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Robustness Analysis of Evolutionary Algorithms to Portfolio Optimization Against Errors in Asset Means

In: Operations Research Proceedings 2013

Author

Listed:
  • Omar Rifki

    (Kyushu University)

  • Hirotaka Ono

    (Kyushu University)

Abstract

The Mean-Variance (MV) optimization is a well-studied model for portfolio optimization. Although the main focus is primarily on finding the best efficient portfolios, the MV model is known to be extremely sensitive to perturbations in asset means. This paper investigates the robustness of MV optimization when solved by Evolutionary Algorithms (EA), in the case of linear constraints, i.e., budget constraints and holding constraints. To this end, comparisons were made on Quadratic Programming (QP), Genetic Algorithms (GA) and Evolution Strategies (ES). In order to identify, for EA, robust portfolios, which are supposed to exhibit low sensitivity to small changes in assets means, we proceed by exploiting the population aspect of EA and computing the performance of some selected ‘good’ individuals under multiple runs subject to perturbations. Comparison of portfolios follows two procedures, the first measures the loss in terms of utility functions, while the second is more practical enabling the decision maker to incorporate a preferred level of robustness. The experimental results using real-world data show that EAs have stronger robustness than QP; many individuals of EA’s population outperform the QP-based optimal portfolio.

Suggested Citation

  • Omar Rifki & Hirotaka Ono, 2014. "Robustness Analysis of Evolutionary Algorithms to Portfolio Optimization Against Errors in Asset Means," Operations Research Proceedings, in: Dennis Huisman & Ilse Louwerse & Albert P.M. Wagelmans (ed.), Operations Research Proceedings 2013, edition 127, pages 369-375, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-07001-8_50
    DOI: 10.1007/978-3-319-07001-8_50
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