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Why do banks fail? An investigation via text mining

Author

Listed:
  • Hanh Hong Le

    (RMIT University Vietnam)

  • Jean-Laurent Viviani

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

  • Fitriya Fauzi

    (RMIT University Vietnam)

Abstract

This study aims to investigate the material loss review published by the Federal Deposit Insurance Corporation (FDIC) on 98 failed banks from 2008 to 2015. The text mining techniques via machine learning, i.e. bag of words, document clustering, and topic modeling, are employed for the investigation. The pre-processing step of text cleaning is first performed prior to the analysis. In comparison with traditional methods using financial ratios, our study generates actionable insights extracted from semi-structured textual data, i.e. the FDIC's reports. Our text analytics suggests that to prevent from being a failure; banks should beware of loans, board management, supervisory process, the concentration of acquisition, development, and construction (ADC), and commercial real estate (CRE). In addition, the primary reasons that US banks went failure from 2008 to 2015 are explained by two primary topics, i.e. loan and management.

Suggested Citation

  • Hanh Hong Le & Jean-Laurent Viviani & Fitriya Fauzi, 2023. "Why do banks fail? An investigation via text mining," Post-Print hal-04223185, HAL.
    • Handle: RePEc:hal:journl:hal-04223185
    DOI: 10.1080/23322039.2023.2251272
    Note: View the original document on HAL open archive server: https://hal.science/hal-04223185
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    References listed on IDEAS

    as
    1. Adam B. Ashcraft, 2005. "Are Banks Really Special? New Evidence from the FDIC-Induced Failure of Healthy Banks," American Economic Review, American Economic Association, vol. 95(5), pages 1712-1730, December.
    2. Martin D. D. Evans & Richard K. Lyons, 2017. "How is Macro News Transmitted to Exchange Rates?," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 14, pages 547-596, World Scientific Publishing Co. Pte. Ltd..
    3. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    4. Zongyun Li & Panteha Farmanesh & Dervis Kirikkaleli & Rania Itani, 2022. "A comparative analysis of COVID-19 and global financial crises: evidence from US economy," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 2427-2441, December.
    5. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
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    Cited by:

    1. William C. Dudley, 2024. "Bank Failures and Contagion Lender of Last Resort, Liquidity, and Risk Management," Working Papers 329, Princeton University, Department of Economics, Center for Economic Policy Studies..

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    More about this item

    Keywords

    text mining; US failed bank; BoW; k-means; topic modeling; hierarchies clustering; G00; G21;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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