IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v254y2016i1p236-252.html
   My bibliography  Save this article

A two-stage classification technique for bankruptcy prediction

Author

Listed:
  • du Jardin, Philippe

Abstract

Ensemble techniques such as bagging or boosting, which are based on combinations of classifiers, make it possible to design models that are often more accurate than those that are made up of a unique prediction rule. However, the performance of an ensemble solely relies on the diversity of its different components and, ultimately, on the algorithm that is used to create this diversity. It means that such models, when they are designed to forecast corporate bankruptcy, do not incorporate or use any explicit knowledge about this phenomenon that might supplement or enrich the information they are likely to capture. This is the reason why we propose a method that is precisely based on some knowledge that governs bankruptcy, using the concept of “financial profiles”, and we show how the complementarity between this technique and ensemble techniques can improve forecasts.

Suggested Citation

  • du Jardin, Philippe, 2016. "A two-stage classification technique for bankruptcy prediction," European Journal of Operational Research, Elsevier, vol. 254(1), pages 236-252.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:1:p:236-252
    DOI: 10.1016/j.ejor.2016.03.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716301369
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.03.008?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
    ---><---

    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:eee:ejores:v:254:y:2016:i:1:p:236-252. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.