Proceeding of: Intelligent Data Engineering and
Automated Learning – IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011The 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.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10- UC3M/TIC-5029).Publicad
