Multi-objective Scatter Search with External Archive for Portfolio Optimization

Khin Lwin, Rong Qu, Jianhua Zheng

Abstract

The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multiobjective Scatter Search approach. In this work, we present a hybrid multiobjective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection problem. We considered the extended mean-variance portfolio model with three practical constraints which limit the number of assets in a portfolio, restrict the proportions of assets held in the portfolio and pre-assign some specific assets in the portfolio. The proposed hybrid metaheuristic algorithm follows the basic structure of the Scatter Search and defines the reference set solutions based on Pareto dominance and crowding distance. New Subset generation and combination methods are proposed to generate efficient and diversified portfolios. Hill Climbing operation is integrated to search for improved portfolios. The performance of the proposed multiobjective Scatter Search algorithm is compared with Non-dominated Sorting Genetic Algorithm (NSGA-II). Experimental results indicate that the proposed algorithm is a promising approach for solving the constrained portfolio selection problem. Measurements by the performance metrics indicate that it outperforms NSGA-II on the solution quality within a shorter computational time.

References

  1. Beasley, J. (1990). Or-library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41(11):1069-1072.
  2. Branke, J., Scheckenbach, B., Stein, M., Deb, K., and Schmeck, H. (2009). Portfolio optimization with an envelope-based multi-objective evolutionary algorithm. European Journal of Operational Research, 199(3):684-693.
  3. Chang, T., Meade, N., Beasley, J., and Sharaiha, Y. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers and Operations Research, 27(13):1271-1302.
  4. Corne, D. W., Jerram, N. R., Knowles, J. D., Oates, M. J., et al. (2001). Pesa-ii: Region-based selection in evolutionary multiobjective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001. Citeseer.
  5. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. Evolutionary Computation, IEEE Transactions on, 6(2):182-197.
  6. Fonseca, C. and Fleming, P. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1-16.
  7. Glover, F., Laguna, M., and Martí, R. (2000). Fundamentals of scatter search and path relinking. Control and cybernetics, 39(3):653-684.
  8. Lwin, K. and Qu, R. (2013). A hybrid algorithm for constrained portfolio selection problems. Applied Intelligence,DOI:10.1007/s10489-012-0411-7.
  9. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1):pp. 77-91.
  10. Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments. John Wiley and Sons, New York.
  11. Moral-Escudero, R., Ruiz-Torrubiano, R., and Suarez, A. (2006). Selection of optimal investment portfolios with cardinality constraints. In Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pages 2382-2388. IEEE.
  12. Robic?, T. and Filipic?, B. (2005). Demo: Differential evolution for multiobjective optimization. In Evolutionary Multi-Criterion Optimization, pages 520-533. Springer.
  13. Sierra, M. R. and Coello, C. A. C. (2005). Improving pso-based multi-objective optimization using crowding, mutation and epsilon-dominance. In EMO'05, pages 505-519.
  14. Skolpadungket, P., Dahal, K., and Harnpornchai, N. (2007). Portfolio optimization using multi-obj ective genetic algorithms. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pages 516-523. IEEE.
  15. Van Veldhuizen, D. A. and Lamont, G. B. (1998). Multiobjective evolutionary algorithm research: A history and analysis. Technical report, Citeseer.
  16. Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K. (1998). Probability and statistics for engineers and scientists, volume 8. Prentice Hall Upper Saddle River, NJ:.
  17. Zitzler, E., Laumanns, M., Thiele, L., Zitzler, E., Zitzler, E., Thiele, L., and Thiele, L. (2001). Spea2: Improving the strength pareto evolutionary algorithm.
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Paper Citation


in Harvard Style

Lwin K., Qu R. and Zheng J. (2013). Multi-objective Scatter Search with External Archive for Portfolio Optimization . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 111-119. DOI: 10.5220/0004552501110119


in Bibtex Style

@conference{ecta13,
author={Khin Lwin and Rong Qu and Jianhua Zheng},
title={Multi-objective Scatter Search with External Archive for Portfolio Optimization},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},
year={2013},
pages={111-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004552501110119},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - Multi-objective Scatter Search with External Archive for Portfolio Optimization
SN - 978-989-8565-77-8
AU - Lwin K.
AU - Qu R.
AU - Zheng J.
PY - 2013
SP - 111
EP - 119
DO - 10.5220/0004552501110119