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Portfolio selection using fuzzy decision theory

Author

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  • Srichander Ramaswamy

Abstract

This paper presents an approach to portfolio selection using fuzzy decision theory. The approach is such that a given target rate of return is achieved for an assumed market scenario. If the assumed market scenario turns out to be incorrect, the portfolio is guaranteed to secure a given minimum rate of return. The methodology is useful in the management of assets against given liabilities or in forming structured portfolios that guarantee a minimum rate of return.

Suggested Citation

  • Srichander Ramaswamy, 1998. "Portfolio selection using fuzzy decision theory," BIS Working Papers 59, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:59
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    Citations

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    Cited by:

    1. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Robust technical trading with fuzzy knowledge-based systems (Forthcoming in "Frontiers in Artificial Intelligence and Applications".)," CARF F-Series CARF-F-413, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Wei Chen, 2009. "Weighted portfolio selection models based on possibility theory," Fuzzy Information and Engineering, Springer, vol. 1(2), pages 115-127, June.
    3. Tsaur, Ruey-Chyn, 2013. "Fuzzy portfolio model with different investor risk attitudes," European Journal of Operational Research, Elsevier, vol. 227(2), pages 385-390.
    4. Mukesh Kumar Mehlawat & Pankaj Gupta, 2014. "Credibility-Based Fuzzy Mathematical Programming Model For Portfolio Selection Under Uncertainty," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 101-135.
    5. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Robust Technical Trading with Fuzzy Knowledge-based Systems," CIRJE F-Series CIRJE-F-1053, CIRJE, Faculty of Economics, University of Tokyo.
    6. A Bilbao & M Arenas & M Jiménez & B Perez Gladish & M V Rodríguez, 2006. "An extension of Sharpe's single-index model: portfolio selection with expert betas," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(12), pages 1442-1451, December.
    7. Li, Ting & Zhang, Weiguo & Xu, Weijun, 2013. "Fuzzy possibilistic portfolio selection model with VaR constraint and risk-free investment," Economic Modelling, Elsevier, vol. 31(C), pages 12-17.
    8. Madlener, Reinhard & Glensk, Barbara & Weber, Veronika, 2011. "Fuzzy Portfolio Optimization of Onshore Wind Power Plants," FCN Working Papers 10/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Jul 2014.
    9. Liu, Yong-Jun & Zhang, Wei-Guo & Zhang, Pu, 2013. "A multi-period portfolio selection optimization model by using interval analysis," Economic Modelling, Elsevier, vol. 33(C), pages 113-119.
    10. Perez Gladish, B. & Jones, D.F. & Tamiz, M. & Bilbao Terol, A., 2007. "An interactive three-stage model for mutual funds portfolio selection," Omega, Elsevier, vol. 35(1), pages 75-88, February.
    11. Bilbao, A. & Arenas, M. & Rodriguez, M.V. & Antomil, J., 2007. "On constructing expert Betas for single-index model," European Journal of Operational Research, Elsevier, vol. 183(2), pages 827-847, December.
    12. Fang, Yong & Lai, K.K. & Wang, Shou-Yang, 2006. "Portfolio rebalancing model with transaction costs based on fuzzy decision theory," European Journal of Operational Research, Elsevier, vol. 175(2), pages 879-893, December.
    13. Jiuping Xu & Xiaoyang Zhou & Desheng Wu, 2011. "Portfolio selection using λ mean and hybrid entropy," Annals of Operations Research, Springer, vol. 185(1), pages 213-229, May.
    14. Jiuping Xu & Xiaoyang Zhou & Steven Li, 2011. "A Class of Chance Constrained Multi-objective Portfolio Selection Model Under Fuzzy Random Environment," Journal of Optimization Theory and Applications, Springer, vol. 150(3), pages 530-552, September.
    15. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2017. "Fuzzy Logic-based Portfolio Selection with Particle Filtering and Anomaly Detection," CIRJE F-Series CIRJE-F-1037, CIRJE, Faculty of Economics, University of Tokyo.

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