Predicting change in sovereign credit ratings using machine learning

Bonomo, Adrian Ricardo (A.A. 2020/2021) Predicting change in sovereign credit ratings using machine learning. Tesi di Laurea in Advanced financial economics, Luiss Guido Carli, relatore Paolo Porchia, pp. 97. [Master's Degree Thesis]

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Background and literature review. The world of credit rating agencies. The link between sovereign credit rating and sovereign risk. The impact of a sovereign credit rating change on the market. The limitations and issues of sovereign credit ratings. The regulation of CRAs. History and development of machine learning. Applications of machine learning for sovereign credit ratings. Baseline statistical method: ordered logistic model. Design and data. Model evaluation. SHAP value. Results. Cross validation accuracy. Determining factors.


Bibliografia: pp. 68-73.

Thesis Type: Master's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56)
Chair: Advanced financial economics
Thesis Supervisor: Porchia, Paolo
Thesis Co-Supervisor: Santucci de Magistris, Paolo
Academic Year: 2020/2021
Session: Extraordinary
Deposited by: Alessandro Perfetti
Date Deposited: 08 Sep 2022 15:44
Last Modified: 08 Sep 2022 15:44


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