IDEAS home Printed from https://ideas.repec.org/a/rsk/journ1/7957482.html
   My bibliography  Save this article

Understanding and predicting systemic corporate distress: a machine-learning approach

Author

Listed:
  • Burcu Hacibedel
  • Ritong Qu

Abstract

evel probabilities of default, covering 55 economies and spanning the last three decades. Systemic corporate distress is identified by elevated probabilities of default across a large portion of the firms in an economy. A machine-learning-based earlywarning system is constructed to predict the risk of systemic distress in one year’s time. Our results show that credit expansion, monetary policy tightening, overvalued stock prices and debt-linked balance-sheet weaknesses predict corporate distress.We also find that systemic corporate distress events are associated with contractions in gross domestic product. Their impacts are milder than those of financial crises.

Suggested Citation

  • Burcu Hacibedel & Ritong Qu, . "Understanding and predicting systemic corporate distress: a machine-learning approach," Journal of Credit Risk, Journal of Credit Risk.
    • Handle: RePEc:rsk:journ1:7957482
    as

    Download full text from publisher

    File URL: https://www.risk.net/journal-of-credit-risk/7957482/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach
    Download Restriction: no
    ---><---

    More about this item

    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:rsk:journ1:7957482. 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: Thomas Paine (email available below). General contact details of provider: https://www.risk.net/journal-of-credit-risk .

    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.