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

作　　者：见静[1] 高岳林[1,2] 

机构地区：[1]宁夏大学数学计算机学院,银川750021 [2]北方民族大学信息与系统科学研究所,银川750021

出　　处：《计算机工程与应用》2012年第27期189-193,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60962006);国家社会科学基金项目资助(No.07XJY038)

摘　　要：投资组合决策面临现实证券市场中的大量数据,是一个复杂的组合优化问题,属于NP难问题,传统的算法难以有效求解。文化算法和粒子群算法是新近出现的两种仿生智能算法,将新提出的动态文化粒子群算法用于求解均值-VaR模型,用罚函数方法处理模型中的不等式约束,选取沪市和深市的十六支股票作为备选股票进行实证分析,数值结果表明该算法可以高效、合理地解决投资组合优化问题。Portfolio decisions faces a great deal of data of the real security market, which is a complicated combina- torial 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 se- curities 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) 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]