The application of echo state network in stock data mining

Authors:
Xiaowei Lin;Zehong Yang;Yixu Song
Affiliations:
State Key Laboratory of Intelligent Technology and System, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and System, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...;State Key Laboratory of Intelligent Technology and System, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, ...
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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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.