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Local Search Techniques for Constrained Portfolio Selection Problems

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  • Schaerf, Andrea

Abstract

We consider the problem of selecting a portfolio of assets that provides the investor a suitable balance of expected return and risk. With respect to the seminal mean-variance model of Markowitz, we consider additional constraints on the cardinality of the portfolio and on the quantity of individual shares. Such constraints better capture the real-world trading system, but make the problem more difficult to be solved with exact methods. We explore the use of local search techniques, mainly tabu search, for the portfolio selection problem. We compare the combine previous work on portfolio selection that makes use of the local search approach and we propose new algorithms that combined different neighborhood relations. In addition, we show how the use of randomization and of a simple form of adaptiveness simplifies the setting of a large number of critical parameters. Finally, we show how our techniques perform on public benchmarks. Copyright 2002 by Kluwer Academic Publishers

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  • Schaerf, Andrea, 2002. "Local Search Techniques for Constrained Portfolio Selection Problems," Computational Economics, Springer;Society for Computational Economics, vol. 20(3), pages 177-190, December.
  • Handle: RePEc:kap:compec:v:20:y:2002:i:3:p:177-90
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    Cited by:

    1. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2015. "Linear vs. quadratic portfolio selection models with hard real-world constraints," Computational Management Science, Springer, vol. 12(3), pages 345-370, July.
    2. Konstantinos Anagnostopoulos & Georgios Mamanis, 2011. "Multiobjective evolutionary algorithms for complex portfolio optimization problems," Computational Management Science, Springer, vol. 8(3), pages 259-279, August.
    3. Marco Corazza & Giacomo Di Tollo & Giovanni Fasano & Raffaele Pesenti, 2015. "A novel initialization of PSO for costly portfolio selection problems," Working Papers 4, Department of Management, Università Ca' Foscari Venezia.
    4. Branke, J. & Scheckenbach, B. & Stein, M. & Deb, K. & Schmeck, H., 2009. "Portfolio optimization with an envelope-based multi-objective evolutionary algorithm," European Journal of Operational Research, Elsevier, vol. 199(3), pages 684-693, December.
    5. Xi-li Zhang & Wei-Guo Zhang & Wei-jun Xu & Wei-Lin Xiao, 2010. "Possibilistic Approaches to Portfolio Selection Problem with General Transaction Costs and a CLPSO Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 36(3), pages 191-200, October.
    6. Woodside-Oriakhi, M. & Lucas, C. & Beasley, J.E., 2011. "Heuristic algorithms for the cardinality constrained efficient frontier," European Journal of Operational Research, Elsevier, vol. 213(3), pages 538-550, September.
    7. Yucheng Kao & Hsiu-Tzu Cheng, 2013. "Bacterial Foraging Optimization Approach to Portfolio Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 453-470, December.
    8. Xiaolou Yang, 2006. "Improving Portfolio Efficiency: A Genetic Algorithm Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 1-14, August.

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