Portfolio optimization problems in different risk measures using genetic algorithm

Authors:
Tun-Jen Chang;Sang-Chin Yang;Kuang-Jung Chang
Affiliations:
Department of International Business, Shih Chien University, Taiwan;Department of Computer Science, Chung Cheng Institute of Technology, National Defense University, Taiwan;Graduate School of Defense Science, Chung Cheng Institute of Technology, National Defense University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper introduces a heuristic approach to portfolio optimization problems in different risk measures by employing genetic algorithm (GA) and compares its performance to mean-variance model in cardinality constrained efficient frontier. To achieve this objective, we collected three different risk measures based upon mean-variance by Markowitz; semi-variance, mean absolute deviation and variance with skewness. We show that these portfolio optimization problems can now be solved by genetic algorithm if mean-variance, semi-variance, mean absolute deviation and variance with skewness are used as the measures of risk. The robustness of our heuristic method is verified by three data sets collected from main financial markets. The empirical results also show that the investors should include only one third of total assets into the portfolio which outperforms than those contained more assets.