Small and medium-sized enterprises’ time to default: an analysis using an improved mixture cure model with time-varying covariates

The analysis of the survival time of small and medium-sized enterprises (SMEs) in credit default is as important as that of their credit defaults. This analysis is complicated by the fact that not all SMEs ultimately default on their loans, making a standard time-to-event analysis unsuitable. Based on our recently proposed mixture cure model with time-varying covariates, we identify issues in earlier studies and, to update the incidence component of the model, we propose a more flexible method that employs a support vector machine to enhance the exploration of nonlinear covariate effects if SMEs never default. In addition, we consider the time-varying and fixed covariates for the incidence and latency of an event. Our estimation method is tested and verified via a simulation study, which shows that our proposed model outperforms existing models. In our empirical study, an association network was established using fuzzy cognitive maps for feature selection. The results of our analysis reveal that several factors have significant impacts on the survival time to default. The proposed model also gives a favorable performance in the prediction of probability of default.