Strategic Disclosure and Debt Covenant Violation∗Thomas Bourveau†

Derrald Stice‡

Rencheng Wang§

October 2015

Preliminary - Do not circulate
Abstract
This study examines how managers change their forecasting behavior as a debt
covenant violation approaches. Using a sample of firms that disclose a debt covenantviolation (DCV) in their annual or quarterly financial statements, we find that management forecast errors are larger and that forecasts are more optimistic leading up toa DCV. Additionally, firms whose management forecasts’ display larger forecast errors
or more optimism before a DCV are more likely to experience increases in dividend
payouts and stock price volatility before the violation, compared to firms that willsoon violate a covenant but whose management forecasts do not exhibit these biases.
Overall, our results are consistent with managers changing their disclosure behavior toconceal upcoming covenant violations from debtholders while also taking actions thatare favorable to equity investors.

Keywords: Debt Covenant Violation, Strategic Disclosure, Risk-Shifting∗

We thank Amir Sufi for sharing data on debt covenant violation on his website.

†

Hong Kong University of Science and Technology, Department of Accounting - actb@ust.hk
Hong Kong University of Science and Technology, Department of Accounting - acstice@ust.hk§
University of Queensland, Business School - r.wang@business.uq.edu.au
‡

1

1

Introduction
This paper examines how managers strategically alter their provision of voluntary disclo-sure to capital market participants before the occurrence of a debt covenant violation. The
accounting literature has extensively studied managers’ incentives to voluntarily disclose partor all of the private information they possess about firms’ future fundamentals.1 Becauseof the repetitive nature of the decision to disclose additional information, managers have
incentives to credibly commit to a truthful disclosure regime. Indeed, prior research showsthat managers with better historical accuracy have been found to have a larger effect on
stock prices and analyst forecast revisions and to achieve more favorable career outcomes(Williams, 1996; Lee et al., 2012; Yang, 2012). Prior studies provide evidence, however, thatmanagers are more likely to intentionally bias the voluntarily disclosed information when
there are benefits in doing so for the firms or managers (e.g., Lang and Lundholm, 2000; Nagar et al., 2003; Rogers and Stocken, 2005) (Hilary et al., 2014b). In this paper, we provide
evidence that managers intentionally bias the information disclosed prior to a debt covenantviolation to delay its discovery and the costs associated with the violation.
A large body of research in accounting and finance has documented the costs and consequences of debt covenant violation (DCV, hereafter). Covenant violations lead to large
negative stock price reactions, increases in interest rates, and difficulty in securing further
financing (Beneish and Press, 1993; Roberts and Sufi, 2009a). Additionally, recent research
provides evidence that covenant violations are associated with a shift of firm control tolenders (see, e.g., Roberts and Sufi (2009b) for a survey of this literature). The shift of control rights is significant, resulting in changes to CEO turnover, corporate restructuring, andreductions in dividend payouts to shareholders (Nini et al., 2012). Prior research provides
strong evidence that managers use both real actions and accrual manipulations to avoid violating a covenant (Sweeney, 1994; DeFond and Jiambalvo, 1994; Dichev and Skinner, 2002;
Beatty and Weber, 2003). However, there is no evidence that managers also modify their1

See Beyer et al. (2010) for a thorough review of this literature.

2

disclosure behavior in their attempts to delay the shift of control rights and decision making
to lenders. This paper intends to fill this gap.
Specifically, we provide evidence that management forecasts issued in the quarter before
a DCV are less accurate (larger forecast error).2 The inaccuracy of the forecast in thequarter before a DCV could be driven by the general uncertainty of a firm experiencing
financial deterioration. We find, however, that forecasts issued in the quarter before a DCV
are more optimistic (relative to the eventually realized earnings) than those issued before
a non-violation quarter.3 These results are both economically and statistically significant
and are consistent with managers attempting to conceal upcoming covenant violations fromdebtholders. Indeed, by issuing more optimistic earnings forecasts, managers are delaying
the disclosure to market participants that earnings will fall below the threshold that triggers
a DCV.4
We next predict that managers who are modifying their forecasts to conceal an upcoming
DCV may also take actions that are beneficial to equity holders and which would likely be
discouraged by debtholders in the event of covenant violation.5 Managers are likely to place
the interests of shareholders ahead of debtholders because it is shareholders who employ
them, and managers themselves are likely to be equity holders, especially in the recent time2
We concentrate our analysis on management earnings forecasts. Indeed, Beyer et al. (2010) suggest thatearnings guidance represents the most important voluntary disclosure channel. They argue that, on average,
it explains approximately 55% of accounting-based information and account for 15.67% of the quarterly
returns variance.
3
To examine the relevance of upcoming covenant violations on current management disclosure choices
we employ the full sample of firms that provide management earnings guidance over our sample period. Inorder to isolate the effects attributable to DCVs for covenant violators, we restrict our sample to those firms
with at least one DCV over our sample period in additional tests and inferences do not change.
4
Our implicit assumption is that firms are attempting to decrease the perceived likelihood of violating
a financial covenant, specifically a covenant containing an earnings component, while in reality firms may
violate both financial and non-financial covenants. Our sample of covenant violators, however, is comprised
solely of financial covenant violators. Moreover, recent studies have documented that the majority of debt
contracts contain an earnings-based debt covenant and that the use of earnings-based covenants has increased
substantially over time (Demerjian, 2011; Demerjian and Owens, 2014). We discuss our sample of covenantviolators in Section 3.
5
After a technical covenant violation, creditors gain the same control rights that they would in the eventof a payment default; and prior studies find that lenders exercise these control rights by decreasing dividend
payments, decreasing capital expenditures, choosing new managers and directors, and influencing other realdecisions of the borrowers (e.g., Chava and Robert, 2008; Nini et al., 2012).

3

period following Reg FD (Hall and Liebman, 1998; Bebchuk and Grinstein, 2005). Consistent
with this prediction, we find that the managers who are modifying their forecasts to be
more optimistic are more likely to authorize large increases (> 50%) in dividend payouts
in the quarter before a DCV is announced, relative to the same quarter in the preceding
year. We also provide evidence that firms whose managers are modifying their forecasts
experience increases in daily stock return volatility. This increase in volatility corresponds
to an increase the riskiness of the firm’s existing assets. Such behavior has been describedin the finance literature as a risk-shifting problem resulting from the conflict of interest
between creditors and equity holders (Jensen and Meckling, 1976; Becker and Stromberg,2012). Taken together, we interpret these findings as providing evidence that managers
attempt to delay the news of upcoming DCVs in order to take actions that benefit equity
holders over debt holders.
We next perform two cross-sectional tests. We first investigate whether the influenceof debtholders over firms affects the characteristics of management forecasts. We find that
our results are more pronounced in low leverage firms, with low leverage firms experiencing
greater forecast inaccuracy and more optimistic bias than high leverage firms. For both
the dividend and stock volatility results, we find a stronger effect for low leverage firms forwhich debt financing is less important. We interpret these results as being consistent withmanagers of firms for whom debt is less vital being more willing to use their forecasts toconceal upcoming DCVs.
Second, we find that our results are concentrated in relatively small firms. That is,
managers at smaller firms alter their earnings guidance towards more optimistic estimatesand use this to act in favor of equity holders over creditors before the occurrence of a DCV
by increasing dividend payouts as well as the riskiness of their firm’s assets. We take thatas evidence that the costs to mislead capital market participants in the short-run to act infavor of equity holders before a DCV are too high for relatively large firms. This is consistent
with the empirical literature in accounting suggesting that larger firms face a higher public4

scrutiny and that size is a major determinant of firms’ securities litigation risk (e.g., Kim
and Skinner, 2012).
Finally, we perform several additional tests to ensure the robustness and validity of ourresults. Our main concern is that our results may be driven by optimistic managers insteadof rational managers deliberately modifying their forecasts before a DCV. A recent stream ofresearch documents that managers exhibit biases in their forecasts, and that such biases may
reflect optimism traits (e.g., Hribar and Yang, 2015). Furthermore, other research suggeststhat agents’ biases in capital markets are endogenous and dynamic (Hilary and Menzly,
2006; Hilary and Hsu, 2011). Additionally, if overconfident managers tend to overestimate
their ability and judgment when managing firms (e.g., Malmendier and Tate, 2005; BenDavid et al., 2013), they may be more likely to violate covenants. The positive correlation
between covenant violations and managerial optimism arising from this omitted variable
could potentially bias our estimated effect upward. As such, our results may be driven by
optimistic managers taking more risk, rather than by rational managers deliberately altering
the characteristics of their own forecasts in order to favor equity holders before the occurrenceof a DCV. To first rule out this concern, we investigate and find that management forecasts’width increases in the quarter preceding the DCV, which is inconsistent with prior researchsuggesting that managers’ optimistic biases are associated with decreased forecasting width(Hilary and Hsu, 2011). Next, we test for and fail to find a difference in the increasein forecast errors and optimism prior to the DCV between firms with prior good versus
bad performance. This again plausibly rules out our concern that our results are driven by
behavioral traits, since prior research finds that optimism in forecasting arises predominantly
in firms that have experienced recent success (Hilary et al., 2014a).
Our study makes three contributions to the literature. First, we add to the literatureinvestigating covenant violation. Prior research has documented that covenant violationis costly and that managers take action to avoid DCVs (e.g., Sweeney, 1994; DeFond andJiambalvo, 1994; Beneish and Press, 1995; Beatty and Weber, 2003), even though the most5common lender response to a DCV is to issue a waiver or renegotiate the contract (Dichev
and Skinner, 2002). We provide evidence consistent with managers altering their disclosure
choices in an attempt to delay the discovery of a DCV by third parties and therefore thecosts associated with violation.
Second, we add to the large literature on voluntary management disclosure (see Beyer
et al., 2010). Specifically, our results complement previous findings that managers intentionally introduce bias in their earnings guidance. For instance, prior works document thatmanagers strategically use earnings guidance to “walk-down” market expectations to a beatable level (e.g., Richarson et al., 2004; Gong et al., 2009). Additionally, Ge and Lennox (2011)
find no evidence of managers issuing optimistic forecasts before an acquisition using stock
and argue that ”deception by commission” is too costly in terms of litigation risk in their
setting. We provide evidence, however consistent with managers perceiving the short-term
benefits of delaying knowledge of a covenant violation using optimistic earnings forecasts tooutweigh the costs of misleading investors.
Lastly, we add interesting insights to our understanding of the agency considerations
that exist as managers choose actions before a covenant violation that affect debt and equity
holders differently. The evidence we provide that managers delay news of an upcoming DCV
using their voluntary forecasts, while taking the exact actions that lenders are likely to discourage after violation is of interest to researchers, practitioners, and regulators. Consistent
with several recent studies in finance that highlight the transfer of control rights to creditors
after a DCV (Roberts and Sufi, 2009a; Nini et al., 2012), we provide evidence that managers
prepare for a loss of control rights by increasing dividends and stock return volatility which
benefits shareholders and managers now to the detriment of creditors.
In the next section we develop our hypotheses. We describe the sample selection procedures and variables used in this study in Section 3. Section 4 presents the main empiricalresults, and Section 5 presents the results of additional analyses. A summary and conclusions
are provided in Section 6.

6

2

Background and Hypothesis Development
As capital providers, lenders focus on ensuring the timely repayment of the principal

and interest that are their claims on a borrower’s future cash flow and assets. Because debt
holders suffer from borrowers’ economic losses but do not share in the upside from economic
gains, they seek to gain control of the firm as soon as possible when their investment is at
risk (see e.g., Aghion and Bolton, 1992). Debt covenants are included in lending contracts inorder to reduce the ability of managers to extract rents from debt holders and to turn controlof the firm over to creditors during bad economic states of the firm (Jensen and Meckling,1976).6 Debt covenants define financial tripwires which shift control rights to lenders when
activated, and they restrict the actions that managers are allowed to take after debt issuance.
Managers accept the costs of including debt covenants, however, because their commitment
to restrict their actions and forfeit control during bad states ex ante generates more favorable
borrowing terms (Bradley and Roberts, 2004).
Covenant violation is costly to shareholders (see e.g., Beneish and Press, 1993, 1995;
Sufi, 2009; Nini et al., 2012; Gao et al., 2015). Following a DCV, lenders are entitled todemand immediate repayment of the loan or they can renegotiate the contract or grant a
waiver. Dichev and Skinner (2002) document that the most common outcomes of a DCV
are obtaining a waiver and renegotiating the contract. Both of these outcomes, however, canbe costly. Beneish and Press (1993) estimate that the average cost of a DCV attributable
to increased interest rates and renegotiation or waiver fees is between one and two percentof the market value of equity for their sample of firms.7
Recent research also provides evidence that DCVs are associated with a shift of firm
control to lenders and other consequences that are costly to managers and equity holders(see, e.g., Roberts and Sufi (2009b) for a survey of this literature). For example, Nini et al.
6
Jensen and Meckling (1976) list unwarranted distributions to shareholders, issuance of higher priority
debt claims, and investments in negative net present value projects for purposes of empire building anddiversification as potential actions that the inclusion of debt covenants attempts to prevent.
7
DCVs may also lead to the costly inclusion of additional covenants to the debt contract during thenegotiation process (Core and Schrand, 1999).

7

(2012) find that DCVs lead to CEO turnover, corporate restructurings, slowdowns in mergers
and acquisitions, and reductions in dividend payouts. Chava and Robert (2008) also reportthat capital investment decreases after financial covenant violation, and Roberts and Sufi
(2009a) find that DCVs increase borrowers’ interest rates and restrict firms’ access to debt
markets. These studies provide evidence that firms that violate debt covenants incur costs
related to the transfer of control to lenders even before formal payment default.8
Management earnings forecasts are voluntary disclosures that provide managers’ estimateof expected earnings over a given period. This channel is one of the main mechanisms for
managers to set or change the market’s earnings expectation. A long literature in accounting
has found that management earnings forecasts provide relevant information to market participants. For example, prior studies have found that management forecasts are associatedwith changes in stock prices (e.g., Nagar et al., 2003), decreases in cost of capital (Frankel
et al., 1995; Coller and Yohn, 1997), and revisions in analysts’ forecasts (Waymire, 1986;
Baginski and Hassell, 1990; Cotter et al., 2006).9
Research shows that prior forecast accuracy affects the credibility, and therefore reaction
to, current management forecasts (Williams, 1996; Hutton, 2007). Over 90 percent of managers surveyed by Graham et al. (2005) confirm that managers issue voluntary forecasts,
including management earnings forecasts, to develop and maintain a reputation for accurate
and transparent reporting. Despite the reputation incentives to be accurate and consistent
when issuing management earnings forecasts, prior research does find that managers canbe strategic in their forecasting behavior. Starting in the 1990s managers are, on average,
8

The severity of the cost of DCV has also been inferred from evidence of the exercise of managerial
reporting discretion. Watts and Zimmerman (1978) posit that managers will choose accounting methods thatwill decrease the probability of debt covenant violation, and several studies have found evidence consistent
with this assertion. Sweeney (1994) finds that firms that are approaching a debt covenant violation respondwith income-increasing accounting changes. DeFond and Jiambalvo (1994) examine a sample of firms that
violated debt covenants and find that in the year before and in the year of the covenant violation, total
accruals and working capital accruals are significantly positive. Beatty and Weber (2003) find that firms
with debt covenants are more likely to adopt income-increasing accounting policies than are firms without
debt covenants. Beneish et al. (2012) find evidence of earnings management and insider trading prior todefault.
9
See Hirst et al. (2008) and Beyer et al. (2010) for a review of the management earnings forecast literature.

8

pessimistic in their quarterly earnings forecasts; and this trend is often explained as a resultof management’s desire to walk-down market, in particular analysts’, earnings expectationin order to increase the likelihood of beating them (Matsumoto, 2002; Cotter et al., 2006;
Bergman and Roychowdhury, 2008).10 Annual earnings forecasts, on the other hand, tend
to be optimistically biased (Rogers and Stocken, 2005; Bergman and Roychowdhury, 2008).
Other factors have been found to influence management forecasting behavior. Nagaret al. (2003) provide evidence that managers holding more firm equity are more likely toissue frequent forecasts in order to avoid mispricing. Lang and Lundholm (2000) show thatmanagement forecasts are more likely to be optimistically biased leading up to an equity
offering, while Aboody and Kaznik (2000) provide evidence that managers issue bad-news
earnings forecasts around stock option award dates in an attempt to temporarily drive down
prices. Similarly, Rogers and Stocken (2005) and Cheng and Lo (2006) provide evidence
that insider trading is related to bad-news management forecasts. Taken together, theseprior studies provide evidence that managers alter their earnings forecasting behavior when
there are personal benefits in doing so that outweigh the costs of reducing their perceived
accuracy and credibility (e.g., Lang and Lundholm, 2000; Nagar et al., 2003; Rogers andStocken, 2005; Ge and Lennox, 2011)
We predict that an upcoming covenant violation will provide a setting in which managers have incentives to modify their forecasting behavior. If managers believe that lenders
are likely to take actions that will be unfavorable to shareholders and themselves when acovenant is violated, they may choose to change their disclosure behavior in order to conceal
an upcoming violation. The first question confronting managers regarding the voluntary
disclosure of an earnings forecast is whether or not to issue one. Nagar et al. (2003) argue
that silence on the part of managers is interpreted as bad news, so it could be that managers
attempting to conceal the likelihood of an upcoming DCV are more likely to issue a management earnings forecast. Consistent with this argument, Houston et al. (2010) provide
10
Early research conducted before the 1990s finds that management earnings forecasts are either unbiased
or optimistic (see e.g., Penman, 1980; McNichols, 1989).

9evidence that managers temporarily stop issuing forecasts when they are unable to meet
analysts’ earnings expectations and start to issue again when they are able.
Conditional on deciding to issue any forecasts, managers must then decide on the frequency of issuance. Prior studies have found that firms with less volatile earnings, better
governance, that consistently meet or beat analysts’ forecasts, or whose earnings are increasing are more likely to increase the frequency of their forecasts (Waymire, 1985; Miller, 2002;
Ajinkya et al., 2005; Houston et al., 2010). At the same time, however, a firm’s management
forecast policy tends to be sticky over time (e.g., Hirst et al., 2008). It could be that managers do not believe they can successfully delay news of an upcoming DCV by deciding toissue a forecast or by increasing the frequency of forecasts. This leads to our first hypothesis,
stated in the null:
H1: Managers change the likelihood and frequency of issuing management forecasts inthe quarter before a debt covenant violation.
After deciding to issue a forecast, managers choose the characteristics of the forecast. If
managers use their forecasts to change the market’s earnings expectation and decrease theperceived likelihood that a covenant will be violated then forecasts issued in the quarter
before a covenant violation will be less accurate. Additionally, because increases in earnings
will unambiguously decrease the likelihood that a covenant is violated, these forecasts will
be optimistically biased. Together, we predict that if managers issue a forecast in the period
before a DCV that the forecast will be less accurate and biased upwards when compared to
the forecasts of managers from firms that will not soon violate a covenant. Formally,
H2: Management forecasts issued before a debt covenant violation are more optimistic anddisplay larger forecast error than forecasts issued before quarters with no covenant violation.

If managers are attempting to conceal an upcoming DCV from debt holders by modifying
their earnings forecasts, we predict that at the same time they will be taking actions that10

benefit shareholders and themselves but that lenders would be likely to prohibit once theygain control rights following a DCV. Nini et al. (2012) show that covenant violations arefollowed immediately by a reduction in dividend payouts. If managers are attempting tobenefit shareholders at the expense of debt holders then paying out a firm’s cash holdings toshareholders is convincing evidence of this. DCVs are not exogenous events, and managers
may very well be taking many actions in their efforts to improve the financial prospectsof the firm in ways that benefit both debt and equity holders. It would be difficult for
managers to argue, however, that increased dividend payments to shareholders help the firm
generally, and debt holders in particular, survive the economic conditions that led to a DCV.
We predict that managers the same managers who display larger forecast errors and more
optimistic bias in their management forecasts before a DCV will also be increasing dividend
payments.
Another channel through which managers can benefit shareholders and themselves is
to increase the riskiness of the projects that they choose. Because equity holders are theresidual claimants on a firm’s assets, they disproportionately benefit from high risk projects
compared to debt holders whose claims on the firm are fixed (see e.g., Aghion and Bolton,
1992). Managers may choose to invest in riskier projects as a last resort, knowing that ahigh payoff would keep the firm from violating a covenant and financially deteriorating evenmore. Given the prior work showing the increases in CEO turnover following a DCV (Niniet al., 2012); managers may feel that this increases their chances of retaining employment at
the firm. Shareholders may not necessarily object to the increased riskiness of the projects
chosen by managers as it increases the value of their claim on the firm (Jensen and Meckling,1976). While we do not observe the portfolio of projects chosen by management, we canobserve the volatility of a firm’s stock. If managers are choosing riskier projects, we willobserve increases in firms’ return volatility in the periods just before a DCV. This leads toour final formal hypothesis:11H3: Managers whose forecasts are more optimistic and display larger forecast error are
more likely to increase dividend payouts and stock price volatility before a debt covenantviolation compared to managers whose forecasts do not exhibit these characteristics.

3

Sample Selection and Empirical Specification

3.1

Sample Selection and Data Source

We use the sample of covenant violation data developed by (Nini et al., 2009) and(Roberts and Sufi, 2009a).11 Next, we merge this dataset with management forecast dataobtained from First Call. We use managerial quarterly earnings per share (EPS) estimatesreported in the First Call database starting from the sample period after the Reg FD. This
sample period allows us to identify management forecasts directly and mitigate the potential
private communications and missing data issues prior to Reg FD (Chuck et al., 2013). For
each fiscal quarter, we keep all forecasts that are issued before the actual earnings releaseto calculate forecast frequency, but only retain the point and range guidance observations
to calculate forecast errors and width, as these variables are less clearly defined for otherforms of guidance (such as open-ended and qualitative guidance).12 Last, we retrieve accounting information from Compustat’s quarterly data files, and stock price and return data
are obtained from the daily files of the Center for Research in Security Prices (CRSP).

3.2

Empirical Specification

To test our first and second hypotheses, we estimate the following regression:11

We retrieve this dataset from Sufi’s website (http://faculty.chicagobooth.edu/amir.sufi/data.html).
Furthermore, in calculating forecast errors and width, we remove any forecasts issued after the fiscal
quarter end (i.e. pre-announcements) to avoid the possibility that pre-announcements may differ in naturefrom manager earnings estimates (e.g., McNichols, 1989; Rogers and Stocken, 2005; Rogers and Van Buskirk,
2013).
12

12

M F V ari,t = β0 + +β1 V ioli,t +nXβx Controli,t + i,t

(1)

x=1

In this empirical model, MFVar stands for a series of management forecast-related variables. Namely, FreqMF, Issue, MFE, and Optim. FreqMF is forecast frequency, measured
as the log of the number of management forecasts issued in quarter t by firm i. Issue is anindicator variable equal to one if a firm issues a forecast in quarter t, and zero otherwise.
MFE is the magnitude of forecast optimism, measured as the difference between management forecast and realized earnings, scaled by price. Optim is forecast optimism, measured
as an indicator variable equal to one if the management forecast is greater than the realized
earnings of quarter t, and zero otherwise. Our variable of interest, Viol, is an indicatorvariable equal to one if a firm violates a debt covenant in quarter t, and zero otherwise.
According to our first hypothesis, we do not expect to find a statistically significant change
in the likelihood of issuing a forecast or in forecasting frequency (i.e. for FreqMF and Issue).
As per our second hypothesis, we expect the coefficient β1 to be positive and statisticallysignificant when the dependent variable is MFE or Optim.
We supplement our model with a series of control variables (Controls). Prior research has
identified various managerial incentives that motivate managers to bias their earnings forecasts, including to support market expectations during financial distress (Frost, 1997; Koch,
2002) (Z-Score), deter potential industry entrants (Newman and Sansing, 1993) (HHI ), facilitate security issuance (Frankel et al., 1995; Lang and Lundholm, 2000) (ExtFin), reduce
expected legal costs (Skinner, 1994, 1997; Baginski et al., 2002) (Litig), and guide analysts’
forecasts to avoid missing expectations at the earnings announcement (Matsumoto, 2002;
Cotter et al., 2006; Gong et al., 2011) (Insto).
Aside from managerial incentives, prior studies document significant relations between
bias and several firm characteristics, including firm performance (ROA, Loss, and Return),

13

accounting accruals (Bloated ), firm size (Size), analyst coverage (Coverage), and growth
opportunity (Btm) (e.g., McNichols, 1989; Rogers and Stocken, 2005; Gong et al., 2009).13
We provide additional details on the above variable definitions in Appendix A. Lastly, we
augment our empirical model with industry and year fixed effects. Year fixed effects accountfor intertemporal changes in management forecast characteristics while industry fixed effects
account for cross-industry differences.14
We winsorize the top and bottom one percentiles of continuous variables to mitigate theinfluence of potential outliers. We employ OLS estimation for models with a continuous
dependent variable, while we use probit estimation for models with a binary dependent
variable. The standard errors are calculated according to the procedure outlined in Cameron
et al. (2011) and are group-wise heteroskedasticity-consistent (i.e., adjusted simultaneously
for heteroskedasticity and the clustering of observations by firm and quarter). Our results
are not affected by calculating the standard errors using one-way clustering (by firm) or
two-way clustering (by firm and year) instead.
To test our third hypothesis, we estimate the following regression:

V ari,t = β0 + β1 V ioli,t + β3 V ioli,t X M F V ari,t + β3 M F V ari,t +nXβx Controli,t + i,t (2)x=1

In this empirical model, the dependent variable, Var, stands for SigDiv and RetVolt.
SigDiv intends to capture significant increases in dividend payouts. It is measured as anindicator variable equal to one if a firm increases the dividend payout to investors by more
13

Our results are not affected when we further control for a firm’s consistency in meeting-or-beating
analyst forecasts (Barton and Simko, 2002; Das et al., 2012; Kross et al., 2011; McInnis and Collins, 2011).
Likewise, our results are not affected when we replace Bloated with either total accruals (Gong et al., 2011)
or discretionary accruals (Kasznik, 1999; Gong et al., 2009). Our results are also robust to controlling forinternal control quality (Feng et al., 2009), fourth-quarter effects, the earnings response coefficient (Daset al., 2011), insider trading (Noe, 1999; Cheng and Lo, 2006), the value-relevance of earnings (Matsumoto,2002; Hutton, 2005), and lagged forecast characteristics (Gong et al., 2011). For the sake of brevity, we do
not include these additional or alternative controls in our baseline models.
14
We define industry fixed effects following the Fama–French 12 industry classification (Gong et al., 2011).

14

than 50% of compared to the amount paid in the same quarter in the previous year (quartert-4), and zero otherwise.15 RetVolt is stock return volatility, measured as the standard
deviation of daily stock return in quarter t. MFVar corresponds to the following set ofmanagement forecast related variables:MFE and Optim. We expect the coefficient on β2 ,the interaction between Viol and MFVar, to be positive and statistically significant. We
include the same set of control variables (Controls) as in Equation (1).

3.3

Descriptive Statistics

Table 1 Panel A reports the descriptive statistics for the full sample of 88,152 firm-quarter
observations used to test the likelihood of managers issuing a forecast before a DCV (H1).
The mean natural log of the number of management forecasts (FreqMF in the quarter before
a DCV is 0.0663. Issue, has a mean of 0.0956, indicating that approximately 10% of firms
have managers that issue of an earnings forecast in any given quarter. AbFreqMF has amean value of -0.0002, indicating that most managers do not issue management forecasts.
Table 1 Panel B reports the descriptive statistics of the 15,698 firm-observations for oursample of firms that issue at least one management forecast over our time period (2000 to2010) used in all subsequent tests. We limit our discussion in the text of the descriptive
statistics of the other variables of interest and of the control variables to the observations inPanel B for brevity.
Viol has a mean of 0.0305, indicating that approximately 3% of firm quarters in our
sample contain a DCV, compared to the 7% violation rate reported by Nini et al. (2012)
for the universe of Compustat firms over their sample period. This difference indicatesthat the types of firms that issue management forecasts are less likely to violate a covenant
than the average firms in Compustat, consistent with prior work that finds that managers
at firms with good financial performance are more likely to issue forecasts Miller (2002).
15

We posit that management forecast bias before a DCV is associated with significant increases in dividend
payouts. The exact amount of increase, however, is an empirical question. Our results are not affected if we
use 40%, 60%, or 100% to define the significance of the change in dividend payout.

15

The average management forecast error (MFE ) in our sample is -0.0004, indicating that,on average, managers are pessimistic in their forecasts. This finding is consistent with priorstudies of management forecasts over quarterly horizon Hirst et al. (2008). Optim has amean value of 0.3722, indicating that roughly 37% of forecasts are higher than the actual
earnings reported. SigDiv has a mean value of 0.0218, indicating that just over 2% of firms
in our management forecast sample increase their dividend payout by at least 50% comparedto the same quarter in the previous year. RetVolt has a mean of 0.0037.
The means of Z-Score, HHI, ExtFin, and Litig are 1.7405, 0.2342, -0.0029, and 0.4320,
respectively. The mean of earnings volatility (EarnVolt) is 0.0200, and the mean institutional
ownership (Insto) is approximately 69%. The average ROA and Return over the last quarteris 0.0114 and -0.0007, respectively. Nini et al. (2012) also documented a negative stock
return in the quarter before a DCV is disclosed. The average firm size (Size) and book-tomarket (Btm) of observations in our sample is 6.9819 (natural log of firm assets) and 0.4788,
respectively. On average, the firms in our sample are covered by 5.67 analysts (Coverage),and have an average net asset bloat (Bloated ) of 2.6259.
Table 2 reports the Pearson correlation matrix of the variables in our sample. Viol is positively correlated with management forecast errors, forecast optimism, significant increases
in dividends, and stock return volatility with statistical significance at the 1% level. These
correlations provide univariate evidence consistent with our predictions. Viol is also significantly correlated with many of the control variables: specifically, it is positively correlatedwith net external financing in the current quarter, earnings volatility, losses, net asset bloat,
and book-to-market, and it is negatively correlated with Z-score, operating in a litigious
industry, ROA, stock return over the previous quarter, firm size, and analyst coverage.

16

4

Empirical Results

4.1

Debt Covenant Violations, Likelihood of Issuing a Forecast,and Forecast Bias

We first examine whether covenant violation effects the likelihood of management forecast issuance (H1). The first two columns of Table 3 report the estimation results of equation(1) with FreqMF (Column 1) and Issue (Column 2) as the dependent variables. The coefficient on Viol is not significant in either specification, suggesting that the likelihood ofand frequency of issuing forecasts do not change as a firm approaches the violation of adebt covenant. Column (3) presents the results of re-estimating equation (1) after replacing the dependent variable with abnormal forecast frequency (AbFreqMF). AbFreqMF ismeasured as the residual value from the estimation results of equation (1) (excluding Viol )and may better capture the effect of DCVs on management forecast issuance. The results,
however, do not change; and the coefficient on Viol remains insignificant. While the results
do not support H1, prior research argues that managers have less discretion over the decision of whether to issue a forecast, because of litigation or reputation concerns and otherpre-existing conditions or antecedents (see e.g., Hirst et al. (2008)).
Next, we examine whether upcoming covenant violations affect the accuracy and bias inmanagement forecasts (H2). Before turning to our multivariate analyses, we present somegraphical evidence. One concern would be that the change in disclosure characteristics isalready present several years before the occurrence of the debt covenant violation, which
would make unlikely that the expected DCV is driving managers’ change in disclosure.
Focusing on the firms with a DCV in quarter t, the first graph in Figure 1 presents theaverage forecast errors around the DCV. The forecast error clearly peaks around the DCV
itself, indicating that managers significantly alter their disclosure right before the DCV. In
the second part of Figure 1, we plot the likelihood to issue optimistic forecast around theDCV. Again, one can see a clear increase compared to the previous periods and a drop
17

after the DCV. Indeed, the likelihood to issue optimistic forecast increases significantly
from 50% two years before the DCV to 60% in the violation quarter. Table 4 reports
results from estimating equation (1) with MFE and Optim as the dependent variables.
Columns (1) and (2) show that the coefficient on Viol is positive and statistically significant
in both specifications with a value of 0.00563 and 0.40047 for MFE and Optim, respectively.
In addition to being statistically significant, the coefficients on Viol are also economicallysignificant in size. The magnitude of the coefficient in Column (1) is approximately 160% of
the magnitude of the effect of Btm.16 The marginal effect of Viol in Column (2) is 14.35%,
which is larger than the effect of Btm (untabulated 5.88%).
Many of the included control variables are statistically significant as well. Management
forecast errors are positively associated with industry concentration, whether or not a firm islocated in a high litigation industry, recent losses, ROA, net asset bloat, and book-to-market.
Forecast errors are negatively associated with earnings volatility and stock returns over theprevious quarter. Forecast optimism is increasing recent net external financing, losses, andbook-to-market and decreasing in earnings volatility, recent stock returns, and firm size.
Taken together, the results presented in Table 4 support H2.
We also perform two cross-sectional tests partitioning the sample on leverage and firmsize. We partition our sample into quintiles of leverage and size and re-estimate equation(1) with MFE and Optim as the dependent variables. Table 5 Panel A present the leverage
results. Columns (1) and (3) present the MFE and Optim results for the lowest quintile offirm leverage while Columns (2) and (4) present the results for the highest quintile. The MFE
effect that we observe in the full sample is still present in both the high and low leverage
partitions with both specifications yielding statistically significant results. The coefficients on
Viol are 0.00772 and 0.00325 for low and high leverage, respectively. As predicted, however,16

It is customary to evaluate the economic effect by comparing it to the mean or median of the dependent
variable. In our case, however, this would result in division by a number very close to zero. Instead, we
compare the effect of Viol to Btm in order to obtain economic significance in a relative manner. To do so,
we multiply the coefficient on Viol by the standard deviation of Viol, and scale this by the corresponding
product for Btm. We continue to use a similar procedure to calculate the economic magnitude of our key
variables in other OLS estimations.

18the coefficient on Viol is larger in the sample of firms with low leverage compared to thosewith high leverage (χ2 = 4.26). The Optim effect follows the same pattern for firms withlow and high leverage. The coefficient on Viol is 0.65656 and 0.17214 for firms with low
and high leverage, respectively, and the coefficient is statistically larger in the low leverage
partition (χ2 = 6.35). We interpret this result as providing evidence that firms with lower
leverage having more bargaining power with lenders; and therefore, managers at low leveragefirms are more willing to manipulate their voluntary forecasts to conceal upcoming DCVs
and delay lenders’ ability to gain control rights. Firms that rely heavily on debt may not be
willing to jeopardize the relationship with their lender, knowing that the costs of switching
to a new lender are substantial (Petersen and Rajan, 1994).
Table 5 Panel B presents the size results. We use firm size to capture the litigation riskof a firm. Larger firms face more public scrutiny and investors in large firms are more likely
to take legal action in the event of the financial deterioration of a firm or of unexpected
bad news (Kim and Skinner (2012). Columns (1) and (3) present the MFE and Optim
results for the lowest quintile of firm size while Columns (2) and (4) present the results forthe highest quintile of firm size. The MFE and Optim effects that we observe in the fullsample are still present in both the small and large firm size partitions with the coefficienton Viol remaining statistically significant in all specifications. The coefficient on Viol in theforecast error specifications is larger in the sample of small firms compared to large firms as
predicted (χ2 = 5.10). We do not find, however, a significant difference in the coefficients
across small and large firms for the forecast optimism results, both coefficients are positiveand statistically significant. These results are consistent with managers being more willingto manipulate their forecasts when litigation risk is lower. Overall, we interpret the twocross-sectional tests in Table 5 as providing evidence that managers are more willing tostrategically alter their earnings forecasts to delay the news of upcoming covenant violations
when the cost of doing so is lower.

19

4.2

Dividend Payouts and Stock Price Volatility

Table 6 reports the estimation results of equation (2) where the dependent variables
are SigDiv and RetVolt. H3 predicts that managers that are strategically altering theirearnings forecasts to delay news of an upcoming DCV will also be taking real actions that
benefit shareholders. Specifically, we investigate whether or not managers that manipulate
their forecasts also significantly increase dividend payouts and increase a firm’s stock pricevolatility (by increasing the riskiness of a firm’s projects). The indicator variable SigDiv takes
a value of one if managers increase a firm’s dividend payout by at least 50% compared to thesame quarter in the previous year, and zero otherwise. Columns (1) and (2) show that thecoefficients on the interaction terms between Viol and the proxies for forecast accuracy (MFEand Optim) are positive and significant, suggesting that managers who issue biased forecasts
are more likely to significantly increase dividend payouts before a DCV. The marginal effectof MFE X Viol is 3.07%, which is greater than all of the other variables included in the
regression; meanwhile, the marginal effect of Optim X Viol is 7.84%.
Columns (3) and (4) show the interaction effects on stock return volatility. The coefficients on MFE X Viol and Optim X Viol are also positive and significant, suggesting thatmanagers who issue biased forecast are more likely to increase stock return volatility before a
DCV. Taken together, the results in Table 6 support the view that managers modify forecasts
are also taking other actions that benefit shareholders at the expense of debt holders.
We investigate the dividend and stock volatility results using the cross-sectional partitions
from Table 5. We partition our sample into quintiles of leverage and size and re-estimate
equation (2) with SigDiv and RetVolt as the dependent variables. Table 7 Panel A present
the leverage results. Columns (1) and (3) present the MFE X Viol and Optim X Viol
results for the lowest quintile of firm leverage while Columns (2) and (4) present the results
for the highest quintile. The MFE X Viol and Optim X Viol effects that we observe in thefull sample are driven exclusively by the low leverage observations, the interaction term is
insignificant in the high leverage specifications. Additionally, the interaction coefficients in20

the low leverage partitions are statistically larger than the coefficients in the high leverage
partitions (χ2 = 2.23 and χ2 = 3.58 for MFE X Viol and Optim X Viol, respectively.)
Table 7 Panel A Columns (5) through (8) provide the leverage results for the stock return
volatility results. We observe a similar pattern of results. The coefficients on the interaction
terms are larger for in the low leverage partition than the high leverage partition. The
difference in coefficients, however, is only significant at conventional levels for the MFE X
Viol specifications (χ2 = 1.99). This is likely due to a loss of power in our tests as wepartition the interaction.
Panel B of Table 7 presents the dividend and stock volatility results after partitioning on
firm size. Columns (1) through (4) present the dividend results. The coefficients on MFE X
Viol and Optim X Viol are both larger when firm size is small (χ2 = 3.24 and χ2 = 4.90,
respectively). We find similar results in the stock volatility tests where the coefficients on
MFE X Viol and Optim X Viol are again both larger when firm size is small (χ2 = 3.59 andχ2 = 2.98, respectively). Together, these cross-sectional tests provide evidence consistent
with our predictions and prior studies in finance (Becker and Stromberg, 2012; Jensen andMeckling, 1976). Managers acting in the behalf of equity holders have incentives to increase
the riskiness of the existing assets, and therefore stock volatility, even in cases when thisreduces the net present value of the firm. This is because the benefits of this higher risk
primarily go to equity holders thanks to their limited liability while the costs are primarilyborn by creditors. This problem is commonly known as the risk-shifting problem. Thesecross-sectional tests show that these effects are stronger When the costs of concealing an
upcoming covenant violation are lower.

5

Robustness Tests
Correlated Omitted Variables
The results from the previous section demonstrate that, on average, managers issue

21

earnings guidance that exhibit larger forecast errors and that are more likely to be optimistic
in the quarter before the occurrence of a debt covenant violation. Throughout the paper, we
argue that managers privately anticipate the incidence of a debt covenant violation and avoid
signaling this violation earlier by issuing more optimistic forecast. Next, they use the time
before the covenant violation to strategically act in favor of shareholders relative to creditors
by increasing dividend payments and the riskiness of firms’ assets. One potential concern andalternative explanation is that our results are driven by managers’ behavioral characteristics
rather than by a rational managers that strategically modify firms’ provision of voluntary
disclosure to deliberately favor equity holders at the expense of creditors. Indeed, a recent
stream of research has highlighted the role of managers’ behavioral traits, such as executives’
overconfidence, in explaining corporate choices (e.g., Malmendier and Tate, 2005; Ben-Davidet al., 2013), which could potentially lead to covenant violation. In this section, we perform
two distinct robustness tests to plausibly rule out the possibility that our results are driven
by underlying behavioral static and/or dynamic characteristics of managers at firms thateventually violate a debt covenant.
A first specific concern would be that firms that violate covenants are run by managers
that are different (i.e. overconfident) compared to firms that do not happen to violatecovenants during our sample period. If this is true and managerial overconfidence is able toexplain the positive relation between DCVs and forecast optimism, we then would expect anegative association between covenant violations and forecast width. Indeed, greater forecast
precision has been identified as one of the necessary conditions of managerial overconfidence(Hilary and Hsu, 2011). We directly test this alternative explanation and report our resultsin column (1) of Table 8. Specifically, we find a positive and statistically significant coefficienton our Viol variable. We measure Width as the difference between the upper- and lower-end
estimates, scaled by price (point estimates then have a range of zero). This indicates that
forecast width is larger for guidance issued by managers in firms that are about to experiencea debt covenant violation. This finding is not consistent with managerial overconfidence. It

22

is however consistent with managers providing vague forecasts in order to conceal news of an
upcoming DCV. Finally, including firm or manager fixed effects to our model does not affectour inferences (untabulated). These results further mitigate the concern that time-invariant
managerial style, especially personal attributes of managers, could explain our findings (e.g.,Bamber et al., 2010).
Second, a recent study by Hilary et al. (2014a) finds that recent past success leads tomore optimistic forecasts in the future. These recent findings suggest that if our results
are driven by dynamic managerial optimism, we should expect them to be concentrated
in firms that have experienced good performance in the recent past. We re-run our tests
exploring these alternative explanations and also provide the results in Table 8. Specifically,
we next re-estimate equation (1) partitioning our sample by using firms’ past performance.
We define a firm as having had relatively bad performance if its averaged return on assets
over the last four quarters is lower than that of the industry median. Columns (2)-(5) provide
evidence that past performance does not effect the association between covenant violationand forecast optimism that we document in Table 4. That is, both firms that experiencepast good and bad operating performance display, on average, increase in forecast error andoptimism before the occurrence of a debt covenant violation. In untabluated analyses, theserobustness tests are not affected if we use consistency or frequency of meeting or beating
earnings benchmarks to categorize past performance in lines with the design in Hilary et al.
(2014a), although the reduced sample size weakens our testing power.
Selection Issues
Managers have discretion over the forecast issuance decision. Hence, the observed association between DCV and forecast bias may not represent the true relationship in a complete
series of management forecasts (including both issued and unissued management forecasts)
due to non-random sample selection. Our main results of forecast bias, however, are lesslikely to be driven by such selection issues for two reasons. First we do not find a significant
change in forecast issuance as a DCV approaches, which mitigates the concern that the ob-

23

served change in forecast bias is potentially related to changes in forecast issuance. Second,
we follow Lennox and Park (2006) and (Gong et al., 2011) to implement a Heckman twostage procedure. Following these two studies, we use two instruments, the earnings response
coefficient (ERC) and the quarterly industry-median values of ERC. Both variables plausibly
explain the management forecast issuance decision (first stage) but have no obvious direct
effect on forecast bias (second stage). Consistent with this view, the correlation between
the instruments and an indicator variable for the forecast issuance is positive and significant(p-values¡0.01). In contrast, the correlation between the instruments and the error terms of
the second stage are low (the p-values range from 0.35 to 0.91). More importantly, the untabulated second-stage regressions continue to demonstrate a positive and significant relation
between forecast bias and covenant violation.

6

Conclusion
This paper studies the relation between strategic voluntary disclosure and debt covenantviolation. Specifically, we find robust evidence that managers issue less accurate and more
optimistic earnings guidance prior to a debt covenant violation in order to conceal the upcoming violation. Furthermore, we document that the managers who increased the strategicallybiased their forecasts then take actions that favor equity holders at the expense of debtholders. Specifically, managers that issue biased forecasts also significantly increase dividend
payouts and the riskiness of firms’ assets. Our results are stronger when firms exhibit lower
leverage, consistent with the intuition that managers are more likely to act in favor of equity holders when they represent a larger source of financing. Furthermore, our results areconcentrated in relatively small firms, whose public scrutiny is weaker. In other words, thetrade-off to mislead capital market participants in the short-run to conceal an upcoming
debt covenant violation is perceived as less costly for small firms that carry a lower litigation
risk.

24

Our results contribute to the accounting literature investigating firms’ behavior in reaction to the incidence of debt covenant violation. Next, our paper adds to the vast literatureon the strategic disclosure of managers by documenting that future debt covenant violations
create incentives for managers to alter their provision of voluntary disclosure. Finally, ourresults highlight how disclosure is used as a tool by managers to act in favor of equity holders relative to creditors, contributing to the rich literature in finance examining risk-shifting
behavior and shareholders-creditor conflicts.

25

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31

Figure 1: Management Forecast Characteristic around Debt Covenant Violation

MFE
0.007
0.006
0.005
0.004
MFE

0.003
0.002
0.001
0
Qt-8~Qt-5

Qt-4~Qt-1

Qt

Qt+1~Qt+4

Optim
0.7
0.6
0.5
0.4
Optim

0.3
0.2
0.1
0
Qt-8~Qt-5

Qt-4~Qt-1

Qt32

Qt+1~Qt+4

Table 1: Descriptive Statistics
This table reports summary statistics for the full sample in Panel A and management forecast
sample in Panel B. All variables are defined in Appendix A.

Panel A: Full Sample
Variable
FreqMF
Issue
AbFreqMF
Viol
ZscoreHHI
ExtFinLitig
EarnVolt
Insto
Loss
ROA
ReturnBloated
Size
Btm
CoverageN
Mean Std. Dev.
88,152 0.0663
0.203988,152 0.0956
0.2941
88,152 -0.0002
0.1957
88,152 0.0591
0.235888,152 1.0026
2.4311
88,152 0.2413
0.1852
88,152 0.0067
0.063888,152 0.3496
0.476988,152 0.0317
0.049888,152 0.4947
0.3052
88,152 0.3403
0.473888,152 -0.0072
0.059588,152 -0.0025
0.258888,152 3.7425
6.627588,152 5.7567
2.0127
88,152 0.6204
0.549088,152 1.1542
0.984833

25th Perc.
0.00000.0000-0.0972
0.00000.51740.1038-0.01170.00000.00670.21810.0000-0.0108-0.1476
1.1936
4.28050.27480.0000Median0.00000.0000-0.0500
0.00001.32320.1939-0.0004
0.00000.01410.51520.00000.0078-0.0175
2.14995.71900.4705
1.098675th Perc.
0.00000.0000-0.00850.00002.14780.31210.00701.00000.03370.76031.00000.02030.11573.78557.09530.7655
1.9459Panel B: Management Forecast Sample
Variable
MFE
Optim
SigDiv
RetVolt
Viol
Z-ScoreHHI
ExtFinLitig
EarnVolt
Insto
Loss
ROA
ReturnBloated
Size
Btm
CoverageN
Mean Std. Dev.
15,698 -0.0004
0.007815,698 0.3722
0.4834
15,698 0.0218
0.146015,698 0.0037
0.0021
15,698 0.0305
0.172015,698 1.7405
1.5994
15,698 0.2342
0.162815,698 -0.0029
0.0433
15,698 0.4320
0.4954
15,698 0.0200
0.0283
15,698 0.6892
0.2252
15,698 0.1757
0.3806
15,698 0.0114
0.0303
15,698 -0.0007
0.2171
15,698 2.6259
2.402915,698 6.9819
1.6192
15,698 0.4788
0.338915,698 1.8981
0.753434

25th Perc.
-0.00200.00000.00000.00230.00001.10270.1113-0.0166
0.00000.0054
0.55360.00000.0041-0.1232
1.16765.87960.25411.3863

Median-0.0006
0.000001.0000
0.00310.00001.73770.1947-0.00140.00000.01010.73180.00000.0140-0.0088
2.0317
6.87580.3962
1.945975th Perc.
0.00001.00001.00000.00450.00002.49550.30420.00521.00000.02150.86020.00000.02480.10833.23187.98430.60702.3979

Table 2: Correlation Matrix

This table reports Pearson correlations. All variables are defined in Appendix. ***, **, and * indicate significance at the 1%,5%, and 10% level, respectively.

35

Variable (N= 15, 698)
(1)
(2)
(3)
(1) MFE
1.00(2) Optim
0.467***
1.00(3) SigDiv-0.0010.019**
1.00(4) RetVolt
0.043*** 0.122*** 0.296***(5) Viol
0.140*** 0.086*** 0.051***(6) Z-Score
0.040*** -0.019** -0.095***(7) HHI
0.017**
0.002-0.150***(8) ExtFin-0.0050.017**
0.096***(9) Litig-0.0050.026*** 0.216***(10) EarnVolt-0.046***0.000
0.194***(11) Insto
0.005-0.057*** -0.034***(12) Loss
0.015*
0.061*** 0.157***(13) ROA
0.017** -0.046*** -0.114***(14) Return-0.137*** -0.155*** -0.020**(15) Bloated0.020**
0.009
0.003(16) Size-0.017** -0.083*** -0.350***(17) Btm
0.073*** 0.079*** 0.061***(18) Coverage-0.020** -0.051*** -0.071***

(4)

(5)

(6)

(7)

(8)

1.00
0.133***-0.190***-0.101***0.102***
0.188***
0.419***-0.345***0.368***-0.357***-0.109***0.075***-0.393***0.214***-0.160***1.00-0.066***0.007
0.015*-0.013*0.052***-0.084***0.123***-0.109***-0.066***0.015*-0.133***0.116***-0.085***

1.00
0.008
-0.075***0.026***-0.352***0.249***-0.368***0.509***-0.042***-0.170***0.318***-0.272***0.209***

1.00-0.011-0.256***-0.071***-0.006-0.064***0.028***
0.01-0.037***0.057***
0.019**-0.091***

1.00
0.01
0.085***-0.044***0.058***-0.088***0.031***
0.029***-0.105***-0.012-0.039***

(9)

(10)

1.00
0.125***
1.00
0.009-0.241***0.125*** 0.344***-0.069*** -0.399***-0.031*** -0.052***-0.098*** 0.106***-0.021*** -0.195***-0.066*** 0.028***0.161*** -0.047***

(11)

1.00
-0.234***0.249***-0.034***-0.053***0.343***-0.204***0.369***

(12)

(13)

(14)

(15)

(16)

1.00
-0.629***1.00-0.064*** 0.072***
1.00
0.154*** -0.207*** -0.060***
1.00-0.264*** 0.260*** -0.042*** 0.075***
1.00
0.244*** -0.280*** 0.096*** 0.184*** -0.453***-0.106*** 0.127*** -0.050*** 0.069*** 0.654***

(17)

(18)

1.00
-0.313***

1.00

Table 3: Debt Covenant Violation and Voluntary DisclosureThis table presents the results from regressions relating voluntary disclosure choices to theviolation of debt covenants for U.S. listed firms from 2000 to 2010. Columns (1) and (3)report the estimation results of the OLS regressions. Column (2) reports the ones of the
Probit regression. All dependent and independent variables are defined in Appendix A. pvalues are reported under brackets and standard errors are corrected for heteroskedasticity
and clustered by firm and quarter. ***, **, and * indicate significance at the 1%, 5%, and
10% level, respectively.

(1)
(2)
(3)
FreqMF
Issue
AbFreqMF
Viol
0.00201
0.026720.00193(0.503)(0.462)(0.516)
Z-Score0.00004
0.00039-0.00217***(0.924)(0.954)(0.000)HHI-0.01117-0.09505-0.02400***(0.134)(0.200)(0.001)
ExtFin0.00572
0.026170.01593(0.570)(0.823)(0.112)
Litig
0.01594***
0.08945** -0.01321***(0.000)(0.017)(0.001)
EarnVolt-0.02709-0.65800***-0.01322(0.118)(0.007)(0.445)
Insto0.01949**
0.32111***0.01295(0.014)(0.000)(0.101)
Loss-0.01962*** -0.19021***0.00153(0.000)(0.000)(0.544)ROA
0.03328**0.68369***
0.02144(0.025)(0.006)(0.148)Return-0.02978*** -0.31205***
0.00144(0.000)(0.000)(0.731)
Bloated-0.00042*** -0.01711*** 0.00066***(0.001)(0.000)(0.000)Size-0.00582*** -0.06544***-0.00075(0.000)(0.000)(0.626)
Btm-0.00874***-0.02223-0.01816***(0.000)(0.425)(0.000)Coverage
0.05535*** 0.54837***
0.00226(0.000)(0.000)(0.535)
Year Fixed Effects
Yes
Yes
Yes
Industry Fixed Effects
Yes
Yes
Yes
Observations
88,152
88,152
88,152
R2
0.095
0.160
0.028
36

Table 4: Debt Covenant Violation and Management Forecast Characteristics
This table presents the results from regressions relating various management forecast characteristics to the violation of debt covenants for U.S. listed firms from 2000 to 2010. Column(1) reports the estimation results of the OLS regression, and column (2) reports the ones of
the Probit regression. All dependent and independent variables are defined in Appendix A.
p-values are reported under brackets and standard errors are corrected for heteroskedasticity
and clustered by firm and quarter. ***, **, and * indicate significance at the 1%, 5%, and
10% level, respectively.

(1)
(2)
MFE
Optim
Viol
0.00563*** 0.40047***(0.000)(0.000)Z-Score0.00012-0.01120(0.184)(0.333)HHI
0.00107*0.13258(0.079)(0.201)
ExtFin0.00172
0.55414**(0.362)(0.027)
Litig
0.00062**
0.09266*(0.018)(0.063)
EarnVolt-0.01273*** -2.64205***(0.001)(0.000)Insto
0.00098**-0.03404(0.037)(0.634)Loss
0.00072**
0.07827*(0.025)(0.065)
ROA
0.01507***0.91051(0.001)(0.120)Return-0.00495*** -1.02189***(0.000)(0.000)Bloated0.00006*-0.00019(0.095)(0.973)Size-0.00001-0.05235***(0.945)(0.002)
Btm
0.00176*** 0.16421***(0.005)(0.003)Coverage-0.00005
0.02443(0.698)(0.347)
Year Fixed Effects
Yes
Yes
Industry Fixed Effects
Yes
Yes
Observations
15,69815,698
R2
0.058
0.049
37

Table 5: Cross-Sectional Analysis
on Debt Covenant Violation and Management Forecast Characteristics
This table presents the results from regressions relating various management forecast characteristics to the violation of debt covenants for U.S. listed firms from 2000 to 2010, groupedby leverage ratio and firm size. Columns (1) and (2) report the estimation results of the OLS
regressions, and columns (3) and (4) report the ones of the Probit regressions. All dependentand independent variables are defined in Appendix A. p-values are reported under brackets
and standard errors are corrected for heteroskedasticity and clustered by firm and quarter.
***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
Panel A: Partition using Leverage

(1)
(2)
(3)
(4)
Low Lev
High Lev
Low Lev
High Lev
MFE
MFE
Optim
Optim
0.00772*** 0.00325*** 0.65656*** 0.17214(0.000)(0.014)(0.000)(0.189)Viol

χ2 -test for difference
Control Variables
Year Fixed Effects
Industry Fixed Effects
Observations
R2

4.26
Yes
Yes
Yes
3,135
0.085

6.35
Yes
Yes
Yes
3,143
0.043

Yes
Yes
Yes
3,135
0.061

Yes
Yes
Yes
3,143
0.052

Panel B: Partition using SizeViol

χ2 -test for difference
Control Variables
Year Fixed Effects
Industry Fixed Effects
Observations
R2

(1)
(2)
Low Size High Size
MFE
MFE
0.00816*** 0.00342(0.000)(0.143)(3)(4)
Low SizeHigh Size
Optim
Optim
0.51888*** 0.69170***(0.008)(0.003)

5.10

1.36

Yes
Yes
Yes
3,592
0.092

Yes
Yes
Yes
3,591
0.039
38

Yes
Yes
Yes
3,592
0.062

Yes
Yes
Yes
3,591
0.069

Table 6: Dividends and Stock Price Volatility
This table presents the results from regressions relating dividend payout policy and stock
price volatility and management forecast characteristics to the violation of debt covenantsfor U.S. listed firms from 2000 to 2010. Columns (1) and (2) report the estimation resultsof the Probit regressions, and columns (3) and (4) report the ones of the OLS regressions.
All dependent and independent variables are defined in Appendix A. p-values are reported
under brackets and standard errors are corrected for heteroskedasticity and clustered by firmand quarter. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

Viol
MFE X Viol
MFE
Optim X Viol
Optim
Z-Score(1)
SigDiv
0.00802(0.930)12.80303**(0.029)-0.07593(0.978)-0.00405(0.895)HHI-0.37340(0.112)
ExtFin1.92331***(0.000)Litig
0.11758(0.324)
EarnVolt
8.72978***(0.000)Insto
0.51647***(0.004)Loss
0.13628*(0.065)ROA
1.08943(0.390)Return-0.00210(0.977)
Bloated
0.01478(0.378)Size-0.46159***(0.000)Btm-0.32096***(0.007)
Coverage
0.36709***(0.000)Year Fixed Effects
Yes
Industry Fixed Effects
Yes
Observations
15,698
R2
0.280

(2)
SigDiv-0.11587(0.329)-(3)
RetVolt
0.00014(0.361)
0.02203***(0.009)-0.00169(0.518)-(4)
RetVolt
0.00025**(0.013)-0.32689**0.00035*(0.028)(0.094)-0.03114
0.00003(0.351)(0.258)-0.00427
0.00010*** 0.00010***(0.889)(0.002)(0.002)-0.37044-0.00021-0.00021(0.114)(0.100)(0.105)1.92575*** 0.00138*** 0.00140***(0.000)(0.001)(0.001)0.118750.00017*** 0.00017***(0.319)(0.006)(0.005)8.69796*** 0.01543*** 0.01541***(0.000)(0.000)(0.000)0.51281*** -0.00077*** -0.00076***(0.004)(0.000)(0.000)0.13602*0.00032*** 0.00032***(0.066)(0.000)(0.000)1.10378-0.00489*** -0.00485***(0.386)(0.000)(0.000)-0.01677-0.00089** -0.00090***(0.817)(0.010)(0.009)0.01476-0.00000-0.00000(0.378)(0.884)(0.904)-0.46235*** -0.00039*** -0.00039***(0.000)(0.000)(0.000)-0.31756***0.000070.00006(0.008)(0.655)(0.670)0.36754*** 0.00034*** 0.00034***(0.000)(0.000)(0.000)
Yes
Yes
Yes
Yes
Yes
Yes
15,69815,69815,698
0.280
0.552
0.552
39

Table 7: Cross-Sectional Analysis on Dividends and Stock Price Volatility
This table presents the results from regressions relating dividend payout policy and stock price volatility and management
forecast characteristics to the violation of debt covenants for U.S. listed firms from 2000 to 2010, grouped by leverage ratio andfirm size. Columns (1)-(4) report the estimation results of the Probit regressions, and columns (5)-(8) report the ones of the
OLS regressions. All dependent and independent variables are defined in Appendix A. p-values are reported under brackets andstandard errors are corrected for heteroskedasticity and clustered by firm and quarter. ***, **, and * indicate significance atthe 1%, 5%, and 10% level, respectively.
Panel A: Partition using Leverage

Viol
40

MFE X Viol
MFE
Optim X ViolOptim
χ2 -test for difference
Control Variables
Year Fixed Effects
Industry Fixed Effects
Observations
R2(1)
Low Lev
SigDiv-0.01551(0.949)34.81956**(0.015)-18.26663***(0.005)-(2)
High Lev
SigDiv-0.10027(0.559)2.70633(0.824)0.87912(0.879)
-

-

-

(3)
Low Lev
SigDiv-0.47463(0.175)
-

(4)
(5)
(6)
High Lev Low Lev High Lev
SigDiv
RetVolt
RetVolt-0.01629 0.00037* 0.00039**(0.946)(0.088)(0.011)0.02658** 0.00020(0.33)(0.988)-0.00194-0.00175(0.672)(0.671)
0.96897** -0.10735(0.043)(0.800)-0.21529** -0.01735(0.016)(0.831)

2.23
Yes
Yes
Yes
3,143
0.31

3.58
Yes
Yes
Yes
3,135
0.26

Yes
Yes
Yes
3,143
0.31

(7)
Low Lev
RetVolt-0.00011(0.680)-(8)High Lev
RetVolt
0.00040*(0.091)---0.00094**(0.022)0.00009(0.101)-0.00003(0.931)-0.00001(0.816)

1.99
Yes
Yes
Yes
3,135
0.26

Yes
Yes
Yes
3,143
0.58

1.31
Yes
Yes
Yes
3,135
0.28

Yes
Yes
Yes
3,143
0.58

Yes
Yes
Yes
3,135
0.58

Panel B: Partition using SizeViol
MFE X Viol
MFE
Optim X Viol
Optim
41

χ2 -test for difference
Control Variables
Year Fixed Effects
Industry Fixed Effects
Observations
R2

(1)
(2)
(3)
(4)
Low Size High Size
Low SizeHigh Size
SigDiv
SigDiv
SigDiv
SigDiv-0.43253** 0.22578 -0.82976*** 0.49999(0.029)(0.417)(0.003)(0.286)85.55468*
1.81443(0.051)(0.907)-7.65561-2.92621(0.178)(0.726)1.01824*** -0.42831(0.008)(0.463)0.01387-0.13202*(0.808)(0.052)
3.24
Yes
Yes
Yes
3,592
0.17

(5)
Low Size
RetVolt
0.00048*(0.060)0.02423(0.241)-0.00646(0.193)
-

4.9
Yes
Yes
Yes
3,550
0.24

Yes
Yes
Yes
3,592
0.17

(6)
(7)
(8)
High Size Low Size High Size
RetVolt
RetVolt
RetVolt-0.00024-0.00010-0.00031(0.307)(0.783)(0.211)-0.02649*(0.098)0.00812(0.468)0.00113** 0.00003(0.033)(0.928)-0.00004
0.00000(0.366)(0.997)

3.59
Yes
Yes
Yes
3,550
0.24

Yes
Yes
Yes
3,592
0.52

2.98
Yes
Yes
Yes
3,550
0.61

Yes
Yes
Yes
3,592
0.52

Yes
Yes
Yes
3,550
0.61

Table 8: Robustness Test - Alternative Stories
This table presents additional results from the regressions relating forecast width and othermanagement forecast characteristics to the violation of debt covenants for U.S. listed firms
from 2000 to 2010. Column (1) reports the results of forecast width. Columns (2)-(54)report the comparative statistics based on past firm performance. p-values are reported
under brackets and standard errors are corrected for heteroskedasticity and clustered by firmand quarter. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
(1)
viol
Z-ScoreHHI
ExtFinLitig
EarnVolt
Insto
Loss
ROA
ReturnBloated
Size
Btm
Coverageχ2 -test for differenceYear Fixed Effects
Industry Fixed Effects
Observations
R2

Width
0.00045**(0.026)-0.00019***(0.000)-0.00037*(0.060)-0.00147***(0.006)0.00010(0.421)0.01039***(0.000)-0.00146***(0.000)0.00077***(0.000)-0.00427*(0.073)0.00014(0.270)-0.00018***(0.000)-0.00024***(0.000)0.00281***(0.000)-0.00007(0.211)
Yes
Yes
15,698
0.378

(2)
(3)
(4)
Bad
Other
Bad
MFE
MFE
Opt
0.38271*** 0.43243*** 0.00598***(0.000)(0.000)(0.000)-0.01851-0.00714
0.00023(0.425)(0.569)(0.295)0.10369
0.127810.00199*(0.538)(0.306)(0.086)0.903070.39046
0.00326(0.111)(0.164)(0.512)
0.09633
0.07860
0.00108**(0.187)(0.184)(0.045)0.73044-3.40543***-0.01283(0.481)(0.000)(0.283)0.19442*-0.12855
0.00095(0.059)(0.141)(0.293)0.13634**
0.05858
0.00133**(0.013)(0.345)(0.013)4.38221***-0.07360
0.02954***(0.000)(0.920)(0.003)-1.02834*** -1.01665*** -0.00769***(0.000)(0.000)(0.000)-0.00383-0.00011
0.00002(0.670)(0.988)(0.792)-0.10504*** -0.03513* -0.00050***(0.000)(0.073)(0.002)0.20801**0.23012***0.00149(0.018)(0.008)(0.125)0.059620.008420.00060*(0.161)(0.787)(0.075)
0.43
Yes
Yes
4,278
0.052

42

(5)
Other
Opt
0.00504***(0.000)0.00011(0.266)0.00048(0.435)0.00059(0.748)0.00040(0.137)-0.00890**(0.014)0.00095*(0.077)0.00028(0.439)0.00792*(0.084)-0.00376***(0.000)0.00009**(0.013)0.00016(0.127)0.00192**(0.030)-0.00030**(0.033)

0.15
Yes
Yes
11,420
0.053

Yes
Yes
4,278
0.077

Yes
Yes
11,4200.052

Appendix A: Variable Definitions
In this Appendix, we describe our empirical measures and provide their sources.
Variable
Variable of Interest
Viol

Definition

SourceIndicator variable equal to 1 if a firm violates the debt covenant in a quarter, zero otherwise

Amir Sufi’s website

Dependent Variables
FreqMF
Log of number of management forecasts issued in a quarter
First Call
Issue
Indicator variable equal to 1 if a firm issues a forecast in a quarter, zero otherwiseFirst Call
AbFreqMF
Abnormal FreqMF, measured as the residual value of the regression of FreqMF
First Call
Optim
Indicator variable equal to 1 if management forecast is greater than realized earnings, zero otherwiseFirst Call
MFEManagement forecast bias, measured as the difference between management forecast and realized earnings, scaled by price First Call
SigDiv
Significant increase in dividend payout, measured as an indicator variable equal to 1 if a firm increases more than 50%
of dividend from the quarter of last year to current quarter, zero otherwise
Compustat
RetVolt
Stock return volatility, measured as the standard deviation of daily stock return in a quarter
CRSP
Width
Management forecast width, measured as the the difference between the upper- and lower-end estimates, scaled by price(point estimates have a range of zero)First Call

43
Control Variables
Z-ScoreHHI
ExtFinLitig

EarnVolt
Insto
Loss
ROA
ReturnBloated
Size
Btm
Coverage
Lev

Financial distress, measured by Altman’s Z-score at the beginning of current quarter (Altman 1968)
Industry concentration, measured by the Herfindahl-Hirschman index and calculated as the sum of squares of firms’
last-quarter market shares of sales within each 4-digit SIC industry
External financing of current quarter, measured as the sum of net equity financing and net debt financing,scaled by total asset
Indicator variable set to one for litigious industries including Biotechnology (SIC 2833 to 2836)
Computer Hardware (SIC 3570 to 3577), Electronics (SIC 3600 to 3674), Retailing (SIC 5200 to 5961),
and Computer Software (SIC 7371 to 7379), and zero otherwise
Earnings volatility, measured as the standard deviation of the quarterly return on assets over the last two years
Institutional investor ownership, measured as the percentage of institutional ownership in a firmat the beginning of current quarter
Negative earnings, an indicator variable equal to one if income before extraordinary items of last quarter is negative,and zero otherwise
Return on firm assets of last quarter, measured as income before extraordinary items divided by total assets
Buy-and-hold size-adjusted return over last quarter
Net asset bloat of quarter t, measured as book value of equity plus debt, minus cash, and scaled by sales
Natural logarithm of the market value of equity at the beginning of current quarter
Book-to-market ratio at the beginning of the current quarter, measured as the book value of equity divided
by the market value of equity at the end of quarter t
Analyst coverage, measured as natural logarithm of one plus the number of analysts following of current quarter
Leverage ratio of last quarter, measured as total debts, scaled by total assets.

Compustat
Compustat
Compustat
Compustat
Compustat
Compustat
Compustat
Compustat
CRSP
Compustat
Compustat
CompustatFirst Callcompustat