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

Francisco Luna (), David Quintana, Sandra García () and Pedro Isasi ()
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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

International Journal of Information Technology & Decision Making (IJITDM), 2016, vol. 15, issue 03, 479-515

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.

Keywords: Portfolio optimization; robustness; multiobjective optimization (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1142/S0219622016500115

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