DP2010-32

Repor ting of Inter nal C ontrol
Def iciencies, Restatements, a nd
Mana gem ent For ecasts
Katsuhiko MURAMI YA
Tom omi TA KA DA
November 10, 2010

Reporting of Internal Control Deficiencies,
Restatements, and Management Forecasts
Katsuhiko Muramiya
Assistant Professor
Research Institute for Economics and Business Administration
Kobe University
Tomomi Takada*
Associate Professor
Graduate School of Business Administration
Kobe University* Corresponding author
Graduate School of Business Administration, Kobe University, 2-1 Rokkodai-cho, Nada-ku,
Kobe, Hyogo 657-8501, Japan+81-78-803-6935
takada@pearl.kobe-u.ac.jp
Acknowledgment
The authors gratefully appreciate the helpful comments and suggestions of Myung-In Kim,
Takatoshi Hayashi, Yoshihiro Machida, Yoshinao Matsumoto, Tae-Young Park, Hiroshi Taki,
Masahiro Yurino, and the participants of the 2010 AAA annual meeting presentation and Kansai
Audit Research Workshop. The authors gratefully acknowledge the financial support from theGrant-in-Aid for Scientific Research (#21530462) from the Ministry of Education, Culture,
Sports, Science and Technology of Japan. All errors are the responsibility of the authors.

1

Reporting of Internal Control Deficiencies,
Restatements, and Management Forecasts

Abstract
We examine the relationship between accuracy in management forecasts and the effectivenessof internal controls by using the unique setting in Japan, where disclosing management forecasts
is effectively mandated. Feng et al. (2009) posit and find that managers of firms reportinginternal control weaknesses under the Sarbanes–Oxley Act (SOX) report less accurate earnings
forecasts compared with other firms in the U.S., where management forecasts are disclosed
voluntarily. In line with this notion, our results show that firms disclosing internal control
deficiencies and those restating financial highlights report less accurate management forecasts
in the Japanese market, where the disclosure of management forecasts are effectively mandated.
Furthermore, we find that manager’s optimistic biases cause such inaccurate management
forecasts. Our results indicate that the effectiveness of internal controls has a significant impact
on internal reports, which are used in forming forecasts; therefore, internal control weaknesses
induce less accurate management forecasts.

1. Introduction
This study investigates the relationship between the accuracy of management forecasts and theeffectiveness of internal controls in the Japanese market. Although a number of studies haveexamined management forecasts and have clarified factors that influence the accuracy of
forecasts, the effectiveness of internal controls has not been discussed as a factor that
determines the accuracy of management forecasts. However, Feng et al. (2009) posit and findthat firms with ineffective internal controls report less accurate management forecasts than2others. Managers of firms with ineffective internal controls are likely to rely on erroneousinternal management reports in forming forecast; therefore, their earnings forecasts might beless accurate than those of firms with effective internal controls. Following Feng et al. (2009),
we regard firms disclosing internal control deficiencies (ICD) as firms with ineffective internal
controls, since they report material weaknesses in internal control systems. This disclosure isrequired under the Financial Instruments and Exchange Law in Japan, which is parallel to the
SOX in the U.S.
In addition to this disclosure, we also regard firms restating their financial highlights
as firms with weak internal controls. Financial highlights are condensed financial statements
publicized by Japanese firms in around 40 days after the fiscal year ends. This report is requiredby Japan’s stock exchange to ensure the timeliness of disclosure. Certainly, this disclosure is theearliest information of a firm’s performance for the market participants, and therefore it is likely
to be very useful for revising their expectations. However, since financial highlights aredisclosed soon after the accounts are settled and are approved by the board of directors, that is
before the audit procedures are completed and before the annual shareholder’s meetings are held,
they are likely to involve errors or fraud stemming from inaccurate internal management reports.
Therefore, some managers ought to restate the released financial highlights when a significant
difference is revealed between disclosed values and newly calculated values as audit procedures
proceed. Therefore, we ascertain that the firms restating financial highlights are the firms thathave weak internal controls. However, internal control weaknesses of such firms may be lesssevere than those disclosing ICD if they do not disclose ICD. Since the effectiveness of internal
control weaknesses is essentially a question of degree, may be several firms that do not haveinternal control weaknesses sufficient to disclose ICD. Nevertheless, even if firms have any
internal control weaknesses, they are likely to report more or less inaccurate management3forecasts. Stated differently, firms restating their financial highlights might report less accurate
management forecast than other firms because of their internal control weaknesses, even though
they do not disclose ICD. We then investigate whether the accuracy of management forecasts
disclosed by firms that restate is lower than those disclosed by firms that do not restate, in
addition to conducting an analysis regarding the accuracy of management forecasts of firms
with ICD. Simultaneously, we compare the accuracy of management forecasts of the firms that
restate the released financial highlights with that of the firms with ICD.
Thus, our study is in line with the findings of Feng et al. (2009), while our view point
is different from theirs in that we regard both firms, those reporting ICD and those restating
financial highlights, as those with weak internal controls. Thereby, we contribute to a series ofmanagers’ forecast examinations as well as to the prior literature concerning internal control
reports and restatements. In addition, disclosure of management forecasts is effectively
mandated in Japan; therefore, whether or not managers disclose earnings forecasts need not be
controlled in this study, while it is required in studies examining other markets. When themarket investigated has a voluntarily disclosure system of management forecasts, the analysis
concerning the accuracy of management forecasts usually involves errors in controlling for the
incentives of the disclosure itself. On the other hand, since we investigate the Japanese market,
where disclosure of management forecasts is effectively mandated, it allows us to clarify the
direct relationship between internal control weaknesses and the accuracy of management
forecasts without controlling for any incentives to disclose the forecasts. Hence, this study has
an advantage in assessing whether the effectiveness of internal controls influence the accuracyof management forecasts, and it provides new evidence.
Our results indicate that firms disclosing ICD and those restating financial highlights
report less accurate management forecasts. This is consistent with the prediction that firms with4

weak internal controls report less accurate management forecasts. Furthermore, we find that amanager’s optimistic bias causes such inaccurate management forecasts. While managers arelikely to report optimistically biased forecasts, internal control weaknesses may accelerate thistendency. Thus, we find that the accuracy of management forecasts is lower among firms withweak internal controls. Although firms that restate probably suffer somewhat less internal
control weaknesses than firms that disclose ICD, the results show that the accuracy and the bias
of management forecasts are indifferent between these two firms.
Our study makes the following contribution to the literature. We provide new evidence
regarding the relationship between a manager’s forecast accuracy and internal control
weaknesses in a market that has an effectively mandated disclosure system regarding
managements’ forecasts. We also examine whether internal control weaknesses relate toopportunistic or pessimistic forecast biases, while Feng et al. (2009) do not. The results indicatethat internal control weaknesses cause opportunistic biases of management forecasts. Since
people use any information disclosed by firms for their investment decisions, inherent biases insuch information are useful for them. Hence, our results contribute to market participants
making better decisions about their investments. Moreover, we contribute to a series of audit
research, as we utilize unaudited financial information disclosed by firms. The financial
information that we use includes management forecasts and financial highlights, both of which
are unaudited. Although control risk is well known as a component of material misstatement
risk in the audit risk model, we usually cannot examine the possible direct effect of control riskon the misstatement of financial information, since financial information before an audit isgenerally inaccessible. Hence, the misstatements that we determine in audited financial
statements are the ones that have not been detected through audit procedures. Therefore, thenumber of misstatements in those statements is likely to be much smaller than the number to5which the control risks are actually related. In contrast, we utilize unaudited financial
information disclosed by firms; therefore, this allows us to examine the direct effect of internal
control weaknesses (control risk) on the misstatement of financial information. Thus, we
contribute to the literature by revealing the possible effect of control risk on unaudited financial
information.
This paper is organized as follows: Section 2 provides a literature review ofmanagement forecasts and Section 3 explains the setting in Japan. Section 4 describes the
hypothesis and examination model for this study. The data and variables’ statistics are presented
in Section 5, and Section 6 reports and explores the empirical results. Section 7 provides
concluding remarks.

2. Literature Review
Several previous studies have examined management earnings forecasts in the U.S. market.
Hirst et al. (2008) provide a comprehensive review of management forecasts of U.S. firms, andthey raise three perspectives from which prior studies have investigated. These are antecedents,characteristics, and consequences. The accuracy of management forecasts that we investigate inthis study is one of the characteristics of management forecasts. A number of factors lead to
management forecast errors. Cotter et al. (2006) and Matsumoto (2002) find that management
utilizes earnings forecasts to guide the market expectations regarding future earnings in order tomeet such expectations when they release actual earnings 1 . In addition, Rogers and Stocken(2005) show that managers misrepresent their forward-looking information as the market’s
ability to detect misrepresentations decreases. These results indicate that managers have several

1

On the other hand, Kasznik (1999) shows that managers are inclined to manage reported earnings
toward their forecasts. His result implies that managers regard forecasted values that they make as abenchmark to either meet or beat.
6

incentives or opportunities to report less accurate forecasts. However, tools exist to prevent
managers from disclosing less accurate forecasts. Ajinkya et al. (2005) and Karamanou andVafeas (2005) show that firms with better governance structures disclose more accurate
management forecasts than others. Thus, previous research has been inclined to shed light on a
manager’s incentives or deterrent mechanism against management’s opportunistic behavior inorder to explore the determinant factors of errors in management forecasts.
On the other hand, Feng et al. (2009) investigate the influence of internal control
efficiency on management earnings forecasts. Managers of firms with ineffective internal
controls are likely to rely on erroneous internal management reports in forming forecasts;
therefore, their earnings forecasts might be less accurate than those of firms with effective
internal controls. Feng et al. (2009) show that the accuracy of management forecasts is lower
among firms reporting ICD under Section 404 of SOX for U.S. companies. This result indicatesthat a manager’s earnings forecasts can be less accurate irrespective of manager’s incentives.
Okuda and Suzuki (2009) also find that the creation of a department to monitor internal controls
reduces errors in management earnings forecasts in the context of the Japanese market. This
result implies that managers are able to utilize more accurate internal management reports when
making forecasts, and therefore the made earnings forecasts are more accurate if a department iscreated to supervise internal control systems. These results support the notion that effective
internal control systems improve the accuracy of management forecasts, while ineffective
systems deteriorate it.

3. The Japanese Setting
3.1 Internal Control Reports in Japan
The importance of companies’ internal controls has been recognized, especially in the U.S. soon

7

after fraudulent behavior by firms such as Enron and World.com occurred. Since SOX wasenacted in 2002, management of Securities and Exchange Commissions (SEC) registrants has
been obligated to prepare the internal control report. Meanwhile, the environment surrounding
the Japanese financial capital market has been changing drastically 2 ; therefore, an urgent needexisted to (1) compile comprehensive and cross-sectional rules for user protection and develop
an environment where users can invest with confidence, (2) enhance fairness and transparency
as well as restore confidence in the market, and (3) enhance the attractiveness of the Japanesemarket as an international market. To respond to these needs, the bills to develop the legislative
framework for the Financial Instruments and Exchange Law, which was amended from the
Securities Exchange Law, were approved in the ordinary Diet session in June 2006 (Financial
Service Agency (FSA), 2006). Enhancing disclosure requirements was one of the policies
involved with the bills, and it included enhancing internal control system. The internal control
reporting system was therefore enforced in September 2007 and was applicable from thebusiness year beginning on or after April 1, 2008. In other words, Japanese firms have been
disclosing an assessment of the validity of their internal controls over financial reporting since
the fiscal year ending in March 2009. Thus, Japan was about six years behind the U.S. in
implementing internal control report disclosure system.
The Japanese internal control report system was designed taking into account thecriticism against the one originally introduced by the SOX in the U.S. (FSA, 2007) Stateddifferently, the FSA took measures to minimize the burden on listed companies in reporting theevaluation of internal control systems. According to FSA (2007, 2010), there are six differences
2

FSA (2006) provides three viewpoints to present a dramatic change in the Japanese environment. From
the user’s viewpoint, although several financial technologies had been developed, regulations for userprotection at that moment had not appeared. From the market’s perspective, it also insisted that a Japanesehousehold’s financial assets should shift from savings to investments, since Japanese people mainly held
financial assets in the form of cash and deposits. Finally, it presented that the Japanese market should
enhance its attractiveness as an international market amid the globalization of financial and capital
markets.
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between the laws in the U.S. and in Japan, which are as follows.

(1) In determining the scope of internal control assessment, a top–down, risk-based approachhas been adopted in Japan. This results in a narrower scope of internal control assessment
relative to American firms.
(2) The classification of internal control deficiencies is simplified. The U.S. has three levels ofinternal control deficiencies, including material weaknesses, significant deficiencies, and control
deficiencies, while in Japan deficiencies are classified only into two levels, including material
weaknesses and deficiencies in Japan.
(3) Japanese auditors are required to solely audit the managerial assessment of internal controls,
and they are not required to carry out the direct reporting that has been adopted in the U.S.
Direct reporting implies that auditors directly audit and report on the effectiveness of internal
controls.
(4) Internal control audits are to be performed by the same auditors responsible for auditing acompany’s financial statement in Japan, while the teams conducting the internal control audit
and the financial statement audit are generally separated in the U.S.
(5) The internal control audit report is prepared in conjunction with the report of financial
statements audit in Japan, while these are allowed to be separately prepared in the U.S.
(6) In Japan, auditors may contact the company’s personnel, such as corporate auditors 3(kansayaku), audit committees, or internal auditors in charge of supervising or monitoring
operations of internal control when necessary. Such coordination is not explicitly specified in
SOX.

3

Large Japanese companies employ two types of auditors: corporate auditors and independent auditors.
For more details, see Matsumoto (1999) and Pong and Kita (2006).
9

Although details differ somewhat between these two countries, the basic concept
regarding the evaluation of the effectiveness of internal controls is identical. However, thepercentage of Japanese firms disclosing ICD and having fiscal year ending in March 2009 wasabout 2% 4 , which is much lower than that of U.S. firms. According to prior studies, the ratio of
U. S. firms disclosing ICD is at least more than 10% (Ashbaugh-Skaife et al., 2007; Doyle et al.,2007). Such a low frequency of Japanese firms’ disclosure of ICD may be explained by thenotion that companies attempt not to disclose material weaknesses to reduce the risk of being
recognized as a company having “serious material weakness” (Yazawa, 2010). Alternatively,
Japanese regulation might force management to have a higher threshold for disclosing material
weaknesses in internal controls because of the FSA measures to minimize the burden on listedcompanies. For whatever reason, the low frequency of Japanese firms’ disclosure of ICD is
distinctive.

3.2 Management Forecasts in Japan
According to the Financial Instruments and Exchange Law, listed Japanese firms
prepare and file annual securities report (Yuka Shoken Hokokusho). Since the reports include avariety of information, such as financial statements, detailed information on business activities,
and corporate governance information, firms take time to prepare it. Hence, the report ispublicly available about three months after a firm’s fiscal year ends. To fill the gap in thetimeliness of information, listed Japanese firms are required by the stock exchange to disclose
condensed financial statements (Kessan Tanshin) immediately after the financial statement
accounts are settled (Kato et al., 2009; Ota, 2010). On average, it takes 40 days to disclose
financial highlights, and this is the earliest information accessible to market participants.

4

In Japan, more than 70% of firms have March fiscal year ends.
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Although audits of financial statement have not been completed when financial highlights are
publicized, the information involved in the report helps market participants revise their
expectations in a timely manner.
Another distinctive feature of Japanese firms’ disclosure is that they provide next
year’s forecast in parallel with realized values in the condensed report. Listed Japanesecompanies disclose estimated values of several accounts, such as sales, operating profit,
ordinary profit (income before special items), net income, earnings per share, and dividends per
share, as per the requirements of the stock exchange. Except for dividend information,
management generally discloses point forecasts. Forecast disclosure is regarded as aneffectively mandated rule in Japan, since the stock exchange strongly encourages managers oflisted firms to provide forecasts. In Japan, more than 90% of listed firms disclose the following
year’s forecast 5 (Kato et al., 2009).
In sum, the Japanese environment provides a unique setting to investigate therelationship between the effectiveness of internal controls and accuracy of management
forecasts for the following two reasons. First, the percentage of firms disclosing ICD in March2009 was about 2%, much lower than that of U.S. firms under SOX. Second, management
forecast disclosure is effectively mandated by Japan’s stock exchange, and such a system isunobserved in other countries, including the U.S. In short, the factors that exist at the time whena manager decides to issue a forecast need not be controlled while examining the Japaneseenvironment. We examine the relationship between the effectiveness of internal controls and theaccuracy of management forecasts by using this unique setting in Japan.

5

The TSE (Tokyo Stock Exchange) allows firms with high uncertainty about future prospects not toreport management forecasts to prevent investors from making decisions on the basis of misleading
information. However, the TSE requires even those firms to report management forecasts by the end of
the fiscal year shortly after removing the uncertainty (TSE, 2006).
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4. Hypothesis Development
We basically follow Feng et al. (2009) to develop a hypothesis with regard to the relationshipbetween the effectiveness of internal controls and management forecast errors. Weak internal
controls will affect the financial inputs to management forecasts because some material
weaknesses likely result in erroneous or incomplete internal management reports (Feng et al.,2009). Accordingly, we posit that the accuracy of management forecasts is relatively lower
among firms with ineffective internal controls compared with those with effective internal
controls.
Weakness in internal controls is not likely to be a question of whether or not suchcontrols exist, rather it is a question of the level of control. A material weakness in internal
controls is defined as “a significant deficiency, or combination of significant deficiencies, that
results in more than a remote likelihood that a material misstatement of the annual or interim
financial statements will not be prevented or detected (Public Company Accounting Oversight
Board (PCAOB), 2004).” However, there are at least two other levels of severity of internal
control problems, which were identified by PCAOB (2004). These are control deficiency andsignificant deficiency, even though these deficiencies per se are not required to be disclosed in
the U.S. (SEC, 2004) Likewise Japanese firms are required to disclose ICD only when theirinternal control systems are materially deficient. Regarding the relationship with the accuracy ofmanagement forecasts, firms with material weaknesses in internal controls are likely to havematerial forecast errors. Although firms are not obliged to disclose internal control deficiencies
unless they are at the “material weakness” level, firms with any level of internal control
deficiencies may report more or less inaccurate forecasts. If so, the distinction of whether or notmaterial weaknesses are reported may not be enough to portray the relationship between theeffectiveness of internal controls and management forecast errors.

12Recent discussions about the effectiveness of internal controls are frequently linked to
the filings regarding the internal control report under Section 302 or Section 404 of SOX.
Ineffective internal controls are, however, sometimes highlighted as a determining factor ofrestatements (Kinney and McDaniel, 1989; DeFond and Jiambalvo, 1991), which are regarded
as a problematic accounting practice. Effective internal control systems can possibly result in
detecting and correcting unintentional errors before financial statements are released. Therefore,a high frequency of restatements may represent the ineffectiveness of a firm’s internal control
systems. Indeed, Ashbaugh-Skaife et al. (2007) find that prior restatements have a significant
positive relationship with the reporting of internal control deficiencies.
To delineate the relationship between the effectiveness of internal controls and theaccuracy of management forecasts in a broader way than done by Feng et al. (2009), we shedlight on firms that restate their financial statements. The Japanese environment provides aninteresting setting for examining this relationship. As discussed in Section 2.2, financial
highlights disclosed by Japanese firms include forecasts as well as the values of accounts
realized. Although realized values represent past accounting transactions and are unlikely tochange after the release, financial highlights are publicized before the auditing procedure.
Therefore, errors or fraud in financial highlights may be detected as auditors perform theprocedure after the release. In addition, timeliness and reliability represent a trade-off
relationship; therefore, timely information has a higher possibility of including unreliable
information. Accordingly, some errors or fraud may be revealed in financial highlights after the
release, and firms then need to restate them. However, firms that restate do not necessarily havesufficient ICD to be disclosed. Although the effectiveness of internal controls is a question of
the level of effectiveness, ICD disclosure is required only when their deficiencies result in morethan a remote likelihood that a material misstatement of financial statements will not be13

prevented or detected. Hence, even firms that need not disclose ICD may well restate released
financial information, as firms not disclosing ICD may still have internal control weaknesses.
We then posit that firms that restate their financial highlights have more or less internal control
weaknesses irrespective of ICD disclosure.
The Japanese stock exchange requires listed companies to disclose details of theamendment in released information immediately after they are revealed 6 . We regard firms thatpublicize amendments to financial highlights as restatement firms, while this definition may besomewhat different from those used in previous studies 7 . According to Table 2, 4.4% of listed
firms restated disclosed financial information. The percentage is relatively higher than that of
ICD firms (2.3% 8 ). In other words, even firms without material weaknesses in internal controls
restate their financial highlights. These statistics indicate that there are several firms whose
internal control systems are not deficient enough to report material weaknesses but are actuallyineffective enough to restate released information. By incorporating the possible influence ofrestatement firms’ internal control weaknesses on management forecast errors in the analysis,
we are able to investigate the influence of different levels of internal control weaknesses on
management forecast errors. We then develop Model (1) to examine the relationship between
internal control weaknesses and management forecast errors.

Abs( MFE ) = α + β1 ICD + β 2 RES + β 3 LOSS + β 4 EQUITY + β 5 SIZE + β 6 DIST + β 7 SD( NI )+ β 8GROW + β 9 IND _ DIV + β10GEO _ DIV + β11 Abs( lagMFE ) + ε ,6(1)

In amending financial highlights, whether such an amendment has a material impact on investors’
investment decisions is taken into account (TSE, 2010, Rule 405).
7Previous research generally specified restatement firms as firms that amended audited financial
statements, while in this study, restatement firms are defined as firms amending condensed financial
statements, which are unaudited.
8This percentage is a little higher than that of the entire set of listed firms that disclosed ICD because of
the data requirements in this study. Details of the data requirements are explained in Section 4.
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The definitions of the variables are presented in Table 1. The dependent variable,Abs(MFE) is higher (lower) if the management forecast is less (more) accurate. The variables
examined in this study are ICD and RES, which are dummy variables for firms disclosing ICD
and restating financial highlights, respectively. As we posit that firms having weak internal
controls report less accurate management forecasts, the expected signs of the coefficient on ICD
and RES are both positive.

(Insert Table 1 around here)

To control for other determinants of the accuracy of management forecasts, we detectnine control variables following the prior literature, especially Ota (2006), who examined thedetermining factors of errors in management forecasts for Japanese firms. LOSS, EQUITY, SIZE,DIST 9 , and GROW are controls for the influence of reporting losses, external financing, size, thedegree of distress, and growth on the accuracy of management forecasts, respectively. Thesefirm characteristics have been specified as the factors that induce managers to report biased
forecasts. Moreover, innate variability and complexity in their operation are likely to make it
difficult for managers to disclose accurate earnings forecasts. Therefore, SD(NI), IND_DIV, and
GEO_DIV are controlled in Model (1). Prior studies present that management forecast errors are
persistent. To control for this persistent effect from management forecast error, we add
Abs(lagMFE) into the model. In addition to these variables, we control for the effect of industry
on the accuracy of management forecasts, while industry dummies are not shown in Model (1)to avoid redundancy.

9

We follow Ohlson (1980) and Ota (2006) to estimate DIST. The details of score loading and scorecoefficients are described in Appendix.
15

5. Data and Descriptive Statistics
5.1 Data
The sample observations are listed Japanese firms having fiscal year end in March 2009. The
listed Japanese firms are required to file the internal control report only after the fiscal year end
in March 2009. Since our analysis needs realized earnings data to calculate the accuracy ofmanagement forecasts, we also use data from the fiscal year end in March 2010. Since we areaccessible to such data until March 2010 fiscal year end, then we limit sample observations tofirms having fiscal year ends in March 2009. Since certain control variables need data fromprevious periods to be calculated, then we went back to the data as necessary. We obtained
sample firms’ financial data from Nikkei NEEDS Financial Quest, which covers realized
financial data as well as management forecasts. We removed firms that did not have the required
data to calculate the variables in Model (1), resulting in a sample of 2214 observations. To
control for the possible effect of outliers on the results, the continuous variables SIZE, SD(NI),and GROW are winsorized at the 1 and 99 percentiles. Regarding abs(MFE) and abs(lagMFE),
signed MFE and lagMFE are winsorized at the 1 and 99 percentiles, and these absolute values
are used in the empirical model estimations.

5.2 Descriptive Statistics and Correlation Matrix
Table 2 provides the descriptive statistics. As discussed in the previous section, the percentage
of ICD firms and restatement firms is 2.3% and 4.4%, respectively. This indicates that theprobability of firms restating financial highlights is higher than that of firms disclosing ICD.
Regarding management signed forecast errors, its mean (median) value of –0.038 (0.001)
implies that the average value is influenced by some firms reporting highly opportunistic
earnings forecasts, while median firms reported earnings forecasts that fell slightly below the16realized value in the next period. This negative mean value is comparable to that obtained by
Kato et al. (2009), while their median value is negative. Sample period differences may cause
this difference in the distribution of management forecast biases, since their sample period isfrom 1997 to 2006.

(Table 2 is inserted around here)

Table 3 presents the correlation matrix. According to Table 3, the univariate
relationship between Abs(MFE) and ICD is positive (0.14, 0.07), and it is consistent with our
hypothesis. The relationship between Abs(MFE) and RES is also positive (0.13 and 0.10). These
results support our expectation that firms with ineffective internal controls report less accurate
earnings forecasts. Except for the correlations between LOSS and DIST of 0.53 and 0.59, thecorrelations do not pose any multicollinearity problems, while the VIF for all of the control
variables is below 10. Therefore, our regression model is free of multicollinearity concerns.

(Table 3 is inserted around here)

6. Empirical Results
6.1 Univariate Analysis
Table 4 presents the result of the univariate analysis. We compare the accuracy of management
forecasts between firms in two ways. The first is between ICD firms (firms that disclosed ICD)
and non-ICD firms (firms that did not disclose ICD), and the second is between restatement
firms (firms that restated financial highlights) and non-restatement firms (firms that did notrestate financial highlights). Table 4 shows that the mean value of abs(MFE) is significantly

17

higher for ICD firms than for non-ICD firms. The median value is also higher for ICD firms
than for non-ICD firms at the 0.01 level (two-tailed). As is the case of a comparison between
ICD firms and non-ICD firms, the mean and median values of abs(MFE) are significantly
higher for restatement firms than for non-restatement firms at the 0.01 level (two-tailed). In
keeping with the results of the correlation matrix, Table 4 shows that firms with ineffective
internal controls report less accurate management forecasts compared with firms with effective
internal controls.

(Table 4 is inserted around here)

6.2 Regression Results
The results of the multivariate analysis are reported in Table 5. The coefficient on ICD is 0.097,
which is significant at the 0.10 level (two-tailed). This result indicates that Japanese firms
disclosing ICD report less accurate management forecasts than other firms. The coefficient on
RES (0.059) also has a positive and significant value at the 0.05 level (two-tailed), which
supports our hypothesis. Since the severity of internal control weaknesses may differ between
firms disclosing ICD and restatement firms, we conducted the analysis to test for the difference
between the coefficients on ICD and RES. The result is stated in the last two rows of Table 4.
According to the table, while the coefficient on ICD exceeds that on RES, the difference is notstatistically significant. These results indicate that although both firms, those reporting ICD andrestating financial highlights, reported less accurate management forecasts, the level of accuracy
between them is statistically indifferent.

(Table 5 is inserted around here)18

Regarding the results of the control variables, the coefficients on SIZE, DIST, GEO_DIV,and Abs(lagMFE) are statistically significant, and these have expected signs. While all of the
independent variables are not statistically significant, the explanatory power of the model (adj.
R2) is approximately 0.29, which is the same or higher compared with other studies regarding
Japanese firms (Ota 2006) and U.S. companies (Ajinkya et al., 2005; Karamanou and Vafeas,
2005; Rogers and Stocken, 2005). Therefore, Model (1) is likely to specify the relevant factorsthat relate to the accuracy of management forecasts.

6.3 Additional Analysis
Although the difference between realized earnings and management forecasts has beenfrequently discussed in terms of accuracy, several studies have explored it in terms of bias (Hirst
et al., 2008). To clarify whether the accuracy of management forecasts is induced by an
optimistic or pessimistic bias, we separately reestimate Model (1) for firms having positive andnegative management forecast errors. These analyses allow us to examine whether
managements’ optimistic or pessimistic biases influence the results.
Prior literature shows that management forecasts generally reflect optimistic biases(Ajinkya et al., 2005; Kato et al., 2009; Rogers and Stocken, 2005). If management forecasts
inherently involve optimistic biases, managers in firms with weak internal controls may report
more optimistic forecasts. Without accurate internal management reports, firms’ managers ought
to disclose their optimistic expectations if they are inherently optimistic in forming theirforecasts. On the other hand, if firms with weak internal controls report less accurate
management forecasts just because of the uncertainty about their future performance,
management forecasts may be biased in both the optimistic and pessimistic directions. If the19former explanation is valid, the coefficients on ICD and RES will be positive only while usingfirms with negative management forecast errors (opportunistic bias). In contrast, if the latter
explanation is valid, the coefficients on ICD and RES will be positive both while using firms
with negative management forecast errors and while using those with equal to or greater thanzero management forecast errors.

(Table 6 is inserted around here)

Table 6 reports the results of Model (1). Column (1) indicates firms with negative
management forecast errors and column (2) indicates those with equal to or greater than zeromanagement forecast errors. The coefficients on ICD (0.128) and RES (0.107) are both positiveand significant at the 0.10 and the 0.05 levels, respectively, as shown in column (1). On theother hand, as shown in column (2) the coefficient on ICD is negative (–0.016), while it isinsignificant. With respect to RES, the coefficient has an insignificant positive value (0.015).
These results indicate that firms with weak internal controls report optimistic management
forecasts and the coefficients are significant at the conventional level. Since the coefficients on
ICD and RES are not statistically significant in column (2), we interpret that internal control
weaknesses do not involve pessimistic biases in management forecasts. As in the results shownin Table 5, the difference between the coefficients on ICD and RES are significant neither incolumn (1) nor in column (2). Collectively, our results imply that less accurate management
forecasts by firms with weak internal controls are likely to be induced by their optimistic biases.

7. Conclusion
In this study, the relationship between the accuracy of management forecasts and the20effectiveness of internal controls was investigated. In Japan, disclosing management forecasts is
effectively mandated; therefore, we need not control for factors that are likely to influence theissuance of forecasts, unlike in other countries such as the U.S. Therefore, the Japanese setting
allows us to examine the determinants of the accuracy of management forecasts without
controlling for incentives to disclose such forecasts.
Following Feng et al. (2009), we posit that managers of firms with weak internal
controls are likely to rely on erroneous internal management reports in forming forecasts;
therefore, their earnings forecasts might be less accurate than those of firms with effective
internal controls. We shed light on firms disclosing ICD as well as firms restating financial
highlights, since they have more or less internal control weaknesses. Consistent with the notionthat firms with weak internal controls report less accurate management forecasts, our results
indicate that firms disclosing ICD and firms restating financial highlights report less accurate
management forecasts. Furthermore, we find that a manager’s optimistic bias causes suchinaccurate management forecasts. While managers are likely to report optimistically biased
forecast, internal control weaknesses may accelerate such tendencies of management.
The limitations of this research include its limited sample period and possible omitted
variable problems. This study examines only firms having fiscal year end in March 2009;
therefore, the results may not be generalizable. Moreover, the explanatory power (around 0.30)
of our regression model is comparable with that of other studies, but it is far from high. There is
still a room to improve this explanatory power by including additional variables. These issues
are left for future research. Although there are a few limitations to this study, our findings
provide new evidence concerning the relationship between the accuracy of management
forecasts and the effectiveness of internal controls.

21

Appendix: Estimation of Distress Risk
Following Ota (2006), we employ the principal component method of factor analysis, which issuggested by Ohlson (1980), and we calculate the degree of distress risk to estimate Model (1).
Ohlson (1980) provides a bankruptcy probability model by using nine variables. We use factor
scores from the first component as a proxy for the degree of financial distress.

(Table A1 is inserted around here)

Table A1 presents the definition of the variables used in the principal component
method of factor analysis. The sample period for which we conduct the analysis is from April
2002 to March 2009. All stock variables, such as liabilities, assets, and cash flows, represent
mean values during the period. The descriptive statistics and the results of the principal
component method of factor analysis are shown in Tables A2 and A3, respectively. The factor
loading sign and the score coefficients of the first principal component are consistent with
Ohlson (1980) and Ota (2006). Therefore, we interpret that the first principal component inTable A3 represents the intensity of financial distress. We then utilize the factor score from thefirst principal component to calculate DIST in Model (1).

(Table A2 and A3 are inserted around here)

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25

Table 1 Variable Definition
The management earnings forecast error defined as the difference between
MFErealized earnings of period t + 1 and the management earnings forecast of
period t + 1 made at the earnings announcement of period t, deflated by themarket value of equity at the end of fiscal year t.

Abs(MFE)
ICD
RES
LOSS
EQUITYThe absolute value of the management earnings forecast error
Dummy variable that is equal to one if the firm discloses internal control
deficiencies and zero otherwise
Dummy variable that is equal to one if the firm restates the disclosed
financial highlights and zero otherwise
Dummy variable that is equal to one if the firm reports negative net income
and zero otherwise
Dummy variable that is equal to one if the firm issues equity during theperiodSIZE

The logarithm of the market value of equity at the end of fiscal year t − 1

DIST

Ohlson O-score (For details, see Appendix.)

SD(NI)GROWTH
IND_DIV
GEO_DIV

The standard deviation of ROA over the prior 7 years (requiring at least three
non-missing observations.)
The average of sales growth (from t − 1 to t) over the prior 7 years(requiring at least three non-missing observations.)
The industry concentration measure defined as the sum of the squares of(firm sales in each industry segment/total sales)
The geographic concentration measure defined as the sum of the squares of(firm sales in each geographic segment/total sales)

lagMFEThe management earnings forecast error in year t − 1

Abs(lagMFE)

The absolute value of management earnings forecast error in year t − 1

26

Table 2 Descriptive Statistics
Obs.

Mean

SD

Q1

Median

Q3

MFE

2,214

−0.0380.224−0.045

0.001

0.038

Abs(MFE)

2,214

0.112

0.198

0.014

0.040

0.113

ICD

2,214

0.023

0.151

0.000

0.000

0.000

RES

2,214

0.044

0.206

0.000

0.000

0.000

LOSS

2,214

0.360

0.480

0.000

0.000

1.000

EQUITY

2,214

0.086

0.280

0.000

0.000

0.000

SIZE

2,214

23.046

1.755

21.78922.81224.156

DIST

2,214

−0.0180.140−0.101−0.0340.039

SD(NI)

2,214

0.036

0.045

0.012

0.022

0.041

GROWTH

2,214

0.050

0.098

0.001

0.031

0.073

IND_DIV

2,214

0.796

0.255

0.568

0.994

1.000

GEO_DIV

2,214

0.892

0.196

0.835

1.000

1.000

lag_MFE

2,214−0.1220.207−0.144−0.054−0.012

lag_ABS(MFE)

2,214

0.131

0.201

0.021

0.059

0.144

The sample consists of Japanese listed companies having fiscal year end in March 2009. Allvariables are defined in Table 1.

27

Table 3 Correlation Matrix

MFE
Abs(MFE)
ICD
RES
LOSS
EQUITYSIZE
DISTSD(NI)GROWTH
IND_DIV
GEO_DIV
lag_MFE
ABS(lagMFE)

1
2
3
4
5
6
7
8
9
10
11
12
13
14

1
1.00
−0.72
−0.14
−0.08
−0.17
−0.05
0.20
−0.23
−0.20
0.02
−0.01
−0.06
0.29
−0.28

2
−0.11
1.00
0.14
0.13
0.31
0.05
−0.35
0.35
0.28
0.02
0.08
0.00
−0.46
0.47

3
−0.08
0.07
1.00
0.11
0.09
0.06
−0.09
0.13
0.17
−0.03
0.03
0.01
−0.10
0.11

4
−0.01
0.10
0.11
1.00
0.10
0.00
−0.08
0.13
0.04
0.00
0.02
−0.03
−0.11
0.11

5
−0.08
0.33
0.09
0.10
1.00
0.04
−0.19
0.53
0.34
−0.05
−0.02
−0.11
−0.60
0.59

6
−0.02
0.01
0.06
0.00
0.04
1.00
0.01
0.11
0.21
0.25
−0.05
−0.01
−0.05
0.05

7
0.17
−0.43
−0.08
−0.08
−0.20
0.01
1.00
−0.21
−0.27
0.01
−0.37
−0.36
0.30
−0.32

8
−0.04
0.28
0.06
0.10
0.59
0.05
−0.18
1.00
0.36
−0.08
−0.09
0.03
−0.59
0.58

9
−0.09
0.34
0.10
0.03
0.45
0.14
−0.28
0.20
1.00
0.22
0.08
0.04
−0.44
0.46

Pearson (Spearman) correlations are reported in the lower (upper) diagonal. All variables are defined in Table 1.

28

10
0.12
−0.01
−0.05
−0.04
−0.13
0.17
0.13
−0.16
0.07
1.00
0.02
−0.01
−0.02
0.03

11
−0.02
0.13
0.03
0.02
−0.02
−0.04
−0.38
−0.14
0.12
0.01
1.00
0.16
−0.02
0.04

12
−0.10
−0.03
0.01
−0.02
−0.10
−0.01
−0.36
0.02
−0.09
−0.11
0.22
1.00
0.04
−0.03

13
0.10
−0.41
−0.08
−0.09
−0.74
−0.03
0.26
−0.56
−0.42
0.08
0.02
0.17
1.00
−0.99

14
−0.07
0.49
0.09
0.10
0.73
0.02
−0.34
0.54
0.49
−0.08
0.03
−0.13
−0.90
1.00

Table 4 Univariate Analysis
Obs.

Mean

Median

Total

2,214

0.112

0.040

non-ICD

2,162

0.107

52

ICD
non-RESRES

t-stat.(p-value)

z-stat.(p-value)

0.040−3.355−3.138

0.294

0.079

(0.00)(0.00)

2,116

0.106

0.039

−3.646−4.824

98

0.231

0.092

(0.00)(0.00)

This table reports mean and median values of management forecast accuracy for the sample of
all firms, non-ICD firms (firms that did not disclose ICD), ICD firms (firms that disclosed ICD),
non-RES firms (firms that did not restate financial highlights), and RES firms (firms that
restated financial highlights). A two-sample t-test (Wilcoxon rank-sum test) is used to test
(two-tailed) for significant differences in means (median) between ICD and non-ICD groups aswell as RES and non-RES groups.

29

Table 5 Regression Result
0.782***(9.34)
ICD(+)
0.097*(1.91)
RES(+)
0.059**(2.17)
LOSS(+)−0.001(−0.049)
EQUITY(+)
0.010(0.65)
SIZE(–)−0.029***(−10.4)
DIST(+)
0.127**(2.05)
SD(NI)(+)
0.091(0.61)
GROW
(+)
0.017
(0.26)
IND_DIV(–)−0.021(−1.22)
GEO_DIV(–)−0.060***(−2.67)ABS(lagMFE)(+)
0.286***(5.69)
Observations
2214
2
0.2867
Adj. R
ICD = RS (F-value)
0.41(p-value)(0.52)
This table reports the parameter estimates and t-statistics from the OLS estimation of Abs(MFE)on the ICD dummy, RES dummy, and other control variables. All variables are defined in Table
1. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 level (two-tailed), respectively.
The t-statistics are based on White’s (1980) heteroskedasticity consistent standard errors andcovariance.
Constant

30

Table 6 Additional Analysis

Constant
ICD

(+)

RES

(+)

LOSS

(+)

EQUITY

(+)

SIZE

(–)

DIST

(+)

SD(NI)

(+)

GROW

(+)

IND_DIV(–)

GEO_DIV(–)

lagMFE(+)

Observations
Adj. R2
ICD = RS (F-value)(p-value)

Column (1)MFE < 01.021***(6.74)
0.128*(1.88)
0.107**(2.21)
0.003(0.18)
0.040(1.35)−0.039***(−7.94)0.213***(2.64)
0.125(0.53)
0.056(0.48)−0.029(−0.90)−0.068(−1.50)0.301***(4.26)
1078
0.32631.68(0.20)

Column (2)MFE ≥ 00.465***(8.29)−0.016(−0.55)0.015(1.15)
0.004(0.49)−0.010(−1.16)−0.017***(−9.01)−0.019(−0.49)−0.090(−0.85)0.057*(1.77)
0.001(0.11)−0.038***(−2.74)0.190***(5.00)
1136
0.30310.06(0.80)

This table reports the parameter estimates and t-statistics from the OLS estimation of Abs(MFE)on the ICD dummy, RES dummy, and other control variables for the sample of firms withnegative management forecast errors (Column 1) and firms with equal to or greater than zeromanagement forecast errors (Column 2). All variables are defined in Table 1. ***, **, and *indicate significance at the 0.01, 0.05, and 0.10 level (two-tailed), respectively. The t-statistics
are based on White’s (1980) heteroskedasticity consistent standard errors and covariance.

31

Table A1 Variable Definition for Principle Component Analysis
Total liabilities/Total assets
Working capital/Total assets
Current liabilities/Current assets
Net income/Total assets
Operating cash flow/Total liabilities
Dummy variable that is equal to one if earnings are negative for the last two years
INTWO
and zero otherwise
Dummy variable that is equal to one if total liabilities exceed total assets and zero
OENEG
otherwise
The difference between earnings in t and those in t − 1 divided by the sum of absolute
CHIN
value of earnings in t and those in t − 1
TLTA
WCTA
CLCA
NITA
FUTL

Table A2 Descriptive Statistics
N
Mean
SD
p10
p25
p50
p75
p90

TLTA
30,119
0.59
2.58
0.23
0.37
0.56
0.72
0.85

WCTA
30,119
0.13
2.57
−0.12
0.01
0.16
0.32
0.47

CLCA
30,119
1.00
5.59
0.27
0.45
0.70
0.99
1.49

NITA
30,119
0.01
0.22
−0.04
0.00
0.02
0.04
0.08

FUTL INTWO OENEG
30,119 30,119
30,119
0.09
0.10
0.01
4.24
0.30
0.12
−0.05
0.00
0.00
0.02
0.00
0.00
0.09
0.00
0.00
0.20
0.00
0.00
0.37
0.00
0.00

CHIN
30,119
0.01
0.56
−1.00
−0.27
0.04
0.29
0.98

The sample consists of Japanese listed companies from April 2002 to March 2009. All variables
are defined in Table A1.

32

Table A3 Result of Principle Component Analysis
TLTA
WCTA
CLCA
NITA
FUTL
INTWO
OENEG
CHIN

Factor Loadings
0.950−0.9500.838−0.371−0.0090.131
0.293−0.051

Eigenvalue
Variation Explained

Score Coefficients
0.345−0.3460.305−0.135−0.0030.048
0.107−0.018

2.749
34.4%

This table reports the factor loadings and score coefficients of principle component analysis.
The variables are from Ohlson (1980) and Ota (2006). All variables are defined in Table A1.

33