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Learning Dynamic Dependencies With Graph Evolution Recurrent Unit for Stock Predictions

文献类型：期刊论文

作者Tian, Hu1,2; Zhang, Xingwei1,2; Zheng, Xiaolong1,2; Zeng, Daniel Dajun1,2
刊名IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
出版日期2023-07-10
页码13
关键词Gated recurrent unit graph representation learning learning dynamic dependencies stock prediction system
ISSN号2168-2216
DOI10.1109/TSMC.2023.3284840
通讯作者Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
英文摘要Investment decisions and risk management require understanding the time-varying dependencies between stocks. Graph-based learning systems have emerged as a promising approach for predicting stock prices by leveraging interfirm relationships. However, existing methods rely on a static stock graph predefined from finance domain knowledge and large-scale data engineering, which overlooks the dynamic dependencies between stocks. In this article, we present a novel framework called graph evolution recurrent unit (GERU), which uses a dynamic graph neural network to automatically learn the evolving dependencies from historical stock features, leading to better predictions. Our approach consists of three parts: first, we develop an adaptive dynamic graph learning (ADGL) module to learn latent dynamic dependencies from stock time series. Second, we propose a clustered ADGL (clu-ADGL) to handle large-scale time series by reducing time and memory complexity. Third, we combine the ADGL/clu-ADGL with a graph-gated recurrent unit to model the temporal evolutions of stock networks. Extensive experiments on real-world datasets show that our proposed methods outperform existing methods in predicting stock movements, capturing meaningful dynamic dependencies and temporal evolution patterns from the financial market, and achieving outstanding profitability in portfolio construction.
WOS关键词NETWORK
资助项目Ministry of Science and Technology of China[2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[72293575]
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:001030654200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Ministry of Science and Technology of China ; Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/53777]  
专题舆论大数据科学与技术应用联合实验室
通讯作者Zheng, Xiaolong
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Tian, Hu,Zhang, Xingwei,Zheng, Xiaolong,et al. Learning Dynamic Dependencies With Graph Evolution Recurrent Unit for Stock Predictions[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2023:13.
APA Tian, Hu,Zhang, Xingwei,Zheng, Xiaolong,&Zeng, Daniel Dajun.(2023).Learning Dynamic Dependencies With Graph Evolution Recurrent Unit for Stock Predictions.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,13.
MLA Tian, Hu,et al."Learning Dynamic Dependencies With Graph Evolution Recurrent Unit for Stock Predictions".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2023):13.

入库方式： OAI收割

来源：自动化研究所

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