IDEAS home Printed from https://ideas.repec.org/a/ids/ijesbu/v43y2021i3p411-437.html
   My bibliography  Save this article

Failure prediction models: development and comparison between the multivariate discriminant analysis and the support vector machine for Tunisian companies

Author

Listed:
  • Fayçal Mraihi
  • Inane Kanzari

Abstract

In this study, we try to develop a model that would predict corporate default using a multivariate discriminant analysis (MDA) and a support vector machine (SVM). The two models are applied on the Tunisian context. Our sample consists of 212 companies operating in different industries, of which 106 are 'performing' companies and 106 are 'failing' companies, observed over the 2005-2010 period. The results of the use of a battery of 87 ratios showed that 16 ratios can build the model and that liquidity and solvency have more weight than profitability and management in predicting distress. Despite the slight superiority of the results provided by the SVM model, on the control sample, the results provided by the two models are good either in terms of correct classification percentage or in terms of the stability of discriminating power over time and space.

Suggested Citation

  • Fayçal Mraihi & Inane Kanzari, 2021. "Failure prediction models: development and comparison between the multivariate discriminant analysis and the support vector machine for Tunisian companies," International Journal of Entrepreneurship and Small Business, Inderscience Enterprises Ltd, vol. 43(3), pages 411-437.
    • Handle: RePEc:ids:ijesbu:v:43:y:2021:i:3:p:411-437
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=115882
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijesbu:v:43:y:2021:i:3:p:411-437. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=74 .

    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.