Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?
AbstractOver 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 classical 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 classical 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.
Download InfoIf 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.
Bibliographic InfoPaper 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 04/249.
Length: 40 pages
Date of creation: Jun 2004
Date of revision:
Other versions of this item:
- Balcaen S. & Ooghe H., 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?," Vlerick Leuven Gent Management School Working Paper Series 2004-16, Vlerick Leuven Gent Management School.
- NEP-ACC-2004-09-05 (Accounting & Auditing)
- NEP-ALL-2004-09-05 (All new papers)
- NEP-BEC-2004-09-05 (Business Economics)
- NEP-HPE-2004-09-05 (History & Philosophy of Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Yochanan Shachmurove, 2002. "Applying Artificial Neural Networks to Business, Economics and Finance," Penn CARESS Working Papers 5ecbb5c20d3d547f357aa1306, Penn Economics Department.
- Neophytou, E. & Molinero, C.M., 2001. "Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach," Papers 01-172, University of Southampton - Department of Accounting and Management Science.
- Altman, Edward I., 1984. "The success of business failure prediction models : An international survey," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 171-198, June.
- Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
- Kahya, Emel & Theodossiou, Panayiotis, 1999. " Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology," Review of Quantitative Finance and Accounting, Springer, vol. 13(4), pages 323-45, December.
- Kaiser, Ulrich, 2001. "Moving in and out of financial distress: evidence for newly founded service sector firms," ZEW Discussion Papers 01-09, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
- Neophytou, E. & Charitou, A. & Charalambous, C., 2001. "Predicting Corporate Failure: Empirical Evidence for the UK," Papers 01-173, University of Southampton - Department of Accounting and Management Science.
- Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
- Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. " Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-91, March.
- Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
- Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
- Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
- Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
- Luoma, M & Laitinen, EK, 1991. "Survival analysis as a tool for company failure prediction," Omega, Elsevier, vol. 19(6), pages 673-678.
- Cecilio Mar-Molinero & Carlos Serrano-Cinca, 2001. "Bank failure: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 7(2), pages 165-183.
- Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007.
- Niemann, Martin & Schmidt, Jan Hendrik & Neukirchen, Max, 2008. "Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity," Journal of Banking & Finance, Elsevier, vol. 32(3), pages 434-446, March.
- Alessandra Amendola & Marialuisa Restaino & Luca Sensini, 2013. "Corporate Financial Distress And Bankruptcy: A Comparative Analysis In France, Italy And Spain," Global Economic Observer, "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences & Institute for World Economy of the Romanian Academy, vol. 1(2), pages 131-142, November.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nathalie Verhaeghe).
If references are entirely missing, you can add them using this form.