The Information Content of Bank Loan Covenants
by
Cem Demiroglu
and
Christopher James
Department of Finance, Insurance, and Real Estate
Warrington College of Business Administration
University of Florida
Gainesville, FL 32611
First Draft: November 30, 2006
The Information Content of Bank Loan Covenants
Abstract
This paper examines the relationship between the restrictiveness of covenants in bank loan agreements
and subsequent operating performance of borrowing firms. Realized future operating performance is used
as a proxy for the borrower’s private information concerning credit quality. The restrictiveness of
financial covenants may be related to future operating performance because restrictive covenants act as a
signaling device that permits high quality firms (e.g., firms with positive private information about their
future performance) to distinguish themselves from observationally similar but lower quality firms. We
measure the restrictiveness of financial covenants by how close the covenant is set relative to the level of
covenant variable at the inception of the loan (covenant tightness) and also by number of covenants in the
loan contract (covenant intensity). We find that tighter covenants are associated with improvements in the
performance as measured by changes in the covenant variable. Consistent with lenders encouraging
revelation of private information via covenant choice, we find that tighter covenants are associated with
lower borrowing costs. We also find a larger stock price reaction to the announcement of loans with
tighter covenants which is consistent with covenant choice signaling favorable private information.
Finally, we find that covenant intensity is associated with better performance but only for the most
informationally opaque firms. Overall, our results suggest that the borrower’s choice of the restrictiveness
of covenants credibly conveys private information about future performance.
I. Introduction
Do borrowers agree to what appear to be onerous contract terms to credibly convey to the lender
their confidence in future improvements in performance? In this paper we address this question by
examining the relationship between the restrictiveness of bank loan covenants and subsequent changes in
the operating performance of borrowing firms. We use realized future operating performance as a proxy
for the borrowers’ private information concerning their creditworthiness at the time of loan. We also
examine empirically the determinants of the design of loan contracts and the link between contract design
and subsequent performance in the presence of agency costs and adverse selection.
The idea that covenants can serve as a signaling device is motivated by theoretical work by Chan
and Kanatas (1985) and Besanko and Thakor (1987) on collateral requirement and more recently by
Gerleanu and Zwiebel (2005) and Dessein (2005) on contract design and the allocation of control rights.
These models suggest that borrowers (or entrepreneurs) use contract terms to credibly convey private
information concerning their future prospects. The basic idea is that borrowers have private information
about either their future performance or the costs of a potential covenant violation. Borrowers agree to
restrictive covenants when they have favorable private information about their performance or when the
costs of covenant violation are expected to be small (for example when ex-post the lenders are expected
to have little incentive to enforce the covenant). In the signaling context, borrowers with negative private
information (so called poor quality borrowers) will find it costly to mimic the contract choices of
borrowers with favorable private information and hence opt for less restrictive covenants. By charging
lower spreads to borrowers that choose tighter covenants and penalizing poor quality borrowers upon
covenant violations, lenders encourage borrowers to reveal their private information. We refer to this
conceptual framework as the Signaling Theory of Covenants (STC).1
Anecdotal evidence also suggests that the choice of covenants conveys information about the
borrower’s expected future performance. For example, Zimmerman (1975) states that through the loan
document and the covenants it contains “…the bank creates a clear understanding to the borrower as to
what is expected”. Consistent with this view, when Lucent announced a new loan agreement on February
22, 2001, the Dow Jones News Service reported an analyst commentary that “… the financial conditions
attached to the loan were the first hints of guidance Lucent has given since its first quarter conference
call.” The analyst also stated that “... the minimum EBITDA requirements that Lucent agreed to reveal the
company has committed to losing no more than $1.525 billion in the ongoing second quarter and $825
million in the third quarter.”
1
Another reason tightness of covenants may be related to subsequent improvements in operating performance is that
tight covenants effect a manager’s financial reporting decisions (See Dichev and Skinner (2002)).
1
Prior empirical work on the importance of signaling in covenant selection is limited. One of the
challenges in testing the STC is distinguishing between observable measures of firm quality (credit risk)
and private information about borrower quality that may be conveyed through covenant choice. In
particular, the most commonly cited reason for the existence of loan covenants and collateral
requirements is that they serve to mitigate agency conflicts between shareholders and debtholders.2 This
explanation for the structure of covenants is often referred to as the Agency Theory of Covenants (ATC).
Since the agency costs of debt are generally thought to be inversely related to the financial condition of
the firm, covenants are expected to be more restrictive in loans to the least creditworthy borrowers (e.g.,
highly levered firms and to firms with significant growth opportunities).3 This suggests that the
relationship between covenant restrictions and potential proxies for private information such as future
changes in operating performance and future loan defaults may be difficult to detect. In particular, since
observationally riskier borrowers are more likely to experience poor future performance, if credit risk
(based on observables) is measured with error then one may observe a negative relationship between the
restrictiveness of covenants and future operating performance even though covenant selection conveys
favorable private information. Finding, however, a positive and significant relationship between the
restrictiveness of covenants and future operating performance is unambiguous evidence consistent with a
signaling story.
The importance of private information and thus signaling is likely to vary inversely with the
precision of the lender’s estimate of the creditworthiness of the borrower.4 This suggests that the
relationship between the restrictiveness of covenants and future performance is likely to be the strongest
among more informationally opaque borrowers. Thus, the relationship between covenant choice and
future performance should be stronger when information problems become more severe.
Another challenge in testing the STC is that the covenant choice set and hence the tightness of a
covenant is likely to be related to the level of the covenant variable and thus underlying credit risk of the
borrower. As a result, the informativeness of a firm’s covenant choice is likely to vary with credit risk.
For example, consider a liquidity covenant that requires a firm maintain a minimum ratio of current assets
to current liabilities. Firms with initially high levels of liquidity are likely to be able to choose from a
broader menu of covenant levels than firms with initially low levels of liquidity. In this case the
information content of the covenant choice depends on the initial level of the covenant variable. To
2
See for example, Jensen and Meckling (1976), Smith and Warner (1979), Myers (1977), and Smith (1993).
See for example, Booth and Booth (2006), Bradley and Roberts (2004), Malitz (1986), and Nash, Netter and
Poulsen (2003). Investigating the choice of bank loan covenants, Bradley and Roberts (2004) find that growth firms
get more restrictive covenants. However, Nash, Netter and Poulsen (2003) find that public debt contracts of high
growth firms are less likely to include restrictions on dividends and debt issuance.
4
Jimenez, Salas and Saurina (2006) make this argument in the context of the choice of securing a loan.
3
2
address this problem we place borrowers in clusters based on their levels of the covenant variables at loan
inception and then examine covenant choice within each cluster.
Our empirical analysis is based on a large sample of bank loans taken out by non-financial U.S.
public firms between January 1995 and December 2001. The primary source for loan contract terms is the
Dealscan database from the Loan Pricing Corporation.
We define the restrictiveness of covenants along two dimensions; covenant tightness and
covenant intensity. For financial covenants we define covenant tightness by comparing the restrictiveness
of each borrower’s covenant choice relative to the choices of borrowers that have similar covenant level
choice sets (i.e. in the same cluster). Any covenant choice that is more restrictive than the cluster median
is classified as tight. The advantage of using this tightness measure is that we can examine changes in
performance along a narrow but presumably value relevant measure. Moreover, it seems reasonable to
assume that the borrower’s private information is closely tied to future performance as measured by
changes in the covenant variable.5 For example, borrowers select coverage or liquidity covenants levels
that they expect to achieve.
A drawback of using the tightness measure is that lenders often make adjustments to GAAP
numbers when defining covenants to account for specific needs or characteristics of the borrower (See
Leftwich (1983)). As a result, definitions of covenant variables may differ from how those variables are
reported or defined in Compustat. This implies that covenant tightness is likely to be measured with error.
As we discuss later, we employ several procedures to minimize measurement error. We focus on two
common and economically important financial covenants whose tightness we are able to measure most
reliably; the current ratio covenant and the Debt/EBITDA covenant.
We also measure the restrictiveness of covenants by the number of covenants in the loan contract.
Following Bradley and Roberts (2004) we define covenant intensity by the number of covenants included
in the loan contract (hereafter we refer to this as the covenant intensity index). More specifically, the
covenant intensity index equals the sum of six covenant indicators (collateral, dividend restriction, more
than 2 financial covenants, asset sales sweep, equity issuance sweep, and debt issuance sweep). The index
consists primarily of covenants that restrict borrower actions or provide lenders rights that are conditioned
on adverse future events.6 This suggests that if covenant intensity serves as a signal of borrower credit
quality then it is likely to provide information concerning the likelihood or cost of adverse events. As a
A commercial lending text describes the criteria for setting target thresholds as follows: “The thresholds are set
based on the company’s historical and projected performance, as well as the banker’s determination of key
performance levels required to provide adequate protection”. Principles of Loan Structure 3-6 Edge Development
Group 2006 available at www.edgedevelopment.com.
6
For example, asset sweeps require that a portion of the proceeds form asset sales to be used to pay down the loan
and collateral requirements provide the lender title to the assets conditional on default.
5
3
result, one way to measure future performance is by the frequency of subsequent covenant violations or
defaults (see for example Jimenez et al. (2006)). The advantage of this performance measure is that it is a
simple measure of credit risk (i.e. the likelihood the borrower is unable to perform according to the loan
agreement). However, a potential problem with this approach is that default is an outcome that may be
mechanically linked to the number of covenants. Moreover, even though borrowers may expect the
likelihood of a covenant violation to increase with the number of covenants, higher quality borrowers may
nevertheless choose more restrictive covenants because they expect the cost of covenant violations to be
low.
To avoid these problems, when examining covenant intensity we use three broader measures of
performance. The first measure is the frequency of CRSP delisting due to poor performance (liquidation,
bankruptcy, or the borrowers failure to meet exchange listing requirements). This provides a measure of
likelihood of poor operating performance (in the spirit of the frequency of defaults measure) that is not
mechanically linked to the number of covenants. The second measure is changes in credit ratings; more
specifically, the frequency of credit rating downgrades. This allows us to measure less extreme changes in
credit quality. Our third and last measure is changes in Altman’s Z-score.
Overall, we find a positive and significant relationship between the tightness of financial
covenants and improvements in operating performance of borrowers. In particular, controlling for credit
risk and other determinants of covenant tightness, we find that tighter financial covenants are associated
with significant improvements in the covenant variable in the three years following the inception of the
loan. Consistent with the selection of tight covenants conveying favorable private information we find
that stock returns on the announcement of loans are significantly higher for loans with tight covenants.
Finally, controlling for the interdependence between the selection of tight covenants and borrowing costs,
we find that borrowers significantly lower their interest costs by choosing tight rather than loose
covenants. These results are consistent with covenant tightness conveying positive private information
concerning the borrower’s unobservable credit quality.
Turning to the covenant intensity results for the entire sample, in univariate analysis, we find
that the covenant intensity is positively related to the likelihood of CRSP delisting due to poor
performance and credit rating downgrades. However, this is at least in part attributable to agency
problems that lead to more intensive covenants for loans to observably riskier firms. To address this issue,
in multivariate analysis, we examine the relationship between covenant choice and performance
controlling for observable risk factors and proxies for the severity of information asymmetries.
Controlling for these factors, we find that the covenant intensity is associated with improved performance
where information asymmetries are most severe. We also find that covenant intensity is priced in the
sense that selectivity adjusted spreads are lower when more intensive covenants are chosen.
4
Overall our empirical analysis indicates that covenant structure is related to observable risk
measures as well as borrower’s private information concerning future changes in credit quality. Loans to
observationally riskier borrowers have more restrictive covenants while at the same time borrowers
appear to convey private information by their choice of covenants. To our knowledge this is the first
paper to provide evidence on the importance of these potentially conflicting influences on contract choice.
The remainder of the paper is organized as follows. In section 2 we describe our sample of bank
loans and our measures of covenant tightness and covenant intensity. In section 3 we outline our
empirical tests and provide summary statistics. We present our empirical results in Section 4. Section 5
provides a summary and conclusion.
II. Sample and Measures of Restrictiveness of Covenants
A. Overall Loan Sample
Our primary data source on the terms of bank loan agreements is Loan Pricing Corporation’s
(LPC) Dealscan database.7 Our sample covers loans made from 1995 though 2001. We focus on dollar
denominated bank loans of non-financial U.S. firms with publicly traded common stocks listed on CRSP
files as of loan activation date.8 We require that borrowers have financial information in Compustat for
the fiscal year preceding the loan agreement. We eliminate debtor-in-possession (DIP) financings and
subordinated loans. Finally, we eliminate loans with maturity (at loan inception) less than one year. We
focus on long-term loans for two reasons. First, for very short term loans the loan renewal or roll over
process serves as a substitute for covenants in controlling moral hazard. Second, we measure performance
by yearly changes in operating performance. As a result, in the case of short-term loans there is
uncertainty as to whether the restrictions in the loan contract are effective throughout the performance
horizon.
The resulting loan sample includes 11,660 loans from 3,689 unique borrowers. Dealscan provides
information on loan maturity, amount, type, purpose, syndication, covenants and pricing. We use
7
A large majority of the loan data in LPC are from SEC filings (13-Ds, 14-Ds, 13-Es, 10-Ks, 10-Qs, 8-Ks and
Registration Statements (e.g. S-series filings)). According to Carey and Hrycay (1999), from 1995 onward, Dealscan
contains the “large majority” of sizable commercial loans in the US. Over 90 percent of all Dealscan loans in this
period are syndicated (i.e. underwritten and financed by a group of banks, insurance companies, and other financing
entities).
8
We hand matched Dealscan to CRSP and Compustat. Dealscan does not report standard borrower identifiers such
as a CUSIP number. Therefore, we identify CRSP records of Dealscan borrowers by using ticker, name, and
industry matching. In order to verify the accuracy of our Dealscan-CRSP matches we compare borrower sales and
locations (i.e. state) from Dealscan to those in the CRSP-Compustat Merged Database. A more detailed description
of our linking procedure is available upon request.
5
Compustat to collect information on the financial condition of borrowers, CRSP for information on
borrower common stocks, and I/B/E/S for analyst forecasts on borrower earnings. A more detailed
description of the variables used in this study will be provided below.
The Dealscan database provides information on a number of financial and other affirmative and
negative covenants. Financial covenants are restrictions on accounting variables (ratios) that must be
maintained over the life of the loan. Dealscan reports information on 17 different financial covenants that
fall into five broad covenant categories: coverage, leverage, liquidity, net worth, and capital
expenditures.9 About two-thirds of the loans include at least one financial covenant and the average loan
includes 2.15 financial covenants. Debt/EBITDA and net worth (including tangible net worth) covenants
are the most popular financial covenants (both are used in 52 percent of loans with financial covenants).10
Dealscan also provides information on covenants restricting dividend payments (restricting dividends
under certain conditions), collateral requirements and prepayments requirements (so called sweeps that
mandate that a portion of the loan be repaid out of excess cash flows, debt and equity financings, or asset
sales proceeds).11
Dealscan also provides information on the all-in-drawn spread. This is a measure of the
borrowing cost per dollar of borrowing expressed as a basis point mark-up over the 6-month LIBOR and
it includes recurring fees associated with the credit facility. LPC computes the spread for non-LIBOR
based loans by converting index used to price the loan into a LIBOR equivalent using the historical
relationship between the index and the LIBOR. All-in-drawn spread has been used as a measure of
borrowing costs in a number of previous empirical studies on loan pricing (see for example, Bradley and
Roberts (2004), Brav et al (2006), Guner (2006), Moerman (2005)).
The basic unit of observation in Dealscan is a loan, also referred to as a “facility”. If a borrower
enters into multiple loan agreements with the same lenders on the same date these loans are grouped
9
Coverage covenants include at least one of the covenants below: interest coverage, fixed charge coverage, debt /
EBITDA, senior debt / EBITDA, and debt service coverage. The liquidity covenants include current ratio or quick
ratio covenants. The leverage covenant group consists of leverage, senior leverage, debt / equity, debt / tangible net
worth, and loan / value covenants. The net worth covenant group includes loans with net worth and tangible net
worth covenants. Capital expenditure covenants that restrict capital expenditures (typically limiting expenditures
based on operating cash flows). A number of financial covenants contain a trend, also called a “build up”, which
changes the covenant threshold to make the covenant more (or less) restrictive over time. For example, a leverage
covenant might stipulate a maximum leverage of 50 percent at loan inception, decreasing to 40 percent at the end of
first year, and 30 percent at the end of second year. Dealscan reports the initial covenant level as well as the highest
(or lowest) covenant thresholds over the life of the loan. Detailed covenant schedules are available only for a limited
number of loans with Tearsheets
10
These frequencies are quite similar to those reported by Sufi (2006). He finds that 49 percent of the loans in
Dealscan include Debt/EBITDA covenants and 46 percent include net worth covenants.
11
For more detailed discussion of these covenants, see Bradley and Roberts (2004).
6
together under a deal “package”. Loan covenants are typically drafted by package and hence all of the
loans in a package are subject to the same covenants. Therefore, a loan-level analysis of covenants overweights characteristics of borrowers taking out multiple facilities. This, however, is a less important
concern in our study since we exclude all loans with maturity (at loan inception) less than one year. In
particular, since most deal packages with multiple loans consist of one long-term and one short-term (i.e.
less than 1-year) facility, after excluding short-term loans only about 25 percent of the loans in our sample
belong to a deal package with multiple facilities. Moreover, when testing the relationship between
covenant tightness and borrowing costs, a loan-level analysis is appropriate since loans that are in the
same package may be priced differently. Therefore, the empirical results we provide in this paper are
based on a loan-level analysis. Note that all of our results are robust to package-level analysis. Moreover,
our multivariate results are robust to clustering standard errors at the package (or borrower) level.
B. Measuring the Restrictiveness of Covenants
Perhaps the most straightforward and potentially informative measure of the restrictiveness of
financial covenants is covenant tightness. By covenant tightness we mean how close the covenant is set
relative to the level of the covenant variable at the time the loan is made. Tighter covenants are more
restrictive in the sense that the borrower has less flexibility with respect to decisions that may adversely
affect the level of the covenant variable. Moreover, since lenders and borrowers presumably set covenants
at levels they expect borrowers to achieve, where the covenants are set is potentially informative of the
borrower’s expectation and confidence concerning future performance. Finally, since covenant tightness
is defined based on the covenant variable, changes in the covenant variable provides a clear and
presumably value relevant benchmark on which to measure change in performance.
Previous empirical studies on the determinants of covenant structure typically measure the
restrictiveness of covenants by the presence of a particular covenant (for example, whether the loan is
secured) or the number of covenants included in the loan contract. (See for example Booth and Booth
(2006), Bradley and Roberts (2004), Billett, King and Mauer (2006) and Nash, Netter and Poulsen
(2003)). Covenant restrictions are measured in this way in part because covenants such as collateral
requirements are thought to be particularly important in controlling agency problems and partly because
the existence and the restrictiveness of different covenants tend to be correlated with one another. The
positive correlation among covenants probably arises because covenants restricting one activity affect
borrower’s incentives to undertake other activities. While we examine the information content measuring
covenant intensity, signaling through covenant intensity is likely to be more difficult to detect because
one is forced to use broad performance benchmarks and because covenant intensity is likely to be closely
linked to observable credit risk that may be measured with error.
7
C. Covenant Tightness Sample
A frequently cited advantage of bank loans is that the covenants can be designed and defined to
meet the specific needs of the borrower.12 However, this also means a lack of uniformity in how
covenants are defined. As a result, covenant tightness calculated using Compustat financials and generic
covenant names in Dealscan may suffer from significant measurement error.
According to Dichev and Skinner (2002) current ratio covenants have the most standard GAAP
based covenant definitions among all financial performance covenants.13 In addition, existing research on
covenant violations indicates that current ratio covenants are among the most frequently violated
covenants, which suggests that the tightness of current ratio covenants is economically important to
borrowers (see e.g., Beneish and Press (1993), Chen and Wei (1993), and Sweeney (1994)). For these
reasons, we include loans with current ratio covenants in our tightness sample.
Current ratio covenants principally condition lender control on changes in the borrower’s
liquidity. Thus, current ratio covenants are different from financial covenants based on cash flows and
leverage which are often cited as critical factors in the lending decisions. As a result 94 percent of
Dealscan loans that include at least one financial covenant include a coverage covenant. Examining cash
flow and leverage covenants requires getting around the measurement error associated with determining
tightness and compliance. One way to get around the problem is to focus on a sample of loans where the
covenants are defined so that tightness and compliance can be determined using financial data in
Compustat (i.e. if there are no non-GAAP adjustments to the definition of the covenant). The covenant
definitions and schedules are available for a sub-sample of Dealscan loans with Tearsheet information.
Because of the high cost of hand collecting information on covenant definitions and quarterly covenant
schedules, we use this methodology to study only the most commonly used financial covenant in bank
loans, Debt/EBITDA covenant.
Loan sample for current ratio and Debt/EBITDA covenants consists of 956 and 559 loans, for 506
and 234 borrowers, respectively.
12
See for example Smith and Warner (1979)
According to Dichev and Skinner (2002) net worth covenants also have standard definitions. We do not examine
net worth covenants for two reasons. First, the restrictiveness of a net worth covenant is jointly determined by where
the base covenant, net income build up, equity issuance build up, and other build ups are set at. Because
restrictiveness of the base covenant is typically inversely related to the restrictiveness associated with the buildups, it
is difficult to measure the “net” tightness of a net worth covenant. Second, it is hard to come up with an appropriate
operating performance benchmark for the net worth covenant. In particular, major changes in net worth are often
associated with a borrower’s pay out and equity issuance decisions which are not necessarily correlated with
improvements (or deteriorations) in a borrower’s operating performance.
13
8
D. Measuring Covenant Tightness
One measure of covenant tightness is the slack (i.e. distance to default) which is simply the
difference between the level of the covenant variable at the initiation of the loan and minimum or
maximum level permitted by the contract. The greater the slack the less restrictive the covenant since the
borrower’s financial condition can deteriorate more before triggering a covenant violation. While the
slack provides a useful measure of covenant tightness when testing agency theories of covenant choice,
slack is not a good measure of tightness when investigating the information content of covenant choice.
The reason is that the slack is likely to be negatively correlated with credit risk since the level the
covenant variable determines the menu of covenants applicable to a particular borrower. For example,
when choosing a minimum current ratio covenant, a borrower with a current ratio of 1.50 has to choose a
covenant below 1.50, or else he will be immediately in violation. The choice set of another borrower with
a current ratio of 2.50 is obviously bigger. This borrower can choose a covenant anywhere between 0.50
(the lowest current ratio covenant in our sample) and 2.50. This heterogeneity of choice sets explains why
firms with lower current ratios are more likely to choose covenants with a lower slack than firms with
high current ratios.14 This in turn implies that the slack measure will be correlated with observable credit
risk measures.
To address this problem we compare the covenant choices of borrowers with the same choice sets
and investigate whether their choices are correlated with subsequent performance conditional on credit
risk. The fact that covenants are clustered at discrete numbers allow us to partition our covenant samples
in order to form clusters in which borrowers have the same menu of covenant choices. Borrowers that
have similar financial ratios are subject to similar covenant menus. Within each cluster we sort borrowers
by their covenant choices. Then we classify covenants that are more restrictive than the cluster median as
tight. Finally, we pool borrowers/loans from all clusters. Note that classifying covenant choices in
discrete tight vs. loose categories serves to lower the measurement error problem associated with
imperfect covenant benchmarks.
For the current ratio covenant sample we use quarterly Compustat files to calculate the ratio of
current assets (QDI # 40) to current liabilities (QDI # 49). Current ratio covenants in our sample range
between 0.50 and 3.50 and are mainly clustered at discrete levels 0.25 from each other. Also, the current
ratios of our sample firms range between 1.00 and 9.30. Therefore, we place borrowers with current ratios
below 3.50 into 10 clusters. Each of these ten clusters has a cluster width of 0.25. For example, the first
14
The borrower’s choice is further restricted by the fact that current ratio covenants are not continuous but are
typically clustered at a few discrete numbers. The most common current ratio covenant clusters below 1.50 are 1.00
and 1.25.
9
cluster includes firms with current ratios between 1.00 and 1.24; the second cluster includes firms with
current ratios between 1.25 and 1.49, and henceforth. All borrowers with current ratios greater than or
equal to 3.50 are placed in an 11th cluster.15 In each cluster, borrowers that choose covenants greater than
or equal to the cluster median covenant are classified as choosing tight covenants. After making the
tight/loose covenant classifications in each cluster, we pool the data from clusters.
We measure the tightness of the Debt/EBITDA covenant choice in a similar manner. As
mentioned before, Debt/EBITDA covenant sample consists of loans with Tearsheet information in
Dealscan. Tearsheets includes a summary of covenant definitions as well as the covenant schedules (e.g.
changes in covenant levels over time). After reading all Tearsheets, we eliminated loans with non-GAAP
adjustments to Debt/EBITDA covenant.16 We define Debt/EBITDA as the ratio of 4-quarter moving
average debt (defined by using long-term debt (QDI # 51) and debt in current liabilities (QDI # 45)) to
four quarter moving sum EBITDA (QDI # 21). We use a 4-quarter moving average because
Debt/EBITDA covenants are most frequently defined as an average of prior quarters. The Debt/EBITDA
covenants range from 2.00 to 7.00 with clustering at discrete intervals of 0.25. As a result we partition the
data in 21 even clusters. The bottom cluster includes firms with Debt/EBITDA ranging from 0 and 2.00
and the top cluster includes firms with Debt/EBITDA greater than 6.75. Borrowers in the bottom cluster
have the greatest menu of covenant options. In each cluster, borrowers that choose covenants less than or
equal to the cluster median covenant are classified as choosing tight covenants.
While measuring covenant tightness based on clusters controls for differences in credit risk we
also conducted the analysis based on slack. As discussed below the main results are similar when we use
this alternative measure of covenant tightness.
E. Measuring Covenant Intensity
As discussed earlier covenant intensity refers to the number of covenant restrictions contained in
the loan document. Our aggregate measure of the covenant structure is Bradley and Robert’s (2004)
covenant intensity index. The index equals the sum of six covenant indicators: collateral, dividend
restrictions, asset sales sweep, debt issuance sweep, equity issuance sweep, and the existence of more
than 2 financial covenants. Therefore, the value of the intensity index ranges between 0 and 6. When the
15
Appendix A contains a detailed description of how the current ratio and Debt/EBITDA covenants were defined as
well as a discussion of how we address obvious measurement problems that arose.
16
Note, however, that our Debt/EBITDA covenant tightness proxy may suffer from a measurement error problem to
the extent that the covenant definitions in Tearsheets are incomplete. Any measurement error in this case, however,
is likely to create attenuation bias on our empirical results, which makes it more difficult draw strong statistical
inferences.
10
value of one of the indicators is missing we set the index to missing and eliminate the loan from the
intensity analysis. There are 3,704 loans from 1,564 borrowers in our covenant intensity sample.
Note that the covenant intensity index equally weights each covenant and hence implicitly
assumes that each covenant is equally restrictive (or important) for borrowers. As a robustness check we
modify the intensity index by counting only once the existence of a sweep (so that the maximum value of
the index is 4) and by including in the count all categories of financial covenants (so that the maximum
count is 10). Our results are similar when we use these alternative measures.
III. Empirical Tests and Summary Statistics
A. Empirical Methodology
Under the STC, covenant selection provides a signal of the borrower’s private information
concerning his credit quality. The basic idea is that covenant violations involve costs to borrowers so that
the selection of more intensive covenants by higher quality borrowers is costly for lower quality
borrowers to mimic.17 Higher quality borrowers have an incentive to choose more restrictive covenants if,
controlling for observable risk characteristics, more restrictive covenants are associated with lower
borrowing costs.
Testing the STC requires a proxy for the borrower’s private information regarding credit quality.
Our empirical tests assume that the borrower’s private information concerning credit quality is correlated
with realized future operating performance. The STC predicts that borrowers with favorable private
information select more restrictive covenants. Thus, finding a positive relationship between the
restrictiveness of covenants and future operating performance is consistent with covenants choice serving
as a signal. We specify our empirical model with future performance as an explanatory variable because
future performance is assumed to be a proxy for the private information the borrower has at the time
terms of the loan agreement are negotiated.18 In testing the STC, controlling for observable risk
characteristics is critical since agency based theories of covenant choice predict that riskier borrowers will
be subject to more restrictive covenants and therefore we would expect future operating performance to
17
The response of lenders to covenant violations varies. Frequently, covenant violations involve waivers or a
renegotiating the terms of the loan agreement and not a demand for repayment or termination of the loan agreement.
Nevertheless the results of previous empirical studies suggest that covenant violations are on average costly (See for
example, Chen and Wei (1993) Beneish and Press (1993) and Chava and Roberts (2006) and Sufi (2006)).
18
Jimenez et al (2006) follow a similar approach. An alternative approach would be to estimate a model of the
determinants of future performance with covenant tightness and intensity as explanatory variables. When we use this
approach we find a positive relationship between future performance and covenant tightness.
11
be negatively correlated with observable credit risk measures (i.e. riskier customers are expected to
perform worse, on average).
When examining the choice of covenant tightness we measure performance by changes in the
ratio that the covenant is written on. In particular, we examine changes in the current ratio and
Debt/EBITDA from the end of the first quarter after loan inception to the end of 5th, 9th, and 13th quarters.
Since covenant intensity is measured by the number of covenants that cover a potentially broad set of
indicators of financial health we use three broad based measures of performance. The first measure is the
frequency of delisting due to poor performance (liquidation, bankruptcy, and the borrowers failure to
meet exchange listing requirements) in the three years following the inception of the loan.19 The second
measure is frequency of rating downgrades. For firms without a credit rating, we measure downgrades by
declines in estimating credit ratings sufficient to move the borrower into a lower alpha based credit rating.
We estimate credit ratings using an ordered probit model developed by Blume, Lim, and MacKinlay
(1998). A detailed explanation of the rating model is provided in Appendix B. Our final broad measure of
performance is changes in Altman’s Z-score as defined in Sufi (2006).
Jimenez et al (2006) argue that the importance of collateral as a signal of borrower quality varies
with the degree of information asymmetries. The basic idea is that private information about credit quality
is more important when lenders evaluate opaque (i.e. less transparent) borrowers. This argument suggests
that the relationship between covenant choice and future performance will vary with the opacity of the
borrower.
If the restrictiveness of covenants conveys favorable private information concerning the borrower
credit quality then we would expect that controlling for observable risk differences, tighter and more
intensive covenants to be associated with relatively lower loan spreads. Note, however, that the prediction
of a negative relationship between borrowing costs and the covenant restrictiveness is not unique to the
signaling story. For example, the ATC also predicts that more restrictive covenants will be associated
with lower borrowing costs.
We examine the relationship between borrowing costs and covenant restrictions using a two-step
procedure similar to the ones used by Booth and Booth (2006) and Bradley and Roberts (2004). The first
step involves estimating a model of the determinants of covenant restrictions. The second step involves
estimating a selectivity bias adjusted model of borrowing costs for both borrowers that select tight
(intensive) and loose covenants under alternative covenant structures. We expect that the lower the
19
Borrowers with CRSP delisting codes equal to 400-499 or 550-599 are classified as poor performers. Including
firms with delisting codes equal to 500-549 does not affect any of our results.
12
likelihood of choosing restrictive covenants (according to the selection model) the lower the borrowing
costs for borrowers that select tight or intensive covenant structures.
While better quality borrowers may select tight covenants, selecting tight covenants exposes the
borrower to a greater likelihood of default for modest deteriorations in performance. Thus, tighter
covenants may be associated with a higher probability of default. However, if tight covenants provide a
positive signal of borrower quality then violating tightly set covenants may be associated with less severe
consequences than if covenants are set loosely. To examine this issue, we investigate the relationship
between the frequency of bankruptcy or delisting due to poor performance, covenant violations and how
tightly covenants are set at loan inception.
Our final empirical test of the STC involves examining the relationship between the stock price
reaction to loan announcements and the intensity and tightness of covenants. If covenant structure
conveys favorable private information we would expect a more positive share price reaction associated
with loans with tight or intensive covenant structures.
B. Summary Statistics.
Table 1 provides summary statistics for the borrowers and loans in overall sample of 11,660 loans
as well as sub-samples of loans with Debt/EBITDA and current ratio covenants. While summary statistics
for the overall sample include loans with Debt/EBITDA and current ratio covenants the statistical tests
are based on comparing borrowers/loans in the Debt/EBITDA and current ratio samples to
borrowers/loans without these covenants in their loan agreements.
20
Our summary statistics for the
overall sample are very similar to those reported in related papers using Dealscan (see for example, Sufi
(2006)).
The summary statistics in Table 1 suggest that smaller firms are more likely to have
Debt/EBITDA and current ratio covenants in their loan contracts. The reported credit ratings are based
on actual credit ratings for senior debt (i.e. historical S&P senior (or long-term) credit ratings in
Compustat) when they are available. When ratings are not available we use estimated ratings from an
ordered probit model developed by Blume, Lim and MacKinlay (1998). As shown, the mean (and
median) credit ratings are significantly lower for firms in the Debt/EBITDA and current ratio samples.21
20
Also, note that Table 1 provides summary statistics for all loans with Debt/EBITDA covenants. As discussed
above, in our empirical analysis, we use a subset of these loans with Tearsheets information. As Dichev and Skinner
(2002) note loans with Tearsheets are larger “bellwether” loans taken out by larger borrowers .
21
The ordered probit model assumes that credit ratings are a function of borrower’s interest coverage ratio, leverage,
operating margin, market value of equity as well as beta and residual volatility of stock returns. We estimate the
model using senior S&P credit ratings and panel data from 1994 to 2004 as reported in Compustat. Details
concerning the estimation are in Appendix B.
13
In particular, notice that while the average letter rating for all firms is BB, mean and median numerical
estimated credit rating is lower for the covenant sub-samples implying greater levels of credit risk for
these firms. Consistent with the differences in credit rating, loans with Debt/EBITDA and current ratio
covenants are associated with higher borrowing costs and higher likelihood of being secured. Finally, the
mean (and median) Z-score for firms in the Debt/EBITDA and current ratio samples are somewhat higher
than the overall sample suggesting these firms are less risky. This, however, is primarily due to the weight
placed on working capital in the Z-score calculation and the fact that firms in the Debt/EBITDA and
current ratio samples have significantly more liquidity.
The pre-loan leverage of firms with Debt/EBITDA covenants is only slightly higher than the
leverage of firms without Debt/EBITDA covenants. However, firms with current ratio covenants have
significantly lower (about 10 percent) pre-loan leverage than firms without current ratio covenants. There
are two potential explanations for these findings. First, loan amount is significantly higher for loans with
Debt/EBITDA and current ratio covenants, which suggests that the decision to include covenants may
among other things be based on pro-forma leverage rather than pre-loan leverage. Second, because of
high earnings volatility, low profitability, and opacity, firms with current ratio covenants may have
limited access to outside capital and relatively lower debt capacity.22 Therefore, the low pre-loan leverage
of firms with current ratio covenants does not necessarily imply better credit quality.
Covenant structure may also be related to severity of information asymmetries because adverse
selection problems may lead more restrictive covenants to control agency problems and because
covenants may play a more important signal role for informationally opaque firms. We measure the
information asymmetries using three proxies. The first proxy is the forecast error in analysts’ earnings
estimates for the fiscal year prior to loan inception.23 Following Christie (1987), we define the analyst
earnings forecast error as the ratio of the absolute difference between predicted earnings and the actual
earnings (per share) to the price per share at the beginning of the month of forecasts. Predicted earnings
are defined as the mean monthly earnings forecast for the month preceding the last month of the fiscal
year prior to the loan.24 Higher forecast errors are assumed to be associated with greater information
22
This also makes borrowers with current ratio covenants more vulnerable to liquidity shocks and leads them to
hold significantly more cash than other borrowers. Consistent with this view, Sufi (2006) finds that firms with lower
cash flows rely more heavily on cash and hence hold more cash out of their cash flows.
23
Analysts’ earnings forecasts are obtained from IBES, which reports monthly summary statistics of analysts’
earnings forecasts for each firm with coverage.
24
Elton et al (1984) investigate the determinants of forecast errors for a wide cross-section of firms and find that
there are three components of these errors: economy-wide factors, industry-wide factors, and firm-specific factors.
They show that 84 percent of the forecast errors during the month preceding fiscal year ends may be attributed to
firm-specific factors, which implies that forecast errors at this time is a particularly good proxy for the level of
asymmetric information about a firm’s cash flows.
14
asymmetries between borrowers and lenders concerning future cash flows. Our second measure is the
standard deviation of abnormal returns on quarterly earnings announcements in the five years before the
initiation of the loan (see Dierkens (1991) and Krishnaswami and Subramaniam (1999)).25 A significant
stock market reaction to an earnings announcement suggests that the market did not anticipate the
information and hence greater information asymmetries about a firm’s cash flows. Our final measure of
asymmetry information is the age of the borrower. Older borrowers are likely to have a more established
track record and hence lenders are likely to be better informed about their prospects. (See Jimenez et al
(2006))26. As shown in Table 1, firms with current ratio covenants are significantly younger and more
opaque than firms in the overall sample of firms. Firms with Debt/EBITDA covenants are also younger
than the overall sample of firms though we have mixed evidence about the relative transparency of the
firms in the two groups.
Bradley and Roberts (2004) and Billet et al (2006) find that the use of one type of covenant tends
to be highly correlated with the use of other types of covenants. Based on these findings we would expect
that loans in the Debt/EBITDA and current ratio samples have more intensive covenant structures than
the overall sample of loans. As shown in Table 1 we find the mean and median covenant intensity index is
higher for loans with Debt/EBITDA covenants. In contrast, the intensity index is lower for loans in the
current ratio covenant sample. However, this appears to be due to the fact that current ratio covenants
substitute for prepayment requirements (i.e. sweeps). For example, 76 percent of loans with
Debt/EBITDA covenants contain asset sales sweeps versus 41 percent of loans without Debt/EBITDA
covenants. In contrast, 39 percent of loans with current ratio covenants have asset sales sweeps versus 63
percent of loans without current ratio covenants. Otherwise, loans with Debt/EBITDA and current ratio
covenants are more likely to include collaterals and restrictions on dividend payments than loans without
these covenants.
Conceptually, the relationship between syndication and covenant choice is unclear. On the one
hand, syndication involves multiple lenders, which increases hold out problems and other impediments to
debt restructuring. To mitigate these problems the syndicated loans may have fewer and looser covenants
similar to what one sees in public debt contracts. Alternatively, since covenant violations are used to
25
The quarterly earnings announcement dates are obtained from Compustat. We use value-weighted CRSP market
index to measure market adjusted abnormal returns at (-1, +1) trading days centered on quarterly earnings
announcements during the five years preceding loan inception. We then calculate the volatility of these abnormal
returns for firms with at least four quarterly earnings announcement returns.
26
Because the “true” incorporation dates of our borrowers are not available in electronic form, we use the number of
months between loan inception and the first CRSP listing date of a borrower as a proxy for age . All of the results
reported in this paper are very similar when we replace CRSP listing date with the first year in Compustat that the
firm’s share price is available.
15
trigger monitoring on the part of lenders (see Rajan and Winton (1995)), free-rider problems associated
with monitoring may lead to more restrictive covenants when loans are syndicated. Drucker and Puri
(2006) argue that loans that are expected to be sold contain more restrictive covenants which may imply
that syndicated loans have more restrictive covenants. As shown in Table 1, we find that loans with
Debt/EBITDA covenants on average have more lenders than the loans in the overall sample. However,
we find that the loans with current ratio covenants have fewer lenders, on average, and are less likely to
be syndicated. One reason for this is that, as shown in Table 1, loans with current ratio covenants are
taken out by much smaller borrowers and are, on average, much smaller (in dollar terms) than loans in the
overall sample.
Table 2 provides summary statistics for our samples of loans with current ratio and
Debt/EBITDA covenants grouped by the tightness of the covenants. As shown, all-in-drawn spreads are
significantly higher for loans with tighter covenants suggesting these loans are riskier. The greater credit
risk appears to stem from lower earnings since we find no significant differences in leverage based on
covenant tightness. It is important to remember that since tightness is measured using clusters of firms
that have similar levels of the covenant variables, we would find much larger differences in credit spreads
and observable risk measures than those in Table 2 if we defined tightness by the distance to default (i.e.
slack). For example, smaller, less profitable, more highly levered firms, as well as firms with high
investment spending are significantly more likely to agree to current ratio and Debt/EBITDA covenants
with lower distance to default (not reported).
One reason firms may choose tighter covenants is that they have less volatility in earnings or
liquidity. Consistent with this argument, mean of the quarterly standard deviation of EBITDA/Sales is
significantly less for firm with tight coverage covenants and the mean and the median of the standard
deviation in quarterly current ratios is significantly lower for firms with tight current ratios.27
As shown in Table 2, we find no systematic relationship between the tightness of covenants and
information asymmetries. For example, while the median analyst forecast error is significantly larger for
the firms with tight covenants borrowers in the tight Debt/EBITDA covenant sample have significantly
lower mean and median volatility in earnings announcement returns. In addition, there are no significant
differences in the age of borrowers based on the tightness of covenants.
Finally, notice that the frequency of loans to finance takeovers is higher in the tight covenant
sample. This suggest that tight covenants may be one way borrowers credibly commit to lenders to
undertake changes designed to improve cash flows or liquidity associated with the acquisition.
27
Sufi (2006) argues that firms with high cash flow volatility may prefer to avoid covenants based on cash flows.
Our finding that, conditional on the inclusion of a cash flow covenant, firms with high cash flow volatility choose
less restrictive cash flow covenants seems to support and extend his findings.
16
Table 3 provides summary statistics based on the intensity of covenant structure. Firms with more
restrictive covenants in their loan agreement are significantly riskier and more informationally opaque
than firms with no or few covenants in their loan agreements. For example, firms in the no covenant
sample have significantly higher earnings, use less leverage and have significantly lower earnings
volatility than firms in the high intensity sample. Credit risk also appears to increase with the intensity of
the covenant structure. In particular, leverage is significantly higher and earnings are significantly lower
and more volatile for firms in the high intensity than for firms in the low intensity sample.
As shown in Table 3, firms with loans that contain more intensive covenants are on average
younger and have higher analyst earnings forecast errors than firms with less intensive structures. Overall,
these results indicate that information asymmetries and observable credit risk are important determinants
of covenant intensity. Comparing the findings in Table 2 to those in Table 3 it may appear as though
differences in covenant intensity are associated with larger differences in credit risk than are differences
in covenant tightness. However, once again, it is important to remember that our tightness measure
accounts at least partly for differences in the credit risk. In particular, by using clusters we are comparing
differences in covenant choice for firms with similar liquidity or coverage ratios and that are therefore
likely to have similar observable credit risk characteristics.
IV. Empirical Results
A. Univariate Analysis of Covenant Choice and Operating Performance
Table 4 provides the results of a univariate analysis of covenant choice and future performance.
Panels A and B provide a comparison of industry adjusted changes in current ratios and Debt/EBITDA by
the tightness of the current ratio and Debt/EBITDA covenants, respectively, at the initiation of the loan.28
Industry adjusted changes are computed as the difference between the change for each firm and the
change in the median firm within the same four-digit SIC code as the sample firm.29 Consistent with the
STC, we find significant increases in mean and median current ratio for firms with tight current ratio
covenants. The percentage increase in current ratios for firms with tight covenants is significantly greater
than the change for firms with loose covenants and firms without liquidity covenants. Indeed, the median
28
As a robustness check, in both univariate and multivariate performance tests we include only loans that were
active as of the period of interest. For most loans, the effective maturity or termination dates are available from deal
and facility remark columns in Dealscan. All our results are robust to this alternative methodology.
29
We report industry adjusted changes to control for industry wide changes in performance. Our results are similar
if we use unadjusted changes.
17
change in current ratios for firms in the loose covenants is negative indicating a reduction in liquidity
after the loan is made.
As shown in Panel B, the performance of firms with tight Debt/EBITDA covenants also
improves. In particular, the mean and median Debt/EBITDA decrease significantly relative to the firms
with loose covenants and relative to firms without coverage covenants. While the performance of firms
with tight Debt/EBITDA covenants improves, the performance of firms with loose covenants deteriorates
(i.e. the amount of debt relative to earnings increases).
In contrast to the improvements in performance for firms with tight covenants, we find that the
overall performance of firms with more intensive covenants deteriorates. As shown in Table 5, firms in
the high intensity sample are more likely to be delisted due to poor performance and are more likely to
experience a credit downgrade in the three years following the initiation of the loan. Moreover, the mean
and median Altman’s Z-score decreases more for firms with intensive covenants than for firms with less
restrictive covenants in their loan agreements.
Overall, the univariate analysis provides no support for the signaling story concerning the choice
of covenant intensity. The negative relationship between covenant intensity and performance may reflect
the role of covenants in controlling agency problems. In particular, agency problems may lead to a
positive relationship between covenant intensity and observable credit risk. Under the signaling
hypothesis, covenant choice allows lenders to distinguish between firms that are indistinguishable based
on observable risk characteristics. In next section we present a multivariate analysis of covenant choice
that attempts to control differences in firm characteristics.
We also examine the relationship between covenant tightness and the broad performance
measures reported in Panel C (not reported for brevity). Overall, we find no systematic relationship
between covenant tightness and declines in overall performance. In particular, we find no significant
difference in the frequency of credit rating downgrades or in changes Z-scores between the firms in the
loose and tight covenant samples. We do however find that the frequency of a delisting in the three years
following the loan agreement is higher for firms in the tight covenant sample. For example, the frequency
of delisting through year 3 is 8.31 percent and 10.49 percent for firms in the tight current ratio and
Debt/EBITDA covenant samples, respectively. In contrast, the frequency of a delisting for firms with
loose current ratio and Debt/EBITDA covenants is 5.23 percent and 2.08 percent, respectively. As we
discuss in the next section, the higher frequency of delisting appears to be related to higher observable
credit risk for firms with tight covenants.
18
B. Multivariate Analysis of Covenant Choice
We control for credit risk and factors other than signaling that may affect covenant choice by
estimating a multivariate model of covenant choice. For covenant tightness, we estimate probit models
relating the likelihood of tight covenant selection to loan characteristics, borrower characteristics, proxies
for information asymmetries and changes in the covenant variable in the year following the loan
agreement. We also estimate the model using performance during the two and three years following the
loan was made and get similar findings (not reported for brevity). For covenant intensity, we estimate a
Poisson regression with the covenant intensity index as the dependent variable.30
Estimates of the probit model relating the likelihood of choosing tight covenants to loan and borrower
characteristics are presented in Table 5. We provide estimates for three specifications of the model based
on three different proxies for information asymmetries (age, volatility in earnings announcement returns,
and analyst earnings forecast errors). Each regression includes an intercept, in addition to industry (based
on one-digit SIC codes), year, loan type, and loan purpose fixed effects (unreported). As shown in Panel
A, smaller firms and firms with lower earnings are more likely to have tight current ratio covenants. This
result is consistent with riskier borrowers receiving more restrictive covenants. We also find a positive
and statistically significant relationship likelihood of choosing tight current ratio covenants and the
amount of investment spending (i.e. capital expenditures plus R&D). This result is consistent with tight
current ratio covenants being used to mitigate agency cost of debt for high growth firms. Specifically,
restrictions on changes in liquidity may serve to limit asset substitution problems that are particularly
acute among high growth firms. Finally, we find a negative but insignificant relationship between
tightness and the historical volatility of borrowers’ current ratios.
Consistent with the signaling hypothesis, we find a positive and statistically significant relationship
between covenant tightness and future change in the borrower’s current ratio (our proxy for the
borrower’s private information at the time the loan is negotiated). This is finding is consistent with the
argument that borrowers select tight current ratio covenants when they expect their performance to
improve.
As shown in Panel B of Table 5, riskier borrowers are more likely to agree to tight Debt/EBITDA
covenants. In particular, notice that tighter Debt/EBITDA covenants are more likely the lower the
earnings of the borrower and the greater the amount of leverage used. This suggests that, not surprisingly,
tight coverage covenants focus on claims dilution problems arising from the issuance of additional debt.
Finally, the likelihood of tight covenants is less likely the more volatile the borrower’s Debt/EBITDA
30
The model of covenant intensity choice is estimated using a Poisson regression because covenant intensity is a
non-negative count variable. Our results are similar using OLS regressions.
19
prior to the origination of the loan. This suggests that covenants are set looser the more difficult it is to
forecast the covenant variable.
The negative and statistically significant relationship between tight Debt/EBITDA covenants and
subsequent changes in the borrower’s industry adjusted Debt/EBITDA is consistent with the signaling
hypotheses. In other words, borrowers that expect their performance to improve in terms of reductions in
the amount of debt relative to earnings (i.e. increased debt coverage) select tighter Debt/EBITDA
covenants.
Finally, we find no relationship between covenant tightness and the severity of information
asymmetries. Note that in Panels A and B of Table 5 the estimated coefficients on our information proxies
are generally not statistically significant. Moreover, we find no evidence that the importance of covenant
tightness as a signal varies with the severity of information problems. Specifically, when we interact
performance changes with our asymmetric information proxies (not reported) we find that the interaction
effects are not statistically significant.
We also estimate the probit models of covenant tightness using our broad measures of
performance. Using a broad based measure of performance as a proxy for the borrowers’ private
information is likely to cause attenuation bias if the private information conveyed in the financial
covenant choice relates narrowly to the covenant variable. Overall, we find no significant relationship
between covenant tightness and the changes Altman Z-score, the likelihood of credit rating downgrades,
or the frequency of delistings due to poor performance. We also find no significant relationship between
covenant tightness and these performance measures interacted with our proxies for information
asymmetries. Thus, the private information conveyed through covenant choice appears to pertain
narrowly to the expected future performance of the covenant variable.
Turning to our analysis of the determinants of covenant intensity, Panels A, B and C in Table 6
provide estimates of Poisson regressions relating covenant intensity to firm and loan characteristics and
our three proxies for the borrower’s private information. Because delistings are infrequent in the first year
following the loan, we report the results using delistings within three years of the initiation of the loan.31
Overall, the results reported in Table 6 indicate that, consistent with previous empirical studies,
agency problems are important determinants of the covenant structure of bank loan agreements (see, for
example, Bradley and Roberts (2004)). In particular, we find a positive and significant relationship
between covenant intensity and leverage and the negative relationship between covenant intensity and
earnings. These results are consistent with the ATC argument that the agency costs of debt are expected to
31
The results are similar to the ones reported in Table 6 for performance measured over one, two and three year
horizons.
20
be greater the riskier the borrower. We also find that covenant intensity is positively related to the
maturity of the loan. This result is consistent with the argument that covenants serve to mitigate agency
problems by reducing the effective maturity of the loan (see Billett et al (2006)). Moreover, for most of
the specifications, we find a positive and significant relationship between covenant intensity and
investment spending. This is consistent with the argument that covenants serve to mitigate asset
substitution problems associated with growth firms. Finally, information asymmetries appear to be
important determinants of covenant intensity. On average, younger firms and firms with less predictable
earnings have loans with more restrictive covenants.
If we ignore the cross effects of the information proxies with our performance measures, we find
covenant intensity is associated with deteriorations in future operating performance. In particular, as
shown in Table 6, we find a positive relationship between covenant intensity and the frequency of rating
downgrades and delistings and a negative relationship between covenant intensity and changes in
Altman’s Z-scores. One explanation for this is that covenant intensity is positively related to observable
credit risk and that our credit risk measures do not completely control for differences observable credit
quality. Again, this is more likely to be a problem when examining covenant intensity than covenant
tightness because we measure covenant tightness by first clustering based on covenant variable.
To address this problem, we include in the model cross effect variables defined as our
performance measures interacted with the various information proxies. The idea is that since private
information regarding credit quality is likely to be more important when lending to younger and more
informationally opaque borrowers, the use of covenant intensity to signal credit quality will be more
prominent among opaque rather than transparent borrowers.32 Consistent with this argument, the
coefficient estimates for the cross effects variable shown in Table 6 are negative and significant when
analyst earning forecast errors or earnings announcement returns are used as information proxies and the
downgrades or delistings performance measures are used and positive and statistically significant when
performance is measured by changes in Altman’s Z-scores. These results are consistent with the argument
that the importance of covenants as a signal increases as information asymmetries become more severe.
The results using age as an information proxy are also consistent with signaling. In particular, since
information asymmetries are assumed to decrease with the age of the borrower, the positive and
significant coefficient for the cross-effects with downgrades and delistings and the negative coefficient
for cross-effects for changes in Altman’s Z-score are consistent with a signaling hypothesis. Overall,
these results are consistent with covenants playing a role in mitigating adverse selection problems.
32
Jimenez et al (2006) make the argument concerning collateral as a signal of quality.
21
C Are Covenant Restrictions Priced?
Borrowers will have an incentive to signal private information concerning credit quality by
choosing more restrictive covenants if they are rewarded for doing so through lower costs of borrowing.
Thus, borrowing costs and the choice of covenant tightness (intensity) are interdependent. We account for
this interdependence using a two step selectivity adjustment procedure described in Lee (1978) and
Heckman (1979) and employed recently by Booth and Booth (2006) to examine loan pricing. This
methodology involves first estimating a probit model of covenant choice. In the case of covenant
tightness the dependent variable equals one if a tight current ratio or Debt/EBITDA covenant is chosen.
For covenant intensity we convert the count variable to a binary intensity variable and then estimate the
probit model of covenant choice. The binary intensity measure equals to one if the covenant index is
greater than 4 and zero if the covenant index is zero. As a robustness check, we also define the binary
intensity variable as one if the index is greater than 4 and zero if the index is zero or one. In the probit
model, we assume that the choice of tight or intense covenants is a function of the same loan and
borrower characteristics described in Tables 5 and 6, respectively.
We use the linear predictors of the first step probit model to compute the inverse Mills ratios. The
inverse Mills ratio is calculated as φ (ψ) / Φ (ψ) when tight (intensive) covenants are selected, and φ (ψ) /
(1 - Φ (ψ)) when the loan agreement contains loose (less intensive) covenants. Here φ is the standard
normal density function, Φ is the standard normal cumulative distribution function and ψ is the estimated
linear predictor from the first-stage probits. The second step involves estimating (via OLS) the
relationship between borrowing costs and observed loan and borrower characteristics conditional on the
selection of tight (intensive) or loose (less intensive) covenant structures. We include in the borrowing
cost regression the inverse Mills ratio as a selectivity variable. Intuitively, the inverse Mills ratio provides
a measure of the lenders updated beliefs regarding credit quality based on the choice of covenants. A
negative coefficient for the inverse Mills ratio implies that the choice of tight (intense) covenants, on the
margin, reduce borrowing costs by increasing the lenders perception of the credit quality of the borrower.
Therefore, we expect the estimated coefficient on the inverse Mills ratio to be negative for borrowers that
choose tight (intense) covenants and positive or insignificant for borrowers that select loose (less intense
covenants).
Table 7 and 8 provide estimates of selectivity corrected loan spread regressions relating
borrowing costs to loan and borrower characteristics as well as the inverse Mills ratio calculated from the
22
first step probit model.33 We measure borrowing costs by log of the all-in-drawn spread. Consistent with
the argument that tighter or more intensive covenants lead to lower borrowing costs the estimated
coefficient on the inverse Mills ratio is negative and statistically significant for the sample of loans with
tight or intense covenants. This is consistent with borrowers having an incentive to signal favorable
private information through the covenant choice. In contrast, the estimated coefficient on the inverse
Mills ratio for loans with loose or less intense covenants is positive (and for most specifications
statistically significant). Note that the positive coefficient on the selectivity variable is consistent with the
ATC in that borrowers that select loose covenants appear to trade greater flexibility for higher borrowing
costs.
E. Covenant Tightness and Covenant Violations
For a given change in operating performance, tighter covenants expose borrowers to a greater
likelihood of violation. However, a finding of more covenant violations among borrowers that choose
tight covenants is not necessarily inconsistent with the signaling hypothesis. In particular, a borrower may
select tight covenants even if they are more likely to violate if they expect the costs of a violation to be
lower when covenants are set tightly. The costs of a violation in turn depend how lenders react to a
violation. Lenders’ reactions to violations can vary in severity from a simple waiver to a demand for
immediate repayment of the loan. We investigate the relationship between the potential consequences of
covenant violations and the tightness of covenants by first identifying firms that violated either the current
ratio or Debt/EBITDA covenants, then examining whether violations are more likely to lead to
bankruptcy, delisting due to performance, or the lenders not granting waivers when covenants are set
tightly.
We focus on violations of financial covenants because using financial data we can determine
when a violation is likely to have occurred. For the current ratio covenant sample, we identify potential
covenant violations by comparing the level of the covenant variable to the minimum threshold required
by the loan agreement. We refer to these as potential violations because of the measurement problems
associated with determining the tightness of financial covenants and because loan agreement contains
adjustments to the covenant level over the life of the loan.34 Moreover, subsequent amendments to the
33
To save space we do not report the estimates of the first stage probit model. The estimated relationships between
tightness (intensity) and loan, borrower risk characteristics and our information proxies are similar to those reported
in Tables 5 and 6. When estimating the first step probit model we do not include the future performance measures.
34
To account for some of these changes for we obtained information on covenant adjustment schedules from
Tearsheets and SEC filings (which often contain as an appendix the loan agreement). Since our Debt/EBITDA
sample consists of loans with Tearsheets information, we have quarterly covenant schedules for all the loans in the
Debt/EBITDA sample. Only about 10 percent of the loans in the current ratio sample have covenant schedules that
23
loan agreement may change the covenant threshold required by the initial loan agreement. Amended loan
agreements are unlikely to impose more restrictive covenant thresholds than the initial loan agreement,
however. If a covenant appears to have been violated then we search SEC quarterly filings (i.e. 10-Qs and
10-Ks) for any discussion of covenant violations or defaults from the first quarter after loan inception up
to three years following the violation or until the maturity date of the loan. For the Debt/EBITDA
covenant sample, because the measurement error problem may potentially be more severe, we identify
covenant violations by searching quarterly SEC filings for discussion of covenant violations for all of the
loans in the sample. Unfortunately, in most cases when SEC filings indicate a covenant violation without
providing a detailed discussion of the specific covenants that were violated. We assume, since the firm is
out of compliance with either the current ratio or Debt/EBITDA covenant, the violation reported in the
SEC filing pertains to Debt/EBITDA or current ratio covenants. If there is not a potential violation (i.e.
the firms appeared to be in compliance with the Debt/EBITDA and/or the current ratio covenants
according to our quarterly tightness measure) or if there is a potential violation but violation is not
reported in the SEC filings, we assume the covenant was not violated.
We collected information on whether the firms in our sample filed for bankruptcy, were delisted
because of poor performance, or had covenant violations that were not cured through a waiver or
amendment to the loan agreement. We obtained this information in SEC filings discussing the covenant
violation and from the CRSP delisting files. If violations are more costly when covenants are set loosely
we expect that these so called bad outcomes are less frequent when firms violate tight rather than loosely
set covenants.
Panel A of Table 9 provides estimates of a probit model relating the frequency of covenant
violations to covenant tightness and firm risk characteristics.35 Loans are defined as having tight
covenants if there is tight Debt/EBITDA and/or current ratio covenants. We include in the probit models
the firm leverage and the other credit risk measures used in our previous analyses. Table 9 shows that
covenant violations are significantly more likely when covenants are set tightly and when more covenants
change over time. Dealscan does not provide a detailed schedule of current ratio covenants. However, it does report
whether there is an increasing, decreasing, or fluctuating trend in the covenant as well as the highest (or lowest)
value that the covenant can take over the life of the loan. If we can not find a detailed covenant schedules from the
sources in the Tearsheets or SEC 10-K filings and Dealscan indicates the current ratio covenant changes over the life
of the loan, we linearly interpolate the covenant thresholds over the projected life of the loan. Chava and Roberts
(2006) use this approach as well.
35
In order to use a uniform sample of loans in alternative specifications and better evaluate the marginal effect of
variables included in each specification, we restrict the covenant violation analysis reported in Table 9 only to loans
with a non-missing covenant intensity index. The results are very similar when we include all loans.
24
are included in the loan agreement. This result is not particularly surprising since, as shown in Tables 4
and 5, credit risk and covenant tightness and intensity are positively related.
Of greater interest is the relationship between so called “bad outcomes” and covenant violations
when covenants are set tightly. To examine this question we estimate a probit model relating the
frequency of bad outcomes to covenant violations. The estimates of the probit model are presented in
Panel B of Table 9.36 Consistent with the argument that the costs of violation are inversely related to how
tightly covenants are set at loan inception, we find a negative and statistically significant relationship
between the frequency of bad outcomes and covenant violations for loans with tight covenants. While this
result is consistent with the signaling story, another explanation is that financial condition of firms with
tight covenants deteriorates less prior to a covenant violation and as a result covenant violations are less
likely to be associated with bad outcomes. Nevertheless, when we include the change in the covenant
variable from the initiation of the loan to the violation we find that loans with tight covenants are still
associated with a lower likelihood of bad outcomes upon a covenant violation though at slightly lower
levels of statistical significance. Moreover, not surprisingly, we find that the likelihood of a bad violation
outcome is positively related to the deterioration in the financial condition of borrowers. Overall, these
results are consistent with the argument that covenant violations are less costly when covenants are set
tightly.
F. The stock market reaction to loan announcements
Our final empirical test involves examining the relationship between the stock price reaction to
loan announcements and the tightness and intensity of the covenant structure in the loan agreement. We
limit this analysis to loans in the current ratio and Debt/EBITDA samples so that we can measure
covenant tightness. To identify loan announcements we searched Factiva new archives for a news report
(including a company press release) for 30 days before and after the loan inception date. As the
announcement date we use the earlier of the date of the press report or the loan inception date (although,
since most of the press reports precede the inception date our results are similar if we use the
announcement date). We were able to identify announcement dates for 415 of the loans in our sample or
about one-third of current ratio and Debt/EBITDA covenant samples. This is similar to the proportion of
the Dealscan loans with news announcements reported in a recent paper by Gonzalez, Houston and James
(2006). For each announced loan we computed market adjusted announcement returns over a three-day
36
It is important to note that the marginal effects and standard errors of the interacted variables in Panel B of Table
9 are only suggestive. Because of the non-linear nature of the estimation procedure, it is not possible to compute
point estimates for the interaction terms when using probit. We get very similar results when we compute corrected
marginal effects and standard errors as suggested by Norton, Wang, and Ai (2004). We do not report these estimates
for brevity.
25
window centered on the announcement date. We use three-day returns because we are uncertain as to the
timing of the announcement and whether the press report is about a loan announcement made during
trading the previous day. Market adjusted returns are simply the difference between the firm’s
announcement day returns and the return on the CRSP value weighted index. Consistent with previous
empirical studies of the announcement effects of bank loan agreements we find a statistically significant
average three-day announcement return of 1.20 percent for the loans in our sample (The z statistic is
2.28).
If the selection of restrictive covenants conveys favorable private information regarding the
prospects of the borrower we would expect to observe higher stock returns when loans with tight
covenants are announced. To examine this issue we estimate a regression that relates announcement day
returns to covenant tightness and controls for loan size and risk characteristics of the borrower. A loan is
considered to have tight covenants if either a tight Debt/EBITDA or current ratio covenant was chosen.
The results of this analysis are reported in Table 10. Consistent with the argument that tight covenants
signal private information we find a positive and statistically significant relationship between
announcement returns and the presence of tight covenants. The second specification in Table 10 also
suggests that the relationship between the announcement returns and covenant tightness is positive and
significant even after controlling for loan size and the risk characteristics of the borrower.
We also examine the relationship between announcement day returns, covenant tightness and the
intensity of covenants. Including covenant intensity in the regression leads to a substantial decrease in the
sample size because often times one or more of the components of the covenant index are not reported.
We find a positive relationship between returns and the tightness of covenants and a negative and
statistically significant relationship between returns and covenant intensity. These results suggest that
while covenant tightness conveys positive private information selecting intensive covenants conveys
negative information. This latter result may arise in part from the fact that covenant intensity is related to
the credit risk characteristics of the borrower and because the private information conveyed by intensive
covenants depends on the severity of information asymmetries.
To address these issues we interact the covenant intensity measure with our proxies for
information asymmetries (as we did in Table 6). In Table 10 we report the results using analyst forecast
errors as an information proxy although the results are similar if we use the dispersion in earning
announcement returns as an information proxy. As shown in the last two columns of Table 10 we find a
positive and significant relationship between announcement returns and covenant tightness. Moreover,
while the coefficient on covenant intensity is negative and statistically significant we find the coefficient
estimate on the interaction variable is positive and statistically significant at the 5 percent level. This
finding is consistent with the results reported in Table 6. Specifically, the extent to which the choice of
26
covenant intensity provides a signal of favorable private information concerning the quality of the
borrower depends on the severity of information problems. In other words the signaling content of
covenant intensity choice varies with the precision with which lenders can estimate credit risk based on
observable firm characteristics.
V. Summary and Conclusions
An intuitively appealing explanation of the choice of tight or intensive covenants is that the
choice conveys information about the borrower’s confidence in future performance. Testing the
information content of covenant choice is challenging however because restrictive covenants are also
used to control agency problems and are therefore likely to be correlated with observable credit risk
characteristics. Testing the signaling hypothesis involves coming up with a methodology to control for
differences in observable risk characteristics and identifying a reasonable proxy for the private
information conveyed through covenant choice. In this paper, we test the signaling hypothesis by
assuming that realized future performance is correlated with the borrowers private information at the time
the loan in originated. We attempt to control for observable risk differences by including in a model of the
determinants of covenant choices market and accounting based measures of credit risk. In the case of
covenant tightness we also use a novel approach of examining covenant choice by clusters of firms
formed based on similar levels of the financial variables on which the covenant is based.
Overall, we find evidence consistent with the signaling hypothesis. The most compelling, in our
views, is the empirical evidence regarding the selection of tight covenants. In particular, consistent with a
signaling story we find a positive relationship between the choice of tight covenants and improvements in
future performance as measured by changes in the covenant variable. Moreover, we find that tight
covenants are associated with incrementally lower borrowing costs for firms that select tight covenants.
Finally, as further support of a signaling story we find that the stock price reaction to bank loan
announcements is greater when the loan agreement contains tight covenants. Taken together this evidence
suggests that the choice of tight covenants convey favorable private information concerning the future
performance of the borrower.
The evidence concerning information content of intensive covenants is more complicated. Our
results suggest that covenant intensity choice conveys favorable information when information
asymmetries are the most severe. In particular, we find that the relationship between intensity and future
performance depends on the severity of information problems. As information asymmetries increase the
selection of intensive covenants is more frequently associated with improvements in performance. The
stock returns associated with loan announcements is also consistent with this finding. Specifically, we
27
find that the relationship between stock returns and the intensity of the covenants in the loan agreement
are increasing in our proxy for information asymmetries.
28
Appendix A: Calculation of Covenant Tightness
1. Current Ratio Tightness
In order to control for the direct effect of the loan on a borrower’s current ratio, we form current
ratio clusters based on the first post-loan fiscal quarter end current ratio of borrowing firms. Though this
introduces a forward looking bias to our analysis, it is quite plausible to assume that pre-loan negotiations
on financial covenants are based on pro-forma balance sheets considering the effect of the loan on
financial ratios. Note that all our results hold when we use last pre-deal quarterly current ratio or average
quarterly current ratio. Second, as suggested by previous studies (e.g., Dichev and Skinner (2002), Chava
and Roberts (2006)) on current ratio covenant, about 10 percent of the time firms are immediately in
violation of their current ratio covenant. Without access to exact covenant definitions, it is difficult to
know whether these violations are due to measurement error or they are actual violations. We took several
steps to minimize the measurement error in our current ratio covenant sample. First, we eliminated all
loans where the ratio of covenant to current ratio greater than or equal to 1.20. All of our results are very
similar when we delete loans with the ratio of covenant to current ratio above 1.10 or 1.00. Second,
inspection of a sub-sample of loan contracts on Edgar indicates that non-GAAP adjustments to current
ratio covenants are very common in loan contracts when the borrower has a current ratio below 1. Also,
immediate violations mentioned before are the most common among these loans. Therefore, we eliminate
all such loans. As a final step, we make a correction to the tightness classification from the cluster
analysis. Specifically, in the top cluster (i.e. cluster of firms with current ratio above 3.50), unlike in any
other cluster, there is a big variation of borrower current ratios (i.e. ranging between 3.50 and 9.30). This
variation creates unexpected problems when a cluster-classification is used: For example, a borrower with
a current ratio of 3.50 and covenant choice of 2.50 is classified as choosing a loose covenant while
another borrower with a current ratio of 9.00 and a covenant choice of 2.75 is classified as choosing a
tight covenant. Obviously, it is difficult to argue that this is a fair classification. Therefore, only for this
top cluster, we re-classify all covenant choices below 2/3 of existing current ratio as loose.37 For example,
regardless of the cluster median covenant, a borrower that has a current ratio of 4.50 is classified as
choosing a loose covenant as long as he agrees to a covenant below 3.00.
2. Debt/EBITDA Tightness
As in the current ratio sample, in order to control for the direct effect of the loan on a borrower’s
debt levels, we form Debt/EBITDA clusters based on a first post-loan fiscal quarter end measure. In order
to clean the sample, we eliminated all loans where the ratio of borrower Debt/EBITDA to the covenant is
above 1.20 at loan inception.38 In addition, we eliminated loans where the borrower had Debt/EBITDA
below zero or above 20, because of concerns about severe measurement error and the difficulty of
assessing performance changes when the initial Debt/EBITDA is negative or very high. As a final step,
we re-assess the tightness of the loans in the bottom cluster. The cluster analysis classifies any borrower
in the bottom cluster (i.e. Debt/EBITDA between 0 and 2.00) that chooses a covenant below 4.00 as
choosing a tight covenant. For example, a borrower which has a Debt/EBITDA of 0.25 and a covenant of
3.00 is classified as choosing a tight covenant by the cluster analysis. This, obviously, is not a very
compelling classification. To correct for this problem, in the bottom cluster only, we re-classify all
covenant choices where the ratio of Debt/EBITDA to the covenant below 2/3 as loose.
37
The results remain the same for a wide range of adjustment levels. Also, imposing the same adjustment to other
cluster does not lead to any re-classifications.
38
Note that Debt/EBITDA covenant is a maximum ratio covenant, unlike current ratio covenant which is a
minimum ratio covenant. Therefore, borrowers in the Debt/EBITDA sample are in violation of the covenant when
their Debt/EBITDA exceeds the covenant specified by their loan contract.
29
Appendix B:
Ordered Probit Estimates of Credit Ratings
For firms without credit ratings, we estimate credit rations using an ordered probit model proposed by
Blume, Lim, and Mackinlay (1998). We start the analysis by using the entire universe of Compustat firms
with historical long-term credit ratings (i.e. senior credit rating before 1998) in the 1994-2004 period.
Blume et al use credit ratings from Warga file, but only because Compustat senior credit ratings are not
available in the earlier parts of their sample period. Because S&P ratings are available from Compustat
for our entire sampling period we use ratings on Compustat.
I. Variables
The accounting ratios used to estimate ratings are: pretax interest coverage, operating income to sales,
long-term debt to assets, and total debt to assets.39 Following, Blume, Lim, and MacKinlay (1998) we use
three-year averages of these ratios. Because pretax interest coverage is highly skewed and negative
coverage ratios are not economically meaningful, we winsorize annual interest coverage ratios at 0 and
100. To control for the non-linearities in the relationship between interest coverage ratio and credit
ratings we calculate four variables based on the three year average coverage ratio:
Cit Є [0,5)
Cit Є [5,10)
Cit Є [10,20)
Cit Є [20,100]
C1it
Cit
5
5
5
C2it
0
Cit - 5
5
5
C3it
0
0
Cit - 10
10
C4it
0
0
0
Cit - 20
Cit is the pretax interest coverage of firm i at the of calendar year t. Cjit represents the jth component of the
coverage ratio as defined in the table above, where j = 1, 2, 3, 4.
We used natural log of inflation adjusted market capitalization to control for the effect of firm size on
credit ratings. Also, using CRSP daily stock files, we calculated beta and residual standard deviation of
common stock returns from the Scholes-Williams market model. For each calendar year we included beta
and residual standard deviation of companies with at least 100 daily stock returns. We use the valueweighted CRSP index as the market index. To control for the variation in the cross-sectional averages of
standard deviation of residuals over time, each year we divided standard deviation of residuals for each
firm by cross-sectional averages of that year.
II. Model
We estimate the model in the same way Blume et al do. Our data panels are organized by calendar, not
fiscal, year. In our pooled probit analysis we assume that alphas change over time (first year's alpha is set
equal to zero), while the slope coefficients remain constant over our sampling period. Assuming
heteroskedasticity we model the error terms as an exponential function of market capitalization. We
assume that the alpha for the residual variance equation is zero. We estimate the model parameters using
Maximum Likelihood Estimation (MLE).
39
The pretax interest coverage is defined as the ratio of [operating income after depreciation (178) + interest
expense (15)] to [interest expense (15)], where the numbers in parentheses are Compustat annual data item numbers.
The ratio of operating income to sales is defined as [operating income before depreciation (13)] to [Net sales (12)].
The ratio of long-term debt to assets is defined as [long-term debt (6)] to [assets (6)]. The ratio of total debt to assets
is defined as [long-term debt (6) + debt in current liabilities (34) + average short-term borrowings (104)] to [assets
(6)].
30
Ordered Probit Model Estimates of Credit Ratings for the Panel Data, 1994 – 2004
The estimates are for the ordered probit model parameters using a panel data sample of 15,269
observations from 1994 through 2004. To conserve space year fixed effects are omitted. The lower
boundaries for rating category parameters are the estimates of the partition parameters for the rating
categories. The variance parameter is the estimate of the coefficient associated with the market value of
equity when the variance of the disturbances is modeled as function of the deflated market value of
equity. The standard errors are calculated under the assumption that the disturbances are uncorrelated.
Coefficient
Standard
Error
t Value
P-value
Approx r > |t|
Beta
Pretax Interest Coverage
Max (5, Coverage)
Max (0, Coverage - 5)
Max (0, Coverage - 10)
Max (0, Coverage - 20)
Operating Margin
LT Debt Leverage
Total Debt Leverage
Market Value
Market Model Beta
Standard Error
0.069
0.013
0.013
-0.002
0.052
-1.619
0.749
0.213
-0.150
-1.017
0.005
0.004
0.002
0.001
0.006
0.073
0.053
0.008
0.010
0.033
14.32
3.44
5.32
-2.90
9.02
-22.32
14.11
27.06
-14.28
-30.59
0.000
0.001
0.000
0.004
0.000
0.000
0.000
0.000
0.000
0.000
Lower Boundary for
Rating Category
C
CC-CCC
B
BB
BBB
A
AA
-2.113
-1.412
0.052
0.834
1.529
2.217
2.675
0.077
0.057
0.040
0.050
0.067
0.089
0.107
-27.31
-24.90
1.32
16.82
22.93
24.90
24.90
0.000
0.000
0.186
0.000
0.000
0.000
0.000
Variance Parameter
Market Value
-0.188
0.009
-21.37
0.000
31
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34
Table 1
Summary Statistics of Bank Loans 1995-2001
The table below presents borrower and loan characteristics for a sample of dollar denominated loans of non-financial U.S. firms with publicly traded common stocks. The period of
analysis is 1995-2001 and the source of loan information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans whose maturity (at loan inception) is less than one year are
excluded from the analysis. Borrower financials are as of the last pre-loan fiscal year and are obtained from Compustat. Information on borrower common stocks is from CRSP. Analyst
forecast errors are calculated by using analysts’ earnings forecasts in I/B/E/S. Market capitalization equals to the number of shares outstanding [Data 25] times fiscal year and closing
stock price [Data 199]. The data in brackets are annual Compustat data item numbers. Another measure of borrower size is Total assets which equals [Data 6]. Estimated credit rating is
an estimate of borrowing firm credit quality from the ordered probit model of Blume, Lim, and MacKinlay (1998). Details of the estimation are in Appendix A. Z-score is an alternative
proxy we use to measure borrower creditworthiness and is defined as in Sufi (2006). EBITDA / Sales is an operating performance measure that is equal to [Data 13] / [Data 12]. Standard
deviation of EBITDA / Sales is the five-year pre-loan volatility of [Data 13] / [Data 12]. Pre-loan leverage of borrowers is Total Debt / Assets, equals [Data 9 + Data 34 + Data 104] /
[Data 6]. A measure of borrower fixed assets is PPE / Assets, which is measured by dividing [Data 30] to [Data 6]. A ratio of leverage to operating profits is Total Debt / EBITDA, which
equals [Data 9 + Data 34 + Data 104] / [Data 13]. A proxy for borrower investment opportunities and potential risk shifting activities is (Capital expenditures + R&D) / Sales, which is
equal to [Data 128 + Data 46] / Data 12. Liquidity is measured by Current ratio, which is the ratio of current assets [Data 4] to current liabilities [Data 5]. Age is the number of years
between the loan inception date and the first date borrower stocks appear on CRSP files. Analyst forecast error is calculated by taking the absolute value of the difference between mean
consensus analysts forecast of borrower earnings regarding the last fiscal year before loan inception and realized borrower earnings, and dividing this difference by borrower common
stock price at the beginning of the month of analyst forecasts. We use analyst forecasts issued one month before the fiscal year end of interest to calculate forecast errors. Standard
deviation of earnings announcement returns is the volatility of abnormal stock returns on borrower common stock (-1, +1) trading days around quarterly earnings announcement dates as
reported by Compustat. The market index used to calculate the abnormal returns is value-weighted CRSP index. We calculate this volatility measure only for borrowers with more than
three pre-loan quarterly earnings announcement dates. All-in-drawn spread is calculated and reported by Dealscan as the total borrowing cost of the drawn portion of a loan over and
above LIBOR. Maturity is the number of months between loan inception and expiration date. Loan amount / Total assets is the maximum amount available to the borrower scaled by
borrower’s total assets. Covenant intensity index equals the sum of six covenant indicators (collateral, dividend restriction, more than 2 financial covenants, asset sales sweep, equity
issuance sweep, and debt issuance sweep) and hence ranges between 0 and 6. When the indicator for one of the six covenants is missing the index is set equal to missing and the loan is
excluded from the analysis. Number of lenders indicates the number of lenders in the loan syndicate. Syndicated loan equals 1 if loan is syndicated (includes more than one lender); 0
otherwise. Secured is an indicator variable which equals 1 if the loan is backed by collateral. Sweep is an indicator variable which equals 1 if the loan includes a(n) asset sales, debt
issuance, equity issuance, or excess cash flow sweep. Performance pricing equals 1 if loan pricing is tied to borrower performance; and 0 otherwise. The summary statistics are reported
for three classes of loans: overall loan sample, sample of loans with a Debt/EBITDA covenant, sample of loans with a current ratio covenant. While summary statistics for the overall
sample include loans with coverage and liquidity covenants the statistical tests are based on comparing borrowers/loans in the coverage and liquidity samples to firms without these
covenants in their loan agreements. We provide loan level summary statistics. We conduct two-tailed mean and median comparison tests to investigate whether there is any difference
between borrower and loan characteristics of covenant samples and non-covenant benchmarks. a, b, and c, indicate significance at the 1%, 5%, and 10% levels, respectively.
Overall
Debt/EBITDA covenant
N=11,660
Mean
Current ratio covenant
N=3,739
Median
Mean
N=956
Median
Mean
Median
Panel A. Borrower Characteristics
Market capitalization ($ 1 millions)
Total assets ($ 1 millions)
Estimated credit rating
Altman’s Z-score
EBITDA / Sales
Standard deviation of EBITDA / Sales
Total debt / Total assets
PPE / Total assets
Total debt / EBITDA
(Capital expenditures + R&D) / Sales
2,900
2,992
0.55 (BB)
1.91
13.54%
315
481
0.59 (BB)
1.93
12.66%
1,033
a
1,259
a
0.45 (BB)
a
2.00
a
14.59%
a
0.50 (BB)
a
1.96
b
13.50%
a
2.36%
b
608
a
79
a
440
a
112
a
0.23 (BB)
a
0.26 (BB)
a
2.37
a
2.35
a
12.24%
a
9.89%
a
4.81%
a
2.98%
a
a
22.39%
a
2.46%
3.90%
32.69%
31.57%
35.68%
a
34.07%
a
23.81%
5.10%
a
8.05%
1.94
a
1.73
5.84%
a
13.00%
1.76
a
7.75%
5.45%
7.27%
a
2.29
1.68
2.58
a
11.55%
a
a
13.23%
6.78%
1.63
2.05
Standard deviation of current ratio
0.36
0.17
0.39
Volatility of earnings announcement returns
398
a
4.22%
1.96
Analyst forecast error
a
a
Current ratio
Age (years)
270
17.5
0.017
6.98%
9.9
0.002
6.13%
a
12.7
a
0.014
a
7.30%
a
0.20
7.0
a
a
0.003
6.63%
a
2.30
5.72%
a
a
0.37
0.85
a
5.96%
a
2.05
a
0.24
a
10.8
a
7.0
a
0.023
a
0.004
a
8.34%
a
7.39%
a
35
Table 1 Cont’d
Panel B. Loan Characteristics
All in drawn spread
Maturity (months)
Loan amount / Total assets
Number of lenders
Covenant intensity index
Secured indicator
174.7
150.0
192.2
a
187.5
a
198.6
a
60.00
a
40.28
a
36.00
45.95%
a
40.65%
a
31.73%
a
3.24
a
1.00
a
2.88
a
2.00
a
86.43%
a
---
42.05
36.00
52.74
a
37.89%
27.46%
56.65%
a
9.45
a
6.00
a
5.00
a
--
7.10
3.00
3.49
4.00
4.18
a
74.87%
--
80.17%
a
200.0
Sweep indicator
67.04%
--
81.01%
a
--
50.51%
a
Syndicated loan indicator
86.77%
--
93.93%
a
--
66.63%
a
--
Performance pricing
43.20%
--
77.88%
a
--
45.92%
c
--
35.56%
--
41.00%
a
--
50.94%
a
--
--
18.93%
a
--
--
15.17%
a
--
--
12.87%
a
--
--
63.49%
a
--
--
30.54%
a
--
Panel C. Loan purpose
Debt repayment
Corporate purpose
24.52%
--
8.83%
a
Takeover/Acquisition
18.46%
--
32.81%
a
8.77%
--
9.09%
Revolver
57.47%
--
56.35%
Term loan
24.67%
--
35.14%
Working capital financing
Panel D. Loan Type
c
36
a
Table 2
Summary Statistics by Covenant Tightness
The table below presents the differences between borrower and loan characteristics for a sample of loans with tight vs. loose current ratio and debt / ebitda covenants.
Our analysis is limited to the dollar denominated loans with of non-financial U.S. firms with publicly traded common stocks. The period of analysis is 1995-2001 and
the source of loan information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans with maturity (at loan inception) less than a year are excluded from the
analysis. Debt / ebitda sample is restricted to loans with Tearsheets. Also, if the covenant definition reported in the Tearsheets include a non GAAP adjustment to the
calculation of debt / ebitda we drop the loan from debt / ebitda analysis. We measure covenant tightness as follows: First, we divide each covenant sample into clusters
considering the choice set of borrowers. Borrowers with similar values of the covenant variable are grouped together. Second, in each cluster we rank borrowers by the
restrictiveness of their covenant choices. We classify those that choose covenants as restrictive as or more restrictive than the cluster median as choosing tight
covenants. After classifying covenants as tight and loose, we pool together loans from all clusters. We provide the summary statistics at the loan level. Letters a, b, and
c, indicate significance levels from two-tailed test of difference from the non-covenant loan benchmarks at the 1%, 5%, and 10% levels, respectively.
Debt / EBITDA Covenant
Tight
N=347
Mean
Current ratio covenant
Loose
N=212
Median
Tight
N=421
Mean
Median
Mean
2,580
860
1,102
Loose
N=535
Median
Mean
Median
221
87
Panel A. Borrower Characteristics
Market capitalization ($ 1 millions)
Total assets ($ 1 millions)
Estimated credit rating
Altman’s Z-score
EBITDA/Sales
Standard deviation of EBITDA/Sales
Total debt / Total assets
PPE / Total assets
Total debt / EBITDA
(Capital expenditures + R&D) / Sales
Current ratio
1,170
a
1,824
0.62 (BB)
770
a
2.05
16.50%
2.79%
3.47
9.67%
0.69 (BB)
a
b
a
2.03
a
a
42.00%
6.52%
433
c
a
c
1.79
2,004
972
692
0.84 (BB)
0.93 (BBB)
0.18 (BB)
b
114
0.34 (BB)
2.35
1.83%
4.30%
2.22%
6.28%
2.66%
21.11%
3.23%
40.97%
39.88%
37.08%
23.85%
22.28%
23.78%
22.59%
7.35%
5.21%
7.70%
5.44%
8.31%
5.89%
2.52
1.91
1.86
0.66
5.70%
11.80%
5.75%
11.71%
1.64
1.82
1.71
2.04
a
5.51%
2.61
7.9
a
0.25
0.16
0.31
14.5
7.7
10.5
b
0.0020
5.29%
c
2.32
242
0.27 (BB)
10.31%
13.2
c
110
0.17 (BB)
1.93
Age (years)
5.95%
a
17.14%
0.15
0.0066
b
69
2.13
0.27
Std of earnings announcement returns
c
20.54%
14.08%
Standard deviation of current ratio
Analyst forecast error
b
a
b
c
a
a
8.28%
a
b
5.67%
1.87
0.21
a
a
7.1
b
2.38
2.41
14.23%
11.47%
1.63
0.95
13.99%
5.96%
2.51
2.31
0.42
0.28
11.1
7.0
0.0141
0.0033
8.18%
6.94%
109.6
175.0
c
0.0056
0.0012
0.0195
0.0042
6.33%
5.76%
8.26%
7.84%
139.8
100.0
208.6
b
Panel B. Loan Characteristics
All in drawn spread
Maturity (months)
Loan amount / Total assets
Number of lenders
Covenant intensity index
200.8
59.35
83.06%
4.86
86.85%
Sweep indicator
88.36%
Performance pricing
b
a
16.44
Secured indicator
Syndicated loan indicator
a
100.00%
a
a
225.0
60.00
83.65%
a
a
200.0
64.07
64.00
39.65
36.00
59.81%
52.47%
42.34%
34.15%
13.00
15.29
13.00
3.22
5.00
3.56
4.00
3.00
--
64.98%
--
70.08%
a
-
a
a
71.67%
b
40.56
36.00
39.32%
29.55%
1.00
3.26
1.00
3.00
2.78
2.00
85.75%
--
86.98%
--
55.00%
--
46.76%
--
c
--
100.00%
--
69.36%
--
64.49%
--
--
85.83%
--
47.74%
--
44.49%
--
--
52.23%
--
50.12%
--
51.59%
--
--
4.45%
--
16.86%
--
20.56%
--
--
21.86%
--
19.48%
--
11.78%
--
--
4.45%
--
10.21%
--
14.95%
--
--
59.92%
--
60.81%
--
65.61%
--
--
29.96%
--
34.68%
--
27.29%
--
Panel C. Loan purpose
Debt repayment
Corporate purpose
Takeover/Acquisition
Working capital financing
25.64%
a
3.85%
61.54%
0.64%
a
a
a
b
Panel D. Loan Type
Revolver
47.44%
Term loan
47.12%
a
a
b
37
Table 3
Summary Statistics by Covenant Intensity
The table below presents the borrower and loan characteristics of a sample of bank loans by covenant intensity. Our analysis is limited to the dollar
denominated loans with of non-financial U.S. firms with publicly traded common stocks. The period of analysis is 1995-2001 and the source of loan
information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans with maturity (at loan inception) less than a year are excluded from the
analysis. Covenant intensity index equals the sum of six covenant indicators (collateral, dividend restriction, more than 2 financial covenants, asset sales
sweep, equity issuance sweep, and debt issuance sweep) and hence ranges between 0 and 6. When the indicator for one of the six covenants is missing the
index is set equal to missing and the loan is excluded from the analysis. We formed three categories of loans by intensity. The first category consists of
loans with no covenants. Low intensity implies the existence of one or two covenants. High intensity on the other hand implies the existence of five or six
covenants. We provide the summary statistics at the loan level. Letters a, b, and c, indicate two-tailed test of difference from the non-covenant loan
benchmarks at the 1%, 5%, and 10% levels, respectively.
No covenant, index = 0
Low intensity, index = 1 or 2
Sample N=238
Sample N= 1,126
Mean
Median
Mean
High intensity, index = 5
or 6
Sample N= 1,387
Median
Mean
Median
833
232
Panel A. Borrower Characteristics
Market capitalization ($ 1 millions)
Total assets ($ 1 millions)
Estimated credit rating
Altman’s Z-score
EBITDA / Sales
Standard deviation of EBITDA / Sales
Total debt / Total assets
PPE / Total assets
Total debt / EBITDA
(Capital expenditures + R&D) / Sales
3,775
5,535
1.31 (BBB)
2.15
19.12%
2.85%
27.20%
7.26%
1.67
11.14%
Current ratio
1.66
Standard deviation of current ratio
0.19
Age (years)
28.2
Analyst forecast error
0.0042
Volatility of earnings announcement returns
4.52%
a,a
a,a
a,a
-,a
a,a
a,a
b,a
a,a,a
a,a,a
a,a
a,a
a,a
a,a
2,092
2,464
1.37 (BBB)
2.09
16.58%
1.89%
28.09%
5.55%
1.45
8.61%
1.52
0.13
24.3
0.0011
4.22%
a,a
a,a
a,a
-,a
a,a
a,a
c,a
-,a
-,a
a,a
a,a
a,a
a,a
a,a
a,a
1,144
1,394
0.55 (BB)
2.08
c
c
a
a
225
335
0.60 (BB)
2.12
14.78%
12.19%
3.82%
2.13%
29.94%
8.64%
2.04
14.04%
a
a
a
a
2.10
0.38
13.9
0.0151
6.45%
28.86%
5.89%
1.50
6.25%
1.83
c
a
a
a
a
a
a
c
a
a
a
a
a
a
0.20
8.3
0.0023
5.69%
a
a
a
1,119
457
0.30 (BB)
0.31 (BB)
1.77
1.74
13.76%
12.85%
3.85%
2.56%
42.60%
40.66%
6.82%
4.79%
3.38
2.75
10.88%
5.25%
2.00
1.66
0.45
0.19
11.8
6.4
0.0224
0.0036
7.32%
6.82%
257.0
250.0
Panel B. Loan Characteristics
All in drawn spread
Maturity (months)
Loan amount / Total assets
Number of lenders
Covenant intensity index
47.1
46.48
24.69%
16.00
0
Secured indicator
0.00%
Sweep indicator
0.00%
Syndicated loan indicator
99.58%
Performance pricing
77.73%
a,a
-,a
a,a
a,a
a,a
a,a
a,a
a,b
a,c
35.0
60.00
20.22%
13.00
0
a,a
b,a
a,a
a,a
a,a
141.4
44.85
40.00%
8.18
1.62
--
59.15%
--
11.72%
--
89.25%
--
64.03%
--
57.37%
--
14.30%
--
17.41%
a
a
a
a
a
a
a
a
a
125.0
41.50
33.14%
4.00
2.00
a
a
a
a
a
62.86
60.00
73.78%
60.56%
10.71
7.00
5.60
6.00
--
99.35%
--
--
100.00%
--
--
98.20%
--
--
72.53%
--
--
29.99%
--
--
4.61%
--
--
47.22%
--
--
5.19%
--
--
40.74%
--
--
55.66%
--
Panel C. Loan purpose
Debt repayment
57.56%
Corporate purpose
15.97%
Takeover/Acquisition
13.45%
Working capital financing
5.04%
-,a
-,a
-,a
-,-
--
6.93%
--
71.49%
--
17.76%
a
a
a
c
Panel D. Loan Type
Revolver
Term loan
71.43%
7.14%
-,a
a,a
a
a
38
Table 4
The Relationship between Covenant Structure and Subsequent Performance: Univariate Analysis
The table below presents the univariate relationship between covenant structure and subsequent borrower performance for a sample of non-financial U.S. public firm bank loans that are activated between
1995 and recorded by the LPC’s Dealscan database. Loans with maturity (at loan inception) less than a year are excluded from the analysis. We report performance associated with covenant intensity as
well as covenant tightness. We classify borrowers that have current ratio and debt / ebitda covenants in their loan contracts into tight vs. loose categories. Then, we compare the performance of the
borrowers in each category to that of borrowers that (i) are in the other tightness category (e.g. tight vs. loose), (ii) do not have the particular covenant in their loan contract. Note that the non-covenant
benchmark for the current ratio sample is restricted to borrowers with no liquidity covenant and similarly the non-covenant benchmark for debt / ebitda covenant is restricted to loans of firms without any
coverage covenant (e.g. interest coverage, fixed charge coverage, debt / ebitda, senior debt / ebitda, and debt service coverage). Covenant intensity index equals the sum of six covenant indicators
(collateral, dividend restriction, more than 2 financial covenants, asset sales sweep, equity issuance sweep, and debt issuance sweep) and hence ranges between 0 and 6. When the indicator for one of the
six covenants is missing the index is set equal to missing and the loan is excluded from the analysis. We formed three categories of loans by intensity. The first category consists of loans with no covenants.
Low intensity implies the existence of one or two covenants. High intensity, on the other hand, implies the existence of five or six covenants. We consider the following performance benchmarks. For the
tightness analysis we first report 1, 2, and 3 year changes in industry-adjusted covenant variable. We also present Edgar reported covenant violations from loan inception until the end of first and third
years for tight and loose categories. We do not have covenant violation data for non-covenant groups. We also present CRSP delisting probabilities (until the end of first and third years) as well as credit
rating downgrade frequencies under both tightness and intensity analyses. CRSP delisting is an indicator variable which is equal to 1 if CRSP delisting code for the firm is (400-499, liquidation) or (550599, delisting) in the particular period of interest; 0 otherwise. Credit rating downgrade indicator equals 1if the “full” S&P senior credit rating of a borrower declines in the period of interest; 0 otherwise.
For firms without a credit rating estimate ratings using the ordered probit approach in Blume, Lim, and MacKinlay (1998). Details of the estimation are in Appendix A. Borrower level averages are
reported in the table. a, b, and c, indicate significance at the 1%, 5%, and 10% levels, respectively.
All Other Non-Financial Dealscan
Loose
Median
N
N
Mean
Panel A. Current ratio covenant
Percentage change in industry adjusted current ratio from pre-deal
to:
Year 1
Year 2
Year 3
7,110
6,605
6,072
1.18%
2.97%
5.66%
-,a
-,a
-,b
-1.13%
-1.45%
0.02%
c,a
-,a
-,b
503
478
437
-0.51%
2.23%
4.74%
a
a
b
-4.93%
-3.31%
-3.45%
a
a
b
385
343
311
11.19%
12.50%
11.91%
4.08%
7.61%
3.67%
Panel B. Debt / EBITDA covenant
Industry adjusted change in Debt/EBITDA from pre-deal to:
Year 1
Year 2
Year 3
3,173
2,980
2,784
-0.24
-0.31
-0.32
a,a
a,a
a,a
-0.12
-0.16
-0.18
a,a
a,a
a,a
205
189
181
0.40
0.41
0.25
a
a
a
0.35
0.32
0.15
a
a
a
317
293
261
-1.15
-1.22
-1.63
-1.01
-1.47
-2.02
No Covenant
N
Panel C. Covenant Intensity
Change in Altman’s Z-score from pre-deal to the end of Year 1
Change in Altman’s Z-score from pre-deal to the end of Year 3
Delisting due to poor performance until the end of Year 1
Delisting due to poor performance until the end of Year 3
Credit rating downgrade from pre-deal to the end of Year 1
Credit rating downgrade from pre-deal to the end of Year 3
225
200
238
238
228
202
Mean
-0.14
-0.20
0.00%
2.10%
14.47%
24.26%
Median
N
Low Intensity
Median
-,a
-,b
a,a
a,a
-,a
-,a
Mean
Tight
-0.09
-0.12
-,a
N
Mean
987
807
1,126
1,126
1,030
835
-0.14
-0.28
1.42%
8.17%
17.96%
28.62%
a
a
a
Median
High Intensity
Median
a
Mean
-0.04
-0.14
a
N
Mean
1,207
948
1,387
1,387
1,235
965
-0.31
-0.34
2.09%
14.92%
25.02%
39.07%
Median
-0.20
-0.23
39
Table 5
Multivariate Analysis of the Determinants of Financial Covenant Tightness
The table below presents the probit regressions (marginal effects reported) for the determinants of covenant tightness for a sample of bank loans
with current ratio and debt / ebitda covenants. Panel A presents the results for the current ratio covenant tightness and Panel B presents probits
for the debt/ebitda sample. Under each panel we present three specifications; one specification for each asymmetric information proxy: Age
equals to the number of years between the loan date and the first date borrower’s stock appeared on CRSP files. Standard deviation of earnings
announcement returns is the volatility of abnormal stock returns on borrower common stock (-1, +1) trading days around quarterly earnings
announcement dates as reported by Compustat. The market index used to calculate the abnormal returns is value-weighted CRSP index. We
calculate the volatility only for borrowers with more than three pre-loan quarterly earnings announcement date. Analyst forecast error is
calculated by taking the absolute value of the difference between mean consensus analysts forecast of borrower earnings regarding the fiscal
year end before loan inception and realized borrower earnings, and dividing this difference by borrower common stock price at the beginning of
the month of analyst forecasts. We use analyst forecasts issued one month before the fiscal year end of interest to calculate forecast errors. Our
analysis is limited to the dollar denominated loans with of non-financial U.S. firms with publicly traded common stocks. Note that all of the
loans in the debt / ebitda sample are syndicated and hence we do not use syndicated indicator as an independent variable in Panel B. The period
of analysis is 1995-2001 and the source of loan information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans with maturity (at
loan inception) less than a year are excluded from the analysis. Debt / ebitda sample is restricted to loans with Tearsheets. Also, if the covenant
definition reported in the Tearsheets include a non GAAP adjustment to the calculation of debt / ebitda we drop the loan from debt / ebitda
analysis. We measure covenant tightness as follows: First, we divide each covenant sample into clusters considering the choice set of borrowers.
Borrowers with similar values of the covenant variable are grouped together. In order to take into account the effect of the loan on borrower
financial ratios, we cluster borrowers by using financial ratios at the end of the first fiscal quarter following loan inception. Second, in each
cluster we rank borrowers by the restrictiveness of their covenant choices. We classify those that choose covenants as restrictive as or more
restrictive than the cluster median as choosing tight covenants. After classifying covenants as tight and loose, we pool together loans from all
clusters. Each regression includes industry, year, loan type, and loan purpose fixed effects (unreported). Robust standard errors are reported.
Letters a, b, and c indicate significance at 1%, 5%, and 10% levels, respectively.
Panel A. Current Ratio Covenant
Standard deviation of
quarterly earning
announcement returns
Age
Analyst forecast error
Coef.
Std Err
Coef.
Std Err
Coef.
Std Err
Loan characteristics:
Maturity (months)
Deal amount / Total assets
Dummy: Syndicated loan
-0.001
-0.021
0.163
0.001
0.069
0.046
-0.001
0.021
0.128
0.001
0.074
0.049
0.000
-0.164
0.103
0.001
Borrower credit risk characteristics:
Log (Market capitalization)
EBITDA / Sales
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Current ratio
Volatility of current ratio
-0.054
-0.753
-0.125
0.162
0.611
-0.152
-0.092
0.017
0.221
0.120
0.367
0.173
0.024
0.062
-0.060
-0.754
-0.129
0.208
0.610
-0.155
-0.095
a
a
0.017
0.222
0.121
0.369
0.173
0.025
0.062
-0.052
-0.566
-0.256
-0.079
0.438
-0.183
-0.129
0.002
-0.851
c
0.517
-0.270
0.053
0.300
a
0.058
0.268
Asymmetric information proxy
0.000
% change in industry adjusted current ratio year 1
0.279
Number of observations
Pseudo R-squared
a
a
a
a
a
a
826
0.156
a
a
a
797
0.159
c
0.085
c
0.056
b
0.022
b
0.253
c
0.150
0.405
b
0.191
a
0.032
c
0.077
0.954
a
597
0.151
40
0.066
Table 5 Cont’d
Panel B. Debt / EBITDA Covenant
Standard deviation of
quarterly earning
announcement returns
Age
Coef.
Loan characteristics
Maturity (months)
Deal amount / Total assets
-0.001
0.217
Borrower credit risk characteristics:
Log (Market capitalization)
EBITDA / Sales
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Volatility of EBITDA / Sales
-0.001
-1.044
0.489
-0.996
0.197
-3.414
Asymmetric information proxy
Change in industry adjusted Debt/EBITDA year 1
Number of observations
Pseudo R-squared
a
a
a
b
0.001
-0.090
a
497
0.271
Std Err
Coef.
0.002
0.077
-0.001
0.265
0.025
0.393
0.109
0.836
0.445
1.654
-0.010
-0.931
0.432
-1.190
0.291
-2.947
0.002
-0.600
0.015
-0.091
a
b
a
b
a
491
0.296
Analyst forecast error
Std Err
Coef.
0.002
0.074
0.000
0.226
0.024
0.368
0.102
0.795
0.410
1.401
-0.021
-0.855
0.344
-1.167
0.126
-2.705
1.147
-0.649
0.015
-0.083
Std Err
a
b
a
c
0.002
0.074
0.026
0.360
0.108
0.810
0.421
1.445
2.042
a
467
0.253
41
0.015
Table 6
Multivariate Analysis of the Determinants of Covenant Intensity
The table below presents poisson regressions of covenant intensity using a sample of non-financial U.S. public firm bank loans that are activated
between 1995 and 2001 and recorded by Dealscan. Loans with maturity (at loan inception) less than a year are excluded from the analysis.
Covenant intensity equals the sum of six covenant indicators (collateral, dividend restriction, more than 2 financial covenants, asset sales sweep,
equity issuance sweep, and debt issuance sweep) and hence ranges between 0 and 6. When the indicator for one of the six covenants is missing
the index is set equal to missing and the loan is excluded from the analysis. Our performance signal is credit rating downgrade in Panel A, CRSP
delisting in Panel B, and change in z-score in Panel C. Change in rating is the change in ratings from Blume, Lim, and MacKinlay (1998) model
from pre-deal to Year 1. We hold leverage at pre-deal levels when estimating Year 1 ratings. Downgrade equals 1 if borrower rating declines
from one alphanumerical rating class to a lower class; 0 otherwise. CRSP delisting is an indicator variable which equals to 1 if the CRSP
delisting code for the borrower equals liquidation (400-499) or exchange delisting (550-600). Each Panel includes three specifications; one for
each asymmetric information proxy. Age is the number of years between the loan inception date and the first date borrower stocks appear on
CRSP files. Standard deviation of earnings announcement returns is the volatility of abnormal stock returns on borrower common stock (-1, +1)
trading days around quarterly earnings announcement dates as reported by Compustat. The market index used to calculate the abnormal returns
is value-weighted CRSP index. We calculate this volatility measure only for borrowers with more than three pre-loan quarterly earnings
announcement dates. Analyst forecast error is calculated by taking the absolute value of the difference between mean consensus analysts
forecast of borrower earnings regarding the last fiscal year before loan inception and realized borrower earnings, and dividing this difference by
borrower common stock price at the beginning of the month of analyst forecasts. We use analyst forecasts issued one month before the fiscal
year end of interest to calculate forecast errors. Each regression includes an intercept, in addition to industry, year, loan type, and loan purpose
fixed effects (unreported). Robust standard errors are reported. Letters a, b, and c indicate significance at 1%, 5%, and 10% levels, respectively.
Panel A. Credit rating downgrade within one year
Standard deviation of
quarterly earning
announcement
returns
Age
Std.
Err.
Coef.
Std.
Err.
Coef.
Analyst forecast error
Std.
Err.
Coef.
Loan characteristics
Maturity (months)
0.002
a
0.000
0.002
a
0.000
0.002
a
0.001
Deal amount / Total assets
0.202
a
0.021
0.236
a
0.021
0.264
a
0.024
Dummy: Syndicated loan
0.114
a
0.044
0.134
a
0.046
0.137
a
0.052
Log (Market capitalization)
-0.048
a
0.006
-0.049
a
0.006
-0.074
a
0.008
EBITDA / Sales
-0.178
a
0.066
-0.172
b
0.068
-0.158
b
0.080
0.413
a
0.032
0.408
a
0.032
0.438
a
0.038
-0.217
0.165
-0.236
0.169
-0.490
a
0.182
0.078
0.070
0.111
0.072
0.285
a
0.079
Volatility of EBITDA / Sales
-0.034
0.203
0.024
0.206
0.286
Asymmetric information proxy
-0.005
0.001
1.416
a
0.279
0.764
a
0.204
0.049
0.200
a
0.043
0.100
a
0.023
0.021
-1.658
a
0.513
-0.787
b
0.360
Borrower credit risk characteristics:
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Dummy: Credit rating downgrade
0.007
Cross effect
0.035
a
c
0.240
Number of observations
3081
3024
2657
Pseudo R-squared
0.127
0.128
0.142
42
Table 6 Cont’d
Panel B. Delisting due to poor performance within 3 years
Standard deviation of
quarterly earning
announcement
returns
Age
Std.
Err.
Coef.
Std.
Err.
Coef.
Analyst forecast error
Std.
Err.
Coef.
Loan characteristics
Maturity (months)
0.002
a
0.000
0.002
a
0.000
0.002
a
0.000
Deal amount / Total assets
0.205
a
0.020
0.229
a
0.020
0.264
a
0.022
Dummy: Syndicated loan
0.112
a
0.040
0.139
a
0.042
0.136
a
0.048
Log (Market capitalization)
-0.039
a
0.006
-0.046
a
0.006
-0.071
a
0.007
EBITDA / Sales
-0.161
a
0.062
-0.150
b
0.064
-0.111
0.357
a
0.031
0.367
a
0.031
0.380
a
0.036
-0.100
0.159
-0.139
0.162
-0.332
c
0.176
0.008
0.069
0.036
0.071
0.184
b
0.079
Volatility of EBITDA / Sales
-0.069
0.197
0.050
0.194
0.182
Asymmetric information proxy
-0.005
0.001
1.351
a
0.257
0.749
a
0.208
Dummy: Delisted due to poor performance
-0.014
0.025
0.267
a
0.044
0.127
a
0.027
0.001
-2.619
a
0.463
-1.177
a
0.343
Borrower credit risk characteristics:
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Cross effects
0.007
a
a
0.077
0.238
Number of observations
3369
3305
2879
Pseudo R-squared
0.125
0.126
0.139
43
Table 6 Cont’d
Panel C. Change in Z-score within one year
Standard deviation of
quarterly earning
announcement
returns
Age
Std.
Err.
Coef.
Std.
Err.
Coef.
Analyst forecast error
Std.
Err.
Coef.
Loan characteristics
Maturity (months)
0.002
a
0.000
0.002
a
0.000
0.002
a
0.001
Deal amount / Total assets
0.194
a
0.021
0.222
a
0.021
0.245
a
0.024
Dummy: Syndicated loan
0.099
b
0.044
0.123
a
0.045
0.129
b
0.052
Log (Market capitalization)
-0.041
a
0.007
-0.048
a
0.006
-0.071
a
0.008
EBITDA / Sales
-0.219
a
0.066
-0.224
a
0.067
-0.201
b
0.082
0.486
a
0.037
0.489
a
0.036
0.520
a
0.042
-0.413
b
0.176
-0.397
b
0.178
-0.664
a
0.193
0.076
0.074
0.114
0.075
0.278
a
0.082
Volatility of EBITDA / Sales
-0.135
0.210
-0.078
0.215
-0.071
a
0.008
Asymmetric information proxy
-0.004
a
0.001
0.972
a
0.249
0.681
a
0.189
Change in Z-score
-0.046
a
0.018
-0.096
a
0.026
-0.105
a
0.016
Cross effects
-0.002
c
0.001
0.233
0.275
0.885
a
0.237
Borrower credit risk characteristics:
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Number of observations
2968
2910
2560
Pseudo R-squared
0.128
0.129
0.14
44
Table 7
Is Covenant Tightness Priced? Selectivity Corrected Loan Spread Regressions.
The table below presents the estimates from second-stage of a switching regression analysis which tests whether loans with tight covenants are associated with lower loan spread than loans with loose
covenants after accounting for selectivity bias in covenant tightness choice. Our loan sample includes dollar denominated loans of non-financial U.S. firms with publicly traded common stocks. The
period of analysis is 1995-2001 and the source of loan information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans with maturity (at loan inception) less than a year are excluded from
the analysis. In the first stage (unreported) we run a probit model to examine the determinants of choosing tight vs. loose covenants based on borrower and loan characteristics. The dependent variable
in the first-stage equals 1 if the borrower chooses a tight covenant; and 0 otherwise. The linear predictor from the first-stage is used the compute the Inverse Mills ratio, which is defined as: φ (ψ) / Φ
(ψ) when tight covenants are selected, and φ (ψ) / (1 - Φ (ψ)) when loose covenants are selected. Here φ is the standard normal density function, Φ is the standard normal cumulative distribution
function and ψ is the estimated linear predictor from the first-stage probits. In the second-stage (presented below), we run selectivity corrected linear spread regressions where the dependent variable
equals the natural logarithm of all-in-drawn spread. Note that the spread regressions of loans with tight vs. loose covenants are run separately. Note that all of the loans in the debt / ebitda sample are
syndicated and hence we do not use syndicated indicator as an independent variable in Panel B. Each regression includes an intercept, in addition to industry, year, loan type, and loan purpose fixed
effects (unreported). Robust standard errors are reported. Letters a, b, and c indicate significance at 1%, 5%, and 10% levels, respectively.
Current ratio covenant
Tight
Coef.
Inverse Mill’s Ratio
-9.420
Loan characteristics
Maturity (months)
Deal amount / Total assets
Dummy: Syndicated loan
0.001
0.029
-0.552
Borrower credit risk characteristics:
Log (Market capitalization)
EBITDA / Sales
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Current ratio
Volatility of current ratio
Volatility of EBITDA/Sales
0.034
2.056
1.368
-1.385
-1.665
0.631
0.512
--
Number of observations
Adjusted R-squared
Loose
Std. Err.
Coef.
a
2.756
1.912
a
0.002
0.083
0.165
0.063
0.857
0.290
0.570
0.682
0.218
0.117
--
b
a
b
b
a
a
Debt/EBITDA covenant
Tight
Std. Err.
Coef.
a
0.640
-2.276
-0.004
0.080
-0.363
a
0.001
0.095
0.083
-0.076
-0.213
0.795
-0.079
-0.289
0.144
0.121
--
a
0.027
0.357
0.157
0.374
0.244
0.058
0.067
--
a
a
b
c
Loose
Std. Err.
Coef.
Std. Err.
a
0.880
0.369
1.803
0.006
-0.063
--
a
0.002
0.072
--
0.007
0.112
--
c
0.004
0.194
--
-0.217
0.396
0.050
-0.738
0.127
--1.496
a
0.027
0.562
0.166
0.967
0.391
--0.939
-0.304
-1.452
0.801
-2.131
1.330
---1.399
a
0.036
1.167
0.305
1.293
0.536
--1.252
a
c
b
332
432
326
200
0.464
0.423
0.601
0.675
45
Table 8
Is Covenant Intensity Priced? Selectivity Corrected Loan Spread Regressions.
The table below presents the estimates from the second-stage of a switching regression analysis which attempts to test whether loans with high
covenant intensity are associated with lower loan spread than loans with low covenant intensity after accounting for selectivity bias in covenant
intensity choice. Our loan sample includes dollar denominated loans of non-financial U.S. firms with publicly traded common stocks. The period
of analysis is 1995-2001 and the source of loan information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans with maturity (at loan
inception) less than a year are excluded from the analysis. Covenant intensity equals the sum of six covenant indicators (collateral, dividend
restriction, more than 2 financial covenants, asset sales sweep, equity issuance sweep, and debt issuance sweep) and hence ranges between 0 and 6.
When the indicator for one of the six covenants is missing the index is set equal to missing and the loan is excluded from the analysis. In both
Panel A and Panel B, high intensity sample consists of loans with covenant intensity equal to 5 or 6. In Panel A low intensity sample consists of
loans without any covenants. In Panel B low intensity sample consists of loans with no covenants or only one covenant. In the first stage
(unreported) we run a probit model to examine the determinants of choosing covenants with high intensity vs. low intensity based on borrower and
loan characteristics. The dependent variable in the first-stage equals 1 if the borrower chooses intense covenants; and 0 otherwise. The linear
predictor from the first-stage is used the compute the Inverse Mills Ratio, which is defined as: φ (ψ) / Φ (ψ) when tight covenants are selected, and
φ (ψ) / (1 - Φ (ψ)) when loose covenants are selected. Here, φ is the standard normal density function, Φ is the standard normal cumulative
distribution function and ψ is the estimated linear predictor from the first-stage. In the second-stage (presented below), we run selectivity corrected
linear loan spread regressions where the dependent variable equals the natural logarithm of all-in-drawn spread. Note that the spread regressions of
loans with high intensity and low intensity loans are run separately. Each regression includes an intercept, in addition to industry, year, loan type,
and loan purpose fixed effects (unreported). Robust standard errors are reported. Letters a, b, and c indicate significance at 1%, 5%, and 10%
levels, respectively.
Panel A. Intense includes BR Index = 5, 6 and not intense includes BR Index = 0
Overall Sample
Intense
Coef.
Inverse Mill’s Ratio
-0.458
Loan characteristics
Maturity (months)
Deal amount / Total assets
Dummy: Syndicated loan
0.000
-0.007
0.132
Borrower credit risk characteristics:
Log (Market capitalization)
EBITDA / Sales
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Volatility of EBITDA / Sales
-0.073
-0.430
0.311
-0.003
0.225
0.038
Number of observations
Adjusted R-squared
b
a
a
a
b
1229
0.297
Not Intense
Std. Err.
Coef.
Std. Err.
0.223
0.809
b
0.395
0.001
0.019
0.101
-0.014
0.024
--
b
0.004
0.211
--
0.009
0.083
0.041
0.191
0.093
0.269
-0.062
-0.422
-0.314
-0.501
0.012
1.751
c
0.038
0.327
0.306
1.170
0.473
0.906
c
226
0.499
46
Table 8 Cont’d
Panel B. Intense includes BR Index = 5, 6 and not intense includes BR Index = 0, 1
Overall Sample
Intense
Coef.
Inverse Mill’s Ratio
-0.612
Loan characteristics
Maturity (months)
Deal amount / Total assets
Dummy: Syndicated loan
0.000
-0.020
0.120
Borrower credit risk characteristics:
Log (Market capitalization)
EBITDA / Sales
Total debt / Total assets
Power, Plant, and Equipment / Total assets
(Capital expenditures + R&D) / Sales
Volatility of EBITDA / Sales
-0.069
-0.402
0.276
0.016
0.214
-0.041
Number of observations
Adjusted R-squared
a
a
a
a
b
1229
0.304
Not Intense
Std. Err.
Coef.
Std. Err.
0.167
0.486
c
0.277
0.001
0.019
0.100
-0.013
0.082
-0.376
a
0.002
0.124
0.208
0.008
0.082
0.039
0.191
0.093
0.269
-0.162
-0.280
0.045
-0.407
0.151
2.351
a
c
a
0.034
0.308
0.189
0.520
0.335
0.826
612
0.550
47
Table 9
Restrictiveness of Covenants, Covenant Violations, and Outcome of Violations
The table below presents probit regressions that explain the relationship between restrictiveness of loan covenants at loan inception and the
probability of subsequent covenant violations as well as violation outcomes. Panel A presents probit regressions (marginal effects reported) that
explain the probability of covenant violations for a sample of loans that include a current ratio or a debt / ebitda covenant (or both). The
dependent variables in Panel A are indicator variables that equal to 1 if there is a covenant violation during the period from loan inception to
years 1 and 3. Panel B presents probit regressions (marginal effects reported) that explain the relationship between ex-ante restrictiveness of
covenants and the outcome (e.g. lender response) of covenant violations. The dependent variable for the probits in Panel B is an indicator
variable that equals 1 if the borrower gets delisted from CRSP due to poor performance, declares bankruptcy, or terminates the loan because of
its inability to comply with loan covenants. Covenant violations for each loan are hand collected from quarterly borrower filings (10-Qs and 10Ks) on Edgar. First, using Compustat we measured quarterly covenant slack for each of the loans in our current ratio and debt/ebitda samples for
the first three years after loan inception and identified “potential” covenant violators. Second, we tracked the condition of each of these
“potential” violators on Edgar from loan inception until the minimum of loan expiration (e.g. maturity or termination), borrower delisting, and
the end of 12th quarter after loan inception. Violations in this table are violations reported in SEC filings, not the violations identified by our
slack measure. Violation outcome information in Panel B is jointly collected from the SEC filings of covenant violators and CRSP delisting
files. Covenant intensity equals the sum of six covenant indicators (collateral, dividend restriction, more than 2 financial covenants, asset sales
sweep, equity issuance sweep, and debt issuance sweep) and hence ranges between 0 and 6. When the indicator for one of the six covenants is
missing the index is set equal to missing and the loan is excluded from the analysis. Robust standard errors are reported. Letters a, b, and c
indicate significance at 1%, 5%, and 10% levels, respectively.
Panel A. Relationship Between Covenant Violation and Restrictiveness of Covenants
Dependent variable
Violation year 1
Std.
Err.
Coef.
Coef.
Dummy: Tight covenant
Covenant intensity index
Log (market capitalization)
EBITDA / Sales
Total debt / Total assets
PPE / Total assets
(Capital expenditures + R&D) / Sales
Number of observations
Pseudo R-squared
0.135
a
0.024
-0.009
-0.394
0.050
-0.096
0.283
b
a
0.004
0.130
0.051
0.257
0.130
a
712
0.072
Std.
Err.
0.104
0.034
-0.012
-0.441
0.006
0.003
0.300
a
a
c
a
0.023
0.006
0.008
0.129
0.048
0.255
0.013
a
Violation year 3
Std.
Err.
Coef.
Coef.
0.171
-0.016
-0.077
0.141
-0.150
0.103
712
0.110
a
0.030
a
-0.012
-0.430
0.059
0.353
0.159
0.132
0.054
-0.018
-0.161
0.067
0.016
0.146
712
0.053
Number of observations
Pseudo R-squared
0.387
0.009
a
0.105
0.017
-0.049
-0.027
-0.070
0.135
0.143
-0.010
a
a
0.013
0.005
-0.064
0.027
0.990
0.053
b
712
0.223
0.376
0.006
0.007
-0.047
-0.027
-0.078
0.121
0.145
0.001
a
a
c
712
0.095
Panel B. Relationship Between Outcome of Covenant Violations and Covenant Tightness
Dependent variable = 1 if company is delisted, bankruptcy or violations of covenant not cured; 0 otherwise.
Coef.
Std. Err.
Coef.
Std. Err.
Dummy: Violation
Dummy: Tight covenant
Covenant intensity index
Tight covenant * Violation
Log (market capitalization)
EBITDA / Sales
Total debt/ Total assets
PPE / Total assets
(Capital expenditures + R&D) / Sales
Std.
Err.
a
a
a
a
0.106
0.017
0.004
0.013
0.005
0.064
0.026
0.143
0.053
712
0.229
48
0.031
0.009
0.011
0.181
0.059
0.361
0.152
Table 10
Announcement Returns and Covenant Structure: Weighted Least Squares Regressions
The table below presents weighted least squares regressions that explain the relationship between restrictiveness (i.e. tightness and intensity) of loan covenants and loan announcement returns using a
sample of bank loan announcements by non-financial U.S. public firms during the 1995-2001 period. We focus only on loans with a current ratio or a debt / ebitda covenant (or both). The source of
loan information is Loan Pricing Corporation’s (LPC) Dealscan database. Loans with maturity (at loan inception) less than a year are excluded from the analysis. Loan announcement dates are hand
searched at Factiva news archives within one-month of loan inception date. Announcement returns are calculated at the (-1, +1) trading day window centered on loan announcement date by using the
CAR approach using the value-weighted CRSP index as the market index. Tight is an indicator variable that equals to 1 if the loan has a tight current ratio or debt / ebitda covenant; 0 otherwise.
Covenant intensity equals the sum of six covenant indicators (collateral, dividend restriction, more than 2 financial covenants, asset sales sweep, equity issuance sweep, and debt issuance sweep) and
hence ranges between 0 and 6. When the indicator for one of the six covenants is missing the index is set equal to missing and the loan is excluded from the analysis. Analyst forecast error is
calculated by taking the absolute value of the difference between mean consensus analysts forecast of borrower earnings regarding the fiscal year end before loan inception and realized borrower
earnings, and dividing this difference by borrower common stock price at the beginning of the month of analyst forecasts. We use analyst forecasts issued one month before the fiscal year end of
interest to calculate forecast errors. Each regression includes an intercept, industry, year, loan type, and loan purpose fixed effects (unreported). In the estimation process each observation is weighted
by the inverse of residual stock return volatility. Robust standard errors are reported. Letters a, b, and c indicate significance at 1%, 5%, and 10% levels, respectively.
Dependent variable: (-1, +1) Market-adjusted return around the loan announcement
Coef.
Dummy: Tight covenant
0.030
Std. Err.
a
0.010
Coef.
0.023
Std. Err.
a
0.009
Covenant intensity
Coef.
Std. Err.
b
0.008
0.014
b
0.007
-0.004
b
0.002
-0.004
b
0.002
0.161
b
0.071
-0.965
a
0.354
0.012
b
0.005
Analyst forecast error
-0.016
0.014
b
-0.015
0.013
0.017
0.000
0.003
0.003
0.002
-0.030
0.032
-0.042
0.043
Total debt / Total assets
0.058
0.052
0.007
0.015
(Capital expenditures + R&D) / Sales
0.004
0.035
-0.007
0.042
PPE / Total assets
0.151
0.097
0.231
Log (market capitalization)
EBITDA / Sales
Number of observations
Adjusted R-squared
Std. Err.
0.016
Covenant intensity * Analyst forecast error
Deal amount / Total assets
Coef.
b
0.006
0.114
415
411
258
231
0.031
0.058
0.162
0.066
49