Event Attention Network for Stock Trend Prediction

Abstract

Different news events have different effects on stock price changes. If they are simply fed to the neural network for prediction, the accuracy will be affected. We propose a method to predict stock price trend based on time series news information. First, we extract events from news text and represent them as dense vectors by event embedding technique. Further-more, we employ attention mechanism to figure out event is the main cause of the price fluctuation. Then, we use a Gated Recurrent Unit to model the influence of events on stock market. Experimental results show that our model achieve a certain improvement on S&P500 index compared to baseline methods.