Building an Early Warning System of Bank Failure with Alternative Methods

An early warning system (EWS) to flag troubled banks has been worked on since the early 1990s. The S&L crisis in the 1980s as well as the financial crisis of 2007-2009 have been extensively worked on to find important variables that help to predict bank failure and the logit model has been the standard workhorse. This paper adopts another strategy to predict bank failure in the machine learning field in order to build a more efficient EWS. Using a logit model and a random forest, I find that the random forest approach significantly outperforms the logit regression in the training (in-sample) data. The random forest also outperforms the logit in bank failure prediction in the testing (out-of-sample) data as the data used to estimate the model is closer in time to the testing sample. One way to make an EWS more effective would be to flag potentially troubled banks a few years in advance using a logit model at the beginning of a crisis, and then quickly update these predictions with a random forest as the crisis evolves and more data readily available