Portfolio Optimization Using Krill Herd Metaheuristic Algorithm Considering Different Measures of Risk in Tehran Stock Exchange

Portfolio optimization is one of the most important issues in investment. Harry Markowitz was the first person who applied risk with this regard. This issue was later studied from different perspectives, using various risk measures, optimization methods, and considering transaction costs. In this research, we aim to use the Krill Herd metaheuristic algorithm in portfolio optimization, and examine its possible advantages. In the present study, we try to solve the portfolio optimization problem and to find the efficient frontier using Krill Herd’s novel algorithm. We also consider three different measures for risk: variance, semi-variance, and expected shortfall. Our data consists of adjusted returns of the top fifty stocks in Tehran Stock Exchange from 2012 to 2018. Atfirst, the efficient frontiers of the optimal portfolios, using different measures for risk were plotted. The relative similarity of the three plots indicates the stability of the Krill Herd Algorithm in obtaining efficient frontiers. Then, we observed that the Sharpe Ratios of this algorithm are higher than those of Imperialist Competitive and Particles Swarm Algorithms. The Krill Herd Algorithm has a better performance finding efficient frontier and optimized portfolios in comparison to the other common algorithms; therefore, it can be used instead of the other algorithms to obtain better results