Alternative methodologies in studies on business failure: do they produce better results than the classic 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 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.
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Bibliographic InfoPaper provided by Vlerick Leuven Gent Management School in its series Vlerick Leuven Gent Management School Working Paper Series with number 2004-16.
Length: 44 pages
Date of creation: 21 Aug 2004
Date of revision:
Other versions of this item:
- S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
- NEP-ALL-2005-02-13 (All new papers)
- NEP-BEC-2005-02-13 (Business Economics)
- NEP-DCM-2005-02-13 (Discrete Choice Models)
- NEP-HPE-2005-02-13 (History & Philosophy of Economics)
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- Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
- Yochanan Shachmurove, 2002. "Applying Artificial Neural Networks to Business, Economics and Finance," Penn CARESS Working Papers 5ecbb5c20d3d547f357aa1306, Penn Economics Department.
- 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.
- 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.
- 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.
- Luoma, M & Laitinen, EK, 1991. "Survival analysis as a tool for company failure prediction," Omega, Elsevier, vol. 19(6), pages 673-678.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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