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

Author

Listed:
  • Francisco Luna

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

  • David Quintana

    (UC3M - Universidad Carlos III de Madrid [Madrid])

  • Sandra García

    (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)

  • Pedro Isasi

    (UC3M - Universidad Carlos III de Madrid [Madrid])

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," Post-Print cea-01849801, HAL.
    • Handle: RePEc:hal:journl:cea-01849801
    DOI: 10.1142/S0219622016500115
    Note: View the original document on HAL open archive server: https://cea.hal.science/cea-01849801
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    References listed on IDEAS

    as
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