IDEAS home Printed from https://ideas.repec.org/a/ids/ijelfi/v2y2008i2p241-255.html
   My bibliography  Save this article

A genetic-based hybrid approach to corporate failure prediction

Author

Listed:
  • Ping-Chen Lin
  • Jiah-Shing Chen

Abstract

This paper proposes a genetic-based hybrid approach to predict the possibility of corporate failure. We use Genetic Algorithm (GA) to select the critical variables set and optimise the weight of each classifier for integrating the best features of several classification approaches (such as discriminant analysis, logistic regression and neural networks) in order to enhance prediction results. GA with nonlinear searching capabilities extracts more critical feature variables if compared with the Stepwise Method. This means that the undesirable variables for classification models will be cleaned out by GA. In addition, our experimental results show that this hybrid approach obtains better prediction performance than when using a single approach effectively.

Suggested Citation

  • Ping-Chen Lin & Jiah-Shing Chen, 2008. "A genetic-based hybrid approach to corporate failure prediction," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 2(2), pages 241-255.
  • Handle: RePEc:ids:ijelfi:v:2:y:2008:i:2:p:241-255
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=17543
    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:ijelfi:v:2:y:2008:i:2:p:241-255. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Darren Simpson). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=171 .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.