Knowledge Commons of Institute of Automation,CAS
Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions | |
Tian, Hu1,2; Zheng, Xiaolong1,2; Zhao, Kang3; Liu, Maggie Wenjing4; Zeng, Daniel Dajun1,2 | |
发表期刊 | INFORMS JOURNAL ON COMPUTING |
ISSN | 1091-9856 |
2022-03-01 | |
页码 | 19 |
摘要 | Co-movement among individual firms' stock prices can reflect complex inter firm relationships. This paper proposes a novel method to leverage such relationships for stock price predictions by adopting inductive graph representation learning on dynamic stock graphs constructed based on historical stock price co-movement. To learn node representations from such dynamic graphs for better stock predictions, we propose the hybrid-attention dynamic graph neural network, an inductive graph representation learning method. We also extended mini-batch gradient descent to inductive representation learning on dynamic stock graphs so that the model can update parameters over mini batch stock graphs with higher training efficiency. Extensive experiments on stocks from different markets and trading simulations demonstrate that the proposed method signifi-cantly improves stock predictions. The proposed method can have important implications for the management of financial portfolios and investment risk. Summary of Contribution: Accurate predictions of stock prices have important implications for financial decisions. In today's economy, individual firms are increasingly connected via different types of relationships. As a result, firms' stock prices often feature synchronous co-movement patterns. This paper represents the first effort to leverage such phenomena to construct dynamic stock graphs for stock predictions. We develop hybridattention dynamic graph neural network (HAD-GNN), an inductive graph representation learning framework for dynamic stock graphs to incorporate temporal and graph attention mechanisms. To improve the learning efficiency of HAD-GNN, we also extend the minibatch gradient descent to inductive representation learning on such dynamic graphs and adopt a t-batch training mechanism (t-BTM). We demonstrate the effectiveness of our new approach via experiments based on real-world data and simulations. |
关键词 | graph representation learning deep learning predictive models business intelligence |
DOI | 10.1287/ijoc.2022.1172 |
关键词[WOS] | BEHAVIOR ; RETURNS ; NETWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Ministry of Science and Technology of China[2020AAA0108401] ; Natural Science Foundation of China[71621002] ; Natural Science Foundation of China[71472175] ; Natural Science Foundation of China[71602184] ; Natural Science Foundation of China[71991462] ; Natural Science Foundation of China[71825007] ; Ministry ofHealth of China[2017ZX10303401-002] ; Strategic Priority Research Programof Chinese Academy of Sciences[XDA27030100] |
项目资助者 | Ministry of Science and Technology of China ; Natural Science Foundation of China ; Ministry ofHealth of China ; Strategic Priority Research Programof Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Operations Research & Management Science |
WOS类目 | Computer Science, Interdisciplinary Applications ; Operations Research & Management Science |
WOS记录号 | WOS:000803709800001 |
出版者 | INFORMS |
七大方向——子方向分类 | 人工智能+金融 |
国重实验室规划方向分类 | 社会系统建模与计算 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49522 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Zheng, Xiaolong |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Univ Iowa, Tippie Coll Business, Dept Business Analyt, Iowa City, IA 52242 USA 4.Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Tian, Hu,Zheng, Xiaolong,Zhao, Kang,et al. Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions[J]. INFORMS JOURNAL ON COMPUTING,2022:19. |
APA | Tian, Hu,Zheng, Xiaolong,Zhao, Kang,Liu, Maggie Wenjing,&Zeng, Daniel Dajun.(2022).Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions.INFORMS JOURNAL ON COMPUTING,19. |
MLA | Tian, Hu,et al."Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions".INFORMS JOURNAL ON COMPUTING (2022):19. |
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