Enhancing Financial Portfolio Robustness with an Objective Based on epsilon-Neighborhoods Articles uri icon

publication date

  • mayo 2016

start page

  • 479

end page

  • 515

issue

  • 3

volume

  • 15

international standard serial number (ISSN)

  • 0219-6220

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 epsilon-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; multiobjective evolutionary algorithm; genetic algorithms; optimization; selection