IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04223185.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04223185/document
    Download Restriction: no

    File URL: https://libkey.io/10.1080/23322039.2023.2251272?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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..

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    2. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    3. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    4. Fatima Alali & Silvia Romero, 2013. "Characteristics of failed U.S. commercial banks: an exploratory study," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(4), pages 1149-1174, December.
    5. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    6. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    7. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    8. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    9. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    10. Aykut Ekinci & Safa Sen, 2024. "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3161-3179, December.
    11. Colvin, Christopher L. & de Jong, Abe & Fliers, Philip T., 2015. "Predicting the past: Understanding the causes of bank distress in the Netherlands in the 1920s," Explorations in Economic History, Elsevier, vol. 55(C), pages 97-121.
    12. Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019. "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series 2019-070, Board of Governors of the Federal Reserve System (U.S.).
    13. Papanikolaou, Nikolaos I., 2018. "To be bailed out or to be left to fail? A dynamic competing risks hazard analysis," Journal of Financial Stability, Elsevier, vol. 34(C), pages 61-85.
    14. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    15. Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
    16. Kolari, James W. & López-Iturriaga, Félix J. & Sanz, Ivan Pastor, 2019. "Predicting European bank stress tests: Survival of the fittest," Global Finance Journal, Elsevier, vol. 39(C), pages 44-57.
    17. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    18. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2018. "Short term prediction of extreme returns based on the recurrence interval analysis," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 353-370, March.
    19. Tascón, María T. & Castaño, Francisco J., 2017. "Selection of Variables in Small Business Failure Analysis: Mean Selection vs. Median Selection || Selección de variables en el análisis de fracaso de empresas pequeñas: selección de medias frente a se," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 24(1), pages 54-88, Diciembre.
    20. Buckmann, Marcus & Gallego Marquez, Paula & Gimpelewicz, Mariana & Kapadia, Sujit & Rismanchi, Katie, 2023. "The more the merrier? Evidence on the value of multiple requirements in bank regulation," Journal of Banking & Finance, Elsevier, vol. 149(C).
    21. Veres Ferrer, Ernesto Jesús & Labatut Serer, Gregorio & Pozuelo Campillo, Jose, 2009. "Hacia una ordenación de las pequeñas empresas atendiendo a su posible situación de fracaso/Towards a Ranking of Smaller Companies According to Their Failure Risk," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 27, pages 775(18á)-77, Diciembre.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04223185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.