Applicability of Portfolio Theory in Nepali Stock Market

Abstract

In the rapidly growing stock market of Nepal, this study tests the applicability of the portfolio creation model and attempts to aware investors about the potential portfolio alternatives they can make to achieve their peculiar risk-return need, through a robust optimization model. A portfolio model using Markowitz mean-variance method is applied to calculate the optimal portfolio and portfolios fitting the investor specific needs, from a sample of 20 Group “A” listed companies on NEPSE. The monthly stock prices between April 2010 and December 2014 of sample companies are used as training data. And, the applicability of the model is tested based on their prices on April 2015. From the analysis it is concluded that such mean-variance optimization is applicable in Nepal. Furthermore, most of the stocks, even from different sectors, are highly correlated to each other illustrating the lack of diversification opportunity at NEPSE. Additionally, the significantly high volatility even at global minimum variance level illustrated the risky nature of business environment in the country. There is an opportunity for high return, but the investor’s willingness to gain this is tested through the high magnitude of minimum risk. These findings call for the policy makers’ immediate attention in creating a favorable environment to bring the real sector companies in the public trading realm and enhancing the commodities and derivatives market in the country, thereby helping stimulate the investment environment in Nepal.

Portfolio Analysis of Financial Institutions in Nepal

“…The poor performance of the average household can be traced to the costs associated with this high level of trading. Adhikari and Jha (2016) performed research to aware investors about the potential portfolio alternatives to achieve their peculiar risk-return need through robust optimization portfolio model. Markowitz mean-variance method applied under the study to reach portfolio model from the data from 2010 to December 2014.…”