The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm

K. Liagkouras, K. Metaxiotis

DSS Lab., Department of Informatics, University of Piraeus, Piraeus, Greece.

DOI: 10.4236/jsea.2013.67B005                

Abstract

The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification measure and the real world constraints imposed to the model make the problem difficult to be solved with exact methods. Thanks to the exploratory mechanism, n-MOEA concentrates the search effort where is needed more and provides a well formed efficient frontier with the solutions spread across the whole frontier. We also provide evidence for the robustness of the produced non-dominated solutions by carrying out, out-of-sample testing during both bull and bear market conditions on FTSE-100.

Keywords

Multiobjective Optimization; Evolutionary Algorithms; Portfolio Optimization

Conflicts of Interest

The authors declare no conflicts of interest.

References

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