Sarah McVay FLM 4.08
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Transcript of Sarah McVay FLM 4.08
Internal Control and Management Guidance*
Mei Feng
Assistant Professor of Accounting
Katz School of Business, University of Pittsburgh
E-mail: [email protected]
Chan Li
Assistant Professor of Accounting
Katz School of Business, University of Pittsburgh
E-mail: [email protected]
Sarah McVay
Assistant Professor of Accounting
David Eccles School of Business, University of Utah
E-mail: [email protected]
Abstract:
We examine the effects of internal control quality on management guidance, and find that
guidance is less accurate in the year of, and the two years preceding, the disclosure of ineffective
internal controls. We find that the less accurate guidance persists if the internal controls remain
ineffective, but is mitigated if the internal control problems are remediated. We also find the
management forecast errors are larger when the internal control problems are most likely to
affect interim numbers and thus guidance. Finally, we find changes in the characteristics of
management guidance following the identification and disclosure of ineffective internal controls;
managers are less likely to issue guidance, and if they do issue guidance, the guidance is less
specific. We conclude that internal control quality has an economically significant effect on
management guidance, providing additional support for Section 404 and expanding our
knowledge on the determinants of management forecast accuracy.
* We would like to thank Michael Ettredge, Harry Evans, Weili Ge, Matt Magilke and workshop participants at the
University of Pittsburgh and the University of Utah for their helpful comments.
1
Internal Control and Management Guidance
Introduction
In this paper, we examine the relation between internal control quality and the accuracy
of management guidance. Disclosures of internal control deficiencies became widely available
for the first time following the Sarbanes-Oxley Act of 2002. Section 404 of this regulation
requires management to document their firm’s internal controls and assess their effectiveness.
Auditors are then required to attest to and report on the management assessment.1 There has
been a heated debate over the relative costs and benefits of Section 404. Empirical evidence on
the costs and benefits of disclosing weak internal controls is mixed, though generally results
suggest that the disclosures under Section 404 tend to be largely uninformative, leading many to
argue that the costs of Section 404 (both in terms of audit fees and employee time) exceed the
related benefits.2 In a recent survey conducted by the U.S. Chamber of Commerce on the cost of
Section 404, 89% of the respondents think the costs exceed the benefits of Section 404
compliance.3 This debate is especially important as Section 404 is currently effective only for
the largest firms (accelerated filers). The deadline for non-accelerated filers has repeatedly been
delayed over the last few years. While the SEC has stood firm on the 2007 deadline for the
management reporting requirement for non-accelerated filers, a bill to delay the effective date for
another year is pending in Congress (Whitehouse, 2007).
1 Section 404 became effective for fiscal years ending after November 15, 2004, and currently applies only to the
largest firms (accelerated filers). Section 302 preceded Section 404 and applies to all SEC registrants, effective in
August of 2002. Section 302 requires that managers publicly disclose changes in their internal control systems. 2 For example, Beneish et al. (2008) and Ogneva et al. (2007) find no relation between cost of capital and Section
404 disclosures after controlling for known determinants of cost of capital, while Ashbaugh-Skaife et al. (2007b) do
find a relation. With respect to earnings quality, while Doyle et al. (2007b) find that accruals quality is lower for
firms with weak internal controls, this association is much weaker for Section 404 disclosures (versus Section 302
disclosures). 3 Approximately 50% of the respondents in this survey, released on November 8, 2007, were managers of small
firms not yet subject to Section 404, while the other 50% were from firms currently operating under Section 404
(http://www.uschamber.com/publications/reports/0711soxsurvey.htm).
2
We add to this debate by examining a significant benefit of Section 404 that has not
previously been addressed in the literature or business press—the association between internal
control quality and management guidance. While voluntary disclosures are not the focus of
Section 404, we argue that internal control problems would result in lower-quality interim
financial inputs, thereby resulting in lower-quality management guidance. In other words, good
internal control can improve the quality of earnings guidance, which potentially brings various
benefits to firms, such as increased analyst following (Lang and Lundholm, 1993) and a better
reputation for transparent and accurate reporting (Graham et al., 2005; Williams, 1996).
We study 2,940 firms that issued earnings guidance and filed Section 404 reports with the
SEC from 2005-2007. Following previous literature (Ajinkya et al., 2005), we measure the
quality of earnings guidance using ex post management forecast errors (the absolute value of the
difference between actual earnings and management forecasts, scaled by the stock price at the
beginning of the period).4 Consistent with our hypothesis, we find that firms disclosing material
weaknesses in internal control tend to have significantly larger management forecast errors than
firms reporting effective internal controls. Specifically, management forecast errors are, on
average, 0.007 higher when a firm reports a material weakness in internal control, after
controlling for earnings characteristics such as losses and volatility that make earnings more
difficult to predict. This magnitude is quite large given the mean (median) forecast error is only
0.01 (0.004). We conduct three additional tests to further validate the link between internal
control quality and the quality of management guidance.
4 The internal control problem could result in both an erroneous forecast and an erroneous reported earnings figure.
To the extent that the errors in the forecasts and earnings are positively correlated, this will bias against finding a
relation between management forecast accuracy and internal control quality. Another possibility is that weak
internal controls allow managers to more easily manage earnings to meet their own forecast. This should also bias
against our findings, and suggests that on average our findings are due to unintentional rather than intentional errors.
3
First, we examine the management forecast errors in the three years prior to the disclosed
internal control deficiency. Because our measure of forecast accuracy relies on reported earnings
as well as the manager’s forecast, additional auditor scrutiny in the year the firm reports a
material weakness might lower reported earnings (Hogan and Wilkins, 2005), thereby resulting
in larger ex post forecast errors solely because of this additional scrutiny. When we re-estimate
our tests with historical forecast accuracy, we find a similar relation between a material
weakness disclosure in year t and forecast accuracy in year t-1. This relation monotonically
declines as we go back in time, and becomes insignificant in year t-3. These findings mitigate
our concern that auditor scrutiny might affect our results. Rather, it appears more likely that the
lower-quality financial information available to the manager is driving the association between
management forecast accuracy and internal control deficiencies.
Second, we examine how the relation between internal control quality and management
forecast accuracy varies by the type of material weakness reported. We first identify weaknesses
related to revenue and cost of goods sold, which we expect to have the greatest impact on the
information used by the manager when forming the guidance (Fairfield et al., 1996). We find
that weaknesses affecting these financial statement accounts are more highly associated with
management forecast errors than other weaknesses. We also tabulate the number of weaknesses
reported, based on the assumption that the more weaknesses present, the more severe the internal
control problems, and the more likely they are to affect the accuracy of the guidance. We find
that the magnitude of the forecast errors increases with the number of material weaknesses
reported.
Third, we examine changes in internal control quality. We find that if an internal control
problem persists (i.e., the problem is reported as a material weakness in year t+1 as well as year
4
t), larger forecast errors also persist. However, if the internal control problems are remediated in
year t+1, the forecast errors in year t+1 for the remediated firms are not statistically different
from the errors for the control sample. Jointly, our tests provide strong support for the
contention that the quality of a firm’s internal control system exerts an economically and
statistically significant impact on the accuracy of the firm’s earnings forecasts.
Finally, we examine how managers’ reporting strategies change following the
identification and disclosure of an internal control problem. We find that if the material
weakness is not remedied in the year following the material weakness disclosure, managers tend
to either stop issuing guidance, or issue less specific guidance. However, if the firm reports an
internal control problem and remedies the problem by the end of the following fiscal year, we do
not find these responses—managers continue to issue guidance and tend to maintain the
specificity of the guidance. Our findings are consistent with managers adjusting their voluntary
disclosure strategies based on the quality of internal control, which is consistent with managers
believing that the quality of internal control affects either the quality or the credibility of their
forecast, or both.
Our paper contributes to both the literature on internal control over financial reporting as
well as the management forecast literature. We first add to the heated debate on the costs and
benefits of Section 404. While the empirical findings for Section 404 firms regarding the
relation between internal control quality and earnings quality have been weak (Doyle et al.
2007b), we document a robust and economically significant association between internal control
quality and the quality of management earnings guidance, indicating that a good internal control
system can help improve the quality of some voluntary disclosures. Management guidance
quality is a more powerful setting than earnings quality in which to test the effect of Section 404
5
because auditors can help mitigate the negative effect of weak controls on earnings, but not on
earnings guidance. Our results indicate that Section 404 provides an important benefit that has
been previously overlooked—higher-quality management guidance. In addition, our findings
have broader implications. If managers use similar inputs for their internal forecasting processes
and decision-making processes, our findings suggest that managers may be making suboptimal
business decisions because they are relying on faulty numbers resulting from weak internal
controls.
Our paper also contributes to the management forecast literature, which has previously
focused primarily on how managers’ incentives, such as litigation concerns and insider trading
motives, affect management forecast characteristics. We are not aware of prior studies
investigating how the quality of the financial information used by the managers affects
management forecast characteristics, such as forecast accuracy, frequency and specificity. We
find that quality of financial information is a statistically and economically significant
determinant of forecast accuracy. In addition, managers appear to change their earnings
forecasting strategies based on the quality of their interim financial information.
Motivation and Hypothesis Development
Prior research on internal control quality
A great deal of research has followed the recent public disclosures of internal control
quality under Sections 302 and 404 of the Sarbanes-Oxley Act. The initial papers were largely
descriptive, providing evidence on the types of firms issuing ineffective internal controls (e.g.,
Ge and McVay, 2005; Ashbaugh-Skaife et al., 2007a; Doyle et al. 2007a; Bryan and Lilien,
2005). These papers find that firms with weak internal controls tend to be smaller, less
profitable, more complex, and/or undergoing changes via rapid growth or restructurings. Other
6
studies examined the impact of ineffective internal controls on audit cost, such as higher audit
fees (e.g., Raghunandan and Rama, 2006; Hoitash et al., 2008), longer audit delays (Ettredge et
al., 2006), and more auditor resignations (Ettredge et al., 2007).
Studies have also begun examining the possible benefits of effective internal controls in
terms of the cost of equity and earnings quality, although the results are mixed. While
Ashbaugh-Skaife et al. (2007b) find a relation between cost of capital and internal control
deficiencies across Section 302 and 404 disclosures, Beneish et al. (2008) find that cost of equity
and price reactions to disclosures are significant for Section 302 disclosures but not for Section
404 disclosures. Ogneva et al. (2007) find no difference in the cost of equity capital among
Section 404 disclosures after controlling for known determinants of cost of equity capital.
Doyle et al. (2007b) find evidence of lower-quality earnings for material weakness firms
filing Section 302 disclosures, but not for Section 404 disclosers, on average. Ashbaugh-Skaife
et al. (2008) and Bedard (2006) find evidence of improvements in earnings quality following
remediations of internal control problems for firms disclosing material weaknesses under Section
404. This mixed evidence on the relation between internal control and earnings quality could be
due to the existence of additional monitoring mechanisms (e.g., auditors, boards of directors, and
institutional investors; Hogan and Wilkins, 2005; Krishnan, 2005; Tang and Xu, 2007). For
example, auditors’ substantive testing can act as a substitute for many internal control
deficiencies, mitigating the negative effects on earnings quality (Doyle et al., 2007b).
While researchers have focused on reported earnings, the effect of internal control
problems on management forecasts remains unexamined.5 Management forecasts are an
extremely important voluntary disclosure. Existing research shows that these forecasts are very
5 As we said before and will discuss in greater detail below, management guidance provides a stronger setting than
reported earnings in which to examine the impact of weak internal controls, as the negative effects are not mitigated
by auditors’ substantive testing.
7
informative. The earnings surprise embedded in a management forecast influences prices (e.g.,
Patell, 1976; Penman, 1980; Waymire, 1984; Pownall and Waymire, 1989) and alters investors’
earnings expectations, as measured by subsequent revisions in analyst forecasts (Jennings, 1987;
Baginski and Hassell, 1990; Williams, 1996). Prior research on management forecast accuracy
has mainly focused on the incentives facing the managers and their firms (e.g., Rogers and
Stocken, 2005). However, regardless of a manager’s incentives or ability to effectively compile
information into a forecast, if the manager is using poor-quality inputs, the forecast will likely
also be of poorer quality. Thus, we expect that the quality of the internal control over financial
reporting will affect the quality of the financial information used by the manager to form the
estimate, thereby affecting the quality of the voluntary disclosure.
We anticipate that weak internal controls will affect the financial reporting inputs to
management guidance in at least two ways. First, weaknesses can result in errors in the financial
statement figures. While auditors’ substantive testing mitigates the effect of these errors on
reported earnings, they do not substantively test the interim numbers used by managers to form
their guidance, and thus we expect the effect to be stronger for management guidance than
reported earnings. Consider the following material weakness disclosure provided by Penn
Treaty American Corporation (whose main business is providing long-term care insurance) in its
10-K filing for the fiscal year ended December 31, 2005:
The Company did not maintain adequate controls over the claims processing and
payment areas to analyze and record appropriate adjustments to the claims
payables and expense or monitor the proper determination and processing of
claim payments. Numerous deficiencies were generally aggregated into two areas:
claims processing (including claim maximum benefits, authority limits, check
processing, and routine payment issues), and claims quality assurance department
(responsible for the identification of errors and fraud).
8
Given the company’s business, the internal control problems over claims noted above
will clearly affect the interim numbers management uses to form their earnings guidance, likely
resulting in less accurate earnings guidance than if the forecast had been based on more accurate
interim numbers.
Second, weaknesses can result in untimely, or stale, financial statement information. For
example, a company may lack personnel with adequate expertise to generate the information
needed by management for forecasting on a timely basis. Dana Corporation filed the following
weakness in their December 31, 2005 10-K filing:
Our financial and accounting organization was not adequate to support our
financial accounting and reporting needs. Specifically, lines of communication
between our operations and accounting and finance personnel were not adequate
to raise issues to the appropriate level of accounting personnel and we did not
maintain a sufficient complement of personnel with an appropriate level of
accounting knowledge, experience and training in the application of GAAP
commensurate with our financial reporting requirements. This control deficiency
resulted in ineffective controls over the accurate and complete recording of
certain customer contract pricing changes and asset sale contracts (both within
and outside of the Commercial Vehicle business unit) to ensure they were
accounted for in accordance with GAAP.
Thus, transactions were not fully recorded during the period, making interim numbers
less informative. Managers using out-of-date information face more uncertainty and may rely on
less accurate estimates when issuing forecasts. As a result, we expect their forecast errors to be
larger. As with errors resulting from weak internal control, timeliness becomes less of an issue
with reported earnings, because the manager is able to wait to file the report until all figures have
been finalized for the period (i.e., the lag between the period end and the filing date allows some
“catch-up” to occur).
Thus, we posit that the better the underlying quality of the internal controls, the better
able managers are to issue more accurate guidance. This leads to our first hypothesis:
9
H1: Management forecast accuracy is higher among firms with effective internal controls.
Specifically, we expect firms reporting material weaknesses in internal control to issue
less accurate management guidance. We conduct three distinct tests of H1. First, we examine
the association between internal control problems and management forecast errors in both
current and prior years, including controls for known determinants of management forecast
accuracy and internal control quality. Second, we partition the material weaknesses by type—
concentrating on weaknesses affecting sales or cost of goods sold, which we expect to have a
greater impact on management forecast accuracy than all other weaknesses, and those we
estimate to have more severe internal control problems, measured by the number of weaknesses
reported. Finally, we examine how changes in the quality of internal control map into
management forecast errors. We expect that firms remediating their internal control problems
mitigate the adverse effects on their management forecast accuracy, while firms that fail to
resolve their problems will continue to exhibit lower forecast accuracy. Jointly, these tests
provide strong evidence for our hypothesis that managers in firms with weak internal controls
will issue less accurate guidance.
Our second hypothesis addresses managers’ responses to the identification and disclosure
of weaknesses in their internal control. Prior to Sarbanes-Oxley and the culmination of Section
404, managers were not required to document their internal control procedures. While they
likely had some idea of the internal control quality of their firm, they had not been required to
conduct a detailed evaluation, and thus they may not have known the extent of their internal
control problems. Moreover, even if they were aware of an internal control problem, they were
not required to publicly disclose material weaknesses. After identifying and publicly disclosing
10
an internal control problem, however, managers may change their guidance behavior, either
because they hesitate to rely on potentially faulty figures, or they feel the market will discount
their guidance in the presence of an internal control problem. Managers might cease to provide
guidance, issue less precise guidance, or perhaps delay issuing guidance until after the internal
control problem has been remediated. This leads to our second hypothesis:
H2: The identification and disclosure of poor-quality internal controls leads managers to
change their guidance behavior.
Specifically, we examine how the likelihood of issuing guidance, the specificity of the
guidance, and the timing of the guidance, change following the initial disclosure of a material
weakness in internal control. We partition our firms by those that have a material weakness in
both years t and t+1 versus those that quickly remediate their internal control problems and issue
a material weakness only in year t.6 We expect the former group to be more likely to change
their voluntary disclosure behavior since managers are likely aware of the t+1 internal control
weakness disclosure throughout the year.7
Data and Sample Selection
We collect our data from Audit Analytics (Section 404 reports), First Call (management
forecasts), Annual Compustat (financial statement variables), and CRSP (stock returns to
generate beta). Table 1 summarizes our sample construction. We begin with all Section 404
reports available on Audit Analytics, 11,528 firm-year observations from January 2005 to
September 2007, corresponding to fiscal 2004 through fiscal 2006. We exclude 712 firm-years
6 As we will discuss further below, we do not conduct our main analysis in year t as it is not clear when in year t the
internal control problem was discovered (see also footnote 16). 7 It is possible that managers believe the internal control problem has been remediated, and a new internal control
problem arises in year t+1; this should bias against our tests.
11
that are not covered by First Call8 and 7,174 observations that did not issue an annual point or
range forecast in the corresponding fiscal year, which results in a sample of 3,642 observations.
We next remove observations without the necessary financial data from the Compustat, analyst
coverage from First Call, and stock return data from CRSP. Our final sample contains 2,940
firm years.9
Variable Definitions and Descriptive Statistics
We create an indicator variable that is equal to one if the firm received an adverse Section
404 report, and zero if the firm received a clean report. Of the 2,940 firm-years in our final
sample, 305 (10.4%) firm-year observations received adverse opinions.10
As noted above, we
define management forecast errors as the absolute value of the difference between reported
earnings and management forecasts scaled by the stock price at the beginning of the fiscal year.
We focus on the absolute value of the forecast error as we are interested in the magnitude, rather
than the direction, of the error. Internal control problems can result in both erroneous forecasts
and erroneous reported earnings. As noted previously, we expect the realized earnings number
to have fewer errors. If reported earnings have similar forecast errors to management forecasts,
management forecast errors will be understated. This should serve to bias against finding any
relation between management forecast accuracy and internal control quality. Table 2 presents
descriptive statistics for the full sample, as well as the adverse and clean report firm-years
separately. On average, the absolute value of management forecast errors is 0.01. Examining the
8 A firm is covered by First Call if the firm is included in any of the following First Call Historical Database files:
company issued guidelines, summary, detailed analysts’ forecasts or actual earnings files. 9 We match all annual forecasts made in a given fiscal year to that year’s internal control data. We do not require
that the year being forecasted is the year of the material weakness disclosure. If a manager issues guidance more
than once in the fiscal year, we take the average of all of the forecast errors. 10
While 10.4% is a slightly smaller percentage than existing studies examining Section 404 reports, we examine
fiscal years 2004–2006 and the number of weaknesses monotonically declines over this time period (143, 104, and
58 for 2004–2006, respectively).
12
errors across the two sub-samples (clean versus adverse opinions), it is clear that the magnitude
of the forecast errors is significantly larger for firms with adverse opinions, providing initial
support for H1. We realize, however, that earnings of firms with internal control weaknesses
tend to be harder to estimate (Doyle et al., 2007b) and that auditors may have increased their
scrutiny in the year of the adverse opinion (Hogan and Wilkins, 2005). Thus, we conduct
multivariate tests and examine prior years’ forecast errors to provide additional evidence on H1.
We include a multitude of control variables in our regression analyses. As noted
previously, firms with poor internal control quality tend to be systematically different from firms
with strong internal controls. For example, they tend to be smaller, less profitable, more highly
levered, and growing rapidly or experiencing a restructuring (Ge and McVay, 2005; Ashbaugh-
Skaife et al., 2007a; Doyle et al., 2007a; Ettredge et al., 2007). Using these known determinants
as a starting point for the inclusion of control variables, we first include firm size as a control
variable (LN_TA), as firm size is also likely associated with management forecast accuracy.
Larger firms may have more experienced and knowledgeable staff, thereby resulting in more
accurate guidance. We next control for profitability (ROA and LOSS) and leverage, as managers
in firms with low profitability and/or high leverage may be less able to allocate resources to
forming their guidance. Moreover, analysts have been shown to have a more difficult time
estimating earnings for loss-making firms (Brown, 2001). We control for sales growth, as
rapidly growing firms, which are less able to maintain strong internal controls, may also have
more difficulty estimating earnings. We include an indicator variable if the firm operates in a
litigious industry (following Francis et al., 1994), as Ashbaugh-Skaife et al. (2007a) hypothesize
a greater concentration of material weaknesses in highly litigious industries; we also include
13
Beta as an alternative proxy for litigation risk (Ajinkya et al., 2005).11
We include the magnitude
of special items scaled by lagged assets (SI) to proxy for both restructurings and large asset
impairments (Ashbaugh-Skaife et al., 2007a; Doyle et al., 2007a; Hogan and Wilkins, 2005);
changes to the organizational structure will likely make earnings more difficult to predict.12
Both Ashbaugh-Skaife et al. (2007a) and Doyle et al. (2007a) find that M&A activity is related
to the disclosure of a material weakness, and thus we include an indicator variable for M&A
activity as we expect these firms may also have harder to predict earnings given these changes.
Finally, we consider the type of auditor, as Ge and McVay (2005) and Ashbaugh-Skaife et al.
(2007a) find that larger auditors have a greater number of material weaknesses under 302
disclosures, although Ettredge et al. (2007) find the opposite results when examining 404
disclosures, probably because Big 4 auditors either required speedy remediations or dropped
their riskiest clients. Firms with large auditors may also have lower forecast errors (Ajinkya et
al., 2005).
We control for additional determinants of management forecast accuracy that may also be
correlated with internal control quality. We include the number of analysts following the firm
(ANALYSTS), as Lang and Lundholm (1993) find that firms with higher analyst following tend
to have better disclosure. We include earnings volatility (STDEARN) as firms with more
volatile earnings may have greater difficulty forecasting earnings, and the dispersion of analyst
forecasts prior to the management guidance (DISPFOR), as this also proxies for uncertainty
about earnings. Finally, we control for both when in the year the forecast is issued (HORIZON)
11
Francis et al. (1994) find an association between litigious industries and the presence of earnings guidance;
however, Ajinkya et al. (2005) do not find an association between the accuracy of the guidance and operating in a
litigious industry. 12
Results are similar if we include an indicator variable for restructuring charges in year t as an alternative to SI.
14
and the magnitude of the revision suggested by the management guidance (REVISION). In both
instances, we expect larger values to be associated with larger errors (Ajinkya et al., 2005).
As Table 2 shows, material weakness firms tend to be smaller and less profitable,
consistent with prior research (e.g., Ge and McVay, 2005). Approximately 28% of our sample
conduct their main operations in a highly litigious industry (biotech, computers, technology and
retail, based on Francis et al., 1994), and there appears to be a greater concentration of firms in
litigious industries among firms issuing adverse reports (38.0% versus 26.8%). This
concentration is consistent with the expectations of Ashbaugh-Skaife et al. (2007a), though they
do not find a similar concentration among Section 302 disclosers. It is possible that in the
Section 404 era, in which managers are required to issue a report, conclude on the effectiveness
of internal control, and have auditors attest to this conclusion, litigation risk plays a greater role.
An alternative proxy of litigation risk (beta) is also higher among material weakness firms, and
these firms tend to have more income-decreasing special items, both consistent with prior
literature (e.g., Bryan and Lilien, 2005).
Approximately 92% of our sample firm-years were audited by Big 4 auditors, with fewer
Big 4 audits among our adverse opinion firm-years (87.5% versus 92.7%). Analyst following is
lower among material weakness firms, consistent with these firms being smaller and less
profitable, and these firms also tend to have more volatile earnings.
Test Design and Results
Main Test of H1
To test H1, that managers in firms with lower-quality internal control have greater
forecast errors (lower forecast accuracy), we first estimate the following OLS regression model:
15
ABSERROR= b0 + b1MW + b2 LN_TA + b3ROA + b4LOSS + b5LEVERAGE
+ b6GROWTH + b7LITIGATE + b8BETA + b9SI + b10MA
+ b11BIG4 + b12ANALYSTS + b13STDEARN + b14DISPFOR
+ b15HORIZON + b16REVISION + ε (1)
where ABSERROR is the absolute value of the management forecast error (scaled by price) and
MW is an indicator variable that is equal to one if the firm filed a contemporaneous adverse 404
report, and zero if the firm filed a clean report. We also include control variables that may be
correlated with both weak internal controls and management forecast accuracy, discussed above:
size, profitability, leverage, sales growth, operating in a litigious industry, beta, incurring special
items (such as restructurings or impairments), undertaking a merger or acquisition, audit quality,
the number of analysts following the firm, earnings volatility, the dispersion of the analyst
forecast prior to the management guidance, when during the year the guidance is issued, and
finally the magnitude of the revision suggested by the management guidance. Each of these
variables is motivated above (see descriptive statistics) and defined in Table 2.
Results are presented in Table 3. The first column of results pools all firm-years
together. In the subsequent three columns, we parse out our sample by fiscal year, so that each
firm is included only once in the estimation procedure. Across each of the four regression
estimations, MW is positive and significant (b1 = 0.006, t-statistic = 6.99 for the full sample).
This indicates that firms disclosing poor internal control quality exhibit significantly larger
management forecast errors (in absolute terms). Turning to our control variables, in all years but
2006, firm size is not significant. This finding is consistent with Ajinkya et al. (2005); size
seems to be a stronger determinant of the occurrence of a forecast than the accuracy of any
resulting forecasts. ROA is not significant, while loss firms tend to have larger forecast errors,
and leverage is largely insignificant (though weakly positively associated with errors in 2006).
16
Growth is negative and significant in two of our four specifications. While growth tends
to be negatively associated with internal control quality, it appears to be associated with lower
forecast errors (akin to the market-to-book ratio’s association in Ajinkya et al., 2005).
LITIGATE is not significant, consistent with Ajinkya et al. (2005), while BETA is positively
associated with management forecast errors in three of our four estimations. Special items do
not appear to be consistently associated with management forecast errors, while M&A activity
appears to be associated with lower forecast errors in two of our four specifications. We
expected the additional complexity involved with forecasting earnings for the newly combined
entity to be associated with larger errors; perhaps M&A also proxies for profitability, or
managers use M&As to help meet their earnings projections (e.g., Tyco).
Being audited by a BIG4 is not significantly different from zero, consistent with Ajinkya
et al. (2005), while ANALYSTS is significantly negatively associated with management forecast
errors in three of our four estimations, consistent with expectations. Earnings volatility
(STDEARN) is not associated with errors, inconsistent with Ajinkya et al. (2005) and our
expectations. Greater uncertainty among analysts (DISPFOR) is positively associated with
larger errors in each of our estimations, while the earlier in the year the forecasts are issued
(HORIZON) and the larger the suggested revision (REVISION), the greater the errors, consistent
with our expectations.
In sum, after controlling for known determinants of ex post management forecast errors
and potentially correlated determinants of internal control problems, we find evidence consistent
with H1—firms reporting internal control problems have less accurate management forecasts,
consistent with the managers in these firms relying on lower-quality interim financial
information when forming their forecasts.
17
Accuracy of Historical Guidance
As noted in Hogan and Wilkins (2005), auditors likely apply lower thresholds for write-
offs and other adjustments in the year a firm discloses a material weakness in internal control.
These adjustments are not necessarily related to the internal control problem. Rather, what might
have passed under the radar in earlier years (such as a possible impairment of equipment) now
results in an impairment due to additional auditor scrutiny, as the auditors may anticipate
additional scrutiny by regulators and investors over firms disclosing material weaknesses in
internal control. Because this additional scrutiny might mechanically lower the forecast
accuracy (as reported earnings might be mechanically lower due to the additional scrutiny), we
also examine the relation between a material weakness in the firm’s initial 404 report, and the
forecast accuracy in years t-1 through t-3, preceding the initial Section 404 report. As noted in
Doyle et al. (2007b), it is likely that the internal control problems, though first disclosed only
recently, have been in existence for some time. In years prior to the disclosure, the confounding
“auditor” effect noted in Hogan and Wilkins (2005) should not be a concern.
Turning to Table 4, we have 1,007 observations for the year preceding the initial 404
report. This number is greater than the number of observations in 2004 alone, as some firms
filed their initial 404 report in 2005; the number of observations decline as we move back in time
as fewer firms have available data. The coefficient on MW continues to be positive and
significant in years t-1 and t-2, while in t-3 the test statistic loses significance. In year t-1, the
coefficient on MW is 0.007, similar to our main test reported in Table 3. Thus, it does not
appear that the additional auditor scrutiny in year t is unduly affecting the ex post management
forecast errors examined in Table 3. The coefficient on MW monotonically declines as we move
back in time, with a coefficient of 0.004 in year t-2 and a coefficient of 0.003 in year t-3. The
18
smaller coefficients are consistent with fewer of the current-year internal control problems
actually being in existence in prior years. Overall, our tests continue to support our conclusion
that firms with internal control problems are more likely to issue less accurate earnings guidance.
Material Weaknesses by Type
While we attempt to control for potentially correlated variables, such as profitability, our
list of control variables is not exhaustive, and some correlated omitted variables, such as
managerial experience, are extremely difficult to measure.13
Thus, in this section, we conduct
cross-sectional tests of our hypothesis. Clearly, not all material weaknesses would be expected
to reduce the quality of the financial information inputs; thus we partition our weaknesses by
type and severity. We expect the greatest impact to be via material weaknesses affecting sales
and cost of goods sold. These two items are very important inputs when managers form their
forecasts. For example, Lundholm and Sloan (2006) note that sales are the single most important
input to a forecasting model, and Fairfield et al. (1996) find that sales and cost of goods sold
have the greatest value for predicting future earnings. We expect errors in these items to result in
large forecast errors. We also expect a greater likelihood of an error in the interim numbers
when more material weaknesses are present, as the number of weaknesses is a proxy for the
overall severity of the internal control problems.
Table 5 presents the two cross-sectional tests. The first compares weaknesses that affect
revenue or cost of goods sold to all other weaknesses. Referring to the first column of results,
13
In addition to the cross-sectional tests performed in this section, which are consistent with the internal control
problems causing the larger errors, and do not support managerial expertise as a correlated omitted variable, our
tests examining years t-1 through t-3 also speak to the viability of this alternative. If expertise were driving the
association between internal control quality and management accuracy, as managers become more experienced with
their firms, their errors should decline. Thus, when examining past years, the errors should be larger. Rather, we
find that as we go back in time, the errors decline, consistent with fewer of the current period weaknesses in
existence the further back in time we go.
19
the weaknesses affecting revenue and/or cost of goods sold increase management forecast errors
by 0.012, which is much larger than the effect of other material weakness, 0.003. The difference
between these two types of weaknesses is also statistically significant (p-value = 0.001; not
tabulated). Therefore, forecast errors are larger when the material weakness affects the quality of
important input numbers that managers use to form their forecasts. In our next cross-sectional
test, we simply examine how the error varies with the number of material weaknesses reported, a
general measure of severity. Consistent with our conjecture, as the number of material
weaknesses increases by one, the error significantly increases by 0.003.14
In addition, both of
our results hold when we examine past years’ errors (the final two columns in Table 5). These
tests provide strong evidence that it is the material weakness driving at least a portion of the
larger management forecast error, rather than some unidentified firm characteristic.
Internal Control Quality Change Analysis
Our final test of H1 examines how our results change as internal control quality
improves, worsens, or stays the same. For example, we would expect that as internal control
quality improves, the accuracy of the management forecast no longer suffers. To perform this
analysis, we break out our sample into four categories, those that issued a clean report in both
years (the benchmark group), those that issued an adverse opinion followed by a clean opinion
(IC_IMPROVE), those that issued two adverse opinions sequentially (IC_ADVERSE), and those
that issued a clean opinion followed by an adverse opinion (IC_WORSE). These categories are
defined using the change from year t to t+1. In Table 6, we present the level of the management
forecast error (in absolute terms) for year t+1 and t, conditional on the firm having issued point
14
When we restrict our sample to only those firms disclosing at least one material weakness, the number of material
weaknesses continues to be significantly and positively related to the management forecast error (coefficient = 0.003
with a t-stat of 4.29).
20
or range forecast in year t+1. IC_IMPROVE firms are associated with larger absolute forecast
errors in year t, consistent with our main finding, but not year t+1. In year t+1, their forecasts are
as accurate, on average, as those firms with clean opinions in both years.15
IC_ADVERSE firms
are associated with larger absolute forecast errors in both years, consistent with the problem
existing in both years. Finally, IC_WORSE firms are associated with larger errors only in t+1,
the year they report the adverse opinion. Note that this result is not inconsistent with our results
in Table 4 (where we examine prior years’ errors, where these prior years largely preceded
Section 404). IC_WORSE firms explicitly concluded effective internal controls in the prior
year, but identified and disclosed a material weakness in the current year. Finding that the errors
of these firms were no worse than those of the control sample in the prior year provides evidence
that they had, on average, truthfully disclosed effective internal controls in the prior year. This
finding links the origination of internal control problems with an increase in management
forecast errors (consistent with faulty interim numbers reducing the accuracy of the forecast).
Overall, across each of our tests, results are consistent with H1, that the internal control
quality has a statistically and economically significant effect on the manager’s forecast accuracy.
Test of H2
Our second hypothesis conjectures that the identification and disclosure of an internal
control problem affect the managers’ guidance behavior. For example, the managers’ (newly
acquired) knowledge that they are relying on potentially faulty figures may reduce their
likelihood of issuing a forecast, or perhaps lead them to decrease the specificity of the forecast or
delay the timing of the forecast. Alternatively, the managers might have previously been aware
15
The change in forecast accuracy from year t to year t+1 among IC_IMPROVE firms is not statistically significant
(p-value = 0.568, two-tailed). Looking at the effect in year t, however, it appears that remediated problems had less
of an effect on errors in the first place (0.002 versus 0.007). Thus, it appears that firms more quickly remediate less
severe problems.
21
of the internal control problem, but the public disclosure of this weakness might affect their
guidance behavior. Because we do not know when during the year the material weakness was
discovered, we examine two years of reports.16
We conjecture that managers who identify and
disclose a material weakness in their firm in year t and do not remediate this problem by the end
of year t+1 are the most likely to change their disclosure strategy in year t+1. Given that they
disclose material weakness in both years, managers probably know throughout year t+1 that they
have a material weakness, and that by issuing guidance in year t+1 they are relying on potentially
inaccurate interim figures. The following models are used to test H2:
∆OCCUR = b0 + b1IC_IMPROVE+ b2IC_ADVERSE + b3∆LN_TA + b4∆ROA
+ b5∆LOSS + b6∆LEVERAGE + b7∆GROWTH + b8LITIGATE + b9∆BETA
+ b10∆SI + b11∆MA + b12∆BIG4 + b13∆ANALYSTS + b14∆STDEARN
+ b15∆RESTATE + b16∆EXECTURN + ε (2)
∆SPECIFICITY = b0 + b1IC_IMPROVE+ b2IC_ADVERSE + b3∆LN_TA + b4∆ROA
+ b5∆LOSS + b6∆LEVERAGE + b7∆GROWTH + b8LITIGATE + b9∆BETA
+ b10∆SI + b11∆MA + b12∆BIG4 + b13∆ANALYSTS + b14∆STDEARN
+ b15∆RESTATE + b16∆EXECTURN + b17∆DISPFOR + b18∆HORIZON + ε (3)
∆HORIZON = b0 + b1IC_IMPROVE+ b2IC_ADVERSE + b3∆LN_TA + b4∆ROA
+ b5∆LOSS + b6∆LEVERAGE + b7∆GROWTH + b8LITIGATE + b9∆BETA
+ b10∆SI + b11∆MA + b12∆BIG4 + b13∆ANALYSTS + b14∆STDEARN
+ b15∆RESTATE + b16∆EXECTURN + b17∆DISPFOR + ε (4)
where ∆OCCUR is an indicator variable that is equal to one if the manager issued a forecast in
year t+1 but did not in year t, negative one if the manager did not issue a forecast in year t+1 but
did in year t, and zero if there was no change in forecast issuance. ∆SPECIFICITY is the change
in the average forecast specificity, where specificity has a value of one if the forecast is
16
We also considered changes in year t. We do not find systematic changes in behavior from year t-1 to year t for
firms that ex post reported a material weakness in internal control for year t. It appears that either managers were
not aware of the problem when providing guidance in year t, or they knew of their weak internal controls but
changed their behavior after the public disclosure of the weakness. It is also possible that our tests lack power, as
managers may have learned of the problem and changed their behavior during the year, however, our forecast
measures are the averages for the year. Finally, we considered the behavior level in year t (i.e., whether guidance
was issued in year t). Again, we do not find a significant association, consistent with the two explanations above.
22
qualitative, two if the forecast is a minimum or maximum, three if the forecast is a range, and
four if the forecast is a point estimate. ∆HORIZON is the change in the average number of days
between the end of the period and the issuance of the management forecast, where a positive
number indicates the forecasts are issued in a more timely fashion.
Our control variables mirror those in Equation (1), but are in changes, rather than levels
(where the changes are annual); these control variables largely follow Ajinkya et al. (2005), who
examine occurrence and specificity, as well as accuracy, which we examined in Equation (1).
We also introduce two new control variables for these tests, to control for alternative reasons that
managers might change their forecasting behavior. The first, following Brochet et al. (2007) is a
management turnover of the CEO or CFO (EXECTURN). Brochet et al. (2007) find that when a
top-level executive turns over, there tends to be an associated break in guidance. Moreover,
among CFO turnovers, if the guidance continues, it tends to be less specific. We also control for
restatements (RESTATE); if there is a pending restatement, managers may wait to issue
guidance until the restatement is resolved.17
Turning to Table 7, managers in firms issuing an adverse report followed by a clean
report (IC_IMPROVE) do not appear to change either their likelihood of issuing a forecast
(∆OCCUR) or the specificity of their forecasts (∆SPECIFICITY). They do, however, appear to
issue guidance (∆HORIZON) later in the year relative to the previous year. Perhaps they wait
until after they remediate their internal control problem in year t+1 to issue guidance. This
finding and the potential explanation complement the improvement in forecast accuracy implied
17
We also include EXECTURN and RESTATE in model (1) (not tabulated). The association between internal
control quality and management forecast errors remain unchanged, while RESTATE is positively associated with
forecast errors (p=0.005), and EXECTURN is insignificant (p=0.148). Results also hold if we consider the
existence of a turnover or restatement in either year t or year t+1 (rather than changes in these occurrences).
23
by Table 6 for IC_IMPROVE firms (i.e., it appears that they wait until they are more confident
in the interim numbers before forming their guidance, resulting in more accurate forecasts).
Managers in firms issuing adverse reports in both year t and year t+1 appear to be much
more likely to stop issuing guidance, and, if they do issue guidance, appear to provide less
specific and less timely guidance. This is likely a result of their reduced confidence in the
numbers they rely on to form their estimates. We test the differences between the improvement
group (IC_IMPROVE) and no-improvement group (IC_ADVERSE) for each change in forecast
behavior (∆OCCUR, ∆SPECIFICITY and ∆HORIZON); the differences are all statistically
significant.18
Turning to the control variables, while size was largely insignificant when
examining forecast accuracy, it is a strong determinant of the choice to issue a forecast,
consistent with Kasznik and Lev (1995), though insignificant when explaining specificity and
horizon. Firms with increasing return on assets tend to issue more specific guidance, but tend to
issue the guidance later in the year, whereas loss firms tend to issue guidance more quickly.
Increases in leverage tend to lead to a decrease in the likelihood of issuing guidance, but when
the guidance is issued, it tends to be released earlier in the quarter. The origination of M&As
tends to increase the likelihood of providing guidance, but it is not associated with the specificity
or timing of the guidance. An increase in the number of analysts following the firm is associated
with an increase in the occurrence of guidance, while an executive turnover is not associated
with the likelihood, specificity, or timing of the guidance in our sample.19
Finally, greater
dispersion in analyst forecasts leads to more timely guidance, while more timely guidance results
in less specific guidance, each consistent with prior research.
18
In Table 6, we examined how the change in internal control quality maps into the accuracy of management
forecasts. However, as noted here, it appears that managers anticipating the use of the poor-quality information
inputs are more likely to stop issuing guidance. 19
Note that Brochet et al. (2007) examine a smaller subset of firms that issue guidance regularly, and are not
constrained to examining accelerated filers.
24
Sensitivity Analyses
The Informativeness of Management Forecasts
We argue that internal control deficiencies result in larger errors in management
guidance, and that these errors are economically important. While our analyses have provided
the economic significance of the magnitude of the errors, if investors and analysts place a lower
reliance on management guidance provided by firms with weak internal controls, these errors
may not have an economic impact on capital markets. In this section, we investigate the
informativeness of the management guidance to determine if these errors are being incorporated
by market participants. We examine the degree of incorporation by analysts and test if this
incorporation is lower for our material weakness sample relative to the control sample. We
examine the first year the firm had an internal control problem (e.g., analyst revisions during
2004 for a firm subsequently disclosing that it had an internal control problem in 2004).20
Following prior research, we regress the revision made by analysts (ANALYST_REV) on the
suggested change made by managers (REVISION) and control variables, as follows:
ANALYST_REV = b0 + b1REVISION+ b2REVISION×MW + b3REVISION×DOWN
+ b4REVISION×REPUTATION + b5REVISION×AGREE + ε (5)
We include each management revision and analyst revision in the estimation, and control
for firm fixed-effects. A positive and significant coefficient on REVISION implies that analysts
are incorporating management guidance when updating their forecasts. Our variable of interest
is REVISION×MW, the incremental incorporation made by analysts for material weakness
20
Prior research has examined both price reactions and analyst forecast revisions to management guidance. We opt
to examine analyst revisions rather than price reactions, as price reactions reflect both the change in expectations in
the numerator, and the discount rate (or risk of the company) in the denominator. We expect material-weakness
firms to have systematic differences in risk (e.g., Ogneva et al., 2007; Bryan and Lilien, 2005). We consider only
the first year of the internal control problem as our intent is to determine if analysts discount these managers’
guidance before the public announcement of an internal control problem.
25
firms. If we find a negative and significant coefficient on this interaction term, this is consistent
with analysts discounting guidance provided by managers in firms with material weaknesses in
internal control. We include three control variables that have been shown to affect the
incorporation of guidance. First, DOWN is an indicator variable that is equal to one of the
manager’s REVISION reduces earnings expectations (relative to the pre-existing analyst
consensus forecast). REPUTATION is the accuracy of the preceding management forecast.
Finally, AGREE is an indicator variable that is equal to one if the price reaction to the guidance
is in the same direction as the manager’s suggested revision.
Results are presented in Table 8. The coefficient on REVISION is 0.451, indicating that
analysts respond, on average, to management guidance by updating their own forecasts in the
suggested direction. The coefficient on the interaction of REVISION and MW is 0.042, which is
not significantly different from zero. Therefore, analysts do not appear to discount management
forecasts issued by firms with internal control weakness after controlling for the known variation
in analyst incorporation. In other words, Table 8 suggests that management guidance is
incorporated by market participants, whether or not the firm has a material weakness in internal
control, ex post, providing additional support for the economic importance of internal control
quality on management guidance.
The Impact of Material Weaknesses among Section 302 Disclosures
Section 302 of the Sarbanes-Oxley Act, effective in August 2002 for all SEC registrants,
also resulted in a large number of material weakness disclosures. Because Section 302
disclosures precede Section 404 disclosures, we investigate the effects of these disclosures for
our accelerated filer sample as follows. First, we replicate our main analysis on the Section 302
material weakness sample of firms. We find that management forecast errors are also larger in
26
years where Section 302 material weaknesses are disclosed (consistent with Table 3); the
coefficient on MW is 0.008 with a t-statistic of 2.77 (not tabulated). Second, we examine
whether the 302 material weakness firms change their forecast behavior following their 302
material weakness disclosures. We find that firms disclosing material weaknesses in two
consecutive years (beginning with the year prior to Section 404 and extending through to their
initial 404 report) are more likely to stop issuing forecasts in the first year of Section 404; the
coefficient on IC_ADVERSE is -0.098 with a t-statistic of 1.86 (not tabulated).21
Third, we
exclude firms that disclose a material weakness under Section 302 from our main analysis on
Section 404 disclosures (as they have disclosed a material weakness prior to their initial 404
report); our results remain unchanged. Thus, our results are consistent across both Section 302
and 404, consistent with material weaknesses affecting management forecast errors and
management guidance behavior.
Management Forecast Accuracy Measure
There are several alternative ways to calculate our measure of forecast quality—the ex
post absolute value of the management forecast error. Our results are robust to these
alternatives. For example, as noted in footnote 9, we take the average error of all forecasts
issued by a firm during the fiscal year. We replicate our analysis (Equation 1) using the last
forecast issued in each fiscal year, and results are similar (the coefficient on MW is 0.006 and the
corresponding t-statistic is 6.38).
21
Ideally we would like to examine the initial 302 disclosures followed by the subsequent year, under either Section
302 or 404. However, for Section 302 disclosures we are using the data made publicly available by Doyle et al.
(2007b). This data includes only the first Section 302 material weakness, thus, if there is a material weakness
disclosure in 2002, we do not know if there was a subsequent 302 material weakness in 2003. Therefore, we
concentrate on material weakness disclosures made in the year prior to the initial 404 report in order to ensure
completeness. We exclude firms filing earlier material weaknesses from our test.
27
In addition, our focus has been on annual guidance for several reasons. First, internal
control reports are released annually, and thus we are best able to identify the affected period and
pinpoint the guidance issued during that period using annual data. Second, our measure of
forecast quality, the ex post management forecast error, is affected by errors in both the
management forecast and reported earnings. Using annual figures allows auditors to help
mitigate effects of internal control problems on reported earnings, concentrating our
investigation on the effects of guidance. However, we replicate our main analysis (Equation 1)
using quarterly data. Results indicate the coefficient on MW is 0.001 with a t-statistic of 2.03.22
Finally, because internal control quality is not exogenous, we econometrically control for
self-selection bias using a two-stage approach and estimate a probit regression of MW on the
determinants of material weaknesses. The independent variables are obtained from model (1)
and similar to Ashbaugh-Skaife et al. (2007a) and Doyle et al. (2007a): LN_TA, GROWTH,
LOSS, LITIGATE, BIG4, ROA, LEVERAGE, MA, SI, STDEARN, BETA, and ANALYSTS.23
From this first-stage regression, which identifies the likelihood of a firm being selected as a
material weakness firm, we calculate the inverse Mills ratio (see Heckman, 1979) and include
this ratio in our main regression (Equation 1). After the inclusion of the inverse Mills ratio, the
coefficient on MW continues to be significant, with a t-statistic of 7.78. Thus, results do not
appear to be driven by firms self-selecting into the material weakness group.
Conclusion
We examine the relation between internal control quality and management guidance
using Section 404 disclosures made by accelerated filers from 2005–2007. We argue that the
22
This weaker result supports our decision to use annual earnings, which are both audited and less affected by issues
with the timeliness of the earnings figures. Using annual earnings, the errors in reported earnings are more likely to
have been corrected, allowing us to concentrate on the error in the management guidance. 23
Variables are defined in Table 2.
28
quality of internal control not only affects reported earnings, as previously documented, but also
likely affects interim numbers used by management to provide earnings guidance. Consistent
with this, we find that within firms reporting ineffective internal controls, management forecast
accuracy is significantly lower, both statistically and economically. We find stronger results
when the weaknesses affect revenue or cost of goods sold, consistent with these balances having
the greatest effect on forecasted earnings (Fairfield et al., 1996). We also find that the
association between management forecast accuracy and internal control is no longer significant
after the internal control problem has been remediated, consistent with management forecast
accuracy having been affected by prior internal control problems. Finally, we provide evidence
that managers change their guidance behavior following the disclosure of a material weakness in
internal control. If a weakness persists, managers are more likely to stop issuing guidance, and,
if they do issue guidance, tend to issue less specific guidance.
Overall, our results strongly support the notion that the quality of the information inputs
to earnings guidance is an important determinant of management forecast accuracy and that the
quality of internal control has a broader impact than previously documented. Internal controls
not only affect reported earnings; they also affect the quality of management guidance. Our
paper adds to the debate on the cost/benefit tradeoff of Section 404, and opens the door to
additional potential effects of internal control, such as management decision-making. If
managers rely on faulty interim numbers when making decisions in firms with internal control
deficiencies, managers may make sub-optimal decisions. These decisions might include choices
related to production, capital investment, M&As, R&D, advertising, and hiring or expansion
decisions. Future research might consider examining the association between managerial
decision-making and internal control. Our findings also highlight how internal control continues
29
to be a challenge following the initial year of Section 404; we find that while many problems
were quickly remediated following their identification and disclosure, new internal control
challenges arose in subsequent years, further supporting the notion that evaluating internal
controls needs to be an ongoing process. Overall, our findings strongly support that there are
benefits to maintaining strong internal controls.
30
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33
Table 1
Sample Selection
Firm-Year
Observations
Firm-years with Section 404 reports from January 2005 to September 2007 11,528
Less:
Those not covered by First Call 712
Those without a point or range management earnings forecast 7,174
Those missing financial information from Compustat 172
Those missing analyst information from First Call 297
Those missing stock information from CRSP 233
Number of firm-years in the final sample (with management forecast errors) 2,940
34
Table 2
Descriptive Statistics
Full Sample
Control
Sample
MW
Sample
Control
Sample
MW
Sample
N =
2940
N =
2635
N =
305
dif
f.
N =
2635
N =
305
dif
f.
M
ean
Med
ian
Min
M
ax
Std
. D
ev.
Mea
n
Mea
n
t-st
at.
Med
ian
Med
ian
Z-s
tat.
AB
SE
RR
OR
0.0
10
0.0
04
0.0
00
0.1
29
0.0
18
0.0
09
0.0
18
-8.5
7
0.0
04
0.0
08
-5.6
2
LN
_T
A
21.0
88
20.9
76
17.2
89
25.7
83
1.6
79
21.1
51
20.5
43
6.0
2
21.0
47
20.2
11
5.7
4
RO
A
0.0
61
0.0
60
-0.6
31
0.3
67
0.1
04
0.0
65
0.0
25
6.4
0
0.0
63
0.0
37
5.9
9
LO
SS
0.0
96
0.0
00
0.0
00
1.0
00
0.2
94
0.0
83
0.2
07
-7.0
2
0.0
00
0.0
00
-6.9
6
LE
VE
RA
GE
0.6
20
0.5
97
0.0
88
1.8
07
0.3
01
0.6
21
0.6
17
0.2
4
0.6
01
0.5
74
0.9
1
GR
OW
TH
0.1
80
0.1
25
-0.3
98
1.7
68
0.2
46
0.1
79
0.1
89
-0.6
7
0.1
25
0.1
23
0.0
6
LIT
IGA
TE
0.2
79
0.0
00
0.0
00
1.0
00
0.4
49
0.2
68
0.3
80
-4.1
7
0.0
00
0.0
00
-4.1
6
BE
TA
1.1
91
1.1
44
0.0
47
2.5
79
0.4
75
1.1
81
1.2
85
-3.6
4
1.1
33
1.2
37
-2.7
2
SI
0.0
12
0.0
02
0.0
00
0.2
18
0.0
31
0.0
11
0.0
22
-5.4
8
0.0
02
0.0
04
-3.2
1
MA
0.1
91
0.0
00
0.0
00
1.0
00
0.3
93
0.1
89
0.2
13
-1.0
3
0.0
00
0.0
00
-1.0
3
BIG
4
0.9
22
1.0
00
0.0
00
1.0
00
0.2
69
0.9
27
0.8
75
3.1
9
1.0
00
1.0
00
3.1
8
AN
AL
YST
S
1.9
42
2.0
79
0.0
00
3.3
67
0.7
79
1.9
58
1.8
00
3.3
7
2.0
79
1.7
92
2.6
0
ST
DE
AR
N
0.0
67
0.0
28
0.0
01
1.0
09
0.1
22
0.0
63
0.0
97
-4.5
7
0.0
26
0.0
46
-5.6
2
DIS
PFO
R
0.0
67
0.0
45
0.0
00
0.5
26
0.0
71
0.0
68
0.0
64
0.8
7
0.0
45
0.0
42
1.1
5
HO
RIZ
ON
210.5
69
204.5
00
-25.0
00
584.5
00
68.1
38
209.7
50
217.6
20
-1.9
1
204.0
00
208.4
29
-1.5
1
RE
VIS
ION
0.0
04
0.0
02
0.0
00
0.0
40
0.0
06
0.0
04
0.0
04
-0.9
60
0.0
02
0.0
02
-0.7
86
Note
: p-v
alues
are
bas
ed o
n tw
o-t
aile
d tes
ts.
35
Table 2, Continued
Variables Definitions:
MW
A
n indic
ator
var
iable
that
is
equal
to o
ne
if the
com
pan
y r
ecei
ves
an a
dver
se S
ecti
on 4
04 o
pin
ion in y
ear
t, a
nd z
ero
oth
erw
ise.
AB
SE
RR
OR
T
he
abso
lute
val
ue
of
the
man
agem
ent fo
reca
st e
rror
(rea
lize
d e
arnin
gs
less
the
man
agem
ent fo
reca
st),
sca
led b
y
lag
ged
sto
ck p
rice
.
LN
_T
A
The
nat
ura
l lo
g o
f to
tal as
sets
(C
om
pust
at #
6).
RO
A
Net
inco
me
(Com
pust
at #
172)
/ la
gged
tota
l as
sets
(C
om
pust
at #
6).
LO
SS
A
n indic
ator
var
iable
that
is
equal
to o
ne
if n
et inco
me
(Com
pust
at #
172)
is n
egat
ive,
and z
ero o
ther
wis
e.
LE
VE
RA
GE
T
ota
l li
abil
itie
s (C
om
pust
at #
181)
/ la
gged
tota
l as
sets
(C
om
pust
at #
6).
GR
OW
TH
S
ales
gro
wth
over
the
pri
or
yea
r (s
ales
(C
om
pust
at #
12)
in y
ear
t le
ss s
ales
in y
ear
t-1 s
cale
d b
y s
ales
in y
ear
t-1).
LIT
IGA
TE
An indic
ator
var
iable
that
is
equal
to o
ne
if the
firm
’s m
ain o
per
atio
ns
are
in a
hig
h-l
itig
atio
n indust
ry
[bio
tech
nolo
gy (
2833-2
836 a
nd 8
731-8
734),
com
pute
rs (
3570-3
577 a
nd 7
370-7
374),
ele
ctro
nic
s (3
600-3
674),
and
reta
il (
5200-5
961)
indust
ries
, an
d z
ero o
ther
wis
e (b
ased
on F
ranci
s et
al., 1994)]
.
BE
TA
T
he
slope
coef
fici
ent fr
om
est
imat
ing S
har
pe’
s (1
964)
mar
ket
model
usi
ng d
aily
ret
urn
dat
a fr
om
yea
r t-
1.
SI
The
abso
lute
val
ue
of
spec
ial it
ems
(Com
pust
at #
17)
scal
ed b
y lag
ged
tota
l as
sets
(C
om
pust
at #
6).
MA
A
n indic
ator
var
iable
that
is
equal
to o
ne
if the
com
pan
y h
as m
erger
s an
d a
cquis
itio
n (
Com
pust
at A
FT
NT
1=
“A
A”)
,
and z
ero o
ther
wis
e.
BIG
4
An indic
ator
var
iable
that
is
equal
to o
ne
if the
audit
or
is a
Big
4 a
udit
or,
and z
ero o
ther
wis
e.
AN
AL
YS
TS
T
he
log o
f th
e num
ber
of
anal
yst
s fo
llow
ing the
firm
at th
e beg
innin
g o
f th
e fi
scal
yea
r.
ST
DE
AR
N
The
stan
dar
d d
evia
tion o
f R
OA
over
the
last
fiv
e yea
rs (
requir
ing a
t le
ast th
ree
non-m
issi
ng o
bse
rvat
ions)
.
DIS
PF
OR
T
he
stan
dar
d d
evia
tion o
f th
e in
div
idual
anal
yst
fore
cast
s fo
r yea
r t, p
rior
to the
man
agem
ent guid
ance
in y
ear
t.
HO
RIZ
ON
The
num
ber
of
days
pri
or
to the
fisc
al p
erio
d-e
nd in w
hic
h the
man
agem
ent fo
reca
st is
issu
ed, w
her
e a
larg
er n
um
ber
indic
ates
a m
ore
tim
ely f
ore
cast
. F
ore
cast
s is
sued
aft
er the
fisc
al p
erio
d-e
nd a
re n
ot ex
cluded
, an
d thus
HO
RIZ
ON
can
be
neg
ativ
e.
RE
VIS
ION
T
he
abso
lute
val
ue
of
the
revis
ion im
pli
ed b
y the
man
agem
ent fo
reca
st: |(
man
agem
ent fo
reca
st –
pre
-exis
ting
med
ian a
nal
yst
fore
cast
)| s
cale
d b
y lag
ged
sto
ck p
rice
.
We
win
sori
ze the
top a
nd b
ott
om
1%
of
each
of
our
conti
nuous
var
iable
s to
avoid
the
infl
uen
ce o
f outl
iers
.
36
Table 3
Internal Control Quality and Management Forecast Accuracy
Dependent Variable = ABSERROR
Full Sample
2004
2005
2006
C
oef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Inte
rcep
t -0
.001
-0.2
9
0.7
75
-0.0
07
-0.9
4
0.3
48
-0.0
04
-0.4
8
0.6
33
0.0
09
1.0
8
0.2
82
MW
0.006
6.99
0.001
0.006
5.00
0.001
0.006
3.68
0.001
0.005
2.85
0.004
LN
_T
A
0.0
00
-0.3
1
0.7
54
0.0
00
0.8
1
0.4
20
0.0
00
0.1
9
0.8
47
-0.0
01
-1.7
8
0.0
76
RO
A
0.0
00
0.0
1
0.9
94
0.0
04
0.8
0
0.4
23
-0.0
02
-0.3
6
0.7
20
0.0
00
0.0
5
0.9
56
LO
SS
0.0
14
11.2
5
0.0
01
0.0
15
7.2
5
0.0
01
0.0
18
7.7
0
0.0
01
0.0
11
5.0
3
0.0
01
LE
VE
RA
GE
0.0
02
1.6
0
0.1
11
-0.0
01
-0.4
6
0.6
46
0.0
02
1.2
8
0.2
00
0.0
03
1.8
9
0.0
59
GR
OW
TH
-0
.003
-2.1
7
0.0
30
0.0
00
-0.2
1
0.8
34
-0.0
02
-0.9
8
0.3
27
-0.0
07
-3.0
2
0.0
03
LIT
IGA
TE
0.0
00
-0.4
6
0.6
45
0.0
00
-0.0
6
0.9
49
-0.0
01
-0.5
3
0.5
97
0.0
00
-0.1
3
0.8
97
BE
TA
0.0
02
3.9
1
0.0
01
0.0
02
1.7
0
0.0
90
0.0
04
3.1
4
0.0
02
0.0
01
1.4
9
0.1
38
SI
0.0
13
1.3
8
0.1
69
0.0
29
1.8
8
0.0
60
0.0
24
1.4
2
0.1
55
-0.0
38
-2.2
0
0.0
28
MA
-0
.001
-1.8
1
0.0
71
-0.0
03
-2.2
7
0.0
23
0.0
00
0.0
4
0.9
65
-0.0
01
-0.6
6
0.5
08
BIG
4
-0.0
01
-0.6
0
0.5
49
0.0
00
0.2
5
0.8
03
-0.0
01
-0.6
2
0.5
33
-0.0
02
-1.0
6
0.2
88
AN
AL
YST
S
-0.0
02
-4.5
1
0.0
01
-0.0
03
-3.9
3
0.0
01
-0.0
03
-3.1
9
0.0
02
0.0
00
-0.1
6
0.8
76
ST
DE
AR
N
0.0
03
1.0
7
0.2
83
0.0
02
0.6
1
0.5
39
0.0
04
0.7
4
0.4
59
0.0
02
0.3
8
0.7
01
DIS
PFO
R
0.0
40
10.0
1
0.0
01
0.0
38
5.8
2
0.0
01
0.0
35
4.7
3
0.0
01
0.0
49
7.1
5
0.0
01
HO
RIZ
ON
0.0
00
6.9
4
0.0
01
0.0
00
4.2
9
0.0
01
0.0
00
3.7
2
0.0
01
0.0
00
4.0
4
0.0
01
RE
VIS
ION
1.0
47
20.2
5
0.0
01
0.8
16
9.4
6
0.0
01
1.1
07
12.1
7
0.0
01
1.2
12
13.3
0
0.0
01
Tota
l O
bs.
2940
941
1028
971
MW
Obs.
305
143
104
58
F-v
alue
104.9
0
0.0
01
41.3
0.0
01
50.4
1
0.0
01
38.8
5
0.0
01
Adju
sted
R2
0.3
61
0.3
68
0.3
83
0.3
54
Note
: p-v
alues
are
bas
ed o
n tw
o-t
aile
d tes
ts. S
ee T
able
2 f
or
var
iable
def
initio
ns.
37
Table 4
The Relation between Internal Control Quality and Management Forecast Accuracy for Fiscal Years Preceding the Disclosure
Dependent Variable = ABSERROR
t-1
t-2
t-3
C
oef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Inte
rcep
t -0
.005
-0.4
8
0.6
30
-0.0
01
-0.1
1
0.9
10
0.0
09
0.6
5
0.5
13
MW
0.007
4.84
0.001
0.004
2.86
0.004
0.003
1.62
0.105
LN
_T
A
0.0
00
-0.2
1
0.8
37
0.0
00
-0.9
3
0.3
54
-0.0
01
-0.8
1
0.4
18
RO
A
0.0
08
1.2
1
0.2
26
0.0
09
1.3
0
0.1
94
0.0
04
0.3
8
0.7
00
LO
SS
0.0
17
6.9
2
0.0
01
0.0
10
5.1
9
0.0
01
0.0
12
4.9
8
0.0
01
LE
VE
RA
GE
0.0
03
1.3
1
0.1
90
0.0
06
2.8
5
0.0
05
0.0
06
2.1
6
0.0
31
GR
OW
TH
-0
.002
-0.9
5
0.3
43
-0.0
03
-1.0
8
0.2
81
-0.0
02
-1.0
9
0.2
74
LIT
IGA
TE
-0
.002
-1.3
9
0.1
64
0.0
00
0.0
2
0.9
84
0.0
01
0.4
2
0.6
77
BE
TA
0.0
06
4.1
9
0.0
01
0.0
03
1.8
7
0.0
61
0.0
00
0.1
8
0.8
55
SI
-0.0
06
-0.3
6
0.7
17
0.0
13
0.8
0
0.4
23
-0.0
15
-0.8
8
0.3
82
MA
0.0
00
-0.3
2
0.7
50
0.0
00
-0.1
0
0.9
18
0.0
01
0.3
0
0.7
68
BIG
4
0.0
01
0.2
8
0.7
78
-0.0
03
-1.0
5
0.2
92
0.0
00
0.1
2
0.9
07
AN
AL
YST
S
-0.0
03
-3.5
2
0.0
01
-0.0
02
-1.7
5
0.0
80
-0.0
04
-3.2
4
0.0
01
ST
DE
AR
N
-0.0
02
-0.6
8
0.4
96
0.0
06
2.0
4
0.0
42
-0.0
08
-2.3
1
0.0
21
DIS
PFO
R
0.0
19
2.0
6
0.0
39
0.0
36
3.5
8
0.0
01
0.0
50
5.3
4
0.0
01
HO
RIZ
ON
0.0
00
6.0
0
0.0
01
0.0
00
8.2
8
0.0
01
0.0
00
7.0
0
0.0
01
RE
VIS
ION
1.2
59
13.7
1
0.0
01
1.2
77
12.5
3
0.0
01
0.6
42
7.1
2
0.0
01
Tota
l O
bs.
1007
911
760
MW
Obs.
160
145
124
F-v
alue
38.8
2
0.0
01
35.2
7
0.0
01
20.9
4
0.0
01
Adju
sted
R2
0.3
76
0.3
76
0.2
96
Note
: p-v
alues
are
bas
ed o
n tw
o-t
aile
d tes
ts. S
ee T
able
2 f
or
var
iable
def
initio
ns.
38
Table 5
The Relation between Types of Internal Control Problems and Management Forecast Accuracy
Dependent Variable = ABSERROR
Y
ear
t Y
ear
t Y
ear
t-1 to t-2
Y
ear
t-1 to t-2
C
oef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Coef
f.
t-st
at.
p-v
alue
Inte
rcep
t -0
.002
-0.4
0
0.6
92
-0.0
01
-0.2
1
0.8
35
-0.0
03
-0.4
7
0.6
35
-0.0
01
-0.1
6
0.8
77
REV/COGS
0.012
8.52
0.001
0.009
6.00
0.001
OTHER
0.003
2.81
0.005
0.003
2.61
0.009
NUMBERMW
0.003
10.90
0.001
0.002
4.90
0.001
LN
_T
A
0.0
00
-0.1
9
0.8
50
0.0
00
-0.4
4
0.6
60
0.0
00
-0.7
7
0.4
40
0.0
00
-1.1
2
0.2
63
RO
A
0.0
00
0.1
2
0.9
03
0.0
01
0.3
1
0.7
55
0.0
08
1.5
7
0.1
16
0.0
07
1.4
5
0.1
47
LO
SS
0.0
14
11.1
6
0.0
01
0.0
14
10.9
6
0.0
01
0.0
12
8.0
5
0.0
01
0.0
12
7.8
1
0.0
01
LE
VE
RA
GE
0.0
02
1.5
8
0.1
14
0.0
02
1.4
6
0.1
44
0.0
04
2.7
2
0.0
07
0.0
04
2.9
8
0.0
03
GR
OW
TH
-0
.002
-2.0
1
0.0
44
-0.0
03
-2.2
6
0.0
24
-0.0
03
-1.8
0
0.0
72
-0.0
03
-1.6
1
0.1
08
LIT
IGA
TE
0.0
00
-0.4
5
0.6
53
0.0
00
-0.3
9
0.6
96
-0.0
01
-1.3
5
0.1
77
-0.0
01
-1.1
8
0.2
36
BE
TA
0.0
02
3.8
5
0.0
01
0.0
02
3.8
4
0.0
00
0.0
05
5.0
4
0.0
01
0.0
06
5.3
9
0.0
01
SI
0.0
15
1.5
9
0.1
12
0.0
14
1.4
6
0.1
44
0.0
02
0.2
0
0.8
38
0.0
04
0.3
3
0.7
41
MA
-0
.001
-1.9
3
0.0
54
-0.0
01
-1.8
8
0.0
61
0.0
00
-0.3
3
0.7
41
0.0
00
-0.3
2
0.7
47
BIG
4
-0.0
01
-0.7
5
0.4
55
0.0
00
-0.2
8
0.7
81
-0.0
01
-0.7
7
0.4
40
-0.0
01
-0.6
2
0.5
32
AN
AL
YST
S
-0.0
02
-4.4
5
0.0
01
-0.0
02
-4.4
2
0.0
01
-0.0
02
-3.6
7
0.0
01
-0.0
02
-3.6
2
0.0
01
ST
DE
AR
N
0.0
02
0.9
7
0.3
32
0.0
02
0.9
2
0.3
56
0.0
01
0.7
3
0.4
64
0.0
01
0.5
9
0.5
52
DIS
PFO
R
0.0
40
10.0
2
0.0
01
0.0
41
10.2
4
0.0
01
0.0
27
4.0
3
0.0
01
0.0
28
4.1
0
0.0
01
HO
RIZ
ON
0.0
00
6.9
3
0.0
01
0.0
00
6.9
5
0.0
01
0.0
00
10.2
4
0.0
01
0.0
00
10.0
8
0.0
01
RE
VIS
ION
1.0
55
20.4
9
0.0
01
1.0
50
20.5
4
0.0
01
1.3
04
19.2
2
0.0
01
1.3
04
19.1
5
0.0
01
Tota
l O
bs.
2940
2940
1930
1930
RE
V/C
OG
S O
bs.
108
125
OT
HE
R O
bs.
197
192
Adju
sted
R2
0.3
67
0.3
76
0.3
82
0.3
77
39
Table 5, Continued
Note
: p-v
alues
are
bas
ed o
n t
wo-t
aile
d t
est
s.
Our
var
iable
s ar
e def
ined
as
follow
s:
RE
V/C
OG
S i
s an
indic
ator
var
iable
that
is
equal
to o
ne
if t
he
firm
rep
ort
s a
mat
eria
l w
eaknes
s in
the
revenue o
r co
st o
f goods
sold
/inven
tory
acc
ounts
, and z
ero o
ther
wis
e.
OT
HE
R i
s an i
ndic
ator
var
iable
that
is
equal
to o
ne
if t
he f
irm
report
s a
mat
eria
l w
eaknes
s in
inte
rnal
contr
ol
and n
one
of
thes
e w
eaknes
ses
are
rela
ted t
o t
he
revenue
or
cost
of
goods
sold
/invento
ry a
ccounts
, and z
ero i
f th
e
firm
rep
ort
s a
mate
rial
wea
knes
s in
the
reven
ue
or
cost
of
goods
sold
/invento
ry a
ccounts
or
does
not
report
a m
ater
ial
wea
knes
s.
NU
BM
ER
MW
is
equal
to t
he
tota
l num
ber
of
mate
rial
wea
knes
ses
in inte
rnal
contr
ol re
port
ed in f
isca
l yea
r t. A
dditio
nal var
iable
def
initio
ns
are
pro
vid
ed in T
able
2.
40
Table 6
The Change of Internal Control Quality and the Level of Management Forecast Accuracy
Dependent Variable = ABSERROR
Year t+1 Year t
Coeff. t-stat. p-value Coeff. t-stat. p-value
Intercept -0.003 -0.46 0.648 0.001 0.14 0.885
IC_IMPROVE 0.002 1.45 0.147 0.002 2.11 0.035
IC_ADVERSE 0.009 3.85 0.001 0.007 4.06 0.001
IC_WORSE 0.004 2.67 0.008 0.000 0.08 0.934
LN_TA 0.000 -0.32 0.749 0.000 -0.63 0.526
ROA 0.000 -0.09 0.930 0.016 3.88 0.000
LOSS 0.016 9.08 0.001 0.013 7.93 0.001
LEVERAGE 0.003 2.09 0.037 0.001 0.79 0.432
GROWTH -0.004 -2.03 0.042 0.000 0.38 0.702
LITIGATE 0.000 0.04 0.970 0.000 -0.01 0.993
BETA 0.002 2.66 0.008 0.001 1.48 0.139
SI -0.012 -0.90 0.371 -0.019 -1.64 0.101
MA -0.001 -0.68 0.499 -0.001 -1.57 0.116
BIG4 0.000 0.17 0.863 0.000 0.39 0.696
ANALYSTS -0.002 -2.79 0.005 -0.001 -2.23 0.026
STDEARN 0.007 1.65 0.099 -0.001 -0.19 0.846
DISPFOR 0.042 8.05 0.001 0.034 7.23 0.001
HORIZON 0.000 4.84 0.001 0.000 4.24 0.001
REVISION 1.119 16.46 0.001 1.042 15.65 0.001
diff. between b1 and b2 (p-value) 0.007 0.012
diff. between b1 and b3 (p-value) 0.210 0.199
diff. between b2 and b3 (p-value) 0.115 0.001
Total Observations 1740 1381
IC_IMPROVE Obs. 141 114
IC_ADVERSE Obs. 43 37
IC_WORSE Obs. 84 767
Adjusted R2 0.374 0.319
Note: p-values are based on two-tailed tests. Our variables are defined as follows: IC_IMPROVE is an indicator
variable that is equal to one if the 404 opinion is adverse in year t and clean in year t+1. IC_ADVERSE is an
indicator variable that is equal to one if the 404 opinions are adverse in both year t and year t+1. IC_WORSE is an
indicator variable that is equal to one if the 404 opinion in is clean in year t and adverse in year t+1. Additional
variable definitions are provided in Table 2.
41
Table 7
The Change of Internal Control Quality and the Change in Management Forecast Behavior
D
epen
den
t V
aria
ble
∆
OC
CU
R
∆
SPE
CIF
ICIT
Y
∆
HO
RIZ
ON
C
oef
f.
t-st
at.
p-v
alue
C
oef
f.
t-st
at.
p-v
alue
C
oef
f.
t-st
at.
p-v
alue
Inte
rcep
t -0
.017
-2.3
4
0.0
20
0.0
06
0.5
3
0.5
99
-3
.161
-1.2
4
0.2
17
IC_IMPROVE
-0.020
-1.08
0.279
0.015
0.52
0.606
-13.850
-2.01
0.045
IC_ADVERSE
-0.114
-4.16
0.001
-0.104
-2.24
0.025
-30.442
-2.83
0.005
∆L
N_A
T
0.0
79
2.9
7
0.0
03
-0
.018
-0.3
9
0.6
93
-3
.269
-0.3
1
0.7
56
∆R
OA
0.0
22
0.3
4
0.7
36
0.4
22
3.4
2
0.0
01
-5
2.4
60
-1.8
2
0.0
68
∆L
OSS
-0.0
12
-0.7
0
0.4
84
0.0
38
1.1
9
0.2
35
13.7
86
1.8
5
0.0
65
∆L
EV
ER
AG
E
-0.0
70
-2.9
2
0.0
04
-0
.011
-0.2
7
0.7
90
33.4
37
3.6
2
0.0
00
∆G
RO
WT
H
-0.0
21
-1.4
3
0.1
54
0.0
40
1.3
0
0.1
94
-3
.981
-0.5
6
0.5
77
LIT
IGA
TE
0.0
06
0.4
8
0.6
28
-0
.010
-0.5
9
0.5
55
0.1
58
0.0
4
0.9
68
∆B
ET
A
0.0
26
2.3
5
0.0
19
-0
.013
-0.6
5
0.5
13
-4
.772
-1.0
7
0.2
84
∆S
I 0.0
86
0.6
5
0.5
18
-0
.023
-0.0
9
0.9
25
-4
0.1
83
-0.7
0
0.4
87
∆M
A
0.0
26
2.1
0
0.0
36
0.0
17
0.9
7
0.3
30
3.0
82
0.7
8
0.4
37
∆B
IG4
-0.0
10
-0.3
5
0.7
30
-0
.009
-0.1
5
0.8
84
15.1
59
1.0
1
0.3
11
∆A
NA
LY
ST
S
0.0
22
1.7
8
0.0
75
0.0
04
0.2
0
0.8
41
-3
.443
-0.7
1
0.4
75
∆ST
DE
AR
N
-0.0
21
-0.3
2
0.7
49
-0
.113
-0.8
9
0.3
71
-1
9.7
55
-0.6
7
0.5
04
∆R
EST
AT
E
-0.0
06
-0.4
8
0.6
29
0.0
09
0.5
1
0.6
12
-0
.288
-0.0
7
0.9
44
∆E
XE
CT
UR
N
-0.0
07
-0.6
6
0.5
10
0.0
18
1.2
2
0.2
21
-2
.172
-0.6
2
0.5
33
∆D
ISPFO
R
-0.0
16
-0.1
3
0.8
98
183.9
83
6.4
9
<.0
001
∆H
OR
IZO
N
0.0
00
-1.7
6
0.0
78
F-t
est on the
dif
f. b
etw
een b
1 a
nd b
2
0.0
03
0.0
20
0.1
74
Tota
l O
bse
rvat
ions
4,9
80
1,6
93
1,6
93
IC_IM
PR
OV
E O
bse
rvat
ions
429
129
129
IC_A
DV
ER
SE
Obse
rvat
ions
184
49
49
Adju
sted
R2
0.0
08
0.0
08
0.0
44
42
Table 7, Continued
Note
: p
-val
ues
are
bas
ed o
n tw
o-t
aile
d tes
ts. O
ur
var
iable
s ar
e def
ined
as
follow
s: ∆
OC
CU
R is
an indic
ator
var
iable
that
is
equal
to o
ne
if the
manager
iss
ued
a
fore
cast
in y
ear
t+1 b
ut
did
not
in y
ear
t, n
egat
ive
one
if t
he
manager
did
not
issu
e a
fore
cast
in y
ear
t+1 b
ut
did
in y
ear
t, a
nd z
ero i
f th
ere
was
no c
han
ge
in
fore
cast
iss
uance
. ∆
SP
EC
IFIC
ITY
is
the
change
in a
ver
age
fore
cast
spec
ific
ity,
wher
e s
pec
ific
ity h
as a
val
ue
of
one
if t
he
fore
cast
is
qual
itat
ive,
two i
f th
e
fore
cast
is
a m
inim
um
or
maxim
um
, th
ree
if t
he
fore
cast
is
a ra
nge,
and f
our
if t
he
fore
cast
is
a poin
t fo
reca
st. ∆
HO
RIZ
ON
is
the
change
in t
he
aver
age
num
ber
of
days
bet
wee
n t
he
issu
ance
of
the
manag
em
ent
fore
cast
and t
he
end o
f th
e p
erio
d, w
her
e a
posi
tive
num
ber
indic
ates
the
fore
cast
s ar
e is
sued
in a
more
tim
ely
fash
ion. ∆
RE
ST
AT
E i
s an
indic
ator
var
iable
that
is
equal
to o
ne
if the
firm
announce
d a
rest
atem
ent in
yea
r t+
1 b
ut did
not in
yea
r t, n
egat
ive
one if
the
firm
did
not
announce
a r
esta
tem
ent
in y
ear
t+1 b
ut
did
in y
ear
t, a
nd z
ero i
f th
ere
was
no c
hange i
n r
esta
tem
ent
announce
men
t.
∆E
XE
CT
UR
N i
s an
indic
ator
var
iable
that
is
equal to
one i
f th
e fi
rm h
ad a
n e
xec
uti
ve
(CE
O o
r C
FO
) tu
rnover
in y
ear
t+1 b
ut
not in
yea
r t, n
egat
ive o
ne
if t
he
firm
did
not
have
an e
xec
utive
turn
over
in y
ear
t+1 b
ut
did
in y
ear
t, a
nd z
ero i
f th
ere
was
no c
hange
in e
xec
uti
ve
turn
over
. E
ach o
f th
e ch
ange
var
iable
s is
mea
sure
d f
rom
the
yea
r of
the
mat
eria
l
wea
knes
s to
the
yea
r fo
llow
ing the
mat
eria
l w
eaknes
s dis
clo
sure
. A
dditio
nal
var
iable
s ar
e def
ined
in T
able
2 a
nd T
able
6.
43
Table 8
Analyst Forecast Revisions Following Management Guidance
Dependent Variable = ANALYST_REV
Coeff. t-stat. p-value
REVISION 0.451 16.72 0.001
REVISION x MW 0.042 1.45 0.147
REVISION x DOWN 0.152 5.42 0.001
REVISION x
MGR_REPUTATION -7.499 -6.15 0.001
REVISION x AGREE 0.214 10.16 0.001
Firm fixed effects Included
Total Observations 2,339
MW Observations 305
Adjusted R2 0.839
Note: p-values are based on two-tailed tests. Our variables are defined as follows: REVISION is the
absolute value of the revision implied by the management forecast: |(management forecast – pre-existing
median analyst forecast)| scaled by lagged stock price. ANALYST_REV is the magnitude of the analyst
forecast revision, the revised consensus analyst forecast less the pre-existing consensus analyst forecast,
scaled by lagged stock price. The pre-existing consensus analyst forecast is the most recent consensus
before the management forecast (within two to 30 days). The revised consensus analyst forecast is the
updated consensus forecast following the management forecast (within 15 days). If there is no updated
analyst posterior consensus forecast, ANALYST_REV is zero. DOWN is an indicator variable that is
equal to one if the management forecast falls below the pre-existing consensus analyst forecast, and zero
otherwise. MGR_REPUTATION is the accuracy of the preceding management forecast, following
Williams (1996). AGREE is an indicator variable that is equal to one if the three-day abnormal return
around the management forecast (–1, +1) has the same sign as the management guidance, and zero
otherwise. The abnormal return is equal to the difference between the firm return and the value-weighted
return.