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Portfolio Optimization Using SPEA2 with Resampling

  • Sandra García
  • David Quintana
  • Inés M. Galván
  • Pedro Isasi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)

Abstract

The subject of financial portfolio optimization under real-world constraints is a difficult problem that can be tackled using multiobjective evolutionary algorithms. One of the most problematic issues is the dependence of the results on the estimates for a set of parameters, that is, the robustness of solutions. These estimates are often inaccurate and this may result on solutions that, in theory, offered an appropriate risk/return balance and, in practice, resulted being very poor. In this paper we suggest that using a resampling mechanism may filter out the most unstable. We test this idea on real data using SPEA2 as optimization algorithm and the results show that the use of resampling increases significantly the reliability of the resulting portfolios.

Keywords

Pareto Front Multiobjective Optimization Portfolio Optimization Portfolio Selection Monthly Return 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sandra García
    • 1
  • David Quintana
    • 1
  • Inés M. Galván
    • 1
  • Pedro Isasi
    • 1
  1. 1.Computer Science DepartmentCarlos III University of MadridLeganesSpain

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