Earnings Quality
in
Restating Firms:
Empirical Evidence
Abstract
The objective of this paper is to compare the earnings quality in
US rms required to restate their nancial statements with the quality
of a matched control group.
More specically, we test whether the earnings quality diers in
the ten years before the (last) restatement event for the two groups.
To examine if the scrutiny of the Securities and Exchange Commission (SEC) improves the nancial reporting of restating rms, we also
test whether the quality diers between the two groups after the last
restatement.
Using a wide portfolio of accounting quality metrics, we predict and
nd that the earnings quality of restating rms is poorer than that of
the control group, already in the years before the restatement. Using a
dierence-in-dierence research design, we also nd that the restating
rms improve the quality of their nancials statements, but surprisingly not signicantly more than the control group. It is therefore not
possible to attribute the improvement to the restatement event alone.
Marie Herly*, Jan Bartholdy & Frank Thinggaard
Aarhus University
Department of Economics and Business
Fuglesangs Alle 4, 8210 Aarhus V
Denmark
[email protected]
0045 4716 5396
* Corresponding Author
1
Introduction
This paper examines earnings quality in rms before and after they restate
their nancial statements.
So far, research within the earnings quality of restating rms has mainly
focused on measuring the quality in the restatement year only, and has made
no inferences on the quality before and after. Thus, little is known on how
restating rms behave before and after they are detected by the Securities
and Exchange Commission (SEC).
This gap is problematic for at least two reasons. First, if earnings quality
is dierent for restating and non-restating rms even before the actual restatement, then SEC may begin the surveillance and scrutiny of these rms
even earlier.
Investors can also determine which rms that are likely to
restate based on the earnings quality of these rms. Second, given the enormous eorts SEC puts into detecting restaters, it is certainly in the interest
of SEC, regulators, and investors to examine if the rms actually improve
afterwards. This can also shed light on the educative role of SEC.
Previous research shows that rms that restate do have lower earnings
quality in the restatement year.
Some studies have found that restaters
have poor corporate governance, high growth, and extreme values of specic
accounting fundamentals in the years before the restatement event. It is also
evident that the nancial markets react very negatively to restatements, and
that management and auditor turnover increases after a restatement.
However, to my knowledge no studies examine the earnings quality before
and after a restatement event. An examination of this will deepen our understanding of restating rms and how they generically dier from non-restating
ones.
The purpose of this paper is twofold: First, a broad portfolio of earnings quality metrics will be thoroughly described, and their advantages and drawbacks
will be discussed. Second, these earnings quality metrics are used to examine
the properties of rms with restated nancial statements and a matched control group. Thus, the paper is a joint test of the earnings quality of restating
rms before and after the restatement event, and whether the accounting
quality proxies actually work.
The paper outlines the following research questions:
Research Question 1. Do restating rms have poor accounting quality
compared to non-restating rms prior to a restatement?
Research Question 2. Do restating rms have poor accounting quality
compared to non-restating rms after a restatement?
Research Question 3. Does a restatement event change accounting quality
for restaters and non-restaters, respectively?
1
The restating rms are identied through the Securities and Exchange
Commission (SEC), and the sample contains prominent cases of earnings
management, such as Xerox, Bristol-Myers Squibb, and QWest. We use a
matched sample research design in which we match each restating rm with a
control rm with similar size and protability, and within a similar industry.
We rst test the earnings quality for both the treatment and the control
group both before and after a restatement event, and then test the relative
change in earnings quality for the two groups with a dierence-in-dierence
design.
The remainder of this paper is structured as follows: Section 2 is a thorough
literature review on the determinants and consequences of accounting quality.
The next section continues with a description of the most commonly used
metrics for measuring accounting quality. Section 4 gives an overview of the
literature within restatements, the sources available for identifying them,
and potential pitfalls in research designs.In Section 5 my three hypotheses
are then developed based on the previous sections, and Section 6 describes
the research design and data. Empirical results are presented in Section 7,
followed by robustness tests and conclusion, as well as suggestions for further
research.
2
Accounting Quality
The notions of accounting and earnings quality have been used both synonymously and as two dierent concepts.
Melumad and Nissim (2008) dene
accounting quality as a generalised view of earnings quality which evaluates
the impact of accounting choices.
This view is shared with Francis et al. (2006), who note that nancial reporting quality is a special case of information quality and dene earnings
quality as a summary indicator of nancial reporting quality. The authors
caution against focusing only on earnings when evaluating nancial reporting quality, since a lack of focus on for example balance sheet information
will mask true dierences in accounting quality.
In the following, accounting quality and earnings quality will be used
interchangeably.
2.1 Denition
Many denitions of accounting quality exist in the literature. As such, earnings quality is a function of both the ability of the accounting system to
measure the rm's fundamental performance and how the accounting system
is implemented, both of which are unobservable. The task of disentangling
these is therefore challenging to say the least. DeFond (2010) in consequence
2
suggests that the inability to directly observe the two constructs of interest
is unlikely to be solved by the literature.
Former chairman of SEC, Arthur Levitt, mentions comparability and
transparency as two main attributes of high quality nancial reporting.
Barth and Schipper (2008) and Bhattacharya et al. (2003) also propose transparency as a desired attribute of high quality earnings.
The notion of decision usefulness as an indicator of high earnings quality is widely accepted and has been used by a number of researchers, e.g.,
Abdelghany (2005), Ball and Shivakumar (2005), and Dechow et al. (2009).
The latter dene earnings quality broadly as decision usefulness, but they
also stress the notion of faithful representation for accounting quality.
Dechow and Schrand (2004) dene earnings quality broader than decision
usefulness.
They view the denition of high quality earnings as threefold:
rst, the reported earnings number should reect current performance, second, it should be a good indicator of future operating performance and,
nally it should accurately annuitise the intrinsic value of the company.
A branch of research sees precision as the main attribute of high quality earnings, implying that earnings should accurately reect the underlying
reality of the rm.
These include for example Francis et al. (2006).
This
denition corresponds well to the qualitative characteristic faithful representation dened by IASB (IASB CF Ÿ33).
Visvanathan (2006) uses the notion of closeness-to-cash as a desirable
property of earnings. Thus earnings that are closer to cash ows, i.e. earnings that contain relatively small amounts of accruals, are of higher quality.
Hence, this view is closely related to for example Dechow et al. (2009) and
Francis et al. (2006).
Conservatism, in the meaning of prudence, has also been put forward as a
characteristic of accounting quality (Basu, 1997). This implies that caution
is exercised when estimation assets and income, and liabilities and expenses,
such that the former are not overstated, and the latter are not understated.
Whether unconditional conservatism increases or decreases decision usefulness is an unresolved issue
1
Barth et al. (2008) dene high quality earnings as those that exhibit less
earnings management, implying that quality is not an innate characteristic,
but rather the absence of manipulation and bias. This corresponds well to
the discussion of Guay et al. (1996) who argue that managerial opportunism
reduces information precision and accounting quality.
A brief review of some of the denitions is shown in Table 1.
As Melumad and Nissim (2008) note, some of the attributes of earnings qual-
1
Distinction between
conditional conservatism (more timely recognition of bad news
unconditional conservatism (policy that results in lower
than of good news in earnings) and
book values of assets/higher book values of liabilities in the early periods of asset/liability
life time).
3
Table 1: Denitions of Accounting Quality
Quality Construct
Selected References
Decision usefulness
Ball and Shivakumar (2005), Schipper and Vin-
Closeness-to-cash
Visvanathan (2006)
Comparability
IASB (2006)
Faithful representation
Francis et al. (2006), EU (1978), IASB (2006)
Persistence
Schipper
Precision
Francis et al. (2006)
Prudence
Basu (1997), Beekes et al. (2004), Watts (2003)
Relevance
IASB (2006)
cent (2003)
and
Vincent
(2003),
Dechow
and
Dichev (2002), Comiskey and Mulford (2000)
Transparency
Levitt (1998a), Bhattacharya et al. (2003)
Understandability
IASB (2006)
Valuation input
Dechow and Schrand (2004), Melumad and Nissim (2008)
ity have contradictory implications. As an example, earnings that are close
to the underlying cash ows are not necessarily predictable or accurately
reecting future performance. Schipper and Vincent (2003) describe a possible contradiction between the persistence and predictive ability of earnings:
Highly persistent earnings will have low predictive ability if the variance of a
typical to the series is large. Consequently, earnings that are of high quality
on the persistence dimension may be of low quality on the predictive ability
dimension. It also seems clear that earnings quality as a construct is contextspecic, partly because the users, to whom the denition is targeted, dier
from situation to situation (Dechow et al., 2009). In a related vein, Schipper
and Vincent (2003) argue that earnings quality diers according to the users
of nancial statements; thus, standard setters and managers with compensation contracts tied to the earnings number may have dierent perceptions
of accounting quality.
2.2 Determinants of Accounting Quality - Dependent Variable
It is widely accepted that the quality of the standards and the diligence of
regulators are important determinants of earnings quality. Soderstrom and
Sun (2007) mention the accounting standards, the tax system, and the legal
and political systems as drivers of earnings quality, and Barth et al. (2008)
suggest that regulators inuence earnings quality. Beuselinck et al. (2009)
also nd positive impacts on earnings informativeness following IFRS adoption, supporting the view that high quality standards have positive eects on
4
earnings quality. Ewert and Wagenhofer (2005) show that tighter accounting
standards increase earnings quality but can do little to restrict real earnings
management. Levitt (1998a) argue that strong accounting framework and
outside auditing will improve earnings quality.
Capital market forces also have an eect on earnings quality. Soderstrom
and Sun (2007) argue that nancial market development inuences earnings
quality, and Ball and Shivakumar (2005) show that UK public companies
have higher accounting quality than private ones, due to the market demand
for information. Burgstahler et al. (2006) examine how capital market pressures and institutional factors inuence rms' incentives to report accurate
earnings.
Some factors of the rm itself also aect the quality of its nancial statements. These are for example the capital and ownership structure (Soderstrom and Sun, 2007), the accounting methods chosen by the rm (Altamuro
et al., 2005), and rm performance (DeFond and Park, 1997). The internal
control regulation and corporate governance mechanisms of the rm also affect the accounting quality (Altamuro and Beatty, 2010).
Dechow and Schrand (2004) argue that the nature of the rm as such can
also inuence the earnings quality.
The authors suggest that high growth
companies, companies with intangible assets or complex transactions, and
companies in volatile business environments can provide earnings numbers
that do not accurately reect rm performance or indicate future cash ows.
In these cases, neither earnings management nor poor monitoring is to blame
for the low earnings quality.
Finally, managerial intent and purely discretionary decisions also inuences the quality of earnings. The case of earnings management is elaborated
in Section 2.5.
2.3 Consequences of Accounting Quality - Independent Variable
The cost of capital is a widely used proxy of market outcomes. In general,
empirical evidence suggests a negative relation between earnings quality and
the cost of equity capital.
Francis et al. (2006) view the cost of capital
as a summary indicator of investors' resource allocation decisions, which
is related to earnings quality since high quality nancial reporting should
assist users in their resource allocation decision. Easley and O'Hara (2004)
show that accounting information of high quality reduces cost of capital by
reducing information risk.
In their much cited paper, the authors extend
the capital asset pricing model to include information asymmetry, and show
that information risk is non-diversiable and thus a priced risk factor. Chen
et al. (2007) and Lambert et al. (2007) nd similar results.
Francis et al.
(2004) show that rms with the least favourable values of seven attributes
of earnings quality generally experience larger cost of equity. Extending this
5
research to the cost of debt as well, Francis et al. (2005) show that rms
with poor accruals quality have larger cost of debt and equity capital. They
assign this to the fact that higher magnitudes of accruals indicate higher
information risk, which demands higher cost of capital.
There is, however, not completely consensus on this issue. Thus, Cohen
(2008) suggests that rms providing nancial information of higher quality
do not necessarily enjoy a lower cost of equity. He attributes this to classical
asset pricing theory which shows that diversiable risk is not priced and
argues that information is not diversiable.
Another branch of research studies how earnings quality inuences stock
prices. Dechow and Schrand (2004) argue that they would expect no response
to low quality earnings if investors are rational in their response to earnings.
However, empirical evidence suggests that the quality of information does
inuence investors. For example, Dechow et al. (2009) report that markets
show negative reactions following a decline in earnings quality.
Francis et al. (2005) show that rms with low earnings quality exhibit
higher price-earnings ratios and equity betas. In addition, the authors nd
that innate earnings quality has larger expected returns eects than discretionary components.
Other market outcomes include the bid-ask spread, which has been used
to measure for example liquidity (Amihud and Mendelson, 1986), information asymmetry (Huang and Stoll, 1997). Research has linked high quality
earnings with reduced bid-ask spread (Francis et al., 2006).
Another branch of research is concerned with non-market outcomes; following Dechow et al. (2009), the stock price reaction to qualied audit opinions
is either negative or non-existing.
The connection between analysts' fore-
casts and earnings quality has also been examined, under the perception
that high quality earnings will yield smaller forecast errors (Ashbaugh and
Pincus, 2001). Francis et al. (2005) show that poor earnings quality results
in worse credit ratings.
2.4 Earnings Management
Earnings management literature is closely related to earnings quality literature since it is clear that earnings management decreases earnings quality
(Dechow and Schrand, 2004). By denition, earnings management induces
an intentional bias in nancial reports (Melumad and Nissim, 2008).
Numerous denitions of earnings management exist, and most circle
around the use of discretion in accounting to achieve a specic goal. Former
SEC chairman, Arthur Levitt describe it as the grey area between legitimacy and outright fraud (...) where earnings reports reect the desires of
management rather than the underlying nancial performance of the company (Levitt, 1998b).
Healy and Wahlen (1999, p.
6
368) dene earnings
management as: ..when managers use judgment in nancial reporting and
in structuring transactions to alter nancial reports to either mislead some
stakeholders about the underlying economic performance of the company
or to inuence contractual outcomes that depend on reported accounting
numbers.
Financial disclosures can be managed in several ways, for example by selecting accounting methods within GAAP or by applying methods in a way
that supports the picture management wishes to display to users (Schipper, 1989). Jiambalvo (1996) describes two ways of manipulating earnings,
real decision and pure accounting decisions.
The rst could be delays or
accelerations of sales or sale of xed assets to aect gains and losses. The
second includes for example changes in accounting principles and changes of
estimate of residual value of xed assets.
The line between earnings management and fraud is ne, and earnings
management does not necessarily equal fraud. According to Levitt (1998b),
earnings management is located in the gap between legitimate accounting
and outright fraud, whereas Melumad and Nissim (2008) term fraud as
an extreme case of earnings management. Some cases of earnings management clearly violate GAAP, whereas other cases are within the borders of
GAAP. Following Dechow and Skinner (2000, p. 238), (...) while nancialreporting choices that clearly violate GAAP can clearly constitute of both
fraud and earnings management, it also seems as if systematic choices made
within GAAP can constitute earnings management. The authors suggest
that within-GAAP choices can be considered earnings management if they
are used to obscure or mask true economic performance. Nelson et al. (2003)
categorise earnings management in three categories: earnings management
consistent with GAAP, earnings management dicult to distinguish from
GAAP, and earnings management clearly violating GAAP.
Dechow and Skinner (2000) observe that although numerous measures of
earnings management have been devised by researchers, none of them have
been very powerful in identifying and predicting the managing of earnings
in practise.
Leuz et al. (2003) acknowledge that earnings management is
dicult to measure because it manifests itself in very dierent forms.
2.4.1 Incentives to Manage Earnings
One group of incentives is capital market expectations and valuation, a group
in which rms manage earnings to inuence short-term stock price performance (Healy and Wahlen, 1999). Examples of this are increasing the share
price prior to seasoned equity oerings or decreasing the stock price before
management buyout (Dechow and Schrand (2004); Ecker et al. (2006)).
Often, managers attempt to report prots (Burgstahler and Dichev,
1997) or sustain recent performance and meet analysts' expectations (Degeorge et al., 1999). However, the opposite could also be true, illustrated for
7
example by Jones (1991), who proves that rms manage earnings to decrease
reported income prior to import relief investigations. Hence, rms can have
incentives to manage earnings both upwards and downwards.
Dechow and Schrand (2004) suggest that earnings management especially occurs after a period of high growth and increasing performance in
booming economies. When the economy slows down, managers nd it dicult to meet the expectations set during the boom and a decline in earnings
can compel managers to use aggressive accounting, earnings management or
even fraud. This view is supported by Richardson et al. (2002), who note
that a history of previously reported positive earnings forces management to
manage earnings, as they are unwilling to break a string of positive earnings.
Dechow and Schrand (2004) also note that managers may create some of the
problems themselves by guiding analysts about future results and thus creating unrealistic expectations. In addition to this, they also suggest that the
corporate culture as such can aect the likelihood of earnings management.
Another group of incentives is contracts written in terms of accounting
numbers, such as lending and compensation contracts. These include avoiding debt covenants (Abdelghany (2005); Ecker et al. (2006)), bonus plans
and compensation packages (Dechow and Schrand (2004); Richardson et al.
(2002)).
Healy and Wahlen (1999) also outline regulatory motivations. These can
for example be industry-specic regulations for nancial institutions and
utilities, or anti-trust regulation (Ecker et al., 2006).
The standards also inuence the degree of earnings management, and
even though tighter standards, that is, standards that leave less room for
discretion, should impede earnings management, the opposite could also be
true. According to Ewert and Wagenhofer (2005), tighter accounting standards may lead to a substitution eect, so that accounting earnings management is met with real earnings management.
Obviously, some of the incentives are interrelated in some way, e.g., management could wish to aect share prices and thus increase the value of their
own bonus plans, which is often attached to the market price of the rm's
stock.
2.4.2 Consequences of Earnings Management
Following Melumad and Nissim (2008) there are two types of costs associated
with earnings management: the costs associated with undetected earnings
management and costs following a detection of earnings management. In the
rst case, an overstatement of earnings will generally lead to an understatement of future earnings
2
2 . In the second case, costs incurred when earnings
For example, if a rm has managed earnings by understating bad debts to increase
net receivables and thereby overstate current earnings, it will most likely be forced to
write-down in the next period, resulting in a large bad debt expense and lower earnings.
8
management is detected include negative eects on reputation, stock prices,
and increased fees to auditors.
Burgstahler and Dichev (1997) note that the extent of earnings management
is likely to be a function of the ex ante costs of earnings management, such
that earnings manipulators are likely to be rms faced with relatively lower
ex ante costs of earnings management.
Following the discussion of the impact from earnings quality on cost of
capital, it seems clear that earnings management is expected to increase the
cost of capital. This is documented by Dechow et al. (1996), who attribute
the increase to investors' decreased estimates of rm value and credibility.
They also show a large, negative stock price reaction after the detection of
earnings management is made public, since investors believe that rm value
has been overstated.
According to Healy and Wahlen (1999), investors are not "fooled" by
earnings management and thus nancial statements provide useful information to users. However, Healy and Wahlen (1999) do acknowledge that some
studies reach opposite conclusions. For example, Teoh et al. (1998) show that
rms with income-increasing abnormal accruals in the year of a seasoned equity oering signicantly underperform in the following years, suggesting
that earnings management prior to equity issues aects share prices.
3
Proxies for Accounting Quality
Following the various denitions of accounting quality, the natural next step
is guring out how to measure the quality of the rm's nancial statements.
This implies the objective measuring of how decision useful the statements
are. Table 2 summarises the metrics described.
Francis et al. (2006) distinguish between two types of proxies for accounting quality: those that reect the innate factors or nature of the rm,
and those that reect the surrounding business environment and accounting choices. Innate sources are the economic fundamentals of the rm, such
as the operational environment, whereas discretionary sources include for
instance managements reporting and accounting choices, auditing and the
quality of reporting standards. The intrinsic factors are slow to change relative to the discretionary ones.
Dechow et al. (2009) separate the proxies in a slightly dierent manner,
as they distinguish between a true component of earnings, reecting the
real underlying cash ows, and an element of error induced by the accounting
choices.
As many of the proxies for quality are tailored to a specic study, the
proxies are highly context specic.
As a result, they can have somewhat
contradictory implications, even though most of them are related. Melumad
and Nissim (2008) provide the example of articially smooth earnings, which
9
Table 2: Overview of Earnings Quality Metrics
Abnormal Accruals
Models
Accruals Quality
Models
Other Accruals
Models
Avoiding Earnings
Decreases and
Small Losses
Asymmetric
Timeliness
Smoothness
The Jones Model
Large amounts of discretionary
The Modied Jones Model
accruals not explained by
The performance-Matched
accounting fundamentals indicate
Modied Jones Model
poor quality
Dechow-Dichev model
Earnings not mapping closely
Modied Dechow-Dichev
into cash ows are of low quality
Magnitude of Accruals
High levels of or changes in
Change in Accruals
accruals indicate poor quality
Avoiding Earnings Decreases
Articially avoiding small
Loss Avoidance
earnings decreases and losses
indicate low quality
Timeliness
Less timely recognition of losses
Timely Loss Recognition
implies poor quality
Variability of Earnings
Articially smooth earnings are
Correlations between Accruals
of low quality
and Cash Flows
Persistence
Persistence
Impersistent earnings indicate
low quality
Predictability
Predictability
Earnings not able to predict
themselves indicate poor quality
10
increase the persistence and predictability, but weaken the relation between
cash ows and earnings.
3.1 Abnormal Accruals Models
A large part of the accounting based metrics examines how accruals directly
inuence earnings quality. Earnings consist of both cash ows and accruals
and since accruals are discretionary and based on estimates, pure cash ows
are generally considered more reliable than earnings (Dechow, 1994). Dechow
and Dichev (2002) state that one role of accruals is to shift or adjust the
recognition of cash ows over time, so the adjusted numbers better measure
rm performance
3.
Dechow and Schrand (2004) note that accruals used correctly mitigate
irrelevant volatility in cash ows and thus improve the decision usefulness
of earnings.
Dechow and Skinner (2000) agree with this and argue that
accrual accounting as such tend to dampen the uctuations of underlying
cash ows, thereby creating a more useful earnings number, than currentperiod cash ows. In line with this view, Melumad and Nissim (2008) argue
that earnings smoothed with accruals increase earnings quality, since they
improve persistence.
Sloan (1996) shows that the accruals component of earnings is less persistent than the cash ow component.
However, this does not imply that
accruals are not decision useful. As Dechow et al. (2010) note, research has
shown that earnings are more persistent than cash ows and that earnings
produce smaller forecasting errors than cash ows in valuation models. This
suggests that accruals indeed can improve decision usefulness, even if they
have lower persistence than cash ows. This also highlights the fact that accruals are useful, although they introduce measurement error and managerial
discretion in nancial statements.
A large body of literature hypothesises that earnings are primarily misstated via the accruals component (Dechow et al., 2009). Since accruals by
nature are subjective judgements, they do open the door for opportunistic,
short-term earnings management, if accruals are used to hide value-relevant
changes in cash ows (Dechow and Schrand, 2004).
The introduction of
estimates also decreases the predictability of earnings, since they are subjective and hard to predict. Francis et al. (2006) note that several proxies of
earnings quality are based on the view that accruals, ceteris paribus, reduce
earnings quality. Richardson et al. (2002) argue that accrual information is
a key determinant of earnings manipulation and that high levels of accruals
should be seen as a red ag indicating an increased likelihood of earnings
3
Following IASC (1989, Ÿ 22), accrual accounting implies that ...the eects of transac-
tions and other events are recognised when they occur (and not as cash or its equivalent
is received or paid) and they are recorded in the accounting records and reported in the
nancial statements in the periods to which they relate."
11
manipulation.
Caution is needed when accruals are used to measure earnings quality.
Dechow and Schrand (2004) suggest that some rms can erroneously be classied as having low accruals quality and predictability, due to the nature of
the business. These are for instance rms with high growth or a large proportion of intangible assets. As mentioned by Dechow and Skinner (2000),
certain forms of earnings management, such as income smoothing, are hard
to distinguish from appropriate accrual accounting choices. Jiambalvo (1996)
shows that accrual models generate low power tests of earnings management
even for fairly high levels of earnings management.
The line between ap-
propriate exercise of managerial discretion through accruals and earnings
management is thus very ne.
DeFond (2010) argue that the abnormal accrual models all suer from the
inherent limitation that researchers are unable to determine whether the estimated discretionary component is a result of management's discretionary
accounting choices or just an artifact of the model used. Thus, every test
using abnormal accrual models is a joint test of the hypothesis tested in
that specic study and the hypothesis that the proxy is a valid measure.
Therefore it is also dicult to evaluate which accrual model is 'best'.
3.1.1 The Jones Model
The abnormal accruals models originally assumed that expected normal accruals are identical to last period's total accruals and that they consist of
both normal, non-discretionary accruals (NA) and abnormal, discretionary
accruals (DA) (DeAngelo, 1986).
This approach thus assumes that the
change in total accruals from one period to the next is due to change in
discretionary accruals.
As noted by Dechow et al. (1995), the DeAngelo model and the somewhat similar Healy model (Healy, 1985) only measure discretionary accruals
without error if non-discretionary accruals are constant and discretionary
accruals have a mean of zero over the estimation period.
The Jones Model (Jones, 1991) relaxes this rather strict assumption. The
general view in her model is that accounting fundamentals, such as revenues
or assets, should explain accruals. The objective is thus to divide accruals
into two components:
Normal, non-discretionary accruals associated with
the rm's fundamental earnings process; and abnormal, discretionary accruals which stem from intentional or unintentional accounting errors (Dechow
et al., 2010).
Higher levels of accruals which are not associated with the
fundamental earnings process of the rm are assumed to reduce the quality of earnings. The accounting fundamentals are thus determinants of unmanipulated accruals.
In order of separating normal from abnormal accruals, Jones develops a
framework in which she controls for changes in property, plant and equip-
12
ment, and revenue. Large amounts of accruals not explained by these fundamentals indicate lower earnings quality, since abnormal accruals are sensitive
to managerial discretion.
(Jones, 1991) calculated total accruals as change in net working capital
adjusted for changes in all current operating accounts (adapted from DeAngelo (1986)). Later studies using the Jones Model often nd accruals directly
as operational cash ows subtracted from earnings.
Jones relaxes the assumption of changes in accruals being due to the discretionary component, by estimating an expectation model for total accruals to
control for the economic circumstances of the rm. Thus, Jones species a
linear relationship between total accruals and change in sales and property,
plant and equipment:
T Ait = αi
1
+ β1i ∆REVit + β2i P P Eit + εit
Ait−1
(1)
where
T Ai t = Total accruals in year t for rm i, scaled by lagged, total assets;
Ait−1 = Total assets in year t-1 for rm i ;
∆REVit = Revenues in year t less revenues in year t-1 for rm i, scaled by lagged,
total assets;
P P Eit = Gross property, plant, and equipment in year t for rm i, scaled by lagged,
total assets.
Then, the prediction error from the OLS regression, dened as
uip = T Aip − (αi
1
Aip−1
+ β1i ∆REVip + β2i P P Eip )
represents the level of discretionary accruals for rm
i
t
at time . To test the
earnings management hypothesis, Jones tests if the average prediction error
is greater than or equal to zero, using Patell (1976). While the prediction
error from the regression is used in the original Jones Model, Schipper and
Vincent (2003) argue that residuals can also be used as a proxy for discretionary accruals.
Jones notes that management naturally cannot hide the
true accrued amount in multiple accounting periods, since rm income must
equal cash ows over all years. However, it is possible to hide the true nature
of a rm's earnings in the short run.
Originally, the Jones Model used time-series data, but the estimation of abnormal accruals can be use both rm-specic, time-specic or cross-sectional
data
4.
Although widely used, the Jones Model has also been much criticised. Numerous studies have shown misspecications and low predictive ability in
4
Subramanyam (1996) prefers the cross-sectional version rather than the time-series,
since the rst generates a considerably larger sample and the long time-interval in the
time-series could cause misspecication in the model due to non-stationarity.
13
the Jones model (see e.g. McNichols (2000)). Francis et al. (2006) question
whether the separation in abnormal and normal accruals reects the true
dierence between discretionary and non-discretionary accruals. They show
that accounting fundamentals such as rm size and standard deviation of
sales revenue explain approximately 65 percent of the variation in the abnormal accruals. McNichols (2002) argues that the Jones Model might be
misspecied since the lagged and future eect of change in sales is ignored
in it. Bernard and Skinner (1996) suggest that the Jones Model treast some
legitimate accruals as abnormal. Beneish (1997) also argues that the separation in discretionary and non-discretionary accruals is not convincing and
that managers can exercise discretion, without intentionally inating earnings. He also provides evidence that accrual models have poor detective performance even among rms with extreme earnings management behaviour.
Since the correlation between discretionary accruals and total accruals
is more than 80 percent, Dechow et al. (2003) note that it might be just
as useful to look at overall level of accruals, rather than dividing them into
abnormal and abnormal. This is the case for all abnormal accruals model,
which can be criticised for misclassifying non-discretionary accruals as discretionary. Finally, Dechow et al. (2009) show that discretionary accruals are
generally less powerful than total accruals at detecting earnings management
in SEC enforcement releases.
3.1.2 The Modied Jones Model
In their 1995 article, Dechow, Sloan and Sweeney criticise the Jones Model
for its implicit assumption on nondiscretionary revenues. They argue that
managers can easily use discretionary revenues for instance to speed up revenues before nancial year-end. Such a situation would lead to an increase in
revenues and accruals but also in receivables. The accruals attached to this
form of earnings management will be classied as non-discretionary accruals
in the setting of the Jones Model, causing the estimate of earnings management to be biased towards zero. Therefore, rms that manage revenue will
not be detected in the Jones Model.
As a response to this critique, Dechow et al. (1995) extend the model
to control for managed revenues, by including accounts receivables when
determining non-discretionary accruals, following the intuition from above
that overstated revenues will lead to boosted receivables:
T Ait = α1
1
+ α2 (∆REVit − ∆RECit ) + α3 P P Eit + εit
Ait−1
where
(2)
∆RECit = Net receivables in year t less net receivables in year t-1 for rm i, scaled
by lagged total assets;
Other variables are as previously dened.
14
Again, the prediction error from the above regression is the estimate on
discretionary accruals.
The modication is designed to eliminate the tendency of measuring discretionary accruals with error when revenues are managed, and thus changes
in revenues are adjusted for changes in receivables.
The inclusion of net
receivables changes the implicit assumption from the Jones Model dramatically. Hence, the Modied Jones Model assumes that all changes in credit
sales result from earnings management.
The empirical evidence on the Modied Jones Model versus the Jones
Model is somewhat ambiguous.
Francis et al. (2006) prefer the Modied
Jones model over the Jones Model, whereas Subramanyam (1996) and Stubben
(2010) nd that the Modied Jones Model shows no real improvement in performance compared to the original.
The Modied Jones Model has like its predecessor been subject to
much criticism. Francis et al. (2005) suggest that the measure of abnormal
accruals contains a substantial amount of uncertainty and believe that the
link to information risk is unclear. Guay et al. (1996) also show imprecision
in the Modied Jones Model, but note that this might be the case for all the
abnormal accrual models.
Following Beneish (1997), the Modied Jones Model could be augmented
with lagged total accruals and a measure of past price performance, to control
for past rm performance. Dechow et al. (2009) suggest that the Modied
Jones Model has higher explanatory power than the original version but
suers from the same performance related problems as the Jones Model.
3.1.3 The Performance-matched Modied Jones Model
From the intuition that rms with extreme performance are likely to engage in earnings management and thus have lower earnings quality, Kothari
et al. (2005) propose a performance-matched discretionary accrual approach.
They match each rm-year observation with another rm from the same industry and year and the closest possible ROA. The paper thus builds on the
work of Dechow et al. (1995), who showed that both the Jones Model and
the Modied Jones model were misspecied when applied to samples with
extreme performances.
As a control, Kothari et al. (2005) also propose a modied version of the
Modied Jones Model, in which they include ROA as an additional regressor
besides sales and PPE, thereby controlling for rm performance on discretionary accruals. The authors describe a non-linear relation between accruals
and performance, and justify the model with the argument that discretionary
accrual models are misspecied under extreme performance. The regression
approach imposes stationarity of the relation between accruals and performance through time or in the cross-section. The authors show that misspecication issues are attenuated, albeit not eliminated, when ROA is included.
15
Using current year ROA rather than lagged ROA yields better results.
Teh inclusion of ROA as an additional regressor does not impose a specic
relation between accruals and performance.
T Ait = δ0 + δ1
1
Ait−1
+ δ2 ∆REVit + δ3 P P Eit + δ4 ROAit + υit
(3)
where
ROAit = Return on assets in year
for rm i.
Other variables are as previously dened.
As with the other models,
υit
t
from the above regression is the measure of
abnormal accruals. As evident, Kothari et al. (2005) include a constant in
the mode, which was not the case for neither the Jones nor the Modied
Jones Model. Dechow et al. (2010) note that the approach of Kothari et al.
(2005) is likely to add noise to the measure of discretionary accruals, and it
is best applied when correlated performance is in fact an important concern.
3.2 Accruals Quality Models
Another group of accrual models measure accruals quality as such.
The
quality of total accruals is of interest, and thus researchers do not attempt
to distinguish between normal and abnormal accruals. Accruals quality is
consistent with the view that low-variance rms have high earnings quality
(Francis et al., 2006).
3.2.1 Dechow-Dichev Model
The rst model introducing accruals quality as a measure of earnings quality
was proposed by Dechow and Dichev (2002). This model is based on the fact
that accruals shift the recognition of cash ows over time to better measure
rm performance. Since accruals are based on estimates, the incorrect estimates must be corrected in future accruals and earnings; as a consequence,
estimation errors are noise that reduces the benecial role of accruals
5 . De-
chow and Dichev predict that the quality of accruals and earnings decreases
when the magnitude of estimation errors increases. They therefore propose
an empirical measure of accruals quality which maps working capital accruals into operating cash ows. A poor match thus signies low accruals
5
When cash ows are received after they are recognised, management must estimate
the expected, received amount.
If the amount is estimated incorrectly, this will natu-
rally be corrected when the accrual is close. However, each period's accrual will contain
an estimation error in the opening accrual and a realised error in the closing accrual.
Since earnings feature an estimation error and its correction, their ability to measure rm
performance is reduced. As a result, a minimisation of the estimation error is desirable.
16
quality. More specically, the model is built up as a regression of the change
in working capital accruals on last year, present, and future cash ows.
Dechow and Dichev measure accruals as the change in working capital,
which yields the following regression:
∆W Cit = β0 + β1 CF Oit−1 + β2 CF Oit + β3 CF Oit+1 + εit
where
∆W Cit is working capital in year
t
less revenues in year
lagged, total assets;
CF Oit is cash ows from operations in year
assets.
t
t-1
(4)
for rm i, scaled by
for rm i, scaled by lagged, total
Changes in working capital is the Dechow-Dichev measure of accruals. The
error term shows the extent to which accruals map into realised cash ows,
and the variance hereof is a proxy for accruals quality. High variance in the
estimation errors yields non-persistent earnings, and it is an inverse measure
of earnings quality. The idea is that systematically small or large estimation
errors do not create problems for users since these still enable them to predict
future earnings. This is intuitively appealing, since a persistent residual does
not necessarily equal low accruals quality but can just be a result of a reality
in the rm. On the other hand, volatile residuals impede investors' prediction
of future earnings, creating an earnings number of low quality.
It is expected that rms with low accrual quality will also have low earnings
persistence.
Dechow and Dichev do not distinguish intentional estimation errors from
the unintentional ones, since all errors signify poor accruals quality, regardless the underlying intent.
The model therefore deviates from the Jones
model, which attempts to capture earnings management, whereas the DechowDichev Model focuses on earnings quality per se.
According to Francis et al. (2004), the Dechow-Dichev model is a powerful
earnings quality measure. Schipper and Vincent (2003) nd that the DechowDichev Model avoids several of the problems associated with the accounting
fundamental approach by Jones (1991). However, the model requires that
working capital accruals lag or lead cash receipts by no more than a year.
In a discussion of Dechow and Dichev (2002), McNichols (2002) notes
that the Dechow-Dichev Model is only applicable where the key element of
accruals is current accruals. In settings where this assumption is not met,
the model is not operational. As opposed to this, Francis et al. (2006) nd
that total accruals make a legitimate proxy for current accruals.
Francis
et al. (2005) nd that Dechow-Dichev Model best captures the uncertainty
in accruals. They also suggest that the Jones Model and the Dechow-Dichev
Model work well together, since the rst does not suer from the limitations
of the second, in terms of using current accruals rather than total accruals. According to Dechow et al. (2010), it is an important limitation to the
17
Dechow-Dichev Model that it focuses on short-term working capital, since
for instance impairments of goodwill and PPE are likely to reect earnings
management. Finally, McNichols (2002) nds misspecication in the model.
This is also the case with the Jones model, though.
3.2.2 Modied Dechow-Dichev Model
In her discussion of the Dechow and Dichev (2002) paper, McNichols (2002)
argues that measurement error in the Dechow-Dichev model may preclude
the model from controlling for the fundamental factors inuencing accruals.
She suggests that the model is misspecied; more specically, the residuals
from the Dechow-Dichev model are correlated with change in sales, indicating that cash ow from operations is a noisy proxy for the cash ow
recognised in the accruals. She thus extends the model to include additional
explanatory variables, which are important in forming expectations about
current accruals. The extent to which accruals map into cash ows, change
in sales and PPE is thus an inverse measure of accruals quality. This yields
the following regression (all variables scaled by average assets):
∆W Cit = β0 + β1 CF Oit−1 + β2 CF Oit + β3 CF Oit+1
+ β4 ∆REVit + β5 P P Eit + εit
(5)
where all variables are as previously specied.
As in the original Dechow-Dichev Model, the variance of
εit
is an inverse
measure of earnings quality.
The Modied Dechow-Dichev Model has shown greater t than the DechowDichev Model (Francis et al., 2005). Kent et al. (2010) nd that DechowDichev and the modied Dechow-Dichev Model perform equally well, but
the latter provides a signicantly larger coecient of determination.
Barth et al. (2008) criticise the Modied Dechow-Dichev Model for not
capturing the perceptions of investors and analysts and the fact that it focuses on current accruals instead of total accruals.
3.3 Other Accruals Models
3.3.1 Magnitude of Accruals
The sheer magnitude of accruals has been used as a measure of earnings
quality.
Bhattacharya et al. (2003) expect that the level of accruals in-
creases with earnings aggressiveness if cash ow realisations are held equal.
Following their intuition, aggressive accounting will lead to fewer negative
and more positive accruals. This will lead to a higher level of accruals over
all since rms are more likely to overstate than to understate earnings. Dechow et al. (2010) argue that extreme accruals are of low quality because
18
they represent a less persistent component of earnings. In accordance with
Dechow and Dichev (2002), their proxy for earnings quality and the level of
accruals are both proxies for the same aspect of unobservable true accrual
quality, as a measure of earnings persistence. They also note that the sheer
level of accruals is simple and easy to use. However, the authors still nd
that the Dechow-Dichev model does capture better the variation in earnings
persistence. An important note made by Dechow and Dichev (2002) is that
rms with large accruals tend to generate larger estimation errors, emphasising the fact that the magnitude of accruals and Dechow-Dichev model might
be two sides of the same coin.
Dechow et al. (2011) examine whether rms that misstate earnings have unusually high working capital accruals. They predict and nd that accruals
are larger in misstating years.
Dechow and Schrand (2004) also note that the magnitude of accruals
can reveal the quality of earnings, but they note that the level cannot be
seen independently.
Thus, high accruals in companies with low cash ow
volatility should "ring the bells", whereas high accruals in for example a
rm in the high growth industry, do not necessarily indicate poor earnings
quality.
Bhattacharya et al. (2003) and Leuz et al. (2003) measure earnings aggressiveness as the level of accruals:
T Ait
Ait−1
(6)
where all variables are as previously dened.
The higher the ratio, the higher the earnings aggressiveness.
This measure of earnings quality is certainly simplistic, and thus it is
rarely used as a single metric for earnings quality. As Dechow and Schrand
(2004) note, some rms will have higher levels of accruals due to the very
nature of their business, despite of otherwise honest management.
3.3.2 Change in Total Accruals
The general idea behind this metric is that accruals should be constant over
time, and thus a signicant change in accruals could indicate managerial
manipulation.
Schipper and Vincent (2003) suggest that as long as some
portion of accruals is both non-manipulated and approximately constant over
time, changes in total accruals could stem from managerial manipulations,
and they may provide an inverse measure of earnings quality.
Hence, the
more accruals change over time, the poorer is the earnings quality.
DeAngelo (1986) argues that large accruals as such do not necessarily indicate low earnings quality or earnings management behaviour, but a jump
in accruals might indicate that managers have deliberately over- or understated earnings. She therefore assumes that a signicant change in accruals
19
from one period to the next indicates low earnings quality. This rests on the
following logic: Given that accruals consist of both normal and discretionary
accruals, DeAngelo assumes that average change in normal accruals is zero.
Therefore, a signicant change in total accruals reects a change in abnormal
accruals.
More specically, DeAngelo (1986) calculates the following fraction:
∆T Ait
Ait−1
(7)
where
Where all are variables as previously dened.
A fraction signicantly dierent from 0 indicates poor earnings quality.
Sloan (1996) shows that the accrual component of earnings is less persistent than cash ows.
One interpretation of this is that current over-
and understatements of accruals are adjusted via accruals in future periods
(Dechow and Schrand, 2004).
Thus, the recording and reversal of accrual
misstatements result in accruals, that are more volatile than cash ows. As
Dechow and Schrand note, these misstatements may simply be due to the
fact that managers have to make judgements and forecasts when determining
accruals. Therefore, accruals that change from year to year do not necessarily
indicate poor EQ.
As with the magnitude of accruals, this approach is somewhat simple,
but it has often been included in earnings quality studies on par with with
other accrual models. In general, it seems to correlate signicantly with other
accruals models, indicating that it is useful as an additional robustness test.
3.4 Avoiding Earnings Decreases & Small Losses
The discussion on avoidance of small losses and earnings decreases is linked
to the reported kink in the distribution of reported earnings, as discussed
by Hayn (1995).
She argues that losses are not expected to perpetuate
since rms have liquidation options. Losses are thus less informative about
future rm performance than prots are. She shows that even though the
overall distribution of earnings is not signicantly dierent from a normal
distribution, there is a point of discontinuity around zero. More specically,
there is a concentration of cases just above zero and fewer than expected
of small losses just below zero.
She suggests ...that rms whose earnings
are expected to fall just below the zero earnings point engage in earnings
manipulation to help them cross the `red line' for the year" (Hayn, 1995, p.
132).
Thus, it is suggested that the kink in earnings is due to an unwillingness
of rms to report losses or earnings decreases.
Likewise, Burgstahler and
Dichev (1997) make two predictions on earnings management. They posit
20
rst, that earnings are managed to avoid earnings decreases and second,
earnings are managed to avoid losses.
Avoidance of small losses and earnings decreases have been used extensively in the research, under many dierent terms, e.g, small loss avoidance
(Leuz et al., 2003), loss avoidance (Bhattacharya et al., 2003), frequency of
small positive earnings (Lang et al., 2003), and managing towards positive
earnings Barth et al. (2008).
There are a number of incentives for managers to produce steadily increasing earnings numbers. Barth et al. (1995) show that rms with increasing earnings during a longer period enjoy higher price-earnings ratios and
higher premiums for the long series of earnings increases.
These benets
vanish immediately when the line of increasing earnings is broken.
These
ndings have been supported in other studies later, and thus there seems to
be strong incentives to keep earnings positive and steadily increasing. According to, for example, Degeorge et al. (1999) and Dechow et al. (2003), just
exactly meeting or beating analysts' forecasts also points towards earnings
management.
Most of the research is connected with examining the frequency of small
increases in earnings or small prots barely over zero.
Leuz et al. (2003)
note that while one might argue that managers are interested in avoiding all
losses and earnings decreases, but, at the same time, they only have limited
reporting discretion, it is not possible to conceal larger losses or earnings
decreases. Therefore, managers may manage earnings to make them seem
increasing or above zero only when possible.
3.4.1 Avoiding Small Earnings Decreases
Burgstahler and Dichev (1997) use small increases in earnings in as a proxy
for earnings management.
They test the hypothesis of avoiding earnings
decreases as:
N Iit
M V Eit−1
(8)
where
N Iit = Net income in year t for rm i ;
M V Eit−1 = market value of equity in year t-1 for rm i.
Under the hypothesis of no earnings management, the expected distribution
of earnings change would be approximately symmetric and normal, following
Hayn (1995).
3.4.2 Loss Avoidance
Burgstahler et al. (2006) estimate the small loss avoidance as the frequency
of small prots compared to small losses:
21
SP N It
SN N It
(9)
where
SP N It = small positive net income, dened as
SN N It = small negative net income, dened as
N It
At−1
N It
At−1
between 0 and 1 %;
between 0 and -1 %.
The higher this ratio is, the higher is the loss avoidance.
The interpretation that small positive earnings indicate earnings management is somewhat controversial, and several researchers have questioned
whether earnings management actually explains the kink in earnings. Dechow et al. (2003) argue that the increase in cash ows around the zero
reference earnings point stems from the positive relation between cash ows
and earnings. therefore an increase in cash ow is actually expected around
the kink.
They therefore reject the notion that the increase in cash ows
is due to earnings management. Dechow et al. (2003) also argue that since
there is a positive relation between working capital accruals and earnings,
Burgstahler and Dichev's ndings concerning an increase around zero do not
necessarily indicate earnings management. Since Dechow et al. (2003) nd
that other accruals decrease for the small prot group relative to the small
loss group, the conclusions of Burgstahler and Dichev (1997) on earnings
management is - according to Dechow et al. (2003) - questionable.
The main criticism of Dechow et al. (2003) pertains to the fact that rms
can take real actions to avoid reporting losses or earnings decreases, and thus
the overrepresentation of these two incidents is not necessarily evidence of
earnings management. One can easily imagine that employees are more motivated when facing a loss and managers may make decisions that increase
cash ows and hence earnings, absent of earnings management.
Coulton et al. (2005) provide similar criticism in their examination of
rms that beat a simple benchmark, such as achieving increasing earnings.
They argue that the kink in earnings is a poor proxy for earnings management and that the kink could just as well be attributable to the scaling of
earnings by for instance lagged assets or price.
However, a group of researchers have shown correlations between small
prots and other earnings management proxies, indicating that small positive
earnings could possibly stem from earnings management.
3.5 Asymmetric Timeliness
The research of timeliness hypothesises that prots and losses are recognised in an asymmetric manner. More specically, losses are recognised on a
more timely basis than prots, leading to less persistent and more reverting
negative earnings changes.
22
For asymmetric timeliness to be an indicator of earnings quality, it is
assumed that the more timely recognition of losses in fact increases decision
usefulness.
According to Dechow et al. (2010), previous literature suggest
that equity markets perceive asymmetric timeliness as increasing earnings
quality. However, research has so far failed to prove that asymmetric timeliness in fact improves decision making. Ball and Shivakumar (2005) note that
timely loss recognition increases the value relevance of nancial reporting.
Asymmetric timeliness is related to conditional conservatism, and a more
timely recognition of losses is often associated with a conservative accounting
system (Basu, 1997).
3.5.1 Timeliness
A frequently used measure of asymmetric timeliness is Basu (1997)'s reverse
earnings-returns regression also used to detect conservatism.
Basu (1997)
proposes a second measure of timeliness which is not based on returns:
∆N It = α0 + α1 N EGDU Mt−1 + α2 ∆N It−1
+ α3 (N EGDU Mt−1 ∗ ∆N It−1 ) + υt
(10)
where
∆N It = change in income from year t-1 to t, scaled by lagged total assets;
N EGDU Mt−1 = an indicator variable equal to 1 if ∆N It is negative.
Timely recognition of economic losses implies that they are less persistent
and tend to reverse faster than prots.
This predicts that
α3
< 0.
Basu
(1997) nds support for this prediction.
3.5.2 Timely Loss Recognition
The main intuition behind timely loss recognition is the fact that rms with
high nancial reporting quality recognise losses as they occur, rather than
deferring them (Lang et al., 2006).
of large losses.
This will lead to a higher frequency
Such a high frequency also indicates that earnings have
not been articially smoothed. If earnings had been smoothed, large losses
should be relatively rare. The opposite can, however, also be true, since a
high frequency of large losses could indicate big bath earnings management.
There is thus a conict between the two.
Ball and Shivakumar (2005) argue that timely loss recognition increases
nancial statement usefulness, in particular in corporate governance and
debt agreements.
The rst is aected because managers are less likely to
make NPV-negative investments. The second is aected because timely loss
recognition provides more accurate information for loan pricing. They also
note that the demand for timely gain recognition is smaller since mangers
have natural incentives to report prots.
23
Timely loss recognition is linked to some of the other metrics for earnings
quality. Barth et al. (2008) suggest that one characteristic of high quality
earnings is that large losses are recognised as they occur, rather than being
deferred to future periods. This characteristic is closely related to smoothing
since large losses should be relatively rare if earnings are smoothed. Therefore, rms reporting (large) losses on a regular basis have higher quality
earnings than those that do not, from the intuition that the latter are expected to have managed earnings.
According to Beekes et al. (2004), timely loss recognition is also linked to
the asymmetric timeliness of earnings and therefore conservatism.
Lang et al. (2006) and Barth et al. (2008) use the frequency of large losses
as an indicator of earnings quality:
N Iit
ATit
(11)
where all variables are as previously dened.
If the above ratio is less than -0.2, it is dened as a large loss.
A high
frequency of those is juxtaposed with high accounting quality, since losses
are recognised as they occur. They use an indicator variable equalling 1 if
annual net income scaled by total assets is less than -0.2, and 0 otherwise.
They use this in a regression where they regress a cross-listing variable on
the negative NI variable -a negative coecient indicates that cross-listing
rms are less likely to report large losses.
3.6 Smoothness
Two conicting views on smooth earnings as an indicator of earnings quality
exist in the literature. One view reects the idea that managers articially
smooth out relevant uctuations. This leads to a less timely and informative earnings number. In this view, smooth earnings indicate poor quality
earnings. The opposite view reects the idea that management uses private
information to smooth out transitory, value irrelevant uctuations in earnings, thereby achieving a more useful earnings number. In this view, smooth
earnings indicate high quality earnings (Francis et al., 2006).
The rst view, that smoothing decreases earnings quality, stems from
the hypothesis that management responds to a negative (positive) cash ow
stream by increasing (decreasing) accruals (Barth et al., 2008). According
to Kirschenheiter and Melumad (2002), managers have several incentives to
report smooth earnings. First, the authors argue that the market rewards
smooth earnings since they are assumed to have higher precision. Therefore,
if the rm reports a large, positive earnings surprise, the positive eect on
stock prices might be dampened since investors prefer smooth, unsurprising
earnings. Second, consistently positive earnings may raise the expectations
24
of cash ows to investors, thereby increasing share prices.
Managers can
also wish to appear less risky and attract inexpensive capital. Francis et al.
(2004) suggest that capital market participants reward smoother earnings
streams with reduced costs of equity and debt.
Bhattacharya et al. (2003) use earnings smoothing as a measure of earnings opacity. They argue that articially smooth earnings fail to depict the
true swings in underlying rm performance. As a consequence, smoothing
increases earnings opacity.
Supporters of the second view argue that very volatile earnings variability could indicate poor accounting quality since it could arise due to big
bath earnings management (Healy, 1985). Big baths refers to an earnings
management technique, where management understate losses to be able to
report future prots. Barth et al. (2008) also note that high earnings variability could indicate low accounting quality since it could be due to errors
in estimating accruals.
Dechow and Skinner (2000) give appealing examples to show that the
line between smoothing as a way to include only material changes and uctuations and smoothing as opportunistic earnings management is subtle. As
a consequence, they note that detecting earnings management via smoothing in large samples is extremely dicult. They also note that to be able to
characterise income smoothing as earnings management, one needs to dene
the point at which managers' accrual decisions result in too much earnings
smoothing. This is naturally not an easy task.
In a related vein, Subramanyam (1996) notes that while some smoothing has
an opportunistic connotation, not all smoothing is necessarily opportunistic,
since managers might smooth earnings to create more persistent earnings.
Thus, smoothing can enhance the value relevance of earnings.
The question thus remains whether or not smoothness per se in fact is a
quality indicator. Smooth earnings are not necessarily desirable attributes
following the concepts statements.
But smoothness is an outcome of an
accrual-based system assumed to improve decision usefulness, and as such
not the ultimate goal of the system. Researchers thus need to dierentiate
fundamental smoothness from smoothness as an outcome of discretionary
accounting choices. Following Dechow et al. (2010) the smoothing measures
known so far only measure if the earnings stream is smooth, not why it is.
3.6.1 Variability of Earnings
Earnings variability is often measured by the standard deviation or the variance of earnings, either as a stand-alone measure or relative to the underlying
cash ows.
Leuz et al. (2003), Lang et al. (2003) and Francis et al. (2004) measure
smoothing as the ratio of standard deviation of earnings to the standard
25
deviation of cash ows:
σ(N Ii /Ait−1 )
σ(CF Oi /Ait−1 )
(12)
where all variables are as previously dened.
If rms use accruals to manage earnings, the variability of change in operating income should be lower than that of cash ows. Low ratios hence
indicate that insiders exercise accounting discretion to smooth reported earnings. Francis et al. (2004) use net income before extraordinary items in the
numerator, whereas Leuz et al. (2003) use operating income.
Barth et al. (2008) test the level of earnings smoothing by testing the
variability of earnings directly:
σ(∆N Iit )
(13)
where all variables are as previously dened.
Higher values indicate less smoothing and thus higher accounting quality
after the rst view described in section 4.5.
3.6.2 Correlations between Accruals and Cash Flows
Dechow (1994) argues that cash ows and accruals are expected to be negatively correlated over time as a natural result of accrual accounting, because
accruals reverse over time. However, she also suggests that a large negative
correlation between accruals and cash ows could indicate that accruals are
used to smooth uctuations in cash ows, suggesting lower accounting quality. Following this intuition, management responds to low (high) cash ows
by increasing (decreasing) accruals, thus boosting (lowering) income.
Leuz et al. (2003) and Bhattacharya et al. (2003) use the measure of
negative correlation between change in accruals and change in cash ows:
ρ(∆T Ait , ∆CF Oit )
(14)
where all variables are as previously dened.
Barth et al. (2008) assume that high quality rms will exhibit a less negative
correlation between accruals and cash ows than low quality rms. Like Dechow (1994), they acknowledge that the proper rule of accruals is to smooth
variability in cash ows, and that the correlation by denition is expected
to be positive, because accruals reverse over time.
26
3.7 Persistence
Persistent earnings can be viewed as desirable since earnings that are able to
predict themselves are more valuable for users, e.g., for valuation purposes.
Persistence or sustainability has therefore been used often as a measure of
high earnings quality (Francis et al., 2006). Regardless of the sign and magnitude of earnings, persistence captures the extent to which the current period innovation becomes a permanent part of the earnings series (Schipper
and Vincent, 2003). The intuition behind persistence as an earnings quality
metric is that persistent earnings will make current earnings a more useful
measure of future performance in perpetuity. Thus, higher earnings persistence is of higher quality when the earnings is also value relevant (Dechow
et al., 2010).
Following Schipper and Vincent (2003) persistent earnings have been associated with larger investor responses to earnings, which supports the hypothesis that persistent earnings are more useful for users, in particular for
valuation purposes (Dechow and Schrand, 2004). Dechow et al. (2009) note
that studies of earnings persistence and cash ow predictability are motivated by an assumption that persistence improves decision usefulness in an
equity valuation context.
A common measure for persistence is the autocorrelation of earnings
where high autocorrelation between current and past income is desirable A
stationary AR1 model with
φ
close to 1 is thus considered persistent (Heij
et al., 2004).
Francis et al. (2004) use an autoregressive model on earnings per share to
measure persisitence:
Xj,t = φ0,j + φ1,j Xj,t−1 + εj,t
(15)
where
Xj,t = Net income before extraordinary items in year t for rm i, scaled by the
weighted average number of outstanding shares during year t ;
Large values of
φ1,j
indicate more persistent earnings.
In continuation of his discussion of conservatism, Basu (1997) argues
that negative earnings changes are less persistent that than positive earnings changes. As a consequence, negative earnings changes do generally not
become a permanent part of future earnings, whereas good earnings changes
will.
The accruals quality measure proposed by Dechow and Dichev (2002) is
related to earnings persistence, since rms with low accrual quality have a
larger amount of accruals that are unrelated to cash ows, which induces
noise and less persistency in earnings. This is natural, since more accrual
estimation errors will lead to less persistent earnings. However, it has been
documented several times (e.g. Sloan (1996)) that accruals are less persistent
27
than cash ows.
The authors also note that it is dicult to distinguish
empirically between the eects of accruals quality and the level of accruals
on earnings persistence.
Schipper and Vincent (2003) argue that highly impersistent earnings can
be the outcome of neutral application of accounting standards in volatile economic environments. Thus, they do not necessarily indicate poor accounting
quality.
3.8 Predictability
The concept of earnings predictability as a desirable attribute is closely connected with persistence.
According to Schipper and Vincent (2003), pre-
dictability is the ability of the nancial statements to improve users' abilities
to forecast items of interest, i.e., the ability of past earnings to predict future earnings. Following this denition, variability decreases predictability,
and the term is therefore connected to both the sustainable and smoothing
literature. Like these two attributes, some ambiguity still exists on whether
predictability is actually a desirable attribute of earnings since it is not directly consistent with representational faithfulness.
It is evident, though,
that predictable earnings are valuable inputs for valuation purposes, such as
DCF analysis.
Schipper and Vincent (2003) note a contradiction between predictability
and persistence; in cases where the variance of the typical shock to the series i
large, highly persistent earnings (a random walk) will have low predictability.
Hence, in this situation earnings that are of high quality under the persistence
view are of poor quality under the predictability view.
Predictability can be measured using the same model used for measuring
persistence.
Another measure often used is the forecast error of analysts'
earnings forecasts.
Dichev and Tang (2009) note that high volatility de-
creases earnings predictability. They calculate absolute predictability from
autoregressive regressions of current on 1-year lagged earnings, i.e., the same
AR1 process as above:
Xj,t = φ0,j + φ1,j Xj,t−1 + εj,t
(16)
where all variables are as previously dened.
Consequently, the variance of
ε
is Dichev and Tang's inverse measure of
predictability, since the variance of the error term captures the variation
in earnings remaining after accounting for the eect of the autoregressive
coecient.
Schipper and Vincent (2003) mention some empirical diculties when
operationalising predictability. The choice of time period is not agreed upon
in the literature, even though many researchers have used one-year-ahead
predictions.
They also note that no consensus exists on what to predict;
28
researchers have used reported net income, cash ows and various subsets
of net income.
Finally, they criticise predictability for suering the same
issues as income smoothing since it has not been claried whether predictable
earnings indicate high quality earnings or opportunistic earnings smoothing.
4
Restatements
A restatement event means a correction of errors or irregularities in the nancial statements.
A broader denition of restatements also exists, e.g.,
providing restated results after adoption of new accounting standards or an
M&A (Scholz, 2008). This study, however, only focuses on correction of misstated nancial statements.
Extensive research has been done on rms restating their nancial statements.
The properties of restatements in an earnings quality context are
indeed compelling.
First of all, an outside source has identied problems
with the quality of the nancial statements of restating rms (Dechow et al.,
2010). Thus, a perceived advantage of restatements is that they are a direct
proxy for poor earnings quality (DeFond, 2010).
Violations could include nancial and accounting fraud, insider trading,
market manipulation, providing false or misleading information, and selling securities without proper registration (GAO, 2009). Thus, irregularities
cover both fraud and earnings management within/outside GAAP.
Following Scholz (2008), the major reasons for restatements include revenue (e.g. improper or questionable recognition of revenue), expenses (e.g.
improper capitalisation of expenditures), and reclassication and disclosure
(e.g. categorisation of debt payments as investments). The number of fraud
cases has been fairly stable during the past 15 years, namely approximately
5 %. However, the number of rms that restate has increased dramatically
in the last ten years - from 90 in 1997 to 1,577 in 2006. This rise is among
other things traceable back the downturn of the American economy in the
beginning of the new millennium, and the enaction of SOX in 2002, as well
as various accounting issues in the mid 2000s.
4.1 Previous Literature
Extensive research exists on both the determinants and consequences of restatements.
The research on which rms that will misstate earnings is extensive. Some
common characteristics of restating rms include high growth prior to restatements (Beneish, 1999a), executive compensation contracts (Burns and
Kedia, 2006), high leverage and high likelihood to violate debt covenants
(Dechow et al., 1996) and poor corporate governance (Farber, 2005), Dechow et al. (1996).
29
Overall, the evidence on opportunistic reporting incentives is mixed, and
the results across researchers are far from unambiguous.
As an example,
Beneish (1999a) do not nd debt covenants to be signicant and Dechow
et al. (1996) only nd signicant inuence from some corporate governance
characteristics, not all.
A range of literature also explores the consequences of SEC restatements.
One would clearly expect market reactions to be negative, as restatements
will aect investors' condence negatively. (GAO, 2002) and (GAO, 2006b)
use a standard event study to determine the impact on stock prices following restatements.
They nd that stock prices decline signicantly in a
two-day window, whereas the long-term impact is more dicult to determine, but it is assumed to be negative as well. Investors respond dierently
to dierent reasons for restating and thus reacted more negatively to the
category restructuring, assets, or inventory than to the category cost or expense. Palmrose et al. (2004) nd similar results.
While it is clear that investors react negatively to misstatements, empirical
research has not been able to clearly determine if restatements equal poor
earnings quality.
Other outcomes of earnings restatements include increased management
turnover (Feroz et al., 1991) and cost of capital (Dechow et al., 1996), Hribar
and Jenkins (2004), signicant negative stock returns (Dechow et al., 1996)
and obviously considerable costs attached to auditors, lawyers etc.
Again, there exists some ambiguity as to the consequences of restatements.
For example Beneish (1999a) examines the incentives and conse-
quences of earnings overstatements and nds that revelation in SEC does
not impose serious enough consequences on managers, and thus the presence
of SEC alone will not prevent managers from engaging in earnings management.
In contrast, Desai et al. (2006) nd that management turnover
increases signicantly following a restatement. This suggests that the "risk
of getting caught" might prevent managers from earnings overstatement.
4.2 Identifying Restatements
Several sources identify restatement events. This study exploits SEC's restatement database which is described in detail below. Some other sources
are described as well, along with my reason for not using these resources.
4.2.1 SEC's AAERs
The US Securities and Exchange Commission (SEC) is the primary federal
agency involved in accounting requirements for publicly traded companies. It
recognises FASB's US GAAP as the general accepted standards which rms
should comply with. The role of SEC is to protect users of nancial statements, in particular investors. It is therefore in the interest of SEC to ensure
30
and maintain a high accounting quality among American corporations, and
hence they monitor American rms for signs of reporting violations. If such
a sign occurs, SEC publishes an Accounting and Auditing Enforcement Release (AAER)
6 on that particular company to inform investors that some
kind of nancial reporting violation has taken place. The AAERs have been
issued since April 1982 and provide information on the nancial statement
quality as a whole, not just earnings.
SEC identies the rms that allegedly misconduct nancial statements through
anonymous tips, public criticism and news reports, voluntary restatements
and random sampling among listed companies.
Since SEC is concerned
mainly with protecting investors, it is more likely to scrutinise rms that
are either large, raising debt or equity, or IPO rms.
Since SEC only has limited funds available, they are likely to pursuit only
the worst cases of earnings manipulation (Hennes et al., 2008), and thus it is
agreed among most researchers that the AAERs include mostly fraudulent
or intentional misstating behaviour (Dechow et al. (2010) and Eilifsen and
Messier Jr. (2000)).
Indeed, in most AAERs, SEC accuses managers of
intentional misstating nancial statements, i.e., fraud.
However, in some
cases SEC acknowledges that management was negligent, i.e., reckless in not
knowing (Dechow et al., 2010).
Dechow et al. (1996) assume that SEC correctly identies overstating
rms and that the rms have knowingly engaged themselves in earnings
overstating.
Under this assumption, the AAERs are a powerful tool for
examining earnings management and accounting quality hypotheses.
The
dataset excludes all restatements for example due to M&As and many unintentional errors.
Since AAERs are likely to include only the cases of intentional misstatements, the sample has a low expected type I error rate (rms that do not
manage earnings are incorrectly classied as managers) (Dechow et al., 2011).
However, given the limited funds of the SEC, the expected type II error rate
might be high as well (many rms are likely to remain undetected).
4.2.2 Other Sources
GAO Database 7
The Government Accountability Oce (GAO) is the audit, evaluation and
investigative arm of the American Congress.
GAO has issued lists of restating rms in 2002 and 2006, respectively.
The restatements were identied searching the database Lexis-Nexis for variations of restate and other relevant words. GAO also searched SEC's ling,
company web sites and compared qualitative features of the rms. Excluded
6
A list of the AAERs is available through the commercial database Audit Analytics
and from www.sec.org/edgar.
7
Available from http://www.gao.gov/special.pubs/gao-06-1079sp/toc.html
31
from the lists are rms not registered with SEC, routine reporting issues
such as mergers or stock splits, simple presentation issues and restatements
following accounting policy changes. Thus, the list consists of restatements
following accounting fraud and accounting errors, by GAO termed aggressive accounting issues (GAO, 2006a).
It is clear that there is an overlap
between the databases of SEC and GAO, because SEC often requires restatements and restatements often trigger SEC's investigation.
The GAO
sample is larger than the SEC sample each year, but it only goes back to
2002 (compared to 1982 for SEC). The GAO list also includes a wider range
of misstatements.
The heterogeneity of the sample has been subject to some criticism. Dechow
et al. (2010) argue that the GAO dataset includes too many unintentional
misstatements and non-material errors which have nothing to do with earnings management. Hennes et al. (2008) suggest that researchers should split
the GAO dataset into intentional earnings management and errors when
doing accounting research.
Another major drawback when working with the GAO dataset is the the
time-lag from the restatement is detected, until it becomes public. This span
varies greatly from case to case. This makes the GAO database well-suited
for research on the consequences of restatements but less appropriate for
research on the determinants of restatements (Dechow et al., 2009).
Standard Law Database on Shareholder Lawsuit
The cases in the shareholder lawsuit database have been used as an indicator
of poor earnings quality. The database includes intentional misstatements,
but lawsuits could also arise due to other issues, for example after a major
decline of stock prices. The sample is thus very heterogeneous, and it has to
be carefully cleaned before it is used to hypothesise about the quality of the
rms involved.
Study Specic Identications
Some researchers have also created data sets specically to the study or research question at hand. Richardson et al. (2002) create their own dataset
(like GAO) from 1971 to 2000, excluding unintentional errors and misapplications. Many of the rms identied were SEC targets as well.
Palmrose et al. (2004) search for words like restate and restatements (like
GAO) and assume that SEC targets are a part of this sample. Abbott et al.
(2004) use rms that have restated but have not received an allegation of
fraud by SEC, also similar to the GAO dataset.
Beneish (1997), Beneish (1999a), and Beneish (1999b) combine SEC's rms
with search in press releases to prevent the time-lag in the SEC database to
inuence results.
32
4.3 Issues when Working with Restatements
One issue when testing accounting quality hypotheses on restating dataset
is that most of the rms identied there have in fact violated GAAP, i.e. the
cases consist of more fraud than earnings management. However, there are
many degrees of earnings management that exercise managerial discretion
within the borders of GAAP, and these rms are not likely to be caught
by the dataset (Dechow and Skinner, 2000).
The fact that only the more
spectacular cases of earnings management or fraud are included limits the
generalisability of the results (Dechow et al., 1996). The view is supported
by Jiambalvo (1996), who mentions that the SEC sample is only suitable for
research on GAAP violating earnings management. Therefore restatements
and AAERs may not be appropriate for studies that wish to capture errors
or earnings management within the boarders of GAAP.
However, since Dechow et al. (1996) expect that rms violating GAAP also
manage earnings within GAAP. This certies the use of restatements as a
proxy for earnings management, because those rms identied by SEC have
most likely also been engaged in the less severe earnings management.
Another issue when working with restatements is highlighted by Dechow
et al. (2010): The misstatement samples are often small, whereas the number
of potential sources of incentives is large. So the tests may not be powerful
enough to detect a true relation. In other words, it can be dicult to show
empirically the exact causality between an incentive to manage earnings and
an actual restatement.
Palmrose et al. (2004) suggest that one of the most important limitations
posed by the dierent restatement samples is the selection bias, namely that
only the rms detected and judged are in the sample.
The selection bias
depends on the specic accounting irregularity and the decision of the rm
to report it.
This corresponds to a high Type II error rate as described
earlier.
5
Hypotheses Development
Previous research that links restatements and earnings quality (e.g. Dechow
et al. (2011); Jones et al. (2008); Richardson et al. (2002)) focuses mainly
on the actual restatement years. In particular, in a time-series analysis of
misstating rms, Dechow et al. (2011) compare each rm's non-misstating
years with misstating ones and thus assume that misstating rms have poor
accounting quality in the restatement year alone.
An underlying assumption is thus that accounting quality is only poorer in
the misstatement years, when the rm was actually detected.
I hypothesise that the poor quality of earnings is not limited to the actual
restatement year, but that rms identied by SEC have continuously poor
earnings quality in the years prior to the restatement(s).
33
I hence test if the restating rms are generically dierent from otherwise
similar, non-restating rms in the years before the restatement.
In consequence, the rst hypothesis to be tested is the following:
Hypothesis 1. Restating rms have poor accounting quality compared to
non-restating rms prior to a restatement event.
I compare earnings quality before the (last) restatement year for both restaters and non-restaters. I predict that the poor earnings quality detected
by previous research in the restatement years is evident already before the
event, and thus restaters are expected to have lower accounting quality than
non-restaters.
The opposite could, however, also be true. As assumed by for instance
Dechow et al. (2011), rms may only exercise improper discretion in the
restatement years.
It could also be the case that rms manage earnings within the boundaries of
GAAP before they are detected by the SEC, but the earnings quality metrics
I apply cannot actually detect it.
Given the severe market reactions following a restatement outlined in Section
4 (e.g. signicant drops in stock prices and increased management turnover),
I expect that restating rms conform following a restatement event, and thus
improve accounting quality after the actual restatement:
Hypothesis 2. Restating and non-restating rms have similar accounting
quality after a restatement event.
The earnings quality of restaters and non-restaters is compared after the
last restatement year. Under the assumption that the SEC enforcement has
an educative role, it is expected that the accounting quality is improved
relatively more for the restaters than the non-restaters, and thus I predict
no dierence in quality between the two groups in the after-period.
Similarly, I compare the change in earnings quality over the periods, for
both groups:
Hypothesis 3. Restating rms improve accounting quality relatively more
than non-restating rms following a restatement event.
It is possible to nd reverse results, though.
As evident from Section 6,
some rms restate more than once which could indicate that an immediate
improvement fails to take place. Again, it is also possible that the metrics I
use are not able to measure a possible improvement.
6
Research Design and Descriptive Statistics
Each restating rm is identied through Audit Analytics' (AA) Non-Reliance
Database, after which the nancial statement information from these rms
34
is found in Compustat Unrestated Quarterly.
Each restating rm is then
matched in the restatement year with a similar rm that has not restated,
based on size, ROA, and industry.
6.1 Sample Selection
The restatement events are identied from Audit Analytics, a commercial
database available through WRDS. The restatement data set from Audit
Analytics covers all SEC registrants (US rms only) that have restated since
January 1st 2001. The sample contains Form 8-K and 8-K/A lings under
the title "4.02: Non-Reliance on Previously Issued Financial Statements or
a Related Audit Report or Completed Interim Review".
Audit Analytics contains detailed information on which year(s) each rm
has restated, the date the restatement became publicly known, and the reason for the restatement. Since only limited accounting information is available in AA, the information has to be downloaded from Compustat or a
similar source.
There is one major caveat when using restatement information from Audit Analytics, namely that there is no direct link between AA and for instance
Compustat and CRSP. This complicates the process of nding the nancial
statement numbers from the rms identied in Audit Analytics. Whereas
AA uses CIK as rm identier, Compustat uses GVKEY, and CRSP uses
CUSIP. Even though all data bases report the Ticker symbol for each company, this cannot be used to match, since it can change over time and be
reused by more than one company.
As suggested by WRDS,
8 We match each restating rm in Audit Ana-
lytics to its nancial statement information in Compustat by the company
name.
9
Of the 7,325 unique restating rms present in Audit Analytics' restatement database, the above matching procedure yields 3,817 rms matched
with their GVKEY in Compustat.
The nancial statement information is taken from the add-on database to
Compustat, Unrestated Quarterly.
This contains both the original, un-
8
http://wrds-web.wharton.upenn.edu/wrds/support/Additional\%20Support/
WRDS\%20Knowledge\%20Base\%20with\%20FAQs.cfm?folder_id=645&article_id=1610
9
More
function
tance
specically,
we
COMPGED(),
between
two
perform
which
distances,
a
returns
i.e.,
the
fuzzy
the
merge
using
generalised
dissimilarity
between
the
SAS
edit
dis-
the
two
(http://support.sas.com/documentation/cdl/en/lrdict/64316/HTML/default/viewer.htma002206133.htm).
To be certain that the rm from Compustat is in fact identical to the restating rm from
Audit Analytics, we accept the match if the distance between the two strings is less than
100. A COMPGED score of 0 indicates perfect match (e.g. BIODEL INC = BIODEL
INC), whereas a COMPGED score of 90 indicates a nearly perfect match (e.g.
HOLDINGS LTD = R.V.B. HOLDING, LTD.)
35
RVB
10
restated data value and the backlled, restated data value for each item.
Even though some researchers (e.g.
Dechow et al. (2011)) have used
restated data values, we prefer using the original values for several reasons:
First, according to Standard & Poor's,
11 the number of rms in Compu-
stat with restated data values is as high as 35%.
Second, the sign and magnitude of the dierence between the restated
and unrestated data values varies enormously and independently on each of
the three dimensions: from rm to rm, from time period to time period,
and from data item to data item. Thus, it is not possible a priori to predict
which data values that will dier and in which direction.
Third, the dierence between the two values is material for some data
items, which can inuence results.
Table 3: Compare original/restated values for Restating Firms
Line Item
Original Value
Restated Value
Dierence
-1284.5***
Assets
2,268.6
3,553.1
COGS
1,331.0
1,324.6
6.4
Debt, long-term
644.0
644.8
-0.8
Debt, short-term
414.8
418.2
-3.4**
8.22
8.31
-0.09
Earnings Per Share
Net Income
Operational Cash Flows
R & D Expense
Revenue
85.5
85.5
0.0
165.2
198.4
-33.2***
91.9
91.5
0.4**
1,496.8
1,841.0
-344.2***
The table depicts the matched restaters in all available years. In millions
USD, except earnings per share.
All items have been winsorised at the 1st and 99th percentile.
*, **, *** denote signicant dierence from 0 at the 10%, 5%, and 1% levels,
respectively. Using paired t-tests.
Table 3 compares specic line items that previous literature has identied as
being the common reasons for restatements. More specically, Scholz (2008)
states that the majority of restatements are due to revenue recognition, core
expenses (e.g. R& D expenses), and disclosure issues (such as reclassication
of debt).
It is clear that material dierences exist between restated and original data
values for some of the line items of interest. In particular, both assets and
revenue show signicant dierences.
This is in line with the argument of
Scholz (2008) that many restatements contain revenue recognition issues, as
described in Section 4.
10
I gratefully acknowledge the nancial support of FSRs Studie- & Understøttelsesfond
to buy access to the Compustat Unrestated Quarterly Database.
11
http://www.charteroaksystems.com/data_products/compustat/unrestated.html
36
Since Compustat Unrestated Quarterly is given in quarterly data, we
convert it to annual data by using the fourth quarter amount for balance
sheet and cash ow amounts and by summarising the four quarters for each
year to one year for the income statement amounts.
Furthermore, all data values are winsorised at the 1st and the 99th percentile.
6.2 Matching Procedure
For each restating rm, we nd a match among all Compustat rms, that
are not identied by SEC. The Compustat rm must meet the following
requirements:
•
Financial statement information in the restatement year
•
Same 1-digit SIC code
•
Total assets within
•
ROA within
±
±
40%
40%
If a restater has more than one match, the joined absolute dierence between
assets and ROA is minimised. Of the 3,817 restating rms, 2,028 obtain a
unique match.
Each restating rm has one exact match in the control group of non-restating
rms, with correspondent restatement and non-restatement years, respectively.
before and after the restatement and non-restatement
before sample consists of ten years before the actual
We split the sample in
year, respectively. The
restatement event. If a rm has restated multiple times, the last restatement
year ends the
before
sample. The
after
sample lasts a minimum of three and
a maximum of ten year after the last restatement/non-restatement event.
The sample thus consists of rm years from 1990 (ten years before the rst
recorded restatement event in 1990) to 2011.
The matching procedure is consistent with previous research; Dechow et al.
(1996) match on industry, year, and rm size, and Beneish (1999b) use industry, year, and rm age.
The underlying assumption behind this research design is naturally that
the matched, non-restating control group on average has higher earnings
quality and/or manages earnings less than the restating rms. If the rms in
the control group also have poor earnings quality but are simply not detected
by the SEC, it will seriously inuence the conclusions. But as mentioned in
Section 5.2, since SEC mostly pursuits larger rms the matching on size
partly deals with this problem, as the control group is likely to have been
scrutinised and approved by the SEC.
37
Kothari et al. (2005) and Dechow et al. (2010) discuss another serious
concern related to a matched sample in accounting quality studies, namely
that rms in the control group have similar incentives to manage earnings. In
particular, Dechow et al. (2010) argue that earnings manipulation and earnings quality issues appear to cluster by industry. The fact that we match very
broad on industry (1-digit SIC code) partly mitigates this issue, even though
it remains a concern that has to be taken into account when interpreting the
results.
If both of the above concerns impact the results, it will increase the
likelihood of maintaining the hypothesis of no dierence in earnings quality
12 .
between the two groups, i.e., Type II errors
6.3 Accounting Quality Metrics
Below is a description of how earnings quality is measured using all 16 metrics. An overview is depicted in Table 4.
Accruals Models
The original Jones Model measured abnormal accruals in only one year and
hypothesised income decreasing earnings management, thereby having an a
priori expectation of the direction of the accruals.
My research design is dierent on these two parameters. First, the interest lies in estimating abnormal accruals in a longer time period, and second,
we have no hypothesis concerning the direction of the earnings management,
and therefore abnormal accruals can take on both positive and negative values.
We therefore alter the Jones Model in two ways to deal with these issues.
To solve the rst issue, namely nding the abnormal accruals in the
two time periods (before and after the restatement event, respectively), we
estimate The Jones Model as xed-eects panel regressions in each of the 48
Fama-French (FF) industries
13 . Under this approach, we assume that the
unobserved heterogeneity inuencing the level of accruals is xed over time
in each FF industry. Thus, only time-varying unobserved factors remain in
the error term which allows us to examine how discretionary accruals for
14
each rm in each of the 48 industries change over time.
To solve the second issue, we use the solutions proposed in the methodological paper by Hribar and Nichols (2007).
12
13
A failure to reject a false null hypothesis
Available
Siccodes48.txt.
14
They describe that discre-
from
http://staff.washington.edu/edehaan/pages/Programming/
It is not possible to use rm-xed eects in the Jones Model. To see this, remember
that xed eects place the unobserved eects, that are constant over time, in a constant
variable capturing the unobserved eect.
Since the unobserved eect is removed from
the residual, and the measure of discretionary accruals is the residual, the approach will
underestimate the level of abnormal accruals.
38
39
The Modied Jones
Model
The Jones Model
T Ait = αi + β1i ∆REVit
+β2i P P Eit + ρ(CF O)it + T Ait−1 + εit
T Ait = αi + β1i ∆REVit − ∆RECit
+β2i P P Eit + ρ(CF O)it + T Ait−1 + εit
= (Income bef. Ex. Items
= Sales (12)/Lagged Assets (6)
= Gross Property, Plant, and
= (Income bef. Ex. Items
= Sales (12)/Lagged Assets (6)
= Gross Property, Plant, and
= Receivables (2)/Lagged Assets
Activites (308)/Lagged Assets (6)
= Cash Flows from Operating
Equipment (7)/Lagged Assets
(6)
REV
REC
PPE
CFO
(124)))/Lagged Assets (6)
Activites (308)-Ex. Items
(123)-(Cash Flows from Operating
TA
Activites (308)/Lagged Assets (6)
= Cash Flows from Operating
Equipment (7)/Lagged Assets
REV
PPE
CFO
(124)))/Lagged Assets (6)
Activites (308)-Ex. Items
(123)-(Cash Flows from Operating
TA
Table 4: Calculation of Earnings Quality Metrics
Residual represents
discretionary
accruals. High levels
indicate poor
earnings quality
Residual represents
discretionary
accruals. High levels
indicate poor
earnings quality
40
Modied
Dechow-Dichev
Model
Dechow-Dichev
Model
The Performancematched Modied
Jones Model
+β4 ∆RECit + β5 P P Eit + εit
+β2 CF Oit + βCF Oit+1
∆W Cit = β0 + β1 CF Oit−1
+β2 CF Oit + β3 CF Oit+1 + εit
∆W Cit = β0 + β1 CF Oit−1
+δ4 ROAit + ρ(CF O)it + T Ait−1 + εit
T Ait = δ0 + δ1 + δ2 ∆REVit + δ3 P P Eit
TA
= (Income bef. Ex. Items
= Sales (12)/Lagged Assets (6)
= Gross Property, Plant, and
= Cash Flows from Operating
= Net income (172)/Total Assets
∆
∆
Taxes Accrued
= Cash Flows from Operating
∆
∆
Taxes Accrued
= Cash Flows from Operating
= Sales (12)/Lagged Assets (6)
Equipment (7)/Lagged Assets (6)
= Gross Property, Plant, and
Activites (308)/Lagged Assets (6)
CFO
PPE
REC
Acc.
Other Assets and Liabilities
(307))/Lagged Assets (6)
(305) +
Payables (304) +
∆
= -(∆ Acc. Receivables (302)
Inventory (303) +
WC
∆
∆
+
Activites (308)/Lagged Assets (6)
CFO
Acc.
Other Assets and Liabilities
(307))/Lagged Assets (6)
(305) +
Payables (304) +
∆
= -(∆ Acc. Receivables (302)
Inventory (303) +
WC
∆
∆
+
Activites (308)/Lagged Assets (6)
(6)
Equipment (7)/Lagged Assets
REV
PPE
ROA
CFO
(124)))/Lagged Assets (6)
Activites (308)-Ex. Items
(123)-(Cash Flows from Operating
High variance in the
residual indicates
poor earnings
quality
High variance in the
residual indicates
poor earnings
quality
Residual represents
discretionary
accruals. High levels
indicate poor
earnings quality
41
Asymmetric
Timeliness
Small Loss
Avoidance
Avoiding Earnings
Decreases
Change in Accruals
Magnitude of
Accruals
(N EGDU Mit−1 ∗ ∆N Iit−1 ) + υit
α2 ∆N Iit−1 + α3
∆N Iit = α0 + α1 N EGDU Mit−1 +
SP N Iit
SN N Iit
N Iit
Ait−1
∆T Ait
Ai,t−1
T Ait
Ai,t−1
TA
= Income bef. Ex. Items
= Indicator variable equalling 1
= Indicator variable equalling 1
∆
NI is less than 0
= Indicator variable
= Net Income (172)/Lagged Assets
equalling 1 if
(6)
NI
NEGDUM
(6)) is between 0 and -0.01
if (Net Income (172)/Lagged Assets
SNNI
(6)) is between 0 and 0.01
if (Net Income (172)/Lagged Assets
SPNI
= Total Assets (6)
= Net Income (172)
= Total Assets (6)
NI
A
A
Activites (308)-Ex. Items (124))
(123)-(Cash Flows from Operating
= Income bef. Ex. Items
= Total Assets (6)
TA
A
Activites (308)-Ex. Items (124))
(123)-(Cash Flows from Operating
accounting quality
α3 < 0 implies high
High ratios indicate
poor earnings
quality
Distribution
dierent from 0
signify poor
earnings quality
High levels indicate
poor earnings
quality
High levels indicate
poor earnings
quality
42
Persistence
Correlation between
Accruals and Cash
Flows
Variability of
Earnings
Variability of
Earnings to Cash
Flows
Timely Loss
Recognition
Xi,t = φ0,i + φ1,i Xi,t−1 + εi,t
ρ(∆N Iit , ∆CF Oit )
σ(∆N Iit )
σ(N Iit )
σCF Oit
N Iit
Ait
= Net income (172)
= Net Income (172)/ Lagged
= Net Income (172)/ Lagged
= Net Income (172)/Common
Shares Outstanding (25)
X
(124))/Lagged Assets (6)
Activites (308)-Ex. Items
= (Cash Flows from Operating
Assets (6)
NI
CFO
Assets (6)
NI
(124))/Lagged Assets (6)
Activites (308)-Ex. Items
= (Cash Flows from Operating
Assets (6)
= Net Income (172)/ Lagged
NI
CFO
= Total Assets (6)
NI
A
Low values of φ0,i
indicate
impersistent
earnings
Large negative
correlations indicate
smoothing
Low variability
indicates smoothing
High ratios indicate
earnings smoothing
High frequency of
large losses indicate
high earnings
quality
43
Predictability
Xi,t = φ0,i + φ1,i Xi,t−1 + εi,t
X
= Net Income (172)/Common
Shares Outstanding (25)
High variance of the
error term indicates
poor earnings
quality
tionary accruals models are increasingly used with unsigned values of abnormal accruals to test for overall dierences in earnings quality.
Using
the absolute rather than the signed value of discretionary accruals obviously changes the distribution of residuals, since they are truncated at 0. In
addition, Hribar and Nichols (2007) show that unsigned discretionary accruals have a dierent probability function than signed discretionary accruals.
More specically, they show that the expected value of absolute discretionary
accruals is an increasing function of the residual variance. Therefore, they
suggest that the driver of the residual variance is controlled for in research
designs. In line with this, we add the volatility of operating cash ows of
each rm as an additional regressor, to control for operating volatility.
In accordance with with prior research (e.g. Louis and White (2007)),
we include lagged accruals in the regression to control for the mean reversion
of accruals.
Consistent with for instance Francis et al. (2005), we estimate discretionary
accruals directly as the residuals, rather than using a two-stage approach.
We predict that restaters have higher levels of absolute discretionary
accruals in the years before the restatement.
Accruals Quality Models
Working capital accruals are estimated using the approach of Dechow and
Dichev (2002).
We follow Francis et al. (2004) in estimating Equation 4 and 5 over
rolling, rm-specic windows, ten years in the
minimum of three years in the
after
before
sub-sample, and a
sub-sample. The panel regressions for
both the original and the modied Dechow-Dichev Model are run with rmxed eects and the standard deviation of residuals for each rm is the
Dechow-Dichev measure of accruals quality.
We predict that restaters have higher standard deviation of residuals
than non-restaters in the years before the restatement.
Accruals Models
We estimate accruals directly as the dierence between cash ows and earnings. Since the reporting of cash ows was made mandatory with the FASB
Statement No.
95 (eective from 1988), this method has by far been the
most widely used.
More specically, my measure of accruals stems from Hribar and Collins
(2002), who nd accruals as earnings before extraordinary items, less operating cash ows from continuing operations. They suggest that this method
contains less measurement error than the balance sheet approach used before.
We test if the level and change in accruals, respectively, are signicantly
dierent for the restaters and non-restaters, and before and after the last
restatement event. In accordance with Dechow and Dichev (2002), the absolute magnitude of accruals is the variable of interest.
44
We expect to nd higher levels of and yearly changes in accruals for
restaters before the restatement event.
Avoiding Earnings Decreases and Small Losses
We follow Burgstahler and Dichev (1997) in comparing distributions of earnings but scale with lagged assets rather than market value of equity.
We
tabulate results for distributions of change in earnings.
It is tested if the distributions of earnings for restaters and non-restaters,
respectively, are signicantly dierent from each other. Table 7 and 8 show
the median of change in earnings and the dierence between the two is tested
with the Wilcoxon Two-Sample Test for dierences in distributions.
When comparing the frequency of small losses and small prots we use
the approach of Burgstahler et al. (2006).
Restaters are predicted to have a higher median of change in earnings
and a smaller proportion of small losses to small prots, than non-restaters
do.
Timeliness
The piecewise regression suggested by Basu (1997) is estimated as a panel
regression with rm-xed eects in both groups and time periods. We follow the approach of Ball and Shivakumar (2005). Table 7 and 8 show the
coecient on
N EGDU M ∗ ∆N I ,
which is expected to be more negative for
non-restaters than restaters.
We tabulate the number of large losses as the percentage of the total
number of observations.
Smoothness
The variability of change in earnings and the variability of earnings to variability of cash ows are calculated on rm level and compared for the two
groups.
Restaters are expected to smooth more, i.e.
have lower variance
than non-restaters.
Spearman correlations are calculated between accruals and cash ows as the
last measure of earnings smoothing. The correlations are anticipated to be
more negative for restaters.
Persistence
In accordance with Francis et al. (2004), a one-order autoregressive regression
is estimated for each rm using maximum likelihood estimation, in the before
and after period, respectively. The parameter estimates on
φ
are outputted
and compared for restaters and non-restaters.
We expect that non-restaters have more persistent earnings, i.e.,
φ closer
to 1.
Predictability
The autoregressive regression is estimated as described above, and we compare the standard deviation of residuals for each rm.
Higher variance of the error term is expected for restaters.
45
6.4 Descriptive Statistics
Table 5 shows all the restatements from Audit Analytics (matched with
unique GVKEY) before each restating rm is matched with a non-restating
counterpart.
This corresponds to 5,336 restatement events shared among
3,817 unique rms. Thus, each rm on average restated 1.4 times.
Table 5: Restatements divided in Years
Restatement Year
Number of Restatements
Percent
2000
283
5.3%
2001
338
6.3%
2002
348
6.5%
2003
466
8.7%
2004
528
9.9%
2005
898
16.8%
2006
878
16.5%
2007
569
10.7%
2008
422
7.9%
2009
299
5.6%
2010
307
5.8%
Sum
5,336
100%
It is clear that the number of restatements increased signicantly in the
mid-2000s. This could be due to change in SEC's identication procedure,
the downturn in the American economy in the beginning of the new millen-
46
nium, and the Sarbanes-Oxley Act (Scholz, 2008).
Table 6 presents a comparison of restating rms in my sample to all Compustat rms. It seems as if restating rms are overrepresented in the service
industry. Apart from this the restating sample is quite heterogeneous. It is
also evident that restaters represent the overall population quite well.
Panel B depicts some of the variables of interest. In particular net income
stands out as it is considerably smaller for restaters. This ts well with the
observation of Scholz (2008), who notes that the vast majority of restatements reduce income.
Table 6: Comparison of Restaters and All Compustat Firms
Panel A: Industry
Industry
Agriculture, Forestry, And
SIC-code
Restaters
Compustat
01-09
0.3
0.4
Fishing
Mining And Construction
10-17
8.2
6.7
Manufacturing
20-39
37.8
37.2
Transportation,
40-49
9.0
10.4
Wholesale And Retail Trade
50-59
9.4
8.9
Finance, Insurance, And Real
60-67
12.1
18.0
Services
70-89
21.3
16.7
Public Administration
91-99
2.0
1.7
100%
100%
Restaters
Compustat
4,733.0
4,872.4
12.8
103.9
168.3
197.1
Communications, Electric,
Gas, And Sanitary Services
Estate
Panel B: Size and ROA
Assets
Net Income
Cash Flows from Operations
Using unrestated data values. All amounts in millions USD.
Table 7 tabulates descriptive statistics for the key variables used in the analyses. It shows that the match is quite good and the two groups resemble each
other. However, earnings is again smaller for restaters, following the same
argument as above that the majority of restatements are income decreasing.
47
Table 7: Descriptive Statistics of Key Variables
Restaters
Variable
A: Total Assets
NI: Net Income
CFO: Operational
Mean
Median
2071.2
73.1
Non-Restaters
Std. Dev.
Mean
Median
Std. Dev.
393.8
5272.0
2287.7
384.4
6047.5
10.7
1232.8
117.3
13.3
1192.4
177.1
25.6
519.1
214.9
31.7
581.8
-109.8
-14.3
1257.5
-97.6
-14.5
1096.8
Cash Flows
TA: Accruals
WC: Working Capital
PPE: Property, Plant
4.5
0.4
72.7
4.4
0.4
82.4
1197.5
142.6
3120.4
1207.6
140.6
3241.2
1679.7
370.6
4133.0
1892.2
367.2
4646.4
221.0
42.9
594.5
245.9
44.9
669.7
0.0
0.0
0.7
0.0
0.1
0.3
& Equipment
REV: Revenue
REC: Receivables
ROA: Return on
Assets
Using unrestated data values, winsorised at the 1st and 99th percentile.
All amounts in millions USD.
7
Empirical Results
7.1 Before Restatement Event
Table 8 compares the earnings quality of restaters and non-restaters before
the (last) restatement event.
The accruals models, the measure of time-
liness, and the persistence and predictability generally show lower quality
for restaters, as predicted. The measures of earnings smoothing, however,
indicate that restaters in fact smooth less than non-restaters, contrary to
expectations.
All the abnormal accruals models are highly signicant in the expected
direction. Thus, the level of discretionary accruals are considerably larger
for restaters than for non-restaters in the years before a restatement. The
same is the case for the accruals quality models. The restaters have larger
standard deviation of residuals, indicating poorer earnings quality, although
this is insignicant for the Modied Dechow-Dichev Model.
In the other accruals models, the absolute value of accruals is greater for
restaters than non-restaters, as expected. Surprisingly, the absolute change
in accruals is larger for non-restaters.
The measure of avoidance of earnings decreases moves in the opposite
direction of the expected. This is possibly connected with results from Table
6 which shows that restaters have considerably smaller earnings than nonrestaters.
The proportion of small prots to small losses shows no real dierences be-
48
Table 8: Restaters and Non-Restaters
Metric
Abnormal
Accruals
Models
Before
Prediction
the Restatement Event
Restaters
Non-Restater
Dierence
Jones Model
>
0.1471
0.1144
0.0328***
Modied Jones
>
0.1527
0.1193
0.0333***
>
0.1380
0.1068
0.0312***
>
0.0223
0.0201
0.0022*
>
0.0231
0.0212
0.0019
|>|
-0.0752
-0.0679
-0.0073
|>|
0.0005
-0.0040
0.0045*
>
0.0367
0.0481
-0.0114***
Loss Avoidance
>
0.26
0.24
0.02
Timeliness
>
0.2167
-0.6104
0.8271
Timely Loss
<
8.3047
7.6465
0.6582**
<
4.5880
4.4629
0.1251
Model
PerformanceMatched Mod.
Jones Model
Accruals
Quality
Models
Dechow-Dichev
Model
Modied
Dechow-Dichev
Other
Accruals
Models
Magnitude in
Accruals
Change in
Accruals
Avoiding
Decreases
and Small
Losses
Asymmetric
Timeliness
Avoiding Small
Earnings
Decreases
Recognition
Var. of Earnings
Smoothness
to Cash Flows
Var. of Earnings
<
0.2507
0.2068
0.0439*
Corr. between
<
-0.4247
-0.4479
0.0233
Persistence
<
0.3254
0.3508
-0.0254
Predictability
>
0.8714
0.8652
0.006
Accruals and Cash
Flows
Persistence
Predictability
The prediction on the relation between restaters and non-restaters follows the hypothesis that the
restaters have lower earnings quality than the non-restaters before the restatement event.
*, **, *** denote signicant dierence from 0 at the 10%, 5%, and 1% levels, respectively, with
two-sample t-test.
49
tween the two groups, and it seems as if both avoid small losses.
There
could be several reasons for this nding: Either all rms manage earnings or
take real actions to avoid small losses, or perhaps it is a part of the accrual
process to report fewer losses than prots.
While non-restaters as anticipated have less persistent negative earnings
changes, they actually report large losses less frequently.
The three components of smoothing all move in the opposite direction
of the expected, although only one of them signicantly so. Variability of
earnings to variability of cash ows shows no dierence between the two
groups, whereas restaters have more volatile earnings, indicating less earnings smoothing. Correlations between accruals and cash ows move opposite
than expected, although highly insignicant (p-value 32%).
Non-restaters have more persistent earnings the restaters, although marginally
insignicant (p-value 12%), whereas predictability is similar for the two
groups.
In sum, the majority of the metrics show that restaters have poorer
accounting quality even in the years before the restatement, supporting my
rst hypothesis. However, some of the metrics reach the opposite conclusion,
but this is often the case in earnings quality studies, since each of the metrics
measures a distinct feature of the nancial statements.
7.2 After Restatement Event
Results for the earnings quality metrics for both groups in the years after
the (last) restatement event are tabulated in Table 9.
While the restaters appear to have improved the quality of earnings on
some dimensions (e.g. asymmetric timeliness and loss avoidance), material
dierences still remain on other measures (e.g. all the accruals models and
persistence).
Signicant dierences remain after the restatement event
between restaters and non-restaters, respectively, measured both with the
abnormal accruals models and the accruals quality models. Thus, restaters
still have lower earnings quality than non-restaters.
The same is the case
for the other accrual models, even though the sign of change in accruals has
changed direction.
In accordance with results from Table 8, the measure of avoiding earnings
decreases is signicantly dierent for the two groups.
For asymmetric timeliness it is seen that restaters and non-restaters report
large losses in an equally timely manner, hence it seems as if restaters have
improved.
Restaters seem to smooth more than non-restaters following earnings to
cash ows, but focusing only on the variability of earnings, restaters in fact
smooth less. Correlation coecients show no real dierences.
Non-restaters have less persistent earnings than non-restaters, although insignicantly so (p-value 11%), whereas predictability - like before - is almost
50
Table 9: Restaters and Non-Restaters
Metric
Abnormal
Accruals
Models
Prediction
After
the Restatement Event
Restaters
Non-Restater
Dierence
Jones Model
=
0.1027
0.0935
0.0092**
Modied Jones
=
0.1070
0.0979
0.0091**
=
0.0964
0.0887
0.0077*
=
0.0085
0.0074
0.0011
=
0.0107
0.0094
0.0014*
|=|
-0.0990
-0.0702
-0.0288*
|=|
-0.0129
0.0133
-0.0262
=
0.0335
0.0475
-0.014***
Loss Avoidance
=
0.20
0.27
-0.07
Timeliness
=
-0.0695
-0.6808
0.6113
Timely Loss
=
8.7219
6.7791
1.9428
=
3.2291
5.1454
-1.9163
Var. of Earnings
=
0.1446
0.0877
0.0569
Corr. between
=
-0.4647
-0.4736
0.0089
Persistence
=
0.2494
0.2829
-0.0335
Predictability
=
0.7928
0.8116
-0.0188
Model
PerformanceMatched Mod.
Jones Model
Accruals
Quality
Models
Dechow-Dichev
Model
Modied
Dechow-Dichev
Other
Accruals
Models
Magnitude in
Accruals
Change in
Accruals
Avoiding
Decreases and
Small Losses
Asymmetric
Timeliness
Avoiding Small
Earnings
Decreases
Recognition
Var. of Earnings
Smoothness
to Cash Flows
Accruals and Cash
Flows
Persistence
Predictability
The prediction follows the prediction that the restaters and non-restaters have identical earnings
quality after the restatement.
*, **, *** denote signicant dierence from 0 at the 10%, 5%, and 1% levels, respectively, with
two-sample t-test.
51
similar.
Even though the restaters seem to have improved on some parameters of
earnings quality, some of the dierences from before the restatement remain
afterwards as well. However, it is neither possible to maintain nor reject the
second hypothesis.
7.3 Dierence-in-Dierence
Despite the strengths of the matched sample design outlined above, it does
not fully control for dierences in the economic environment.
also compare the
change
Therefore I
in earnings quality before and after a restatement
event for both restaters and non-restaters. The dierence-in-dierence test
is thus a test of Hypothesis 3.
In this dierence-in-dierence setting each
rm acts as its own control. This also means that each restatement rm and
its control are aected equally by outside factors.
Under the expectation that restaters improve earnings quality following
a restatement, it is expected that the change towards higher earnings quality
is higher for restaters than for non-restaters.
Table 10 shows results of the dierence-in-dierence tests. The specic
tests are combined in headers by averaging each metrics, except Avoiding
Earnings Decreases and Timely Loss Recognition, where only the respective
tests are tabulated.
15
For almost all dimensions of earnings quality, restaters have improved. However, this is also the case for most of the non-restaters, and on no dimensions
is the change between the two groups signicant.
Similar with the ndings in Table 8 and 9, restaters have larger amounts
of discretionary accruals than non-restaters before and after the restatement
event.
However, both groups experience a drop in discretionary accruals
between the two periods. It is therefore not possible to attribute the change
to the restatement event only.
The same is the case in the accruals quality models.
Here both groups
experience signicant increases in earnings quality, but the increase is not
signicantly larger for restaters than for non-restaters.
While restaters decrease their level and change of accruals from before the
restatement event to after, the opposite in fact happens for non-restaters.
The dierence between the two is not signicant at conventional levels, but
it still seems as if restaters increase earnings quality in the period, while
non-restaters experience a decrease.
15
Each of the two groups (avoiding earnings decreases and small loss avoidance, and
timeliness and timely loss recognition) is too heterogeneous to combine into a single metric.
Both dierence-in-dierence for the untabulated metrics move in the expected direction,
although this is insignicantly dierent for restaters and non-restaters
52
53
i > ii
Persistence
Predictability
i
-0.0678
-0.1073***
-0.6497
-1.8696
-0.0312**
-0.0183
-0.0112***
-0.0345***
After-Before: Restaters ( )
ii )
0.0357
-0.0734***
0.0851
-2.5946*
-0.0090
0.0064
-0.0097***
-0.0286***
After-Before: Non-Restaters (
*, **, *** denote signicant dierence from 0 at the 10%, 5%, and 1% levels, respectively. P-value in parentheses.
Restater vs. non-restater tested with two-sample t-test, before vs. after with paired t-test.
i < ii
i < ii
Smoothness
i > ii
i < ii
i > ii
Other Accruals Models
Timely Loss Recognition
i > ii
Avoiding Earnings Decreases
i > ii
Accruals Quality Models
Prediction
Abnormal Accruals Models
Metric
Table 10: Dierence-in-Dierence
i-ii )
-0.1035 (0.30)
-0.0339 (0.39)
-0.7348 (0.60)
0.7250 (0.22)
-0.0222 (0.18)
-0.0247 (0.12)
-0.0015 (0.21)
-0.0059 (0.60)
Dierence (
Restaters experience a signicant increase in earnings quality as measured
by avoiding earnings decreases, while non-restaters only experience a very
small, insignicant increase. However, the dierence between the two is not
signicant at conventional levels.
Both restaters and non-restaters report large losses less frequently after
the restatement event, the latter group in fact signicantly so.
Hence, it
seems as if both groups increase earnings quality, but this increase is not
signicantly larger for restaters than non-restaters.
The smoothing metrics all move in the opposite direction of what is
expected.
Hence, it actually seems as if restaters smooth earnings more
after a restatement, whereas non-restaters smooth slightly less.
Both groups experience signicant drops in persistence over the time
span. Restaters have more predictable earnings in the after-period, whereas
the opposite is true for non-restaters. Although the dierence is insignicant,
it does seem as if restaters improve more than non-restaters in the time
period.
In sum, restaters improve their accounting quality over the time period
using some of the metrics, but not more than non-restaters. It is not possible
to attribute the improvement to the restatement event for the restaters, since
the matched control group was subject to an improvement as well.
The fact that accounting quality seems to improve over the period for
both groups on some parameters can be attributed to a number of factors.
For instance, Singer and You (2011) nd that earnings quality improves after
the enactment of SOX which could inuence the results. Macro economic
factors or the economic downturn in the middle of the period could also
change the earnings quality.
Finally, anecdotal evidence suggests that while the number of restatements has increased, their severity has decreased.
This could be due to
SOX, increased scrutiny by the SEC in the post-Enron period, or that rms
generally are less afraid of the market reactions to restatements. This might
also drive the improvement in earnings quality for the accruals models.
Recall, though, that other metrics in fact move in the opposite direction. For instance, earnings seem to have become less persistent, as is also
suggested by Dechow and Schrand (2004).
8
Robustness Tests
The fact that the restatement events happen in dierent calender years (from
2000 until 2010) mitigates a possible eect from changes in macro economic
variables and the overall economic environment.
These eects are further
controlled for by using the dierence-in-dierence design.
A common concern when working with restatements in an accounting
quality context is the fact that endogeneity issues might be present. This is
54
55
0.3254
Persistence
0.3508
-0.4479
0.2068
4.4629
0.0481
0.0212
-0.0679
0.1068
0.1193
0.1144
Non-Restaters
3.2291
0.1446
-0.4647
0.2494
Var. of Earnings
Corr. between Accruals and Cash Flows
Persistence
0.0335
Avoiding Earnings Decreases
Var. of Earnings to Cash Flows
-0.0990
-0.0129
Change in Accruals
Performance-Matched Mod. Jones Model
Magnitude in Accruals
0.1070
0.0964
Modied Jones Model
0.1027
Jones Model
Restaters
0.2829
-0.4736
0.0877
5.1454
0.0475
0.0133
-0.0702
0.0887
0.0979
0.0935
Non-Restaters
Normal Sample
-0.4247
Corr. between Accruals and Cash Flows
Panel B: After Sample
0.2507
Var. of Earnings
0.0005
Change in Accruals
0.0367
-0.0752
Magnitude in Accruals
4.5880
0.1380
Performance-Matched Mod. Jones Model
Var. of Earnings to Cash Flows
0.1527
Avoiding Earnings Decreases
0.1471
Restaters
Normal Sample
Modied Jones Model
Before Sample
Jones Model
Panel A:
0.3018
-0.4374
0.2522
7.6135
0.0291
0.0115
-0.0726
0.1042
0.1178
0.1147
Non-Restaters
0.2494
-0.4647
0.1446
3.2291
0.0335
-0.0129
-0.0990
0.0964
0.1070
0.1027
Restaters
0.2829
-0.4736
0.0877
5.1454
0.0475
0.0133
-0.0702
0.0887
0.0979
0.0935
Non-Restaters
Delete First Restatement Year
0.3117
-0.4356
0.3254
2.3050
0.0013
0.0236
-0.0801
0.1449
0.1602
0.1567
Restaters
Delete First Restatement Year
Table 11: Robustness Tests
0.3508
-0.4479
0.2068
4.4629
0.0481
0.0212
-0.0679
0.1068
0.1193
0.1144
Non-Restaters
0.2312
-0.4726
0.1818
2.7620
-0.006
-0.0723
-0.1595
0.0997
0.1089
0.1050
Restaters
0.2496
-0.4717
0.1323
3.5505
0.0236
0.0120
-0.0722
0.0958
0.1047
0.1012
Non-Restaters
Delete First Year After Restatement
0.3254
-0.4247
0.2507
4.5880
0.0367
0.0005
-0.0752
0.1380
0.1527
0.1471
Restaters
Delete First Year After Restatement
the case if the SEC also uses some of the earnings quality metrics to identify
the restaters which might lead to reversed causality, so that the likelihood of
restating inuences the outcome of the earnings quality metric. Even though
this explanation cannot be entirely ruled out, it is strongly impeded by the
large amount of earnings quality metrics included in the tests. Given that
they measure dierent aspects of earnings quality, aggregated they are likely
to measure some underlying construct of the quality of nancial statements.
Table 17 tabulates sensitivity checks using a sample of the quality metrics,
specically the abnormal accruals models, other accruals models, avoiding
earnings decreases, smoothing, and persistence.
One possible concern is whether the poor quality detected in the years
before the restatement is driven by any restatements in the
before
sample.
We therefore remove these for the rms that have restated more than once.
Results are tabulated in the second column of Panel A, along with the results
from table 8 and 9 for comparative purposes.
Overall, this control does not seem to make material dierences to the
results. However, the two smoothness measures of variability of earnings are
larger when removing the rst restatement years. Most likely, this is merely
a consequence of the fact that we exclude one year in the period. Annual
variances are then articially enlarged.
Another issue is connected with measuring the quality of earnings after
a restatement. One can imagine that there is a time lag before the improvement takes place or before it can actually be measured. Therefore we remove
the rst year after the last restatement in the
after
sample to see if it in-
uences results. Results are shown in the third column of Table 11 Panel
B.
No material dierences seem to exist after this control and hence it is
unlikely that the earnings quality of restaters has improved signicantly more
than non-restaters, even when allowing a one-year time lag.
9
Conclusion
Using a broad portfolio of earnings quality metrics and a sample of rms
required to restate by the SEC, we nd the following: My results indicate
that the poor earnings quality in the restatement year detected in previous
studies is evident up to ten years before the actual restatement.
It also
seems as if the dierence in earnings quality between restaters and nonrestaters is smaller after a restatement event.
However, we show with a
dierence-in-dierence research design that restaters do not improve
more
than non-restaters. It is therefore not possible to attribute the improvement
to the actual restatement event.
To my knowledge, this is the rst study to examine the educative role of
a restatement event, measured on the accounting quality of restating rms.
56
The fact that we cannot statistically isolate the eect of a restatement in the
quality of earnings is quite surprising. In particular, given the severe market
reactions to restatements, the nancial markets clearly take restatements
very serious. Yet, this does not lead to an immediate, material improvement
in the earnings quality of restating rms.
Assuming that the metrics we
apply accurately measure accounting quality, my results indicate that the
scrutiny and intervention of SEC does not necessarily lead to a change in
the behaviour of the restating rms. This point is further underlined by the
fact that several of the rms in my sample restate more than once.
This paper complements previous research on the consequences of restatements (e.g. (GAO, 2006b); (Dechow et al., 1996); (Dechow et al., 2011)) and
adds further to our knowledge on how rms behave after a restatement event.
It also elaborates on research on the quality of nancial statements in the
actual restatement year (Jones et al. (2008); Richardson et al. (2002)).
The practical implications of these ndings are of particular importance to
the SEC and regulators. First, it is evident that the poor earnings quality
present in the restatement year can be measured up to ten years before the
restatement. This knowledge can be used when SEC selects rms to examine. Second, as several of the restating rms restate more than once and do
not improve the quality of earnings signicantly more than a group of similar
rms, restating rms might need the surveillance of SEC, even in the years
after they restate.
The ndings of this paper open up to a number of interesting research
questions. Suggestions for further research thus include an examination of
how rms behave after a restatement, on other parameters than earnings
quality.
It could also be interesting to study what drives the quality of
earnings in the post-restatement period.
Finally, it remains an open question if the ndings from this study will
also hold outside the US, where dierent regulatory oversight is present.
57
References
Abbott, L. J., S. Parker, and G. F. Peters (2004). Audit committee characteristics and restatements.
Auditing 23 (1), 6987.
Abdelghany, K. E. (2005). Measuring the quality of earnings.
Auditing Journal 20 (8-9), 10011015.
Altamuro, J. and A. Beatty (2010).
aect nancial reporting?
Managerial
How does internal control regulation
Journal of Accounting Economics 49 (1-2),
5858.
Altamuro, J., A. L. Beatty, and J. Weber (2005). The eects of accelerated
revenue recognition on earnings management and earnings informativeness:
Evidence from sec sta accounting bulletin no. 101.
Review 80 (2), 373401.
Accounting
Amihud, Y. and H. Mendelson (1986). Asset pricing and the bid-ask spread.
Journal of Financial Economics 17 (2), 223249.
Ashbaugh, H. and M. Pincus (2001). Domestic accounting standards, international accounting standards, and the predictability of earnings.
of Accounting Research 39 (3), 417434.
Ball, R. and L. Shivakumar (2005).
Journal
Earnings quality in uk private rms:
comparative loss recognition timeliness.
nomics 39, 83128.
Journal of Accounting Eco-
Barth, M. E., W. H. Beaver, and W. R. Landsman (2001). The relevance
of the value relevance literature for nancial accounting standard setting:
Another view.
Journal of Accounting Economics 31 (1-3), 77104.
Barth, M. E., J. A. Elliott, and M. J. Finn (1995). Market rewards associated
with increasing earnings patterns.
SSRN eLibrary .
Barth, M. E., W. R. Landsman, and M. H. Lang (2008).
accounting standards and accounting quality.
search 46, 467498.
Barth, M. E. and K. Schipper (2008).
International
Journal of Accounting Re-
Financial reporting transparency.
Journal of Accounting, Auditing Finance 23 (2), 173190.
Basu, S. (1997). The conservatism principle and the asymmetric timeliness
of earnings.
Journal of Accounting Economics 24 (1), 337.
Beekes, W., P. Pope, and S. Young (2004). The link between earnings timeliness, earnings conservatism and board composition: evidence from the
uk.
Corporate Governance: An International Review 12 (1), 4759.
58
Beneish, M. D. (1997). Detecting gaap violation: Implications for assessing
earnings management among rms with extreme nancial performance.
Journal of Accounting and Public Policy 16 (3), 271309.
Beneish, M. D. (1999a). The detection of earnings manipulation.
Analysts Journal 55 (5), 24.
Financial
Beneish, M. D. (1999b). Incentives and penalties related to earnings overstatements that violate gaap.
Accounting Review 74 (4), 425457.
Bernard, V. L. and D. J. Skinner (1996). What motivates managers' choice
of discretionary accruals?
Journal of Accounting Economics 22, 313325.
Beuselinck, C., P. Joos, I. K. Khurana, and S. Van der Meulen (2009).
Mandatory ifrs reporting and stock price informativeness.
Bhattacharya, U., H. Daouk, and M. Welker (2003).
earnings opacity.
Accounting Review 78 (3), 641678.
The world price of
Board, F. A. S. (2008). Conceptual framework for nancial reporting: The
objective of nancial reporting and qualitative characteristics and constraints of decision-useful nancial reporting information. exposure draft.
Burgstahler, D. C. and I. D. Dichev (1997). Earnings management to avoid
earnings decreases and losses.
Journal of Accounting Economics 24 (1),
99126.
Burgstahler, D. C., L. Hail, and C. Leuz (2006). The importance of reporting
incentives: Earnings management in european private and public rms.
The Accounting Review 81 (5), 9831016.
Burns, N. and S. Kedia (2006). The impact of performance-based compensation on misreporting.
Journal of Financial Economics 79 (1), 3567.
Chen, S., T. Shevlin, and H. Tong Yen (2007). Does the pricing of nancial
reporting quality change around dividend changes?
Research 45 (1), 140.
Journal of Accounting
Cohen, D. A. (2008). Does information risk really matter? an analysis of the
determinants and economic consequences of nancial reporting quality.
Asia Pacic Journal of Accounting and Economics, Vol. 15, No. 2, pp.
69-90, August 2008 .
Comiskey, E. and C. W. Mulford (2000).
Analysis.
Guide to Financial reporting and
John Wiley Sons.
Coulton, J., S. Taylor, and S. Taylor (2005).
Is 'benchmark beating' by
australian rms evidence of earnings management?
nance 45 (4), 553576.
59
Accounting and Fi-
DeAngelo, L. E. (1986).
Accounting numbers as market valuation substi-
tutes: A study of management buyouts of public stockholders.
counting Review 61 (3), 400421.
The Ac-
Dechow, Patricia, M., G. Sloan, Richard, and P. Sweeney, Amy (1995). Detecting earnings management.
Accounting Review 70 (2), 193225.
Dechow, P., W. Ge, and C. Schrand (2010). Understanding earnings quality: A review of the proxies, their determinants and their consequences.
Journal of Accounting and Economics 50 (2-3), 344401.
Dechow, P. M. (1994). Accounting earnings and cash ows as measures of
rm performance - the role of accounting accruals.
and Economics 18 (1), 342.
Journal of Accounting
Dechow, P. M. and I. D. Dichev (2002). The quality of accruals and earnings:
The role of accrual estimation errors.
Accounting Review 77, 3559.
Dechow, P. M., W. Ge, C. R. Larson, and R. G. Sloan (2009). Predicting
material accounting misstatements.
SSRN eLibrary .
Dechow, P. M., W. Ge, C. R. Larson, and R. G. Sloan (2011).
ing material accounting misstatements.
search 28 (1), 1782.
Predict-
Contemporary Accounting Re-
Dechow, P. M., W. Ge, and C. M. Schrand (2009).
Understanding earn-
ings quality: A review of the proxies, their determinants and their consequences.
SSRN eLibrary .
Dechow, P. M., S. A. Richardson, and I. Tuna (2003).
Why are earnings
kinky? an examination of the earnings management explanation.
of Accounting Studies 8 (2/3), 355384.
Dechow, P. M. and C. M. Schrand (2004).
Dechow, P. M. and D. J. Skinner (2000).
Earnings Quality.
Review
CFA Institute.
Earnings management: Recon-
ciling the views of accounting academics, practitioners, and regulators.
Accounting Horizons 14 (2), 235250.
Dechow, P. M., R. G. Sloan, and A. P. Sweeney (1996).
Causes and con-
sequences of earnings manipulation: An analysis of rms subject to enforcement actions by the sec.
Contemporary Accounting Research 13 (1),
137.
DeFond, M. L. (2010). Earnings quality research: Advances, challenges and
future research.
Journal of Accounting and Economics 50 (2-3), 402409.
DeFond, M. L. and C. W. Park (1997). Smoothing income in anticipation of
future earnings.
Journal of Accounting and Economics 23 (2), 115139.
60
Degeorge, F., J. Patel, and R. Zeckhauser (1999). Earnings management to
exceed thresholds.
Journal of Business 72 (1), 133.
Desai, H., C. E. Hogan, and M. S. Wilkins (2006).
penalty for aggressive accounting:
ment turnover.
Accounting Review 81 (1), 83112.
Dichev, I. D. and V. W. Tang (2009).
predictability.
The reputational
Earnings restatements and manage-
Earnings volatility and earnings
Journal of Accounting Economics 47 (1-2), 160160.
Easley, D. and M. O'Hara (2004). Information and the cost of capital.
nal of Finance 59 (4), 15531583.
Jour-
Ecker, F., J. Francis, I. Kim, P. M. Olsson, and K. Schipper (2006).
returns-based representation of earnings quality.
view 81 (4), 749780.
A
The Accounting Re-
Eilifsen, A. and W. F. Messier Jr. (2000). The incidence and detection of
misstatements: A review and integration of archival research.
Accounting Literature 19, 1.
EU (1978).
Journal of
Fourth council directive 78/660/eec of 25 july 1978 based on
article 54 (3) (g) of the treaty on the annual accounts of certain types of
companies.
Ewert, R. and A. Wagenhofer (2005).
Economic eects of tightening ac-
counting standards to restrict earnings management.
view 80 (4), 11011124.
The Accounting Re-
Farber, D. B. (2005). Restoring trust after fraud: Does corporate governance
matter?
The Accounting Review 80 (2), 539561.
FASB (1980). Con 2: Qualitative characteristics of accounting information.
Feroz, E. H., K. Park, and V. S. Pastena (1991). The nancial and market
eects of the sec's accounting and auditing enforcement releases.
of Accounting Research 29 (3), 107143.
Journal
Francis, J., R. Lafond, P. Olsson, and K. Schipper (2005). The market pricing
of accruals quality.
Journal of Accounting Economics 39, 295327.
Francis, J., R. LaFond, P. M. Olsson, and K. Schipper (2004). Costs of equity
and earnings attributes.
The Accounting Review 79 (4), 9671010.
Francis, J., P. Olsson, and K. Schipper (2006). Earnings quality.
and Trends in Accounting 1 (4), 259340.
GAO (2002).
Financial restatements:
Foundations
Update of public company trends,
market impacts, and regulatory enforcement activities. Technical report,
United States Government Accountability Oce.
61
GAO (2006a).
Financial restatement database.
Technical report, United
States Government Accountability Oce.
GAO (2006b).
Financial restatements: Update of public company trends,
market impacts, and regulatory enforcement activities. Technical report,
United States Government Accountability Oce.
GAO (2009).
Greater atttention needed to enhance communication and
utilization of resources in the division of enforcement. Technical report,
United States Goverment Accountability Oce.
Guay, W., S. P. Kothari, and R. L. Watts (1996). A market-based evaluation
of discretionary accrual models.
Journal of Accounting Research 34,
83
105.
Hayn, C. (1995). The information content of losses.
Economics 20 (2), 125154.
Healy, P. M. (1985).
sions.
Journal of Accounting
The eect of bonus schemes on accounting deci-
Journal of Accounting and Economics 7 (1-3),
85107. doi: DOI:
10.1016/0165-4101(85)90029-1.
Healy, P. M. and J. M. Wahlen (1999).
A review of the earnings man-
agement literature and its implications for standard setting.
Horizons 13 (4), 365383.
Accounting
Heij, C., P. de Boer, P. H. Franses, T. Kloek, and H. K. van Dijk (2004).
Econometric Methods with Applications in Business and Economics
(1st
ed.). Oxford University Press.
Hennes, K. M., A. J. Leone, and B. P. Miller (2008).
The importance of
distinguishing errors from irregularities in restatement research: The case
of restatements and ceo/cfo turnover.
Accounting Review 83 (6),
1487
1519.
Holthausen, R. W. and R. L. Watts (2001).
The relevance of the value-
relevance literature for nancial accounting standard setting.
Accounting Economics 31 (1-3), 375.
Hribar, P. and D. W. Collins (2002).
plications for empirical research.
Journal of
Errors in estimating accruals:
Im-
Journal of Accounting Research 40 (1),
105134.
Hribar, P. and N. T. Jenkins (2004). The eect of accounting restatements on
earnings revisions and the estimated cost of capital.
Studies 9 (2-3), 337356.
62
Review of Accounting
Hribar, P. and C. D. Nichols (2007). The use of unsigned earnings quality
measures in tests of earnings management.
search 45 (5), 10171053.
Journal of Accounting Re-
Huang, R. D. and H. R. Stoll (1997). The components of the bid-ask spread:
A general approach.
Review of Financial Studies 10 (4), 9951034.
IASB (2006). Preliminary views on an improved conceptual framework for
nancial reporting: The objective of nancial reporting and qualitative
characteristics of decision-useful nancial reporting information.
IASC (1989). Framework for the preparation and presentation of nancial
statements.
Jiambalvo, J. (1996).
Discussion of "causes and consequences of earnings
manipulation: An analysis of rms subject to enforcement actions by the
sec".
Contemporary Accounting Research 13 (1), 3748.
Jones, J. (1991). Earnings management during import relief investigations.
Journal of Accounting Research 29 (2), 193229.
Jones, K. L., G. V. Krishnan, and K. D. Melendrez (2008). Do models of
discretionary accruals detect actual cases of fraudulent and restated earnings?
an empirical analysis.
Contemporary Accounting Research 25 (2),
499531.
Kent, P., J. Routledge, and J. Stewart (2010).
accruals quality and corporate governance.
Innate and discretionary
Accounting and Finance 50 (1),
171171.
Kirschenheiter, M. and N. D. Melumad (2002). Can "big bath" and earnings
smoothing co-exist as equilibrium nancial reporting strategies?
of Accounting Research 40 (3), 761796.
Journal
Kothari, S. P., A. J. Leone, and C. E. Wasley (2005). Performance matched
discretionary accrual measures.
Journal of Accounting Economics 39 (1),
163197.
Krisement, V. (1997). An approach for measuring the degree of comparability
of nancial accounting information.
European Accounting Review 6 (3),
465485.
Lambert, R., C. Leuz, and R. E. Verrecchia (2007). Accounting information,
disclosure, and the cost of capital.
Journal of Accounting Research 45 (2),
385420.
Lang, M., J. S. Raedy, and W. Wilson (2006). Earnings management and
cross listing: Are reconciled earnings comparable to us earnings?*.
of Accounting Economics 42 (1-2), 255255.
63
Journal
Lang, M., J. S. Raedy, and M. H. Yetman (2003). How representative are
rms that are cross-listed in the united states? an analysis of accounting
quality.
Journal of Accounting Research 41 (2), 363386.
Leuz, C., D. Nanda, and P. D. Wysocki (2003). Earnings management and
investor protection:
an international comparison.
Economics 69 (3), 505527.
Levitt, A. (1998a).
Journal of Financial
The importance of high quality accounting standards.
Accounting Horizons 12 (1), 7982.
Levitt, A. (1998b). The "numbers game".
Louis, H. and H. White (2007). Do managers intentionally use repurchase
tender oers to signal private information? evidence from rm nancial
reporting behavior.
Journal of Financial Economics 85 (1), 205233.
McNichols, M. F. (2000).
studies.
Research design issues in earnings management
Journal of Accounting and Public Policy 19 (4-5), 313345.
McNichols, M. F. (2002). Discussion of quality of accruals and earnings: The
role of accrual estimation errors.
The Accounting Review , 6169.
Melumad, N. and D. Nissim (2008). Line-item analysis of earnings quality.
Foundations and Trends in Accounting 3 (4), 87221.
Nelson, M. W., J. A. Elliott, and R. L. Tarpley (2003). How are earnings
managed? examples from auditors.
Accounting Horizons 17, 1736.
Palmrose, Z.-V., V. J. Richardson, and S. Scholz (2004). Determinants of
market reactions to restatement announcements.
and Economics 37 (1), 5989.
Patell, J. M. (1976).
price behavior:
Journal of Accounting
Corporate forecasts of earnings per share and stock
Empirical test.
Journal of Accounting Research 14 (2),
246276.
Penman, S. H. and X.-J. Zhang (2002). Accounting conservatism, the quality
of earnings, and stock returns.
Accounting Review 77 (2), 237264.
Richardson, S. A., A. I. Tuna, and M. Wu (2002). Predicting earnings management: The case of earnings restatements.
SSRN eLibrary .
Schipper, K. (1989).
Commentary on earnings management.
Accounting
Schipper, K. (2003).
Principles-based accounting standards.
Accounting
Horizons 3 (4), 91103.
Horizons 17 (1), 6172.
64
Schipper, K. and L. Vincent (2003).
zons 17, 97111.
Earnings quality.
Accounting Hori-
The Changing Nature and Consequences of Public Company Financial Restatements. The Department of the Treasury.
Scholz, S. (2008).
Singer, Z. and H. You (2011). The eect of section 404 of the sarbanes-oxley
act on earnings quality.
Journal of Accounting, Auditing Finance 26 (3),
556589.
Sloan, R. G. (1996). Do stock prices fully reect information in accruals and
cash ows about future earnings?
Accounting Review 71 (3), 289315.
Soderstrom, N. and K. J. Sun (2007). Ifrs adoption and accounting quality:
A review.
European Accounting Review 16 (4), 675702.
Stubben, S. R. (2010).
management.
Discretionary revenues as a measure of earnings
The Accounting Review 85 (2), 695717.
Subramanyam, K. R. (1996). The pricing of discretionary accruals.
of Accounting and Economics 22 (1-3), 249281.
Journal
Teoh, S. H., I. Welch, and T. J. Wong (1998). Earnings management and
the long-run market performance of initial public oerings.
Finance 53 (6), 19351974.
Journal of
Velury, U. and D. S. Jenkins (2006). Institutional ownership and the quality
of earnings.
Journal of Business Research 59 (9), 10431051.
Visvanathan, G. (2006). An empirical investigation of 'closeness to cash' as
a determinant of earnings response coecients.
Research 36 (2), 109120.
Accounting and Business
Wagenhofer, A. and H. Ducker (2007). Die messung von "earnings"-qualität.
Journal für Betriebswithschaft 57 (3-4), 263297.
Watts, R. L. (2003).
Conservatism in accounting part ii:
research opportunities.
Evidence and
Accounting Horizons 17 (4), 287302.
65