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.
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.
1
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.
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.
3
2
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
3
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.
4
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.
5
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.
6
(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 accrualor 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-toEBITDA covenants. Other studies, such as Bradley and Roberts (2004) and Billett et al. (2007),
7
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
8
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.
9
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,
10
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-toassets 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.
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.
10
11
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 ratiobased. 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
12
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.
13
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.
14
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.
15
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-toEBITDA 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.
16
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
17
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.
18
covenants, there is only one statistically significant difference, where the Senior Debt-toEBITDA 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
19
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.
20
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).
21
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.
22
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.
23
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-overquarter 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.
24
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.
25
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.
26
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:
Pr (VIOLATION ) =
1
,
1 + e− z
(3)
z = β 0 + β1STRICTNESSl + β 2 NCOVl + β 3 INVGRADEi ,l + β 4 BSMPROBi ,l
+ LoanControls + CovenantControls + YearFixedEffects + IndustryFixedEffects + 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
27
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
28
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.
29
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.
30
Appendix A
Categorization of Tearsheets covenants into Dealscan categories
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Covenant
Min. Interest Coverage
Min. Cash Interest Coverage
Min. Debt Service Coverage
Min. Fixed Charge Coverage
Max. Debt to EBITDA
Max. Sr. Debt to EBITDA
Max. Leverage
Max. Sr. Leverage
Max. Debt to Tangible Net Worth
Max. Debt to Equity
Min. Current Ratio
Min. Quick Ratio
Min. EBITDA
Min.Net Worth
Min. Tangible Net Worth
Numerator
Earnings
Earnings
Earnings
Earnings
Debt
Sr. Debt
Debt
Sr. Debt
Debt
Debt
Current Assets
Any not in (11)
Earnings
Shareholders’ Equity
Shareholders’ Equity – Intangibles
31
Denominator
Interest Expense
Interest Paid
Interest Expense + Principal
Any not in (1) through (3)
Earnings
Earnings
Assets
Assets
Tangible Net Worth
Shareholders’ Equity
Current Liabilities
Current Liabilities
n/a
n/a
n/a
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
45.4%
Covenant Frequency
953
Numerator: Primary
Elements
EBITDA
EBIT
Other *
789
146
18
82.8%
15.3%
1.9%
* net income, operating cash flow, EBITDAR, cash flow, revenue,
EBITDAEX, free cash flow, positive net income
Numerator: Secondary
Elements
Capital Expenditures
Interest Expense
Other *
49
15
35
5.1%
1.6%
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, noncash items, extraordinary items
Denominator
Interest Expense
Senior Interest Expense
952
1
99.9%
0.1%
725
76.0%
144
38
15.1%
4.0%
46
4.8%
* includes 31 different definitions
Definitions
EBITDA / Interest
Expense**
EBIT / Interest Expense
(EBITDA-capex) / Interest
Expense
Other *
Panel B: Min. Cash Interest Coverage
Deals Frequency
Covenant Frequency
69
3.3%
Numerator: Primary
Elements
EBITDA
EBIT
Net Income
63
5
1
91.3%
7.2%
1.6%
Numerator: Secondary
Elements
Capital Expenditures
Interest Paid
Other *
8
3
2
11.6%
4.3%
2.9%
* taxes, amortization of goodwill
Denominator
32
Interest Paid
Definitions
EBITDA / Interest Paid**
(EBITDA-Capex) / Interest
Paid
Other*
69
100.0%
53
7
76.8%
10.1%
9
13.0%
* 4 different definitions
Panel C: Min. Fixed Charge Coverage
Deals Frequency
Covenant Frequency
592
28.2%
Numerator: Primary
Elements
EBITDA
EBIT
EBITDAR
Operating Cash Flow
Net Income
Cash Flow
Other *
417
63
37
26
21
14
13
70.4%
10.6%
6.3%
4.4%
3.5%
2.4%
2.2%
* EBITR, EBT, EBITAM, operating income, excess cash flow, EBITDAEX
Numerator: Secondary
Elements
Rent
Capital Expenditures
Interest
Operating Lease Payments
Taxes
Taxes Paid
Cash and Equivalents
Non-Cash Items
Other *
140
104
27
24
22
20
10
10
63
23.6%
17.6%
4.6%
4.1%
3.7%
3.4%
1.7%
1.7%
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
Principal Payments
Rent
Capital Expenditures
Dividends Paid
Interest Paid
Dividends
Taxes Paid
Capital Lease Payments
Operating Lease Payments
Taxes
473
348
202
153
92
91
80
80
78
73
61
79.9%
58.8%
34.1%
25.8%
15.5%
15.4%
13.5%
13.5%
13.2%
12.3%
10.3%
33
Preferred Dividends
Other *
Definitions
(EBITDA+rent) /
(interest+rent)
EBITDA /
(interest+principal+rent)
(EBIT+rent) /
(interest+rent)
Other *
26
70
4.4%
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
25
4.2%
16
2.7%
15
2.5%
536
90.5%
* includes 353 different definitions
Panel D: Min. Debt Service Coverage
Deals Frequency
Covenant Frequency
145
6.9%
Numerator: Primary
Elements
EBITDA
Other *
129
16
89.0%
11.0%
* EBIT, operating cash flow, net income, cash flow, EBIDA, EBI
Numerator: Secondary
Elements
Capital Expenditures
Taxes Paid
Taxes
Other *
54
16
14
25
34.2%
11.0%
9.7%
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
Interest
Interest Paid
Other *
145
126
19
14
100.0%
86.9%
13.1%
9.7%
* capital lease payments, redemption of preferred stock, total principal, letter
of credit fees, commitment fees
55
37.9%
15
10.3%
Definitions
EBITDA / (principal +
interest)
(EBITDA-capex) /
(principal+interest)
34
Other *
75
Panel E: Max. Debt to EBITDA
Deals
Covenant Frequency
865
51.7%
* includes 46 different definitions
Frequency
41.2%
Numerator: Primary
Elements
Long-term Debt
Other *
861
4
99.5%
0.5%
* secured debt, unsecured debt
Numerator: Secondary
Elements
Rent
Cash and Equivalents
Other *
24
10
8
2.8%
1.2%
0.9%
* subordinated debt, capital leases, convertible subordinated debt, subsidiary
debt, revolving line of credit outstanding
Denominator: Primary
Elements
EBIDTA
Operating Cash Flow
Cash Flow
Other *
821
26
14
4
94.9%
3.0%
1.6%
0.5%
* EBITDAR, net income, EBIT
Denominator: Secondary
Elements
Capital Expenditures
Rent
Other *
8
5
7
0.9%
0.6%
0.8%
* interest, dividends, preferred dividends, interest paid, SEC expenses, asset
sales proceeds
787
91.0%
23
2.7%
14
1.6%
41
4.7%
* includes 21 different definitions
Definitions
Long-term Debt /
EBITDA**
Long-term Debt /
Operating Cash Flow
Long-term Debt / Cash
Flow
Other *
Panel F: Max. Senior Debt to EBITDA
Deals Frequency
Covenant Frequency
161
7.7%
Numerator: Primary
Elements
Senior Debt
Senior Secured Debt
159
2
98.8%
1.2%
35
Numerator: Secondary
Elements
Cash and Equivalents
Subordinated Debt
1
1
0.6%
0.6%
Denominator: Primary
Elements
EBITDA
Operating Cash Flow
Other *
150
8
3
93.2%
5.0%
1.8%
* cash flow, EBITDAR
Denominator: Secondary
Elements
Rent
Capital Expenditures
16
2
9.9%
1.2%
144
8
89.4%
5.0%
Definitions
Senior Debt / EBITDA**
Senior Debt / Operating
Cash Flow
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
Liabilities
Secured Debt
Other *
463
20
10
5
93.0%
4.0%
2.0%
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
Tangible Assets
471
27
94.6%
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%
36
Debt / Tangible Assets
Liabilities / Assets
Other *
23
19
35
4.6%
3.8%
7.0%
* includes 22 different definitions
Panel H: Max. Senior Leverage
Deals
Covenant Frequency
53
Frequency
2.5%
Numerator
Senior Debt
Senior Secured Debt
50
3
94.3%
5.7%
Denominator
Assets
Tangible Assets
49
4
92.5%
7.5%
46
4
86.8%
7.5%
3
5.7%
Definitions
Senior Debt / Assets**
Senior Debt / Tangible
Assets
Senior Secured Debt /
Assets
Panel I: Max. Debt to Tangible Net Worth
Deals Frequency
Covenant Frequency
153
7.3%
Numerator: Primary
Elements
Debt
Liabilities
Senior Debt
100
40
13
65.4%
26.1%
8.5%
Numerator: Secondary
Elements
Operating Lease
Other *
10
7
6.5%
4.6%
* cash and equivalents, subordinated debt, letter of credit
Denominator: Primary
Elements
Tangible Net Worth
153
100.0%
Denominator: Secondary
Elements
Debt
Subordinated Debt
Other *
12
8
3
7.8%
5.2%
2.0%
* senior notes, convertible subordinated debt, senior debt
37
Definitions
Debt / Tangible Net
Worth**
Liabilities / Tangible Net
Worth
Other *
81
52.9%
37
24.2%
35
22.9%
* includes 13 different definitions
Covenant Frequency
Deals
309
Frequency
14.7%
Numerator: Primary
Elements
Debt
Liabilities
Senior Debt
Other *
224
45
26
14
72.5%
14.6%
8.4%
4.5%
* subordinated debt, long-term debt, secured debt, senior notes, term loan
Numerator: Secondary
Elements
Rent
Cash and Equivalents
Other *
5
4
8
1.6%
1.3%
2.6%
* operating leases, preferred equity, capital leases, revolving line of credit,
shareholders’ equity
Denominator: Primary
Elements
Net Worth
Capitalization
Shareholders’ Equity
Other *
265
27
14
3
85.8%
8.7%
4.5%
1.0%
* adjusted net worth, capital stock
Denominator: Secondary
Elements
Debt
Subordinated Debt
Other *
40
20
16
12.9%
6.5%
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**
Liabilities / Net Worth
Debt / Capitalization
Other *
147
37
21
104
47.6%
12.0%
6.8%
33.7%
* includes 37 different definitions
Deals
283
Frequency
13.5%
Panel J: Max. Debt to Equity
Panel K: Min. Current Ratio
Covenant Frequency
38
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
270
95.4%
13
5.6%
* includes 9 different definitions
Covenant Frequency
Deals
15
Frequency
0.7%
Numerator:
Receivables
Cash and Equivalents
Others *
13
13
5
86.7%
86.7%
33.3%
* inventory, long-term inventory, prepaid expenses
Denominator
Current Liabilities
Accounts Payable
14
1
93.3%
6.7%
10
66.7%
5
33 .3%
* includes 4 different definitions
Covenant Frequency
Deals
156
Frequency
8.6%
Primary Elements
EBITDA
Other *
154
2
98.7%
1.3%
* 85% prior year EBITDA, "adjusted"
Definitions
Current Assets / Current
Liabilities**
Other *
Panel L: Min. Quick Ratio
Definitions
Receivables + Cash /
Current Liabilities**
Other *
Panel M: Min. EBITDA
Secondary Elements
39
Gain on Asset Sales
Non-cash Items
Operating Lease Payments
Capital Lease Payments
LIFO Reserve
1
2
2
1
1
0.6%
1.3%
1.3%
0.6%
0.6%
Definitions
EBITDA**
Other*
152
4
97.4%
2.6%
* includes 4 different definitions
Covenant Frequency
Deals
670
Frequency
31.9%
Primary Elements
Net Worth
Other *
649
21
96.9%
3.1%
* starting net worth, alternative net worth
Panel N: Net Worth
Secondary Elements:
Escalators
Positive Cumulative Net
Income
Equity Issuance Proceeds
Cumulative Net Income
Other *
276
41.2%
254
122
50
37.9%
18.2%
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**
NW + CNI(+) + EqPro
NW + CNI(+)
NW + CNI
NW + CNI + EqPro
NW + EqPro
Other *
226
144
101
54
50
29
66
33.7%
21.5%
15.1%
8.1%
7.5%
4.3%
9.9%
* includes 50 different definitions
Panel O: Tangible Net Worth
Deals
Covenant Frequency
372
Frequency
17.7%
Primary Elements
Tangible Net Worth
372
100.0%
157
154
42.2%
41.4%
Secondary Elements:
Escalators
Equity Issuance Proceeds
Positive Cumulative Net
Income
40
Cumulative Net Income
Other *
74
32
19.9%
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**
TNW + CNI(+) + EqPro
TNW + CNI(+)
TNW + CNI + EqPro
TNW + CNI
TNW + EqPro
Other *
121
82
55
34
30
21
29
32.5%
22.0%
14.8%
9.1%
8.1%
5.6%
7.8%
* includes 25 different definitions
41
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 accountingrelated 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.
42
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), 132151.
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.
43
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.
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
121.254
100.678
0.000
36.000
88.000
200.000
878.380
2.622
1.013
1.000
2.000
3.000
3.000
6.000
16.708
14.232
1.000
7.000
13.000
22.000
149.000
727.287
0.685
23.000
81.864
250.000
25,000.000
SPREAD
NCOV
SYNDSIZE
Min.
P25
Median
P75
Max.
Panel B: Dealscan-Compustat Intersection
FACILITIES
277.736
43.603
25.787
1.000
24.333
36.533
60.867
365.933
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
MATURITY
SPREAD
44
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 DealscanCompustat 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.
P25
Median
P75
Max.
ASSETS
4,431.680
12,755.520
Min.
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
0.043
86.080
282.937
966.779
689,600.000
Panel B: Dealscan-Compustat Intersection
ASSETS
1,788.970
11,558.110
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
45
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.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Covenant
IC
CIC
FCC
DSC
DE
SrDE
LEV
SrLEV
DTNW
DEq
CR
QR
EBITDA
NW
TNW
Obs.
953
69
592
145
865
161
498
53
153
309
283
15
156
670
372
Freq.
45.4%
3.3%
28.2%
6.9%
41.2%
7.7%
23.7%
2.5%
7.3%
14.7%
13.5%
0.7%
8.6%
31.9%
17.7%
Numerator
Primary
Secondary
10
16
3
4
12
30
7
19
3
7
2
2
5
7
2
0
3
4
8
7
1
5
5
n/a
3
5
3
15
1
21
46
Denominator
Primary
Secondary
2
n/a
1
n/a
36
n/a
8
n/a
6
8
4
2
2
10
2
0
1
5
5
12
1
2
2
n/a
n/a
n/a
n/a
n/a
n/a
n/a
Definitions
34
6
356
48
24
8
25
3
15
40
10
5
5
56
31
Variation
Index
28.0
11.5
1.7
3.0
36.0
20.1
19.9
17.7
10.2
7.7
28.3
3.0
31.2
12.0
12.0
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
Min.Interest
Coverage
Standard Definition
Compustat Implementation
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-toEBITDA
Debt / EBITDA
DLTTQ + DLCQ / OIBDPQ
91.0%
Max. Senior Debt-toEBITDA
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-toTangible 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)
47
Frequency
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
Min. Interest Coverage
Min. Cash Interest Coverage
Min Fixed Charge Coverage
Min. Debt Service Coverage
Max. Debt to EBITDA
Max. Senior Debt to EBITDA
Max. Leverage Ratio
Max. Senior Leverage
Max. Debt to Tangible Net Worth
Max. Debt to Equity
Min. Current Ratio
Min. Quick Ratio
Min. EBITDA
Net Worth
Tangible Net Worth
Average
Consistency
0.883
0.975
0.958
0.943
0.929
0.975
0.916
0.973
0.955
0.938
0.986
0.997
0.941
0.911
0.944
In TS, Not DS
0.047
0.013
0.009
0.019
0.028
0.015
0.037
0.013
0.014
0.058
0.010
0.002
0.056
0.052
0.017
Not in TS, In DS
0.070
0.012
0.033
0.038
0.043
0.010
0.048
0.010
0.031
0.004
0.004
0.001
0.003
0.037
0.039
0.949
0.024
0.027
48
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
Min. Interest Coverage
Min. Cash Interest Coverage
Min. Debt Service Coverage
Max. Debt to EBITDA
Max. Senior Debt to EBITDA
Max. Leverage Ratio
Max. Senior Leverage
Max. Debt to Tangible Net Worth
Max. Debt to Equity
Min. Current Ratio
Min. Quick Ratio
Min. EBITDA
Net Worth
Tangible Net Worth
All Covenants
Tearsheets
Standard
Frequency
0.760
0.768
0.423
0.910
0.894
0.845
0.865
0.529
0.476
0.954
0.800
0.974
0.969
1.000
Non-Tearsheets
Standard
Frequency
0.806
0.667
0.400
0.875
1.000
0.909
0.833
0.429
0.667
0.818
1.000
0.889
0.955
1.000
0.834
0.851
49
Difference
T-statistic
-0.046
0.101
0.023
0.035
-0.106
-0.064
0.032
0.100
-0.191
0.136
-0.200
0.085
0.014
0.000
-0.67
0.30
0.09
0.71
-3.01***
-0.99
0.08
0.49
-0.57
1.11
-1.87
0.76
0.31
0.00
-0.017
-0.46
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.
Column:
Min. Threshold Covenants
Interest Coverage
Cash Interest Coverage
Fixed Charge Coverage
Debt Service Coverage
Current Ratio
Quick Ratio
EBITDA
Net Worth
Tangible Net Worth
Max. Threshold Covenants
Debt to EBITDA
Sr. Debt to EBITDA
Leverage
Senior Leverage
Debt to TNW
Debt to Equity
Observations
Total Error
(1)
(2)
Initial SLACKSTD
Mean
Median
(3)
(4)
Initial SLACKTS
Mean
Median
(5)
(6)
ERROR
Meanwin
Meanraw
(7)
(8)
953
69
592
145
283
15
139
670
373
226
17
584
83
17
3
4
28
2
3.761
5.727
2.049
15.768
1.378
1.374
3.303
5.298
3.520
1.788
1.859
1.147
1.785
1.305
1.497
1.213
1.202
1.206
3.500
5.594
1.632
13.969
1.371
1.392
3.315
5.423
3.520
1.631
1.705
1.146
1.371
1.299
1.319
1.221
1.189
1.208
0.262***
0.132**
0.417
1.799
0.008
0.341
-0.011
-0.125
-0.000
0.270***
0.132**
0.491
0.654***
0.010
-0.018
-0.011
0.875
0.042
865
161
498
53
153
309
72
42
72
7
66
122
0.882
1.868
0.561
0.753
0.573
0.463
0.658
0.659
0.512
0.518
0.551
0.628
0.849
0.853
0.560
0.653
0.587
0.271
0.663
0.571
0.512
0.487
0.570
0.611
0.033
1.015
0.001
0.100
-0.014
0.192*
0.032
0.364**
0.008
0.100
0.004
0.883
50
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 amountweighted 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
51
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.:
Column:
STRICTNESS
(1)
1.063***
(3.35)
(2)
VIOLATION
(3)
0.928***
(2.75)
0.068
(1.09)
(4)
NCOV
0.124**
(2.11)
SLACKNW
-0.013
(-0.81)
-0.369**
-0.339**
-0.458**
(-2.47)
(-2.28)
(-2.09)
3.082***
3.067***
2.240**
(3.15)
(3.20)
(2.08)
0.034
0.024
-0.067
(0.38)
(0.26)
(-0.58)
-0.254***
-0.222***
-0.272***
(-4.84)
(-4.18)
(-3.81)
0.690***
0.653***
0.689***
(6.20)
(5.85)
(4.66)
TNW, DEBT2TNW, FIXEDCC, CRATIO
I, Y
I, Y
I, Y
2,638
2,638
1,336
0.133
0.136
0.130
INVGRADE
BSMPROB
log(MATURITY)
log(FACILITIES)
SECURE
Covenant Controls Included
Fixed Effects
N
Pseudo R2
-0.351**
(-2.37)
3.015***
(3.21)
0.036
(0.41)
-0.214***
(-4.08)
0.655***
(5.88)
I, Y
2,638
0.136
52
(5)
1.375***
(3.00)
0.044
(0.55)
-0.007
(-0.48)
-0.389*
(-1.77)
2.416**
(2.24)
-0.112
(-0.96)
-0.236***
(-3.22)
0.621***
(4.16)
I, Y
1,336
0.138
Table 9, continued
Predictive ability for actual covenant violations
Panel B: Violations during the first year following loan inception
Dep. Var.:
Column:
STRICTNESS
(1)
1.457***
(4.23)
NCOV
SLACKNW
INVGRADE
BSMPROB
log(MATURITY)
log(FACILITIES)
SECURE
Covenant Controls Included
Fixed Effects
N
Pseudo R2
-0.597***
(-3.37)
3.991***
(5.62)
-0.140
(-1.36)
-0.120**
(-2.44)
0.675***
(4.67)
I, Y
3,729
0.128
VIOLATION1YR
(3)
(4)
1.748***
(4.74)
0.012
-0.130*
(0.20)
(-1.82)
-0.043
(-1.10)
-0.668***
-0.608***
-0.775***
(-3.79)
(-3.41)
(-2.99)
3.771***
3.781***
2.526***
(5.10)
(5.19)
(2.97)
-0.099
-0.116
-0.125
(-0.96)
(-1.12)
(-0.94)
-0.163***
-0.103**
0.954***
(-3.39)
(-2.12)
(4.67)
0.749***
0.679***
0.160
(5.31)
(4.69)
(0.75)
TNW, DEBT2TNW, FIXEDCC, CRATIO
I, Y
I, Y
I, Y
3,729
3,729
1,509
0.119
0.128
0.124
(2)
53
(5)
1.984***
(3.86)
-0.165*
(-1.65)
-0.026
(-0.85)
-0.629**
(-2.39)
2.441***
(2.81)
-0.128
(-0.95)
0.844***
(4.07)
0.116
(0.54)
I, Y
1,509
0.137