earnings-based and accrual-based market anomalies

8/20/2019 Earnings-Based and Accrual-Based Market Anomalies

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Earnings-Based and Accrual-Based Market Anomalies: One Effect or Two?

Daniel W. Collins*Henry B. Tippie Research Chair in Accounting

Paul Hribar

Ph.D. Student

College of Business AdministrationUniversity of Iowa

Iowa City, IA 52246-1000

May 25, 1999

Abstract: This paper investigates whether the accrual pricing anomaly documented by Sloan

(1996) for annual data holds for quarterly data and whether this form of market mispricing isdistinct from the post-earnings announcement drift anomaly. We find that the market appears to

overestimate (underestimate) the persistence of the accrual (cash flow) component of quarterlyearnings and, therefore, tends to overprice (underprice) accruals (cash flows). Moreover, the

accrual (cash flow) mispricing appears to be distinct from post-earnings announcement drift. Ahedge portfolio trading strategy that exploits both forms of market mispricing generates

abnormal returns in excess of those based on unexpected earnings, accruals, or cash flowinformation alone.

Key Words: Market Efficiency, Accruals, Post-Earnings Announcement Drift

JEL Classification: G14, M41

*Corresponding Author. Phone (319) 335-0910; Fax: (319) 335-1956; e-mail: daniel-

[email protected].

We gratefully acknowledge the insightful comments and suggestions made by Kevin Den Adel,Bruce Johnson, Mort Pincus, Shyam Sunder, Richard Tubbs, Charles Wasley, Greg Waymire

and workshop participants at the University of Utah and Carnegie Mellon University.


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Earnings-based and accrual-based market anomalies: one effect or two?

1. Introduction

The efficiency of capital markets has received a great deal of attention in both the finance

and accounting literature. Research in this area most commonly examines market anomalies,

which are defined simply as predictable abnormal returns (Ball 1992). Prominent examples of

market anomalies include post-earnings announcement drift (e.g. Foster et al. 1984; Bernard and

Thomas 1989, 1990), the book-to-market anomaly (Stattman 1980), the earnings-to-price

anomaly (Basu 1977), and more recently the accrual anomaly (Sloan 1996). This study

compares two prominent earnings-based accounting anomalies -- post-earnings announcement

drift and the accrual anomaly -- to determine whether they capture the same market inefficiency

or whether they represent different anomalies that in combination reveal more extreme market

mispricing than has been documented in the literature to date.1

The literature on post-earnings announcement drift demonstrates that prices continue to

drift in the direction of the initial market response to quarterly earnings surprises (standardized

unexpected earnings or SUEs) for at least 120 trading days following the earnings

announcement, with much of the price adjustment occurring in the days surrounding the

subsequent two quarters’ earnings releases (e.g. Foster, Olsen, Shevlin 1984; Rendleman, Jones,

Latane 1987; Bernard and Thomas 1989; 1990; Freeman and Tse 1989). The results of this

stream of research suggest that the market fails to fully appreciate and price the future earnings

1 An example of related research is Greig (1992), who forms portfolios along two dimensions, using both size and

Ou and Penman’s (1989) fundamental analysis statistic, and finds the size effect dominates Ou and Penman’sstatistic. Similarly, Jaffe, Keim and Westerfield (1989) form portfolios on size and earnings/price (E/P), and show

that earnings/price is more robust predictor of future abnormal returns when controlling for the January effect.


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large negative (positive) accrual component? If so, hedge portfolios based on various

combinations of unexpected earnings and accrual rankings should generate abnormal returns that

exceed those that can be earned by exploiting any one of the anomalies in isolation. Analogous

predictions hold for combinations of unexpected earnings and cash flow strategies.

Our results provide evidence of statistically significant abnormal returns associated with

quarterly accrual and cash flow-based trading strategies as well as unexpected earnings-based

strategies. More importantly, the unexpected earnings and accrual (cash flow) strategies appear

to capture different mispricing phenomenon. Thus, combining the earnings-based SUE strategy

with either the accrual or the cash flow-based strategy significantly increases the magnitude of

abnormal returns that can be earned. Moreover, there does not appear to be significant additional

risk involved with the combined strategies in terms of magnitude or frequency of losses. In

summary, we find the unexpected earnings and accruals/cash flow anomalies, when combined,

reveal a more extreme form of market mispricing than previously documented in the literature.

This mispricing can be exploited to generate abnormal returns in excess of those based on

unexpected earnings, accruals or cash flow rankings alone.

The remainder of the paper proceeds as follows. Section two summarizes the extant

research on earnings-based and accrual-based (cash flow) anomalies and develops the

hypotheses. Section three explains sample selection procedures and provides descriptive

statistics. Section four outlines the Mishkin test (1983) and hedge portfolio tests for market

mispricing and gives the empirical results. Section five provides diagnostic tests, and Section six

gives our summary and conclusions and discusses the future research opportunities suggested by

our findings.


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2. Earnings-Based and Accrual-Based Market Anomalies:

Post-earnings announcement drift -- the phenomenon where stock prices continue to drift

in the direction of the initial price response to an earnings announcement -- is one of the most

prominent and perplexing market anomalies documented in the accounting literature. More

generally, post-earnings announcement drift can be viewed as a manifestation of what Bernard,

Thomas and Whalen (1997) label the standardized unexpected earnings (SUE) effect.2 This

broader characterization refers to all market anomalies designed and tested using the SUE metric

as a proxy for unexpected earnings. Therefore, it includes the literature on post-earnings

announcement drift as well as the related work on the time series properties of SUEs (e.g.

Bernard and Thomas 1990, Ball and Bartov 1996). In the current paper, these anomalies are

referred to as earnings-based SUE anomalies.

The central puzzle underlying this group of anomalies is that stock prices act as if

investors use a simple seasonal random walk with drift expectations model in forming quarterly

earnings expectations. However, earnings forecast errors conditional on such a model exhibit

strong and predictable autocorrelation patterns. Thus, when subsequent quarterly earnings are

announced, stock prices adjust to a component of the earnings surprise that should have been

predictable given the past time series of earnings. The upshot of these findings is that stock

prices do not appear to fully impound the implications of current quarterly earnings surprises for

future earnings. While there is some skepticism that SUE-based anomalies truly reflect a

departure from market efficiency, rather than just an artifact of research design, Bernard et al.

2 To compute SUE, expected earnings are first computed using either a seasonal random walk, a first-order

autoregressive process, or the Brown-Rozeff model. For example, Foster et al. (1984) and Bernard and Thomas

(1989) calculate expected earnings as: E(Qi,t) = Qi,t-4 + φi( Qi,t-1 - Qi,t-5 ) + δi . SUEs are then computed as thedifference between actual earnings and expected earnings, scaled by the standard deviation of the past earnings

series.


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(1997) argue that of six prominent anomalies, this group is the most likely to reflect market

mispricing.3

Sloan’s (1996) results suggest the market fails to properly price the accruals component

of earnings. He shows that the market erroneously overestimates the persistence of the accruals

component of annual earnings while underestimating the persistence of the cash flow

component. Moreover, accruals exhibit negative serial correlation or mean reversion tendencies.

Consequently, the market responds as if surprised when seemingly predictable earnings reversals

occur in the following year.

In order to exploit this overreaction to accruals, Sloan forms zero net investment hedge

portfolios that take a long position in firms with the largest negative accruals (standardized by

total assets) and an offsetting short position in firms with the largest positive accruals. This

strategy earns positive annual excess returns of 10.4% and incurs losses in only two of the thirty

years examined. Although Sloan’s hypotheses are stated in terms of accounting accruals, he

notes that similar predictions could be made based on investors underestimating the persistence

of cash flows (p.292, footnote 4). Thus, a trading strategy taking a long position in firms with

the highest level of operating cash flows and an offsetting short position in firms with the lowest

level of operating cash flows also appears to generate positive abnormal returns.

One complication that arises when comparing the earnings-based anomaly with an

accrual or cash flow anomaly is that the SUE-based anomaly has been examined on a quarterly

basis while Sloan documents the accrual-based anomaly only for annual data. Accordingly, to

facilitate a comparison between the unexpected earnings and accrual anomalies, we first

implement the accrual-based and related cash flow-based trading strategies on a quarterly basis

3 The six anomalies tested are: (1) earnings momentum (SUE), (2) returns momentum, (3) book to market ratio, (4)

E/P ratio, (5) Ou and Penman fundamental analysis, (6) Holthausen and Larcker fundamental analysis.


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to determine whether the mispricing documented by Sloan generalizes to quarterly data. In a

quarterly setting, it is unclear how long it will take for any mispricing which may be associated

with quarterly accruals and cash flows to be ‘corrected’ when subsequent quarters’ earnings are

realized. Because post-earnings announcement drift largely manifests itself in the two quarters

immediately following the earnings announcement, the primary tests will focus on the two

quarters immediately following an extreme quarterly accrual or cash flow realization.

A second objective of this study is to determine if the unexpected earnings-based

anomaly is distinct from the accrual or cash flow anomalies and whether one dominates or is

subsumed by the other. We do this by forming portfolios based on rankings along both

dimensions (unexpected earnings and either accruals or cash flows). The resultant contingency

tables allow us to examine conditional and marginal frequencies to determine the degree of

overlap between the unexpected earnings and accruals/cash flow rankings and to test predictions

about the abnormal return performance of various portfolio subgroups within the contingency

table.

If, in fact, unexpected earnings and accruals/cash flow anomalies reflect different forms

of mispricing and are largely independent of one another, we predict that a new trading strategy

which takes advantage of both forms of mispricing will yield even larger abnormal returns than

those associated with each individual anomaly. Specifically, formulating a strategy that takes a

long (short) position in firms with extreme positive (negative) unexpected earnings and extreme

negative (positive) accruals is expected to yield larger abnormal returns than a strategy that

exploits only one of the anomalies. Similarly, a strategy that takes a long (short) position in

firms with extreme positive (negative) unexpected earnings and extreme high (low) operating


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cash flows is expected to dominate a strategy that exploits only the SUE or cash flow strategy

alone.

2. Sample Selection and Data.

Quarterly COMPUSTAT and daily CRSP data are collected for all NYSE/AMEX firms

on either the Primary, Supplementary and Tertiary Industrial File or the Research File over the

years 1988-1997.4 In contrast to Sloan (1996), we estimate quarterly accruals as the difference

between earnings and cash flows from operations.5 Specifically, the accruals component of

quarterly earnings is computed as follows:

Accrualst = Earningst - CFOt (1)

where

Earningst = earnings from continuing operations (COMPUSTAT #8)

CFOt = cash flow from operations (COMPUSTAT #108).6

4 This restriction is necessary in order to compute accruals as in equation (1). COMPUSTAT reports quarterly

“funds flow” data based on either a working capital statement, a source and use of funds statement, or a cash

statement by activity starting in 1984, but these data are often incomplete and/or incorrect with respect to the

‘Working capital changes – Other ’ account (Quarterly item #73). Since this item is necessary to compute cash fromoperations we focus only on the post SFAS 95 period for which cash flow data are reported.5 Sloan (1996) and most prior studies that test the pricing implications of accruals use the period-to-period change in

current asset and current liability accounts, adjusted for changes in cash and reclassifications of currently maturing

portions of long-term debt, to estimate the accrual component of earnings. Drtina and Largay (1985) and Revsine,

Collins and Johnson (1999) demonstrate this balance sheet approach to calculating accruals can lead to seriouserrors, particularly when a firm has been involved in mergers, acquisitions, or divestitures. When these events

occur, the articulation between the changes in working capital balance sheet accounts and the accrual components of

earnings is destroyed. Collins and Hribar (1999) assess the implications of this measurement problem associatedwith the balance sheet approach to estimating accruals for studies on earnings management and the pricing of

discretionary versus non-discretionary accruals.6 For cash flow statement items, COMPUSTAT reports data for the cumulative interim period year-to-date.

Therefore, for the 2nd

, 3rd

, and 4th

quarters, the differences between the reported amounts in quarter t and quarter t-1must be computed to arrive at the correct amount of cash flow from operations for the three months ended in the

current quarter.


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As in Sloan (1996), earnings, cash flows and accruals are all standardized by average total assets

to enhance cross-sectional comparability.

To provide evidence on post-earnings announcement drift and the earnings-based SUE

anomalies, expected earnings are calculated as a seasonal random walk with drift

(Bernard and Thomas’ 1989, 1990):

E (Qi,t) = Qi,t-4 + δi (2)

where δi is the drift term estimated using a minimum of 12 quarters and a maximum of 36

quarters. The SUE for firm i in quarter t is then calculated as:

SUEi,t = (Qi,t – E(Qi,t)) / σ [Qi,t – E(Qi,t)] (3)

where σ [Qi,t – E(Qi,t)] is the standard deviation of the forecast error. Note that equation (2)

excludes the first-order autoregressive term used in Bernard and Thomas (1989). Foster, Olsen,

Shevlin (1984), however, report no substantive difference from using either estimation equation,

and Bernard, Thomas, Whalen (1997) use equation (2) in their analysis of the SUE-based

anomaly.7 Additionally, while the standard deviation of the forecast error is used as a scaling

variable when replicating prior studies, later empirical tests use unexpected earnings

standardized by average total assets in order to maintain a consistent scaling variable across all

variables.8 The final sample of quarterly accruals, operating cash flows and SUE realizations

contains 41,237 firm-quarters.

Tables 1a and 1b provide descriptive statistics for the final sample based on quarterly

partitions of both accruals and cash flows. In Table 1a, quarterly accruals are used as the

7 As a robustness check, expected earnings with a first order autoregressive term are estimated for firms with

continuous data from 1976-1987. The correlation between expected earnings calculated in this manner and expected

earnings calculated using equation (2) is 0.956. Thus, because of the additional data requirements needed to obtain

stable firm specific AR(1) estimates, the seasonal random walk with drift model in equation (2) is used instead in aneffort to increase sample size.8 None of the results are qualitatively affected by the choice of a scaling variable.


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partitioning variable. The negative correlation between accruals and cash flows is evident in the

mean cash flow realizations, which appear to be monotonically decreasing with respect to the

accrual ranking. Similarly, in Table 1b when cash flows are used as the partitioning variable,

mean accrual realizations appear to be monotonically decreasing with respect to the cash flow

ranking. This is confirmed by a strong negative Spearman rank correlation of –0.75 (p-value <

0.001) between accrual and cash flow decile classifications.

The SUE variable appears to be positively correlated with both accruals and cash flows,

although the relation is not linear. This is confirmed by Spearman rank correlations of 0.078 (p-

value < 0.001) between SUE and accrual deciles, and 0.125 (p-value < 0.001) between SUE and

cash flow deciles. It should be noted that because of the documented post-earnings

announcement drift effect, the correlation between unexpected earnings and either accruals or

cash flows might confound a trading strategy based on these latter variables. For example, a

quarterly accrual strategy predicts positive (negative) excess returns for firms in the lowest

(highest) accrual decile. The positive correlation of accruals with SUE, however, implies that

there will be a disproportionate number of firms with large negative accruals that will belong to

the lowest unexpected earnings decile, for which we predict negative excess returns. Thus,

quarterly accrual trading strategies that do not control for unexpected earnings will likely be

understated. With cash flows the relation is just the opposite. Cash flow deciles tend to be

positively correlated with unexpected earnings deciles. Thus, the highest cash flow decile will

tend to be populated by a disproportionate number of firms in the highest unexpected earnings

decile. Accordingly, the abnormal returns to a cash flow strategy that fails to control for

unexpected earnings are likely to be overstated. Our subsequent joint tests control for both SUE

decile membership and accrual/cash flow decile membership.


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With respect to the risk proxies listed in Panel B of Tables 1a and 1b, the extreme accrual

and cash flow deciles on average contain smaller firms as well as firms with slightly higher

betas. Table 1a reveals that the mean betas for the first and tenth accrual deciles are 1.08 and

1.10 respectively, while the mean beta for firms in accrual deciles 2 through 9 is 1.04. Similarly,

mean betas for the first and tenth cash flow deciles are 1.12 and 1.08 respectively, while the

mean beta for firms in cash flow deciles 2 through 9 is 1.08. The average size based on market

value decile ranking for the first and tenth accrual deciles are 6.6 and 6.6 respectively, while the

average for accrual deciles 2 through 9 is 7.7. Similarly, the average market value decile ranking

for the first and tenth cash flow deciles are 6.3 and 7.3 respectively, while the average for deciles

2 through 9 is 7.7.

As noted in Sloan (1996), the symmetric relationship between firm characteristics in the

extreme accrual and cash flow deciles results in negligible exposure to either beta or size pricing

effects when offsetting long and short positions are taken in firms in these extreme deciles.

Nevertheless, to control for potential risk or size-based explanations for our results, returns will

be adjusted for both size and beta before computing excess returns

3.1 Abnormal return calculations

Previous anomaly studies typically calculate abnormal returns using a companion portfolio

approach (e.g. Foster, Olsen and Shevlin, 1984; Bernard and Thomas, 1989; Sloan 1996). Under

this approach, size-adjusted abnormal returns are computed as:

AR i,t = R i,t - R p,t (4)

where AR it = size-adjusted abnormal return for firm i, day t.

R it = the raw return for firm i, on day t.R pt = value weighted mean return on the NYSE/AMEX

size decile that firm i is a member of in the quarter examined.

CRSP computes size decile returns (R p,t) by ranking firms on market capitalization at the


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beginning of the portfolio formation year and then dividing these firms into ten equal portfolios.

The decile classifications are rebalanced annually. As indicated in Table 1, the extreme accrual

(cash flow) deciles (1 and 10) tend to be composed of smaller firms and firms with higher betas.

Moreover, the full sample has a mean beta exceeding 1, indicating that on average, the entire

sample contains somewhat riskier firms (mean beta = 1.04). While there is a known negative

relation between size and beta, basic exploratory tests for the sample show statistically positive

abnormal returns for the entire sample of approximately 1.8% over the two years following

portfolio formation when using equation (4) to compute abnormal returns. This suggests that

implicitly assuming a coefficient of one on R p,t in equation (4) may ignore firm-specific

covariation with the an appropriately chosen benchmark (e.g. the size control portfolio).

Therefore, to control for both size and covariation as sources of risk, the following regression is

estimated over the 60 months prior to the month of the portfolio formation:

where the variables are as defined above. Abnormal returns are then computed as prediction

errors using the following equation:

Using size portfolio average returns as the independent variable controls for the well-known

size effect (Banz, 1981 and Reinganum, 1982). Moreover, allowing the coefficient on the size

portfolio return to vary by firm helps control for firm-specific systematic risk. Using equation

(6) results in abnormal returns that are insignificantly different from zero for the entire sample

over the two years following portfolio formation, suggesting that risk and size effects have been

(6) )ˆˆ( ,,, t pt it i R R AR βα +−=

(5) ,,, t it pt i R R εβα ++=


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effectively controlled.9

Abnormal returns are cumulated from 18 days after the earnings announcement for quarter t

to 17 days after the earnings announcement for quarter t+2 (approximately 120 trading days).10

The hedge portfolio return is computed by subtracting the average abnormal return on the short

portfolio from the average abnormal return on the long portfolio. Thus, for example, taking the

average cumulative abnormal return of firms in the highest SUE decile and subtracting the

cumulative abnormal return of firms in the lowest SUE decile produces the SUE hedge portfolio

return.

4. Tests of Market Mispricing

Following Sloan (1996), we use the Mishkin (1983) test and hedge portfolio tests to

determine whether the market efficiently impounds accounting information (e.g., earnings

surprises (SUEs), accruals, or cash flows from operations) into the price structure.

4.1 Mishkin test

Mishkin (1983) develops a framework to test the rational expectations hypothesis in

macroeconomics. We adapt this framework to examine whether the market’s valuation of

quarterly unexpected earnings, accruals or cash flows rationally anticipates the implications of

these signals for future earnings. Mispricing is indicted if the weight the market assigns to these

items in valuation is significantly larger (smaller) than the weight that these items receive in

predicting future earnings.

9 All primary tests are replicated using both standard size-adjusted returns (constraining the coefficient to equal 1)

as well as market model returns, with no qualitative change in the results.10

Easton and Zmijewski (1993), using a sample of 193,283 10-Q filings, estimate that on average the 10-Q becomes

publicly available 44.7 days after the fiscal quarter-end, or 14.7 days after the earnings announcement date. To provide a conservative estimate of when accrual and cash flow data become publicly available, 18 days after the

earnings announcement date is used as the start of the accumulation period.


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In the present setting, an earnings forecasting equation is combined with a rational

pricing model to yield the following system of equations that are jointly estimated:11

Qt+1 = Qt-3 + α (Qt – Qt-4) + δ + υt (7)

CSAR t+1 = R t+1 - Et(R t+1θt) = β[Qt+1 – Qt-3 - δ - α* (Qt – Qt-4)] + εt, (8)

where θt is the set of information available to the market at the end of period t.

Notice that the forecasting equation is essentially Foster’s (1977) model of quarterly

earnings. Conditional on equation (7) being correctly specified, market efficiency implies that

expected size-adjusted abnormal returns (CSAR t+1 ) should be zero. Therefore, market efficiency

imposes the constraint that α = α*. This nonlinear constraint requires that the market correctly

anticipate the implications of seasonal differences for the most recent quarter for updating

forecasts of next quarter’s earnings. The evidence on post-earnings announcement drift suggests

that the market systematically fails to do this and, therefore, that α* < α.

To test whether Sloan’s (1996) findings of the market’s overpricing of accruals carries

over to a quarterly setting, the current quarter’s earnings (Qt) are decomposed into its accruals

and cash flow components and the above system of equations is rewritten as follows:

Qt+1 = Qt-3 + γ 1Accrualst + γ 2 CFOt – αQt-4 + δ + υt (9)

CSAR t+1 = β[Qt+1 – Qt-3 - δ - γ 1* Accrualst - γ 2* CFOt + α*Qt-4] + εt, (10)

The benefits of this specification are two-fold. First, decomposing the current quarter’s

earnings into accrual and cash flow components allows the coefficients on the two components

to differ, thereby highlighting any differential persistence between the accrual and cash flow

11

For purposes of conducting the Mishkin tests, we initially limit the return accumulation period to 18 days after the

earnings announcement for quarter t to 17 days after the earnings announcement for quarter t+1 (approximately 60days) to parallel the forecasting horizon in the forecasting equation. Extending the return accumulation period to

120 days, does not significantly alter any of the results reported here.


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components. Second, allowing the α coefficient to vary between the two equations in essence

captures the effect of post-earnings announcement drift. Thus, we are able to investigate if

accruals and/or cash flows are systematically mispriced on a quarterly basis after controlling for

post-earnings announcement drift.12

If the market appropriately prices the future earnings

implications of current accruals and cash flows, then we expect γ 1 = γ 1* and γ 2 = γ 2*. If, however,

Sloan’s findings that the market over-prices accruals and under-prices cash flows carries over to

a quarterly setting, then we expect γ 1 < γ 1* and γ 2 > γ 2*. Moreover, if post-earnings

announcement drift exists after allowing the coefficients on accruals and cash flows to differ, we

expect α > α*.

Mishkin shows that the following likelihood ratio statistic is distributed asymptotically

χ2(q) under the null hypothesis of market efficiency:

2*n*ln(SSR c/SSR

u),

whereq = the number of constraints imposed by market efficiency,

n = the number of observations in the sample,

SSR c = the sum of squared residuals from the constrained weighted system, andSSR

u = the sum of squared residuals from the unconstrained weighted system.

Market efficiency is rejected in favor of market mispricing if the likelihood ratio statistic

is sufficiently large; i.e. if imposing the market efficiency constraint substantially increases SSR c

as compared to SSR u.

Table 2 presents the results of the Mishkin tests. Panel A provides the results of

estimating equations (7) and (8). Consistent with the evidence in Bernard and Thomas (1989,

1990) and Ball and Bartov (1996), the market appears to systematically under-react to quarterly

12 An equivalent specification of equation (9) is: Qt+1 = Qt-3 + α1(φ1Accrualst + φ2 CFOt –Qt-4 ) + δ + υt, whichhighlights more readily the post-earnings announcement drift parameter α1. In our equivalent specification given ineqn. (9), γ 1 = α1φ1 and γ 2 = α2φ2.


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earnings surprises. Specifically, the α1 coefficient in the forecasting equation equals 0.306 while

the α1* coefficient in the returns equation equals 0.093. The likelihood ratio statistic for the test

of market efficiency (α1 = α1*) is 43.55, which is significant at the α=0.001 level.

Table 2, Panel B presents the results of estimating equations (9) and (10) where we

decompose the current quarter’s (Qt) earnings into its cash flow and accruals component. This

analysis allows us to determine if the accrual or cash flow mispricing is distinct from the post-

earnings announcement drift effect captured by parameters α1 and α1*. Consistent with Sloan’s

annual results, Panel B shows in the forecasting equation that quarterly accruals (γ 1 = 0.234) are

less persistent than quarterly cash flows (γ 2 = 0.247) with respect to next quarter’s earnings.

However, the difference is less pronounced than on an annual basis. More importantly, the

market appears to systematically over-estimate the persistence of accruals (γ 1* = 0.295), and

under-estimate the persistence of cash flows (γ 2* = 0.134). The likelihood ratio test statistic for

market efficiency, which is a joint test of whether γ 1 = γ 1* and γ 2 = γ 2*, is 113.44 which is

significant at the 0.001 level. Finally, the unexpected earnings coefficient in the forecasting

equation (α1 = 0.301) is still larger than the unexpected earnings coefficient implied in the

returns equation (α1* = 0.092). This result demonstrates that post-earnings announcement drift is

still present after allowing for differential pricing implications of the accrual and cash flow

components. In summary, it appears that post-earnings announcement drift as well as accrual

and cash flow mispricing occurs on a quarterly basis. These results provide the basis for hedge

portfolio tests that attempt to simultaneously exploit both of these anomalies.

4.2 Hedge portfolio tests

SUE and accrual or cash-based hedge portfolios are constructed to determine if the

abnormal returns documented in Bernard and Thomas (1990) can be replicated, and whether or


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not the accrual anomaly documented by Sloan (1996) holds on a quarterly basis. We form hedge

portfolios taking corresponding long and short positions in firms from extreme decile rankings

for either SUE, accruals or cash flows. As noted earlier, the abnormal returns are calculated

from 18 days after the earnings announcement date for quarter t to 17 days after the earnings

announcement date for quarter t+2. SUE deciles are numbered 1 through 10 with SUE1

representing firms with the most negative unexpected earnings and SUE10 representing firms

with the most positive unexpected earnings. Likewise, accrual deciles are numbered 1 through

10 with ACC1 representing firms with the largest income decreasing (negative) accruals and

ACC10 representing firms with the largest income increasing (positive) accruals. For cash flow

portfolios, CFO1 represents firms with the lowest level of operating cash flows and CFO10

represents firms with the highest level of operating cash flows. For the tests using quintiles

rather than deciles, a similar convention is used with 1 and 5 representing the lowest and highest

level of the respective partitioning variable.

To ensure the hedge portfolio strategy is implementable, the entire distribution of the

partitioning variable must be known prior to the portfolio formation date. For example, the

distribution of SUEs is unknown until the last earnings announcement has been made in a given

quarter, and the distributions of accruals and cash flows are unknown until all financial

statements have been publicly released in a given quarter. To mitigate this concern, the decile

cut-offs for the quarterly SUE portfolios are based on the previous quarter’s SUE distribution as

in Bernard and Thomas (1989). For the accrual or cash flow classifications, firms are classified

into deciles or quintiles based on the distribution of accruals from the same quarter of the

previous year. This better reflects the effect of the ‘integral’ approach to quarterly reporting,

whereby certain expenses are estimated during the first three quarters and the cumulative effect


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which is somewhat higher than the frequency of losses reported by Bernard and Thomas (1989).

As noted in their study, this loss frequency compares favorably to that yielded by a unit beta

portfolio, where the incidence of loss quarters is 39% over the years 1935-1968 (see Fama and

MacBeth 1973). Moreover, for the current sample the most extreme loss is only – 2.1%, which

is smaller than the most extreme loss in Bernard and Thomas (1989). Finally, losses occur only

once in adjacent quarters, and the cumulative abnormal return over these two quarters is –2.85%.

Thus, there is not a sustained negative effect of implementing this strategy over an extended

period. The sum of all negative quarters is only -11.6% while the sum of all positive quarters is

approximately 262%. These results corroborate the Bernard and Thomas (1989, 1990) findings

of post-earnings announcement drift that is not likely explained by omitted risk factors.

4.2.2 Examination of quarterly accrual and cash flow anomalies

To provide for a direct comparison between the SUE and accrual (cash flow) anomalies,

we next attempt to replicate Sloan's (1996) annual accrual (cash flow) pricing anomalies with

quarterly data.

Hedge portfolios are formed using quarterly decile classifications based on the magnitude

of accruals or operating cash flows with abnormal returns accumulated over the two quarters

after the portfolio formation date. The hedge portfolios are formed by taking a long position in

ACC1 firms and a short position in ACC10 firms. Figure 2a depicts these two-quarter abnormal

returns associated with each of the thirty-six quarters in our sample period. The average (median)

two-quarter excess return accruing to this strategy is 5.56% (5.50%) with a standard error of

0.771% (significant at .001). As shown, the accruals-based hedge portfolio yields negative

returns in only 5 of 36 quarters or 13.9% of the time. Moreover, the largest negative return is

7.95% and the sum of all negative returns is only 10.7%, while the sum of all positive returns is


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211%. Thus, it appears the market misprices quarterly accruals and this mispricing can be

exploited to generate significantly positive abnormal returns on average.

Figure 2b presents returns to a quarterly cash flow strategy where we take a long position

in CFO10 firms and a short position in CFO1 firms. The mean (median) excess return accruing

to this strategy is 3.77% (3.64%) with a standard error of 0.848% (significant at .01). Figure 2b

reveals that this strategy generates negative excess returns in 7 of 36 quarters or 19.4% of the

time. While considerably less viable than the quarterly accruals strategy, these results

nevertheless suggest that the market fails to fully impound into prices the future earnings

implications of current operating cash flow information.

4.2.3 Joint SUE/accrual and SUE/cash flow strategies.

To this point, the SUE, accruals, and cash flow strategies have been examined

independently of one another. If, indeed, the market’s mispricing of accruals (cash flows) is

distinct from the post-earnings announcement drift phenomenon, then it should be possible to

form trading strategies that capitalize on both forms of mispricing to yield even larger excess

returns than previously documented.

For example, some firms with large positive unexpected earnings shocks may attempt to

mitigate these shocks by creating large negative (income decreasing) accruals that shift expenses

forward or delay revenues. Given the negative autocorrelation tendencies for large accruals

coupled with Sloan’s and our finding of market over-reaction to accruals, these firms would

likely exhibit positive earnings changes and positive abnormal returns in the subsequent

quarter(s). Conversely, firms faced with large negative unexpected earnings shocks may attempt

to smooth earnings and/or make their situation look better by using large income increasing

accruals. If the market over-estimates the persistence of these income increasing accruals, then


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these firms would be expected to exhibit negative abnormal returns in the subsequent

quarter(s).15

Note that in both of these situations, the subsequent market correction for accruals

mispricing reinforces the post-earnings announcement drift that would be present in the absence

of large accruals.

The results of the joint strategies are summarized in two contingency tables in Table 3

and graphically in Figure 3. Panel A of Table 3 presents the abnormal returns earned from

portfolios constructed by grouping firms according to unexpected earnings and accrual

realizations, while Panel B presents results where firms are partitioned by unexpected earnings

and cash flow realizations.

16

The top number in each cell represents the mean cumulative size-

and risk-adjusted return earned over the two quarters subsequent to the portfolio formation date

averaged across the 36 quarters in our sample period. The number in parentheses represents the

number of firm/quarters that comprise each cell. To simplify our presentation, quintiles 2

through 4 have been condensed into a single cell, while the extreme quintiles (1 and 5) are

presented separately. Shaded cells in Table 3 represent cells where both of the partitioning

variables give congruent signals for future earnings and, hence, would be used in a hedge

portfolio strategy. This presentation format allows for several insights into the individual and

joint anomalies.

First, it is immediately apparent that joint strategies will be more profitable than the

individual strategies outlined in the previous section. For example, in the unexpected earnings

15 A similar logic applies to cash flows, although in this case we are looking for firms with large positive unexpected

earnings accompanied by large positive cash flows, versus firms with large negative unexpected earnings and large

negative cash flows.16 The hedge portfolios in this section utilize extreme quintiles instead of deciles. The rationale for switching to

quintiles is that focusing on the intersection of extreme deciles essentially reduces the number of firms in each

portfolio by a factor of 10 which introduces more variability into the estimates. Using the intersection of extreme

SUE/accrual quintiles adds stability to our estimates and enhances the power of our tests. Bernard and Thomas(1989) also utilize quintiles when comparing the continuously balanced strategy to the companion portfolio

approach (p.21, Figure 5).


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and accruals matrix in panel A, the average abnormal returns to the long position (SUE5/ACC1 =

5.84%) and the short position (SUE1/ACC5 = -6.11%) are each roughly as large as the total

hedge portfolio returns generated by the individual strategies reported earlier (6.88% for SUE,

5.56% for accruals). Thus a hedge portfolio strategy formed by taking a long position in

SUE5/ACC1 firms and a short position in SUE1/ACC5 firms will earn an abnormal return of

11.94% that is roughly double that of either individual strategy.

Second, notice that post-earnings announcement drift is not present when the accruals

ranking signals mispricing in the opposite direction. Specifically, firms’ returns in the lowest

SUE group do not drift downward if earnings contain large income decreasing accruals

(SUE1/ACC1 = -0.29%, insignificantly different from zero). Similarly, firms’ returns in the

highest SUE group do not drift upward if earnings contain large income increasing accruals

(SUE5/ACC5=0.76%, insignificantly different from zero). Thus, the effect of post-earnings

announcement drift is affected by the magnitude of accruals associated with the earnings that are

announced.

Finally, note that the average abnormal returns associated with the firms falling into a

given SUE grouping (column) always decrease as we move down the table while the abnormal

returns for a given ACC grouping (row) always increase as we move from left to right in the

table. The consistency in the pattern of abnormal returns across cells suggests that the

differences in returns are more likely due to the mispricing associated with the joint

SUE/accruals signals than to some omitted variable(s).

Similar observations can be made for the abnormal returns associated with unexpected

earnings and cash flow groups reflected in Panel B of Table 3. Consistent with our earlier

evidence that the cash flow mispricing is less pronounced, the average abnormal return to the long


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position (SUE5/CFO5) is 4.10% while average return associated with the short position

(SUE1/CFO1) is -4.43%. Both of these average returns are smaller than their respective

counterparts in Panel A. Nevertheless, a strategy combining unexpected earnings with cash flow

rankings earns average abnormal returns of 8.53% over the subsequent two quarters which is

larger than the abnormal returns generated by either of the individual strategies. Moreover, as

was the case for accruals, the effect of post-earnings announcement drift is affected by the

magnitude of cash flows. As shown, firms with large positive unexpected earnings but large

negative cash flows earn returns insignificantly different from zero (SUE5/CFO1 = 0.99%) as do

firms with large negative unexpected earnings but large positive cash flows (SUE1/CFO5 = -

1.10%).

The effect of accruals mispricing on the magnitude of the post-earnings announcement

drift is even more evident in Figure 3, which plots cumulative daily abnormal returns over 120

trading days starting 18 days past the quarterly earnings announcement. The dark solid lines in

Figure 3 depict the standard post-earnings announcement drift in the highest and lowest SUE

quintiles, yielding a hedge portfolio abnormal return of approximately six percent. The abnormal

return plots change substantially, however, if accruals are added as an additional partitioning

variable. Heavy dashed lines in Figure 3 depict the average abnormal return associated with firm-

quarters in the highest (lowest) SUE quintile and the largest income decreasing (increasing)

accrual quintile. Notice that firms with large positive unexpected earnings that use income-

decreasing accruals to mitigate the magnitude of the positive earnings surprise exhibit

substantially larger upward drift than the average for all SUE5 firms. Similarly, firms with large

negative unexpected earnings that use income-increasing accruals to mitigate the level of the

negative earnings surprise experience a significantly larger downward drift than the average for


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all SUE1 firms. Finally, the thin dashed lines in Figure 3 depict the abnormal returns accruing to

firms in the highest (lowest) SUE quintile and the largest income increasing (decreasing) accrual

quintile. In these cases, where the earnings surprise is driven by a large accruals component,

there is essentially no post-earnings announcement drift. Thus, it is clear that the level of accruals

embedded within an earnings surprise significantly alters the abnormal return behavior over the

following quarters. As shown in Figure 3, the mispricing of accruals can either magnify or

mitigate the drift in prices subsequent to earnings announcements.

Figures 4a and 4b graphically present the two-quarter average abnormal returns to

implementable hedge portfolio strategies based jointly on unexpected earnings and either

accruals or cash flow rankings across the 36 quarters in our sample. Figure 4a depicts quarterly

abnormal returns to the joint SUE and accrual strategy (SUE/ACC strategy). As shown in the

contingency table, taking a long position in firms with positive unexpected earnings and income

decreasing accruals (i.e. SUE5/ACC1) and an offsetting short position in firms with negative

unexpected earnings and income increasing accruals (i.e. SUE1/ACC5) earns average abnormal

returns of 11.94% with a standard error of 1.68% (significant at .0001). Of the 36 quarters in our

sample, 32 quarters yield positive abnormal returns while negative abnormal returns occur in

only four quarters. Moreover, the sum of all positive quarters is 448% while the sum of all

negative quarters is -22.5% with the bulk of the negative abnormal returns coming in the fourth

quarter of 1993 (-15.0%).17 Thus, combining the two individual anomalies clearly generates

additional abnormal returns, with minimal (if any) increase in risk.18

17 One potential explanation is the change in corporate tax rates in the 3rd quarter of 1993 and the subsequent ‘hit’ to

earnings induced accrual based earnings management in the fourth quarter of that year (See Weiss 1999 for an

example).18 Even greater abnormal returns are used when combining extreme deciles, although these are riskier in terms of

standard deviation and frequency of losses (See table 5).


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The abnormal returns accruing to the joint SUE and cash flow strategy (SUE/CFO

strategy) presented in Figure 4b, while significantly positive, are less dramatic than those

observed for the joint SUE/accruals strategy. Taking a long position in firms with large positive

unexpected earnings and high cash flows (i.e. SUE5/CFO5) and a short position in firms with

large negative unexpected earnings and low cash flows (i.e. SUE1/CFO1) yields average

abnormal returns of 8.53%, with a standard error of 1.46% (significant at .001). This strategy is

negative in only 5 of the 36 quarters suggesting no increase in risk over the individual SUE or

CF strategies reported earlier. Moreover, the sum of the excess returns across all positive

quarters is 324%, while the sum across all negative quarters is only –31.9%. Again, the largest

single loss occurs in the fourth quarter of 1993 (-16.9%). Thus, the joint SUE/CFO strategy also

appears to be viable, although not as impressive as the SUE/ACC strategy. This suggests that the

market mispricing of cash flow information is not as pervasive and/or as dramatic as the

mispricing of accruals.

4.3 Regression based portfolio tests

A comparison between the individual and joint strategies can also be made in a regression

framework. To examine the hedge portfolios using a regression approach, the following models

are estimated:

CSAR t, t+2 = α + β1 SUE1 + β2 SUE5 + β3 ACC1 + β4 ACC5 + β5 SUE1*ACC1+ β6 SUE1*ACC5 + β7 SUE5*ACC1 + β8 SUE5*ACC5 (11)

CSAR t, t+2 = α + β1 SUE1 + β2 SUE5 + β3 CFO1 + β4 CFO5 + β5 SUE1*CFO1+ β6 SUE1*CFO5 + β7 SUE5*CFO1 + β8 SUE5*CFO5 (12)

where SUE1(5) = 1 if firm j is in the lowest (highest) SUE quintile, zero otherwise.

ACC1(5) = 1 if firm j is in the lowest (highest) accrual quintile, zero otherwise.CFO1(5) = 1 if firm j is in the lowest (highest) cash flow quintile, zero otherwise.


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The rationale for equations (11) and (12) is that the indicator variables pick up the mean

effect of being in a specific quintile. Thus, by taking appropriate linear combinations of the

parameters, different hedge portfolios can be examined. The benefit to this approach is that

additional factors posited to affect the excess returns can be added as control variables to rule out

other potential explanations for our results.19

Furthermore, because the regression equation

examines the effect of SUE, accruals, and cash flows simultaneously, estimated abnormal returns

can be computed after controlling for the correlation among the partitioning variables.

Equations (11) and (12) are estimated on a quarterly basis and the mean values over time

are reported in Table 4. To mitigate potential bias, t-statistics are computed using the sampling

distribution of the parameter estimates over time, and p-values are computed using a t-

distribution with 35 degrees of freedom.

The main effects that are presented would be the same as the accrual hedge portfolio if

there were no significant interaction effects (although based on quintiles instead of deciles).

With respect to the accrual and cash flow strategy in Panel A, the magnitude of the joint SUE

and accrual strategy (11.94%) is comprised of both SUE and accrual main effects. Moreover, as

indicated by the β6 coefficient, there is a significantly negative interaction effect of

approximately –1.89% for firms with large negative unexpected earnings and large income

increasing accruals. Similarly, in Panel B the magnitude of the joint SUE and cash flow strategy

(8.53%) is comprised of both SUE and cash flow main effects, and there is a significantly

negative interaction (β5 = –2.10%) for firms with large negative unexpected earnings and large

negative cash flows. We also see a significantly negative interaction (β7 =–2.00%) for firms

19

As a robustness check, the natural log of the ratio of book value of common equity to market value of commonequity is added as a control variable. This variable is statistically significant in both the accrual and cash flow

regressions, however abnormal returns accruing to the individual and joint strategies are essentially unchanged.


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with large positive unexpected earnings but large negative cash flows. Thus, it appears that

significant post-earnings announcement drift documented in earlier studies is mitigated to a large

degree when the mispricing associated with accruals (cash flows) moves in the opposite

direction.

5. Diagnostic Tests

5.1 Sensitivity to the choice of a starting date.

As mentioned previously, 18 days after the earnings announcement was chosen as the start

of the accumulation period. While this is a conservative estimate, there may be some concern

that the returns to either the cash flow based or accrual based strategy are earned by trading on

information before it is actually known. If this is indeed the case, the bulk of the abnormal

returns should accrue at the start of the accumulation period when seemingly unknowable

information is being traded on. To explore this issue, daily cumulative abnormal returns for the

joint SUE and accrual strategy are plotted over the following two quarters. The results (not

presented) show a smooth upward and downward drift that appears unaffected by the starting

date chosen. Results for the joint SUE and cash flow anomaly are qualitatively similar.

5.2 Continuously balanced strategy

A problem in formulating an implementable trading strategy arises when using the hedge

portfolio approach. As noted in Bernard and Thomas (1989), implementing this strategy requires

taking new positions in size control portfolios on a daily basis. This raises the question of

whether the abnormal returns would remain under an easily implementable zero investment

strategy. To address this concern, Bernard and Thomas (1989) also use an alternative to the

companion portfolio approach. Specifically, they compute abnormal returns using what they call


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a continuously balanced approach. This approach begins by identifying all firms that announce

earnings on a given day. If firms in both extreme SUE, accruals or cash flow deciles report

earnings on the same day, appropriately weighted offsetting long and short positions are taken in

these firms. If firms from only one extreme portfolio announce on a given day, then no position

is taken in these firms until an offsetting match in the other extreme portfolio becomes available.

Buy and hold returns are then computed on each individual hedge portfolio from that point

forward.20

To investigate this concern, the individual accrual strategy and the joint SUE and accrual

strategy (based on quintiles) are replicated using the continuously balanced approach.

Untabulated results show 120-day abnormal returns of 7.84% for the individual accrual strategy,

and 12.91% for the joint SUE and accrual strategy. These returns are of comparable magnitude

to the returns accruing to the equally weighted hedge portfolio approach reported in Table 5.

Thus it appears that equivalent abnormal returns could be earned using a much less costly

approach.

5.3

Returns around subsequent quarterly earnings announcement dates

A final robustness check examines the three-day returns surrounding the subsequent

quarterly earnings announcement dates. Bernard, Thomas, and Whalen (1997) argue that

abnormal returns clustered at future earnings announcement dates are less likely to be due to

omitted risk factors and more likely to reveal a market anomaly with respect to accounting

signals. To examine this issue, three-day returns beginning two days prior to the COMPUSTAT

earnings announcement date are computed for the following two quarterly announcements.

Results show that while the earnings announcement period constitutes 5% (6/120days) of the

20 For the most restrictive strategy (i.e. the joint SUE and Accrual strategy), 90.5% of the time a match becomes

available within 3 days. For the individual accrual strategy, a match is found 98.5% of the time.


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total accumulation period, 22.0% of the abnormal returns for the quarterly accrual strategy and

21.9% of the abnormal returns for the joint SUE and accrual strategy are earned in the three-day

windows surrounding the next two quarters’ earnings announcements. While these percentages

are lower than the 40% reported by Sloan (1996), the difference is likely due to the difference in

sample periods and the rise in voluntary pre-earnings disclosures being issued by firms in recent

years, particularly when earnings are expected to fall below analysts' expectations. For example,

The Wall Street Journal (June 23, 1997) reports that earnings guidance rose from around 250

firms in 1994 to about 2000 firms in 1996 (and more than 700 firms through the first quarter of

1997). Given that our sample spans 1988-1997, earnings announcements during this time period

are more likely to be preempted by pre-announcements than in Sloan’s sample, which ends in

1991.

As a check on the validity of earnings pre-announcements as a potential explanation,

returns to the long and short portfolios are examined separately, as it is more likely that bad news

is disclosed early (e.g. Skinner 1994; The Wall Street Journal 1997). Results show that on

average, 46.3% of returns to the long position are earned in the three-day windows surrounding

the future earnings announcement dates, while essentially zero abnormal returns are earned in the

three-day windows for the short (bad news) position. Thus, it appears that voluntary

predisclosure of information by management, particularly for “bad news” situations, may be

affecting the returns earned at the earnings announcement date.

6. Summary and Conclusion

This paper provides evidence that the market systematically misestimates the future

earnings implications of the accrual and cash flow components of current quarterly earnings.


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The market’s over-estimation (under-estimation) of the persistence of accruals (cash flows) leads

to mispricing that can be exploited to generate significant positive abnormal returns over the two

quarters after the portfolio formation date. The accruals/cash flow mispricing appears to be

largely independent of the post-earnings announcement drift phenomenon widely documented in

previous literature. Thus, trading on the joint SUE and accruals (cash flow) signals earns greater

abnormal returns than trading on any of the individual anomalies. Moreover, there appears to be

little or no additional risk associated with trading on these joint signals.

Our findings have several important implications for extant research and suggest a

number of areas for future research. Information content studies relating security returns to

accruals and cash flows assume correct market pricing of earnings components. Yet evidence in

Sloan (1996) and this paper suggests the market systematically overprices (underprices) the

accrual (cash flow) component of earnings. Thus, studies demonstrating a stronger

contemporaneous association between returns and accrual earnings relative to cash flows should

be reexamined in light of this mispricing. Future information content studies should explicitly

control for this mispricing by allowing for possible price reversals in the research design.

Much of the extant earnings forecasting literature relies on time series models that

extrapolate past earnings. Our findings suggest that one may be able to exploit the different

persistence properties of accrual and cash flow components of earnings to improve upon extant

forecasting models that rely exclusively on past earnings.

Understanding sources of intertemporal differences in market anomalies is largely

unexplored in the literature. Our results also show that post-earnings announcement drift is

exaggerated or mitigated based on the level of accruals embedded within the earnings surprise.

This finding suggests that intertemporal differences in post-earnings announcement drift are


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likely due, in part, to intertemporal differences in the magnitude of accruals contained within

earnings surprises.

While our results suggest that accruals, in general, are mispriced, we do not address

whether there is greater mispricing for certain types of accruals (e.g., current versus long-term

accruals) which warrants further attention. Moreover, investigating whether the discretionary

component of accruals suffers from greater mispricing than the non-discretionary component is

another interesting area for future research. Finally, understanding what causes the market to fail

to fully impound the future earnings implications of current accrual (cash flow) signals is a

perplexing question that deserves further investigation.


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Table 1a. Mean (median) values of selected characteristics for ten portfolios formed quarterly on the magnitude of accruals. Sampleconsists of 41,237 firm quarters over the years 1988-1997.

Quarterly Portfolio Accrual Ranking

I ncome decreasing I ncome increasing

1 2 3 4 5 6 7 8 9 10

Panel A: Components of Earnings

Accruals -0.105(-0.073)

-0.041(-0.035)

-0.027(-0.025)

-0.019(-0.017)

-0.013(-0.012)

-0.007(-0.006)

-0.001(-0.001)

0.007(0.007)

0.020(0.020)

0.070(0.054)

Cash Flows 0.082(0.069)

0.047(0.045)

0.037(0.036)

0.030(0.029)

0.024(0.023)

0.019(0.018)

0.014(0.012)

0.007(0.006)

-0.005(-0.006)

-0.051(-0.039)

Standardized Unexpected Earnings (SUE) -1.39(-0.066)

-0.259(0.048)

-0.078(0.066)

0.048(0.094)

0.084(0.083)

0.171(0.122)

0.188(0.132)

0.260(0.145)

0.292(0.147)

0.547(0.207)

Panel B: Risk Proxies

Beta 1.08(1.08)

1.05(1.05)

1.03(1.02)

1.02(1.02)

1.00(1.01)

1.01(1.02)

1.03(1.04)

1.07(1.06)

1.09(1.09)

1.10(1.09)

Mean Size Decile 6.6 7.4 7.8 8.0 8.0 7.9 7.7 7.6 7.3 6.6Median Market-to-Book 1.70 1.80 1.86 1.84 1.78 1.74 1.74 1.80 1.83 1.81

Accruals = Earnings minus cash from operations, scaled by average total assets.

Cash Flows = Cash from operations, as reported on the statement of cash flows, scaled by total assets.SUE = Seasonally differenced quarterly earnings divided by the standard deviation of the forecast error.Beta = firm specific coefficients from a time series regression of the individual firm return over the risk free rate on the corresponding size decile return over the risk free rate. Regressions

are based on the 60 months prior to the month of the earnings announcement.Size Decile = average decile based on classifications of all NYSE/AMEX firms, where 1 contains the smallest firms and 10 contains the largest firms.Mkt-to-Book = Ratio of market Value of Common Equity to Book Value of Common Equity, measured at the end of the prior quarter.


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Table 1b. Mean (median) values of selected characteristics for ten portfolios formed quarterly on the magnitude of operating cash

flows. Sample consists of 41,237 firm quarters over the years 1988-1997

Quarterly Portfolio Cash Flow Ranking

1 2 3 4 5 6 7 8 9 10


Accruals 0.053

(0.049)

0.012

(0.016)

0.002

(0.004)

-0.004

(-0.003)

-0.010

(-0.007)

-0.014

(-0.012)

-0.019

(-0.017)

-0.024

(-0.022)

-0.035

(-0.031)

-0.077

(-0.057)

Cash Flows -0.060(-0.046)

-0.011(-0.009)

0.002(0.003)

0.010(0.011)

0.017(0.017)

0.024(0.023)

0.031(0.030)

0.039(0.038)

0.052(0.049)

0.102(0.082)

Standardized Unexpected Earnings

(SUE)

-0.200(-0.039)

-0.158(0.041)

-0.112(0.075)

-0.047(0.066)

-0.040(0.069)

-0.040(0.106)

-0.000(0.106)

0.101(0.138)

0.121(0.171)

0.245(0.221)


Beta 1.12(1.11)

1.09(1.09)

1.06(1.06)

1.03(1.04)

1.01(1.02)

1.03(1.04)

1.00(1.01)

1.03(1.02)

1.05(1.05)

1.08(1.05)

Mean Size Decile 6.3 6.9 7.4 7.6 7.8 8.0 8.1 8.1 7.9 7.3Median Market-to-Book 1.61 1.54 1.52 1.56 1.66 1.81 1.94 2.09 2.17 2.22


Cash Flows = Cash from operations, as reported on the statement of cash flows, scaled by total assets.SUE = Seasonally differenced quarterly earnings divided by the standard deviation of the forecast error.Beta = firm specific coefficients from a time series regression of the individual firm return over the risk free rate on the corresponding size decile return over the risk free rate. Regressions

are based on the 60 months prior to the month of the earnings announcement.Size Decile = average decile based on classifications of all NYSE/AMEX firms, where 1 contains the smallest firms and 10 contains the largest firms.Mkt-to-Book = ratio of market Value of Common Equity to Book Value of Common Equity, measured at the end of the prior quarter.


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Table 2. Results from non-linear generalized least-squares estimation of the stock price reaction

to information in current financial statement information. CSAR is the cumulative size and risk adjusted return following the release of financial statements; Qt+i equals earnings for quarter i;

Accruals equals earnings minus cash from operations; CashFlow equals cash from operations asreported on the statement of cash flows. Variables are scaled by average total assets to ensure

cross-sectional comparability.

Panel A: Post-Earnings Announcement Drift Specification

Qt+1 = Qt-3 + α0 + α1(Qt - Qt-4) + vt+1CSAR t+1 = β0 + β1(Qt+1 – Qt-3 - α0 - α1*(Qt - Qt-4)) + εt+1

Parameter Estimate Asymptotic Std. Error

α1 0.306 0.005

α1*

0.093 0.033β1 1.819 0.001

Test of Market Efficiency α1 = α1*

Likelihood ratio statistic 43.55

Marginal Significance level 0.001

Panel B: Decomposition of current earnings into accrual and cash flow components

Qt+1 = Qt-3 + γ 0 + γ 1Accrualst + γ 2CashFlowt - α1Qt-4 + vt+1CSAR t+1 = β0 + β1(Qt+1 - Qt-3 - γ 0 - γ 1*Accrualst - γ 2*CashFlowt + α1*Qt-4) + εt+1

Parameter Estimate Asymptotic Std. Error

γ 1 0.234 0.006 γ 1* 0.295 0.037γ 2 0.247 0.006

γ 2* 0.134 0.037α1 0.301 0.007

α1* 0.092 0.042

β11.776 0.058

Test of Market Efficiency γ 1 = γ 1* and γ 2 = γ 2*

Likelihood ratio statistic 113.44Marginal Significance level 0.001


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Table 3. Abnormal returns for a holding period of two quarters based upon quarterly rankings

of unexpected earnings (SUE) and either accruals or cash flows. Quintiles 2 through 4 have beencondensed into one cell. Mean values reported are computed as a mean across 36 quarters. The

number of observations per cell is reported in parentheses and is based on the total number of firm quarters in a given cell spanning the years 1988-1997. Shaded cells represent observations

that have congruent signals for future unexpected earnings.

Panel A: Unexpected Earnings (SUE) and Accrual classifications

SUE Quintile

SUE1 SUE2-4 SUE5

ACC1

-0.29%(2612)

1.70%*(3751)

5.84%*(1705)

2.16%*(8068)

Accrual Quintile

ACC2-4

-2.55%*

(4029)

0.09%

(16764)

3.08%*

(4156)

0.10%

(24949)

ACC5

-6.11%*(1302)

-1.57%*(4792)

0.76%(2126)

-1.74%*(8220)

-2.40%*

(7943)

0.02%

(25307)

3.11%*

(7987)

Panel B: Unexpected Earnings (SUE) and Cash Flow classifications

SUE Quintile

SUE1 SUE2-4 SUE5

CFO1

-4.43%*(2081)

-0.45%(4253)

0.99%(1760)

-1.19%*(8094)

Cash Flow Quintile

CFO2-4

-1.89%*

(4409)

-0.00%

(16328)

3.44%*

(4133)

0.23%

(24870)

CFO5

-1.10%(1453)

0.61%(4726)

4.10%*(2094)

1.22%*(8273)

-2.40%*

(7943)

0.02%

(25307)

3.11%*

(7987)

* Significantly different from zero at α=0.01 level


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Table 4. Regression-based estimates of size-adjusted abnormal returns over the two quarters

following a quarterly earnings announcement. Portfolios formed using extreme SUE and Accrualquintiles, ranked on a quarterly basis. Return accumulation begins 18 days after the quarter t

earnings announcement date and ends 17 days after the quarter t+2 earnings announcement.Parameter estimates are computed as the mean estimate across all 36 quarters, and t-statistics are

computed using the sampling distribution of the parameter estimates. Significance levels are based on a t-distribution with 35 degrees of freedom.

Panel A: Joint SUE and Accruals Regression Parameter Estimates

CSAR t, t+2 = α + β1t SUE1 + β2t SUE5 + β3t ACC1 + β4t ACC5 +β5t SUE1*ACC1 + β6t SUE1*ACC5 + β7t SUE5*ACC1 + β8t SUE5*ACC5 + εt

Parameter αα ββ1 ββ2 ββ3 ββ4 ββ5 ββ6 ββ7 ββ8

Mean Estimate: .0009 -.0265 .0298 .0160 -.0166 -.0066 -.0189 .0114 -.0065

(t -Statistic) (0.19) (3.99)** (4.20)** (2.95)** (3.32)** (0.55) (1.66)* (0.802) (0.60)

Accrual and SUE hedge portfolio: 11.94% (t=7.13)**

Panel B: Joint SUE and Cash Flow Regression Parameter Estimates

CSAR t, t+2 = α + β1t SUE1 + β2t SUE5 + β3t CFO1 + β4t CFO5 +β5t SUE1*CFO1 + β6t SUE1*CFO5 + β7t SUE5*CFO1 + β8t SUE5*CFO5 + εt

Parameter αα ββ1 ββ2 ββ3 ββ4 ββ5 ββ6 ββ7 ββ8

Mean Estimate: .0001 -.0187 .0345 -.0044 .0061 -.0210 .0017 -.0200 .0004

(t -Statistic) (0.01) (2.65)** (4.94)** (0.75) (1.23) (1.90)* (0.16) (1.67)* (0.05)

Cash flow and SUE hedge portfolio: 8.53% (t=5.83)

* Significant at α = 0.05, one tailed** Significant at α = 0.01, one tailedResults are based upon 41,237 observations.


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Table 5. Cumulative size and risk adjusted returns accruing to hedge portfolios. Means are

computed as a mean across 36 fiscal quarters over the years 1988-1997. Individual strategies are based on taking offsetting long and short positions in the appropriate extreme deciles. Joint

strategies are computed using intersection of extreme deciles as well as extreme quintiles.

Hedge portfolio partition 1Q – Abnormal Returns(Standard Deviation) 2Q – Abnormal Returns(Standard Deviation)

I ndividual strategies:

SUE 4.24%*

(4.63%)

6.88%*

(6.03%)

Accrual 2.76%*

(2.98%)5.56%*

(4.63%)

Cash Flows 2.09%*

(3.48%)

3.77%*

(5.06%)

Join t Strategies :Accruals & SUE

Deciles 9.35%*

(12.94%)15.87%

*

(17.48%)

Quintiles 7.05%*

(6.01%)

11.94%*

(10.06%)

Cash Flows & SUE

Deciles 5.83%

*

(7.23%) 12.73%

*

(14.05%)

Quintiles 5.05%*

(5.54%)8.53%

*

(8.77%)

* Significantly different from zero at the 0.01 level


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-0.3

-0.2

-0.1

0

0.1

0.2

0.3

8 8

8 9

8 9

9 0

9 0

9 1

9 1

9 2

9 2

9 3

9 3

9 4

9 4

9 5

9 5

9 6

9 6

9 7

Year

A b

n o r m a l R e t u r n

Figure 1. Two-quarter abnormal returns to a strategy taking a long

(offsetting short) position in firms with the largest positive (negative)

unexpected earnings. Based on 36 quarters over the years 1988-1997.

Mean Ret

6.88%

Sum Pos

262%

Sum Neg

-11.6%

Minimum

-2.10%


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Table 1a. Mean (median) values of selected characteristics for ten portfolios formed quarterly on the maconsists of 41,237 firm quarters over the years 1988-1997.

Quarterly Portfolio Accrual Ranking

Income decreasing1 2 3 4 5 6 7


Accruals -0.105(-0.073)

-0.041

(-0.035)

-0.027

(-0.025)

-0.019

(-0.017)

-0.013

(-0.012)

-0.007

(-0.006)

-0.001

(-0.001

Cash Flows 0.082(0.069)

0.047

(0.045)

0.037

(0.036)

0.030

(0.029)

0.024

(0.023)

0.019

(0.018)

0.014

(0.012


(SUE)

-1.39

(-0.066)

-0.259

(0.048)

-0.078

(0.066)

0.048

(0.094)

0.084

(0.083)

0.171

(0.122)

0.188

(0.132


Beta 1.08(1.08)

1.05

(1.05)

1.03

(1.02)

1.02

(1.02)

1.00

(1.01)

1.01

(1.02)

1.03

(1.04)

Mean Size Decile 6.6 7.4 7.8 8.0 8.0 7.9 7.7

Median Market-to-Book 1.70 1.80 1.86 1.84 1.78 1.74 1.74

Accruals = Earnings minus cash from operations, scaled by average total assets.Cash Flows = Cash from operations, as reported on the statement of cash flows, scaled by total assets.

SUE = Seasonally differenced quarterly earnings divided by the standard deviation of the forecast error.Beta = firm specific coefficients from a time series regression of the individual firm return over the risk free rate on the corresponding size decile r

are based on the 60 months prior to the month of the earnings announcement.

Size Decile = average decile based on classifications of all NYSE/AMEX firms, where 1 contains the smallest firms and 10 contains the largest firms.

Mkt-to-Book = Ratio of market Value of Common Equity to Book Value of Common Equity, measured at the end of the prior quarter.


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Table 1b. Mean (median) values of selected characteristics for ten portfolios formed quarterly on the m

flows. Sample consists of 41,237 firm quarters over the years 1988-1997.

Quarterly Portfolio Cash Flow Rankin

1 2 3 4 5 6 7Panel A: Components of Earnings

Accruals 0.053(0.049)

0.012

(0.016)

0.002

(0.004)

-0.004

(-0.003)

-0.010

(-0.007)

-0.014

(-0.012)

-0.019

(-0.017

Cash Flows -0.060(-0.046)

-0.011

(-0.009)

0.002

(0.003)

0.010

(0.011)

0.017

(0.017)

0.024

(0.023)

0.031

(0.030


(SUE)

-0.200

(-0.039)

-0.158

(0.041)

-0.112

(0.075)

-0.047

(0.066)

-0.040

(0.069)

-0.040

(0.106)

-0.000

(0.106


Beta 1.12(1.11)

1.09

(1.09)

1.06

(1.06)

1.03

(1.04)

1.01

(1.02)

1.03

(1.04)

1.00

(1.01)

Mean Size Decile 6.3 6.9 7.4 7.6 7.8 8.0 8.1

Median Market-to-Book 1.61 1.54 1.52 1.56 1.66 1.81 1.94


Cash Flows = Cash from operations, as reported on the statement of cash flows, scaled by total assets.

SUE = Seasonally differenced quarterly earnings divided by the standard deviation of the forecast error.Beta = firm specific coefficients from a time series regression of the individual firm return over the risk free rate on the corresponding size decile r

are based on the 60 months prior to the month of the earnings announcement.Size Decile = average decile based on classifications of all NYSE/AMEX firms, where 1 contains the smallest firms and 10 contains the largest firms.Mkt-to-Book = ratio of market Value of Common Equity to Book Value of Common Equity, measured at the end of the prior quarter.


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-0.3

-0.2

-0.1

0

0.1

0.2

0.3

8 8

8 9

8 9

9 0

9 0

9 1

9 1

9 2

9 2

9 3

9 3

9 4

9 4

9 5

9 5

9 6

9 6

9 7

Year

A b n o r m a l R e t u r n

Figure 2a. Two-quarter abnormal returns to a strategy taking a long

(offsetting short) position in firms with the largest income increasing

(income decreasing) accruals. Based on 36 quarters over the years

1988-1997.

Mean Ret

5.56%

Sum Pos

211%

Sum Neg

-10.7%

Minimum

-7.95%

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

8 8

8 9

8 9

9 0

9 0

9 1

9 1

9 2

9 2

9 3

9 3

9 4

9 4

9 5

9 5

9 6

9 6

9 7

Year

A b n o

r m a l R e t u r n

Figure 2b. Two-quarter abnormal returns to a strategy taking a long

(offsetting short) position in firms with the largest positive (negative)

operating cash flows. Based on 36 quarters over the years 1988-1997.

Mean Ret

3.77%

Sum Pos158%

Sum Neg

-20.3%

Minimum

-7.07%


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-0.08

-0.06

-

earnings-based and accrual-based market anomalies

Documents