Portfolio Optimization Modelling with R for Enhancing Decision Making and Prediction in Case of Uganda Securities Exchange

Abstract

Portfolio Optimization involves choosing proportions of assets to be held in a portfolio, so as to make the portfolio better than any other. In this research, we use a software for statistical computing R to analyse the performance of portfolio optimization models which include; Markowitz’s Mean-Variance (MV) model, the VaR model, and Konno and Yamazaki’s Mean-Absolute Deviation (MAD) model. We start by analysing multi-asset data for the major indexes in the world followed by historical data of 16 constituent shares listed on the Uganda Securities Exchange (USE) covering 6.5 years. The paper then tests the stock performance of the models using R. We found that GREXP bonds dominated the world market as they accounted for more than 60% of the Maximum Diversified Portfolio (MDP). For the USE, we generated more risk measures like volatility, Sharpe Ratio (SR), Risk Parity (RP), Expected Shortfall (ES) or CVaR which we used to assess stock performance. UMEME, NVL, BATU, JHL, DFCU, EBL, EABL, KCB, SBU and CENT were the best-performing stocks. By understanding the performance of portfolio optimization models in R, Ugandan investors will develop a better view of the latest performance of the stocks listed on the USE. This will help them to decide on which stocks to include in their investment portfolios, thus prevent wrong investment decisions.