Alternative methodologies in studies on business failure: do they produce better results than the classic statistical methods?
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- 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.
References listed on IDEAS
- R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, 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.
- J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
- Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
- 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.
- 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.
- 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.
- 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 323-345, December.
- 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.
- 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.
- Yochanan Shachmurove, 2002. "Applying Artificial Neural Networks to Business, Economics and Finance," Penn CARESS Working Papers 5ecbb5c20d3d547f357aa1306, Penn Economics Department.
- 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.
- 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.
- 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.
- Luoma, M & Laitinen, EK, 1991. "Survival analysis as a tool for company failure prediction," Omega, Elsevier, vol. 19(6), pages 673-678.
- Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, 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 269-291, March.
CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Luca Sensini, 2016. "An Empirical Analysis of Financially Distressed Italian Companies," International Business Research, Canadian Center of Science and Education, vol. 9(10), pages 75-85, October.
- Zeineb Affes & Rania Hentati-Kaffel, 2016.
"Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis,"
Documents de travail du Centre d'Economie de la Sorbonne
16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01281948, HAL.
- 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.
- 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.
- 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.
More about this item
NEP fieldsThis paper has been announced in the following NEP Reports:
- 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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