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Portfolio implementation risk management using evolutionary multiobjective optimization

Author

Listed:
  • David Quintana

    (LCC - Departamento Lenguajes y Ciencias de la Computación - Universidad de Málaga [Málaga] = University of Málaga [Málaga])

  • Roman Denysiuk

    (Universidade do Minho = University of Minho [Braga])

  • Sandra García-Rodríguez

    (LADIS (CEA, LIST) - Laboratoire d'analyse des données et d'intelligence des systèmes (CEA, LIST) - DM2I (CEA, LIST) - Département Métrologie Instrumentation & Information (CEA, LIST) - LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies - DRT (CEA) - Direction de Recherche Technologique (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay)

  • Antonio Gaspar-Cunha

    (Universidade do Minho = University of Minho [Braga])

Abstract

Portfolio management based on mean-variance portfolio optimization is subject to different sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancy between target and present portfolios, caused by trading strategies, may expose investors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user's preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance-covariance matrix.

Suggested Citation

  • David Quintana & Roman Denysiuk & Sandra García-Rodríguez & Antonio Gaspar-Cunha, 2017. "Portfolio implementation risk management using evolutionary multiobjective optimization," Post-Print hal-01881379, HAL.
    • Handle: RePEc:hal:journl:hal-01881379
    DOI: 10.3390/app7101079
    Note: View the original document on HAL open archive server: https://hal.science/hal-01881379
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    References listed on IDEAS

    as
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    Cited by:

    1. Paolo Pagliuca & Stefano Nolfi, 2019. "Robust optimization through neuroevolution," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.

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