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A chance constrained recourse approach for the portfolio selection problem

Author

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  • Meryem Masmoudi

    (University of Bahrain)

  • Fouad Ben Abdelaziz

    (NEOMA Business School, Boulevard André Siegfried)

Abstract

This paper deals with the stochastic portfolio selection problem when the loss in the portfolio return is considered as a recourse cost. We suppose that the investor would penalize infeasible solutions for uncertain constraints with the most probable highest recourse cost rather than with the expected recourse cost as in the traditional recourse approach. This novel approach which is mixed with a goal programming approach is used to solve a multi-objective stochastic portfolio selection model. We illustrate the paper results by an empirical example using the weekly returns of the Standard & Poor’s 100 securities between January 2001 and November 2011.

Suggested Citation

  • Meryem Masmoudi & Fouad Ben Abdelaziz, 2017. "A chance constrained recourse approach for the portfolio selection problem," Annals of Operations Research, Springer, vol. 251(1), pages 243-254, April.
    • Handle: RePEc:spr:annopr:v:251:y:2017:i:1:d:10.1007_s10479-015-1844-2
    DOI: 10.1007/s10479-015-1844-2
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    References listed on IDEAS

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    1. Abdelaziz, Fouad Ben & Aouni, Belaid & Fayedh, Rimeh El, 2007. "Multi-objective stochastic programming for portfolio selection," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1811-1823, March.
    2. Abdelaziz, Fouad Ben, 2012. "Solution approaches for the multiobjective stochastic programming," European Journal of Operational Research, Elsevier, vol. 216(1), pages 1-16.
    3. Ralph Steuer & Yue Qi & Markus Hirschberger, 2007. "Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection," Annals of Operations Research, Springer, vol. 152(1), pages 297-317, July.
    4. A. Charnes & W. W. Cooper, 1963. "Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints," Operations Research, INFORMS, vol. 11(1), pages 18-39, February.
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    Cited by:

    1. Hanxiao Wang & Huizi Ma, 2022. "Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China," Mathematics, MDPI, vol. 10(16), pages 1-18, August.

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