Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning

Pike, Tyler; Sapriza, Horacio; Zimmermann, Tom

doi:10.17016/FEDS.2019.070

Working Paper

Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning

Abstract: This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.

Keywords: Corporate Default; Early Warning Indicators; Economic Activity; Machine Learning;

JEL Classification: C53; E32; G33;

https://doi.org/10.17016/FEDS.2019.070