Modeling credit risk with a multi-stage hybrid model: An alternative statistical approach

Abstract

This paper examines the impact of hybridizations on the classification performances of sophisticated machine learning classifiers such as gradient boosting (GB, TreeNet®) and random forest (RF) using multi-stage hybrid models. The empirical findings confirm that, overall, hybrid model GB (X*Di; ŶDi, LR), which consists of TreeNet® combined with logistic regression along with a new dependent variable, offers significantly superior accuracy compared to the baselines and other hybrid classifiers. However, the performances of hybrid classifiers are not consistent across all types of datasets. For low-dimensional data, the constructed models consistently outperform the base classifiers; however, on high dimensional data, the classification outcomes provide little evidence of improvement and in certain cases, they underperform the baseline models. These findings have relevance for the analysis of high- and low-dimensional credit risk, small and medium enterprises, agricultural credits, and so on. Furthermore, the example credit risk scenario and its outcomes provide an alternative path for hybrid and machine learning approaches to be applied to more general applications in accounting and finance fields.