Developments in Mean-Variance Efficient Portfolio Selection (eBook)

Megha Agarwal is an Assistant Professor at the University of Delhi, India. She gained her education from Kings College, London, Delhi School of Economics, Shri Ram College of Commerce and Delhi Public School in India. She is extensively engaged in research and teaching at the university and has published articles in a number of indexed/peer reviewed journals.

Cover 1
Half-Title 2
Title 4
Copyright 5
Conetnts 6
List of Figures 10
List of Tables 11
Foreword 13
Preface 15
Acknowledgements 17
1 Introduction 19
1.1 Introduction 19
1.2 Review of trends in the Indian economy and Indian capital markets 24
1.3 Research gaps 26
1.4 Raison d’être of the book 27
1.5 Problem statement 29
1.6 Research objectives 30
1.7 Research hypotheses 31
1.8 Research methodology 32
1.9 Sources of data 34
1.10 Chapter plan 35
1.11 Limitations of the study 37
2 Advances in Theories and Empirical Studies on Portfolio Management 38
2.1 Literature on mean-variance efficient portfolio 40
2.2 Literature on asset pricing theories 45
2.3 Literature on diversification of portfolios 51
2.4 Literature on portfolio optimisation and variance-covariance matrix 54
2.5 Literature on the impact of behavioural and systemic factors on an investor’s portfolio choice 60
2.6 Literature on the lead-lag relationship between the stock and futures market 66
2.7 Summary and conclusions 71
3 Contributions to the Portfolio Theory 74
3.1 The standard mean-variance portfolio selection model 74
3.2 Advances in portfolio selection theories 75
3.3 Emerging issues and challenges in Indian equity markets 82
3.3.1 Risk management 83
3.3.2 Disclosures and accounting standards 83
3.3.3 Investor protection and education 84
3.3.4 Wireless trading and co-location 84
3.3.5 Algorithmic trading and high frequency trading 84
3.3.6 Smart order routing 85
3.3.7 Minimum public shareholding 86
4 Mean-Variance Efficient Portfolio Selection: Model Development 89
4.1 Multi-objective quadratic programming 90
4.2 Model building and application 91
4.2.1 The objective function 91
4.2.2 Calculation of risk/variance of portfolio 91
4.2.3 Evaluation criteria and constraint set 91
4.2.4 Modelling constraints for an investor 104
4.3 Multivariate regression: model formulation 107
4.3.1 Multiple regression model 1 107
4.3.2 Multiple regression model 2 108
4.4 Granger causality tests 109
4.5 A utility approach 109
4.6 Performance measures for portfolios 114
4.7 Tests for equality 115
4.7.1 Mean equality test 115
4.7.2 Variance equality tests 116
4.8 To sum up 116
5 Mean-Variance Quadratic Programming Portfolio Selection Model: An Empirical Investigation of India’s National Stock Exchange 119
5.1 Sample size and data collection 120
5.2 Software used 121
5.3 Mean-variance portfolio selection model: empirical testing 121
5.4 Descriptive statistics – returns 123
5.5 Data inputs 125
5.6 Model formulations 126
5.7 Mean-variance efficient portfolio selection model formulations: analysis and interpretations 130
5.7.1 Diversifier’s portfolio 130
5.7.2 Satisficer’s portfolio 135
5.7.3 Plunger’s portfolio 135
5.7.4 Market trend portfolio 138
5.7.5 Capital gain bias portfolio 142
5.7.6 Dividend gain bias portfolio 146
5.7.7 Equal priority portfolio 149
5.7.8 Ideal portfolio 152
5.7.9 Markowitz’s portfolio selection model 153
5.8 Comparison of alternate portfolio selection models 159
5.9 Markowitz’s efficient frontier and mean-variance efficient portfolios 163
5.10 Multivariate regression analysis: estimating equations 167
5.11 Granger causality analysis 179
5.12 Utility analysis 180
5.13 Performance evaluation of portfolios: ranking the model formulations 186
5.14 Hypotheses testing: tests for equality 187
5.15 To sum up 194
6 Mean-Variance Portfolio Analysis Using Accounting, Financial and Corporate Governance Variables-Application on London Stock Exchange’s FTSE 100 199
6.1 Securities and evaluation criteria 199
6.1.1 Data and software used 201
6.1.2 Modelling constraints for an investor 206
6.1.3 Alternative model formulations 208
6.2 Results and discussion 211
6.2.1 Formation of Pareto optimal portfolios 211
6.2.2 Portfolio performance evaluation 211
6.3 Out of the sample tests 219
7 Summary, Conclusions and Suggestions for Future Research 222
7.1 Model development 225
7.1.1 A general model 226
7.1.2 Alternate portfolio selection model formulations 227
7.1.3 Multiple regression analysis 230
7.1.4 Granger causality interpretations 231
7.1.5 Portfolio utility analysis 231
7.1.6 Performance evaluation of portfolios 232
7.1.7 Tests for equality: main findings 232
7.1.8 Out of the sample tests 233
7.2 Conclusions 234
7.3 Suggestions for future research 236
Annex 1
1 Programming for the multi-criteria portfolio selection Model 237
2 Programming for Markowitz’s portfolio selection model 242
Notes 244
References 247
Index 258