Voluntary Disclosure during Credit Watches: Do Credit ...
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Voluntary Disclosure during Credit Watches: Do Credit Rating Agencies Concern about Disclosure Quality? Presented by Dr Kai Wai Hui Associate Professor Hong Kong University of Science and Technology #2012/13-13 The views and opinions expressed in this working paper are those of the author(s) and not necessarily those of the School of Accountancy, Singapore Management University.
Transcript of Voluntary Disclosure during Credit Watches: Do Credit ...
Voluntary Disclosure during Credit Watches: Do Credit Rating
Agencies Concern about Disclosure Quality?
Presented by
Dr Kai Wai Hui
Associate Professor Hong Kong University of Science and Technology
#2012/13-13
The views and opinions expressed in this working paper are those of the author(s) and not necessarily those of the School of Accountancy, Singapore Management University.
Voluntary Disclosure during Credit Watches: Do Credit Rating Agencies
Concern about Disclosure Quality?
Kai Wai Hui*
Department of Accounting
Hong Kong University of Science and Technology
Zhu Lui
Department of Accounting and Law
University at Albany, SUNY
October 2012
Preliminary
Voluntary Disclosure during Credit Watches: Do Credit Rating Agencies
Concern about Disclosure Quality?
Abstract:
This paper investigates managers’ voluntary disclosure during credit watch periods. A credit
watch warns investors of a possible rating revision and the uncertainty in a firm’s future
creditworthiness and, therefore, is accompanied by intense demand for information. We
investigate 1) whether managers disclose more information during credit watches; 2) whether
managers strategically disclose biased information in response to credit watches, and 3) whether
and how effectively credit rating agencies monitor managers’ voluntary disclosure in such a
setting. Using credit watch data from Moody’s, we report that 1) management earnings forecast
frequency is higher during credit watches, 2) compared with non-watch periods, management
earnings forecasts disclosed during credit watches are more optimistically biased and less
accurate in case of downward watches, but less optimistically biased and more accurate in the
case of upward watches, and 3) optimistically biased and less accurate forecasts issued during
credit watches are not associated with resolutions of downgrade watches, but are associated with
less favorable resolutions of upgrade watches. Our findings suggest that managers’ voluntary
disclosure increases during credit watches, but the credibility of forecasts depends on the
direction of credit watch. Rating agencies play an important but limited role in monitoring the
credibility of voluntary disclosures.
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Voluntary Disclosure during Credit Watches: Do Credit Rating Agencies
Concern about Disclosure Quality?
I. INTRODUCTION
The accounting literature suggests that management forecasts are an important channel
used by the management to adjust market expectations (Ajinkia and Gift 1986) which accounts
for 66% of accounting-based information provided to the market (Beyer et al. 2010). However,
managers may issue biased information, especially when there is significant uncertainty about
future performance (Roger and Stocken 2005). This paper examines management forecasts
during credit watches. Given that credit rating agencies engage in private information production
to discover managers’ superior information (e.g. Healy and Palepu 2001), we also examine
whether credit rating agencies monitor the disclosure quality during credit watches.
A credit watch is a rating procedure publicly announced by credit rating agencies. When
credit rating agencies expect a firm’s creditworthiness to undergo a significant change but cannot
make an immediate rating decision, they place the firm on a credit watch for a possible rating
revision. Literature shows that credit watches account for a significant portion of rating revisions
(Chung et al. 2012). Since a rating revision has significant impact on a company’s stock/bond
price and future financing costs (Hand et al. 1992; Dhaliwal and Reynolds 1994; Dichev and
Piotroski 2001), being put on credit watch triggers significant demand for additional information
from outsiders. Yet little is known on whether and how managers respond to this information
demand via voluntary disclosure amid such a critical event. Nor is there any evidence about
whether credit rating agencies, important information intermediaries in the financial market with
information advantages compared to many market participants (Healy and Palepu 2001; Jorion et
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al. 2005), monitor the quality of managers’ voluntary disclosure.1 Therefore, we study managers’
voluntary disclosure during credit watch periods, including disclosure frequency, content and
bias, for better understanding of managers’ behaviors of voluntary disclosure and the monitoring
role potentially played by the rating agencies.
To assess management disclosure during credit watch and rating agencies’ monitoring of
disclosure quality, we investigate three related research questions. First, we study whether
managers of on-watch firms disclose more information in response to increased information
demand by issuing management earnings forecasts during credit watch periods. Since credit
watch may soon lead to rating changes, credit watch placements draw investors’ attention
regarding firms’ future performance and creditworthiness. Therefore, we predict that firms are
more likely to issue management earnings forecasts in response to the increased information
demand after being put on credit watches.
We further examine whether managers are more likely to issue earnings forecasts when the
credit watch is for downgrade than in cases of upgrade. Extant literature suggests that bond
investors have limited upside payoffs and are more sensitive to negative news than to positive
news (Plummer and Tse 1999). Equity investors also react to downgrades more than to upgrades.
For example, Hand et al. (1992) report that stock/bond price reactions to downgrades are much
stronger than to upgrades. Given the significant impacts of rating downgrades on firms’ future
financing costs and investment constraints, failing to warn investors about the deterioration of
creditworthiness in advance before rating downgrades may significantly increase on-watch
firms’ litigation risk (e.g. Skinner 1994). We, therefore, predict that firms are more likely to issue
management earnings forecasts in response to credit watches for rating downgrades.
1 Ajinkia et al. (2005) establish the critical role of corporate governance in maintaining credibility of voluntary
disclosures.
3
Second, we investigate the quality of management earnings forecasts issued during credit
watch periods by examining forecast bias and accuracy. The uncertainty accompanying credit
watches makes it difficult for outsiders to assess the truthfulness of managers’ voluntary
disclosures (Roger and Stocken 2005). This difficulty in assessing the credibility of managers’
disclosures along with managers’ incentives to avoid the significant negative impact of rating
downgrades (or to benefit more from rating upgrades) on firm value may induce bias in
voluntary disclosures.
In addition, given the substantial cost of downgrades (e.g., the large negative market
reaction to downgrades, higher future financing costs and more stringent debt/loan covenants,
etc.) and investors’ asymmetric reactions to bad/good news in the bond market, managers may
have stronger incentives to issue earnings forecasts biased upwards during credit watches for
downgrades than during upgrades. Overall, we predict that management earnings forecasts
issued in credit watch periods are less accurate/more biased (relative to actual earnings) than
management earnings forecasts issued in other periods, especially when credit watches are for
possible downgrades.
However, as an alternative hypothesis, rating agencies may deter managers from issuing
biased forecasts. Credit rating agencies are considered to be sophisticated users of financial
information and engage in private information production to uncover managers’ private
information, thereby helping mitigate agency problems between managers and outsiders (Healy
and Palepu 2001). More importantly, credit rating agencies have private communications with
managers and, after the passage of Regulation FD, have the privilege to access private
information that may be unavailable to other outsiders (Jorion et al., 2005). Credit rating
agencies thus are better equipped to detect management misbehavior. Therefore, credit rating
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agencies may be able to undo biases in management disclosures when formulating watch
resolutions. Credit watch resolutions released after credit reviews can serve as an effective
verification of adequacy and reliability of management disclosures. Rating agencies may even
consider the accuracy of management forecasts as signals of managers’ talent (e.g. Truemen
1986; Kasznik 1999; Healy and Palepu 2001) when assessing future credit risks. Therefore,
credit rating agencies can play a monitoring role in improving the transparency and quality of
management disclosure. We expect that management forecasts issued during watch periods are
less optimistically biased and more accurate compared with those issued in non-watch periods.
Lastly, we investigate whether rating agencies consider disclosure quality when reaching
credit watch resolutions. To this end, we examine two properties of credit watches, the watch
duration and watch resolution. Timely credit rating revisions are important to investors and
regulators (Cheng and Neamtiu 2008). While rating agencies claim to maintain, on average, a
90-day review period for credit watches, actual descriptive statistics reported by prior research
show considerable variation in watch durations (Keenan et al. 1998; Bannier and Hirsch 2010;
Chung et al. 2012). Since information provided by managers during credit watch periods is an
important input for watch resolutions (Keenan et al. 1998; S&P Corporate Rating Criteria 2006),
we expect that management forecasts of higher quality (i.e., less biased and more accurate)
should facilitate rating agencies’ credit analysis and therefore lead to shorter watch durations.
We further study the relationship between rating resolution and forecast quality. Prior
studies (e.g., Truemen 1986; Kasznik 1999; Healy and Palepu 2001) suggest a reputation effect
of disclosure. That is, accurate management forecasts indicate managers’ ability to accurately
forecast future performance and to manage business in a challenging environment. Similarly,
credit rating agencies may consider whether managers deliver the promises made in management
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forecasts as an indicator of the ability to uphold their promises to debt holders. As a result, we
expect that firms issuing more accurate and less biased forecasts are more likely to receive
favorable watch resolutions.
Moreover, prior research shows that downgrade watches are, on average, resolved in
shorter periods than upgrade watches (Keenan et al. 1998; Chung et al. 2012), which suggests
that rating agencies provide more timely resolutions for downgrade watches than for upgrade
watches. The trade-off between the accuracy and timeliness of rating revisions has always been a
concern for agencies (Cheng and Neamtiu 2008). A shorter watch duration constrains the efforts
put in to resolve the uncertainty of a firm’s future credit risk and results in greater difficulty to
detect bias in management disclosures. To the extent the relatively longer duration of upgrade
watches enables rating agencies to better monitor quality of disclosure, we expect the
relationship between forecast bias/accuracy and favorable rating resolutions to be stronger during
upgrade watches than during downgrade watches. In sum, we predict that there is a positive
association between the accuracy of management earnings forecasts and favorable watch
resolutions, especially during upgrade credit watches.
We identify a sample of 519 forecasts issued during Moody’s credit watch reviews (named
Watchlist by Moody’s) from 1996 to 2009 for 251 firms. We first document that firms are more
likely to release management earnings forecasts in credit watch periods than in non-watch
periods. This is consistent with the notion that firms use management earnings forecasts to meet
investors’ information demand and to reduce the information asymmetry between managers and
outsiders. We also find that firms are more likely to issue management earnings forecasts during
credit watches for downgrades than during credit watches for upgrades, consistent with the
notion that firms face stronger information demand during downgrade watch periods.
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We next examine the quality of management earnings forecasts issued during Moody’s
Watchlist review periods. First, we find that management earnings forecasts are on average more
optimistically biased during credit watch periods than in non-watch periods (i.e. managers’
forecast earnings are higher than the realized earnings when firms are on watch for possible
rating changes). This is consistent with managers’ incentive to optimistically bias their public
disclosure during credit watches to manage public expectations. Second, we find that the quality
of managers’ earnings forecasts is associated with the direction of the intended rating changes
announced upon watch placements. Compared with non-watch periods, managers issue more
optimistically biased and less accurate earnings forecasts during downgrade watches but less
optimistically biased and more accurate earnings forecasts during upgrade watches. Together,
our findings suggest that managers strategically determine the quality of their disclosure during
the watch periods. However, we also provide evidence that rating agencies seem to monitor the
disclosure quality and their monitoring is more effective in cases of upgrade watches than for
downgrades. This suggests that the emphasis on timeliness in downgrade decisions may have
impaired rating agencies’ monitoring of the quality of forecast earnings information issued by
on-watch firms.
We further investigate rating agencies’ monitoring role by examining the association
between the quality of management earnings forecasts and two properties of credit watches – the
duration and the resolution. We find that lower quality earnings forecasts issued during credit
watch periods prolong the duration of the watches, especially for upgrades. This negative
association is observed only for upgrade watches, and not for downgrade watches. These
findings suggest that accurate forecasts facilitate timely watch resolution and have a positive
impact on obtaining favorable resolution of upgrade watches. Combined with our observation
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that management earnings forecasts issued during upgrade watches are less optimistically biased
and are more accurate, credit rating agencies apparently do consider the quality of managers’
voluntary disclosure during watch periods and their monitoring is effective in cases of upgrades.
Our findings support the notion that credit rating agencies play a governance role and help
improve disclosure quality.
Our paper contributes to the literature in the following ways. First, our paper addresses an
important question of whether managers disclose credible information in response to the
market’s demand for additional information during credit watches, an important event that
reveals critical information to both creditors and equity investors. We provide for the first time
the evidence that managers do respond to such demand by disclosing additional information but
also take the opportunity to influence outsiders’ perspectives of their firms. By examining firms’
voluntary disclosures in an event-like setting, our paper provides additional evidence for
understanding managers’ incentives for voluntary disclosures, as well as insights to investors on
how to interpret the publicly disclosed information during credit watches.
Second, our paper is the first to explore credit rating agencies’ role in monitoring the
quality of voluntary financial disclosures. Prior studies have mainly focused on the role of rating
agencies as information intermediaries in financial markets. We investigate the frequency and
quality of voluntary disclosures probably triggered by credit watches and its consequences for
rating actions. We find increased amount of voluntary disclosure following rating agencies’
watch placements. We also find that rating agencies consider disclosure quality. Specifically,
optimistically biased and low accuracy forecasts lead to longer credit watch durations whereas
firms on upgrade watches are less likely to be upgraded when their earnings forecasts released
during watches are upwardly biased and less accurate. Accordingly, we find improved disclosure
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quality during upgrade watches. Nevertheless, while credit rating resolutions are apparently not
affected by disclosures, the overall magnitude of bias in management earnings forecasts during
watch periods, especially during downgrade watches, suggests that rating agencies play a limited
role in monitoring disclosure quality.
The rest of the paper is organized as follows. Section 2 reviews the related literature and
develops our main hypotheses. Section 3 presents the research methodology. Section 4 reports
descriptive statistics of the sample and results of the empirical analysis. Section 5 concludes.
II. CREDIT WATCH AND PROPERTIES OF MANAGEMENT FORECAST
2.1. Likelihood of Issuing Management Earnings Forecasts during Credit Watches
We first examine the likelihood of managements issuing earnings forecasts during credit
watches. Firms are more likely to issue management earnings forecasts during credit watches for
two reasons. First, credit watch placements cause intense demand for information from both
credit rating agencies and investors. The rating agencies claim that putting firms on credit
watches instead of direct change in rating is mainly driven by the uncertainty in firms’ future
creditworthiness. Rating agencies collect additional information, including inputs from managers
of the firms under review, for analysis during credit watch reviews.2 Therefore, a credit watch
placement itself represents rating agencies’ demand for additional information.
In addition, a credit watch is a public “warning” from credit rating agencies that signals to
investors a significant likelihood of a change in an on-watch firm’s credit rating. In credit watch
announcements, rating agencies express their concerns about the changes in the on-watch firm’s
2 For example, Moody’s states that “during the course of a rating review, Moody’s solicits information from the
issuer in order to understand plans either for addressing the problem, or for taking advantage of the opportunities
that have inspired the review” (Keenan et al. 1998, 3).
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financial and risk profile, which may draw investors’ attention and induce them to demand
additional information. Empirical evidence shows that rating agencies are more likely to put a
firm on watch before rating change if there is greater demand for information by investors
(Chung et al., 2012). Moreover, a negative rating change may significantly increase a firm’s
financing cost and prior studies show significant negative reactions in both equity and bond
markets (Hand et al., 1992).
As such, a credit watch accompanies strong demand for additional information during the
watch period by credit rating agencies to decide whether a rating change is warranted, as well as
by general investors to investigate whether a firm’s creditworthiness has significantly changed
and to predict a possible rating change. Managers acting on behalf of shareholders, therefore,
have incentives to disclose additional information regarding potential changes in firms’
creditworthiness in response to demand from either rating agencies or investors in general.
Second, credit watch may increase the tendency to make voluntary disclosure by affecting
managers’ costs/benefits assessments. A credit watch reveals rating agencies’ private
information of a firm’s future performance. Its impact is two-fold: 1) it reduces the potential
benefits that managers may earn by withholding information which can be inferred, at least to
some extent, from rating agencies’ announcement of watch placement; and 2) it alleviates
managers’ concerns about the proprietary cost of disclosure because credit watch at least partly
publicizes managers’ private information.3 Therefore, credit watch induces managers to issue
3 Managers might be reluctant to issue management forecasts to correct market expectations if they worry that
management forecasts may disclose sensitive information to competitors (Bamber and Cheon 1998).
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management forecasts by reducing the benefit of non-disclosure as well as the cost of
disclosure.4
In summary, credit watch placements trigger demand for information from market
participants and lower the hurdles of voluntary disclosures and, therefore, may encourage
managers to provide to the market their own assessments of firms’ future performance. We
present our first hypothesis as follows.
H1A. Firms are more likely to issue management earnings forecasts after being placed on credit
watch.
Prior research has shown that managers are more likely to issue voluntary disclosures in
anticipation of negative performance shock (Kasznik and Lev 1995; Skinner 1994). Litigation
risk is a major concern of managers when considering timely disclosure to pre-empt the bad
news (Skinner 1997). Managers are more likely to issue management forecasts conveying bad
news than good news, especially when facing greater litigation risk (Roger and Stocken 2005).
To this end, credit watches for possible downgrades may imply additional litigation risk.
Empirical studies show that downgrades, but not upgrades, have significant impacts on firms’
stock returns at the time of and during periods after the rating changes (Hauthousan and Leftwich
1984; Hand et al. 1992; Dichev and Piotroski 2001). Rating downgrades also increase firms’
future financing costs and constrain firms’ future investment activities. Failing to warn investors
about the downside risk in advance may, therefore, significantly increase firms’ litigation risk.
As such, downgrade credit watches may further increase the propensity of disclosure of
additional information by managers during watch periods. We present our second hypothesis as
follows.
4 The greater information demand during credit watches may increase disclosure cost by increasing the litigation risk
of presenting misleading information, although prior studies suggests that litigation cost of non-disclosure
dominants (e.g. Field et al 2005).
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H1B. Firms are more likely to issue management earnings forecasts after being placed on credit
watches for downgrades than upgrades.
2.2. Biases in Management Earnings Forecasts during Credit Watches
Extant literature suggests that although in general, management earnings forecasts are
credible (Pownall and Waymire 1989), managers can make biased forecasts, especially when the
market’s ability to detect bias is limited (Bamber et al., 2009). The literature also suggests that
managers may strategically use voluntary disclosure to influence stock prices amid certain
important corporate events, for instance, before seasonal equity offerings (Lang and Lundholm
2000; Jo and Kim 2007). During credit watch periods, managers are more likely to provide
biased forecasts because of two reasons. First, the cost of issuing optimistically biased forecasts
is lower. Compared to non-watch periods, there is greater uncertainty during credit watch
periods, which increases the difficulty that rating agencies and investors face in detecting and
filtering the bias in the issued forecasts. Second, the benefit of issuing biased forecast is higher.
In the debt market, management earnings forecasts have a greater impact on investors’
expectations of future performance when there is greater uncertainty about future performance
(Shivakumar et al. 2011). Optimistic forecasts issued during credit watch periods may help shape
investors’ perspectives on the issue at stake, clarify rating agencies’ concern over future
performance and control the damage caused by unfavorable comments released by rating
agencies. Therefore, we present our hypothesis regarding the potential bias in management
earnings forecasts issued in credit watch periods as follows.
H2A. Compared to actual earnings, management earnings forecasts issued during credit watch
periods are more optimistically biased and less accurate than forecasts issued in other periods.
In addition, managers may have stronger tendency to issue optimistically biased earnings
forecasts during downgrade watches than during upgrade watches. Rating downgrades result in
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significant costs to the firm, including higher future financing costs, more stringent debt/loan
covenants and a smaller pool of institutional investors. This leads to significant negative
stock/bond price reactions. On the contrary, market reactions to upgrades are usually of much
smaller magnitude. The asymmetric stock/bond valuation consequences of rating downgrades
and upgrades offer greater incentives for managers to issue optimistically biased earnings
forecasts to nudge market expectations upwards (or prevent them from further sliding). We
therefore predict a higher optimistic bias and lower forecast accuracy of management earnings
forecasts issued during downgrade watch periods.5
H2B. Compared to actual earnings, management earnings forecasts issued during downgrade
credit watch periods are more optimistically biased and less accurate than forecasts issued in
other periods.
2.3. Rating Agencies’ Monitoring Role during Credit Watches
A notable factor in examining management earnings forecasts during credit watches is the
potential monitoring role played by the involved credit rating agencies. According to the rating
methodology published by rating agencies, rating change decisions are based on public
information, private information from managers and rating agencies’ own research. Credit rating
agencies are arguably sophisticated users of financial information having the capability to
interpret the obtained information effectively. Credit rating agencies carry the risk of losing
reputation if they make incorrect rating revisions based on biased information provided by
managers of on-watch firms.
The passage of Regulation FD has further enhanced credit rating agencies’ role as
important information intermediaries by granting them the privilege to access managers’ private
5 Prior literature suggests that firms may face litigation risk if the forecasts are misleading. However, Field et al.
(2005) finds that the litigation risk of not warning the investors dominate the disclosure decision and the
implications of litigation from misleading the market is not significant in the derterminates of forecast issuance.
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information unavailable to other market participants. The rating agencies’ privileged access to
private information and sophisticated information processing capabilities, therefore, enable them
to detect bias in management forecasts when formulating rating resolutions. The potential
inconsistency between optimistically biased forecasts and unfavorable watch resolutions thus
helps deter managers from issuing biased forecasts by increasing the costs of misleading
forecasts. As such, rating decisions at watch resolutions can serve as a mechanism for
verification of the credibility of managers’ earnings forecasts.
In addition, prior literature suggests that disclosure accuracy is an important indicator of
managers’ ability to manage the business (e.g. Truemen 1986; Healy and Palepu 2001). If rating
agencies use this valuable information when formulating their resolutions, failing to provide
accurate forecasts results in significant reputation costs to the management and may lead to less
favorable rating resolutions.
Overall, we expect that effective monitoring by rating agencies during credit watches
induces more accurate and less biased management earnings forecasts during credit watch
periods. We present an alternative hypothesis to H2A, on disclosure quality, as follows:
H3A. Compared to actual earnings, management earnings forecasts issued during credit watch
periods are less optimistically biased and more accurate than forecasts issued in other periods.
Given bond holders’ asymmetry payoffs between upgrades and downgrades and the
regulation requirement to maintain a minimum rating for the firms, rating agencies are under
substantial pressure to provide timely revisions of downgrade watches. Consistent with this, prior
research shows that it takes a significantly longer period for rating agencies to resolve upgrade
watches than downgrade watches (Keenan et al. 1998; Chung et al. 2012). The demand for
timely rating revisions raises the concerns on the trade-offs between timely rating revisions and
the accuracy of ratings by the rating agencies and regulators (Cheng and Neamtiu 2008). Longer
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review periods in cases of upgrade watches enable rating agencies to exert more effort and
caution when evaluating on-watch firms’ creditworthiness. We expect that rating agencies’
monitoring of disclosure quality, if any, is more effective during upgrade watches than during
downgrades. Therefore, optimistically biased management earnings forecasts may be less
effective in influencing rating agencies’ decisions during upgrade watches, which consequently
reduces on-watch firms’ incentive to issue upward biased management forecasts. We present
Hypothesis H3B as follows.
H3B. Compared to actual earnings, management earnings forecasts issued during upgrade
credit watch periods are less optimistically biased and more accurate than forecasts issued in
other periods.
If rating agencies monitor disclosures, quality of management forecasts may affect how
rating agencies proceed with credit analysis during credit watches. We therefore further examine
two properties of credit watches – the watch duration and whether the watch resolution is
favorable to an on-watch firm.
The duration of a credit watch is the length of time that credit rating agencies take for
information collection, analysis and resolution of the uncertainty in on-watch firms’
creditworthiness. Watch duration may be related to availability and quality of information
required for credit analysis (Keenan et al. 1998; Bannier and Hirsch 2010; Chung et al. 2012).
Other things being equal, the lower the quality of information provided by managers to rating
agencies during credit watches, the more are the time and effort rating agencies need to spend on
resolving credit watches, and hence a longer watch duration. We expect that management
forecasts that are less biased and more accurate facilitate better analysis and, therefore, lead to
shorter watch duration. We present our hypothesis on watch duration as follows.
H4A. The credit watch duration is shorter if management earnings forecasts are more accurate
and less optimistically biased.
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In addition, following our expectation in H3B that rating agencies’ monitoring may be
more effective during upgrade watches, we expect the relationship between forecast
bias/accuracy and watch duration to be stronger during upgrade watches.
H4B. The credit watch duration is shorter if management earnings forecasts are more accurate
and less optimistically biased during upgrade watches.
The second credit watch property we examine is the watch resolution. We focus on
whether the likelihood of receiving a favorable watch resolution is related to the quality of
management earnings forecasts issued during credit watches.6 Prior studies (e.g. Truemen, 1986;,
Kasznik 1999, Healy and Palepu 2001) suggest a reputation effect of disclosure. That is, accurate
management forecasts indicate managers’ ability to accurately forecast future performances and
to manage business in a challenging environment. If rating agencies are sophisticated and do
discount overly optimistic information from managers, following the reputation hypothesis, we
would expect rating agencies to incorporate the perceived quality of management forecasts in
their credit resolutions. In other words, if credit agencies consider disclosure quality as an
indicator of the ability of the management team to deliver their promises to the debt holders, a
firm issuing more accurate forecasts is more likely to have a favorable resolution. Therefore, we
develop our Hypothesis H5A as follows.
H5A. The credit watch resolution is more favorable if management forecast is more accurate
and less optimistically biased.
6 One argument is that biased forecasts may be effective in influencing rating decisions only if the realized earnings
are announced after watch resolutions. We therefore conduct a robustness check for this test by excluding forecast-
watches with earnings announced in the watch periods. Our result is robust if we limit forecasts to those with actual
earnings announcements after the watch resolutions.
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In addition, following our expectation in H3B that rating agencies play a stronger
monitoring role when concluding upgrade credit watches, we expect the relationship between
forecast bias/accuracy and rating resolution to be stronger.
H5B. The credit watch resolution is more favorable if management earnings forecast is more
accurate and less optimistically biased during upgrade watches.
III. RESEARCH DESIGN
3.1 Sample selection
We collect credit watch data over the period from 1996 to 2009 from Moody’s Default
Risk Service. This database includes Moody’s credit rating actions on bond issuers and issues,
including Moody’s credit watch actions – Watchlist reviews. Each Watchlist review is uniquely
identified with the firm under review, the starting and resolution dates of the review, the intended
rating action announced at the Watchlist placement date, and the actual rating action at the
Watchlist resolution date. Therefore, we are able to identify the unique credit watch period for
each sample firm.
There are two types of Watchlist reviews, one for bond issuers (i.e. possible rating changes
at the firm level for bond issuers as business entities) and the other for bond issues (i.e. possible
rating changes at the security level for individual bonds). In this paper we focus only on
Watchlist reviews for bond issuers at the firm level because such reviews indicate significant
changes in the overall creditworthiness of firms and, therefore, provide stronger incentives for
firms to disclose management earnings forecasts.
We collect management earnings forecasts along with analyst earnings forecasts and actual
earnings from Thomson Financial’s First Call database. We only include management earnings
forecasts of earnings per share (EPS) issued by US firms. Given that whether to provide
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management earnings forecasts can be affected by corporate disclosure policy, we only focus on
firms that issued at least one management earnings forecast between 1996 and 2009. If a
management earnings forecast is issued during credit watch (i.e. after the starting date and before
the resolution date of a Watchlist review), we classify the management earnings forecast as a
watch-period forecast; otherwise we classify it as a non-watch-period forecast. To control for the
policy of credit rating agencies in following and selecting watch firms, we also restrict our
analysis to firms that had ever received credit watches. Since we are comparing characteristics of
management forecasts for the same group of firms between watch and non-watch periods, this
helps control for time-invariant firm effects that may affect corporate disclosure and focus on the
possible impact of credit watch on management earnings forecast.
3.2 Propensity of management earnings forecast
To examine the association between credit watches and management earnings forecasts,
we first investigate the probability of issuance of management earnings forecasts, defined by a
dummy variable, ISSUE. ISSUE = 1 if a firm issues a management earnings forecast during the
credit watch period, and 0 otherwise. If credit watches represent rating agencies’ and investors’
demand for information and firms on watch respond to such demand by issuing management
earnings forecasts, we should find a positive association between credit watch placements and
the issuance of management earnings forecasts. In addition, we examine the propensity of
issuing management earnings forecasts in upgrade and downgrade watch periods to test if firms
are more likely to issue forecasts in downgrade watch periods than in upgrade watch periods. .
We conduct both univariate and multivariate tests to examine these associations. We first
compare the likelihood of issuing management earnings forecasts in fiscal quarters that overlap
with credit watch periods, with the likelihood of issuing management earnings forecasts in fiscal
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quarters that do not overlap with credit watch periods. We then follow Ajinkya et al. (2005) and
use the following regression specifications to control for other determinants of voluntary
disclosure.
ISSUE = α0 + α1WATCH + α2SIZE + α3MB + α4RD + α5ROASTD + α6IO +α7AF + α8FESTD +
α9LEV + α10REGFD + α11LITI + α12New+ α13Loss + α14QCAR + Year Dummies + ε,
(1)
ISSUE = α0 + α1DWATCH + α2UWATCH + α3SIZE + α4MB + α5RD
+ α6ROASTD + α7IO +α8AF + α9FESTD + α10LEV + α11REGFD + α12LITI
+ α13New+ α14Loss + α15QCAR + Year Dummies + ε,
(2)
where:
WATCH = 1 if a fiscal quarter overlaps with a credit watch period, and 0 otherwise;
DWATCH = 1 if a fiscal quarter overlaps with a credit watch period for downgrade, and 0
otherwise;
UWATCH = 1 if a fiscal quarter overlaps with a credit watch period for upgrade, and 0
otherwise;
SIZE = log of market value of common equity;
MB = market-to-book ratio;
RD = R&D expense scaled by total assets;
ROASTD = Standard deviation of ROA in the past 6 years;
IO = total institutional ownership;
AF = number of analysts following a firm in the previous quarter;
FESTD = standard deviation of analyst forecasts, scaled by the median forecast;
LEV = financial leverage;
REGFD = 1 if a management earnings forecast is issued in a post-Reg FD period.
LITI = 1 if a firm is in one of the following industries defined by SIC4 (biotechnology 2833–
2836 and 8731–8734, computers 3570–3577 and 7370–7374, electronics 3600–3674 and
retail 5200–5961), and 0 otherwise (Francis, Philbrick and Schipper, 1994).
New = 1 if earnings growth from last year and 0 otherwise.
Loss = 1 if the firm has loss and 0 otherwise.
QCAR = Abnormal stock returns during the quarter.
We control relevant firm characteristics including size (SIZE), market-to-book ratio
(MB), R&D intensity (RD), and control for the predictability of future earnings using earnings
volatility (ROASTD). To control for the information environment and governance role played by
sophisticated investors and analysts, institutional ownership (IO), analyst following (AF), analyst
forecast dispersion (FESTD) and financial leverage (LEV) are also added as controls. We also
19
control for litigation risk (LITI). Since Reg. FD affects firms’ voluntary disclosure, we also
include a dummy variable, REGFD, to identify management earnings forecasts issued in the
post-Reg. FD periods. In the end, we also add the control for the total amount of information
revealed in the quarter to control for any special information events that might drive our findings,
which may also related to the watch.
3.3 Accuracy and bias of watch-period management earnings forecasts
We assess the quality of management earnings forecasts issued in credit watch periods
based on forecast accuracy and bias. We define accuracy, AFE, as the absolute value of the
difference between actual earnings per share and management forecast of earnings per share,
scaled by stock price at the beginning of the fiscal quarter. We define bias, FE, as management
forecast of earnings per share minus actual earnings per share, scaled by stock price at the
beginning of the fiscal quarter. A management earnings forecast is optimistically biased if FE>0.
We run the following regressions to examine the accuracy and bias of management
earnings forecasts to see if firms issue opportunistic management earnings forecasts to influence
rating agencies’ decisions and investors’ perception of future performance:
FE = η0 + η1WATCH + η2SIZE + η3MB + η4RD + η5ROASTD + η6IO + η7AF + η8FESTD +
η9LEV + η10REGFD + η11Liti + η12New + η13Loss + η14ANNUAL + Year Dummies + ε,
(3)
AFE = δ0 + η1WATCH + η2SIZE + η3MB + η4RD + η5ROASTD + η6IO + η7AF + η8FESTD +
η9LEV + η10REGFD + η11Liti + η12New + η13Loss + η14ANNUAL + Year Dummies + ε,
(4)
where the explanatory variables are defined in Equations (1) and (2). ANNUAL = 1 if a forecast
is on annual earnings, and 0 otherwise;
3.4 Watch resolution and management earnings forecasts
20
Finally, we investigate whether there is an association between watch duration/resolution
and quality of management earnings forecasts. If credit rating agencies are sophisticated to assess
the quality of management earnings forecasts, we expect that firms issuing high-quality
management earnings forecasts are more likely to have short watch durations and receive
favorable watch resolutions than firms issuing low-quality management earnings forecasts, all
else being equal. We run the following regressions to examine the effect of management
earnings forecasts on the durations and resolutions of credit watches. The dependent variable,
WDUR, is the number of calendar days between a credit watch addition and its resolution;
WRSLT, is a dummy variable equal to 1 if the resolution of a credit watch is favorable to on-
watch firms (i.e. no downgrade after a negative credit watch and upgrade after a positive credit
watch) and 0 otherwise. The explanatory variables of our interest are forecast bias (FE) and
forecast accuracy (AFE).7
WDUR = θ0 + θ1FE or AFE + θ2SIZE + θ3MB + θ4RD + θ5ROASTD + θ6IO + θ7AF + θ8LEV
+ θ9LITI + θ10NEW + θ12 LOSS + θ13 ANNUAL + Year Dummies + ε, (5)
WRSLT = θ0 + θ1FE or AFE + θ2SIZE + θ3MB + θ4RD + θ5ROASTD + θ6IO + θ7AF + θ8LEV
+ θ9LITI + θ10NEW + θ12 LOSS + θ13 ANNUAL + Year Dummies + ε, (6)
where the explanatory variables are defined in Equations (1) to (4).
IV. EMPIRICAL RESULTS
Table 1 summarizes the sample selection procedures and provides descriptive statistics of
the sample of 7,277 management earnings forecasts. We use Moody’s Watchlist data. Panel A
shows that we start with 10,502 management earnings forecasts issued by US firms that issued at
7 The actual earnings that management forecasts are forecasting may be realized during the watch period, or they
may be realized after the watch period. For forecasts that are realized during the watch period, the observed forecast
errors are used by the rating agencies to access the reputation of the managements. For forecasts that are unrealized
during the watch period, the rating agencies use the expected forecast errors in their decisions. In the second case,
we use the ex-pose forecast errors as an empirical proxy. Our results are robust if we measure forecast errors using
the difference between analyst consensus at watch period ends and management forecasts.
21
least one forecast over the period from 1996 to 2009, and had at least one credit watch. After
merging the data with IBES, CRSP and Compustat, we are able to identify 519 watch-period and
6,766 non-watch-period management earnings forecasts.8
We also examine the distribution of the sample and the type of management earnings
forecasts issued during credit watch periods and in other periods, and report the descriptive
statistics in Panel B of Table 1. In general, we observe similar patterns in the format and horizon
of management earnings forecasts issued in watch periods and non-watch periods. This suggests
that firms do not issue more precise or longer horizon forecasts during watch periods. We also
observe similar industry distributions of management earnings forecasts.
Table 2 provides summary statistics of the variables for the sample management earnings
forecasts used in our analysis. We also provide descriptive statistics of characteristics of sample
firms that are related to management earnings forecasts as identified in the literature.
4.1 Likelihood of issuing management earnings forecasts
We first examine the association between credit watch and the likelihood of on-watch
firms issuing management earnings forecasts. Table 3 reports the frequency of management
earnings forecasts in credit watch periods and in other periods. Panel A shows that overall,
regardless of whether a watch is for a downgrade or an upgrade, firms are more likely to issue
management earnings forecasts in credit watch periods than in other periods. For firms that
issued at least one management earnings forecast from 1996 to 2009, they issued management
earnings forecasts in about 46% of credit-watch-related fiscal quarters while in only 35% of non-
watch fiscal quarters. The difference in the frequency of issuing management earnings forecasts
is economically and statistically significant (11.3% with p-value less than 0.01).
8 We only focus on point and range forecasts in order to calculate the forecast errors.
22
We further break down the watch periods into upgrade watches and downgrade watches.
48% firms issue management forecasts during downgrade watches and 43% firms issue forecasts
during upgrade watches. Both results are significantly higher than the non-watch firm quarters at
1% level. In addition, consistent with concerns about litigation risk, firms are more likely to
voluntarily make disclosures when they are put on watch for downgrades than upgrades. The
difference is 5.5% significant at two-tail 1% level. The results reported in Panel A of Table 3 are
in support of Hypothesis 1 that firms are more likely to voluntarily disclose information when
put on watch by credit rating agencies for possible rating changes, especially for downgrades.
Since prior literature suggests that whether to issue management earnings forecasts might
be related to other firm characteristics, we further perform multivariate analyses to examine the
association between credit watch and the issuance of management earnings forecasts. Table 3
Panel B reports the results of regressing the issuance of management earnings forecasts on credit
watch placement, controlling for relevant firm characteristics including size (SIZE), firm growth
using market-to-book ratio (MB) and R&D intensity (RD), and uncertainty to predict future
earnings using earnings volatility (ROASTD). RD is significantly positive and ROASTD is
significantly negative, suggesting R&D intensive firms issue more forecasts to reveal
information on developments, and volatile earnings indicates less predictable future earnings.
We also control for governance using institutional ownership (IO), other information
intermediaries – analyst following (AF). Consistent with Ajinkia et al. (2005), institution holders
improve management forecast quality. Uncertainty in analysts’ earnings forecasts – analyst
forecast dispersion (FESTD) is negative, which indicates the high uncertainty in predicting
future earnings reduces forecast frequency. Firms issue management forecasts to mitigate the
litigation risks and LITI is significantly positive. Since Reg. FD affects firms’ voluntary
23
disclosure, we also include a dummy variable, REGFD, to identify management earnings a
forecast issued in the post-Reg FD periods and is significantly positive. Loss firms issue fewer
forecasts due to high uncertainty associated with future earnings and LOSS is significantly
negative. QCAR is significantly positive suggesting firms with more positive news in the quarter
issues more forecasts. Columns A and B of Panel B reports the regression results for all credit
watch placements regardless of the direction of possible rating changes. The coefficient on
WATCH is 0.254 and significant at 1% level. This is consistent with the univariate comparison
result in Panel A and supports our first hypothesis that firms are more likely to issue
management earnings forecasts after being put on credit watch.
We further exam whether firms are more likely to issue management earnings forecasts
during downgrade watches. Specifically, we run multivariate regressions by separating
downgrade watches (DWATCH) and upgrade watches (UWATCH), and report the results in
Columns C and D of Panel B Table 3. Two observations emerge. First, both coefficients on
DWATCH and UWATCH are significantly positive, further supporting Hypothesis 1 that firms
are more likely to issue management earnings forecasts during watch periods regardless of the
direction of watch. Second, the coefficient on DWATCH is significantly more positive (0.306
with t-statistics 6.65) than that on UWATCH (0.153 with t-statistics 2.52). The difference is
reported at the bottom of the table with F-statistics of 4.21, significant at 5% level. This is
consistent with firms being more likely to issue management earnings forecasts during
downgrade watches. The results in Table 3 suggest that firms on credit watch, especially those
on downgrade watch, are more likely to use voluntary disclosure to reveal additional information
to the market.
4.2 Bias and accuracy of watch-period management earnings forecasts
24
As we previously suggested, we expect firms on credit watch to have conflicting incentives
when providing management earnings forecasts. On the one hand, managers want to provide
credible information to the market to satisfy investors’ demand for information and to mitigate
the risk of litigation against withholding material information. On the other hand, managers have
incentives to strategically disclose optimistically biased information to influence investors’ and
rating agencies’ perceptions about their firms. To test how management earnings forecasts are
affected by the two conflicting incentives, we first compare the accuracy (AFE) and the bias (FE)
of management earnings forecasts issued in watch and non-watch periods. Panel A of Table 4
shows that compared with management earnings forecasts issued in non-watch periods, forecasts
issued in watch periods are more optimistic and less accurate. The average forecast error (FE) of
management earnings forecasts issued in watch periods is about twice of the error of forecasts
issued in non-watch periods (-0.0026 vs. -0.0013 and the difference is statistically significant at
1% level with t-statistics of 2.42). Management earnings forecasts issued in watch periods are
also less accurate than those issued in non-watch periods (0.0073 vs. 0.0055 with t-statistics of
3.26).
Panel B of Table 4 reports the results of multivariate regression of forecast bias and
forecast accuracy on watch placement and other control variables. The coefficient on WATCH is
positive and marginally significant in the forecast bias regression (0.001 with t-statistics 1.74)
and significantly positive in the forecast accuracy regression (0.001 with t-statistics 2.15). Table
4 thus shows result consistent with Hypothesis 3 that firms on credit watch seem to be overly
optimistic and less accurate in their voluntary disclosures.
We also examine if firms’ voluntary disclosures in watch periods are related to the
direction of suggested rating changes and report the findings in Table 5. Panel A of Table 5
25
shows that management earnings forecasts issued in downgrade watch periods are significantly
more optimistic (e.g. the average FE is 0.0040 for downgrade watches and 0.0013 for upgrade
watches) and significantly less accurate (e.g. the average AFE is 0.0088 for downgrade watches
and 0.0055 for upgrade watches). Further analysis shows that the average bias and accuracy of
forecasts in upgrade watch quarters are not significantly different from those of forecasts in non-
watch quarters. Specifically, the difference between downgrade watch quarters and non-watch
quarters is statistically significant with t-statistics of 3.05 and 3.34. On the contrary, the
difference between upgrade watch quarters and non-watch quarters are statistically insignificant
with t-statistics of 1.44 and 1.29 respectively. This suggests that the differences in forecast bias
and accuracy between management earnings forecasts issued in watch periods and non-watch
periods are mainly driven by downgrade watches.
Panel B of Table 5 shows that these findings still hold after controlling for other related
firm characteristics. The coefficient on DWATCH is significantly positive in the forecast error
regression (0.002 with t-statics of 2.58) and significantly positive in the forecast accuracy
regression (0.003 with t-statistics of 3.19). On the other hand, the coefficient of UWATCH is
significantly negative in the forecast error regression (-0.001 with t-statics of -1.84) and
significantly negative in the forecast accuracy regression (-0.001 with t-statistics of -2.05). The
coefficients of DWATCH and UWATCH are significantly different with F-statistics of 10.10
and 14.18 respectively. Together, results in Table 5 are consistent with Hypothesis 4 that
compared to non-watch periods, firms issue more optimistic and less accurate management
earnings forecasts in downgrade watch periods and issue more conservative and accurate
management earnings forecasts in upgrade watch periods. The result is consistent with the notion
that managers have different disclosure incentive during downgrade watch and upgrade watch
26
periods. Specifically, they are likely to optimistically bias their forecasts to avoid downgrade
watches, but issue credible forecasts in upgrade watch quarters.
4.3. Management earnings forecasts and credit watch duration
To provide evidence that the perceived forecast accuracy matters in rating agencies’ rating
decisions during credit watches, we first test whether watch duration is associated with the bias
and accuracy of management earnings forecasts issued in the watch periods.
Table 6 shows the result of the tests on watch duration and the bias/accuracy of
management earnings forecasts issued during credit watches. The first three columns of Table 6
report the results of the forecast bias regressions. The coefficient on FE is significantly positive
in Columns 1 and 3, suggesting longer watch durations if the management forecasts issued
during the watch periods are more optimistically biased, especially for upgrade watches. The last
three columns of Table 6 report the results of the forecast accuracy regressions. The coefficient
on AFE is significantly positive with t-statistics 2.01 in the last column. This supports our
expectation that low-quality management earnings forecasts send to rating agencies noisier
information on future performances and risk, and it takes more time for rating agencies reach
resolutions. However, the association between forecast accuracy and watch duration only
prevails in upgrade watches. 9 Overall, the finding in Table 6 is consistent with the notion that
rating agencies try to provide timely downgrade actions to the market, which impairs their
monitoring in downgrade watches.
4.4 Credit watch resolutions and management earnings forecasts
9 The adjusted R-square in upgrade watches sample is about 5 times higher than downgrade watches. This suggests
that rating decisions in upgrade watches are more likely to be explained by measureable public information than
downgrade watches.
27
A unique feature of firms’ voluntary disclosures in credit watch periods is the involvement
of credit rating agencies. That is, such voluntary disclosure is likely triggered by rating agencies’
credit watch additions and the disclosed information is used as an important input for watch
resolutions. Rating agencies, therefore, may play a disciplinary role regarding firms’ voluntary
disclosure in the credit watch periods. We next examine if there is an association between
resolutions of credit watches and accuracy/bias of management earnings forecasts.
Panel A of Table 7 shows the comparison of management earnings forecasts issued in
watch periods by firms that receive favorable resolutions (i.e. no downgrade for a downgrade
watch or an upgrade for an upgrade watch) and firms that receive unfavorable resolutions (i.e. a
downgrade for a downgrade watch or no upgrade for an upgrade watch). Overall, as indicated by
the univariate comparison in Panel A of Table 7, firms receiving favorable watch resolutions
issue less optimistically biased management earnings forecasts in watch periods (the difference is
0.0027 and significant at 5% level). Further analyses in the second and third columns show that
the finding is driven by incidents of upgrade watches. Both forecast error and bias are not
significantly different between favorable and unfavorable resolutions under downgrade watches
(t-statistics of 0.45). However, the management forecasts issued in upgrade watch periods is
significantly more accurate and less optimistically biased, when the watch solution is favorable
(with t-statistic of 2.65 and 1.66 respectively).
Panel B of Table 7 reports the regression results of watch resolutions on forecast accuracy
and forecast bias, controlling for other firm characteristics. The coefficients on FE or AFE are of
the expected sign but not statistically significant for the overall credit watch/management
earnings forecast sample. When we separate the downgrade watch subsample from the upgrade
watch subsample, we find that, for upgrade watches, the coefficients on FE and AFE are
28
significantly negative (with t-statistics of -2.21 and -2.22 respectively). This is consistent with
the results in Panel A of Table 7 that firms receiving upgrades after being placed on upgrade
watches issue less optimistic and more accurate forecasts. The findings thus suggest that rating
agencies seem to be able to discern the credibility of voluntary disclosure released during
upgrade watches, and their decision of granting favorable rating decisions are positively
associated with more credible forecasts, controlling for other factors.
However, rating agencies’ governance role is limited in the downgrade watches as we
observe no significant association between watch resolutions and forecast bias/accuracy in the
downgrade watch subsample (t-statistic of 0.27 and 0.74 respectively). Together with the
findings in Tables 5 that firms are more likely to issue overoptimistic management earnings
forecasts after being placed on downgrade watches. The insignificant relationship between
forecast bias/accuracy and downgrade watch resolution in Table 7 suggests that more
optimistically biased management earnings forecasts do not lead to more favorable resolutions of
downgrade watches.10
Nevertheless, the overall optimistically biased management earnings
forecasts issued during downgrade watches suggests that rating agencies play a limited
governance role on disclosure quality.
V. CONCLUSIONS
In this paper, we examine management earnings forecasts issued during credit watch, an
important credit rating event. There are several notable features about this setting. First, firms’
voluntary disclosure of information about future earnings is likely triggered by credit watch
placements that are initiated by rating agencies, not managers. This creates a setting to
10
This finding is consistent with managers issuing upwards biased earnings forecasts during downgrade watches for
“damage control” instead of, rating manipulation.
29
investigate voluntary disclosure in respond to investors’ information demand. Second, credit
watch placements indicate great uncertainty in firms’ future performance, which not only leads
to great demand for additional disclosure but also makes it more difficult to detect
misrepresentation of information. Last, since credit rating agencies have private information and
the outcome of the credit watch depends on their assessment of future performance, management
earnings forecasts are monitored by rating agencies. This creates an experiment-like setting for
us to investigate managers’ disclosure strategies, and the role of credit rating agencies in
monitoring the quality of voluntary disclosures. Overall, the empirical evidence on management
earnings forecasts during credit watches enhances our understanding of firms’ disclosure
behavior as well as the role of credit rating agencies in disclosure decisions.
Using credit watch data from Moody’s, we report that firms are more likely to issue
management earnings forecasts during credit watch, which suggests that management earnings
forecasts are used to reduce information asymmetry and to satisfy outsiders’ demand for
information. We also show that management earnings forecasts disclosed during credit watch are
biased, implying that they are used not only to reduce information gap between managers and
outsiders but also to influence rating agencies’ and/or investors’ perceptions of firms’ future
performance/risk. Further, we show that favorable rating changes are associated with
lower/higher optimistic bias/accuracy in upgrade watches, suggesting that rating agencies help
discipline management earnings forecasts in upgrade watches. However, their monitoring role is
limited and does not prevent firms from issuing optimistically biased earnings forecasts when
facing the risk of rating downgrades.
30
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32
Table 1. Sample Selection and Distribution
Panel A. Sample selection procedures
# of firms # of forecasts
Ann Qtr All Ann Qtr All
All management earnings forecasts for
fiscal quarters ended between 1996 and
2009 for firms ever issued forecasts and
ever had credit watches
295
289
320
6,180
4,322
10,502
Keep those with a prior analyst forecast for
the same quarter
288 283 318 4,476 3,379 7,855
Usable sample after merge with CRSP and
Compustat
284 270 313 4,204 3,073 7,277
Watch-period forecasts 170 131 251 302 217 519
Non-watch-period forecasts 274 257 305 3,902 2,856 6,758
Panel B. Distribution of forecasts
Watch-period forecasts Non-watch-period
forecasts
% of forecasts % of forecasts
by format:
point estimate 17.2% 17.6%
range estimate 74.5% 77.2%
open-end estimate 3.1% 1.4%
qualitative estimate 5.2% 3.8%
by horizon:
annual estimate 56.5% 57.0%
quarterly estimate 43.5% 43.0%
by industry (highest five - SIC2):
49 – Electric, Gas, Sanitary 11.9% 13.5%
28 – Chemicals & Allied Products 10.3% 10.4%
36 – Electr, Oth Elec Eq, Ex Cmp 6.6% 6.8%
35 – Indus, Comm Mach, Comp Equip 6.1% 5.2%
20 – Food & Kindred Products 5.9% 5.4%
33
Table 2. Variable Definitions a
Panel A: Variable Definitions and Descriptive Statistics
Variables Definitions Mean Median STDEV
FE Management earnings forecast -
Actual earnings per share, deflated by
stock price -0.001
0.000
0.012
AFE Absolute value of FE 0.006 0.002 0.012
Size Log of beginning total assets 8.695 8.580 1.349
MB Market to book ratio at the beginning
of period 3.699
2.633
4.126
RD R&D expenses deflated by total
assets 0.020
0.000
0.057
Roastd Standard deviation of ROA in the
past 6 years 0.037
0.020
0.057
IO % of institutional holdings of
outstanding shares 0.542
0.657
0.345
AF Log of number of analysts following
a firm 2.532
2.565
0.563
FESTD Std. of analyst forecasts, scaled by the
median forecast 0.326
0.059
2.928
LEV Leverage 0.237 0.231 0.141
NEW 1 if earnings growth from last year
and 0 otherwise 0.556
1.000
0.497
LOSS 1 if the firm has loss and 0 otherwise 0.068 0.000 0.251
REGFD 1 if post regulation FD period 0.891 1.000 0.312
ANNUAL 1 if annual forecasts and 0 otherwise 0.578 1.000 0.494
LITI 1 if high litigation industry and 0
otherwise 0.286
0.000
0.452
QCAR Abnormal returns during the quarter -0.001 -0.001 0.194 a The above descriptive statistics are based on the 7,277 sample forecasts.
34
Table 3. Frequency of Management Earnings Forecast: Watch vs. Non-watch Periods
Panel A: Watch vs. Non-watch a
Probability of forecast % of periods with management earnings forecasts
(1) Non-watch firm-quarters 0.350
(2) Watch firm-quarters 0.463
(3) Downgrade watch firm-quarters 0.482
(4) Upgrade watch firm-quarters 0.427
Difference
(2) – (1): Watch vs. Nonwatch 0.113***
(t-statistics) (9.01)
(3) – (1): Downgrade vs. Nonwatch 0.133***
(t-statistics) (8.58)
(4) – (1): Upgrade vs. Nonwatch 0.077***
(t-statistics) (3.85)
(3) – (4): Downgrade vs. Upgrade 0.055**
(t-statistics) (2.16)
Panel B: Management Forecast Frequency in Credit Watch b
Variables Prediction (A) (B) (C) (D)
WATCH + 0.254*** 0.255***
(6.79) (6.78)
DWATCH + 0.306*** 0.305***
(6.65) (6.61)
UWATCH + 0.153** 0.160**
(2.52) (2.61)
SIZE + 0.000 0.000 -0.000 0.000
(-0.58) (-0.60) (-0.58) (-0.61)
MB + 0.000 0.000 -0.000 0.000
(-1.42) (-1.55) (-1.42) (-1.55)
RD + 7.535*** 7.559*** 7.536*** 7.560***
(3.68) (3.65) (3.68) (3.65)
ROASTD - -1.266** -1.253** -1.258*** -1.245
(-2.52) (-2.45) (-2.51) (-2.44)
IO + 0.561*** 0.515*** 0.562*** 0.516***
(4.78) (4.22) (4.79) (4.23)
AF + 0.006 0.005 0.006* 0.005
(1.49) (1.37) (1.50) (1.38)
FESTD - -1.082*** -1.104*** -1.085*** -1.107
(-6.13) (-6.21) (-6.14) (-6.23)
LEV ? -0.233 -0.228 -0.233 -0.228
(-1.60) (-1.55) (-1.60) (-1.55)
REGFD + 0.684*** 0.692*** 0.683*** 0.691***
(11.00) (11.10) (10.99) (11.09)
LITI + 0.328*** 0.325 0.328*** 0.325
(4.27) (4.22) (4.27) (4.22)
NEW +/- -0.026 -0.024 -0.024 -0.022
(-1.12) (-1.01) (-1.04) (-0.93)
LOSS ? -0.403*** -0.402*** -0.405*** -0.404
(-8.56) (-8.42) (-8.60) (-8.45)
QCAR -0.292*** -0.289***
(-7.90) (-7.82)
35
Year dummies included
Adj. R2 15.07% 15.24% 15.09% 15.25%
No. of Obs. 28,696 28,696 28,696 28,696
F-Test:
DWATCH - UWATCH 4.21** 3.78* a Table 3 Panel A compares the probability of managers issuing earnings forecasts in the watch and non-watch fiscal
quarters. A watch fiscal quarter is a quarter when the quarter overlaps with credit watch period, otherwise a fiscal quarter is
defined as non-watch fiscal quarter. b Table 3 Panel B reports Probit regression results that regress the occurrence of management earnings forecasts in a fiscal
quarter on watch placement and other firm characteristics. The dependent variable is a dummy variable, equal to 1 if the firm
issued management forecast during the firm quarter. Year dummies are included and standard error terms are adjusted for
potential clustering by firms. p-values for two-tail tests are reported in parentheses. See Table 1 for variable definitions.
36
Table 4. Management Earnings Forecast Error/Accuracy and Credit Watch Placement
Panel A: Forecast Error/Accuracy Comparison between Watch and Non-watch Quarters
Signed Forecast
Error (FE)
Unsigned Forecast Error
(AFE)
Watch Firm Quarters 0.0026 0.0073
Non-watch Firm Quarters 0.0013 0.0055
Difference 0.0013*** 0.0018***
(t-statistics) (2.42) (3.26)
Panel B: Multivariate Analysis of Forecast Errors (FE) and Forecast Accuracy (AFE) a
Variable Predicted
Sign
Forecast Errors (FE) Forecast Accuracy (AFE)
WATCH +/- 0.001* 0.001**
(1.74) (2.15)
SIZE + 0.000 0.000
(0.04) (-0.03)
MB + 0.000 0.000*
(0.06) (-1.68)
RD + -0.006* -0.012***
(-1.82) (-3.28)
ROASTD - -0.002 0.001
(-0.56) (0.24)
IO + -0.001 -0.001
(-1.57) (-1.28)
AF + 0.000 -0.001***
(-0.91) (-2.57)
FESTD 0.000* 0.000*
(1.70) (1.68)
LEV + 0.002 0.006**
(0.95) (2.35)
REGFD + -0.004 -0.002
(-1.09) (-0.45)
LITI + 0.000 0.001
(0.59) (0.71)
NEW ? -0.001** 0.000
(-2.01) (-0.71)
LOSS + 0.000 0.004***
(0.06) (3.21)
ANNUAL + 0.003*** 0.006***
(6.93) (11.54)
Year dummies included
Adj. R2 4.09% 11.15%
No. of Obs. 7,277 7,277 a The table reports OLS regression using the management forecast errors (FE) as dependent variable in the 1st
column, and forecast accuracy (AFE) as dependent variable in the 2nd column. Year dummies are included and
standard error terms are adjusted for potential clustering by firms. p-values for two-tail tests are reported in
parentheses.
37
Table 5. Watch Direction and Management Earnings Forecast Error/Accuracy
Panel A: Forecast Error Comparison between Watch and Non-watch Quarters
Forecast Error
(FE) Forecast Accuracy
(AFE)
(1) Non-watch Firm Quarters 0.0013 0.0055
(2) Downgrade Watch Firm Quarters 0.0040 0.0088
(3) Upgrade Watch Firm Quarters 0.0001 0.0044
Differences:
(1) – (2) -0.0026*** -0.0033***
(-4.02) (-4.98)
(1) – (3) 0.0012 0.0011
(1.44) (1.29)
(2) – (3) 0.0039*** 0.0044***
(3.05) (3.34)
Panel B: Multivariate Analysis of Forecast Errors (FE) and Forecast Accuracy (AFE) a
Variables Prediction
Forecast Error
(FE)
Forecast Accuracy (AFE)
DWATCH +/- 0.002** 0.003***
(2.58) (3.19)
UWATCH +/- -0.001* -0.001**
(-1.84) (-2.05)
SIZE + 0.000 0.000
(0.08) (0.01)
MB + 0.000 0.000
(0.05) (-1.68)
RD + -0.007* -0.013***
(-1.88) (-3.32)
ROASTD - -0.002 0.002
(-0.43) (0.37)
IO - -0.001 -0.001
(-1.56) (-1.25)
AF + 0.000 -0.001**
(-0.89) (-2.54)
FESTD + 0.000* 0.000*
(1.70) (1.68)
LEV + 0.002 0.006**
(0.97) (2.37)
REGFD + -0.004 -0.002
(-1.10) (-0.45)
LITI + 0.000 0.001
(0.63) (0.75)
NEW + -0.001 0.000
(-1.94) (-0.63)
LOSS + 0.000 0.004***
(0.04) (3.20)
ANNUAL + 0.003*** 0.006***
(6.93) (11.53)
Year dummies included
38
Adj. R2 4.25% 10.40%
No. of Obs. 7,277 7,277
F-Test
DWATCH - UWATCH 10.10*** 14.18*** a The table reports OLS regression results. The dependent variable is either management forecast errors (FE) in the 1
st
column or forecast accuracy (AFE) in the 2nd
column. Year dummies are included and standard error terms are adjusted
for potential clustering by firms. p-values for two-tail tests are reported in parentheses. See Table 1 for variable
definitions.
39
Table 6. Forecast Error/Accuracy, and Watch Duration a
Forecast Error Forecast Accuracy
Variables Ex.
Sign
(1)
All
(2)
Downgrade
Watch
(3)
Upgrade
Watch
(4)
All
(5)
Downgrade
Watch
(6)
Upgrade
Watch
FE or AFE +/- 6.868* 5.474 13.910** 1.623 0.957 9.113**
(1.85) (1.40) (2.24) (0.42) (0.23) (2.01)
SIZE + -0.071 0.048 -0.137* -0.078 0.047 -0.150*
(-1.12) (0.56) (-1.79) -(1.18) (0.53) (-1.93)
MB + -0.010 0.003 -0.074*** -0.010 0.003 -0.070***
(-0.36) (0.13) (-2.77) -(0.36) (0.11) (-2.65)
RD + 1.714 1.947 -1.497 1.722 1.827 -1.376
(1.05) (1.19) (-0.62) (1.03) (1.11) (-0.55)
ROASTD - 1.438 3.279 -0.990 1.276 3.191 -1.266
(0.98) (0.92) (-0.77) (0.90) (0.89) (-0.97)
IO + -0.382 -0.526 -0.301 -0.400 -0.531 -0.340*
(-1.14) (-1.00) (-1.42) -(1.20) (-1.00) (-1.65)
AF + -0.017 -0.255 0.302 -0.022 -0.250 0.288*
(-0.12) (-1.59) (1.88) -(0.17) (-1.56) (1.77)
FESTD -0.030 -0.010 -0.041 -0.015 0.000 -0.026
(-0.78) (-0.26) (-0.33) -(0.37) (-0.01) (-0.18)
LEV + -0.745 -0.988 -0.127 -0.741 -0.953 -0.098
(-1.11) (-1.15) (-0.29) -(1.03) (-1.08) (-0.22)
REGFD + 0.242 -1.356*** -4.111*** 0.323 -1.340*** -4.167***
(0.78) (-3.24) (-12.12) (0.98) (-3.23) (-12.47)
LITI + -0.199 -0.266 -0.101 -0.186 -0.260 -0.087
(-1.31) (-1.23) (-0.61) -(1.21) (-1.20) (-0.52)
NEW + 0.116 0.049 0.067 0.115 0.055 0.070
(0.99) (0.29) (0.35) (0.96) (0.32) (0.37)
LOSS + 0.000 -0.146 0.281 0.007 -0.125 0.299
(0.00) (-0.48) (1.06) (0.03) (-0.40) (1.15)
ANNUAL + -0.135 -0.265 -0.039 -0.374 -0.228 -0.045
(-0.99) (-1.39) (-0.34) -(0.70) (-1.21) (-0.40)
Year dummies included
Adj. R2 8.21% 11.36% 49.50% 10.87% 10.87% 48.67%
# of Obs. 519 339 180 519 339 180
a The table reports regression results of credit watch duration on forecast bias/accuracy and other control variables. The
dependent variable is the number of days between a credit watch placement and its resolution. Year dummies are included
and standard error terms are adjusted for potential clustering by firms. p-values for two-tail tests are reported in
parentheses. See Table 1 for variable definitions.
40
Table 7. Forecast News, Forecast Error/Accuracy, and Watch Resolution
Panel A. Descriptive statistics and univariate comparison
a) Forecast Error (FE)
Watch resolution All Downgrade Watch Upgrade Watch
Unfavorable 0.0040 0.0043 0.0027
Favorable 0.0013 0.0035 -0.0009
Difference 0.0027** 0.0008 0.0036***
t-stat. (2.13) (0.45) (2.65)
b) Forecast Accuracy (AFE)
Watch resolution All Downgrade Watch Upgrade Watch
Unfavorable 0.0084 0.0090 0.0063
Favorable 0.0062 0.0086 0.0037
Difference 0.0022* 0.0004 0.0026*
t-stat. (1.76) (0.22) (1.66)
Panel B. Multivariate regression of watch resolution on forecast news and forecast error a
Forecast Error Forecast Accuracy
Variables Ex.
Sign
All Downgrade
Watch
Upgrade
Watch
All Downgrade
Watch
Upgrade Watch
FE or AFE +/- -5.876 1.561 -40.222** -3.542 4.397 -29.174**
(-1.03) (0.27) (-2.21) (-0.65) (0.74) (-2.22)
SIZE + -0.041 -0.244** 0.519*** -0.039 -0.243** 0.517***
(-0.53) (-2.50) (2.88) (-0.49) (-2.47) (2.91)
MB + 0.032 0.022 0.160* 0.031 0.023 0.137
(1.08) (0.79) (1.81) (1.06) (0.84) (1.56)
RD + -2.390* 0.339 -23.576*** -2.399* 0.526 -23.756***
(-1.69) (0.19) (-3.68) (-1.69) (0.29) (-3.60)
ROASTD - 0.605 0.571 3.903* 0.675 0.597 4.191*
(0.40) (0.21) (1.65) (0.44) (0.22) (1.88)
IO + -0.366 -0.403 -1.291** -0.370 -0.378 -1.237**
(-1.34) (-1.16) (-2.05) (-1.35) (-1.09) (-2.01)
AF + 0.296* 0.460** -0.246 0.291* 0.459** -0.174
(1.74) (2.21) (-0.61) (1.70) (2.19) (-0.45)
FESTD -0.041 -0.013 -0.238 -0.048 -0.020 -0.160
(-0.68) (-0.20) (-0.65) (-0.81) (-0.31) (-0.44)
LEV + -0.828 -1.511** 1.398 -0.828 -1.568** 1.321
41
(-1.51) (-2.19) (1.18) (-1.50) (-2.26) (1.14)
REGFD + 0.406 -4.807*** 0.133 0.408 -4.892*** 0.854
(0.97) (-8.39) (0.15) (0.98) (-8.50) (0.85)
LITI + -0.258 -0.337 -0.047 -0.259 -0.349 -0.074
(-1.20) (-1.34) (-0.08) (-1.20) (-1.38) (-0.13)
NEW + 0.119 -0.163 0.975*** 0.122 -0.177 0.945**
(0.74) (-0.84) (2.65) (0.77) (-0.91) (2.64)
LOSS + -0.127 -0.191 0.238 -0.123 -0.232 0.193
(-0.42) (-0.51) (0.37) (-0.41) (-0.62) (0.31)
ANNUAL + -0.066 -0.242 0.165 -0.081 -0.260 0.147
(-0.43) (-1.27) (0.62) (-0.52) (-1.36) (0.53)
Year dummies included
Adj. R2 10.68% 9.64% 47.42% 10.52% 9.80% 46.96%
# of Obs. 519 339 180 519 339 180
a The table reports Probit regression results. The dependent variable is a dummy variable, equal to 1 if the resolution of a credit watch
is favorable (i.e. no downgrade after a negative credit watch and upgrade after a positive credit watch) and 0 otherwise. Year dummies
are included and standard error terms are adjusted for potential clustering by firms. p-values for two-tail tests are reported in
parentheses. See Table 1 for variable definitions.
