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

TL;DR

This paper introduces a graph-based multi-view model that integrates long-term trends, short-term fluctuations, and sudden events to improve trading volume movement prediction, addressing limitations of previous methods.

Contribution

It proposes a novel temporal heterogeneous graph approach combined with deep canonical analysis to effectively fuse diverse information sources for better prediction accuracy.

Findings

Outperforms existing baselines significantly

Effectively integrates multi-view information

Demonstrates robustness across different market scenarios

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 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.