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A fuzzy portfolio selection model with background risk

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  • Li, Ting
  • Zhang, Weiguo
  • Xu, Weijun

Abstract

In financial markets, the presence of background risk may affect investors’ investments. This article develops a fuzzy portfolio selection model with background risk, based on the definitions of the possibilistic return and possibilistic risk. For the returns of assets obey LR-type possibility distribution, we propose a specific portfolio selection model with background risk. Then, a numerical study is carried out by using the data concerning some stocks. Based on the data, we obtain the efficient frontier of the possibilistic portfolio with background risk, and compare it with the efficient frontier of the portfolio without background risk. Finally, we conclude that the background risk can better reflect the investment risk of the real economy environment which make the investors choose a more suitable portfolio to them.

Suggested Citation

  • Li, Ting & Zhang, Weiguo & Xu, Weijun, 2015. "A fuzzy portfolio selection model with background risk," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 505-513.
    • Handle: RePEc:eee:apmaco:v:256:y:2015:i:c:p:505-513
    DOI: 10.1016/j.amc.2015.01.007
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    Cited by:

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    2. Yuanyuan Zhang & Xiang Li & Sini Guo, 2018. "Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature," Fuzzy Optimization and Decision Making, Springer, vol. 17(2), pages 125-158, June.
    3. Apichat Chaweewanchon & Rujira Chaysiri, 2022. "Markowitz Mean-Variance Portfolio Optimization with Predictive Stock Selection Using Machine Learning," IJFS, MDPI, vol. 10(3), pages 1-19, August.
    4. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
    5. Deng Xiong & Liu Yanli, 2018. "A High-Moment Trapezoidal Fuzzy Random Portfolio Model with Background Risk," Journal of Systems Science and Information, De Gruyter, vol. 6(1), pages 1-28, February.
    6. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    7. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
    8. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.

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