Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions

Hu Tian (), Xiaolong Zheng (), Kang Zhao (), Maggie Wenjing Liu () and Daniel Dajun Zeng ()
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Hu Tian: The State of Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Science, Beijing 100190, China
Xiaolong Zheng: The State of Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Science, Beijing 100190, China
Kang Zhao: Department of Business Analytics, Tippie College of Business, The University of Iowa, Iowa City, Iowa 52242
Maggie Wenjing Liu: School of Economics and Management, Tsinghua University, Beijing 100084, China
Daniel Dajun Zeng: The State of Key Laboratory of Management and Control for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Science, Beijing 100190, China

INFORMS Journal on Computing, 2022, vol. 34, issue 4, 1940-1957

Abstract: Co-movement among individual firms’ stock prices can reflect complex interfirm 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 significantly 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 hybrid-attention 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 mini-batch 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.

Keywords: graph representation learning; deep learning; predictive models; business intelligence (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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