Predicting corporate defaults using machine learning with geometric-lag variables

Abstract

This study examines whether corporate default prediction techniques based on machine learning can achieve better performance by using geometrically declining weighted average values of the time series variables, that is, geometric-lag variables. We test four machine learning algorithms: logistic regression, random forest, support vector machine, and feedforward neural network. The geometric-lag financial variables capture each company's historical financial information. Using such variables reduces the computation time and improves the prediction performance. The actual default rates increase with the predicted default probabilities, suggesting that our model predictions can help investors make better investment decisions.