Abstract
AbstractDefault prediction is the primary goal of credit risk management. This problem has long been tackled using well-established statistical classification models. Still, nowadays, the availability of large datasets and cheap software implementations makes it possible to employ machine learning techniques. This paper uses a large sample of small Italian companies to compare the performance of various machine learning classifiers and a more traditional logistic regression approach. In particular, we perform feature selection, use the algorithms for default prediction, evaluate their accuracy, and find a more suitable threshold as a function of sensitivity and specificity. Our outcomes suggest that machine learning is slightly better than logistic regression. However, the relatively small performance gain is insufficient to conclude that classical statistical classifiers should be abandoned, as they are characterized by more straightforward interpretation and implementation.
Funder
Università degli Studi di Trento
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Economics and Econometrics,Finance,Business and International Management