Predict Stock Price with Financial News Based on Recurrent Convolutional Neural Networks

Abstract

People have been interested in making profits from financial stock market prediction. However, stock market forecast has always been a challenging problem because of its uncertainty and volatility. We take a different approach by a model called recurrent convolutional neural networks (RCN) that combines the advantages of convolutions, sequence modeling, word embedding for stock price analysis and information extraction from financial news. We then combine RCN with technical analysis indicators to predict stock price. The results show that the technical analysis model combining with RCN performs better than the technical analysis alone. Besides, the prediction error of RCN is lower than that of Long-short term memory networks.