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Dynamic correlation graph convolution network with embedded temporal correlation extraction for stock price forecasting
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AI
DOI：10.1016/j.engappai.2025.112370.png)
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Abstract
En 中文
Stock price forecasting has become a significant and complex research area within financial technology. The dynamic correlations among stocks and the inherent noise in price volatility present considerable challenges in accurately forecasting stock prices and enhancing investment returns. This paper introduces a novel Dynamic Correlation Graph Convolution Network (DyCGCN) with embedded temporal correlation extraction. First, we propose a dual-scale dynamic graph generation method to capture the topological relationships among stocks. Second, we develop a dynamic correlation-temporal convolution module that extracts high-level temporal correlations. Third, we introduce a prospect theory-guided multi-strategy loss function that accommodates the diverse risk preferences of investors. Furthermore, we present a joint regression-classification learning method to extract and leverage stock trend information. Experiments conducted on four real-world datasets demonstrate the superiority of DyCGCN, achieving an average 24.7% reduction in prediction error and a 10.5% improvement in predictive accuracy over baseline models, underscoring its strong potential for practical stock price forecasting.
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