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| Title: | A hybrid particle swarm optimization approach and its application to solving portfolio selection problems |
| Authors: | Shamshul Bahar, Yaakob, Prof. Madya
Watada, Junzo |
| E-mail: | shamshul@fuji.waseda.jp |
| Keywords: | Genetic algorithm
Hybrid particle swarm optimization
Modern portfolio theory
Particle swarm optimization |
| Issue Date: | Jun-2011 |
| Publisher: | Fuji Technology Press |
| Citation: | Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 15 (4), 2011, pages 473-478 |
| Abstract: | 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. |
| Description: | Link to publisher's homepage at http://www.fujipress.jp/ |
| URI: | http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001500040011.xml
http://hdl.handle.net/123456789/14027 |
| ISSN: | 1343-0130 |
| Appears in Collections: | School of Electrical Systems Engineering (Articles)
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