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A novel portfolio selection model in a hybrid uncertain environment

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  • Li, Jun
  • Xu, Jiuping
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    Abstract

    The future returns of each securities cannot be correctly reflected by the securities data in the past, therefore the statistical techniques and the experts' judgement and experience are combined together to estimate the security returns in the future. In this paper, the returns of each securities are assumed to be fuzzy random variables, then following the ideas of mean variance model a new portfolio selection model in a hybrid uncertain environment is proposed. Moreover, the [lambda]-mean variance efficient frontiers and [lambda]-mean variance efficient portfolios are defined, and the properties of [lambda]-mean variance efficient portfolios located on different [lambda]-mean variance efficient frontiers are discussed. Finally, a numerical example is presented to illustrate the proposed portfolio selection model. On the basis of the results, we can conclude that the proposed model can provide the more flexible results.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 37 (2009)
    Issue (Month): 2 (April)
    Pages: 439-449

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    Handle: RePEc:eee:jomega:v:37:y:2009:i:2:p:439-449

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    Related research

    Keywords: Portfolio selection Fuzzy random variable Expectation Variance Efficient frontier;

    References

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    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
    2. 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.
    3. Huang, Xiaoxia, 2007. "Two new models for portfolio selection with stochastic returns taking fuzzy information," European Journal of Operational Research, Elsevier, vol. 180(1), pages 396-405, July.
    4. Arenas Parra, M. & Bilbao Terol, A. & Rodriguez Uria, M. V., 2001. "A fuzzy goal programming approach to portfolio selection," European Journal of Operational Research, Elsevier, vol. 133(2), pages 287-297, January.
    5. 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.
    6. Zmeskal, Zdenek, 2001. "Application of the fuzzy-stochastic methodology to appraising the firm value as a European call option," European Journal of Operational Research, Elsevier, vol. 135(2), pages 303-310, December.
    7. Yoshida, Yuji, 2003. "The valuation of European options in uncertain environment," European Journal of Operational Research, Elsevier, vol. 145(1), pages 221-229, February.
    8. Zmeskal, Zdenek, 2005. "Value at risk methodology under soft conditions approach (fuzzy-stochastic approach)," European Journal of Operational Research, Elsevier, vol. 161(2), pages 337-347, March.
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    Citations

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    Cited by:
    1. Hasuike, Takashi & Ishii, Hiroaki, 2009. "On flexible product-mix decision problems under randomness and fuzziness," Omega, Elsevier, vol. 37(4), pages 770-787, August.
    2. Zhang, Xili & Zhang, Weiguo & Xiao, Weilin, 2013. "Multi-period portfolio optimization under possibility measures," Economic Modelling, Elsevier, vol. 35(C), pages 401-408.
    3. Li, Deng-Feng, 2011. "Linear programming approach to solve interval-valued matrix games," Omega, Elsevier, vol. 39(6), pages 655-666, December.
    4. Tsaur, Ruey-Chyn, 2013. "Fuzzy portfolio model with different investor risk attitudes," European Journal of Operational Research, Elsevier, vol. 227(2), pages 385-390.
    5. Bell, John E. & Griffis, Stanley E. & Cunningham III, William A. & Eberlan, Jon A., 2011. "Location optimization of strategic alert sites for homeland defense," Omega, Elsevier, vol. 39(2), pages 151-158, April.

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