A hybrid particle swarm optimization approach and its application to solving portfolio selection problems

Shamshul Bahar Yaakob, Junzo Watada

    研究成果査読

    抄録

    In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposedmethod is a viable approach to portfolio selection.

    本文言語English
    ページ(範囲)473-478
    ページ数6
    ジャーナルJournal of Advanced Computational Intelligence and Intelligent Informatics
    15
    4
    出版ステータスPublished - 2011 6

    ASJC Scopus subject areas

    • 人工知能
    • コンピュータ ビジョンおよびパターン認識
    • 人間とコンピュータの相互作用

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