A portfolio selection model using genetic relation algorithm and genetic network programming

In this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.