Measuring Financial Covenant Strictness in Private Debt Contracts

Peter R. Demerjian† Foster School of Business, University of Washington

Edward L. Owens Simon School of Business, University of Rochester

January 2014

Abstract

We examine the measurement of financial covenant strictness in private debt contracts using Dealscan data. Based on analysis of detailed covenant definitions from the Tearsheets dataset, for each Dealscan covenant type we specify a "standard" covenant definition that can be computed using Compustat. For most covenants, the average error induced by using our standard definition rather using the precise covenant definitions is insignificant. Applying these findings, we compute a single Dealscan-based comprehensive measure of financial covenant strictness that utilizes information about slack, volatility of underlying covenant parameters, and their covariance across the entire set of financial covenants included in a contract. We provide evidence that this measure is superior to alternative measures of covenant strictness used in prior literature. Although measurement error undoubtedly exists, our evidence endorses a comprehensive approach to measuring covenant strictness using the full breadth of covenant slack data available in Dealscan.

* We appreciate the helpful comments of Dan Amiram, Anna Costello, Ilia Dichev, Valeri Nikolaev, Regina Wittenberg-Moerman, Jerry Zimmerman, and participants at the AAA FARS 2014 Midyear meeting. Demerjian gratefully acknowledges the financial support of the Goizueta Business School and the Foster School of Business. Owens gratefully acknowledges the financial support of the Simon School of Business. † Corresponding author. Phone: 206-221-1648; email: pdemerj@uw.edu.

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1. Introduction

Recent years have seen a renewed interest in accounting research on debt contracting, and

particularly on studies examining the inclusion and implications of accounting-based debt

covenants. These questions are not new to the research; debt contracts and covenants play an

important role in the development of positive accounting theory (Watts and Zimmerman 1978,

1986). The increase in research volume can be attributed, at least in part, to the introduction of

LPC/Dealscan, a database of private loan agreements, in the late 2000s. Dealscan provides wide

coverage of private loan issuance starting in the 1990s, and includes details such as lender and

loan type, collateral requirements, and covenant inclusion. Prior to Dealscan, studies examining

debt covenants required hand-collection from SEC filings, which generally resulted in small

samples of less than 150 observations (e.g. El-Gazzar and Pastena 1991; Beneish and Press 1993,

1995; DeFond and Jiambalvo 1994; Sweeney 1994). Dealscan provides details on thousands of

private loan contracts, and given the breadth of Dealscan’s coverage of private loans, has

allowed researchers to test hypotheses in large-sample, generalizable settings (e.g., Dichev and

Skinner 2002).1

Many studies on financial covenants require a measure of the strictness, or slack, of

covenants. Covenant slack is defined as the difference between the required threshold value and

the actual value of the covenant measure. For example, an interest coverage covenant may

require a borrower to maintain earnings at a minimum of three times interest, indicating a

threshold value of three. If the actual ratio of earnings to interest is six, this ratio can decline by

three before the borrower is in default on the covenant. If the actual ratio is nine, the borrower

1 Chava and Roberts (2008) find that Dealscan covers 50%-75% of private loans in the early-1990s, and that their coverage rate increases in the mid-1990s.

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has further space to decline—that is, more slack—before default. 2 Covenant slack holds a

significant place in the positive theory of accounting: as initially described in Watts and

Zimmerman (1986), the debt covenant hypothesis predicts that borrowers close to covenant

thresholds will make income-increasing accounting choices to avoid costly technical default.

More generally, covenant slack is considered an ex post proxy for borrower riskiness or the

degree of agency conflicts.

While Dealscan is an effective data source for research questions examining covenant

inclusion, it is considerably less so for those exploring slack. This is because, while Dealscan

provides information on what general type of covenant is used and its threshold value, it does not

provide sufficient detail to calculate the actual value of the covenant measure. For example,

interest coverage is generally defined as the ratio of earnings to interest expense. However,

earnings can be net income, EBIT, EBITDA, or some other measure, and interest expense can be

accrual- or cash-basis. Given Dealscan’s fairly coarse set of categories—it includes 15 classes of

financial covenants in all—and the presumed variation in covenant measures within each group,

measuring covenant slack with Dealscan data has generally been avoided due to the potential for

measurement error. A number of studies have mitigated this concern by focusing on one or two

financial covenants where measurement error is assumed to be the lowest. 3 A significant

drawback of this sort of analysis is that, by focusing on a subset of covenants and ignoring

others, overall covenant strictness could be under- or overreported. Moreover, it ignores the

customization of covenants that is common in private loans (Leftwich 1983).

2 There are a number of ways to calculate slack; we discuss ours in Section 4. 3 Dichev and Skinner (2002) and Frankel and Litov (2007) focus on current ratio and net worth. Kim (2010) and Kim et al. (2010) study net worth alone. Franz et al. (2012) examine only current ratio. Demiroglu and James (2012) study current ratio and debt-to-EBITDA covenants.

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In this study, we propose a way to use Dealscan to measure covenant strictness for all

covenants, not just those with relatively homogeneous measurement. The analysis which we use

to justify this approach proceeds in several steps: documenting the variation in covenant

measures, determining “standard” covenant measures based on empirical observation, and

quantifying the measurement error induced by using our standard measures. To document

measurement variation, we start by collecting a sample of loans from Dealscan’s Tearsheets

database. Tearsheets, a database covering a subset of Dealscan loans, provides more detailed

information, including specific covenant definitions that allow calculation of precise covenant

slack. However, the data in Tearsheets is not machine-readable, making it difficult to use for

large-sample studies. We hand-collect and code data on 5,278 financial covenants from 2,100

loans. Using the same 15 covenant definitions as Dealscan, we find considerable variation in the

degree of homogeneity in covenant measurement. For example, Current Ratio covenants feature

just ten different definitions across 283 covenants, including 270 (95.4%) measured as current

assets over current liabilities. In contrast, we document 356 different definitions across 592

Fixed Charge Coverage covenants, suggesting this measure is extremely heterogeneous and

likely customized to the features of the borrower. This descriptive evidence is consistent with

prior evidence that there is significant variation in measurement across individual covenants

(Leftwich 1983; El-Gazzar and Pastena 1990).

Next, we quantify the measurement error induced by heterogeneity in covenant

measurement. For each of the 15 financial covenant categories from Dealscan, we determine a

“standard” definition which will be applied to Dealscan covenant information: this standard

definition is the most common definition for the covenant used in Tearsheets. For example, of

the 953 interest coverage covenants in Tearsheets, 725 (76.1%) are defined as EBITDA divided

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by accrual-basis interest expense, making this definition the standard for interest coverage.4 We

proceed to measure slack for each covenant in Tearsheets using both our standard definition and

the true definition as revealed by loan-specific detail in Tearsheets. Returning to interest

coverage, we know that 76% of these covenants are actually measured with the standard

measure—meaning these covenants have no measurement error. For the remaining 24% of

interest coverage covenants with actual definitions that deviate from the standard, we document

the measurement error obtained by using the standard measure instead of the actual Tearsheets

definition.

Next, we analyze the error in measurement of initial slack, that is, the slack at contract

inception. We tabulate the average differences between the Tearsheets-based slack and the

standard definition-based slack. We find that for most covenants, the average error is

insignificantly different from zero, suggesting that in most cases the standard measure serves as a

reasonable proxy for measuring the initial slack of covenants when the precise definition is not

known.

This evidence suggests that, although measurement error undoubtedly exists, it is likely

not as serious as the prior literature has generally presumed. Further, we believe the benefits of

measuring covenant slack for all loans in Dealscan—using the standard measures presented in

our study—outweighs the cost of measurement error. Finally, these results suggest that

researchers can develop a comprehensive measure of covenant slack based on all the covenants

in the loan package (rather than analyzing covenants individually, or measuring just a few). One

such comprehensive approach has been offered by Murfin (2012), who uses Dealscan data to

4 We are able to adopt a standard definition for fourteen of the fifteen Dealscan covenant categories based solely on an ex ante analysis of Tearsheets. The exception is fixed charge coverage, which displays sufficient definitional heterogeneity that we cannot initially select a standard measure. We select a standard for fixed charge coverage that minimizes measurement error; we discuss this in Section 4.

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compute an aggregate measure of covenant strictness incorporating the number of covenants,

covenant slack, and the covariance of change in covenant values. We compute a similar measure

of aggregate strictness, using Dealscan data and applying our standard covenant measures.5 As a

validation test, we examine whether our aggregate strictness measure is associated with financial

covenant violations. Using a sample of actual technical defaults provided by Nini et al. (2012),

we show that our Dealscan-based aggregate strictness measure is a significant predictor of

technical default. Further, after controlling for aggregate strictness, neither the number of

financial covenants nor the net worth covenant slack are associated with technical default over

the life of the loan. Perhaps more strikingly, after controlling for aggregate strictness, the number

of covenants attached to a loan is negatively associated with actual covenant violation during the

first year of a loan.

We acknowledge that the lack of precise covenant definitions is not the sole source of

potential measurement error in using Dealscan to compute covenant slack. For instance, even if

Dealscan provided exact, precise definitions for every contractual component, some financial

statement data are not readily available to researchers (i.e. in Compustat) that would enable

computation of the precise covenant definition.6 Also, as is the case with any data source that

requires data entry from source documents, it is possible that Dealscan may misclassify or omit

covenants from actual loan contracts. Further, we use Tearsheets data as a proxy for actual loan

contract data. To the extent that Tearsheets does not provide all contractual information (e.g.

precise definitions of the components of covenant measures), the may also introduce

measurement error. Finally, we analyze Tearsheets data to make inferences about the broader

5 Our formulation, while following Murfin (2012), differs in several important ways, including in the number of covenant categories and the measurement of slack; these are discussed in greater detail in Section 5. 6 Some covenant definitions feature elements not disclosed in Compustat, such as junior interest expense, investment fees, unamortized bond discount, and interest associated with capital leases. Additionally, covenants can sometimes be defined with vague parameters, such as “non-cash items” that cannot be accurately measured.

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population of Dealscan loans. To the extent that the Tearsheets and Dealscan loan universes

differ, evidence based on Tearsheets may not be generalizable to Dealscan loans. We explore

these potential problems in Section 3.4, and conclude that they introduce minimal additional

measurement error into our analysis.

The paper proceeds as follows: Section 2 discusses the background and motivation for

our study. Section 3 describes the data. Section 4 discusses our general research design and

empirical findings. Section 5 presents a measure of aggregate covenant strictness based on the

full breadth of covenant data in Dealscan, along with associated validity tests. Section 6

concludes.

2. Background

Financial covenant slack—the difference between the threshold value and the initial

value of the financial covenant measure—serves an important role in positive accounting theory.

Watts and Zimmerman (1986) propose the debt covenant hypothesis, which predicts that firms

close to covenant thresholds (i.e. with low slack) have incentives to make income-increasing

accounting choices. Early empirical work in this area operationalized overall covenant strictness

with borrower financial leverage; since contract terms were unobservable, these studies assumed

firms with high leverage were closer to violating financial covenants (Duke and Hunt 1990).7

The introduction of the Dealscan database in the late 1990s facilitated a new wave of

research on private lending agreements, with a focus on financial covenant structure. A key early

paper is Dichev and Skinner (2002), which examines the debt covenant hypothesis using

Dealscan data. In their study, they discuss the advantages that Dealscan provides to researchers:

the database has broad coverage of private loans, provides details on many contract provisions

7 Watts and Zimmerman (1986) originally describe the “debt/equity hypothesis” and use leverage as an empirical proxy for covenant strictness.

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(such as loan amount, interest rate, collateral data, covenants), and most important for

researchers is machine-readable. Recent studies examining the types of financial covenants in

private loan contracts include Demerjian (2011), Zhang (2011), and Christensen and Nikolaev

(2012).

While Dealscan has allowed researchers to address a number of research questions

related to debt and covenants, it is not without drawbacks. The most serious one, in terms of

research involving financial covenants, is a lack of data on the exact definitions of covenants.

For example, Dealscan may indicate a loan includes an interest coverage covenant with a

threshold value of three. To calculate slack, the researcher must be able to measure the value of

interest coverage and compare this to the required threshold. However, Dealscan does not

provide the specific definition of interest coverage employed in the contract. So, while interest

coverage is generally defined as the ratio of earnings to interest expense, earnings could take on

many different definitions (e.g. net income, EBIT, EBITDA, etc.) and interest could be accrual-

or cash-basis. Given that covenants are frequently customized (Leftwich 1983; El Gazzar and

Pastena 1990), not knowing the exact definition used in the contract introduces potential

measurement error.

Dichev and Skinner (2002) acknowledge this shortcoming of the data and adjust their

research design accordingly. Specifically, they conduct their analysis using only current ratio and

net worth covenants, which they consider most homogenously measured. A number of studies

follow the same approach. Frankel and Litov (2007) also study current ratio and net worth

covenants; Kim (2010) and Kim et al. (2010) examine net worth alone; Franz et al. (2012) study

only the current ratio; and Demiroglu and James (2010) focus on current ratio and debt-to-

EBITDA covenants. Other studies, such as Bradley and Roberts (2004) and Billett et al. (2007),

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do not try to measure slack, but rather use the number of covenants as a proxy for overall

financial covenant strictness. While it is possible that more covenants may mean more overall

strictness, this is not necessarily the case. For example, a loan with one tightly set covenant could

be more likely to enter technical default than a loan with three loosely set covenants.

Murfin (2012) consolidates these two approaches into a single, aggregate measure of

covenant strictness. Specifically, for each loan he uses the number of covenants, the estimated

slack of these covenants, and the covariance between different covenant measures to compute the

probability of default for any covenant in the loan package. To estimate slack, Murfin (2012)

relies on one definition per covenant type— potentially introducing the measurement error noted

in Dichev and Skinner (2002). Murfin (2012) acknowledges this problem (p. 1573), but suggests

that any measurement error will be absorbed in the model’s error since he uses aggregate

strictness as a dependent variable. This provides little comfort to researchers wanting to use

aggregate strictness as an independent variable, as is often the case.

3. Data sources and descriptive analysis

3.1. Data sources

Our main sources of loan data are Dealscan and Tearsheets. Dealscan is machine-

readable and provides a variety of details on loans, including the use of specific financial

covenants (but not sufficient detail to calculate precise slack). Our computation of aggregate

covenant strictness and its validation tests use Dealscan data. Tearsheets covers a subset of

Dealscan loans. This database provides more precise information about loans, including detail

sufficient to calculate precise financial covenant slack. Tearsheets is not machine-readable, so we

hand-code data from the records into a machine-readable format. Our descriptive evidence on

financial covenant measurement, our assessment of standard covenant measures, and tests

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quantifying measurement error using the standard measure all use the Tearsheets data.

Additionally, we use Compustat quarterly data for accounting data (used to calculate slack and

assess measurement errors), CRSP daily for stock return data (to calculate distance to default),

and data on actual covenant violations provided by Nini et al. (2012).

3.2. Tearsheets overview

Tearsheets provides detailed loan information for a subset of deals from the Dealscan

universe. According to LPC (the company that originally produced Dealscan and Tearsheets), a

Tearsheets report is “available for the more complex or uniquely structured deals in the market.”8

Beyond this brief description, there is little information indicating why a deal is selected for

inclusion in Tearsheets. Dichev and Skinner (2002) report that Tearsheets includes “bellwether”

loans of particular importance.

Tearsheets provides many parameters of the loan contract, including the number and

type of facilities, the maturities and interest spreads of these facilities, agency ratings for the

borrower or loan, the number and identities of lenders (including their interest in the loan

syndication), negative covenants, financial covenants, performance pricing, and collateral

requirements. While data on many of these provisions is available from Dealscan, Tearsheets

often provides a greater level of detail. Important for our purposes, Tearsheets provides sufficient

detail on financial covenant definitions to calculate precise slack. For example, Dealscan may

indicate that a loan features an interest coverage covenant, and give the initial threshold, but not

say how the interest coverage ratio is defined. Tearsheets, in contrast, will indicate how both

earnings and interest expense are defined.

8 This quote was taken from the LPC website in May 2008. LPC has since been sold to Thomson/Reuters, and there is no mention of Tearsheets on their webpage, though the data are still available to Dealscan subscribers.

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Tearsheets includes records of 2,683 loan packages from 1,773 borrowers, which we

match to Compustat to generate a sample of 2,100 loans. The loans in this Tearsheets-Compustat

intersection sample are issued between 1987 and 2004. Tearsheets coverage is highest from 1992

through 2000, with at least 150 deals each year and 88% of aggregate deals issued during this

period. Each loan package consists of individual loan facilities. The average package has 1.67

facilities, with a maximum of six. The most common facility is a revolving line of credit (in

91.7% of packages), which gives the borrower access to a line of credit with no required

drawdowns or scheduled repayments. Term loans, which generally require immediate drawdown

of funds and fixed repayment, are also common (32% of sample loan packages). Backup

financing sources, such as letters of credit (55%) and swingline options (33%) are likewise

common. Other facility types include revolvers that convert into term loans, bridge loans, and

other types of private financing. None of these other types are included in more than five percent

of loan packages. The most common stated purposes of sample loans are “corporate purposes”

(76.3%) and “debt” (64.2%), while several other purposes are listed less frequently (e.g.

“working capital,” “takeover,” “acquisition”).

Table 1, Panel A provides descriptive statistics on the Tearsheets loan sample. The

average deal’s principal amount (FACILITY) is $722M, with an average stated term to maturity

(MATURITY) of about four and a half years (53 months). The facility-weighted average spread

over LIBOR (SPREAD) is 121 basis points. Most of the loans are syndicated, where the average

loan package has over 16 lenders (SYNDSIZE), with as few as one and as many as 149. For

comparison, Table 1, Panel B presents corresponding statistics for the intersection of Dealcan

and Compustat for the sample period. Consistent with their status as “bellwether” loans,

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Tearsheets loans are larger than Dealscan on average; however, covenant use is similar across

these two samples.

Table 2, Panel A presents summary Compustat data on Tearsheets borrowers, where we

match Tearsheets observations to the Compustat fiscal quarter-end most closely preceding loan

initiation. For comparison, in Panel B we present corresponding statistics for all Dealscan

borrowers over the sample period. The Tearsheets borrowers are on average large (total assets of

$4,432M), have high sales ($3,104M), are profitable (ROA of 3.0%), and growing (asset growth

of 17.0%). Relative to the full Dealscan population, these firms have high leverage (debt-to-

assets of 0.38 vs. 0.29).

3.3. Dealscan financial covenant classifications and contract-level variation

In the Dealscan database, financial covenant data is contained in the ‘FinancialCovenant’

and ‘NetWorthCovenant’ datasets.9 These two datasets combined report 15 distinct classes of

financial covenants (with general definitions): 10

1. Min. Interest Coverage (earnings / interest expense) 2. Min. Cash Interest Coverage (earnings / interest paid) 3. Min. Fixed Charge Coverage (earnings / fixed charges) 4. Min. Debt Service Coverage (earnings / (interest expense + principal paid)) 5. Max. Debt to EBITDA (debt / EBITDA) 6. Max. Senior Debt to EBITDA (senior debt / EBITDA) 7. Max. Leverage Ratio (debt / assets) 8. Max. Senior Leverage (senior debt / assets) 9. Max. Debt to Tangible Net Worth (debt / (shareholders’ equity – intangibles) 10. Max. Debt to Equity (debt / shareholders’ equity) 11. Min. Current Ratio (current assets / current liabilities) 12. Min. Quick Ratio ((cash + ST investments + A/R) / current liabilities) 13. Min. EBITDA (EBITDA)

9 These are the dataset names used in Dealscan downloaded via WRDS. 10 Dealscan also includes Max. Capex and Max. Loan to Value covenants in the 'financialcovenant' dataset. However, in our view, these are conceptually different from the notion of financial covenants that we analyze. Recent vintages of Dealscan also include the following covenants: Max. Long-term Investment to Net Worth, Max. Net Debt to Assets, Max. Total Debt (including Contingent Liabilities) to Tangible Net Worth, Min. Equity to Asset Ratio, Min. Net Worth to Total Asset, and Other Ratio. However, each of these covenants appears in an immaterial percent (i.e., < 0.05%) of Dealscan loan contracts, and did not exist as separate Dealscan categories during the Tearsheet coverage period; therefore, we omit them from our study.

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14. Net Worth (shareholders’ equity) 15. Tangible Net Worth (shareholders’ equity – intangibles)

In order to document the variation in financial covenant measurement, we sort financial

covenants from Tearsheets into the 15 Dealscan classes. This is to make our classification of

Tearsheets covenants consistent, and to aid in quantifying measurement error. However, since

Dealscan does not provide guidance on how financial covenants are classified (beyond the names

of the categories given in their datasets), we must use judgment in classifying Tearsheets

covenants into the 15 categories. In Appendix A we show our general rubric for classifying

Tearsheets covenants, and we discuss the error this potentially introduces in Section 3.4.

We provide detailed data on the measurement of covenants from each category in

Appendix B. For the sake of parsimony, we summarize these data in Table 3, which presents the

observations and frequency of covenants from each of the 15 classes. For example, interest

coverage covenants (IC) are included 953 in the 2,100 Tearsheets deals (45.4%). The subsequent

columns show the number of different measures used in defining these covenants. We separately

document numbers used in the numerator and denominator, as many of the covenants are ratio-

based. Further, we sort each of these into “primary” and “secondary” elements. We classify

measures as primary when they are part of the fundamental definition of the covenant. For

example, interest coverage is defined generally as the ratio of earnings to interest. As such, the

primary element of the numerator is the selected measure of earnings (e.g. EBITDA, EBIT, net

income, etc.) and the primary element of the denominator is the selected measure of interest (e.g.

interest expense). Secondary elements are those outside of the general definition of the covenant.

For interest coverage, certain items are added or subtracted from earnings in the numerator, such

as taxes or capital expenditures; these are the secondary elements. It is important to note that

some covenants do not have some elements by definition. Specifically, we consider all

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denominator elements in coverage covenants (interest, cash interest, fixed charge, and debt

service) as primary, and naturally there are no denominator elements for the non-ratio covenants

(EBITDA, net worth, tangible net worth).

The next column shows the number of different definitions used across all covenants in

the class. For interest coverage, there are 34 definitions in the 953 instances this covenant is

included in Tearsheets contracts. The final column shows the Variation Index, which we define

as the observations of a covenant divided by the number of definitions. This index provides a

means of quantifying and comparing the degree of heterogeneity in a covenant’s measurement. A

value of one indicates that each observation of a covenant has its own definition (i.e. complete

heterogeneity), while higher values indicate less variation in measurement.

The results in Table 3 provide a variety of descriptive facts about the measurement of

financial covenants. First, even those covenants generally considered in the literature to be

homogenously measured (e.g. current ratio, net worth, and tangible net worth) show some

variation. Current ratio covenants feature 10 different definitions, while net worth and tangible

net worth have 56 and 31 definitions respectively.11,12 Second, as expected, some covenants

feature a great deal of variation. Most striking is fixed charge coverage, with 356 definitions for

just 592 covenants. No other covenant has even 100 different definitions; however, debt service

coverage (48) and debt-to-equity (40) both feature a large number of definitions relative to their

frequency of use. Third, some covenants that have generally been presumed to have high

variation in measurement feature relatively few definitions. These include debt-to-EBITDA (24

11 Much of the variation in definitions for net worth and tangible net worth can be attributed to the secondary elements, or “escalators” (Beatty et al. 2008). These provisions add values, such as net income or equity issuance proceeds, to the threshold of the covenant in the periods following loan inception. As such, if researchers are simply interested in measuring the initial slack in net worth or tangible net worth covenants, there are considerably fewer definitions used. 12 Dichev and Skinner (2002) identify this variation, and use Tearsheets data to supplement Dealscan in measuring net worth covenant slack.

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definitions for 865 covenants), and interest coverage (34 definitions for 953 covenants). The

Variation Index illustrates this homogeneity in measurement: both debt-to-EBITDA (36.0) and

interest coverage (28.0) have high measures, similar to current ratio (28.3). In contrast, the Index

for fixed charge coverage is very low (1.7), suggesting each definition is used on average in less

than two covenants. In total, we document that there is considerable variation in how financial

covenants are measured, but the degree of heterogeneity differs across covenant classes.

One upshot of documenting the various measurement rules for financial covenants is that

we can observe whether certain definitions are used more frequently than others in defining

financial covenants. Using the data in Appendix B, we identify the modal definition for each

covenant class, which we term the “standard” definition of that covenant. This standard allows us

to assess another dimension of the variation in financial covenant measurement. Specifically,

even if a covenant class features a large number of definitions, if a large percentage of

observations are concentrated in a single measure the risk of measurement error is low.

We present the data on covenant standard definitions in Table 4. The table includes each

covenant class, the standard definition, how the standard definition is implemented with

Compustat variables, and the frequency with which the standard definition is used (conditional

on the covenant being included in a loan). The results show that standard definitions are indeed

used frequently for many of the covenants. Specifically, over the 15 covenant classes, 10 have

the standard definition used over 75% of the time, and 12 have it used at least 50% of the time.13

Debt-to-equity (47.6%) and debt service coverage (37.9%) both have modal definitions used in

less than half the observations. However, the next most frequent definition for each is used less

than three times as frequently (12.0% and 10.3% respectively). We consider the modal

13 The frequencies for net worth and tangible net worth are for definitions including and excluding escalators respectively. The second, larger number can be interpreted as the frequency with which a loan uses the standard for initial slack for that covenant.

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definitions as the standard when assessing measurement error, even if it does not comprise the

majority of observations.

The only covenant that has no clear ex ante standard definition is fixed charge coverage.

The most frequent definition is only used in 2.7% of covenants, and the majority of definitions

(276 of 353) are used in a single covenant. As such, rather than assigning an ex ante standard

definition, we attempt to determine ex post the best way to measure fixed charge coverage to

minimize measurement error. We discuss this procedure in Section 4.14

3.4. Additional measurement issues

There are a number of measurement issues that potentially limit the inferences of our

study. We describe these issues, and discuss their potential seriousness, below.

3.4.1. Data items not available on Compustat

We rely on Compustat to compute covenant actual values and hence slack. However, as

noted by Murfin (2012), some financial statement data are not readily available to researchers.

For example, some covenants are written on junior interest expense, a variable not disclosed

separately in Compustat. Hence, even if Dealscan provided precise covenant definitions, we

would not be able to completely accurately calculate slack using Compustat. Fortunately for

researchers, there are relatively few elements we cannot measure with Compustat, and such

elements are used relatively infrequently. For example, fixed charge coverage (the covenant with

the greatest number of such “unmeasurables”) has seven of 36 denominator elements that we

cannot measure, where these elements are included in just 14 loans. Due to the relative

infrequency of this problem, we do not expect it to affect our results.

3.4.2. Consistency between Dealscan and Tearsheets

14 We consider four candidate definitions for fixed charge coverage; each has EBITDA in the numerator.

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We use data on covenants in Tearsheets to make inferences on covenant measurement in

Dealscan. As such, it is important that both databases report the same covenants and classify

them the same way. This second point is particularly relevant because Dealscan provides no

guidance on how covenants are classified; if Dealscan systematically classifies covenants

differently than we do, this could introduce error into our analysis.15

Using loans at the intersection of Dealscan and Tearsheets, we examine the consistency

in covenant reporting and classification between the two databases. We report these results in

Table 5. For each covenant class, we report three statistics: consistency (either in both databases

or in neither), the frequency that a covenant is in Tearsheets but not Dealscan, and the frequency

that a covenant is not in Tearsheets but in Dealscan. Using the minimum current ratio covenant

as an illustration, the consistency of 0.986 indicates that in 98.6% of loans current ratio

covenants are either listed in both Dealscan and Tearsheets or not listed in either. For the

remaining 1.4% of cases, either Tearsheets lists a current ratio covenant while Dealscan does not

(1.0%), or Dealscan lists it while Tearsheets does not (0.4%).

Overall consistency between the two databases ranges from 0.883 (Interest coverage) to

0.997 (Quick ratio), with consistency across all covenants at 94.9%. Errors most likely result

from Dealscan misclassifying a covenant that appears in Tearsheets (e.g., coding a Debt-to-

EBITDA covenant as Leverage). On average, the incidence of consistency errors is relatively

minor. More important, they are split fairly evenly between the two types of errors (2.4% and

2.7%), suggesting that this error does not inject systematic bias into our analysis.

3.4.3. Tearsheets and actual contract detail

A third source of potential measurement error stems from using Tearsheets to capture

contract detail. Ideally, we would extract data from actual loan contracts in order to document 15 Drucker and Puri (2009) find that Dealscan sometimes incorrectly excludes covenants from their loan records.

17

the detailed covenant definitions. However, data on private loans is difficult to collect directly

from contracts; only some contracts are disclosed publicly, and those that are publicly available

are reported in a variety of SEC filings (10-k, 10-q, 8-k). Moreover, the contract detail in SEC

filings does not have a standard format.

We implicitly assume that Tearsheets data is equivalent to actual contract data. This

assumption is true for some aspects of the contract (such as the elements included in the

covenant definition), but not true for others. For instance, when Tearsheets indicates that

EBITDA is used in a particular covenant, it does not provide detail on precisely how EBITDA is

defined in the contract. While it is not clear how much variation there is (if any) in measurement

of the elements of covenants, there is evidence that some definitions are commonly adjusted in

contracts (Li 2010).

To determine whether this assumption induces any further measurement error, we collect

actual contract detail from 10-k, 10-q, and 8-k filings for a random sample of 100 the Tearsheets

loans. We examine the contract-specific definitions of the most common elements of financial

covenants—EBITDA, interest expense, debt, senior debt, net worth, tangible net worth, current

assets, and current liabilities—and measure the difference (if any) between the actual definition

and our assumed definition (from the standard definitions listed in Table 4). In most cases there

is no error; that is, the element as defined in the actual contract is identical to how we define it

based on Tearsheets. When there are errors, they are small, infrequent, and statistically

insignificant. This suggests that using Tearsheets instead of actual contracts does not introduce

any further measurement error into our analysis.

3.4.4. Generalizability of Tearsheets evidence to Dealscan data

18

Tables 1 and 2 illustrate the differences between Tearsheets and the broader Dealscan

population. Tearsheets borrowers are larger, more profitable, have lower growth, are more highly

levered, and have larger loans that are more widely syndicated. This raises the concern that

evidence based on Tearsheets data may not be generalizable to the full Dealscan universe.

Specifically, if covenant measurement differs systematically between the different deals, the

inferences we draw from Tearsheets data cannot be generalized to Dealscan deals.

One way to assess the differences between Tearsheets and non-Tearsheets deals in

Dealscan, in terms of covenant measurement, is to compare the frequency that the standard

covenant measures are used in each. If the Tearsheets deals use standard covenant definitions

less frequently, this suggests that the standards we have documented likely apply to all Dealscan

deals. However, if the Tearsheets deals use the standard covenant definitions more frequently,

using Tearsheets as a proxy for all Dealscan deals may not be appropriate: by using a less

customized, more standardized subset of loans we could be missing detail from the broader

Dealscan population.

Since Dealscan does not provide sufficient detail to determine if the standard measure

was used, we collect contract detail from 100 loan packages included in Dealscan that are not

part of Tearsheets. We measure the frequencies of standard definitions for this set of loans and

compare it with the frequencies from the Tearsheets sample. This data is presented in Table 6.16

The second column shows the frequency of standard measurement for each covenant type (and in

the bottom row, the aggregate frequency for all covenants) for our study’s main sample of

Tearsheets loans. The third column shows the frequency for the 100 non-Tearsheets loans, while

the fourth and fifth columns show the difference and t-statistic respectively. For individual

16 We do not include fixed charge coverage covenants in this analysis, as we cannot derive a standard measure as we do for other covenants.

19

covenants, there is only one statistically significant difference, where the Senior Debt-to-

EBITDA covenant is less likely to be standardized in the Tearsheets deals. On an aggregate

basis, 83.4% of covenants in the Tearsheets are measured with the standard measure, compared

to 85.1% for the non-Tearsheets sample, a statistically insignificant difference of 1.7%. Overall,

this data suggests that our Tearsheets sample features standardization of covenants at a similar

rate to the Dealscan population at large. This gives us confidence in using Tearsheets-based

covenant definitions as proxies for the broader Dealscan population of loans, and particularly in

drawing inferences on the most common measures of covenants.

4. Measurement and Analysis of Initial Slack

In this section we examine the error in initial slack that arises from applying our standard

covenant definitions rather than using the more precise actual definitions from Tearsheets,

employing the full Tearsheets-Compustat intersection sample of 2,100 loans. Initial covenant

slack measures the gap between the threshold level of the covenant financial measure and the

borrower’s actual value of the financial measure at loan initiation. The interpretation of a slack

measure is slightly different based on whether the covenant imposes a minimum (e.g. current

ratio) or maximum (e.g. leverage) threshold. We calculate slack in a form that allows an

expression of the allowable movement of the underlying financial statement variable (before

violation occurs) as a percentage of the threshold, as follows:

������ =��� �� ���

������� �� (1)

Accordingly, SLACK for minimum (maximum) threshold covenants indicates how much the

covenant financial measure can decrease (increase) before the threshold is reached. For example,

consider a minimum interest coverage covenant with a minimum threshold of 3.0. A borrower

20

with actual interest coverage of 4.0 has SLACK = 1.33, i.e. slack of 33% above the minimum

threshold. Next, consider a maximum leverage covenant with a threshold of 0.5. A borrower with

actual leverage of 0.4 has SLACK = 0.80, i.e. slack of 20% under the maximum threshold. For

covenants with minimum thresholds, SLACK of less than 1.0 indicates violation, while SLACK

over 1.0 indicates violation for maximum threshold covenants.

In order to quantify the error induced by using our standard covenant definition, we

calculate two slack measures, one based on the detailed Tearsheets definition and one based on

the standard definition shown in Table 4. This leads to two slack measures for each borrower i

and covenant c, which we term SLACKi,c,TS and SLACKi,c,STD respectively. Note that the threshold

is the same regardless of the definition of the actual value of the covenant measure; in other

words, the threshold is not subject to measurement error. We calculate measurement error

induced by using our standard covenant definition as:

������, = ������,,��� − ������,,�� (2)

We measure the initial SLACK using the actual value of the ratios for the quarter-end

most closely preceding loan inception.17 We present descriptive statistics on initial slack and

measurement error in Table 7. We divide covenants into those with minimum and those with

maximum thresholds. Columns 1 and 2 show the number of observations for the specific

covenant and the number of observations with errors, i.e. where the standard and Tearsheets

slack measures differ. Columns 3 and 4 (5 and 6) present the mean and median initial slack for

the standard (Tearsheets) measures.

17 We use the most recent quarter-end to capture the information that was available during contracting. Significant changes to the firm between the prior quarter and loan initiation, particularly for longer gaps, may introduce measurement error into our slack calculation. However, we expect any measurement error to affect “standard” slack and “Tearsheets” slack equally, hence not affecting our error computation.

21

Based on our analysis of the Tearsheets data, we are able to assign standard definitions

for all covenant categories except fixed charge coverage; covenants in this class are so

heterogeneously measured that no natural “standard” emerges from the data. Since our objective

in developing standards is to provide useful definitions for researchers, we turn to the data to

determine the best way to measure fixed charge coverage. We start by considering common

numerator and denominator elements. The most common primary numerator element for this

covenant is EBITDA, so we use this as the numerator; since no secondary elements are used in

more than 25% of fixed charge coverage covenants, we do not include any secondary elements.

For the denominator, we consider the four most commonly used elements: interest, principal

payment, capital expenditures, and rent expense. We then measure alternate definitions of fixed

charge coverage, including each element (and combinations of each), and compute the error as in

Eq. (2). We acknowledge that this will lead to a relatively noisy standard definition, so we

attempt to minimize the average error to at least remove any bias. We find that the ratio EBITDA

/ (interest + principal + rent) yields the lowest absolute error, with a mean and median value

insignificantly different from zero. Therefore, we select this as our standard.18

Focusing first on the minimum threshold covenants in Table 7, the mean initial

Tearsheets slack ranges from a low of 37.1% above the threshold (Current Ratio) to a high of

1,296.9% above the threshold (Debt Service Coverage). However, the medians show a tighter

range (from 14.6% for Fixed Charge Coverage to 70.5% for Cash Interest Coverage), suggesting

the means are inflated due to outliers.19 The distributions of initial standard slack are similar,

with a wide range for means (37.4% for Quick Ratio to 1,476.8% for Debt Service Coverage)

18 There are likely further steps that can be taken to remove noise from this measure. For example, cross-sectional variation in different common charges (e.g. capex, rent, taxes, dividends) may allow for more precise measurement. We leave this sort of refinement for future research. 19 We neither truncate nor winsorize the slack measures in columns (3) and (5).

22

and a narrower one for medians (14.7% for Fixed Charge Coverage to 85.9% for Cash Interest

Coverage). Turning to maximum threshold covenants, we find similarly skewed distributions of

slack. Focusing on median Tearsheets SLACK reveals a range from 33.7% (Debt-to-EBITDA) to

51.3% (Senior Leverage) below the threshold. Median standard slack shows a similar range,

from 34.1% (Senior Debt-to-EBITDA) to 48.8% (Leverage) below the threshold.

Columns 7 and 8 present the mean ERRORc. Based on our calculations, ERROR

represents measurement error induced by using the standard slack calculation rather than the

more precise Tearsheets definition. For the minimum (maximum) threshold covenants, a positive

error indicates that the standard measure overstates (understates) slack, while a negative error

indicates understatement (overstatement).

Among all minimum threshold covenants, there exists statistically significant

measurement error in just two covenants, Interest Coverage and Cash Interest Coverage. Just one

maximum threshold covenant, Debt-to-Equity, has a significant error. One concern in measuring

the statistical significance of ERROR is the effect of outliers; specifically, since we do not

truncate or winsorize the two slack measures, large differences between the two may be driven

by outliers.20 In column 8, we recompute ERROR after winsorizing both SLACK measures at 1%

and 99% (results are substantively similar when we truncate rather than winsorize). ERROR for

Interest Coverage and Cash Interest Coverage remain significant, while Debt-to-Equity shifts to

be insignificant. Further, ERROR for Debt Service Coverage and Senior Debt-to-EBITDA

become significant.

5. A Dealscan-based measure of overall financial covenant strictness

The results of our analysis thus far suggest that a set of standard covenant definitions

applied to the Dealscan universe can yield useful inferences for researchers studying financial 20 We do not test the difference in medians, as many of the covenants have a majority of the errors equal to zero.

23

covenants. As an application, we use the broad Dealscan population of loans and compute a

loan-level measure of aggregate covenant strictness. Then, as a validation test, we examine the

relation between this aggregate measure and actual technical defaults. We find this aggregate

measure predicts future technical defaults, and does so more effectively than other strictness

proxies that are common in extant literature (e.g., the number of financial covenants).

5.1. Aggregate covenant strictness measure

We adapt work by Murfin (2012) in calculating our aggregate strictness measure. This

measure incorporates the four features that intuitively determine the overall strictness of the

covenants attached to a loan: the number of covenants, their slack, their scale, and the variances

and covariances of the underlying financial ratios. To fix intuition, consider a loan with a single

minimum net worth covenant. The probability of covenant violation is a function of initial slack

and the variance of net worth (i.e., similar to option pricing, greater volatility in the underlying

measure of net worth results in a greater likelihood of technical default, ceteris paribus).21

Generalizing to a loan with N covenants, the probability that at least one covenant enters

technical default is a function of the slack of each individual covenant, the variance of each

underlying financial measure, and the covariance between each of the N financial measures. As

an extreme example, consider two financial measures whose changes are perfectly correlated.

There is no benefit to including covenants written on both measures, as the second adds no

incremental likelihood of technical default. However, adding covenants with less than perfectly

correlated measures increase the likelihood a loan enters technical default; and this incremental

effect is increasing inversely to the covariance of covenant measures.

21 Dichev and Skinner (2002) find that the variance of the underlying measure and the covenant slack are inversely related. The measure developed by Murfin (2012) accommodates this correlation.

24

5.2. Computation

The aggregate covenant strictness measure, combining the four features discussed in the

prior section, presents the probability that at least one covenant in a loan package will enter

technical default subsequent to loan inception. To compute this probability, we assume the

financial measures underlying covenants follow a multivariate normal distribution, which allows

us to compute the probability of covenant violation using the multivariate normal cumulative

distribution function.

We use simulation based on our standard slack measures to compute the aggregate

measure. To illustrate the simulation mechanics, consider a loan with a single current ratio

covenant whose actual value at loan inception is 1.5 and threshold value is 1.2, yielding a slack

of 1.25 (1.5/1.2). Using historical data, we compute the mean and variance of quarter-over-

quarter changes in the current ratio, where we specify changes in ratio form.22 For the first

iteration of the simulation, we randomly generate a “change in current ratio” using the mean and

variance parameterized from actual data and apply it to borrower i’s quarter t current ratio.

Suppose the randomly generated current ratio “change ratio” is 0.75, then borrower i’s simulated

quarter t+1 current ratio will be 0.75*1.5 = 1.125, which indicates that a covenant violation

would occur (as 1.125 < 1.2, the threshold value). We repeat this process 1,000 times; the

calculated probability of technical default is the proportion of simulation iterations indicating

technical default.

This process can be generalized to the multiple covenant setting. Rather than just

computing the mean and variance of one “financial ratio change” to feed into simulation, we use

historical data to generate means, variances, and covariances among changes in the financial

measures underlying all the loan’s covenants. Having this covariance matrix in place, a single 22 For example, if the current ratios decreases from 1.5 to 1.3, then the “change ratio” is 1.3/1.5 = 0.867.

25

simulation iteration begins by drawing (again, applying the covariance matrix under the

assumption of multivariate normality) a set of change ratios for each financial measure in the

covenant set. Then, we apply these randomly drawn change ratios to borrower i’s loan inception

date financial measures to obtain a simulated quarter t+1 outcome for each covenant. Simulated

covenant violation probability is calculated at the loan level; if any one covenant would have

breached its threshold, the loan is marked as in technical default.

We use borrower-level data to compute the means, variances, and covariances of

covenant financial measure changes. We merge all firms at the intersection of Compustat

quarterly and Dealscan, and compute the 15 financial measures underlying Dealscan covenants

for each firm-quarter beginning in 1986, applying the standard definitions described in Table 4.

We next compute borrower-level quarter-over-quarter change ratios for each of the 15 covenant

financial measures, after deleting observations with negative financial measures (the change in a

negative ratio has ambiguous meaning). We delete all observations with missing data for any of

the 15 change ratios, and truncate all change ratios at the upper and lower percentile, leaving

183,518 borrower-quarter observations for use in matrix computations. As in Murfin (2012), we

wish to allow for cross-sectional variation in the covariance structure among financial measures.

Accordingly, we estimate covariance matrices by firm size groupings based on total assets.

Specifically, we rank firms in each year into thirty size groups, and then estimate one covariance

matrix for each size group using all borrower-quarters of data.23

With the thirty size-based multivariate normal covariance matrices and variable means in

place, we begin forming the Dealscan loan sample over which we compute our strictness

23 Rather than using size groupings, Murfin (2012) estimates covariance matrices using one-digit SIC industry groups. He further allows for variation in covariance structures over time by estimating distinct matrices each year using rolling 10-year windows of backward looking data. However, Murfin (2012) reports that his results are not materially different if he uses one single pooled covariance matrix across all firms and time. Accordingly, we do not incorporate time-series variation into our covariance matrices.

26

measure. We begin with all Dealscan loan packages for which we can obtain a Computat link,

and merge these with the financial statement measures that underlie each of the 15 Dealscan

financial covenants using the most recent quarterly data preceding loan inception.24 Consistent

with our calculation of covariance matrices, we delete any observations with negative values for

either the covenant threshold or the underlying financial measure. Finally, we delete observations

if any covenant in the loan package is already in violation at loan inception.25 The final sample

includes 8,282 loan package observations.

Using the size-based covariance matrices previously computed with Compustat data, we

run the simulation to compute the strictness measure for each of the 8,282 sample loan packages.

We define our aggregate financial covenant strictness measures, STRICTNESS, as the proportion

of the 1,000 simulation iterations where any one of the loan covenant thresholds would be

breached. By construction, STRICTNESS ranges between zero and one, with higher values

corresponding to more frequent violations in the simulation, and hence higher levels of covenant

strictness. From the 8,282 sample loan packages, we delete observations with missing data for

loan facility amount, maturity, security requirements, interest spread, or number of covenants,

leaving 7,751 loan observations.

5.3. Empirical results

Panel A of Table 8 presents descriptive statistics for the 7,751 observations in our

STRICTNESS loan sample. The median loan package has a STRICTNESS of 0.069 (i.e. a 7%

chance of violating a covenant during the quarter following loan inception). The overall sample

24 If the most recent accounting data available is more than five months prior to loan inception date, we delete the observation. 25 Alternatively, rather than deleting these observations we could simply set their strictness measure to 1.0 (i.e. if a loan is already in violation at inception, then the probability of the loan being in violation within the quarter following inception is 1.0 by definition). Our results are not sensitive to this choice.

27

shows wide variation in strictness, with STRICTNESS ranging from 0.000 (no expected chance

of violation) to 0.780 (relatively high expected likelihood of violation).

Table 8, Panel B presents descriptive statistics for STRICTNESS, sorted by the number of

covenants in the loan. A few noteworthy observations emerge. Both mean and median

STRICTNESS are monotonically increasing in the number of loan covenants, consistent with

more covenants providing greater protection to the lender. However, examining the tails of the

distribution across subsamples suggests that relying on the number of covenants as a proxy for

covenant strictness is potentially problematic. This point can be made through a number of

comparisons, but let us consider just one. Note that 25% of loans with four covenants have a

probability of violation of less that 12%. In contrast, 25% of loans with only two covenants have

a greater than 13% chance of violation. This suggests that, even while the number of covenants is

positively correlated with the likelihood of technical default, there is significant variation within

groups.

We offer validation of our measure following Murfin (2012). We expect that, if

STRICTNESS is a superior measure of financial covenant strictness compared to the number of

covenants, then STRICTNESS should be more useful in predicting future violations. Using data

on actual covenant violations supplied by Nini et al. (2012), we estimate the following logit

regression:

1

( ) ,1 z

Pr VIOLATIONe

−=

+ (3)

0 1 2 3 , 4 ,

.

l l i l i lz STRICTNESS NCOV INVGRADE BSMPROB

LoanControls CovenantControls YearFixedEffects IndustryFixedEffects

β β β β β

ε

= + + + +

+ + + + +

VIOLATION is an indicator variable that equals one if there was a covenant violation during the

term of loan package l and equals zero otherwise, and STRICTNESS and NCOV are as previously

28

defined. INVGRADE is an indicator that equals one if firm i's most recently available (at the time

of loan inception) long-term S&P credit rating is in an investment grade category, and equals

zero otherwise. BSMPROB is the Black-Scholes-Merton estimated default probability for firm i

as of the end of the month immediately preceding the inception of loan l. Loan controls include

the natural log of MATURITY, the natural log of FACILITIES, and SECURE. Covenant controls

include firm i's most recently available (at loan inception) tangible net worth, debt-to-tangible

net worth ratio, fixed charge coverage ratio, and current ratio. When estimating Eq. (3), we

cluster standard errors by firm.

We present the estimation results for Eq. (3) in Table 9, Panel A. As reported in column

(1), STRICTNESS has a significant positive association with covenant violation over the life of

the loan (coefficient: 1.06; z-statistic 3.35). Column (2) reports a similar specification, but

replaces STRICTNESS with NCOV (the number of financial covenants), which also has a

significantly positive coefficient (0.12; z-statistic 2.11). In column (3) we include both

STRICTNESS and NCOV; we find that STRICTNESS maintains its positive, significant

association with future technical default, but that NCOV is insignificant. In terms of economic

significance, a shift in STRICTNESS over its interquartile range (holding other variables at their

means, and using the estimated coefficient in column 3) increases the likelihood of technical

default by approximately 5% (untabulated).

The prior literature commonly uses net worth covenant slack as a measure of covenant

strictness, since this covenant is frequently used and homogeneously defined. Columns (4) and

(5) of Table 9, Panel A present regression results using new worth slack as a determinant of

future technical default. The results in column (4) show that net worth slack does not predict

29

future technical default. In column (5), which includes STRICTNESS, NCOV, and net worth

slack, only STRICTNESS is statistically significant.

All of the analyses in our study are conditioned on the covenant terms at loan inception,

as captured by Dealscan. However, evidence suggests that loans are frequently renegotiated

during their term (e.g., Roberts and Sufi 2009) and contract provisions may change. Accordingly,

in Table 9, Panel B, we repeat the preceding analysis, but restrict the independent variable to be

covenant violations in the first year after loan inception (VIOLATION1YR), a period over which

contract modification is relatively unlikely. The key inferences remain: STRICTNESS is the

strongest predictor of covenant violations during the first year following loan inception. Also

notable is that when controlling for aggregate covenant strictness, the number of covenants

(NCOV) is negatively associated with the probability of covenant violation during the first year

of a loan's tenure.

6. Conclusion

In this study, we examine the measurement of financial covenant strictness in private debt

contracts. The selection and strictness of covenants should play a complementary role in

providing protection to the creditor, specifically through the channel of technical default. While

covenant selection has received considerable attention in the literature, there have been fewer

papers examining strictness. This is largely due to insufficient data from the databases commonly

used in studies of private debt. Notably, the LPC/Dealscan database of private loans provides

sufficient data to determine when a covenant is used in a loan, but does not provide precise

definitions for use in calculating a covenant’s slack. This perceived shortcoming has impeded

progress in the debt contracting literature, as researchers typically presume that associated

measurement error from using Dealscan to compute covenant strictness would be severe.

30

We document and quantify the measurement error that is induced by using Dealscan data

to estimate covenant slack. Using Tearsheets, a database of detailed loan agreements, we

document a wide range of measures used in covenants in practice, consistent with the

customization of contracts described in Leftwich (1983). Using these data, we determine a

“standard” definition—the most common definition used in Tearsheets loans—for the 15 types

of covenants reported in Dealscan. Comparing slack calculated precisely from Tearsheets detail

and more coarsely using our standard definitions applied to the general Dealscan database, we

quantify the degree of measurement error related to using our standard definitions. We find that,

for most of the 15 covenant classes, the average error in initial slack calculations is close to zero.

Finally, we compute a Dealscan-based measure of aggregate covenant strictness. This

measure, using the entire Dealscan universe and based on our standard covenant slack

computations, is a strong predictor of future technical default. Moreover, it dominates other

measures used as proxies for covenant strictness in extant literature. Our analysis suggests that,

on the whole, measurement error undoubtedly exists when using Dealscan data to calculate

covenant slack. However, these errors appear to be unbiased and not particularly large. We

conclude that the benefits of using the entire breadth of Dealscan covenant data in studies

examining covenant slack likely outweighs the cost of potential measurement error. Our

evidence endorses a comprehensive approach to measuring covenant strictness (using our

standard definitions) with the full breadth of covenant data presented in Dealscan. We encourage

future research to pursue refinements to such a measure.

31

Appendix A

Categorization of Tearsheets covenants into Dealscan categories

Covenant Numerator Denominator

1 Min. Interest Coverage Earnings Interest Expense 2 Min. Cash Interest Coverage Earnings Interest Paid 3 Min. Debt Service Coverage Earnings Interest Expense + Principal 4 Min. Fixed Charge Coverage Earnings Any not in (1) through (3) 5 Max. Debt to EBITDA Debt Earnings 6 Max. Sr. Debt to EBITDA Sr. Debt Earnings 7 Max. Leverage Debt Assets 8 Max. Sr. Leverage Sr. Debt Assets 9 Max. Debt to Tangible Net Worth Debt Tangible Net Worth 10 Max. Debt to Equity Debt Shareholders’ Equity 11 Min. Current Ratio Current Assets Current Liabilities 12 Min. Quick Ratio Any not in (11) Current Liabilities 13 Min. EBITDA Earnings n/a 14 Min.Net Worth Shareholders’ Equity n/a 15 Min. Tangible Net Worth Shareholders’ Equity – Intangibles n/a

32

Appendix B Detailed descriptives for the covenants attached to the 2,100 deals from the intersection of Tearsheets and Compustat.

Panel A: Min. Interest Coverage

Frequency Covenant Frequency 953 45.4% Numerator: Primary Elements

EBITDA 789 82.8% EBIT 146 15.3% Other * 18 1.9% * net income, operating cash flow, EBITDAR, cash flow, revenue,

EBITDAEX, free cash flow, positive net income Numerator: Secondary Elements

Capital Expenditures 49 5.1% Interest Expense 15 1.6% Other * 35 3.7% * taxes, taxes paid, dividends, dividends paid, interest income, gain/loss on

asset sales, change in working capital, restructuring charges, amortization of goodwill, principal payments, unamortized investment fee, depreciation, non-cash items, extraordinary items

Denominator Interest Expense 952 99.9% Senior Interest Expense 1 0.1% Definitions EBITDA / Interest Expense**

725 76.0%

EBIT / Interest Expense 144 15.1% (EBITDA-capex) / Interest Expense

38 4.0%

Other * 46 4.8% * includes 31 different definitions

Panel B: Min. Cash Interest Coverage

Deals Frequency Covenant Frequency 69 3.3% Numerator: Primary Elements

EBITDA 63 91.3% EBIT 5 7.2% Net Income 1 1.6% Numerator: Secondary Elements

Capital Expenditures 8 11.6% Interest Paid 3 4.3% Other * 2 2.9% * taxes, amortization of goodwill Denominator

33

Interest Paid 69 100.0% Definitions EBITDA / Interest Paid** 53 76.8% (EBITDA-Capex) / Interest Paid

7 10.1%

Other* 9 13.0% * 4 different definitions

Panel C: Min. Fixed Charge Coverage

Deals Frequency Covenant Frequency 592 28.2% Numerator: Primary Elements

EBITDA 417 70.4% EBIT 63 10.6% EBITDAR 37 6.3% Operating Cash Flow 26 4.4% Net Income 21 3.5% Cash Flow 14 2.4% Other * 13 2.2% * EBITR, EBT, EBITAM, operating income, excess cash flow, EBITDAEX Numerator: Secondary Elements

Rent 140 23.6% Capital Expenditures 104 17.6% Interest 27 4.6% Operating Lease Payments 24 4.1% Taxes 22 3.7% Taxes Paid 20 3.4% Cash and Equivalents 10 1.7% Non-Cash Items 10 1.7% Other * 63 10.6% * dividends, commitment fees, dividends paid, capital lease payments,

interest income, preferred dividends, change in working capital, assets sales proceeds, change in deferred taxes, interest paid, equity issuance proceeds, amortization of intangibles, restructuring charges, extraordinary items, repurchases, amortization of goodwill, extraordinary losses, LIFO reserve, depreciation, revolving line of credit outstanding, capital lease interest, amortization of bond discount

Denominator Interest 473 79.9% Principal Payments 348 58.8% Rent 202 34.1% Capital Expenditures 153 25.8% Dividends Paid 92 15.5% Interest Paid 91 15.4% Dividends 80 13.5% Taxes Paid 80 13.5% Capital Lease Payments 78 13.2% Operating Lease Payments 73 12.3% Taxes 61 10.3%

34

Preferred Dividends 26 4.4% Other * 70 11.8% * cash capital expenditures, debt, lease payments, accrued preferred

dividends, preferred stock redemption, revolving line of credit outstanding, management fee, repurchases, senior interest, interest income, senior principal payments, total principal, change in deferred taxes, restructuring charges, prepayments, amortization of bond discount, capital lease interest, junior interest, subordinated principal payments, senior debt, commitment fee, SEC expenses, sale / leaseback proceeds, current portion of long-term debt

Definitions (EBITDA+rent) / (interest+rent)

25 4.2%

EBITDA / (interest+principal+rent)

16 2.7%

(EBIT+rent) / (interest+rent)

15 2.5%

Other * 536 90.5% * includes 353 different definitions

Panel D: Min. Debt Service Coverage Deals Frequency Covenant Frequency 145 6.9% Numerator: Primary Elements

EBITDA 129 89.0% Other * 16 11.0% * EBIT, operating cash flow, net income, cash flow, EBIDA, EBI Numerator: Secondary Elements

Capital Expenditures 54 34.2% Taxes Paid 16 11.0% Taxes 14 9.7% Other * 25 17.2% * rent, cash and equivalents, non-cash items, dividends, commitment fees,

dividends paid, capital lease payments, interest income, change in working capital, assets sales proceeds, change in deferred taxes, interest paid, equity issuance proceeds, restructuring charges, equity repurchases, amortization of goodwill

Denominator Principal Payments 145 100.0% Interest 126 86.9% Interest Paid 19 13.1% Other * 14 9.7% * capital lease payments, redemption of preferred stock, total principal, letter

of credit fees, commitment fees Definitions EBITDA / (principal + interest)

55 37.9%

(EBITDA-capex) / (principal+interest)

15 10.3%

35

Other * 75 51.7% * includes 46 different definitions

Panel E: Max. Debt to EBITDA

Deals Frequency Covenant Frequency 865 41.2% Numerator: Primary Elements

Long-term Debt 861 99.5% Other * 4 0.5% * secured debt, unsecured debt Numerator: Secondary Elements

Rent 24 2.8% Cash and Equivalents 10 1.2% Other * 8 0.9% * subordinated debt, capital leases, convertible subordinated debt, subsidiary

debt, revolving line of credit outstanding Denominator: Primary Elements

EBIDTA 821 94.9% Operating Cash Flow 26 3.0% Cash Flow 14 1.6% Other * 4 0.5% * EBITDAR, net income, EBIT Denominator: Secondary Elements

Capital Expenditures 8 0.9% Rent 5 0.6% Other * 7 0.8% * interest, dividends, preferred dividends, interest paid, SEC expenses, asset

sales proceeds Definitions Long-term Debt / EBITDA**

787 91.0%

Long-term Debt / Operating Cash Flow

23 2.7%

Long-term Debt / Cash Flow

14 1.6%

Other * 41 4.7% * includes 21 different definitions

Panel F: Max. Senior Debt to EBITDA

Deals Frequency Covenant Frequency 161 7.7% Numerator: Primary Elements

Senior Debt 159 98.8% Senior Secured Debt 2 1.2%

36

Numerator: Secondary Elements

Cash and Equivalents 1 0.6% Subordinated Debt 1 0.6% Denominator: Primary Elements

EBITDA 150 93.2% Operating Cash Flow 8 5.0% Other * 3 1.8% * cash flow, EBITDAR Denominator: Secondary Elements

Rent 16 9.9% Capital Expenditures 2 1.2% Definitions Senior Debt / EBITDA** 144 89.4% Senior Debt / Operating Cash Flow

8 5.0%

Other * 9 * includes 6 different definitions

Panel G: Max. Leverage Ratio (i.e., Debt to Assets)

Deals Frequency Covenant Frequency 498 23.7% Numerator: Primary Elements

Long-term Debt 463 93.0% Liabilities 20 4.0% Secured Debt 10 2.0% Other * 5 1.0% * subordinated debt, restricted debt Numerator: Secondary Elements

Other * 15 3.0% * cash and equivalents, rent, net worth, preferred equity, capital leases,

revolving line of credit outstanding, tax liability Denominator: Primary Elements

Assets 471 94.6% Tangible Assets 27 5.4% Denominator: Secondary Elements

Other * 20 4.0% * debt, operating lease, cash and equivalents, rent, short-term liabilities,

cumulative net income, equity issuance proceeds, reserves, revenue, minority interest

Definitions Debt / Assets** 421 84.5%

37

Debt / Tangible Assets 23 4.6% Liabilities / Assets 19 3.8% Other * 35 7.0% * includes 22 different definitions

Panel H: Max. Senior Leverage Deals Frequency Covenant Frequency 53 2.5% Numerator Senior Debt 50 94.3% Senior Secured Debt 3 5.7% Denominator Assets 49 92.5% Tangible Assets 4 7.5% Definitions Senior Debt / Assets** 46 86.8% Senior Debt / Tangible Assets

4 7.5%

Senior Secured Debt / Assets

3 5.7%

Panel I: Max. Debt to Tangible Net Worth

Deals Frequency Covenant Frequency 153 7.3% Numerator: Primary Elements

Debt 100 65.4% Liabilities 40 26.1% Senior Debt 13 8.5% Numerator: Secondary Elements

Operating Lease 10 6.5% Other * 7 4.6% * cash and equivalents, subordinated debt, letter of credit Denominator: Primary Elements

Tangible Net Worth 153 100.0% Denominator: Secondary Elements

Debt 12 7.8% Subordinated Debt 8 5.2% Other * 3 2.0% * senior notes, convertible subordinated debt, senior debt

38

Definitions Debt / Tangible Net Worth**

81 52.9%

Liabilities / Tangible Net Worth

37 24.2%

Other * 35 22.9% * includes 13 different definitions

Panel J: Max. Debt to Equity Deals Frequency Covenant Frequency 309 14.7% Numerator: Primary Elements

Debt 224 72.5% Liabilities 45 14.6% Senior Debt 26 8.4% Other * 14 4.5% * subordinated debt, long-term debt, secured debt, senior notes, term loan Numerator: Secondary Elements

Rent 5 1.6% Cash and Equivalents 4 1.3% Other * 8 2.6% * operating leases, preferred equity, capital leases, revolving line of credit,

shareholders’ equity Denominator: Primary Elements

Net Worth 265 85.8% Capitalization 27 8.7% Shareholders’ Equity 14 4.5% Other * 3 1.0% * adjusted net worth, capital stock Denominator: Secondary Elements

Debt 40 12.9% Subordinated Debt 20 6.5% Other * 16 5.2% * operating leases, junior debt, cash and equivalents, rent, senior notes,

preferred equity, long-term debt, liabilities, deferred taxes, extraordinary items

Definitions Debt / Net Worth** 147 47.6% Liabilities / Net Worth 37 12.0% Debt / Capitalization 21 6.8% Other * 104 33.7% * includes 37 different definitions

Panel K: Min. Current Ratio

Deals Frequency Covenant Frequency 283 13.5%

39

Numerator: Primary Elements

Current Assets 283 100.0% Numerator: Secondary Elements

Others * 9 3.2% * cash and equivalents, revolving line of credit outstanding, LIFO reserve,

inventory, current portion of long-term debt Denominator: Primary Elements

Current Liabilities 283 100.0% Denominator: Secondary Elements

Other * 3 1.1% * revolving line of credit outstanding, current long-term debt Definitions Current Assets / Current Liabilities**

270 95.4%

Other * 13 5.6% * includes 9 different definitions

Panel L: Min. Quick Ratio

Deals Frequency Covenant Frequency 15 0.7% Numerator: Receivables 13 86.7% Cash and Equivalents 13 86.7% Others * 5 33.3% * inventory, long-term inventory, prepaid expenses Denominator Current Liabilities 14 93.3% Accounts Payable 1 6.7% Definitions Receivables + Cash / Current Liabilities**

10 66.7%

Other * 5 33 .3% * includes 4 different definitions

Panel M: Min. EBITDA

Deals Frequency Covenant Frequency 156 8.6% Primary Elements EBITDA 154 98.7% Other * 2 1.3% * 85% prior year EBITDA, "adjusted" Secondary Elements

40

Gain on Asset Sales 1 0.6% Non-cash Items 2 1.3% Operating Lease Payments 2 1.3% Capital Lease Payments 1 0.6% LIFO Reserve 1 0.6% Definitions EBITDA** 152 97.4% Other* 4 2.6% * includes 4 different definitions

Panel N: Net Worth

Deals Frequency Covenant Frequency 670 31.9% Primary Elements Net Worth 649 96.9% Other * 21 3.1% * starting net worth, alternative net worth Secondary Elements: Escalators

Positive Cumulative Net Income

276 41.2%

Equity Issuance Proceeds 254 37.9% Cumulative Net Income 122 18.2% Other * 50 7.5% * fixed amount, debt to equity conversion, acquisitions, subordinated debt,

dividends, repurchases, intangibles, change in net worth, preferred equity, acquisitions for equity, debt issuance proceeds, acquired intangibles, IPO proceeds, acquisitions

Definitions NW** 226 33.7% NW + CNI(+) + EqPro 144 21.5% NW + CNI(+) 101 15.1% NW + CNI 54 8.1% NW + CNI + EqPro 50 7.5% NW + EqPro 29 4.3% Other * 66 9.9% * includes 50 different definitions

Panel O: Tangible Net Worth

Deals Frequency Covenant Frequency 372 17.7% Primary Elements Tangible Net Worth 372 100.0% Secondary Elements: Escalators

Equity Issuance Proceeds 157 42.2% Positive Cumulative Net Income

154 41.4%

41

Cumulative Net Income 74 19.9% Other * 32 8.6% * fixed amount, debt to equity conversion, acquisitions, subordinated debt,

dividends, repurchases, cumulative dividends, asset sales proceeds, alternative equity issuance proceeds, change in net worth, preferred dividends, subsidiary net worth, operating income, subordinated debt proceeds, treasury stock, goodwill, restructuring charges, debt redemption

Definitions TNW** 121 32.5% TNW + CNI(+) + EqPro 82 22.0% TNW + CNI(+) 55 14.8% TNW + CNI + EqPro 34 9.1% TNW + CNI 30 8.1% TNW + EqPro 21 5.6% Other * 29 7.8% * includes 25 different definitions

42

REFERENCES

Beatty, A., Weber, J., Yu, J., 2008. Conservatism and debt. Journal of Accounting and Economics 45, 154-174.

Beneish, M., Press, E., 1993. Costs of technical violation of accounting-based debt covenants.

The Accounting Review 68(2): 233-257.

Beneish, M., Press, E., 1995. The resolution of technical default. The Accounting Review 70(2):

337-353.

Billett, M., King, T., Mauer, D., 2007. Growth opportunities and the choice of leverage, debt

maturity, and covenants. Journal of Finance 62(2): 697-730.

Bradley, M., Roberts, M., 2004. The structure and pricing of corporate debt covenants. Working

Paper. Duke University and the University of Pennsylvania.

Chava, S., Roberts, M., 2008. How does financing impact investment? The role of debt covenants. Journal of Finance 63, 2085-2121.

Christensen, H., Nikolaev, V., 2012. Capital versus performance covenants in debt contracts.

Journal of Accounting Research 50 (1), 75-116. DeFond, M., Jiambalvo, J., 1994. Debt covenant violation and manipulation of accruals. Journal

of Accounting and Economics 17: 145-176. Demerjian, P., 2011. Accounting standards and debt covenants: has the "balance sheet approach"

led to a decline in the use of balance sheet covenants. Journal of Accounting and Economics 52, 178-202.

Demiroglu, C., James, C., 2010. The information content of bank loan covenants. Review of

Financial Studies 23 (10), 3700-3737. Duke, J., Hunt, H., 1990. An empirical examination of debt covenant restrictions and accounting-

related debt proxies. Journal of Accounting and Economics 12, 45-63. Dichev, I., Skinner, D., 2002. Large-sample evidence on the debt covenant hypothesis. Journal of

Accounting Research 40 (4), 1091-1123. Drucker, S., Puri, M., 2009. On loan sales, loan contracting, and lending relationships. Review of

Financial Studies 22(7): 2835-2872. El-Gazzar, S. Pastena, V., 1990. Negotiated accounting rules in private financial contracts.

Journal of Accounting and Economics 12: 381-396.

43

El-Gazzar, S., Pastena, V., 1991. Factors affecting the scope and initial tightness of covenant restrictions in private lending agreements. Contemporary Accounting Research 8 (1), 132-151.

Frankel, R., Litov, L., 2007. Financial accounting characteristics and debt covenants. Working

paper, Washington University in St. Louis. Franz, D., HassabElnaby, H., Lobo, G., 2012. Impact of proximity of to debt covenant violation

on earnings management. Working paper, University of Toledo and University of Houston. Kim, B., 2010. Evidence on conservatism change after debt contracts. Working paper, American

University. Kim, B., Lei, L., Pevzner, M., 2010. Debt covenant slack and real earnings management.

Working paper, American University and George Mason University. Leftwich, R., 1983. Accounting information in private markets: evidence from private lending

agreements. The Accounting Review 58 (1), 23-42. Li, N., 2010. Negotiated measurement rules in debt contracts. Journal of Accounting Research 48

(5), 1103-1144. Murfin, J., 2012. The supply-side determinants of loan contract strictness. Journal of Finance 67

(5), 1565-1601. Nini, G., Smith, D., Sufi, A., 2012. Creditor control rights, corporate governance, and firm value.

Review of Financial Studies 25(6), 1713-1761. Roberts, M., Sufi, A., 2009. Renegotiation of financial contracts: evidence from private credit

agreements. Journal of Financial Economics 93 (2), 159-184. Sweeney, A., 1994. Debt-covenant violations and managers’ accounting responses. Journal of

Accounting and Economics 17: 281-308. Watts, R., Zimmerman, J., 1986. Positive Accounting Theory. Englewood Cliffs, NJ: Prentice

Hall. Zhang, J., 2011. The impact of changes in competition in the syndicated loan market on financial

covenant use. Working paper, McGill University.

44

Table 1

Descriptive statistics Panel A of Table 1 presents the Tearsheets-Compustat intersection sample descriptive statistics (2,100 loan packages). Panel B of Table 1 presents the Dealscan-Compustat intersection sample spanning the same sample period. FACILITIES is the aggregate face amount of all loan facilities on loan package l in millions of U.S. dollars. MATURITY is the facility amount-weighted average loan maturity in months for loan package l. SPREAD is the facility amount-weighted average interest rate on loan package l in excess of LIBOR, in basis points. NCOV is the number of distinct financial and net worth covenants attached to the loan package. SYNDSIZE is the number of distinct lenders participating in the loan.

Panel A: Tearsheets-Compustat Intersection

Mean Std. Dev. Min. P25 Median P75 Max.

FACILITIES 722.447 1,188.650 7.000 200.000 350.000 750.000 15,000.000

MATURITY 52.995 23.533 6.133 36.500 59.750 63.900 121.733

SPREAD 121.254 100.678 0.000 36.000 88.000 200.000 878.380

NCOV 2.622 1.013 1.000 2.000 3.000 3.000 6.000

SYNDSIZE 16.708 14.232 1.000 7.000 13.000 22.000 149.000

Panel B: Dealscan-Compustat Intersection

FACILITIES 277.736 727.287 0.685 23.000 81.864 250.000 25,000.000

MATURITY 43.603 25.787 1.000 24.333 36.533 60.867 365.933

SPREAD 178.810 112.046 1.500 87.500 162.500 250.000 1,071.000

NCOV 2.697 1.099 1.000 2.000 3.000 3.000 7.000

SYNDSIZE 6.601 8.459 1.000 1.000 3.000 9.000 108.000

45

Table 2

Borrower characteristics Panel A of Table 2 presents descriptive statistics for borrowing firms in the Tearsheets-Compustat intersection sample (2,100 loan packages) Panel B of Table 2 presents descriptive statistics for borrowing firms at the Dealscan-Compustat intersection for the same sample period. ASSETS are total assets in millions of U.S. dollars, SALES is annual sales in millions of U.S. dollars, ROA is return on assets, GROWTH is the annual sales growth rate, MTB is the market-to-book ratio, and LEVERAGE is total debt-to-assets.

Panel A: Tearsheets-Compustat Intersection

Mean Std. Dev. Min. P25 Median P75 Max.

ASSETS 4,431.680 12,755.520 0.206 501.473 1,277.130 3,257.740 209,204.000

SALES 3,103.830 6,311.770 0.000 438.366 1,076.170 3,011.600 91,241.000

ROA 0.030 0.087 -1.078 0.007 0.036 0.066 0.358

GROWTH 0.170 0.343 -1.421 0.002 0.090 0.240 2.069

MTB 1.718 1.012 0.482 1.172 1.431 1.938 13.319

LEVERAGE 0.378 0.242 0.000 0.222 0.351 0.507 1.946

Panel B: Dealscan-Compustat Intersection

ASSETS 1,788.970 11,558.110 0.043 86.080 282.937 966.779 689,600.000

SALES 1,152.950 3,854.560 0.000 76.352 229.419 771.304 137,352.170

ROA 0.012 0.173 -5.880 0.001 0.035 0.038 0.760

GROWTH 0.336 0.601 -0.509 0.039 0.171 0.414 4.216

MTB 1.845 1.344 0.577 1.114 1.416 2.041 13.648

LEVERAGE 0.293 0.215 0.000 0.119 0.276 0.427 1.053

46

Table 3

Summary of covenant measurement Table 3 presents summary data on the measurement of financial covenants in Tearsheets. "Covenant" refers to the covenant class, based on the Dealscan classification; these include Min. Interest Coverage (IC), Min. Cash Interest Coverage (CIC), Min. Fixed Charge Coverage (FCC), Min. Debt Service Coverage (DSC), Max. Debt to EBITDA (DE), Max. Senior Debt to EBITDA (SrDE), Max. Leverage Ratio (LEV), Max. Senior Leverage (SrLEV), Max. Debt to Tangible Net Worth (DTNW), Max. Debt to Equity (DEq), Min. Current Ratio (CR), Min. Quick Ratio (QR), Min. EBITDA (EBITDA), Net Worth (NW), and Tangible Net Worth (TNW). "Obs." is the number of times the covenant is used in Tearsheets; "Freq." is the frequency of the covenant over the 2,100 Tearsheets loans. "Primary" and "Secondary" refer to the number of primary and secondary elements for the Numerator and Denominator, respectively. Entries of “n/a” indicate that element does not exist by definition (e.g. all denominator elements in IC, CIC, FCC, and DSC are classified as “Primary”). "Definitions" refers to the number of different definitions found in the Tearsheets observations. "Variation Index" is the number of covenant observations divided by the number of definitions; higher values indicate greater homogeneity in measurement of that covenant.

Numerator Denominator Variation Covenant Obs. Freq. Primary Secondary Primary Secondary Definitions Index

1 IC 953 45.4% 10 16 2 n/a 34 28.0 2 CIC 69 3.3% 3 4 1 n/a 6 11.5 3 FCC 592 28.2% 12 30 36 n/a 356 1.7 4 DSC 145 6.9% 7 19 8 n/a 48 3.0 5 DE 865 41.2% 3 7 6 8 24 36.0 6 SrDE 161 7.7% 2 2 4 2 8 20.1 7 LEV 498 23.7% 5 7 2 10 25 19.9 8 SrLEV 53 2.5% 2 0 2 0 3 17.7 9 DTNW 153 7.3% 3 4 1 5 15 10.2 10 DEq 309 14.7% 8 7 5 12 40 7.7 11 CR 283 13.5% 1 5 1 2 10 28.3 12 QR 15 0.7% 5 n/a 2 n/a 5 3.0 13 EBITDA 156 8.6% 3 5 n/a n/a 5 31.2 14 NW 670 31.9% 3 15 n/a n/a 56 12.0 15 TNW 372 17.7% 1 21 n/a n/a 31 12.0

47

Table 4

Covenant standard definitions Table 4 presents the most common definitions of fifteen covenants based on data from Tearsheets. Definitions are all based on quarterly Compustat; all flow variables are annualized (summing the current plus prior three quarters) for both income statement and statement of cash flow variables. For the Min. Fixed Charge Coverage covenant, we present the definition that minimizes measurement error based on subsequent analysis, as no ex ante standard definition arises. For Min. Net Worth and Min. Tangible Net Worth, we report two frequencies: including and excluding the effects of escalators.

Dealscan Covenant Standard Definition Compustat Implementation Frequency

Min.Interest Coverage

EBITDA / Interest Expense OIBDPQ / XINTQ 76.0%

Min. Cash Interest Coverage

EBITDA / Interest Paid OIBDPQ / INTPNY 76.8%

Min. Fixed Charge Coverage

EBITDA / (Interest Expense + Principal + Rent Expense)

OIBDPQ / XINTQ + lag(DLCQ) + XRENT

2.7%

Min. Debt Service Coverage

EBITDA / (Interest Expense + Principal)

OIBDPQ / XINTQ + lag(DLCQ)

37.9%

Max. Debt-to-EBITDA

Debt / EBITDA DLTTQ + DLCQ / OIBDPQ 91.0%

Max. Senior Debt-to-EBITDA

Senior Debt / EBITDA DLTTQ + DLCQ – DS / OIBDPQ

89.4%

Max. Leverage Debt / Assets DLTTQ + DLCQ / ATQ 84.5%

Max. Senior Leverage

Senior Debt / Assets DLTTQ + DLCQ – DS / ATQ 86.8%

Max. Debt-to-Tangible Net Worth

Debt / TNW DLTTQ + DLCQ / ATQ – INTANQ – LTQ

52.9%

Max. Debt-to-Equity Debt / NW DLTTQ + DLCQ / ATQ – LTQ

47.6%

Min. Current Ratio Current Assets / Current Liabilities ACTQ / LCTQ 95.4%

Min. Quick Ratio Account Receivable + Cash & Equivalents / Current Liabilities

RECTQ + CHEQ / LCTQ 66.7%

Min. EBITDA EBITDA OIBDPQ 97.4%

Min. Net Worth NW ATQ – LTQ 33.7% / 96.9%

(excl. escalators)

Min. Tangible Net Worth

TNW ATQ – INTANQ - LTQ 32.5% / 100.0% (excl. escalators)

48

Table 5

Consistency of the recording of covenant existence across Tearsheets and Dealscan Table 5 presents concordance statistics for the recording of particular covenants on loan packages in the Tearsheets detail versus the Dealscan database. Consistency of 1.000 would indicate that for every loan in Tearsheets on which a particular covenant type is recorded, that covenant is recorded as the identical covenant type on the corresponding loan observation in the Dealscan dataset, and vice versa. The final two columns present the direction of error where overall consistency is not 1.000 (i.e., covenant recorded in the Tearsheets record but not in Dealscan, and vice versa). Covenant Consistency In TS, Not DS Not in TS, In DS

Min. Interest Coverage 0.883 0.047 0.070

Min. Cash Interest Coverage 0.975 0.013 0.012

Min Fixed Charge Coverage 0.958 0.009 0.033

Min. Debt Service Coverage 0.943 0.019 0.038

Max. Debt to EBITDA 0.929 0.028 0.043

Max. Senior Debt to EBITDA 0.975 0.015 0.010

Max. Leverage Ratio 0.916 0.037 0.048

Max. Senior Leverage 0.973 0.013 0.010

Max. Debt to Tangible Net Worth 0.955 0.014 0.031

Max. Debt to Equity 0.938 0.058 0.004

Min. Current Ratio 0.986 0.010 0.004

Min. Quick Ratio 0.997 0.002 0.001

Min. EBITDA 0.941 0.056 0.003

Net Worth 0.911 0.052 0.037

Tangible Net Worth 0.944 0.017 0.039

Average 0.949 0.024 0.027

49

Table 6

Frequency of Standard Covenant Definition between Tearsheets and non-Tearsheets Loans Table 6 shows the frequency with which each type of covenant is measured with the standard measure (from Table 4) for the sample of all Tearsheets loans (column 2) and a random sample of 100 loans included in Dealscan but not in Tearsheets (column 3). The difference between the two frequencies and the t-statistic of this difference are presented in the fourth and fifth columns. We exclude fixed charge coverage from the table. *** indicates statistical significance at the 1% level.

Covenant

Tearsheets

Standard

Frequency

Non-Tearsheets

Standard

Frequency

Difference T-statistic

Min. Interest Coverage 0.760 0.806 -0.046 -0.67

Min. Cash Interest Coverage 0.768 0.667 0.101 0.30

Min. Debt Service Coverage 0.423 0.400 0.023 0.09

Max. Debt to EBITDA 0.910 0.875 0.035 0.71

Max. Senior Debt to EBITDA 0.894 1.000 -0.106 -3.01***

Max. Leverage Ratio 0.845 0.909 -0.064 -0.99

Max. Senior Leverage 0.865 0.833 0.032 0.08

Max. Debt to Tangible Net Worth 0.529 0.429 0.100 0.49

Max. Debt to Equity 0.476 0.667 -0.191 -0.57

Min. Current Ratio 0.954 0.818 0.136 1.11

Min. Quick Ratio 0.800 1.000 -0.200 -1.87

Min. EBITDA 0.974 0.889 0.085 0.76

Net Worth 0.969 0.955 0.014 0.31

Tangible Net Worth 1.000 1.000 0.000 0.00

All Covenants 0.834 0.851 -0.017 -0.46

50

Table 7

Initial slack error Table 6 measures the measurement error in initial covenant slack. The "Observations" columns include the total

number of observations of the covenant in the sample, and the number of those observations where there is

measurement error (i.e., where the "standard" definition does not exactly match the contract-level details). Initial

SLACK is the ratio of the initial value of the covenant measure to the threshold value of the covenant measure,

where measurement of the initial value uses the most recently available Compustat data prior to contract inception.

Initial SLACKTS is the initial slack measured based on the Tearsheets definition of the covenant. Initial SLACKSTD is

the initial slack based on the “standard” definition for the covenant as presented in Table 4. ERROR is the difference

between SLACKSTD and SLACKTS. The table presents mean ERROR based on unadjusted mean initial SLACKs

(Meanraw), with the mean initial SLACKs winsorized at the top and bottom 1% (Meanwin), and with the mean initial

SLACKs truncated at the top and bottom 1%. Median errors are not reported as these are generally zero. ***, **, and

* indicate the mean error is different from zero at the 1%, 5%, and 10% levels, respectively.

Observations Initial SLACKSTD Initial SLACKTS ERROR

Total Error Mean Median Mean Median Meanraw Meanwin

Column: (1) (2) (3) (4) (5) (6) (7) (8)

Min. Threshold Covenants

Interest Coverage 953 226 3.761 1.788 3.500 1.631 0.262*** 0.270*** Cash Interest Coverage 69 17 5.727 1.859 5.594 1.705 0.132** 0.132** Fixed Charge Coverage 592 584 2.049 1.147 1.632 1.146 0.417 0.491 Debt Service Coverage 145 83 15.768 1.785 13.969 1.371 1.799 0.654*** Current Ratio 283 17 1.378 1.305 1.371 1.299 0.008 0.010 Quick Ratio 15 3 1.374 1.497 1.392 1.319 0.341 -0.018 EBITDA 139 4 3.303 1.213 3.315 1.221 -0.011 -0.011 Net Worth 670 28 5.298 1.202 5.423 1.189 -0.125 0.875 Tangible Net Worth 373 2 3.520 1.206 3.520 1.208 -0.000 0.042

Max. Threshold Covenants

Debt to EBITDA 865 72 0.882 0.658 0.849 0.663 0.033 0.032 Sr. Debt to EBITDA 161 42 1.868 0.659 0.853 0.571 1.015 0.364** Leverage 498 72 0.561 0.512 0.560 0.512 0.001 0.008 Senior Leverage 53 7 0.753 0.518 0.653 0.487 0.100 0.100 Debt to TNW 153 66 0.573 0.551 0.587 0.570 -0.014 0.004 Debt to Equity 309 122 0.463 0.628 0.271 0.611 0.192* 0.883

51

Table 8

Descriptive statistics - Dealscan strictness sample Table 8 presents descriptive statistics for the Dealscan strictness sample (7,751 loan packages). STRICTNESS is the aggregate measure of loan package financial covenant strictness, interpreted as the probability that at least one covenant will be violated over the quarter immediately following loan inception. FACILITIES is the aggregate face amount of all loan facilities on loan package l in millions of U.S. dollars. MATURITY is the facility amount-weighted average loan maturity in months for loan package l. SPREAD is the facility amount-weighted average interest rate on loan package l in excess of LIBOR, in basis points. SECURE is an indicator variable that equals one if loan package l requires collateral, and equals zero otherwise. NCOV is the number of distinct financial and net worth covenants attached to the loan package. Panel A presents descriptive statistics for a number of package level variables across the entire sample. Panel B presents descriptive statistics for STRICTNESS for subsamples based on the number of covenants attached to the loan package (1 through 7). Panel A: Sample descriptive statistics N Mean Std Min. P25 P50 P75 Max.

STRICTNESS 7,751 0.149 0.177 0.000 0.001 0.069 0.265 0.780

FACILITIES 7,751 478.99 1,045.55 0.14 59.00 200.00 500.00 18,971.20

MATURITY 7,751 43.416 22.32 0.196 24.000 44.000 60.000 276.000

SECURE 7,751 0.524 0.496 0.000 0.000 1.000 1.000 1.000

SPREAD 7,751 157.767 117.409 1.500 62.500 125.000 225.000 1,055.00

NCOV 7,751 2.661 0.949 1.000 2.000 3.000 3.000 7.000

Panel B: STRICTNESS descriptives by NCOV subsamples NCOV N Mean Std Min P25 P50 P75 Max

1 895 0.059 0.099 0.000 0.000 0.002 0.079 0.520

2 2,309 0.084 0.132 0.000 0.000 0.006 0.129 0.688

3 3,303 0.168 0.181 0.000 0.008 0.097 0.298 0.774

4 1,036 0.278 0.186 0.000 0.117 0.272 0.414 0.771

5-7 208 0.321 0.198 0.000 0.154 0.309 0.482 0.780

52

Table 9

Predictive ability for actual covenant violations Table 9 presents results of logit estimation of Eq. (3). Panel A presents results where the dependent variable (VIOLATION) is an indicator that equals one if the firm realized a covenant violation during the loan tenure, and equals zero otherwise. Panel B presents results where the dependent variable (VIOLATION1YR) is an indicator that equals one if the firm realized a covenant violation during the first year following loan inception, and equals zero otherwise. STRICTNESS is the aggregate measure of loan package financial covenant strictness, interpreted as the probability that at least one covenant will be violated over the quarter immediately following loan inception. NCOV is the number of distinct financial and net worth covenants attached to the loan package. SLACKNW is the loan inception date net worth covenant slack. INVGRADE is an indicator that equals one if firm i had an investment grade S&P long-term debt rating immediately prior to loan inception. BSMPROB is the market-based probability of default for firm i measured in the month preceding loan inception. FACILITIES is the aggregate face amount of all loan facilities on loan package l in millions of U.S. dollars. MATURITY is the facility amount-weighted average loan maturity in months for loan package l. SECURE is an indicator variable that equals one if loan package l requires collateral, and equals zero otherwise. Firm and industry fixed effects, as well as an intercept, are included but not reported. Robust z-statistics based on clustered standard errors at the firm level are reported in parentheses. *, **, and *** indicate significance (two-sided) at the 10%, 5%, and 1% levels, respectively.

Panel A: Violations over the entire life of the loan

Dep. Var.: VIOLATION

Column: (1) (2) (3) (4) (5)

STRICTNESS 1.063*** 0.928*** 1.375***

(3.35) (2.75) (3.00)

NCOV 0.124** 0.068 0.044

(2.11) (1.09) (0.55)

SLACKNW -0.013 -0.007

(-0.81) (-0.48)

INVGRADE -0.351** -0.369** -0.339** -0.458** -0.389*

(-2.37) (-2.47) (-2.28) (-2.09) (-1.77)

BSMPROB 3.015*** 3.082*** 3.067*** 2.240** 2.416**

(3.21) (3.15) (3.20) (2.08) (2.24)

log(MATURITY) 0.036 0.034 0.024 -0.067 -0.112

(0.41) (0.38) (0.26) (-0.58) (-0.96)

log(FACILITIES) -0.214*** -0.254*** -0.222*** -0.272*** -0.236***

(-4.08) (-4.84) (-4.18) (-3.81) (-3.22)

SECURE 0.655*** 0.690*** 0.653*** 0.689*** 0.621***

(5.88) (6.20) (5.85) (4.66) (4.16)

Covenant Controls Included TNW, DEBT2TNW, FIXEDCC, CRATIO

Fixed Effects I, Y I, Y I, Y I, Y I, Y

N 2,638 2,638 2,638 1,336 1,336

Pseudo R2 0.136 0.133 0.136 0.130 0.138

53

Table 9, continued

Predictive ability for actual covenant violations Panel B: Violations during the first year following loan inception

Dep. Var.: VIOLATION1YR

Column: (1) (2) (3) (4) (5)

STRICTNESS 1.457*** 1.748*** 1.984***

(4.23) (4.74) (3.86)

NCOV 0.012 -0.130* -0.165*

(0.20) (-1.82) (-1.65)

SLACKNW -0.043 -0.026

(-1.10) (-0.85)

INVGRADE -0.597*** -0.668*** -0.608*** -0.775*** -0.629**

(-3.37) (-3.79) (-3.41) (-2.99) (-2.39)

BSMPROB 3.991*** 3.771*** 3.781*** 2.526*** 2.441***

(5.62) (5.10) (5.19) (2.97) (2.81)

log(MATURITY) -0.140 -0.099 -0.116 -0.125 -0.128

(-1.36) (-0.96) (-1.12) (-0.94) (-0.95)

log(FACILITIES) -0.120** -0.163*** -0.103** 0.954*** 0.844***

(-2.44) (-3.39) (-2.12) (4.67) (4.07)

SECURE 0.675*** 0.749*** 0.679*** 0.160 0.116

(4.67) (5.31) (4.69) (0.75) (0.54)

Covenant Controls Included TNW, DEBT2TNW, FIXEDCC, CRATIO

Fixed Effects I, Y I, Y I, Y I, Y I, Y

N 3,729 3,729 3,729 1,509 1,509

Pseudo R2 0.128 0.119 0.128 0.124 0.137