Predicting bankruptcies using rough set approach: the case of Turkish banks

Evaluation of the reasons for business failures has been a major preoccupation of researchers and practitioners for many years. A large number of methods including multiple regression analysis, discriminant analysis, logit analysis, recursive partitioning algorithm, and several other techniques have been used for the prediction of business failures. This study has followed a different approach using the Rough Set theory to investigate the reasons of bankruptcies in the Turkish banking system. For this purpose, financial ratios of 29-41 commercial banks were examined for the 1995-2007 period. The results showed that this model is a promising alternative to the conventional methods for bankruptcy prediction.

[1]  Byeong Seok Ahn,et al.  The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .

[2]  E. Altman.  The success of business failure prediction models: An international survey , 1984 .

[3]  Constantin Zopounidis,et al.  A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .

[4]  J. Komorowski,et al.  FAULT DIAGNOSIS IN ROTATING MACHINERY USING ROUGH SETS AND ROSETTA : EXTENDED ABSTRACT , 2008 .

[5]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[6]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[7]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[8]  P.P.M. Joos,et al.  Financial distress models in Belgium: the results of a decade of empirical research , 1995 .

[9]  C. Zopounidis,et al.  Rough-Set Sorting of Firms According to Bankruptcy Risk , 1994 .

[10]  Kar Yan Tam,et al.  Neural network models and the prediction of bank bankruptcy , 1991 .

[11]  J. Krysiński,et al.  Application of the rough sets theory to the analysis of structure-activity-relationships of antimicrobial pyridinium compounds. , 1995, Die Pharmazie.

[12]  Zuleyka Díaz Martínez,et al.  Machine learning and statistical techniques. An application to the prediction of insolvency in spanish non-life insurance companies , 2005 .

[13]  Salvatore Greco,et al.  Rough approximation of a preference relation by dominance relations , 1999, Eur. J. Oper. Res..

[14]  M. Beynon,et al.  Variable precision rough set theory and data discretisation: an application to corporate failure prediction , 2001 .

[15]  Constantin Zopounidis,et al.  Application of the Rough Set Approach to Evaluation of Bankruptcy Risk , 1995 .

[16]  Sofie Balcaen,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Alternative Methodologies in Studies on Business Failure: Do They Produce Better Results than the Classical Statistical Methods? , 2022 .

[17]  Anne Carroll,et al.  Using Best's Ratings in Life Insurer Insolvency Prediction , 1994 .

[18]  Massimo Paruccini,et al.  Applying multiple criteria aid for decision to environmental management. , 1994 .

[19]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .