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Enhancing Financial Portfolio Robustness with an Objective Based on ϵ-Neighborhoods

Author

Listed:
  • Francisco Luna

    (Departmento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, E.T.S.I. Informática, Campus de Teatinos 29071, Spain)

  • David Quintana

    (Department of Computer Science, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganes, Madrid 28911, Spain)

  • Sandra García

    (CEA Saclay, DRT/LIST/DM2I/LADIS, 91191 Gif-sur-Yvette Cedex, France)

  • Pedro Isasi

    (Department of Computer Science, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganes, Madrid 28911, Spain)

Abstract

Financial portfolio optimization is a challenging task. One of the major difficulties is managing the uncertainty arising from different aspects of the process. This paper suggests a solution based on ϵ-neighborhoods that, combined with a time-stamped resampling mechanism, increases the robustness of the solutions. The approach is tested on four of the most popular evolutionary multiobjective algorithms over a long period of time. This results in a significant enhancement in the reliability of the estimated efficient frontier.

Suggested Citation

  • Francisco Luna & David Quintana & Sandra García & Pedro Isasi, 2016. "Enhancing Financial Portfolio Robustness with an Objective Based on ϵ-Neighborhoods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 479-515, May.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:03:n:s0219622016500115
    DOI: 10.1142/S0219622016500115
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    References listed on IDEAS

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