Neural Network Method to Predict Stock Price Movement Based on Stock Information Entropy

  • Xun Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


In this paper, the neural network is employed to learn the complicated association of the stock information stream with stock price movement. In general, the positive stream of stock information is assumed to stimulate buying and increase the stock prices, and the negative stream of stock information is assumed to result in selling and decrease the stock prices. Based on the stock information entropy, the intensity for stock information stream is measured and applied to associating with the stock price movement with the aid of neural networks. Experiments illustrate the associations statistically. The results are helpful in probing the microstructure of the stock markets from a new angle.


Stock Market Stock Price Natural Language Processing Earning Announcement Trained Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xun Liang
    • 1
    • 2
  1. 1.Institute of Computer Science and TechnologyPeking UniversityBeijingChina
  2. 2.Department of Economics and Operations ResearchStanford UniversityPalo AltoUSA

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