Evaluation of empirical attributes for credit risk forecasting from numerical data
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DOIhttp://dx.doi.org/10.21511/imfi.14(1).2017.01
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Article InfoVolume 14 2017, Issue #1, pp. 9-18
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In this research, the authors proposed a new method to evaluate borrowers’ credit risk and quality of financial statements information provided. They use qualitative and quantitative criteria to measure the quality and the reliability of its credit customers. Under this statement, the authors evaluate 35 features that are empirically utilized for forecasting the borrowers’ credit behavior of a Greek Bank. These features are initially selected according to universally accepted criteria. A set of historical data was collected and an extensive data analysis is performed by using non parametric models. Our analysis revealed that building simplified model by using only three out of the thirty five initially selected features one can achieve the same or slightly better forecasting accuracy when compared to the one achieved by the model uses all the initial features. Also, experimentally verified claim that universally accepted criteria can’t be globally used to achieve optimal results is discussed.
- Keywords
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JEL Classification (Paper profile tab)E5, C63, M41
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References27
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Tables3
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Figures1
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- Fig. 1. Tenfold cross-validation
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- Table 1. Key steps in a bank’s loan decision
- Table 2. Areas of business analysis
- Table 3. The selected attributes presented in descending order according to their |r| value
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- D’ Agostino, R. B. (1971). An omnibus test of normality for moderate and large size samples. Biometrika, 58, 341-348.
- Basel Committee on Banking Supervision. (2006). Observed range of practice in key elements of Advanced Measurement Approaches (AMA).
- Benos, A., and Papanastasopoulos, G. (2007). Extending the Merton model: A hybrid approach to assessing credit quality. Mathematical and computer modelling, 46, 47-68.
- Bowman, K. O., and Shenton, L. R. (1975). Omnibus test contours for departures from normality based on b1 and b2, Biometrika, 62, 243-250.
- Corder, G. W., and Foreman, D. I. (2009). Nonparametric statistics for non-statisticians: a step-by-step approach. John Wiley and Sons.
- Daniels, K., and Ramirez, G. (2008). Information, credit risk, lender specialization and loan pricing: Evidence from the DIP financing market. Journal of Financial Services Research, 34, 35-59.
- Diamantaras, K. I., and Kung, S. Y. (1996). Principal component neural networks: theory and applications, John Wiley and Sons, Inc.
- Doumpos, M., and Zopounidis, C. (2001). Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment. Omega, 29, 97-109.
- Duff, A., and Einig, S. (2009). Credit ratings quality: The perceptions of market participants and other interested parties. The British Accounting Review, 41, pp. 141-53.
- Fernandes, J. (2005). Corporate credit risk modeling: Quantitative rating system and probability of default estimation. Available at SSRN 722941.
- Garefalakis, A. Dimitras, A. Floros, C., Lemonakis, C. (2016). How Narrative Reporting changed the Business World: Providing a new measurement tool. Corporate Ownership and Control, 13, 317-334.
- Grimm, L. G., and Yarnold, P. R. (1995) Reading and understanding multivariate statistics. American Psychological Association.
- Hall, M. A., and Smith, L. A. (1998). Practical feature subset selection for machine learning.
- Huang, C. L., Chen, M. C., and Wang, C.J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33(4), 847-56.
- Kohavi, R., and John, G.H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97, 273-324.
- Kosmidou, K., Pasiouras, F., Tsaklanganos, A. (2007). Domestic and multinational determinants of foreign bank profits: The case of Greek banks operating abroad. Journal of Multinational Financial Management, 17, 1-15.
- Kung, S. Y., and Diamantaras, K. I. (1991). Neural networks for extracting unsymmetric principal components. Neural Networks for Signal Processing [1991], Proceedings of the 1991 IEEE Workshop (pp. 50-59). IEEE.
- Pasiouras, F., Gaganis, C., Zopounidis, C. (2006). The impact of bank regulations, supervision, market structure and bank characteristics on individual bank ratings: a cross-country analysis. Review of Quantitative Finance and Accounting, 27(4), 403-438.
- Pasiouras, F., Tanna, S. (2010). The Prediction of bank acquisition targets with discriminant and logit analyses: methodological issues and empirical evidence Research in International Business and Finance, 24, 39-61.
- Sieczka, P., and Hołyst, J. A. (2009). Collective firm bankruptcies and phase transition in rating dynamics. The European Physical Journal B-Condensed Matter and Complex Systems, 71(4), 461-466.
- Smola, A. J., and Schölkopf, B. (1998). Learning with kernels. Citeseer.
- Sola, J., and Sevilla, J. (1997). Importance of input data normalization for the application of neural networks to complex industrial problems. Nuclear Science, IEEE Transactions on, 44, 1464-1468.
- Song, F., Guo, Z., and Mei, D. (2010). Feature selection using principal component analysis, 1, 27-30. Presented at the System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2010 International Conference on, IEEE.
- Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge.
- Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.
- Vapnik, V. (1992). Principles of risk minimization for learning theory. Advances in neural information processing systems, 831-838.
- Vapnik, V. (2000). The nature of statistical learning theory. Springer Science and Business Media.