Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning
Yan Chen, Shingo Mabu, Kaoru Shimada, and Kotaro Hirasawa
Graduate school of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka
-  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.
-  T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “Study of evolutionary multiagent models based on symbiosis,” IEEE Trans. Syst., Man and Cybern. B, Vol.36, No.1, pp. 179-193, 2006.
-  J. H. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor, University of Michigan Press, 1975.
-  D. E. Goldberg, “Genetic Algorithm in search, optimization and machine learning,” Addison-Wesley, 1989.
-  J. R. Koza, “Genetic Programming, on the programming of computers by means of natural selection,” Cambridge, Mass., MIT Press, 1992.
-  J. R. Koza, “Genetic Programming II, Automatic Discovery of Reusable Programs,” Cambridge, Mass., MIT Press, 1994.
-  R. S. Sutton and A. G. Barto, “Reinforcement Learning -An Introduction,” Cambridge, Massachusetts, London, England, MIT Press, 1998.
-  N. Baba, N. Inoue, and Y. Yanjun, “ Utilization of soft computing techniques for constructing reliable decision support systems for dealing stocks,” in Proc. of Int. Joint Conf. on Neural Networks, 2002.
-  J.-Y. Potvin, P. Soriano, and M. Vallee, “Generating trading rules on the stock markets with genetic programming,” Computers & Operations Research, Vol.31, pp. 1033-1047, 2004.
-  K. J. Oh, T. Y. Kim, S.-H. Min, and H. Y. Lee, “Portfolio algorithm based on portfolio beta using genetic algorithm,” Expert Systems with Application, Vol.30, pp. 527-534, 2006.
-  S. Mabu, H. Hatakeyama, M. T. Thu, K. Hirasawa, and J. Hu, “Genetic Network Programming with Reinforcement Learning and Its Application to Making Mobile Robot Behavior,” IEEJ Trans. EIS, Vol.126, No.8, pp. 1009-1015, 2006.
-  K. H. Lee and G. S. Jo, “Expert system for predicting stock market timing using a candlestick chart,” Expert Systems with Applications, Vol.16, pp. 357-364, 1999.
-  Y. Izumi, T. Yamaguchi, S. Mabu, K. Hirasawa, and J. Hu, “Trading Rules on the Stock Market using Genetic Network Programming with Candlestick Chart,” 2006 IEEE Congress on Evolutionary Computation, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, pp. 8531-8536, July 16-21, 2006.
-  S. Mabu, Y. Izumi, K. Hirasawa, and T. Furuzuki, “Trading Rules on Stock Markets Using Genetic Network Progamming with Candle Chart,” T. SICE, Vol.43, No.4, pp. 317-322, 2007 (in Japanese).
-  Y. Izumi, K. Hirasawa, and T. Furuzuki, “Trading Rules on the Stock Markets Using Genetic Network Progamming with Importance Index,” T. SICE, Vol.42, No.5, pp. 559-566, 2006 (in Japanese).
-  V. Dhar, “A Comparison of GLOWER and Other Machine Learning Methods for Investment Decision Making,” Springer Berlin Press, pp.208-220, 2001.
-  S. Duerson, F. S. Khan, V. Kovalev, and A. H. Malik, “Reinforcement Learning in Online Stock Trading Systems,” 2005.
-  S. Pafka, M. Potters, and I. Kondor, “Exponential Weighting and Random-Matrix-Theory-Based Filtering of Financial Covariance Matrices for Portfolio Optimization,” arXiv:cond-mat/0402573v1, 2004. Quantitative Finance, (to be appeared).
-  N. Basalto, R. Bellotti, F. De Carlo, P. Facchi, and S. Pascazio, “Clustering stock market companies via chaotic map synchronization,” Physica A, 345, p. 196, arXiv:cond-mat/0404497v1, 2005.
-  W. Huang, Y. Nakamori, and S. Y. Wang, “Forecasting stock market movement direction with support vector machine Source,” Computers and Operations Research, Vol.32, Issue 10, pp. 2513-2522, 2005.
-  M. B. Porecha, P. K. Panigrahi, J. C. Parikh, C. M. Kishtawal, and S. Basu, “Forecasting non-stationary financial time series through genetic algorithm,” arXiv:nlin/0507037v1, 2005.
-  M. H. Jensen, A. Johansen, F. Petroni, and I. Simonsen, “Inverse Statistics in the Foreign Exchange Market,” Physica A, 340, p. 678, arXiv:cond-mat/0402591v2, 2004.
-  T. Mikosch and C. Starica, “Stock Market Risk-Return Inference. An Unconditional Non-parametric Approach,” SSRN Working Paper Series, 2004.
-  H. Iba and T. Sasaki, “Using Genetic Programming to Predict Financial Data,” Proc. of the Congress of Evolutionary Computation, pp. 244-251, 2001.
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