基于粒子群算法的证券投资组合优化研究

Zehong Li, Weice Ni
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引用次数: 3

摘要

Markowitz于1952年通过证券投资组合的均值-方差模型提出了资产组合理论。到目前为止,证券投资问题仍然是一个具有挑战性的问题。近年来,智能算法蓬勃发展。粒子群优化算法(PSO)的灵感来自于鸟群或鱼群的社会行为。采用协作的、基于群体的全局搜索群智能元启发式方法求解该模型。基于上述理论粒子群算法,建立了中国证券市场中考虑摩擦因素的多因素组合投资最优模型。并将该模型应用于指数30指数股票的实证研究,结果可为证券投资提供科学依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Optimizing Security Investment Combination Based on PSO
Markowitz proposed the theory of asset portfolio via a mean-variance model for security investment combination in 1952. The issue of security investment is still challenging till now. Intelligence algorithms were flourishing in resent years. Particle Swarm Optimization (PSO) is inspired by social behavior of bird flocking or fish schooling. It is co-operative, population-based global search swarm intelligence meta-heuristics and is applied to solve the model. Based on the theory PSO algorithm mentioned above, a multi-factor and optimal model for portfolio investment in the condition of considering friction factors in China’s security market is established. Additionally, the model is implemented on the demonstrated research of the index stock of index 30, the result could provide scientific foundation for security investment.
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