Risk-Return Relationship and Portfolio Management

There is conflicting evidence on the applicability of Capital Asset Pricing Model in the Indian stock market. Data for 158 stocks listed on the Bombay Stock Exchange was analysed using a number of tests from 1991 to 2002, the period which roughly coincides with the period after liberalization and initiation of capital market reforms. Taken in aggregate the various empirical tests show that CAPM is not valid for the Indian stock market for the period studied.

The arbitrage pricing theory (APT) has been proposed as an alternative to the capital asset pricing model (CAPM). Using principal components analysis to estimate the factors that influence stock returns. Analysis of the Indian stock market using monthly and weekly returns for 1991–2002 shows that APT with multiple factors provides a better indication of asset risk and estimates of required rate of return than CAPM which uses beta as the single measure of risk.

Up to the beginning of the last decade, financial economics was dominated by linear paradigm, which assumed that economic time series conformed to linear models or could be well approximated by a linear model. However, there is increasing evidence that asset returns may be better characterized by a model which allows for non-linear behaviour. Though more efforts are now being directed towards the Asian stock markets in the light of their increasing importance to the investment world and the world economy, there is an extremely sparse literature, which utilizes recent advances in non-linear dynamics to examine the data generating process of the South Asian stock markets. This study investigates the presence of non-linear dependence in three major markets of South Asia: India, Sri Lanka and Pakistan. It was, however, realized that merely identifying non-linear dependence was not enough. Previous research has shown that the presence of non-linear characteristics usually takes the form of ARCH/GARCH (Autoregressive Conditional Heteroscedasticity or Generalized Autoregressive Conditional Heteroscedasticity) type conditional heteroscedasticity. Keeping this in view, this study investigates whether the non-linear dependence is caused by predictable conditional volatility. It has been found that the simple GARCH (1, 1) model has fitted all the market return series adequately and accounted for the non-linearity found in the series.

Overreaction Effect can be traced back to 1980s when DeBondt and Thaler (The Journal of Finance XL:793–805, 1985) argued that there existed a strong tendency for both low- and high-performing securities in one period to experience a reversal in the following years. Since then, it has become one of the grey areas in finance and leads to an ongoing debate on its existence. The study critically evaluates the work of various authors discussing the possible causes of the effect and its behavioural aspects.

Modern portfolio theory began with the postulation of Capital Asset Pricing Model (CAPM). It provides how a risky security is priced in competitive capital market. It is the theory of equilibrium between risk and return. It postulates a positive and linear relationship between risk and return, and maintains that non-market risk successively declines with the process of diversification. The study examines the monthly return of composite portfolio of 100 stocks of BSE 100 for the period from June 1996 to May 2005. The findings are in favour of the model by asserting a positive and linear relationship between risk and return. The study also reports that as diversification is carried out, non-market risk successfully declines. The findings support CAPM in Indian stock market in establishing a trade-off between risk and return.

This study investigates the stock price behaviour of Indian stock market using BSE Sensex as well as 30 individual underlying shares included in the Sensex. Variance Ratio test for the market index suggests dependency of the aggregate market series, which violates the assumption of Random Walk Hypothesis (RWH). However, the test results manifest mixed behaviour of return generating process for individual companies. The study has also developed one forecasting model for the market index using the ARIMA process. The AR (9) model has been found to be an appropriate model for forecasting future returns to the Sensex, the validity of which is of course, subject to real-world experiments.

Emerging stock market returns have been extensively studied by academic community over the past two decades. However, there is still no consensus among the researchers and practitioners as to which asset pricing models should be used to explain returns in these markets. The basic objective of the study is to evaluate the power and performance of multifactor asset pricing models (three and four factor model) over the traditional one factor CAPM, using the data from one of the fastest growing emerging market: India. The study using a large sample data of 470 listed stocks over a period of 16 years stretching from January 1997 to March 2013, evaluate the relevance of Fama and French three factor model as well as liquidity augmented four factor model in explaining the stock return variations in the Indian stock market. The study employs time series regression approach to examine the impact of market risk, size risk, value risk and liquidity risk on stock returns. The overall results of the study provide support to the multi-dimensional nature of risk and suggest the use of multifactor asset pricing models for consideration in investment decisions. Both Fama and French three factor model and liquidity augmented four factor model were found to be superior than traditional one factor CAPM. Though, liquidity augmented four factor model was found to be slightly better in explaining Indian stock returns as compared to Fama and French three factor model.

This study examines the concept of variable efficiency (time-varying levels of efficiency) and time-varying return predictability in the Indian stock market, which are the implications of Adaptive Markets Hypothesis (AMH). We apply linear tests to examine the time-varying dependence in two different indices of the Bombay Stock Exchange (BSE) in India, i.e. Sensex (benchmark) and BSE 500 (broad-based) Index. We utilize rolling window approach to analyse the impact of observation period, time horizon and data frequency, on the weak form level of market efficiency. The results suggest patterns that are consistent with the implications of Adaptive Markets Hypothesis for both the indices. The broad-based BSE 500 Index has been found to be more inefficient than the benchmark Sensex.

Efficient capital market theory postulates the random walk behaviour of stock market, i.e. risk and return are normally distributed. Capital asset pricing models, which assume the normality in risk and return, deal with how risky securities are valued in an efficient capital market. The present study applies a set of parametric and non-parametric tests to examine the normality of return and risk of daily, weekly, monthly and annual returns in the Indian stock market. The study examines the prices of the Bombay Stock Exchange (BSE)-listed indices: Sensex, BSE 100 and BSE 500 for the period 1996–2006, and three sub-periods (January 1996–December 1999, January 2000–December 2002, January 2003–December 2006) and reports the significant findings. The returns are negatively skewed for all the indices over the period. Asymmetry is found in risk and return in case of daily and weekly returns. Monthly and annual returns, however, are found normally distributed for all three indices over the period of time. These findings bring out the importance of time horizon in investment strategy for the Indian stock market.

The study investigates the presence of conditional heteroskedasticity in time series of US stock market returns, and the asymmetric effect of good and bad news on volatility. Further, the study also analyses the relationship between stock returns and conditional volatility, and standard residuals. The daily opening and closing prices of the S&P 500 and NASDAQ 100 are used for the period January 1990–December 2007. The study applies GARCH (1, 1) and T-GARCH (1, 1) to examine the heteroskedasticity and the asymmetric nature of stock returns, respectively. The results of the study suggest the presence of the heteroskedasticity effect and the asymmetric nature of stock returns. Further, analysing the relationship, the study reports a negative significant relationship between stock returns and conditional volatility. However, the relationship between stock returns and standardized residuals is found to be significant. This study provides a robustness test of the conditional volatility and asymmetric impact of good and bad news. These findings bring out that investors adjust their investment decisions with regard to expected volatility, however, they expect extra risk premium for unexpected volatility.

Capital market efficiency is a matter of great interest for policymakers and investors in designing investment strategy. If efficient market hypothesis (EMH) holds true, it will prevent the investors to realize extra return by utilizing the inherent information of stocks. They will realize extra returns only by incorporating the extra risky stocks in their portfolios. While empirical tests of EMH and risk–return relationship are plentiful for developed stock markets, the focus on emerging stock markets like India, Pakistan, Sri Lanka, etc., began with the liberalization of financial systems in these markets. With globalization and deregulation, the enormous opportunities of investment in South Asian stock markets have attracted the domestic and foreign institutional investors in general, and to reduce their portfolio risk by diversifying their funds across the markets in particular.

In the age of globalization, foreign capital has become the wheel of economic development. If a country wants to walk with the rest of the world, foreign capital contributes in achieving a competitive edge. In order to attract foreign capital, all developing countries are working on the principles of liberalization and globalization. India is no exception to it. Being the fifth largest economy of the world with huge potential in all sectors. India can be the most prospective destination for foreign investors. The risk and return are the two parameters of the economy, which attract the fancy of the investors. We do an assessment of these two parameters in Indian Stock market for the period from June 1996 to May 2005. During this 10-year period, the Indian economy has not remained stable throughout, instead passed through three distinct phases successively, i.e. decline, recession and growth. We examine the risk and return profile of the Indian stock market under these different economic conditions, and their futuristic scenario.

The study attempts to validate efficient market hypothesis in Indian stock market by examining the relationship between risk and return. It also examines the possibility of diversification effect on portfolio risk, which is the composite of market and non-market risk. The study takes daily, weekly and monthly adjusted opening and closing prices of BSE 100 composite portfolios for the period of June1996 through May 2005. The findings suggest that the relationship between portfolio return and risk is very weak, based on daily return. It is moderate in the case of weekly return. However, portfolio risk and return exhibit a high degree of positive relationship when monthly return is used. Portfolio non-market risk shows a declining tendency with diversification.

Price earning ratios are widely applied by investors to make investment decisions and to determine the future behaviour of stock price. We measure the performance of a set of portfolios which are based on P/E of stocks. The study examines the monthly price-earning ratios of BSE 100 companies, for the period June 1996–May 2005, and three non-overlapping sub-periods (June 1996–December 1999, January 2000–December 2002, and January 2003–May 2005). It found no consistency between the portfolios’ expected return and their corresponding P/E ratios. It is observed that the stock market failed to reflect instantaneous response pertaining to earnings information. However, during the pool and sub-periods’, the relationship between portfolio expected return and market risk is found to be positive and significant. These findings question the efficient market hypothesis but hold the application of the capital asset pricing model in the Indian stock market.

The study attempts to measure the relationship between risk and return, and the effect of diversification on non-market risk in Indian stock market by applying Market Index Model. For the analysis, monthly adjusted opening and closing prices of composite portfolio of BSE 100 companies for the period June 1996 through May 2005 have been taken. We find a high positive correlation between portfolio return and risk. It also signifies that portfolio non-market risk declines with diversification. The results, so obtained, are fully coinciding with the generalization of market index model, and thereby hold it applicable in Indian stock market, in establishing the trade-off between risk and return.

We make an attempt to examine the performance of portfolios formulated on the basis of Mean–Variance approach. For the analysis, monthly adjusted opening and closing prices of composite portfolio of BSE 100 companies have been taken for the period ranging from June 1996 to May 2005. The study has wide-ranging implications for finance professionals and policy makers. Ten portfolios have, first, been formulated and then evaluated by using Sharpe’s excess return to beta approach. Nine portfolios’ expected returns out of ten are significant at 5% level of significance. A cross-sectional analysis of the same set of ten portfolios carried out for three non-overlapping sub-periods (June 1996–December 1999, Jan 2000–December 2002, and Jan 2003–May 2005). The three sub-periods exhibit successive different economic conditions in the Indian economy, viz. decline, recession and growth, respectively. The results so obtained exhibit that portfolio-expected return of all ten portfolios, in three different economic conditions, are optimal.

Islamic finance is one of the most rapidly growing segments of the global financial system. The emergence of Islamic finance can be traced back to 1963 in Egypt, while its importance comes to the global financial system only after the global financial crisis occurred in 2008. It has been reported that the continuing volatility in bond and equity markets, combined with the uncertainty surrounding the Euro Zone, has opened up the Islamic finance industry to a new segment of potential investors looking to diversify away from traditional investments. However, despite the increasing importance of Islamic finance, particularly in developing economies in the Middle East and Southeast Asia, religious and social complexity has acted against a holistic understanding by policymakers, researchers and practitioners. The study provides a review and analysis of the definition, principles, and instruments of Islamic finance that is provided by most Islamic banks. Also, the study tries to answer the question as to what are the key principles of Islamic finance, which led to economic growth. We find that the Islamic finance principles are conducive to the growth of the economy as they help in reducing inflation, monetary volatility, and unemployment, besides in achieving social justice and optimum allocation of resources.

G-30, Basel Committee on Banking Supervision, Bank of International Settlements and most Central Banks across the globe have endorsed Value at Risk (VaR) as a standard for measuring risk. Though VaR is widely accepted as a true measure of risk for the banking industry, it is yet to find enough acceptance in the investment industry. VaR reporting on a periodic basis could help investors in better understanding of risks of loss to their investments. We have tried to review different methods of estimating VaR, and their applications. Many variants of VaR propagated by researchers seem to work in patches. Risk Metrics developed by J. P. Morgan, which uses exponentially weighted moving average (EWMA) method, has become a standard tool for VaR estimation. Till the time another more effective method is developed, VaR is likely to continue attracting a lot of interest.

The purpose of the study is to critically examine the empirical evidence of Efficient Market Hypothesis (EMH) that pose challenges to the concept of perpetual informational efficiency of financial markets and to provide a context in which a better understanding of behavioural biases can be attained through the evolutionary perspective provided by Adaptive Market Hypothesis (AMH). The defence proffered to various anomalies of EMH has been examined and the weaknesses in the justification provided to reinstate the confidence in the concept of informational efficiency of markets have been re-emphasized. We find that EMH is a description of an ideal scenario of stock market functionality; however, real world is rarely as ideal. Financial markets are a creation of human beings without any restrictions to the selection of market participants. EMH is very abstract in its framework and does not accommodate the possibility of an alternative to informational efficiency in which market inefficiency can persist. It is observed that AMH provides a better financial paradigm than EMH to describe the behaviour of stock returns.

We develop an investor sentiment index that captures the investor behaviour and analyses its suitability in explaining asset prices after augmenting it in multifactor asset pricing models. Seven different proxies including Sensex P/E ratios, dividend premium, modified advances to declines ratio, number of new equity issues, ratio of total equity issues to total equity and debt issues, turnover of BSE and volatility premium have been utilized. The investor sentiment index thus created mimics the movement of Sensex. Investor sentiment finds significance in explaining the returns for most of the portfolio under the different multifactor models. Fama–French three-factor model again lags in explaining the portfolio returns while Carhart four-factor model and residual momentum factor model match in performance for explaining stock returns.