Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?
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DOI: 10.1007/s10614-015-9534-3
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Cited by:
- Aykut Ekinci & Halil İbrahim Erdal, 2017. "Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 677-686, April.
- Bart H. L. Overes & Michel van der Wel, 2021. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers 2101.12684, arXiv.org, revised Jul 2021.
- Duygun, Meryem & Ozturk, Huseyin & Shaban, Mohamed, 2016. "The role of sovereign credit ratings in fiscal discipline," Emerging Markets Review, Elsevier, vol. 27(C), pages 197-216.
- Chen, Jian & Katchova, Ani L. & Zhou, Chenxi, 2021.
"Agricultural loan delinquency prediction using machine learning methods,"
International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(5), May.
- Chen, Jian & Katchova, Ani, 2019. "Agricultural Loan Delinquency Prediction Using Machine Learning Methods," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290745, Agricultural and Applied Economics Association.
- Guneren Genc, Elif & Deniz Basar, Ozlem, 2019. "Comparison of Country Ratings of Credit Rating Agencies with MOORA Method," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 10(2), pages 391-404, April.
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Keywords
Classification and regression trees (CART); Multilayer perceptron (MP); Support vector machines (SVM); Bayes net; and naïve Bayes; Sovereign credit ratings;All these keywords.
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