Uncovering Financial Constraints

74 Pages Posted: 2 May 2019 Last revised: 16 Apr 2020

See all articles by Matthew Linn

Matthew Linn

Isenberg School of Management, University of Massachusetts

Daniel Weagley

Georgia Institute of Technology - Scheller College of Business

Date Written: April 19, 2019

Abstract

We classify firms’ financial constraints using a random forest model trained on the Hoberg and Maksimovic (2015) text-based constraint measures. Our model uses only financial variables to predict constraints, allowing us to significantly expand the cross- section and time-series of classified firms compared to the text-based measures. We conduct a number of tests to validate the informativeness of our measures. Using our classifications, we provide evidence that returns of debt (equity) constrained firms are more sensitive to shocks to the cost of equity (debt) financing. The results highlight the importance of financial flexibility in determining a firm’s exposure to financing shocks.

Keywords: financial constraints, machine learning, random forests, financial flexibility

JEL Classification: G00, G30, G35, G1, G3

Suggested Citation

Linn, Matthew and Weagley, Daniel, Uncovering Financial Constraints (April 19, 2019). Available at SSRN: https://ssrn.com/abstract=3375048 or http://dx.doi.org/10.2139/ssrn.3375048

Matthew Linn

Isenberg School of Management, University of Massachusetts ( email )

Amherst, MA 01003
United States

Daniel Weagley (Contact Author)

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States
(404) 385-3015 (Phone)

HOME PAGE: http://www.danielweagley.com

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