Abstract
In the financial markets, accurate prediction of stocks is crucial for formulating investment strategies. Previous research predominantly relied on a stock's historical information for prediction, but overlooked the cross-effects between stocks. However, stocks are closely connected rather than independent of each other. This work introduces a deep learning framework named StockGCN for stock prediction, which can be easily extended by integrating other modules. By constructing a stock graph structure, the model transforms the prediction of individual stocks into the prediction of the entire graph. Experiments show that StockGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale stock networks and consistently outperforms state-of-the-art baselines on real-world stock datasets.
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Acknowledgements
This work was supported by the Natural Science Foundation of Tianjin (No. 21JCYBJC00640) and by the 2023 CCF-Baidu Songguo Foundation (CCF-BAIDU 202311).
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Xin, C., Han, Q., Pan, G. (2024). Correlation Matters: A Stock Price Predication Model Based on the Graph Convolutional Network. In: Huang, DS., Si, Z., Pan, Y. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14864. Springer, Singapore. https://doi.org/10.1007/978-981-97-5588-2_20
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DOI: https://doi.org/10.1007/978-981-97-5588-2_20
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