research-article

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction

Authors:

Chencheng Shang,

Yuqi LiangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 3584 - 3593

https://doi.org/10.1145/3511808.3557089

Published: 17 October 2022 Publication History

Abstract

The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relation graphs are usually constructed by handcraft human labeling or nature language processing, which are suffering from heavy resource requirement and low accuracy. Besides, they cannot effectively response to the dynamic changes in relation graphs. Therefore, in this paper, we propose a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series. In particular, we first generate the company relation graph for each trading day according to their historic price. Then we leverage a transformer encoder to encode the price movement information into temporal representations. Afterward, we propose a heterogeneous graph attention network to jointly optimize the embeddings of the financial time series data by transformer encoder and infer the probability of target movements. Finally, we conduct extensive experiments on the stock market in the United States and China. The results demonstrate the effectiveness and superior performance of our proposed methods compared with state-of-the-art baselines. Moreover, we also deploy the proposed THGNN in a real-world quantitative algorithm trading system, the accumulated portfolio return obtained by our method significantly outperforms other baselines.

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Cited By

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Chen HEldardiry H(2024)Graph Time-series Modeling in Deep Learning: A SurveyACM Transactions on Knowledge Discovery from Data10.1145/363853418:5(1-35)Online publication date: 28-Feb-2024
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Index Terms

Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Huang S(2024)Design and Implementation of an Intelligent Financial Management System Based on Enterprise Legal SystemJournal of Organizational and End User Computing10.4018/JOEUC.35022436:1(1-25)Online publication date: 12-Aug-2024
https://doi.org/10.4018/JOEUC.350224
Chen HEldardiry H(2024)Graph Time-series Modeling in Deep Learning: A SurveyACM Transactions on Knowledge Discovery from Data10.1145/363853418:5(1-35)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3638534
Wang JWang KLin XZhang WZhang Y(2024)Neural Attributed Community Search at Billion ScaleProceedings of the ACM on Management of Data10.1145/36267381:4(1-25)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3626738
Liu HZhou YZhou YHu B(2024)Heterogeneous Dual-Dynamic Attention Network for Modeling Mutual Interplay of StocksIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33742695:7(3595-3606)Online publication date: Jul-2024
https://doi.org/10.1109/TAI.2024.3374269
You ZZhang PZheng JCartlidge J(2024)Multi-Relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends ClassificationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447394(6545-6549)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447394
Salamai A(2024)Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysisNeural Networks10.1016/j.neunet.2024.106537179(106537)Online publication date: Nov-2024
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Gao KLiu CWu JDu BHu W(2024)Towards a better negative sampling strategy for dynamic graphsNeural Networks10.1016/j.neunet.2024.106175(106175)Online publication date: Feb-2024
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Ying ZCheng DChen CLi XZhu PLuo YLiang Y(2024)Predicting stock market trends with self-supervised learningNeurocomputing10.1016/j.neucom.2023.127033568:COnline publication date: 14-Mar-2024
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Dakalbab FTalib MNasir QSaroufil T(2024)Artificial intelligence techniques in financial trading: A systematic literature reviewJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10201536:3(102015)Online publication date: Mar-2024
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Ma DYuan DHuang MDong L(2024)VGC-GANExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121204236:COnline publication date: 1-Feb-2024
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