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Mean-chance model for portfolio selection based on uncertain measure

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  • Huang, Xiaoxia
  • Zhao, Tianyi

Abstract

This paper discusses a portfolio selection problem in which security returns are given by experts’ evaluations instead of historical data. A factor method for evaluating security returns based on experts’ judgment is proposed and a mean-chance model for optimal portfolio selection is developed taking transaction costs and investors’ preference on diversification and investment limitations on certain securities into account. The factor method of evaluation can make good use of experts’ knowledge on the effects of economic environment and the companies’ unique characteristics on security returns and incorporate the contemporary relationship of security returns in the portfolio. The use of chance of portfolio return failing to reach the threshold can help investors easily tell their tolerance toward risk and thus facilitate a decision making. To solve the proposed nonlinear programming problem, a genetic algorithm is provided. To illustrate the application of the proposed method, a numerical example is also presented.

Suggested Citation

  • Huang, Xiaoxia & Zhao, Tianyi, 2014. "Mean-chance model for portfolio selection based on uncertain measure," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 243-250.
    • Handle: RePEc:eee:insuma:v:59:y:2014:i:c:p:243-250
    DOI: 10.1016/j.insmatheco.2014.10.001
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    Cited by:

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    2. Tingting Yang & Xiaoxia Huang, 2022. "A New Portfolio Optimization Model Under Tracking-Error Constraint with Linear Uncertainty Distributions," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 723-747, November.
    3. Wang, Xiantao & Zhu, Yuanguo & Tang, Pan, 2024. "Uncertain mean-CVaR model for portfolio selection with transaction cost and investors’ preferences," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    4. Dong-Hwa Lee & Joo-Ho Sung, 2024. "Dynamic Liability-Driven Investment under Sponsor’s Loss Aversion," Risks, MDPI, vol. 12(2), pages 1-14, February.
    5. Lin Chen & Jin Peng & Bo Zhang & Isnaini Rosyida, 2017. "Diversified models for portfolio selection based on uncertain semivariance," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(3), pages 637-648, February.
    6. Huang, Xiaoxia & Yang, Tingting, 2020. "How does background risk affect portfolio choice: An analysis based on uncertain mean-variance model with background risk," Journal of Banking & Finance, Elsevier, vol. 111(C).
    7. Huang, Xiaoxia & Di, Hao, 2016. "Uncertain portfolio selection with background risk," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 284-296.

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