Portfolio Selection Optimization Problem Under Systemic Risks

Portfolio selection is of great importance among financiers, who seek to invest in a financial market by selecting a portfolio to minimize the risk of investment and maximize their profit. Since there is a covariant among portfolios, there are situations in which all portfolios go high or down simultaneously, known as systemic risks. In this study, we proposed three improved meta-heuristic algorithms namely, genetic, dragonfly, and imperialist competitive algorithms to study the portfolio selection problem in the presence of systemic risks. Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection.