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35 years of studies on business failure: an overview of the classical statistical methodologiesand their related problems

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  • S. BALCAEN

    ()

  • H. OOGHE

    ()

Abstract

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 ‘classical cross-sectional statistical’ methods have appeared to be most popular. Numerous ‘singleperiod’ 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 classical 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 classical 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 classical statistical business failure prediction models and their related problems.

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Bibliographic Info

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 04/248.

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Length: 62 pages
Date of creation: Jun 2004
Date of revision:
Handle: RePEc:rug:rugwps:04/248

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Cited by:
  1. 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.
  2. Ooghe, H. & De Prijcker, S., 2006. "Failure processes and causes of company bankruptcy: a typology," Vlerick Leuven Gent Management School Working Paper Series 2006-21, Vlerick Leuven Gent Management School.
  3. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  4. Korol, Tomasz & Korodi, Adrian, 2011. "An Evaluation of Effectiveness of Fuzzy Logic Model in Predicting the Business Bankruptcy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 92-107, September.
  5. du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.
  6. Malcolm Smith & Yun Ren & Yinan Dong, 2011. "The predictive ability of “conservatism” and “governance” variables in corporate financial disclosures," Asian Review of Accounting, Emerald Group Publishing, vol. 19(2), pages 171-185, September.
  7. De Cleyn S. & Braet J., 2006. "The evolution and performance of spin-off ventures: integration and elaboration of existing models," Working Papers 2006031, University of Antwerp, Faculty of Applied Economics.
  8. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
  9. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classificati," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.
  10. Erkki Laitinen, 2011. "Assessing viability of Finnish reorganization and bankruptcy firms," European Journal of Law and Economics, Springer, vol. 31(2), pages 167-198, April.
  11. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
  12. Alessandra Amendola & Marialuisa Restaino & Luca Sensini, 2010. "Variable Selection In Forecasting Models For Corporate Bankruptcy," Working Papers 3_216, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
  13. Mirabelle Muûls, 2012. "Exporters, Importers and Credit Constraints," CEP Discussion Papers dp1169, Centre for Economic Performance, LSE.
  14. Abbas, Qaiser & Rashid, Abdul, 2011. "Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan," MPRA Paper 28161, University Library of Munich, Germany.
  15. Janet Mitchell & Patrick Van Roy, 2007. "Failure prediction models : performance, disagreements, and internal rating systems," Working Paper Research 123, National Bank of Belgium.
  16. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
  17. Ovidiu CAPRARIU, 2010. "The Bankrupt Risk In Feed Distribution Branch In Dolj District – Fdr Model," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 0(S1), pages S156-S169, June.
  18. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.

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