Using the European Commission country recommendations to predict sovereign ratings: A topic modeling approach

Abstract

This paper examines the role of textual and unstructured data in the credit risk assessment of sovereigns. Specifically, in this paper, a novel approach to understand and predict sovereign ratings is proposed. For that purpose, information embedded in the annual country reports issued by the European Commission is used. The model employs a neural-network-based document embedding known as document to vector (Doc2Vec) to convert each country report into a numerical vector, which is then used as features into a logistic regression. The model is trained using information from 2011 to 2019 and it correctly predicts the 70.27% of country ratings in the test sample, improving slightly the results obtained using only macroeconomic variables.