Uncovering Financial Constraints

63 Pages Posted: 2 May 2019 Last revised: 29 Jun 2022

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 tests to validate the informativeness of our measures. Using the measures, we show the equity issuance, investment, and institutional ownership of equity-focused, constrained firms is especially sensitive to investor sentiment. In the cross-section, institutional ownership is negatively associated with equity-related constraints. Among 1990s discount brokerage investors and Robinhood investors, the stock of equity-focused constrained firms is preferred relative to unconstrained firms.

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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