Abstract
As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN‐BiSLSTM to predict the closing price of the stock. Bidirectional special long short‐term memory (BiSLSTM) improved on bidirectional long short‐term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN‐BiSLSTM. CNN‐BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short‐term memory (LSTM), BiLSTM, CNN‐LSTM, and CNN‐BiLSTM. The experimental results show that the mean absolute error (MAE), root‐mean‐squared error (RMSE), and R‐square (R2) evaluation indicators of the CNN‐BiSLSTM are all optimal. Therefore, CNN‐BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.
Highlights
Stock predicting research is an applied research direction of financial big data
To predict stock closing price more accurately, this paper proposes a stock prediction model based on CNNBiSLSTM, which uses stock data of the last five trading days to predict the closing price of the trading day
From the errors between the predicted value and the true value, we can conclude that the convolutional neural network (CNN)-Bidirectional special long short-term memory (BiSLSTM) has the best fitting degree, and multilayer perceptron (MLP) is the worst. e MLP is not suitable for processing time series data. e mean absolute error (MAE), root-mean-squared error (RMSE), and R2 performance of the MLP are all worse than other models
Summary
With the rapid growth of China’s economy and the continuous expansion of the financial market, more and more investors have begun to pay attention to the methods to improve return on investment and effectively avoid certain risks. Among these methods, the stock price prediction is of great significance in the commercial and financial fields [1, 2]. E traditional analysis method is to use the existing stock data and relevant technical charts, combined with the investor’s own experience to predict the stock price This method is not applicable in today’s increasingly large and complex stock market. In addition to low efficiency and excessive reliance on manual experience, there are a series of problems such as poor integrity of stock content information and feature data redundancy. e utilization rate of stock data is low, and the effect is not good, so it is difficult to meet the needs of market development
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