In investment market, investors often pay attention to investment portfolio selection and asset allocation under market risk. Thus, this study presents a two-stage method of investment portfolio based on soft computing techniques. The first stage uses data envelopment analysis to select most profitable funds, while hybrid of genetic algorithm (GA) and particle swarm optimization (PSO)is proposed to conduct asset allocation in the second stage.The evaluation results show that Sharpe value of portfolio based on the proposed method is superior to those of portfolio based on GA, PSO and market index. The proposed method really can help investors robustly obtain gains.
J. Kuo, R. and W. Hong, C.
"Integration of Genetic Algorithm and Particle Swarm Optimization for Investment Portfolio Optimization,"
Applied Mathematics & Information Sciences: Vol. 07:
6, Article 33.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss6/33