Özet:

This study examines the performance of modern portfolio theory optimization options and the dataset where optimization options best achieve investors' goals. The research can enable investors to manage their portfolios during various crises, such as the health crisis caused by the COVID-19 pandemic. In this context, we used 6-month, 12-month, 24-month, and 36-month daily returns of 29 stocks in the pre-COVID-19 BIST-30 Index. We applied portfolio optimization options to these returns (equal weighting, risk-constrained return maximization, return-constrained risk minimization, direct risk minimization, and Sharpe ratio maximization methods) and obtained 20 portfolios. Then, we compared these portfolios' return, risk, and Sharpe ratios over the one-year investment period of the COVID-19 pandemic. According to the findings, investors should use the Sharpe ratio maximization method to maximize their return and performance. In addition, we concluded that it would be more beneficial for risk-averse investors to use the direct risk minimization method. Investors using the risk-constrained return maximization method should choose a long-term data set to create a portfolio. We recommended that investors prefer long-term data sets for return constrained risk minimization and direct risk minimization methods. On the other hand, we could not establish a relationship between the data sets used in the Sharpe ratio maximization method and the risk, return, and Sharpe ratio values.

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