{"type":"ScholarlyArticle","title":"Subsequence-based Graph Routing Network for Capturing Multiple Risk Propagation Processes","authors":[{"name":"Rui Cheng","slug":"rui-cheng","given":"Rui","family":"Cheng","affiliation":"School of Computing and Artificial Intelligence , Southwestern University of Finance and Economics rcheng"},{"name":"Qing Li","slug":"qing-li","given":"Qing","family":"Li","affiliation":"School of Computing and Artificial Intelligence , Southwestern University of Finance and Economics rcheng"}],"abstract":"In finance, the risk of an entity depends not only on its historical information \r\nbut also on the risk propagated by its related peers. Pilot studies rely on Graph Neural Networks (GNNs) to model this risk propagation, where each entity is treated as a node and represented by its time-series information.\r\nHowever, conventional GNNs are constrained by their unified messaging mechanism with an assumption that the risk of a given entity only propagates to its related peers with the same time lag and has the same effect, which is against the ground truth. In this study, we propose the subsequence-based graph routing network (S-GRN) for capturing the variant risk propagation processes among different time-series represented entities. In S-GRN, the messaging mechanism between each node pair is dynamically and independently selected from multiple messaging mechanisms based on the dependencies of variant subsequence patterns. The S-GRN is extensively evaluated on two synthetic tasks and three real-world datasets and demonstrates state-of-the-art performance.","doi":"10.24963/ijcai.2022/529","url":"https://ijcai.org/proceedings/2022/529","pdf":"https://ijcai.org/proceedings/2022/0529.pdf","markdown":"https://ijcai.org/proceedings/2022/529.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/529","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":529,"pages":{"first":3810,"last":3816},"keywords":["Machine Learning: Sequence and Graph Learning","Multidisciplinary Topics and Applications: Finance"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987069","length_sec":83,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985212","length_sec":705,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Ali and Hirshleifer, 2020] Usman Ali and David Hirsh- leifer. Shared analyst coverage: Unifying momen- tum spillover effects. Journal of Financial Economics, 136(3):649-675, 2020.","title":"Shared analyst coverage: Unifying momentum spillover effects","authors":"Usman Ali, David Hirshleifer","year":"2020","venue":"Journal of Financial Economics","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Campbell et al., 2010] John Y Campbell, Christopher Polk, and Tuomo Vuolteenaho. Growth or glamour? fundamen- tals and systematic risk in stock returns. The Review of Financial Studies, 23(1):305-344, 2010.","title":"Growth or glamour? fundamentals and systematic risk in stock returns","authors":"Christopher John Y Campbell, Tuomo Polk, Vuolteenaho","year":"2010","venue":"The Review of Financial Studies","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chang et al., 2017] Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael J. Witbrock, Mark A. Hasegawa-Johnson, and Thomas S. Huang. Dilated recurrent neural networks. In Advances in Neural Information Processing Systems, pages 77-87, 2017.","title":"Dilated recurrent neural networks","authors":"Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael J Witbrock, A Mark, Thomas S Hasegawa-Johnson, Huang","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng and Li, 2021] Rui Cheng and Qing Li. Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 35, pages 55-62, 2021.","title":"Modeling the momentum spillover effect for stock prediction via attribute-driven graph attention networks","authors":"Rui Cheng, Qing Li","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chiu and Nichols, 2016] Jason PC Chiu and Eric Nichols. Named entity recognition with bidirectional lstm-cnns. Transactions of the Association for Computational Lin- guistics, 4:357-370, 2016.","title":"Named entity recognition with bidirectional lstm-cnns","authors":"Jason Pc Chiu, Eric Nichols","year":"2016","venue":"Transactions of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chung et al., 2014] Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evalua- tion of gated recurrent neural networks on sequence mod- eling. arXiv preprint arXiv:1412.3555, 2014.","title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","authors":"Chung","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chung et al., 2017] Junyoung Chung, Sungjin Ahn, and Yoshua Bengio. Hierarchical multiscale recurrent neural networks. In International Conference on Learning Rep- resentations, 2017.","title":"Hierarchical multiscale recurrent neural networks","authors":"Chung","year":"2017","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Costello, 2020] Anna M Costello. Credit market disruptions and liquidity spillover effects in the supply chain. Journal of Political Economy, 128(9):3434-3468, 2020.","title":"Credit market disruptions and liquidity spillover effects in the supply chain","authors":"Anna M Costello","year":"2020","venue":"Journal of Political Economy","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Edwards et al., 2018] Robert D Edwards, John Magee, and WH Charles Bassetti. Technical analysis of stock trends. 2018.","title":"Technical analysis of stock trends","authors":"John Robert D Edwards, Charles Magee, Bassetti","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Feng et al., 2019] Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, and Tat-Seng Chua. Temporal re- lational ranking for stock prediction. ACM Transactions on Information Systems, 37(2):1-30, 2019.","title":"Temporal relational ranking for stock prediction","authors":"Fuli Feng, Xiangnan He, Xiang Wang, Cheng Luo, Yiqun Liu, Tat-Seng Chua","year":"2019","venue":"ACM Transactions on Information Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Genc ¸ay et al., 2003] Ramazan Genc ¸ay, Faruk Selc ¸uk, and Brandon Whitcher. Systematic risk and timescales. Quan- titative Finance, 3(2):108, 2003.","title":"Systematic risk and timescales","authors":"Genc ¸ay","year":"2003","venue":"Quantitative Finance","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Hou, 2007] Kewei Hou. Industry information diffusion and the lead-lag effect in stock returns. The Review of Finan- cial Studies, 20(4):1113-1138, 2007.","title":"Industry information diffusion and the lead-lag effect in stock returns","authors":"Kewei Hou, Hou","year":"2007","venue":"The Review of Financial Studies","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hsu et al., 2021] Yi-Ling Hsu, Yu-Che Tsai, and Cheng-Te Li. Fingat: Financial graph attention networks for recom- mending top-k profitable stocks. IEEE Transactions on Knowledge and Data Engineering, 2021.","title":"Fingat: Financial graph attention networks for recommending top-k profitable stocks","authors":"Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li","year":"2021","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jang et al., 2017] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations, 2017.","title":"Categorical reparameterization with gumbel-softmax","authors":"Eric Jang, Shixiang Gu, Ben Poole","year":"2017","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kipf and Welling, 2017] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Rep- resentations, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"Thomas N Kipf, Max Welling","year":"2017","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2021] Yongli Li, Chao Liu, Tianchen Wang, and Baiqing Sun. Dynamic patterns of daily lead-lag networks in stock markets. Quantitative Finance, pages 1-14, 2021.","title":"Dynamic patterns of daily lead-lag networks in stock markets","authors":"Yongli Li, Chao Liu, Tianchen Wang, Baiqing Sun","year":"2021","venue":"Quantitative Finance","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lo et al., 2000] Andrew W Lo, Harry Mamaysky, and Jiang Wang. Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implemen- tation. The Journal of Finance, 55(4):1705-1765, 2000.","title":"Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation","authors":"Andrew W Lo, Harry Mamaysky, Jiang Wang","year":"2000","venue":"The Journal of Finance","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Menzly and Ozbas, 2010] Lior Menzly and Oguzhan Ozbas. Market segmentation and cross-predictability of returns. The Journal of Finance, 65(4):1555-1580, 2010.","title":"Lior Menzly and Oguzhan Ozbas. Market segmentation and cross-predictability of returns","authors":"Ozbas Menzly","year":"2010","venue":"The Journal of Finance","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Osadchiy et al., 2016] Nikolay Osadchiy, Vishal Gaur, and Sridhar Seshadri. Systematic risk in supply chain net- works. Management Science, 62(6):1755-1777, 2016.","title":"Systematic risk in supply chain networks","authors":"Nikolay Osadchiy, Vishal Gaur, Sridhar Seshadri","year":"2016","venue":"Management Science","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Qin et al., 2017] Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison W. Cottrell. A dual-stage attention-based recurrent neural network for time series prediction. In Carles Sierra, editor, Interna- tional Joint Conference on Artificial Intelligence, pages 2627-2633, 2017.","title":"A dual-stage attention-based recurrent neural network for time series prediction","authors":"Dongjin Yao Qin, Haifeng Song, Wei Chen, Guofei Cheng, Garrison W Jiang, Cottrell","year":"2017","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/366"},{"position":22,"raw":"[Scarselli et al., 2009] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE Trans. Neural Networks, 20(1):61-80, 2009.","title":"The graph neural network model","authors":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini","year":"2009","venue":"IEEE Trans. Neural Networks","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Scholes, 2000] Myron S Scholes. Crisis and risk manage- ment. American Economic Review, 90(2):17-21, 2000.","title":"Crisis and risk management","authors":"Myron S Scholes","year":"2000","venue":"American Economic Review","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Serrano et al., 2018] Alejandro Serrano, Rogelio Oliva, and Santiago Kraiselburd. Risk propagation through payment distortion in supply chains. Journal of Operations Man- agement, 58:1-14, 2018.","title":"Risk propagation through payment distortion in supply chains","authors":"Alejandro Serrano, Rogelio Oliva, Santiago Kraiselburd","year":"2018","venue":"Journal of Operations Management","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Sys- tems, pages 5998-6008, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xu et al., 2015] Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li. Empirical evaluation of rectified activations in con- volutional network. CoRR, abs/1505.00853, 2015.","title":"Empirical evaluation of rectified activations in convolutional network","authors":"Bing Xu, Naiyan Wang, Tianqi Chen, Mu Li","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}