IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-642-18062-0_7.html
   My bibliography  Save this book chapter

Learning machines supporting bankruptcy prediction

In: Statistical Tools for Finance and Insurance

Author

Listed:
  • Wolfgang Karl Härdle

    (Humboldt Universität zu Berlin and National Central University, Center for Applied Statistics and Economics)

  • Linda Hoffmann

    (Humboldt Universität zu Berlin, Center for Applied Statistics and Economics)

  • Rouslan Moro

    (Brunel University)

Abstract

This work presents one of the more recent and efficient learning systems – support vector machines (SVMs). SVMs are mainly used to classify various specialized categories such as object recognition (Schölkopf (1997)), optical character recognition (Vapnik (1995)), electric load prediction (Eunite (2001)), management fraud detection (Rätsch and Müller (2004)), and early medical diagnostics. It is also used to predict the solvency or insolvency of companies or banks, which is the focus of this work. In other words, SVMs are capable of extracting useful information from financial data and then label companies by giving them score values. Furthermore, probability of default (PD) values for companies can be calculated from those score values. The method is explained later.

Suggested Citation

  • Wolfgang Karl Härdle & Linda Hoffmann & Rouslan Moro, 2011. "Learning machines supporting bankruptcy prediction," Springer Books, in: Pavel Cizek & Wolfgang Karl Härdle & Rafał Weron (ed.), Statistical Tools for Finance and Insurance, chapter 7, pages 225-250, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-18062-0_7
    DOI: 10.1007/978-3-642-18062-0_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-642-18062-0_7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.