Developments in Mean-Variance Efficient Portfolio Selection

Mean-variance efficient portfolio selection was originally identified by Nobel Laureate Harry Markowitz (1952) and to this day remains one of the most popular approaches to portfolio selection. However the turmoil suffered by stock exchanges as a result of the financial crises in the United States and later in Europe has evoked new interest across the globe for better portfolio management within the existing mean variance framework. Substantial improvements in the availability of large data sets, real time information and software capable of performing complex computations contributes towards improved research work in portfolio selection. Better understanding of the markets and evolving economic models provide the means to add further to modern portfolio theory. This book discusses a variety of new determinants for optimal portfolio selection. It reviews the existing modelling framework for portfolio selection developed by Markowitz, Sharpe, Fama and French and Ross and creates mean-variance efficient portfolios from the available pool of securities companies listed on the National Stock Exchange (NSE). The crucial role of portfolio attributes such as expected return, variance, the responsiveness of stock's index returns, market capitalisation, book-to-equity ratio and other such factors are identified in the creation of efficient portfolios. The resulting portfolios created using alternate portfolio selection model formulations are compared using the Sharpe and Treynor ratios. Quantitative and qualitative comparisons enable researchers to rank them in terms of their effectiveness in the present day Indian securities market. The mean-variance analysis undertaken in this book will be of immense use to individual and institutional investors, brokerage houses, mutual fund managers, banks, high net worth individuals, portfolio management service providers, financial advisors, regulators, stock exchanges and research scholars in the area of portfolio selection.