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Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

Author

Listed:
  • Yu Zhao
  • Huaming Du
  • Ying Liu
  • Shaopeng Wei
  • Xingyan Chen
  • Fuzhen Zhuang
  • Qing Li
  • Ji Liu
  • Gang Kou

Abstract

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

Suggested Citation

  • Yu Zhao & Huaming Du & Ying Liu & Shaopeng Wei & Xingyan Chen & Fuzhen Zhuang & Qing Li & Ji Liu & Gang Kou, 2022. "Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks," Papers 2201.04965, arXiv.org, revised Jan 2022.
    • Handle: RePEc:arx:papers:2201.04965
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    References listed on IDEAS

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    1. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    2. Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018. "Enhancing Stock Movement Prediction with Adversarial Training," Papers 1810.09936, arXiv.org, revised Jun 2019.
    3. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    4. Xianchao Wu, 2020. "Event-Driven Learning of Systematic Behaviours in Stock Markets," Papers 2010.15586, arXiv.org.
    5. Jiexia Ye & Juanjuan Zhao & Kejiang Ye & Chengzhong Xu, 2020. "Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction," Papers 2005.04955, arXiv.org, revised Oct 2020.
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    Cited by:

    1. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.

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