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A novel initialization of PSO for costly portfolio selection problems

Author

Listed:
  • Marco Corazza

    (Dept. of Economics, Università Ca' Foscari Venice)

  • Giacomo Di Tollo

    (Dept. of Management, Università Ca' Foscari Venice)

  • Giovanni Fasano

    (Dept. of Management, Università Ca' Foscari Venice)

  • Raffaele Pesenti

    (Dept. of Management, Università Ca' Foscari Venice)

Abstract

In this paper we propose an efficient initialization of a deterministic Particle Swarm Optimization (PSO) scheme. PSO has showed to be promising for solving several unconstrained global optimization problems from real applications, where derivatives are unavailable and the evaluation of the objective function tends to be costly. Here we provide a theoretical framework which motivates the use of a deterministic version of PSO, in place of the standard stochastic iteration currently adopted in the literature. Then, in order to test our proposal, we include a numerical experience using a realistic complex portfolio selection problem. This numerical experience includes the application of PSO to a parameter dependent unconstrained reformulation of the considered portfolio selection problem. The parameters are either adaptively updated as in an exact penalty framework, or they are tuned by the code REVAC. We show that in both these settings our PSO initialization is preferable with respect to the standard proposal from the literature.

Suggested Citation

  • 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.
    • Handle: RePEc:vnm:wpdman:105
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    References listed on IDEAS

    as
    1. Marco Corazza & Giovanni Fasano & Riccardo Gusso, 2011. "Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem," Working Papers 2011_10, Department of Economics, University of Venice "Ca' Foscari".
    2. Emilio Fortunato Campana & Giovanni Fasano & Daniele Peri, 2012. "Penalty function approaches for ship multidisciplinary design optimisation (MDO)," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(6), pages 765-784.
    3. E.F. Campana & Matteo Diez & Giovanni Fasano & Daniele Peri, 2013. "Initial particles position for PSO, in Bound Constrained Optimization," Working Papers 6, Department of Management, Università Ca' Foscari Venezia.
    4. Renato Leone & Giovanni Fasano & Massimo Roma & Yaroslav D. Sergeyev, 2020. "Iterative Grossone-Based Computation of Negative Curvature Directions in Large-Scale Optimization," Journal of Optimization Theory and Applications, Springer, vol. 186(2), pages 554-589, August.
    5. 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.
    6. Chen, Zhiping & Wang, Yi, 2008. "Two-sided coherent risk measures and their application in realistic portfolio optimization," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2667-2673, December.
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    Cited by:

    1. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2021. "MURAME parameter setting for creditworthiness evaluation: data-driven optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 295-339, June.
    2. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2017. "PSO-based tuning of MURAME parameters for creditworthiness evaluation of Italian SMEs," Working Papers 04, Department of Management, Università Ca' Foscari Venezia.

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    More about this item

    Keywords

    Deterministic PSO; Global Optimization; Portfolio Selection Problems; Exact Penalty functions.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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