A model based LSTM and graph convolutional network for stock trend prediction
Stock market is a complex system characterized by collective activity, where interdependencies between stocks have a significant influence on stock price trends. It is widely believed that modeling these dependencies can improve the accuracy of stock trend prediction and enable investors to earn mor...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-09-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2326.pdf |
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| author | Xiangdong Ran Zhiguang Shan Yukang Fan Lei Gao |
| author_facet | Xiangdong Ran Zhiguang Shan Yukang Fan Lei Gao |
| author_sort | Xiangdong Ran |
| collection | DOAJ |
| description | Stock market is a complex system characterized by collective activity, where interdependencies between stocks have a significant influence on stock price trends. It is widely believed that modeling these dependencies can improve the accuracy of stock trend prediction and enable investors to earn more stable profits. However, these dependencies are not directly observable and need to be analyzed from stock data. In this paper, we propose a model based on Long short-term memory (LSTM) and graph convolutional network to capture these dependencies for stock trend prediction. Specifically, an LSTM is employed to extract the stock features, with all hidden state outputs utilized to construct the graph nodes. Subsequently, Pearson correlation coefficient is used to organize the stock features into a graph structure. Finally, a graph convolutional network is applied to extract the relevant features for accurate stock trend prediction. Experiments based on China A50 stocks demonstrate that our proposed model outperforms baseline methods in terms of prediction performance and trading backtest returns. In trading backtest, we have identified a set of effective trading strategies as part of the trading plan. Based on China A50 stocks, our proposed model shows promising results in generating desirable returns during both upward and downward channels of the stock market. The proposed model has proven beneficial for investors to seeking optimal timing and pricing when dealing with shares. |
| format | Article |
| id | doaj-art-9993d19d06fc4094b1fb66e34cee2e57 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-9993d19d06fc4094b1fb66e34cee2e572025-08-20T01:55:12ZengPeerJ Inc.PeerJ Computer Science2376-59922024-09-0110e232610.7717/peerj-cs.2326A model based LSTM and graph convolutional network for stock trend predictionXiangdong Ran0Zhiguang Shan1Yukang Fan2Lei Gao3Beijing Information Technology College, Beijing, ChinaInformatization and Industry Research Department, State Information Center, Beijing, ChinaCollege of Arts and Science, New York University, New York, United States of AmericaStandards and Safety Department, Beijing Big Data Center, Beijing, ChinaStock market is a complex system characterized by collective activity, where interdependencies between stocks have a significant influence on stock price trends. It is widely believed that modeling these dependencies can improve the accuracy of stock trend prediction and enable investors to earn more stable profits. However, these dependencies are not directly observable and need to be analyzed from stock data. In this paper, we propose a model based on Long short-term memory (LSTM) and graph convolutional network to capture these dependencies for stock trend prediction. Specifically, an LSTM is employed to extract the stock features, with all hidden state outputs utilized to construct the graph nodes. Subsequently, Pearson correlation coefficient is used to organize the stock features into a graph structure. Finally, a graph convolutional network is applied to extract the relevant features for accurate stock trend prediction. Experiments based on China A50 stocks demonstrate that our proposed model outperforms baseline methods in terms of prediction performance and trading backtest returns. In trading backtest, we have identified a set of effective trading strategies as part of the trading plan. Based on China A50 stocks, our proposed model shows promising results in generating desirable returns during both upward and downward channels of the stock market. The proposed model has proven beneficial for investors to seeking optimal timing and pricing when dealing with shares.https://peerj.com/articles/cs-2326.pdfLong short-term memoryGraph convolutional networkStock trend predictionStock trading decisions |
| spellingShingle | Xiangdong Ran Zhiguang Shan Yukang Fan Lei Gao A model based LSTM and graph convolutional network for stock trend prediction PeerJ Computer Science Long short-term memory Graph convolutional network Stock trend prediction Stock trading decisions |
| title | A model based LSTM and graph convolutional network for stock trend prediction |
| title_full | A model based LSTM and graph convolutional network for stock trend prediction |
| title_fullStr | A model based LSTM and graph convolutional network for stock trend prediction |
| title_full_unstemmed | A model based LSTM and graph convolutional network for stock trend prediction |
| title_short | A model based LSTM and graph convolutional network for stock trend prediction |
| title_sort | model based lstm and graph convolutional network for stock trend prediction |
| topic | Long short-term memory Graph convolutional network Stock trend prediction Stock trading decisions |
| url | https://peerj.com/articles/cs-2326.pdf |
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