Stock portfolio optimization using Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) under Conditional Value at Risk (CVaR)
Subject Areas : Financial engineeringArezou Karimi 1 , sara goodarzi dahrizi 2
1 - Department of mathematics, Faculty of Basic Sciences, Univercity of Ayatollah Boroujerdi, Boroujerd, Iran
2 - Department of mathematics, Faculty of Basic Sciences, Univercity of Ayatollah Boroujerdi, Boroujerd, Iran
Keywords: particle swarm optimization, Imperialist Competitive Algorithm, Stock Portfolio, Conditional Value at Risk,
Abstract :
The choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selection models. The conditional value at Risk, which is one of the models of portfolio selection, follows the Quadratic Programming. Given that Quadratic Programming requires extensive computations, the use of metaheuristic algorithms in solving these problems increases the speed and accuracy of computations. The aim of this study is to minimize the Conditional Value at Risk by using two algorithms of Imperialist Competitive Algorithm and Particle Swarm Optimization. Therefore, using 800 days of data from 12 companies listed on the Tehran Stock Exchange in the period of 2/5/92 to 1/28/98, portfolio has been formed, and the weight of each stock in the optimal portfolio and the risk and return of the portfolio has been calculated using MATLAB2018 software. Then, using SPSS software, the average difference between risk and return of the two algorithms was tested.The results showed that the risk and return of the two algorithms were not statistically significant,.
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