Business failure prediction: simple-intuitive models versus statistical models

Hubert Ooghe, Christophe Spaenjers and P. Vandermoere ()
Additional contact information
P. Vandermoere: -

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

Abstract: We give an overview of the shortcomings of the most frequently used statistical techniques in failure prediction modelling. The statistical procedures that underpin the selection of variables and the determination of coefficients often lead to ‘overfitting’. We also see that the ‘expected signs’ of variables are sometimes neglected and that an underlying theoretical framework mostly does not exist. Based on the current knowledge of failing firms, we construct a new type of failure prediction models, namely ‘simple-intuitive models’. In these models, eight variables are first logit-transformed and then equally weighted. These models are tested on two broad validation samples (1 year prior to failure and 3 years prior to failure) of Belgian companies. The performance results of the best simple-intuitive model are comparable to those of less transparent and more complex statistical models.

Pages: 41 pages
Date: 2005-10
New Economics Papers: this item is included in nep-bec, nep-ecm, nep-ent and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://wps-feb.ugent.be/Papers/wp_05_338.pdf (application/pdf)

Related works:
Working Paper: Business failure prediction: simple-intuitive models versus statistical models (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:05/338

Access Statistics for this paper

More papers in Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration Contact information at EDIRC.
Bibliographic data for series maintained by Nathalie Verhaeghe ().