• 35 Years of studies on business failure: an overview of the classic statistical methodologies and their related problems

      Balcaen, Sofie; Ooghe, Hubert (Vlerick Business School, 2004)
      Over the last 35 years, the topic of business failure prediction has developed to a major research domain in corporate finance. A gigantic number of academic researchers from all over the world have been developing corporate failure prediction models, based on various modelling techniques. The ‘classic cross-sectional statistical' methods have appeared to be most popular. Numerous ‘single-period' or ‘static' models have been developed, especially multivariate discriminant models and logit models. As to date, a clear overview and discussion of the application of the classic cross-sectional statistical methods in corporate failure prediction is still lacking, this paper extensively elaborates on the application of (1) univariate analysis, (2) risk index models, (3) multivariate discriminant analysis, and (4) conditional probability models, such as logit, probit and linear probability models. It discusses the main features of these methods and their specific assumptions, advantages and disadvantages and it gives an overview of a large number of academically developed corporate failure prediction models. Despite the popularity of the classic statistical methods, there have appeared to be several problems related to the application of these methods to the topic of corporate failure prediction. However, in the existing literature there is no clear and comprehensive analysis of the diverse problems. Therefore, this paper brings together all criticisms and problems and extensively enlarges upon each of these issues. So as to give a clear overview, the diverse problems are categorized into a number of broad topics: problems related to (1) the dichotomous dependent variable, (2) the sampling method, (3) non-stationarity and data instability, (4) the use of annual account information, (5) the selection of the independent variables, and (6) the time dimension. This paper contributes towards a thorough understanding of the features of the classic statistical business failure prediction models and their related problems.
    • Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?

      Balcaen, Sofie; Ooghe, Hubert (Vlerick Business School, 2004)
      Over the last 35 years, the topic of company failure prediction has developed to a major research domain in corporate finance. Academic researchers from all over the world have been developing a gigantic number of corporate failure prediction models, based on various types of modelling techniques. Besides the classic cross-sectional statistical methods, which have produced numerous failure prediction models, researchers have also been using several alternative methods for analysing and predicting business failure. To date, a clear overview and discussion of the application of alternative methods in corporate failure prediction is still lacking. Moreover, frequently, different designations or names are used for one method. Therefore, this study aims to provide a clear overview of the alternative research methods, attributing each of them a fixed designation. More in particular, this paper extensively elaborates on the most popular methods of survival analysis, machine learning decision trees and neural networks. Furthermore, it discusses several other alternative methods, which can be considered to have a certain value added in the empirical literature on business failure: the fuzzy rules-based classification model, the multi-logit model, the CUSUM model, dynamic event history analysis, the catastrophe theory and chaos theory model, multidimensional scaling, linear goal programming, the multi-criteria decision aid approach, rough set analysis, expert systems and self-organizing maps. This paper discusses the main features of these methods and their specific assumptions, advantages and disadvantages and it gives an overview of a number of academically developed corporate failure prediction models. Several issues viewed in isolation by earlier studies are here considered together, which is of major importance for gaining a clear insight into the possible alternative methods of corporate failure modelling and their corresponding features. A second aim of this paper is to find an answer to the question whether the more sophisticated, alternative modelling methods produce better performing failure prediction models than the rather simple classic statistical methods. The analysis of the conclusions of a large number of empirical studies comparing the classification results and/or the prediction abilities of failure prediction models based on different techniques seems to indicate that we may question the benefits to be gained from using the more sophisticated alternative methods.
    • Failure prediction models from different countries : empirical testing on Belgian companies and possible explanations

      Ooghe, Hubert; Camerlynck, Jan; Balcaen, Sofie (UGent, Fac. Economie & Bedrijfskunde, 2001)
    • From distress to exit: determinants of the time to exit

      Balcaen, Sofie; Manigart, Sophie; Ooghe, Hubert (2009)
    • The Ooghe-Joos-De Vos failure prediction models : a cross-industry validation

      Ooghe, Hubert; Camerlynck, Jan; Balcaen, Sofie (UGent, Fac. Economie & Bedrijfskunde, 2001)
      This study tests the validity of the Belgian Ooghe-Joos-De Vos (1991) failure prediction models (1 and 3 years prior to failure) across 18 different industries and different size classes. Firstly, the performance results and the trade-off functions reveal a wide range of performances for the different industries. However, we notice that the OJD models perform best for the classical manufacturing industries and financial services, while they show the worst performance results for the service industries and the no-industry category. Furthermore, when using new, industry specific cut-off points, the error rates of the models are significantly reduced. Secondly, the OJD model 1 year prior to failure seems to perform best for large companies and companies with complete form annual accounts. Finally, the performance differences between the various subgroups with respect to industry, size class and form of annual account of the model 3 years prior to failure the are much smaller than those of the model 1 year prior to failure.