IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v7y1998i3p187-197.html
   My bibliography  Save this article

Using neural networks to predict corporate failure

Author

Listed:
  • Daniel E. O’Leary

Abstract

Predicting corporate failure or bankruptcy is one of the most important problems facing business and government. The recent Savings and Loan crisis is one example, where bankruptcies cost the United States billions of dollars and became a national political issue. This paper provides a ‘meta analysis’ of the use of neural networks to predict corporate failure. Fifteen papers are reviewed and compared in order to investigate ‘what works and what doesn’t work’. The studies are compared for their formulations including aspects such as the impact of using different percentages of bankrupt firms, the software they used, the input variables, the nature of the hidden layer used, the number of nodes in the hidden layer, the output variables, training and testing and statistical analysis of results. Then the findings are compared across a number of dimensions, including, similarity of comparative solutions, number of correct classifications, impact of hidden layers, and the impact of the percentage of bankrupt firms. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Daniel E. O’Leary, 1998. "Using neural networks to predict corporate failure," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(3), pages 187-197, September.
    • Handle: RePEc:wly:isacfm:v:7:y:1998:i:3:p:187-197
    DOI: 10.1002/(SICI)1099-1174(199809)7:33.0.CO;2-7
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/(SICI)1099-1174(199809)7:33.0.CO;2-7
    Download Restriction: no
    File URL: https://libkey.io/10.1002/(SICI)1099-1174(199809)7:33.0.CO;2-7?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
    ---><---

    Citations

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


    Cited by:

    1. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.

    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:wly:isacfm:v:7:y:1998:i:3:p:187-197. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.