DEBT COVENANTS AND RISK TAKING
Huijing Fu
Texas Christian University
Jieying Zhang*
University of Southern California
January 2011
*Corresponding author. Tel: (213) 7408705
Email: jieying@marshall.usc.edu
Acknowledgments: We thank Kevin Murphy, Oguzhan Ozbas, K.R. Subramanyam, workshop
participants at USC Finance brownbag for helpful comments and suggestions.
Debt Covenants and Risk Taking
ABSTRACT
This paper investigates whether debt covenants protect lenders from borrowers’ risk taking.
Using a large sample of private credit agreements, we find that firms with more restrictive
covenants spend less on R&D, diversify more, and their CEOs have less risk-taking incentives
from option compensation. We use a change specification, a simultaneous regression, and a bond
market reaction test to address the endogeneity. In particular, we find a positive bond market
reaction to additional covenants, suggesting that covenant restrictions do not simply capture
expected lower risk taking. Additional analyses show that (1) covenant violations trigger a
further reduction in risk-taking activities; (2) the effect of covenants in mitigating risk taking is
not driven by short maturity, and (3) the results from private debt covenants can be replicated
with public debt covenants. Overall, our findings provide important evidence that debt
covenants are effective in mitigating risk taking, an agency problem that cannot be explicitly
contracted on.
Keywords: debt covenants, risk taking, risk-return tradeoff, covenant violation
1.Introduction
Agency theory suggests that debt covenants exist to mitigate debtholder-stockholder conflict
by restricting wealth expropriation from borrowers (Jensen and Meckling, 1976; Myers, 1977;
Smith and Warner, 1979). One such action involves borrowers undertaking suboptimal risky
projects after debt is in place. 1 However, there is no empirical evidence on whether debt
covenants are effective in restricting borrowers’ risk taking and thereby protecting lenders from
wealth expropriation.2 In this paper, we fill this void by investigating whether stronger debt
covenants lead to lower risk taking.
It is important to study the effectiveness of debt covenants because debt covenants are widely
used to reduce the agency cost of debt (Tirole, 2006). While theoretically covenants are designed
to protect lenders from wealth expropriation including risk taking, incomplete contracting
suggests that covenants cannot explicitly specify the exact projects that a firm may or may not
undertake. Therefore it is unclear whether covenants are effective in mitigating risk taking
through a combination of indirect restrictions (McDaniel, 1986).
Our primary hypothesis is that when debt covenants are more restrictive, borrowers engage in
less risk taking. We expect this negative relation for two reasons. First, restrictive covenants
limit borrower’s abilities to engage in risk taking (Smith and Warner, 1979). For example, asset
sweep covenant limits borrowers’ ability to replace less risky assets with more risky ones; debt
sweep and equity sweep covenant limit borrowers’ ability to finance risky projects from raising
additional capital; and secured debt covenants limit borrowers’ ability to substitute the pledged
assets for risky ones. Second, restrictive covenants reduce borrowers’ incentives to take risk
1
Other actions include unauthorized distribution, claim dilution, under or over investment, etc.
The empirical evidence is also limited on whether covenants are effective in addressing other value destroying
actions. There exists some evidence on whether dividend covenant curbs underinvestment (DeAngelo and DeAngelo,
1990) and whether capital expenditure covenant reduces over investment (Nini, Smith, and Sufi, 2009).
2
through the threat of covenant violations. Risk taking increases the odds of covenant violations.
For example, risky projects produce more volatile financial performance and financial covenants
are more likely to be violated. As a result, borrowers would be less willing to take excess risk to
avoid the anticipated cost associated with covenant violations.3
However, two factors work against our hypothesis. First, debt contracts are incomplete and
covenants can only restrict risk taking indirectly. Because none of the covenants mentioned
above can specify which projects the borrower may or may not undertake, these covenants can
only restrict risk taking indirectly through reducing borrowers’ incentives and abilities to take
additional risk. Second, the threat of covenant violation may not be considered substantial to
prevent risk taking. In that case, borrowers would only cut down risk taking after a covenant is
violated and the contract is renegotiated, not before. Therefore, it is an open empirical question
whether restrictive covenants are associated with lower risk taking in the absence of a control
transfer.
We measure the restrictiveness of debt covenants following Bradley and Roberts (2004).
Specifically, we define covenant intensity of a private debt contract as the sum of six dummy
variables representing the existence of a debt sweep covenant, an equity sweep covenant, an
asset sweep covenant, a dividend covenant, at least two financial covenants, and a secure
covenant. This measure ranges from zero (the least restrictive) to six (the most restrictive). We
measure risk taking using two sets of variables. The first set is risk-taking activities, captured by
R&D expenditure, number of segments, and segment Herfindahl index.4 The second set is risktaking incentives, captured by the sensitivity of CEO option portfolio to stock return volatility
3
Beneish and Press (1993) document that the cost associated with covenant violation is economically significant.
Note that risk taking increases with R&D expenditure and segment Herfindahl index, but decreases with the
number of segments because diversification lowers risk.
4
2
(vega). We use both vega from newly granted options and total vega, which is the sum of vega
from newly granted options, vega from unexercisable options, and vega from exercisable options.
Our sample is the intersection of Dealscan and Compustat database. Essentially we include
all Dealscan loan contracts to the U.S. public corporations for which the risk taking variables are
available. Our risk taking activity sample (hereafter, the full sample) includes 37,456 firm-year
observations from 1987 to 2008, representing 6,102 firms. The data requirement for vega reduces
the risk-taking-incentive sample (hereafter, the reduced sample) to 13,086 firm-year observations
(2,014 firms).
We first document a negative relation between risk taking and covenant intensity. Univariate
analysis indicates that all five risk taking measures, i.e., R&D, number of segments, segment
Herfindahl, vega from newly granted options, and total vega, monotonically decrease with the
covenant intensity. Multivariate regression analysis finds the same pattern, that is, borrowers’
risk-taking activities and incentives decrease with covenant intensity, after controlling for
leverage, size, ROA, Intangibles, sales growth, M/B, stock return, return volatility, cash balance,
cash surplus, and dividend cut. Nonparametric analysis also yields consistent results that high
covenant intensity firms have significantly lower risk taking than low covenant intensity firms
that have the closest covenant propensity score. Collectively, these results suggest that covenants
are effective in reducing risk taking.
We then address the potential endogeneity between debt covenants and risk taking. We first
employ a change specification to examine whether an increase in covenant intensity leads to a
decrease in risk taking, using the borrower itself as the control. We find consistent results that
borrowers take less risk when there is an increase in covenant intensity, for both a short time
series that includes only the year before and the year of loan initiation, and a longer time series
3
that spans all the years before and after loan initiation. Then we run a two-stage-least-square
regression where the instrumental variable is the average covenant intensity of the lending
bank(s). We find strong and consistent results that borrowers’ risk-taking activities and
incentives decrease with covenant intensity in the second stage. Finally and most importantly, we
exploit the bond market reaction to additional covenants to gauge whether additional covenants
simply capture expected reduction in risk taking. If only borrowers that expect lower risk taking
in the future agree on restrictive covenants, we expect the bond market not to react positively to
additional covenants because the lower risk taking is anticipated. Using daily bond prices from
TRACE, we find a positive bond market reaction to the additional covenants introduced by new
bank loans, suggesting that additional covenant restrictions do not simply capture expected lower
risk taking. Taken together these three tests mitigate the concern of the endogeneity between
covenants and risk taking.
We also conduct four additional analyses. First, we address the concern that covenant
intensity is a surrogate for leverage; that is, borrowers with high leverage have lower risk taking
through other mechanisms, such as short maturity.5 After sorting the sample into four groups by
leverage and covenant intensity, we find that firms with low leverage but high covenant intensity
have lower risk taking than firms with high leverage but low covenant intensity. This indicates
that leverage does not drive the negative relation between covenant and risk taking. In addition,
covenant intensity has a consistently negative relation with risk taking after controlling for
leverage, yet leverage does not have a consistently negative association with risk taking,
suggesting that leverage has a more complex relation with risk taking.
5
Although Coles et al. (2004) find that high leverage is associated with high vega, it is still possible that other
contracting mechanisms such as short maturity may restrict the risk taking of the borrowers with higher leverage.
We explore the effect of short maturity separately.
4
Second, we examine whether covenant violations further limit risk taking. While it is
important that the existence of covenants mitigates risk taking, covenant violations are important
control transfer mechanisms that allow lenders further influence on firm decisions. We find a
decrease in both risk-taking activities and incentives after covenant violations in the univariate
analysis. However, in the multivariate analysis, we find that risk-taking activities drop
significantly in the four years after covenant violations, but not risk-taking incentives. Overall,
our evidence suggests that covenant violations have a further impact on curbing risk taking.
Third, we explore the role of short maturity in curbing risk taking. Unlike state contingent
covenants, short maturity allows frequent renegotiation and thereby could also curb agency
problems including risk taking. While Brockman, Martin, and Unlu (2009) find that short term
debt constrains managerial risk preference, we find that covenants continue to reduce risk taking
after controlling for short maturity. In fact, in most of the regressions, covenants dominate short
maturity in reducing risk taking.
Lastly, we test the role of bond covenants in mitigating risk taking. We use loan covenants in
our primary analyses because loan covenants are more binding due to lower renegotiation costs
between banks and borrowers. Nevertheless, we provide evidence that bond covenants are also
effective in reducing risk taking.
Our study contributes to the literature in several ways. First, we are the first study to directly
examine whether debt covenants are effective in indirectly restricting borrowers’ risk taking.
Chava and Roberts (2008) and Roberts and Sufi (2009) study how covenant violations restrict
borrower’s subsequent investments and debt financing and covenants serve as “tripwires” in both
cases. These papers do not address the question of whether restrictive covenants cause borrowers
5
to reduce risk taking absent of a control transfer.
The primary focus of this paper is to
investigate whether stronger covenants restrict risk taking.
Second, we add to the literature on how debtholder-stockholder conflict impacts operating
and investing decisions. In a closely related study, Nini, Smith, and Sufi (2009) document that
capital expenditure restrictions in private debt contracts cause a reduction in firm investment.
Our paper complements Nini et al. (2009) by investigating whether debtholder-stockolder
conflict impacts operating decisions. 6 Instead of examining a specific covenant (i.e. capital
expenditure restriction) and a specific wealth transfer action (i.e., over-investment), we study the
overall covenant restriction (six major covenants collectively) and a broad set of risk taking
behavior, including R&D, focus, and CEO risk-taking incentives. Overall, our findings highlight
the importance of debtholder-shareholder conflict in impacting operating decisions and the
importance of debt governance in mitigating risk taking.
The remainder of the paper is organized as follows. The next section summarizes related
literature and develops the hypotheses. Section 3 describes our data, empirical proxies and
research design. Section 4 presents the empirical results of our analyses and Section 5 concludes
the study.
2. Literature and hypotheses
The early empirical literature on debt covenants examines the ex ante efficiency in the tradeoff between the benefit and cost of including restrictive covenants. If the benefit of reducing the
cost of debt outweighs the cost of loosing future flexibility, then the borrower would agree on
6
In another related paper, Coles, Daniel and Naveen (2006) find that manager risk-taking incentives are positively
related to more debt financing. Our paper complements Coles et al. (2006) by demonstrating that although debt
financing increases risk-taking incentives, debt governance particularly debt covenants mitigates risk-taking
incentives.
6
restrictive covenants. Consistent with the theoretical predictions, firms are more likely to include
restrictive covenants in the debt contracts if the leverage is high (Malitz, 1986), or the
shareholder-bondholder conflicts are high (Begley and Feltham, 1999), or managerial
entrenchment and the likelihood of committing fraud is high (Chava, Kumar, and Warga, 2009).
On the benefit of covenants, Billett, King, and Mauer (2007) document that the negative relation
between leverage and growth opportunities is attenuated by covenant protection. Their paper
shows that covenants can mitigate the agency cost of debt for high growth firm through an
increase in debt financing. However, all these papers study the ex ante contract design and are
silent about whether and how covenants protect lenders ex post after debt is in place. 7
Recently there is an upsurge of studies that investigate how covenants violations affect firm
investing and financing. Chava and Roberts (2008) document that capital investment declines
sharply following a financial covenant violation. Roberts and Sufi (2009) show that net debt
issuing activity experiences a sharp and persistent decline following covenant violations. In both
papers covenants serve as a trigger that transfers control rights to lenders, and lenders influence
firm investing and financing using their right to accelerate the debt, increase interest rates, etc.
While the tripwire function of covenants is undoubtedly important, these papers do not address
the question whether covenants are effective in protecting lenders via the threat of a control
transfer without an actual control transfer.
We are aware of only one recent paper that investigates the impact of covenants on firm
behavior in the absence of a control transfer. Nini, Smith, and Sufi (2009) find that capital
expenditure restrictions in private debt contracts are effective in reducing borrowers’
7
See Robert and Sufi (2009a) for a review of the theory and empirical evidence on debt contracting.
7
investment.8 Our paper complements Nini et al. (2009) by studying whether six major covenant
restrictions collectively (as opposed to the capital expenditure restriction) mitigate borrowers’
risk taking (as opposed to borrower’s over-investment). While the effect of capital expenditure
restriction on over-investment is direct and less obscure, we focus on risk taking because
incomplete contracting makes it unclear whether covenants are effective in limiting risk taking
indirectly and there is no empirical evidence on this issue.
We expect covenants to mitigate risk taking because theory suggests that covenants exist to
protect debtholders from agency problems and risk taking is one of the agency problems that
transfer wealth from debtholders to shareholders. Debt covenants can mitigate risk taking in two
ways. First, restrictive covenants limit borrower’s abilities to engage in risk taking (Smith and
Warner, 1979). For example, asset sweep covenant restrict borrowers from disposing existing
assets and thereby limiting borrowers’ ability to dispose less risky assets and replace them with
more risky ones. Debt sweep and equity sweep covenant prevents borrowers from raising
additional capital to finance risky projects. Secured debt covenants reduce borrowers’ abilities to
substitute the pledged assets for risky ones because debtholders hold the title to these assets.
Second, restrictive covenants reduce borrowers’ incentives to take risk through the threat of
covenant violations. Risky projects produce more volatile financial performance and as a result,
financial covenants are more likely to be violated. Take Maximum Debt/EBITDA, the most
frequently used financial covenant as an example. If the borrower chooses an overly risky project,
then EBITDA becomes more volatile, and there is a higher chance of violating the Debt/EBITA
covenant. Previous literature establishes that the cost of covenant violation is economically
significant. Beneish and Press (1993) estimate that the cost of increased interest and early
8
DeAngelo and DeAngelo (1990) study the impact of dividend covenant on distressed firms. In contrast, our paper
investigates the role of covenants in firms’ normal business condition.
8
retirement of debt resulting from the covenant violation amounts to 1.2 to 2 percent of market
value of equity, or alternatively 4.4 to 7.3 percentage of the debt outstanding. Chava and Roberts
(2008) and Roberts and Sufi (2009) further document that there is a significant drop in
borrower’s investments and debt financing subsequent to covenant violations, imposing real
operating and financing costs on the borrowers. There is also evidence that firms manage
earnings to avoid covenant violation (Dichev and Skinner, 2002), implying that the cost of
covenant violation is non-trivial. In anticipation of these costs associated with covenant
violations, borrowers would constrain themselves from taking excess risk to avoid covenant
violation.
However, it is also possible that covenants may not be effective in mitigating risk taking for
the following reasons. Debt contracts are incomplete in the sense that they do not address every
possible future contingency. It can be very costly and often impossible to write a complete
contract that specifies all future contingencies (Grossman and Hart, 1986; Hart and Moore, 1988
& 1990; Hart, 1995; Tirole, 1999). Therefore covenants may not be effective in protecting
lenders from every dimension, especially the risk-taking activities and incentives that are
contingent on future outcomes. In addition, the threat of covenant violation may not be
substantial enough to prevent risk taking. If that is the case, borrowers would only cut down risk
taking after covenants are violated and lenders exercise control. Therefore, it is an empirical
question whether restrictive covenants mitigate borrower’s risk taking without a control transfer
from covenant violation. This leads to our first hypothesis:
H1a: Debt covenant intensity is not associated with borrower’s risk taking.
3. Data and empirical proxies
9
3.1 Sample construction
We start from Reuters Loan Pricing Corporation’s Dealscan database. We focus on bank
loans because covenants in bank loan agreements are more binding compared to public bonds
due to relatively low renegotiation cost. Since Dealscan is constructed at package and facility
level and a borrower may have multiple loan packages and each loan package may have multiple
facilities, we first aggregate the facility-level data into package level. Specifically, we sum the
facility loan amount as the package loan amount. We use the longest facility maturity as the
package maturity. 9 We average the all-in-drawn spreads of individual facility as the package
spread. And we define a package to have performance pricing as long as one facility has
performance pricing. 10, 11 Then we expand the package level data to package-year level based on
the package initiation and ending date. Lastly, we purge the package-year data into firm-year by
keeping the package with maximum covenant intensity and the accompanying package
characteristics, because we intend to study the most binding covenants.12
Then we manually merge the firm-year data from Dealscan with Compustat by company
names. We supplement our manual match with the Dealscan-Compustat link file used in Chava
and Roberts (2008) to ensure a comprehensive match. 13 We exclude non-US borrowers,
borrowers with negative assets, negative equity, negative sales, or missing risk taking variables.
Our full sample includes 37,456 firm-year observations from 1987 to 2008, representing 6,102
9 We use the longest maturity because most covenants apply to the whole package, and violation of one covenant
often put the whole deal in technical default even though only one or more specific facilities are renegotiated
(Bradley and Robert, 2004).
10
Unlike other covenants, the secure covenant is at the facility level. We define a loan package to have the secure
covenant as long as one of the facilities has the secure covenant.
11
Our results are robust if we aggregate the facility level data to package level by taking the maximum or minimum
of loan amount, spread, maturity, etc.
12
If a firm has only one package outstanding in a particular year, then naturally the covenants of that package are the
only covenants binding. However, it is sometimes the case that a firm has multiple packages outstanding in the same
year. This happens when different loans overlap in borrowing period.
13
We thank Michael Roberts for sharing with us the Dealscan-Compustat link file.
10
borrowers. We use this sample to test the association between borrowers’ risk-taking activities
and covenants. We further merge the Dealscan/Compustat combined sample with Compustat
Executive Compensation database to obtain the data needed to construct risk-taking-incentive
variables such as vega, delta, tenure, etc. This step generates a reduced sample of 13,086 firmyear observations (2,014 firms). We use this reduced sample to test the association between
borrowers’ risk-taking incentives and covenants.
3.2 Measuring debt covenants
We measure the restrictiveness of debt covenants following Bradley and Roberts (2004).
Specifically, we define the covenant intensity of a bank loan contract as the sum of six dummy
variables: 1) a dummy variable equal to one if there is a debt sweep covenant, 2) a dummy
variable equal to one if there is an equity sweep covenant, 3) a dummy variable equal to one if
there is an asset sweep covenant, 4) a dummy variable equal to one if there is a dividend
covenant, 5) a dummy variable equal to one if there are at least two financial covenants, and 6) a
dummy variable equal to one if there is a secure covenant. This measure ranges from zero (the
least restrictive) to six (the most restrictive).
We study a firm’s overall covenant restriction instead of individual covenants, because
although some covenants are not designed specifically to mitigate risk taking, they often have
indirect effect on mitigating risk taking. For example, while restricting additional debt financing,
debt sweep covenants indirectly mitigate risk taking by constraining the debt financing available
to overinvest in risky projects. Also, the main purpose of financial covenants is to monitor the
default risk as reflected in financial performance, but risk taking would increase the likelihood of
breaching the financial covenant through the occurrence of an extreme negative outcome. The
11
indirect effect of these covenants on risk taking suggests that we examine a firm’s overall
covenant restrictiveness.
3.3 Measuring risk taking behavior
We measure borrowers’ risk taking using two sets of variables. The first set measures risktaking activities, proxied by R&D, number of segments, and segment Herfindahl index. R&D
expenditures, often investments in intangible assets, are considered as high risk compared to
capital expenditures on tangible assets. For example, Kothari et al. (2001) document that R&D
investments generate more uncertain future benefits than capital expenditures. The number of
segments and segment Herfindahl index are used to capture the risk related to diversification.
Lewellen (1971) suggests a coinsurance effect, i.e., the imperfect correlation among the cash
flows of a diversified firm’s business units reduces default risk. Amihud and Lev (1981) and
May (1995) further document that risk averse managers tend to diversify more. Thus we use a
high number of segments and a low segment Herfindahl index to proxy for low risk taking
behavior from diversification. To ensure consistency among the risk taking variables, we
multiply the number of segments by -1 so that a higher value of this transformed variable proxies
for high risk taking. However, in descriptive statistics and univariate analyses we still use the
original positive number of segments so that the interpretation is more intuitive.
The second set of variables measures risk-taking incentives, proxied by the sensitivity of
CEO option portfolio to stock return volatility (vega). Following Core and Guay (2001), we use
two measures of vega, i.e., vega from newly granted options and total vega, where total vega is
the sum of vega from newly granted options, vega from unexercisable options, and vega from
12
exercisable options. 14 We use vega to capture CEO’s risk-taking incentives because theory
suggests that option compensation provides managers with incentives to take risk. For example,
Haugen and Senbet (1981) point out that option-based compensation mitigates a risk-averse
manager's reluctance to take risky investment projects (see also, Amihud and Lev, 1981; May,
1995). A number of empirical studies find that firm risk is positively related to managerial
incentives from option compensation (e.g., Guay, 1999; Rajgopal and Shevlin, 2002). Recent
evidence further suggests a relationship between managerial risk-taking incentives (Vega) and
the riskiness of corporate decisions (Coles, Daniel, and Naveen, 2006; Chava and Purnanandam,
2010). There is evidence that the cost of debt increases with the risk-taking incentives embedded
in a manager’s compensation (Daniel, Martin, and Naveen, 2004), indicating that debtholders
understand the relation between risk and option compensation. 15 Therefore, we investigate
whether debt covenants mitigate managers’ risk-taking incentives arising from option
compensation.
3.4 Control variables
Appendix 1 provides detailed definition of all variables used in the analyses. In the
regressions with risk-taking activities as dependent variables, we include the following variables
to control for the expected level of risk taking in the absence of restrictive covenants.
Leverage: Everything else equal, firms with high leverage have stronger incentive to engage
in risk taking. However, leverage could also be correlated with contracting mechanisms beyond
covenants that mitigate risk taking. We explicitly model one such mechanism, i.e., short maturity,
14
We study the vega from newly granted options separately because we expect it to provide CEOs the strongest
incentive to take risk. However, we do not test whether different vega responds to covenants differently because it is
not central to our hypothesis.
15
Bertsch and Mann (2005) find that large, positive, unexplained bonus and option awards are predictive of both
default and large rating downgrades and they conjecture that one possible reason is that performance-based
compensation induces managers to pursue high risk strategies.
13
in additional analyses. In addition, prior literature finds that debt financing can influence
investment. In particular, Lang, Ofek, and Stulz (1996) show that firms with high leverage tend
to invest less. Size: larger firms have more resources to invest in R&D but at the same time may
have less growth potential. Larger firms are also more likely to be diversified. ROA and Stock
return measure firm performance.
Sales growth and market-to-book: both proxy for investment opportunity set. Firms with
higher sales growth and market-to-book have more investment opportunities and therefore may
invest more in R&D for future growth. Intangibles: the level of existing intangibles could affect
the R&D expenditure in two ways. On one hand, firms with large intangibles tend to operate in
industries in which R&D is important. Therefore a higher level of intangibles predicts higher
R&D. On the other hand, firms with large intangibles may have less demand to make further
investment in R&D. Standard deviation of daily return controls for firm risk. Cash balance and
cash surplus captures the capacity to invest in R&D. Dividend cut indicates cash constraints and
thus less cash available for further investment in R&D.
In the regression with risk-taking incentives as dependent variables, we add several
additional control variables: delta, CEO turnover, and CEO tenure. We control for delta because
existing papers use delta as a weaker proxy for risk-taking incentives, i.e., higher delta provides
managers with less risk-taking incentives (Coles, Daniel, and Naveen, 2006; Brockman, Martin,
and Unlu 2009). We control for CEO turnover and tenure because CEOs with longer tenures are
more likely to be entrenched and risk averse (Berger et al., 1997).
4. Empirical Analyses
4.1 Descriptive statistics and univariate analyses
14
Table 1 presents the descriptive statistics. Panel A of Table 1 reports the distributions of the
variables used in the analyses. Our sample firms on average spend 2.24% of total assets on R&D
and have 1.81 segments. One percent increase in return volatility on average provides the CEOs
about $101,480 increase in the value of their total option compensation. The average covenant
intensity of the sample firms is 2.08, indicating that the average firm has at least two restrictive
covenants.
Penal B of Table 1 shows the detailed distributions of covenant intensity and its components.
There are 22.04% of the firms that have zero covenant intensity, meaning that these firms do not
have any of the six major restrictive covenants in their loan contracts. On the other end of the
distribution, there are 8.61% of the firms that have all six covenants present in their loan
contracts. When we decompose the covenant intensity into its components, we observe that the
secured covenant is the most frequently used covenant, with 57.97% of the firms using it. The
next popular covenants are financial covenants and dividend covenants, present in about 50% of
the borrowers’ loan contracts. The three sweep covenants, i.e., asset sweep, debt sweep, and
equity sweep, are less popular and used by 20% of the borrowers or less.
[Insert Table 1]
4.2 Risk taking and covenant intensity
Table 2 Panel A presents the univariate analysis. In Panel A of Table 2, we sort the sample
firms into seven covenant intensity groups, ranging from 0 to 6. There is a nearly monotonic
pattern that as covenant intensity increases, R&D and segment Herfindahl decreases, the number
of segment increases, and the two vegas decrease.
To test the how debt covenants impact borrower’s risk taking, we estimate the following
pooled regressions separately for risk-taking activities and risk-taking incentives.
15
Risk-taking activities =α0+ α1Cov_intensity + α2Leverage + α3Size + α4ROA + α5Intangibles +
α6Sale_Growth + α7M/B + α8Stock Ret + α9Std. Ret + α10Cash + α11Cash_Surplus +
α12Dividend_Cut +Firm/Year Fixed Effects + εit;
(1)
Risk-taking incentives =α0+ α1Cov_intensity + α2Leverage + α3Size + α4ROA + α5Intangibles +
α6Sale_Growth + α7M/B + α8Stock Ret + α9Std. Ret + α10Cash + α11Cash_Surplus +
α12Dividend_Cut + α13New Delta + α14Total_Delta + α15 Turnover + α16 Tenure +Firm/Year
Fixed Effects + εit;
(2)
Panle B of Table 2 reports the regression results. We find significantly negative associations
between covenant intensity and R&D, negative number of segments, and segment Herfindahl
index, consistent with the hypothesis that borrowers with more restrictive covenants invest less
in R&D and diversify more. For example, R&D has a significantly negative coefficient of -0.11,
indicating that firms that are restricted by all six covenants spend 0.66% (of total assets) less than
firms that are not restricted by any covenant. We also find covenant intensity has a significantly
negative association with vega from newly granted options and vega from all options, consistent
with managers bound by more restrictive covenants have lower risk-taking incentives from their
option compensation. Note that these results obtain after controlling for a variety of firm
characteristics that might affect risk taking, such as leverage, growth opportunities (as captured
by sales growth and M/B), performance (ROA and stock return); and a variety of firm
characteristics that might affect R&D investment and diversification, such as cash constraint
(cash balance, cash surplus, and dividend cut). Lastly, we include firm and year fixed effects and
report robust standard errors clustered by firm.
To mitigate the concern on model misspecification and address the selection bias, we
perform propensity score matching in Panel C of Table 2. We first construct a propensity score
as the predicted probability from a logistic regression of a high covenant intensity dummy
(defined as one if covenant intensity is greater than or equal to 2, the sample median) on the
16
covariates in equation (1) or (2). We then match firms in high covenant intensity group with
firms in low covenant intensity group with the closest propensity score. Out of 37,456
observations, we are able to match 29,550 observations, with 14,775 observations each for high
and low covenant intensity group. The insignificant difference between propensity scores
indicates that the match is very close. Untabulated results indicate that after matching our
covariates are balanced between high covenant and low covenant firms. After matching firms on
their propensity scores, we find that firms with high covenant intensity have significantly lower
risk taking than firms with low covenant intensity, consistently across all five measures of risk
taking.16 For example, firms with high covenant intensity on average spend 2.06% of total assets
on R&D while firms with low covenant intensity but everything else similar spend 2.54%. This
analysis further confirms that covenant intensity is negatively related to risk taking.
[Insert Table 2]
4.3 Endogeity between covenants and risk taking
The negative association between covenants and risk taking in the cross section could be
driven by endogeneity. Specifically, it is possible that more restrictive covenants and lower risk
taking are simultaneously determined by underlying firm characteristics, or firms that expect to
have lower risk taking choose to have more restrictive covenants. To address these concerns, we
perform three distinct tests, that is, a change specification, a simultaneous equation, and a bond
market reaction test.
4.3.1 The change specification
If an increase in covenant intensity is accompanied by a decrease in risk taking, then it is
more likely that the increase in covenant intensity causes the decrease in risk taking. To test this,
16
The results from the propensity score matching are robust to the choice of the closeness of the match and the
choice of the cutoff for high covenant intensity.
17
we run two time series regressions. The first regression compares the risk taking in the years
prior to loan initiation to the risk taking in the year of loan initiation. This test requires the
borrowers to adjust their risk taking rather quickly after the covenants are in place. The second
regression compares the average risk taking over firm years without covenant restrictions to the
average risk taking over firm years with covenant restrictions. This second test weakens the
requirement on the speed of risk adjustment.
Panel A of Table 3 report the results of the first regression. We find that in the year of loan
initiation, segment diversification increases and total vega decreases compared to the year before.
In addition, the increase in diversification and decrease in vega is a function of the restrictiveness
of the covenants. For R&D and vega from newly granted option, we also find a negative relation
with the increase in covenant intensity; however, the coefficients are not statistically significant.
The evidence indicates that the introduction of new covenant restrictions have an immediate
impact on the diversification risk and managers’ risk-taking incentives.
Panel B of Table 3 reports the results of the second regression. When we relax the
requirement on the speed of risk adjustment, we find that all risk taking variables have a
significantly lower value in the years with covenant restrictions than in the years without the
restrictions, and the reduction in risk taking increases with the restrictiveness of the covenants.
Taken together, the time series regressions suggest that an increase in covenant intensity is
accompanied by both an immediate and a long-term reduction in risk taking, thus partially
alleviating the concern that covenant intensity and risk taking are endogenously determined.
[Insert Table 6]
4.3.2 Simultaneous equations
18
In this section, we estimate a system that models covenant intensity and risk-taking as jointly
determined. Concerns about endogeneity arise from the supposition that the firm or managerial
risk-taking and covenant restrictions might be simultaneously determined by some underlying
firm characteristics. If this is the case, then ordinary least squares estimation can lead to biased
coefficient estimates. Our two stage least square system is specified as follows.
When risk-taking activities are the dependent variables,
Covenant Intensity =γ0+ γ1Lcov + γ2Lmatu + γ3Lspread + γ4Size+ γ5Leverage + γ6ROA +
γ7Intangibles + γ8Std. Return + γ9Logamt + γ10LogSpread + γ11Log Matu + γ12PP +
γ13Rating_aaa + γ14Rating_aa + γ15Rating_a + γ16Rating_bbb + γ17Rating_bb+ γ18Rating_b +
γ19Rating_ccc + γ20Rating_cc + γ21R&D + γ22Log_nseg + γ23seg_herf +Firm/Year Fixed Effects
+ εit;
(3)
Risk-taking activities =α0+ α1Cov_intensity_predicted + α2Leverage + α3Size + α4ROA +
α5Intangibles + α6Sale_Growth + α7M/B + α8Stock Ret + α9Std. Ret + α10Cash +
α11Cash_Surplus + α12Dividend_Cut +Firm/Year Fixed Effects + εit;
(4)
When risk-taking incentives are the dependent variables,
Covenant Intensity =γ0+ γ1Lcov + γ2Lmatu + γ3Lspread + γ4Size+ γ5Leverage + γ6ROA +
γ7Intangibles + γ8Std. Return + γ9Logamt + γ10LogSpread + γ11Log Matu + γ12PP +
γ13Rating_aaa + γ14Rating_aa + γ15Rating_a + γ16Rating_bbb + γ17Rating_bb+ γ18Rating_b +
γ19Rating_ccc + γ20Rating_cc + γ21New Vega + γ22Total Vega +Firm/Year Fixed Effects + εit; (5)
Risk-taking Incentives =α0+ α1Cov_intensity_predicted + α2Leverage + α3Size + α4ROA +
α5Intangibles + α6Sale_Growth + α7M/B + α8Stock Ret + α9Std. Ret + α10Cash +
α11Cash_Surplus + α12Dividend_Cut + α13 New_Delta + α14Total_Delta + α15Turnover +
α16Tenure +Firm/Year Fixed Effects + εit;
(6)
We use bank covenant preference as our main instrumental variable. We measure bank
covenant preference as the average covenant intensity of all borrowers from the same bank. If a
loan package involves multiple banks, which is usually the case in syndicated loans, we further
take the average of the bank-specific covenant intensity for the multiple banks involved. While
covenants are potentially endogenous determined with other loan and firm characteristics, it is
less likely that the average level of covenant restrictiveness imposed by a bank to all its
19
borrowers is endogenously determined with a particular borrower’s covenant restrictiveness.
Thus, we treat lender covenant intensity as exogenous. In addition, we compute bank average
maturity and bank average spread as two additional instruments to further capture the risk
preference of the banks that might affect their covenant designs.
We rely on earlier theoretical studies to guide our selection of control variables in the first
stage regression. Studies have shown that the debt contract terms such as borrowing amount
(Logamt), spread (Logspread), and maturity (LogMatu) are important determinants of how
restrictive covenants are in debt contracts. In addition, we include firm long-term debt rating and
other firm characteristics.
Panel A of Table 4 presents the first-stage regression. We find that bank covenant preference
has a substantial impact on the individual firm covenant intensity. The correlation between bank
covenant preference (bank average spread) and borrowers’ covenant intensity is 0.40 (0.16).
Collectively, the three instruments can explain 17% if the variation in firm covenant intensity.
We also find that more restrictive covenants exist for borrowers with smaller total assets, lower
leverage, higher intangibles, and higher return volatility. In addition, more restrictive covenants
are accompanied by larger loan amount, higher loan spread, longer loan maturity, and
performance pricing clause. We do not find that risk taking variables explain the restrictiveness
of the covenants. Panel B of Table 4 presents the second stage regression where the treatment
variable is the predicted covenant intensity from the first stage. We observe significantly
negative coefficients on the predicted covenant intensity for all five risk taking variables.
Therefore, the 2sls results confirm the earlier results and demonstrate that debt covenant is an
effective mechanism that mitigates borrowers’ risk taking.
[Insert Table 4]
20
4.3.3 Bond market’s reaction to additional covenant restrictions
Note that the two tests above do not rule out the possibility that only borrowers that can
afford lower risk taking in the future agree on taking restrictive covenants. In other words,
covenant intensity may capture the expected reduction in risk taking. To address this possibility,
we exploit the bond market’s reaction to additional covenant restrictions introduced by a new
bank loan. If the firm’s risk taking and the restrictiveness of covenants are in equilibrium, then
additional covenant restrictions would deviate from this equilibrium and thus negatively affect
bond value. If additional covenants introduce lower risk taking instead of merely reflecting an
expected reduction in risk taking, then the externality would extend to the current bondholders
and lead to a positive bond market reaction.
We begin our sample selection with the new bank loans from 2002 to 2008, and we measure
the change in covenant intensity by taking the difference between the covenant intensity of the
new loan in its starting year and that of the existing loan. We obtain daily bond prices from
TRACE. Our sample includes 1,650 firm year observations.
We use two measures of abnormal bond returns, treasury adjusted and factor-adjusted,
measured over the (-3, 1) where 0 is the new loan initiation date. We choose this window
because the event days are the initiation dates of new loan contracts and there is hardly any news
at day 0 if there is any preceding announcement of the new loan. Nevertheless, our results are
robust to a variety of different windows such as (-2, 2). We first control for the covenant
intensity of the existing loans to capture the overall restrictiveness of debt covenants prior to the
new loan. We also control for firm characteristics such as size, leverage, ROA, R&D, speculative
grade dummy, and new loan characteristics including spread and maturity. In addition, we
21
control for bond liquidity using average daily trading volume during the event window as the
proxy.
Table 5 presents the regression estimates. We find that the estimated coefficients for change
in covenant intensity are positive and significant across raw returns, treasury-adjusted abnormal
return, and factor-adjusted abnormal return. The results show that bondholders in general react
positively to the increase of covenant intensity introduced by the issue of new loans, suggesting
that additional covenant restrictions do not simply capture an expected reduction in risk taking
that should be already embedded in the bond return.
[Insert Table 5]
Taken together our three tests above alleviate the concern that endogeneity drives the
negative association between risk taking and covenant intensity.
4.4 Additional Analyses
4.4.1 The effect of leverage
To examine whether leverage drives the negative relation between risk taking and covenant
intensity, in Table 6 we sort the sample into four groups by the median leverage and median
covenant intensity. We find that firms with low leverage but high covenant intensity have less
risk-taking activities (except for R&D) and risk-taking incentives than firms with high leverage
but low covenant intensity. This suggests that the negative relation between risk taking and
covenant intensity is unlikely driven by leverage.
[Insert Table 6]
4.4.2 Risk taking after covenant violation
While we provide evidence that firms reduce risk taking in response to covenant restrictions,
it is also likely that firm further reduce risk taking after covenants are violated and debt contracts
22
are renegotiated. Consistent with covenant violation triggers the transfer of control right to
creditors, Chava and Robert (2008) find that firms have lower investments after covenant
violations. Robert and Sufi (2009) document that firms reduce net debt issuance after covenant
violations. Following the arguments in these two papers, we test whether firms also engage in
less risk taking after covenant violations, as required by newly negotiated contract terms, or
tightened covenants.17
We test the impact of covenant violation on risk taking by comparing the risk taking levels
before and after covenant violations. We first merge our sample with the covenant violation data
used in Nini, Smith, and Sufi (2009) with our sample.18 Note that we restrict this analysis to
firms that have reported at least one covenant violation during 1996-2008. 19
Panel A of Table 7 plots the risk-taking activities and incentives four years before and four
years after covenant violation. There is an observable decline in all five risk taking proxies after
covenant violation.20 Panel B of Table 7 report the multivariate regression that regresses risk
taking proxies on five dummy variables representing the year of covenant violation and each of
the four years after covenant violation, covenant intensity, and control variables. We find that
there is a significantly decline in R&D investment and a significant increase in segment
diversification in year of and the four years after covenant violations. However, we do not find a
significant decline in the two vega measures after including the control variables. Overall, our
17
Even though lenders often amend the contract to loosen the violated covenants so that the borrower gets out of
technical default, such amendment is costly and often contain other stronger restrictions on borrowers (Nini, Smith,
and Sufi, 2009).
18
We thank the authors in Nini, Smith, and Sufi (2009), Robert and Sufi (2009), and Nini, Smith, and Sufi (2009)
for making their data publicly available. We note that since their original data are quarterly observations, we purge
the quarterly data into annual data before merging with our sample. Specifically, we define a firm year to be in
violation as long as the firm reports at least one covenant violation in the year.
19
Our results are similar if we include all firm years in our sample so that the risk taking level of firms that do not
have any covenant violations enter the analysis as baseline.
20
Note that higher number of segments indicates lower risk.
23
analyses demonstrate that covenants not only prevent risk taking before covenant violations, but
also further curb risk taking after covenant violations.
[Insert Table 7]
4.4.3 Maturity vs. covenant intensity
Short maturity is a commonly used alternative mechanism to mitigate the agency cost of debt
(Barnea, Haugen, and Senbet, 1980; Leland and Toft, 1996). Brockman, Martin, and Unlu (2009)
find evidence consistent with that short maturity constrain managerial risk preference as proxied
by vega and delta. Unlike state contingent covenants, short maturity allows frequent
renegotiation and thereby gives lenders more flexibility. However, covenants provide lenders
incentives to monitor because lenders need to gather information on covenants, while short term
debt does not provide such incentives (Rajan and Winton, 1995). Therefore, ex ante, it is unclear
whether covenants or short maturity is more effective in reducing the agency cost of debt.
To ensure that our results are not driven by short term debt, and to provide insights on the
effectiveness of both mechanisms, we further incorporate short maturity into the analysis. First,
we add loan maturity to the regression of risk taking on covenant intensity. A negative
coefficient on loan maturity is consistent with short maturity mitigating risk taking.21 However,
Table 8 shows that the coefficient on maturity is only significantly negative in the regression of
the number of segments and segment Herfindahl index, while the coefficients on covenant
intensity are consistently negative for all six risk taking proxies after controlling for maturity.
These results are consistent with covenants dominate short maturity in mitigating borrowers’ risk
taking.
[Insert Table 8]
21
We multiply loan maturity by -1 so that a negative coefficient on this transformed maturity variable is consistent
with short maturity reducing risk taking.
24
4.4.4 The role of bond covenants
We study loan covenants in our primary analysis because loan covenants are more binding
due to lower renegotiation cost. However, as an additional analysis, we replace the loan
covenants with bond covenants to examine whether bond covenants are also effective in
mitigating risk taking. We collect bond covenants from Mergent Fixed Income Securities
Database (FISD). We measure bond covenant intensity following Billett, King, and Mauer
(2007). Specifically, bond covenant intensity is defined as the sum of fifteen dummy variables
capturing the existence of restrictions on dividend payment, share repurchase, funded debt,
subordinate debt, senior debt, secured debt, total leverage, sales and lease-back, stock issuance,
asset sale, investment policy, and merger, as well as rating and net worth triggered covenants,
cross-default provisions, and poison put. Table 9 reports that bond covenant intensity also has a
significant negative association with all five risk taking proxies, after controlling for other firm
characteristics.
[Insert Table 9]
5. Conclusion
Agency theory suggests that the debtholder-stockholder conflicts result in actions undertaken
by shareholders that reduce the value of debt, including risk taking after the debt is the place. In
this paper, we examine whether restrictive covenants are effective in mitigating the borrowers’
risk-taking behavior. We find that firms with more restrictive covenants spend less in R&D,
diversify more, and their CEOs have less less risk-taking incentives from option compensation.
These results obtain after addressing the endogeneity between risk taking and covenants. In
particular, we find that the bond market reacts positively to additional loan covenants around the
loan initiation days, suggesting that additional covenants do not simply capture expected
25
reduction in risk taking. Overall, our findings provide important evidence that debt covenants are
effective in mitigating risk taking, an agency problem that cannot be explicitly contracted on.
Our study is the first to provide comprehensive evidence that debt covenants mitigate risktaking activities and managerial risk-taking incentives. In addition, our study adds to the
literature on how debtholder-stockholder conflict impacts operating and investing decisions (Nini
et al, 2009). Our findings highlight the important role of debt covenants in mitigating agency
costs of debt.
Our findings show that closer attention need to be paid on the design of debt covenants to
better mitigate borrowers’ incentives to expropriate wealth from debtholders. While corporate
risk taking behavior is a complex decision, debt covenants seem very important in shaping this
decision. Overall, our findings have important implications on optimal debt contract design and
debt governance.
26
References
Amihud, Y., Lev, B., 1981. Risk reduction as a managerial motive for conglomerate mergers. Bell Journal
of Economics 12, 605–617.
Barnea, Amir, Robert A. Haugen, and Lemma W. Senbet, 1980, A rationale for debt maturity structure
and call provisions in the agency theoretic framework, Journal of Finance 35, 1223-1234.
Begley, J., Feltham, G., 1999, An empirical examination of the relation between debt contracts and
management incentives, Journal of Accounting & Economics 27, 229-259.
Beneish, M., Press, E., 1993. Costs of technical violation of accounting- based debt covenants. The
Accounting Review 68, 233–257.
Berger, P G., Ofek, E., and Yermack, D.L., 1997. Managerial entrenchment and capital structure. Journal
of Finance 52, 1411-1438.
Bertsch, K., Mann, C., 2005. CEO compensation and credit risk. Moody’s Investor Service Global Credit
Research, Special Comments July 2005.
Bradley, M., Roberts, M.R., 2004. The structure and pricing of corporate debt covenants. Working paper,
Duke University.
Billett, M., King, D., and Mauer, D., 2007. Growth opportunities and the choice of leverage, debt
maturity, and covenants. Journal of Finance 62, 697-730.
Brockman, P., Martin, X., Unlu, E., 2009. Executive compensation and the maturity structure of corporate
debt. Journal of Finance, forthcoming.
Chava S., Kumar, P., Warga, A., 2009. Managerial agency and bond covenants. The Review of Financial
Studies, forthcoming.
Chava, S., Roberts, M.R., 2008. How does financing impact investment? The role of debt covenants.
Journal of Finance 63, 2085-2121.
Chava, S., Purnanandam, A., 2010. Is default risk negatively related to stock returns? The Review of
Financial Studies 23, 2523-2559.
Coles, J.L., Daniel, N.D., Naveen, L., 2006. Managerial incentives and risk-taking. Journal of Financial
Economics 79, 431-468.
Core, J., Guay, W., 2002. Estimating the value of employee stock option portfolios and their sensitivities
to price and volatility. Journal of Accounting Research 40, 613-630.
Daniel, N.D., Martin, J.S., Naveen, L., 2004. The Hidden Cost of Managerial Incentives: Evidence from
the Bond and Stock Markets. Working paper.
DeAngelo, H., and L. DeAngelo, 1990. Dividend policy and financial distress: an empirical investigation
of troubled NYSE firms. Journal of Finance 45, 1415-1432.
27
Demiroglu, C., James, C., 2007. The information content of bank loan covenants. Proceedings, Federal
Reserve Bank of Chicago, issue May, 148-182.
Dichev, I., Skinner, D., 2002. Large sample evidence on the debt covenant hypothesis. Journal of
Accounting Research 40, 1091–1123.
Garleanu, N., Zwiebel, J., 2008. Design and renegotiation of debt covenants. Review of Financial Studies
22, 749-781.
Grossman S, Hart O. 1986. The costs and benefits of ownership: A theory of vertical and lateral
integration. Journal of Political Economy 94: 691-719.
Hart, O., 1995. Firms, Contracts, and Financial Structure. Oxford University Press.
Hart O, Moore J. 1988. Incomplete Contracts and Renegotiation. Econometrica 56: 755-785.
Hart O, Moore J. 1990. Property rights and the nature of the firm. Journal of Political Economy 98: 11191158.
Haugen R.A., Senbet, L.W., 1981. Resolving the agency problems of external capital through options.
Journal the Finance 36, 629-647.
Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs, and ownership
structure. Journal of Financial Economics 3, 305–360.
Johnson, S. A., 2003. Debt maturity and the effects of growth opportunities, managerial discretion, and
the security issue decision, Review of Financial Studies 16, 209-236.
Kothari, S., Laguerre, T., Leone, A., 2002. Capitalization versus expensing: evidence on the uncertainty
of future earnings from capital expenditures versus R&D outlays. Review of Accounting Studies 7,
355-382.
Leland, Hayne E., and Klaus Bjerre Toft, 1996, Optimal capital structure, endogeneous bankruptcy, and
the term structure of credit spreads, Journal of Finance 51, 987-1019.
Lewellen, Wilbur G., 1971, A pure financial rationale for the conglomerate merger, Journal of Finance
26, 527–537.
Malitz, I., 1986. On financial contracting: the determinants of bond covenants. Financial Management 15,
18-25.
May, D., 1995. Do managerial motives influence firm risk reduction strategies? Journal of Finance 50,
1291–1308.
McDaniel, M., 1986. Bondholders and corporate governance. Business Lawyer 41, 413-460.
Myers, S. C., 1977. Determinants of corporate borrowing, Journal of Financial Economics 5:147-145.
Nini, G., Smith, D.C., Sufi, A., 2009. Creditor control rights and firm investment policy. Journal of
Financial Economics 92, 400-420.
28
Rajan, Raghuram, and Andrew Winton, 1995, Covenants and collateral as incentives to monitor, Journal
of Finance 50, 1113-1146.
Rajgopal, S., Shevlin, T., 2002. Empirical evidence on the relation between stock option compensation
and risk taking. Journal of Accounting and Economics 33, 145–171.
Roberts, M.R., Sufi, A., 2009. Control rights and capital structure: an empirical investigation. Journal of
Finance 64, 1657-1695.
Shumway, T., 2001. Forecasting bankruptcy more accurately: a simple hazard model. Journal of Business
74, 101-124.
Smith, C., 1993. A perspective on accounting-based debt covenant violations. The Accounting Review 68,
289–303.
Smith C.W., Warner, J.B., 1979. On financial contracting: an analysis of bond covenants. Journal of
Financial Economics 7, 117-161.
Tirole, J., 1999. Incomplete contracts: where do we stand? Econometrica 67, 741-781.
Tirole, J., 2006. The Theory of Corporate Finance. Princeton University Press, Princeton.
29
Appendix A. Variable definitions
A.1. Risk taking variables
R&D
Nseg
Lognseg
Segherf
Vega new
Vega total
=
=
=
=
=
Research and development expenditure scaled by assets (Compustat XR&D/AT). Missing values are coded as 0.
The number of business segments. For single segment firm, the number of segments is equal to one.
-1 × The natural log of the number of business segments. For single segment firm, the number of segments is equal to one.
Sum (segment sale/firm sale)2. For single segment firm, seg_herf equals to one.
Vega for newly granted option, where vega is the change in the executive’s option portfolio value (in thousand $) for a 1%
change in the annualized standard deviation of stock returns.
= The sum of vega for newly granted option, vega for exercisable options, and vega for non-exercisable options.
A.2. Firm performance variables
ROA
Sales growth
Tobin’s Q
= Earnings before interest, tax, depreciation and amortization divided by total assets (Compustat EBITDA/AT)
= Change in sales divided by total assets (Compustat (SALEt-SALEt-1)/AT)
= Market value of equity plus book value of debt divided by book value of asset (Compustat ((PRCC_F*CSHO)+AT-CEQ)
/AT)
A.3. Firm characteristics
Leverage
Assets (million $)
Size
M/B
Stock return
Dividend cut
PPE
Std. return
Cash balance
Cash surplus
Highrisk
Rating
Rating dummies
Speculative
Delta new
Delta total
Turnover
Tenure
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
Total liabilities divided by total assets (Compustat LT/AT) at the end of the fiscal year.
Total assets (Compustat AT) at the end of the fiscal year.
Natural log of total assets.
Market value of equity divided by book value of equity (Compustat ((PRCC_F*CSHO) /CEQ).
Annual stock return over the fiscal year.
Dummy variable equal to one if this year’s dividend (Compustat DVC) is lower than last year’s.
Total Property, Plant, and Equipment divided by total assets (Compustat PPENT/AT).
StandaR&D deviation of daily stock return over the fiscal year.
Cash balance divided by total assets (Compustat CHE/AT).
Cash from assets-in-place divided by total assets (Compustat (OANCF-DPC+XR&D)/AT)
A dummy variable equal to one if the Shumay bankruptcy score is in the top quartile for a firm year, and zero otherwise.
S&P domestic long term issuer credit rating (Compustat SPLTICRM). We code AAA as 1 and C as 21.
Dummy variables equal to one for AAA, AA, A, BBB, BB, B, CCC, and CC rating, respectively, and zero otherwise.
A dummy variable equal to one if for non-investment grade ratings (i.e., RATING is lower than BBB-).
Delta for newly granted options, where delta is the change in the executive’s option portfolio value (in thousand $) for a
1% change in stock price.
= The sum of Delta for newly granted options, delta for exercisable options, and delta for not exercisable options.
= A dummy variable equal to one if the firm changes its CEO and 0 otherwise.
= The number of years that the CEO has been in the position.
A.4. Loan and bond characteristics
30
Covenant intensity
Lag Covenant intensity
Δ Covenant intensity
Maturity
Logmaturity
Spread
Logspread
Loan amount (million $)
Logamount
Performance pricing
Log(years to maturity)
Raw bond return
Treasury-adjusted
abnormal bond return
Factor-adjusted abnormal
bond return
Volume
V0-V4
= The sum of the six dummy variables: a) a dummy variable equal to one if there exists a debt sweep covenant; b) a dummy
variable equal to one if there exists an equity sweep covenant; c) a dummy variable equal to one if there exists an asset
sweep covenant; d) a dummy variable equal to one if there exists a dividend restriction; e) a dummy variable equal to one
if any of the facility in the package is secured; f) a dummy variable equal to one if there exists two or more financial
covenants. If a firm has multiple packages outstanding at the same time, then the highest covenant intensity measure is
chosen.
= The covenant intensity of the existing loan when a new loan is initiated.
= The difference between the covenant intensity of a new loan and that of the existing loan.
= Loan maturity in months.
= The natural log of one plus the loan maturity.
= Loan spread measured in basis points over LIBOR.
= The natural log of loan spread.
= Loan amount in million $s.
= The natural log of loan amount.
= A dummy variable equal to one if the loan contract contains performance pricing clause.
= -1 × The natural log of years to maturity, where year to maturity is the distance between current year and the year in which
the loan matures.
= Daily bond return from TRACE average daily trading prices, accumulated over the event window.
= Raw bond return minus maturity-matched treasury bond return, accumulated over the event window.
= Residual from regressing raw bond return on five Fama-French factors, accumulated over the event window.
= Bond daily trading volume from TACE, in million dollars. If a firm has multiple bonds trading on the same day, then
volume is the total trading volume of those bonds.
= Dummy variables indicating the year of covenant violation (V0) and each of the four subsequent years (V1-V4).
A.5. Instrumental variables
Lcov
Lmatu
Lspread
= Average covenant intensity of the lender(s). We first calculate average covenant intensity of each bank in the Dealscan
database. Then we further calculate the average covenant intensity of all banks in the syndication.
= Average loan maturity of the lenders, computed in a similar fashion as Lcov.
= Average loan spread of the lenders, computed in a similar fashion as Lcov.
31
Table 1
Summary statistics
The full sample is consisted of 37,456 observations from the intersection of Dealscan and Compustat. The reduced sample is consisted of 13,086 observations
from further merging the full sample with ExcuComp. All variables are defined in Appendix A. All variables except covenant intensity, performance pricing, and
ratings are winsorized at the 1st and 99th percentile levels. Panel A shows the descriptive statistics of all variables used in the analyses. Panel B presents the
distribution of the covenant intensity measure and its six components.
Panel A Descriptive statistics
Variable
N
Mean
Std
Q1
Median
Q3
R&D (%)
37,456
2.24
5.03
0.00
0.00
1.80
Nseg
37,456
1.81
1.67
1.00
1.00
1.00
Segherf
37,456
0.90
0.22
1.00
1.00
1.00
ROA (%)
37,456
11.47
10.94
7.50
12.14
17.10
Sales growth (%)
37,456
10.12
27.41
-0.18
7.88
20.93
Tobin’s Q
37,456
1.64
0.98
1.06
1.33
1.84
Vega new (in thousand $s)
13,086
29.85
59.09
0.00
8.10
29.93
Vega total (in thousand $s)
13,086
101.48
200.56
0.00
28.11
103.59
37,456
2.08
1.84
1.00
2.00
3.00
Dependent Variables
Treatment Variable
Covenant intensity
Control Variables
Leverage
37,456
0.56
0.20
0.42
0.57
0.70
Total assets (in million $s)
37,456
3,426
27,120
108
404
1,644
Intangible assets
37,456
0.13
0.17
0.00
0.05
0.20
Cash balance
37,456
0.09
0.13
0.01
0.04
0.12
Cash surplus
37,456
0.04
0.10
-0.01
0.04
0.09
Dividend cut
37,456
0.10
0.31
0.00
0.00
0.00
M/B
37,456
2.74
3.03
1.18
1.88
3.09
Stock Return
37,456
0.14
0.60
-0.24
0.05
0.36
32
Std. return
37,456
0.03
0.02
0.02
0.03
0.04
Delta_new (in thousand $s)
13,086
38.73
78.97
0.00
10.80
39.19
Delta_total (in thousand $s)
13,086
259.05
489.88
26.34
90.00
261.33
CEO turnover
13,086
0.11
0.31
0
0
0
CEO tenure
13,086
6.49
7.22
2.00
4.00
9.00
Maturity
37,456
46.86
22.75
33.00
47.00
60.00
Spread
37,456
184.25
124.36
75.00
162.50
262.50
Loan amount (million $)
37,456
288.00
581.00
20.00
85.00
275.00
Performance pricing
37,456
0.44
0.50
0.00
0.00
1.00
Rating
13,781
10.23
3.33
8.00
10.00
13.00
(BBB+)
(BBB-)
(BB-)
Instrumental Variables
Lcov
37,284
1.41
0.57
1.05
1.33
1.66
Lmatu
37,284
50.08
7.53
47.11
50.34
53.29
Lspread
37,284
182.92
62.12
148.36
164.77
191.70
Panel B The distribution of covenant intensity
Covenant
intensity
0
1
2
3
4
5
6
Total
Covenant intensity
Number of
Percent (%)
firm-years
8,254
22.04
9,603
25.64
6,360
16.98
5,928
15.83
1,892
5.05
2,193
5.85
3,226
8.61
37,456
Components of covenant intensity
Covenant dummy
Number of
Percent (%)
firm-years
Asset sweep
7,626
20.36
Equity sweep
5,557
14.84
Debt sweep
5,646
15.07
Dividend
18,634
49.75
Secured
21,713
57.97
>= two financial covenant
18,820
50.25
100
33
Table 2
Risk taking and covenant intensity: Pooled regression
Panel A presents the regression estimates from regressing risk taking variables on covenant intensity. The dependent variables are research and development
expenditures scaled by assets (R&D), -1 × logarithm of the number of segments (Lognseg), segment Herfindahl index (Segherf), the dollar change in CEO’s
newly granted option value or total option value for a 0.01 change in standard deviation of returns (Vega new or Vega total). The treatment variable is the
covenant intensity, which is the sum of six dummy variables capturing the existence of debt sweep, asset sweep, equity sweep, dividend, security, and financial
covenant. Control variables are defined in Appendix A. P-values are based on robust Z-statistics adjusted for firm level clustering. All regressions include firm
and year fixed effects. Panel B reports the mean difference in risk taking measures between high and low covenant intensity groups after propensity score
matching, where high covenant intensity group is consisted of firms with covenant intensity >= 2. The propensity score is the predicted probability from
regressing high covenant intensity dummy on the covariates in Panel A.
Panel A Risk-return tradeoff across various covenant intensity groups
Covenant intensity
0
1
2
3
4
5
6
N
8,254
9,603
6,360
5,928
1,892
2,193
3,226
Risk-taking activities
R&D (%)
Nseg
2.22
1.65
2.38
1.46
2.89
1.97
2.71
1.86
1.12
1.99
1.16
2.28
1.15
2.51
Risk-taking incentives
N
Vega_new
Vega_total
3,466
42.07
144.68
2,526
32.05
109.38
2,647
26.95
93.86
1,781
18.10
61.66
681
27.27
85.37
922
22.64
69.32
1,063
19.58
65.80
Segherf
0.93
0.94
0.88
0.89
0.87
0.84
0.81
Panel B Regressions of risk taking measures on covenant intensity
Intercept
Covenant intensity
Leverage
Size
ROA
Intangibles
Sales growth
M/B
Stock return
R&D
Coeff.
p-value
3.40
<.0001
-0.11
<.0001
-1.24
<.0001
-0.10
<.0001
-0.17
<.0001
-1.60
<.0001
0.01
<.0001
0.21
<.0001
-0.36
<.0001
Risk-taking activities
Lognseg
Coeff.
p-value
0.19
0.00
-0.06
<.0001
0.16
<.0001
-0.10
<.0001
0.00
<.0001
-0.50
<.0001
0.00
<.0001
0.00
0.96
-0.01
0.29
Segherf
Coeff.
p-value
1.09
<.0001
-0.02
<.0001
0.05
<.0001
-0.03
<.0001
0.00
<.0001
-0.18
<.0001
0.00
<.0001
0.00
0.92
0.00
0.07
34
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
p-value
-26.63
<.0001 -131.10
<.0001
-0.61
0.00
-3.04
0.00
2.33
0.35
8.17
0.55
5.40
<.0001
22.47
<.0001
0.26
<.0001
0.58
0.04
3.88
0.08
22.56
0.08
-0.20
0.25
-2.84
0.00
0.00
0.91
-0.15
0.03
-19.45
<.0001 -64.96
<.0001
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year fixed
effect
N
R2
12.25
5.62
14.43
-0.41
<.0001
<.0001
<.0001
<.0001
1.04
-0.23
-0.23
0.01
<.0001
<.0001
<.0001
0.42
-0.01
-0.04
-0.07
0.00
Included
Included
Included
37,456
55.2%
37,456
19.2%
37,456
14.9%
0.93
0.01
<.0001
0.74
-91.37
0.15
1.35
1.86
0.60
0.00
0.00
1.26
Included
0.01
0.97
0.80
0.02
<.0001
0.57
0.39
0.18
13,086
74.9%
-237.31
35.99
40.86
10.39
1.17
0.12
0.28
-23.73
Included
13,086
59.3%
Panel C Comparison of risk taking measures between high and low covenant intensity group after propensity score matching
N
Low covenant
intensity
High covenant
intensity
High – Low
(p value)
14,775
14,775
Risk-taking activities
Propensity
R&D
Log_nseg
score
0.51
2.54
-0.23
Seg_herf
N
0.93
4,238
4,238
0.51
2.06
-0.44
0.88
0.00
(0.94)
-0.48
(0.00)
-0.21
(0.00)
-0.05
(0.00)
35
Risk-taking incentives
Propensity
Vega
score
_new
0.53
28.80
Vega
_total
98.07
0.53
26.61
90.70
0.00
(0.95)
-2.19
(0.07)
-7.37
(0.07)
0.09
0.04
0.07
0.00
<.0001
<.0001
<.0001
<.0001
Table 3
Risk taking and covenant intensity: A change specification
This table presents the regression estimates from regressing risk taking variables on covenant intensity. The sample used in Panel A is consisted of the year prior
to the loan initiation and the year of loan initiation. Panel B is based on a larger sample including all the years prior to the loan initiation and the years of and
after the loan initiation. The dependent variables are research and development expenditures scaled by assets (R&D), -1 × logarithm of the number of segments
(Lognseg), segment Herfindahl index (Segherf), the dollar change in CEO’s newly granted option value or total option value for a 0.01 change in standard
deviation of returns (Vega new or Vega total). The treatment variable is the covenant intensity, which is the sum of six dummy variables capturing the existence
of debt sweep, asset sweep, equity sweep, dividend, security, and financial covenant. In the years prior to loan initiation, covenant intensity is defined to be 0.
Control variables are defined in Appendix A. P-values are based on robust Z-statistics adjusted for firm level clustering. All regressions include industry firm and
year fixed effects. Panel B reports the mean difference in risk taking measures between high and low covenant intensity groups after propensity score matching,
where high covenant intensity group is consisted of firms with covenant intensity >= 2. The propensity score is the predicted probability from regressing high
covenant intensity dummy on the covariates in Panel A.
Panel A Loan initiation year compared to the previous year
Intercept
Covenant intensity
Leverage
Size
ROA
Intangibles
Sales growth
M/B
Stock return
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year
fixed effect
N
R2
R&D
Coeff.
p-value
4.04
<.0001
-0.06
0.20
-2.17
<.0001
-0.04
0.41
-0.24
<.0001
-3.20
<.0001
0.01
<.0001
0.19
<.0001
-0.38
0.01
9.37
0.05
5.77
<.0001
17.48
<.0001
-0.49
0.00
Risk-taking activities
Lognseg
Coeff.
p-value
0.11
0.14
-0.04
<.0001
0.16
<.0001
-0.06
<.0001
0.00
0.06
-0.51
<.0001
0.00
0.00
0.00
0.75
0.00
0.91
-0.01
0.97
-0.12
0.06
-0.06
0.25
0.03
0.19
Segherf
Coeff.
p-value
1.04
<.0001
-0.01
<.0001
0.04
0.00
-0.01
<.0001
0.00
0.39
-0.16
<.0001
0.00
0.06
0.00
0.65
0.00
0.34
-0.12
0.32
-0.02
0.18
0.00
0.95
0.00
0.93
Included
Included
Included
4,260
58.5%
4,260
11.8%
4,260
8.9%
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
p-value
-13.17
0.20
-62.49
0.07
-0.66
0.13
-4.63
0.00
11.34
0.12
27.66
0.19
3.49
<.0001
12.17
<.0001
0.23
0.05
0.83
0.03
5.25
0.40
54.41
0.03
-0.38
0.35
-1.39
0.11
0.06
0.07
-0.10
0.29
-18.71
<.0001 -53.04
<.0001
-269.13
0.01
-382.90
0.15
9.07
0.21
32.70
0.22
12.27
0.33
64.60
0.07
0.88
0.66
-0.25
0.97
0.53
<.0001
0.66
<.0001
0.00
0.75
0.14
<.0001
1.85
0.47
-11.04
0.16
-0.01
0.88
-0.09
0.74
Included
Included
1,417
73.9%
36
1,417
59.0%
Panel B All years included
Intercept
Covenant intensity
Leverage
Size
ROA
Intangibles
Sale_growth
M/B
Stock return
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year fixed
effect
N
R2
R&D
Coeff.
p-value
2.97
<.0001
-0.08
<.0001
-1.10
<.0001
-0.06
0.01
-20.25
<.0001
-1.99
<.0001
0.99
<.0001
0.27
<.0001
-0.43
<.0001
12.99
<.0001
4.32
<.0001
17.04
<.0001
-0.41
<.0001
Risk-taking activities
Lognseg
Coeff.
p-value
0.20
<.0001
-0.05
<.0001
0.11
<.0001
-0.09
<.0001
0.22
<.0001
-0.61
<.0001
0.08
<.0001
0.00
0.66
-0.01
0.03
0.00
0.99
-0.23
<.0001
-0.08
0.02
0.00
0.65
Segherf
Coeff.
p-value
1.08
<.0001
-0.01
<.0001
0.03
<.0001
-0.03
<.0001
0.07
<.0001
-0.21
<.0001
0.03
<.0001
0.00
0.46
0.00
0.54
-0.20
0.00
-0.05
<.0001
-0.03
0.00
0.00
0.53
Included
Included
Included
68,839
58.8%
68,839
18.1%
68,839
15.4%
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
p-value
-26.72
<.0001
-135.36
<.0001
-0.67
<.0001
-3.18
<.0001
4.28
0.06
13.51
0.25
4.92
<.0001
22.43
<.0001
22.78
<.0001
52.69
0.03
5.66
0.01
34.21
0.01
-0.34
0.04
-2.75
0.00
1.01
0.44
-8.77
0.14
-18.58
<.0001
-61.58
<.0001
-127.40
<.0001
-279.83
0.02
3.45
0.21
40.72
0.00
0.10
0.98
35.67
0.07
1.28
0.07
6.59
0.03
0.60
<.0001
1.02
<.0001
0.00
0.88
0.13
<.0001
1.76
0.06
-22.98
<.0001
-0.07
0.05
-0.37
0.05
Included
Included
19,528
74.9%
37
19,528
59.3%
Table 4
Risk taking and covenant intensity: Simultaneous equations (2sls)
This table presents the simultaneous equation regression estimates. Panel A reports the first stage regression where covenant intensity is regressed on instruments
and control variables. Panel B reports the second stage regression where risk taking variables are regressed on the predicted value of covenant intensity from the
first stage and the control variables. The dependent variables are research and development expenditures scaled by assets (R&D), -1 × logarithm of the number of
segments (Lognseg), segment Herfindahl index (Segherf), the dollar change in CEO’s newly granted option value or total option value for a 0.01 change in
standard deviation of returns (Vega new or Vega total). The treatment variable is the predicted covenant intensity from the first stage. Control variables are
defined in Appendix A. P-values are based on robust Z-statistics adjusted for firm level clustering. All regressions in Panel B include firm and year fixed effects.
Panel A First stage regression of covenant intensity on instruments
Intercept
Lcov
Lmatu
Lspread
Size
Leverage
ROA
Intangibles
Std. return
Logamt
Logspread
Logmatu
PP
R&D
Lognseg
Segherf
Vega new
Vega total
Rating dummies
N
R2
Risk-taking activities
-8.77
<.0001
0.68
<.0001
0.01
<.0001
0.00
<.0001
-0.03
0.03
-0.26
0.00
0.00
0.16
1.35
<.0001
3.81
<.0001
0.25
<.0001
0.85
<.0001
0.06
0.00
1.42
<.0001
0.00
0.25
-0.17
<.0001
0.07
0.38
Included
37,284
53.6%
Risk-taking incentives
-11.02
<.0001
0.77
<.0001
0.03
<.0001
0.00
0.06
-0.17
<.0001
0.00
1.00
0.01
0.01
1.21
<.0001
10.09
<.0001
0.33
<.0001
0.99
<.0001
0.03
0.34
1.09
<.0001
0.00
0.00
Included
13,044
57.1%
0.22
0.02
38
Panel B Second stage regression of risk-taking activities on the predicted value of covenant intensity from the first stage
Intercept
Predicated Covenant
intensity
Leverage
Size
ROA
Intangibles
Sales growth
M/B
Stock return
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year fixed
effect
N
R2
R&D
Coeff.
p-value
3.51
<.0001
-0.19
<.0001
-1.21
-0.10
-0.17
-1.33
0.01
0.21
-0.36
13.17
5.63
14.45
-0.41
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Risk-taking activities
Lognseg
Coeff.
p-value
0.26
<.0001
-0.13
<.0001
0.20
-0.10
0.00
-0.26
0.00
0.00
0.00
1.80
-0.20
-0.23
0.00
<.0001
<.0001
<.0001
<.0001
<.0001
0.21
0.94
<.0001
<.0001
<.0001
0.79
Segherf
Coeff.
p-value
1.11
<.0001
-0.03
<.0001
0.06
-0.03
0.00
-0.11
0.00
0.00
0.00
0.19
-0.03
-0.07
0.00
<.0001
<.0001
<.0001
<.0001
<.0001
0.35
0.02
0.03
0.03
<.0001
0.40
Included
Included
Included
37,284
55.3%
37,284
22.3%
37,284
16.6%
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
p-value
-25.51
<.0001 -78.28
0.05
-1.06
<.0001
-7.91
<.0001
2.90
5.30
0.26
5.66
0.00
-0.22
-19.32
-81.74
0.28
1.20
1.77
0.61
0.00
1.15
-0.07
Included
13,044
74.9%
39
0.25
<.0001
<.0001
0.02
0.82
0.21
<.0001
0.02
0.94
0.82
0.03
<.0001
0.72
0.23
0.09
14.72
17.32
0.48
27.30
-0.17
-4.01
-88.37
77.82
-1.89
32.87
20.08
1.25
0.22
-26.90
-0.84
Included
13,044
40.4%
0.59
<.0001
0.32
0.36
0.11
0.03
<.0001
0.71
0.96
0.44
0.09
<.0001
<.0001
<.0001
0.03
Table 5
Risk taking and covenant intensity: Bond market’s reaction to additional covenant restrictions
This table presents the regression estimates of the bond market’s reaction to additional covenant restrictions introduced by a new bank loan. The sample is
consisted of 1,650 new bank loans from 2002 to 2008, with daily bond prices available from TRACE Database. The dependent variable is abnormal bond returns,
treasury adjusted or market-adjusted, measured over the five-day window (-3, 1) where 0 is the new loan initiation date. The treatment variable is the change in
covenant intensity defined as the covenant intensity of the new loan in its starting year and minus the covenant intensity of the prior year (if there is no loan in the
prior year, the covenant intensity of the prior year is zero). P-values are based on t-statistics from the pooled regression.
Raw bond return
Intercept
Δ Covenant intensity
Lag covenant intensity
Size
Leverage
ROA
R&D
Speculative
Spread
Maturity
Volume
N
R2
Coeff.
-3.66
0.89
0.03
0.25
1.01
1.96
4.34
-0.06
0.00
0.00
-0.01
1,650
0.54%
p-value
0.01
0.02
0.77
0.04
0.30
0.33
0.34
0.88
0.04
0.29
0.08
Treasury-adjusted
abnormal bond return
Coeff.
p-value
-3.75
0.01
0.89
0.03
0.03
0.79
0.25
0.04
1.03
0.30
1.99
0.33
4.32
0.35
-0.07
0.87
0.00
0.04
0.00
0.32
-0.01
0.08
1,650
0.53%
40
Fama-French factor adjusted
abnormal bond return
Coeff.
p-value
-3.20
0.02
0.83
0.03
0.02
0.84
0.23
0.06
0.83
0.39
0.64
0.75
2.82
0.53
0.26
0.52
0.00
0.31
0.00
0.27
-0.01
0.10
1,650
0.26%
Table 6
Risk taking across various covenant intensity and leverage groups
R&D
Low leverage
High leverage
Low
covenant
3.50
1.41
High
covenant
2.57
1.14
Nseg
Low
High
covenant
covenant
1.60
2.08
1.71
2.13
Segherf
Low
High
covenant
covenant
0.93
0.86
0.92
0.86
41
Vega new
Low
High
covenant
covenant
29.98
20.54
38.66
21.06
Vega total
Low
High
covenant
covenant
106.49
66.97
130.08
68.78
Table 7
Risk taking and covenant violations
This table presents the analyses on risk taking around covenant violations. Panel A plots the changes in risk taking four years before and four years after covenant
violations. Panel B reports regression estimates of regressing risk taking variables on five dummy variables capturing the year of the covenant violation (V0) and
each of the four subsequent years to the covenant violation (V1-V4). Control variables are defined in Appendix A. P-values are based on robust Z-statistics
adjusted for firm level clustering. All regressions include firm and year fixed effects.
Panel A The impact of covenant violation on risk taking
Risk taking incentives
before and after covenant violation
Risk taking activities
3
2.5
2
1.5
rd
1
nseg
0.5
herf
0
-4
-3
-2
-1
0
1
2
3
Risk taking incentives
Risk taking activities
before and after covenant violation
100
80
60
vega
40
totvega
20
0
-4 -3 -2 -1
4
0
1
2
3
4
Event time in years (0=violation)
Event time in years (0=violation)
42
Panel B Regression analysis
Intercept
V0
V1
V2
V3
V4
Covenant intensity
Leverage
Size
ROA
Intangibles
Sales growth
M/B
Stock return
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year
fixed effect
N
R2
R&D
Coeff.
p-value
3.85
<.0001
-0.29
0.01
-0.25
0.02
-0.12
0.31
-0.39
0.00
-0.26
0.09
-0.04
0.21
-1.23
0.00
-0.19
0.00
-0.18
<.0001
-1.99
<.0001
0.01
<.0001
0.23
<.0001
-0.34
<.0001
15.15
<.0001
7.28
<.0001
13.89
<.0001
-0.46
0.00
Risk-taking activities
Lognseg
Coeff.
p-value
-0.11
0.33
-0.06
<.0001
-0.15
<.0001
-0.24
<.0001
-0.32
<.0001
-0.39
<.0001
0.02
0.00
-0.03
0.58
0.09
<.0001
0.00
0.02
0.38
<.0001
0.00
<.0001
0.00
0.27
0.04
<.0001
-4.06
<.0001
0.29
0.00
0.27
0.00
0.08
0.01
Segherf
Coeff.
p-value
1.02
<.0001
-0.02
0.00
-0.04
<.0001
-0.07
<.0001
-0.09
<.0001
-0.11
<.0001
-0.01
0.02
0.01
0.78
-0.02
<.0001
0.00
0.00
-0.13
<.0001
0.00
<.0001
0.00
0.22
-0.01
0.00
0.91
<.0001
-0.02
0.52
-0.09
0.00
-0.04
0.00
Included
Included
Included
8,491
53.5%
8,491
23.3%
8,491
14.4%
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
p-value
-8.68
0.52
-106.97
0.00
-0.28
0.78
1.14
0.83
-0.60
0.71
10.16
0.08
-0.13
0.91
3.19
0.64
-0.35
0.83
-3.40
0.64
-1.79
0.19
-3.73
0.62
-0.63
0.02
-1.56
0.27
5.56
0.21
20.21
0.52
5.33
<.0001
19.27
0.00
0.14
0.15
0.10
0.80
-0.73
0.86
18.66
0.45
-0.19
0.49
-2.06
0.13
0.01
0.72
-0.01
0.85
-14.57
<.0001
-46.46
<.0001
-177.98
0.00
-742.84
0.00
-2.63
0.64
-11.32
0.64
-0.37
0.96
28.10
0.41
1.48
0.36
0.91
0.87
0.55
<.0001
1.02
<.0001
0.00
0.16
0.12
0.00
-0.02
0.99
-28.27
<.0001
-0.07
0.32
-0.68
0.12
Included
Included
2,731
67.3%
43
2,731
51.7%
Table 8
Short maturity as an alternative mechanism to mitigate risk taking
This table presents the regression estimates of risk taking variables or return metrics on covenant intensity and short maturity. The dependent variables in Panel A
are R&D, Lognseg, Segherf, Vega new and Vega total. The dependent variables in Panel B are the three return metrics. In both panels, the first treatment variable
is the covenant intensity, which is the sum of six dummy variables capturing the existence of debt sweep, asset sweep, equity sweep, dividend, security, and
financial covenant. The second treatment variable is the natural log of the years to maturity (multiplied by -1 so that higher value of this variable corresponds to
shorter maturity), where the year to maturity is the distance between current year and the year in which the loan matures. Control variables are defined in
Appendix A. P-values are based on robust Z-statistics adjusted for firm level clustering. All regressions include firm and year fixed effects.
Risk-taking activities
Lognseg
R&D
Coeff.
Intercept
covenant intensity
Log(years to
maturity)
Leverage
Size
ROA
Intangibles
Sales growth
M/B
Stock return
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year
fixed effect
N
R2
p-value
Coeff.
p-value
Segherf
Coeff.
p-value
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
3.49
-0.11
0.09
<.0001
<.0001
0.02
0.11
-0.06
-0.08
0.07
<.0001
<.0001
1.06
-0.02
-0.02
<.0001
<.0001
<.0001
-24.50
-0.55
1.86
<.0001
0.00
0.00
-76.72
-2.82
9.94
-1.22
-0.10
-0.17
-1.59
0.01
0.21
-0.36
11.94
5.58
14.43
-0.41
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.14
-0.10
0.00
-0.51
0.00
0.00
-0.01
1.31
-0.19
-0.23
0.01
<.0001
<.0001
<.0001
<.0001
<.0001
0.78
0.27
<.0001
<.0001
<.0001
0.49
0.05
-0.03
0.00
-0.18
0.00
0.00
0.00
0.07
-0.03
-0.07
0.00
<.0001
<.0001
<.0001
<.0001
<.0001
0.76
0.07
0.40
0.07
<.0001
0.66
0.30
<.0001
<.0001
0.07
0.25
0.94
<.0001
0.00
0.79
0.80
0.02
<.0001
0.69
0.21
0.11
8.44
18.66
0.46
10.86
-3.79
-0.15
-89.45
-113.64
-11.00
34.99
20.73
1.26
0.22
-26.93
-0.83
Included
Included
Included
Included
2.59
5.42
0.26
4.00
-0.20
0.00
-19.44
-105.21
-1.00
1.35
1.84
0.61
0.00
1.18
-0.06
Included
37,456
55.2%
37,456
19.7%
37,456
15.3%
13,086
74.9%
44
13,086
40.4%
p-value
0.06
0.01
0.04
0.75
<.0001
0.33
0.70
0.04
0.17
<.0001
0.59
0.78
0.41
0.08
<.0001
<.0001
<.0001
0.03
Table 9
Risk taking and bond covenant intensity
This table presents the regression estimates from regressing risk taking variables or return metrics on BOND covenant intensity. Bond covenants are collected
from Mergents’ Fixed Income Securities Database. Bond covenant intensity is defined as the sum of fifteen dummy variables capturing the existence of
restrictions on dividend payment, share repurchase, funded debt, subordinate debt, senior debt, secured debt, total leverage, sales and lease-back, stock issuance,
asset sale, investment policy, and merger, as well as rating and net worth triggered covenants, cross-default provisions, and poison put (Billett, King, and Mauer,
2007). Control variables are defined in Appendix A. P-values are based on robust Z-statistics adjusted for firm level clustering. All regressions include firm and
year fixed effects.
Intercept
Bond covenant
intensity
Leverage
Size
ROA
Intangibles
Sales growth
M/B
Stock return
Std. return
Cash balance
Cash surplus
Dividend cut
Delta new
Delta total
Turnover
Tenure
Firm and year fixed
effect
N
R2
R&D
Coeff.
p-value
2.22
<.0001
-0.09
<.0001
-0.37
-0.06
-0.12
-0.89
0.00
0.13
-0.28
12.11
3.19
13.88
-0.30
0.19
0.08
<.0001
0.00
0.50
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
Risk-taking activities
Lognseg
Coeff.
p-value
0.57
<.0001
-0.04
<.0001
0.16
-0.12
0.00
-0.83
0.00
0.00
0.00
0.18
-0.66
-0.38
0.00
0.02
<.0001
0.01
<.0001
0.00
0.06
0.92
0.73
<.0001
0.00
0.93
Segherf
Coeff.
p-value
1.18
<.0001
-0.01
<.0001
0.04
-0.04
0.00
-0.30
0.00
0.00
0.01
-0.31
-0.14
-0.14
0.00
0.08
<.0001
0.00
<.0001
0.00
0.10
0.09
0.08
<.0001
0.00
0.75
Risk-taking incentives
Vega new
Vega total
Coeff.
p-value
Coeff.
p-value
-39.16
<.0001
-187.62
<.0001
-0.65
0.00
-4.41
<.0001
Included
Included
Included
10.43
5.97
0.34
3.70
-0.41
-0.02
-25.42
-169.38
-7.76
8.55
1.80
0.63
0.00
0.01
-0.14
Included
18,812
63.4%
18,812
18.5%
18,812
15.5%
9,088
75.4%
45
0.01
<.0001
<.0001
0.24
0.11
0.50
<.0001
0.00
0.13
0.36
0.14
<.0001
0.86
0.99
0.04
37.36
28.30
0.55
31.11
-4.14
-0.17
-86.65
-373.01
8.27
90.23
12.56
1.05
0.14
-34.88
-0.87
Included
9,088
59.1%
0.08
<.0001
0.24
0.09
0.00
0.20
<.0001
0.08
0.73
0.02
0.03
<.0001
<.0001
<.0001
0.01