Corpus ID: 231728501

Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques

@article{Overes2021ModellingSC,
  title={Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques},
  author={B. Overes and M. V. Wel},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.12684}
}
Sovereign credit ratings summarize the creditworthiness of countries. These ratings have a large influence on the economy and the yields at which governments can issue new debt. This paper investigates the use of a Multilayer Perceptron (MLP), Classification and Regression Trees (CART), and an Ordered Logit (OL) model for the prediction of sovereign credit ratings. We show that MLP is best suited for predicting sovereign credit ratings, with an accuracy of 68%, followed by CART (59%) and OL (33… Expand

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References

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