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A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs

Author

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  • Zhang, Wei-Guo
  • Liu, Yong-Jun
  • Xu, Wei-Jun

Abstract

This paper deals with a multi-period portfolio selection problem with fuzzy returns. A possibilistic mean-semivariance-entropy model for multi-period portfolio selection is presented by taking into account four criteria viz., return, risk, transaction cost and diversification degree of portfolio. In the proposed model, the return level is quantified by the possibilistic mean value of return, the risk level is characterized by the lower possibilistic semivariance of return, and the diversification degree of portfolio is measured by the originally presented possibilistic entropy. Furthermore, a hybrid intelligent algorithm is designed to obtain the optimal portfolio strategy. Finally, the comparison analysis between the possibilistic entropy model and the proportion entropy model is provided by two numerical examples to illustrate the efficiency of the proposed approaches and the designed algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:222:y:2012:i:2:p:341-349
    DOI: 10.1016/j.ejor.2012.04.023
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    References listed on IDEAS

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    5. Guo, Sini & Yu, Lean & Li, Xiang & Kar, Samarjit, 2016. "Fuzzy multi-period portfolio selection with different investment horizons," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1026-1035.
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    10. Yong-Jun Liu & Wei-Guo Zhang, 2019. "Possibilistic Moment Models for Multi-period Portfolio Selection with Fuzzy Returns," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1657-1686, April.
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    17. K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
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