IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-04159762.html
   My bibliography  Save this paper

What do bankrupcty prediction models tell us about banking regulation? Evidence from statistical and learning approaches

Author

Listed:
  • Pierre Durand

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Gaëtan Le Quang

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

Prudential regulation is supposed to strengthen financial stability and banks' resilience to new economic shocks. We tackle this issue by evaluating the impact of leverage, capital, and liquidity ratios on banks default probability. To this aim, we use logistic regression, random forest classification, and artificial neural networks applied on the United-States and European samples over the 2000-2018 period. Our results are based on 4707 banks in the US and 3529 banks in Europe, among which 454 and 205 defaults respectively. We show that, in the US sample, capital and equity ratios have strong negative impact on default probability. Liquidity ratio has a positive effect which can be justified by the low returns associated with liquid assets. Overall, our investigation suggests that fewer prudential rules and higher leverage ratio should reinforce the banking system's resilience. Because of the lack of official failed banks list in Europe, our findings on this sample are more delicate to interpret.

Suggested Citation

  • Pierre Durand & Gaëtan Le Quang, 2021. "What do bankrupcty prediction models tell us about banking regulation? Evidence from statistical and learning approaches," Working Papers hal-04159762, HAL.
    • Handle: RePEc:hal:wpaper:hal-04159762
    Note: View the original document on HAL open archive server: https://hal.science/hal-04159762
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04159762/document
    Download Restriction: no
    ---><---

    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:wpaper:hal-04159762. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.