Machine Learning Models for Early Warning of Financial Crises in the U.S. Economy Using Macro-Financial Indicators

  • Authors

    • Md Khalilor Rahman MBA, Business analytics, Gannon University, Erie, PA, USA
    • Md Sazzad Hossain MBA, Business analytics, Gannon University, Erie, PA, USA
    • Sayem Ul Haque MBA- Business Analytics, Gannon University, PA, USA
    • Kazi Abu Jahed Master of Science- Business Intelligence and Analytics, Saint Joseph's University, PA, USA
    • Md Sharif Robbani School of Engineering & Technology at Western Illinois University, Macomb, IIllinois, USA
    • Mahamuda Akter Shati Master's in Business Analytics, Grand Canyon University
    • Tanjina Tuly MSc in Business Analytics, Trine University
    • Md Toushif Pramanik Master of Embedded Software Engineering, Gannon University, Erie, USA
    https://doi.org/10.14419/wkr3pj23

    Received date: February 20, 2026

    Accepted date: March 10, 2026

    Published date: March 16, 2026

  • Early Warning Systems; Financial Crisis Prediction; Macro-Financial Indicators; Probability Calibration; Structural Breaks

  • Abstract

    Early identification of U.S. recessions is critically important for policymakers, financial institutions, and investors; however, accurate prediction remains challenging. The data is sparse, things in the economy shift in ways that aren’t always predictable, and macro-financial indicators don’t always move in straight lines. This study looks at a set of machine learning models, logistic regression, random forest, and ‎XGBoost, to see how well they can flag recessions six months ahead. We use a monthly dataset stretching from 1970 to 2025, and define ‎recession periods using the National Bureau of Economic Research dates. The problem is set up as a binary classification: will a recession ‎happen or not? To ensure methodological rigor, the models are trained using strict time-series cross-validation, and we evaluate their performance using ROC-AUC, precision-recall curves, Brier scores, calibration curves, and the lead time to issue a warning. We find that non-‎linear models like XGBoost and random forests tend to beat logistic regression by 5–10% on ROC-AUC. These models capture nonlinear ‎interactions between indicators that simpler linear models may fail to detect. The yield curve spread comes out as the most reliable signal, ‎followed by unemployment and the VIX volatility index. Credit indicators add a little extra, but not much. The models can give useful warning signals about 5–6 months before a recession starts, though accuracy drops when you look further ahead or during times when the economy goes through big changes, like after 2008 or post-COVID-19. Calibration tests show that predicted probabilities aren’t perfect; turning ‎them into reliable risk estimates needs some care. The study also points out gaps in previous ML-based early warning work: people often ‎rely on random cross-validation, don’t test models over different forecast horizons, and don’t focus enough on interpretability. By tackling ‎these issues, our approach is more robust and respects the time dimension, making it more suitable for real-world policy use. Overall, the ‎results suggest that well-calibrated, interpretable ML models can work alongside traditional econometrics to help policymakers act before a ‎recession hits.

  • References

    1. Aashish, K. C., Zamil, M. Z. H., Mridul, M. S. I., Akter, L., Sharmin, F., Ayon, E. H., … & Malla, S. (2025). Towards eco-friendly cybersecurity: Machine learning-based anomaly detection with carbon and energy metrics. International Journal of Applied Mathematics, 38(9s). https://doi.org/10.12732/ijam.v38i9s.822.
    2. Alam, M., Shil, S. K., Sharmin, F., KC, A., Md, A. H., Ali, M., ... & Malla, S. (2026). Hybrid deep learning models for equipment failure prediction in US industrial systems. International Journal of Applied Mathematics, 39(1s). https://doi.org/10.12732/ijam.v39i1s.1609.
    3. Al Montaser, M. A., & Bhuiyan, M. A. I. (2025). Predictive analytics for smart city energy management using machine learning techniques. Fron-tiers in Computer Science and Artificial Intelligence, 4(4), 71–82. https://doi.org/10.32996/fcsai.2025.4.4.6.
    4. Bhowmik, P. K., Subha, D. T., Rahim, A., Mohammed, A. A., Begum, M., Chowdhury, R., ... & Shati, M. A. Self-adaptive machine learning mod-els for financial risk forecasting: Handling non-stationarity in banking and cryptocurrency time series.
    5. Bluwstein, K., Buckmann, M., Joseph, A., Kang, M., Kapadia, S., & Simsek, Ö. (2020). Credit growth, the yield curve, and financial crisis predic-tion: Evidence from a machine learning approach. Journal of International Economics, 103773. https://doi.org/10.2139/ssrn.3520659.
    6. Cai, Y. (2026). The methodological evolution of macroeconomic early warning systems: From econometric models to real-time data analytics. Fi-nancial Economics Insights, 3(1), 38–46. https://doi.org/10.70088/p1agfe73.
    7. Chauvet, M., & Potter, S. (2005). Forecasting recessions using the yield curve. Journal of Forecasting, 24(2), 77–103. https://doi.org/10.1002/for.932.
    8. Chouksey, A., Dola, A., Antara, U. K., Begum, S., Ahmed, T., Sultana, T., & Zabin, N. (2025). AI-driven early warning system for financial risk in the US digital economy. International Journal of Applied Mathematics, 38(9s). https://doi.org/10.12732/ijam.v38i9s.838.
    9. Chung, S. (2023). Inside the black box: Neural network-based real-time prediction of US recessions. arXiv.
    10. Coffinet, J., & Kien, J.-N. (2020). Detection of rare events: A machine learning toolkit with an application to banking crises. Journal of Finance and Data Science, 5(4), 183–207. https://doi.org/10.1016/j.jfds.2020.04.001.
    11. Das, B. C., et al. (2025). AI-driven cybersecurity threat detection: Building resilient defense systems using predictive analytics. arXiv preprint arXiv:2508.01422. https://doi.org/10.14419/hysdg957.
    12. Debnath, S., et al. (2025). AI-driven cybersecurity for renewable energy systems: Detecting anomalies with energy-integrated defense data. Inter-national Journal of Applied Mathematics, 38(5s). https://doi.org/10.12732/ijam.v38i5s.367.
    13. Dola, A., Begum, S., Antara, U. K., Islam, M. R., Sultana, T., & Zabin, N. (2024). Machine learning models for detecting hidden collusion net-works in US corporate finance. Journal of Economics, Finance and Accounting Studies, 6(1), 143–154. https://doi.org/10.32996/jefas.2024.6.1.14.
    14. Dueker, M. J. (1997). Strengthening the case for the yield curve as a predictor of U.S. recessions. Federal Reserve Bank of St. Louis Review, 79(1), 41–51. https://doi.org/10.20955/r.79.41-51.
    15. Estrella, A., & Mishkin, F. S. (1998). Predicting U.S. recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45–61. https://doi.org/10.1162/003465398557320.
    16. Hasan, M. R., Rahman, M. A., Gomes, C. A. H., Nitu, F. N., Gomes, C. A., Islam, M. R., & Shawon, R. E. R. (2025). Building robust AI and ma-chine learning models for supplier risk management: A data-driven strategy for enhancing supply chain resilience in the USA. Advances in Con-sumer Research, 2(4).
    17. Hasan, M. S., et al. (2025). Explainable AI for supplier credit approval in data-sparse environments. International Journal of Applied Mathematics, 38(5s). https://doi.org/10.12732/ijam.v38i5s.380.
    18. Holopainen, M., & Sarlin, P. (2015). Toward robust early-warning models: A horse race, ensembles and model uncertainty. arXiv. https://doi.org/10.2139/ssrn.2584343.
    19. Islam, M. R., Pramanik, M. T., & Zeeshan, M. A. F. (2025). Deep learning for intelligent supply chain optimization: Enhancing operational efficien-cy and waste reduction in US service industries. Frontiers in Computer Science and Artificial Intelligence, 4(2), 45–62. https://doi.org/10.32996/fcsai.2025.4.2.5.
    20. Islam, M. Z., Sumsuzoha, M., Islam, M. R., Kawsar, M., Mithu, M. F. H., Pant, S., ... & Al Helal, M. A. Graph neural networks for systemic finan-cial risk forecasting: Modeling cross-market contagion between banking systems and cryptocurrency markets.
    21. Jakir, T. (2025). Signal-to-noise analysis of crisis indicators in global finance using artificial intelligence. International Journal of Applied Mathemat-ics, 38(10s), 1815–1836. https://doi.org/10.12732/ijam.v38i10s.1075.
    22. Maas, B. (2019). Nowcasting and forecasting US recessions: Evidence from the Super Learner. MPRA Paper No. 96408.
    23. Miah, M. N. I., Uddin, M. J., & Kakumani, M. (2026). Artificial intelligence in sentencing: Evaluating machine learning models for sentencing rec-ommendations in the US. Frontiers in Computer Science and Artificial Intelligence, 5(4), 30–43. https://doi.org/10.32996/fcsai.2025.4.3.5x.
    24. Namaki, A., Eyvazloo, R., & Ramtinnia, S. (2023). A systematic review of early warning systems in finance. SSRN Electronic Journal.
    25. Rahman, M. S. (2025). Machine learning–enabled early warning system for detecting micro-inflation clusters in the US economy. International Journal of Applied Mathematics, 38(12s), 2743–2769. https://doi.org/10.12732/ijam.v38i12s.1585.
    26. Ray, R. K. (2025). Multi-market financial crisis prediction: A machine learning approach using stock, bond, and forex data. International Journal of Applied Mathematics, 38(8s), 706–738. https://doi.org/10.12732/ijam.v38i8s.602.
    27. Ren, T. (2024). Stock market extreme risk prediction based on machine learning. Journal of Business Research. https://doi.org/10.1016/j.najef.2024.102241.
    28. Reza, S. A., et al. (2025). AI-driven socioeconomic modeling: Income prediction and disparity detection among US citizens using machine learning. Advances in Consumer Research, 2(4).
    29. Reza, S. A., et al. (2025). Machine learning enabled early warning system for financial distress using real-time digital signals. arXiv preprint arXiv:2510.22287.
    30. Samitas, A. (2020). Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71, 101507. https://doi.org/10.12732/ijam.v38i4s.225.
    31. Shawon, R. E. R., et al. (2025). Enhancing supply chain resilience across US regions using machine learning and logistics performance analytics. International Journal of Applied Mathematics, 38(4s). https://doi.org/10.12732/ijam.v38i4s.225.
    32. Shawon, R. E. R., Buiya, M. R., Pant, S., Al Jobaer, M. A., Chowdhury, M. S. R., Kawsar, M., … & Ali, M. (2025). Detecting illicit cross-chain fund movement: Behavioral machine learning models for bridge-based laundering patterns. International Journal of Applied Mathematics, 38(12s). https://doi.org/10.12732/ijam.v38i12s.1399.
    33. Shovon, M. S. S. (2025). Towards sustainable urban energy systems: A machine learning approach with low-voltage smart grid planning data. In-ternational Journal of Applied Mathematics, 38(8s), 1115–1155. https://doi.org/10.12732/ijam.v38i8s.631.
    34. Sizan, M. M. H., et al. (2025). Machine learning-based unsupervised ensemble approach for detecting new money laundering typologies in transac-tion graphs. International Journal of Applied Mathematics, 38(2s). https://doi.org/10.12732/ijam.v38i2s.88.
    35. Vrontos, S. D., Galakis, I., & Vrontos, J. (2021). Modeling and predicting U.S. recessions using machine learning techniques. International Journal of Forecasting, 37(2), 647–671. https://doi.org/10.1016/j.ijforecast.2020.08.005.
    36. Wang, Z., Li, K., Xia, S. Q., & Liu, H. (2021). Economic recession prediction using deep neural network. arXiv.

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  • How to Cite

    Rahman, M. K., Hossain , M. S., Haque, S. U., Jahed, K. A., Robbani, M. S., Shati, M. A., Tuly, T., & Pramanik , M. T. . (2026). Machine Learning Models for Early Warning of Financial Crises in the U.S. Economy Using Macro-Financial Indicators. International Journal of Accounting and Economics Studies, 13(2), 513-525. https://doi.org/10.14419/wkr3pj23