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Fuzzy Chance-Constrained Integer Programming Models for Portfolio Investment Selection and Optimization Under Uncertainty

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  • Shayarath Srizongkhram

    (Sirindhorn International Institute of Technology, Thammasat University, Thailand)

  • Kittitath Manitayakul

    (Sirindhorn International Institute of Technology, Thammasat University, Thailand)

  • Pisacha Suthamanondh

    (Sirindhorn International Institute of Technology, Thammasat University, Thailand)

  • Navee Chiadamrong

    (Sirindhorn International Institute of Technology, Thammasat University, Thailand)

Abstract

Portfolio investment optimization is the process of selecting the best portfolio out of the set of all projects being considered. A high financial return is not the only concern since minimization of associated risk is as important. Its objective should be set to maximize the expected return and minimize the risk in the investment as most data need to be justified based on vagueness and future values. Thus, the portfolio investment optimization problem under a fuzzy environment is studied here by incorporating a classical mathematical optimization model with the fuzzy theory. It is solved with the fuzzy chance-constrained integer programming model by linear programming under predetermined conditions and limitations. This study also uses both the credibility index and credibilistic risk index for measuring the investment return and investment risk. A numerical example is illustrated to demonstrate the effectiveness and benefits of the proposed algorithm.

Suggested Citation

  • Shayarath Srizongkhram & Kittitath Manitayakul & Pisacha Suthamanondh & Navee Chiadamrong, 2020. "Fuzzy Chance-Constrained Integer Programming Models for Portfolio Investment Selection and Optimization Under Uncertainty," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 11(3), pages 33-58, July.
    • Handle: RePEc:igg:jkss00:v:11:y:2020:i:3:p:33-58
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