Corporate rating forecasting using Artificial Intelligence statistical techniques
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Author(s)Daniel CaridadLink to ORCID Index: https://orcid.org/0000-0003-3843-8246
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Jana HančlováLink to ORCID Index: https://orcid.org/0000-0002-0939-6852
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Hosn el Woujoud BousselmiLink to ORCID Index: https://orcid.org/0000-0001-9544-8657
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Lorena Caridad López del RíoLink to ORCID Index: https://orcid.org/0000-0002-3406-9917
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DOIhttp://dx.doi.org/10.21511/imfi.16(2).2019.25
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Article InfoVolume 16 2019, Issue #2, pp. 295-312
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141 Downloads
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Creative Commons Attribution 4.0 International License
Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.
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JEL Classification (Paper profile tab)C45, G17
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References32
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Tables17
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Figures6
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- Figure 1. ANN estimated with sample I for S&P’s ratings
- Figure 2. ROC curve for the S&P prediction model with sample I
- Figure 3. Network to estimate Moody’s ratings (Sample I)
- Figure 4. ROC chart for the Moody’s model
- Figure 5. Network MLP (47, 13, 1) to estimate S&P rating
- Figure 6. ROC curve
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- Table 1. Total default probability for each rating level
- Table 2. S&P and Moody’s rating distributions in samples I and II
- Table 3. Rating’s codes
- Table 4. Distribution of rates in sample I
- Table 5. Sample I description of the X variables
- Table 6. S&P and Moody’s distributions
- Table 7. Areas under the ROC curves for the S&P ratings prediction (sample I)
- Table 8. Relative importance of the exogenous variables
- Table 9. Areas under ROC curves
- Table 10. Relative importance of the exogenous variables of the network
- Table 11. Classification with a network in three classes (sample I, S&P).
- Table 12. Classification with a network in three classes (sample I, S&P)
- Table 13. Descriptive statistics for basic variables
- Table 14. Fisher’s discriminant functions classification
- Table 15. Area under the ROC curve for each rating
- Table 16. Importance and normalized importance of each explanatory variable
- Table 17. Importance and relative importance of the input variables
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- Angelini, El., di Tollo, G. & Roli, A. (2008). A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4), 733-755.
- Bissoondoyal-Bheenick, E., & Treepongkaruna, S. (2011). Analysis of the determinants of bank ratings: comparison across rating agencies. Australian Journal of Management, 36(3), 405-424.
- Bongaerts, D. (2014). Alternatives for issuer-paid Credit Rating Agencies (W.P. 1-45).
- Chaveesuk, R., Srivaree-Ratana, C., & Smith, A. (1999). Alternative Neural Network Approaches to Corporate Bond Rating. Journal of Engineering Valuation and Cost Analysis, 2, 1-17.
- Devasena, L. (2014). Competency comparison between logistic classifier and partial decision tree classifier for credit risk prediction. Operations Research and Applications, 1(1), 31-40.
- Dima, A. M., & Vasilache, S. (2016). Credit risk modeling for companies default prediction using Neural Networks. Romanian Journal of Economic Forecasting XIX(3), 127-143.
- Dutta, S., & Shekhar, S. (1988). Bond rating: A non-conservative application of neural networks. In Proceedings of the IEEE Int. Conf. on Neural Networks (pp. 443-450).
- Financial Stability Board (2017). Artificial intelligence and machine learning in financial services (45 p.).
- Fitch Ratings (2018). Procedures and methodologies for determining credit ratings Form 25-101F1 (103 p.).
- Gangolf, C., Dochow, R., Schmidt, G., & Tamisier, T. (2016). Automated credit rating prediction in a competitive framework. RAIRO-Operations Research, 50(4-5), 749-765.
- Gogas, P., Papadimitriou, T., & Agrapetidou, A. (2013). Forecasting bank credit ratings. The Journal of Risk Finance, 15(2), 195-209.
- Horrigan, J. (1966). The determination of long-term credit standing with financial ratios. Journal of Accounting Research, 4, 44-62.
- Huang, Z., Chen, H., Hsu, C., Chen, W., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems, 37(4), 543-558.
- Irmatova, E. (2017). RELARM: a rating model based on relative PCA attributes and k-means clustering. Russian Journal of Entrepeneurship, 10, 1-20.
- Jayadev, M. (2006). Predictive power of Financial Risk Factors: an empirical analysis of default companies. Vikalpa: The Journal for Decisions Makers, 31(3), 45-56.
- Kaplan, R. S., & Urwitz, G. (1979). Statistical models of bond rating: a methodological inquiry. The Journal of Business, 52(2), 231-261.
- Karminsky, A., & Khromova, E. (2016). Extended modeling of banks’ credit ratings. Procedia Computer Science, 91, 201-210.
- Khemakhem, S., & Boujelbène, Y. (2015). Credit risk prediction: A comparative study between discriminant analysis and the neural network approach. Accounting and Management Information Systems, 14(1), 60-78.
- Kumar, K., & Haynes, J. D. (2003). Forecasting credit ratings using an ANN and statistical techniques. International Journal of Business Studies, 11(1), 91-108.
- Lee, M., & Lin, S. (2014). Integrating Genetic Algorithm and Rough Set Theory for Credit Rating Forecasting. Research and development, 46-47(3), 177-184.
- Maher, J., & Sen, T. K. (1997). Predicting Bond Ratings Using Neural Networks: A Comparison with Logistic Regression. Intelligent Systems in Accounting Finance & Management, 6(1), 59-72.
- Mayer, M., Resch, F., & Sauer, S. (2017). Validating point-in-time vs. through-the-cycle credit rating systems (Preliminary paper).
- Metz, A. (2006). Moody’s Credit Rating Prediction Model. Moody’s Investors Service Global Credit Research Report, 100722, 1-20.
- Novotna, M. (2012). The use of different approaches for credit rating prediction and their comparison. In Proceedings of the 6th International Conference on Managing and Modelling of Financial Risks (pp. 448-457).
- Pinches, G. E., & Mingo, K. A. (1973). A Multivariate Analysis of Industrial Bond Ratings. Journal of Finance, 28(1), 1-18.
- Ptak-Chmielewska, A. (2016). Statistical models for corporate credit risk assessment-rating models. Acta Universitatis Lodziensis Folia Oeconomica, 3(322), 87-111.
- Ruiz-Gándara, A., & Caridad y Ocerin, J. M. (2014). Computational Methods in the Identification of Forecasting Time Series Models. International Journal of Scientific Management and Tourism, 0, 5-17.
- Saha, S., & Waheed, S. (2017). Credit Risk of Bank Customers can be Predicted from Customer’s Attribute using Neural Networks. International Journal of Computer Applications, 161(3), 39-44.
- Shin, K. S., Lee, T. S., & Kim, H. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127-135.
- Surkan, A., & Singleton, J. C. (1990). Neural networks for bond rating improved by multiple hidden layers. IJCNN International Joint Conference on Neural Networks, 2, 157-162.
- Tsai, C-F., & Wu, J-W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4), 2639-2649.
- Zhao, Z., Xu, S., Ho Kang, B., Jahangir Kabir, M. M., Liu, Y., & Wasinger, R. (2015). Investigation and improvement of multi-layer perceptron neural networks for credit scoring. Expert Systems with Applications, 42(7), 3508-3516.
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