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 meanvariance model in cardinality constrained efficient frontier. To achieve this objective, we collected three different risk measures based upon meanvariance 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 meanvariance, 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.
portfolio optimization genetic algorithm meanvariance cardinality constrained efficient frontier mean absolute deviation variance with skewness semi-variance