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Can Text-Based Statistical Models Reveal Impending Banking Crises?

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  • Emile du Plessis

    (University of Hamburg)

Abstract

This paper introduces statistical models Wordscores and Wordfish to study and predict banking crises. While Wordscores is akin to supervised learning, Wordfish is analogous to unsupervised learning. Both methods estimate the position of banking distress on a tranquil-to-crisis spectrum. Findings suggest that the two statistical methods signal banking crisis up to two-years in advance, with robust results from AUROC, Granger causality and VAR impulse responses. Both methods outperform random forests in predicting crises using textual data. The Wordscores index highlights increased usage of banking sector nomenclature two years preceding a crisis, and Granger causes a crisis series with one and two lag lengths. Results from the Wordfish technique, a statistical model with Poisson distribution, show the index spikes before and during the Global Financial Crisis, when a large share of the countries in the world encountered banking crises. This paper contributes to literature on text-based models of banking crises by bolstering the preemptive policy responses available to policy makers. Given their early warning signals, both Wordscores and Wordfish can be considered a part of the toolset to monitor the stability and resilience of the banking sector.

Suggested Citation

  • Emile du Plessis, 2025. "Can Text-Based Statistical Models Reveal Impending Banking Crises?," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1265-1298, March.
  • Handle: RePEc:kap:compec:v:65:y:2025:i:3:d:10.1007_s10614-024-10594-5
    DOI: 10.1007/s10614-024-10594-5
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    1. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    2. Mr. Luc Laeven & Mr. Fabian Valencia, 2010. "Resolution of Banking Crises: The Good, the Bad, and the Ugly," IMF Working Papers 2010/146, International Monetary Fund.
    3. Xin Chen & Yang Wang & Yifei Zhang, 2023. "Detecting Financial Statement Fraud Using Machine-Learning Methods," World Scientific Book Chapters, in: Daisy Chou & Conall O'Sullivan & Vassilios G Papavassiliou (ed.), FinTech Research and Applications Challenges and Opportunities, chapter 6, pages 235-263, World Scientific Publishing Co. Pte. Ltd..
    4. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    5. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    6. Luc Laeven & Fabian Valencia, 2020. "Systemic Banking Crises Database II," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 68(2), pages 307-361, June.
    7. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    8. Tongyu Wang & Shangmei Zhao & Guangxiang Zhu & Haitao Zheng, 2021. "A machine learning-based early warning system for systemic banking crises," Applied Economics, Taylor & Francis Journals, vol. 53(26), pages 2974-2992, June.
    9. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    10. Carmen M. Reinhart & Kenneth S. Rogoff, 2009. "Varieties of Crises and Their Dates," Introductory Chapters, in: This Time Is Different: Eight Centuries of Financial Folly, Princeton University Press.
    11. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "An alternative approach to predicting bank credit risk in Europe with Google data," Finance Research Letters, Elsevier, vol. 35(C).
    12. Gorton, Gary, 1988. "Banking Panics and Business Cycles," Oxford Economic Papers, Oxford University Press, vol. 40(4), pages 751-781, December.
    13. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    14. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," International Finance Discussion Papers 1374, Board of Governors of the Federal Reserve System (U.S.).
    15. Mary Chen & Matthew DeHaven & Isabel Kitschelt & Seung Jung Lee & Martin J. Sicilian, 2023. "Identifying Financial Crises Using Machine Learning on Textual Data," JRFM, MDPI, vol. 16(3), pages 1-28, March.
    16. Gresnigt, Francine & Kole, Erik & Franses, Philip Hans, 2015. "Interpreting financial market crashes as earthquakes: A new Early Warning System for medium term crashes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 123-139.
    17. Nitish Ranjan Sinha, 2016. "Underreaction to News in the US Stock Market," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-46, June.
    18. Wicker, Elmus, 1980. "A Reconsideration of the Causes of the Banking Panic of 1930," The Journal of Economic History, Cambridge University Press, vol. 40(3), pages 571-583, September.
    19. Steven L. Heston & Nitish Ranjan Sinha, 2017. "News vs. Sentiment: Predicting Stock Returns from News Stories," Financial Analysts Journal, Taylor & Francis Journals, vol. 73(3), pages 67-83, July.
    20. Emile du Plessis, 2022. "Dynamic forecasting of banking crises with a Qual VAR," Journal of Applied Economics, Taylor & Francis Journals, vol. 25(1), pages 477-503, December.
    21. Romain Rancière & Aaron Tornell & Frank Westermann, 2008. "Systemic Crises and Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(1), pages 359-406.
    22. Jonathan B. Slapin & Sven‐Oliver Proksch, 2008. "A Scaling Model for Estimating Time‐Series Party Positions from Texts," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 705-722, July.
    23. Mark Joy & Marek Rusnák & Kateřina Šmídková & Bořek Vašíček, 2017. "Banking and Currency Crises: Differential Diagnostics for Developed Countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 44-67, January.
    24. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    25. du Plessis, Emile, 2022. "Multinomial modeling methods: Predicting four decades of international banking crises," Economic Systems, Elsevier, vol. 46(2).
    26. Reinhart, Karmen & Rogoff, Kenneth, 2009. ""This time is different": panorama of eight centuries of financial crises," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 77-114, March.
    27. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    28. Gerard Hoberg & Gordon Phillips, 2010. "Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 23(10), pages 3773-3811, October.
    29. Kenneth Benoit & Michael Laver & Christine Arnold & Paul Pennings & Madeleine O. Hosli, 2005. "Measuring National Delegate Positions at the Convention on the Future of Europe Using Computerized Word Scoring," European Union Politics, , vol. 6(3), pages 291-313, September.
    30. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    31. Charles W. Calomiris & Joseph R. Mason, 2003. "Fundamentals, Panics, and Bank Distress During the Depression," American Economic Review, American Economic Association, vol. 93(5), pages 1615-1647, December.
    32. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    33. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    34. Christina D. Romer & David H. Romer, 2017. "New Evidence on the Aftermath of Financial Crises in Advanced Countries," American Economic Review, American Economic Association, vol. 107(10), pages 3072-3118, October.
    35. Charles W. Calomiris & Harry Mamaysky, 2018. "How News and Its Context Drive Risk and Returns Around the World," NBER Working Papers 24430, National Bureau of Economic Research, Inc.
    36. Mr. Burkhard Drees & Ceyla Pazarbasioglu, 1998. "The Nordic Banking Crisis: Pitfalls in Financial Liberalization: Pitfalls in Financial Liberalization," IMF Occasional Papers 1998/007, International Monetary Fund.
    37. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    38. Tsai, Ming-Feng & Wang, Chuan-Ju, 2017. "On the risk prediction and analysis of soft information in finance reports," European Journal of Operational Research, Elsevier, vol. 257(1), pages 243-250.
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    More about this item

    Keywords

    Quantitative analysis of textual data; Banking crises; Text-based models; Early warning signal;
    All these keywords.

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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