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JACIII Vol.20 No.3 pp. 484-491
doi: 10.20965/jaciii.2016.p0484
(2016)

Paper:

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Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta

Yan Chen and Zhihui Shi

School of Statistics and Management, Shanghai University of Finance and Economics
Shanghai 200433, China

Received:
September 1, 2015
Accepted:
April 9, 2016
Published:
May 19, 2016
Creative Commons License
Keywords:
portfolio beta, genetic relation algorithm, robust genetic network programming, stock trading
Abstract
In this paper, Robust Genetic Network Programming (R-GNP) for generating trading rules for stocks is described. R-GNP is a new evolutionary algorithm, where solutions are represented using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid model, R-GNP is applied to generating stock trading rules with variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness functions with the original data and a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps: priority selection by the portfolio β, optimization by the Genetic Relation Algorithm (GRA), and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands on the Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods even in the world-wide financial crisis of 2007. Hence, its effectiveness has been confirmed.
Cite this article as:
Y. Chen and Z. Shi, “Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.3, pp. 484-491, 2016.
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