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Hybrid search for cardinality constrained portfolio optimization

Published: 08 July 2006 Publication History

Abstract

In this paper, we describe how a genetic algorithm approach added to a simulated annealing (SA) process offers a better alternative to find the mean variance frontier in the portfolio selection process. The nonlinear mixed integer quadratic programming model is considerably more difficult to solve than the original model; but some computational experiments have shown that hybrid heuristics offer a good alternative for these types of problems.

References

[1]
Chang T.J., Meade N., Beasley J.E. and Sharaiha Y.M., Heuristics for cardinality constrained portfolio optimisation, Computers & Operations Research, 27 (2000) pp. 1271--1302.
[2]
Crama, Y., & Schyns, M. Simulated annealing for complex portfolio selection problems. European Journal of Operational Research, 150(3), 2003, pp. 546--571.
[3]
Kellerer, Mansini, & Speranza. Selecting portfolios with fixed costs and minimum transaction lots. Annals of Operations Research 99, 2000, 287--304.
[4]
Loraschi A., Tettamanzi A., Tomassini M., Verda P., Distributed genetic algorithms with an application to portfolio selection problems, in: Proc. Int. Conf. on Artificial Neural Networks and Genetic Algorithms (ICANNGA95, D.W. Pearson, N.C. Steele and R.F. Albrecht (eds.), Springer-Verlag), 1995, pp. 384--387.
[5]
Mansini, R., & Speranza, M. G. Heuristic algorithms for the portfolio selection problem with minimum transaction lots. European Journal of Operational Research(114), 1999, 219--233.
[6]
Maringer, D., & Kellerer, H. Optimization of cardinality constrained portfolios with a hybrid local search algorithm. Or Spectrum, 25(4), 2003, 481--495.
[7]
Markowitz, H. M. Portfolio selection. Cambridge, MA.: BlackWell. 1959.

Cited By

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  • (2022)Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07526-627:7(3921-3939)Online publication date: 8-Nov-2022
  • (2017)Solving cardinality constrained mean-variance portfolio problems via MILPAnnals of Operations Research10.1007/s10479-017-2447-x254:1-2(47-59)Online publication date: 2-Mar-2017
  • (2017)A Novel Hybrid Nature-Inspired Scheme for Solving a Financial Optimization ProblemHybrid Artificial Intelligent Systems10.1007/978-3-319-59650-1_14(161-172)Online publication date: 2-Jun-2017
  • Show More Cited By

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    cover image ACM Conferences
    GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation
    July 2006
    2004 pages
    ISBN:1595931864
    DOI:10.1145/1143997
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 July 2006

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    Author Tags

    1. Markowitz model
    2. mixed integer programming
    3. portfolio selection

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    GECCO06
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    GECCO06: Genetic and Evolutionary Computation Conference
    July 8 - 12, 2006
    Washington, Seattle, USA

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    GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    Cited By

    View all
    • (2022)Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07526-627:7(3921-3939)Online publication date: 8-Nov-2022
    • (2017)Solving cardinality constrained mean-variance portfolio problems via MILPAnnals of Operations Research10.1007/s10479-017-2447-x254:1-2(47-59)Online publication date: 2-Mar-2017
    • (2017)A Novel Hybrid Nature-Inspired Scheme for Solving a Financial Optimization ProblemHybrid Artificial Intelligent Systems10.1007/978-3-319-59650-1_14(161-172)Online publication date: 2-Jun-2017
    • (2013)A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics ApplicationsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2012.219680017:3(321-344)Online publication date: 1-Jun-2013
    • (2012)A new method for mean-variance portfolio optimization with cardinality constraintsAnnals of Operations Research10.1007/s10479-012-1165-7205:1(213-234)Online publication date: 13-Jun-2012
    • (2011)Hybrid metaheuristics for constrained portfolio selection problemsQuantitative Finance10.1080/1469768090346016811:10(1473-1487)Online publication date: Oct-2011
    • (2010)Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimizationProceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications10.1007/978-3-642-12842-4_14(101-111)Online publication date: 4-May-2010
    • (2009)Active Portfolio Management under a Downside Risk FrameworkProceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems10.1007/978-3-642-02319-4_85(702-712)Online publication date: 22-Jun-2009
    • (2008)Evolutionary Stochastic Portfolio OptimizationNatural Computing in Computational Finance10.1007/978-3-540-77477-8_5(67-87)Online publication date: 2008
    • (2007)A realistic approach to evolutionary multiobjective portfolio optimization2007 IEEE Congress on Evolutionary Computation10.1109/CEC.2007.4424473(204-211)Online publication date: Sep-2007
    • Show More Cited By

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