Proceedings

COGRASP: Co-Occurrence Graph Based Stock Price Forecasting

Zhengze Li, Zilin Song, Tingting Yuan, Xiaoming Fu

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7527-7535.

https://doi.org/10.24963/ijcai.2025/837

Keywords

  • Multidisciplinary Topics and Applications: MTA: Finance
  • Data Mining: DM: Applications

Abstract

Forecasting stock prices is complex and challenging. Uncovering correlations among stocks has proven to enhance stock price forecasting. However, existing correlation discovery methods, such as concept-based methods, are slow, inaccurate, and limited by their reliance on predefined concepts and manual analysis. In this paper, we propose COGRASP, a novel approach for stock price forecasting that constructs stock co-occurrence graphs automatically by analyzing rapidly updated sources such as reports, newspapers, and social media. Besides, we aggregate forecasts across multiple timescales (i.e., long-, medium-, and short-term) to capture multi-timescale trends fluctuations, thereby enhancing price forecasting accuracy. In experiments with real-world open-source stock market data, COGRASP outperforms state-of-the-art methods.

Zhengze Li, Zilin Song, Tingting Yuan, and Xiaoming Fu. 2025. COGRASP: Co-Occurrence Graph Based Stock Price Forecasting. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7527–7535. https://doi.org/10.24963/ijcai.2025/837
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