Artificial Intelligence Methods for Detecting Financial Contagion and Market Interdependence
Abstract
The increasing complexity and interconnectedness of global financial systems have rendered traditional econometric methods insufficient for identifying the non-linear pathways of financial contagion. This paper provides a comprehensive systems-level analysis of artificial intelligence (AI) methods designed to detect and model market interdependence and the subsequent propagation of shocks across heterogeneous asset classes. We examine the structural trade-offs inherent in large-scale predictive architectures, specifically focusing on the tension between the representational depth of deep learning models and the operational transparency required for regulatory oversight. The discussion extends into the socio-technical dimensions of AI deployment, addressing the physical requirements of high-performance computing, the necessity of robust data governance, and the environmental sustainability of compute-intensive financial modeling. Furthermore, we explore the policy implications of algorithmic convergence, where the widespread adoption of similar predictive frameworks among systemically important financial institutions may inadvertently synchronize market behaviors and amplify fragility. The research also scrutinizes the ethical imperatives of fairness and equity in capital distribution, arguing that contagion detection systems must be audited for historical biases to prevent the automated marginalization of specific economic sectors. By synthesizing perspectives from systems engineering, computational finance, and public policy, this work offers a roadmap for the development of resilient, transparent, and socially responsible contagion monitoring infrastructures. We conclude that while AI offers unprecedented capabilities for navigating the uncertainties of the twenty-first-century economy, its success is contingent upon a holistic approach that integrates technical precision with institutional accountability and environmental stewardship.
References
1.Abadie, A. (2021). Using machine learning for volatility estimation and prediction. Journal of Economic Literature, 59(2), 606-640.
2.Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
3.Battiston, S., et al. (2012). DebtRank: Too central to fail? Financial networks, the FED and systemic risk. Scientific Reports, 2, 541.
4.Qi, R. (2025, August). Interpretable Slow-Moving Inventory Forecasting: A Hybrid Neural Network Approach with Interactive Visualization. In Proceedings of the 2025 International Conference on Generative Artificial Intelligence for Business (pp. 41-46).
5.Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
6.Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
7.Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
8.Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223-236.
9.Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53(9), 1375-1388.
10.Zhou, D. (2026). AI-Driven Hybrid SAST–DAST–SCA–IAST Framework for Risk-Based Vulnerability Prioritization in Microservice Architectures.
11.Devlin, J., et al. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
12.Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial institutions. Journal of Econometrics, 182(1), 119-134.
13.Elliott, M., Golub, B., & Jackson, M. O. (2014). Financial networks and cascading failures. Econometrica, 82(6), 2099-2153.
14.Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
15.Yi, X. (2026). Privacy-Enhanced Ad Targeting for Social E-Commerce: A Federated Learning Framework with Zero-Knowledge Verification for Creator Monetization. Frontiers in Business and Finance, 3(1), 102-113.
16.Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535-574.
17.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
18.Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
19.He, K., et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
20.Zhang, T. (2025, November). A Neuro-Symbolic and Blockchain-Enhanced Multi-Agent Framework for Fair and Consistent Cross-Regulatory Audit Intelligence. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 254-261).
21.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
22.Hull, J. C. (2021). Machine Learning in Business: An Introduction to the World of Data Science. Pearson.
23.Qi, R. (2025, June). Enterprise financial distress prediction based on machine learning and SHAP interpretability analysis. In Proceedings of the 2025 International Conference on Artificial Intelligence and Digital Finance (pp. 76-79).
24.Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
25.Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.
26.Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.
27.Lopez de Prado, M. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
28.Li, H., & Liu, T. (2023). Portfolio optimization based on the LSTM forecasting model. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (Vol. 48, No. 1, pp. 97-106).
29.Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The Journal of Finance, 66(1), 35-65.
30.Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
31.Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.
32.Paszke, A., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems.
33.Rossi, G. (2018). Socio-Technical Systems and the Finance Industry. Routledge.
34.Yi, X. (2025, October). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1602-1607).
35.Schwartz, R., et al. (2020). Green AI. Communications of the ACM, 63(12), 54-63.
36.Shiller, R. J. (2015). Irrational Exuberance. Princeton University Press.
37.Liu, T. (2026). Volatility Forecasting and Early-Warning Market Stress Detection: A Leakage-Safe Evaluation with Tree Ensembles and Transformers.
38.Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
39.Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.
40.Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
41.Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
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