Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry
Probability of default prediction is one of the important tasks of rating agencies as well as of banks and other financial companies to measure the default risk of their counterparties. Knowing predictors that significantly contribute to default prediction provides a better insight into fundamentals of credit risk analysis. Default prediction and default predictor selection are two related issues, but many existing approaches address them separately. We employed a unified procedure, a regularization approach with logit as an underlying model, which simultaneously selects the default predictors and optimizes all the parameters within the model. We employ Lasso and elastic-net penalty functions as regularization approach. The methods are applied to predict default of companies from industry sector in Southeast Asian countries. The empirical result exhibits that the proposed method has a very high accuracy prediction particularly for companies operating Indonesia, Singapore, and Thailand. The relevant default predictors over the countries reveal that credit risk analysis is sample specific. A few number of predictors result in counter intuitive sign estimates.
|Date of creation:||Aug 2013|
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- Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
- Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
- Wolfgang Karl HÃ¤rdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
- Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
- repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
- Racine, Jeffrey & Su, Liangjun & Ullah, Aman, 2014. "The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics," OUP Catalogue, Oxford University Press, number 9780199857944.
- Zhang, Junni L. & Härdle, Wolfgang K., 2010.
"The Bayesian Additive Classification Tree applied to credit risk modelling,"
Computational Statistics & Data Analysis,
Elsevier, vol. 54(5), pages 1197-1205, May.
- Junni L. Zhang & Wolfgang Härdle, 2008. "The Bayesian Additive Classification Tree Applied to Credit Risk Modelling," SFB 649 Discussion Papers SFB649DP2008-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Shiyi Chen & W. K. Hardle & R. A. Moro, 2011. "Modeling default risk with support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 11(1), pages 135-154.
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
- S. Balcaen & H. Ooghe, 2004. "35 years of studies on business failure: an overview of the classical statistical methodologiesand their related problems," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/248, Ghent University, Faculty of Economics and Business Administration.
- Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
- repec:bla:joares:v:4:y:1966:i::p:71-111 is not listed on IDEAS
- Maria Vassalou & Yuhang Xing, 2004. "Default Risk in Equity Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 831-868, April.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320.
- Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March. Full references (including those not matched with items on IDEAS)
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