Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
This work addresses stock market prediction for investors and analysts, but it is incremental as it applies existing NLP and deep learning techniques to a financial domain.
The paper tackled stock price prediction by applying word embeddings and deep neural networks to financial news, resulting in significantly improved accuracy over a baseline using only historical price data on a standard financial database.
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.