Work

Bankruptcy prediction of privately held SMEs using feature selection methods

Year: 2021

Type: article

Abstract: We test alternative feature selection methods for bankruptcy prediction and illustrate their superiority versus popular models used in the literature. We apply these methods to a comprehensive dataset of more than one million financial statements covering the entire universe of privately held Norwegian SMEs in 2006-2017. We find that input variables chosen by an embedded least absolute shrinkage and selection operator (LASSO) method yield the best in-sample fit, out-of-sample performance, and stability. This finding holds across different time periods and is robust to using either discrete hazard models with logistic regression or a deep artificial neural network in the estimation. We also contribute to the literature by illustrating the superiority of LASSO in a real-world simulation of a competitive credit market. Specifically, using the variable set chosen by LASSO generates bank profits that are about 50% higher than those resulting from using the second best performing set of bankruptcy predictors.

Source: Journal of Empirical Finance

DOI: 10.1016/j.jempfin.2026.101725

Authors: Florentina Paraschiv, Markus Schmid, Ranik Raaen Wahlstrøm