A Stock Closing Price Prediction Model Based on CNN-BiSLSTM
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5360828 |
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| author | Haiyao Wang Jianxuan Wang Lihui Cao Yifan Li Qiuhong Sun Jingyang Wang |
| author_facet | Haiyao Wang Jianxuan Wang Lihui Cao Yifan Li Qiuhong Sun Jingyang Wang |
| author_sort | Haiyao Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-45140dfb9ca944d9b796e716c5f0d9ab |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-45140dfb9ca944d9b796e716c5f0d9ab2025-02-03T06:12:10ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/53608285360828A Stock Closing Price Prediction Model Based on CNN-BiSLSTMHaiyao Wang0Jianxuan Wang1Lihui Cao2Yifan Li3Qiuhong Sun4Jingyang Wang5School of Ocean Mechatronics, Xiamen Ocean Vocational College, Xiamen 361100, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaThe 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaAs 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.http://dx.doi.org/10.1155/2021/5360828 |
| spellingShingle | Haiyao Wang Jianxuan Wang Lihui Cao Yifan Li Qiuhong Sun Jingyang Wang A Stock Closing Price Prediction Model Based on CNN-BiSLSTM Complexity |
| title | A Stock Closing Price Prediction Model Based on CNN-BiSLSTM |
| title_full | A Stock Closing Price Prediction Model Based on CNN-BiSLSTM |
| title_fullStr | A Stock Closing Price Prediction Model Based on CNN-BiSLSTM |
| title_full_unstemmed | A Stock Closing Price Prediction Model Based on CNN-BiSLSTM |
| title_short | A Stock Closing Price Prediction Model Based on CNN-BiSLSTM |
| title_sort | stock closing price prediction model based on cnn bislstm |
| url | http://dx.doi.org/10.1155/2021/5360828 |
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