Default prediction using a tree-boosted Tobit model

Vortrag von Dr. Fabio Sigrist

Datum: 25.05.18  Zeit: 15.15 - 17.00  Raum: ETH HG G 19.1

We consider the task of predicting whether loans are paid back or not. An often encountered problem in default prediction is the fact that there is relatively little default data since bankruptcies are usually uncommon events. We show how this issue can be alleviated by using a tree-boosted Tobit model in cases where there is additional data for the non-default events that is related to the default mechanism. Such additional data can consist of, for instance, number of days of delay by which loans were paid back, stock returns, or distance to default measures. We apply our proposed model for predicting defaults on loans made to Swiss small and medium-sized enterprises and obtain a large improvement in predictive performance compared to other state-of-the-art approaches.