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Applying Data Mining Techniques for the Stock Price Prediction

Wang Ying, Zhou Yan, Qi Fei, Zhang Haifeng


This research aims to provide intelligent tool to predict stock price which shall provide service for the investment decision-making of stock traders. The data sets of Beijing Shougang Company from 2012 to 2013 were employed for the analysis and prediction as sample data. The combined model of principal component analysis algorithm and BP artificial neural networks algorithm were established. At last, we got the predicted stock price of Beijing Shougang Company from 2012 to 2013 by using the built model and prepared data. The predicted stock price was used to compare with the real value of stock price. The comparison indicated that data mining technique is an appropriate and sufficiently sensitive method to predict stock price.


Data mining, stock price prediction, Principal component analysis, BP artificial neural networks.

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