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Uncertain portfolio selection with mental accounts

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  • Xiaoxia Huang
  • Hao Di

Abstract

Since the security market is so complex, in real life, there are situations where the future security returns cannot be reflected by the past data and are given by experts' estimations according to their knowledge and judgement rather than by historical data. This paper discusses a portfolio selection problem in such an uncertain environment. In the paper, in order to reflect different attitudes towards risk that vary by goal in one portfolio investment, we apply mental account to the investment. Using uncertainty theory, we propose a new mean–variance uncertain portfolio selection model with mental accounts. Furthermore, we discuss the shape of the mean–standard deviation efficient frontier of the subportfolios of each mental account when security returns are normal uncertain variables and further give the condition where the optimal aggregate portfolio is on the mean–standard deviation efficient frontier. In addition, we compare the optimal portfolio with mental accounts with that without mental accounts. Finally, a numerical example is given as an illustration.

Suggested Citation

  • Xiaoxia Huang & Hao Di, 2020. "Uncertain portfolio selection with mental accounts," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(12), pages 2079-2090, September.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:12:p:2079-2090
    DOI: 10.1080/00207721.2019.1648706
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    Cited by:

    1. Yang, Tingting & Huang, Xiaoxia, 2022. "Active or passive portfolio: A tracking error analysis under uncertainty theory," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 309-326.
    2. Li, Bo & Zhang, Ranran, 2021. "A new mean-variance-entropy model for uncertain portfolio optimization with liquidity and diversification," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. Li, Bo & Huang, Yayi, 2023. "Uncertain random portfolio selection with different mental accounts based on mixed data," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    4. 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.
    5. Yang, Tingting & Huang, Xiaoxia, 2022. "Two new mean–variance enhanced index tracking models based on uncertainty theory," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).

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