Neural Network Prediction of Stock Price Trend Based on RS with Entropy Discretization
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Huang Haiqing, Gan Xusheng, Lei Lei
To accurately predict the stock price trend, an integration prediction method based on Rough Set (RS) and BP neural network are proposed. In the method, RS is firstly applied to reduce the features of stock price trend, and an entropy-based discretization algorithm is introduced to process the continuous attribute data, then, on this basis, BP neural network is used to establish the prediction model of stock price trend. The validation result indicates that, by RS attribute reduction, the prediction model of BP neural network for stock price trend can be simplified with performance improvement. The prediction result is better than those of traditional neural network and RBF neural network. This verifies its feasibility and effectiveness.
BP Neural Network, Rough Set, Stock Price Trend, Discretization, Attribute Reduction.