Abstract
Financial news shows significant impacts on stock movement. Previous stock movement prediction models mainly incorporated textual information without considering text quality, resulting in irrelevant text misleading prediction. Meanwhile, the models do not provide key news about the stock market to help investors make more rational investment decisions based on textual information. In this paper, we propose a framework for incorporating news summaries into stock predictions (SumSP) via graphical learning. It uses a graph-clustering mechanism to extract financial news closely related to stock price fluctuations as summaries and then predicts stock price movement based on the impact of the summaries. The model ultimately yields meaningful, thematically diverse and economically meaningful summaries, as well as better prediction results. Experiments demonstrate the effectiveness of our model, comparing to state-of-the-art methods (The code will be released at https://github.com/JinHanLei/SumSP).
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Acknowledgements
This work has been supported by the National Natural Science Foundation of China (NSFC) (71671141, 71873108), Fundamental Research Funds for the Central Universities (JBK 171113, JBK 170505, JBK 1806003), Sichuan Province Science and Technology Department (2019YJ0250), and the Financial Innovation Center of the Southwestern University of Finance and Economics.
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Jin, H., Wang, J., Tan, J., Chen, J., Shu, T. (2022). Incorporating News Summaries for Stock Predictions via Graphical Learning. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_29
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