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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.

Transcript of Sarah McVay FLM 4.08

Page 1: 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.

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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).

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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.

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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

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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

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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

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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.

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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).

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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:

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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

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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.

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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).

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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

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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.

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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:

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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).

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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.

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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

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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.

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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).

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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.

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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.

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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).

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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.

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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.

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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

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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.

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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.

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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

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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.

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Penman, S. 1980. “An empirical investigation of voluntary disclosure of corporate earnings

forecasts.” Journal of Accounting Research (Spring): 132–160.

Pownall, G. and G. Waymire. 1989. “Voluntary disclosure credibility and securities prices:

Evidence from management earnings forecasts, 1969–73.” Journal of Accounting

Research (Autumn): 227–245.

Raghunandan K. and D. Rama. 2006. “SOX Section 404 material weakness disclosures and audit

fees.” Auditing: A Journal of Practice & Theory 25: 99–114.

Rogers, J. and P. Stocken. 2005. “Credibility of management forecasts.” The Accounting Review

80, 1125–1162.

Sharpe, W. 1964. “Capital asset prices: A theory of market equilibrium under conditions of risk.”

Journal of Finance (19): 425–442.

Tang, A. and L. Xu. 2007. “Institutional ownership, internal control material weakness and firm

performance.” Working paper, Morgan State University, (ssrn).

Waymire, G. 1984. “Additional evidence on the information content of management earnings

forecasts.” Journal of Accounting Research (Autumn): 703–718.

Whitehouse, T. 2007. “How small companies can use AS5.” Compliance Week, September 18.

Williams, P. 1996. “The relation between a prior earnings forecast by management and analyst

response to a current management forecast.” The Accounting Review 71 (1): 103–113.

Page 34: Sarah McVay FLM 4.08

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

Page 35: Sarah McVay FLM 4.08

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.

Page 36: Sarah McVay FLM 4.08

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

.

Page 37: Sarah McVay FLM 4.08

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.

Page 38: Sarah McVay FLM 4.08

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.

Page 39: Sarah McVay FLM 4.08

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

Page 40: Sarah McVay FLM 4.08

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.

Page 41: Sarah McVay FLM 4.08

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.

Page 42: Sarah McVay FLM 4.08

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

Page 43: Sarah McVay FLM 4.08

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.

Page 44: Sarah McVay FLM 4.08

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.