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Fuzzy portfolio selection model with real features and different decision behaviors

Author

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  • Yong-Jun Liu

    (South China University of Technology)

  • Wei-Guo Zhang

    (South China University of Technology)

Abstract

In the ever changing financial markets, investor’s decision behaviors may change from time to time. In this paper, we consider the effect of investor’s different decision behaviors on portfolio selection in fuzzy environment. We present a possibilistic mean-semivariance model for fuzzy portfolio selection by considering some real investment features including proportional transaction cost, fixed transaction cost, cardinality constraint, investment threshold constraints, decision dependency constraints and minimum transaction lots. To describe investor’s different decision behaviors, we characterize the return rates on securities by LR fuzzy numbers with different shape parameters in the left- and right-hand reference functions. Then, we design a novel hybrid differential evolution algorithm to solve the proposed model. Finally, we provide a numerical example to illustrate the application of our model and the effectiveness of the designed algorithm.

Suggested Citation

  • Yong-Jun Liu & Wei-Guo Zhang, 2018. "Fuzzy portfolio selection model with real features and different decision behaviors," Fuzzy Optimization and Decision Making, Springer, vol. 17(3), pages 317-336, September.
    • Handle: RePEc:spr:fuzodm:v:17:y:2018:i:3:d:10.1007_s10700-017-9274-z
    DOI: 10.1007/s10700-017-9274-z
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    References listed on IDEAS

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    1. Xidonas, Panagiotis & Mavrotas, George & Zopounidis, Constantin & Psarras, John, 2011. "IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection," European Journal of Operational Research, Elsevier, vol. 210(2), pages 398-409, April.
    2. Zhang, Wei-Guo & Zhang, Xi-Li & Xu, Wei-Jun, 2010. "A risk tolerance model for portfolio adjusting problem with transaction costs based on possibilistic moments," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 493-499, June.
    3. Dickson,David C. M., 2016. "Insurance Risk and Ruin," Cambridge Books, Cambridge University Press, number 9781107154605.
    4. Mansini, Renata & Speranza, Maria Grazia, 1999. "Heuristic algorithms for the portfolio selection problem with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 114(2), pages 219-233, April.
    5. Zhang, Wei-Guo & Liu, Yong-Jun & Xu, Wei-Jun, 2012. "A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs," European Journal of Operational Research, Elsevier, vol. 222(2), pages 341-349.
    6. Angelelli, Enrico & Mansini, Renata & Speranza, M. Grazia, 2008. "A comparison of MAD and CVaR models with real features," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1188-1197, July.
    7. Tsaur, Ruey-Chyn, 2013. "Fuzzy portfolio model with different investor risk attitudes," European Journal of Operational Research, Elsevier, vol. 227(2), pages 385-390.
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    Cited by:

    1. Li, Bo & Huang, Yayi, 2023. "Uncertain random portfolio selection with different mental accounts based on mixed data," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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