Paper:

# 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

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.

- [1] D. Enke and S. Thawornwong, “The use of data mining and neural networks for forecasting stock market returns,” Expert Systems with Applications, Vol.29, No.4, pp. 927-940, 2005.
- [2] A. Fernandez and S. Gomez, “Portfolio selection using neural networks,” Computers & Operations Research, Vol.34, No.4, pp. 1177-1191, 2007.
- [3] C. C Lin and Y. T. Liu, “Genetic algorithms for portfolio selection problems with minimum transaction lots,” European J. of Operational Research, Vol.185, No.1, pp. 393-404, 2008.
- [4] N. Harnpornchai, K. Autchariyapanitkul, J. Sirisrisakulchai, and S. Sriboonchitta, “Optimal Outpatient Appointment System with Uncertain Parameters Using Adaptive-Penalty Genetic Algorithm,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.5, pp. 585-592, 2015.
- [5] W. Z. Dai and K. Xia, “Approach to Hybrid Flow-Shop Scheduling Problem Based on Self-Guided Genetic Algorithm,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.3, pp. 365-371, 2015.
- [6] J. Dan, W. Guo, W. R. Shi, B. Fang, and T. P. Zhang, “PSO Based Deterministic ESN Models for Stock Price Forecasting,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.2, pp. 312-318, 2014.
- [7] S. D. F. Hilado, L. A. G. Lim, R. N. G. Naguib, E. P. Dadios, and J. M. C. Avila, “Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, No.5, pp. 792-797, 2014.
- [8] K. G. Abistado, C. N. Arellano, and E. A. Maravillas, “Weather Forecasting Using Artificial Neural Network and Bayesian Network,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.18, No.5, pp. 812-817, 2014.
- [9] S. Yokoyama, H. Iizuka, and M. Yamamoto, “Priority Rule-Based Construction Procedure Combined with Genetic Algorithm for Flexible Job-Shop Scheduling Problem,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.6, pp. 892-899, 2015.
- [10] T. Watanabe, T. Kamai, and T. Ishimaru, “Robust Estimation of Camera Homography by Fuzzy RANSAC Algorithm with Reinforcement Learning,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.19, No.6, pp. 833-842, 2015.
- [11] S. Mabu, K. Hirasawa, and J. Hu, “A graph-based evolutionary algorithm: Genetic network programming and its extension using reinforcement learning,” Evolutionary Computation, MIT Press, Vol.15, No.3, pp. 369-398, 2007.
- [12] K. Hirasawa, T. Eguchi, J. Zhou, L. Yu, J. Hu, and S. Markon, “A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming,” IEEE Trans. on Systems, Man and Cybernetics, Part C, Vol.38, No.4, pp. 535-550, 2008.
- [13] Y. Chen, S. Mabu, K. Shimada, and K. Hirasawa, “Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.12, No.4, pp. 383-392, 2008.
- [14] Y. Chen and X. C. Wang, “A Hybrid Stock Trading System Using Genetic Network Programming and Mean Conditional Value-at-Risk,” European J. of Operational Research, Vol.240, pp. 861-871, 2014.
- [15] E. Gonzales, K. Taboada, K. Shimada, S. Mabu, and K. Hirasawa, “Evaluating Class Association Rules using Genetic Relation Programming,” Proc. of the IEEE Congress on Evolutionary Computation 2008, pp. 731-736, 2008.
- [16] Y. Chen and K. Hirasawa, “A Portfolio Selection Model using Genetic Relation Algorithm and Genetic Network Programming,” IEEJ Trans. on Electrical and Electronic Engineering, Vol.6, No.5, pp. 403-413, 2011.
- [17] K. Hirasawa, X. Wang, J. Murata, J. Hu, and C. Z. Jin, “Universal Learning Network and Its Application to Chaos Control,” Neural Networks, Vol.13, No.2, pp. 239-253, 2000.
- [18] K. Hirasawa, J. Murata, J. Hu, and C. Z. Jin, “Universal Learning Network and Its Application to Robust Control,” IEEE Trans. on Systems Man and Cybernetics, Part B, Vol.30, No.3, pp. 419-430, 2000.
- [19] J. R. Koza, Genetic Programming, on the programming of computers by means of natural selection, Cambridge, Mass.: MIT Press, 1992.
- [20] Y. Chen, C. Yue, S. Mabu, and K. Hirasawa, “A Genetic Relation Algorithm with Guided Mutation for the Large-Scale Portfolio Optimization,” Proc. of the ICROS-SICE Int. Joint Conf. 2009, pp. 2579-2584, 2009.
- [21] R. S. Sutton, A. G. Barto, Reinforcement Learning-An Introduction, Cambridge: Massachusetts, London, England, MIT Press, 1998.