Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.
REF used a multi layer neural network to predict the stock market and found that incorporating textual features from financial news can improve the accuracy of prediction.
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
ArXiv
ArXiv
Computer Science Mathematics