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

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Listed:
  • Liang Zhao
  • Wei Li
  • Ruihan Bao
  • Keiko Harimoto
  • YunfangWu
  • Xu Sun

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.

Suggested Citation

  • Liang Zhao & Wei Li & Ruihan Bao & Keiko Harimoto & YunfangWu & Xu Sun, 2021. "Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling," Papers 2108.11318, arXiv.org.
    • Handle: RePEc:arx:papers:2108.11318
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    File URL: http://arxiv.org/pdf/2108.11318
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    References listed on IDEAS

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    1. Ye Xunyu & Yan Rui & Li Handong, 2014. "Forecasting trading volume in the Chinese stock market based on the dynamic VWAP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(2), pages 125-144, April.
    2. Deli Chen & Yanyan Zou & Keiko Harimoto & Ruihan Bao & Xuancheng Ren & Xu Sun, 2019. "Incorporating Fine-grained Events in Stock Movement Prediction," Papers 1910.05078, arXiv.org.
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