Impact of News on the Trend of Stock Price Change: an Analysis based on the Deep Bidirectiona LSTM Model

Abstract

With the in-depth study of the stock market, the impact of news media on stock prices has gradually been paid attention to. However, in previous studies, most of them used the real-time rise and fall of stock prices to reflect the impact of news on stock prices, ignoring that it takes time for investors to react to news. In this paper, considering the lag of investors’ response to stock price, we choose BIAS as a measure index after news happened for a period of time to analyze the impact of news media on stock price trends. Based on the DBLSTM (Deep Bidirectional Long Short-Term Memory) model, we establish a model to predict the short-term trend of stock prices using news text data. Experiments show that the model we adopted outperforms other models in terms of prediction accuracy.

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