Performance investigation and comparison of two evolutionary algorithms in portfolio optimization: Genetic and particle swarm optimization

Contrary to the growing use of portfolios and in spite of its rich literature, there are some problems and unanswered questions. The aim of this work is to be a useful instrument for helping fin anco practitioners and researchers with the portfolio selection problem. This study first reviews Modern Portfolio Theory's literature and describes the investment selection process. Second, this paper specifically aims at applying efficient optimization methods for solving the portfolio selection problem. Hence, the genetic algorithm and the particle swarm optimization (PSO) approaches to resolve the portfolio selection problem with the objective of simultaneous risk minimization/return maximization are applied. Computational analyses are provided so as to investigate the performance of the algorithms and the input data. Therefore, at one step, four different portfolios are selected from Tehran Stock Exchange market, 50 top companies (Annual and monthly-based portfolios). Finally, this paper applies the selected portfolios to the stock market data of a 10-month period proceeding portfolios constructions. The results indicate that, the genetic annual-based portfolio has the best performance in contrast to its other counterparts; it outperforms the average return of market portfolio in a relatively short period. Moreover, PSO annual-based portfolio has a relatively good performance and could achieve the market portfolio's return during the test period. Findings show that using annual data is more efficient than the monthly data. On the whole, the results show that the evolutionary methods of this paper with annual data, can consistently handle the practical portfolio selection problem.