COMPARISON OF SEVERAL METHODS FOR PARETO SET GENERATION IN MULTI-CRITERIA PORTFOLIO OPTIMIZATION
DOI:
https://doi.org/10.47839/ijc.7.3.520Keywords:
Heuristic algorithms, adjustable weights, multi-objective portfolio optimization, performance metricsAbstract
Pareto set generation methods are considered with respect to their application for multi criteria portfolio selection. Several such methods were compared experimentally including some recently proposed evolutionary methods and the method of adjustable weights. Test problems were based on standard portfolio quality criteria and data on stocks of 10 Lithuanian companies. The experimental data on the performance of the considered algorithms in different metrics are presented and discussed.References
H. Markowitz. Portfolio selection. Journal of Finance, 7:77-91, 1952.
J. Li, S. Taiwo. Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming. Proceedings of IEEE Congress on Evolutionary Computation, 16-21 July 2006, 2171- 2178.
T.-J. Chang, N. Meade, J. E. Beasley, and Y. M. Sharaiha. Heuristics for cardinality constrained portfolio optimisation. Computers and Operations Research, 27: 1271-1302, 2000.
S.-M. Wang, J.-C. Chen, H. M. Wee, K. J. Wang. Non-linear Stochastic Optimization Using Genetic Algorithm for Portfolio Selection. International Journal of Operations Research, Vol. 3, No. 1, 16-22, 2006.
M. Ehrgott, K. Klamroth, and C. Schwehm. Decision aiding an MCDM approach to portfolio optimization. European Journal of Operational Research, 155: 752-770, 2004.
Y. Xia, S. Wang, X. Deng. Theory and methodology: a compromise solution to mutual funds portfolio selection with transaction costs. European Journal of Operation Research, 134: 564-581, 2001.
I. Radziukyniene, A. Zilinskas. On Pareto set generation in multi-criteria portfolio optimization, Proceedings of the Fifth International Conference ICNNAI’2008, Minsk, 2008, 282-287.
C. Stummer, M. Sun. New Multiobjective Metaheuristic Solution Procedures for Capital Investment Planning. Journal of Heuristics, 11: 183–199, 2005.
Ehrgott, M., C. Waters, R. N. Gasimov, O. Ustun. Multiobjective Programming and Multiattribute Utility Functions in Portfolio Optimization. 2006. Available on http://www.esc.auckland.ac.nz/research/tech/esc-tr-639.pdf
Mukerjee, A., R. Biswas, K. Deb, and A. P. Mathur. Multi-objective evolutionary algorithm for the risk-return trade-off in bank loan management. International Transactions in Operational research, 9, 2002, 583-597.
Steuer, R. E., Y. Qi, and M. Hirschberger. Portfolio Selection in the Presence of Multiple Criteria. In C. Zopounidis, M. Doumpos, P. M. Pardalos (Eds) Handbook of Financial Engineering, Springer, 2008.
H. Eskandari and C.D. Geiger. A Fast Pareto Genetic Algorithm Approach for Solving Expensive Multiobjective Optimization Problems, In press, Journal of Heuristics.
A.J. Nebro, J.J. Durillo, F. Luna, B. Dorronsoro, E. Alba. A Cellular Genetic Algorithm for Multiobjective Optimization. Proceedings of NICSO 2006, pp. 25-36. Granada, Spain, June 2006.
A.J. Nebro, F. Luna, E. Alba, A. Beham, B. Dorronsoro. AbYSS: Adapting Scatter Search for Multiobjective Optimization. Tech Rep. ITI-2006-2, Departamento de Lenguajes y Ciencias de la Computacion, University of Malaga, 2006.
J.D. Knowles, D.W. Corne. Approximating the nondominated front using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2), 2000, pp. 149-172.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation, 6 (2), 2002, 182– 197.
D. A. Van Veldhuizen and G. B. Lamont, “Multiobjective Evolutionary Algorithm Research: A History and Analysis,” Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst. Technol., Wright-Patterson, AFB, OH, Tech. Rep. TR-98-03, 1998.
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