Uncovering Financial Constraints

64 Pages Posted: 2 May 2019 Last revised: 15 Mar 2023

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 a random forest model to classify firms' financial constraints using only financial variables. Our methodology expands the range of classified firms compared to text-based measures while maintaining similar levels of informativeness. We construct two versions of our constraint measures, one using many firm characteristics and the other using a small set of more primitive characteristics. Using our measures, we find that institutional investors hold a lower percentage of shares in equity-focused constrained firms, while retail investors show a preference for them. Equity issuance and investment of constrained firms also increases during periods of high investor sentiment.

Keywords: financial constraints, machine learning, random forests, institutional investors, retail investors

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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