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

Tyler Pike, Horacio Sapriza and Tom Zimmermann

No 2019-070, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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 (search for similar items in EconPapers)
JEL-codes: C53 E32 G33 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2019-09-20
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2019-70

DOI: 10.17016/FEDS.2019.070

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