{"type":"ScholarlyArticle","title":"Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling","authors":[{"name":"Liang Zhao","slug":"liang-zhao","given":"Liang","family":"Zhao","affiliation":""},{"name":"Wei Li","slug":"wei-li","given":"Wei","family":"Li","affiliation":""},{"name":"Ruihan Bao","slug":"ruihan-bao","given":"Ruihan","family":"Bao","affiliation":""},{"name":"Keiko Harimoto","slug":"keiko-harimoto","given":"Keiko","family":"Harimoto","affiliation":""},{"name":"Yunfang Wu","slug":"yunfang-wu","given":"Yunfang","family":"Wu","affiliation":""},{"name":"Xu Sun","slug":"xu-sun","given":"Xu","family":"Sun","affiliation":""}],"abstract":"Trading volume movement prediction is the key in a variety of financial applications.  Despite its importance, there is few  research  on  this  topic  because  of  its  requirement for comprehensive understanding of information  from  different  sources.   For  instance,  the  relation between multiple stocks,  recent transaction data and suddenly released events are all essential for understanding trading market.  However, most of the previous methods only take the fluctuation information  of  the  past  few  weeks  into  consideration, thus yielding poor performance.  To handle this issue, we propose a graph-based approach that can incorporate multi-view information, i.e., long-term stock trend, short-term fluctuation and sudden events information jointly into a temporal heterogeneous graph.   Besides,  our method is equipped with deep canonical analysis to highlight the correlations between different perspectives of fluctuation for better prediction.  Experiment results show that our method outperforms strong baselines by a large margin.","doi":"10.24963/ijcai.2021/518","url":"https://ijcai.org/proceedings/2021/518","pdf":"https://ijcai.org/proceedings/2021/0518.pdf","markdown":"https://ijcai.org/proceedings/2021/518.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/518","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":518,"pages":{"first":3764,"last":3770},"keywords":["Machine Learning: Time-series; Data Streams","Multidisciplinary Topics and Applications: Economic and Finance"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}