra Bank Fraud 202 6.2.10.5 Residual Diagnostics - Andhra Bank Fraud 204 6.2.10.6 Model Stability Diagnostics - Andhra Bank Fraud 205 6.2.10.7 Regression Analysis - Bank of Maharashtra Fraud 207 6.2.10.8 Residual Diagnostics - Bank of Maharashtra Fraud 209 6.2.10.9 Model Stability Diagnostics - Bank of Maharashtra Fraud 210 6.2.10.10 Regression Analysis - Canara and Vijaya Bank Fraud 212 6.2.10.11 Residual Diagnostics - Canara and Vijaya Bank Fraud 214 6.2.10.12 Model Stability Diagnostics - Canara and Vijaya Bank Fraud 215

6.2.10.13 Regression Analysis - SBI Fraud 217

6.2.10.14 Residual Diagnostics - SBI Fraud 219

XII

6.2.10.15 Model Stability Diagnostics - SBI Fraud 220 Chapter VII Findings, Conclusions and Recommendations 222-238

7.1 Findings of the Study 222

7.2 Conclusions of the Study 230

7.3 Recommendations of the Study 237

7.4 Policy Implications of the Study 238

7.5 Contribution of the Study 239

7.6 Scope for Further Research 240

Research Paper Publications 241

Research Paper Presentations 242

References 243

Annexure

LIST OF TABLES

Table No. Table Description Page No.

1 List of Economic Events 21

2 Summary statistics results of stock market returns and macroeconomic variables

93

3 Results of Augmented Dickey-Fuller Test 94

4 Results of Correlation Analysis 96

5 Results of Granger Causality Test 97

6 Results of Bai-Perron Test 99

7 Results of CUSUM Test 102

8 Results of Serial Correlation and Heteroscedasticity 110 9 Results of Summary Statistics of stock returns, traditional and

modern performance measures

113 10 Results showing Panel Unit Root Test of stock returns,

traditional and modern performance measures

117 11 Results showing relationship of Stock Returns with traditional

and modern performance measures

120

XIII

12 Results of Hausman Test for model selection 122

13 Results of Fixed Effects Model 123

14 Results of Random Effects Model 125

15 Regression result showing the impact of Demonetization on stock market returns

132 16 Residual Diagnostic results of the models examining the impact

of Demonetization on stock market returns

133 17 Regression result showing the impact of Brexit Referendum on

stock market returns

137 18 Residual Diagnostic results of the models examining the impact

of Brexit Referendum on stock market returns

138 19 Regression result showing the impact of Chinese Stock Market

Meltdown on stock market returns

143 20 Residual Diagnostic results of the models examining the impact

of Chinese Stock Market Meltdown on stock market returns

144 21 Regression result showing the impact of Major Depreciation of

Indian Rupee on stock market returns

149 22 Residual Diagnostic results of the models examining the impact

of Major Depreciation of Indian Rupee on stock market returns

150 23 Regression result showing the impact of Announcement of

LTCG Tax on stock market returns

155 24 Residual Diagnostic results of the models examining the impact

of Announcement of LTCG Tax on stock market returns

156 25 Regression result showing the impact of Bank Recapitalization

Announcement on stock market returns

161 26 Residual Diagnostic results of the models examining the impact

of Bank Recapitalization Announcement on stock market returns

162

27 Regression result showing the impact of US Subprime Mortgage Crisis on stock market returns

167 28 Residual Diagnostic results of the models examining the impact

of US Subprime Mortgage crisis on stock market returns

168 29 Regression result showing the impact of GST Passed in Rajya 174

XIV

Sabha on stock market returns

30 Regression result showing the impact of GST Passed in Lok Sabha on stock market returns

175 31 Regression result showing the impact of Commencement of

GST on stock market returns

176 32 Residual Diagnostic results of the models examining the impact

of GST Passed in Rajya Sabha on stock market returns

177 33 Residual Diagnostic results of the models examining the impact

of GST Passed in Lok Sabha on stock market returns

178 34 Residual Diagnostic results of the models examining the impact

of Commencement of GST on stock market returns

179 35 Regression result showing the impact of RERA Passed in Rajya

Sabha on stock market returns

187 36 Regression result showing the impact of RERA Passed in Lok

Sabha on stock market returns

188 37 Regression result showing the impact of Commencement of

RERA on stock market returns

189 38 Residual Diagnostic results of the models examining the impact

of RERA Passed in Rajya Sabha on stock market returns

190 39 Residual Diagnostic results of the models examining the impact

of RERA Passed in Lok Sabha on stock market returns

191 40 Residual Diagnostic results of the models examining the impact

of Commencement of RERA on stock market returns

192 41 Regression result showing the impact of PNB Fraud on stock

market returns

198 42 Residual Diagnostic results of the models examining the impact

of PNB Fraud on stock market returns

199 43 Regression result showing the impact of Andhra Bank Fraud on

stock market returns

203 44 Residual Diagnostic results of the models examining the impact

of Andhra Bank Fraud on stock market returns

204 45 Regression result showing the impact of Bank of Maharashtra

Fraud on stock market returns

208

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46 Residual Diagnostic results of the models examining the impact of Bank of Maharashtra Fraud on stock market returns

209 47 Regression result showing the impact of Canara Bank and

Vijaya Bank Fraud on stock market returns

213 48 Residual Diagnostic results of the models examining the impact

of Canara Bank and Vijaya Bank Fraud on stock market returns

214 49 Regression result showing the impact of SBI Fraud on stock

market returns

218 50 Residual Diagnostic results of the models examining the impact

of SBI Fraud on stock market returns

219

LIST OF FIGURES

Figure No. Title Page No.

1 Macroeconomic Variables 22 Factor Wagon Wheel 11

2 Trend in Gold Prices and Nifty 50 Index 85

3 Trend in Silver Prices and Nifty 50 Index 86

4 Trend in Foreign Exchange Reserve and Nifty 50 Index 86

5 Trend in Inflation Rate and Nifty 50 Index 86

6 Trend in Interest Rate and Nifty 50 Index 87

7 Trend in Crude Oil Prices and Nifty 50 Index 87 8 Trend in Real Effective Exchange Rate and Nifty 50 Index 87 9 Trend in Foreign Direct Investment and Nifty 50 Index 88 10 Trend in Foreign Portfolio Investment and Nifty 50 Index 88

11 Trend in Broad Money and Nifty 50 Index 88

12 Trend in Narrow Money and Nifty 50 Index 89

13 Trend in Imports of Goods and Services and Nifty 50 Index 89 14 Trend in Exports of Goods and Services and Nifty 50 Index 89 15 Trend in Gross Domestic Product and Nifty 50 Index 90 16 Trend in Gross Fixed Capital Formation and Nifty 50 Index 90 17 Trend in Private Final Consumption Expenditure and Nifty 90

XVI

50 Index 18

Trend in Government Final Consumption Expenditure and

Nifty 50 Index 91

19 Trend in Current Account Balance and Nifty 50 Index 91

20 Trend in Tax Revenue and Nifty 50 Index 91

21 Trend in Agriculture Value Added and Nifty 50 Index 92

22 Industry Value Added and Nifty 50 Index 92

23 Trend in Services Value Added and Nifty 50 Index 92 24

Results of CUSUM Test for models examining the impact of

Demonetization on stock market returns 135

25

Results of CUSUM Test for models examining the impact of

Brexit Referendum on stock market returns 140 26

Results of CUSUM Test for models examining the impact of

Chinese Stock Market Meltdown on stock market returns 146 27

Results of CUSUM Test for models examining the impact of

Major Depreciation of Indian Rupee on stock market returns 152 28

Results of CUSUM Test for models examining the impact of

Announcement of LTCG Tax on stock market returns 158

29

Results of CUSUM Test for models examining the impact of Bank Recapitalization Announcement on stock market

returns 164

30

Results of CUSUM Test for models examining the impact of

US Subprime Mortgage Crisis on stock market returns 170 31

Results of CUSUM Test for models examining the impact of

GST Passed in Rajya Sabha on stock market returns 181 32

Results of CUSUM Test for models examining the impact of

GST Passed in Lok Sabha on stock market returns 182 33

Results of CUSUM Test for models examining the impact of

Commencement of GST on stock market returns 183 34

Results of CUSUM Test for models examining the impact of

RERA Passed in Rajya Sabha on stock market returns 194 35

Results of CUSUM Test for models examining the impact of

RERA Passed in Lok Sabha on stock market returns 195

XVII

36

Results of CUSUM Test for models examining the impact of

Commencement of RERA on stock market returns 196 37

Results of CUSUM Test for models examining the impact of

PNB Fraud on stock market returns 201

38

Results of CUSUM Test for models examining the impact of

Andhra Bank Fraud on stock market returns 206 39

Results of CUSUM Test for models examining the impact of

Bank of Maharashtra Fraud on stock market returns 211 40

Results of CUSUM Test for models examining the impact of

Canara Bank and Vijaya Bank Fraud on stock market returns 216 41

Results of CUSUM Test for models examining the impact of

SBI Fraud on stock market returns 221

LIST OF ABBREVIATIONS

ADF Augmented Dickey-Fuller

AIM Alternative Investment Market

APT Asset Pricing Theory

ARCH Autoregressive Conditional Heteroscedasticity

AVA Agriculture Value Added

BoP Balance of Payment

BRICS Brazil, Russia, India, China, and South Africa

BSE Bombay Stock Exchange

CAB Current account Balance

CAPM Capital Asset Pricing Model CBI Central Bureau of Investigation CEOs Chief Executive Officers

CLRM Classical Linear Regression Model

CPI Consumer Price Index

CUSUM Cumulative Sum

ESA European System of Accounts

XVIII

ETFs Exchange Traded Funds

EU European Union

EVA Economic Value Added

FDI Foreign Direct Investment

FER Foreign Exchange Reserve

FOREX Foreign Exchange

FPI Foreign Portfolio Investment.

FRED Federal Reserve Economic Database

GARCH Generalized Autoregressive Conditional Heteroscedasticity GARCH-MIDAS

Generalized Autoregressive Conditional Heteroscedasticity- Mixed Data Sampling

GDP Gross Domestic Product

GFCE Government Fixed Capital Expenditure GFCF Gross Fixed Capital Formation

GST Goods and Service Tax

ICCL Indian Clearing Corporation Ltd.

IIP Index for Industrial Production IMF International Monetary Fund IPO Initial Public Offer

IVA Industry Value Added

LAVA Log of Agriculture Value Added

LBM Log of Broad Money

LCAB Log of Current Account Balance LCOIL Log of Crude Oil Prices

LEXP Log of Exports of Goods and Services LFDI Log of Foreign Direct Investment LFER Log of Foreign Exchange Reserves LFPI Log of Foreign Portfolio Investment LGDP Log of Gross Domestic Product

LGFCE Log of Government Final Capital Expenditure LGFCF Log of Gross Fixed Capital Formation

LGOLD Log of Gold Prices

XIX

LIMP Log of Imports of Goods and Services LINFL Log of Inflation Rate

LINT Log of Interest Rate

LIVA Log of Industry Value added

LM Lagrange Multiplier

LNAIR Log of Nifty Auto Index Return LNFIR Log of Nifty FMCG Index Return

LNFSIR Log of Nifty Financial Services Index Return LNIR Log of Nifty 50 Index Return

LNITIR Log of Nifty IT Index Return

LNM Log of Narrow Money

LNMEDIR Log of Nifty Media Index Return LNMETIR Log of Nifty Metal Index Return LNPHIR Log of Nifty Pharma Index Return LNPSUBIR Log of Nifty PSU Bank Index Return LNPVTBIR Log of Nifty Private Bank Index Return LNRIR Log of Nifty Realty Index Return

LPFCE Log of Private Final Capital Expenditure LREER Log of Real Effective Exchange Rate

LSE London Stock Exchange

LSILVER Log of Silver Prices

LSVA Log of Services Value Added

LTAX Log of Tax Revenue

LTCG Long-Term Capital Gains

M1 Narrow Money

M3 Broad Money

MFDFA Multi Fractal De-trended Fluctuation Analysis

NASDAQ National Association of Securities Dealers Automated Quotations NBER National Bureau of Economic Research

NEER Nominal Effective Exchange Rate NIPA National Income and Product Accounts NOPAT Net Operating Profit After Tax

XX

NSCCL National Securities Clearing Corporation Ltd.

NSE National Stock Exchange

NYSE New York Stock Exchange

OECD Organization for Economic Co-operation and Development OMXBBGI OMX Baltic Benchmark Gross Index

OMXR OMX Riga Index

OMXT OMX Tallinn Index

OMXV OMX Vilnius Index

OPEC Organization of the Petroleum Exporting Countries PFCE Private Fixed Capital Expenditure

PP Phillips Perron

PPI Producer Price Index

PVT Private

RBI Reserve Bank of India

REER Real Effective Exchange Rate RERA Real Estate Regulatory authority

ROA Return on Asset

ROE Return on Equity

ROIC Return on Invested Capital S&P Standard & Poor’s

SDRs Special Drawing Rights

SEBI Securities and Exchange Board of India

SGX Singapore Exchange

SVA Services Value Added

UK United Kingdom

UNSNA United Nations System of National Accounts VECM Vector Error Correction Model

WCS Western Canadian Select

WPI Wholesale Price Index

WTI West Texas Intermediate

Chapter I

1

Chapter I

Introduction

______________________________________________________________

The present section introduces the research topic, highlights the need for this research study and provides the scope of the study. The objectives of the study, research methodology focusing on every objective, and necessary hypotheses developed for the purpose of study have also been incorporated. This section also provides significance of study, the possible limitations and finally the organization of chapters which will act as guidance for remaining part of the thesis.

1.1 Introduction:

The Dow Theory¹ says that “Stock Market Discounts Everything.” This signifies, whatever events which occur in an economy get reflected through the stock market. The events may be; rise or fall in inflation rates, interest rates, changes in government policies, government budget, changes in the balance of payment (BoP) structure, currency appreciation or depreciation, recession or depression, natural calamities like floods, cyclones, and draughts or terrorist attacks and so on. Also, the events may be related to companies such as dividend announcements, the appointment of new directors, or CEOs, mergers, amalgamations, acquisitions, internal and external reconstructions. The stock market acts as a barometer or mirror and thus reflects the impact of all these economic and financial events. Considering the significant importance of macroeconomic variables with regards to stock prices, many researchers have attempted to study these relationships using various models. How well the macroeconomic variables can explain the changes in stock ________________________________________

1The Dow Theory was derived from the 255 editorials of Wall Street Journal written by Charles Dow (1851-1902)

2

equity returns in the stock market is explained by Bilson, Brailsford & Hooper (1999).

Adam & Tweneboah (2008) examined the impact of macroeconomic variables on stock prices with reference to Ghana and proved the long run relationship between stock prices and macroeconomic variables using Johansen’s multivariate co-integration test. The researchers also showed that in the short run, the exchange rates and inflation also influence stock prices. Sariannidis, Giannarakis, Litinas & Konteos (2009) used GARCH Model to study the impact of various macroeconomic variables on Dow Jones Wilshire 5000 and Dow Jones Sustainability Indexes. The researchers concluded that the US stock market gets negatively affected by the returns in crude oil prices and positively affected by the returns of bond (10-year Maturity). Also, the association of stock prices with the exchange rate was found to be negative. Rasiah (2010) also investigated the long run and short-run relationships between various macroeconomic factors and stock market using time series analysis. The researchers applied Co-integration Test and Vector Error Correction Model (VECM) to demonstrate the results and proved the influence of money supply, consumer price index, and exchange rates in predicting stock returns. This proves the research area to be of eminent interest for many researchers.

Besides macroeconomic variables, the stock market also witnesses numerous economic events. The economic events here refer to the events which occur in the economy of the country and also across the globe. These events can be expected or sometimes unexpected by the investors and other market participants. For example, the event like demonetization on 8^th November 2016 was not expected by the nation.

However, the reforms like Good and Service Tax (GST) were into the discussion for many years. In the era of globalization, there has been a significant rise in trade relations between countries. As such a country is bound to get affected by international economic events. Market efficiency has been a prominent area of research for the past several

3

decades. An efficient stock market is one which accurately reflects all the information which is relevant in determining the prices of security (Malkiel, 1989). The researcher also commented that in the market valuation process, there is room for hopes and fears of market participants. Thus, the present study will examine the impact of macroeconomic variables on stock market returns and also evaluate the impact of some of the key economic events in Indian stock market.

The world is dynamic and so also its constituents. The information dissemination in today’s world has become immense faster and accurate with technological advancement.

Investors look out for various aspects to support their investment decision. Traditionally, the net profits, sales or revenue from operations, debt structure emerged as important indicators to analyze the performance of companies fundamentally. Over the time, the investors considered the performance measures like Return on Equity (ROE), Return on Asset (ROA), Return on Invested Capital (ROIC) and so on to make the investment decisions accordingly. With the emergence of Economic Value Added (EVA) as a modern performance measurement technique which was coined by Stern Stewart and Co. in 1990s, it was interesting to see which performance measures explain variations in stock returns efficiently. Many researchers contributed to evaluating the efficiency of traditional and modern performance measures in predicting stock market returns. The traditional measures concerned more with the earnings and profitability aspect of the company. Whereas modern measures like Economic Value Added gave importance to shareholders value creation along with profits of the company. When Economic Value Added was invented, few companies took its cognizance. Now with the growth of significant research in the area, investors and companies are realizing the importance of reporting Economic Value Added in their Financial Statements. Although it still remains a debatable question whether Economic Value Added is a better financial performance measure than traditional ones or

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not? As such the issue will be investigated in the current study. The present study aims to examine the impact of traditional (Return on Asset, Return on Equity, Return on Invested Capital) and modern performance measures (Economic Value Added) on stock returns and investigate if there exists any relationship between the said variables. Such a relationship needs to be examined to assist the investors to understand which performance indicators have a significant impact on stock returns in this dynamic world. The term “Dynamic”

here refers to the constant changes, activities, or progress as defined by Oxford Dictionary.

It evolved in early 19^th Century from the French ‘dynamique’ and from Greek

‘dunamikos’.

1.2 Need for the Study:

It is evident from the previous literature on stock returns and macroeconomic variables as elaborated in Chapter II that, many researchers have significantly contributed to this area of study. The prime focus has been found studying the United States stock market. The United States is associated with almost every country in the world, which have bilateral ties with regards to trade, policies, and decisions. Therefore the majority of research studies are attracted by United States stock markets, followed by European stock markets (Reddy & Narayan, 2016). India, being an emerging economy with untapped financial resources is growing significantly with regards to stock markets. The Indian stock Exchanges, i.e. Bombay Stock Exchange (BSE) and National Stock Exchange (NSE) attract a large pool of domestic as well as foreign investors. Hence the study concerning the influence of various macroeconomic variables affecting stock markets is vital. Many Indian, as well as foreign authors, have addressed this issue. But since the economic conditions are dynamic, a more complex study considering larger samples and analysis through effective econometric models becomes necessary. The present research study will

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try to overcome the limitations of previous studies relating to stock returns and macroeconomic variables and analyze the influence of these variables. Also, the modern performance measures are evolving, and the investors are getting better measures in analyzing the financial performance of the companies in which they are planning to invest.

The modern performance measures like Economic Value Added (EVA) although focuses on shareholders’ wealth creation is yet to gain significant importance. In the present dynamic world, it is vital to understand whether traditional or modern performance measures affect stock market returns. The stock market is also bound to get affected by various expected and unexpected economic events which occur in an economy and across the world. The stock market of any country is expected to get affected by economic events not only arising in its economy, but also in world economies due to increased trade relations after globalization and formation of various trade blocks. All countries are dependent on each other for various means. It is important to understand how these economic events affect the stock market returns, which sector gets severely affected by these events and how an investor can deal with such events. Thus there was a need to address these issues and the present study shall server this purpose.

1.3 Scope of the Study:

The present study is aimed to examine the impact of macroeconomic variables on stock market returns. The analysis will be performed considering monthly data of 257 months i.e. January 1996 – May 2017, quarterly data of 85 quarters i.e. Quarter II 1996 – Quarter II 2017 and annual data of 20 years i.e. 1996 – 2016, which includes macroeconomic variables such as Gold Prices, Silver Prices, Foreign Exchange Reserve (FER), Inflation Rate, Interest Rate, Crude Oil Prices, Real Effective Exchange Rate (REER), Foreign Direct Investment (FDI), Foreign Portfolio Investment (FPI), Broad

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Money (M3), Narrow Money (M1), Imports of Goods and Services and Exports of Goods and Services which are in monthly series, Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFCF), Private Final Consumption Expenditure (PFCE), Government Final Consumption Expenditure (GFCE) and Current Account Balance (CAB) in quarterly series, and Tax Revenue, Agriculture Value Added (AVA), Industry Value Added (IVA) and Services Value Added (SVA) in annual series data. The stock returns will be computed in monthly, quarterly and annual series while analyzing with the respective type of macroeconomic variable. Also, the purpose of the present study is to examine the impact of traditional (Return on Asset, Return on Equity, Return on Invested Capital) and modern performance measures (Economic Value Added) on stock returns and investigate if there exists any relationship between the said variables in this dynamic world by considering 408 companies listed on Nifty 500 Index spread over 18 sectors for the period 2002-2017. The sectors to which these 408 companies belong include Automobile, Cement and Cement Products, Chemical, Construction, Consumer Goods, Energy, Fertilizers and Pesticides, Financial Services, Healthcare Services, Industrial Manufacturing, Information Technology, Media and Entertainment, Metals, Paper, Pharma, Services, Telecom, and Textiles. The study also aims to examine the impact of various economic events on Indian stock market returns. The economic events considered for the study are Demonetization, Brexit Referendum, Chinese stock market meltdown, Major depreciation of Indian Rupee, Announcement of Long Term Capital Gains (LTCG) Tax, Bank Recapitalization Announcement, US Subprime mortgage crisis, Goods and Service Tax, Real Estate (Regulation and Development) Act 2016, and select Bank frauds. The bank frauds considered for study are Punjab National Bank fraud, Andhra Bank fraud, Bank of Maharashtra fraud, Canara Bank and Vijaya Bank fraud, and State Bank of India fraud.

The analysis with respect to Goods and Services Tax and Real Estate (Regulation and

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Development) Act 2016 is performed considering when they were passed in Rajya Sabha, Lok Sabha and commencement of such regulation. Thus the present study will focus on macroeconomic variables, financial performance indicators, and economic events and examine their impact on stock market returns in this dynamic world. Considering the three focus areas of the present study, the objectives are set pertaining to every area which are presented in next section.

1.4 Objectives of the Study:

The present study examines the impact of select macroeconomic variables, financial performance indicators and economic events on stock market returns. The objectives are further categorized into sub objectives. An insight about objectives of the study is presented as follows:

1.4.1 Objective I: To Study the Dynamics of Macroeconomic Variables and Stock Market Returns.

Sub Objectives:

1.4.1.1 To examine the trends in select macroeconomic variables and stock market returns.

1.4.1.2 To analyze the relationship between select macroeconomic variables and stock market returns.

1.4.1.3 To investigate the causation effect between select macroeconomic variables and stock market returns.

1.4.1.4 To study the impact of select macroeconomic variables on stock market returns.

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1.4.2 Objective II: To Study the Dynamics of Financial Performance Indicators and Stock Market Returns.

Sub Objectives:

1.4.2.1 To evaluate the relationship between select financial performance indicators and stock market returns.

1.4.2.2 To study the impact of select financial performance indicators on stock market returns.

1.4.3 Objective III: To Study the Dynamics of Economic Events and Stock Market Returns.

Sub Objective:

1.4.3.1 To examine the impact of select economic events on stock market returns.

1.5 Research Methodology:

The present study utilized various statistical and econometric tools and techniques to support the analysis of objectives set for the purpose of study. A detailed overview of the methodology used in the current study is elaborated in the present section which focuses on the period considered for study, sample design, data variables and data sources, and various statistical and econometrics techniques employed using econometric softwares. Such techniques are briefly explained to get an understanding about the relevance of these techniques in the present study and equations are incorporated to support the analysis.

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1.5.1 Examining Impact of Macroeconomic Variables on Stock Market Returns:

1.5.1.1 Period of study:

The study considers a monthly data of 257 months i.e. January 1996 – May 2017, quarterly data of 85 quarters i.e. Quarter II 1996 – Quarter II 2017 and annual data of 20 years i.e. 1996 – 2016 to examine the impact of macroeconomic variables on stock market returns. As the period of study is spread over two decades and includes financial crisis in the year 2008 and other economic events, study suspects the presence of structural breaks.

The structural break presence may deteriorate the results. Therefore, the analyses are performed considering this aspect.

1.5.1.2 Sample Design:

The present study identifies and selects various macroeconomic variables through in-depth content analysis and thereby constructs a 22-factor macroeconomic variables wagon wheel. Using the data available from official websites of World Bank, International Monetary Fund (IMF), Organization for Economic Co-operation and Development (OECD), Federal Reserve Economic Data (FRED) and Reserve Bank of India (RBI), the study identified 1523 macroeconomic variables. The variables were further reduced to 40 based on their relevance. And finally, 22 variables were identified using extensive literature review and economic theories supporting the expected impact of the macroeconomic variable on stock market returns.

1.5.1.3 Data Variables and Data Sources:

The study has considered Nifty 50 index returns as a proxy of Indian stock market.

The National Stock Exchange (NSE) being India’s largest stock exchange in equity transactions and world’s 11th biggest stock exchange (as of April 2018 as per reports of

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World Federation of Exchanges) in terms of market capitalization, the study considered prominent index of NSE i.e. Nifty 50 Index. The 22 macroeconomic variables identified are represented in Figure 1.

The macroeconomic variables considered for the study includes Gold Prices, Silver Prices, Foreign Exchange Reserve, Inflation Rate, Interest Rate, Crude Oil Prices, Real Effective Exchange Rate, Foreign Direct Investment, Foreign Portfolio Investment, Broad Money (M3), Narrow Money (M1), Imports of Goods and Services and Exports of Goods and Services which are in monthly series, Gross Domestic Product, Gross Fixed Capital Formation, Private Final Consumption Expenditure, Government Final Consumption Expenditure and Current Account Balance in quarterly series, and Tax Revenue, Agriculture Value Added, Industry Value Added and Services Value Added in annual series data.

Likewise, the stock returns are computed in monthly, quarterly and annual series while analyzing with the respective type of macroeconomic variable. Such data pertaining to closing prices of Nifty 50 Index were obtained from the official website of National Stock Exchange (NSE) of India. The required data relating to macroeconomic variables have been extracted from the official websites of World Bank, Federal Reserve Economic Data (FRED) and Reserve Bank of India (RBI).

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Figure 1: Macroeconomic Variables 22 Factor Wagon Wheel.

Source: Author’s Compilation 1.5.1.4 Statistical and Econometric Techniques:

The present study attempts to investigate the association of various macroeconomic variables with stock returns. The returns from the stocks will be computed using formula Ln(Po/P1). Where, Po is the price at the end of the period, and P1 signifies price at the beginning of the period. The returns are converted into log form for normality purpose.

The study utilizes various statistical and econometric techniques such as Graphical Analysis, Summary Statistics, Correlation Analysis, Granger Causality Test, Bai-Perron Test, and CUSUM Test.

The study uses Graphical Analysis to examine the trends in variables. The macroeconomic variables are compared with Nifty 50 Index closing prices. Such trends are

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displayed using simple line charts. Summary Statistics considered for the study include Mean, Standard Deviation, Skewness and Kurtosis. The mean which is a measure of central tendency, also known as Average is used as a performance measure. Higher the value of mean is regarded as better depending on the nature of variable. In contrast, Standard Deviation is a measure of dispersion which signifies variation in the data. Lower the value of standard deviation is treated as better as it signifies less variation. Skewness is a measure of symmetry of the data. It gives an idea whether the data is symmetrical, positively skewed or negatively skewed. In case of skewness if the β is equal to 0, it is regarded as symmetrical, β more than 1 then positively skewed and β less than 1, it is called as negatively skewed. And Kurtosis focuses on the flatness of the data. It assumes all the bell-shaped curves to be symmetrical but with different heights. Here, if the β is equal to 0 or 3, it is regarded as Mesokurtic, β more than 0 or 3, it will be Leptokurtic and β less than 0 or 3, it will be Platykurtic. Comparison of β with 0 or 3 depends on the nature of the output. As such Skewness and Kurtosis gives an idea normality of the data and overall summary statistics gives an understanding about nature of the data. Correlation Analysis has been utilized to evaluate the relationship between selected variables.

The study utilizes widely used and accepted measure of Pearson’s Correlation to achieve such correlation analysis. Pearson’s Correlation not only signifies how strong the relationship is, but it also shows the direction, that is whether the relationship is positive or negative. If Pearson’s Correlation (r) is equal to 0, it indicates existence of no relationship, if r more than 0, it shows positive relationship and when r is less than 0 it signifies negative relationship. At no point, the relationship can be less than -1 or more than +1. P- values can be used to support the Pearson’s Correlation to show whether such relationships are significant or not.

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To examine the impact of macroeconomic variables on stock market returns, study utilizes Bai-Perron test instead of normal Ordinary Least Squares as it suspects the presence of structural breaks. The structural changes or structural breaks are unexpected shifts which occur in a time series data due to any event, which may bring in forecasting errors or make the model unreliable. Such a situation is dealt by using Bai-Perron test. The Bai-Perron test is better as compared to the Chow test in evaluating structural breaks as it allows for more than one structural break and automatically detects it. Bai-Perron test also provides the results for various periods as identified by breaks. This gives an understanding about impact during different periods.

The stability of the model is examined using CUSUM (Cumulative Sum) test, also referred to as Cumulative Sum Control Chart. The test gives the results in the form of chart which consist of Upper Control Limit (UCL) and Lower Control Limit (LCL). When a particular model lies between UCL and LCL at selected level of significance, the model is considered to be stable.

The correlation analysis provides the extent of relationship between two or more variables and regression analysis gives an understanding about presence of any significant impact, however both fails to determine which variable causes the other. Causality is important as to evaluate the causation effect between selected variables. The study utilizes one of the widely used Granger Causality Test to serve this purpose. The Granger Causality Test will reveal the presence of unidirectional or bidirectional causality between the variables.

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Following models were developed for the purpose of study:

LNIR = α1 + β₁ LGOLD + ε₁ (1)

Where, LNIR is the Log of Nifty 50 Index Returns, α1 is constant term, β1 is slope coefficient, LGOLDrepresents Log of Gold Prices and ε₁ symbolizes disturbance term of the model.

LNIR = α₂ + β₂ LSILVER + ε₂ (2)

Where, LNIR is the Log of Nifty 50 Index Returns, α2 is constant term, β₂ is slope coefficient, LSILVERrepresents Log of Silver Prices and ε2 symbolizes disturbance term of the model.

LNIR = α3 + β3 LFER + ε3 (3)

Where, LNIR is the Log of Nifty 50 Index Returns, α3 is constant term, β3 is slope coefficient, LFER represents Log of Foreign Exchange Reserves and ε3 symbolizes disturbance term of the model.

LNIR = α4 + β₄ LINFL + ε₄ (4)

Where, LNIR is the Log of Nifty 50 Index Returns, α4 is constant term, β4 is slope coefficient, LINFLrepresents Log of Inflation Rates and ε4 symbolizes disturbance term of the model.

LNIR = α5 + β5 LINT + ε5 (5)

Where, LNIR is the Log of Nifty 50 Index Returns, α₅ is constant term, β₅ is slope coefficient, LINTrepresents Log of Interest Rates and ε5 symbolizes disturbance term of the model.

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LNIR = α₆ + β₆ LCOIL + ε₆ (6)

Where, LNIR is the Log of Nifty 50 Index Returns, α6 is constant term, β₆ is slope coefficient, LCOILrepresents Log of Crude Oil Prices and ε6 symbolizes disturbance term of the model.

LNIR = α7 + β7 LREER + ε7 (7)

Where, LNIR is the Log of Nifty 50 Index Returns, α7 is constant term, β7 is slope coefficient, LREERrepresents Log of Real Effective Exchange Rate and ε7 symbolizes disturbance term of the model.

LNIR = α8 + β₈ LFDI + ε₈ (8)

Where, LNIR is the Log of Nifty 50 Index Returns, α8 is constant term, β₈ is slope coefficient, LFDI represents Log of Foreign Direct Investment and ε8 symbolizes disturbance term of the model.

LNIR = α9 + β9 LFPI + ε9 (9)

Where, LNIR is the Log of Nifty 50 Index Returns, α₉ is constant term, β₉ is slope coefficient, LFPI represents Log of Foreign Portfolio Investment and ε9 symbolizes disturbance term of the model.

LNIR = α10 + β10 LBM + ε10 (10)

Where, LNIR is the Log of Nifty 50 Index Returns, α10 is constant term, β10 is slope coefficient, LBMrepresents Log of Broad Money and ε10 symbolizes disturbance term of the model.

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LNIR = α₁₁ + β₁₁ LNM + ε₁₁ (11)

Where, LNIR is the Log of Nifty 50 Index Returns, α11 is constant term, β₁₁ is slope coefficient, LNMrepresents Log of Narrow Money and ε11 symbolizes disturbance term of the model.

LNIR = α12 + β12 LIMP + ε12 (12)

Where, LNIR is the Log of Nifty 50 Index Returns, α12 is constant term, β12 is slope coefficient, LIMP represents Log of Imports of Goods and Services and ε12 symbolizes disturbance term of the model.

LNIR = α13 + β₁₃ LEXP + ε₁₃ (13)

Where, LNIR is the Log of Nifty 50 Index Returns, α₁₃ is constant term, β₁₃ is slope coefficient, LEXP represents Log of Exports of Goods and Services and ε13 symbolizes disturbance term of the model.

LNIR = α14 + β14 LGDP + ε14 (14)

Where, LNIR is the Log of Nifty 50 Index Returns, α₁₄ is constant term, β₁₄ is slope coefficient, LGDP represents Log of Gross Domestic Product and ε14 symbolizes disturbance term of the model.

LNIR = α15 + β15 LGFCF + ε15 (15)

Where, LNIR is the Log of Nifty 50 Index Returns, α15 is constant term, β15 is slope coefficient, LGFCFrepresents Log of Gross Fixed Capital Formation and ε15 symbolizes disturbance term of the model.

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LNIR = α₁₆ + β₁₆ LPFCE + ε₁₆ (16)

Where, LNIR is the Log of Nifty 50 Index Returns, α16 is constant term, β₁₆ is slope coefficient, LPFCE represents Log of Private Final Consumption Expenditure and ε16

symbolizes disturbance term of the model.

LNIR = α17 + β17 LGFCE + ε17 (17)

Where, LNIR is the Log of Nifty 50 Index Returns, α17 is constant term, β17 is slope coefficient, LGFCErepresents Log of Government Final Consumption Expenditure and ε17

symbolizes disturbance term of the model.

LNIR = α18 + β₁₈ LCAB + ε_{1 8} (18)

Where, LNIR is the Log of Nifty 50 Index Returns, α18 is constant term, β₁₈ is slope coefficient, LCAB represents Log of Current Account Balance and ε18 symbolizes disturbance term of the model.

LNIR = α19 + β19 LTAX + ε19 (19)

Where, LNIR is the Log of Nifty 50 Index Returns, α₁₉ is constant term, β₁₉ is slope coefficient, LTAXrepresents Log of Tax Revenue and ε19 symbolizes disturbance term of the model.

LNIR = α20 + β20 LAVA + ε20 (20)

Where, LNIR is the Log of Nifty 50 Index Returns, α20 is constant term, β20 is slope coefficient, LAVA represents Log of Agricultural Value Added and ε20 symbolizes disturbance term of the model.

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LNIR = α₂₁ + β₂₁ LIVA + ε₂₁ (21)

Where, LNIR is the Log of Nifty 50 Index Returns, α21 is constant term, β₂₁ is slope coefficient, LIVArepresents Log of Industry Value Added and ε21 symbolizes disturbance term of the model.

LNIR = α22 + β22 LSVA + ε22 (22)

Where, LNIR is the Log of Nifty 50 Index Returns, α22 is constant term, β22 is slope coefficient, LSVArepresents Log of Services Value Added and ε₂₂ symbolizes disturbance term of the model.

The study will make sure that all the Classical Linear Regression Model (CLRM) assumptions are fulfilled during the research. For this purpose study will make use of Augmented Dickey-Fuller Test to examine the stationarity of data, Breusch-Godfrey Serial Correlation LM Test to check the presence of autocorrelation and Heteroscedasticity Test:

ARCH to test the presence of Heteroscedasticity in the data. For analysis purpose, the stock returns are considered as dependent variable and macroeconomic variables as regressors. Independent analyses are performed, and the results are sorted using MS Excel.

The study utilized econometric software E-views to obtain the results.

1.5.2 Examining Impact of Financial Performance Indicators on Stock Market Returns:

1.5.2.1 Period of study:

The study considers a period of 16 years, i.e. from 2002 to 2017 to examine the impact of financial performance indicators on stock market returns. The data is annual in nature.

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1.5.2.2 Sample Design:

The study identifies and selects various financial performance indicators based on extensive review of literature. The data pertaining to study consists of 408 companies listed in the Nifty 500 Index and further sorted to 18 sectors in India. Initially, study selected 500 companies listed on Nifty 500 Index, however due to non-availability of data for a sufficient number of years for some companies; the companies were reduced to 408.

The study performed the analysis for all the companies combined as well as sector-wise analysis. The sectors considered are Automobile, Cement and Cement Products, Chemical, Construction, Consumer Goods, Energy, Fertilizers and Pesticides, Financial Services, Healthcare Services, Industrial Manufacturing, Information Technology, Media and Entertainment, Metals, Paper, Pharma, Services, Telecom, and Textiles.

1.5.2.3 Data Variables and Data Sources:

The study considers Return on Asset (ROA), Return on Equity (ROE), Return on Invested Capital (ROIC) as traditional performance measures and Economic Value Added (EVA) as a modern performance measure. Nifty 50 Index returns have been used as a proxy to Indian stock market. The data pertaining to Return on Asset (ROA), Return on Equity (ROE), Return on Invested Capital (ROIC) and Economic Value Added (EVA) have been extracted from Bloomberg Terminal Database. And the data relating to closing prices of 408 companies have been obtained from the CMIE Prowess Database.

1.5.2.4 Statistical and Econometric Techniques:

The study implemented Panel Data Analysis (REM and FEM Model) and Correlation Analysis to get the results. Also, Summary Statistics and Panel Unit Toot Tests were performed to understand the nature of the data. The required analyses were