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.
| Original language | English |
|---|---|
| Pages (from-to) | 473-478 |
| Number of pages | 6 |
| Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
| Volume | 15 |
| Issue number | 4 |
| Publication status | Published - 2011 Jun |
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Keywords
- Genetic algorithm
- Hybrid particle swarm optimization
- Modern portfolio theory
- Particle swarm optimization
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
Cite this
A hybrid particle swarm optimization approach and its application to solving portfolio selection problems. / Yaakob, Shamshul Bahar; Watada, Junzo.
In: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 4, 06.2011, p. 473-478.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A hybrid particle swarm optimization approach and its application to solving portfolio selection problems
AU - Yaakob, Shamshul Bahar
AU - Watada, Junzo
PY - 2011/6
Y1 - 2011/6
N2 - 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.
AB - 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.
KW - Genetic algorithm
KW - Hybrid particle swarm optimization
KW - Modern portfolio theory
KW - Particle swarm optimization
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M3 - Article
VL - 15
SP - 473
EP - 478
JO - Journal of Advanced Computational Intelligence and Intelligent Informatics
JF - Journal of Advanced Computational Intelligence and Intelligent Informatics
SN - 1343-0130
IS - 4
ER -