Multiobjective Algorithms with Resampling for Portfolio Optimization

Authors

  • Sandra García Computer Science Department, Carlos III University of Mardid, Leganés
  • David Quintana Computer Science Department, Carlos III University of Mardid, Leganés
  • Inés M. Galván Computer Science Department, Carlos III University of Mardid, Leganés
  • Pedro Isasi Computer Science Department, Carlos III University of Mardid, Leganés

Keywords:

Financial portfolio optimization, robust portfolio, multiobjective evolutionary algorithms

Abstract

Constrained financial portfolio optimization is a challenging domain where the use of multiobjective evolutionary algorithms has been thriving over the last few years. One of the major issues related to this problem is the dependence of the results on a set of parameters. Given the nature of financial prediction, these figures are often inaccurate, which results in unreliable estimates for the efficient frontier. In this paper we introduce a resampling mechanism that deals with uncertainty in the parameters and results in efficient frontiers that are more robust. We test this idea on real data using four multiobjective optimization algorithms (NSGA-II, GDE3, SMPSO and SPEA2). The results show that resampling significantly increases the reliability of the resulting portfolios.

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Published

2014-01-20

How to Cite

García, S., Quintana, D., Galván, I. M., & Isasi, P. (2014). Multiobjective Algorithms with Resampling for Portfolio Optimization. COMPUTING AND INFORMATICS, 32(4), 777–796. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/1971