Earnings Quality

in

Restating Firms:

Empirical Evidence

Abstract

The objective of this paper is to compare the earnings quality inUS �rms required to restate their �nancial statements with the qualityof a matched control group.

More speci�cally, we test whether the earnings quality di�ers inthe ten years before the (last) restatement event for the two groups.To examine if the scrutiny of the Securities and Exchange Commis-sion (SEC) improves the �nancial reporting of restating �rms, we alsotest whether the quality di�ers between the two groups after the lastrestatement.

Using a wide portfolio of accounting quality metrics, we predict and�nd that the earnings quality of restating �rms is poorer than that ofthe control group, already in the years before the restatement. Using adi�erence-in-di�erence research design, we also �nd that the restating�rms improve the quality of their �nancials statements, but surpris-ingly not signi�cantly more than the control group. It is therefore notpossible to attribute the improvement to the restatement event alone.

Marie Herly*, Jan Bartholdy & Frank Thinggaard

Aarhus UniversityDepartment of Economics and Business

Fuglesangs Alle 4, 8210 Aarhus VDenmark

mherly@asb.dk0045 4716 5396

* Corresponding Author

1 Introduction

This paper examines earnings quality in �rms before and after they restatetheir �nancial statements.

So far, research within the earnings quality of restating �rms has mainlyfocused on measuring the quality in the restatement year only, and has madeno inferences on the quality before and after. Thus, little is known on howrestating �rms behave before and after they are detected by the Securitiesand Exchange Commission (SEC).

This gap is problematic for at least two reasons. First, if earnings qualityis di�erent for restating and non-restating �rms even before the actual re-statement, then SEC may begin the surveillance and scrutiny of these �rmseven earlier. Investors can also determine which �rms that are likely torestate based on the earnings quality of these �rms. Second, given the enor-mous e�orts SEC puts into detecting restaters, it is certainly in the interestof SEC, regulators, and investors to examine if the �rms actually improveafterwards. This can also shed light on the educative role of SEC.

Previous research shows that �rms that restate do have lower earningsquality in the restatement year. Some studies have found that restatershave poor corporate governance, high growth, and extreme values of speci�caccounting fundamentals in the years before the restatement event. It is alsoevident that the �nancial markets react very negatively to restatements, andthat management and auditor turnover increases after a restatement.

However, to my knowledge no studies examine the earnings quality beforeand after a restatement event. An examination of this will deepen our under-standing of restating �rms and how they generically di�er from non-restatingones.

The purpose of this paper is twofold: First, a broad portfolio of earnings qual-ity metrics will be thoroughly described, and their advantages and drawbackswill be discussed. Second, these earnings quality metrics are used to examinethe properties of �rms with restated �nancial statements and a matched con-trol group. Thus, the paper is a joint test of the earnings quality of restating�rms before and after the restatement event, and whether the accountingquality 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?

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The restating �rms are identi�ed through the Securities and ExchangeCommission (SEC), and the sample contains prominent cases of earningsmanagement, such as Xerox, Bristol-Myers Squibb, and QWest. We use amatched sample research design in which we match each restating �rm with acontrol �rm with similar size and pro�tability, and within a similar industry.We �rst test the earnings quality for both the treatment and the controlgroup both before and after a restatement event, and then test the relativechange in earnings quality for the two groups with a di�erence-in-di�erencedesign.

The remainder of this paper is structured as follows: Section 2 is a thoroughliterature review on the determinants and consequences of accounting quality.The next section continues with a description of the most commonly usedmetrics for measuring accounting quality. Section 4 gives an overview of theliterature within restatements, the sources available for identifying them,and potential pitfalls in research designs.In Section 5 my three hypothesesare then developed based on the previous sections, and Section 6 describesthe research design and data. Empirical results are presented in Section 7,followed by robustness tests and conclusion, as well as suggestions for furtherresearch.

2 Accounting Quality

The notions of accounting and earnings quality have been used both synony-mously and as two di�erent concepts. Melumad and Nissim (2008) de�neaccounting quality as a generalised view of earnings quality which evaluatesthe impact of accounting choices.This view is shared with Francis et al. (2006), who note that �nancial re-porting quality is a special case of information quality and de�ne earningsquality as a summary indicator of �nancial reporting quality. The authorscaution against focusing only on earnings when evaluating �nancial report-ing quality, since a lack of focus on for example balance sheet informationwill mask true di�erences in accounting quality.

In the following, accounting quality and earnings quality will be usedinterchangeably.

2.1 De�nition

Many de�nitions of accounting quality exist in the literature. As such, earn-ings quality is a function of both the ability of the accounting system tomeasure the �rm's fundamental performance and how the accounting systemis implemented, both of which are unobservable. The task of disentanglingthese is therefore challenging to say the least. DeFond (2010) in consequence

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suggests that the inability to directly observe the two constructs of interestis unlikely to be solved by the literature.

Former chairman of SEC, Arthur Levitt, mentions comparability andtransparency as two main attributes of high quality �nancial reporting.Barth and Schipper (2008) and Bhattacharya et al. (2003) also propose trans-parency as a desired attribute of high quality earnings.

The notion of decision usefulness as an indicator of high earnings qual-ity 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 de�ne earnings quality broadly as decision usefulness, but theyalso stress the notion of faithful representation for accounting quality.

Dechow and Schrand (2004) de�ne earnings quality broader than decisionusefulness. They view the de�nition of high quality earnings as threefold:�rst, the reported earnings number should re�ect current performance, sec-ond, 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 qual-ity earnings, implying that earnings should accurately re�ect the underlyingreality of the �rm. These include for example Francis et al. (2006). Thisde�nition corresponds well to the qualitative characteristic �faithful repre-sentation� de�ned by IASB (IASB CF �33).

Visvanathan (2006) uses the notion of closeness-to-cash as a desirableproperty of earnings. Thus earnings that are closer to cash �ows, i.e. earn-ings that contain relatively small amounts of accruals, are of higher quality.Hence, this view is closely related to for example Dechow et al. (2009) andFrancis et al. (2006).

Conservatism, in the meaning of prudence, has also been put forward as acharacteristic of accounting quality (Basu, 1997). This implies that cautionis 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 useful-ness is an unresolved issue 1

Barth et al. (2008) de�ne high quality earnings as those that exhibit lessearnings management, implying that quality is not an innate characteristic,but rather the absence of manipulation and bias. This corresponds well tothe discussion of Guay et al. (1996) who argue that managerial opportunismreduces information precision and accounting quality.

A brief review of some of the de�nitions is shown in Table 1.

As Melumad and Nissim (2008) note, some of the attributes of earnings qual-

1Distinction between conditional conservatism (more timely recognition of bad newsthan of good news in earnings) and unconditional conservatism (policy that results in lowerbook values of assets/higher book values of liabilities in the early periods of asset/liabilitylife time).

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Table 1: De�nitions of Accounting Quality

Quality Construct Selected References

Decision usefulness Ball and Shivakumar (2005), Schipper and Vin-cent (2003)

Closeness-to-cash Visvanathan (2006)Comparability IASB (2006)Faithful representation Francis et al. (2006), EU (1978), IASB (2006)Persistence Schipper and Vincent (2003), Dechow and

Dichev (2002), Comiskey and Mulford (2000)Precision Francis et al. (2006)Prudence Basu (1997), Beekes et al. (2004), Watts (2003)Relevance IASB (2006)Transparency Levitt (1998a), Bhattacharya et al. (2003)Understandability IASB (2006)Valuation input Dechow and Schrand (2004), Melumad and Nis-

sim (2008)

ity have contradictory implications. As an example, earnings that are closeto the underlying cash �ows are not necessarily predictable or accuratelyre�ecting future performance. Schipper and Vincent (2003) describe a possi-ble contradiction between the persistence and predictive ability of earnings:Highly persistent earnings will have low predictive ability if the variance of atypical to the series is large. Consequently, earnings that are of high qualityon the persistence dimension may be of low quality on the predictive abilitydimension. It also seems clear that earnings quality as a construct is context-speci�c, partly because the users, to whom the de�nition is targeted, di�erfrom situation to situation (Dechow et al., 2009). In a related vein, Schipperand Vincent (2003) argue that earnings quality di�ers according to the usersof �nancial statements; thus, standard setters and managers with compen-sation contracts tied to the earnings number may have di�erent perceptionsof accounting quality.

2.2 Determinants of Accounting Quality - Dependent Vari-

able

It is widely accepted that the quality of the standards and the diligence ofregulators are important determinants of earnings quality. Soderstrom andSun (2007) mention the accounting standards, the tax system, and the legaland political systems as drivers of earnings quality, and Barth et al. (2008)suggest that regulators in�uence earnings quality. Beuselinck et al. (2009)also �nd positive impacts on earnings informativeness following IFRS adop-tion, supporting the view that high quality standards have positive e�ects on

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earnings quality. Ewert and Wagenhofer (2005) show that tighter accountingstandards increase earnings quality but can do little to restrict real earningsmanagement. Levitt (1998a) argue that strong accounting framework andoutside auditing will improve earnings quality.

Capital market forces also have an e�ect on earnings quality. Soderstromand Sun (2007) argue that �nancial market development in�uences earningsquality, and Ball and Shivakumar (2005) show that UK public companieshave higher accounting quality than private ones, due to the market demandfor information. Burgstahler et al. (2006) examine how capital market pres-sures and institutional factors in�uence �rms' incentives to report accurateearnings.

Some factors of the �rm itself also a�ect the quality of its �nancial state-ments. These are for example the capital and ownership structure (Soder-strom and Sun, 2007), the accounting methods chosen by the �rm (Altamuroet al., 2005), and �rm performance (DeFond and Park, 1997). The internalcontrol regulation and corporate governance mechanisms of the �rm also af-fect the accounting quality (Altamuro and Beatty, 2010).Dechow and Schrand (2004) argue that the nature of the �rm as such canalso in�uence the earnings quality. The authors suggest that high growthcompanies, companies with intangible assets or complex transactions, andcompanies in volatile business environments can provide earnings numbersthat do not accurately re�ect �rm performance or indicate future cash �ows.In these cases, neither earnings management nor poor monitoring is to blamefor the low earnings quality.

Finally, managerial intent and purely discretionary decisions also in�u-ences the quality of earnings. The case of earnings management is elaboratedin Section 2.5.

2.3 Consequences of Accounting Quality - Independent Vari-

able

The cost of capital is a widely used proxy of market outcomes. In general,empirical evidence suggests a negative relation between earnings quality andthe cost of equity capital. Francis et al. (2006) view the cost of capitalas a summary indicator of investors' resource allocation decisions, whichis related to earnings quality since high quality �nancial reporting shouldassist users in their resource allocation decision. Easley and O'Hara (2004)show that accounting information of high quality reduces cost of capital byreducing information risk. In their much cited paper, the authors extendthe capital asset pricing model to include information asymmetry, and showthat information risk is non-diversi�able and thus a priced risk factor. Chenet al. (2007) and Lambert et al. (2007) �nd similar results. Francis et al.(2004) show that �rms with the least favourable values of seven attributesof earnings quality generally experience larger cost of equity. Extending this

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research to the cost of debt as well, Francis et al. (2005) show that �rmswith poor accruals quality have larger cost of debt and equity capital. Theyassign this to the fact that higher magnitudes of accruals indicate higherinformation 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 qualitydo not necessarily enjoy a lower cost of equity. He attributes this to classicalasset pricing theory which shows that diversi�able risk is not priced andargues that information is not diversi�able.

Another branch of research studies how earnings quality in�uences stockprices. Dechow and Schrand (2004) argue that they would expect no responseto low quality earnings if investors are rational in their response to earnings.However, empirical evidence suggests that the quality of information doesin�uence investors. For example, Dechow et al. (2009) report that marketsshow negative reactions following a decline in earnings quality.

Francis et al. (2005) show that �rms with low earnings quality exhibithigher price-earnings ratios and equity betas. In addition, the authors �ndthat innate earnings quality has larger expected returns e�ects than discre-tionary components.

Other market outcomes include the bid-ask spread, which has been usedto measure for example liquidity (Amihud and Mendelson, 1986), informa-tion asymmetry (Huang and Stoll, 1997). Research has linked high qualityearnings with reduced bid-ask spread (Francis et al., 2006).

Another branch of research is concerned with non-market outcomes; follow-ing Dechow et al. (2009), the stock price reaction to quali�ed audit opinionsis either negative or non-existing. The connection between analysts' fore-casts and earnings quality has also been examined, under the perceptionthat high quality earnings will yield smaller forecast errors (Ashbaugh andPincus, 2001). Francis et al. (2005) show that poor earnings quality resultsin worse credit ratings.

2.4 Earnings Management

Earnings management literature is closely related to earnings quality liter-ature since it is clear that earnings management decreases earnings quality(Dechow and Schrand, 2004). By de�nition, earnings management inducesan intentional bias in �nancial reports (Melumad and Nissim, 2008).

Numerous de�nitions of earnings management exist, and most circlearound the use of discretion in accounting to achieve a speci�c goal. FormerSEC chairman, Arthur Levitt describe it as �the grey area between legiti-macy and outright fraud (...) where earnings reports re�ect the desires ofmanagement rather than the underlying �nancial performance of the com-pany� (Levitt, 1998b). Healy and Wahlen (1999, p. 368) de�ne earnings

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management as: �..when managers use judgment in �nancial reporting andin structuring transactions to alter �nancial reports to either mislead somestakeholders about the underlying economic performance of the companyor to in�uence contractual outcomes that depend on reported accountingnumbers.�

Financial disclosures can be managed in several ways, for example by se-lecting accounting methods within GAAP or by applying methods in a waythat supports the picture management wishes to display to users (Schip-per, 1989). Jiambalvo (1996) describes two ways of manipulating earnings,real decision and pure accounting decisions. The �rst could be delays oraccelerations of sales or sale of �xed assets to a�ect gains and losses. Thesecond includes for example changes in accounting principles and changes ofestimate of residual value of �xed assets.

The line between earnings management and fraud is �ne, and earningsmanagement does not necessarily equal fraud. According to Levitt (1998b),earnings management is located in the gap between legitimate accountingand outright fraud, whereas Melumad and Nissim (2008) term fraud asan extreme case of earnings management. Some cases of earnings manage-ment clearly violate GAAP, whereas other cases are within the borders ofGAAP. Following Dechow and Skinner (2000, p. 238),� (...) while �nancial-reporting choices that clearly violate GAAP can clearly constitute of bothfraud and earnings management, it also seems as if systematic choices madewithin GAAP can constitute earnings management.� The authors suggestthat within-GAAP choices can be considered earnings management if theyare used to obscure or mask true economic performance. Nelson et al. (2003)categorise earnings management in three categories: earnings managementconsistent with GAAP, earnings management di�cult to distinguish fromGAAP, and earnings management clearly violating GAAP.

Dechow and Skinner (2000) observe that although numerous measures ofearnings management have been devised by researchers, none of them havebeen very powerful in identifying and predicting the managing of earningsin practise. Leuz et al. (2003) acknowledge that earnings management isdi�cult to measure because it manifests itself in very di�erent forms.

2.4.1 Incentives to Manage Earnings

One group of incentives is capital market expectations and valuation, a groupin which �rms manage earnings to in�uence short-term stock price perfor-mance (Healy and Wahlen, 1999). Examples of this are increasing the shareprice prior to seasoned equity o�erings or decreasing the stock price beforemanagement buyout (Dechow and Schrand (2004); Ecker et al. (2006)).

Often, managers attempt to report pro�ts (Burgstahler and Dichev,1997) or sustain recent performance and meet analysts' expectations (De-george et al., 1999). However, the opposite could also be true, illustrated for

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example by Jones (1991), who proves that �rms manage earnings to decreasereported income prior to import relief investigations. Hence, �rms can haveincentives to manage earnings both upwards and downwards.

Dechow and Schrand (2004) suggest that earnings management espe-cially occurs after a period of high growth and increasing performance inbooming economies. When the economy slows down, managers �nd it di�-cult to meet the expectations set during the boom and a decline in earningscan compel managers to use aggressive accounting, earnings management oreven fraud. This view is supported by Richardson et al. (2002), who notethat a history of previously reported positive earnings forces management tomanage earnings, as they are unwilling to break a string of positive earnings.Dechow and Schrand (2004) also note that managers may create some of theproblems themselves by �guiding� analysts about future results and thus cre-ating unrealistic expectations. In addition to this, they also suggest that thecorporate culture as such can a�ect the likelihood of earnings management.

Another group of incentives is contracts written in terms of accountingnumbers, such as lending and compensation contracts. These include avoid-ing debt covenants (Abdelghany (2005); Ecker et al. (2006)), bonus plansand compensation packages (Dechow and Schrand (2004); Richardson et al.(2002)).

Healy and Wahlen (1999) also outline regulatory motivations. These canfor example be industry-speci�c regulations for �nancial institutions andutilities, or anti-trust regulation (Ecker et al., 2006).

The standards also in�uence the degree of earnings management, andeven though tighter standards, that is, standards that leave less room fordiscretion, should impede earnings management, the opposite could also betrue. According to Ewert and Wagenhofer (2005), tighter accounting stan-dards may lead to a substitution e�ect, so that accounting earnings manage-ment is met with real earnings management.

Obviously, some of the incentives are interrelated in some way, e.g., man-agement could wish to a�ect share prices and thus increase the value of theirown bonus plans, which is often attached to the market price of the �rm'sstock.

2.4.2 Consequences of Earnings Management

Following Melumad and Nissim (2008) there are two types of costs associatedwith earnings management: the costs associated with undetected earningsmanagement and costs following a detection of earnings management. In the�rst case, an overstatement of earnings will generally lead to an understate-ment of future earnings 2. In the second case, costs incurred when earnings

2For example, if a �rm has managed earnings by understating bad debts to increasenet receivables and thereby overstate current earnings, it will most likely be forced towrite-down in the next period, resulting in a large bad debt expense and lower earnings.

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management is detected include negative e�ects on reputation, stock prices,and increased fees to auditors.Burgstahler and Dichev (1997) note that the extent of earnings managementis likely to be a function of the ex ante costs of earnings management, suchthat earnings manipulators are likely to be �rms faced with relatively lowerex ante costs of earnings management.

Following the discussion of the impact from earnings quality on cost ofcapital, it seems clear that earnings management is expected to increase thecost of capital. This is documented by Dechow et al. (1996), who attributethe increase to investors' decreased estimates of �rm value and credibility.They also show a large, negative stock price reaction after the detection ofearnings management is made public, since investors believe that �rm valuehas been overstated.

According to Healy and Wahlen (1999), investors are not "fooled" byearnings management and thus �nancial statements provide useful informa-tion to users. However, Healy and Wahlen (1999) do acknowledge that somestudies reach opposite conclusions. For example, Teoh et al. (1998) show that�rms with income-increasing abnormal accruals in the year of a seasoned eq-uity o�ering signi�cantly underperform in the following years, suggestingthat earnings management prior to equity issues a�ects share prices.

3 Proxies for Accounting Quality

Following the various de�nitions of accounting quality, the natural next stepis �guring out how to measure the quality of the �rm's �nancial statements.This implies the objective measuring of how decision useful the statementsare. Table 2 summarises the metrics described.

Francis et al. (2006) distinguish between two types of proxies for ac-counting quality: those that re�ect the innate factors or nature of the �rm,and those that re�ect the surrounding business environment and account-ing choices. Innate sources are the economic fundamentals of the �rm, suchas the operational environment, whereas discretionary sources include forinstance management�s reporting and accounting choices, auditing and thequality of reporting standards. The intrinsic factors are slow to change rel-ative to the discretionary ones.

Dechow et al. (2009) separate the proxies in a slightly di�erent manner,as they distinguish between a �true� component of earnings, re�ecting thereal underlying cash �ows, and an element of error induced by the accountingchoices.

As many of the proxies for quality are tailored to a speci�c study, theproxies are highly context speci�c. As a result, they can have somewhatcontradictory implications, even though most of them are related. Melumadand Nissim (2008) provide the example of arti�cially smooth earnings, which

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Table 2: Overview of Earnings Quality Metrics

Abnormal Accruals

Models

The Jones ModelThe Modi�ed Jones ModelThe performance-MatchedModi�ed Jones Model

Large amounts of discretionaryaccruals not explained byaccounting fundamentals indicatepoor quality

Accruals Quality

Models

Dechow-Dichev modelModi�ed Dechow-Dichev

Earnings not mapping closelyinto cash �ows are of low quality

Other Accruals

Models

Magnitude of AccrualsChange in Accruals

High levels of or changes inaccruals indicate poor quality

Avoiding Earnings

Decreases and

Small Losses

Avoiding Earnings DecreasesLoss Avoidance

Arti�cially avoiding smallearnings decreases and lossesindicate low quality

Asymmetric

Timeliness

TimelinessTimely Loss Recognition

Less timely recognition of lossesimplies poor quality

Smoothness Variability of EarningsCorrelations between Accrualsand Cash Flows

Arti�cially smooth earnings areof low quality

Persistence Persistence Impersistent earnings indicatelow quality

Predictability Predictability Earnings not able to predictthemselves indicate poor quality

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increase the persistence and predictability, but weaken the relation betweencash �ows and earnings.

3.1 Abnormal Accruals Models

A large part of the accounting based metrics examines how accruals directlyin�uence earnings quality. Earnings consist of both cash �ows and accrualsand since accruals are discretionary and based on estimates, pure cash �owsare generally considered more reliable than earnings (Dechow, 1994). Dechowand Dichev (2002) state that one role of accruals is to shift or adjust therecognition of cash �ows over time, so the adjusted numbers better measure�rm performance 3.

Dechow and Schrand (2004) note that accruals used correctly mitigateirrelevant volatility in cash �ows and thus improve the decision usefulnessof earnings. Dechow and Skinner (2000) agree with this and argue thataccrual accounting as such tend to dampen the �uctuations of underlyingcash �ows, thereby creating a more useful earnings number, than current-period cash �ows. In line with this view, Melumad and Nissim (2008) arguethat earnings smoothed with accruals increase earnings quality, since theyimprove persistence.

Sloan (1996) shows that the accruals component of earnings is less per-sistent than the cash �ow component. However, this does not imply thataccruals are not decision useful. As Dechow et al. (2010) note, research hasshown that earnings are more persistent than cash �ows and that earningsproduce smaller forecasting errors than cash �ows in valuation models. Thissuggests that accruals indeed can improve decision usefulness, even if theyhave lower persistence than cash �ows. This also highlights the fact that ac-cruals are useful, although they introduce measurement error and managerialdiscretion in �nancial statements.

A large body of literature hypothesises that earnings are primarily mis-stated via the accruals component (Dechow et al., 2009). Since accruals bynature are subjective judgements, they do open the door for opportunistic,short-term earnings management, if accruals are used to hide value-relevantchanges in cash �ows (Dechow and Schrand, 2004). The introduction ofestimates also decreases the predictability of earnings, since they are subjec-tive and hard to predict. Francis et al. (2006) note that several proxies ofearnings quality are based on the view that accruals, ceteris paribus, reduceearnings quality. Richardson et al. (2002) argue that accrual information isa key determinant of earnings manipulation and that high levels of accrualsshould be seen as a red �ag indicating an increased likelihood of earnings

3Following IASC (1989, � 22), accrual accounting implies that �...the e�ects of transac-tions and other events are recognised when they occur (and not as cash or its equivalentis received or paid) and they are recorded in the accounting records and reported in the�nancial statements in the periods to which they relate�."

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manipulation.Caution is needed when accruals are used to measure earnings quality.

Dechow and Schrand (2004) suggest that some �rms can erroneously be clas-si�ed as having low accruals quality and predictability, due to the nature ofthe business. These are for instance �rms with high growth or a large pro-portion of intangible assets. As mentioned by Dechow and Skinner (2000),certain forms of earnings management, such as income smoothing, are hardto distinguish from appropriate accrual accounting choices. Jiambalvo (1996)shows that accrual models generate low power tests of earnings managementeven for fairly high levels of earnings management. The line between ap-propriate exercise of managerial discretion through accruals and earningsmanagement is thus very �ne.DeFond (2010) argue that the abnormal accrual models all su�er from theinherent limitation that researchers are unable to determine whether the es-timated discretionary component is a result of management's discretionaryaccounting choices or just an artifact of the model used. Thus, every testusing abnormal accrual models is a joint test of the hypothesis tested inthat speci�c study and the hypothesis that the proxy is a valid measure.Therefore it is also di�cult to evaluate which accrual model is 'best'.

3.1.1 The Jones Model

The abnormal accruals models originally assumed that expected normal ac-cruals are identical to last period's total accruals and that they consist ofboth normal, non-discretionary accruals (NA) and abnormal, discretionaryaccruals (DA) (DeAngelo, 1986). This approach thus assumes that thechange in total accruals from one period to the next is due to change indiscretionary accruals.

As noted by Dechow et al. (1995), the DeAngelo model and the some-what similar Healy model (Healy, 1985) only measure discretionary accrualswithout error if non-discretionary accruals are constant and discretionaryaccruals have a mean of zero over the estimation period.The Jones Model (Jones, 1991) relaxes this rather strict assumption. Thegeneral view in her model is that accounting fundamentals, such as revenuesor assets, should explain accruals. The objective is thus to divide accrualsinto two components: Normal, non-discretionary accruals associated withthe �rm's fundamental earnings process; and abnormal, discretionary accru-als which stem from intentional or unintentional accounting errors (Dechowet al., 2010). Higher levels of accruals which are not associated with thefundamental earnings process of the �rm are assumed to reduce the qual-ity of earnings. The accounting fundamentals are thus determinants of un-manipulated accruals.

In order of separating normal from abnormal accruals, Jones develops aframework in which she controls for changes in property, plant and equip-

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ment, and revenue. Large amounts of accruals not explained by these funda-mentals indicate lower earnings quality, since abnormal accruals are sensitiveto managerial discretion.

(Jones, 1991) calculated total accruals as change in net working capitaladjusted for changes in all current operating accounts (adapted from DeAn-gelo (1986)). Later studies using the Jones Model often �nd accruals directlyas operational cash �ows subtracted from earnings.Jones relaxes the assumption of changes in accruals being due to the discre-tionary component, by estimating an expectation model for total accruals tocontrol for the economic circumstances of the �rm. Thus, Jones speci�es alinear relationship between total accruals and change in sales and property,plant and equipment:

TAit = αi1

Ait−1+ β1i∆REVit + β2iPPEit + εit (1)

where

TAit = 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;

PPEit = Gross property, plant, and equipment in year t for �rm i, scaled by lagged,

total assets.

Then, the prediction error from the OLS regression, de�ned as

uip = TAip − (αi1

Aip−1+ β1i∆REVip + β2iPPEip)

represents the level of discretionary accruals for �rm i at time t. To test theearnings management hypothesis, Jones tests if the average prediction erroris greater than or equal to zero, using Patell (1976). While the predictionerror from the regression is used in the original Jones Model, Schipper andVincent (2003) argue that residuals can also be used as a proxy for discre-tionary accruals. Jones notes that management naturally cannot hide thetrue accrued amount in multiple accounting periods, since �rm income mustequal cash �ows over all years. However, it is possible to hide the true natureof a �rm's earnings in the short run.Originally, the Jones Model used time-series data, but the estimation of ab-normal accruals can be use both �rm-speci�c, time-speci�c or cross-sectionaldata 4.

Although widely used, the Jones Model has also been much criticised. Nu-merous studies have shown misspeci�cations and low predictive ability in

4Subramanyam (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 thetime-series could cause misspeci�cation in the model due to non-stationarity.

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the Jones model (see e.g. McNichols (2000)). Francis et al. (2006) questionwhether the separation in abnormal and normal accruals re�ects the truedi�erence between discretionary and non-discretionary accruals. They showthat accounting fundamentals such as �rm size and standard deviation ofsales revenue explain approximately 65 percent of the variation in the ab-normal accruals. McNichols (2002) argues that the Jones Model might bemisspeci�ed since the lagged and future e�ect of change in sales is ignoredin it. Bernard and Skinner (1996) suggest that the Jones Model treast some�legitimate� accruals as abnormal. Beneish (1997) also argues that the sepa-ration in discretionary and non-discretionary accruals is not convincing andthat managers can exercise discretion, without intentionally in�ating earn-ings. He also provides evidence that accrual models have poor detective per-formance even among �rms with extreme earnings management behaviour.

Since the correlation between discretionary accruals and total accrualsis more than 80 percent, Dechow et al. (2003) note that it might be justas useful to look at overall level of accruals, rather than dividing them intoabnormal and abnormal. This is the case for all abnormal accruals model,which can be criticised for misclassifying non-discretionary accruals as dis-cretionary. Finally, Dechow et al. (2009) show that discretionary accruals aregenerally less powerful than total accruals at detecting earnings managementin SEC enforcement releases.

3.1.2 The Modi�ed Jones Model

In their 1995 article, Dechow, Sloan and Sweeney criticise the Jones Modelfor its implicit assumption on nondiscretionary revenues. They argue thatmanagers can easily use discretionary revenues for instance to speed up rev-enues before �nancial year-end. Such a situation would lead to an increase inrevenues and accruals but also in receivables. The accruals attached to thisform of earnings management will be classi�ed as non-discretionary accrualsin the setting of the Jones Model, causing the estimate of earnings manage-ment to be biased towards zero. Therefore, �rms that manage revenue willnot be detected in the Jones Model.

As a response to this critique, Dechow et al. (1995) extend the modelto control for managed revenues, by including accounts receivables whendetermining non-discretionary accruals, following the intuition from abovethat overstated revenues will lead to boosted receivables:

TAit = α11

Ait−1+ α2(∆REVit − ∆RECit) + α3PPEit + εit (2)

where

∆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 de�ned.

14

Again, the prediction error from the above regression is the estimate ondiscretionary accruals.

The modi�cation is designed to eliminate the tendency of measuring dis-cretionary accruals with error when revenues are managed, and thus changesin revenues are adjusted for changes in receivables. The inclusion of netreceivables changes the implicit assumption from the Jones Model dramat-ically. Hence, the Modi�ed Jones Model assumes that all changes in creditsales result from earnings management.

The empirical evidence on the Modi�ed Jones Model versus the JonesModel is somewhat ambiguous. Francis et al. (2006) prefer the Modi�edJones model over the Jones Model, whereas Subramanyam (1996) and Stubben(2010) �nd that the Modi�ed Jones Model shows no real improvement in per-formance compared to the original.

The Modi�ed Jones Model has � like its predecessor � been subject tomuch criticism. Francis et al. (2005) suggest that the measure of abnormalaccruals contains a substantial amount of uncertainty and believe that thelink to information risk is unclear. Guay et al. (1996) also show imprecisionin the Modi�ed Jones Model, but note that this might be the case for all theabnormal accrual models.

Following Beneish (1997), the Modi�ed Jones Model could be augmentedwith lagged total accruals and a measure of past price performance, to controlfor past �rm performance. Dechow et al. (2009) suggest that the Modi�edJones Model has higher explanatory power than the original version butsu�ers from the same performance related problems as the Jones Model.

3.1.3 The Performance-matched Modi�ed Jones Model

From the intuition that �rms with extreme performance are likely to en-gage in earnings management and thus have lower earnings quality, Kothariet al. (2005) propose a performance-matched discretionary accrual approach.They match each �rm-year observation with another �rm from the same in-dustry and year and the closest possible ROA. The paper thus builds on thework of Dechow et al. (1995), who showed that both the Jones Model andthe Modi�ed Jones model were misspeci�ed when applied to samples withextreme performances.As a control, Kothari et al. (2005) also propose a modi�ed version of theModi�ed Jones Model, in which they include ROA as an additional regressorbesides sales and PPE, thereby controlling for �rm performance on discre-tionary accruals. The authors describe a non-linear relation between accrualsand performance, and justify the model with the argument that discretionaryaccrual models are misspeci�ed under extreme performance. The regressionapproach imposes stationarity of the relation between accruals and perfor-mance through time or in the cross-section. The authors show that misspec-i�cation 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 speci�c

relation between accruals and performance.

TAit = δ0 + δ11

Ait−1+ δ2∆REVit + δ3PPEit + δ4ROAit + υit (3)

where

ROAit = Return on assets in year t for �rm i.

Other variables are as previously de�ned.

As with the other models, υit from the above regression is the measure ofabnormal accruals. As evident, Kothari et al. (2005) include a constant inthe mode, which was not the case for neither the Jones nor the Modi�edJones 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 itis 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. Thequality of total accruals is of interest, and thus researchers do not attemptto distinguish between normal and abnormal accruals. Accruals quality isconsistent 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 qualitywas proposed by Dechow and Dichev (2002). This model is based on the factthat accruals shift the recognition of cash �ows over time to better measure�rm performance. Since accruals are based on estimates, the incorrect esti-mates must be corrected in future accruals and earnings; as a consequence,estimation errors are noise that reduces the bene�cial role of accruals 5. De-chow and Dichev predict that the quality of accruals and earnings decreaseswhen the magnitude of estimation errors increases. They therefore proposean empirical measure of accruals quality which maps working capital ac-cruals into operating cash �ows. A poor match thus signi�es low accruals

5When cash �ows are received after they are recognised, management must estimatethe 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 containan 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 �rmperformance is reduced. As a result, a minimisation of the estimation error is desirable.

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quality. More speci�cally, the model is built up as a regression of the changein 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:

∆WCit = β0 + β1CFOit−1 + β2CFOit + β3CFOit+1 + εit (4)

where

∆WCit is working capital in year t less revenues in year t-1 for �rm i, scaled by

lagged, total assets;

CFOit is cash �ows from operations in year t for �rm i, scaled by lagged, total

assets.

Changes in working capital is the Dechow-Dichev measure of accruals. Theerror 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 theestimation errors yields non-persistent earnings, and it is an inverse measureof earnings quality. The idea is that systematically small or large estimationerrors do not create problems for users since these still enable them to predictfuture earnings. This is intuitively appealing, since a persistent residual doesnot necessarily equal low accruals quality but can just be a result of a realityin the �rm. On the other hand, volatile residuals impede investors' predictionof future earnings, creating an earnings number of low quality.It is expected that �rms with low accrual quality will also have low earningspersistence.

Dechow and Dichev do not distinguish intentional estimation errors fromthe unintentional ones, since all errors signify poor accruals quality, regard-less the underlying intent. The model therefore deviates from the Jonesmodel, which attempts to capture earnings management, whereas the Dechow-Dichev Model focuses on earnings quality per se.

According to Francis et al. (2004), the Dechow-Dichev model is a powerfulearnings quality measure. Schipper and Vincent (2003) �nd that the Dechow-Dichev Model avoids several of the problems associated with the accountingfundamental approach by Jones (1991). However, the model requires thatworking capital accruals lag or lead cash receipts by no more than a year.

In a discussion of Dechow and Dichev (2002), McNichols (2002) notesthat the Dechow-Dichev Model is only applicable where the key element ofaccruals is current accruals. In settings where this assumption is not met,the model is not operational. As opposed to this, Francis et al. (2006) �ndthat total accruals make a legitimate proxy for current accruals. Franciset al. (2005) �nd that Dechow-Dichev Model best captures the uncertaintyin accruals. They also suggest that the Jones Model and the Dechow-DichevModel work well together, since the �rst does not su�er from the limitationsof the second, in terms of using current accruals rather than total accru-als. 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, sincefor instance impairments of goodwill and PPE are likely to re�ect earningsmanagement. Finally, McNichols (2002) �nds misspeci�cation in the model.This is also the case with the Jones model, though.

3.2.2 Modi�ed 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 precludethe model from controlling for the fundamental factors in�uencing accruals.She suggests that the model is misspeci�ed; more speci�cally, the residualsfrom the Dechow-Dichev model are correlated with change in sales, indi-cating that cash �ow from operations is a noisy proxy for the cash �owrecognised in the accruals. She thus extends the model to include additionalexplanatory variables, which are important in forming expectations aboutcurrent accruals. The extent to which accruals map into cash �ows, changein sales and PPE is thus an inverse measure of accruals quality. This yieldsthe following regression (all variables scaled by average assets):

∆WCit = β0 + β1CFOit−1 + β2CFOit + β3CFOit+1

+ β4∆REVit + β5PPEit + εit (5)

where all variables are as previously speci�ed.

As in the original Dechow-Dichev Model, the variance of εit is an inversemeasure of earnings quality.The Modi�ed Dechow-Dichev Model has shown greater �t than the Dechow-Dichev Model (Francis et al., 2005). Kent et al. (2010) �nd that Dechow-Dichev and the modi�ed Dechow-Dichev Model perform equally well, butthe latter provides a signi�cantly larger coe�cient of determination.

Barth et al. (2008) criticise the Modi�ed Dechow-Dichev Model for notcapturing the perceptions of investors and analysts and the fact that it fo-cuses 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 earningsquality. 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 negativeand more positive accruals. This will lead to a higher level of accruals overall since �rms are more likely to overstate than to understate earnings. De-chow et al. (2010) argue that extreme accruals are of low quality because

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they represent a less persistent component of earnings. In accordance withDechow and Dichev (2002), their proxy for earnings quality and the level ofaccruals are both proxies for the same aspect of unobservable �true� accrualquality, as a measure of earnings persistence. They also note that the sheerlevel of accruals is simple and easy to use. However, the authors still �ndthat the Dechow-Dichev model does capture better the variation in earningspersistence. An important note made by Dechow and Dichev (2002) is that�rms with large accruals tend to generate larger estimation errors, emphasis-ing the fact that the magnitude of accruals and Dechow-Dichev model mightbe two sides of the same coin.Dechow et al. (2011) examine whether �rms that misstate earnings have un-usually high working capital accruals. They predict and �nd that accrualsare larger in misstating years.

Dechow and Schrand (2004) also note that the magnitude of accrualscan reveal the quality of earnings, but they note that the level cannot beseen independently. Thus, high accruals in companies with low cash �owvolatility should "ring the bells", whereas high accruals in for example a�rm in the high growth industry, do not necessarily indicate poor earningsquality.

Bhattacharya et al. (2003) and Leuz et al. (2003) measure earnings ag-gressiveness as the level of accruals:

TAit

Ait−1(6)

where all variables are as previously de�ned.

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 verynature 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 overtime, and thus a signi�cant change in accruals could indicate managerialmanipulation. Schipper and Vincent (2003) suggest that as long as someportion of accruals is both non-manipulated and approximately constant overtime, changes in total accruals could stem from managerial manipulations,and they may provide an inverse measure of earnings quality. Hence, themore accruals change over time, the poorer is the earnings quality.

DeAngelo (1986) argues that large accruals as such do not necessarily in-dicate low earnings quality or earnings management behaviour, but a jumpin accruals might indicate that managers have deliberately over- or under-stated earnings. She therefore assumes that a signi�cant change in accruals

19

from one period to the next indicates low earnings quality. This rests on thefollowing logic: Given that accruals consist of both normal and discretionaryaccruals, DeAngelo assumes that average change in normal accruals is zero.Therefore, a signi�cant change in total accruals re�ects a change in abnormalaccruals.

More speci�cally, DeAngelo (1986) calculates the following fraction:

∆TAit

Ait−1(7)

where

Where all are variables as previously de�ned.

A fraction signi�cantly di�erent from 0 indicates poor earnings quality.Sloan (1996) shows that the accrual component of earnings is less per-

sistent 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 accrualmisstatements result in accruals, that are more volatile than cash �ows. AsDechow and Schrand note, these misstatements may simply be due to thefact that managers have to make judgements and forecasts when determiningaccruals. Therefore, accruals that change from year to year do not necessarilyindicate 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 withother accrual models. In general, it seems to correlate signi�cantly with otheraccruals 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 linkedto the reported kink in the distribution of reported earnings, as discussedby Hayn (1995). She argues that losses are not expected to perpetuatesince �rms have liquidation options. Losses are thus less informative aboutfuture �rm performance than pro�ts are. She shows that even though theoverall distribution of earnings is not signi�cantly di�erent from a normaldistribution, there is a point of discontinuity around zero. More speci�cally,there is a concentration of cases just above zero and fewer than expectedof small losses just below zero. She suggests �...that �rms whose earningsare expected to fall just below the zero earnings point engage in earningsmanipulation 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 unwillingnessof �rms to report losses or earnings decreases. Likewise, Burgstahler andDichev (1997) make two predictions on earnings management. They posit

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�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 exten-sively in the research, under many di�erent terms, e.g, small loss avoidance(Leuz et al., 2003), loss avoidance (Bhattacharya et al., 2003), frequency ofsmall positive earnings (Lang et al., 2003), and managing towards positiveearnings Barth et al. (2008).

There are a number of incentives for managers to produce steadily in-creasing earnings numbers. Barth et al. (1995) show that �rms with increas-ing earnings during a longer period enjoy higher price-earnings ratios andhigher premiums for the long series of earnings increases. These bene�tsvanish immediately when the line of increasing earnings is broken. These�ndings have been supported in other studies later, and thus there seems tobe strong incentives to keep earnings positive and steadily increasing. Ac-cording to, for example, Degeorge et al. (1999) and Dechow et al. (2003), justexactly meeting or beating analysts' forecasts also points towards earningsmanagement.

Most of the research is connected with examining the frequency of smallincreases in earnings or small pro�ts barely over zero. Leuz et al. (2003)note that while one might argue that managers are interested in avoiding alllosses and earnings decreases, but, at the same time, they only have limitedreporting discretion, it is not possible to conceal larger losses or earningsdecreases. Therefore, managers may manage earnings to make them seemincreasing 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 proxyfor earnings management. They test the hypothesis of avoiding earningsdecreases as:

NIitMVEit−1

(8)

where

NIit = Net income in year t for �rm i ;

MVEit−1 = market value of equity in year t-1 for �rm i.

Under the hypothesis of no earnings management, the expected distributionof earnings change would be approximately symmetric and normal, followingHayn (1995).

3.4.2 Loss Avoidance

Burgstahler et al. (2006) estimate the small loss avoidance as the frequencyof small pro�ts compared to small losses:

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SPNItSNNIt

(9)

where

SPNIt = small positive net income, de�ned as NItAt−1

between 0 and 1 %;

SNNIt = small negative net income, de�ned as NItAt−1

between 0 and -1 %.

The higher this ratio is, the higher is the loss avoidance.The interpretation that small positive earnings indicate earnings man-

agement is somewhat controversial, and several researchers have questionedwhether earnings management actually explains the kink in earnings. De-chow et al. (2003) argue that the increase in cash �ows around the zeroreference earnings point stems from the positive relation between cash �owsand earnings. therefore an increase in cash �ow is actually expected aroundthe kink. They therefore reject the notion that the increase in cash �owsis due to earnings management. Dechow et al. (2003) also argue that sincethere is a positive relation between working capital accruals and earnings,Burgstahler and Dichev's �ndings concerning an increase around zero do notnecessarily indicate earnings management. Since Dechow et al. (2003) �ndthat �other accruals� decrease for the small pro�t group relative to the smallloss group, the conclusions of Burgstahler and Dichev (1997) on earningsmanagement is - according to Dechow et al. (2003) - questionable.The main criticism of Dechow et al. (2003) pertains to the fact that �rmscan take real actions to avoid reporting losses or earnings decreases, and thusthe overrepresentation of these two incidents is not necessarily evidence ofearnings management. One can easily imagine that employees are more mo-tivated when facing a loss and managers may make decisions that increasecash �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 manage-ment and that the kink could just as well be attributable to the scaling ofearnings by for instance lagged assets or price.

However, a group of researchers have shown correlations between smallpro�ts and other earnings management proxies, indicating that small positiveearnings could possibly stem from earnings management.

3.5 Asymmetric Timeliness

The research of timeliness hypothesises that pro�ts and losses are recog-nised in an asymmetric manner. More speci�cally, losses are recognised on amore timely basis than pro�ts, leading to less persistent and more revertingnegative earnings changes.

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For asymmetric timeliness to be an indicator of earnings quality, it isassumed that the more timely recognition of losses in fact increases decisionusefulness. According to Dechow et al. (2010), previous literature suggestthat equity markets perceive asymmetric timeliness as increasing earningsquality. However, research has so far failed to prove that asymmetric timeli-ness in fact improves decision making. Ball and Shivakumar (2005) note thattimely loss recognition increases the value relevance of �nancial reporting.

Asymmetric timeliness is related to conditional conservatism, and a moretimely recognition of losses is often associated with a conservative accountingsystem (Basu, 1997).

3.5.1 Timeliness

A frequently used measure of asymmetric timeliness is Basu (1997)'s reverseearnings-returns regression also used to detect conservatism. Basu (1997)proposes a second measure of timeliness which is not based on returns:

∆NIt = α0 + α1NEGDUMt−1 + α2∆NIt−1

+ α3(NEGDUMt−1 ∗ ∆NIt−1) + υt (10)

where

∆NIt = change in income from year t-1 to t, scaled by lagged total assets;

NEGDUMt−1 = an indicator variable equal to 1 if ∆NIt is negative.

Timely recognition of economic losses implies that they are less persistentand tend to reverse faster than pro�ts. 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 withhigh �nancial reporting quality recognise losses as they occur, rather thandeferring them (Lang et al., 2006). This will lead to a higher frequencyof large losses. Such a high frequency also indicates that earnings havenot been arti�cially smoothed. If earnings had been smoothed, large lossesshould be relatively rare. The opposite can, however, also be true, since ahigh frequency of large losses could indicate big bath earnings management.There is thus a con�ict between the two.

Ball and Shivakumar (2005) argue that timely loss recognition increases�nancial statement usefulness, in particular in corporate governance anddebt agreements. The �rst is a�ected because managers are less likely tomake NPV-negative investments. The second is a�ected because timely lossrecognition provides more accurate information for loan pricing. They alsonote that the demand for timely gain recognition is smaller since mangershave natural incentives to report pro�ts.

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Timely loss recognition is linked to some of the other metrics for earningsquality. Barth et al. (2008) suggest that one characteristic of high qualityearnings is that large losses are recognised as they occur, rather than beingdeferred to future periods. This characteristic is closely related to smoothingsince large losses should be relatively rare if earnings are smoothed. There-fore, �rms reporting (large) losses on a regular basis have higher qualityearnings than those that do not, from the intuition that the latter are ex-pected to have managed earnings.According to Beekes et al. (2004), timely loss recognition is also linked tothe asymmetric timeliness of earnings and therefore conservatism.

Lang et al. (2006) and Barth et al. (2008) use the frequency of large lossesas an indicator of earnings quality:

NIitATit

(11)

where all variables are as previously de�ned.

If the above ratio is less than -0.2, it is de�ned as a large loss. A highfrequency of those is juxtaposed with high accounting quality, since lossesare recognised as they occur. They use an indicator variable equalling 1 ifannual 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 onthe negative NI variable -a negative coe�cient indicates that cross-listing�rms are less likely to report large losses.

3.6 Smoothness

Two con�icting views on smooth earnings as an indicator of earnings qualityexist in the literature. One view re�ects the idea that managers arti�ciallysmooth out relevant �uctuations. This leads to a less timely and informa-tive earnings number. In this view, smooth earnings indicate poor qualityearnings. The opposite view re�ects the idea that management uses privateinformation to smooth out transitory, value irrelevant �uctuations in earn-ings, thereby achieving a more useful earnings number. In this view, smoothearnings indicate high quality earnings (Francis et al., 2006).

The �rst view, that smoothing decreases earnings quality, stems fromthe hypothesis that management responds to a negative (positive) cash �owstream by increasing (decreasing) accruals (Barth et al., 2008). Accordingto Kirschenheiter and Melumad (2002), managers have several incentives toreport smooth earnings. First, the authors argue that the market rewardssmooth earnings since they are assumed to have higher precision. Therefore,if the �rm reports a large, positive earnings surprise, the positive e�ect onstock prices might be dampened since investors prefer smooth, unsurprisingearnings. Second, consistently positive earnings may raise the expectations

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of cash �ows to investors, thereby increasing share prices. Managers canalso wish to appear less risky and attract inexpensive capital. Francis et al.(2004) suggest that capital market participants reward smoother earningsstreams with reduced costs of equity and debt.Bhattacharya et al. (2003) use earnings smoothing as a measure of earn-ings opacity. They argue that arti�cially smooth earnings fail to depict thetrue swings in underlying �rm performance. As a consequence, smoothingincreases earnings opacity.

Supporters of the second view argue that very volatile earnings variabil-ity could indicate poor accounting quality since it could arise due to bigbath earnings management (Healy, 1985). Big baths refers to an earningsmanagement technique, where management understate losses to be able toreport future pro�ts. Barth et al. (2008) also note that high earnings vari-ability could indicate low accounting quality since it could be due to errorsin estimating accruals.

Dechow and Skinner (2000) give appealing examples to show that theline between smoothing as a way to include only material changes and �uc-tuations and smoothing as opportunistic earnings management is subtle. Asa consequence, they note that detecting earnings management via smooth-ing in large samples is extremely di�cult. They also note that to be able tocharacterise income smoothing as earnings management, one needs to de�nethe point at which managers' accrual decisions result in �too much� earningssmoothing. This is naturally not an easy task.In a related vein, Subramanyam (1996) notes that while some smoothing hasan 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 aquality indicator. Smooth earnings are not necessarily desirable attributesfollowing the concepts statements. But smoothness is an outcome of anaccrual-based system assumed to improve decision usefulness, and as suchnot the ultimate goal of the system. Researchers thus need to di�erentiatefundamental smoothness from smoothness as an outcome of discretionaryaccounting choices. Following Dechow et al. (2010) the smoothing measuresknown 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 vari-ance of earnings, either as a stand-alone measure or relative to the underlyingcash �ows.

Leuz et al. (2003), Lang et al. (2003) and Francis et al. (2004) measuresmoothing as the ratio of standard deviation of earnings to the standard

25

deviation of cash �ows:σ(NIi/Ait−1)

σ(CFOi/Ait−1)(12)

where all variables are as previously de�ned.

If �rms use accruals to manage earnings, the variability of change in op-erating income should be lower than that of cash �ows. Low ratios henceindicate that insiders exercise accounting discretion to smooth reported earn-ings. Francis et al. (2004) use net income before extraordinary items in thenumerator, whereas Leuz et al. (2003) use operating income.

Barth et al. (2008) test the level of earnings smoothing by testing thevariability of earnings directly:

σ(∆NIit) (13)

where all variables are as previously de�ned.

Higher values indicate less smoothing and thus higher accounting qualityafter 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 nega-tively correlated over time as a natural result of accrual accounting, becauseaccruals reverse over time. However, she also suggests that a large negativecorrelation between accruals and cash �ows could indicate that accruals areused to smooth �uctuations in cash �ows, suggesting lower accounting qual-ity. Following this intuition, management responds to low (high) cash �owsby increasing (decreasing) accruals, thus boosting (lowering) income.

Leuz et al. (2003) and Bhattacharya et al. (2003) use the measure ofnegative correlation between change in accruals and change in cash �ows:

ρ(∆TAit,∆CFOit) (14)

where all variables are as previously de�ned.

Barth et al. (2008) assume that high quality �rms will exhibit a less negativecorrelation between accruals and cash �ows than low quality �rms. Like De-chow (1994), they acknowledge that the proper rule of accruals is to smoothvariability in cash �ows, and that the correlation by de�nition is expectedto be positive, because accruals reverse over time.

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3.7 Persistence

Persistent earnings can be viewed as desirable since earnings that are able topredict themselves are more valuable for users, e.g., for valuation purposes.Persistence or sustainability has therefore been used often as a measure ofhigh earnings quality (Francis et al., 2006). Regardless of the sign and mag-nitude of earnings, persistence captures the extent to which the current pe-riod innovation becomes a permanent part of the earnings series (Schipperand Vincent, 2003). The intuition behind persistence as an earnings qualitymetric is that persistent earnings will make current earnings a more usefulmeasure of future performance in perpetuity. Thus, higher earnings persis-tence is of higher quality when the earnings is also value relevant (Dechowet al., 2010).Following Schipper and Vincent (2003) persistent earnings have been asso-ciated with larger investor responses to earnings, which supports the hy-pothesis that persistent earnings are more useful for users, in particular forvaluation purposes (Dechow and Schrand, 2004). Dechow et al. (2009) notethat studies of earnings persistence and cash �ow predictability are moti-vated by an assumption that persistence improves decision usefulness in anequity valuation context.

A common measure for persistence is the autocorrelation of earningswhere high autocorrelation between current and past income is desirable Astationary AR1 model with φ close to 1 is thus considered persistent (Heijet al., 2004).Francis et al. (2004) use an autoregressive model on earnings per share tomeasure persisitence:

Xj,t = φ0,j + φ1,jXj,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 earn-ings changes. As a consequence, negative earnings changes do generally notbecome a permanent part of future earnings, whereas good earnings changeswill.

The accruals quality measure proposed by Dechow and Dichev (2002) isrelated to earnings persistence, since �rms with low accrual quality have alarger amount of accruals that are unrelated to cash �ows, which inducesnoise and less persistency in earnings. This is natural, since more accrualestimation errors will lead to less persistent earnings. However, it has beendocumented several times (e.g. Sloan (1996)) that accruals are less persistent

27

than cash �ows. The authors also note that it is di�cult to distinguishempirically between the e�ects of accruals quality and the level of accrualson earnings persistence.

Schipper and Vincent (2003) argue that highly impersistent earnings canbe the outcome of neutral application of accounting standards in volatile eco-nomic environments. Thus, they do not necessarily indicate poor accountingquality.

3.8 Predictability

The concept of earnings predictability as a desirable attribute is closely con-nected with persistence. According to Schipper and Vincent (2003), pre-dictability is the ability of the �nancial statements to improve users' abilitiesto forecast items of interest, i.e., the ability of past earnings to predict fu-ture earnings. Following this de�nition, variability decreases predictability,and the term is therefore connected to both the sustainable and smoothingliterature. Like these two attributes, some ambiguity still exists on whetherpredictability is actually a desirable attribute of earnings since it is not di-rectly consistent with representational faithfulness. It is evident, though,that predictable earnings are valuable inputs for valuation purposes, such asDCF analysis.

Schipper and Vincent (2003) note a contradiction between predictabilityand persistence; in cases where the variance of the typical shock to the series ilarge, highly persistent earnings (a random walk) will have low predictability.Hence, in this situation earnings that are of high quality under the persistenceview are of poor quality under the predictability view.

Predictability can be measured using the same model used for measuringpersistence. 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 fromautoregressive regressions of current on 1-year lagged earnings, i.e., the sameAR1 process as above:

Xj,t = φ0,j + φ1,jXj,t−1 + εj,t (16)

where all variables are as previously de�ned.

Consequently, the variance of ε is Dichev and Tang's inverse measure ofpredictability, since the variance of the error term captures the variationin earnings remaining after accounting for the e�ect of the autoregressivecoe�cient.

Schipper and Vincent (2003) mention some empirical di�culties whenoperationalising predictability. The choice of time period is not agreed uponin the literature, even though many researchers have used one-year-aheadpredictions. They also note that no consensus exists on what to predict;

28

researchers have used reported net income, cash �ows and various subsetsof net income. Finally, they criticise predictability for su�ering the sameissues as income smoothing since it has not been clari�ed whether predictableearnings 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 de�nition of restatements also exists, e.g.,providing restated results after adoption of new accounting standards or anM&A (Scholz, 2008). This study, however, only focuses on correction of mis-stated �nancial statements.Extensive research has been done on �rms restating their �nancial state-ments. The properties of restatements in an earnings quality context areindeed compelling. First of all, an outside source has identi�ed problemswith the quality of the �nancial statements of restating �rms (Dechow et al.,2010). Thus, a perceived advantage of restatements is that they are a directproxy for poor earnings quality (DeFond, 2010).

Violations could include �nancial and accounting fraud, insider trading,market manipulation, providing false or misleading information, and sell-ing securities without proper registration (GAO, 2009). Thus, irregularitiescover both fraud and earnings management within/outside GAAP.

Following Scholz (2008), the major reasons for restatements include rev-enue (e.g. improper or questionable recognition of revenue), expenses (e.g.improper capitalisation of expenditures), and reclassi�cation and disclosure(e.g. categorisation of debt payments as investments). The number of fraudcases has been fairly stable during the past 15 years, namely approximately5 %. However, the number of �rms that restate has increased dramaticallyin the last ten years - from 90 in 1997 to 1,577 in 2006. This rise is amongother things traceable back the downturn of the American economy in thebeginning of the new millennium, and the enaction of SOX in 2002, as wellas various accounting issues in the mid 2000s.

4.1 Previous Literature

Extensive research exists on both the determinants and consequences of re-statements.

The research on which �rms that will misstate earnings is extensive. Somecommon characteristics of restating �rms include high growth prior to re-statements (Beneish, 1999a), executive compensation contracts (Burns andKedia, 2006), high leverage and high likelihood to violate debt covenants(Dechow et al., 1996) and poor corporate governance (Farber, 2005), De-chow et al. (1996).

29

Overall, the evidence on opportunistic reporting incentives is mixed, andthe results across researchers are far from unambiguous. As an example,Beneish (1999a) do not �nd debt covenants to be signi�cant and Dechowet al. (1996) only �nd signi�cant in�uence from some corporate governancecharacteristics, not all.

A range of literature also explores the consequences of SEC restatements.One would clearly expect market reactions to be negative, as restatementswill a�ect investors' con�dence negatively. (GAO, 2002) and (GAO, 2006b)use a standard event study to determine the impact on stock prices fol-lowing restatements. They �nd that stock prices decline signi�cantly in atwo-day window, whereas the long-term impact is more di�cult to deter-mine, but it is assumed to be negative as well. Investors respond di�erentlyto di�erent reasons for restating and thus reacted more negatively to thecategory restructuring, assets, or inventory than to the category cost or ex-pense. Palmrose et al. (2004) �nd similar results.While it is clear that investors react negatively to misstatements, empiricalresearch has not been able to clearly determine if restatements equal poorearnings quality.

Other outcomes of earnings restatements include increased managementturnover (Feroz et al., 1991) and cost of capital (Dechow et al., 1996), Hribarand Jenkins (2004), signi�cant 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 restate-ments. For example Beneish (1999a) examines the incentives and conse-quences of earnings overstatements and �nds that revelation in SEC doesnot impose serious enough consequences on managers, and thus the presenceof SEC alone will not prevent managers from engaging in earnings man-agement. In contrast, Desai et al. (2006) �nd that management turnoverincreases signi�cantly following a restatement. This suggests that the "riskof getting caught" might prevent managers from earnings overstatement.

4.2 Identifying Restatements

Several sources identify restatement events. This study exploits SEC's re-statement database which is described in detail below. Some other sourcesare 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 federalagency involved in accounting requirements for publicly traded companies. Itrecognises FASB's US GAAP as the general accepted standards which �rmsshould comply with. The role of SEC is to protect users of �nancial state-ments, in particular investors. It is therefore in the interest of SEC to ensure

30

and maintain a high accounting quality among American corporations, andhence they monitor American �rms for signs of reporting violations. If sucha sign occurs, SEC publishes an Accounting and Auditing Enforcement Re-lease (AAER)6 on that particular company to inform investors that somekind of �nancial reporting violation has taken place. The AAERs have beenissued since April 1982 and provide information on the �nancial statementquality as a whole, not just earnings.SEC identi�es the �rms that allegedly misconduct �nancial statements throughanonymous tips, public criticism and news reports, voluntary restatementsand random sampling among listed companies. Since SEC is concernedmainly with protecting investors, it is more likely to scrutinise �rms thatare either large, raising debt or equity, or IPO �rms.

Since SEC only has limited funds available, they are likely to pursuit onlythe worst cases of earnings manipulation (Hennes et al., 2008), and thus it isagreed among most researchers that the AAERs include mostly fraudulentor intentional misstating behaviour (Dechow et al. (2010) and Eilifsen andMessier Jr. (2000)). Indeed, in most AAERs, SEC accuses managers ofintentional misstating �nancial statements, i.e., fraud. However, in somecases SEC acknowledges that management was negligent, i.e., reckless in notknowing (Dechow et al., 2010).

Dechow et al. (1996) assume that SEC correctly identi�es overstating�rms and that the �rms have knowingly engaged themselves in earningsoverstating. Under this assumption, the AAERs are a powerful tool forexamining earnings management and accounting quality hypotheses. Thedataset excludes all restatements for example due to M&As and many unin-tentional errors.

Since AAERs are likely to include only the cases of intentional misstate-ments, the sample has a low expected type I error rate (�rms that do notmanage earnings are incorrectly classi�ed as managers) (Dechow et al., 2011).However, given the limited funds of the SEC, the expected type II error ratemight be high as well (many �rms are likely to remain undetected).

4.2.2 Other Sources

GAO Database 7

The Government Accountability O�ce (GAO) is the audit, evaluation andinvestigative arm of the American Congress.

GAO has issued lists of restating �rms in 2002 and 2006, respectively.The restatements were identi�ed searching the database Lexis-Nexis for vari-ations of �restate� and other relevant words. GAO also searched SEC's �ling,company web sites and compared qualitative features of the �rms. Excluded

6A list of the AAERs is available through the commercial database Audit Analyticsand from www.sec.org/edgar.

7Available from http://www.gao.gov/special.pubs/gao-06-1079sp/toc.html

31

from the lists are �rms not registered with SEC, routine reporting issuessuch as mergers or stock splits, simple presentation issues and restatementsfollowing accounting policy changes. Thus, the list consists of restatementsfollowing accounting fraud and accounting errors, by GAO termed aggres-sive accounting issues (GAO, 2006a). It is clear that there is an overlapbetween the databases of SEC and GAO, because SEC often requires re-statements and restatements often trigger SEC's investigation. The GAOsample is larger than the SEC sample each year, but it only goes back to2002 (compared to 1982 for SEC). The GAO list also includes a wider rangeof misstatements.The heterogeneity of the sample has been subject to some criticism. Dechowet al. (2010) argue that the GAO dataset includes too many unintentionalmisstatements and non-material errors which have nothing to do with earn-ings management. Hennes et al. (2008) suggest that researchers should splitthe GAO dataset into intentional earnings management and errors whendoing accounting research.

Another major drawback when working with the GAO dataset is the thetime-lag from the restatement is detected, until it becomes public. This spanvaries greatly from case to case. This makes the GAO database well-suitedfor research on the consequences of restatements but less appropriate forresearch 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 indicatorof poor earnings quality. The database includes intentional misstatements,but lawsuits could also arise due to other issues, for example after a majordecline of stock prices. The sample is thus very heterogeneous, and it has tobe carefully cleaned before it is used to hypothesise about the quality of the�rms involved.

Study Speci�c Identi�cations

Some researchers have also created data sets speci�cally to the study or re-search question at hand. Richardson et al. (2002) create their own dataset(like GAO) from 1971 to 2000, excluding unintentional errors and misappli-cations. Many of the �rms identi�ed were SEC targets as well.Palmrose et al. (2004) search for words like restate and restatements (likeGAO) 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 offraud by SEC, also similar to the GAO dataset.Beneish (1997), Beneish (1999a), and Beneish (1999b) combine SEC's �rmswith search in press releases to prevent the time-lag in the SEC database toin�uence results.

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4.3 Issues when Working with Restatements

One issue when testing accounting quality hypotheses on restating datasetis that most of the �rms identi�ed there have in fact violated GAAP, i.e. thecases consist of more fraud than earnings management. However, there aremany degrees of earnings management that exercise managerial discretionwithin the borders of GAAP, and these �rms are not likely to be caughtby the dataset (Dechow and Skinner, 2000). The fact that only the morespectacular cases of earnings management or fraud are included limits thegeneralisability of the results (Dechow et al., 1996). The view is supportedby Jiambalvo (1996), who mentions that the SEC sample is only suitable forresearch on GAAP violating earnings management. Therefore restatementsand AAERs may not be appropriate for studies that wish to capture errorsor earnings management within the boarders of GAAP.However, since Dechow et al. (1996) expect that �rms violating GAAP alsomanage earnings within GAAP. This certi�es the use of restatements as aproxy for earnings management, because those �rms identi�ed by SEC havemost likely also been engaged in the less severe earnings management.

Another issue when working with restatements is highlighted by Dechowet al. (2010): The misstatement samples are often small, whereas the numberof potential sources of incentives is large. So the tests may not be powerfulenough to detect a true relation. In other words, it can be di�cult to showempirically the exact causality between an incentive to manage earnings andan actual restatement.

Palmrose et al. (2004) suggest that one of the most important limitationsposed by the di�erent restatement samples is the selection bias, namely thatonly the �rms detected and judged are in the sample. The selection biasdepends on the speci�c accounting irregularity and the decision of the �rmto report it. This corresponds to a high Type II error rate as describedearlier.

5 Hypotheses Development

Previous research that links restatements and earnings quality (e.g. Dechowet al. (2011); Jones et al. (2008); Richardson et al. (2002)) focuses mainlyon the actual restatement years. In particular, in a time-series analysis ofmisstating �rms, Dechow et al. (2011) compare each �rm's non-misstatingyears with misstating ones and thus assume that misstating �rms have pooraccounting quality in the restatement year alone.An underlying assumption is thus that accounting quality is only poorer inthe misstatement years, when the �rm was actually detected.

I hypothesise that the poor quality of earnings is not limited to the actualrestatement year, but that �rms identi�ed by SEC have continuously poorearnings quality in the years prior to the restatement(s).

33

I hence test if the restating �rms are generically di�erent from otherwisesimilar, 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 re-staters and non-restaters. I predict that the poor earnings quality detectedby previous research in the restatement years is evident already before theevent, and thus restaters are expected to have lower accounting quality thannon-restaters.

The opposite could, however, also be true. As assumed by for instanceDechow et al. (2011), �rms may only exercise improper discretion in therestatement years.It could also be the case that �rms manage earnings within the boundaries ofGAAP before they are detected by the SEC, but the earnings quality metricsI apply cannot actually detect it.

Given the severe market reactions following a restatement outlined in Section4 (e.g. signi�cant drops in stock prices and increased management turnover),I expect that restating �rms conform following a restatement event, and thusimprove 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 thelast restatement year. Under the assumption that the SEC enforcement hasan educative role, it is expected that the accounting quality is improvedrelatively more for the restaters than the non-restaters, and thus I predictno di�erence in quality between the two groups in the after-period.

Similarly, I compare the change in earnings quality over the periods, forboth 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 immediateimprovement fails to take place. Again, it is also possible that the metrics Iuse are not able to measure a possible improvement.

6 Research Design and Descriptive Statistics

Each restating �rm is identi�ed through Audit Analytics' (AA) Non-RelianceDatabase, after which the �nancial statement information from these �rms

34

is found in Compustat Unrestated Quarterly. Each restating �rm is thenmatched 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 identi�ed from Audit Analytics, a commercialdatabase available through WRDS. The restatement data set from AuditAnalytics covers all SEC registrants (US �rms only) that have restated sinceJanuary 1st 2001. The sample contains Form 8-K and 8-K/A �lings underthe title "4.02: Non-Reliance on Previously Issued Financial Statements ora Related Audit Report or Completed Interim Review".

Audit Analytics contains detailed information on which year(s) each �rmhas restated, the date the restatement became publicly known, and the rea-son for the restatement. Since only limited accounting information is avail-able in AA, the information has to be downloaded from Compustat or asimilar source.

There is one major caveat when using restatement information from Au-dit Analytics, namely that there is no direct link between AA and for instanceCompustat and CRSP. This complicates the process of �nding the �nancialstatement numbers from the �rms identi�ed in Audit Analytics. WhereasAA uses CIK as �rm identi�er, Compustat uses GVKEY, and CRSP usesCUSIP. Even though all data bases report the Ticker symbol for each com-pany, this cannot be used to match, since it can change over time and bereused 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 companyname.9

Of the 7,325 unique restating �rms present in Audit Analytics' restate-ment database, the above matching procedure yields 3,817 �rms matchedwith their GVKEY in Compustat.

The �nancial statement information is taken from the add-on database toCompustat, Unrestated Quarterly. This contains both the original, un-

8http://wrds-web.wharton.upenn.edu/wrds/support/Additional\%20Support/

WRDS\%20Knowledge\%20Base\%20with\%20FAQs.cfm?folder_id=645&article_id=16109More speci�cally, we perform a fuzzy merge using the SAS

function COMPGED(), which returns the generalised edit dis-tance between two distances, i.e., the dissimilarity between 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 fromAudit Analytics, we accept the match if the distance between the two strings is less than100. A COMPGED score of 0 indicates perfect match (e.g. BIODEL INC = BIODELINC), whereas a COMPGED score of 90 indicates a nearly perfect match (e.g. RVBHOLDINGS LTD = R.V.B. HOLDING, LTD.)

35

restated data value and the back�lled, restated data value for each item.10

Even though some researchers (e.g. Dechow et al. (2011)) have usedrestated 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 di�erence between the restatedand unrestated data values varies enormously and independently on each ofthe 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 predictwhich data values that will di�er and in which direction.

Third, the di�erence between the two values is material for some dataitems, which can in�uence results.

Table 3: Compare original/restated values for Restating Firms

Line Item Original Value Restated Value Di�erence

Assets 2,268.6 3,553.1 -1284.5***COGS 1,331.0 1,324.6 6.4Debt, long-term 644.0 644.8 -0.8Debt, short-term 414.8 418.2 -3.4**Earnings Per Share 8.22 8.31 -0.09Net Income 85.5 85.5 0.0Operational Cash Flows 165.2 198.4 -33.2***R & D Expense 91.9 91.5 0.4**Revenue 1,496.8 1,841.0 -344.2***

The table depicts the matched restaters in all available years. In millionsUSD, except earnings per share.All items have been winsorised at the 1st and 99th percentile.*, **, *** denote signi�cant di�erence from 0 at the 10%, 5%, and 1% levels,respectively. Using paired t-tests.

Table 3 compares speci�c line items that previous literature has identi�ed asbeing the common reasons for restatements. More speci�cally, Scholz (2008)states that the majority of restatements are due to revenue recognition, coreexpenses (e.g. R& D expenses), and disclosure issues (such as reclassi�cationof debt).It is clear that material di�erences exist between restated and original datavalues for some of the line items of interest. In particular, both assets andrevenue show signi�cant di�erences. This is in line with the argument ofScholz (2008) that many restatements contain revenue recognition issues, asdescribed in Section 4.

10I gratefully acknowledge the �nancial support of FSRs Studie- & Understøttelsesfondto buy access to the Compustat Unrestated Quarterly Database.

11http://www.charteroaksystems.com/data_products/compustat/unrestated.html

36

Since Compustat Unrestated Quarterly is given in quarterly data, weconvert it to annual data by using the fourth quarter amount for balancesheet and cash �ow amounts and by summarising the four quarters for eachyear to one year for the income statement amounts.

Furthermore, all data values are winsorised at the 1st and the 99th per-centile.

6.2 Matching Procedure

For each restating �rm, we �nd a match among all Compustat �rms, thatare not identi�ed by SEC. The Compustat �rm must meet the followingrequirements:

• Financial statement information in the restatement year

• Same 1-digit SIC code

• Total assets within ± 40%

• ROA within ± 40%

If a restater has more than one match, the joined absolute di�erence betweenassets and ROA is minimised. Of the 3,817 restating �rms, 2,028 obtain aunique match.

Each restating �rm has one exact match in the control group of non-restating�rms, with correspondent restatement and non-restatement years, respec-tively.

We split the sample in before and after the restatement and non-restatementyear, respectively. The before sample consists of ten years before the actualrestatement event. If a �rm has restated multiple times, the last restatementyear ends the before sample. The after sample lasts a minimum of three anda maximum of ten year after the last restatement/non-restatement event.The sample thus consists of �rm years from 1990 (ten years before the �rstrecorded 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 in-dustry, year, and �rm age.

The underlying assumption behind this research design is naturally thatthe matched, non-restating control group on average has higher earningsquality and/or manages earnings less than the restating �rms. If the �rms inthe control group also have poor earnings quality but are simply not detectedby the SEC, it will seriously in�uence the conclusions. But as mentioned inSection 5.2, since SEC mostly pursuits larger �rms the matching on sizepartly deals with this problem, as the control group is likely to have beenscrutinised and approved by the SEC.

37

Kothari et al. (2005) and Dechow et al. (2010) discuss another seriousconcern related to a matched sample in accounting quality studies, namelythat �rms in the control group have similar incentives to manage earnings. Inparticular, Dechow et al. (2010) argue that earnings manipulation and earn-ings quality issues appear to cluster by industry. The fact that we match verybroad on industry (1-digit SIC code) partly mitigates this issue, even thoughit remains a concern that has to be taken into account when interpreting theresults.

If both of the above concerns impact the results, it will increase thelikelihood of maintaining the hypothesis of no di�erence in earnings qualitybetween the two groups, i.e., Type II errors12.

6.3 Accounting Quality Metrics

Below is a description of how earnings quality is measured using all 16 met-rics. An overview is depicted in Table 4.

Accruals Models

The original Jones Model measured abnormal accruals in only one year andhypothesised income decreasing earnings management, thereby having an apriori expectation of the direction of the accruals.

My research design is di�erent on these two parameters. First, the inter-est 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 val-ues.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 thetwo time periods (before and after the restatement event, respectively), weestimate The Jones Model as �xed-e�ects panel regressions in each of the 48Fama-French (FF) industries13. Under this approach, we assume that theunobserved heterogeneity in�uencing the level of accruals is �xed over timein each FF industry. Thus, only time-varying unobserved factors remain inthe error term which allows us to examine how discretionary accruals foreach �rm in each of the 48 industries change over time.14

To solve the second issue, we use the solutions proposed in the method-ological paper by Hribar and Nichols (2007). They describe that discre-

12A failure to reject a false null hypothesis13Available from http://staff.washington.edu/edehaan/pages/Programming/

Siccodes48.txt.14It is not possible to use �rm-�xed e�ects in the Jones Model. To see this, remember

that �xed e�ects place the unobserved e�ects, that are constant over time, in a constantvariable capturing the unobserved e�ect. Since the unobserved e�ect is removed fromthe residual, and the measure of discretionary accruals is the residual, the approach willunderestimate the level of abnormal accruals.

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Table4:

Calculation

ofEarnings

QualityMetrics

TheJones

Model

TA

it=αi+β1i∆REVit

+β2iPPE

it+ρ(CFO

) it

+TA

it−1

+ε i

t

TA

=(Incomebef.Ex.Item

s(123)-(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s(124)))/Lagged

Assets(6)

REV

=Sales(12)/Lagged

Assets(6)

PPE=

Gross

Property,Plant,and

Equipment(7)/Lagged

Assets

CFO

=Cash

Flowsfrom

Operating

Activites

(308)/Lagged

Assets(6)

Residualrepresents

discretionary

accruals.Highlevels

indicate

poor

earningsquality

TheModi�ed

Jones

Model

TA

it=αi+β1i∆REVit−

∆REC

it

+β2iPPE

it+ρ(CFO

) it

+TA

it−1

+ε i

t

TA

=(Incomebef.Ex.Item

s(123)-(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s(124)))/Lagged

Assets(6)

REV

=Sales(12)/Lagged

Assets(6)

REC

=Receivables(2)/Lagged

Assets

(6)

PPE=

Gross

Property,Plant,and

Equipment(7)/Lagged

Assets

CFO

=Cash

Flowsfrom

Operating

Activites

(308)/Lagged

Assets(6)

Residualrepresents

discretionary

accruals.Highlevels

indicate

poor

earningsquality

39

ThePerform

ance-

matched

Modi�ed

Jones

Model

TA

it=δ 0

+δ 1

+δ 2

∆REVit

+δ 3PPE

it

+δ 4ROA

it+ρ(CFO

) it

+TA

it−1

+ε i

t

TA

=(Incomebef.Ex.Item

s(123)-(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s(124)))/Lagged

Assets(6)

REV

=Sales(12)/Lagged

Assets(6)

PPE=

Gross

Property,Plant,and

Equipment(7)/Lagged

Assets

ROA

=Net

income(172)/TotalAssets

(6)

CFO

=Cash

Flowsfrom

Operating

Activites

(308)/Lagged

Assets(6)

Residualrepresents

discretionary

accruals.Highlevels

indicate

poor

earningsquality

Dechow

-Dichev

Model

∆WC

it=β0

+β1CFO

it−1

+β2CFO

it+β3CFO

it+1

+ε i

t

∆WC

=-(∆

Acc.Receivables(302)

+∆

Inventory

(303)+

∆Acc.

Payables(304)+

∆Taxes

Accrued

(305)+

∆Other

AssetsandLiabilities

(307))/Lagged

Assets(6)

CFO

=Cash

Flowsfrom

Operating

Activites

(308)/Lagged

Assets(6)

Highvariance

inthe

residualindicates

poorearnings

quality

Modi�ed

Dechow

-Dichev

Model

∆WC

it=β0

+β1CFO

it−1

+β2CFO

it+βCFO

it+1

+β4∆REC

it+β5PPE

it+ε i

t

∆WC

=-(∆

Acc.Receivables(302)

+∆

Inventory

(303)+

∆Acc.

Payables(304)+

∆Taxes

Accrued

(305)+

∆Other

AssetsandLiabilities

(307))/Lagged

Assets(6)

CFO

=Cash

Flowsfrom

Operating

Activites

(308)/Lagged

Assets(6)

PPE=

Gross

Property,Plant,and

Equipment(7)/Lagged

Assets(6)

REC

=Sales(12)/Lagged

Assets(6)

Highvariance

inthe

residualindicates

poorearnings

quality

40

Magnitudeof

Accruals

TA

it

Ai,t−

1

TA

=Incomebef.Ex.Item

s(123)-(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s(124))

A=

TotalAssets(6)

Highlevelsindicate

poorearnings

quality

Changein

Accruals

∆TA

it

Ai,t−

1

TA

=Incomebef.Ex.Item

s(123)-(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s(124))

A=

TotalAssets(6)

Highlevelsindicate

poorearnings

quality

AvoidingEarnings

Decreases

NI i

t

Ait−1

NI=

Net

Income(172)

A=

TotalAssets(6)

Distribution

di�erentfrom

0signifypoor

earningsquality

SmallLoss

Avoidance

SPNI i

t

SNNI i

t

SPNI=

Indicatorvariableequalling1

if(N

etIncome(172)/Lagged

Assets

(6))

isbetween0and0.01

SNNI=

Indicatorvariableequalling1

if(N

etIncome(172)/Lagged

Assets

(6))

isbetween0and-0.01

Highratiosindicate

poorearnings

quality

Asymmetric

Timeliness

∆NI i

t=α0

+α1NEGDUM

it−1+

α2∆NI i

t−1

+α3

(NEGDUM

it−1∗

∆NI i

t−1)

+υit

NI=

Net

Income(172)/Lagged

Assets

(6)

NEGDUM

=Indicatorvariable

equalling1if∆

NIisless

than0

α3<0implies

high

accountingquality

41

TimelyLoss

Recognition

NI i

t

Ait

NI=

Net

income(172)

A=

TotalAssets(6)

Highfrequency

of

largelosses

indicate

highearnings

quality

Variabilityof

Earningsto

Cash

Flows

σ(NI i

t)

σCFO

it

NI=

Net

Income(172)/

Lagged

Assets(6)

CFO

=(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s

(124))/Lagged

Assets(6)

Highratiosindicate

earningssm

oothing

Variabilityof

Earnings

σ(∆NI i

t)

NI=

Net

Income(172)/

Lagged

Assets(6)

Low

variability

indicatessm

oothing

Correlationbetween

AccrualsandCash

Flows

ρ(∆NI i

t,∆CFO

it)

NI=

Net

Income(172)/

Lagged

Assets(6)

CFO

=(C

ash

Flowsfrom

Operating

Activites

(308)-Ex.Item

s

(124))/Lagged

Assets(6)

Largenegative

correlationsindicate

smoothing

Persistence

Xi,t

=φ0,i

+φ1,iX

i,t−

1+ε i

,t

X=

Net

Income(172)/Common

SharesOutstanding(25)

Low

values

ofφ0,i

indicate

impersistent

earnings

42

Predictability

Xi,t

=φ0,i

+φ1,iX

i,t−

1+ε i

,t

X=

Net

Income(172)/Common

SharesOutstanding(25)

Highvariance

ofthe

errorterm

indicates

poorearnings

quality

43

tionary accruals models are increasingly used with unsigned values of ab-normal accruals to test for overall di�erences in earnings quality. Usingthe absolute rather than the signed value of discretionary accruals obvi-ously changes the distribution of residuals, since they are truncated at 0. Inaddition, Hribar and Nichols (2007) show that unsigned discretionary accru-als have a di�erent probability function than signed discretionary accruals.More speci�cally, they show that the expected value of absolute discretionaryaccruals is an increasing function of the residual variance. Therefore, theysuggest that the driver of the residual variance is controlled for in researchdesigns. In line with this, we add the volatility of operating cash �ows ofeach �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 reversionof accruals.Consistent with for instance Francis et al. (2005), we estimate discretionaryaccruals directly as the residuals, rather than using a two-stage approach.

We predict that restaters have higher levels of absolute discretionaryaccruals in the years before the restatement.

Accruals Quality Models

Working capital accruals are estimated using the approach of Dechow andDichev (2002).

We follow Francis et al. (2004) in estimating Equation 4 and 5 overrolling, �rm-speci�c windows, ten years in the before sub-sample, and aminimum of three years in the after sub-sample. The panel regressions forboth the original and the modi�ed Dechow-Dichev Model are run with �rm-�xed e�ects and the standard deviation of residuals for each �rm is theDechow-Dichev measure of accruals quality.

We predict that restaters have higher standard deviation of residualsthan non-restaters in the years before the restatement.

Accruals Models

We estimate accruals directly as the di�erence between cash �ows and earn-ings. Since the reporting of cash �ows was made mandatory with the FASBStatement No. 95 (e�ective from 1988), this method has by far been themost widely used.

More speci�cally, my measure of accruals stems from Hribar and Collins(2002), who �nd accruals as earnings before extraordinary items, less oper-ating cash �ows from continuing operations. They suggest that this methodcontains less measurement error than the balance sheet approach used before.

We test if the level and change in accruals, respectively, are signi�cantlydi�erent for the restaters and non-restaters, and before and after the lastrestatement event. In accordance with Dechow and Dichev (2002), the ab-solute magnitude of accruals is the variable of interest.

44

We expect to �nd higher levels of and yearly changes in accruals forrestaters before the restatement event.

Avoiding Earnings Decreases and Small Losses

We follow Burgstahler and Dichev (1997) in comparing distributions of earn-ings but scale with lagged assets rather than market value of equity. Wetabulate results for distributions of change in earnings.It is tested if the distributions of earnings for restaters and non-restaters,respectively, are signi�cantly di�erent from each other. Table 7 and 8 showthe median of change in earnings and the di�erence between the two is testedwith the Wilcoxon Two-Sample Test for di�erences in distributions.

When comparing the frequency of small losses and small pro�ts we usethe approach of Burgstahler et al. (2006).

Restaters are predicted to have a higher median of change in earningsand a smaller proportion of small losses to small pro�ts, than non-restatersdo.

Timeliness

The piecewise regression suggested by Basu (1997) is estimated as a panelregression with �rm-�xed e�ects in both groups and time periods. We fol-low the approach of Ball and Shivakumar (2005). Table 7 and 8 show thecoe�cient on NEGDUM ∗∆NI, which is expected to be more negative fornon-restaters than restaters.

We tabulate the number of large losses as the percentage of the totalnumber of observations.

Smoothness

The variability of change in earnings and the variability of earnings to vari-ability of cash �ows are calculated on �rm level and compared for the twogroups. Restaters are expected to smooth more, i.e. have lower variancethan non-restaters.Spearman correlations are calculated between accruals and cash �ows as thelast measure of earnings smoothing. The correlations are anticipated to bemore negative for restaters.

Persistence

In accordance with Francis et al. (2004), a one-order autoregressive regressionis estimated for each �rm using maximum likelihood estimation, in the beforeand after period, respectively. The parameter estimates on φ are outputtedand compared for restaters and non-restaters.

We expect that non-restaters have more persistent earnings, i.e., φ closerto 1.

Predictability

The autoregressive regression is estimated as described above, and we com-pare 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 withunique GVKEY) before each restating �rm is matched with a non-restatingcounterpart. This corresponds to 5,336 restatement events shared among3,817 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 signi�cantly in themid-2000s. This could be due to change in SEC's identi�cation 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 Com-

pustat �rms. It seems as if restating �rms are overrepresented in the serviceindustry. Apart from this the restating sample is quite heterogeneous. It isalso evident that restaters represent the overall population quite well.Panel B depicts some of the variables of interest. In particular net incomestands out as it is considerably smaller for restaters. This �ts well with theobservation of Scholz (2008), who notes that the vast majority of restate-ments reduce income.

Table 6: Comparison of Restaters and All Compustat Firms

Panel A: Industry

Industry SIC-code Restaters Compustat

Agriculture, Forestry, AndFishing

01-09 0.3 0.4

Mining And Construction 10-17 8.2 6.7Manufacturing 20-39 37.8 37.2Transportation,Communications, Electric,Gas, And Sanitary Services

40-49 9.0 10.4

Wholesale And Retail Trade 50-59 9.4 8.9Finance, Insurance, And RealEstate

60-67 12.1 18.0

Services 70-89 21.3 16.7Public Administration 91-99 2.0 1.7

100% 100%

Panel B: Size and ROA

Restaters Compustat

Assets 4,733.0 4,872.4Net Income 12.8 103.9Cash Flows from Operations 168.3 197.1

Using unrestated data values. All amounts in millions USD.

Table 7 tabulates descriptive statistics for the key variables used in the anal-yses. It shows that the match is quite good and the two groups resemble eachother. However, earnings is again smaller for restaters, following the sameargument as above that the majority of restatements are income decreasing.

47

Table 7: Descriptive Statistics of Key Variables

Restaters Non-Restaters

Variable Mean Median Std. Dev. Mean Median Std. Dev.

A: Total Assets 2071.2 393.8 5272.0 2287.7 384.4 6047.5NI: Net Income 73.1 10.7 1232.8 117.3 13.3 1192.4CFO: OperationalCash Flows

177.1 25.6 519.1 214.9 31.7 581.8

TA: Accruals -109.8 -14.3 1257.5 -97.6 -14.5 1096.8WC: Working Capital 4.5 0.4 72.7 4.4 0.4 82.4PPE: Property, Plant& Equipment

1197.5 142.6 3120.4 1207.6 140.6 3241.2

REV: Revenue 1679.7 370.6 4133.0 1892.2 367.2 4646.4REC: Receivables 221.0 42.9 594.5 245.9 44.9 669.7ROA: Return onAssets

0.0 0.0 0.7 0.0 0.1 0.3

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 beforethe (last) restatement event. The accruals models, the measure of time-liness, and the persistence and predictability generally show lower qualityfor restaters, as predicted. The measures of earnings smoothing, however,indicate that restaters in fact smooth less than non-restaters, contrary toexpectations.

All the abnormal accruals models are highly signi�cant in the expecteddirection. Thus, the level of discretionary accruals are considerably largerfor restaters than for non-restaters in the years before a restatement. Thesame is the case for the accruals quality models. The restaters have largerstandard deviation of residuals, indicating poorer earnings quality, althoughthis is insigni�cant for the Modi�ed Dechow-Dichev Model.

In the other accruals models, the absolute value of accruals is greater forrestaters than non-restaters, as expected. Surprisingly, the absolute changein accruals is larger for non-restaters.

The measure of avoidance of earnings decreases moves in the oppositedirection of the expected. This is possibly connected with results from Table6 which shows that restaters have considerably smaller earnings than non-restaters.The proportion of small pro�ts to small losses shows no real di�erences be-

48

Table 8: Restaters and Non-Restaters Before the Restatement Event

Metric Prediction Restaters Non-Restater Di�erence

Abnormal

Accruals

Models

Jones Model > 0.1471 0.1144 0.0328***Modi�ed JonesModel

> 0.1527 0.1193 0.0333***

Performance-Matched Mod.Jones Model

> 0.1380 0.1068 0.0312***

Accruals

Quality

Models

Dechow-DichevModel

> 0.0223 0.0201 0.0022*

Modi�edDechow-Dichev

> 0.0231 0.0212 0.0019

Other

Accruals

Models

Magnitude inAccruals

|>| -0.0752 -0.0679 -0.0073

Change inAccruals

|>| 0.0005 -0.0040 0.0045*

Avoiding

Decreases

and Small

Losses

Avoiding SmallEarningsDecreases

> 0.0367 0.0481 -0.0114***

Loss Avoidance > 0.26 0.24 0.02

Asymmetric

Timeliness

Timeliness > 0.2167 -0.6104 0.8271Timely LossRecognition

< 8.3047 7.6465 0.6582**

Smoothness

Var. of Earningsto Cash Flows

< 4.5880 4.4629 0.1251

Var. of Earnings < 0.2507 0.2068 0.0439*Corr. betweenAccruals and CashFlows

< -0.4247 -0.4479 0.0233

Persistence Persistence < 0.3254 0.3508 -0.0254

Predictability Predictability > 0.8714 0.8652 0.006

The prediction on the relation between restaters and non-restaters follows the hypothesis that therestaters have lower earnings quality than the non-restaters before the restatement event.*, **, *** denote signi�cant di�erence from 0 at the 10%, 5%, and 1% levels, respectively, withtwo-sample t-test.

49

tween the two groups, and it seems as if both avoid small losses. Therecould be several reasons for this �nding: Either all �rms manage earnings ortake real actions to avoid small losses, or perhaps it is a part of the accrualprocess to report fewer losses than pro�ts.

While non-restaters as anticipated have less persistent negative earningschanges, they actually report large losses less frequently.

The three components of smoothing all move in the opposite directionof the expected, although only one of them signi�cantly so. Variability ofearnings to variability of cash �ows shows no di�erence between the twogroups, whereas restaters have more volatile earnings, indicating less earn-ings smoothing. Correlations between accruals and cash �ows move oppositethan expected, although highly insigni�cant (p-value 32%).

Non-restaters have more persistent earnings the restaters, although marginallyinsigni�cant (p-value 12%), whereas predictability is similar for the twogroups.

In sum, the majority of the metrics show that restaters have pooreraccounting 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 metricsmeasures a distinct feature of the �nancial statements.

7.2 After Restatement Event

Results for the earnings quality metrics for both groups in the years afterthe (last) restatement event are tabulated in Table 9.

While the restaters appear to have improved the quality of earnings onsome dimensions (e.g. asymmetric timeliness and loss avoidance), materialdi�erences still remain on other measures (e.g. all the accruals models andpersistence). Signi�cant di�erences remain after the restatement eventbetween restaters and non-restaters, respectively, measured both with theabnormal accruals models and the accruals quality models. Thus, restatersstill have lower earnings quality than non-restaters. The same is the casefor the other accrual models, even though the sign of change in accruals haschanged direction.

In accordance with results from Table 8, the measure of avoiding earningsdecreases is signi�cantly di�erent for the two groups.For asymmetric timeliness it is seen that restaters and non-restaters reportlarge losses in an equally timely manner, hence it seems as if restaters haveimproved.

Restaters seem to smooth more than non-restaters following earnings tocash �ows, but focusing only on the variability of earnings, restaters in factsmooth less. Correlation coe�cients show no real di�erences.Non-restaters have less persistent earnings than non-restaters, although in-signi�cantly so (p-value 11%), whereas predictability - like before - is almost

50

Table 9: Restaters and Non-Restaters After the Restatement Event

Metric Prediction Restaters Non-Restater Di�erence

Abnormal

Accruals

Models

Jones Model = 0.1027 0.0935 0.0092**Modi�ed JonesModel

= 0.1070 0.0979 0.0091**

Performance-Matched Mod.Jones Model

= 0.0964 0.0887 0.0077*

Accruals

Quality

Models

Dechow-DichevModel

= 0.0085 0.0074 0.0011

Modi�edDechow-Dichev

= 0.0107 0.0094 0.0014*

Other

Accruals

Models

Magnitude inAccruals

|=| -0.0990 -0.0702 -0.0288*

Change inAccruals

|=| -0.0129 0.0133 -0.0262

Avoiding

Decreases and

Small Losses

Avoiding SmallEarningsDecreases

= 0.0335 0.0475 -0.014***

Loss Avoidance = 0.20 0.27 -0.07

Asymmetric

Timeliness

Timeliness = -0.0695 -0.6808 0.6113Timely LossRecognition

= 8.7219 6.7791 1.9428

Smoothness

Var. of Earningsto Cash Flows

= 3.2291 5.1454 -1.9163

Var. of Earnings = 0.1446 0.0877 0.0569Corr. betweenAccruals and CashFlows

= -0.4647 -0.4736 0.0089

Persistence Persistence = 0.2494 0.2829 -0.0335

Predictability Predictability = 0.7928 0.8116 -0.0188

The prediction follows the prediction that the restaters and non-restaters have identical earningsquality after the restatement.*, **, *** denote signi�cant di�erence from 0 at the 10%, 5%, and 1% levels, respectively, withtwo-sample t-test.

51

similar.Even though the restaters seem to have improved on some parameters of

earnings quality, some of the di�erences from before the restatement remainafterwards as well. However, it is neither possible to maintain nor reject thesecond hypothesis.

7.3 Di�erence-in-Di�erence

Despite the strengths of the matched sample design outlined above, it doesnot fully control for di�erences in the economic environment. Therefore Ialso compare the change in earnings quality before and after a restatementevent for both restaters and non-restaters. The di�erence-in-di�erence testis thus a test of Hypothesis 3. In this di�erence-in-di�erence setting each�rm acts as its own control. This also means that each restatement �rm andits control are a�ected equally by outside factors.

Under the expectation that restaters improve earnings quality followinga restatement, it is expected that the change towards higher earnings qualityis higher for restaters than for non-restaters.

Table 10 shows results of the di�erence-in-di�erence tests. The speci�ctests are combined in headers by averaging each metrics, except AvoidingEarnings Decreases and Timely Loss Recognition, where only the respectivetests are tabulated.15

For almost all dimensions of earnings quality, restaters have improved. How-ever, this is also the case for most of the non-restaters, and on no dimensionsis the change between the two groups signi�cant.

Similar with the �ndings in Table 8 and 9, restaters have larger amountsof discretionary accruals than non-restaters before and after the restatementevent. However, both groups experience a drop in discretionary accrualsbetween the two periods. It is therefore not possible to attribute the changeto the restatement event only.The same is the case in the accruals quality models. Here both groupsexperience signi�cant increases in earnings quality, but the increase is notsigni�cantly larger for restaters than for non-restaters.While restaters decrease their level and change of accruals from before therestatement event to after, the opposite in fact happens for non-restaters.The di�erence between the two is not signi�cant at conventional levels, butit still seems as if restaters increase earnings quality in the period, whilenon-restaters experience a decrease.

15Each of the two groups (avoiding earnings decreases and small loss avoidance, andtimeliness and timely loss recognition) is too heterogeneous to combine into a single metric.Both di�erence-in-di�erence for the untabulated metrics move in the expected direction,although this is insigni�cantly di�erent for restaters and non-restaters

52

Table10:Di�erence-in-Di�erence

Metric

Prediction

After-Before:

Restaters

(i)

After-Before:

Non-Restaters

(ii)

Di�erence

(i-ii)

AbnormalAccrualsModels

i>ii

-0.0345***

-0.0286***

-0.0059(0.60)

AccrualsQualityModels

i>ii

-0.0112***

-0.0097***

-0.0015(0.21)

Other

AccrualsModels

i>ii

-0.0183

0.0064

-0.0247(0.12)

AvoidingEarnings

Decreases

i>ii

-0.0312**

-0.0090

-0.0222(0.18)

TimelyLossRecognition

i<ii

-1.8696

-2.5946*

0.7250

(0.22)

Smoothness

i<ii

-0.6497

0.0851

-0.7348(0.60)

Persistence

i<ii

-0.1073***

-0.0734***

-0.0339(0.39)

Predictability

i>ii

-0.0678

0.0357

-0.1035(0.30)

*,**,***denote

signi�cantdi�erence

from

0atthe10%,5%,and1%

levels,respectively.

P-valuein

parentheses.

Restatervs.non-restatertested

withtwo-samplet-test,before

vs.after

withpaired

t-test.

53

Restaters experience a signi�cant increase in earnings quality as measuredby avoiding earnings decreases, while non-restaters only experience a verysmall, insigni�cant increase. However, the di�erence between the two is notsigni�cant at conventional levels.

Both restaters and non-restaters report large losses less frequently afterthe restatement event, the latter group in fact signi�cantly so. Hence, itseems as if both groups increase earnings quality, but this increase is notsigni�cantly larger for restaters than non-restaters.

The smoothing metrics all move in the opposite direction of what isexpected. Hence, it actually seems as if restaters smooth earnings moreafter a restatement, whereas non-restaters smooth slightly less.

Both groups experience signi�cant drops in persistence over the timespan. Restaters have more predictable earnings in the after-period, whereasthe opposite is true for non-restaters. Although the di�erence is insigni�cant,it does seem as if restaters improve more than non-restaters in the timeperiod.

In sum, restaters improve their accounting quality over the time periodusing some of the metrics, but not more than non-restaters. It is not possibleto attribute the improvement to the restatement event for the restaters, sincethe matched control group was subject to an improvement as well.

The fact that accounting quality seems to improve over the period forboth groups on some parameters can be attributed to a number of factors.For instance, Singer and You (2011) �nd that earnings quality improves afterthe enactment of SOX which could in�uence the results. Macro economicfactors or the economic downturn in the middle of the period could alsochange the earnings quality.

Finally, anecdotal evidence suggests that while the number of restate-ments has increased, their severity has decreased. This could be due toSOX, increased scrutiny by the SEC in the post-Enron period, or that �rmsgenerally are less afraid of the market reactions to restatements. This mightalso drive the improvement in earnings quality for the accruals models.

Recall, though, that other metrics in fact move in the opposite direc-tion. For instance, earnings seem to have become less persistent, as is alsosuggested by Dechow and Schrand (2004).

8 Robustness Tests

The fact that the restatement events happen in di�erent calender years (from2000 until 2010) mitigates a possible e�ect from changes in macro economicvariables and the overall economic environment. These e�ects are furthercontrolled for by using the di�erence-in-di�erence design.

A common concern when working with restatements in an accountingquality context is the fact that endogeneity issues might be present. This is

54

Table11:RobustnessTests

PanelA:Before

Sample

NormalSam

ple

DeleteFirst

RestatementYear

DeleteFirst

YearAfter

Restatement

Restaters

Non-Restaters

Restaters

Non-Restaters

Restaters

Non-Restaters

Jones

Model

0.1471

0.1144

0.1567

0.1147

0.1471

0.1144

Modi�ed

Jones

Model

0.1527

0.1193

0.1602

0.1178

0.1527

0.1193

Perform

ance-M

atched

Mod.Jones

Model

0.1380

0.1068

0.1449

0.1042

0.1380

0.1068

Magnitudein

Accruals

-0.0752

-0.0679

-0.0801

-0.0726

-0.0752

-0.0679

Change

inAccruals

0.0005

0.0212

0.0236

0.0115

0.0005

0.0212

AvoidingEarnings

Decreases

0.0367

0.0481

0.0013

0.0291

0.0367

0.0481

Var.of

Earnings

toCashFlows

4.5880

4.4629

2.3050

7.6135

4.5880

4.4629

Var.of

Earnings

0.2507

0.2068

0.3254

0.2522

0.2507

0.2068

Corr.betweenAccrualsandCashFlows

-0.4247

-0.4479

-0.4356

-0.4374

-0.4247

-0.4479

Persistence

0.3254

0.3508

0.3117

0.3018

0.3254

0.3508

PanelB:AfterSample

NormalSam

ple

DeleteFirst

RestatementYear

DeleteFirst

YearAfter

Restatement

Restaters

Non-Restaters

Restaters

Non-Restaters

Restaters

Non-Restaters

Jones

Model

0.1027

0.0935

0.1027

0.0935

0.1050

0.1012

Modi�ed

Jones

Model

0.1070

0.0979

0.1070

0.0979

0.1089

0.1047

Perform

ance-M

atched

Mod.Jones

Model

0.0964

0.0887

0.0964

0.0887

0.0997

0.0958

Magnitudein

Accruals

-0.0990

-0.0702

-0.0990

-0.0702

-0.1595

-0.0722

Change

inAccruals

-0.0129

0.0133

-0.0129

0.0133

-0.0723

0.0120

AvoidingEarnings

Decreases

0.0335

0.0475

0.0335

0.0475

-0.006

0.0236

Var.of

Earnings

toCashFlows

3.2291

5.1454

3.2291

5.1454

2.7620

3.5505

Var.of

Earnings

0.1446

0.0877

0.1446

0.0877

0.1818

0.1323

Corr.betweenAccrualsandCashFlows

-0.4647

-0.4736

-0.4647

-0.4736

-0.4726

-0.4717

Persistence

0.2494

0.2829

0.2494

0.2829

0.2312

0.2496

55

the case if the SEC also uses some of the earnings quality metrics to identifythe restaters which might lead to reversed causality, so that the likelihood ofrestating in�uences the outcome of the earnings quality metric. Even thoughthis explanation cannot be entirely ruled out, it is strongly impeded by thelarge amount of earnings quality metrics included in the tests. Given thatthey measure di�erent aspects of earnings quality, aggregated they are likelyto measure some underlying construct of the quality of �nancial statements.

Table 17 tabulates sensitivity checks using a sample of the quality metrics,speci�cally the abnormal accruals models, other accruals models, avoidingearnings decreases, smoothing, and persistence.

One possible concern is whether the poor quality detected in the yearsbefore 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 resultsfrom table 8 and 9 for comparative purposes.

Overall, this control does not seem to make material di�erences to theresults. However, the two smoothness measures of variability of earnings arelarger when removing the �rst restatement years. Most likely, this is merelya consequence of the fact that we exclude one year in the period. Annualvariances are then arti�cially enlarged.

Another issue is connected with measuring the quality of earnings aftera restatement. One can imagine that there is a time lag before the improve-ment takes place or before it can actually be measured. Therefore we removethe �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 PanelB.

No material di�erences seem to exist after this control and hence it isunlikely that the earnings quality of restaters has improved signi�cantly morethan non-restaters, even when allowing a one-year time lag.

9 Conclusion

Using a broad portfolio of earnings quality metrics and a sample of �rmsrequired to restate by the SEC, we �nd the following: My results indicatethat the poor earnings quality in the restatement year detected in previousstudies is evident up to ten years before the actual restatement. It alsoseems as if the di�erence in earnings quality between restaters and non-restaters is smaller after a restatement event. However, we show with adi�erence-in-di�erence research design that restaters do not improve more

than non-restaters. It is therefore not possible to attribute the improvementto the actual restatement event.

To my knowledge, this is the �rst study to examine the educative role ofa restatement event, measured on the accounting quality of restating �rms.

56

The fact that we cannot statistically isolate the e�ect of a restatement in thequality of earnings is quite surprising. In particular, given the severe marketreactions to restatements, the �nancial markets clearly take restatementsvery serious. Yet, this does not lead to an immediate, material improvementin the earnings quality of restating �rms. Assuming that the metrics weapply accurately measure accounting quality, my results indicate that thescrutiny and intervention of SEC does not necessarily lead to a change inthe behaviour of the restating �rms. This point is further underlined by thefact that several of the �rms in my sample restate more than once.

This paper complements previous research on the consequences of restate-ments (e.g. (GAO, 2006b); (Dechow et al., 1996); (Dechow et al., 2011)) andadds further to our knowledge on how �rms behave after a restatement event.It also elaborates on research on the quality of �nancial statements in theactual restatement year (Jones et al. (2008); Richardson et al. (2002)).The practical implications of these �ndings are of particular importance tothe SEC and regulators. First, it is evident that the poor earnings qualitypresent in the restatement year can be measured up to ten years before therestatement. This knowledge can be used when SEC selects �rms to exam-ine. Second, as several of the restating �rms restate more than once and donot improve the quality of earnings signi�cantly more than a group of similar�rms, restating �rms might need the surveillance of SEC, even in the yearsafter they restate.

The �ndings of this paper open up to a number of interesting researchquestions. Suggestions for further research thus include an examination ofhow �rms behave after a restatement, on other parameters than earningsquality. It could also be interesting to study what drives the quality ofearnings in the post-restatement period.

Finally, it remains an open question if the �ndings from this study willalso hold outside the US, where di�erent regulatory oversight is present.

57

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