Mean-Variance-Skewness Portfolio Selection Model Based on RBF-GA

Yuanyuan LU, Jiaming LI

Abstract


The classical Markowitz’s mean-variance model in modern investment science uses variance as risk measure while it ignores the asymmetry of the return distribution. This article introduces skewness, V-type transaction costs, cardinality constraint and initial investment proportion, and builds a new class of nonlinear multi-objective portfolio model (mean-variance-skewness portfolio selection model). To solve the model, we develop a genetic algorithm(GA) which contains radial basis function(RBF) neural network, called RBF-GA. The experimental results show that the proposed model is more effective and more realistic than others.


Keywords


Portfolio model; Skewness; RBF-GA

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References


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DOI: http://dx.doi.org/10.3968/n

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