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BlackwellPublishingLtdOxford,UKACFIAccountingand Finance0810-53911467-629XTheAuthorsJournalcompilation 2008 AFAANZXXX

ORIGINAL ARTICLE

A.S. Ahmedet al./AccountingandFinanceXX (2008)XXXXXXA.S. Ahmedet al.

Earnings characteristics and analysts differential

interpretation of earnings announcements:

An empirical analysis

Anwer S. Ahmed

a

, Minsup Song

b

, Douglas E. Stevens

c

a

Mays Business School, Texas A&M University, College Station, 77843-4113, USA

b

College of Business Administration, Sogang University, Seoul, 121-742, South Korea

c

College of Business, Florida State University, Tallahassee, 32306-1110, USA

Abstract

This study provides empirical evidence on factors that drive differential inter-

pretation of earnings announcements. We document that Kandel and Pearsons

forecast measures of differential interpretation are decreasing in proxies for

earnings quality and pre-announcement information quality. This evidence yields

new and useful insights regarding which earnings announcements are less likely

to generate newfound disagreement among analysts and investors. Recent research

suggests that investor disagreement can increase investment risk, increase the cost

of capital, and cause stock prices to deviate from fundamental value. Therefore,

our results support prior intuition that increasing the quality of earnings and pre-announcement information can improve the efficiency of capital markets.

Key words

: Differential interpretation; Earnings announcements; Analyst forecasts

JEL classification

: G14, M41

doi

: 10.1111/j.1467-629X.2008.00292.x

1. Introduction

Researchers have long recognized the potential for public announcements to

be differentially interpreted by market participants (Bachelier, 1900). However,

We thank Orie Barron, David Harris, Gerry Lobo, Stan Markov, Yongtae Kim and SenyoTse; workshop participants at Concordia University, University of Idaho, SeongkyunkwanUniversity, Syracuse University, Wilfried Laurier University and York University; and

participants at the 2006 American Accounting Association Annual Meeting for their helpfulcomments. This study is based on Professor Songs dissertation at Syracuse University.We thank Thomson Financial I/B/E/S for providing data on analysts earnings forecasts.

Received 26 June 2008; accepted 8 November 2008 by Robert Faff (Editor)

.

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it is only recently that researchers have provided empirical evidence of this

differential interpretation (see e.g. Kandel and Pearson, 1995; Bamber

et al.

,

1999). However, to date, the driving factors behind differential interpretation

remain unknown. We address this gap in the literature by empirically investigat-ing factors that are likely to drive the differential interpretation of earnings

announcements.

To develop potential determinants of differential interpretation, we use

intuition from the rational expectations model of Kim and Verrecchia (1994).

Rational expectations models characterize investor information as comprised of

public and private information signals that are normally distributed with mean

and precision (reciprocal of variance).

1

In these models, precision captures the

quality or informedness of the information signal (Verrecchia, 2001). Kim and

Verrecchias (1994) model suggests that differential interpretation of an earningsannouncement is decreasing in: (i) the precision of the earnings announce-

ment; (ii) the precision of pre-announcement information; and (iii) the cost of

acquiring private information. Intuitively, these three information constructs

reduce differential interpretation by reducing the return to acquiring private

information to interpret the earnings idiosyncratically. Therefore, we select

earnings and firm characteristics that are likely to proxy for these information

constructs. In particular, we use earnings predictability, earnings persistence,

earnings surprise and negative earnings to proxy for the precision of the earnings

announcement. We use firm size to proxy for the quality of pre-announcement

information and, controlling for firm size, use analyst coverage and price-to-book

ratio to proxy for the cost of acquiring private information.

2

Following Kandel and Pearson (1995), we measure differential interpretation

by comparing pairs of analysts forecast revisions of annual earnings around the

preceding quarterly earnings announcements. Financial analysts represent an

important group of market agents who are motivated to take full account of others

information and acquire private information where it is profitable (Kandel and

Pearson, 1995, p. 833). Therefore, their forecasts of earnings provide an ideal

dataset by which to examine factors driving differential interpretation of earn-

ings announcements. Kandel and Pearson (1995) present a model of Bayesianupdating with two agents who observe a public announcement. They show that

if two traders have identical likelihoods regarding the public announcement,

1

Rational expectations models assume that investor expectations are conditional on theprecision of their information and the price at which markets clear (Verrecchia, 2001).

2

We argue that with firm size in the model, analyst coverage and the price-to-book ratio

are likely to capture the cost of private information acquisition in Kim and Verrecchias(1994) model. In particular, we argue that after controlling for firm size, analyst cover-age is likely to capture the return to private information acquisition and the price-to-book ratio is likely to capture the difficulty of valuing a firm with high intangible assets(see Section 2.2).

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their mean beliefs will never flip or diverge if they move in different directions

as a result of the announcement. Upon finding a relatively high proportion of

forecast pairs that exhibit these patterns around quarterly earnings announce-

ments, Kandel and Pearson (1995) conclude that earnings announcements aredifferentially interpreted.

Using logistic regression models and analyst forecast data from 19832004

inclusive, we examine the proportion of analyst forecast revision pairs exhibit-

ing Kandel and Pearsons differential interpretation patterns around quarterly

earnings announcements. We find that Kandel and Pearsons forecast measures

for differential interpretation are: (i) negatively related to earnings predictability,

firm size and price-to-book ratio, and (ii) positively related to earnings surprise,

negative earnings and analyst coverage. Our findings support Kim and Verrec-

chias (1994) intuition that differential interpretation is decreasing in earningsquality, pre-announcement information quality, and the cost of acquiring private

information.

This evidence yields new and useful insights. Theoretical and empirical

research suggests that investor disagreement caused by information asymmetry

can increase investment risk and, thereby, increase a firms cost of capital (Kim

and Verrecchia, 1994, 1997; Verrecchia, 2001; Botosan

et al.

, 2004).

3

In addition,

research suggests that differences in investors beliefs, together with short

selling constraints, can cause stock prices to deviate from their fundamental

value (Miller, 1977; Harrison and Kreps, 1978) and potentially form market

bubbles (Scheinkman and Wei, 2003; Hong

et al.

, 2006). The conventional

wisdom has been that earnings announcements reduce information asymmetry

by substituting a public announcement for private pre-announcement information.

4

However, empirical evidence suggests that some earnings announcements

generate newfound disagreement by generating additional private information

gathering (Kandel and Pearson, 1995; Bamberet

al

., 1999). We extend this line

of research by documenting which earnings announcements are less likely to

generate newfound disagreement. Our results support prior intuition that increas-

ing the quality of earnings and pre-announcement information can improve the

efficiency of capital markets.

3

Conceptually, the cost of capital increases when some subset of investors gain an informa-tional advantage over other investors and those with inferior information face an adverse-selection problem (Kim and Verrecchia, 1994, 1997; Verrecchia, 2001). Empirical evidencein Botosan

et al.

(2004) supports this positive relation between information asymmetry anda firms cost of capital.

4

Penman (2004, 77) writes, Bubbles work like a pyramiding chain letter. Speculativebeliefs feed rising stock prices that beget even higher prices, spurred on by further specula-tion. Momentum investing displaces fundamental investing, promoting the rise in prices.The role of accounting is to cut the chain letter, to challenge speculative beliefs, and soanchor investors.

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The remainder of the present paper is organized as follows. In the following

section, we identify potential determinants of differential interpretation and relate

them to earnings and firm characteristics. In Section 3, we describe our sample and

explain the Kandel and Pearson (1995) measures of differential interpretation.In Section 4, we present our empirical results and, in Section 5, we conduct

sensitivity tests. We conclude in Section 6 by discussing the implications of our

results.

2. Determinants of differential interpretation and choice of proxies

Theorists have modelled differential interpretation of a public announcement

in various ways. In Kandel and Pearsons (1995) model of Bayesian updating,

agents interpret the announcement differently because they possess different like-lihoods regarding the mean error in the announcement. In Kim and Verrecchias

(1994) rational expectations model, agents interpret the announcement differently

because they acquire private information about the precision of the announce-

ment. Both models capture the intuition that agents apply differential knowledge

or expertise when processing a public announcement. However, in Kandel

and Pearsons model, the source of differential interpretation (differential

likelihoods) is exogenous, whereas the source of differential interpretation

in Kim and Verrecchias model (private information) is endogenously determined.

Therefore, we rely on Kim and Verrecchias model to identify potential determi-

nants of differential interpretation and empirical proxies.

Similar to Kim and Verrecchia (1994), we assume that differential inter-

pretation of an earnings announcement arises when investors (or analysts) acquire

private information regarding the precision of the earnings announcement.

In their model, Kim and Verrecchia show that the number of agents who

acquire private information leading to differential interpretation of an earnings

announcement is decreasing in: (i) the precision of the earnings announcement;

(ii) the precision of pre-announcement information; and (iii) the cost of acquir-

ing the private information (see Lemma 1). Intuitively, the more precise the

earnings announcement or pre-announcement information, the lower the advantageof interpreting the earnings idiosyncratically (becoming an information processor).

That is, the quality of publicly available information reduces the net benefit from

acquiring private information to differentially interpret the earnings. Similarly,

the cost of acquiring the private information reduces differential interpretation

by reducing the net benefit of differential information processing.

We select empirical proxies that are likely to capture the determinants of

differential interpretation suggested by Kim and Verrecchia (1994). In particular,

we examine the effect of eight variables on differential interpretation of earnings

announcements: four variables related to earnings characteristics and four variablesrelated to firm characteristics. The four earnings variables we examine include

earnings predictability, earnings persistence, earnings surprise and negative

earnings. The four firm variables we examine include firm size, analyst coverage,

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volatility of operating cash flows and price-to-book ratio. Below we describe

these variables and present intuition regarding their potential effect on differential

interpretation.

2.1. Earnings characteristics

We examine four earnings variables that are likely to reflect the precision or

quality of the earnings announcement. First, more predictable earnings tell us

more about future earnings and thereby have a greater impact on stock price

than less predictable earnings (Lipe, 1990). Because one of analysts main con-

cerns is to make accurate forecasts of earnings (Mikhail

et al.

, 1997; Hong

et al.

, 2000; Hong and Kubik, 2003; Penman, 2004), analysts are more likely to

rely on accounting earnings with high predictability than on earnings with lowpredictability (Lee, 1999). In particular, analysts are less likely to acquire private

information to differentially interpret the earnings when such earnings contain

high predictability. Therefore, we expect a negative relation between earnings

predictability and differential interpretation.

Second, earnings with higher persistence are regarded as having higher quality.

This is because persistent earnings are sustainable (i.e. they are more likely

to recur in the future). Persistent earnings are also regarded as a desirable

attribute by analysts (Francis et

al

., 2004) and have a greater impact than earn-

ings that are transitory (Lipe, 1990). Therefore, as with predictability, analysts

are more likely to rely on accounting earnings with high persistence than on

earnings with low persistence. In particular, analysts are less likely to acquire

private information to differentially interpret the earnings when such earnings

contain high persistence. Therefore, we expect a negative relation between

earnings persistence and differential interpretation.

Third, earnings surprise captures the average inaccuracy of analysts prior

belief about the earnings announcement. The larger the surprise in the earnings,

the more uncertainty there is likely to be about its implications for future earnings.

Brown and Han (1992) find that earnings announcements with large magnitudes

of earnings surprise decrease the convergence of analysts forecasts about futureearnings. Furthermore, Freeman and Tse (1992) provide evidence that the

marginal response of stock price to unexpected earnings (Earnings Response

Coefficient) declines as the absolute magnitude of unexpected earnings increases.

This non-linear relationship rests on the premise that the magnitude of unexpected

earnings is negatively correlated with earnings persistence. Hence, we expect a

positive relation between the magnitude of earnings surprise and differential

interpretation.

Finally, losses are less informative about a firms future prospects than

profits. This is because of the liquidation option (Stickel, 1990; Hayn, 1995),conservatism (Klein and Marquardt, 2006) and the tendency for firms to load up

negative accruals in a down year and take a bath (Penman, 2004). Therefore,

losses are likely to be temporary and exhibit low persistence. Hence, similar to

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large magnitudes of earnings surprise, we expect there to be greater differential

interpretation when a firm announces losses than when it announces profits.

2.2. Firm characteristics

We examine four firm characteristics that are likely to reflect pre-announcement

information quality as well as the complexity of a firms business. First, firm

size is widely cited as a proxy for the richness of a firms information environ-

ment (Atiase, 1985; Bhushan, 1989; Brennan and Hughes, 1991). In addition,

larger firms tend to have better disclosure policies (Lang and Lundholm, 1993,

1996). Therefore, holding other things constant, analysts have less incentive to

acquire private information to differentially interpret the earnings of large firms.

Therefore, we expect there to be less differential interpretation for large firmsrelative to small firms.

Second, analyst coverage is another widely used proxy for the information

environment of the firm. Although analyst coverage reflects an increase in the

amount of information available to investors (Shores, 1990; Stickel, 1990; Lang

and Lundholm, 1993), analyst coverage also reflects the number of informed

traders in the market (Brennan

et al.

, 1993; Brennan and Subrahmanyam, 1995).

Furthermore, financial analysts are motivated to take full account of others

information and acquire private information where it is profitable (Kandel and

Pearson, 1995). Therefore, after controlling for firm size, we expect a positive

relation between analyst coverage and differential interpretation.

Third, we use the volatility of operating cash flows as a measure of under-

lying operational uncertainty. The operating cash flows number is less subject

to management discretion than earnings (Sloan, 1996; Dechow

et al.

, 1998),

and the volatility of operating cash flows is therefore more likely to reflect

uncertainty in the underlying business operation. Another result in Kim and

Verrecchia (1994) is that increased uncertainty in the cash flows of the firm (for

which earnings is a predictor) leads to more private information processing leading

to differential interpretation of earnings. Because uncertainty in the cash flows

of the firm adds to the underlying uncertainty of the earnings signal, this resultis consistent with their result that less precise earnings leads to more private

information processing. Therefore, holding other things constant, we expect a

positive relation between the volatility of operating cash flows and differential

interpretation.

Finally, firms that have higher intangible assets are likely to be more difficult

to value and analyse all else held constant (Barth et al.

2001). This implies that

the cost of acquiring private information for these firms is likely to be high

relative to firms with low intangible assets. Firms with higher intangible assets

may also operate in an environment where there is greater operational uncer-tainty or higher benefit (return) to private information acquisition. However,

with volatility of operating cash flows and analyst following in the model, we

expect the effect of the higher cost of acquiring private information to dominate

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these alternative effects. Using the price-to-book ratio as a measure of intangible

assets (Fama and French, 1995), we therefore expect a negative relation between

the price-to-book ratio and the acquisition of private information leading to

differential interpretation.

3. Sample, data and variable measurement

3.1. Sample selection

The sample consists of firm-year observations between 1983 and 2004, inclusive,

meeting the following data requirements:

5

1

The quarterly earnings announcement date is available on the quarterly

Compustat files or Institutional Brokers Estimate System (I/B/E/S) Actual files.

6

2

Earnings, assets and other financial statement data are available on Compustat

over the 10 years preceding the year of the earnings announcement.

3

Analysts forecasts of annual and quarterly earnings and actual earnings

data are available on the I/B/E/S Detailed and Actual files. To be included in the

sample, an analyst must issue forecasts of annual earnings within both pre- and

post-announcement periods.

7

We examine individual analysts 1 year ahead earnings forecast revisions

around quarterly earnings announcements.

8

To avoid the problem of stale

forecasts (Barron, 1995), we limit individual analyst forecasts from I/B/E/S

Detailed files to those issued immediately before and after the earnings

announcement. Panel A of Table 1 shows three windows that we use to examine

forecast revisions around quarterly earnings announcements. In Window 1, we

define the pre-announcement period as 15 days to 1 day prior to the quarterly

earnings announcement, and the post-announcement period as the earnings

announcement date to 14 days after the announcement. In Window 2, we

increase the pre-announcement period to 30 days before the quarterly earnings

announcement date because analysts are less likely to issue their forecasts in

the 2 weeks before the earnings announcement (Stickel, 1989; Ivkovi

and

Jegadeesh, 2004). In Window 3, we increase the pre-announcement periodfurther to 42 days before the announcement and increase the post-announcement

5 From the year 1983 onward, significantly large numbers of individual analyst forecast dataare available in the I/B/E/S Detail Tape.

6 If the Compustat earnings announcement date is not available, the I/B/E/S earningsannouncement date is used.

7 In rare cases in which multiple forecasts are available from the same individual analyst

within a pre- or post-announcement period, we use the forecast closest to the earningsannouncement date.

8 For the analyst forecast revision around quarter 4 earnings announcements, we use changesin the 2 year ahead earnings forecast.

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period to 30 days after the announcement. This window is widely used by

previous empirical studies that examine analyst forecast revisions (e.g. Barron,1995; Bamberet

al

., 1999; Barron

et al.

, 2002).

Panel B of Table 1 presents the number of sample firms and sample firm

observations for each of the three windows in each fiscal year used in the

Table 1

Time windows and descriptive information on firm observations

Panel A: Time windows used to examine forecast revisions around quarterly earnings announcements

Windows

Announcement samples

Pre-announcement period Post-announcement period

1 days 15 to 1 days 0 to 14



Panel B: Descriptive information on firm observations by year and time window

Year

Window 1 Window 2 Window 3

Number

of firms

Number

of firm

observations

Number

of firms

Number

of firm

observations

Number

of firms

Number

of firm

observations

1983 45 45 112 131 333 522

1984 89 100 217 298 477 923

1985 63 69 159 204 444 788

1986 65 72 157 198 454 859

1987 97 119 184 278 487 960

1988 121 155 219 328 478 9571989 144 183 277 438 522 1 087

1990 178 258 326 577 554 1 207

1991 165 237 326 579 577 1 226

1992 171 210 304 473 517 942

1993 189 255 350 599 559 1 138

1994 219 304 406 712 613 1 243

1995 236 377 432 786 646 1 334

1996 271 397 460 834 668 1 370

1997 310 459 531 994 734 1 549

1998 324 490 532 1 016 716 1 503

1999 291 426 505 877 676 1 2892000 397 648 673 1 309 796 1 720

2001 453 766 714 1 443 852 1 885

2002 505 878 764 1 565 904 2 027

2003 591 1 069 881 1 859 1 059 2 379

2004 523 785 796 1 294 982 1 668

Total 5 447 8 302 9 325 16 792 14 048 28 576

Mean 248 377 424 763 639 1 299

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study (19832004). Window 1 includes on average 248 firms per year with 377observations. Increasing the pre- and post-announcement periods increases

the average number of firms in our sample to 424 in Window 2 and 639 in

Window 3.

3.2. Differential interpretation measures

We use empirical proxies for differential interpretation developed by Kandel

and Pearson (1995). Kandel and Pearson use the change in forecasts of year t

earnings around preceding quarterly q

earnings announcements to capturedifferential interpretation. Figure 1 shows that we use forecasts of annual

earnings from the pre-announcement period, , and the post-announcement

period, , to form our forecast proxies. Following a Bayesian process, two

analysts i

and j

observing the quarterly earnings announcement,y

q

, update their

forecast of annual earnings as follows:

where i is the analyst-specific weight placed on the prior belief, , in theupdating process and i,q reflects the analyst-specific error term. Kandel andPearson (1995) interpret this analyst-specific error term as differential inter-

pretation of the quarterly earnings announcement.9

Kandel and Pearson (1995) propose that if the error term of the quarterly earnings

signalyq is common to all analysts that is, i,q=j,q=q the following restrictions

9 Similar intuition is found in the models of Kim and Verrecchia (1994, 1997), where aprivate signal is available that can only be used in combination with the earnings signal. Thekey characteristic of this private signal is that it provides a unique context or interpretationto the earnings announcement.

Figure 1 Forecasts used to capture differential interpretation of quarterly earnings announcements.

Fi q t, ,pre

Fi q t, ,post

Analyst s revision post pre : ( ) ( ), , , , ,i F F yi q t i i q t i q i q= + +1

Analyst s revisionpost pre

: ( ) ( ),, , , , ,j F F yj q t j j q t j q j q= + +1

Fi q t, ,pre

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The AuthorsJournal compilation 2009 AFAANZ

must be satisfied: (i) the distance between two analysts posterior beliefs

cannot exceed that between their priors (i.e. ); and

(ii) the relative pessimist cannot turn into the optimist (i.e. if , then

). Therefore, analyst forecast pairs that either diverge or flip after aquarterly earnings announcement are possible if and only if the error terms aredifferent across the analysts (i,qj,q). Therefore, forecast pairs that exhibit thebehaviour in Case (i) and Case (ii) demonstrate differential interpretation of

the quarterly earnings announcement.

Following Kandel and Pearson (1995), we measure differential interpretation

of an earnings announcement by comparing pairs of analysts forecast revisions

of year tannual earnings around the preceding quarterly q earnings announce-ments. Specifically, for analyst pairs whose forecast revisions move in opposite

directions, a Flip or a Divergence case is recognized when a pair of analystforecasts shows the revision pattern of Case (i) or Case (ii), respectively. Finally,anInconsistence case is recognized when the two forecasts exhibit either a FliporDivergence. For each earnings announcement, the proportion ofFlips,Diver-gences and Inconsistencies serve as our dependent variables capturing differentialinterpretation.

3.3. Measurements of earnings and firm characteristics

Following Lipe (1990), we measure the predictability and persistence of

earnings using forecast errors from a time-series model. More specifically, we

measure earnings predictability as the standard deviation of forecast errors from

an autoregressive model of order one (AR1) for annual earnings before extra-

ordinary items scaled by average assets:yt=0+1yt1+t. For each firm in yeart, we estimate equation (1) using ordinary least-squares regression and rolling10 year windows.10 Predictability (Predict) is measured by the standard devia-tion of the errors () from the AR1 model. We multiply the standard deviation bynegative one (1) so that a higher value of this measure reflects higher earnings

predictability. Our measure of persistence (Persist) is also derived from the firm-specific value of1 from the above AR1 model.11

The magnitude of earnings surprise is the absolute value of mean forecast

error using forecasts of quarterly earnings issued within 45 days before the quarter

q earnings announcement. Following previous studies (e.g. Lang and Lundholm,1993, 1996), we deflate absolute mean forecast error by the fiscal quarter qending stock price. The negative earnings dummy variable (NegEPS) is determinedby the sign of earnings per share reported in the I/B/E/S Actual file.

10 We require a minimum of eight observations in 10 years.

11 In sensitivity tests, we also use higher order time series models, such as the IMA (1, 0),ARIMA (1, 1, 0) and ARIMA (2, 1, 0), and achieve similar results.

| | | |, , , , , , , ,F F F F i q t j q t i q t j q t pre pre post post

F Fi q t j q t , , , ,pre pre>

F Fi q t j q t , , , ,post post

>

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The volatility of cash flows is captured by the standard deviation of operating

cash flows over the preceding 10 years (StdCash).12 Because cash flow informa-tion from the statement of cash flows is only available after the Statement of

Financial Accounting Standards No. 95 effective in 1987, we measure cashflows from financial information in the balance sheet following Sloan (1996).Firm size is measured by the log of the market value of equity (lnMV) at eachquarterq ending date, and the analyst coverage variable (Coverage) is measuredby the log of the number of analysts following the firm in a given year. The price-

to-book ratio (P/B) is measured by the total market value of equity divided bytotal stockholders equity at the end of each quarter q.

Finally, we include the change in aggregate analyst beliefs as a control variable.

Kandel and Pearson (1995) emphasize that their proxy measures are likely to

understate differential interpretation under large earnings surprises because ana-lysts are more likely to revise their forecasts in the same direction.13 Similarly,

Bamberetal. (1999) argue that the greater the change in aggregate beliefs, thegreater the measurement error in the Kandel and Pearson (1995) proxies for dif-

ferential interpretation. Following Bamberetal., we control for this potentialmeasurement error by including the absolute mean forecast revision,AbsRev,in the model. Consistent with other variables, we deflate AbsRev by quarter-endstock price. In all regression models, we also include dummy variables of fiscal

quarters and fiscal years to control for potential unknown effects.

3.4. Logit regression model

Our dependent variable has a binary form, which is equal to 1 if the pair

exhibits a pattern of Kandel and Pearson (1995) differential revision, and 0

otherwise. Because all pairs of forecast revisions issued by analysts following

the same firm have the same independent variables, our dependent variables

form grouped binary response data. In this grouped data setting, proportional

dependent variables are often used in the logistic (or Poisson) regression model

to investigate the relationship between these discrete responses and a set of

explanatory variables (Greene, 1999). In our tests, we use the proportions ofdifferential revisions showing a Flip, Divergence and Inconsistence pattern asthe dependent variables in our logit regression model.

Because we do not know the particular relation between the dependent and

independent variables, we present our regression results in steps. In the first

regression model, we examine the relation between the Kandel and Pearson

(1995) measures and the earnings-related variables. In the second regression

12 We require a minimum of eight observations in 10 years.13 This measurement error will result in downward-biased estimates of the proportions ofpairwise inconsistencies due to differential interpretation, which will reduce the power ofour tests.

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model, we examine the relation between the Kandel and Pearson (1995) measures

and the firm-related variables. In the third regression model, we include all of

the independent variables to examine the incremental effects of each variable.

4. Empirical results

4.1 Descriptive statistics and simple correlations

Table 2 presents the average proportion of the Kandel and Pearson (1995)

differential interpretation measures for each of our three time windows. We multiply

each Kandel and Pearson measure by 100 for the purposes of this table. The

average proportion ofFlips and Divergences are 8.88 and 7.98 per cent in

Window 1 and 8.82 and 7.94 per cent in Window 2, respectively. In Window 3,the proportions increase to 9.73 and 8.83 per cent, respectively. These numbers

are comparable to those of Kandel and Pearson (1995) over a similar time

window (9.24 and 11.39 per cent, respectively, in Window 3). The average

proportion of Inconsistencies range from 13.51 per cent in Window 1 to14.75 per cent in Window 3. The value ofInconsistencies is not equal to thesum ofFlips andDivergences because a single observation might be both aFlip

Table 2

Descriptive statistics of frequencies ofFlips,Divergences andInconsistencies

The proportion of differential revision pairs

Flips Divergences Inconsistencies

Window 1 (15, +14)a

Mean 8.88 7.98 13.51

N 8 302 8 302 8 302Window 2 (30, +14)

Mean 8.82 7.94 13.42

N 16 792 16 792 16 792

Window 3 (42, +30)Mean 9.73 8.83 14.75

N 28 576 28 576 28 576

aSee Table 1 for the definition of pre- and post-announcement periods for each Window. Flips is theproportion of analyst pairs that move in opposite directions and flip. A Flip = 1 if

for two analysts ijand ,

where for a given quarterq earnings announcement in fiscal year t+ 1, = analyst is yeartannualearnings announcement in quarterq pre-announcement (if superscript =pre) or post-announcement (ifsuperscript =post).Divergences is the proportion of analyst pairs that move in opposite directions and

diverge. ADivergence= 1 if

Inconsistencies is the proportion of analyst pairs that move in opposite directions and either flip ordiverge.

sign F F sign F F F F F Fi q t i q t j q t j q t i q t j q t i q t j q t ( ) ( ),, , , , , , , , , , , , , , , ,pre post pre post pre pre post postand > < F Fi q t j q t , , , ,

pre pre>

Fi q t, ,pre

sign F F sign F F F F F Fi q t i q t j q t j q t i q t j q t i q t j q t ( ) ( ), ;, , , , , , , , , , , , , , , ,pre post pre post pre pre post post < | | | |

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and a Divergence (i.e. a forecast pair that moves in opposite directions mightboth flip and end up further apart). In the tables below, we present results using

Window 3 forecast revisions. Our inferences are unchanged when we use either

of the shorter time windows.Panel A of Table 3 reports summary statistics on earnings characteristics,

firm characteristics and the control variables we use in our analysis. The mean

(median) value of earnings predictability, Predict, is 0.0467 (0.0285). Notethat we use the negative value of the standard deviation of errors from an AR(1)

model to facilitate the interpretation of our results. The mean (median) value of

earnings persistence, Persist, is 0.3929 (0.4129) and the mean (median) value ofthe absolute forecast error, AbsFE, is 0.0058 (0.0013). About 11.34 per cent ofour sample observations report negative quarterly EPS (NegEPS) in the I/B/E/S

Actual file. The mean (median) value of the standard deviation of annual operatingcash flows, StdCash, is 0.0742 (0.0558) and that of the price-to-book ratio, P/B,is 3.64 (2.13). On average, our sample firms are followed by 18.43 analysts

(Coverage). The absolute value of mean forecast revision divided by stockprice,AbsRev, has a mean (median) value of 0.0076 (0.0029). Not surprisingly,our sample firms are very large, with mean (median) market value of equity,

MV, of 8334 (2065) million dollars. This is because of our data requirements ofat least 8 years of historical financial data and at least two analysts who revise

their forecasts around the quarterly earnings announcements. Hence, caution is

needed in generalizing our empirical results for small firms.

Panel B of Table 3 presents simple correlations among our dependent, inde-

pendent and control variables using Window 3. Following Lang and Lundholm

(1996), we use decile-ranked values of the independent variables because we do

not predict the particular functional form of the relation between the dependent

and independent variables and in order to control for outliers. We rank inde-

pendent variables from original data within their industry-year and convert the

ranks to deciles. Note that our results are robust to using continuous variables.

The first two columns report the correlations among Flips, Divergences andInconsistencies. As expected, the three measures are highly positively corre-lated. The next eight columns report correlations between Flips, Divergencesand Inconsistencies and the earnings and firm characteristics. Although the sim-ple correlations do not support any of our predictions, they must be interpreted

with care because they do not control for other variables in the model. Further-

more, Flips, Divergences and Inconsistencies are all negatively correlated withthe absolute mean forecast revision (AbsRev), suggesting that the Kandel andPearson (1995) measures underestimate the true extent of differential interpreta-

tion around the quarterly earnings announcements.

4.2. Regression results

Our initial analysis of the logit regressions indicated that the variance in the

data was greater than that predicted by the binomial model (an over-dispersion

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Table 3

Summary descriptive statistics of earnings characteristics, firm characteristics and control variables

Panel A: Descriptive statistics

a

N Mean SD 1st 25th

Earnings characteristics

Predict 14 043 0.0467 0.0687 0.2938 0.0527Persist 14 043 0.3929 0.3903 0.4854 0.1456AbsFE 28 576 0.0053 0.0296 0.0000 0.0005NegEPS 28 576 0.1134 0.3171 0 0

Firm characteristicsMV 28 576 8334 23 758 69 730StdCash 14 043 0.0742 0.0780 0.0113 0.0358P/B 28 576 3.64 50.76 0.52 1.44Coverage 14 043 18.4323 10.77 3 10

Control variables

AbsRev 28 576 0.0076 0.0243 0.0000 0.0011

Panel B: Simple correlations of the percentage of analysts differential revisions and decile rank of independent v

Divergences Inconsistencies Predict Persist AbsFE NegEPS lnMV S

Flips 0.3789 0.7966 0.0202 0.0174 0.0728 0.0058 0.0240

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problem). One way of correcting for over-dispersion is to multiply the covariance

matrix by a dispersion parameter estimation (McCullagh and Nelder, 1989).

Therefore, we followed the method in Williams (1982) to estimate the dispersion

parameter and correct for over-dispersion.14

Table 4 reports the results of ourcross-sectional logit regressions of the Kandel and Pearson (1995) forecast

measures of differential interpretation. Panel A presents the results forFlips,Panel B presents the results forDivergences, and Panel C presents the resultsforInconsistencies. The results for all three dependent variables are essentiallythe same. Regarding the earnings-related variables, we find that the Kandel and

Pearson (1995) measures are consistently negatively associated with Predictand positively associated withAbsFE andNegEPS (allp < 0.01) after controllingforAbsRev. These results are consistent with our predictions and suggest that

differential interpretation is decreasing in the precision of earnings. The coefficientforPersist, in contrast, is insignificant in the regression models except theregression model forDivergences with earnings-related variables in the model.

Regarding the firm-related variables, we find that the three Kandel and Pearson

(1995) measures are consistently negatively associated with lnMV (p < 0.05 orp < 0.01) andP/B (p < 0.01), and positively associated with Coverage (p < 0.05 orp < 0.01). The results for lnMV and P/B suggest that, consistent with expecta-tions, differential interpretation is lower when a firm has a richer information

environment or when it is costly to analyse a firms value. The results for

Coverage suggest that differential interpretation is higher when there are moreinformation processors following a firm. However, the coefficient on StdCashswitches from being significantly positive (p < 0.01) when included with justthe firm variables to being marginally negative (p < 0.10) in the full model. Thisresult appears to be due to multicollinearity between StdCash and ourPredictvariable. When we remove Predict from the full model, we get a significantlypositive coefficient on StdCash as predicted.

The evidence in Table 4 supports the evidence in Kandel and Pearson (1995),

Barron etal. (2002) and Barron et al. (2005) that analysts use private, idio-syncratic information to interpret public earnings announcement. However, our

evidence extends this line of research by showing that differential interpretationof earnings is a function of earnings and firm characteristics. In particular,

we find that differential interpretation is reduced by earnings characteristics

reflecting the quality of the earnings and by firm characteristics reflecting the

quality of pre-announcement disclosure and the cost of analysing a firms value.

We now present tests examining the sensitivity and robustness of our results.

14 The William method is appropriate for the proportional form of data with unequal size.As an alternative, we also estimate the dispersion parameter based on the Pearson chi-squared statistic and the deviation for the fitted model, but the results are similar. We useSAS software for these tests.

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Table 4

Results of cross-sectional logit regression of forecast revisions reflecting differential interpretation a,b

Proportion of differential revisions=+1Predictj,t+2Persistj,t+3AbsFEj,q,t+4NegEPSj,q,t+5 ln MVj,q,t+6StdCashj,t+7P/Bj,q,t+8Coveragej,t+9AbsREVj,q,t+Quarter D

+Year Dummies+j,q,t.

Panel A: Results of Flips on decile ranks of characteristics of firms and earnings around quarterly earnings anno

Intercept Predict Persist AbsFE NegEPS lnMV StdCash P/B

1.688*** 0.039*** 0.001 0.060*** 0.491***

(1079.6) (43.5) (0.0) (135.5) (156.3)

0.6609*** 0.0818** 0.0266*** 0.1251(27.4) (21.7) (21.5) (402.1)

0.5958*** 0.0632*** 0.0034 0.0359*** 0.3949*** 0.0417** 0.0190** 0.1060

(18.22) (65.8) (0.6) (47.3) (97.8) (5.6) (6.6) (274.7)

Panel B: Results of divergences on decile ranks of characteristics of firms and earnings around quarterly earning


1.978*** 0.034*** 0.012** 0.089*** 0.462***

(1182.4) (27.2) (5.5) (247.1) (110.8)

1.1027*** 0.0917*** 0.0228*** 0.139(59.9) (21.8) (12.5) (402.7)

1.1489*** 0.0625*** 0.0070 0.0642*** 0.3688*** 0.0482** 0.0235*** 0.111

(53.21) (51.6) (2.0) (120.7) (67.2) (6.0) (8.1) (242.5)

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Panel C: Results of inconsistencies on decile ranks of characteristics of firms and earnings around quarterly ear


1.199*** 0.040*** 0.007 0.078*** 0.543***

(620.5) (54.2) (2.8) (264.3) (213.2)

0.0962 0.0929*** 0.0278*** 0.142

(0.7) (32.9) (26.8) (591.7)

0.0520 0.0700*** 0.0023 0.0520*** 0.4441*** 0.0474** 0.0234*** 0.119

(0.16) (92.4) (0.3) (111.5) (137) (8.4) (11.5) (393.3)

***, ** and * denote statistical significance at the 1, 5 and 10 per cent levels, respectively. aAll variables other th

ranked and then computed as deciles within the industry-year, bChi-squared statistics are presented in parenthese

errors from an autoregressive model of order one (AR1) for earnings before extraordinary item scaled by the avera

is firm-specific value of the coefficient on lagged earnings in time-series AR1 model over the preceding 10 years; divided by quarterq end stock price;NegEPS is dummy variable equalling 1 if negative EPS reported in I/B/E/S Ameasured by fiscal year ending stock price multiplied by the number of shares outstanding; StdCash is standapreceding 10 years; P/B is price-to-book ratio at quarter ending date; Coverage is (log of) number of analyst follmean analyst forecasts around an earnings announcement divided by quarter q end stock price.

Table 4 (continued)

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5. Sensitivity and robustness tests

5.1. Subsample of analyst forecasts revised on the same day

Prior evidence shows that analysts learn from prior forecasts or stock price

(Stickel, 1990). This additional information acquisition by analysts who issue

forecasts later in the post-announcement period can lead to different informa-

tion sets among analysts and result in differential forecast revisions. Although

our 15 days of post-announcement period is short enough to assume that analysts

revise their forecasts to interpret earnings announcement, we cannot exclude the

possibility that analysts additional information acquisition in the post-announcement

period causes analysts to revise their forecasts differently.

To mitigate this concern, we repeat our analyses with forecasts that are evenmore restricted in their timing. In particular, we compose forecast revision pairs

with forecasts that are revised on the same date in the post-announcement

period. Because these forecasts are revised on the same date, analysts in each

pair are more likely to observe the same prior public information. This will

better control for information differences due to different forecast timing across

analysts. The results, reported in Table 5, are similar to those reported in

Table 4. We find consistently negative coefficients on Predict, P/B and AbsRev.We also find consistently positive coefficients onAbsFE andNegEPS. Inconsistentwith Table 4, the coefficient on lnMV is insignificant for all three Kandel andPearson (1995) measures and the coefficient on Coverage is only marginallysignificantly positive forDivergences. This evidence suggests that our resultsare unlikely to be driven by forecast timing differences.

5.2. Analyst forecast dispersion

Analyst forecast dispersion in the pre-announcement period is widely used

to proxy for earnings predictability or uncertainty (e.g. Imhoff and Lobo, 1992;

Atiase and Bamber, 1994). We compute analyst forecast dispersion using the

standard deviation of annual earnings forecasts in the pre-announcement perioddeflated by quarter-end stock price. We find that forecast dispersion in the

pre-announcement period is highly positively correlated to our earnings predict-

ability variable,Predict. Furthermore, when we replace the variable Predictwithforecast dispersion, we find a strongly positive coefficient on forecast dispersion

in all regression models. This is consistent with the notion that analysts are more

likely to rely on private information to revise their forecasts when the public

information environment is less precise.

5.3. Forecast bias

Prior research suggests that analysts systematically issue optimistic forecasts

for annual earnings (e.g. Abarbanell and Bernard, 1992; Francis and Philbrick,

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Table 5

Results of cross-sectional logit regression of forecast revisions reflecting differential interpretation with subsample

announcement period (n= 16 235)a,b

Proportion of differential revisions=+1Predictj,t+2Persistj,t+3AbsFEj,q,t+4NegEPSj,q,t+5 lnMVj,q,t

+6StdCashj,t+7P/Bj,q,t+8Coveragej,t+9AbsREVj,q,t+Quarter D+Year Dummies+j,q,t.


Panel A: Flips0.6744** 0.0622*** 0.0011 0.0370*** 0.4354*** 0.0074 0.0238* 0.1140*

(8.62) (24.5) (0.0) (19.3) (49.0) (0.1) (4.1) (122.1)

Panel B: Divergences1.2894*** 0.0714*** 0.0166* 0.0658*** 0.4108*** 0.0292 0.0373** 0.1133*(23.09) (24.1) (4.0) (45.8) (32.5) (0.8) (7.5) (91.9)

Panel C: Inconsistencies0.2223 0.0759*** 0.0069 0.0533*** 0.4710*** 0.0162 0.0333** 0.1238*

(1.11) (42.8) (1.1) (46.6) (65.9) (0.4) (9.4) (168)

***, ** and * denote statistical significance at the 1, 5 and 10 per cent levels, respectively. aAll variables other th

ranked and then computed as deciles within the industry-year. bChi-squared statistics are presented in parenthese

errors from an autoregressive model of order one (AR1) for earnings before extraordinary item scaled by the avera

is firm-specific value of the coefficient on lagged earnings in time-series AR1 model over the preceding 10 years; divided by quarter q end stock price; NegEPS is dummy variable equalling 1 if negative EPS reported in I/B/E/value measured by fiscal year ending stock price multiplied by the number of shares outstanding; StdCash is standpreceding 10 years; P/B is price-to-book ratio at quarter ending date; Coverage is (log of) number of analyst follmean analyst forecasts around an earnings announcement divided by quarter q end stock price.

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1993; Easterwood and Nutt, 1999). However, this forecast bias is unlikely to

affect our results. First, recent evidence in Brown (2001) and Matsumoto (2002)

suggests that analysts apparent optimistic bias is diminishing, that analysts

appear to be optimistic for only some periods, and that much of analysts optim-ism arises from firms reporting losses. We repeat our tests with sample firms

that do not have losses, and the results are similar. Second, the Kandel and

Pearson (1995) measures are less likely to be affected by analysts forecast bias

because they capture forecast revisions that move in different directions. There-

fore, systematic optimism (or pessimism) in analysts forecasts is unlikely to

affect our measures of differential interpretation.

5.4. Analyst self-selection

Analysts might simply drop a firm rather than issue a negative forecast, even

though they observe significant new information from an earnings announcement.

Empirical evidence shows that analysts tend not to follow firms when there is

bad news (McNichols and OBrien, 1997). This analyst self-selection could

potentially affect our results by limiting differential interpretation of bad news.

This concern is mitigated by two results. First, we find that our variable

NegEPS is positively related to the Kandel and Pearson (1995) measures of dif-ferential interpretation. Second, as mentioned above, limiting our sample firms

to positive EPS announcements does not change our main results.

6. Conclusion

Although empirical researchers have documented the existence of differential

interpretation of earnings announcements (Kandel and Pearson, 1995; Bamber

etal., 1999; Barron etal., 2002, 2005), the factors that drive this differentialinterpretation remain unknown. We fill this gap in the literature by providing

evidence on the determinants of differential interpretation of an earnings

announcement. We find that differential interpretation of earnings is: (i) reduced

by earnings characteristics reflecting the quality of the earnings; (ii) reduced byfirm characteristics reflecting the quality of pre-announcement disclosure; and

(iii) reduced by firm characteristics reflecting the cost of acquiring private

information to interpret earnings idiosyncratically.

Our study has several limitations. First, we use forecast measures of differential

interpretation that require recent forecast updates around earnings announce-

ments. Because of this data requirement, our sample includes relatively large

firms with a large analyst following. Therefore, it is not clear how our results

would generalize to a sample of firms with different characteristics. Second, our

measure of differential interpretation captures more extreme cases of newfounddisagreement among financial analysts. Hence, our results might not generalize

to less extreme cases of differential interpretation or less sophisticated market

participants. These issues are fruitful areas for future research. Despite these

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limitations, our results should be of interest to researchers and regulators. By

identifying earnings and firm characteristics that affect differential interpreta-

tion, we are able to provide insights as to the conditions under which an earnings

announcement is less likely to generate newfound disagreement among analystsand investors. This is important because recent theory and evidence suggests that

investor disagreement can increase investment risk, increase the cost of capital,

and cause stock prices to deviate from fundamental value.

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