Neural network method to predict stock price movement based on stock information entropy

Authors:
Xun Liang
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
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Abstract

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.