The Use of Different Approaches for Credit Rating Prediction and Their Comparison

Abstract

The objective of the paper is to apply three selected approaches for credit rating prediction, linear discriminant analysis, multinomial logistic regression and decision trees. The models are based on data of European companies’ financial indicators and MORE rating. A special attention is paid to comparison of models and interpretation of results. The analysis is also focused on the identification of variables with the most significant impact on credit rating assessment. The estimated models can be used for credit rating prediction and can serve as a useful tool in the investment decision process.

Keywords: credit rating, decision trees, discriminant analysis, logistic regression, modelling, prediction

JEL Classification: C35, C38, G24