{"type":"ScholarlyArticle","title":"COGRASP: Co-Occurrence Graph Based Stock Price Forecasting","authors":[{"name":"Zhengze Li","slug":"zhengze-li","given":"Zhengze","family":"Li","affiliation":""},{"name":"Zilin Song","slug":"zilin-song","given":"Zilin","family":"Song","affiliation":""},{"name":"Tingting Yuan","slug":"tingting-yuan","given":"Tingting","family":"Yuan","affiliation":""},{"name":"Xiaoming Fu","slug":"xiaoming-fu","given":"Xiaoming","family":"Fu","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/837","url":"https://ijcai.org/proceedings/2025/837","pdf":"https://ijcai.org/proceedings/2025/0837.pdf","markdown":"https://ijcai.org/proceedings/2025/837.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/837","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":837,"pages":{"first":7527,"last":7535},"keywords":["Multidisciplinary Topics and Applications: MTA: Finance","Data Mining: DM: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}