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HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

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  • Raehyun Kim
  • Chan Ho So
  • Minbyul Jeong
  • Sanghoon Lee
  • Jinkyu Kim
  • Jaewoo Kang

Abstract

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods.

Suggested Citation

  • Raehyun Kim & Chan Ho So & Minbyul Jeong & Sanghoon Lee & Jinkyu Kim & Jaewoo Kang, 2019. "HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction," Papers 1908.07999, arXiv.org, revised Nov 2019.
    • Handle: RePEc:arx:papers:1908.07999
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    References listed on IDEAS

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    Cited by:

    1. Hanshuang Tong & Jun Li & Ning Wu & Ming Gong & Dongmei Zhang & Qi Zhang, 2024. "Ploutos: Towards interpretable stock movement prediction with financial large language model," Papers 2403.00782, arXiv.org.
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    3. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org.
    4. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    5. Qinkai Chen & Christian-Yann Robert, 2021. "Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data," Papers 2107.10941, arXiv.org, revised Dec 2021.
    6. Wentao Xu & Weiqing Liu & Chang Xu & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "REST: Relational Event-driven Stock Trend Forecasting," Papers 2102.07372, arXiv.org, revised Feb 2021.
    7. 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.
    8. Xianchao Wu, 2020. "Event-Driven Learning of Systematic Behaviours in Stock Markets," Papers 2010.15586, arXiv.org.
    9. Luke Sanborn & Matthew Sahagun, 2023. "Media Moments and Corporate Connections: A Deep Learning Approach to Stock Movement Classification," Papers 2309.06559, arXiv.org.
    10. Thanh Trung Huynh & Minh Hieu Nguyen & Thanh Tam Nguyen & Phi Le Nguyen & Matthias Weidlich & Quoc Viet Hung Nguyen & Karl Aberer, 2022. "Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction," Papers 2211.07400, arXiv.org, revised Nov 2022.
    11. Wentao Xu & Weiqing Liu & Lewen Wang & Yingce Xia & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information," Papers 2110.13716, arXiv.org, revised Jan 2022.
    12. Alireza Namdari & Tariq S. Durrani, 2021. "A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.
    13. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Yen-Lin Chen, 2022. "Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
    14. Alireza Jafari & Saman Haratizadeh, 2022. "GCNET: graph-based prediction of stock price movement using graph convolutional network," Papers 2203.11091, arXiv.org, revised Aug 2022.
    15. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.

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