论文信息 - The Application of Echo State Network in Stock Data Mining

The Application of Echo State Network in Stock Data Mining

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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