The Valuation Premium for a String of Positive Earnings Surprises:

The Role of Earnings Manipulation

Jenny Chu

Department of Finance and Accounting Judge Business School

University of Cambridge Cambridge, UK

j.chu@jbs.cam.ac.uk

Patricia Dechow* Department of Accounting Haas School of Business

University of California, Berkeley Berkeley, USA

Patricia_Dechow@haas.berkeley.edu

Kai Wai Hui Department of Accounting

Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong

ackw@ust.hk

Annika Yu Wang Department of Accounting Haas School of Business

University of California, Berkeley Berkeley, USA

annika_wang@haas.berkeley.edu

April 16, 2016 * Corresponding Author. We thank Brad Badertscher, Ted Christensen, Michelle Hanlon, Bjorn Jorgensen, Alastair Lawrence, Christian Leuz, Reuven Lehavy, Hai Lu, Jeffrey Ng, Peter Pope, K. Ramesh, Catherine Schrand, Hollis Skaife (discussant), Richard Sloan, Jake Thomas, and workshop participants at UC Berkeley, HKUST Accounting Research Symposium (2015), London School of Economics, Norwegian School of Economics, Singapore Management University, Rice University, and the Yale Accounting Research Conference (2015) for helpful comments. We thank the UC Berkeley Center for Financial Reporting and Management for research funding and for sharing the Accounting and Auditing Enforcement Releases dataset.

The Valuation Premium for a String of Positive Earnings Surprises:

The Role of Earnings Manipulation

ABSTRACT

Prior research finds that firms that consistently report positive earnings surprises earn a valuation premium (a higher price-to-earnings ratio). However, the role of earnings management is not definitive because of the high correlation between positive earnings surprises, economic growth, and accruals. We first provide unambiguous evidence linking manipulation to the consistent reporting of positive earnings surprises. Specifically, we show that an unusually high proportion of firms subject to SEC enforcement actions for earnings manipulation report consecutive positive earnings surprises. We next examine whether manipulating managers are motivated by the fear of losing an existing valuation premium or out of greed to obtain a valuation premium. Consistent with fear being the primary motivation, we document that manipulating firms already have high market expectations and are valued at a premium before manipulation starts. This premium is maintained during the manipulation period but is completely lost after the manipulation is discovered. Additional analyses suggest that (i) achieving an earnings string through manipulation is short-lived, lasting only one or two years; and (ii) manipulating managers do not use earnings guidance as a secondary tool to walk down analysts’ forecasts.

Keywords: strings of positive earnings surprises; earnings manipulation; overvalued equity; price-to-earnings ratio; valuation premium; management guidance.

JEL classification: G12, M41

Data availability: All data is available from sources identified in the text.

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“The process, known as gallon pushing, saw Japanese bottlers offered credit terms to buy more concentrate between 1997 and 1999. The SEC said the practice contributed 1-2 cents to Coca-Cola's quarterly earnings per share and was the difference in eight out of 12 quarters between Coke meeting and missing analysts' estimates.”

The Financial Times (2005)

I. INTRODUCTION

Existing research documents that firms that consistently beat benchmarks, such as prior

year’s earnings and analysts’ forecasts, have higher price-to-earnings multiples than firms that

have similar growth in underlying fundamentals but do not consistently beat benchmarks (e.g.,

Barth et al., 1999; Kasznik and McNichols, 2002). Several explanations have been offered for

why firms are rewarded with this valuation premium. These explanations generally require some

form of investor irrationality. For example, Barth et al. (1999) suggest that investors could prefer

less volatile earnings and are willing to pay a premium for them. However, this preference is

difficult to explain when investors hold diversified portfolios. Kasznik and McNichols (2002)

hypothesize that investors pay a premium because they believe consistent beaters will have

higher future growth opportunities than matched counterparts. However, they find that consistent

beaters do not deliver the level of high future performance that would justify the current

premium.

If investors reward consecutive beaters with a higher valuation premium, managers may

have incentives to manipulate earnings in order to obtain the valuation premium (we term this

the greed hypothesis). Researchers face a number of challenges when testing this hypothesis.

First, firms that consistently report positive earnings surprises tend to be growing firms with

strong economic performance. This underlying fundamental growth makes partitioning accruals

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into growth and manipulation components problematic since it is well known that accruals are

correlated with growth. Second, the expected proportion of firms with positive earnings strings

in the absence of earnings management is unknown. This makes constructing a control group

difficult since it is hard to identify firms that are similar in all other aspects to consistent beaters

but do not have consecutive positive earnings surprises.

As a consequence of these problems, most existing research does not directly focus on

the greed hypothesis. For example, Matsumoto (2002) indirectly tests the importance of the

greed hypothesis in her investigation of whether managers use guidance as a way to frequently

beat analysts’ forecasts. She finds that discretionary accruals are higher for consecutive beaters

in univariate tests but not in multivariate analysis. Similarly, Bartov et al. (2002) find weak

support for guidance but insignificant results for discretionary accruals in their earnings

management tests on habitual beaters. The paper that focuses most on consistent beaters and

earnings management is Myers et al. (2007). The study examines a sample of firms that have had

five years of quarterly EPS increases. The authors argue that these firms are managing earnings

because their simulations predict fewer consistent beaters. This argument hinges heavily on the

validity of the simulations in reflecting the “true” earnings process. They also compare various

earnings management metrics to control firms that have similar average annual EPS growth over

five years. These tests rely heavily on the adequacy of the control group. Therefore, as Myers et

al. (2007) acknowledge in concluding remarks, many of their results could be reinterpreted as

being due to the uniqueness of the sample as well as underlying economic growth.

Our first research question therefore seeks to determine whether earnings management is

used to achieve a string of positive earnings surprises. To avoid the interpretation problems of

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prior research, we focus on a set of firms for which there is almost no ambiguity that earnings

manipulation has taken place. We use firms identified by the Securities and Exchange

Commission (SEC) as having manipulated earnings (i.e., firms subject to Accounting and

Auditing Enforcement Releases, or AAER firms). The advantage of the AAER sample is that it

helps mitigate the two previously mentioned interpretation problems. First, there is clear

evidence identified by the SEC that managers manipulated earnings and violated Generally

Accepted Accounting Principles (GAAP). Second, we avoid the problem of identifying an

adequate control sample that is similar in all aspects to treatment firms but did not consistently

beat earnings. In our setting, we can compare the propensity of manipulating firms to

consistently beat quarterly earnings benchmarks with that of the population. Thus we compare

manipulating firms’ propensity to consistently beat analysts’ forecasts to that of the I/B/E/S

population; and manipulating firms’ propensity to consistently beat seasonally adjusted (year

over year) earnings to that of the Compustat population. In addition, we can compare

manipulating firms to propensity score matched samples that control for other characteristics.

These advantages come with costs. One potential disadvantage of the AAER sample is

that these firms were caught while other companies in the population could be manipulating to

consistently beat expectations but were never caught. This problem reduces the power of our test.

A second concern is that the SEC could target firms that consistently report positive earnings

surprises as a screen for further investigation. To address this concern we search news releases to

determine the triggers that caused the SEC investigations. We found that the SEC is mainly

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reactive rather than proactive in discovering manipulating firms.1 We find no examples of

AAERs that were issued as a consequence of the SEC investigating firms that consistently beat

earnings expectations.

Our results suggest that a significantly greater proportion of manipulating firms report

positive earnings strings than both the population and the propensity score matched sample. We

find that during manipulation years, 53.35 percent of manipulating firms consistently beat

analysts’ forecasts for four consecutive quarters, compared to 43.34 percent for the population,

and 45.45 percent for the propensity score matched sample. We obtain similar results when

examining management’s incentive to consistently beat seasonally adjusted earnings. We find

31.31 percent of manipulating firms have four quarters of consecutive beats versus 26.42 percent

for the population and 26.38 percent for the propensity score matched sample. We examine

whether the incentive to report positive earnings strings has incremental explanatory power over

beating annual earnings benchmarks and other determinants of misstatements. Our results are

robust to the inclusion of the annual earnings surprise, the fraud score developed in Dechow et

al. (2011), along with year and industry fixed effects.

Having established that manipulating firms appear to boost earnings to report positive

earnings strings, our second research question investigates the relation between reporting a

positive earnings string via manipulation and the valuation premium. In particular, we investigate

1 In 2009 the SEC created the Division of Economic and Risk Analysis (DERA) with the objective of developing a more rigorous approach to identifying deviant behavior by individuals and firms. In 2011 under the directorship of Craig Lewis, DERA began developing an Accounting Quality Model (AQM) to identify firms with unusual discretionary accruals. The development of this model was publicly announced in 2012 (see: http://www.sec.gov/News/Speech/Detail/Speech/1365171491988#.UhJov9LMB8E). In our private conversations with SEC officials, they acknowledged that there was no systematic usage of formal analytical models prior to 2012.

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two hypothesized links: The first is the greed hypothesis discussed earlier - managers manipulate

earnings to obtain the valuation premium. The second is the fear hypothesis - managers

manipulate earnings because they fear losing an existing valuation premium. Although these

hypotheses are not mutually exclusive, they have different implications for understanding the

motivation of managers and interpreting the results from prior research. Under the greed

prediction, managers intentionally manipulate earnings to fool the market in order to obtain a

higher market valuation. In contrast, under the fear hypothesis managers manipulate earnings to

maintain their status quo (Jensen 2005). Research in behavioral economics suggests that people

measure their happiness based on reference points (Tversky and Kahneman, 1991). When

managers have experienced the benefits of the valuation premium, which include easier access to

capital markets, higher compensation packages, and greater investor recognition, they feel a

strong sense of loss from losing these existing benefits. In order to maintain these benefits,

managers feel compelled to continue meeting market expectations. As a result managers have an

escalating commitment to meet expectations, and this escalation moves them from manipulating

earnings within GAAP to outside of GAAP.

Our results suggest that the fear hypothesis appears to better explain the manipulation in

the AAER sample. However, we do find some support for the greed hypothesis. We document

that consecutive beaters have a valuation premium prior to the manipulation period (the median

forward price-to-earnings ratio is over 40 compared to the population ratio of around 20). We

also find that market expectations are high for these firms: a greater proportion of manipulating

firms have "buy" recommendations and have high long-term growth forecasts relative to control

firms. During the manipulation period these firms are able to maintain their valuation premium.

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However, after the manipulation is discovered, the price-to-earnings ratios of these firms decline

to the population average. We also provide supporting evidence consistent with managers being

concerned with market expectations. First, we show that consistently meeting analyst

expectations is a stronger motivation for earnings manipulation than demonstrating seasonally

adjusted quarterly earnings growth. Second, we show that manipulating firms obtain positive but

declining stock returns at earnings announcement during the manipulation period. The positive

returns are completely offset by the negative return when the firm first misses expectations. The

combined evidence suggests that managers rationally feared missing market expectations and

these fears were realized with a loss of the valuation premium once the firm missed expectations.

We also provide additional analyses to answer two related questions. First, how long are

firms able to manipulate earnings to report positive earnings strings? We find that 42 percent of

manipulating firms have positive earnings strings of eight quarters based on analysts’ consensus

forecasts and that this proportion is significantly higher than the population (32.49%). We find

similar results using seasonally adjusted earnings as the benchmark. However, results for longer

horizons are weak or insignificant. This suggests that because accruals must reverse,

manipulating earnings to report a positive string is difficult beyond two years. This result has

implications for future research. It suggests that firms with long strings of consecutive positive

earnings surprises are likely to initially have strong fundamental performance. This strong

economic performance is rewarded with a valuation premium. Our results suggest researchers

should not use the entire string period to detect earnings management. The most powerful time to

detect earnings management is in the year or two immediately before the string breaks.

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The second question relates to management guidance. Prior research suggests that

managers prefer to guide down analysts’ forecasts rather than engage in earnings management to

consecutively beat earnings expectations (e.g., Matsumoto 2002 and Bartov et al 2002). In our

setting we can ask a slightly different question. Specifically, conditional on managers engaging

in earnings management, do they also use guidance as a secondary tool to help them

consecutively beat earnings expectations? Our results suggest that the answer to this question is

“no.” We find that managers do not provide consistent earnings guidance during manipulation

years. Only 22 percent of the AAER sample provide guidance, and of these firms about a third

stop guiding during the manipulation period. Of the sample that did provide guidance during the

manipulation period, half of these provided guidance for only one quarter of the year. Thus,

management guidance is rare during manipulation years. This analysis suggests that: (i) guiding

analysts’ forecasts down is not an actively used tool by these firms; and (ii) managers appear to

stop providing guidance when they are manipulating earnings. This result is consistent with the

conclusions of Chen et al. (2011) and suggests that managers stop guiding when they anticipate

future bad news.

The results of our paper have implications for standard setters, regulators, auditors, and

investors. Consistent with prior research, we show that when firms miss quarterly earnings

targets after consistently meeting them, there is on average a significant decline in stock price.

This suggests that auditors should not think of materiality thresholds as just a percentage of

earnings or assets, but should also consider the path the firm has taken in reporting their

earnings. A firm that has consecutively beaten prior earnings expectations is potentially under

much more pressure to manipulate earnings and faces stronger valuation consequences for

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seemingly small misses around certain earnings thresholds. Our results also suggest that

regulators such as the SEC should take note of firms that have consistently met or beaten

quarterly consensus earnings forecasts or seasonally adjusted earnings during the year and

scrutinize the disclosures in the annual 10-K in more detail. In addition, standard setters could

consider whether firms should include the “risk” of a potential large stock price decline from

missing an earnings target in their risk factors. Finally, regulators could consider whether more

disclosures in the Management Discussion and Analysis (MD&A) section explaining how the

firm was able to consistently deliver earnings growth would encourage investors to scrutinize

management’s accrual decisions more carefully.

II. BACKGROUND AND RESEARCH QUESTIONS

Prior literature provides empirical evidence on the valuation effects of achieving and

breaking positive earnings strings. One line of research focuses on strings of positive earnings

growth. Barth et al. (1999) find increasing earnings multiples for longer strings of annual

earnings increases, after controlling for growth and risk proxies identified in prior research.

Similarly, Myers et al. (2007) document that firms reporting at least twenty quarters of

seasonally adjusted earnings increases have higher abnormal returns before their positive strings

are broken. A second line of research focuses on earnings strings based on beating analysts’

forecasts. Kasznik and McNichols (2002) find that the market assigns a higher valuation to firms

that have longer strings of meeting analysts’ expectations of annual earnings. Therefore, firms

appear to earn a valuation premium whether they consistently beat analysts’ forecasts or

consistently beat prior earnings. Thus, we investigate both of these “earnings expectations”

models when we perform our analysis.

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Researchers have also examined the consequences of missing earnings expectations.

Results suggest that when investors have high expectations about future growth, missing

earnings expectations can result in a large stock price decline. Skinner and Sloan (2000) show

that firms with high market-to-book ratios have larger responses to bad news earnings

announcement than firms with low market-to-book ratios. Myers et al. (2007) show that firms

with longer strings of consecutive growth in quarterly earnings have stronger negative stock

price responses when growth in earnings stops. Similar findings are also documented in Barth et

al. (1999).

The negative stock price decline forms the basis of the fear hypothesis. As Jensen (2005)

suggests, if for some reason investors over-extrapolate past performance and assume firms will

continue to grow at the same rate, then it is possible for firms to become overvalued. This

overvaluation provides management with benefits such as greater analyst coverage, higher

trading volume and stock liquidity, easier access to capital markets, and higher executive

compensation. However they come at a cost. In order to maintain these benefits, managers must

continue to convince investors that the firm is worth its high valuation. This puts pressure on

managers to focus on meeting market expectations rather than improving the core business.

Under such circumstances, if fundamental performance begins to slow, managers feel compelled

to take actions to hide the slowdown from investors by aggressively engaging in earnings

management. Thus, once a firm is overvalued, managers face an escalating commitment to

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consistently beat benchmarks in order to maintain their valuation premium, out of fear that

missing quarterly earnings expectations can result in significant stock price declines.2

As noted in the introduction, prior research has predominantly investigated the greed

hypothesis. Specifically, researchers have examined whether firms engage in earnings

management to earn the valuation premium. The results suggest that walking down analysts’

forecasts, rather than engaging in earnings management, is a more popular tool used to

consistently beat analysts’ forecasts (e.g., Matsumoto 2002 and Bartov et al. 2002).3 Myers et al.

(2007) specifically investigate whether firms that consistently beat quarterly earnings

expectations (where the expectation is the same quarter in the prior year) for five years have

engaged in earnings management. The study analyzes earnings management over the entire five-

year window immediately before the string breaks, and does not examine the relation between

the formation of the valuation premium and the timing of the earnings management. Therefore

Myers et al.’s (2007) tests do not attempt to distinguish whether the greed hypothesis or the fear

hypothesis is a more important motivation for earnings management. Our paper seeks to better

understand managers’ motivations by formally examining these hypotheses.

2 Schrand and Zechman (2012) suggest that managerial overconfidence results in unintentional upward earnings management that escalates into increasingly intentional misstatements. In contrast, Jensen’s (2005) escalating commitment explanation for earnings manipulation does not require managers to be overconfident. 3 Other studies also provide indirect evidence related to the role of earnings manipulation in string patterns. For example, McInnis and Collins (2011) find that firms that have higher accrual quality are less likely to beat earnings forecasts once analysts start providing cash flow forecasts. Ghosh et al. (2005) find that the magnitude of abnormal accruals for firms with consistent earnings growth and no revenue growth tend to have higher abnormal accruals than firms with both consistent earnings and revenue growth. However, they provide no formal statistical tests of accrual quality between firms with consistent earnings growth and firms without consistent earnings growth, since accrual quality is not the focus of their paper. Similarly, Burgstahler and Eames (2006) compare discretionary accrual levels across small negative, zero, and small positive earnings surprise firms based on analysts’ forecasts. The authors suggest that earnings manipulating firms have higher discretionary accruals at the median, but they do not provide formal statistical tests.

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One paper that specifically focuses on the fear hypothesis is Badertscher (2011).

However his research question differs from ours. Badertscher (2011) seeks to determine whether

there is a link between overvaluation and future earnings management. Such evidence is

consistent with overvalued firms attempting to improve earnings to meet market expectations.

However, Badertscher (2011) does not examine whether firms are engaging in earnings

management to beat market expectations or to report consecutive positive earnings surprises (i.e.,

show continuing “growth”). His focus is on whether managers engage in accrual or real earnings

management and whether the choice of tool is contingent on how long the firm has been

overvalued. Therefore his objective, research design and tests are different from ours. However,

his results are complementary to ours since he provides evidence of both accrual and real

earnings management in overvalued firms.

As mentioned above, Myers et al. (2007) focus on whether firms that consistently show

growth in quarterly earnings per share (EPS) engage in earnings management. Their research

design and approach vary substantially from ours. They identify a treatment sample of firms that

consistently beat for five years, and compare various earnings management proxies to a control

sample of firms that showed similar growth in annual EPS over the five-year period but did not

consistently report positive quarterly EPS surprises. Their earnings management tests have

interpretation challenges. First, the control firms appear to be quite different from the treatment

firms on many dimensions.4 Second, since the treatment firms are selected based on consistently

4 The control firms in Table 2 of Myers et al (2007) show similar EPS growth to treatment firms, but it is not clear whether this is because both groups have similar future economic performance or because control firms have temporarily low EPS at the beginning of the five year period (perhaps because of transitory negative special items). Control firms’ higher price-to-earnings ratio at the start of the period is suggestive of temporarily low EPS. The control firms also show lower future sales growth, lower growth in their asset bases, and lower market-to-book

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reporting positive earnings increases, other tests are difficult to interpret due to the lack of a

clearly defined counterfactual (i.e., what should be expected in the absence of earnings

management).5 Therefore Myers et al. (2007) is interesting and provocative, but the results are

open to interpretation.

The focus of our research is to examine the following predictions:

Prediction 1: Firms engage in earnings manipulation to report strings of positive earnings surprises.

Conditional on finding support for Prediction 1, our next prediction investigates the

motives of management.

Prediction 2: Greed hypothesis: Firms engage in earnings manipulation to report strings of positive earnings surprises to earn a valuation premium.

Prediction 3: Fear hypothesis: Firms engage in earnings manipulation to report strings of positive earnings surprises to avoid losing a valuation premium.

We use firms identified by the SEC as having manipulated earnings to examine

Prediction 1.6 The advantage of using AAERs to identify accounting manipulation is that we

require no estimation model to identify earnings management, and thus we are able to minimize

ratios than the treatment firms. This is consistent with control firms having less growth opportunities than treatment firms. 5 Some tests in Myers et al (2007) are difficult to interpret due to the research design that requires the treatment firms to have consecutive increases in quarterly EPS. For example, the authors argue that firms engage in share repurchases to boost EPS when earnings growth is flat or negative. However, a firm with flat or negative earnings growth can only be in the treatment sample when it repurchased shares and reduced the number of shares outstanding. Similarly, if a treatment firm reports a negative special item, the only way it could be part of the treatment group is if its earnings before special items are sufficiently high so as to offset the negative special item. Otherwise, this firm would not be able to report a positive earnings increase. Even if special items were randomly assigned to firms, one might expect to find a higher occurrence of special items when firms are selected based on a history of EPS increases. Although earnings management is likely to be important, it is hard to know what to expect for the null hypothesis in the absence of earnings management. 6 See also, Feroz et al., 1991; Dechow et al., 1996; Beneish, 1997 and 1999; and Dechow et al., 2011.

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type I errors. Nonetheless the cost of using this sample is potential selection bias concerns. The

SEC only pursues cases where it is confident it will win. Therefore, this sample is likely to

consist of firms that engaged in extreme earnings management. Another concern is whether the

SEC uses consistent beating behavior as a detection device for potential frauds. If this were true,

then our tests would be identifying a SEC selection criterion rather than the motives for earnings

management. We view this possibility as unlikely. Prior research suggests that the SEC is more

reactive than proactive in discovering frauds. For example, Pincus et al. (1988) report that the

SEC obtains leads from several sources, such as public complaints, tips, and referrals from other

law enforcement agencies. Similarly, Miller (2006) suggests that the SEC pursues firms that are

identified by the press as having suspicious earnings or firms with financial restatements and

large asset write-offs. Nevertheless to combat this concern, we hand-collected discovery triggers

for the AAERs to determine whether a string of positive earnings surprises is used as a screen for

identifying fraud firms.

III. SAMPLE SELECTION AND RESEARCH DESIGN

We study two earnings benchmarks: analysts’ forecasts and seasonally adjusted earnings.

We measure analysts’ forecast errors as the difference between actual EPS and the most recent

median EPS forecast before an earnings announcement from I/B/E/S. We obtain seasonally

adjusted earnings from Compustat Unrestated Quarterly Updates. Specifically, we calculate

seasonal earnings change as the difference between current earnings-per-share and the prior

year’s earnings-per-share for the same quarter. Following Myers et al. (2007), we calculate

earnings-per-share as unrestated basic EPS excluding extraordinary items (item EPSPXQR)

divided by the cumulative adjustment factor (item AJEX).

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3.1 The AAER sample

Panel A of Table 1 summarizes the AAER sample selection process. We obtain the

AAER database from the Center for Financial Reporting and Management at UC Berkeley (see

Dechow et al. 2011 for details on the database). We begin with a total of 3,323 observations of

AAERs issued between May 1982 and September 2013. We exclude redundant AAERs related

to the same firm and incident, AAERs that are unrelated to financial statement fraud, and

AAERS for which we cannot find Compustat, I/B/E/S, or Compustat Unrestated identifiers. We

require quarterly earnings data for each 4-quarter string and exclude firms in the financial sector

(SIC codes 6000-6999). 7 We also restrict the sample to firm-year observations without missing

financial data. This results in a final sample of 343 (119) unique AAER firm-year (firm)

observations in the analysts’ forecasts sample and 693 (252) unique AAER firm-year (firm)

observations for the seasonally adjusted earnings sample. The final AAER sample’s

misstatement period spans from 1985 to 2010 for the analysts’ forecasts sample, and covers the

period from 1988 to 2010 for the seasonally adjusted earnings sample. As the unrestated earnings

data from Compustat Unrestated Quarterly Updates is not available until 1987, the seasonally

adjusted earnings sample starts in 1988. We also examine management guidance. We start with

708 AAER firms that are also found in the FirstCall guidance database. We match the final

AAER sample with the FirstCall guidance database and obtain 268 firm-year observations from

1994 to 2010.

7 We exclude firms in the financial sector since in logistic regression analysis we include variables that require details on accruals which is more difficult to measure for financial firms.

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We construct our earnings strings based on firms’ beating or missing two earnings

benchmarks: analysts’ forecasts and seasonally adjusted earnings. A firm-quarter observation

receives a “1” for meeting or beating the benchmark and a “0” for missing the benchmark. A

string observation consists of a series of ones and zeros based on whether the firm beats an

earnings benchmark in a series of quarters.

Exhibit 1 illustrates how we construct earnings strings using the AAER firm Coca-Cola

Company, Ltd. (Coca-Cola). Exhibit 1 presents Coca-Cola’s complete earnings strings based on

both analysts’ forecasts and seasonally adjusted earnings over its alleged manipulation period

from fiscal year 1997 to 1999 (see Appendix A for a discussion of SEC AAER No. 2232).

Throughout this period, Coca-Cola consistently beat analysts’ forecasts but had missed

seasonally adjusted earnings subsequent to 1997; therefore its complete strings based on each

earnings benchmark are (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) and (1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0),

respectively. We use the first fiscal quarter of the fiscal year to begin our string formation in

order to facilitate comparison with the population. We break a firm’s complete strings into

multiple 4-quarter strings, 8-quarter strings, and so forth depending on their durations. For

example, over its alleged manipulation period, Coca-Cola’s complete string contributes three 4-

quarter string observations and one 8-quarter string observation to the AAER sample.

3.2 The population

Our population consists of U.S. firms on Compustat from 1985 to 2010, excluding all

AAER firms identified by the SEC from May 1982 to September 2013. Table 1, Panel B

summarizes the sample selection process. For the analysts’ forecasts sample, we begin with

52,724 firm-year observations from Compustat and delete 3,034 observations that are AAER

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firms. We then exclude firms in financial industries and firm-year observations with missing

financial data. The final analysts’ forecasts population sample consists of 37,546 firm-year

observations. We follow the same procedure for the seasonally adjusted earnings sample and

arrive at a final sample of 100,748 firm-year observations.

Panel A of Exhibit 2 illustrates how we construct strings of various lengths for the

population. Strings begin in the first available fiscal quarter one (Q1) for each firm. Each fiscal

year with earnings data for all four quarters is classified as one 4-quarter string. A string of eight

quarters is constructed by adding together two consecutive fiscal years. Panel B illustrates that if

a firm’s first available fiscal year is 2000, then the first 8-quarter string will consist of fiscal

years 2000 and 2001. The next 8-quarter string will start in 2002 and finish at the end of 2003.

Note that we do not allow overlapping strings. For example, there is no 8-quarter string starting

in 2001 and ending in 2002. We avoid overlapping strings to make it easier to determine the size

and proportions of the population for strings of various lengths. Furthermore, avoiding

overlapping strings facilitates the fraud score calculation as some of the score generating

variables only exist in the Compustat Annual File.

3.3 The propensity score matched sample

An important issue is that differences in economic fundamentals (e.g., growth) could

drive firms’ benchmark beating patterns. We therefore match each AAER string observation to

five observations from the population based on their propensity to achieve positive earnings

strings (i.e., all-one strings). The propensity score is estimated using firm size (SIZE), book-to-

market ratio (BTM), leverage (LEV), and profitability improvement (∆ROA) measured at the

beginning of each earnings string. We then create an indicator variable for all-one strings

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STRING4 that is equal to one if the firm consistently beats the earnings benchmark for four

quarters in a row, and zero otherwise. The matched sample is generated without replacement

under caliper level of 3 percent following Lawrence et al. (2011).

Table 2 reports the propensity score estimation results and then evaluates the

effectiveness of the matching procedure. Firms of larger size, higher growth in profitability,

lower book-to-market and lower leverage are more likely to achieve all-one strings of four

quarters. All of these factors are statistically significant at the 0.01 level. Panel B and Panel C

present that, after our matching procedure, the AAER sample is not significantly different from

the propensity score matched sample with respect to innate firm characteristics at the beginning

of earnings strings. Therefore, the matching procedure appears to be effective in generating a

control sample that is similar in firm size, growth opportunities, profitability growth, and

leverage to the treatment (AAER) sample.

IV. EMPIRICAL RESULTS

4.1 Strings of positive earnings surprises and earnings management

Our first research question is whether firms manipulate earnings in order to achieve a

string of meeting or beating quarterly earnings benchmarks. Our main analyses focus on 4-

quarter string observations from the AAER sample, the population, and a propensity score

matched sample. For a 4-quarter string, there are sixteen possible permutations, with each having

an unconditional distribution probability of 6.25 percent. We identify the sixteen possible

permutations of 4-quarter earnings strings based on whether a firm beats earnings benchmarks in

each of the four quarters. Next, we use Chi-square tests to compare the distribution of string

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permutations in the AAER sample versus the unconditional probability, the distribution in the

population, as well as the distribution in the propensity score matched sample. We provide this

level of detail to determine whether earnings strings are unusual in any sample. We focus on 4-

quarter strings since the number of possible permutations increases exponentially for strings

longer than four quarters. For example, there are 256 different permutations for a string of eight

quarters, compared to 16 permutations for a string of four quarters.

Table 3 compares the distribution of the sixteen binary permutations across the AAER

sample, the population, and the propensity score matched sample, and we also conduct Chi-

square tests on permutation distributions. Panel A reports the distributional properties of strings

that beat analysts’ forecasts. Although the unconditional distribution of each earnings

permutation should account for 6.25 percent of the overall sample, we find that the all-one string

(1, 1, 1, 1) is the most dominant pattern out of the sixteen possible permutations in all three

samples. Indeed, 53.35 percent of AAER firms have an all-one string, compared to 43.34 percent

for the population and 45.54 percent for the propensity score matched sample.8 The Chi-square

tests indicate that the distribution of the AAER sample is significantly different from the

unconditional probability, the population distribution, and the distribution of the propensity score

matched sample. Finally, we note that no other string pattern appears to be particularly unusual

in its frequency.

We find similar results in Panel B of Table 3 using seasonally adjusted earnings as an

alternative earnings benchmark. This benchmark appears more difficult to meet than analysts’

8 In unreported tests, we also find that AAER financials firms are also more likely than the population to beat four consecutive analysts’ forecasts (65.31% versus 41.00%) and seasonally adjusted earnings benchmarks (43.75% versus 26.75%).

19

forecasts. Specifically, we find that 31.31 percent of AAER 4-quarter strings correspond to the

permutation (1, 1, 1, 1) versus 26.42 percent for the population and 26.38 percent for the

propensity score matched strings. The differences in the all-one string distribution between the

AAER sample and its comparison groups (i.e., the population and the propensity score matched

sample) are statistically significant.9

One concern with these findings is whether the SEC uses a string of consecutive earnings

beats as a screen for identifying potential fraud firms. We investigate this issue in Exhibit 3. We

searched press coverage for all firms that reported positive all-one earnings strings to determine

what triggered the SEC investigation. The results in Exhibit 3 suggest that the SEC is more

reactive than proactive. We find that 53 percent of AAERs are triggered by financial

restatements and in another 14 percent the SEC investigations are triggered by shareholder

lawsuits. None of the SEC investigations is triggered by the firm reporting consecutive positive

earnings surprises. Therefore, selection bias does not appear to explain our findings.

A second concern with the findings in Table 3 is whether a string of four quarterly beats

is merely a reflection of firms’ concerns with beating annual earnings benchmarks. The results in

Dechow et al. (2011) suggest this is unlikely to be a major concern since AAER firms are not

more likely to beat annual earnings than other firms. However, it is important to establish

whether a string of quarterly positive earnings surprises is incremental to beating annual

surprises, and we investigate this issue in Table 4.

9 In untabulated tests we examine the magnitude of positive earnings surprises for all-one string firms. We find that AAER firms have smaller positive earnings surprises than the population of firms that report all-one strings for both the analysts’ forecasts and the seasonally adjusted earnings samples. This result suggests that managers appear to manipulate earnings to beat but not overly exceed the benchmark.

20

Table 4 revisits the analysis in Table 3, but includes only AAER firms and control firms

that beat their annual earnings benchmarks. For simplicity we only report distributional

properties for the all-one string (1, 1, 1, 1). Results in Panel A of Table 4 indicate that for firms

covered by sell-side analysts, the all-one string is more prevalent for AAER firms (68.67%) than

for the population (59.81%) and the propensity score matched sample (62.39%). Chi-square tests

confirm that the differences in string patterns between manipulating firms and their comparison

groups are statistically significant. Thus in the sub-sample of firms that also have annual beats,

AAER firms are more likely than the population and the matched firms to report a string of

consecutive beats. Panel A also reveals that 31.33 percent (100% ‒ 62.39%) of the AAER firms

with an annual beat fail to achieve a 4-quarter string of consecutive beats. Thus annual beats do

not necessarily imply a string of beats for four quarters. Some firms are able to consistently beat

quarterly forecasts but do not beat annual forecasts while others are able to beat annual forecasts

but do not consistently beat quarterly forecasts. Panel B of Table 4 presents similar results using

the seasonally adjusted earnings benchmark. AAER firms are more likely to report a string of

quarterly beats than the population and the matched sample. In addition, approximately half of

the AAER firms (47.10%) that achieve annual earnings growth do not have a string of positive

growth in all four quarters.

We next investigate whether our string results hold in a multivariate setting where we

control for other factors that are correlated with the likelihood of misstatement. Dechow et al.

(2011) develop a composite fraud score (F-score) to detect earnings misstatement. We thus use

the decile rank of the F-score (RANKFSCORE) as a control variable in our logistic regression

analysis. We calculate the value of F-score for the fiscal year covered by each 4-quarter string

21

and then transform the F-score value into decile ranks. Following Dechow et al. (2011), the F-

score is generated from change in net operating assets (RSSTACC), change in receivables

(∆REC), change in inventory (∆INV), percentage of soft assets (SOFT), percentage change in

cash sales (∆CSALE), change in return on assets (∆ROA), and an indicator variable for external

financing (ISSUE). We also include an indicator variable for annual beat (ANNBEAT), along

with year and industry fixed effects. To address time-series and cross-sectional residual

dependence, we base statistical inferences on standard errors two-way clustered by both firm and

year.

Table 5 presents the logistic regression results. Panel A compares AAER firms to the

population. We document that information contained in the 4-quarter strings is incrementally

indicative of earnings misstatement. For both the analysts’ forecasts and the seasonally adjusted

earnings samples, the coefficient on STRING4 is significantly positive after controlling for the F-

score (RANKFSCORE) which summarizes the information contained in other financial variables

that correlate with financial misstatement. 10 Additionally, the coefficient on ANNBEAT is

insignificant in the analysts’ forecasts sample and significantly negative in the seasonally

adjusted earnings sample. Therefore, STRING4 appears to subsume managers’ incentives to beat

10 In terms of economic significance, the odds ratio for STRING4 is 1.35 for the analysts’ forecasts sample. The odds ratio for RANKFSCORE is 1.21. We find similar results using seasonally adjusted earnings.

22

annual earnings benchmarks. 11 We find similar logistic regression results using the AAER

sample versus the propensity score matched sample (Panel B of Table 5).12

In summary, the results in Tables 3 to 5 indicate that firms manipulate earnings to

consistently beat both analysts’ consensus forecasts and seasonally adjusted earnings

benchmarks.

4.2 The valuation premium and earnings manipulation to report consecutive positive

earnings surprises

We next examine how the valuation premium (measured as the price-to-earnings

multiple) relates to earnings management. If managers manipulate earnings to report a positive

string of consecutive earnings surprises, we expect price-to-earnings multiples to start at normal

levels and increase during the manipulation period (greed hypothesis). Alternatively, if managers

manipulate earnings to report positive earnings surprises to avoid disappointing the market, then

we expect to see high price-to-earnings multiples prior to the manipulation period followed by

similar price-to-earnings multiples during the manipulation period (fear hypothesis).

Figure 1 compares the time-series pattern of forward price-to-earnings between AAER

firms with positive strings of beating analysts’ forecasts and the population. For this sample,

forward P/E is measured as the fiscal year beginning price divided by the most recent EPS

11 Note that when STRING4 is excluded from the regression, the coefficient on ANNBEAT is positively significant for the seasonally adjusted earnings sample but remains insignificant for the analysts’ forecasts sample. This finding is consistent with Dechow et al.’s (2011) finding that beating analysts’ annual forecasts is not significantly associated with financial misstatement. 12 When we use alternative model specifications by replacing RANKFSCORE with F-score generating variables (i.e., RSSTACC, ∆REC, ∆INV, SOFT, ∆CSALE, ∆ROA, and ISSUE) in the logistic regression analysis, we obtain similar results.

23

consensus forecast from I/B/E/S before the earnings announcement of the first fiscal quarter.

Year t is defined as the first fiscal year that an AAER firm achieves an all-one 4-quarter string

during the manipulation period. Year t-1 and t-2 are the two years immediately prior to the

manipulation period. Year t+1 and t+2 are the first two years subsequent to the manipulation

period.13 The population is matched to the AAER sample by year. Before the manipulation

period, AAER firms that consistently beat analysts’ forecasts during the manipulation period

have significantly higher forward P/E multiples than the population. Specifically, the median of

P/E for AAER firms is 43 and 38 in year t-2 and t-1, whereas the population median in year t-2

and t-1 is 24 and 22, respectively. This valuation premium reflected in P/E is maintained during

the manipulation period starting year t (35 for AAER firms, compared to 21 for the population),

but disappears after the misstatement period in year t+1 and t+2. In year t+2, the median of P/E

(22) for the AAER sample declines to a level close to that of the population (20). We observe

similar patterns in the lower and upper quartile of P/E. These results therefore appear to be more

consistent with the fear hypothesis.

Figure 2 focuses on the seasonally adjusted earnings. For this sample, P/E is measured as

price-to-reported earnings-per-share at the fiscal year end of each time period. AAER firms that

consistently beat seasonally adjusted earnings have a significantly higher price-to-earnings

valuation both before and during the manipulation period. The median of P/E for AAER firms is

13 If a firm manipulates earnings strings for more than one year, then we use the price-to-earnings ratio in its first year of achieving an all-one string. For example, if an AAER firm manipulates earnings for 2006 and 2007 and achieves positive strings in both years, then we plot the price-to-earnings ratio for (i) year 2004 and 2005 as the two years prior to the manipulation period, (ii) for year 2006 as the year during the manipulation period, and (iii) year 2008 and 2009 as the two years subsequent to the manipulation period. As a second example, if an AAER firm manages earnings for 2006 and 2007 but achieves positive string in 2006 only, then we plot the price-to-earnings ratio for (i) year 2004 and 2005 as the two years prior to the manipulation period, (ii) for year 2006 as the year during the manipulation period, and (iii) year 2008 and 2009 as the two years subsequent to the manipulation period.

24

12 in both year t-2 and t-1, whereas the population median is 10 over these same periods. During

the manipulation period, the median P/E of AAER firms increases to 15 while it stays flat at 10

for the population. For the AAER sample, their P/E suffers a decline after the manipulation

period to a median of 13 in year t+1, and then converges to the population median of 10 in year

t+2. We observe similar patterns in the lower and upper quartile of P/E. These results are

consistent with both the fear and greed hypothesis. Manipulating firms have higher multiples

before the manipulation period. However during the manipulation period they not only maintain

the higher multiples (consistent with the fear hypothesis) but also increase them (consistent with

the greed hypothesis). The valuation benefits are completely lost once the manipulation is

revealed.14

In Table 6, we corroborate the results in Figure 1 and Figure 2 with logistic regressions of

AAER occurrence on an indicator variable of past overvaluation along with other firm

characteristics. Panel A reports results for the analysts’ forecasts sample and Panel B for the

seasonally adjusted earnings sample. HIGHPE is an indicator variable that equals one if the

firm’s P/E is in the top quintile by year across the AAER sample and the population, and zero

otherwise. Column (1) of Panel A and Panel B indicates that the coefficient on HIGHPE is

significantly positive in both samples after controlling for size, leverage, and annual profitability

growth.

14 When we isolate AAER firms that achieve all-one strings for both analysts’ forecasts and seasonally adjusted earnings, we find results consistent with the fear prediction. These firms already sell for a high market premium before the manipulation period and perhaps feel stronger pressure to deliver consistent growth under both definitions. In contrast, when we restrict the seasonally adjusted earnings sample to firms that do not have analyst coverage, the results suggest that managers engage in earnings manipulation to earn a valuation premium. This suggests that these firms could be manipulating in order to obtain greater investor and analyst recognition.

25

Column (2) and (3) of Panel A and B of Table 6 investigates whether it is AAER firms

with consecutive positive earnings strings that drive the results reported in Column (1). We

therefore divide the sample of AAER firms into two sub-samples. In Column (2) we report

results for AAER firms that do not have all-one strings during the manipulation period, and in

Column (3) we report results for AAER firms that have at least one all-one strings during the

manipulation period. If overvalued firms are more likely to manipulate earnings to report

consecutive positive earnings surprises, we expect a significant coefficient on HIGHPE in

Column (3) but not in Column (2). The results are consistent with this prediction. HIGHPE is

significant in Column (3) but insignificant or weakly significant in Column (2) in Table 6.

These results provide insights into Badertscher’s (2011) findings. Badertscher (2011)

provides evidence that overvalued firms have future earnings management. However, he does

not investigate whether the earnings management results in positive earnings surprises. Our

results suggest that the earnings management that he documents is likely to be part of a bigger-

picture objective by management to report a string of consecutive positive earnings surprises.

4.3 Market pressure to report consecutive positive earnings surprises

We next provide supplementary evidence that managers of firms that manipulate earnings

to report consecutive positive earnings surprises face strong market pressure to perform. Table 7

presents various proxies of market pressure for AAER firms with all-one strings, compared to

three groups (i) other AAER firms, (ii) the population of firm-years that do not achieve an all-

one string, and (iii) firm-years in the population that report an all-one string. We examine the

forward P/E, the actual P/E, analysts’ long term growth forecasts (LTGR), the average analyst

recommendation (RECMD) with a scale from 1 to 5, where a 5 is a strong-buy recommendation,

26

and the percentage of analysts that give buy recommendation (BUYPCT). Panel A analyzes the

sample of firms with analysts' forecasts. The results indicate that AAER firms that manipulate

earnings to report consecutive positive earnings have significantly higher forward P/E ratios

(52.63 versus 29.54, 22.87 and 47.64), higher long-term growth forecasts (21.38% versus

20.00%, 17.37% and 17.65%), higher average recommendations, and a greater proportion of buy

recommendations. Similar but weaker results are reported in Panel B. These findings are

consistent with managers reporting consecutive positive earnings surprises due to market

pressure.

We next examine how the market responds to the earnings announcements during the

manipulation period. Figure 3 Panel A provides the 3-day stock price response to earnings for the

AAER firm that consistently beat analysts’ forecasts and for those that consistently beat

seasonally adjusted earnings. We compare their stock price response to firms in the population

that also consistently beat these benchmarks. Figure 3 Panel A indicates that AAER firms on

average earn positive returns. Figure 3 Panel B investigates what happens when the firm finally

disappoints the market and misses the benchmark. For comparative purposes we also plot the

cumulative returns earned during the four quarters reported in Panel A for each group. Figure 3

Panel B indicates that for the analysts’ forecasts benchmark, 12-day (3-day return over 4

quarters) cumulative returns are 4.99% for AAER firms and 5.24% for the population (the

difference is not statistically significant). In contrast, the 3-day announcement returns when the

string breaks is -6.08% for AAER firms and -3.16% for the population. The difference of 2.92%

is statistically significant. Results are similar when we use seasonally adjusted earnings as the

benchmark. Consistent with the fear hypothesis, the market response results reported in Figure 3

27

suggest that by manipulating earnings, managers are able to delay a large negative stock price

decline.

In Table 8 we provide our third test of market pressure. Table 8 examines whether

managers have a preference to manipulate earnings to consistently beat analysts’ forecasts or to

consistently beat previously reported earnings. Under the market pressure story, we expect

managers to be more concerned with beating analysts’ forecasts. This table focuses only on firms

that are followed by analysts. The results indicate that beating analysts’ forecasts (STRING4AF)

is positively associated with the incidence of misstatement (with a p-value lower than 0.01)

whereas beating seasonally adjusted earnings (STRING4SADJ) is not related with financial

misstatement.15 These results suggest that managers are more concerned with meeting market

expectations than reporting earnings growth. The results are also in line with prior evidence on

the relative importance of analysts’ forecasts over seasonally adjusted earnings (e.g., Dechow et

al., 2003; Brown and Caylor, 2005).16

4.4. Additional analyses

4.4.1 Earnings manipulation and longer strings of positive earnings surprises

Table 9 presents the univariate analysis of probabilities of achieving all-one strings for

four, eight, twelve, sixteen, and twenty continuous quarters (i.e., equivalent to one to five

consecutive fiscal years). Panel A provides the number of possible permutations for each string

15 Untabulated results indicate that the coefficient on STRING4AF is even higher when we include an additional indicator variable for firms that beat both the analysts’ forecasts and seasonally adjusted earnings in the regression. 16 Analysts’ forecasts are increasingly important in recent years since they directly affect market sentiment (e.g., Dechow et al., 2003; Brown and Caylor, 2005). In contrast, earlier evidence from DeGeorge et al. (1999) suggests that managers perceive seasonally adjusted earnings as more important benchmarks than analysts’ forecasts.

28

length. The unconditional probability of achieving any particular string is extremely rare for any

period longer than twelve continuous quarters; for example, a 16-quarter string presents 65,536

possible permutations, whereas a 20-quarter string presents 1,048,576 possible permutations.

Nevertheless, the actual probabilities of achieving all-one strings for four to twenty consecutive

quarters in both the population and the AAER sample are significantly higher than the

unconditional probabilities, which is consistent with strong economic growth in the US economy

and the empirical findings in Myers et al. (2007). Panel A focuses on analysts’ forecasts and

reports the proportion of AAER firms and the population that have all-one strings of different

lengths (ranging from consistently beating analysts’ forecasts for four quarters to twenty

quarters). Chi-square tests indicate that a significantly greater proportion of AAER firms report

strings of four and eight than the population. Power could be a concern for longer strings since

the sample size of AAER firms is small. Therefore, we also provide the minimal number of

AAER observations required to obtain significance. This test suggests that a lack of power could

play a role in the low significance level for strings of sixteen quarters, but not for strings of

twelve or twenty quarters.

Panel B of Table 9 presents the proportions of all-one seasonally adjusted earnings

strings for the AAER sample and the population. We find that for strings of four, eight, and

twelve quarters, the distributions of all-one strings are significantly higher in the AAER sample

than in the population. However, the positive differences between the AAER sample and the

population are no longer significant for strings of sixteen or twenty quarters. Power does not

appear to be an issue for strings of length sixteen, although it could play a role in strings of

twenty quarters.

29

Table 10 examines longer earnings string patterns using logistic regression analysis.

Panel A reports results for strings of beating analysts’ forecasts. Our results reveal that, after

controlling for the annual earnings benchmark, F-score decile rankings, as well as year and

industry fixed effects, an all-one string is predictive of manipulation only for strings of four

quarters. Panel B presents results for strings of beating seasonally adjusted earnings and reveal

that all-one strings of four, eight, and twelve quarters are incrementally predictive of

manipulation. Therefore, the interpretations of logistic regression results are similar to the

univariate results.

Taken together, our results suggest that firms can potentially manipulate earnings up to

two years, but appear challenged to beat benchmarks through earnings management for periods

beyond two years. Only a few firms are able to manipulate earnings and consistently achieve

benchmarks for longer periods. This evidence is consistent with underlying performance, rather

than earnings management, being a more plausible explanation for longer strings of positive

earnings surprises in the population. Our results are in line with the inferences drawn by Bartov

et al. (2002) that economic growth appears to explain longer strings of positive earnings

surprises.

4.4.2 The role of management guidance in manipulating firms

Prior research suggests that managers guide analysts' forecasts down to help them meet

and beat expectations (e.g., Matsumoto, 2002; Bartov et al, 2002). Figure 4 investigates whether

managers of manipulating firms that consecutively beat analysts’ forecasts also use guidance as a

tool to help them meet and beat expectations. The figure indicates that voluntary disclosure of

earnings guidance is infrequent during earnings manipulation years, and most manipulating firms

30

do not provide guidance. Specifically, the majority of the 708 AAER firms (78%) in our sample

never provide management guidance. Only 86 firms (12%) of our sample guide before and

continue to do so during the misstatement period. A further 48 firms (7%) of the AAER sample

stop guiding altogether during the misstatement period.

Panel B of Figure 4 illustrates the frequency of quarterly earnings guidance by the 110

AAER firms that do provide guidance during manipulation years. Among these firms, 50 percent

of firm-year observations provide guidance for only one quarter throughout the fiscal year. Only

15 percent of the AAER sample (41 firm-year observations) consistently guide in all four

quarters. These results suggest that guidance does not appear to be an actively used tool for our

manipulating firms. Instead the results suggest that management guidance is rare during

manipulation years. This is consistent with Chen et al.’s (2011) evidence that firms stop

providing earnings guidance when they do not expect to report future good news. The scarcity of

management guidance during the manipulation period could be related to managers’ concerns

with litigation risks and duties to update. Alternatively, it could simply stem from their private

knowledge about the actual performance of the firm and thus having little incentive to openly lie

to market participants about the future.

V. CONCLUSION

This paper examines managers’ incentive to manipulate earnings to achieve a string of

consecutive positive earnings surprises. We analyze a sample of firms subject to SEC

enforcement actions for manipulating earnings (AAER firms). The advantage of the AAER

sample is that these firms have unambiguously manipulated earnings, and therefore our research

design avoids the limitations of discretionary accrual models in this setting. We find that AAER

31

firms are more likely to consistently beat quarterly earnings benchmarks than the population of

publicly-listed firms and a propensity score matched sample. We show that the information in

positive quarterly earnings strings is incremental to other determinants for predicting

misstatements.

We investigate the relation between reporting strings of consecutive positive earnings

surprises and the valuation premium. Our results suggest that managers appear to manipulate

earnings to report positive strings in order to maintain their valuation premium. This is consistent

with managers manipulating earnings because they fear losing the market premium and is

supportive of Jensen’s (2005) argument concerning agency costs and overvalued equity. We add

to the literature on the relative importance of beating different earnings benchmarks. Our results

suggest that consistently beating analysts’ forecasts is a more important determinant of

manipulation than consistently beating seasonally adjusted earnings. We also document that

management guidance is rare during manipulation years. Our results appear more consistent with

Chen et al.’s (2011) conclusion that firms stop providing earnings guidance when they do not

anticipate reporting future good news.

In additional analyses we find that managers are able to manipulate earnings to

consistently beat analysts’ forecasts for up to two years and seasonally adjusted earnings for up

to three years. Only a few AAER firms manipulate earnings and consistently beat benchmarks

for periods beyond eight to twelve quarters. This evidence suggests that long strings of consistent

beats are likely to be initially driven by strong economic performance, and it is only later in the

string when earnings manipulation will play a role. We suggest that more powerful tests of

32

earnings management can be constructed by focusing the tests on the one or two years

immediately before the string breaks.

The results of our paper have implications for standard setters, regulators, auditors, and

investors. Our results indicate that managers have incentives to manipulate earnings and commit

fraud to achieve positive earnings strings. Therefore, auditors and regulators such as the SEC

should take note of firms that have consistently met or beaten quarterly analysts’ forecasts or

seasonally adjusted earnings during the year, and scrutinize their financial disclosures in more

detail. In addition, standard setters could consider whether managers of firms with long strings of

positive earnings surprises should make specific disclosures to make investors aware of the risk

of a potential large stock price decline should they miss an earnings target.

33

REFERENCES

Badertscher, B. 2011. Overvaluation and the choice of alternative earnings management mechanisms. The Accounting Review 86 (5): 1491-1518.

Barth, M., J. Elliott, and M. Finn. 1999. Market rewards associated with patterns of increasing earnings. Journal of Accounting Research 37 (2): 387-413.

Bartov, E., D. Givoly, and C. Hayn. 2002. The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics 33 (2): 173-204.

Beneish, M. 1997. Detecting GAAP violations: implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy 16 (3): 271-309.

Beneish, M. 1999. Incentives and penalties related to earnings overstatements that violate GAAP. The Accounting Review 74 (4): 425-457.

Brown, L., and M. Caylor. 2005. A temporal analysis of quarterly earnings thresholds: propensities and valuation consequences. The Accounting Review 80 (2): 423-440.

Burgstahler, D. and M. Eames. 2006. Management of earnings and analysts' forecasts to achieve zero and small positive earnings surprises. Journal of Business Finance & Accounting 33(5-6): 633-652.

Chen, S., D. Matsumoto, and S. Rajgopal. 2011. Is silence golden? An empirical analysis of firms that stop giving quarterly earnings guidance. Journal of Accounting and Economics 51 (1): 134-150.

Dechow, P., W. Ge, C. Larson, and R. Sloan. 2011. Predicting material accounting misstatements. Contemporary Accounting Research 28 (1): 17-82.

Dechow, P., S. Richardson, and I. Tuna. 2003. Why are earnings kinky? An examination of the earnings management explanation. Review of Accounting Studies 8 (2-3): 355-384.

Dechow, P., R. Sloan, and A. Sweeney. 1996. Causes and consequences of earnings manipulation: an analysis of firms subject to enforcement actions by the sec. Contemporary Accounting Research 13 (1): 1-36.

DeGeorge, F., J. Patel, R. Zeckhauser. 1999. Earnings management to exceed thresholds. Journal of Business 72 (1): 1-33.

Fama, E., and K. French. 1997. Industry costs of equity. Journal of Financial Economics 43 (2): 153-193.

Feroz, E., K. Park, and V. Pastena. 1991. The financial and market effects of the SEC’s accounting and auditing enforcement releases. Journal of Accounting Research 29 (s): 107-142.

Ghosh, A., Z. Gu, and P. Jain. 2005. Sustained earnings and revenue growth, earnings quality, and earnings response coefficients. Review of Accounting Studies 10 (1): 33-57.

Jensen, M. 2005. Agency costs of overvalued equity. Financial Management 34 (1): 5-19.

34

Kasznik, R. and M. McNichols. 2002. Does meeting earnings expectations matter? Evidence from analyst forecast revisions and share prices. Journal of Accounting Research 40 (3): 737-759.

Lawrence, A., M. Minutti-Meza, P. Zhang. 2011. Can Big 4 versus non-big 4 differences in audit-quality proxies be attributed to client characteristics? The Accounting Review 86 (1): 259-286.

Matsumoto, D. 2002. Management's incentives to avoid negative earnings surprises. The Accounting Review 77 (3): 483-514.

McInnis, J. and D. Collins. 2011. The effect of cash flow forecasts on accrual quality and benchmark beating. Journal of Accounting and Economics 51 (3): 219-239.

Miller, G. 2006. The press as a watchdog for accounting fraud. Journal of Accounting Research 44 (5): 1001-1033.

Myers, J., L. Myers, and D. Skinner. 2007. Earnings momentum and earnings management. Journal of Accounting, Auditing and Finance 22 (2): 249-284.

Holder, W., K. Pincus, and T. Mock. 1988. The SEC and fraudulent financial reporting. Research in Accounting Regulation 2: 167-188.

Schrand, C., S. Zechman. 2012. Executive overconfidence and the slippery slope to financial misreporting. Journal of Accounting and Economics 53 (1): 311-329.

Securities and Exchange Commission (SEC), 2005. Accounting and auditing enforcement release No. 2232. Available at: http://www.sec.gov/litigation/admin/33-8569.pdf

Skinner, D. and R. Sloan. 2002. Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies 7 (2-3): 289-312.

Tversky, A. and D. Kahneman. 1991. Loss aversion in riskless choice: a reference-dependent model. Quarterly Journal of Economics 106 (4): 1039-1061.

The Financial Times, 2005. Coca-Cola settles Japan accounting probe. April 18, 2005.

35

APPENDIX A Coca Cola and its Incentives to Report Positive Earnings Surprises

The discussion below is extracted from SEC’s Accounting and Auditing Enforcement

Release (No. 2232) for Coca Cola.

“From 1990 through 1996, Coca-Cola consistently met or exceeded earnings expectations while achieving a compound annual earnings per share growth rate of 18.3 percent – more than twice the average growth rate of the S&P 500. Coca-Cola’s superior earnings performance resulted in its common stock trading at a price to earnings multiple (“P/E Ratio”) of 38.1 by the end of 1996, as compared to the S&P 500’s P/E Ratio of 20.8.

In the mid-1990s, Coca-Cola began experiencing increased competition and more difficult economic environments. Nevertheless, Coca-Cola publicly maintained between 1996 and 1999 that it expected its earnings per share to continue to grow between 15 percent and 20 percent annually. At or near the end of each reporting period between 1997 and 1999, Coca-Cola, through its officers and employees implemented a “channel stuffing” practice in Japan known as “gallon pushing.” In connection with this practice, CCJC asked bottlers in Japan to make additional purchases of concentrate for the purpose of generating revenue to meet both annual business plan and earnings targets. The income generated by gallon pushing in Japan was the difference between Coca-Cola meeting or missing analysts’ consensus or modified consensus earnings estimates for 8 out of 12 quarters from 1997 through 1999.”

The Coca Cola channel-stuffing example nicely illustrates the incentives and possible

implementation of financial misstatement in the service of meeting or beating earnings

benchmarks. Our empirical tests examine whether the incentive to manipulate earnings for this

motive is prevalent among AAER firms.

36

EXHIBIT 1 String Example of AAER Firm Coca-Cola Company, Ltd.

This appendix presents Coca-Cola’s earnings strings based on both analysts’ forecasts and seasonally adjusted earnings over its alleged manipulation period from fiscal year 1997 to 1999. A fiscal quarter observation receives a “1” for meeting or beating earnings benchmark and a “0” for missing the benchmark (i.e., analysts’ forecasts or seasonally adjusted earnings). Analysts’ forecast error is calculated as the difference between actual EPS and the median EPS forecast from I/B/E/S. Seasonal earnings change is the difference between current earnings-per-share and the prior year’s earnings-per-share of the same quarter using unrestated earnings data from Compustat Unrestated Quarterly Updates. Earnings-per-share is measured as unrestated basic EPS excluding extraordinary items (item EPSPXQR) divided by cumulative adjustment factors (item AJEXQ) following Myers et al. (2007). Over Coca-Cola’s alleged manipulation period from 1997 to 1999, its complete string contributes three 4-quarter string observations and one 8-quarter observation to the AAER sample.

11 1

1 1

1

11

1 1 1 1

1 1

1 1

0 0 0 0 0 00

0

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30$ Analyst forecast error Seasonal earnings change

37

EXHIBIT 2 Population Construction Illustration Using a Hypothetical Firm

Panel A: String of beating earnings benchmarks.

Panel B: Construction of string observations without overlapping quarters.

String length Time periods and strings based on either benchmark

4 quarters (N = 5)

2000 Q1 - 2000 Q4

2001 Q1 - 2001 Q4

2002 Q1 - 2002 Q4

2003 Q1 - 2003 Q4

2004 Q1 - 2004 Q4

(1, 1, 1, 0) (0, 1, 0, 0) (0, 1, 1, 1) (0, 1, 0, 0) (1, 1, 0, 0) 8 quarters (N = 2)

2000 Q1 - 2001 Q4 2002 Q1 - 2003 Q4 (1, 1, 1, 0, 0, 1, 0, 0) (0, 1, 1, 1, 0, 1, 0, 0)

12 quarters (N = 1)

2000 Q1 - 2002 Q4 (1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1)

16 quarters (N = 1)

2000 Q1 - 2003 Q4 (1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0)

20 quarters (N = 1)

2000 Q1 - 2004 Q4 (1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)

This appendix illustrates the population construction using a hypothetical firm. Figure C.1 presents the complete earnings string of this hypothetical firm from its fiscal year 1999 to 2004. A fiscal quarter observation receives a “1” for meeting or beating earnings benchmark and a “0” for missing the benchmark (i.e., analysts’ forecasts or seasonally adjusted earnings). Table C.1 illustrates how we use firm observations from the population to construct string observations of various lengths without overlapping quarters. It presents the length, time period, and string permutation of each string observation constructed from the complete string of this hypothetical firm.

0

1

2003 Q2 2004 Q3 Meet or beat earnings benchmarksMiss earningsbenchmarks

38

EXHIBIT 3 Triggers for SEC Investigation

Number of unique AAERs in samples 158 100% Financial restatements 83 53% SEC-initiated by factors other than strings (e.g., third-party transactions, asset write-offs, etc.,) 27 17%

Class-action/shareholder/M&A lawsuits 22 14% Other government agencies (e.g. FBI and Justice department) 13 8% Press-initiated 6 4% Whistle-blower to SEC/external agencies 3 2% Analyst-initiated 2 1% Investor/short-seller initiated 2 1% SEC-initiated after observing positive earnings strings 0 0% This Exhibit presents the reasons triggering SEC investigation for AAER firms that reported at least one year of positive quarterly earnings surprises. There are 158 unique AAER firms that appeared in either the sample of analysts’ forecasts or the sample of seasonally adjusted earnings. We obtain this information from news and press releases on Factiva, and we use Google Search for supplementary information.

39

FIGURE 1 Time-series Comparison of Price-to-earnings Ratios between AAER Firms with All-one Earnings

Strings and the Population for Analysts’ Forecasts Sample

Panel A of this figure plots the time-series of price-to-earnings for the AAER sample and the population using analysts’ forecasts as the earnings benchmark. We focus on AAER firms that achieve at least one all-one 4-quarter string during the manipulation period, which results in a subsample of 80 unique AAER firms. Year t is defined as the first fiscal year that a firm achieves an all-one 4-quarter string during the manipulation period. Year t-1 and t-2 are the two years immediately prior to the manipulation period. Year t+1 and t+2 are the first two years subsequent to the manipulation period. The population is matched to the AAER sample by year. We use forward P/E measured as the fiscal year beginning price divided by the most recent EPS consensus forecast from I/B/E/S before the earnings announcement of the first fiscal quarter.

0

5

10

15

20

25

30

35

40

45

50

t-2 t-1 t t+1 t+2

Med

ian

of fo

rwar

d P/

E

AAER all-one strings Population

Pre-manipulation First year of manipulation

Post-manipulation

0

5

10

15

20

25

30

t-2 t-1 t t+1 t+2

Lowe

r qua

rtile

of f

orwa

rd P

/E

AAER all-one strings Population

0

20

40

60

80

t-2 t-1 t t+1 t+2

Upp

er q

uart

ile of

for

ward

P/E

AAER all-one strings Population

40

FIGURE 2 Time-series Comparison of Price-to-earnings Ratios between AAER Firms with All-one Earnings

Strings and the Population for Seasonally Adjusted Earnings Sample

Panel B of this figure plots the time-series of price-to-earnings for the AAER sample and the population using seasonally adjusted earnings as the earnings benchmark. We focus on AAER firms that achieve at least one all-one 4-quarter string during the manipulation period, which results in a subsample of 136 unique AAER firms. Year t is defined as the first fiscal year that a firm achieves an all-one 4-quarter string during the manipulation period. Year t-1 and t-2 are the two years immediately prior to the manipulation period. Year t+1 and t+2 are the first two years subsequent to the manipulation period. The population is matched to the AAER sample by year. P/E is measured as price over actual earnings-per-share at the fiscal year end.

0

2

4

6

8

10

12

14

16

18

t-2 t-1 t t+1 t+2

Med

ian

of y

ear-

end

P/E

AAER all-one strings Population

Pre-manipulation Post-manipulationFirst year of manipulation

0

2

4

6

8

10

t-2 t-1 t t+1 t+2

Lowe

r qua

rtile

of y

ear-

end

P/E

AAER all-one strings Population

0

5

10

15

20

25

30

35

t-2 t-1 t t+1 t+2

Lowe

r qua

rtile

of y

ear-

end

P/E

AAER all-one strings Population

41

FIGURE 3 Stock Market Response to Earnings String and its Break around Earnings Announcements

Panel A: Stock market response to quarterly earnings beats.

Panel B: Cumulative stock market response to earnings strings and its break.

This figure presents the positive stock market response to achieving an all-one 4-quarter string and the negative stock market response to string breaks. Panel A reports the 3-day market adjusted return around earnings announcement date for each quarter during the 4-quarter string period. We measure the string-achievement response by cumulating the four 3-day returns over the fiscal period (12-day cumulative return). Panel B compares the string-achievement response to the string-break response measured as the 3-day market adjusted earnings announcement return of the first quarter when the all-one string breaks (i.e., misses analysts’ forecasts or seasonally adjusted earnings). T-tests suggest that (1) both the 12-day cumulative return and the string-break return are significantly different from zero, and (2) for analysts’ forecasts, string-break returns of AAER firms are significantly lower than that of the population.

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

AAER firms: consistently beat

analysts' forecasts

AAER firms: consistently beat

seasonally adjusted earnings

Population: consistently beat

analysts' forecasts

Population: consistently beat

seasonally adjusted earnings

3-da

y mar

ket a

djus

ted

retu

rns

arou

nd ea

rnin

gs an

noun

cem

ent

Q1

Q2

Q3

Q4

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

AAER firms: achieve and break a string of analysts' forecasts

AAER firms: achieve and break a string of seasonally adjusted

earnings

Population: achieve and break a string of analysts' forecasts

Population: achieve and break a string of seasonally adjusted

earnings

4.99%3.48%

5.24%

7.22%

-6.08%

-3.84% -3.16%-2.59%

Cum

ulat

ive m

arke

t adj

uste

d ret

urns

ar

ound

earn

ings

anno

unce

men

t

String-achievement response String-break response

42

FIGURE 4 Management Guidance Behavior of AAER Firms

Panel A: Management guidance by 708 AAER firms (N = number of firms).

Panel B: Frequency of quarterly guidance for the 110 AAER firms that guided during manipulation years (N is the number of firm-year observations).

This figure presents the distribution of the AAER sample by the frequency of management guidance. In Panel B, there are a total of 268 AAER firm-year observations from the FirstCall guidance database from 1994 to 2010. The 135 firm-year observations that guide one quarter only represent 72 unique firms; the 58 firm-year observations that guide for two quarters represent 42 unique firms; the 34 firm-year observations that guide three quarters represent 26 unique firms; the 41 firm-year observations that