Integration of Genetic Algorithm and Particle Swarm Optimization for Investment Portfolio Optimization

被引：8
|
作者：
Kuo, R. J. [1 ]
Hong, C. W. [2 ]
机构：
[1] Natl Taiwan Univ Sci & Technol, Dept Ind Management, Taipei, Taiwan
[2] Innolux Corp, Tao Yuan, Tainan County, Taiwan
来源：
APPLIED MATHEMATICS & INFORMATION SCIENCES | 2013年 / 7卷 / 06期
关键词：
Decision support system; Investment portfolio; Genetic algorithm; Particle swarm optimization; DATA ENVELOPMENT ANALYSIS; MUTUAL FUND PERFORMANCE; EFFICIENCY; INFORMATION; NETWORK; MODELS; SYSTEM;
D O I：
10.12785/amis/070633
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
引用
收藏
页码：2397 / 2408
页数：12
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