A CNN-LSTM-Based Model to Forecast Stock Prices
Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6622927 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567735339450368 |
|---|---|
| author | Wenjie Lu Jiazheng Li Yifan Li Aijun Sun Jingyang Wang |
| author_facet | Wenjie Lu Jiazheng Li Yifan Li Aijun Sun Jingyang Wang |
| author_sort | Wenjie Lu |
| collection | DOAJ |
| description | Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. |
| format | Article |
| id | doaj-art-7aee1b40204a487890cd0f6d38fa9b59 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7aee1b40204a487890cd0f6d38fa9b592025-02-03T01:00:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66229276622927A CNN-LSTM-Based Model to Forecast Stock PricesWenjie Lu0Jiazheng Li1Yifan Li2Aijun Sun3Jingyang Wang4Business School, Jiangsu Second Normal University, Nanjing 210000, 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, ChinaBusiness School, Jiangsu Second Normal University, Nanjing 210000, ChinaSchool of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, ChinaStock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.http://dx.doi.org/10.1155/2020/6622927 |
| spellingShingle | Wenjie Lu Jiazheng Li Yifan Li Aijun Sun Jingyang Wang A CNN-LSTM-Based Model to Forecast Stock Prices Complexity |
| title | A CNN-LSTM-Based Model to Forecast Stock Prices |
| title_full | A CNN-LSTM-Based Model to Forecast Stock Prices |
| title_fullStr | A CNN-LSTM-Based Model to Forecast Stock Prices |
| title_full_unstemmed | A CNN-LSTM-Based Model to Forecast Stock Prices |
| title_short | A CNN-LSTM-Based Model to Forecast Stock Prices |
| title_sort | cnn lstm based model to forecast stock prices |
| url | http://dx.doi.org/10.1155/2020/6622927 |
| work_keys_str_mv | AT wenjielu acnnlstmbasedmodeltoforecaststockprices AT jiazhengli acnnlstmbasedmodeltoforecaststockprices AT yifanli acnnlstmbasedmodeltoforecaststockprices AT aijunsun acnnlstmbasedmodeltoforecaststockprices AT jingyangwang acnnlstmbasedmodeltoforecaststockprices AT wenjielu cnnlstmbasedmodeltoforecaststockprices AT jiazhengli cnnlstmbasedmodeltoforecaststockprices AT yifanli cnnlstmbasedmodeltoforecaststockprices AT aijunsun cnnlstmbasedmodeltoforecaststockprices AT jingyangwang cnnlstmbasedmodeltoforecaststockprices |