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A CNN-BiLSTM-AM method for stock price prediction

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Abstract

In recent years, with the rapid development of the economy, more and more people begin to invest into the stock market. Accurately predicting the change of stock price can reduce the investment risk of stock investors and effectively improve the investment return. Due to the volatility characteristics of the stock market, stock price prediction is often a nonlinear time series prediction. Stock price is affected by many factors. It is difficult to predict through a simple model. Therefore, this paper proposes a CNN-BiLSTM-AM method to predict the stock closing price of the next day. This method is composed of convolutional neural networks (CNN), bi-directional long short-term Memory (BiLSTM), and attention mechanism (AM). CNN is used to extract the features of the input data. BiLSTM uses the extracted feature data to predict stock closing price of the next day. AM is used to capture the influence of feature states on the stock closing price at different times in the past to improve the prediction accuracy. In order to prove the effectiveness of this method, this method and other seven methods are used to predict the stock closing price of the next day for 1000 trading days of the Shanghai Composite Index. The results show that the performance of this method is the best, MAE and RMSE are the smallest (which are 21.952 and 31.694). R2 is the largest (its value is 0.9804). Compared with other methods, the CNN-BiLSTM-AM method is more suitable for the prediction of stock price and for providing a reliable way for investors’ to make stock investment decisions.

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Funding

This work was funded by Key projects of Humanities and Social Sciences in Colleges and universities of Hebei Province, Grant SD201010, Soft science special project of Hebei Province innovation ability improvement program, Grant 205576142D, and Foundation of Hebei University of Science and Technology, Grant 2019-ZDB02.

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Authors and Affiliations

  1. School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Wenjie Lu & Lele Qin

  2. Business School, Jiangsu Second Normal University, Nanjing, 210000, China

    Wenjie Lu

  3. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Jiazheng Li & Jingyang Wang

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  1. Wenjie Lu
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  2. Jiazheng Li
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Correspondence to Wenjie Lu.

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Lu, W., Li, J., Wang, J. et al. A CNN-BiLSTM-AM method for stock price prediction. Neural Comput & Applic 33, 4741–4753 (2021). https://doi.org/10.1007/s00521-020-05532-z

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