Predicting Financial Distress Using Machine Learning Models

Abstract

This paper examines prediction of financial distress using machine learning techniques. It uses accounting and equity price variables found to be associated with financial distress in Campbell et al. (2008) as input variables to predict output variables of corporate bankruptcy and issuers’ credit rating. By training logistic regression based on the training method in machine learning, I find that the model offers a high ratio of true positive rate to false positive rate in identifying bankrupt firms, which implies its effectiveness to serve as a screener against distressed se- curities. For credit rating prediction, I find statistical evidences that the trained machine learning models provide improved accuracy over the base rate model of weighted random predictions. Lastly, while these models provide relatively low accuracy for predicting the exactly correct rating, its accuracy is much higher for predicting the correct direction of rating changes, which offers a potential for im- proved trading strategy.