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Business failure prediction: simple - intuitive models versus statistical models
Hubert Ooghe,Christophe Spaenjers,Pieter Vandermoere +2 more
- Vol. 6, pp 1-55
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In this paper, the authors give an overview of the shortcomings of the most frequently used statistical techniques in failure prediction modeling and show that the statistical procedures that underpin the selection of variables and the determination of coefficients often lead to overfitting.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.read more
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Journal ArticleDOI
Predicting business failure under the existence of fraudulent financial reporting
Fen‐May Liou,Chien‐Hui Yang +1 more
TL;DR: The one- stage NN model has a higher accuracy rate in identifying failed firms than the discriminant analysis, while the two-stage NN approach has an evenHigher accuracy rate than the one-stageNN model.
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