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A Hybrid Approach of Optimization and Sampling for Robust Portfolio Selection

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Operations Research Proceedings 2015

Part of the book series: Operations Research Proceedings ((ORP))

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Abstract

Dealing with ill-defined optimization problems, where the actual values of the input parameters are unknown or not directly measurable, is generally not an easy task. In order to enhance the robustness of the final solutions, we propose in the current paper a hybrid metaheuristic approach that incorporates a sampling-based simulation module. Empirical application to the classical mean-variance portfolio optimization problem, which is known to be extremely sensitive to noises in asset means, is provided through a genetic algorithm solver. Results of the proposed approach are compared with that specified by the baseline worst-case scenario and the two approaches of stochastic programming and robust optimization.

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Authors and Affiliations

  1. Department of Economic Engineering, Kyushu University, Fukuoka, 812-8581, Japan

    Omar Rifki & Hirotaka Ono

Authors
  1. Omar Rifki
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  2. Hirotaka Ono
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Corresponding author

Correspondence to Omar Rifki .

Editor information

Editors and Affiliations

  1. Department of Business Administration, University of Vienna, Vienna, Austria

    Karl Franz Dörner

  2. Decision Sciences and Statistics (IDS) Department, ESSEC Business School of Paris, Cergy Pontoise Cedex, France

    Ivana Ljubic

  3. Department of Statistics and Operations Research, University of Vienna, Vienna, Austria

    Georg Pflug

  4. Operations Research and Control Systems, TU Wien, Vienna, Austria

    Gernot Tragler

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Rifki, O., Ono, H. (2017). A Hybrid Approach of Optimization and Sampling for Robust Portfolio Selection. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_43

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