Knowledge Graph-Based Modeling of Corporate Risk Factors for Financial Contagion Analysis
Keywords:
financial contagion, corporate risk factors, knowledge graphs, semantic modeling, systemic risk, regulatory infrastructureAbstract
Financial contagion research has traditionally focused on direct interbank exposures and market price co-movement, but the underlying corporate risk factor landscape remains underspecified. This paper develops a knowledge graph-based modeling framework for representing corporate risk factors extracted from regulatory filings, news, and structured firm attributes, and for analyzing how these factors propagate instability across economic networks. We discuss system-level design choices, including entity resolution, semantic normalization, relation extraction, and graph representation. The approach treats risk factors as first-class nodes linked to firms, sectors, geographies, and event types, allowing analysts to trace indirect transmission channels that are not visible in balance-sheet data. We examine structural trade-offs among coverage, precision, latency, and interpretability. We further consider governance, fairness, deployment, and regulatory acceptability. The paper argues that knowledge graph-based methods can complement established network models of contagion by enriching the causal and semantic fabric in which financial distress unfolds. It also identifies key infrastructure requirements for production deployment, including data lineage, auditability, and sustainability. Through this lens, the paper contributes to both financial stability research and the design of regulatory technology systems. Implications for future research and supervisory practice are highlighted.
References
1. Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1–33.
2. Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608.
3. Battiston, S., Puliga, M., Kaushik, R., Tasca, P., & Caldarelli, G. (2012). DebtRank: Too central to fail? Scientific Reports, 2, 541.
4. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J. E., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), Article 71.
5. Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data—The story so far. International Journal on Semantic Web and Information Systems, 5(3), 1–22.
6. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35–65.
7. Campbell, J. L., Chen, H., Dhaliwal, D. S., Lu, H.-M., & Steele, L. B. (2014). The information content of mandatory risk factor disclosures in corporate filings. Journal of Accounting and Economics, 58(1), 1–33.
8. Hoberg, G., & Phillips, G. (2016). Text-based network industries and endogenous product differentiation. Journal of Political Economy, 124(5), 1423–1465.
9. Sun, F., He, S., Wang, R., Ke, L., Shen, H., & Liao, Q. (2026). Modeling Structural Deviation in 10-K Risk Factors: A Semantic Anomaly Detection and Explainable AI Approach. Risks, 14(4), 87.
10. Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134.
11. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
12. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440–442.
13. Caccioli, F., Shrestha, M., Moore, C., & Farmer, J. D. (2014). Stability analysis of financial contagion due to overlapping portfolios. Journal of Banking & Finance, 46, 233–245.
14. Upper, C. (2011). Simulation methods to assess the danger of contagion in interbank markets. Journal of Financial Stability, 7(3), 111–125.
15. Eisenberg, L., & Noe, T. H. (2001). Systemic risk in financial systems. Management Science, 47(2), 236–249.
16. Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11–33.
17. Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743.
18. Noy, N. F., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges. Communications of the ACM, 62(8), 36–43.
19. Christen, P. (2012). Data matching: Concepts and techniques for record linkage, entity resolution, and duplicate detection. Springer.
20. Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. Proceedings of the 31st International Conference on Machine Learning, 32, II-1188–II-1196.
21. Gai, P., & Kapadia, S. (2010). Contagion in financial networks. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 466(2120), 2401–2423.
22. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. MIT Press.
23. Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507.
24. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28.
25. Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech, RegTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law & Business, 37(3), 371–413.
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