An alternative investment strategy evaluation of Indian stocks

Abstract

The individual investor is faced with numerous financial securities (Mutual Fund, Equity, Derivatives) for investment. The challenge is appropriate selection of asset class and securities within the asset class that achieve the target return aligned with investor risk appetite. In India 57% of household savings are in bank deposits with just 3% invested in stocks. This study evaluates different stock allocation methods to construct an optimal portfolio. Modern Portfolio Theory (MPT) quantitative models like mean-variance (MV) framework, Single Index Model (SIM) are evaluated alongside Post MPT model like mean-lower partial moment (LPM) models that account for asymmetric distribution. In addition, heuristic models like equal weights and heuristic LPM are empirically evaluated using both BSE and NSE monthly stock data. These active investment strategies are compared against the passive strategy of investing in stock index and buy-hold strategy. Based on the empirical evidence, the hypothesis examined: Natural log returns of stock prices are normally distributed is rejected, Efficient Market hypothesis (EMH) that benchmark can t be consistently beaten is rejected, Buy-Hold strategy gives superior returns over long term periods is accepted while portfolio optimization models provide superior returns compared to heuristic models is rejected. Apart from the insight that the market can be beaten using the quantitative model, quarterly rebalancing during a bear market is recommended. The buy-hold investment strategy (with initial weights determined by quantitative models) gives better return over the investment time horizon of ten years and more of this study. The R implementation of the portfolios allows easy reuse to scale and expand (to other models like CVaR, MaxDD) or refine (constraint max weight); as well as evaluate over different stock universe sets (small-cap, a sector or industry). The portfolios can also be integrated and used in conjunction with fundamental and technical research techniques. The investor can better evaluate the trade-offs between required returns and risk. Constructing goal based portfolios is simplified