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A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime

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  • M. Naresh Kumar
  • V. Sree Hari Rao

Abstract

Credit risk estimation and bankruptcy prediction methods have utilized Altman’s Z-score method for the last several years. It is reported in many studies that Z-score is sensitive to changes in accounting figures. Researchers have proposed different variations to conventional Z-score that can improve the prediction accuracy. In this paper, we develop a new multivariate nonlinear model for computing the Z-score. In addition, we develop a new credit risk index by fitting a Pearson type 3 distribution to the transformed financial ratios. The results of our study have shown that the new Z-score can predict the bankruptcy with an accuracy of 98.6 % as compared to 93.5 % by Altman’s Z-score. Also, the discriminate analysis revealed that the new transformed financial ratios could predict the bankruptcy probability with an accuracy of 93.0 % as compared to 87.4 % using the weights of Altman’s Z-score. Copyright Springer Science+Business Media New York 2015

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  • M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    • Handle: RePEc:kap:compec:v:46:y:2015:i:1:p:83-102
    DOI: 10.1007/s10614-014-9452-9
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    2. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    3. Bhanu Pratap SINGH & Alok Kumar MISHRA, 2019. "Sensitivity of bankruptcy prediction models to the change in econometric methods," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(620), A), pages 71-86, Autumn.
    4. Eduardo Acosta-González & Fernando Fernández-Rodríguez & Hicham Ganga, 2019. "Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 227-257, January.

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