--- title: "Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling" authors: - "Liang Zhao" - "Wei Li" - "Ruihan Bao" - "Keiko Harimoto" - "Yunfang Wu" - "Xu Sun" doi: "10.24963/ijcai.2021/518" url: "https://ijcai.org/proceedings/2021/518" pdf: "https://ijcai.org/proceedings/2021/0518.pdf" venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence" year: 2021 pages: "3764-3770" track: "Main Track" keywords: - "Machine Learning: Time-series; Data Streams" - "Multidisciplinary Topics and Applications: Economic and Finance" --- # Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling Liang Zhao, Wei Li, Ruihan Bao, Keiko Harimoto, Yunfang Wu, Xu Sun *Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3764-3770. DOI: [10.24963/ijcai.2021/518](https://doi.org/10.24963/ijcai.2021/518) ## 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.