{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T04:55:04Z","timestamp":1781499304118,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/837","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"7527-7535","source":"Crossref","is-referenced-by-count":1,"title":["COGRASP: Co-Occurrence Graph Based Stock Price Forecasting"],"prefix":"10.24963","author":[{"given":"Zhengze","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Computer Science, University of G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilin","family":"Song","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Yuan","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of G\u00f6ttingen, Germany"},{"name":"Institute of Digitalisation and Informatics, IMC Krems University of Applied Sciences, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:16Z","timestamp":1758627316000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/837"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/837","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}