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Optimal Portfolio Selection With A Shortfall Probability Constraint: Evidence From Alternative Distribution Functions

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  • Yalcin Akcay
  • Atakan Yalcin

Abstract

We propose a new approach to optimal portfolio selection in a downside risk framework that allocates assets by maximizing expected return subject to a shortfall probability constraint, reflecting the typical desire of a risk‐averse investor to limit the maximum likely loss. Our empirical results indicate that the loss‐averse portfolio outperforms the widely used mean‐variance approach based on the cumulative cash values, geometric mean returns, and average risk‐adjusted returns. We also evaluate the relative performance of the loss‐averse portfolio with normal, symmetric thin‐tailed, symmetric fat‐tailed, and skewed fat‐tailed return distributions in terms of average return, risk, and average risk‐adjusted return.

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  • Yalcin Akcay & Atakan Yalcin, 2010. "Optimal Portfolio Selection With A Shortfall Probability Constraint: Evidence From Alternative Distribution Functions," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(1), pages 77-102, March.
  • Handle: RePEc:bla:jfnres:v:33:y:2010:i:1:p:77-102
    DOI: 10.1111/j.1475-6803.2009.01263.x
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    Cited by:

    1. 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.
    2. Houda Hafsa, 2015. "CVaR in Portfolio Optimization: An Essay on the French Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 101-111, April.
    3. 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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