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JACIII Vol.15 No.4 pp. 473-478
doi: 10.20965/jaciii.2011.p0473
(2011)

Paper:

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A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems

Shamshul Bahar Yaakob*,** and Junzo Watada*

*Graduate School of IPS, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu 808-0135, Japan

**School of Electrical Systems Engineering, Universiti Malaysia Perlis, 02600 Perlis Malaysia

Received:
January 7, 2011
Accepted:
March 3, 2011
Published:
June 20, 2011
Creative Commons License
Keywords:
particle swarm optimization, hybrid particle swarm optimization, modern portfolio theory, genetic algorithm
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 proposed method is a viable approach to portfolio selection.
Cite this article as:
S. Yaakob and J. Watada, “A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.4, pp. 473-478, 2011.
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References
  1. [1] H. Markowitz, “Portfolio selection,” J. of Finance, Vol.7, No.1, pp. 77-91, 1952.
  2. [2] D. G. Luenberger, “Investment sciences,” Oxford University press, New York, pp. 137-162 and 481-483, 1998.
  3. [3] J. Board and C. Sutcliffe, “The application of operation research technique to financial markets,” Stochastic programming e-print series, 1999.
    http://www.speps.info
  4. [4] A. F. Perold, “Large-scale portfolio optimization,” Management Sciences, Vol.30, No.10, pp. 1143-1160, 1984.
  5. [5] F. J. Jones, “An overview of institutional fixed income strategies,” Professional perspectives on fixed income portfolio management, John Wiley & Sons, pp. 1-13, 2000.
  6. [6] T.-J. Chang, N.Meade, E. Beasley, and M. Sharaiha, “Heuristics for cardinality constrained portfolio optimization,” Computer & Operational Research, Vol.27, No.13, pp. 1271-1302, 2000.
  7. [7] A. Schaerf, “Local search techniques for constrained portfolio selection problems,” Computational Economics, Vol.20, No.3, pp. 177-190, 2002.
  8. [8] Y. Crama and M. Schyns, “Simulated annealing for complex portfolio selection problems,” European J. of Operational Research, Vol.150, No.3, pp. 546-571, 2003.
  9. [9] S. B. Yaakob and J. Watada, “Maintenance of a traffic signal lights based on portfolio model,” Int. J. of Simulation Systems, Science & Technology, Vol.9, No.2, pp. 11-22, 2008.
  10. [10] A. Fernandez and S. Gomez, “Portfolio selection using neural networks, Computers & Operations Research,” Vol.34, No.4, pp. 1177-1191, 2007.
  11. [11] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. of the IEEE Int. Conf. on Neural Network, pp. 1942-1948, 1995.
  12. [12] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” Proc. 6th Int. Symposium on Micro Machine and Human Science, pp. 39-43, 1995.
  13. [13] A. Salman, I. Ahmad, and S. Al-Madani, “Particle swarm optimization for task assignment problem,” J. of Microprocessors and Microsystems, Vol.26, No.8, pp. 363-371, 2002.
  14. [14] D. Y. Sha and C.-Y. Hsu, “A hybrid particle swarm optimization for job shop scheduling problem,” J. of Computers & Industrial Engineering, Vol.51, No.4, pp. 791-808, 2006.
  15. [15] P. Angeline, “Evolutionary optimization versus particle swarm optimization philosophy and performance difference,” Proc. 7th Annual Conf. Evolutionary Program, pp. 601-610, 1998.
  16. [16] R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” Evolutionary Programming VII, New York: Springer-Verlag, Vol.1447, Lecture Notes in Computer Science, pp. 611-616, 1998.
  17. [17] F. Van den Bergh and A. P. Engelbrecht, “A new locally convergent particle swarm optimizer,” Proc. IEEE Int. Conf. Systems, Man, Cybernetics, pp. 96-101, 2002.
  18. [18] M. Lovbjerg, T. K. Rasmussen, and T. Krink, “Hybrid particle swarm optimizer with breeding and subpopulations,” Proc. Genet. Evolutionary Computing Conf., Vol.1, pp. 469-476, 2001.
  19. [19] Y. Shi and R. Eberhart, “Empirical study of particle swarm optimization,” Proc. of the Congress on Evolutionary Computation (CEC99), pp. 1945-1950, 1999.
  20. [20] W. Chen, R.-T. Zhang, Y.-M. Cai, and F.-S. Xu, “Particle swarm optimization for constrained portfolio selection problems,” Proc. of the Fifth Int. Conf. on Machine Learning and Cybernetics, pp. 2425-2429, 2006.
  21. [21] A. A. A. Esmin, G. Lambert-Torres, and A. C. Zambroni de Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans. on Power Systems, Vol.20, No.2, pp. 859-866, 2005.
  22. [22] T. O. Ting, M. V. C. Rao, and C. K. Loo, “A novel approach for unit commitment problem via an effective hybrid particle swarm optimization,” IEEE Trans. on Power Systems, Vol.21, No.1, pp. 411-418, 2006.
  23. [23] A. P. Engelbrecht, “Computational Intelligence,” John Wiley, 2002.

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