AN INTEGRATED FRAMEWORK FOR CLASSIFICATION AND SELECTION OF STOCKS FOR PORTFOLIO CONSTRUCTION: EVIDENCE FROM NSE, INDIA

Abstract

Investment decision making is a complex process, influenced by a number of conflicting objectives. Investors want to maximize their wealth through investing in the stock market while offsetting the risk to the extent possible. To a common investor, risk is an important aspect to be minimized. In this paper we present a distant framework of stock selection for portfolio construction combining Bayesian classifier and a widely used Multi-Criteria Decision Making (MCDM) technique such as the Technique for order of performance by similarity to ideal solution (TOPSIS) along with Entropy method. The study period is 2013 to 2020. We formulate our research design by considering risk adjusted ratios like Sharpe Ratio, Treynor Ratio, Information Ratio, Jensen Ratio, and Calmar Ratio to compare the NSE 100 listed stocks. Using DP omnibus test, the desired sample of companies following the non-normal distribution was achieved. Using financial beta, we have selected the outcome based on the nature of their ‘return’ and ‘risk'. The Entropy-TOPSIS framework has been used to study the profitability of stocks, rank wise for each year, and finally, the Bayes portfolio model help to select the overall profitability associate with low risk for the construction of the portfolio. We notice year-wise inconsistency among the performance of the stocks.