Finance and Economics Discussion Series (FEDS)

Twitter Share RSS

September 2019

Bottom-up leading macroeconomic indicators: An application to non-financial corporate defaults using machine learning

Tyler Pike, Horacio Sapriza, and Tom Zimmermann

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.

Accessible materials (.zip)

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

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

PDF: Full Paper

Back to Top
Last Update: April 02, 2020