Estimating Financial Constraints with Machine Learning

57 Pages Posted: 2 May 2019

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 use random decision forests, a statistical machine learning technique, to classify firms’ equity and debt constraints using only firm-level financial information. We train our model on the Hoberg and Maksimovic (2015) text-based measures, which are informative, but lack coverage. By mapping to financial variables we are able to extend the coverage of the text-based measures both in the cross-section and the time series, increasing the number of classified firm-years by 245%. Our method captures important non-linearities and interactions between financial variables and constraints and exhibits significant out-of-sample performance. We assess the informativeness of our constraint classifications using multiple tests – tests that commonly-used indices have previously been shown to fail. Our classifications perform well. They even outperform the Hoberg and Maksimovic (2015) measures over a similar sample period likely due to the increased coverage.

Keywords: financial constraints, machine learning, random forests, equity recycling

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

Suggested Citation

Linn, Matthew and Weagley, Daniel, Estimating Financial Constraints with Machine Learning (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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