Amendment Thresholds and Voting Rules in Debt Contracts

51 Pages Posted: 16 Sep 2021 Last revised: 18 Apr 2022

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: September 13, 2021

Abstract

We study the voting rules to modify, amend, and renegotiate syndicated loan contracts. We base our hypotheses on a model that shows how amendment thresholds mitigate agency conflicts within the lending syndicate, and discourage strategic defaults by the borrower. Consistent with our model’s predictions, we find that voting rules are more lenient when the lead lender has prior syndicate relationships with non-lead lenders. We also find that voting rules are more stringent when the lead lender has potential conflicts of interest with non-lead lenders, as evidenced by a prior underwriting relationship with the borrower. Lastly, we show that loan amendment thresholds are negatively associated with default risk, suggesting that contracting parties set the thresholds to avoid inefficient liquidations. Overall, our results indicate that voting rules in loan contracts are shaped by agency problems within the lending syndicate and strategic default considerations.

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 (September 13, 2021). 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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