Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction
Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/12/743 |
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| author | Ying Li Xiaosha Xue Zhipeng Liu Peibo Duan Bin Zhang |
| author_facet | Ying Li Xiaosha Xue Zhipeng Liu Peibo Duan Bin Zhang |
| author_sort | Ying Li |
| collection | DOAJ |
| description | Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships, existing methods are unable to handle both dynamic and static relationships simultaneously. To address the limitations of prior research, we introduce a novel stock trend forecasting framework capable of mining the causal relationships that affect changes in companies’ stock prices and simultaneously extracts both dynamic and static features to enhance the forecasting performance. Extensive experimental results in the Chinese stock market demonstrate that the proposed framework achieves obvious improvement against multiple state-of-the-art approaches. |
| format | Article |
| id | doaj-art-7ccbbf2d610b46b688b3eb6e816ae557 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-7ccbbf2d610b46b688b3eb6e816ae5572025-08-20T02:50:59ZengMDPI AGInformation2078-24892024-11-01151274310.3390/info15120743Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock PredictionYing Li0Xiaosha Xue1Zhipeng Liu2Peibo Duan3Bin Zhang4College of Software, Northeastern University, Shenyang 110819, ChinaCollege of Software, Northeastern University, Shenyang 110819, ChinaCollege of Software, Northeastern University, Shenyang 110819, ChinaCollege of Software, Northeastern University, Shenyang 110819, ChinaCollege of Software, Northeastern University, Shenyang 110819, ChinaAccurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships, existing methods are unable to handle both dynamic and static relationships simultaneously. To address the limitations of prior research, we introduce a novel stock trend forecasting framework capable of mining the causal relationships that affect changes in companies’ stock prices and simultaneously extracts both dynamic and static features to enhance the forecasting performance. Extensive experimental results in the Chinese stock market demonstrate that the proposed framework achieves obvious improvement against multiple state-of-the-art approaches.https://www.mdpi.com/2078-2489/15/12/743stock predictiongraph neural networksGranger causality |
| spellingShingle | Ying Li Xiaosha Xue Zhipeng Liu Peibo Duan Bin Zhang Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction Information stock prediction graph neural networks Granger causality |
| title | Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction |
| title_full | Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction |
| title_fullStr | Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction |
| title_full_unstemmed | Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction |
| title_short | Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction |
| title_sort | implicit causality exploration enabled graph neural network for stock prediction |
| topic | stock prediction graph neural networks Granger causality |
| url | https://www.mdpi.com/2078-2489/15/12/743 |
| work_keys_str_mv | AT yingli implicitcausalityexplorationenabledgraphneuralnetworkforstockprediction AT xiaoshaxue implicitcausalityexplorationenabledgraphneuralnetworkforstockprediction AT zhipengliu implicitcausalityexplorationenabledgraphneuralnetworkforstockprediction AT peiboduan implicitcausalityexplorationenabledgraphneuralnetworkforstockprediction AT binzhang implicitcausalityexplorationenabledgraphneuralnetworkforstockprediction |