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.
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