A Stock Closing Price Prediction Model Based on CNN‐BiSLSTM

Haiyao Wang · Jianxuan Wang · Lihui Cao · Yifan Li · Qiuhong Sun · Jingyang Wang

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

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 ( R 2 ) 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.

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References

2017Deep learning with long short-term memory networks for financial market predictions 2017Stock market's price movement prediction with LSTM neural networks 2019CNNpred: CNN-based stock market prediction using a diverse set of variables 2020Stock Market Prediction Using LSTM Recurrent Neural Network 2020A CNN-LSTM-Based Model to Forecast Stock Prices 2019Literature review: Machine learning techniques applied to financial market prediction 2019Stock closing price prediction based on sentiment analysis and LSTM 2016Deep learning for stock prediction using numerical and textual information

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2023Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models 2023Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices 2022Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions 2024Enhancing Stock Market Prediction: A Robust LSTM-DNN Model Analysis on 26 Real-Life Datasets 2024Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

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