Abstract
Sovereign credit ratings are major indicators of a country’s financial structure as they provide an assessment of the creditworthiness of a country and its capability to meet its financial obligations. On the other side, how they are established by credit rating agencies (CRAs) is considered as not transparent and objective enough. This study aims to suggest a prediction framework for sovereign credit ratings based on machine learning (ML) algorithms using the following predictors: Credit Default Swap, Government Bond Yield, GDP/Capita, Consumer Price Index, Currency Volatility, and Political Risk.
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Ekmekcioglu, M., Kaya, T., Tokmakcioglu, K. (2023). Predicting Sovereign Credit Ratings Using Machine Learning Algorithms. In: Calisir, F., Durucu, M. (eds) Industrial Engineering in the Covid-19 Era. GJCIE 2022. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-25847-3_6
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DOI: https://doi.org/10.1007/978-3-031-25847-3_6
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