Uncovering Financial Constraints

66 Pages Posted: 2 May 2019 Last revised: 19 Jan 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 classify firms’ financial constraints using a random forest model that uses only financial variables to predict constraints. Our methodology significantly expands the cross-section and time series of classified firms compared to text-based measures, while maintaining similar levels of informativeness. We construct two versions of the constraint measures. One version uses many firm characteristics to identify constraints. The other uses a small set of arguably exogenous characteristics. Using our measures, we provide two novel facts related to equity constraints: (1) institutional investors hold a relatively lower percentage of the shares of equity-focused constrained firms, while retail investors (Robinhood and 1990s discount brokerage account holders) display a relative preference for equity-focused constrained firms, and (2) the equity issuance and investment of equity-focused, constrained firms 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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