Cardinality-constrained Value at Risk based Portfolio Optimization Using Krill Herd Metaheuristic Algorithm(Case study: Tehran Stock Exchange)

One of the most fundamental problems in investment decisions and portfolio optimization is choosing a suitable measure for risk assessment and management. In this study, the performance of the krill herd Algorithm is investigated for solving the mean-value at risk and mean-conditional value at risk portfolio optimization models considering the cardinality constraints, among 35 active companies in Tehran Stock Exchange. For algorithm training, the roller window method has been used in 2011-2018 and 2012-2019. The Sharpe ratio and the conditional Sharpe ratio of the models have been evaluated and they are compared using the Wilcoxon test. According to the numerical results, the mean–conditional value at risk model outperforms the mean–value at risk model in terms of the rate of return. Also, the model’s profitability improved using cardinality constraint with 5 stocks. Based on the empirical studies, we concluded that there is no significant difference between the performance of the value at risk and conditional value at risk based models. Furthermore, the portfolios with lower number of stocks have shown the better performance.