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
Cite this paper
K. Liagkouras and K. Metaxiotis, "The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm," Journal of Software Engineering and Applications, Vol. 6 No. 7, 2013, pp. 22-29. doi: 10.4236/jsea.2013.67B005.
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