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
Source PublicationINFORMS JOURNAL ON COMPUTING
ISSN1091-9856
2022-03-01
Pages19
Corresponding AuthorZheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
AbstractCo-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.
Keywordgraph representation learning deep learning predictive models business intelligence
DOI10.1287/ijoc.2022.1172
WOS KeywordBEHAVIOR ; RETURNS ; NETWORK
Indexed BySCI
Language英语
Funding ProjectMinistry 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]
Funding OrganizationMinistry of Science and Technology of China ; Natural Science Foundation of China ; Ministry ofHealth of China ; Strategic Priority Research Programof Chinese Academy of Sciences
WOS Research AreaComputer Science ; Operations Research & Management Science
WOS SubjectComputer Science, Interdisciplinary Applications ; Operations Research & Management Science
WOS IDWOS:000803709800001
PublisherINFORMS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49522
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorZheng, Xiaolong
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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