Portfolio optimization problems in different risk measures using genetic algorithm

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...

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Bibliographic Details
Published in:Expert systems with applications 2009-09, Vol.36 (7), p.10529-10537
Main Authors: Chang, Tun-Jen, Yang, Sang-Chin, Chang, Kuang-Jung
Format: Article
Language:English
Subjects:
Cardinality constrained efficient frontier
Genetic algorithm
Mean absolute deviation
Mean–variance
Portfolio optimization
Semi-variance
Variance with skewness
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Summary: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.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2009.02.062