Abstract
Stock data, which is among the most complicated time series, is difficult to analyze and mine. Neural network has been a popular method for data mining in financial area since last decade. In this paper, we explore the use of Echo State Networks (ESNs) to perform time-series mining on stock markets. The Hurst exponent is applied to adaptively determine initial transient and choose sub-series with greatest predictability before training. With the capability of short-term memory provided by ESN, a stock prediction system is built to forecast the close price of the next trading day based on history prices and technical indicators. The experiment results on S&P 500 data set suggest that ESN outperforms other conventional neural networks in most cases and is a suitable and effective way for stock price mining.
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References
Kim, H.-j., Shin, K.-s.: A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Applied Soft Computing 7(2), 569–576 (2007)
Tan, T.Z., Quek, C., Geok See, N.G.: Biological brain-inspired genetic complementary learning for stock market and bank failure prediction. Computational Intelligence 23(2), 236–261 (2007)
Jaeger, H., Haas, H.: Harnessing Nolinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communications. Science 3 (2004)
Jaeger, H.: Short term memory in echo state networks GMD-Report 152. GMD-German National Research Institute for Computer Science (2002)
Ishii, K., van der Zant, T., Becanovic, V., Ploger, P.: Optimization of Parameters of Echo State Network and Its Application to Underwater Robot. In: SICE Annual Conference in Sapporo, vol. 3, pp. 2800–2805 (2004)
Jaeger, H., Lukosevicius, M., Popovici, D.: Optimization and applications of echo State networks with leaky integrator neurons. Neural Networks 20, 335–352 (2007)
Skowronski, M.D., Harris, J.G.: Automatic speech recognition using a predictive echo state network classifier. Neural Networks 20, 414–423 (2007)
Jaeger, H.: The Echo State Approach to Analyzing and Training Recurrent Neural Networks GMD-German National Research Institute for Computer Science, vol. GMD Report 148 (2001)
Hurst, H.E.: Long-term storage of reservoirs: an experimental study. Trans Amer. Soc. Civil Engi. 116, 770–799 (1951)
Qian, B., Rasheed, K.: Stock market prediction with multiple classifiers. Applied Intelligence 26(1), 25–33 (2007)
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© 2008 Springer-Verlag Berlin Heidelberg
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Lin, X., Yang, Z., Song, Y. (2008). The Application of Echo State Network in Stock Data Mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_95
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DOI: https://doi.org/10.1007/978-3-540-68125-0_95
Publisher Name: Springer, Berlin, Heidelberg
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