The Role of Matching in Detecting Post-Earnings-Announcement Drift:
An Intertemporal Analysis

Abstract
We show that temporal attenuation of post-earnings-announcement drift is much lesspronounced when earnings news is measured using earnings components with greater
matching (e.g., gross or operating profit). Notably, “well-matched” news measurescontinue to predict significant excess returns for firms with poorly matched bottom-line
earnings whereas the excess return predictability of SUE (i.e., news in bottom-line
earnings) has largely disappeared for these firms. Regression results show that wellmatched news measures retain their autocorrelation structure over time and gainincremental power over SUE in predicting future profitability. Finally, transientinstitutional investors—who prior evidence suggests should be relatively sophisticated intheir pricing of earnings news—do not appear to significantly exploit SUE signals on thebasis of matching. Overall, our results suggest that markets are still relatively inefficientat pricing matching information in earnings news and that traditional news measures such
as SUE are much less likely to detect this inefficiency in recent years.

1. Introduction
A long-standing literature in accounting examines an anomalously positive
relation between earnings news and future stock returns, commonly known as postearnings-announcement drift, or “PEAD” (Ball and Brown, 1968; Foster et al., 1984;
Bernard and Thomas, 1989, 1990). However, recent years have witnessed a pronounced
decline in PEAD (Johnson and Schwartz, 2001; Richardson et al., 2010; Chordia et al.,2014) coinciding with evidence of investor learning from research on stock return
predictability (McLean and Pontiff, 2015; Milian, 2015) and diminishing transaction
costs to exploit PEAD and other forms of mispricing (Chordia et al., 2014). Since a
popular explanation of the drift suggests that predictable returns reflect earnings
expectations that underweight the serial properties of earnings news (Bernard andThomas, 1990; Ball and Bartov, 1996), PEAD attenuation appears to be consistent with
an improved pricing of the implications of earnings news for future profitability.
In this paper, we take a closer look at this pricing process by examining investors’
understanding of how fundamental accounting principles facilitate serial correlation inearnings and its components. Our investigation focuses on the matching principle,
whereby income is determined based on a temporal “matching” of expenses to revenues
in the period revenues are earned. A consideration of matching as it pertains to thepricing of earnings news is important for two reasons. First, matching has clear
implications for the temporal properties of earnings. Dichev and Tang (2008) model poor
matching as adding noise to the economic relation of advancing expenses to earnrevenues, and they show empirically that poor matching increases volatility and decreases
persistence in earnings, with the latter effect inducing negative autocorrelation in1

earnings changes.1 Schutt (2012) finds that greater matching results in an accounting rateof return that more closely approximates a firm’s internal rate of return (IRR). He further
shows that better IRR approximations make earnings more informative to investors about
the firm’s underlying economic profitability. Thus, an efficient pricing of earnings news
should reflect matching’s role in facilitating the temporal properties of earnings.
Second, recent studies document a marked decline in matching over the past 40
years along with significant changes to earnings’ temporal properties (Dichev and Tang,
2008; Donelson et al., 2011; Srivastava, 2014). 2 These changes pose challenges tointerpretations of PEAD attenuation because some attenuation could arise mechanicallyas a result of declining serial correlation in earnings news. Recognizing the potential formechanical attenuation is important for assessments of temporal gains in earnings news
pricing efficiency, as a failure to control for evolving serial properties may lead to an
overstatement of efficiency gains achieved over time.
However, a bigger and more immediate concern is that declining matching
weakens the ability of traditional PEAD tests to detect inefficiencies in the pricing of
earnings news. Evidence of a temporal decline in GAAP earnings informativeness(Collins, Maydew, and Weiss, 1997), along with a rise in the informativeness of “street”
earnings that often exclude “mismatched” one-time items (Bradshaw and Sloan, 2002),
suggests that declining matching renders bottom-line earnings increasingly inadequate forforecasting future profitability. Nevertheless, to the extent that “matched” earnings
components have retained their forecasting abilities over time, reformulations of the1

Cao and Narayanamoorthy (2012) show that these annual properties translate to the quarterly setting andthat quarterly earnings persistence is positively related with autocorrelation in quarterly earnings news.
2
By “decline in matching,” we mean the empirical decline in the proportion of earnings in which matchingis the guiding recognition principle. Different explanations for this decline are discussed in Section 2.

2

traditional PEAD research design that focus on these components can provide suitable
tests for earnings news pricing efficiency even in the presence of declining matching.
We begin our analysis by first examining how matching facilitates the earnings
forecasting exercise. To this end, we track the evolution of serial correlation patterns fortwo classes of earnings news measures for the same firm: (a) “bottom-line” measures
such as SUE (standardized unexpected earnings)3 and (b) “well-matched” measures that
we argue are less susceptible to temporal declines in matching, such as gross or operating
profit.4 According to the “degrees of matching success” outlined in Dichev and Tang
(2008) gross profit and operating profit, relative to net income, should contain a greater
composition of expenses guided by “direct matching” (e.g., cost of goods sold) and alower composition of expenses that are recognized “as incurred” without regard formatching (e.g., goodwill impairment). Donelson et al. (2011) and Dichev (2013) showthat “as incurred” expenses drive much of the documented changes to the properties of
earnings, which suggests that gross profit and operating profit should exhibit relatively
stable properties over time. Furthermore, Novy-Marx (2013) and Ball et al. (2015) findthat gross profit and operating profit outperform net income as proxies for expected
profitability in the context of dividend discount valuation models, which suggests that ourwell-matched measures should be informative about future profitability. Considering
these factors, we expect our well-matched measures to exhibit (a) less deterioration in
their serial correlation structure over time and (b) an increasing incremental ability toforecast future earnings news relative to SUE.

3

Following prior PEAD literature, we exclude extraordinary items from “bottom-line” earnings.
Throughout this paper, gross profit is defined as net sales minus cost of goods sold, and operating profit isdefined as gross profit minus selling, general and administrative expense. See the Appendix for variable
definitions.
43

Our empirical tests confirm these expectations. Over a sample period spanning
1979-2012, operating profit news (hereafter MSUE for “modified” or better “matched”
SUE) sustains its ability to forecast future operating profit news in both univariate andmultivariate regressions. The multivariate coefficient on MSUE increases from 0.56 in
the pre-1990 period to 0.62 in the post-2000 period (about an 11% increase).
Furthermore, MSUE gains incremental power over SUE in predicting future SUE.
MSUE’s coefficient increases from 0.19 pre-1990 to 0.23 post-2000 (about a 20%
increase), whereas SUE’s coefficient decreases from 0.42 to 0.33 (about a 20% decrease).
We also examine serial correlation patterns in samples of firms that we suspect
have relatively poor matching in bottom-line earnings (e.g., firms with special items,
losses, fourth quarter reporting, and extreme SUE realizations without corresponding
extreme MSUE realizations). MSUE’s incremental abilities are even stronger in thesesamples, which suggests that the benefits of extracting matching information from
earnings increases when matching disparities between our news measures are greater.
We next examine the extent to which the pricing of earnings news has evolved in
capturing matching’s implications for future profitability. Since there is an overlap
between SUE and MSUE, we expect the excess return predictability of both SUE andMSUE to attenuate over time as trading costs diminish and as information about thetemporal properties of earnings becomes richer and more accessible to investors.
However, if investors do not fully appreciate the role of matching in facilitating the serial
properties of earnings, then we expect MSUE to continue to predict significant excess
returns with magnitudes that increasingly outweigh those of SUE’s excess returns.

4When we construct hedge portfolios that take long (short) positions in the highest(lowest) decile of each of our news measures, we find that MSUE portfolios increasingly
outperform SUE portfolios over time. For example, in the post-2000 period, MSUE’ssize-adjusted three-month hedge return is 3.29% (t-stat = 4.41), as compared to SUE’s
hedge return of 2.06% (t-stat = 2.54). Differences in portfolio returns are even morestriking in our poorly matched earnings subsamples, where MSUE portfolios continue topredict significant excess returns while the return predictability of SUE portfolios
dissipates and largely disappears in the post-2000 period. These results suggest that theexcess return predictability of earnings news largely derives from news in “wellmatched” components and that the excess return predictability of “well-matched” news
remains significant even when the degree of matching in bottom-line earnings is poor.
To further solidify the notion that stock prices impound information provided bymatching with a delay, we conduct two additional tests to corroborate our main results.
First, we explore whether transient institutional investors use matching to extract stronger
SUE signals on which to trade. We extend the main regression in Ke andRamalingegowda (2006) by interacting SUE with indicators for well-matched earnings
news, which should have stronger implications for predicting future earnings news andthus should induce more trading in well-matched stocks. While we see some support for
the use of matching early in our sample period, results from the 1990s and later—when
the benefits of identifying matching information are higher—do not support thesignificant use of matching by transient investors to extract stronger SUE signals.
Second, we perform hedge portfolio tests using levels of earnings and operating
profit (rather than changes). Balakrishnan et al. (2010), Novy-Marx (2013) and Ball et al.

5(2015) document long-term return predictability using levels specifications of the news
measures we examine in our paper. Since matching disparities between our news
measures should be reflected in their corresponding levels specifications, we expect toobserve less attenuation in excess returns for the levels specifications of our wellmatched measures. A levels analysis therefore provides a natural out-of-sample test ofwhether matching information underlies disparities in excess return predictability among
our accounting variables. We find that hedge returns for bottom-line earnings levels
declined about 50% in the post-2000 period relative to the pre-1990 period, while thehedge return gap for operating profit increased fourfold over the same period.
We run a battery of additional tests to confirm the robustness of our results.
Fama-MacBeth regressions show that the return predictability of MSUE is consistently
and increasingly incremental to that of SUE after we control for risk characteristics thatare known to predict returns. We also show that our main hedge portfolio results are notexplained by ex ante earnings volatility (Cao and Narayanamoorthy, 2012) or revenue
news (Jegadeesh and Livnat, 2006). Lastly, our hedge portfolio results continue to holdunder alternative MSUE definitions, news scalars, sub-period partitions and hedge
portfolio holding window specifications.
Overall, our results suggest that news in “well-matched” earnings components
continues to predict significant excess returns several months after earnings news isreleased. Thus, even as markets have evolved over time to more efficiently incorporate
serial properties of earnings news into stock prices (Johnson and Schwartz, 2001;
Richardson et al., 2010; Chordia et al., 2014), such gains do not appear to reflect a richer
appreciation of matching’s role in facilitating these properties.

6Our study contributes to the literature in several ways. First, it contributes to the
PEAD literature by identifying a potential source of time series variation in stock pricedrift that has strong implications for interpretations of PEAD attenuation. In particular,
we find that the excess return predictability of earnings news stems largely from news in“well-matched” earnings components and that such predictability is surprisingly
persistent over time, even in recent years. A key implication of this finding is that PEAD
research designs that use bottom-line news measures such as SUE have less power todetect pricing inefficiencies as matching in bottom-line earnings declines. Consequently,
studies that quantify PEAD attenuation using SUE-based research designs are more likely
to provide “upper bound” estimates of efficiency gains achieved in the pricing of
earnings news (e.g., Hung et al. 2014, Chordia et al. 2014, Richardson et al. 2010).5
Second, our study responds to a concern expressed in Richardson et al. (2010)
about “how knowledge of the accounting system itself is not fully exploited (byresearchers) to link accounting information to stock prices and returns” (p. 444). We
exploit a fundamental and widely known accounting principal—matching—and showthat it provides significant information about future profitability that is not fully
impounded into stock prices by investors in a timely manner. By contrast, prior studies in
the fundamental analysis literature tend to offer anecdotal or ad hoc explanations for the
return predictability of their accounting variables (e.g., Ou and Penman, 1989; Lev andThiagarajan, 1993; Abarbanell and Bushee, 1997; Piotroski, 2000).
Lastly, our study makes a case for the continued relevance of matching forforecasting future profitability. While evolving earnings properties threaten the5

We do not attempt to formally quantify matching’s effect on PEAD attenuation in relation to otherdocumented drivers. We leave a comparative analysis of the various drivers of PEAD attenuation identified
in the literature for future research.

7

informativeness of bottom-line earnings (Collins, Maydew and Weiss, 1997; Givoly andHayn, 2000; Dichev and Tang, 2008), we show that news measures that are lesssusceptible to declining matching exhibit robust serial correlation over time and havegained incremental power in predicting earnings news even when matching in current
earnings is poor. These features likely underlie the recent rise in interest in gross andoperating profit among investment professionals,6 so it is instructional to highlight therole of evolving earnings characteristics in dictating the course of current practice.
The remainder of the paper is organized as follows. Section 2 reviews priorliterature on PEAD and matching and presents our hypotheses. Section 3 describes ourdata. Section 4 outlines our tests and presents our results. Section 5 discusses andassesses alternative interpretations of our results. Section 6 offers concluding remarks.

2. Prior Literature and Hypothesis Development
2.1 Post-Earnings-Announcement Drift and Its Attenuation
Accounting researchers have long examined stock price drift in the direction of
past earnings news (Ball and Brown, 1968; Foster et al., 1984; Bernard and Thomas,
1989, 1990). So pervasive is evidence of post-earnings-announcement drift (PEAD) thateven staunch supporters of market efficiency concede that its place as an empiricalregularity is “above suspicion” (Fama, 1998). Bernard and Thomas (1990) propose that
PEAD reflects naïve earnings expectations that assume quarterly earnings news follows aseasonal random walk. Since successive realizations of earnings news are typically
6

Providing anecdotal support for this claim, Brian Nowack, an analyst at Nomura Equity Research who
upgraded his stock recommendation for Amazon from “neutral” to “buy” following Amazon’s Q1 2012
earnings announcement, remarked that he was “beginning to see clouds part in the (Amazon) investmentcase as we believe we and the Street are under-appreciating the growing and expansive drivers within(Amazon’s) gross margin” (Ray, 2012).

8

positively correlated, the authors argue that excess returns can be predicted based on thesign and magnitude of quarterly earnings news. While variants of this theory have been
advanced over the years, the basic idea that PEAD captures an under-reaction to theimplications of current earnings news for future earnings news has been widely adopted
in the literature (Ball and Bartov, 1996; Fama, 1998; Soffer and Lys, 1999; DellaVigna
and Pollet, 2009; Hirshleifer et al., 2009).
More recent evidence, however, suggests that PEAD has substantially declined
over time. For example, Chordia et al. (2014) document a 55% decline in thecharacteristic premium for SUE in their post-2000 sample relative to their pre-2000
sample. The authors link PEAD attenuation to increases in liquidity and decreases intransaction costs that previously inhibited an arbitrageur’s ability to exploit mispricing.7
Johnson and Schwartz (2001), Richardson et al. (2010) and Milian (2015) suggest that
PEAD attenuation reflects investor learning about temporal patterns in earnings news,
consistent with more general evidence of investors learning from academic research on
stock return predictors (McLean and Pontiff, 2015).8 Hung et al. (2014) find that IFRS
adoption, by enhancing reporting quality, significantly contributes to PEAD attenuationinternationally. Collectively, these studies suggest that the pricing of earnings news has
experienced significant efficiency gains over time as market participants learn about andhave greater opportunities to profit from the temporal properties of earnings.
While PEAD attenuation evidence is consistent with an underlying investor
learning effect, not much is known about the nature of investor learning as it pertains to
7

Mendenhall (2004) and Ke and Ramalingegowda (2006) find that PEAD is not significantly exploitedwhen arbitrage risks and transaction costs are sufficiently high.
8Richardson et al. (2010) suggest that PEAD attenuation may be consistent with adaptive market efficiency(e.g., Grossman and Stiglitz, 1980; Lo, 2004) whereby “capital market participants learn about therelevance of information for security prices, and prices adjust accordingly” (p. 442).

9

PEAD. For example, if investors learn about PEAD through academic research that
documents autocorrelation patterns in seasonally differenced earnings (e.g., Bernard andThomas, 1990), it is unclear whether investors simply use autocorrelation patterns inearnings changes to forecast future earnings or if they seek out the underlying drivers ofthese patterns to enhance the quality of their forecasts. The former learning activity issatisfactory from an efficiency perspective as long as autocorrelation data arerepresentative of future data. However, to the extent that temporal patterns evolve overtime, a deeper understanding of what causes predictable changes in earnings may benecessary for an efficient pricing of earnings news.
2.2 Matching in Earnings
The matching principle—which calls for expenses to be recognized concurrent
with their associated revenues in the determination of net income—has long guided
accounting practice and the formation of formal accounting standards across the globe.9
Since many economic transactions occur in periods divorced from their related cash
flows, matching enhances the ability of earnings to communicate how operations
contributed to earned capital in a given period. To the extent that outsiders basecontracting and capital allocation decisions on information conveyed by earnings,
matching should enhance the efficiency of decision making among outsiders byproviding a more transparent measure of the underlying economic profitability of a firm.
Academic research sheds light on the channels through which matching enhances
the usefulness of earnings. Schutt (2012) finds that greater matching in earnings makes
accounting rates of return closer approximations of a firm’s internal rate of return.
9

Early accounting texts and academic research that discuss matching include Paton and Littleton (1940),
Blocker (1949), and Jarrett (1970).

10

Consequently, earnings become more informative to investors about the firm’s
underlying economic profitability. Dichev and Tang (2008) model mismatching inearnings as a form of noise in the economic relation of advancing expenses to earnrevenues. Using analytical and archival methods, they show that mismatching increases
volatility and decreases persistence in annual earnings, with the latter effect inducing
negative autocorrelation in earnings changes. Cao and Narayanamoorthy (2012) showthat these annual properties hold in the quarterly setting and that quarterly earnings
persistence is positively related with positive autocorrelation in quarterly earnings news.
This latter finding is particularly relevant for pricing earnings news as it suggests thatmatching strengthens the autocorrelation structure of quarterly earnings news, thereby
enhancing the predictability of next quarter’s earnings.
Nevertheless, as with PEAD, matching in earnings has been on the decline inrecent years. Consistent with their analytical model, Dichev and Tang (2008), using asample of the 1,000 largest U.S. firms, document a 15% drop in the contemporaneous
correlation between revenues and expenses, a doubling in earnings volatility, and aroughly 30% decline in earnings persistence over a 40-year period. While this evidencecoincides with evolving U.S. accounting standards that increasingly shy away frommatching considerations, researchers have linked declining matching to multiple causal
sources. Donelson et al. (2011) find that an increased incidence of large special items
driven by real economic events (rather than evolving standards per se) is largely
responsible for declining matching.10 Srivastava (2014) argues that evidence of evolving

10

Dichev (2013) finds that large special items reported in economic downturns significantly contribute toincreasing GAAP earnings volatility, but such volatility is considerably dampened when earnings aremeasured following National Income and Product Accounts (NIPA) standards used by the U.S. Bureau of
Economic Analysis (BEA).

11

earnings properties predominately reflects a changing composition of firms over time. Heshows that newer firms exhibit greater intangible intensity, creating earnings streams
characterized by greater mismatching, higher volatility and lower relevance. Regardlessof the source, evidence suggests that declining matching has had profound effects on thetemporal properties of earnings, calling into question earnings’ continuing role as a gauge
of a firm’s underlying economic profitability (Dichev and Tang, 2008).
2.3 The Role of Matching in Detecting PEAD
Given the documented effects of declining matching on earnings properties, we
argue that gauging investors’ understanding of matching and its implications for future
profitability is increasingly important for assessments of earnings news pricing
efficiency. Prior research suggests that declining matching is likely to diminish
autocorrelation in earnings news, complicating an investor’s ability to forecast future
earnings based on temporal patterns in bottom-line earnings alone. Therefore, to theextent that matching continues to guide the recognition of economically significant
components of earnings, an efficient pricing of earnings news will increasingly depend
on knowledge of how matching facilitates predictable earnings time series patterns.
A central feature of our analysis is that we exploit within-firm variation inmatching over time by analyzing two types of earning news measures with varying
susceptibility to the temporal decline in matching. The first type is a “bottom-line”
measure of news using earnings before extraordinary items. Studies of declining
matching in earnings typically focus on bottom-line earnings (Dichev and Tang, 2008;
Donelson et al., 2011; Srivastava, 2014), so we expect bottom-line earnings news
measures to be highly susceptible to declining matching. By contrast, the second type12

consists of “well-matched” measures of news using gross profit or operating profit, bothof which we argue should be less susceptible to declining matching relative to bottomline earnings.
There are two reasons we expect our well-matched measures to be less susceptible
to declining matching. First, both gross and operating profit should have a greater
composition of expenses that are “directly matched” to their related revenues in the
period in which revenues are recognized (e.g., cost of goods sold, sales commissions).11
Second, both measures should have a lower composition of expenses that are recognized“as incurred,” meaning that recognition of the expense is not contingent on a temporal
matching with its associated revenues. Much of the evolution in earnings’ temporal
properties has been attributed to the proliferation of large income-decreasing special
items that are predominately recognized “as incurred” on the income statement(Donelson et al., 2011; Dichev, 2013). These items (e.g., goodwill impairments,
restructuring charges) are not considered recurring components of income and so areconventionally excluded from gross profit and operating profit.12
Equipped with two types of quarterly news measures, we form our first set of
hypotheses around predicted differences in their serial correlation characteristics.
Following Dichev and Tang (2008) and Cao and Narayanamoorthy (2012), we expect toobserve less decay in the first-order autocorrelation of well-matched news measures
11

Dichev and Tang (2008) discuss three degrees of matching success: “direct matching" for costs that are
directly and specifically matched to associated revenue (e.g., cost of goods sold), "indirect matching" forcosts that are matched indirectly by allocating them to periods (e.g., depreciation, taxes), and "no matching"or "expense as incurred" for costs where matching considerations are entirely absent (e.g., most R&D andadvertising).
12
While Compustat attempts to isolate many of these “as incurred” expenses in the variable Special Items,
we note that GAAP does not specifically define special items, and their classification in Compustat issomewhat arbitrary (Burgstahler, Shevlin and Jiambalvo, 2002). While this implies that operating profit
may contain some of these items, this should bias against finding matching disparities between our
measures.

13

relative to bottom-line measures if declining matching deteriorates the persistence ofbottom-line measures at a faster rate than the persistence of well-matched measures.
H1: The first-order autocorrelation of well-matched quarterly earnings news
measures will exhibit less decay over time relative to first-order autocorrelationin bottom-line quarterly earnings news measures.
In addition, we expect well-matched measures to gain incremental power overbottom-line measures in predicting future bottom-line earning news as matching declines
over time. While we expect part of this gain to reflect stronger persistence in wellmatched earnings components, news in well-matched measures may also provide a morereliable signal of innovations in the firm’s underlying economic profitability. Novy-Marx(2013) and Ball et al. (2015) show that gross profit and operating profit outperformbottom-line earnings as proxies for expected profitability in the context of dividend
discount models of equity valuation. Therefore, even as bottom-line earnings becomesincreasingly mismatched, news in well-matched measures likely contains information
about underlying profitability that will persist in future earnings streams.
H2: Well-matched quarterly earnings news measures, relative to bottom-line
news measures, will gain incremental power over time in predicting onequarter-ahead bottom-line earnings news.
Our last hypothesis concerns the evolution of investors’ use of matching in thepricing of earnings news. Supporting the case for efficiency gains in the pricing ofmatching is evidence that investors have learned over time about the forecast relevance ofrelatively nuanced accounting variables such as accruals (Richardson et al., 2010; Greenet al., 2011). Furthermore, with diminishing limits to arbitrage over time, sophisticated14users of financial information (e.g., sell-side analysts, institutional investors) should be
increasingly capable of capitalizing on any lingering excess return predictability.13
Supporting the case for prevailing inefficiencies in the pricing of matching isevidence in Prakash and Sinha (2013) that investors and analysts do not fully understandthe implications of changes in deferred revenue liabilities for future profit margins.
Changes in deferred revenue liabilities tend to be negatively associated with current profit
margins because of mismatching in earnings (e.g., recognizing expenses associated with“unearned” cash receipts). In turn, statement users fail to anticipate future reversals in
profit margins when mismatching gets corrected over time. Penman and Zhang (2002)
document a similar phenomenon for investments (e.g., R&D, advertising) that areconservatively expensed in periods preceding recognition of their associated revenues.
While we do not have strong priors on the evolution of the pricing of matching
information in earnings news, we find that the body of evidence on PEAD attenuation
weakens support for the view that matching information in earnings news is significantly
mispriced in today’s market. Even as there are indications that learning about PEAD canlead to overreaction to the implications of earnings news (Milian, 2015), diminishingtransaction costs over time suggest that sophisticated users should be in an increasingly
good position to quickly abate temporary inefficiencies in the pricing of earnings news.
H3: Investor learning about PEAD encompasses an improved understanding ofmatching and its implications for future profitability.

3. Sample Data and Descriptive Statistics

13

Cross-sectional evidence of a negative association between financial statement user sophistication and
PEAD include Bartov et al. (2000), Mikhail et al. (2003), and Ke and Ramalingegowda (2006).

15

We draw our sample from the CRSP monthly returns database and the Compustat
quarterly database for fiscal years spanning 1979 through 2012. We require firms to havestock prices exceeding $1 per share. Following Ball et al. (2015), we require each firmquarter to have non-missing Compustat data necessary to compute both well-matched andbottom-line earnings news measures to ensure a consistent sample across our two classesof news measures (both are defined below and in the Appendix).
Table 1 provides data on sample size by year. Our total sample size from 19792012 is 388,249 firm-quarters. We see a steady rise in observations from 5,396 firmquarters (1.39% of the sample) in 1979 to a peak of 15,869 firm-quarters (4.09%) in
1999, followed by a slight leveling off to 13,091 firm-quarters (3.37%) in 2012.
Our measure of “bottom-line” earnings news is SUE (i.e., standardized
unexpected earnings) defined as quarterly earnings per share minus expected earnings per
share scaled by the standard deviation of quarterly earnings growth over the previous
eight quarters, as in Jegadeesh and Livnat (2006).14 Expected earnings are assumed tofollow a seasonal random walk with drift. The drift term is measured as the average ofquarterly earnings growth over the previous eight quarters. Our primary measure of“well-matched” earnings news, MSUE, is analogous to SUE except that MSUE’s news
variable is operating profit, which we define as sales minus cost of goods sold and sales,
general and administrative expense, following Ball et al. (2015).15
As in Thomas and Zhang (2011), our return window is the three-month period
beginning in the first month of the calendar quarter that is at least three months
14News measure results are insensitive to the choice of the earnings expectation model employed.
Results are qualitatively unaffected when we replace operating profit with gross profit. Firms haveconsiderable discretion over whether to include certain items in “cost of goods sold” or “sales, general andadministrative expense” (Weil, Schipper, and Francis, 2014). Therefore, it is largely an empirical question
whether gross profit or operating profit is “better matched” in practice.

15

16

subsequent to fiscal quarter-end to ensure that earnings information is released to themarket before the holding period begins. 16 Throughout the paper, we use ADJ_RET,
which is the size-adjusted buy-and-hold three-month stock return calculated as describedin Barber et al. (1999) to demonstrate our results. In multiple regression tests, we also
employ a set of known risk characteristics: SIZE is measured as the natural log of themarket capitalization at the end of the most recent fiscal quarter for which data are
available; BM is the book-to-market ratio, calculated as the book value of equity divided
by the market value of equity at the end of the most recent fiscal quarter for which data
are available; MOM is the buy-and-hold six month raw stock return leading up to themonth prior to the return holding window.
Table 2 provides descriptive statistics for our primary variables of interest. Allvariables except ADJ_RET are winsorized quarterly at the 1st and 99th percentiles. Panel
A provides summary statistics for variables used in our regressions. SUE has a mean
(median) of -0.440 (-0.046) and a standard deviation of 5.118. By comparison, MSUE has
a mean (median) of -0.117 (0.037) and a standard deviation of 4.095. MSUE’s lower
volatility is consistent with theoretical implications in Dichev and Tang (2008) that linkbetter matching in earnings to lower earnings volatility. Distribution statistics for
ADJ_RET, SIZE, BM and MOM are in line with values tabulated in prior literature.
Table 2, Panel B documents the degree of overlap between the SUE and MSUE
decile portfolios (formed quarterly), reported by sub-period. The purpose of this panel is
to highlight the differential composition of firm-quarters within each decile portfolio andto check for trends in the degree of overlap. As expected, the degree of overlap is highest
16 With regard to our portfolio tests, we implement the model with the most flexible design rather than themodel with the maximum return. Results are qualitatively unchanged under various holding windows andportfolio formation dates. Section 4.5 further discusses portfolio test considerations.

17

for the extreme decile portfolios in all three sub-periods (1979-1989, 1990-1999, 20002012).17 However, there is a decreasing trend in the degree of overlap in the extreme
decile portfolios. For example, the highest (lowest) decile portfolios report a 52.96%(57.95%) overlap from 1979-1989, a 50.69% (55.22%) overlap from 1990-1999, and a
47.58% (51.66%) overlap from 2000-2012. These trends suggest that extreme SUE
values are increasingly driven by larger non-operating earnings components over time.
Table 2, Panel C presents the correlations of our news measures (SUE and MSUE)
with future size-adjusted returns (ADJ_RET) by sub-period. We observe attenuation in
return predictability for both SUE and MSUE, though the attenuation for MSUE
(correlation coefficient declining from 0.060 to 0.031) is much milder than theattenuation for SUE (correlation coefficient declining from 0.056 to 0.009). These trends
provide a preliminary indication that earnings news driven by news in “well-matched”
components continues to predict economically significant excess returns.

4. Empirical Results
4.1 Serial Correlation in SUE and MSUE
Before we examine the evolution of the pricing of matching information inearnings news, we first examine temporal properties of our bottom-line news measure(SUE) and our well-matched news measure (MSUE). Recall that we expect MSUE toexperience less decay in its first-order autocorrelation structure relative to SUE (H1) andan increasing incremental ability to forecast one-quarter-ahead SUE (H2), consistent with
the idea that well-matched quarterly news measures are less susceptible to temporal
17

Higher overlap in the extreme deciles is expected because as news in operating profit becomes more
extreme (good or bad), it is more likely to comprise a larger portion of “overall” earnings news and thus
less likely to be diluted by opposite-sign news in non-operating profit.

18

declines in matching relative to bottom-line news measures. Evidence supporting H1 andH2 would provide stronger motivation for assessing whether investors price earnings
news using information provided by matching.
To test H1 and H2, we estimate the following two regression models for the fullsample and by sub-period using the Fama-MacBeth approach (1973):
_

,
,

_

_

,
,

_

,
,

,
,

(1)
(2)

To facilitate comparison between coefficients, we rank-transform our news
variables in ascending order from 0 to 9 each calendar quarter. Hence, R_MSUE (R_SUE)
is the rank-transformed version of MSUE (SUE). Our test for H1 is to compare the trend
in α1 over our three sub-periods to the trend in β2, and we expect the rate of decline in α1
to be smaller than the rate of decline in β2. Our test for H2 is to examine the trend in β1
over our three sub-periods, and we expect β1 to increase over time.
Table 3 provides Fama-MacBeth regression results for our estimations ofequations 1 and 2. Panel A presents the results for equation 1, which regresses onequarter-ahead MSUE on R_MSUE and R_SUE, for the full sample (“Overall” column)
and for each sub-period of our analysis (“1979-1989,” “1990-1999,” “2000-2012”).
Looking at the Overall column, we see that the coefficient on R_MSUE is 0.587 andhighly significant, while the coefficient on R_SUE is much lower at 0.025. These results
support the idea that there is strong serial correlation in MSUE and that SUE does not add
significant value to predicting MSUE beyond what is already provided by current MSUE.
Looking at the sub-period columns, the coefficient on R_MSUE exhibits an increasing

19

trend, starting with a value of 0.559 from 1979-1989 and increasing to a value of 0.616
from 2000-2012, amounting to an 11% increase over time.
Table 3, Panel B presents the results for equation 2, which regresses one-quarterahead SUE on R_MSUE and R_SUE for the full sample and for each sub-period of ouranalysis. Looking first at the coefficient on R_SUE, we see a decreasing trend over the 3
sub-periods, starting with a value of 0.423 in the pre-1990 period and decreasing to avalue of 0.336 in the post-2000 period, amounting to a roughly 20% decrease. Considered
in combination with the increasing coefficient trend for R_MSUE in Panel A, theevidence in Table 3 supports H1 in that bottom-line earnings news loses more serial
predictive power over time than well-matched earnings news.18 Turning to the trend in
the R_MSUE coefficient in Panel B, we see an overall increase from 0.191 in the pre1990 period to 0.231 in the post-2000 period, which amounts to a roughly 20% increaseover time. This trend, along with the decreasing trend for the coefficient on R_SUE,
supports H2, suggesting that well-matched earnings news has gained incremental ability
to forecast future bottom-line earnings news beyond what can be forecast from current
bottom-line earnings news. Looking at the Overall column, we note that while thecoefficient on R_SUE is 0.389, the corresponding coefficient on R_MSUE is only 48%
smaller at 0.202 (both coefficients are highly significant). This suggests that wellmatched earnings news contributes significant incremental information to predicting
bottom-line earnings news throughout our sample period, highlighting the added benefit
that well-matched earnings components provide to the earnings forecasting exercise.19

18

Univariate versions of equations 1 and 2 (untabulated) provide similar evidence consistent with H1.
We also adapt the regression in Dichev and Tang (2008) Table 3 to the quarterly setting (untabulated)
and find that the key temporal patterns from that table (i.e., a decreasing contemporaneous revenue-expense
relation and an increasing relation between revenues and past and future expenses) continue to hold.

19

20

4.2 Return Predictability of SUE and MSUE
Table 4 reports the time-series means of future size-adjusted stock returns(ADJ_RET) for decile portfolios formed on MSUE (Panel A) and SUE (Panel B). At theend of each calendar quarter, firms are sorted into ten portfolios based on the value of thesorting variable (e.g., in Panel A, firms with the lowest MSUE values belong to decile 1,
while firms with the highest MSUE values belong to decile 10). Size-adjusted buy-andhold returns for each stock are calculated over the three months subsequent to theportfolio formation date, and an equal-weighted mean return is computed for eachportfolio. We then form a zero-investment hedge portfolio for each variable by going
long (short) in the highest (lowest) decile portfolio, and we compute Fama-MacBeth tstatistics based on the time-series distribution of the mean hedge portfolio returns.
Panel A of Table 4 reports MSUE decile portfolio returns for the full sample (i.e.,in the “Overall” column) and by sub-period. In the Overall column, there is a smoothmonotonic increase in returns, ranging from a -1.55% return for decile 1 to a 2.17%
return for decile 10. The MSUE hedge portfolio return for the full sample is 3.72%, which
is highly significant (t-stat = 9.90). Panel B of Table 4 reports the corresponding SUE
results. In the Overall column, we see a similarly smooth monotonic increase in returns,
with a hedge portfolio return of 3.05% that is highly significant (t-stat = 7.69). Thus, overthe full sample, MSUE generates superior hedge returns to SUE.
In the sub-period columns for MSUE (Panel A) and SUE (Panel B), there is an
overall temporal decline in portfolio returns, consistent with findings in the PEAD
attenuation literature. However, temporal attenuation in returns is much lower for MSUE

21

portfolios than for SUE portfolios. For example, the hedge return for SUE (Panel B)
decreases from 3.64% in the 1979-1989 sub-period to 2.06% in the 2000-2012 subperiod, a 43% decline, while the hedge return for MSUE (Panel A) decreases from 3.77%
in the 1979-1989 sub-period to 3.29% in the 2000-2012 sub-period, a 13% decline.20
Furthermore, the MSUE hedge return strictly dominates the SUE hedge return in every
sub-period with a progressively increasing excess return gap ranging from a 0.13% gap in
the 1979-1989 sub-period to a 1.23% gap in the 2000-2012 sub-period. Also note that inthe 2000-2012 sub-period, the decile 1 return for MSUE is more negative than thecorresponding return for SUE (-1.31% versus -0.64%), while the decile 10 return for
MSUE is more positive than the corresponding SUE return (1.98% versus 1.42%), whichsuggests that the MSUE portfolio sort is superior in identifying winners and losers.
Figure 1 plots, for each hedge portfolio, the value of a dollar invested at the
beginning of the period over various time intervals. The overall picture that emerges fromthe figure is that the MSUE hedge strategy, once virtually indistinguishable from the SUE
strategy, gradually yields superior returns, especially from 2000 onward, with noindication of reversion in the immediate future. These results, along with the results inTable 4, do not provide strong support for H3, which predicted that investor learning
about PEAD encompasses learning about matching’s implications for future profitability.
Figure 2 plots the trend in SUE and MSUE’s trailing five-year Sharpe ratios overthe course of our sample period. It shows that the trailing five-year Sharpe ratio for
MSUE (the smooth line) largely dominates the corresponding ratios for SUE (the dottedline) starting from the mid-1990s. Also note that the MSUE ratios exhibit a much less
20

There are a few reasons hedge returns for well-matched measures might decline over time: (a) chance,(b) inadequate controls for time varying risk in our tests and/or (c) efficiency gains from learning about
PEAD and exploiting it.

22

precipitous decline than the SUE ratios during the crisis of 2008-2009. In untabulated
results, the full sample Sharpe ratio for SUE is 0.56, while the corresponding ratio for
MSUE is 0.74, or 30% higher than the SUE value. Moreover, the MSUE strategy also
mitigates crash risk. For example, the worst three-month return for the SUE hedge
portfolio is -25.6%, while the worst return for the MSUE hedge is only -10.2%, which is
2.5 times smaller than the SUE decline. Overall, these patterns speak to the relative
stability of the MSUE hedge strategy over time.

4.3 Serial Correlation and Return Predictability in “Mismatched” Samples
Tables 5 and 6 repeat our serial correlation and return predictability analyses for
subsets of firms where matching disparities between MSUE and SUE are suspected to berelatively large. We examine these subsets for two reasons. The first is to better highlightthe distinct information conveyed by MSUE that continues to predict excess returns intoday’s market. The second is to show that much of SUE’s excess return predictability
stems from information conveyed by MSUE, especially in recent years.
We begin by examining a “non-overlapping” subset of our firm-quarters. To form
this subset, we drop firm-quarters with identical extreme decile ranks (i.e., ranks of 1 or10) for SUE and MSUE. Thus, the SUE hedge portfolio is less likely to be driven by
extreme news in MSUE, and vice versa. Table 5, Panel A shows that serial correlation
properties of MSUE and SUE are largely unaffected by exclusion of extreme overlap
firm-quarters, which simply reflects their relatively small representation in the sample(note that extreme overlap firm-quarters comprise only about 10% of the sample).

23

However, when we turn to Table 6, Panel A, which looks at excess returns to nonoverlapping hedge portfolios formed on MSUE and SUE, we see that excess returns to the
MSUE hedge strategy do not attenuate over time. In fact, hedge returns slightly increase
from 2.72% (t-stat = 4.89) in the 1979-1989 sub-period to 2.88% (t-stat = 4.45) in the
2000-2012 sub-period. In stark contrast, SUE hedge returns sharply attenuate from 2.50%(t-stat = 4.04) in the 1979-1989 sub-period to 0.54% (t-stat = 0.73) in the 2000-2012 subperiod. These results suggest that MSUE conveys information distinct from that conveyed
by SUE that is mispriced throughout our sample period. Meanwhile, SUE’s return
predictability, after we purge information conveyed by MSUE, sharply dissipates overtime and largely disappears after 2000. Since the non-overlapping SUE hedge portfolio
likely contains poorer matching relative to the full sample SUE portfolio (since extreme
SUE can be driven by extreme MSUE in the full sample), the sharp drop in hedge returns
here suggests that SUE’s remaining return predictability in recent years likely stems frominformation conveyed by earnings components that are well-matched.21
Next, we consider income-decreasing special item firms and firms reporting
losses. We suspect that special item firms have relatively poor matching in bottom-line
earnings because special items are recognized “as incurred,” and therefore are notexplicitly matched to revenues. Loss firms are likely to report special items as well, butlosses could also arise from investments that are expensed as incurred without a temporal
matching to revenues (e.g., research and development expense, advertising expense).22
21

Note that the decline in SUE returns for non-overlapping firms could be interpreted as investors learning
about temporal implications of mismatching, which can be viewed as a form of learning about matching.
However, our view is that using matching information efficiently implies having an understanding of thetemporal implications of both matching and mismatching. See Section 5 for additional discussion.
22
Our definition of MSUE (tabulated using operating profit) may include certain “as incurred” expenses
such as R&D expense. We find that our MSUE results are insensitive to the exclusion of R&D, which we
attribute largely to our use of quarterly data.

24

Table 5 presents serial correlation results for special item firms (Panel B) and loss
firms (Panel C). In contrast to the full sample results, these results show that thecoefficient on MSUE for both subsets dominates the coefficient on SUE when predicting
future SUE, though the coefficient gap is decreasing somewhat over time. We also notethat the coefficients on MSUE are much larger for these subsets than they are for the fullsample. These differences are consistent with poor matching being intrinsic to thebottom-line earnings of special item and loss firms in all sub-periods. The decreasing
coefficient gap may reflect a rise in the number of firms reporting “serial” special items
and losses, as well as a rise in firms misclassifying routine operating expense items as
special items or losses from discontinued operations (McVay, 2006; Fan et al., 2010).
Table 6, Panels B and C report the corresponding hedge portfolio results for
special item firms and loss firms. In Panel B (special item firms), we observe some return
attenuation for the MSUE portfolio, though excess returns remain significant in the post2000 sub-period (return = 2.61%, t-stat = 2.86). By contrast, the returns to the SUE
portfolio are at least 50% lower in all sub-periods and are no longer significant in the
post-2000 sub-period (return = 0.76%, t-stat = 0.90). In Panel C (loss firms), we observe
similar results, as the MSUE hedge return remains strong (return = 3.42% from 20002012, t-stat = 3.45), while the SUE hedge return is always much smaller and neversignificant (return = 1.18% from 2000-2012, t-stat = 1.00). Again, these results suggestthat well-matched components of earnings news continue to predict excess returns, evenwhen matching in bottom-line earnings is likely to be poor.
Lastly, we consider serial correlation and return predictability of SUE and MSUE
for firms that are reporting results for their fourth fiscal quarter. In terms of intrinsic poor

25

matching factors, the fourth quarter contains many year-end adjustments that “true up”
revenue and expense items to their annual figures (e.g., adjustments to effective tax rateestimates that impact income tax expense). Since adjustments partially reflect correctionsof estimation errors made during interim periods, they are less likely to arise based onmatching considerations. In terms of susceptibility to declining matching factors,
researchers have found that large special items—whose reporting frequency has increased
over time (Donelson et al., 2011)—are disproportionately recognized in the fourth quarter(Kinney and Trezevant, 1997; Burgstahler et al., 2002).
Table 5, Panel D shows evidence of both intrinsic mismatching (likely due toyear-end adjustments) and susceptibility to declining matching over time (likely due toincreased special items). Consistent with intrinsic mismatching effects, the serial
correlation of SUE and MSUE is noticeably lower in the fourth quarter than in the fullsample (consistent with evidence in Sloan and Rangan, 1998). Nevertheless, unlike SUE,MSUE shows a temporal rise in its serial correlation, and it also gains incremental
predictive power for SUE, consistent with bottom-line earnings’ added susceptibility to
declining matching in the fourth fiscal quarter.
Table 6, Panel D shows that the MSUE hedge returns for fourth quarter firms are
consistently large and significant (the 2000-2012 return is 5.03%, t-stat = 2.11), while thecorresponding SUE hedge returns have sharply dissipated over time and are no longer
statistically significant (the 2000-2012 return is 1.73%, t-stat = 0.59).
Overall, the results in Tables 5 and 6 strongly suggest that excess return
predictability in earnings news largely emanates from news in well-matched earnings
components. Furthermore, differences between MSUE and SUE hedge returns appear to26

be more pronounced in recent years (i.e., 2000-2012), which is inconsistent with theconjecture that efficiency gains from investor learning about PEAD capture an improved
understanding of the implications of matching for future profitability (H3). Figure 3 plots
the widening hedge return differential among mismatched subsample firms since 2000.

4.4 The Use of Matching by Transient Institutional Investors
We next examine whether the trading behavior of transient institutional investors
is sensitive to matching information embedded in earnings news. Ke andRamalingegowda (2006) find that transient institutional investors—who hold highly
diversified portfolios and actively trade stocks for short-term gain—exploit SUE signals
and significantly mitigate PEAD as a result. This evidence suggests that transientinvestors are relatively sophisticated users of financial information, so if investor learning
about PEAD encompasses learning about the temporal implications of matching,transient institutions should be among the learned.
To test whether transient institutions use matching to extract stronger SUE
signals, we estimate the following model adapted from Ke and Ramalingegowda (2006):
∆

_

,

_

,

,

∗ _

,

_

,

,

_

,

(3)

,

∆Transient is the change in the percentage of shares owned by transientinstitutions over the calendar quarter in which earnings are announced. D is an indicatorvariable for firm-quarters where we suspect SUE is driven by well-matched earnings
components. We estimate (3) using two different indicator variables. In our first
specification, D = 1 when SUE and MSUE fall in the same top or bottom decile,27

otherwise D = 0. In our second specification, D = 1 when SUE is in the top or bottom
decile and no special items are reported, otherwise D = 0. All other variables are ranktransformations of variables that we defined earlier. Therefore, if transient institutions
exploit matching information when trading on earnings news, then we expect β3 > 0. 23
Table 7 presents the results of our estimations of equation (3). Panel A shows theresults when D indicates overlapping extreme SUE and MSUE firm-quarters. Consistent
with Ke and Ramalingegowda (2006), we find that the coefficient on R_SUE is positiveand significant for the full sample and in each sub-period, which suggests that transientinstitutions trade more heavily on extreme earnings news as indicated by SUE. For our
test variable, D*R_SUE, we find some support for transient institutions exploiting
matching information over the full sample (coefficient = 0.0122, t-stat = 2.40). However,when we look at the sub-period results, D*R_SUE is not significant in the latter two subperiods, i.e., when extracting matching information from earnings news matters most.
Furthermore, the coefficient on D*R_SUE in Panel B, where D indicates extremeSUE
firm-quarters without special items, is not significant in the full sample or in any of thethree sub-periods. Overall, the results in Table 7 do not offer strong evidence thattransient institutions significantly exploit matching information when trading.

4.5 Time-Series Return Predictability in Earnings Levels
Next, we repeat our hedge portfolio analysis from Section 4.2 using quarterlylevels of “bottom-line” earnings (i.e., earnings before extraordinary items) and “well-23

We do not consider loss or fourth quarter firms in this test for the following reasons. First, removing loss
firms will likely result in a disproportionate removal of bottom decile firms, making D*R_SUE difficult tointerpret. Second, use of an interim quarter dummy makes a specious assumption that trading behavior ininterim quarters is not different from behavior in the fourth quarter (absent matching considerations).

28

matched” earnings (i.e., operating profit). Balakrishnan et al. (2010), Novy-Marx (2013)
and Ball et al. (2015) show that levels of both bottom-line and well-matched earnings
predict significant returns over long horizons. Since matching information in our news
variables derives from matching information present in their levels components, a levels
design provides a natural out-of-sample test of investor efficiency with respect tomatching information in earnings. Table 8 presents the excess hedge returns for levels of
operating profit (OP) and earnings (IB). The operating profit hedge yields returns ranging
from 5.45% in the pre-1990 sub-period to 4.39% in the post-2000 sub-period (all highly
significant), while the corresponding returns to the earnings hedge range from 5.00% to
2.79% (the latter return is only weakly significant). These results further support the ideathat the excess return predictability of earnings news stems from matching information.

4.6 Additional Tests and Robustness Checks
We wrap up our analysis with a series of robustness tests. In this subsection, we
test alternative explanations of our main findings and perform sensitivity checks for our
main hedge portfolio results. In Section 5, we discuss the virtues of our matchingframework, and we further contrast our results with related results in prior literature.
First, an alternative explanation of our main hedge portfolio results is that firms
selected into our MSUE portfolios tend to exhibit less earnings volatility over time thanSUE portfolio firms. Therefore, the widening excess return gap that we document merelyreflects lower ex ante earning volatility for MSUE portfolio firms (Cao andNarayanamoorthy, 2012). To rule out this explanation, we partition our sample into three
volatility groups (low, medium, and high) based on the variance of each firm’s bottom-

29

line earnings (scaled by average assets) over the previous eight quarters. Table 9 presents
the returns to MSUE and SUE hedge portfolios formed within each ex ante earnings
volatility group. We find that SUE hedge returns generally diminish as ex ante earning
volatility increases, consistent with the findings of Cao and Narayanamoorthy (2012). By
contrast, the MSUE hedge returns generally increase with increasing ex ante earnings
volatility with significant returns in each tercile in each sub-period. Therefore, it isunlikely that ex ante earnings volatility differences between portfolios explain our results.
Second, we run Fama-MacBeth regressions to test whether our main findings arerobust to controls for known risk characteristics. Table 10 presents the results of thisanalysis. Our dependent variable is three-month buy-and-hold raw returns, and our
independent variables are rank-transformations of MSUE, SUE, Size, BM, and MOM.
Note that the rank-transformations restrict our independent variables to vary from 0 to 1,
so we can interpret coefficients as three-month buy-and-hold hedge returns on the highlow portfolio for each variable. In the Overall column, we see that the coefficients on
R_MSUE and R_SUE are both positive and highly significant, suggesting that eachvariable predicts incremental excess returns over the other. Turning to the sub-period
results, we see that the coefficient on R_MSUE during the post-2000 period is 0.021,
which implies a roughly 8.4% excess return on an annualized basis, while the coefficienton R_SUE is 0.008, which translates to a roughly 3.2% annualized excess return.
Therefore, MSUE’s widening dominance over SUE in recent years remains robust to theinclusion of known risk characteristic controls.
We also run Fama-MacBeth regressions to test whether our results are subsumed
by controls for quarterly revenue news. Jegadeesh and Livnat (2006) show that quarterly30

revenue news predicts excess returns incremental to returns predicted by quarterly
earnings news. Since revenue news excludes poorly matched earnings components, wewant to be sure that our results aren’t simply a manifestation of the return predictabilityof revenue news. In untabulated analysis, we find that the excess return predictability ofrevenue news is subsumed by our well-matched news measures.
Lastly, we perform a series of sensitivity checks for our main hedge portfolio
analysis and tabulate the results in Table 11. In Panel A, we form MSUE portfolios using
gross profit and find that the same patterns observed in Table 4 continue to hold.24 In
Panel B, we rescale both SUE and MSUE by total assets and find that our results arelargely unaffected. In Panel C, we re-partition our full sample into four equally spaced
sub-periods and continue to see significant excess return predictability for MSUE acrossall sub-periods, whereas SUE’s return predictability is insignificant in the last sub-period(i.e., 2004-2012). In Panel D, we adjust the portfolio-holding period to the three days
surrounding next quarter’s earnings announcement and use cumulative market-adjusted
returns over that window. The results show that returns generally decline over time;
however, by the post-2000 period, SUE’s hedge return is insignificant whereas MSUE’sreturn remains highly significant. Finally, in untabulated analyses, we experiment withvarying portfolio formation dates (e.g., one month after earnings announcement) andportfolio holding windows (e.g., 3 to 12 months). We find that the tenor of our results
holds under various formation date and holding window specifications. Therefore, our24

We find similar results when we define MSUE as news in earnings before special items.

31

results cannot be fully explained by earlier dissemination of bottom-line earnings news
over our sample period.25

5. Discussion
Throughout the paper, we have argued that an efficient pricing of earnings news
should incorporate the implications of matching for future profitability. Ultimately, ouranalysis addresses a more fundamental question concerning the extent to which investors
use information provided by accounting conventions to price earnings news. Our choice
to address this question in the context of matching follows from theoretical and empiricalconsiderations. On the theoretical front, matching has clear implications for earnings’
serial properties. On the empirical front, evidence of a temporal decline in matchingcoincides with evidence of a temporal decline in PEAD, so a consideration of matchinglends structure to assessments of the drivers of PEAD attenuation. Thus, we view
matching as an important and useful framework for gauging the scope of investor
learning activities as they pertain to the pricing of earnings news.
Other studies attribute evolving earnings properties to phenomena that are closely
related to matching. Givoly and Hayn (2000) link evolving earnings, accruals and cash
flow properties to an increase in financial reporting conservatism over time. Meanwhile,
Bushman et al. (2014) document a temporal weakening in the negative contemporaneous
correlation of accruals and cash flows, driven by temporal increases in economic shocks
and non-timing-related accruals. Our view is that neither phenomenon—increasingconservatism or the weakening accrual/cash flow relation—can be cleanly isolated from25 We also find that our return patterns hold for (a) samples of the top 3,000 firms by market capitalizationand (b) analyst-covered firms where earnings news is measured based on analyst earnings expectations.

32

matching considerations. In particular, increasing conditional conservatism requires
timelier recognition of expenses (i.e., it creates “mismatching” in earnings), while aweakening accrual/cash flow relation suggests a weakening role of accruals as a“correction mechanism” for timing mismatches between related cash flow and earnings
realizations. We therefore believe that return predictability attributable to either
phenomenon supports the position that matching information relevant for predicting
earnings is not efficiently priced at the time of earnings announcement.
Another related concept underlying evolving earnings properties is the moregeneral notion of earnings quality (e.g., Dechow, Ge and Schrand, 2010; Dichev,
Graham, Harvey and Rajgopal, 2013). An alternative framing of our return predictability
results could be that investors underweight earnings quality (EQ) characteristics such as
persistence when pricing earnings news (e.g., Jegadeesh and Livnat, 2006). Similarly,
returns could reflect an overweighting of low persistence components such as special
items (Burgstahler, Jiambalvo and Shevlin, 2002; Dechow and Ge, 2006). While ourresults may be viewed through a persistence lens, we believe the matching frameworkprovides a more comprehensive perspective on the inefficiencies we document. For
example, an efficient pricing of matching requires investors to recognize both (a) the
higher persistence of “well-matched” earning components and (b) the lower persistenceof “mismatched” earnings components. This joint condition allows investors to recognize
EQ signals (e.g., they understand (a) or (b) individually) without being fully efficientwith respect to quality information in earnings. Furthermore, our matching frameworkoffers guidance on how to extract high quality information from overall “low quality”
earnings, a feature that the EQ literature has not extensively examined to date.

33

Our selection of “well-matched” news measures evokes findings from thefundamental analysis literature (e.g., Lipe, 1986; Ou and Penman, 1989; Lev andThiagarajan, 1993; Abarbanell and Bushee, 1997; Piotroski, 2000; Amir et al., 2010;
Akbas et al., 2014). We emphasize that our choice of measures is guided by theory
relating matching to the serial properties of earnings and that our primary interest is toassess the extent to which the pricing of earnings news impounds these theoretical links.
By contrast, in the bulk of the fundamental analysis literature, researchers motivate theirselection of accounting measures based on ad-hoc or anecdotal factors that typically lacktheoretical grounding. In addition, our analysis is mainly intertemporal in nature as wetackle a research question with implications for interpretations of PEAD attenuation overtime, whereas fundamental analysis is most typically deployed in cross-sectional settings.
Finally, Doyle, Lundholm and Soliman (2006) and Livnat and Mendenhall (2006)
use research designs that compare drift arising from time-series-based earnings news
measures (e.g., SUE) to drift arising from IBES-based news measures. Both papers (DLSand LM) find that the magnitude of drift is larger for IBES-based measures. We note a
few key distinctions between the DLS and LM analyses and our own. First, DLS and LM
compare their news measures using cross-sectional analysis, whereas we employ bothcross-sectional and time-series analysis. Second, in untabulated analysis, we find that
IBES-based measures do not predict significant excess returns in the last sub-period of
our sample (2000-2012), whereas our “well-matched” measures continue to predictsignificant excess returns (for firms with or without analyst following). Third, DLS andLM necessarily use samples of firms with analyst following, so it is not clear whether
their sources of differential drift would generalize to the full Compustat population.

34

6. Conclusion
In this paper, we find that “well-matched” measures of earnings news continue topredict significant excess returns even as “bottom-line” news measures lose return
predictability over time. Widening excess return disparities between well-matched andbottom-line earnings news coincide with increasing incremental abilities of well-matchedmeasures to predict future news in bottom-line earnings. Excess return disparities are
greater when bottom-line earnings exhibit poorer matching andbottom-line news forpoorly matched firms does not predict significant excess returns after 2000. Matching
information in earnings news does not appear to be significantly exploited by transientinstitutional investors in recent years, and our return predictability patterns show up inlevels specifications of our earnings measures. Our results are insensitive to scalar, subperiod, and holding window choices; alternative measures of “well-matched” news; andcontrols for risk, revenue news, and ex ante earnings volatility.
Collectively, our results suggest that investors remain relatively inefficient at
pricing earnings news driven by well-matched earnings components. This inefficiency ismuch less apparent when we focus on bottom-line earnings news in recent years, likely
because bottom-line news is increasingly driven by mismatched earnings componentsthat tend to have limited implications for future profitability. Therefore, recent findings ofsignificant PEAD attenuation do not appear to reflect efficiency gains from investors
learning about matching. Nevertheless, our results suggest that matching information inearnings continues to contribute significantly to predicting future profitability, even as thedegree of matching in bottom-line earnings erodes over time.

35

Appendix
Variable Definitions
Variable Name

MSUE

SUE

Size

Definition
Standardized unexpected operating profit, calculated asquarterly operating profit per share minus expected
operating profit, scaled by the standard deviation ofquarterly operating profit growth over the previous eightquarters. Operating profit is defined as revenue minus=
cost of goods sold and selling, general andadministrative expense, as in Ball et al. (2015). Expected
operating profit follows a seasonal random walk withdrift. The drift term is the average of quarterly operating
profit growth over the previous eight quarters.
Standardized unexpected earnings, calculated asquarterly earnings per share minus expected earnings per
share scaled by the standard deviation of quarterly
earnings growth over the previous eight quarters, as in=
Jegadeesh and Livnat (2006). Expected earnings are
assumed to follow a seasonal random walk with drift.
The drift term is the average of quarterly earnings
growth over the previous eight quarters.
Firm size, calculated as the natural log of the market
capitalization as of the end of the most recent fiscal=
quarter for which data are available (in millions).

BM

Book-to-market ratio, calculated as book value of equity= divided by market value of equity at the end of the mostrecent fiscal quarter for which data are available.

MOM=

The buy-and-hold six month stock return ending one
month prior to the portfolio formation date.

R_MSUE=The decile ranking of MSUE based on the distribution
for each calendar quarter.

R_SUE

=

The decile ranking of SUE based on the distribution foreach calendar quarter.

R_Size=The decile ranking of Size based on the distribution foreach calendar quarter.

36

R_BM

=

The decile ranking of BM based on the distribution foreach calendar quarter.

R_MOM

=

The decile ranking of MOM based on the distribution foreach calendar quarter.

ADJ_RET

Size-adjusted return over the three-month period
beginning in the first month of the calendar quarter that= is at least three months subsequent to fiscal quarter-end.
The methodology to construct size-adjusted portfolios isbased on Barber et al. (1999).

37

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42

Figure 1 Comparison of hedge returns on MSUE and SUE strategies.
Panel A 1979-1989
6
5
4
3
2
1
0
1978

1980

1981

1983

1984

1985

SUE

MSUE

1987

1988

1989

Panel B 1990-1999
6
5
4
3
2
1
0
1989

1991

1992

1994

1995
SUE

43

1996
MSUE

1998

1999

Panel C 2000-2012
6
5
4
3
2
1
0
1999

2001

2002

2004

2005

2006
SUE

2008

2009

2010

2012

2013

MSUE

Panel D 1979-2012
140
120
100
80
60
40
20
0
1978

1984

1989

1995
SUE

2000

2006

2011

MSUE

Figure 1 plots the value of a dollar invested at the beginning of three successive sub-periods (Panels A-C)
and the full sample (Panel D) in SUE (dashed blue line) and MSUE (solid red line) hedge portfolios. At theend of each calendar quarter, firms are sorted into deciles based on the value of the sorting variable (SUE or
MSUE). We then form zero-investment hedge portfolios by going long (short) in the highest (lowest) decileof SUE and MSUE, and we calculate size-adjusted buy-and-hold equal-weighted returns for each portfolio(following the methodology described in Barber et al. (1999)) over the three months subsequent to theportfolio formation date. MSUE is standardized unexpected operating profit, calculated as quarterly
operating profit per share minus expected operating profit, scaled by the standard deviation of quarterly
operating profit growth over the previous eight quarters. Operating profit is defined as revenue minus costof goods sold and selling, general and administrative expense, as in Ball et al. (2015). Expected operating44

profit follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit
growth over the previous eight quarters. SUE is standardized unexpected earnings, calculated as quarterly
earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings
growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed
to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth overthe previous eight quarters.

45

Figure 2 Trailing five-year Sharpe ratios for SUE and MSUE hedge portfolios.
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1982
‐0.2

1987

1993

1998
SUE

2004

2009

2014

MSUE

Figure 2 plots trailing five-year Sharpe ratios for SUE (dashed blue line) and MSUE (solid red line) hedge
portfolios. At the end of each calendar quarter, firms are sorted into deciles based on the value of thesorting variable (SUE or MSUE). We then form zero-investment hedge portfolios by going long (short) inthe highest (lowest) decile of SUE and MSUE, and we calculate size-adjusted buy-and-hold equal-weighted
returns for each portfolio (following the methodology described in Barber et al. (1999)) over the threemonths subsequent to the portfolio formation date. MSUE is standardized unexpected operating profit,
calculated as quarterly operating profit per share minus expected operating profit, scaled by the standard
deviation of quarterly operating profit growth over the previous eight quarters. Operating profit is defined
as revenue minus cost of goods sold and selling, general and administrative expense, as in Ball et al.
(2015). Expected operating profit follows a seasonal random walk with drift. The drift term is the averageof quarterly operating profit growth over the previous eight quarters. SUE is standardized unexpected
earnings, calculated as quarterly earnings per share minus expected earnings per share scaled by thestandard deviation of quarterly earnings growth over the previous eight quarters, as in Jegadeesh and Livnat(2006). Expected earnings are assumed to follow a seasonal random walk with drift. The drift term is theaverage of quarterly earnings growth over the previous eight quarters.

46

Figure 3 Comparison of mismatched subsample hedge returns of MSUE and SUE
strategies from 2000-2012.
Panel A: Non-overlapping subsample
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
2000

2001

2002

2004

2005

2006

SUE

2008

2009

2011

2012

2009

2011

2012

MSUE

Panel B: Special items subsample
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
2000

2001

2002

2004

2005

2006

SUE

47

2008
MSUE

Panel C: Loss subsample
6
5
4
3
2
1
0
2000

2001

2002

2004

2005

2006

2008

SUE

MSUE

2006

2008

SUE

MSUE

2009

2011

2012

Panel D: 4th quarter subsample
2
1.8
1.6
1.4
1.2
1
0.8
2000

2002

2004

2010

2012

Figure 3 plots excess returns to SUE (dashed blue line) and MSUE (solid red line) hedge portfolios for four
“mismatched” subsamples over the 2000-2012 sub-period. The subsample in Panel A comprises all firmquarters excluding those with identical extreme calendar quarter decile rankings for SUE and MSUE. The
subsample in Panel B comprises all firm-quarters that report special items (as identified by Compustat).
The subsample in Panel C comprises all firm-quarters that report negative income before extraordinary
items. The subsample in Panel D comprises all fourth fiscal quarter observations. At the end of eachcalendar quarter, firms are sorted into deciles based on the value of the sorting variable (SUE or MSUE).
We then form zero-investment hedge portfolios by going long (short) in the highest (lowest) decile of SUE
and MSUE, and we calculate size-adjusted buy-and-hold equal-weighted returns for each portfolio

48

(following the methodology described in Barber et al. (1999)) over the three months subsequent to theportfolio formation date. MSUE is standardized unexpected operating profit, calculated as quarterly
operating profit per share minus expected operating profit, scaled by the standard deviation of quarterly
operating profit growth over the previous eight quarters. Operating profit is defined as revenue minus costof goods sold and selling, general and administrative expense, as in Ball et al. (2015). Expected operating
profit follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit
growth over the previous eight quarters. SUE is standardized unexpected earnings, calculated as quarterly
earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings
growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed
to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth overthe previous eight quarters.

49

Table 1 Sample size by year
Year
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
All Year

# Obs
5,396
6,058
5,991
5,875
5,873
6,715
9,066
9,569
9,515
9,110
9,251
9,314
9,457
10,09910,82911,668
12,217
13,19015,106
15,846
15,86915,655
14,24814,00814,19014,462
14,234
14,248
14,277
13,70012,99913,654
13,46913,091
388,249% of sample
1.39
1.56
1.54
1.51
1.51
1.73
2.34
2.46
2.45
2.35
2.38
2.40
2.44
2.60
2.79
3.01
3.15
3.40
3.89
4.08
4.09
4.03
3.67
3.61
3.65
3.72
3.67
3.67
3.68
3.53
3.35
3.52
3.47
3.37
100

We draw our sample from the CRSP monthly returns database and the Compustat quarterly database forfiscal years spanning 1979 through 2012. We require firms to have stock prices exceeding $1 per share.
Following Ball et al. (2014), we require each firm-quarter to have non-missing Compustat data necessary tocompute MSUE and SUE to ensure a consistent sample across our two classes of news measures. MSUE isstandardized unexpected operating profit, calculated as quarterly operating profit per share minus expected50

operating profit, scaled by the standard deviation of quarterly operating profit growth over the previous
eight quarters. Operating profit is defined as revenue minus cost of goods sold and selling, general andadministrative expense, as in Ball et al. (2015). Expected operating profit follows a seasonal random walkwith drift. The drift term is the average of quarterly operating profit growth over the previous eightquarters. SUE is standardized unexpected earnings, calculated as quarterly earnings per share minus
expected earnings per share scaled by the standard deviation of quarterly earnings growth over the previous
eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed to follow a seasonalrandom walk with drift. The drift term is the average of quarterly earnings growth over the previous eightquarters.

51

Table 2 Descriptive statistics
Panel A: Descriptive statistics for the variables in the regression
Variable

Mean

Std

25th Pctl

50th Pctl

75th Pctl

MSUE
SUE
ADJ_RET
SIZE
BM
MOM

-0.117-0.440
0.004
5.262
0.704
0.089

4.095
5.118
0.267
2.072
0.587
0.415

-2.234-1.991-0.1313.742
0.335-0.1450.037-0.046-0.014
5.134
0.569
0.036

2.257
1.815
0.107
6.654
0.912
0.243

Panel B: Overlap between MSUE and SUE for each portfolio by sub-period
Decile
1
2
3
4
5
6
7
8
9
10

1979-1989

1990-1999

2000-201257.95%
35.85%
28.73%
25.22%
23.50%
24.82%
25.05%
27.66%
34.49%
52.96%

55.22%
35.44%
28.52%
25.16%
24.56%
24.39%
23.39%
26.72%
33.93%
50.69%

51.66%
34.15%
26.91%
24.55%
22.29%
21.81%
21.80%
24.73%
31.65%
47.58%

Panel C: Correlation between different surprises (SUE and MSUE) and future sizeadjusted stock returns (ADJ_RET) by sub-period.
Variable
MSUE
SUE

1979-1989

1990-1999

2000-2012

0.060
0.056

0.039
0.031

0.031
0.009

MSUE is standardized unexpected operating profit, calculated as quarterly operating profit per share minus
expected operating profit, scaled by the standard deviation of quarterly operating profit growth over theprevious eight quarters. Operating profit is defined as revenue minus cost of goods sold and selling, generaland administrative expense, as in Ball et al. (2015). Expected operating profit follows a seasonal random
walk with drift. The drift term is the average of quarterly operating profit growth over the previous eightquarters. SUE is standardized unexpected earnings, calculated as quarterly earnings per share minus
expected earnings per share scaled by the standard deviation of quarterly earnings growth over the previous
eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed to follow a seasonalrandom walk with drift. The drift term is the average of quarterly earnings growth over the previous eight52

quarters. ADJ_RET is the three-month size-adjusted buy-and-hold stock return following the methodology
described in Barber et al. (1999). Our return window begins in the first month of the calendar quarter that is
at least three months subsequent to fiscal quarter-end. SIZE is firm size, calculated as the natural log of themarket capitalization as of the end of the most recent fiscal quarter for which data are available (inmillions). BM is the book-to-market ratio, calculated as book value of equity divided by market value ofequity at the end of the most recent fiscal quarter for which data are available. MOM is momentum,
calculated as the six-month buy-and-hold stock return ending one month prior to the portfolio formation
date.

53

Table 3 Fama-MacBeth regressions of future MSUE or SUE on current MSUE andSUE by sub-period
Panel A: Regression of MSUEt+1 on R_MSUEt and R_SUEt
Intercept
R_MSUE
R_SUE
Adj R2

1979-1989

1990-1999

2000-2012

Overall-2.782(-21.45)0.559(36.07)0.035(3.64)-2.785(-32.56)0.581(45.63)0.021(3.08)-3.043(-22.28)0.616(49.52)0.018(3.33)-2.883(-40.10)0.587(72.75)0.025(5.72)

0.174

0.181

0.211

0.190

Panel B: Regression of SUEt+1 on R_MSUEt and R_SUEt
Intercept
R_MSUE
R_SUE
Adj R2

1979-1989

1990-1999

2000-2012

Overall-3.084(-20.04)
0.191(16.10)0.423(24.54)-3.198(-28.21)0.178(21.55)0.420(29.37)-3.033(-13.89)0.230(19.38)0.336(20.73)-3.098(-30.30)0.202(30.31)
0.389(39.12)

0.135

0.106

0.119

0.120

Table 3 reports results for regressions of one-quarter-ahead MSUE (Panel A) and one-quarter-ahead SUE(Panel B) on current quarter R_MSUE and R_SUE. The “R_” prefix denotes decile rank transformations
performed each calendar quarter for our variables of interest. MSUE is standardized unexpected operating
profit, calculated as quarterly operating profit per share minus expected operating profit, scaled by thestandard deviation of quarterly operating profit growth over the previous eight quarters. Operating profit isdefined as revenue minus cost of goods sold and selling, general and administrative expense, as in Ball etal. (2015). Expected operating profit follows a seasonal random walk with drift. The drift term is theaverage of quarterly operating profit growth over the previous eight quarters. SUE is standardized
unexpected earnings, calculated as quarterly earnings per share minus expected earnings per share scaledby the standard deviation of quarterly earnings growth over the previous eight quarters, as in Jegadeesh andLivnat (2006). Expected earnings are assumed to follow a seasonal random walk with drift. The drift termis the average of quarterly earnings growth over the previous eight quarters.

54

Table 4 Future size-adjusted returns for portfolios formed based on MSUE andSUE by sub-period.
Panel A: Decile portfolio returns based on MSUE
Decile1979-1989

1990-1999

2000-2012

Overall

1
2
3
4
5
6
7
8
9
10
D10-D1-1.60%-0.79%-0.19%-0.01%
0.79%
0.73%
0.99%
1.40%
1.83%
2.17%
3.77%
(6.97)-1.81%-0.97%-1.31%-0.42%-0.20%
0.28%
0.51%
0.94%
0.97%
0.95%
1.23%
1.98%
3.29%
(4.41)-1.55%-0.70%-0.31%-0.01%
0.44%
0.67%
0.89%
1.27%
1.43%
2.17%
3.72%
(9.90)-0.60%-0.38%-0.04%
0.25%
0.66%
1.55%
1.24%
2.42%
4.23%
(7.16)

Panel B: Decile portfolio returns based on SUE
Decile1979-1989
1990-1999
2000-2012
1
2
3
4
5
6
7
8
9
10
D10-D1-1.46%-1.30%
0.03%
0.13%
0.62%
1.13%
1.22%
1.34%
1.43%
2.18%
3.64%
(6.18)-1.61%-1.33%-0.98%-0.43%
0.17%
0.19%
1.02%
1.39%
1.84%
2.07%
3.68%
(7.28)-0.64%-0.58%-0.31%
0.28%
0.63%
0.65%
1.20%
1.15%
1.12%
1.42%
2.06%
(2.54)Overall-1.19%-1.03%-0.40%
0.02%
0.49%
0.67%
1.15%
1.28%
1.43%
1.86%
3.05%
(7.69)

Table 4 reports three-month buy-and-hold size-adjusted stock returns to portfolios formed on deciles of
MSUE and SUE for the full sample and by sub-period. At the end of each calendar quarter, firms are sorted
into deciles based on the value of the sorting variable (SUE or MSUE). Size-adjusted buy-and-hold returns
for each stock are calculated over the three months subsequent to the portfolio formation date, and an
equal-weighted mean return is computed for each portfolio. We then form a zero-investment hedge
portfolio for each variable by going long (short) in the highest (lowest) decile portfolio, and we compute55

Fama-MacBeth t-statistics based on the time-series distribution of the mean hedge portfolio returns. MSUE
is standardized unexpected operating profit, calculated as quarterly operating profit per share minus
expected operating profit, scaled by the standard deviation of quarterly operating profit growth over theprevious eight quarters. Operating profit is defined as revenue minus cost of goods sold and selling, generaland administrative expense, as in Ball et al. (2015). Expected operating profit follows a seasonal random
walk with drift. The drift term is the average of quarterly operating profit growth over the previous eightquarters. SUE is standardized unexpected earnings, calculated as quarterly earnings per share minus
expected earnings per share scaled by the standard deviation of quarterly earnings growth over the previous
eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed to follow a seasonalrandom walk with drift. The drift term is the average of quarterly earnings growth over the previous eightquarters.

56

Table 5 Fama-MacBeth regressions of future MSUE or SUE on current MSUE andSUE by sub-period for “mismatched” subsamples
Panel A: Non-overlapping subsamples
Top Panel: Regression of MSUEt+1 on R_MSUEt and R_SUEt
Bottom Panel: Regression of SUEt+1 on R_MSUEt and R_SUEt

Intercept
R_MSUE
R_SUE
Adj R2

Intercept
R_MSUE
R_SUE
Adj R2

1979-1989

1990-1999

2000-2012

Overall-2.740(-20.48)0.556(37.44)0.032(3.20)-2.771(-31.14)0.581(47.76)0.022(2.90)-3.022(-21.34)0.615(49.50)0.017(3.02)-2.857(-38.39)0.586(74.31)
0.023(5.20)

0.144

0.156

0.183

0.162

1979-1989

1990-1999

2000-2012

Overall-3.022(-18.90)0.185(14.85)0.418(25.17)-3.082(-28.17)0.169(20.50)0.410(30.27)-2.927(12.76)0.221(16.98)0.326(20.63)-3.003(-28.27)0.194(27.48)0.381(39.31)

0.105

0.082

0.092

0.093

57

Panel B: Special item subsamples
Top Panel: Regression of MSUEt+1 on R_MSUEt and R_SUEt
Bottom Panel: Regression of SUEt+1 on R_MSUEt and R_SUEt

Intercept
R_MSUE
R_SUE
Adj R2

Intercept
R_MSUE
R_SUE
Adj R2

1979-1989

1990-1999

2000-2012

Overall-2.076(-13.04)
0.523(29.96)-0.032(-1.84)-2.207(-20.03)
0.517(52.42)-0.015(-1.47)-2.885(-20.30)0.610(43.01)0.012(1.60)-2.424(-27.89)0.555(52.92)-0.010(-1.45)

0.146

0.151

0.216

0.175

1979-1989

1990-1999

2000-2012

Overall-2.155(-11.92)
0.343(13.04)0.105(3.88)-1.921(-14.89)0.271(17.36)0.122(7.65)-2.582(-10.35)
0.257(20.64)0.238(14.41)-2.250(-18.78)
0.289(25.83)0.161(12.55)

0.078

0.054

0.092

0.076

58

Panel C: Loss subsamples
Top Panel: Regression of MSUEt+1 on R_MSUEt and R_SUEt
Bottom Panel: Regression of SUEt+1 on R_MSUEt and R_SUEt

Intercept
R_MSUE
R_SUE
Adj R2

Intercept
R_MSUE
R_SUE
Adj R2

1979-1989

1990-1999

2000-2012

Overall-1.849(-14.46)0.550(23.62)-0.044(-2.40)-1.954(-22.26)0.532(28.37)-0.033(-2.84)-2.409(-20.02)0.583(37.94)0.002(0.50)-2.094(-29.92)
0.557(50.21)-0.023(-2.83)

0.140

0.140

0.198

0.162

1979-1989

1990-1999

2000-2012

Overall-2.238(-13.94)0.304(9.51)
0.224(6.89)-2.111(-17.73)
0.248(16.99)0.222(11.54)-2.258(-13.21)0.255(19.27)0.240(16.01)-2.208(-24.53)
0.269(21.74)0.229(17.43)

0.090

0.070

0.090

0.084

59

Panel D: Fourth quarter subsamples
Top Panel: Regression of MSUEt+1 on R_MSUEt and R_SUEt
Bottom Panel: Regression of SUEt+1 on R_MSUEt and R_SUEt

Intercept
R_MSUE
R_SUE
Adj R2

Intercept
R_MSUE
R_SUE
Adj R2

1979-1989

1990-1999

2000-2012

Overall-1.765(-6.10)0.337(11.50)0.050(2.04)-1.758(-11.60)
0.343(24.34)0.012(0.74)-2.410(-9.75)0.472(33.46)0.018(2.29)-2.009(-13.70)
0.390(24.59)0.027(2.71)

0.088

0.075

0.136

0.103

1979-1989

1990-1999

2000-2012

Overall-2.078(-6.14)0.241(13.18)0.172(7.89)-1.792(-12.06)
0.164(10.68)0.149(11.05)-2.057(-10.34)0.259(15.79)0.133(6.83)-1.986(-14.41)0.225(19.18)0.150(13.60)

0.081

0.045

0.083

0.071

Table 5 reports results for regressions of one-quarter-ahead MSUE (top panels) and one-quarter-ahead SUE(bottom panels) on current quarter R_MSUE and R_SUE for four “mismatched” subsamples. The
subsample in Panel A comprises all firm-quarters excluding those with identical extreme calendar quarter
decile rankings for SUE and MSUE. The subsample in Panel B comprises all firm-quarters that reportspecial items (as identified by Compustat). The subsample in Panel C comprises all firm-quarters thatreport negative income before extraordinary items. The subsample in Panel D comprises all fourth fiscal
quarter observations. The “R_” prefix denotes decile rank transformations performed each calendar quarterfor our variables of interest. MSUE is standardized unexpected operating profit, calculated as quarterly
operating profit per share minus expected operating profit, scaled by the standard deviation of quarterly
operating profit growth over the previous eight quarters. Operating profit is defined as revenue minus costof goods sold and selling, general and administrative expense, as in Ball et al. (2015). Expected operating
profit follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit
growth over the previous eight quarters. SUE is standardized unexpected earnings, calculated as quarterly
earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings
growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed
to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth overthe previous eight quarters.

60

Table 6 Future size-adjusted returns for portfolios formed based on MSUE andSUE by sub-period for “mismatched” subsamples
Panel A: Non-overlapping subsamples
1979-1989
1990-1999

2000-2012

Overall

2.72%(4.89)
2.50%(4.04)

2.88%(4.45)
0.54%(0.73)

2.83%(8.29)
1.50%(4.01)

Panel B: Special item subsamples
1979-1989
1990-1999

2000-2012

Overall

3.98%(2.92)
1.77%(2.00)

3.48%(3.90)
1.45%(2.07)

2.61%(2.86)
0.76%(0.90)

3.31%(5.35)
1.29%(2.71)

1990-1999

2000-2012

Overall

3.29%(3.32)
1.03%(1.18)

3.42%(3.45)
1.18%(1.00)

3.88%(5.63)
0.70%(0.99)

Panel D: Fourth quarter subsamples
1979-1989
1990-1999

2000-2012

Overall

5.03%(5.08)
4.55%(6.19)

5.03%(2.11)
1.73%(0.59)

4.44%(4.24)
3.04%(2.56)

R_MSUE
R_SUE

R_MSUE
R_SUE

Panel C: Loss subsamples
1979-1989
R_MSUE
R_SUE

R_MSUE
R_SUE

4.96%(3.22)-0.18%(-0.12)

2.87%(5.43)
1.63%(3.96)

3.03%(1.98)
3.09%(2.46)

Table 6 reports three-month buy-and-hold size-adjusted stock returns to portfolios formed on deciles of
MSUE and SUE for four “mismatched” subsamples. The subsample in Panel A comprises all firm-quarters
excluding those with identical extreme calendar quarter decile rankings for SUE and MSUE. The subsample
in Panel B comprises all firm-quarters that report special items (as identified by Compustat). The
subsample in Panel C comprises all firm-quarters that report negative income before extraordinary items.
The subsample in Panel D comprises all fourth fiscal quarter observations. At the end of each calendar
quarter, firms are sorted into deciles based on the value of the sorting variable (SUE or MSUE). Sizeadjusted buy-and-hold returns for each stock are calculated over the three months subsequent to theportfolio formation date, and an equal-weighted mean return is computed for each portfolio. We then forma zero-investment hedge portfolio for each variable by going long (short) in the highest (lowest) decile
portfolio, and we compute Fama-MacBeth t-statistics based on the time-series distribution of the mean
hedge portfolio returns. MSUE is standardized unexpected operating profit, calculated as quarterly
operating profit per share minus expected operating profit, scaled by the standard deviation of quarterly
operating profit growth over the previous eight quarters. Operating profit is defined as revenue minus cost61

of goods sold and selling, general and administrative expense, as in Ball et al. (2015). Expected operating
profit follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit
growth over the previous eight quarters. SUE is standardized unexpected earnings, calculated as quarterly
earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings
growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed
to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth overthe previous eight quarters.

62

Table 7 Changes in the proportion of shares held by transient institutional investors
responding to matching information in earnings news
Panel A: D = 1 if RSUE and RMSUE are both in top and bottom decile
1979-1989
1990-1999
2000-2012
Intercept
D*R_SUE
R_SUE
D
R_SIZE
R_BM
R_MOM
Adj R2-0.6008(-10.64)0.0185(1.93)
0.0118(2.18)-0.0026(-0.06)-0.0139(-1.56)0.0176(3.18)
0.1482(18.76)-1.3694(-22.25)
0.0063(0.75)
0.0194(3.24)-0.0214(-0.34)-0.0303(-3.40)0.0138(2.35)
0.3112(52.87)-1.1798(-13.47)
0.0121(1.42)
0.0118(2.24)
0.0004(0.75)-0.0075(-0.51)
0.0219(3.88)
0.2682(30.98)-1.0689(-21.03)
0.0122(2.40)
0.0141(4.42)
0.0090(0.29)-0.0164(-2.34)0.0182(5.50)
0.2464(33.64)

0.047

0.104

0.072

0.075

2000-2012

Overall

Panel B: D = 1 if special items equals zero
1979-1989
1990-1999
Intercept
D*R_SUE
R_SUE
D
R_SIZE
R_RBM
R_MOM
Adj R2Overall-0.6208(-9.30)-0.0016(-0.18)0.0168(2.39)
0.0473(1.05)-0.0140(-1.59)0.0172(3.07)
0.1486(18.65)-1.3618(-22.78)0.0064(0.81)
0.0191(3.12)-0.0636(-1.23)-0.0302(-3.38)0.0139(2.37)
0.3109(53.02)-1.1745(13.14)0.0091(1.09)
0.0116(2.04)-0.0216(-0.45)-0.0071(-0.48)0.0221(3.88)
0.2681(30.90)-1.0703(-20.74)
0.0052(1.06)
0.0154(4.31)-0.0144(-0.51)-0.0162(-2.32)0.0181(5.45)
0.2463(33.68)

0.048

0.104

0.072

0.075

Table 7 reports results for tests of whether transient institutional investors use matching information to
extract stronger SUE signals around earnings announcement (adapted from the model in Ke and63Ramalingegowda (2006)). In Panel A, we use an indicator variable D that equals 1 when SUE and MSUE
are in the same extreme decile, zero otherwise. In Panel B, we use an indicator variable D that equals 1
when SUE is in an extreme decile and no special items are reported, zero otherwise. The dependent variable
is ∆Transient, which is the change in the percentage of shares owned by transient institutions over thecalendar quarter in which earnings are announced. The “R_” prefix denotes decile rank transformations
performed each calendar quarter for our variables of interest. MSUE is standardized unexpected operating
profit, calculated as quarterly operating profit per share minus expected operating profit, scaled by thestandard deviation of quarterly operating profit growth over the previous eight quarters. Operating profit isdefined as revenue minus cost of goods sold and selling, general and administrative expense, as in Ball etal. (2015). Expected operating profit follows a seasonal random walk with drift. The drift term is theaverage of quarterly operating profit growth over the previous eight quarters. SUE is standardized
unexpected earnings, calculated as quarterly earnings per share minus expected earnings per share scaledby the standard deviation of quarterly earnings growth over the previous eight quarters, as in Jegadeesh andLivnat (2006). Expected earnings are assumed to follow a seasonal random walk with drift. The drift termis the average of quarterly earnings growth over the previous eight quarters. SIZE is firm size, calculated as
the natural log of the market capitalization as of the end of the most recent fiscal quarter for which data are
available (in millions). BM is the book-to-market ratio, calculated as book value of equity divided bymarket value of equity at the end of the most recent fiscal quarter for which data are available. MOM is
momentum, calculated as the six-month buy-and-hold stock return ending one month prior to the portfolio
formation date.

64

Table 8 Future size-adjusted hedge returns based on levels of operating profit (OP)and earnings (IB) by sub-periodOPIB

1979-1989

1990-1999

2000-2012

Overall

5.45%(6.53)
5.00%(5.69)

4.22%(3.10)
3.87%(3.13)

4.39%(3.00)
2.79%(1.95)

4.68%(6.37)
3.82%(5.34)

Table 8 reports three-month buy-and-hold size-adjusted stock returns to portfolios formed on deciles of OP
and IB for the full sample and by sub-period. At the end of each calendar quarter, firms are sorted into
deciles based on the value of the sorting variable (OP or IB). Size-adjusted buy-and-hold returns for eachstock are calculated over the three months subsequent to the portfolio formation date, and an equalweighted mean return is computed for each portfolio. We then form a zero-investment hedge portfolio foreach variable by going long (short) in the highest (lowest) decile portfolio, and we compute FamaMacBeth t-statistics based on the time-series distribution of the mean hedge portfolio returns. OP is thelevel of operating profit, calculated as quarterly operating profit scaled by average total assets. Operating
profit is defined as revenue minus cost of goods sold and selling, general and administrative expense, as inBall et al. (2015). IB is the level of earnings, calculated as quarterly earnings before extraordinary items
scaled by average total assets.

65

Table 9 Future size-adjusted hedge returns for portfolios formed based on MSUE
and SUE by different subsamples by each decade
Low Volatility

1979-1989

1990-1999

2000-2012

Overall

3.41%(5.82)
3.93%(6.19)

3.63%(5.63)
3.08%(4.96)

2.77%(4.28)
2.27%(3.07)

3.23%(8.88)
3.05%(7.69)

1979-1989

1990-1999

2000-2012

Overall

4.14%(7.08)
3.97%(6.13)

4.08%(4.94)
3.69%(5.64)

2.53%(2.80)
1.51%(1.46)

3.51%(7.56)
2.94%(5.97)

1979-1989

1990-1999

2000-2012

Overall

4.28%(4.83)
3.15%(3.24)

4.65%(5.21)
4.45%(4.85)

3.98%(3.60)
1.06%(0.90)

4.28%(7.49)
2.74%(4.41)MSUE
SUE

Medium Volatility

MSUE
SUE

High Volatility

MSUE
SUE

Table 9 reports three-month buy-and-hold size-adjusted stock returns to portfolios formed on deciles of
MSUE and SUE by ex ante earning volatility tercile group for the full sample and by sub-period. Firms are
sorted into tercile groups each calendar quarter based on whether ex ante earnings volatility is low, medium
or high. Ex ante earnings volatility is calculated as the variance of income before extraordinary items scaledby average assets over the previous eight quarters, following Cao and Narayanamoorthy (2012). At the endof each calendar quarter, firms are sorted into deciles based on the value of the sorting variable (SUE or
MSUE). Size-adjusted buy-and-hold returns for each stock are calculated over the three months subsequent
to the portfolio formation date, and an equal-weighted mean return is computed for each portfolio. We thenform a zero-investment hedge portfolio for each variable by going long (short) in the highest (lowest)
decile portfolio, and we compute Fama-MacBeth t-statistics based on the time-series distribution of themean hedge portfolio returns. MSUE is standardized unexpected operating profit, calculated as quarterly
operating profit per share minus expected operating profit, scaled by the standard deviation of quarterly
operating profit growth over the previous eight quarters. Operating profit is defined as revenue minus costof goods sold and selling, general and administrative expense, as in Ball et al. (2015). Expected operating
profit follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit
growth over the previous eight quarters. SUE is standardized unexpected earnings, calculated as quarterly
earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings
growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed
to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth overthe previous eight quarters.

66

Table 10 Fama-MacBeth regressions of future returns on MSUE and SUE andcontrol variables by sub-period

Intercept
R_MSUE
R_SUE
R_SIZE
R_BM
R_MOM
Adj R2

1979-1989

1990-1999

2000-2012

Overall

0.012(0.51)
0.016(4.32)
0.017(4.15)-0.001(-0.04)0.016(1.52)
0.020(2.12)

0.002(0.09)
0.016(3.88)
0.020(5.61)-0.003(-0.20)0.012(0.76)
0.031(2.74)

0.004(0.16)
0.021(3.27)
0.008(1.99)-0.010(1.95)
0.023(-0.63)0.005(0.31)

0.006(0.42)
0.179(6.07)
0.015(6.21)-0.005(-0.57)0.017(2.41)
0.017(2.31)

0.044

0.026

0.031

0.034

Table 10 reports results for Fama-MacBeth regressions of three-month buy-and-hold raw stock returns on
MSUE and SUE and control variables for the full sample and by sub-period. The “R_” prefix denotes decile
rank transformations performed each calendar quarter for our variables of interest. MSUE is standardized
unexpected operating profit, calculated as quarterly operating profit per share minus expected operating
profit, scaled by the standard deviation of quarterly operating profit growth over the previous eightquarters. Operating profit is defined as revenue minus cost of goods sold and selling, general andadministrative expense, as in Ball et al. (2015). Expected operating profit follows a seasonal random walkwith drift. The drift term is the average of quarterly operating profit growth over the previous eightquarters. SUE is standardized unexpected earnings, calculated as quarterly earnings per share minus
expected earnings per share scaled by the standard deviation of quarterly earnings growth over the previous
eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed to follow a seasonalrandom walk with drift. The drift term is the average of quarterly earnings growth over the previous eightquarters. SIZE is firm size, calculated as the natural log of the market capitalization as of the end of themost recent fiscal quarter for which data are available (in millions). BM is the book-to-market ratio,
calculated as book value of equity divided by market value of equity at the end of the most recent fiscal
quarter for which data are available. MOM is momentum, calculated as the six-month buy-and-hold stock
return ending one month prior to the portfolio formation date.

67

Table 11 Other robustness checks
Panel A: MSUE based on gross profit (sales minus cost of goods sold)1979-1989
1990-1999
2000-2012
Overall
MSUE
SUE

4.01%
(6.37)
3.64%(6.18)

4.11%(7.71)
3.68%(7.28)

Panel B: Scaled by total assets
1979-1989
1990-1999
MSUE
SUE

5.16%
(6.57)
4.84%(6.84)

3.03%(4.13)
2.06%(2.54)

3.66%(9.64)
3.05%(7.69)

2000-2012

Overall

3.32%(4.22)
2.07%(2.41)

4.28%(9.62)
3.58%(7.80)

4.57%(6.62)
4.17%(5.94)

Panel C: Four equally spaced sub-periods
1979-1986
1987-1994
1995-2003
MSUE
SUE

3.63%
(5.52)
3.76%(5.27)

4.62%(6.66)
3.81%(6.49)

4.05%(5.58)
3.58%(5.05)

2004-2012

Overall

2.68%(3.08)
1.20%(1.21)

3.72%(9.90)
3.05%(7.69)

Panel D: Three-day returns around next earnings announcement
MSUE
SUE

1979-1989

1990-1999

2000-2012

Overall

1.30%(10.61)
1.22%(9.88)

0.95%(7.93)
0.56%(3.75)

0.78%(5.58)
0.27%(1.66)

1.00%(12.89)0.66%(7.10)

Table 11 reports three-month buy-and-hold size-adjusted stock returns to portfolios formed on deciles of
MSUE and SUE under various specifications. Panel A defines MSUE as news in gross profit (sales minus
cost of goods sold). Panel B scales news variables by total assets at quarter-end. Panel C employs four
equally spaced sub-periods. Panel D uses three-day cumulative market-adjusted stock returns centered overthe subsequent quarter’s earnings announcement. At the end of each calendar quarter, firms are sorted into
deciles based on the value of the sorting variable (SUE or MSUE). Size-adjusted buy-and-hold returns foreach stock are calculated over the three months subsequent to the portfolio formation date, and an equalweighted mean return is computed for each portfolio. We then form a zero-investment hedge portfolio foreach variable by going long (short) in the highest (lowest) decile portfolio, and we compute Fama-MacBeth
t-statistics based on the time-series distribution of the mean hedge portfolio returns. MSUE is standardized
unexpected operating profit (except in Panel A, where it is gross profit), calculated as quarterly operating
profit per share minus expected operating profit, scaled by the standard deviation of quarterly operating
profit growth over the previous eight quarters. Operating profit is defined as revenue minus cost of goods

68sold and selling, general and administrative expense, as in Ball et al. (2015). Expected operating profit
follows a seasonal random walk with drift. The drift term is the average of quarterly operating profit
growth over the previous eight quarters. SUE is standardized unexpected earnings, calculated as quarterly
earnings per share minus expected earnings per share scaled by the standard deviation of quarterly earnings
growth over the previous eight quarters, as in Jegadeesh and Livnat (2006). Expected earnings are assumed
to follow a seasonal random walk with drift. The drift term is the average of quarterly earnings growth overthe previous eight quarters.

69