Badawi & de Fontenay Paper on EBITDA Definitions

10/05/19

What are you looking for?

I confess that, on its face, this did not strike me as the most exciting topic to read about (and that comes from someone who writes about the incredibly obscure world of sovereign debt contracts).  After all, who even knows what EBITDA definitions are?  Sounds like something from the tax or bankruptcy code.  But don’t let the topic be off putting.  This is a wonderfully interesting project; and elegantly executed (here).  By the way, EBITDA stands for earnings before interest, taxes, depreciation blah blah. Turns out it is especially important for young companies, where potential investors want to know about the cash flow being generated (Matt Levine has been writing about it recently in the context of the WeWork debacle - here). It is also very important because it generally ties into the covenants in the debt instrument and can impact whether or not the covenants are violated.

Using machine learning techniques, Adam and Elisabeth look at the EBITDA definitions in thousands of supposedly boilerplate debt contracts.  And they find a huge amount of variation in this supposedly boilerplate term; variation that can end up making a big difference to the parties involved. (For those interested, there is a nice prior study by Mark Weidemaier in the on how supposedly boilerplate dispute resolution terms in sovereign bonds are often not really all that close (here); and John Coyle’s recent work on choice-of-law provisions in corporate bonds is also along these lines (here))

The question that naturally arises here is whether the variation in these EBITDA definitions is the product of conscious and smart lawyering or just random variation that arises as contracts are copied and pasted over generations. (for more on this, see here (Anderson & Manns) and here (Anderson)). My understanding of the results is that these definitions are definitely not the product of random variation; instead, there seems to be a lot of sneaky lawyering to inflate the supposedly standard EBITDA measure.

Adam and Elisabeth don’t explicitly come out and say that better lawyers draft better (sneakier?) EBITDA contracts for their clients.  But their results on how private equity firms (with their fancier lawyers) seem to get better definitions, may suggest just that.

There are a number of other intriguing findings too – such as the fact that the expansiveness of the definitions are reflected in the spreads (so, are they fully priced in?) and the fact that the best predictor of the definition used in contract X is what definition was used in the immediately prior contract X-1 (so, these contracts are sticky? But private equity contracts are less sticky?).

And then there is the question of how much lawyers matter -- do the sneakier law firms draft dodgier EBITDA definitions?  And is it worth paying more for lawyer sneakiness or does the price mechanism adjust for it?

For me to say more about EBITDA and sneaky lawyering will risk revealing my lack of understanding of both EBITDA and machine learning research.  Maybe also my unhealthy interest in figuring out how to measure the degree of sneaky lawyering. But seriously, this is a cool study.

Here is the abstract:

The definition of EBITDA is among the most important parts of a credit agreement. This concept matters to borrowers and creditors because it frequently determines whether a borrower is in breach of its covenants in the loan, and it matters to regulators because it determines the amount of leverage a loan entails. While credit analysts and debt lawyers have commented on the differences in the definition of EBITDA, existing research on debt agreements has almost entirely ignored this variation and the consequences it has for understanding how debt agreements operate. We use supervised learning of income definitions in thousands of credit agreements to show that there is, indeed, massive variation in the definition of EBITDA and that a substantial proportion of these agreements inflate EBITDA by adding back income. In further analysis we show that expansive EBITDA definitions are more common among private equity borrowers and that banks appear to have allowed more permissive EBITDA definitions for non-private equity borrowers in the wake of the Federal Reserve’s restrictive guidance on lending leverage. We show that there is a negative relationship between the permissiveness of EBITDA definitions and the amount of covenant slack in loans and that more permissive definitions are associated with higher loan spreads. Finally, we demonstrate that the best predictors of the content of income definitions are the past credit agreements of the borrower.

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