Creditor Control and Product-Market Competition
Matthew T. Billett*, Burcu Esmer**, and Miaomiao Yu***
August 2014
Abstract: We explore how rival firms respond when firms in their industry violate debt covenants. We
find that rival firms increase advertising expense, and that this increase is proportional to the size of
industry violators’ pre-existing market share. We also find that rival firm product-market share
increases in the industry market share of violators, and that this relation is more pronounced when
products are more substitutable. Rival firm operating performance also increases in proportion to the
industry market share of violators. Overall, these findings suggest that the increased creditor control
associated with covenant violations has a significant influence on rival firms and product-market
competition.
JEL Classification: G21, G30, M30
Keywords: debt covenants, covenant violations, creditor control, rivals, product market competition
We thank Redouane Elkamhi, Jon Garfinkel, Dave Mauer, Phuong-Anh Nguyen, Raunaq Pungaliya, David Smith,
Xuan Tian, and seminar participants at Bilkent University, the University of Saskatchewan, and participants at
the 2013 Financial Management Association meetings for helpful comments and discussions. All errors remain
our own.
*Kelley School of Business, Indiana University. Email: [email protected]
** Faculty of Business Administration, Bilkent University. Email: [email protected]
*** Edwards School of Business, University of Saskatchewan. Email: [email protected]
Electronic copy available at: http://ssrn.com/abstract=2307031
1. Introduction
We know from existing research that a firm’s capital structure decisions influence competition
and product-market outcomes. Firms elect to engage in price wars when their rivals are constrained by
high debt loads and the resultant need to service high interest payments (Chevalier 1995a,1995b), and
more generally, we know that firm leverage plays a significant role in its overall competitive position
(Brander and Lewis 1986; Maksimovic 1988; Philips 1995; Zingales 1998; Campello 2003). 1 One
common link among these works is the notion that financial flexibility, or lack thereof, may significantly
affect firm competitiveness and accompanying product -market outcomes. Financial flexibility certainly
depends on the quantity and price of leverage; however, flexibility may also depend on debt contract
features, like restrictive covenants which may impede a firm’s ability to respond to rivals’ actions. In
fact, firms may strategically alter their competitive practices when competitors face such constraints.
Consistent with this notion, we find that rivals of firms that violate private debt covenants
significantly increase their advertising expense. They experience significant market share gains and
improvements in profitability. If these are indicative of strategic actions, then we would expect to see
these effects increase with the potential size of the competitive opportunity to gain share – i.e., the
size of the violating firms’ industry market share. Indeed, we find rivals’ advertising, market share
gains, and profitability all increase with the pre-violation market share of the violating firms in their
industry.
We would also expect to see these relations vary with the degree to which rival products can
substitute for violators’ products. Using product durability, R&D intensity, and product fluidity
1
Related work shows firms with excess cash gain product market share at the expense of their relatively cash
poor rivals (Fresard 2010).
1
Electronic copy available at: http://ssrn.com/abstract=2307031
measures we find rival market share gains are more pronounced when violator firms sell less unique
products.
Debt covenants can place direct restrictions on a firm’s ability to borrow and invest, as well as
indirect restrictions resulting from maintenance covenants where the firm must maintain certain
financial ratios or face debt renegotiation. These covenant restrictions are designed to alleviate agency
costs by reducing adverse selection and moral hazard by screening borrowers and by lowering
monitoring costs. In an incomplete contracts setting, however, covenants may trigger suboptimally,
reducing managers’ ability to pursue first-best outcomes. This notion is put in simple terms in a recent
practitioner publication: “loan covenants can backfire if they are too inflexible or restrictive by slowing
a borrower's growth and development. Borrowers may end up managing the loan covenants, rather
than their business.” 2 In such a circumstance, firms may consider their rivals’ covenant restrictions
when making competitive decisions in the product market. 3
Prior studies document that financial covenant violations lead to increased creditor control
over borrowing firms that significantly changes firms’ financial, investment, and payout policies. 4 Nini,
Smith, and Sufi (2009, 2012) document that firms experience a decline in investment, sales growth and
market share following a violation. We build upon this literature by exploring the actions of industry
rivals to see how they respond to their peers’ violations.
2
“What you should know about loan covenants”, Spring 2005, Financial Lending Notes.
3
Covenants may lead rivals to act regardless of whether covenants are optimally or suboptimally constraining
borrowing firms. It may be optimal for the violator firm to give up market share and increased creditor control
may facilitate this.
4
Beneish and Press (1993, 1995), Chen and Wei (1993), Sweeney (1994), Dichev and Skinner (2002), Chava and
Roberts (2008), Roberts and Sufi (2009), Nini et al. (2009, 2012).
2
Electronic copy available at: http://ssrn.com/abstract=2307031
We begin by examining changes in sales growth around covenant violations for the industry
rivals of violating firms (violators) to capture changes in market share (following Fresard, 2010).
Consistent with Nini et al. (2012), we find industry rivals gain significant market share from their
covenant violating peers in the year following the violation. 5 Specifically, we find violators and
matched rivals have similar sales growth patterns prior to the violation; however, rival firms experience
an average (statistically insignificant) change in sales growth of -1.8% from pre- to post-violation, while
the violators in the industry experience a -6.1% change, significant at the 1% level. The difference in
these two changes (i.e., difference-in-differences, DID) is 4.2%, significant at the 1% level. Given previolation sales growth averages 11% for matched rival firms, the sales growth difference-in-differences
of 4.2% represents a 38.2% change for rivals relative to violators.
5
One primary challenge is to isolate the effect of violations from expected changes in sales growth related to
fundamental differences between violators and nonviolator rival firms. Since violations are not random events,
we need to separate the expected outcomes in the absence of a violation and identify the effect of the violation
by comparing the actual and expected outcomes. If deteriorating performance triggers a violation, and sales
growth is serially correlated, then nonviolator firms with similar performance should also exhibit similar future
sales growth. In order to address this issue, we use matching procedures that include matching on pre-violation
sales growth. Rival firms are identified using industry-propensity score matching. We estimate a probit
regression of the probability (i.e. the “propensity score”) of a firm violating a covenant. We include a large set of
observable characteristics consisting of our full set of control variables in equation (1), higher order covenant
controls, the four quarter lags of covenant controls, calendar quarter-year, fiscal quarter-year and industry fixed
effects. We then take all potential matches that are in the same industry (three-digit SIC code) and that have
pre-violation average sales growth within one-half of a standard deviation of the violator firm’s pre-violation
average sales growth and choose the one with the closest propensity score to that of the violator firm. Using
one-quarter of a standard deviation of the violator firm’s pre-violation average sales growth does not change
the results, therefore not reported.
3
We then explore how these changes in market share relate to the nature of the product
market. First, we stratify the sample by the size of the violator firms’ pre-violation market share. We
find market share gains for rivals is only significant (DID=4.8%) when violators have a relatively large
market share. We also explore how product uniqueness affects the product -market outcomes. If
violator firms sell unique products, e.g. produced to a customer’s specification, then it may be costly
for buyers to change suppliers. In this case, there is less opportunity for the industry rivals to take
advantage of the covenant violation. We find this is indeed the case. When violators produce
nondurable goods, are less R&D intensive, and have high product fluidity, rival firms gain more market
share (with DIDs of 4.6%, 3.2% and 6.8% respectively).
We conduct multivariate tests to see the change in sales growth of industry rivals following the
covenant violations. The regression analysis confirms the univariate observations: rivals of firms that
violate a covenant gain market share that increase in the violators’ market share. 6 The regression
results showing rivals’ market share gains increase in the industry market share of violators suggest
that when violators market share is 10% preceding the violation quarter, rivals’ market share grows by
7.5% in the year following the violation. We also find the relation between rival market share and
violators’ market share is more pronounced when products are more similar.
One way industry rivals may attempt to gain market share is by increasing advertising expense.
We find that industry rivals increase their advertising budget following violations, and these changes in
advertising increase in proportion to the market share of the violators. We explore rival firms’
operating performance following violations. We show significant increases in operating cash flow
6
In multivariate tests, we aggregate the market share of all violators within an industry quarter to obtain
violators’ market share.
4
scaled by assets, ROA and ROE for the rival firms. Moreover, we find that all of these post-violation
operating performance changes increase in magnitude with the market share of the violators.
This study contributes to the literature on the link between firms’ financing decisions and
product-market behavior. Although several studies show how the level of debt in firms’ capital
structure affects their product-market strategies, it has been less clear how different features of debt
contacts influence these decisions. 7 Our paper adds to the growing body of literature on the effect of
financial covenant violations on firm behavior (Beneish and Press 1993, 1995; Chen and Wei 1993;
Sweeney 1994; Dichev and Skinner 2002, Chava and Roberts 2008; Roberts and Sufi 2009; Nini et al.
2009, 2012; Esmer 2013). These studies document that financial covenant violations lead to increased
creditor control over borrowing firms that significantly changes firms’ financial, investment, and payout
policies. To our knowledge, our paper is the first to document how industry rivals respond to their
peers’ violations and the product-market outcomes of such actions.
Our paper is related to the literature exploring the effect of bankruptcy and financial distress on
product-market competition (Lang and Stulz 1992; Hertzel, Zhi, Officer, and Rodgers 2008; Hotchkiss
1995; Eberhart, Altman and Aggarwal 1999; Zhang 2010). Relative to these studies, we focus on
financial covenant violations that occur well outside of financial distress and rarely lead to default
(Gopalakrishnan and Parkash 1995). 8 Moreover, financial covenant violations are very common. 9 Thus,
7
See Titman (1984), Brander and Lewis (1986), Maksimovic (1998), Chevalier and Scharfstein (1996), Rotemberg
and Scharfstein (1990) for theoretical work and Chevalier (1995a, 1995b), Phillips (1995), Zingales (1998),
Chevalier and Scharfstein (1996), Campello (2003) for empirical evidence.
8
Firms which violate financial covenants may have performance deterioration leading to the violation. However,
the median firm in violation of a financial covenant has comparable liquidity and valuation measures compared
to the median firm in the full sample.
5
the potential impact covenant violations have on product-market strategies is not limited to a small
fraction of firms facing unique circumstances. By studying financial covenant violations, we are able to
document the changes in product-market outcomes and how the industry rivals respond when
creditors have the opportunity to exert control over corporate behavior in solvent firms on a frequent
basis.
2. Literature Review
A large number of papers examine the link between firms’ capital structure and product-market
behavior. Theoretical papers have mixed predictions about how leverage affects competition.
Fudenberg and Tirole (1986), Bolton and Scharfstein (1990), Phillips (1995), Chevalier and Scharfstein
(1996) argue that competition becomes softer when leverage increases. However, other theoretical
papers such as Brander and Lewis (1986), Maksimovic (1998), Rotemberg and Scharfstein (1990)
predict that leverage changes managerial and shareholder incentives in a way that influences both firm
and rival product-market strategies.
The link between financing and product-market outcomes has also been investigated
empirically. Chevalier (1995a) examines competition in the supermarket industry after one firm
undergoes a leveraged buyout (LBO) and shows that LBOs make firms weaker competitors, as
measured by the entry and expansion decisions and stock price reaction of rival firms. In her second
paper, Chevalier (1995b) finds that LBOs have a significant impact on supermarket prices. She finds
that when LBO firm’s rivals are less leveraged, LBOs lead to decreased prices, consistent with
opportunistic predation by less financially constrained rivals. When rival firms are highly leveraged,
9
Nini et al. (2012) show that between 10% and 20% of public firms were in violation of a covenant during any
particular quarter and more than 40% of the firms were in violation at some point during the 1996- 2007 period.
6
prices rise following LBOs, consistent with LBOs softening product-market competition. Phillips (1995)
finds that in three out of four industries he examined, high leverage weakens competition. Kovenock
and Philips (1997) add to Philips (1995) by showing that high leverage makes firms more passive,
increasing plant closures and decreasing investment in highly concentrated markets. Zingales (1998)
analyzes deregulation in the trucking industry and finds that leverage negatively affects the probability
that a firm survives following the increase in competition.
Complementing these studies, several papers examine competitive responses to shocks in
competitive environments. Chevalier and Scharfstein (1996) and Campello (2003) show that highly
leveraged firms are weaker competitors during market downturns, consistent with Opler and Titman
(1994). Khanna and Tice (2000) study how Walmart’s expansion affects incumbent firms and show that
high leverage associates with more passive responses. Khanna and Tice (2005) expand their earlier
work and show that Walmart places its stores closer to less efficient and highly leveraged rival stores,
consistent with the idea that leverage weakens competition.
Another body of literature investigates the effect of bankruptcy and financial distress on
product-market competition. Lang and Stulz (1992) find that, on average, industry rivals suffer negative
stock price effects (contagion effects) around the time that a competitor files for bankruptcy. However,
the stock price effect is positive for rivals in highly concentrated industries with low leverage
(competitive effects). Hertzel et al. (2008) also show that rivals experience negative announcement
reactions when the filing firm is highly levered. Hotchkiss (1995) finds that reorganized firms earn
operating profit margin lower than the industry median. Eberhart et al. (1999) assess the long-term
stock return performance of 131 firms emerging from Chapter 11, and find large positive excess
returns over the 200 days after emergence, even though these firms exhibit poor operating
7
performance. Zhang (2010) shows rivals of firms that emerge from Chapter 11 have negative long-run
equity returns and deteriorating financial performance.
The extant literature on capital structure and product-market competition has mostly focused
on how the level of leverage impacts product-market outcomes. However, there are other important
features of leverage that may have similar effects on product-market outcomes, one of which is
covenant restrictions in debt agreements. Restrictive covenants define the circumstances under which
debt holders may intervene in management (Aghion and Bolton 1992; Hart and Moore 1995). Building
on this view, several papers investigate how a firm behavior changes once control rights are
transferred to creditors by examining bank loan covenant violations (Beneish and Press 1993, 1995;
Chen and Wei 1993; Sweeney 1994; Dichev and Skinner 2002; Chava and Roberts 2008; Roberts and
Sufi 2009; Nini et al. 2009, 2012; Esmer 2013). Chava and Roberts (2008) and Nini et al. (2009) show a
sharp decrease in investment following violations. Roberts and Sufi (2009) find that firms violating
covenants significantly decrease their net debt issuing activity and their leverage. Nini et al. (2012)
confirm these results and also show that violations are followed by a decrease in shareholder payouts,
a decrease in sales growth, an increase in CEO turnover, an increase in the incidence of corporate
restructurings, and an increase in the likelihood of hiring turnaround specialists. These studies show
creditors exert significant influence over borrowing firms following violations which leads to changes in
borrowing firms’ financial, investment, and payout policies. Our paper explores whether creditor
control threats associated with covenant violations affect the product-market behaviors of rival firms.
8
3. Data
Financial covenant violation data is obtained from Amir Sufi’s website. 10 The sample
construction below follows Nini et al. (2012) using the period 1997 through 2008. To be included in the
sample, we require firms to have data available on the Compustat database, and have average book
assets greater than $10 million in 2000 dollars. We exclude financial firms and firms with missing
information on total assets, sales, common shares outstanding, and closing share price. Imposing these
restrictions leaves a sample of 8,199 firms and 180,335 firm-quarter observations. We follow Nini et al.
(2012) and focus our analysis on new financial covenant violations, which are defined as financial
covenant violations for firms that have not violated a covenant in the previous four quarters. 11 The
reason to focus on the new covenant violations is that they help pinpoint the initiation of creditor
intervention. Therefore, how industry rivals respond when another firm in the industry is the subject of
creditor control will be more clearly identified. In our sample, we have 3,604 new violations between
1997 and 2008. 12
10
This data is available at http://faculty.chicagobooth.edu/amir.sufi/data.htm. Nini, Smith, and Sufi extract
information from every 10-Q and 10-K filing on SEC Edgar website. Using a text-searching algorithm, they
determine whether a firm is in violation of a covenant. Then they match this information to COMPUSTAT file.
For more information on the data, please see Appendix of “Creditor Control Rights, Corporate Governance and
Firm Value” by Nini, Smith and Sufi (2012).
11
Based on the violation sample downloaded from Sufi’s website, we assume that a firm is not in violation of a
covenant during the quarter if there is missing violation variable in the dataset after the starting quarter of the
collection for each firm.
12
See Appendix A.2 and Table A.1 to see the descriptive statistics of variables for firms in new covenant
violations and non-violator rival firms.
9
4. Results
4.1. Univariate Results
Nini et al. (2012) find that firms that violate covenants experience negative industry adjusted
sales growth following the violation. Using a difference-in-differences framework, we start by
corroborating their results by analyzing sales growth of the rival firms when their peers in the same
industry violate a financial covenant. One concern is whether the differences in sales growth patterns
are truly due to creditor control threats and not simply due to fundamental differences between the
rivals and covenant violators. Violations may be preceded by deteriorating performance, suggesting
they are not random events. If sales growth is serially correlated then pre-violation performance may
be driving the decline in sales growth for violators relative to the rivals and not the creditor
intervention. To see if this is the case, we need to compare violators’ sales growth with that of
nonviolator rival firms with similar pre-violation performance. To address this issue, we conduct
difference-in-differences tests which control for both time-invariant firm-level effects that may be
different between rival firms and the violators as well as the expected changes in sales growth
following deterioration in violators’ performance.
We match peer firms using industry-propensity score matching. 13,14 We estimate a probit
regression of the probability (i.e. the “propensity score”) of a firm violating a covenant. We include a
large set of observable characteristics consisting of our full set of control variables in equation (1),
higher order covenant controls, the four quarter lags of covenant controls, calendar quarter-year, fiscal
quarter-year and industry fixed effects. We then take all potential matches that are in the same
13
Table A.2 reports the mean and median statistics of firm characteristics for the violators and their rivals.
14
Violators are matched with non-violator rivals at the violation quarter.
10
industry (three-digit SIC code) and that have pre-violation average sales growth within one-half of a
standard deviation of the violator firm’s pre-violation average sales growth and choose the one with
the closest propensity score to that of the violator firm. 15,16
Table 1 reports the average sales growth for the violators and their rivals for the four quarters
preceding the violation quarter and the four quarters following the violation. Both the rivals and
violating firms have similar sales growth pattern before the violation. Pre-violation average sales
growth is around 11% both for the rivals and violating firms and the difference is insignificantly
different from zero. The rival firms experience an average change in sales growth of -1.8% from pre- to
post-violation, while their covenant violating peers experience a 6.1% decrease, significant at the 1%
level. Moreover, the difference in these two changes (i.e., difference-in-differences, DID) is 4.2%,
significant at the 1% level. Given sales growth averages 11% for rivals, the sales growth difference-indifferences of 4.2% represents a 38.2% change in sales growth for the rivals relative to their peer
violating firms. These results are consistent with Nini et al. (2012).
In panels B through E, we explore changes in sales growth for subsamples of firms. In Panel B
we divide the sample into small and big violators based on their industry market share. In Panel B,
15
The standard deviation of pre-violation average sales growth is about 74%. As robustness, we identify the rival
firm as the firm within the same 3-digit SIC code industry which has a pre-violation average sales growth within
one-quarter of the standard deviation above and below the violator firm’s pre-violation average sales growth
and choose the one with the closest propensity score to that of the violator firm. The results do not change,
therefore not reported.
16
We also match rivals and violators on industry and size. In this case, to identify the matched rival firm, we use
a non-violator rival firm with the same three-digit SIC code and has a pre-violation average sales growth within
one-half (one-quarter) of the standard deviation above and below the violator firm’s pre-violation average sales
growth that has book value of assets closest to that of the violator firm. The results are robust to using these
matching criteria.
11
Small (Big) violators are defined as new violators having the ratio of sales to total industry sales lower
(greater) than the median ratio for the sample of new violators. Specifically, the ratio of sales to total
industry sales is equal to the sum of the past four quarters’ sales (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of
violators, divided by the sum of the past four quarters’ sales of that industry (Sales t-1 +Sales t-2 +Sales t3 +Sales t-4 ).
These results show that the DID is negative and significant for the big violator sample but
insignificant for the small violator sample. One interpretation is that the rivals have little market share
to gain from small violators.
We next explore how different characteristics of violator firms’ products affect the changes in
rival firm market share. If the violating firm is selling more unique products then it may be costly for
buyers to change suppliers, making it difficult for the rival firms to take advantage of the violation. To
test this argument, we define product uniqueness using three different measures: research and
development (R&D) intensity, product durability, and product fluidity. Titman and Wessels (1988)
argue that R&D intensity measures product uniqueness because firms selling products with close
substitutes spend less on R&D due to easy duplication of their technology. Moreover, firms spending
more on R&D are more likely to produce more specialized products (Hertzel et al., 2008). If a violator
firm’s non-missing annual R&D spending (scaled by average assets) is more than the median ratio for
the new violators in the year preceding the violation, that firm is considered high R&D
intensive. 17 Titman and Wessels (1988) argue that firms manufacturing machines and equipment
require specialized servicing and spare parts. In this case, it may be more costly for the customers to
17
Firms are not required to report R&D expenses separately from sales and general administrative (SG&A)
expenses if they are less than 10% of SG&A (SEC Regulation 5-03.2). If a firm’s annual R&D spending prior to the
violation year is missing, the firm is also considered as a low R&D intensive firm, on the assumption that these
firms have low R&D spending (Gentry and Shen, 2013).
12
switch suppliers when their suppliers violate covenants. Durable goods industries are defined as those
with an SIC industry code between 3400 and 4000 (firms producing machines and equipment). Hoberg,
Phillips and Prabhala (2014) use a text-based algorithm to define product fluidity as a “measure of the
competitive threats faced by a firm in its product market, which captures changes in rival firms'
products relative to the firm’s products.” If product fluidity is high, then the firm is considered to be
producing less unique products. We use Hoberg et al. (2014)’s classifications to measure product
fluidity. If a firm’s product fluidity is more (less) than the median level for the sample of new violators
prior to the violation year, the firm is considered as a firm with High (Low) product fluidity.
We create subsamples based on these three measures of product uniqueness and report the
results in panels C, D, and E. In Panel C, we see the sales growth DID is -3.2%, significant at the 5%
level, for the low R&D intensity sample. We also find the DID for the high R&D intensity sample is
negative and marginally significant. Thus R&D intensity provides weak support for the notion that
product similarity leads to greater rival market share gains.
In Panel D, we divide the sample based on nondurable and durable goods industries. We see
that rival firms’ market share gains are only significant for the nondurable subsample where the DID is 4.6%, significant at the 5% level. Lastly, Panel E stratifies the sample based on product fluidity. We see
insignificant rival firm gains for the low fluidity group; however, rival firms gain significant share in the
high fluidity group where we find the DID is -6.8%, significant at the 5% level. Overall the results in
Panels B through E suggest the market share gains of rival firms are more pronounced when the
violators have a greater share to lose and when their products less unique. Next we explore these
relations in a multivariate setting.
13
4.2. Multivariate Results
We estimate various outcomes for the rivals of firms that violate a covenant using the following
regression:
Δ outcome variable = β 1 * Industry market share of violators + Θ 1 *Covenant Controls i,t +
+ Θ 2 *(Covenant Controls i,t-4 ) + Calendar Quarter-Year i,t + Fiscal Quarter-Year i,t + ε i,t
(1)
Industry market share of violators measures the aggregate market share of all violators in a given
industry for a given quarter. If gains by rivals come at the expense of violators then the magnitude of
rival gains should relate to the size of the violators’ market share. We compute Industry market share
of violators as annualized total sales of new violators within the three-digit SIC code industry prior to
the violation quarter divided by the annualized industry sales prior to the violation quarter. More
specifically, it is equal to the sum of past four quarters’ sales (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of peer
violators, divided by the sum of past four quarters’ sales (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of the
industry, where subscript t refers to new covenant violation quarter. In the regressions, we limit the
sample to rival firms, i.e, firms that are not in a covenant violation. 18
We include calendar quarter-year and fiscal quarter-year indicator variables because seasonal
patterns may exist and financial covenant variables are more common in 10-K filings than 10-Q filings.
Following Nini et al. (2012), we include the most common ratios used in debt agreements in the
analysis as Covenant Controls. These ratios include: operating cash flow to average assets, leverage
(debt-to-assets), interest expense to average assets, net worth to assets, and the current ratio (current
assets/current liabilities). We also include market-to-book ratio as it is an indicator of many firm
18
To be included in the sample, we require firms to have available covenant control variable items. We have
76,624 firm-quarter observations in our sample.
14
outcomes. In some specifications, we include four-quarter lagged value of these variables. Since we
have overlapping observations that induce a mechanical serial correlation in the dependent variable,
we cluster our standard errors by firm. 19
Table 2 presents the results for nonviolating rival firms. The dependent variable is Δ Market
Share, annualized sales growth minus the industry average, where the average excludes the firm itself,
or equivalently, is a proxy for market share growth (following Fresard 2010). Columns (1) to (3) show
the regression results using the set of variables in equation (1) as well as the level and first differences
of ln(assets) and the level and first differences of net fixed assets scaled by total assets. We restrict our
sample to industry-quarters which have at least one firm in a new covenant violation. 20
We find the rival firm’s market share increases in the market share of violators in their industry.
In each of the first three specifications we find a positive and significant coefficient on Industry market
share of violators. In Column (3) of Table 2, the coefficient of 0.753 on Industry market share of
violators indicates that the rival firms’ market share grows by 0.753% when the combined industry
market share of violators is 1%.
We also analyze the change in rival firms’ sales growth when the industry market share of
violators is above (below) the median for the sample of rival firms. Columns (4) to (6) of Table 2 show
that rival firms’ market share economically and significantly increases following the violation if the
industry market share of the violators is above the median. If the market share of the violators is small,
however, the increase in market share of rival firms is not statistically significant at a reasonable level.
19
Please see Peterson (2009) for detailed information on clustered standard errors.
20
The results are robust if we include industry-quarters which do not have any new covenant violations.
15
These results are consistent with the notion that the rivals see their peers’ covenant violations as
providing an opportunity to increase their competitive position in the product market.
4.3. Changes in market share and product similarity
The previous section shows that the rival firms’ market share significantly increases in
proportion to the industry market share of violators following a financial covenant violation. In this
section, we explore how product uniqueness influences the relation between changes in rival firms’
market share and the industry market share of violators. The results are presented in Table 3. In the
first three columns, the Product Uniqueness Dummy is one for durable goods industries and zero
otherwise. In columns 4-6, Product Uniqueness Dummy is defined using R&D intensity. If a firm’s
annual R&D spending is more than the median level for the full sample in the year preceding the
violation, that firm is considered as R&D intensive. If more than half of the violator firms in the industry
are R&D intensive, product uniqueness dummy is defined as one, zero otherwise. In columns 7-8,
Product Uniqueness Dummy is defined using product fluidity. Using Hoberg et al. (2014)’s classifications
to measure product fluidity, we categorize a firm’s products as having low fluidity when it is below the
median level for the full sample in the year preceding the violation. If more than half of the violator
firms in the industry have low product fluidity then the Product Uniqueness Dummy equals one.
We include both the Product Uniqueness Dummy and the interaction of Product Uniqueness
Dummy with Industry market share of violators in the regressions. We conjecture that the interaction
term will have a negative coefficient, consistent with the notion that rivals will not be able to take as
much market share from violators when violators’ products are more unique. This is indeed what we
find. In all nine specifications, we find negative coefficients on the interaction term that are significant
at the 1% level. This result supports our conjecture that if peer violators are producing more unique
16
and customer specific products, there is less room for rival firms to take advantage of the violation. We
next explore how rival firms’ advertising expense relates to violators’ industry market share. If rivals
are indeed actively attempting to capture share then we would expect to see advertising expense to
rise as a function of the violators’ share.
4.4. Advertising expense and industry market share of violators
In Table 4, we examine the growth in the annual advertising expense, adjusted annual
advertising expense (advertising expense/lagged sales), and the growth in the adjusted annual
advertising expense as a function of industry market share of violators. 21 Our specifications are similar
to those in Table 2 but with our advertising expense measures as the dependent variables and with
annual values of covenant controls. In the first two specifications where the dependent variable is
change in natural log of advertising expense, we find the coefficient on Industry market share of
violators is 0.575 and 0.522, both significant at the 1% level. The later coefficient suggests that for each
1% of market share held by industry violators’, rival firms grow their advertising budget by 5.22%.
When we explore the adjusted advertising expense and the growth in adjusted advertising expense,
the results are similar. The coefficient of Industry market share of violators is positive and statistically
significant in all specifications. 22 Overall these findings suggest that increases in advertising expense of
21
To explore the annual change in the advertising expense, we restrict the sample to covenant violations that
are reported in the fourth quarter of each fiscal year. We use the fourth quarter since financial covenant
violation announcements are more common in 10-K filings than in 10-Q filings and advertising expense is
reported annually.
22
We explore the relation between advertising expense and product uniqueness; however, there are
countervailing factors that drive the predictions. On the one hand, we would expect advertising to increase
more when products are less unique given the greater product substitutability – leading to a negative relation
between advertising and product uniqueness. On the other hand, firms may need to spend more on advertising
17
the rivals may explain the increase in sales growth with respect to the peer violators, and may be
indicative of strategic actions of the rivals. These results suggest that covenants play an important role
in industry competitiveness and product-market outcomes.
4.5. Operating performance and industry market share of violators
The results above suggest that creditor control threats over borrowing firms following
violations have positive effects on rival firms. To provide evidence on the valuation consequences of
the increased market share for the rivals following covenant violations, we examine rival firms’
performance following violations.
We use three different variables as dependent variables to measure operating performance in
equation (1). We explore, change in operating cash flow scaled by average assets, where operating
cash flow is defined as operating income before depreciation and amortization, change in return on
assets (ROA), where ROA is defined as operating income before depreciation minus depreciation and
amortization divided by average assets, and change in return on equity (ROE), where ROE is defined as
net income divided by average common equity.
Table 5 reports the results. We see in all nine specifications, using three measures of
profitability, the relation between rival firm profits and industry market share of violators is positive
and significant. In other words, not only do the rival firms experience significant improvements in these
profitability measures relative to the violators in their industry, they also experience these increases in
proportion to the violators’ industry market share.
5. Conclusion
to persuade rival customers when products are more unique – leading to a negative relation. In general we find
the relation is statistically insignificant.
18
There is a large body of work which examines how firms’ capital structure affects productmarket behavior. While previous studies focus on the level of leverage, there are other dimensions of
leverage that may also affect product-market outcomes. In this study, we investigate how creditor
control threats impact product-market competition. Restrictive covenants might lead “managers to
manage the covenants, rather than their business”, resulting in competitive opportunities for the rival
firms, and commensurate changes in product-market outcomes. Covenant violations may also lead to
greater creditor control and a decreased emphasis on market share and an increased emphasis on
profits. Regardless of whether these actions benefit shareholders, the influence of covenants on
product-market actions may be an important channel that affects overall industry competitiveness.
The rival firms may consider their peers’ exposure to control threats when developing product-market
strategies. To explore the mechanism between creditor control threats and product-market outcomes,
we look at financial covenant violations. Recent papers show that following financial covenant
violations, creditors increase their control over borrowing firms in ways that significantly affect
borrowing firms’ corporate policies.
Our paper is the first to thoroughly examine the product-market outcomes following financial
covenant violations. We show that the rival firms gain market share when a firm in their industry
violates a financial covenant, this gain increases with the industry market share of violators. The rival
firms gain less market share if peer violators sell more unique goods. We also show that rival firms
increase advertising spending significantly after covenant violations. ROA, ROE, and operating cash
flow significantly increase for rival firms once their peers violate a financial covenant. These findings
indicate that the rivals follow more aggressive strategies to improve their positions in the product
market when another firm in their industry violates a covenant.
19
We find evidence that the rival firm actions (i.e., changes in market share and advertising
spending) are indicative of strategic actions. Namely we see their effects increase with the potential
size of the competitive opportunity – i.e., the size of the violating firms’ market share and the nature of
the product violating firms sell. Taken together, results suggest that creditor control threats can indeed
have dramatic effects on product-market outcomes. Rival firms may be considering their peers’
covenant violations when making competitive decisions in the product market.
20
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24
A.1 Variable definitions
All cash flow statement variables are first disaggregated into quarterly flows.
Sales growth = (Sales t - Sales t-4 )/Sales t-4 .
where Sales = saleq
Industry market share of violators = (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of violators / (Sales t-1 +Sales t2 +Sales t-3
+Sales t-4 ) of the three-digit SIC code industry.
Change in Market Share= ((Sales t+4 -Sales t )/Sales t ) adjusted by the three-digit SIC code industry average
sales growth (excluding the firm itself)
Average assets = (Total assets + lagged total assets) / 2
where total assets = atq
Market-to-book ratio = Market value / total assets
where market value = Market value of equity – book value of equity + total assets
Market value of equity = Price close quarterly * common shares outstanding (prccq*cshoq)
Book value of equity = Total assets – total liabilities (ltq) + deferred taxes and investment tax credit
(txditcq)
Total debt = Debt in current liabilities (dlcq) + total long-term debt (dlttq)
Leverage ratio = Total debt / total assets
Net worth / assets = Total shareholders' equity (seqq) / total assets
Current ratio = Current assets (actq) / current liabilities (lctq)
Interest expense / average assets = Interest and related expense (xintq) / average assets
If interest expense is missing, we calculate the interest expense using this equation:
25
Operating income before depreciation (oibdpq) – depreciation and amortization (dpq) + non-operating
income (nopiq) – pretax income (piq) +impairment of goodwill pretax (gdwlipq) +settlement
pretax (setpq) + writedowns pretx (wdpq) +other special items pretax (spiopq) + restructuring
cost pretax (rcpq)+ gain/loss pretax (glpq)
If non-operating income (nopiq), pretax income (piq), impairment of goodwill pretax (gdwlipq),
settlement pretax (setpq), writedowns pretx (wdpq), other special items pretax (spiopq),
restructuring cost pretax (rcpq), gain / loss pretax (glpq) are missing, we set them equal to zero.
Operating cash flow / average assets = Operating income before depreciation quarterly (oibdpq) /
average assets
Net fixed assets / total assets = Plant, property and equipment (Ppentq) / total assets (atq)
Advertising Expense / lagged sales= Annual advertising Expense (xad) / annual sales (sale)
Return on assets (ROA) = Operating income after depreciation quarterly (oibdpq) / average assets
where operating income after depreciation= Operating income before depreciation quarterly
(oibdpq)-depreciation and amortization (dpq)
Return on equity (ROE) = Net income (niq) / average common equity
where average common equity= (Common equity (ceqq) + lagged common equity) / 2
26
A.2 Descriptive Statistics
Since we use the data of Amir Sufi and follow the data construction of Nini et al. (2012), we
refer the reader to Nini et al. (2012) for the data characteristics. As Nini et al. (2012) show in Table 2 of
their paper, violations are common events, 40 percent of firms violate a financial covenant in 19972008 period, although small firms are more likely to violate a covenant, violations are also common
among large firms (25 percent of firms with greater than $5 billion in assets are in violation in the
sample). We find that violations are more common among manufacturing firms. As Nini et al. (2012)
show in Figure 1 of their paper, violations are more likely to occur in bad times (e.g. 2001 crises), the
incidence of violations declines in the later part of the sample.
Table A.1 displays the distribution of performance measures for new violator firms and the nonviolating rival firms. A new covenant violation is a financial covenant violation for a firm that has not
experienced a financial covenant violation in the previous four quarters. We confirm Table 3 of Nini et
al. (2012) showing that although violator firms have high leverage and interest expense scaled by
average assets compared to rivals, they are not highly leveraged. The median violator firm has debt
ratio of 0.34, compared to 0.21 of the median non-violator rival firm. Operating cash flow/average
assets are lower for the median violator firm compared to the median rival firm; however, violators are
not experiencing deep liquidity problems. The median violator has a reasonably high market-to-book
ratio, 1.5, relative to the median non-violator rival firm’s market-to-book ratio of 2.3. Operating
performance of the median violator is lower than the median rival firm. As Nini et al. (2012) point out
“Overall, financial covenant violations appear to serve more as an indicator of a change in
performance, rather than as an indicator of a low level of performance” (p. 1730).
27
Table A.1 also shows that the median new violator firm’s change in market share in the year
preceding the violation and advertising expense/lagged sales are similar to that of the median nonviolator rival firm.
28
Table 1
Differences-in-differences (DID) tests of sales growth of rival firms and covenant violators
This table presents changes in sales growth of rival firms and violators around new financial covenant violations. A new covenant violation is a financial
covenant violation for a firm that has not experienced a financial covenant violation in the previous four quarters. Sales growth is defined as sales growth with
respect to same quarter last year i.e. (Sales t -Sales t-4 )/Sales t-4 , where subscript t refers to the new covenant violation quarter. A rival firm is identified as a nonviolator firm with the same three-digit SIC code, a pre-violation average sales growth within one-half of the standard deviation above and below the violator
firm’s pre-violation average sales growth, and a propensity score of violating a covenant closest to that of the violator firm. In Panel B, Small (Big) violators are
defined as new violators having the ratio of sales to total industry sales lower (greater) than the median ratio for the sample of new violators. Specifically, the
ratio of sales to total industry sales is equal to the sum of the past four quarters’ sales (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of violators, divided by the sum of the
past four quarters’ sales of that industry (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ). From Panel C to Panel E, product uniqueness is defined by three measures:
Research and Development (R&D) Intensity, Non-durable/Durable Goods Industry and Product Fluidity. If a violator firm’s non-missing annual R&D spending
(scaled by average assets) is less than the median ratio for the new violators in the year preceding the violation, the firm is considered as a Low (High) R&D
intensity firm. If a firm’s annual R&D spending prior to the violation year is missing, the firm is also considered as a Low R&D intensity firm. If a firm’s SIC
industry code is between 3400 and 4000 (firms producing machines and equipment), then it is defined as a durable goods firm; otherwise it is defined as a nondurable goods firm. If a firm’s product fluidity is more (less) than the median level for the sample of new violators prior to the violation year, this firm is
considered as a High (Low) product fluidity firm. In each panel, first rows show the average sales growth of firms in the four quarters prior to the violation.
Second rows show the average sales growth of firms in the four quarters following the violation. Last rows show the difference between average sales growth
for the post-violation and pre-violation periods both for the rivals and their peer violator firms. The difference-in-differences estimator is the difference
between the average difference of the rivals and peer violator firms. ***, ** and * denote 1%, 5% and 10 % levels of significance, respectively.
Panel A
Pre-violation average sales growth
(Quarters -4,-3,-2, -1)
Post-violation average sales growth
(Quarters +1,+2,+3,+4)
Post Violation - Pre Violation
N
1896
New
Violators
0.116***
Rival
Firms
0.110***
1896
0.055***
0.091***
1896
-0.061***
-0.018
DID
0.006
-0.036**
-0.042***
29
Panel B
Pre-violation average sales growth
(Quarters -4,-3,-2, -1)
Post-violation average sales growth
(Quarters +1,+2,+3,+4)
Post Violation - Pre Violation
N
933
933
933
Small Violators
New
Rival
Violators
Firms
0.150***
0.138***
0.104***
-0.047*
0.128***
-0.010
DID
0.012**
N
963
-0.024
963
-0.036
963
Big Violators
New
Rival
Violators
Firms
0.083***
0.082***
0.009
-0.074***
0.056***
-0.026**
DID
0.001
-0.048***
-0.048***
Panel C
Pre-violation average sales growth
(Quarters -4,-3,-2, -1)
Post-violation average sales growth
(Quarters +1,+2,+3,+4)
Post Violation - Pre Violation
N
1364
Low R&D intensity
New
Rival
Violators
Firms
0.099***
0.100***
1364
0.045***
0.078***
1364
-0.054***
N
1299
Non-Durable Goods
New
Rival
Violators
Firms
0.128***
0.128***
-0.021*
High R&D intensity
New
Rival
Violators
Firms
0.160***
0.136***
DID
-0.001
N
532
-0.033**
532
-0.032**
532
-0.079**
N
597
Durable Goods
New
Rival
Violators
Firms
0.091***
0.070***
0.081***
0.125***
-0.010
DID
0.025***
-0.044
-0.069*
Panel D
Pre-violation average sales growth
(Quarters -4,-3,-2, -1)
Post-violation average sales growth
(Quarters +1,+2,+3,+4)
Post Violation - Pre Violation
1299
0.064***
1299
-0.064***
0.110***
-0.018
DID
0.000
-0.046**
597
0.037**
-0.046**
597
-0.054**
0.051***
-0.018
DID
0.021***
-0.014
-0.035
30
Panel E
Pre-violation average sales growth
(Quarters -4,-3,-2, -1)
Post-violation average sales growth
(Quarters +1,+2,+3,+4)
Post Violation - Pre Violation
N
932
Low Fluidity
New
Rival
Violators
Firms
0.069***
0.070***
932
0.027***
932
-0.042***
0.051***
-0.019
High Fluidity
New
Rival
Violators
Firms
0.169***
0.156***
DID
0.000
N
860
-0.023
860
0.084***
-0.023
860
-0.085***
0.139***
-0.017
DID
0.013**
-0.055*
-0.068**
31
Table 2
Rival firm sales growth and the industry market share of covenant violators
This table presents changes in sales growth of rival firms when firms in their industry violate a financial covenant. The dependent variable is Change in Market
Share defined as the sales growth in the year following the violation ((Sales t+4 -Sales t )/Sales t ) adjusted by the three-digit SIC code industry average sales
growth, where the average excludes the firm itself. Industry market share of violators is the total sales prior to the new covenant violation quarter of violators
within the industry (three-digit SIC code) divided by the total corresponding industry sales. Specifically, it is equal to the sum of the past four quarters’ sales
(Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of violators, divided by the sum of the past four quarters’ sales of that industry (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ), where
subscript t refers to the new covenant violation quarter. We limit the sample to rival firms, i.e, firms that are not in a covenant violation. We include industryquarters which have at least one firm in a new covenant violation. Covenant controls include the following: operating cash flow scaled by average assets,
leverage ratio, interest expense scaled by average assets, net worth scaled by assets, current ratio and market-to-book ratio. All specifications include calendar
quarter-year fixed effects, fiscal quarter-year fixed effects, the level and first difference of Ln(assets), and the level and first difference of the ratio of net fixed
assets to total assets. The first difference is computed as the value in quarter t minus the value in quarter t-4. In some specifications, we include four-quarter
lag of covenant controls (i.e. Covenant Control t-4 ). Columns (4) to (6) show the results for the sample where Industry market share of violators is above the
sample median for rival firms. Columns (7) to (9) show the results for the sample where Industry market share of violators is below the sample median for rival
firms. Definitions of the control variables are described in the appendix. Standard errors are clustered by firm. *, **, and *** indicate significance at the 10%,
5%, and 1% levels, respectively.
32
Industry market share of violators
Operating cash flow / average assets
Leverage ratio
Interest expense / average assets
Net worth / assets
Current ratio
Market-to-book ratio
Change in Market Share t–t-4
Industry market share of violators is
Whole Sample
above the median
1
2
3
4
5
6
0.769*** 0.754*** 0.753***
0.588***
0.573*** 0.559***
(10.31)
(10.09)
(10.10)
(7.78)
(7.56)
(7.39)
-2.569*** -2.605*** -2.608***
-2.018***
-2.054*** -2.105***
(-15.54)
(-13.59)
(-13.56)
(-10.47)
(-8.87)
(-8.88)
0.213*** 0.163** 0.164**
0.212***
0.191*** 0.196***
(5.35)
(2.45)
(2.45)
(5.21)
(2.94)
(3.03)
-0.849
-0.819
-0.818
0.299
0.382
0.369
(-1.02)
(-0.98)
(-0.98)
(0.23)
(0.29)
(0.28)
0.130*** 0.206*** 0.206***
0.140***
0.204*** 0.210***
(4.06)
(3.96)
(3.98)
(3.89)
(3.63)
(3.72)
0.017*** 0.010** 0.010**
0.018***
0.019*** 0.020***
(5.15)
(2.17)
(2.14)
(4.45)
(2.95)
(3.06)
0.025*** 0.036*** 0.036***
0.024***
0.035*** 0.035***
(5.24)
(7.08)
(7.08)
(4.30)
(5.21)
(5.21)
0.001
0.017
(0.09)
(1.26)
Four-quarter lag of covenant controls
No
Calendar quarter-year and fiscal quarter-year fixed effects
Yes
Number of observations
76,624
Adjusted R2
0.055
Yes
Yes
76,624
0.059
Yes
Yes
76,624
0.059
No
Yes
38,180
0.056
Yes
Yes
38,180
0.059
Yes
Yes
38,180
0.059
Industry market share of violators is
below the median
7
8
9
1.813
4.704
4.719
(0.59)
(1.55)
(1.55)
-3.008***
-3.023*** -2.960***
(-13.97)
(-11.60)
(-11.37)
0.196***
0.115
0.111
(3.34)
(1.14)
(1.10)
-1.462
-1.502
-1.524
(-1.56)
(-1.59)
(-1.62)
0.125***
0.197***
0.188**
(2.93)
(2.65)
(2.57)
0.017***
0.005
0.004
(4.25)
(0.84)
(0.65)
0.025***
0.037***
0.038***
(4.36)
(6.12)
(6.15)
-0.014
(-1.28)
No
Yes
38,444
0.063
Yes
Yes
38,444
0.068
Yes
Yes
38,444
0.068
33
Table 3
Rival firm sales growth, product uniqueness, and the industry market share of covenant violators
This table presents changes in sales growth of rival firms when firms in their industry violate a financial covenant based on different characteristics of products
peer violators sell. The dependent variable is Change in Market Share defined as the sales growth in the year following the violation ((Sales t+4 -Sales t )/Sales t )
adjusted by the three-digit SIC code industry average sales growth, where the average excludes the firm itself. Industry market share of violators is the total
sales prior to the new covenant violation quarter of violators within the industry (three-digit SIC code) divided by the total corresponding industry sales.
Specifically, it is equal to the sum of the past four quarters’ sales (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of violators, divided by the sum of the past four quarters’
sales of that industry (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ), where subscript t refers to the new covenant violation quarter. We limit the sample to rival firms, i.e,
firms that are not in a covenant violation. We include industry-quarters which have at least one firm in a new covenant violation. We use three measures for
product uniqueness. In columns (1) to (3), we define product uniqueness by the nature of the industry. The product uniqueness dummy equals one for firms
with SIC industry code between 3400 and 4000 (firms producing machines and equipment) and zero otherwise. In columns (4) to (6), we define product
uniqueness by Research and Development (R&D) Intensity. If a firm’s annual R&D spending is more than the median level for the full sample in the year
preceding the violation, that firm is considered as R&D intensive. If more than half of the violator firms in the industry are R&D intensive, product uniqueness
dummy is defined as one, zero otherwise. In columns (7) to (9), we define product uniqueness by product fluidity. If a firm’s product fluidity is less than the
median level in the full sample in the year preceding the violation, the firm’s product fluidity is defined as low. If more than half of the violator firms in the
industry have low product fluidity, the product uniqueness dummy is defined as one, zero otherwise. Covenant controls include the following: operating cash
flow scaled by average assets, leverage ratio, interest expense scaled by average assets, net worth scaled by assets, current ratio and market-to-book ratio. All
specifications include calendar quarter-year fixed effects, fiscal quarter-year fixed effects, the level and first difference of Ln(assets), and the level and first
difference of the ratio of net fixed assets to total assets. The first difference is computed as the value in quarter t minus the value in quarter t-4. In some
specifications, we include four-quarter lag of covenant controls (i.e. Covenant Control t-4 ). Definitions of the control variables are described in the appendix.
Standard errors are clustered by firm. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
34
Industry market share of violators
Industry market share of violators
*Product Uniqueness Dummy
Product Uniqueness Dummy
Operating cash flow/ average assets
Leverage ratio
Interest expense / average assets
Net worth / assets
Current ratio
Market-to-book ratio
1
1.012***
(11.25)
Durable Goods
2
3
0.991***
0.992***
(11.00)
(11.01)
R&D Intensity
5
0.615***
(7.75)
6
0.616***
(7.78)
7
0.882***
(6.78)
Product Fluidity
8
9
0.874***
0.880***
(6.73)
(6.80)
-0.984***
(-5.77)
0.088***
(7.27)
-2.621***
(-15.84)
0.208***
(5.25)
-0.819
(-0.98)
0.120***
(3.82)
0.016***
(4.94)
0.025***
(5.42)
-0.958***
(-5.63)
0.086***
(7.09)
-2.638***
(-13.75)
0.160**
(2.42)
-0.802
(-0.96)
0.197***
(3.84)
0.009**
(2.03)
0.037***
(7.20)
-0.959***
(-5.64)
0.086***
(7.05)
-2.636***
(-13.69)
0.160**
(2.42)
-0.802
(-0.96)
0.197***
(3.84)
0.009**
(1.98)
0.037***
(7.20)
-0.001
(-0.08)
-0.726***
(-3.53)
-0.130***
(-11.79)
-2.618***
(-15.74)
0.199***
(4.97)
-0.765
(-0.92)
0.145***
(4.39)
0.017***
(5.18)
0.027***
(5.70)
-0.697***
(-3.40)
-0.123***
(-11.24)
-2.619***
(-13.67)
0.159**
(2.37)
-0.758
(-0.91)
0.214***
(4.07)
0.010**
(2.14)
0.038***
(7.32)
-0.697***
(-3.41)
-0.124***
(-11.21)
-2.613***
(-13.59)
0.158**
(2.36)
-0.759
(-0.91)
0.213***
(4.07)
0.010**
(2.08)
0.038***
(7.33)
-0.002
(-0.19)
-0.480***
(-3.29)
0.161***
(17.42)
-2.725***
(-15.38)
0.200***
(4.83)
-0.527
(-0.57)
0.137***
(4.00)
0.015***
(4.56)
0.028***
(5.65)
-0.484***
(-3.32)
0.157***
(16.77)
-2.653***
(-12.93)
0.170**
(2.35)
-0.584
(-0.63)
0.226***
(3.97)
0.010**
(1.97)
0.038***
(7.08)
-0.486***
(-3.34)
0.157***
(16.68)
-2.635***
(-12.81)
0.169**
(2.34)
-0.589
(-0.63)
0.223***
(3.95)
0.009*
(1.88)
0.038***
(7.09)
-0.005
(-0.52)
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
76,624
0.057
Yes
76,624
0.061
Yes
76,624
0.061
Yes
76,624
0.060
Yes
76,624
0.063
Yes
76,624
0.063
Yes
67,781
0.064
Yes
67,781
0.067
Yes
67,781
0.067
Change in Market Share t–t-4
Four-quarter lag of covenant controls
Calendar quarter-year and fiscal
quarter-year fixed effects
Number of observations
Adjusted R2
4
0.619***
(7.85)
35
Table 4
Rival firm advertising expense and the industry market share of covenant violators
This table presents changes in advertising expense of rival firms when firms in their industry violate a financial covenant. The dependent variable is the change
in natural logarithm of annual advertising expense in the year following the violation, the change in annual advertising expense divided by last year’s sales in
the year following the violation, and the change in natural logarithm of the annual advertising expense divided by last year’s sales in the year following the
violation. Industry market share of violators is the total sales of violators within the industry (three-digit SIC code) prior to the new covenant violation year
divided by the total corresponding industry sales. We limit the sample to rival firms, i.e, firms that are not in a covenant violation. We include industry-years
which have at least one firm in a new covenant violation. In this regression, covenant controls are computed using annual data. Covenant controls include the
following: operating cash flow scaled by average assets, leverage ratio, interest expense scaled by average assets, net worth scaled by assets, current ratio and
market-to-book ratio. All specifications include year fixed effects, the level and first difference of Ln(assets), and the level and first difference of the ratio of net
fixed assets to total assets. The first difference is computed as the value in year t minus the value in year t-1, where t is the violation year. In some
specifications, we include one-year lag of covenant controls (i.e. Covenant Control t-1 ). Standard errors are clustered by firm. *, **, and *** indicate significance
at the 10%, 5%, and 1% levels, respectively.
36
Industry market share of violators
Operating cash flow / average assets
Leverage ratio
Interest expense / average assets
Net worth / assets
Current ratio
Market-to-book ratio
Level of the dependent variable
First difference of the dependent variable
Change in Market Share t–t-1
One-year lag of covenant controls
Year fixed effects
Number of observations
Adjusted R2
Change in
Ln(Advertising Expense)
1
2
0.575***
0.522***
(3.35)
(3.07)
0.594***
0.454***
(8.90)
(4.27)
-0.029
-0.095
(-0.44)
(-1.02)
0.045
0.161
(0.11)
(0.37)
-0.027
-0.031
(-0.63)
(-0.52)
-0.000
0.003
(-0.04)
(0.59)
0.029***
0.045***
(6.00)
(7.28)
-0.047***
-0.045***
(-6.88)
(-6.72)
-0.094***
-0.093***
(-4.33)
(-4.23)
-0.018
-0.005
(-0.96)
(-0.25)
No
Yes
7,054
0.131
Yes
Yes
7,054
0.140
Change in
(Advertising Expense/Lagged Sales)
3
4
0.091***
0.087***
(2.71)
(2.83)
-0.139***
-0.147*
(-2.67)
(-1.69)
0.008
-0.021
(0.51)
(-1.28)
0.044
0.053
(0.31)
(0.35)
0.013
0.008
(0.94)
(0.52)
-0.001
-0.001
(-0.90)
(-0.65)
0.012**
0.013**
(2.53)
(2.32)
-0.875***
-0.874***
(-34.62)
(-34.04)
-0.004
-0.006
(-0.50)
(-0.75)
-0.032***
-0.031***
(-3.92)
(-4.27)
No
Yes
7,015
0.817
Yes
Yes
7,015
0.817
Change in
Ln(Advertising Expense/Lagged
Sales)
5
6
0.619***
0.525***
(3.62)
(3.14)
0.363***
-0.069
(4.59)
(-0.52)
-0.028
-0.046
(-0.39)
(-0.46)
-0.047
-0.223
(-0.10)
(-0.44)
-0.029
0.066
(-0.67)
(1.11)
0.007
0.020***
(1.60)
(2.95)
0.018***
0.037***
(3.12)
(5.16)
-0.086***
-0.075***
(-11.99)
(-10.76)
0.003
-0.024
(0.14)
(-1.19)
-0.319***
-0.290***
(-13.72)
(-12.64)
No
Yes
7,015
0.215
Yes
Yes
7,015
0.233
37
38
Table 5
Rival firm operating performance and the industry market share of covenant violators
This table presents changes in operating performance of rival firms when firms in their industry violate a financial covenant. Columns (1) - (3)
show operating cash flow (operating income before depreciation and amortization) scaled by average assets. Columns (4) to (6) show return on
assets (ROA), calculated by operating income before depreciation minus depreciation and amortization divided by average assets. Columns (7) to
(9) show return on equity (ROE), calculated by net income divided by average common equity. Industry market share of violators is the total
sales prior to the new covenant violation quarter of new violators within the industry (three-digit SIC code) divided by the total corresponding
industry sales. Specifically, it is equal to the sum of the past four quarters’ sales (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ) of violators, divided by the
sum of the past four quarters’ sales of that industry (Sales t-1 +Sales t-2 +Sales t-3 +Sales t-4 ), where subscript t refers to the new covenant violation
quarter. We limit the sample to rival firms, i.e, firms that are not in a covenant violation. We include industry-quarters which have at least one
firm in a new covenant violation. Covenant controls include the following: operating cash flow scaled by average assets, leverage ratio, interest
expense scaled by average assets, net worth scaled by assets, current ratio and market-to-book ratio. All specifications include calendar quarteryear fixed effects, fiscal quarter-year fixed effects, the level and first difference of Ln(assets), the level and first difference of the ratio of net
fixed assets to total assets, the level and first difference of the corresponding dependent variable. The first difference is computed as the value
in quarter t minus the value in quarter t-4. Definitions of the control variables are described in the appendix. In some specifications, we include
four-quarter lag of covenant controls (i.e. Covenant Control t-4 ). Standard errors are clustered by firm. *, **, and *** indicate significance at the
10%, 5%, and 1% levels, respectively.
39
Industry market share of violators
Operating cash flow / average assets
Leverage ratio
Interest expense / average assets
Net worth / assets
Current ratio
Market-to-book ratio
Change in
Operating cash flow/average assets
1
2
3
0.015***
0.012***
0.012***
(4.11)
(3.33)
(3.37)
-0.261***
-0.377***
-0.376***
(-23.55)
(-28.50)
(-27.92)
-0.002
-0.011**
-0.011**
(-0.59)
(-2.22)
(-2.23)
-0.017
0.033
0.032
(-0.34)
(0.70)
(0.69)
-0.002
-0.010**
-0.010**
(-0.61)
(-2.36)
(-2.37)
-0.001***
-0.001***
-0.001***
(-7.59)
(-4.48)
(-4.51)
-0.001***
0.000*
0.000*
(-3.26)
(-1.72)
(-1.72)
Level of the dependent variable
First difference of the dependent variable
Change in Market Share t–t-4
Four-quarter lag of covenant controls
Calendar quarter-year and fiscal
quarter-year fixed effects
Number of observations
Adjusted R2
-0.000
(-0.42)
Change in
Return on Assets
4
5
6
0.018***
0.017***
0.017***
(4.62)
(4.56)
(4.64)
0.267***
0.256***
0.258***
(5.53)
(4.73)
(4.77)
-0.006*
-0.014*** -0.014***
(-1.81)
(-2.71)
(-2.73)
0.006
0.022
0.022
(0.12)
(0.47)
(0.46)
-0.006**
-0.013*** -0.013***
(-2.20)
(-2.67)
(-2.70)
-0.001***
0.000**
0.000**
(-4.88)
(-2.44)
(-2.55)
0.000**
-0.000
-0.000
(-2.09)
(-0.78)
(-0.76)
-0.464*** -0.489*** -0.488***
(-9.91)
(-10.40)
(-10.39)
-0.161*** -0.129*** -0.129***
(-15.04)
(-4.29)
(-4.30)
-0.000
(-0.81)
Change in
Return on Equity
7
8
9
0.067***
0.062***
0.063***
(3.07)
(2.89)
(2.90)
1.147***
1.076***
1.078***
(22.33)
(16.81)
(16.61)
-0.125*** -0.135*** -0.135***
(-8.51)
(-4.84)
(-4.85)
-0.291
-0.254
-0.254
(-1.48)
(-1.29)
(-1.29)
-0.020*
-0.001
-0.001
(-1.69)
(-0.05)
(-0.06)
0.002***
0.003***
0.003***
(5.42)
(4.80)
(4.79)
-0.001*
0.000
0.000
(-1.79)
(0.48)
(0.48)
-0.688*** -0.704*** -0.704***
(-29.17)
(-28.68)
(-28.67)
-0.104*** -0.093*** -0.093***
(-8.55)
(-6.51)
(-6.51)
-0.000
(-0.16)
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
72,628
0.108
Yes
72,628
0.132
Yes
72,628
0.132
Yes
67,745
0.140
Yes
67,745
0.141
Yes
67,745
0.141
Yes
66,850
0.211
Yes
66,850
0.212
Yes
66,850
0.212
40
41
Table A.1
Descriptive Statistics
This table shows the descriptive statistics of variables for new covenant violation firms and non-violator rival firms. A new covenant violation is a financial
covenant violation for a firm that has not experienced a financial covenant violation in the previous four quarters. Rival firms are firms that are not in a
covenant violation. Please see the appendix for a detailed description of the variables.
Operating cash flow /average assets
Leverage ratio
Interest expense / average assets
Net worth / assets
Current ratio
Market-to-book ratio
Log(assets)
Log(PPE/assets)
Sales growth
ROA
ROE
Change in Ln(advertising expense)
Advertising expense/Lagged sales
Change in Market Share
Industry market share of violators
N
3563
3526
3522
3604
3511
3604
3604
3599
3561
3413
3333
453
511
3218
New Violators
Mean
-0.004
0.335
0.009
0.382
1.942
1.507
5.024
0.285
0.181
-0.021
-0.150
0.013
0.055
-0.197
Median
0.009
0.302
0.006
0.411
1.508
1.173
4.840
0.210
-0.015
-0.004
-0.033
0.012
0.017
-0.193
N
76624
76624
76624
76624
76624
76624
76624
76624
76624
74213
72661
7054
7015
76624
76624
Rival Firms
Mean
0.011
0.212
0.005
0.525
3.220
2.257
5.229
0.248
0.262
-0.004
-0.041
0.031
0.062
-0.175
0.015
Median
0.024
0.141
0.002
0.565
2.188
1.599
4.970
0.162
0.066
0.012
0.012
0.035
0.018
-0.224
0.004
42
Table A.2
Firm characteristics of industry-propensity score matched rival firms and covenant violators
This table reports mean and median statistics for industry-propensity score matched rival firms and new covenant violators. A rival firm is identified as a nonviolator firm with the same three-digit SIC code, a pre-violation average sales growth within one-half of the standard deviation above and below the violator
firm’s pre-violation average sales growth, and a propensity score of violating a covenant closest to that of the violator firm. The last two columns report the
differences in means and medians. Definitions of the control variables are described in the appendix. ***, ** and * denote 1%, 5% and 10 % levels of
significance, respectively.
N
New Violators
Mean
Median
Industry-propensity score matched
Rival Firms
Mean
Median
Mean
Difference
-0.007***
0.016**
Median
Operating cash flow /average assets
Leverage ratio
1896
1896
0.002
0.318
0.012
0.281
0.009
0.303
0.019
0.285
-0.007***
-0.005
Interest expense / average assets
1896
0.008
0.005
0.007
0.005
0.001***
Net worth / assets
1896
0.405
0.431
0.428
0.430
-0.023***
Current ratio
1896
2.017
1.587
2.085
1.704
-0.068
-0.118**
Market-to-book ratio
1896
1.460
1.134
1.440
1.185
0.020
-0.051***
Log(assets)
1896
5.158
4.981
5.417
5.216
-0.260***
-0.235***
Log(PPE/assets)
1896
0.292
0.218
0.295
0.225
-0.003
-0.008
Sales growth
1896
0.053
-0.024
0.055
0.004
-0.002
-0.027**
ROA
ROE
1785
1676
-0.014
-0.097
-0.001
-0.025
-0.006
-0.071
0.006
0.002
-0.008***
-0.027***
-0.007***
-0.027***
0.0004**
0.001
43