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Information Journal Paper

Title

PORTFOLIO OPTIMIZATION BASED ON NONPARAMETRIC ESTIMATION METHODS

Pages

  175-184

Abstract

 One of the major issues that investors are facing with in capital markets is decision making about selecting an appropriate stock exchange for investment and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand, in portfolio selection problems if the assets' expected returns are normally distributed, variance and standard deviation are used as a risk measure. However, the expected returns on assets are not necessarily normal and sometimes have significant differences from normal distribution. This paper offers an optimal portfolio by introducing CONDITIONAL VALUE AT RISK (CVaR) as a measure of risk in a nonparametric framework considering a given expected return. This method is compared with the linear programming method.The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393.The results of this study show the superiority of the nonparametric method over the linear programming method while the nonparametric method is much faster than the linear programming method.

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    APA: Copy

    GHANDEHARI, MAHSA, SHAMSHIRI, AZIMEH, & FATHI, SAEED. (2017). PORTFOLIO OPTIMIZATION BASED ON NONPARAMETRIC ESTIMATION METHODS. PRODUCTION AND OPERATIONS MANAGEMENT, 8(1 (14) ), 175-184. SID. https://sid.ir/paper/217582/en

    Vancouver: Copy

    GHANDEHARI MAHSA, SHAMSHIRI AZIMEH, FATHI SAEED. PORTFOLIO OPTIMIZATION BASED ON NONPARAMETRIC ESTIMATION METHODS. PRODUCTION AND OPERATIONS MANAGEMENT[Internet]. 2017;8(1 (14) ):175-184. Available from: https://sid.ir/paper/217582/en

    IEEE: Copy

    MAHSA GHANDEHARI, AZIMEH SHAMSHIRI, and SAEED FATHI, “PORTFOLIO OPTIMIZATION BASED ON NONPARAMETRIC ESTIMATION METHODS,” PRODUCTION AND OPERATIONS MANAGEMENT, vol. 8, no. 1 (14) , pp. 175–184, 2017, [Online]. Available: https://sid.ir/paper/217582/en

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