Advanced Search
MyIDEAS: Login to save this article or follow this journal

A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

Contents:

Author Info

  • Juliana Yim

    ()
    (RMIT University)

  • Heather Mitchell

    (RMIT University)

Registered author(s):

    Abstract

    This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.face.ufmg.br/novaeconomia/sumarios/v15n1/150103.pdf
    Download Restriction: no

    Bibliographic Info

    Article provided by Economics Department, Universidade Federal de Minas Gerais (Brazil) in its journal Nova Economia.

    Volume (Year): 15 (2005)
    Issue (Month): 1 (January-April)
    Pages: 73-93

    as in new window
    Handle: RePEc:nov:artigo:v:15:y:2005:i:1:p:73-93

    Contact details of provider:
    Postal: Av. Antonio Carlos, 6627 - Predio da FACE Belo Horizonte, 31270-901 Brazil
    Phone: +55 31 3409-7000
    Email:
    Web page: http://www.face.ufmg.br/
    More information through EDIRC

    Order Information:
    Postal: Av. Antonio Carlos, 6627 - Predio da FACE Belo Horizonte, 31270-901 Brazil
    Email:
    Web: http://www.face.ufmg.br/novaeconomia/

    Related research

    Keywords: hybrid neural networks; corporate failures;

    Find related papers by JEL classification:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

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

    Cited by:
    1. Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:nov:artigo:v:15:y:2005:i:1:p:73-93. 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: (Sibelle Diniz).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.