Stock Market Prediction with BP Networks
Cheng Wu · Basic Automation · 2002
In this paper the performance of back propagation neural networks applied to the problem of predicting stock market prices is evaluated. In contrast to previous investigations, the training data used in our experiments is not exclusively based on stock market prices, but also incorporates a variety of other economical factors. By the analysis of different network architectures, the best prediction is chosen.