Amendment Thresholds and Voting Rules in Debt Contracts

68 Pages Posted: 16 Sep 2021 Last revised: 21 Feb 2024

See all articles by Judson Caskey

Judson Caskey

University of California, Los Angeles (UCLA) - Accounting Area

Kanyuan (Kevin) Huang

Chinese University of Hong Kong, Shenzhen

Daniel Saavedra

UCLA Anderson School of Management

Date Written: February 20, 2024

Abstract

Most loan contracts in the US contain a provision for lender voting rules. We study the optimal voting rules to modify, waive or renegotiate syndicated loan contracts using an extension of Gârleanu and Zwiebel (2009). In the model, we allow lenders to waive a covenant violation based on a pre-specified voting rule. We show that the optimal voting rule limits lenders’ ability to extract the surplus of profitable projects, thereby improving contracting efficiency. The model predicts that the optimal voting rule is (i) increasing in the agency conflicts within the lending syndicate, and (ii) decreasing in the default risk of the borrower. We test these predictions empirically and find consistent results. Lastly, we extend our model to analyze how the preference for conservative accounting and covenant choices varies with the optimal voting rule. Overall, our results shed light on the economic incentives behind the wide use of voting rules in loan contracts.

Keywords: Debt contracting, voting rule, syndicated loan

JEL Classification: D86, G21, G32, K12, M41

Suggested Citation

Caskey, Judson and Huang, Kanyuan and Saavedra, Daniel, Amendment Thresholds and Voting Rules in Debt Contracts (February 20, 2024). Available at SSRN: https://ssrn.com/abstract=3922893 or http://dx.doi.org/10.2139/ssrn.3922893

Judson Caskey (Contact Author)

University of California, Los Angeles (UCLA) - Accounting Area ( email )

D410 Anderson Complex
Los Angeles, CA 90095-1481
United States

HOME PAGE: http://sites.google.com/site/judsoncaskey/

Kanyuan Huang

Chinese University of Hong Kong, Shenzhen ( email )

2001 Longxiang Boulevard, Longgang District
Shenzhen, 518172

Daniel Saavedra

UCLA Anderson School of Management ( email )

Los Angeles, CA
United States

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