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Forecasting stock prices with long-short term memory neural network based on attention mechanism

Author

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  • Jiayu Qiu
  • Bin Wang
  • Changjun Zhou

Abstract

The stock market is known for its extreme complexity and volatility, and people are always looking for an accurate and effective way to guide stock trading. Long short-term memory (LSTM) neural networks are developed by recurrent neural networks (RNN) and have significant application value in many fields. In addition, LSTM avoids long-term dependence issues due to its unique storage unit structure, and it helps predict financial time series. Based on LSTM and an attention mechanism, a wavelet transform is used to denoise historical stock data, extract and train its features, and establish the prediction model of a stock price. We compared the results with the other three models, including the LSTM model, the LSTM model with wavelet denoising and the gated recurrent unit(GRU) neural network model on S&P 500, DJIA, HSI datasets. Results from experiments on the S&P 500 and DJIA datasets show that the coefficient of determination of the attention-based LSTM model is both higher than 0.94, and the mean square error of our model is both lower than 0.05.

Suggested Citation

  • Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    • Handle: RePEc:plo:pone00:0227222
    DOI: 10.1371/journal.pone.0227222
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    References listed on IDEAS

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    1. Huicheng Liu, 2018. "Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network," Papers 1811.06173, arXiv.org.
    2. Diego Ardila & Didier Sornette, 2016. "Dating the Financial Cycle: A Wavelet Proposition," Swiss Finance Institute Research Paper Series 16-29, Swiss Finance Institute, revised May 2016.
    3. Ardila, Diego & Sornette, Didier, 2016. "Dating the financial cycle with uncertainty estimates: a wavelet proposition," Finance Research Letters, Elsevier, vol. 19(C), pages 298-304.
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