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A Review on Graph Neural Network Methods in Financial Applications

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  • Jianian Wang
  • Sheng Zhang
  • Yanghua Xiao
  • Rui Song

Abstract

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due to the complexity and volatility of the financial market, the graph constructed on the financial data is often heterogeneous or time-varying, which imposes challenges on modeling technology. Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.

Suggested Citation

  • Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
    • Handle: RePEc:arx:papers:2111.15367
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    References listed on IDEAS

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    1. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
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    3. Hong Pan & Hanxun Zhou, 2020. "Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce," Electronic Commerce Research, Springer, vol. 20(2), pages 297-320, June.
    4. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    5. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    6. Mark Weber & Giacomo Domeniconi & Jie Chen & Daniel Karl I. Weidele & Claudio Bellei & Tom Robinson & Charles E. Leiserson, 2019. "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics," Papers 1908.02591, arXiv.org.
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    Cited by:

    1. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Lai, Wenzhe & Zhen, Zhao & Wang, Fei & Fu, Wenjie & Wang, Junlong & Zhang, Xudong & Ren, Hui, 2024. "Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations," Energy, Elsevier, vol. 288(C).
    3. Dragos Gorduza & Xiaowen Dong & Stefan Zohren, 2022. "Understanding stock market instability via graph auto-encoders," Papers 2212.04974, arXiv.org.
    4. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    5. Sahab Zandi & Kamesh Korangi & Mar'ia 'Oskarsd'ottir & Christophe Mues & Cristi'an Bravo, 2024. "Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction," Papers 2402.00299, arXiv.org, revised Jun 2024.

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