A CNN-BiLSTM-AM method for stock price prediction
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A CNN-BiLSTM-AM method for stock price prediction Wenjie Lu1,2
•
Jiazheng Li3 • Jingyang Wang3 • Lele Qin1
Received: 29 August 2020 / Accepted: 11 November 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
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 CNNBiLSTM-AM method is more suitable for the prediction of stock price and for providing a reliable way for investors’ to make stock investment decisions. Keywords CNN BiLSTM AM Stock price prediction
1 Introduction
& Wenjie Lu [email protected] Jiazheng Li [email protected] Jingyang Wang [email protected] Lele Qin [email protected] 1
School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China
2
Business School, Jiangsu Second Normal University, Nanjing 210000, China
3
School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
The stock market is a place where stocks can be transferred, traded, and circulated. It has a history of 400 years and can be used as a channel for companies to raise funds [1]. By issuing stocks, a large amount of capital flows into the stock market. This promotes the concentration of capital, improves the organic composition of enterprise capital and greatly promotes the development of the commodity economy. Therefore, the stock market is regarded as a barometer of economic and financial activities in a country or region [2]. The Chinese stock market started later than the western stock market. The Chinese stock market was established in the early 1990s. Although the Chinese stock market star
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