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Please note that all times are shown in the time zone of the conference. The current conference time is: 22nd Dec 2024, 05:19:07am CET
CovenantAI - New Insights into Covenant Violations
Vanessa S. Krockenberger1, Anthony Saunders2, Sascha Steffen1, Paulina Maria Verhoff1
1Frankfurt School of Finance & Management, Germany; 2NYU Stern School of Business
Discussant: Tom Griffin (Villanova University)
We create a natural language processing (NLP) machine learning model to identify covenant violations using 10K and 10Q filings provided by the Securities and Exchange Commission (SEC). Our sample includes more than 580,000 filings comprising the universe of all publicly listed U.S. firms and spanning the entire 1996 to 2022 period. We use the MPNET Sentence Transformer as a classification algorithm and obtain a model accuracy of 94.4%, considerably higher than conventional manual approaches. Importantly, we are able to differentiate between firms that obtain an amendment after or before a covenant violation occurs and those that remain in technical default. Covenant violations have significantly declined over the past two decades. During this time, the percentage of firms that received an amendment increased while those in technical default decreased. The decline in violations was driven by non-investment-grade rated and unrated firms and intensified during the COVID-19 pandemic. Firms in technical default perform considerably worse on a number of dimensions, such as leverage or liquidity, and they draw down a larger percentage of their credit lines in the eight quarters before a covenant violation. They recover quickly after a violation, specifically in terms of leverage ratio and operating income. Moreover, they make larger changes in their investment and financial policies as compared to firms that obtain amendments. Again, the effects are mainly driven by non-investment-grade rated and unrated firms. Not surprisingly, non-investment-grade rated firms are about 17% more likely to declare bankruptcy in the eight quarters following a covenant violation. This effect is substantially muted in the sample of firms that obtain loan amendments.
Anticipating Binding Constraints: An Analysis of Financial Covenants
Ken Teoh
International Monetary Fund, United States of America
Discussant: Kristine Hankins (University of Kentucky)
This paper studies the extent to which financial covenants are an important consideration for firm decisions outside of violation events. Applying textual analysis to earnings call transcripts, I construct a novel measure of covenant concerns by distinguishing between discussions of covenants that relate to the future as opposed to the past or present. The measure predicts future violations, and covaries intuitively with earnings and leverage. Covenant concerns are associated with significant reductions in investment and financing activity. These responses persist even after controlling for standard measures of investment opportunities and are economically large relative to the effects of actual violations.
