CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction
Recent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2402 |
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| author | Shanghui Jia Han Gao Jiaming Huang Yingke Liu Shangzhe Li |
| author_facet | Shanghui Jia Han Gao Jiaming Huang Yingke Liu Shangzhe Li |
| author_sort | Shanghui Jia |
| collection | DOAJ |
| description | Recent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlook charts from other indicators and their relationships, resulting in underutilized information for predicting stock. Therefore, we design a novel framework to address the underutilized information limitations within technical charts generated by different indicators. Specifically, different sequences of stock indicators are used to generate various technical charts, and an adaptive relationship graph learning layer is employed to learn the relationships among technical charts generated by different indicators. Finally, by applying a GNN model combined with the relationship graphs of diverse technical charts, temporal patterns of stock indicator sequences are captured, fully utilizing the information between various technical charts to achieve accurate stock price predictions. Additionally, we further tested our framework with real-world stock data, showing superior performance over advanced baselines in predicting stock prices, achieving the highest net value in trading simulations. Our research results not only complement the existing applications of non-singular technical charts in deep learning but also offer backing for investment applications in financial market decision-making. |
| format | Article |
| id | doaj-art-2e31d10f79cf494cb790c5b081d13b45 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-2e31d10f79cf494cb790c5b081d13b452025-08-20T03:36:31ZengMDPI AGMathematics2227-73902025-07-011315240210.3390/math13152402CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price PredictionShanghui Jia0Han Gao1Jiaming Huang2Yingke Liu3Shangzhe Li4School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, ChinaSchool of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, ChinaSchool of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, ChinaInstitute of Beijing Digital Economy Development, Capital University of Economics and Business, Beijing 100070, ChinaSchool of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, ChinaRecent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlook charts from other indicators and their relationships, resulting in underutilized information for predicting stock. Therefore, we design a novel framework to address the underutilized information limitations within technical charts generated by different indicators. Specifically, different sequences of stock indicators are used to generate various technical charts, and an adaptive relationship graph learning layer is employed to learn the relationships among technical charts generated by different indicators. Finally, by applying a GNN model combined with the relationship graphs of diverse technical charts, temporal patterns of stock indicator sequences are captured, fully utilizing the information between various technical charts to achieve accurate stock price predictions. Additionally, we further tested our framework with real-world stock data, showing superior performance over advanced baselines in predicting stock prices, achieving the highest net value in trading simulations. Our research results not only complement the existing applications of non-singular technical charts in deep learning but also offer backing for investment applications in financial market decision-making.https://www.mdpi.com/2227-7390/13/15/2402technical chartsgraph neural networkstock price predictioncross-chart relationshipsadaptive graph learning |
| spellingShingle | Shanghui Jia Han Gao Jiaming Huang Yingke Liu Shangzhe Li CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction Mathematics technical charts graph neural network stock price prediction cross-chart relationships adaptive graph learning |
| title | CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction |
| title_full | CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction |
| title_fullStr | CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction |
| title_full_unstemmed | CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction |
| title_short | CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction |
| title_sort | cirgnn leveraging cross chart relationships with a graph neural network for stock price prediction |
| topic | technical charts graph neural network stock price prediction cross-chart relationships adaptive graph learning |
| url | https://www.mdpi.com/2227-7390/13/15/2402 |
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