IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2108.11318.html
   My bibliography  Save this paper

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

Author

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2108.11318
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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 1-20, 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    2. Yuan Gao & Haokun Chen & Xiang Wang & Zhicai Wang & Xue Wang & Jinyang Gao & Bolin Ding, 2024. "DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation," Papers 2402.06656, arXiv.org.
    3. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2108.11318. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.