---
title: "COGRASP: Co-Occurrence Graph Based Stock Price Forecasting"
authors:
  - "Zhengze Li"
  - "Zilin Song"
  - "Tingting Yuan"
  - "Xiaoming Fu"
doi: "10.24963/ijcai.2025/837"
url: "https://ijcai.org/proceedings/2025/837"
pdf: "https://ijcai.org/proceedings/2025/0837.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "7527-7535"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: MTA: Finance"
  - "Data Mining: DM: Applications"
---

# COGRASP: Co-Occurrence Graph Based Stock Price Forecasting

Zhengze Li, Zilin Song, Tingting Yuan, Xiaoming Fu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 7527-7535. DOI: [10.24963/ijcai.2025/837](https://doi.org/10.24963/ijcai.2025/837)

## 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.