






































46 

Earnings Quality and Investor Reactions to 
Restatement Announcements 

Robin N. Romanus, Ph.D. 

Appalachian State University 

The purpose of this research is to help clarify the ambiguity surrounding market participants’ pricing 
of earnings quality using one clearly observable indicator of low-quality earnings -accounting 
restatements. This study examines the effect that pre-restatement earnings quality has on short-
window returns following restatement announcements using a cross-section of 719 publicly traded 
firms that announced restatements between 1997 and 2004.  Accrual metrics are used to proxy for 
earnings quality.  Results indicate that the market reaction to restatement announcements is 
significantly influenced by pre-restatement earnings quality.  Specifically, the accrual measure of 
earnings quality is significantly and negatively related to the market reaction. This finding provides 
evidence that investors are attuned to the implications of poor earnings quality communicated by 
accruals quality and that this awareness is reflected in stock prices prior to the announcement of 
accounting restatements. 

Keywords:  Restatements; Market Reaction; Earnings Quality 

Introduction 
Publicly reported accounting data have widespread capital market implications for a broad 

spectrum of users.  Misinterpreted or misleading accounting information may instigate inefficient 
resource allocation, obfuscate the standard setting process, and erode investor confidence (General 
Accounting Office, 2002; Schipper & Vincent, 2003; Kalelkar & Nwaeze, 2011).  Consequently, the 
quality of reported earnings has triggered substantial interest by regulators and accounting researchers, 
resulting in an emergent body of research that examines the market pricing of earnings quality (e.g. 
Sloan, 1996;  Xie 2001; GAO, 2002; Aboody, Hughes, & Liu, 2005; Francis et al., 2005; Kalelkar & 
Nwaeze, 2011; Perotti & Wagenhofer, 2014).   

Rigorous efforts to expound on market participants’ aptitudes for discerning the intricacies 
associated with earnings quality have resulted in two distinct perspectives (Dechow & Schrand, 2004). 
The first perspective views market participants as naïve users of accounting information who fail to 
understand the differential persistence in accruals and cash flows resulting in temporary security 
mispricing (e.g. Sloan, 1996; Xie, 2001).   Conversely, the second perspective suggests that in the wake 
of accounting scandals, market participants scrutinize earnings reports carefully and subsequently 
discern and price the quality of earnings (Balsam, Bartov, & Marquardt, 2002; Francis et al., 2005; 
Baber, Chen, & Kang, 2006; Kalelkar & Nwaeze, 2011; Perotti & Wagenhofer, 2014).   The purpose 
of this research is to help clarify the ambiguity surrounding market participants’ pricing of earnings 
quality using one distinct indicator of low-quality earnings - accounting restatements. 

Accounting restatements provide a unique and innovative environment for investigating the 
pricing of earnings quality because they represent relatively direct evidence of low-quality earnings 

http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 
2019, Volume 9, pages 46-63 



47 

 

(DeFond & Francis, 2005).  Additionally, despite heightened awareness of financial reporting quality 
from regulators, auditors, market participants, and the popular press, the number of restatements 
escalated to an all-time high of 1,876 in 2006 (Reilly, 2007).  Numerous studies have used restatements 
as an indicator of either low-quality earnings or earnings management (Richardson, Tuna, & Wu, 2002; 
Abbott, Parker, & Peters, 2004; Hribar & Jenkins, 2004; Kinney, Palmrose, & Scholz, 2004; Blankley, 
Hurtt, & MacGregor, 2012; Francis et al., 2013) and provide clear evidence that firms suffer a 
substantial loss in market value following restatement announcements (Dechow, Sloan, & Sweeney, 
1996; Anderson & Yohn, 2002; GAO, 2002; Palmrose & Scholz, 2004).  Despite the severe market 
repercussions associated with the negative signal communicated via earnings restatements, prior 
research has not empirically addressed to what extent the quality of pre-restatement earnings affects 
market participants’ reactions to these subsequent, more demonstrative indicators of low earnings 
quality.  The present research fills this gap by examining the effect pre-restatement earnings quality 
has on market participants’ reactions to the more visible signal of low-quality earnings conveyed by 
the issuance of a restatement.  
 Palmrose et al. (2004) (hereinafter PRS) provide evidence that numerous factors are associated 
with the market reaction to restatement announcements, including fraud, the number and type of 
account groups affected, and the directionality (income increasing vs. decreasing) of the restatement.  
This research extends their model to include an accrual proxy for earnings quality.  In the framework 
of the model, and using a cross-section of publicly traded firms that announced restatements between 
1997 and 2004, this study examines the effect that pre-restatement earnings quality has on short-
window returns following restatement announcements.   
 The results of this research indicate that pre-restatement earnings quality is significantly 
negatively related to the market reaction to restatement announcements.   Firms with better pre-
restatement earnings quality experience more negative returns in response to restatement 
announcements than firms with worse pre-restatement earnings quality.   The identification of this 
relationship contributes to both the earnings quality and restatement literatures by providing support 
for the contention that earnings quality is, at least in part, understood and priced by market 
participants.  This finding also provides additional insights into the factors associated with market 
participants’ extremely heterogeneous reactions to financial report restatements.   
 The remainder of this paper is organized as follows:  the next section reviews the restatement 
and earnings quality literature and develops the associated hypothesis. The third section describes the 
research design and method and section four presents the analyses and results.  Section five closes 
with a discussion of limitations and conclusions.   
 

Literature Review 
Restatement Research  
 Academic research, governmental reports, and the popular press have documented extensively 
the dramatic increases in the numbers of restatements over the past 20 years (GAO, 2002 & 2006; 
Huron Consulting,  2003 & 2004; Palmrose et al., 2004; Reilly, 2007; Scholz, 2014).  Restatements are 
required when previously issued financial reports are later discovered to not be in accordance with 
Generally Accepted Accounting Principles (GAAP).  Although many restatements do not have 
immediate cash flow implications, they convey important information regarding firms’ potential for 
future earnings and the accuracy and reliability of financial data.  As such, they send a potentially 
negative signal to market participants regarding earnings quality.  The effect this signal has on capital 
markets is substantiated by the strong negative reaction that occurs when companies announce their 
intention to restate earnings (GAO, 2002 & 2006; Palmrose et al., 2004; He & Chiang, 2013; Gordon 
et al., 2014; Du, 2017).  The severe capital market repercussions associated with the decision to restate 
earnings, coupled with the fact that restatements are de facto evidence of poor earnings quality, make 
this a distinctive combination for researching investors’ ability to interpret less pervasive earnings 



48 

 

quality signals.  Additionally, the recent escalation in the number of restatements has created a 
heightened interest in both the causes and consequences of restatements.   
 The negative financial implications of restatements are widely documented. Based on a study 
of SEC enforcement actions resulting in restatements between 1981 and 1992, Dechow et al., (1996) 
report an average negative return of 6 percent.  Examining restatement announcements between 1995 
and 1999, Palmrose et al. (2004) similarly document an average negative return of 9 percent.  In 
addition to firm-specific market reactions, Gleason, Jenkins, and Johnson (2004) note that 
restatements induce a reduction in share prices of firms in the same industry.  Xu, Najand, and 
Ziegenguss (2006) also report that restatements affect the equity valuations of competing firms.  They 
find that firms having cash flow characteristics similar to restating firms experience a negative return 
of 0.76 percent when the restatement reduces the stock price of the restating firm.  
 In addition to eroding market capitalization, restatement firms are subject to other economic 
penalties.  Firms issuing restatements are also frequently the subject of class action lawsuits (Jones & 
Weingram, 1997).  Palmrose and Scholz (2004) investigate the relationship between restatement 
characteristics and litigation.  They indicate that core restatements and those involving a greater 
number of accounts (more pervasive) increase both the likelihood of litigation and plaintiff success.  
Hribar and Jenkins (2004) determine that restatements are associated with increases in the cost of 
equity capital, particularly restatements initiated by the auditor or for companies with high leverage.  
These studies provide support for the view that restatements have economically meaningful 
implications that go far beyond documented deteriorations in market capitalization. 
 Although these studies explore a variety of factors associated with restatements, PRS (2004) 
extend this research and attempt to empirically model the capital market effect of restatement 
announcements.  PRS provides evidence that the severity of the abnormal negative returns following 
restatement announcements varies greatly depending on the characteristics of the restatement.  For 
example, restatements associated with management fraud elicit the most severe stock price reactions.  
Additionally, restatements initiated by the auditor or related to core operating accounts such as 
revenue also result in more substantial negative market reactions.  Extending PRS, Salavei and Moore 
(2005) classify restatements according to the reason for the restatement and examine the market 
reaction according to nine different categories of restatements, including revenue, cost, securities, and 
transaction related.  They determine that market reactions differ significantly by both the type of 
restatement and its magnitude.  Specifically, they find that restatements of noisy information items, 
such as securities and restructurings, increase investors’ information sets and therefore result in 
reactions that are more negative.  Conversely, restatements that are transaction-based increase the 
noisiness of investors’ information sets and do not influence the market reaction as strongly.  

While restatement announcements have obvious negative market consequences, there is a 
limited amount of research investigating what factors lead to the broad variability in returns following 
the announcement event.  Examining the relationship between pre-restatement earnings quality and 
severity of the market reaction can potentially help clarify the substantial heterogeneity in market 
responses.    
 
The Market Pricing of Earnings Quality 
 Evidence is mixed as to the extent to which earnings quality indicators are used by investors 
to extract value relevant information.  Some research provides evidence that market participants 
consider larger accruals or book-tax differences to be indicative of poor earnings quality, resulting in 
a contemporaneous reduction in stock prices (Balsam et al,. 2002; Francis et al., 2005; Hanlon, 2005; 
Baber et al., 2006).  Other research indicates that market participants overprice the discretionary 
component of income, thus temporarily over-inflating stock prices (Sloan, 1996; Subramanyam, 1996; 
Bradshaw, Richardson, & Sloan, 2001; Xie, 2001).  Widely publicized accounting scandals, however, 
coupled with new regulatory requirements, have resulted in an increased focus on earnings quality 



49 

 

issues.  Consistent with a heightened emphasis on earnings quality, recent research indicates investors 
and analysts have become more cognizant of potential earnings management techniques and the 
subsequent implications for future earnings (Balsam et al., 2002; Francis et al., 2005; Hanlon, 2005; 
Baber et al. 2006).  These findings imply market participants may be capable of identifying earnings 
quality signals, which may affect the market reaction to restatement announcements.   
 Francis et al. (2005), provide convincing empirical evidence suggesting investors price accruals 
quality.  They find that lower accruals quality is associated with higher costs of debt, larger equity 
betas, and larger earnings-price ratios.  Other studies corroborate these findings, suggesting that 
investors utilize information concerning the quality of earnings during price formation.  For example, 
Balsam et al. (2002) report a negative association between unexpected discretionary accruals and 
cumulative abnormal returns over a 17-day window following the release of the Form 10-Q.  Similarly, 
Baber et al. (2006) examine the influence of the voluntary disclosure of balance sheet and cash flow 
information on price reactions to earnings announcements.  They provide evidence that investors 
discount earnings when contemporaneous supplemental disclosures contain information suggesting 
earnings have been managed via discretionary accruals.  DeFond and Park (2001) also provide 
evidence suggesting market participants adjust, at least in part, for earnings quality at the time earnings 
are announced.  They report that firms announcing positive earnings surprises have lower earnings 
response coefficients when income-increasing accruals exaggerate the magnitude of the earnings 
surprises.  Collectively, these studies indicate that the earnings quality information conveyed in accruals 
is to some degree reflected in contemporaneous security prices.  
 The above research provides evidence that investors are capable of extracting earnings quality 
information from the information contained in earnings quality proxies such as accruals. As such, 
signals of low-quality earnings in accruals should be reflected in stock prices prior to firms announcing 
restatements.  Restatements, to some degree, may confirm investors’ prior suspicions regarding poor 
earnings quality.  Thus, this signal provides investors with less new or surprising information, resulting 
in a less substantial negative stock price reaction.  Conversely, investors may not expect firms with 
good pre-restatement earnings quality to restate, thereby triggering a more substantial negative price 
reaction. These arguments lead to the following hypothesis: 
 
H1:  Firms with low earnings quality will experience a less severe market reaction to the announcement of a restatement 

relative to firms with high earnings quality.  

 
Methodology 

Variable Development 
 Research by PRS (Palmrose et al. 2004) provides the foundation for the model to test the 
hypothesis.  The model relates various characteristics of restatements and restating firms to the 
cumulative abnormal returns on the day of and the day following a restatement announcement.  Their 
model consists of nine variables, all of which are used as control variables in the model.  The PRS 
model is enhanced by additional variables taken from related literature, most notably the earnings 
quality proxy.  In addition to the earnings quality proxy, two additional control variables are added to 
the PRS model.  These variables, as well as the dependent variables, are developed in the following 
sections. 
 The first set of variables developed by PRS (2004) provides information as to managements’ 
competence and integrity.   Restatements resulting from fraud are one primary indicator that 
management may lack integrity.   FRAUD is an indicator variable equal to 1 in instances where the 
restating firm was subject to an SEC Accounting and Auditing Enforcement Release (AAER) and 0 
otherwise; instances of fraud are expected to be negatively related to the market reaction.   Restatement 
attribution (ATTRIB) designates the party that identified the need for restatement: auditor, SEC, or 
company management.  Restatements attributed to the external parties such as the auditor or SEC are 



50 

 

expected to introduce a greater amount of uncertainty about the reliability of financial information 
and be associated with more negative market reactions.  Instances where the firm identified the need 
for restatement may provide a heightened level of assurance vis-à-vis the ability of management to 
identify and correct potential misstatements, and are thus expected to be positively associated with 
market reactions to the restatement announcements (Palmrose et al., 2004).  Attribution is captured 
as a series of indicator variables, one for each attributing party, external auditor, SEC, and 
management.  Restatement announcements with no attribution serve as a no-information baseline.   
 The PRS (2004) model also includes variables associated with the aspects of the material 
impact of the restatement.  Restatements affecting core (CORE) accounts such as revenue, cost of 
goods sold, and operating expenses, are expected to be associated with a more negative market 
reaction because they represent changes in on-going operating income.  CORE is an indicator variable 
equal to 1 if the restatement affected one or more core account groups and 0 otherwise. Materiality 
(MAT) represents the impact of the restatement on net income and is computed by subtracting 
restated net income from original income over all restated periods and scaling the difference by total 
assets.   PRS (2004) indicate that MAT is significantly positively related to the market reaction 
surrounding the announcement date.  The pervasiveness (PERVAS) of the restatement is defined as 
the number of general account groups affected by the restatement and is expected to be negatively 
related to the market reaction.  The persistence (YEARS) of the misstatement provides information 
about the competence of management and the reliability of the firm’s internal control structure.  
Persistence is measured as the number of periods impacted by the restatement, where a quarter is 
equal to 0.25, and is expected to be negatively related to the market reaction. 
   The PRS (2004) model includes three variables that control for company-specific 
characteristics of the restating firms.  An interaction term between firm size, measured as the natural 
log of the book value of total assets at year-end in the period prior to restatement, and the materiality 
variable (SIZEMAT) is used to control for variations in the relative size of the restating firm.  
Consistent with PRS (2004), SIZEMAT is expected to be negatively related to the market reaction.  
An interaction term between the ratio of long-term debt to total assets and the materiality measure 
(LEVMAT) is used to control for variations in debt levels across the restating firms.  Long-term debt 
and total assets are measured at year-end in the period prior to the earliest restated period.   LEVMAT 
is expected to be negatively related to the market reaction to restatement announcements.  The final 
control variable in the PRS (2004) model captures the returns of the restating firm in the 120-day 
period preceding the restatement announcement (PRIORRET).  Prior returns are expected to be 
positively associated with the market reaction. 
 The PRS (2004) model is enhanced by two additional control variables.  An indicator variable 
(BIGN) is set equal to 1 if the firm engaged a Big N auditor in the year(s) of restatement and 0 
otherwise.  This variable is included to control for variations in audit quality between big N and non-
big N firms (Francis et al,. 1999).  As Big N auditors are associated with better audit quality, BIGN is 
expected to be negatively associated with the market reaction.   In addition to type of auditor, Lazer, 
Livnat, and Tan (2004) report a higher incidence and magnitude of quarterly restatements for firms 
that switched auditors as compared to those that did not.  The authors contend that these restatements 
are a function of the incoming auditor attempting to manage the litigation risk associated with the new 
client.  If restatements following auditor changes are the result of audit firms’ attempts to manage 
litigation risk, then the signal conveyed to the market by the restatement announcement may not be 
considered as indicative of low-quality earnings.  Therefore, an indicator variable (AUDCHANGE) 
equal to 1 if the firm changed auditors in the year of the restatement announcement and 0 otherwise 
is also included in the model and expected to be positively associated with the market reaction. 
 
Model 
 The PRS (2004) model combined with earnings quality (EQ) and the two additional  



51 

 

control variables (BIGN and AUDCHNG) is represented by the following regression equation: 
 
  CAR = β0 – β1EQ – β2FRAUD ± β3ATTRIB – β4CORE + β5MAT – β6PERVAS – β7YEARS – 

β8SIZEMAT – β9LEVMAT + β10PRIORRET – β11BIGN+ β12AUDCHANGE    (1)  

             

Where:  

CAR DAILYRET INDRETi

t

t

i= −
=

=

 ( )
0

1

                      (2) 

 DAILYRET       =   Daily Return for company i for days 0 and +1;  
 INDRET       =   CRSP Market Index Return; 
 
 

  

For the test of the hypothesis, cumulative abnormal returns are measured on days 0 and 1 where day 
0 is the day of the restatement announcement.   
   
Accrual Measure of Earnings Quality 

DDEQ (-) = Dechow and Dichev (2002) modified accruals quality (DDEQ) proxy for 
earnings quality, multiplied by negative one (-1); 

FRAUD (-) = Indicator variable 1 for fraud 0 for no fraud (determined by issuance of 
an SEC Accounting and Auditing Enforcement Release); 

ATTRIB (+/-) = Series of three indicator variables where 1 equals auditor, management or 
SEC attribution and 0 indicates either no or other attribution.  The three 
attribution indicator variables are: Attribution Auditor, Attribution 
Management and Attribution SEC;   

CORE (-) = Indicator variable 0 for restatements not involving core accounts, 1 for 
restatements involving core accounts (revenue, cost of sales and on-going 
operating expenses);  

MAT (+) = Originally reported income less restated income for all years summed 
over all restatement periods and scaled by total assets in the year 
immediately prior to the restatement; 

PERVAS (-) = Pervasiveness as the number of account groups from 1 to 7 that 
represent the focus of the restatement;  

YEARS (-) = Persistence of restatement defined as the number of years the financial 
statements were restated (a restated quarter equals .25); 

SIZEMAT (-) = Interaction between firm size [natural log of the book value of total 
assets reported the last fiscal year-end prior to restatement (SIZE)] and 
the earnings change measure (MAT); 

LEVMAT(-) = Interaction between ratio of long term debt to total assets (based on 
book values at fiscal year-end prior to restatement) and the earnings 
change measure (MAT); 

PRIORRET(+)  = Buy and hold returns over the last 120 days prior to the restatement 
announcement (day –120 to day –1); 

BIGN (-) = Indicator variable equal to 1 if firm engaged a Big N auditor in year or 
quarter of restatement and 0 otherwise; 

AUDCHANGE 
(+) 

= Indicator variable equal to 1 if restating firm changed auditors from the 
restated period up to the period ending with the announcement date and 
0 otherwise. 



52 

 

 Consistent with Francis et al. (2005), a measure of accruals quality developed by Dechow and 
Dichev (2002) (hereinafter DD) is utilized.  Accruals quality in the DD model is measured by the 
extent to which working capital accruals map into operating cash flow realizations in the prior, current, 
and future periods. The DD model is based on the assumption that accruals reflect managements’ 
temporary adjustments to either delay or anticipate the receipt of cash. The error term, thus, captures 
the extent to which managements’ estimates did not accurately capture operating cash flow 
realizations.  Therefore, the DD model reflects both intentional estimation errors arising from earning 
manipulations and unintentional accrual estimation errors resulting from uncertainty in the operating 
environment.  Wherein the DD model is based on current accruals, Francis et al. (2005) enhance the 
reliability of the DD model by adding accrual proxies from the modified Jones model (Dechow, Sloan, 

& Sweeny, 1995; Jones, 1991) for revenue changes (Rev) and property, plant and equipment (PPE).  
This adaptation results in the following accruals model, which estimates the proxy for earnings quality: 
 

 
TCA

Assets

CFO

Assets

CFO

Assets

CFO

Assets

v

Assets

PPE

Assets

j t

j t

j j

j t

j t

j

j t

j t

j

j t

j t

j

j t

j t

j

j t

j t

j t

,

,

, ,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

,

Re
= + + + + + +

− +
      0 1

1

2 3

1

4 5


 (3)  

Where: 

 

  

These estimations produce firm and year-specific residuals that form the basis of the earnings quality 

metric: DDEQ = )( 1, −tj


.  The metric is equal to the rolling five-year standard deviation of firm j’s 

(calculated over the years t - 4 through t) estimated residuals with larger standard deviations indicating 
lower earnings quality.  Because the measurement of accruals quality is the standard deviation of the 
residual, rather than the residual itself, companies with consistently large or small residuals have small 
standard deviations and thus, good earnings quality. The notion being that this consistency is 
associated with considerably less uncertainty (Francis et al., 2005).   

The residual in the Equation (3) is estimated for each year for each one of the Fama and 
French 48 industry classifications for which there are at least five total observations.  This metric is 
hereinafter identified as DDEQ.  Consistent with prior research, the extreme values of the distribution 
are winsorized to the 1 and 99 percentiles (Francis et al., 2005). 

Interpretation of the regression coefficient on earnings quality is intuitively complicated by the 
fact that there is an inverse relationship between the DDEQ statistic and earnings quality (i.e., greater 

t = Year prior to latest year or quarter restated; 
j = Firm; 
TCAj,t = Firm j’s total current accruals for year t (CAj,t - CL,j,t, - Cashj,t+ STDEBTj,t); 

Assetsj,t = Firm j’s average total assets for year t and t-1; 
CFOj,t  = Firm j’s cash flow from operations for year t;  Net income before extraordinary 

items less total accruals (where TAj,t = CAj,t - CLj,t - Cashj,t + STDEBTj,t – 
DEPNj,t); 

DEPNj,t = Firm j’s depreciation and amortization expense for year t; 

CAj,t = Firm j’s change in current assets from year t-1 to year t;  

CLj,t = Firm j’s change in current liabilities from year t-1 to year t; 

Cashj,t = Firm j’s change in cash from year t-1 to year t; 

STDEBTj,t = Firm j’s change in short term debt from year t-1 to year t;  

Revj,t = Firm j’s change in revenue between year t-1 and year t;  

PPEj,t = Firm j’s gross value of PPE in year t. 



53 

 

standard deviations equal worse earnings quality).  For this reason, the values of DDEQ have been 
multiplied by negative one (-1).  This transformation allows a more direct intuitive interpretation of 
the results in that larger values (less negative) of the DDEQ estimates are associated with better 
earnings quality. 
 
Sample and Data 
 The sample consists of companies announcing restatements between January 1, 1997, and 
December 31, 2004.  These sample firms were obtained from the GAO database and from searches 
on the Lexis-Nexis News Library.  The GAO (2002) database contains a list of all companies 
announcing restatements between January 1, 1997, and June 30, 2002.   To locate firms that announced 
restatements after June 30, 2002, keyword searches were performed (e.g. adjust, error, misstate, restate, 
revise) in the Lexis-Nexis News Library.    
 There were 1,368 restatement announcements identified during the sample window.  This 
number was reduced by 30 firms that issued more than one restatement within a six-month period, 
213 firms that restated for reasons other than to correct previously misstated income, and 74 firms 
that did not ultimately issue a restatement.  Of the remaining 1,051 firms, 332 were eliminated due to 
missing data items.  This resulted in a final sample of 719 restatement announcements.  The 
breakdown of sample attrition is presented in Table 1. 
 Additional information was taken directly from the restatement announcements.   The 
restatement announcements generally contains information indicating to whom the restatement is 
attributed  (ATTRIB), the total expected impact on net income (MAT), the fiscal period(s) affected 
(YEARS), the account groups impacted and/or the reason for the restatement (i.e. revenue 
recognition timing)(CORE, PERVAS).  Hand collected data items not found in the restatement 
announcements were taken from a combination of 10-K, 10-Q and 8-K reports.  Additional data was 
collected electronically from COMPUSTAT, CRSP, and Thomson Financial (13F) Filings.   
 

Results 

Descriptive Statistics and Univariate Results 
 Table 2 presents a breakdown of restatement announcements by industry and year of 
announcement.   Consistent with increases noted in other studies (GAO, 2002 & 2006; Huron 
Consulting, 2003 & 2004; Palmrose et al., 2004), the sample increases, although not consistently, across 
years with the largest number of announcements in 2004.  Although over 50 percent of restatement 
announcements are clustered in the business and services industry grouping, the distribution of 
restatements across both calendar quarters (not tabulated) and years is independent of industry.  This 



54 

 

 
 
suggests market anticipation of announcements due to industry clustering is not likely to be an 
important factor in the observed market reactions.   
 Table 3 presents the summary statistics for the CAR associated with restatement 
announcements across four different return windows. In addition to providing returns on all 
restatements in Panel A, the table classified restatements into two distinct subsets. Panel B contains 
those restatements announced in conjunction with earnings releases and Panel C contains those 
restatements announced separately.  The mean CAR for the full restatement sample on days (0, 1) is 
negative 4.5 percent.  As the reaction window is expanded for the full sample, returns become 
increasingly negative up to 5.6 percent for days (-1 to 3). The mean CAR on days (0, 1) for restatements 
announced in earnings releases is negative 3.0 percent, which is significantly greater than the mean 
CAR of restatements announced separately of negative 5.4 percent (z-statistic 3.06).   This supports 
the conjecture that information contained in earning releases may attenuate reactions to the 
restatement announcements.   As expected, the CAR are significantly negative across all four different 
return windows and all three classifications of announcement groupings.  The CAR in the first column 
of Table 3 represents the primary dependent variable for the regressions associated with hypotheses 
one.   

Table 4 presents descriptive statistics and univariate results for the control variables.  In most 
cases, the results are similar to the findings of PRS (2004).  Fraud observations (12 percent, 84 of 719) 
have an average CAR of negative 11 percent, significantly lower than the negative 3.6 percent CAR 
associated with non-fraud observations (t-statistic = -2.83).  Restatements involving core accounts  

Announcements of all potential restatements
a

1368

Duplicate restatement announcements
b

(30)                    

Restatements for technical reasons not amounting to misstatements
c

(213)                  

Announcements that did not result in actual restatements
d

(74)                    

Restatements eliminated by missing data
e

(332)                  

Restatements included in analysis 719

d
Announcements of potential restatements later determined to be unnecessary. 

c
Restatements for technical reasons such as change in accounting policy, discontinued operations, 

mergers/acquisitions, and stock splits were eliminated because they are not properly classified as 

misstatements.

e
Restatements with missing data items are those primarily missing CRSP returns and the five years of 

data required to compute Dechow & Dichev (2002) earnings quality metric.  

Table 1

Sample Attrition

a
Number of initial restatement announcements identified through key word searches on Lexis Nexis (July 

2002 - December 2004) and the GAO database (January 1997 - June 2002).
b
Firms that announced a restatement within a six month period of a prior announcement were eliminated 

from the sample if the restated periods were the same as in the original announcement.



55 

 

 
 
 
also have significantly lower average CAR of negative 6.5 percent compared to negative 0.9 percent 
for non-core restatements (t-statistic = -6.38).   As in the PRS study, the 97 restatements attributed 
(ATTRIB) to auditors result in the most negative returns (10.4 percent) and the 357 attributed to 
management the second most negative returns (4.9 percent).  Of those restatements attributed to only 
one party, the SEC initiated restatements resulted in the least negative returns (2.8 percent).  Despite 
the somewhat logical presumption that SEC involvement should elicit a more negative reaction, the 
above findings are corroborated by Dechow et al. (1996), who document auditor identified accounting 
problems as being associated with more negative reactions than those identified by the SEC.  

The materiality (MAT) measure indicates that restatements associated with larger decreases in 
net income are associated with returns that are more negative.  Returns decrease significantly as the 
number of account groups (PERVAS) increases (F-statistic = 21.55, p-value < .01).   Although the 
majority of the full restatement sample affects only one account group (75 percent, 541 out of 713) 
and is associated with a negative reaction of 3 percent, restatements affecting more than four account 
groups elicit a considerably larger negative return of 19 percent.   These results are also consistent with 
those in the PRS (2004) study.  The number of years (YEARS) that were restated averages 1.4 for the 
entire sample.  The average 120-day return for the sample firms is negative 4.3 percent. 
 
   

Industry
a

Food Products 5 1% 2 <1% 5 1% 1 <1% 13 2%

Recreation 2 <1% 4 1% 7 1% 2 <1% 15 2%

Printing and Publishing 0 <1% 1 <1% 2 <1% 1 <1% 4 1%

Consumer Goods 3 <1% 2 <1% 1 <1% 2 <1% 8 1%

Apparel 3 <1% 1 <1% 1 <1% 2 <1% 7 1%

Healthcare & Medical Products 15 2% 11 2% 15 2% 17 2% 58 8%

Construction & Materials 1 <1% 0 0% 2 <1% 1 <1% 4 1%

Steel Works 2 <1% 1 <1% 1 <1% 1 <1% 5 1%

Fabricated Products & Machinery 10 1% 5 1% 6 1% 11 2% 32 4%

Electrical Equipment 1 <1% 0 0% 3 <1% 0 0% 4 1%

Automobiles & Trucks 0 0% 2 <1% 2 <1% 2 <1% 6 1%

Petroleum & Natural Gas 10 1% 5 1% 5 1% 4 1% 24 3%

Utilities 8 1% 3 <1% 4 1% 6 1% 21 3%

Communications 12 2% 7 1% 6 1% 7 1% 32 4%

Personal & Business Services 116 16% 67 9% 94 13% 99 14% 376 52%

Business Equipment 3 <1% 0 0% 1 <1% 2 <1% 6 1%

Computer Software 33 5% 21 3% 25 3% 25 3% 104 14%

Total 224 31% 132 19% 180 25% 183 25% 719 100%

Table 2

Distribution of Restatement Sample by Industry and Announcement Quarter

Total

a
Industries are defined by SIC code based on a modified  version of Fama and French's classification for 30 

industries.  

1 432

Announcement Quarter



56 

 

 
 

The number of firms that engaged a Big N (BIGN) auditor is substantially higher than those 
that did not, 89 percent compared to 11 percent.   This representation remains consistent across both 
sub-samples.  In the sub-sample of no earnings release announcements, however, the returns are more 

Market-adjusted CAR %
a

0, 1 -1, 0, 1 -1, 0, 1, 2 -1, 0, 1, 3

Mean -4.50 -5.16 -5.42 -5.59

Standard deviation 0.14 0.15 0.15 0.16

t -statistic
c

(-8.77) (-9.54) (-9.42) (-9.64)

First quartile -7.69 -8.85 -9.73 -9.92

Median -1.60 -2.11 -2.74 -2.76

Third quartile 1.62 1.49 1.47 1.47

Panel B: Restatements announced in earnings releases
d

Mean -3.00 -3.71 -4.02 -4.04

Standard deviation 0.12 0.13 0.15 0.15

t -statistic
c

(-3.98) (-4.65) (-4.46) (-4.47)

First quartile -6.10 -8.12 -9.73 -8.08

Median -0.67 -1.30 -1.85 -2.41

Third quartile 3.13 2.85 2.78 3.04

Mean -5.43 -6.04 -6.28 -6.54

Standard deviation 0.14 0.15 0.16 0.16

t -statistic
c

(-7.93) (-8.40) (-8.44) (-8.71)

First quartile -8.44 -9.03 -9.58 -10.30

Median -2.12 -2.59 -3.02 -2.99

Third quartile 0.87 1.02 1.37 1.25

c
Null hypothesis for each window is CAR = 0.  T -tests are two-tailed.

d
Subset of 273 restatements announced in earnings releases.

e
Subset of 446 restatements announced separately from earnings releases.

Panel C: Restatements not announced in earnings releases
e

Panel A: All Restatements
b

Table 3

Event window surrounding announcement day 0

b
Announcements of 719 restatements to correct misstatements of annual and quarterly 

financial reports announced between 1997 and 2004. 

Cumulative Abnormal Returns

a
Market-adjusted CAR calculated using equally weighted index (Palmrose et al. 2004).  

Calculated as the summation of firm i's daily return less CRSP market index return over 

each of the four event windows. 



57 

 

negative for Big N firms (-5.6 percent compared to -3.7 percent), but not significantly so (t-statistic = 
0.86).  The reverse relationship exists for the sub-sample of earnings release announcements, wherein 
less negative returns are associated with Big N firms (-1.8 percent compared to -3.1 percent).   Of the 
719 firms, 154 (21 percent) changed auditors between the latest restated period and the restatement 
announcement date.  Both auditor changes and non-changes are associated with negative returns 
across the primary sample and sub-samples.  CAR are not significantly different between groups in 
the full sample or either sub-sample.   
     
Multivariate Results 
 Results of hypothesis one that tests the impact earnings quality has on the market reaction to 
restatement announcements and examines the earnings quality proxy within the context of the 
multivariate model are presented in Table 5. Hypothesis one results for the earnings quality proxy 
(DDEQ) are presented and discussed below.   
 Results for the OLS regression for the full sample indicate the model is highly significant (F-
statistic = 7.65, adjusted R2 = 11%).  Earnings quality is significantly negatively related to restatement 
announcement market reactions (t-statistic = -2.57, p value < .01) in the full sample of restatement 
announcements.  This result holds in the sub-sample of restatements announced separately from 
earnings releases, (t-statistic = -1.84, p value < .05) and in the sub-sample announced concurrent with 
earnings announcements (t-statistic = -1.89, p value < .05).     

To summarize, the above results for the DDEQ measure of earnings quality support 
hypothesis one in the context of both the full restatement sample and the sub-samples announced 
both separately from and with earnings releases.  In each of these samples, DDEQ is significantly 
negatively related to the market reaction to restatement announcements.  This finding provides strong 
support for hypothesis one and the contention that investors are, to some degree, impounding 
earnings quality information into stock prices.    

In addition to the primary finding for hypothesis one, there are numerous results of note for 
the control variables included in the PRS (2004) model.  Not surprisingly, FRAUD is significantly 
negatively related to the market reaction across the primary and both sub-samples of restatement 
announcements (t-statistics -3.28, -1.78, and -2.91, p values < .01, .05 and .01 respectively).   Similarly, 
restatements which impact core operating accounts (CORE) are also significantly negatively related to 
the market reaction (t-statistics -2.86, -3.25, and -1.64, p values < .01, .01, and .05, respectively).  The 
negative effect the pervasiveness (PERVAS) of the restatement has on the market reaction is driven 
by the sub-sample of restatements not announced in earnings release (t-statistic -6.95, p-value .01).  
The finding that pervasiveness is not significantly negatively related to the market reaction in the sub-
sample of announcements included with earnings releases may be due in part to more complex 
restatements warranting separate announcements.   

The additional variables added to the PRS (2004) model, BIGN and AUDCHANGE, are only 
marginally significant in the sub-samples.  BIGN is negatively related to the market reaction to 
restatement announcements in only the sample of restatements not announced in earnings releases (t-
statistic -1.38, p-value < .10). This finding is not surprising given that 89% of both the full and each 
sub-sample were audited by large firms.  Similarly, AUDCHANGE is only moderately significant in 
the sub-sample of firms that announced restatements in earnings releases (t-statistic -1.31, p-value .10).   

 
 



58 

 

 
 

#

Percent 

or Mean

CAR
e 

Days 

(0,1) #

Percent 

or Mean

CAR
e 

Days 

(0,1) #

Percent 

or Mean

CAR
e 

Days (0,1)

719 273 446

Control Variables

  Fraud 84 12% -0.111 23 8% -0.074 61 14% -0.124

  No Fraud 635 88% -0.036 250 92% -0.026 385 86% -0.043

  (t- statistic) (-2.83) *** (-1.25) (-2.45) **

  (z- statistic) (-3.30) ** (-1.30) (-3.01) ***

  Attribution
f

     AuditorAuditor 97 13% -0.104 30 11% -0.079 67 15% -0.109

     ManagementManagement 357 50% -0.049 143 52% -0.039 214 48% -0.055

     SECSEC 72 10% -0.028 29 11% -0.026 43 10% -0.030

     NoneUnattributed 164 23% -0.018 66 24% 0.003 98 22% -0.032

     Auditor & MgmtAuditor & Mgmt 25 3% -0.018 4 1% 0.067 21 5% -0.034

     Auditor & SECAuditor & SEC 1 <1% 0.009 1 <1% 0.009 0 0% 0.000

     Mgmt & SECMgmt & SEC 3 <1% 0.043 0 0% 0.000 3 <1% 0.043

  (F- statistic) (4.39) *** (2.53) ** (3.14) ***

  (χ
2
) (19.56) *** (15.89) *** (8.25)

 Core earnings 465 65% -0.065 182 67% -0.049 283 63% -0.075

  Non-core earnings 254 35% -0.009 91 33% 0.007 163 37% -0.019

  (t- statistic) (6.38) *** (4.30) *** (4.91) ***

  (z- statistic) (4.74) *** (3.06) *** (3.79) ***

  Materiality

Overall Mean -0.040 -0.001 -0.065

Quintile Means 144 -0.263 -0.090 54 -0.105 -0.062 89 -0.361 -0.102

144 -0.023 -0.085 55 -0.017 -0.051 89 -0.027 -0.115

143 -0.006 -0.025 55 -0.005 -0.030 90 -0.007 -0.028

144 -0.001 -0.024 55 -0.001 -0.006 89 -0.001 -0.023

144 0.081 -0.001 54 0.104 -0.001 89 0.069 -0.003

  (F- statistic) (13.26) *** (2.55) ** (12.04) ***

  (χ
2
) (56.11) *** (11.42) ** (49.04) ***

  Pervasiveness

1 541 75% -0.032 210 77% -0.026 332 74% -0.036

2 112 16% -0.056 43 16% -0.048 69 15% -0.061

3 36 5% -0.086 11 4% -0.006 25 6% -0.121

4 18 3% -0.128 6 2% -0.101 12 3% -0.141

5 6 1% -0.154 1 0% -0.004 5 1% -0.184

6 5 1% -0.463 2 1% -0.041 3 1% -0.745

7 0 0% 0.000 0% 0.000 0 0% 0.000

  (F- statistic) (14.19) *** (0.72) (21.55) ***

  (χ
2
) (23.96) *** (4.12) (24.46) ***

  Years

Mean 1.42 1.15 1.58

Median 1.00 0.75 1.00

Table continued on the next page

Table 4

All Restatements
b

Restatements announced in 

earnings releases
c

Restatements not announced 

in earnings releases
d

Descriptive Statistics for Control Variables
a



59 

 

 
 

#

Percent 

or Mean

CAR
e 

Days 

(0,1) #

Percent 

or Mean

CAR
e 

Days 

(0,1) #

Percent 

or Mean

CAR
e 

Days (0,1)

719 273 446

  SizeMat

Mean -0.07 -0.013 -0.105

Median -0.012 -0.001 -0.016

  LevMat

Mean -0.022 -0.013 -0.035

Median -0.001 -0.001 -0.001

 Prior Returns

Mean -0.043 -0.027 -0.052

Median -0.001 -0.001 -0.001

  BigN

BigN 641 89% -0.046 243 89% -0.018 398 89% -0.056

Non-BigN 78 11% -0.029 30 11% -0.031 48 11% -0.037

(t- statistic) (1.03) (0.55) (0.86)

(z- statistic) (1.58) (1.14) (1.11)

  Auditor Change

Changed 154 21% -0.053 49 18% -0.048 105 24% -0.056

No Change 565 79% -0.043 224 82% -0.026 341 76% -0.054

(t- statistic) (0.84) (0.93) (0.88)

(z- statistic) (0.93) (0.23) (0.81)

f
Instances of restatements attributed to multiple parties in the restatement announcement were coded as such.

a
Control variables defined as follows (in table order).  Fraud: SEC issued an accounting and auditing enforcement release (AAER). Core 

earnings: Restatements involving revenue, cost of goods sold, or on-going operations.  Non-core earnings:  Restatements involving transitory 

items such as merger accounting, tax accounting, non-operating gains or losses and others.  Attribution Auditor, Management, SEC or combination 

of multiple entities:  Restatement attributed to specific entity in press release, baseline case is Unattributed.   Materiality : Restated income (loss) 

less originally reported net income over restated period scaled by book value of total assets at the year end prior to the earliest restated period.   

Pervasiveness:  Number of account groups involved in restatement. The seven account groups are revenue, cost of sales, operating expenses, one-

time/special items, merger-related, non-operating expenses and other.  The sub-account groups used in these classifications are defined in Appendix 

X. Years:  Sum of periods restated where a fiscal year = 1 and a fiscal quarter = 0.25. SizeMat : Interaction between materiality variable and the 

natural log of the book 

e
Cumulative abnormal returns (CAR) calculated using market adjusted model and equally weighted index on days 0 and 1 where day 0 is the date of 

the restatement announcement.

b
Announcements of 719 restatements to correct misstatements of annual and quarterly financial reports announced between 1997 and 2004. 

c
Subset of 273 restatements announced in earnings releases.

d
Subset of 446 restatements announced separately from earnings releases.

value of total assets in at year-end in the year prior to the earliest restated period.  LevMat : Interaction between materiality variable and ratio of 

book value of long term debt to book value of total assets at year-end in the year prior to the earliest restated period.  Prior Returns :  Buy and hold 

returns over last 120 prior to restatement announcement.  BigN:  Indicator variable equal to 1 if restating firm engaged a BigN auditor during the 

period of restatement and 0 otherwise.  Auditor Change :  Indicator variable if restating firm changed auditors from the restated period up the 

period ending with the announcement date. 

*,**,*** Difference across quintiles is significant at 0.10, 0.05, 0.01 levels respectively.  T -tests are two-tailed.  Non-parametric results are based 

on the Mann-Whitney z-statistic (two group comparisons) or the Kruskal-Wallis χ2  (quintile and multiple group comparisons).

Descriptive Statistics for Control Variables
a

Table 4 (continued)

All Restatements
b

Restatements announced in 

earnings releases
c

Restatements not announced 

in earnings releases
d



60 

 

 
 

Limitations and Conclusion 
 As is the case with many studies employing various earnings quality metrics, there is a potential 
survivorship bias related to the number of years of data required to estimate earnings quality.  The 
final sample is likely skewed towards larger, more successful firms. In addition to possible survivorship 
bias, while the accounting literature includes numerous definitions of earnings quality, the primary 
earnings quality metric (DDEQ) used in this study estimates earnings quality based on the relationship 
between accruals and cash flows.  Signals regarding earnings quality are not limited to the information 

Independent variables

Expected 

Sign

Coeff. t -stat Coeff. t -stat Coeff. t -stat

Panel A: DDEQ

Earnings Quality

DDEQ - -0.05 -2.57 *** -0.061 -1.89 ** -0.045 -1.84 **

Control Variables
f

Fraud - -0.050 -3.28 *** -0.049 -1.78 ** -0.055 -2.91 ***

Auditor attribution - -0.031 -2.17 ** -0.036 -1.46 * -0.022 -1.23

Management attribution + 0.001 0.01 -0.029 -1.67 ** 0.182 1.23

SEC attribution - 0.018 1.00 -0.036 -1.32 0.052 2.25 **

Core - -0.03 -2.86 *** -0.053 -3.25 *** -0.023 -1.64 **

Materiality + 0.206 1.67 ** 0.348 0.65 0.223 1.68 **

Pervasiveness - -0.034 -5.75 *** -0.001 -0.12 -0.051 -6.95 ***

Years - 0.006 1.36 * 0.010 1.29 * 0.004 0.82

SizeMat - -0.106 -1.49 * -0.179 -0.52 -0.115 -1.52 *

LevMat - -0.061 -1.71 ** -3.424 -0.75 -0.067 -1.69 **

120 Day return + 0.026 2.06 ** 0.028 1.41 * 0.025 1.53 *

BigN - -0.018 -1.16 -0.004 -0.15 -0.029 -1.38 *

Auditor change + -0.018 -0.14 -0.027 -1.31 * 0.013 0.79

Intercept 0.034 1.77 0.023 0.78 0.025 1.65 **

Model Statistics

n 719 273 446

Adjusted R
2

11% 5% 16%

F -Statistic 7.65 *** 2.09 ** 7.25 ***

*,**,*** Coefficient or model is significant at 0.10, 0.05, 0.01 levels repectively.  Results are one-tailed.

f
Control variables defined in Table 4.

b
Cummulative abnormal returns (CAR) calculated using market adjusted model and equally weighted index on days 0 and 1 

where day 0 is the date of the restatement announcement.
c
Announcements of 719 restatements to correct misstatements of annual and quarterly financial reports.  Restatements 

announced between 1997 and 2004. 
d
Subset of 273 restatements announced in earnings releases.

e
Subset of 446 restatements announced separately from earnings releases.

All sample 

restatements
c

Restatements 

announced in 

earnings releases
d

Restatements not 

announced in 

earnings releases
e

Table 5

OLS regression results for Hypothesis 1 for all sample restatements and subsets that were/were not 

announced in earnings releases 

a
Earnings quality variable defined as follows:  DDEQ  = standard deviation of firm j 's residuals, from year t -4 to year t  from 

annual cross sectional estimations of the modifed Dechow-Dichev (2002) model.  Variable is multiplied by negative one (-1).

  CAR = β0 – β1EQ – β2FRAUD ± β3ATTRIB – β4CORE + β5MAT – β6PERVAS – β7YEARS – 

β8SIZEMAT – β9LEVMAT + β10PRIORRET – β11BIGN+ β12AUDCHANGE 



61 

 

conveyed by accruals, however.   Market participants likely extract earnings quality data from a variety 
of sources that are outside the scope of this study.    
  One primary motivation for undertaking this research was to provide additional insights into 
market participants’ abilities to discern and subsequently price the implications of earnings quality.  
Unlike prior studies that have relied on associating earnings quality proxies with future returns and 
cash flows, this study associates earnings quality with returns following an observable signal that 
earnings are of poor-quality - accounting restatements.  This study examined the association between 
an accruals earnings quality proxy and the market reaction to restatement announcements.  Results 
indicate that earnings quality is negatively related to investors’ reactions to announcements of 
restatements. Associating pre-restatement earnings quality with the market reaction to restatement 
announcements provides more demonstrative evidence that market participants are extracting 
insightful information regarding firms’ future performance from earnings quality indicators and 
subsequently pricing that information.  As such, this research helps elucidate our understanding of 
investors’ use of key accounting information, specifically the information contained in accruals. 

 
 
 

References 
Abbott, L .J., Parker, S., & Peters, G. F. (2004). Audit committee characteristics and restatements.  

Auditing: A Journal of Practice and Theory, 23(1): 69-87. 
Aboody, D., Hughes, J., & Liu, J. (2005). Earnings quality, insider trading, and cost of capital. Journal 

of Accounting Research, 43(5): 651-673. 
Anderson, K. L., & Yohn, T. L. (2002). The effect of 10-K restatements on firm value, information 

asymmetries, and investors' reliance on earnings. Working Paper. Georgetown University.  
Baber, W.R., Chen, S., & Kang, S. (2006). Stock price reaction to evidence of earnings management: 

Implications for supplementary financial disclosure. Review of Accounting Studies, 11: 5-19. 
Balsam, S., Bartov, E., & Marquardt, C. (2002). Accruals management, investor sophistication, and 

equity valuation: Evidence from 10-Q filings. Journal of Accounting Research,  40(4): 987-1012. 
Blankley, A.I., Hurtt, D.N., & MacGregor, J.E. (2012). Abnormal audit fees and restatements.  

Auditing: A Journal of Practice and Theory, 31(1): 79-96. 
Bradshaw, M. T., Richardson, S. A., & Sloan, R. G. (2001). Do analysts and auditors use information 

in accruals? Journal of Accounting Research, 39(1): 45-74. 
Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The 

Accounting Review, 70(2): 193-225. 
Dechow, P.M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings 

manipulations: An analysis of firms subject to enforcement actions by the SEC. Contemporary 
Accounting Research, 13(1): 

Dechow, P. M., & Dichev, I. D. (2002). The quality of accruals and earnings: The role of accrual 
estimation errors. The Accounting Review, 77 (Supplement): 35-59. 

Dechow, P. M., & Schrand, C. M. (2004).The Research Foundation of CFA Institute. Earnings 
Quality.  

DeFond, M. L., & Francis, J. R. (2005). Audit research after Sarbanes-Oxley. Auditing: A Journal of 
Practice and Theory, 24 (Supplement): 5-30. 

DeFond, M. L., & Park, C. W. (2001). The reversal of abnormal accruals and the market valuation of 
earnings surprises. The Accounting Review, 76(3): 375-404. 

Du. L. (2017). The CDS market reaction to restatement announcements. Journal of Business, Finance & 
Accounting, (7-8): 1015-1035. 

Francis, J. R., Maydew, E. L., & Sparks, H. C. (1999). The role of big 6 auditors in the credible 
reporting of accruals. Auditing: A Journal of Practice and Theory, 18(2): 17-34. 



62 

 

Francis, J., LaFond, R., Olsson, P., & Schipper, K. (2005). The market pricing of accruals quality.  
Journal of Accounting and Economics, 39: 295-327. 

Francis, J.R., Michas, P.N., & Yu, M.D. (2013). Office size of big 4 auditors and client restatements. 
Contemporary Accounting Research, 30(4): 1626-1661. 

General Accounting Office. (2002). Financial statement restatements - Trends, market impacts, 
regulatory responses and remaining challenges. 

General Accounting Office. (2006). Financial restatements – Update of public company trends, 
market impacts, and regulatory enforcement activities.  

Gleason, C. A., Jenkins, N. T., & Johnson, W. B. (2004). Financial statement credibility: The 
contagion effects of accounting restatements. Working Paper. University of Iowa.  

Gordon, E.A., Henry, E., Peytcheva, M. & Sun, L. (2013). Discretionary disclosure and the market 
reaction to restatements.  Review of Quantitative Finance and Accounting, 41(1): 75-110. 

Hanlon, M. (2005). The persistence and pricing of earnings, accruals, and cash flows when firms 
have large book-tax differences. The Accounting Review, 80(1): 137-166. 

 
He, L. & Chiang, H. (2013). Market reaction to financial statement restatement: A study on the 

information and insurance role of auditors. Advances in Management and Applied Economics, (3-
4): 37-50. 

Hribar, P., & Jenkins, N. T. (2004). The effect of accounting restatements on earnings revisions and 
the estimated cost of capital. Review of Accounting Studies, 9(2-3): 337-356. 

Huron Consulting Group, LLC. (2003). 2003 Annual Review of Financial Reporting Matters. 
Huron Consulting Group, LLC. (2004). 2004 Annual Review of Financial Reporting Matters. 
Jones, J. (1991). Earnings management during import relief investigations. Journal of Accounting 

Research, 29(2): 193-228. 
Jones, C. L., & Weingram S. E. (1997). The effects of insider trading, seasoned equity offerings, 

corporate announcements, accounting restatements and SEC enforcement actions on 10b-5 
litigation risk. Working Paper. Stanford University.  

Kalelkar, R., & Nwaeze E.T. (2011). Sarbanes-Oxley Act and the quality of earnings and accruals: 
Market based evidence.  Journal of Accounting and Public Policy, 30: 275-294. 

Kinney, W. R., Palmrose, Z-V., & Scholz, S. (2004). Auditor independence, non-audit services, and 
restatements: Was the U.S. government right? Journal of Accounting Research, 42(3): 561-588. 

Lazer, R., Livnat, J., & Tan, C. E. L. (2004). Restatements and accruals after auditor changes. 
Working Paper.  New York University.  

Palmrose, Z.-V., Richardson, V. J., & Scholz, S. (2004). Determinants of market reactions to 
restatement announcements. Journal of Accounting and Economics, 37: 59-89. 

Palmrose, Z-V., & Scholz, S. (2004). The circumstances and legal consequences of non-GAAP 
reporting: Evidence from restatements. Contemporary Accounting Research, 21(1): 139-180. 

Perotti, P. & Wagenhofer, A. (2014). Earnings quality measures and excess returns. Journal of Business, 
Finance & Accounting, 41 (5): 545-571. 

Reilly, D. (2007). Restatements still bedevil firms. The Wall Street Journal (February 12): C7. 
Richardson, S., Tuna, I., & Wu, M. (2002). Predicting earnings management: The case of earnings 

restatements. Working Paper. University of Pennsylvania.  
Salavei, K., & Moore, N. (2005). Signals sent by financial statement restatements. Working Paper. 

University of Connecticut.  
Schipper, K., & Vincent, L. (2003). Earnings quality. Accounting Horizons, 17(Supplement): 97-110. 
Scholz, S. (2014). Financial statement restatement trends 2003 – 2012.  The Center for Audit 

Quality. https://www.thecaq.org/financial-restatement-trends-united-states-2003-2012 
Sloan, R. G. (1996). Do stock prices fully reflect information in accruals and cash flows about future 

earnings? The Accounting Review, 71(3): 289-315. 



63 

Subramanyam, K. R. (1996). The pricing of discretionary accruals. Journal of Accounting and Economics, 
22 (1-3): 249-281.  

Xie, H. (2001). The mispricing of abnormal accruals. The Accounting Review, 76(3): 357-373. 
Xu, T., Najand M., & Ziegenfuss, D. (2006).  Intra-industry effects of earnings restatements due to 

accounting irregularities.  Journal of Business, Finance and Accounting, 33(5): 696-714. 




