  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 graphbased
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.
