计算机工程与应用2012，Vol.48Issue(27)：189-193,5.DOI:10.3778/j.issn.1002-8331.2012.27.040
基于DCA-PSO算法的均值-VaR投资组合优化
Mean-VaR portfolio optimization based on DCA-PSO algorithm
摘要
Abstract
Portfolio decisions faces a great deal of data of the real security market, which is a complicated combinatorial optimization problem, and is a NP-hard problem, which is difficult to be solved by traditional algorithm. Cultural algorithm and particle swarm optimization are emerging bionic intelligence algorithms. This paper introduces a new dynamic particle swarm optimization based on cultural algorithm for solving the mean-VaR model, and uses the penalty function approach to the inequality constraints in the model. An empirical analysis is done by sixteen securities chosen from Shanghai and Shenzhen security markets as the alternative securities. The numerical results show that the problem of portfolio optimization can be solved more reasonably and efficiently by this algorithm.关键词
粒子群优化算法/文化算法/投资组合/均值-风险价值(VaR)Key words
particle swarm optimization/ cultural algorithm/ portfolio/ mean-Value at Risk(VaR)分类
信息技术与安全科学引用本文复制引用
见静,高岳林..基于DCA-PSO算法的均值-VaR投资组合优化[J].计算机工程与应用,2012,48(27):189-193,5.基金项目
国家自然科学基金(No.60962006) （No.60962006）
国家社会科学基金项目资助(No.07XJY038). （No.07XJY038）
