Bankruptcy Prediction of Privately Held SMEs Using Feature Selection Methods

Abstract

In this paper, we test alternative feature selection methods for bankruptcy prediction and illustrate their superiority versus popular models used in the literature. We test these methods using a comprehensive dataset of more than one million financial statements from privately held Norwegian SMEs in 2006-2017. Our methods are allowed to choose among 155 accounting-based input variables derived from prior literature. We find that the input variables chosen by an embedded least absolute shrinkage and selection operator (LASSO) feature selection method yield the best in-sample fit, out-of-sample performance, and stability. Our findings are robust to using discrete hazard models with either a deep artificial neural network (DNN) or logistic regression (LR) in the estimation and hold across different time periods. We show in a simulation which mimics a real-world competitive credit market that using LASSO to choose bankruptcy predictors improves credit risk pricing and decision making, resulting in significantly higher bank profits.