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Business failure prediction: simple-intuitive models versus statistical models

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Author Info
H. OOGHE ()
C. SPAENJERS ()
P. VANDERMOERE ()

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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.

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Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 05/338.

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Length: 41 pages
Date of creation: Oct 2005
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Handle: RePEc:rug:rugwps:05/338

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  1. Janet Mitchell & Patrick Van Roy, 2007. "Failure prediction models : performance, disagreements, and internal rating systems," Research series 200712-18, National Bank of Belgium. [Downloadable!]
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