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Trading volume movement prediction is the key in a variety of financialapplications. Despite its importance, there is few research on this topicbecause of its requirement for comprehensive understanding of information fromdifferent sources. For instance, the relation between multiple stocks, recenttransaction data and suddenly released events are all essential forunderstanding trading market. However, most of the previous methods only takethe fluctuation information of the past few weeks into consideration, thusyielding poor performance. To handle this issue, we propose a graphbasedapproach that can incorporate multi-view information, i.e., long-term stocktrend, short-term fluctuation and sudden events information jointly into atemporal heterogeneous graph. Besides, our method is equipped with deepcanonical analysis to highlight the correlations between different perspectivesof fluctuation for better prediction. Experiment results show that our methodoutperforms strong baselines by a large margin.

Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling