Bottom-Up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults Using Machine Learning

40 Pages Posted: 22 Oct 2019

See all articles by Tyler Pike

Tyler Pike

Board of Governors of the Federal Reserve System

Horacio Sapriza

Board of Governors of the Federal Reserve System

Tom Zimmermann

University of Cologne

Date Written: 2019-09-20

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

Suggested Citation

Pike, Tyler and Sapriza, Horacio and Zimmermann, Tom, Bottom-Up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults Using Machine Learning (2019-09-20). FEDS Working Paper No. 2019-070. Available at SSRN: https://ssrn.com/abstract=3473056 or http://dx.doi.org/10.17016/FEDS.2019.070

Tyler Pike (Contact Author)

Board of Governors of the Federal Reserve System

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Horacio Sapriza

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Tom Zimmermann

University of Cologne ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

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