A Stock Closing Price Prediction Model Based on CNN‐BiSLSTM

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.

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

“…Hybrid DL approaches frequently combine DL techniques with traditional methods [71][72][73][74][75] or DL architectures with each other, such as CNN-LSTM, LSTM or BiLSTM with attention mechanisms (AMs), transformer models, and graph convolutional neural network (GraphCNN). [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90] These hybrid DL models prove to be efficient in identifying complex patterns and relationships in data due to the high capacity and adaptability of DL architectures, especially in applications like SPF. Chandar 71 proposed a new method for stock trading by combining technical indicators and CNNs, termed TI-CNN.…”

Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning

“…Concerning previous studies [58], conducted a study on sales forecasting by using tabular data, such as e-commerce transaction history, and the highest prediction accuracy among all classifiers used in the study (ARIMA, FE+GBRT, DNN, and CNN) was achieved using the CNN. [59] researched stock price prediction by using tabular data, such as stock trading volume, closing price, and market price, and the highest prediction accuracy among all classifiers used in the study (MLP, RNN, LSTM, and CNN) was achieved using the CNN. [60] conducted a study on predicting treatment behavior in patients by using tabular data, such as patient information, and the highest prediction accuracy among all classifiers used in the study (ANN, LR, SVM, DT, RFT, CNN) was achieved using the CNN.…”

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

“…Gao et al [17] innovatively integrated multiple technical indicators, including financial data, to compare the stock market prediction performance between LSTM and GRU under different parameters. Wang et al [18] proposed a convolutional neural network (CNN)-bidirectional special long short-term memory (BiSLSTM) composite model to predict the closing price of stocks in the coming day. e CNN is in charge of capturing the characteristics of the input data in this model, and the bidirectional long short-term memory (BiLSTM) is employed to consider the changing patterns of the historical data.…”