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GNN4Fintech

This is the repository for the collection of Graph-based Deep Learning for Financial Applications.

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Stock Market Prediction

2022

Journal

• Cheng D, Yang F, Xiang S, et al. <b>Financial time series forecasting with multi-modality graph neural network[J]</b>. Pattern Recognition, 2022, 121: 108218. Link
• Yin T, Liu C, Ding F, et al. <b>Graph-based stock correlation and prediction for high-frequency trading systems[J]</b>. Pattern Recognition, 2022, 122: 108209. Link
• Feng S, Xu C, Zuo Y, et al. <b>Relation-aware dynamic attributed graph attention network for stocks recommendation[J]</b>. Pattern Recognition, 2022, 121: 108119. Link

2021

Journal

• Chen W, Jiang M, Zhang W G, et al. <b>A novel graph convolutional feature based convolutional neural network for stock trend prediction[J]</b>. Information Sciences, 2021, 556: 67-94. Link
• Hsu Y L, Tsai Y C, Li C T. <b>FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks[J]</b>. IEEE Transactions on Knowledge and Data Engineering, 2021. Link Code
• Gao J, Ying X, Xu C, et al. <b>Graph-Based Stock Recommendation by Time-Aware Relational Attention Network[J]</b>. ACM Transactions on Knowledge Discovery from Data (TKDD), 2021, 16(1): 1-21. Link Code
• Xiong K, Ding X, Du L, et al. <b>Heterogeneous graph knowledge enhanced stock market prediction[J]</b>. AI Open, 2021, 2: 168-174. Link
• Hou X, Wang K, Zhong C, et al. <b>ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement[J]</b>. IEEE/CAA Journal of Automatica Sinica, 2021, 8(5): 1015-1024. Link

Conference

• Wei T, You Y, Chen T. <b>AR-Stock: Deep Augmented Relational Stock Prediction[C]</b>. The AAAI21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services 2021. Link
• Sawhney R, Agarwal S, Wadhwa A, et al. <b>Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading[C]</b>//Proceedings of the Web Conference 2021. 2021: 11-22. Link
• Zhao L, Li W, Bao R, et al. <b>Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling[C]</b>. IJCAI, 2021. Link Code
• Cheng R, Li Q. <b>Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks[C]</b>. AAAI, 2021. Link Code

Preprint

• Chen Q, Robert C Y. <b>Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data[J]</b>. arXiv preprint arXiv:2107.10941, 2021. Link
• Xu W, Liu W, Wang L, et al. <b>HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information[J]</b>. arXiv preprint arXiv:2110.13716, 2021. Link Code
• Wu J, Xu K, Chen X, et al. <b>Price graphs: Utilizing the structural information of financial time series for stock prediction[J]</b>. arXiv preprint arXiv:2106.02522, 2021. Link Code

2020

Journal

• Long J, Chen Z, He W, et al. <b>An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market[J]</b>. Applied Soft Computing, 2020, 91: 106205. Link

Conference

• Li H Y, Tseng V S, Philip S Y. <b>Enhancing Stock Trend Prediction Models by Mining Relational Graphs of Stock Prices[C]</b>//2020 International Conference on Pervasive Artificial Intelligence (ICPAI). IEEE, 2020: 110-117. Link
• Cheng D, Yang F, Wang X, et al. <b>Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments[C]</b>//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020: 2221-2230. Link
• Li W, Bao R, Harimoto K, et al. <b>Modeling the stock relation with graph network for overnight stock movement prediction[C]</b>//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Special Track on AI in FinTech. 2020: 4541-4547. Link Code
• Ye J, Zhao J, Ye K, et al. <b>Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction[C]</b>//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 6702-6709. Link Code
• Ying X, Xu C, Gao J, et al. <b>Time-aware Graph Relational Attention Network for Stock Recommendation[C]</b>//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020: 2281-2284. Link

Preprint

• Romain D D. <b>Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input[J]</b>. arXiv preprint arXiv:2011.13113, 2020. Link

2019

Journal

• Liu Y, Zeng Q, Ordieres Meré J, et al. <b>Anticipating stock market of the renowned companies: A knowledge graph approach[J]</b>. Complexity, 2019, 2019. Link

Conference

• Liu J, Lu Z, Du W. <b>Combining enterprise knowledge graph and news sentiment analysis for stock price prediction[C]</b>//Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019. Link

Preprint

• Matsunaga D, Suzumura T, Takahashi T. <b>Exploring graph neural networks for stock market predictions with rolling window analysis[J]</b>. arXiv preprint arXiv:1909.10660, 2019. Link
• Kim R, So C H, Jeong M, et al. <b>Hats: A hierarchical graph attention network for stock movement prediction[J]</b>. arXiv preprint arXiv:1908.07999, 2019. Link Code

2018

Conference

• Chen Y, Wei Z, Huang X. <b>Incorporating corporation relationship via graph convolutional neural networks for stock price prediction[C]</b>//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018: 1655-1658. Link

2016

Conference

• Ding X, Zhang Y, Liu T, et al. <b>Knowledge-driven event embedding for stock prediction[C]</b>//Proceedings of coling 2016, the 26th international conference on computational linguistics: Technical papers. 2016: 2133-2142. Link

Anti-money Laundering

2020

Conference

• Alarab I, Prakoonwit S, Nacer M I. <b>Competence of graph convolutional networks for anti-money laundering in bitcoin blockchain[C]</b>//Proceedings of the 2020 5th International Conference on Machine Learning Technologies. 2020: 23-27. Link

2019

Preprint

• Weber M, Domeniconi G, Chen J, et al. <b>Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics[J]</b>. arXiv preprint arXiv:1908.02591, 2019. Link
• Hu Y, Seneviratne S, Thilakarathna K, et al. <b>Characterizing and detecting money laundering activities on the bitcoin network[J]</b>. arXiv preprint arXiv:1912.12060, 2019. Link

Credit Scoring

2020

Conference

• Sukharev I, Shumovskaia V, Fedyanin K, et al. <b>EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data[C]</b>//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 1268-1273. Link

Default Prediction

2021

Journal

• Chi M, Hongyan S, Shaofan W, et al. <b>Bond Default Prediction Based on Deep Learning and Knowledge Graph Technology[J]</b>. IEEE Access, 2021, 9: 12750-12761. Link
• Lee J W, Lee W K, Sohn S Y. <b>Graph convolutional network-based credit default prediction utilizing three types of virtual distances among borrowers[J]</b>. Expert Systems with Applications, 2021, 168: 114411. Link

Financial Event Prediction

2020

Journal

• Su Z, Jiang J. <b>Hierarchical gated recurrent unit with semantic attention for event prediction[J]</b>. Future Internet, 2020, 12(2): 39. Link Code and Data

2019

Conference

• Yang Y, Wei Z, Chen Q, et al. <b>Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks[C]</b>//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 2161-2164. Link

Financial Risk Analysis

2020

Conference

• Yang S, Zhang Z, Zhou J, et al. <b>Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining[C]</b>//IJCAI. 2020: 4661-4667. Link

Fraud Detection

2021

Conference

• Ren Y, Zhu H, Zhang J, et al. <b>Ensemfdet: An ensemble approach to fraud detection based on bipartite graph[C]</b>//2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021: 2039-2044. Link

2020

Journal

• Kurshan E, Shen H. <b>Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook[J]</b>. International Journal of Semantic Computing, 2020, 14(04): 565-589. Link

Conference

• Dou Y, Liu Z, Sun L, et al. <b>Enhancing graph neural network-based fraud detectors against camouflaged fraudsters[C]</b>//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020: 315-324. Link Code
• Kurshan E, Shen H, Yu H. <b>Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook[C]</b>//2020 Second International Conference on Transdisciplinary AI (TransAI). IEEE, 2020: 125-130. Link
• Zhu Y N, Luo X, Li Y F, et al. <b>Heterogeneous Mini-Graph Neural Network and Its Application to Fraud Invitation Detection[C]</b>//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 891-899. Link Code

Preprint

• Rao S X, Zhang S, Han Z, et al. <b>xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs[J]</b>. arXiv preprint arXiv:2011.12193, 2020. Link