CIRGNN: Leveraging Cross-Chart Relationships with a Graph Neural Network for Stock Price Prediction

Shanghui Jia, Han Gao, Jiaming Huang, Yingke Liu and Shangzhe Li ()
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Shanghui Jia: School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, China
Han Gao: School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, China
Jiaming Huang: School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, China
Yingke Liu: Institute of Beijing Digital Economy Development, Capital University of Economics and Business, Beijing 100070, China
Shangzhe Li: School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 102206, China

Mathematics, 2025, vol. 13, issue 15, 1-20

Abstract: 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.

Keywords: technical charts; graph neural network; stock price prediction; cross-chart relationships; adaptive graph learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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