Stock closing price prediction based on sentiment analysis and LSTM

Abstract

Stock market prediction has been identified as a very important practical problem in the economic field. However, the timely prediction of the market is generally regarded as one of the most challenging problems due to the stock market’s characteristics of noise and volatility. To address these challenges, we propose a deep learning-based stock market prediction model that considers investors’ emotional tendency. First, we propose to involve investors’ sentiment for stock prediction, which can effectively improve the model prediction accuracy. Second, the stock pricing sequence is a complex time sequence with different scales of fluctuations, making the accurate prediction very challenging. We propose to gradually decompose the complex sequence of stock price by adopting empirical modal decomposition (EMD), which yields better prediction accuracy. Third, we adopt LSTM due to its advantages of analyzing relationships among time-series data through its memory function. We further revised it by adopting attention mechanism to focus more on the more critical information. Experiment results show that the revised LSTM model can not only improve prediction accuracy, but also reduce time delay. It is confirmed that investors’ emotional tendency is effective to improve the predicted results; the introduction of EMD can improve the predictability of inventory sequences; and the attention mechanism can help LSTM to efficiently extract specific information and current mission objectives from the information ocean.

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Acknowledgements

This work was supported by National Nature Science Foundation of China (NSFC) under Project 71502125.

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Correspondence to Yuhong Liu.

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Jin, Z., Yang, Y. & Liu, Y. Stock closing price prediction based on sentiment analysis and LSTM. Neural Comput & Applic 32, 9713–9729 (2020). https://doi.org/10.1007/s00521-019-04504-2

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Keywords

  • Stock market prediction
  • Long short-term memory
  • Attention mechanism
  • Empirical mode decomposition