Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 38, Issue 11, Pages 14346-14355Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.04.222
Keywords
Wavelet de-noising; BP neural network; WDBP neural network; Stock prices
Categories
Funding
- Ministry of Education overseas cooperation [Z2007-1- 62012]
- Fundamental Research Fund for Physics and Mathematics of Lanzhou University [LZULL200910]
- NSERC of Canada [RGPIN197319]
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Stock prices as time series are non-stationary and highly-noisy due to the fact that stock markets are affected by a variety of factors. Predicting stock price or index with the noisy data directly is usually subject to large errors. In this paper, we propose a new approach to forecasting the stock prices via the Wavelet De-noising-based Back Propagation (WDBP) neural network An effective algorithm for predicting the stock prices is developed. The monthly closing price data with the Shanghai Composite Index from January 1993 to December 2009 are used to illustrate the application of the WDBP neural network based algorithm in predicting the stock index. To show the advantage of this new approach for stock index forecast, the WDBP neural network is compared with the single Back Propagation (BP) neural network using the real data set. (C) 2011 Elsevier Ltd. All rights reserved.
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