is analyst earnings forecast ability only firm specific?

Is Analyst Earnings Forecast Ability Only

Firm Specific?*

LAWRENCE D. BROWN, Georgia State University

EMAD MOHAMMAD, McMaster University

1. Introduction

The state of the art in the earnings forecasting literature is that analyst earn-ings forecast ability is only firm specific. Finding that investors ignore earn-ings forecast ability with respect to firm k when reacting to analyst earningsforecast revisions of firm j, Park and Stice (2000, 259) state, ‘‘we are unable tofind any spillover effect. As a result, we conclude that the specific forecastingability that we document is with respect to the earnings of a specific firm.’’Chen, Francis, and Jiang (2005, 11) state, ‘‘We focus on analyst-firm pairings,as opposed to all forecasts made by an analyst, based on Park and Stice’s(2000) finding that learning about analysts’ forecasting ability is firm-specific(i.e., investors do not believe an analyst is good at predicting firm A simplybecause she is good at predicting firm B).’’ Chen and Jiang (2006, 336) state,‘‘[Our measures] are both analyst-firm specific to account for Park and Stice’s(2000) finding that analysts’ ability is firm-specific.’’

‘‘Spillover’’ is a narrow measure of ‘‘general’’ ability as it considers ana-lyst ability with respect to only one other firm the analyst follows.1 Weexamine if analyst earnings forecast ability is only firm specific by using abroader measure of general ability that considers analyst ability with respectto all other firms the analyst follows. We show that our broader measure ofgeneral ability is incremental to firm-specific ability for the dual purposes ofexplaining earnings forecast accuracy in holdout periods and for explaininghow investors react to earnings forecast revisions.

Our findings have important implications. First, they shift the state of theart in the literature from the notion that earnings forecast ability is only firmspecific to one that it possesses a general aspect, providing researchers, inves-tors, and practitioners with a better understanding of why some analysts arebetter at forecasting earnings than others. Second, researchers using earnings

* Accepted by Patricia O’Brien. We thank Marcus Caylor, Artur Hugon, Nan Liu, Arianna

Pinello, Pat O’Brien, Bill Richardson, two anonymous referees, and workshop part-

icipants at Bentley College and McMaster University for helpful comments. Emad

Mohammad thanks the Social Sciences and Humanities Research Council of Canada for

its financial support.

1. Park and Stice (2000) use the term spillover, not general ability. For expositional conve-

nience we refer to spillover as a narrow measure of general ability.

Contemporary Accounting Research Vol. 27 No. 3 (Fall 2010) pp. 727–750 � CAAA

doi:10.1111/j.1911-3846.2010.01025.x

forecast ability as an input to their empirical models have focused on firm-spe-cific ability: (a) determinants of firm-specific earnings forecast ability (Mik-hail, Walther, and Willis 1997; Clement 1999; Jacob, Lys, and Neale 1999);(b) studies of analyst access to managers using earnings forecast ability toproxy for management access (Chen and Matsumoto 2006; Ke and Yu 2006);(c) research linking earnings forecast ability to the quality of analysts’ stockrecommendations (Loh and Mian 2006; Mikhail, Walther, and Willis 2004);and (d) studies relating forecast ability to the stock market reaction to ana-lysts’ earnings forecast revisions (Clement and Tse 2003; Gleason and Lee2003). Our findings should prompt researchers to include general earningsforecast ability in their models. Third, practitioners seek better earnings fore-casts and investment advisory firms sell them earnings forecasts that are moreaccurate than the consensus estimate.2 Our findings suggest that advisoryfirms can improve the earnings prediction models they sell to practitioners byincorporating analyst general earnings forecast ability into their models.

We proceed as follows. We introduce hypotheses in section 2, and wepresent our sample and methodology in section 3. We present our results insection 4 and we provide a summary and implications in section 5.

2. Hypotheses

The earnings forecasting task is complicated by firm dynamics and manag-ers’ gaming behavior. Examples of firm dynamics include changing productmixes, entering new markets, and introducing new production technologies.Examples of managers’ gaming behavior are upward earnings managementand guidance of analysts’ earnings forecasts (Bartov, Givoly, and Hayn2002; Matsumoto 2002; Brown and Pinello 2007). Because analysts exhibitdifferential earnings forecast ability (Stickel 1992; Sinha, Brown, and Das1997), analysts who are better at working their way through these complica-tions for one firm should be more accurate in predicting future earnings forother firms.

Each firm has a set of complexities which: (a) at a given point in timepartially overlaps that of other firms and (b) changes for individual firmsover time (e.g., during years t and t + 1, firms j and k change their productmixes; firms v and j introduce new production technologies; firms k and bmanage earnings upwards; firms k and x enter new stages of their lifecycles). Thus, firm-specific ability cannot fully capture analyst ability to pre-dict any one firm’s earnings, as no specific firm has experienced all measuresof complexity that it might encounter in the future. Analysts follow firmsexperiencing different complexities which are likely to pertain to other firmsin the future. Their ability to deal with these complexities can be measuredusing a broad measure of general ability. A broad measure of forecast

2. To meet this need, StarMine, a product of Thomson Reuters, predicts future earnings

and analyst earnings forecast revisions by placing more weight on recent forecasts by

top-rated analysts.

728 Contemporary Accounting Research

CAR Vol. 27 No. 3 (Fall 2010)

ability should be relevant to analysts for predictive purposes and to inves-tors for determining how much attention to pay to analyst revisions.

We examine two measures of general ability, a narrow measure and abroad one. Our narrow measure is inspired by the spillover effect of Parkand Stice (2000), which we define as analyst earnings forecast ability for theone other firm for which the analyst has issued the most forecasts. Our broadmeasure is analyst earnings forecast ability of all other firms the analyst fol-lows. We denote our narrow and broad general ability measures as G-Narrowand G-Broad, respectively. We expect both measures of general ability to beincremental to firm-specific ability for explaining analyst accuracy in holdoutperiods, and we expect G-Broad to be more useful than G-Narrow forpredictive ability purposes. More formally, our first hypothesis is:

Hypothesis 1(a). General ability in year t is incremental to firm-specificability in year t for explaining analyst earnings forecast accuracy inyear t + 1.

Hypothesis 1(b). A broad measure of general ability is relatively morelikely than a narrow measure of general ability to reflect this effect.

Investors respond more strongly to earnings forecast revisions by analystswith a track record of making more accurate firm-specific earnings forecasts(Stickel 1992; Park and Stice 2000; Clement and Tse 2003; Gleason and Lee2003) so we expect our forecasting accuracy results to carry over to ourcapital market results. Formally, our second hypothesis is:

Hypothesis 2(a). When reacting to analyst earnings forecast revisions forfirm j in year t + 1, investors utilize information concerning theanalyst’s general ability in addition to information concerning theanalyst’s firm-specific ability.

Hypothesis 2(b). A broad measure of general ability is relatively morelikely to reflect this capital markets effect than a narrow measure ofgeneral ability.

3. Sample and methodology

Sample

We obtain analysts’ annual earnings per share forecasts and actual earningsper share data from the I ⁄B ⁄E ⁄S U.S. Detail file for the 23-year period1983–2005. We delete observations if either the forecasted or actual earningsper share exceeds (is less than) the 99th (1st) percentile of its distribution.We use the last forecast an analyst makes before the earnings announcement(O’Brien 1990; Mikhail et al. 1997; Sinha et al. 1997; Clement 1999; Jacobet al. 1999), and we exclude firms without December fiscal year-ends(O’Brien 1990; Sinha et al. 1997). To obtain meaningful measures of firm-specific and general ability, respectively, we require that at least five analysts

Is Analyst Earnings Forecast Ability Only Firm Specific? 729


follow the included firm and each included analyst follows at least five firmsin a given year.3 We obtain pricing data and SIC codes from the Center forResearch in Security Pricing (CRSP). Our data requirements yield 264,195analyst-firm-year observations for our predictive ability tests.4 We controlfor potential confounding events in our market reaction tests in two ways.First, we omit cases where firms announce earnings or declare dividends dur-ing the five trading days surrounding the earnings revision date (Park andStice 2000). Second, in our multivariate tests, we include revisions made byother analysts during the three trading days surrounding the revision date.The revisions made by the other analysts need not be their last revisions.

Methodology

Firm-specific and general ability metrics

We use the Hong and Kubik (2003) metric to measure firm-specific ability.We begin by defining absolute forecast error as follows:

ERRORi;j;t ¼jFi;j;t � Aj;tj

Pj;tð1Þ

where:

ERRORi,j,t is the absolute forecast error for analyst i, following firm j inyear t;Fi,j,t is the last one-year ahead forecast of annual earnings of firm j for fiscalyear t issued by analyst i;Aj,t is the actual annual earnings for firm j in year t; andPj,t is the stock price of firm j at the beginning of the fiscal year t.

We adjust for differences in firm coverage by ranking all analysts cover-ing a firm in a given year by forecast accuracy, so that the most (least)accurate analyst is ranked 1 (N) for firms followed by N analysts. In casesof ties, each analyst is assigned the middle point of the adjacent ranks.5

3. To clarify our procedure, consider the following two examples. First, assume that analyst

John Doe follows three firms, one of which is XYZ Inc., covered by seven analysts. We

calculate the variables for the seven analysts following XYZ Inc. including John Doe, but

we do not calculate John Doe’s firm-specific or general ability. Second, assume that ana-

lyst Jane Doe covers 10 firms, one of which is ABC Inc, covered by two analysts. Because

we require at least five analysts to cover the firm, we exclude ABC Inc., but we keep Jane

Doe in our sample if she follows at least one other firm covered by at least four other

analysts.

4. If we relax the criteria to include firms followed by three analysts and analysts following

at least three firms, our sample size increases to 322,304 firm-year-analyst observations,

an increase of 22 percent. If we include firms followed by two analysts and analysts fol-

lowing at least two firms, our sample size grows to 344,850 firm-year-analyst observa-

tions, an increase of about 30.6 percent. Our primary empirical findings are unchanged

using these larger sample sizes.

5. For example, if two analysts have the second lowest forecast errors, both are ranked 2.5

(i.e., [2+3] ⁄ 2). If three analysts have the third lowest forecast errors, all three are ranked 4

(i.e., [3+4+5] ⁄ 3). See Hong, Kubik, and Solomon 2000 (127) for more details.



We use an ability measure that standardizes rankings allowing for moremeaningful comparisons among analysts:6

Firm� Specific Abilityi;j;t ¼ 100� Ranki;j;t � 1

Analyst Coveragej;t � 1

� �� 100 ð2Þ

Analyst Coveragej,t is the number of analysts issuing earnings forecastsfor firm j in year t. The most (least) accurate analyst i following firm j inyear t has a Firm-Specific Ability (hereafter F-Ability) score of 100 (0). Eachanalyst has two metrics for general ability: G-Broadi,j,t is the average of allF-Ability scores for analyst i in year t excluding the one for firm j.7,8

G-Narrowi,j,t is the F-Ability score of the one firm besides firm j for whichanalyst i issued the most forecasts in year t. Analysts are placed in threeF-Ability groups: Superior if F-Ability is at least 66.66, Inferior if F-Abilityis at most 33.33, and Average for all others; they are placed in threeG-Broad groups: Superior if G-Broad is at least 66.66, Inferior if G-Broadis at most 33.33, and Average otherwise; and they are placed in threeG-Narrow groups: Superior if G-Narrow is at least 66.66, Inferior ifG-Narrow is at most 33.33, and Average otherwise.9

Table 1, panel A reports frequencies of all nine firm-specific ⁄broad gen-eral ability combinations. Table 1, panel B reports frequencies of all ninefirm-specific ⁄narrow general ability combinations. While, by construction,analysts are divided almost equally among three firm-specific and broadgeneral ability dimensions when they are considered separately, they are notdivided almost equally among the nine firm-specific and broad general abil-ity combinations unless firm-specific ability and broad general ability areindependent. It is evident that these two dimensions are not independent.Of the 83,331 cases of inferior broad general ability, there are about 80 per-cent more cases of inferior versus superior firm-specific ability (38,843 ver-sus 21,522). And of the 85,203 cases of superior broad general ability, thereare about 62 percent more cases of superior versus inferior firm-specificability (33,321 versus 20,523). Similarly, the nine firm-specific and narrow

6. Analyst coverage varies among firms so a simple ranking of analysts does not control

for differences. For example, in a simple ranking scheme, the rank of the least accurate

analyst in a firm covered by three analysts will have the same rank as that of the third

most accurate analyst in a firm covered by 20 analysts.

7. For example, assume that analyst Y follows five firms in year t, X1, X2, X3, X4, and

X5, with F-Ability scores of 40, 50, 60, 70, and 80, respectively. His ⁄ her broad general

accuracy score for firm X1 in year t is 65 ([50 + 60 + 70 + 80] ⁄ 4), and for firm X2 in

year t is 62.5 ([40 + 60 + 70 + 80] ⁄ 4).8. As a robustness check we include firm j in the calculations of G-Broad. Our results are

qualitatively similar so we do not report these results separately.

9. Park and Stice (2000) have three ability groups when examining market reaction to

firm-specific ability, which they refer to as Superior, Normal, and Inferior. When exam-

ining the spillover effect, Park and Stice define only two groups of analysts: Superior in

one group, and Normal and Inferior in the other group. We obtain qualitatively similar

results to Park and Stice when we use two groups similar to theirs.



TABLE 1

Frequency Distributions of Firm-Specific Ability, and Broad and Narrow General Ability

Panel A: Firm-Specific Ability and Broad General Ability

G-Broad

F-Ability Superior Average Inferior Total

Superior Count 33,321 33,535 21,522 88,378

Percent 12.61 12.69 8.15 33.45

Average Count 31,359 33,810 22,966 88,135

Percent 11.87 12.80 8.69 33.36

Inferior Count 20,523 28,316 38,843 87,682

Percent 7.77 10.72 14.70 33.19

Total Count 85,203 95,661 83,331 264,195

Percent 32.25 36.21 31.54 100

Panel B: Firm-Specific Ability and Narrow General Ability

G-Narrow

F-Ability Superior Average Inferior Total

Superior Count 38,521 28,583 21,274 88,378

Percent 14.58 10.82 8.05 33.45

Average Count 28,395 34,797 24,943 88,135

Percent 10.75 13.17 9.44 33.36

Inferior Count 22,604 25,780 39,298 87,682

Percent 8.56 9.76 14.87 33.19

Total Count 89,520 89,160 85,515 264,195

Percent 33.88 33.75 32.37 100

Notes:

We measure firm-specific ability as follows:

Firm� Specific Abilityi;j;t ¼ 100� Ranki;j;t � 1

Analyst Coveragej;t � 1

� �� 100

Ranki,j,t is analyst i’s rank on the basis of his forecast accuracy for firm j in year t. Analyst

Coveragej,t is the number of analysts issuing earnings forecasts for firm j in year t. The

most (least) accurate analyst, i, following firm j in year t has a Firm-Specific Ability

(F-Ability) score of 100 (0). Analyst i’s broad general ability score (G-Broad) is the

average of all the F-Ability scores of the other firms he follows in a given year besides

firm j. Analyst i’s narrow general ability score (G-Narrow) is the F-Ability score of the

one other firm for which he has issued the most forecasts.

Analysts are placed in one of three F-Ability groups: Superior if F-Ability is at least 66.66, Inferior

if F-Ability is at most 33.33, and Average for all others; in one of three G-Broad groups:

Superior if G-Broad is at least 66.66, Inferior if G-Broad is at most 33.33, and Average

otherwise; and in one of three G-Narrow groups: Superior if G-Narrow is at least 66.66,

Inferior if G-Narrow is at most 33.33, and Average otherwise.



general ability combinations in panel B reveal that firm-specific ability andnarrow general ability are not independent.10

Table 2, panel A reports mean analysts’ forecast characteristics whereanalysts are partitioned into three ability groups (firm specific, broad gen-eral, and narrow general) and three performance groups (superior, average,and inferior). We include the usual analyst determinants of earnings fore-cast error (ERROR), namely forecast age (AGE), firm experience (FEXP),general experience (GEXP), number of firms followed (NCOS), number ofindustries followed (NIND), brokerage size (BSIZE), and forecast frequency(FREQ). To control for firm-year effects (Clement 1999), we present the rel-ative measures of these variables, where the relative measure of each vari-able is the raw measure of that variable for the analyst minus the averageraw measure of the same variable for all analysts issuing an earnings fore-cast for the same firm in the same year.

The results in panel A of Table 2 are consistent with the literature on thedeterminants of analyst forecast accuracy. All forecast accuracy determinants,except for general experience, are monotonically related to forecasting abilityfor the firm-specific forecast accuracy measure. Forecast age, number ofindustries followed, brokerage firm size, and forecast frequency are monotoni-cally related to forecasting ability for both general ability forecasting accuracymeasures. Finally, general experience is monotonically related to forecastingability only for the narrow general ability forecast accuracy measure.

Table 2, panel B reports the Pearson correlation matrix consisting ofour forecast ability measures (F-Ability, G-Broad, and G-Narrow) and theirdeterminants. Consistent with past research, forecast error is significantlypositively related to forecast age, number of companies followed, and num-ber of industries followed; and it is significantly negatively related to firmexperience, forecast frequency, and brokerage firm size. Forecast error isnot significantly related to general experience. Most importantly, it is signifi-cantly negatively related to our three measures of analyst ability in the fol-lowing descending order: G-Broad ()6.01 percent), F-Ability ()4.50percent), and G-Narrow ()3.09 percent). All correlations among the threeability metrics are positive and significant. F-Ability and G-Broad have thelowest correlation (11.85 percent); G-Broad and G-Narrow have the highestone (37.94 percent). Many of the determinants of our forecast ability mea-sures are correlated with each other so we include them all in our multivari-ate tests.

Research design

Sinha et al. (1997) examine the persistence of analyst earnings forecast abil-ity in holdout periods, finding that superior analysts remain superior butinferior analysts do not remain inferior. They control for forecast age in

10. A chi-square test of independence for each panel rejects independence at the one percent

level.



TABLE

2

Descriptivestatistics

Panel

A:Meananalysts’forecast

characteristics

Variable

F-A

bility

G-Broad

G-N

arrow

Superior

Average

Inferior

Superior

Average

Inferior

Superior

Average

Inferior

R_ERROR

)0.6927

)0.2745

0.9740

)0.2268

)0.1122

0.3607

)0.1802

)0.0950

0.2877

R_AGE

)23.5159

)16.8142

40.6398

)21.7882

)11.5024

35.4824

)12.8495

)7.9252

21.7124

R_FEXP

0.0302

0.0157

)0.0462

)0.0055

0.0779

)0.0838

0.0469

)0.0306

)0.0172

R_GEXP

)0.0057

0.0143

)0.0087

)0.1440

0.2029

)0.0857

0.0798

)0.0262

)0.0563

R_NCOS

)0.2883

)0.1773

0.4692

)1.7732

1.4153

0.1883

)0.6813

0.3887

0.3079

R_NIN

D)0.1027

)0.0669

0.1709

)0.4271

0.1985

0.2088

)0.1576

0.0180

0.1462

R_BSIZ

E1.6573

0.8275

)2.5042

3.8002

0.1558

)4.0643

2.3078

)0.1189

)2.2917

R_FREQ

0.3184

0.2010

)0.5234

0.3411

0.1392

)0.5086

0.2314

0.0501

)0.2944

Panel

B:Pearsoncorrelationmatrix

Variable

R_F-A

bility

R_G-Broad

R_G-N

arrow

R_AGE

R_FEXP

R_GEXP

R_NCOS

R_NIN

DR_BSIZ

ER_FREQ

R_ERROR

)0.0450

)0.0601

)0.0309

0.4306

)0.0132

0.0013

0.0288

0.0495

)0.0504

)0.2564

R_F-A

bility

0.1185

0.1671

)0.0339

0.0090

)0.0015

)0.0344

)0.0385

0.0416

0.0430

R_G-Broad

0.3794

)0.0488

0.0107

)0.0141

)0.0653

)0.0886

0.0911

0.0928

R_G-N

arrow

)0.0250

0.0110

0.0090

)0.0467

)0.0485

0.0491

0.0377

R_AGE

0.0140

0.0250

0.0090

0.0365

)0.0185

)0.4404

R_FEXP

0.5597

0.0088

0.0184

0.0451

0.1419

R_GEXP

0.1617

0.1000

0.0693

0.0652

(Thetable

iscontinued

onthenextpage.)



TABLE

2(C

ontinued)

Variable

R_F-A

bility

R_G-Broad

R_G-N

arrow

R_AGE

R_FEXP

R_GEXP

R_NCOS

R_NIN

DR_BSIZ

ER_FREQ

R_NCOS

0.6486

)0.1047

)0.0023

R_NIN

D)0.1697

)0.0646

R_BSIZ

E0.0699

Notes:

Bolded

correlationsare

significantlydifferentfrom

zero

atthe5percentlevel

(two-tailed

test)orbetter.

See

Notesto

Table

1fordefinitionsofF-A

bility,G-Broad,andG-N

arrow.

Variable

definitions:

R_F-A

bility(relativefirm

-specificability)istheanalyst’sfirm

-specificabilityforthefirm

minustheaverage‘‘firm

-specificability’’forall

analystsfollowingthesamefirm

thatyear.

R_G-Broad(relativebroadgeneralability)istheanalyst’sbroadgeneralabilityminusaverage‘‘broadgeneralability’’forallanalysts

followingthesamefirm

thatyear.

R_G-N

arrow

(relativenarrow

generalability)istheanalyst’snarrow

generalabilityminusaverage‘‘narrow

generalability’’forall

analystsfollowingthesamefirm

thatyear.

R_ERROR

(relativeforecast

error)

istheanalyst’sforecast

errorforthefirm

minustheaverageforecast

errorforallanalystsfollowing

thesamefirm

inthesameyear,divided

bytheaverageforecast

errorofallanalystsfollowingthesamefirm

thatyear.Forecast

erroristhe

absolute

valueofthedifference

betweentheanalyst’sforecast

andtheactualearnings,scaledbystock

price

atthebeginningofthefiscalyear.

(Thetable

iscontinued

onthenextpage.)



TABLE

2(C

ontinued)

R_FEXP

(relativefirm

experience)isthenumber

ofyears

(includingthecurrentyear)

theanalyst

hasfollowed

thefirm

minustheaverage‘‘firm

experience’’ofallanalystsfollowingthesamefirm

thatyear.

R_GEXP

(relativegeneralexperience)isthenumber

ofyears

(includingthecurrentyear)

theanalyst

hasbeenin

theIB

ESdatabase

minusthe

average‘‘generalexperience’’ofallanalystsfollowingthesamefirm

thatyear.

R_NCOS(relativefirm

complexity)isthenumber

offirm

stheanalyst

followed

duringtheyearminustheaverage‘‘firm

complexity’’ofallanalysts

followingthesamefirm

thatyear.

R_NIN

D(relativeindustry

complexity)isthenumber

ofindustries

theanalyst

followed

duringtheyearminustheaverage‘‘industry

complexity’’of

allanalystsfollowingthesamefirm

thatyear,whereindustry

isdefined

via

theCRSPtw

o-digitSIC

code.

R_BSIZ

E(relativebrokeragefirm

size)isthenumber

ofanalystsworkingforthebrokeragefirm

oftheanalyst

duringtheyearminustheaverage

‘‘brokeragefirm

size’’ofallanalystsfollowingthesamefirm

thatyear.

R_FREQ

(relativeforecast

frequency)isthenumber

offorecaststheanalyst

madeforthefirm

duringtheyearbefore

theearningsannouncement,

minustheaverage‘‘forecast

frequency’’ofallanalystsfollowingthesamefirm

thatyear.



two ways: (a) require all forecasts to be made 5 to 180 days before theannual earnings announcement and (b) match each superior (inferior) ana-lyst with an average analyst who made the closest forecast within a 5-daywindow. They omit superior and inferior analysts who could not bematched with average analysts.11 These data requirements are very restric-tive, so in order to attain a larger sample size we control for forecast age inour multivariate analysis.

In every year, we identify analysts who are superior, average, and inferiorbased on our three ability metrics. We then examine forecast accuracy andinvestor reaction to earnings forecast revisions during the subsequent year(our holdout period). We define accuracy relative to all other analysts follow-ing the same firm that year. We provide a univariate analysis of relative fore-cast errors defined as analyst forecast error minus the average (mean) forecasterror for all analysts following the same firm in the same year, divided by theaverage forecast error of all analysts following the same firm in the same year(Clement 1999). We present univariate results for firm-specific ability, broadgeneral ability, and narrow general ability, combined firm-specific ⁄broadgeneral ability and combined firm-specific ⁄narrow general ability.

We use a multivariate approach relating relative forecast errors to ourability metrics, controlling for other determinants of individual forecast accu-racy. To determine the relative importance of firm-specific ability versus eithergeneral ability metric we begin by estimating the following regression:

R ERRORi;j;t ¼ a0 þ a1R F � Abilityi;j;t�1 þ a2R G� Abilityi;j;t�1

þ a3R AGEi;j;t þ a4R FEXPi;j;t þ a5R GEXPi;j;t

þ a6R NCOSi;j;t þ a7R NINDi;j;t þ a8R BSIZEi;j;t

þ a9R FREQi;j;t þ xi;j;t ð3Þ

where:R_F-Abilityi,j,t)1 (relative firm-specific ability) is analyst i’s firm-specific

ability for firm j in year t)1 minus average ‘‘firm-specific ability’’ for allanalysts following firm j in year t)1;

R_G-Ability i,j,t-1 (relative general ability) is R_G-Broadi,j,t-1 or R_G-Nar-rowi,j,t-1 or both;

R_G-Broadi,j,t-1 is analyst i’s broad general ability in year t)1 minusaverage ‘‘broad general ability’’ for all analysts following firm j in year t)1;

R_G-Narrowi,j,t-1 is analyst i’s narrow general ability in year t)1 minusaverage ‘‘narrow general ability’’ for all analysts following firm j in year t)1;

11. In untabulated results, we replicated Sinha et al. 1997 both over their sample period

(1984–1990) and ours. We obtained similar results to theirs in their sample period,

namely superior analysts remained superior but inferior analysts did not remain inferior.

However, in our sample period, superior analysts remained superior and inferior ana-

lysts remained inferior.



R_ERRORi,j,t (relative forecast error) is analyst i’s forecast error forfirm j in year t minus the average forecast error for all analysts followingthe same firm in the same year, divided by the average forecast error of allanalysts following the same firm that year;

R_AGEi,j,t (relative forecast age) is the number of calendar daysbetween analyst i’s last annual earnings forecast for firm j for year t priorto the earnings announcement and the earnings announcement date minusthe average ‘‘forecast age’’ of all analysts following firm j in year t;

R_FEXPi,j,t (relative firm experience) is the number of years (includingyear t) analyst i has followed firm j minus the average ‘‘firm experience’’ ofall analysts following firm j in year t;

R_GEXPi,j,t (relative general experience) is the number of years (includ-ing year t) analyst i has been in the I ⁄B ⁄E ⁄S database minus the average‘‘general experience’’ of all analysts following firm j in year t;

R_NCOSi,,j,t (relative firm complexity) is the number of firms analyst ifollowed during year t minus the average ‘‘firm complexity’’ of all analystsfollowing firm j in year t;

R_NINDi,j,t (relative industry complexity) is the number of industriesanalyst i followed during year t minus the average ‘‘industry complexity’’ ofall analysts following firm j in year t, where industry is defined via theCRSP two-digit SIC code;

R_BSIZEi,j,t (relative brokerage firm size) is the number of analystsworking for the brokerage firm of analyst i in year t minus the average‘‘brokerage firm size’’ of all analysts following firm j in year t; and

R_FREQi,j,t (relative forecast frequency) is the number of forecasts ana-lyst i made for firm j for year t before the earnings announcement, minusthe average ‘‘forecast frequency’’ of all analysts following firm j in year t.

Gleason and Lee (2003), who also examine multiple forecast revisionsfor the firm in a given year, raise the issue of cross-sectional dependence,where the ordinary least squares (OLS) assumption that the residuals areindependent and identically distributed is violated. When that assumption isviolated, the standard errors of the OLS estimates are biased downwardsand the test statistics are inflated. In the case of analyst forecast revisions,the cross-sectional dependence arises from having multiple observationsfrom the same firm (Gleason and Lee 2003, 203). Therefore, we correct forcross-sectional dependence. Our standard errors are based on a varianceand covariance matrix that assumes a common component for all the fore-cast revisions that come from the same firm.12

12. Petersen (2008), who provides an excellent review of the treatment of standard errors in

the finance literature, discusses these procedures in detail in the section entitled ‘‘Esti-

mating Standard Errors in the Presence of a Fixed Firm Effect’’. Moreover, he provides

programming advice on his website to correct for cross-sectional dependence: http://

www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/se_programming.htm#_

SAS_Programming_Instructions (accessed February 7, 2009).



We examine mean cumulative abnormal returns (CAR) around forecastrevisions based on the previous year’s forecasting ability classifications.CAR is the three-day cumulative abnormal return ()1, +1), where eventday zero is the forecast revision day. We calculate CAR by first estimatingthe market model over the period ()300, )46):

Rj;t ¼ b0 þ b1Rm;t þ vj;t ð4Þ

where Rj,t is the day t stock return for firm j and Rm,t is the value-weightedmarket return on day t. We then calculate CAR as the prediction error ofthe market model over the three-day trading event period ()1, +1):

CARj;t ¼ Rj;t � ðb̂0 þ b̂1Rm;tÞ ð5Þ

Similar to Park and Stice 2000, we run a multivariate regression whereour dependent variable is the three-day stock price reaction to the analystforecast revision and the forecast ability groups are interacted with the ana-lyst revisions. More precisely, we estimate:

CARi;j ¼ c0 þ c1FRi;j þ c2FR Otheri;j þXr

k¼0

c3;kFRi;j � Ability Indicatori;j;t�1

þXr

k¼0

c4;kAbility Indicatori;j;t�1 þ �i;j ð6Þ

where the new terms are as follows:Ability_Indicatori,j,t-1 is an indicator variable that equals 1 if analyst i is

classified in k accuracy group last year, and 0 otherwise, where k = supe-rior, average or inferior when we use three performance groups, or k =superior ⁄ superior, superior ⁄average, superior ⁄ inferior, average ⁄ superior,average ⁄average, average ⁄ inferior, inferior ⁄ superior, inferior ⁄average, orinferior ⁄ inferior when we use nine performance groups;

CARi,j is the three-day CAR ()1, +1), where day zero is the dayanalyst i makes his ⁄her final forecast for firm j;

FRi,j is analyst i’s forecast revision for firm j. We measure forecast revi-sion as the final forecast for firm j by analyst i minus his ⁄her previous fore-cast for the same firm, divided by firm j’s stock price two days before therevision; and

FR_Otheri,j is the average of all other forecast revisions of firm j madewithin the three-day window around analyst i’s forecast revision. FR_Otheris set equal to 0 if there are no other forecast revisions in the three-daywindow.

FR_Other controls for potential confounding effects of other forecastrevisions in the three-day window around the earnings forecast revision ofanalyst i. We correct all standard errors for cross-sectional dependence.



4. Results

Univariate analyses: Forecast accuracy

Table 3 presents relative forecast errors conditional on classifications ofanalysts in the previous year into three forecast ability groups: superior,average, and inferior. Panel A presents results for firm-specific ability, broadgeneral ability, and narrow general ability. Panel B presents results for thenine firm-specific ⁄general ability combinations, where general ability is

TABLE 3

Analysts’ relative forecast errors conditional on last year’s classifications

Panel A: Firm-specific ability, broad general ability, and narrow general ability

Ability Group F-Ability G-Broad G-Narrow

Superior )0.0382 )0.0619 )0.0246Average )0.0112 0.0058 0.0016

Inferior 0.1093 0.1355 0.0700

Superior – Inferior )0.1475 )0.1974 )0.0946t-value 19.13*** 24.40*** 12.50***

Panel B: Firm-specific ⁄broad general ability and firm-specific ⁄narrow general ability

F-Ability ⁄G-Broad F-Ability ⁄G-Narrow

Superior ⁄Superior )0.0886 )0.0551Superior ⁄Average )0.0302 )0.0490Superior ⁄ Inferior 0.0478 0.0122

Average ⁄Superior )0.0681 )0.0335Average ⁄Average )0.0128 )0.0194Average ⁄ Inferior 0.0941 0.0304

Inferior ⁄Superior )0.0019 0.0542

Inferior ⁄Average 0.0819 0.1061

Inferior ⁄ Inferior 0.2567 0.1556

Superior ⁄Superior - Inferior ⁄ Inferior )0.3452 )0.2107t-value 24.26*** 16.70***

Notes:

See notes to Table 1 for definitions of F-Ability, G-Broad, and G-Narrow.

Relative error is the analyst’s error minus the average error of all analysts following

the firm during that year divided by the average error of all analysts following the

firm during that year. Error is the absolute value of the difference between the ana-

lyst’s annual earnings forecast and actual earnings reported by the firm, deflated by

beginning of the year stock price.

*** Denotes significance (two-tailed test) at a probability level below 0.01.



defined first as broad general ability and second as narrow general ability.Panel A shows that the forecast errors are monotonic for all the forecastability metrics. Superior analysts are more accurate than average analysts,and average analysts are more accurate than inferior analysts for eachability measure. The spreads in relative forecast errors of superior versusinferior analysts are 0.1475, 0.1974, and 0.0946, for firm-specific ability,broad general ability, and narrow general ability, respectively (all differencesare significant at the 1 percent level).13 Significance tests (untabulated forsimplicity) reveal the following: (a) broad general ability has a significantlygreater spread than both firm-specific ability and narrow general ability(t-value = 6.17 and 12.71, respectively; p-value < 0.01 for both) and (b)firm-specific ability has a significantly greater spread than narrow generalability (t-value = 6.86, p-value < 0.01).

The panel B results suggest that general ability is incremental to firm-specific ability for explaining analyst forecast accuracy in holdout periodsand that broad general ability is more important than narrow general abil-ity for this purpose. To see this, we compare the spread in relative forecasterrors of superior versus inferior analysts based on firm-specific ability inpanel A with that based on firm-specific ⁄broad general ability in panel B.The firm-specific ability’s superior versus inferior spread of 0.1475 is lessthan half the magnitude of the combined firm-specific ⁄broad general abilityspread of 0.3452 of superior ⁄ superior versus inferior ⁄ inferior (untabulatedt-value = 13.89, p-value < 0.01). In contrast, the firm-specific ability’ssuperior versus inferior spread of 0.1475 is 70 percent the magnitude of thecombined firm-specific ⁄narrow general ability spread of 0.2107 for thecomparison of superior ⁄ superior with inferior ⁄ inferior analysts (untabulatedt-value = 5.01, p-value < 0.01).

Multivariate analyses: Forecast accuracy

Table 4 presents four sets of multivariate regression results estimating (3),showing incremental significance of ability after including control variablesknown to impact individual analyst forecast accuracy.14 The base model hasonly one measure of ability, namely firm-specific ability. The second (third)model adds our broad (narrow) measure of general ability to firm-specificability, while the fourth model adds both measures of general ability to

13. Positive (negative) relative error means that the individual analysts’ forecast error is

greater (lower) than the mean forecast error of all analysts following the same firm dur-

ing the same year. Therefore, superior (inferior) analysts always have negative (positive)

relative errors.

14. Because panel B of Table 2 shows high correlations among some of our independent

variables (e.g., R_FEXP and R_GEXP, R_NCOS and R_NIND), we calculate the vari-

ance inflation factor (VIF) in all the regressions in this sections. VIF is a multicollineari-

ty diagnostic, and a VIF greater than 10 suggests that multicollinearity might be a

problem. None of our variables has a VIF greater than 2.



TABLE

4

Multivariate

regressionsforrelativeforecast

errors

Variable

Expected

Sign

Base

Model

R_G-Broad

R_G-N

arrow

R_G-Broadand

R_G-N

arrow

Coefficient

t-value

Coefficient

t-value

Coefficient

t-value

Coefficient

t-value

R_F-A

bility

))0.0013

)11.25***

)0.0012

)10.15***

)0.0013

)10.4***

)0.0012

)9.95***

R_G-Broad

))0.0033

)11.98***

)0.0033

)10.81***

R_G-N

arrow

))0.0007

)5.39***

)0.0001

)0.84

R_AGE

+0.0053

59.35***

0.0053

59.31***

0.0053

59.32***

0.0053

59.31***

R_FEXP

))0.0035

)2.96***

)0.0034

)2.86***

)0.0035

)2.96***

)0.0034

)2.86***

R_GEXP

))0.0002

)0.23

)0.0003

)0.47

)0.0001

)0.18

)0.0003

)0.44

R_NCOS

+0.001

3.36***

0.001

3.22***

0.001

3.25***

0.001

3.21***

R_NIN

D+

0.0081

5.57***

0.0074

5.11***

0.008

5.51***

0.0074

5.11***

R_BSIZ

E)

)0.001

)14.16***

)0.0009

)13.53***

)0.001

)14.02***

)0.0009

)13.53***

R_FREQ

))0.0473

)29.20***

)0.046

)28.42***

)0.0471

)29.11***

)0.046

)28.41***

Adj.

R-Square

0.1937

0.1943

0.1937

0.1975

R_G-Broad-

R_F-A

bility

)0.0022

)7.91***

)0.0021

7.31***

(Thetable

iscontinued

onthenextpage.)



TABLE

4(C

ontinued)

Variable

Expected

Sign

Base

Model

R_G-Broad

R_G-N

arrow

R_G-Broadand

R_G-N

arrow

Coefficient

t-value

Coefficient

t-value

Coefficient

t-value

Coefficient

t-value

R_G-N

arrow

-

R_F-A

bility

0.0006

3.55***

0.0011

6.37***

R_G-Broad-

R_G-N

arrow

)0.0031

9.64***

Notes:

Weestimate

thefollowingregression:

where:

RER

RO

Ri;j;t¼

a 0þ

a 1R

F�

Abi

lity

i;j;t�

1þ

a 2R

G�

Abi

lity

i;j;t�

1þ

a 3R

AG

Ei;j;tþ

a 4R

FEX

Pi;j;tþ

a 5R

GEX

Pi;j;tþ

a 6R

NC

OS

i;j;t

þa 7

RN

IND

i;j;tþ

a 8R

BSIZ

Ei;j;tþ

a 9R

FR

EQ

i;j;tþ

xi;j;t

R_F-A

bilityi,j,t-1(relativefirm

-specificability)isanalyst

i’sfirm

-specificabilityforfirm

jin

yeart)1minustheaverage‘‘firm

-specific

ability’’forallanalystsfollowingfirm

jin

yeart)1;

R_G-A

bility,j,t-1(relativegeneralability)isR_G-Broadi,j,t-1orR_G-N

arrowi,j,t-1orboth.R_G-Broadi,j,t-1isanalyst

i’sbroadgeneralability

inyeart)1minusaverage‘‘broadgeneralability’’forallanalystsfollowingfirm

jin

yeart)1,defined

astheaverageofallthe

F-A

bilityscoresin

yeart)1excludingthescore

forfirm

j.R_G-N

arrowi,j,t-1isanalyst

i’snarrow

generalabilityin

yeart)1minus

average‘‘narrow

generalability’’forallanalystsfollowingfirm

jin

yeart)1defined

astheR_F-A

bilityscore

oftheoneother

firm

forwhichtheanalyst

issued

themost

forecastsduringyeart.

See

Notesto

Table

2fordefinitionsofthedependentvariable

andthecontrolvariables.

***

Denotessignificance

(two-tailed

test)ataprobabilitylevel

below

0.01.t-statisticsare

basedonstandard

errors

thatare

corrected

forcross-sectionaldependence.



firm-specific ability. Except for general experience, all the control variablesare significant with their expected signs in all four regressions.

Consistent with past research (Stickel 1992; Sinha et al. 1997), the coef-ficient on F-Ability of )0.0013 is significant in the base model (t-value =)11.25, p-value < 0.01), and its coefficient and t-value are similar in theother three models. Consistent with Hypothesis 1(a), general ability is incre-mental to firm-specific ability for explaining analyst accuracy in holdoutperiods. G-Broad and G-Narrow have coefficients of )0.0033 (t-value =)11.98, p-value < 0.01) and )0.0007 (t-value = )5.39, p-value < 0.01) inthe G-Broad and G-Narrow models, respectively, revealing that general abil-ity is incremental to firm-specific ability. The magnitude of the coefficienton F-Ability in the G-Broad model is only about 36 percent of the magni-tude of the coefficient of G-Broad, suggesting that broad general ability ismore important than firm-specific ability for explaining analyst accuracy inholdout periods (t-value of the difference between the coefficients = )7.91;p-value < 0.01). In contrast, the magnitude of the coefficient on F-Abilityin the G-Narrow model is nearly double that of G-Narrow, suggesting thatfirm-specific ability is more important than narrow general ability forexplaining analyst accuracy in holdout periods (t-value of the differencebetween the two coefficients = 3.55, p-value < 0.01).15

The combined G-Broad and G-Narrow general model provides directevidence consistent with Hypothesis 1(b). It reveals that, while both G-Broad and F-Ability remain significant with exactly the same coefficientsand nearly the same t-values as in the G-Broad model, the coefficient on G-Narrow of )0.0001 is only one-seventh of its magnitude in the G-Narrowmodel, and it is no longer significant. Direct tests among coefficients of thecombined G-Broad and G-Narrow model reveal the following: G-Broad issignificantly more important than both F-Ability and G-Narrow for explain-ing analyst accuracy in holdout periods (t-values of the differences betweenthe coefficients are )7.31 and )9.64, respectively; p-value < 0.01 for both).In contrast, F-Ability is significantly more important than G-Narrow forexplaining analyst forecast accuracy in holdout periods (t-value of the dif-ference between the two coefficients = 6.37, p-value < 0.01).

Stock price reactions to analyst revisions

We present our stock price reaction to analyst revision results in Table 5based on (5), which was derived for comparability with Park and Stice(2000). Panel A presents results for superior, average, and inferior analystsclassified by a single ability dimension, either firm specific, broad general, or

15. Recall that our narrow general ability measure is similar (but not identical) to the Park

and Stice 2000 spillover measure. While Park and Stice did not examine whether their

spillover measure possesses predictive ability, our finding that G-Narrow provides infor-

mation incremental to F-Ability suggests that their spillover measure may possess pre-

dictive ability.



TABLE

5

Market

reactionregressions

Panel

A:Firm-specificability,broadgeneralability,andnarrow

generalability

Variable

F-A

bility

G-Broad

G-N

arrow

Coefficient

t-value

Coefficient

t-value

Coefficient

t-value

FR

0.355

13.13***

0.356

13.14***

0.355

13.13***

FR

Other

0.373

6.75***

0.373

6.75***

0.373

6.75***

Superior

·FR

0.064

2.20**

0.083

2.89***

0.016

0.60

Average

·FR

0.046

1.69*

0.026

0.95

0.087

2.90***

Inferior

·FR

0.021

0.72

0.017

0.58

0.036

1.29

Adj.R-Square

0.016

0.016

0.016

Superior

·FR

-Inferior

·FR

0.043

2.08**

0.066

3.04***

)0.019

)0.95

Panel

B:Firm-specific

⁄broadgeneralability,andfirm

-specific

⁄narrow

generalability

F-A

bility

⁄G-Broad

F-A

bility

⁄G-N

arrow

FR

0.356

13.14***

0.355

13.13***

FR

Other

0.373

6.75***

0.373

6.75***

Superior

⁄Superior

·FR

0.079

2.17***

0.029

0.87

Superior

⁄Average

·FR

0.050

1.47

0.114

2.58***

Superior

⁄Inferior

·FR

0.059

1.26

0.068

1.62*

Average

⁄Superior

·FR

0.080

2.27***

0.011

0.33

Average

⁄Average

·FR

0.009

0.26

0.089

2.54**

Average

⁄Inferior

·FR

0.050

1.31

0.023

0.58

Inferior

⁄Superior

·FR

0.094

2.13**

)0.006

)0.14

(Thetable

iscontinued

onthenextpage.)



TABLE

5(C

ontinued)

F-A

bility

⁄G-Broad

F-A

bility

⁄G-N

arrow

Inferior

⁄Average

·FR

0.016

0.43

0.044

1.07

Inferior

⁄Inferior

·FR

)0.056

)1.47

0.021

0.57

Adj.R-Square

0.016

0.016

Superior

⁄Superior

·FR

-Inferior

⁄Inferior

·FR

0.135

3.75***

0.008

0.25

Notes:

Weestimate

thefollowingregression:

CA

Ri;j¼

c 0þ

c 1FR

i;jþ

c 2FR

Oth

eri;jþPr k¼

0

c 3;k

FR

i;j�

Abi

lity

Indi

cato

r i;j;t�

1þPr k¼

0

c 4;k

Abi

lity

Indi

cato

r i;j;t�

1þ� i;j

where:

Ability_Indicator i,j,t-1isanindicatorvariable

equalto

1ifanalyst

iisclassified

inkabilitygroupin

yeart)1,and0otherwise,

wherek

=superior,averageorinferior,ork

=superior

⁄superior,superior

⁄average,

superior

⁄inferior,average

⁄superior,

average

⁄average,

average

⁄inferior,inferior

⁄superior,inferior

⁄average,

orinferior

⁄inferior;

CARi,jisthethree-daycumulativeabnorm

alreturn

()1,+

1),wheredayzero

isthedayanalyst

imakes

his

⁄her

finalforecast

forfirm

j;

FRi,jisanalyst

i’sforecast

revisionforfirm

j.Wemeasure

forecast

revisionasthefinalforecast

forfirm

jbyanalyst

iminushis

⁄her

previousforecast

forthesamefirm

,divided

byfirm

j’sstock

price

twodaysbefore

therevision;and

FR_Other

i,jistheaverageofallother

forecast

revisionsoffirm

jmadewithin

thethree-daywindow

aroundanalyst

i’sforecast

revision.

FR_Other

issetatzero

ifthereare

noother

forecast

revisionsin

thethree-daywindow.

*,**,***denote

significance

(two-tailed

test)ataprobabilitylevel

below

0.10,0.05and0.01,respectively.t-statisticsare

basedon

standard

errors

thatare

correctedforcross-sectionaldependence.

Forsimplicity,wedonottabulate

theresultsfortheinterceptorthemain

effectsoftheabilityindicators.Wetabulate

theirinteractive

effectswithFR

astheseare

ourvariablesofinterest.



narrow general. Panel B presents results for superior, average, and inferioranalysts classified by two ability dimensions, firm specific in conjunctionwith either broad general or narrow general.

The panel A results regarding stock price reaction to analyst forecastrevisions based on firm-specific ability are similar to Park and Stice 2000:(a) prices move in the same direction as analyst forecast revisions and (b)stock prices move most (least) when superior (inferior) analysts revise theirforecasts. The difference in revision response coefficients between firm-spe-cific superior (Superior · FR) and firm-specific inferior (Inferior · FR) ana-lysts of 0.043 is significant at the 5 percent level (p-value = 0.038). Similarevidence is seen for revisions based on broad general ability, but the magni-tude of the spread is far greater. The spread between broad general inferiorand broad general superior analysts is about 53 percent larger than thatbetween firm-specific inferior and firm-specific superior analysts (0.066versus 0.043).

In contrast to firm-specific ability and broad general ability, stock pricesdo not react more to revisions by superior analysts than they do to revi-sions by inferior analysts with narrow general ability. Indeed, stock pricesreact most to revisions by average ‘‘narrow general ability’’ analysts andleast to revisions by superior ‘‘narrow general ability’’ analysts. Our resultsare consistent with Park and Stice 2000, who do not find a spillover effectfor stock market reactions to analyst earnings forecast revisions.16

Panel B of Table 5 shows results for the two groups, firm-spe-cific ⁄broad general and firm-specific ⁄narrow general. Our findings areconsistent with Hypothesis 2(a) that investors act as if general ability isincremental to firm-specific ability when using our broad measure of gen-eral ability, but inconsistent with Hypothesis 2(a) when using our narrowmeasure of general ability. Our findings are consistent with Hypothesis2(b) that investors pay more attention to general ability when it is basedon a broad than a narrow measure of this metric. To see these points,consider the last row of panels A and B of Table 5. The spread in stockprice reaction to forecast revisions by analysts who are superior on thecombined firm-specific and broad general ability dimensions is 0.135,more than triple (double) that of 0.043 (0.066) based on firm-specific(broad general) ability alone. In contrast, the firm-specific ⁄narrow generalability measure reveals a spread in stock price reaction to forecast revi-sions by analysts who are superior on both dimensions and analysts who

16. The result of strong reaction to forecast revisions by average G-Narrow analysts is sali-

ent in Table 5. In panel A, the coefficient for average (but not superior or inferior) ana-

lysts is significant. Moreover, in panel B, analysts with average G-Narrow and superior

firm-specific ability have a returns coefficient of 0.114 (t-statistic = 2.58), and analysts

with average G-Narrow and average firm-specific ability have a returns coefficient of

0.089 (t-statistic = 2.54). These coefficients and t-statistics suggest that investors per-

ceive analysts with average G-Narrow to differ from other analysts. We offer no expla-

nation for this anomalous result.



are inferior on both dimensions of 0.008, which is insignificantly differentfrom zero.17

5. Summary and implications

We show that analyst ability is not only firm specific. We document that abroad measure of general ability is incrementally relevant to firm-specific abil-ity on the two dimensions the literature has employed to evaluate analystearnings forecasts: predictive ability and stock price reaction (Foster 1977;Fried and Givoly 1982). Similar to Park and Stice (2000), we find that a nar-row definition of general ability does not explain stock price reaction to ana-lyst earnings forecast revisions. Our predictive ability results provide aplausible explanation for why this is so. We show that, while both a broadand a narrow measure of general ability are incremental to firm-specific abilityfor explaining analyst forecast accuracy in holdout periods, the relative mag-nitude of the broad (narrow) measure of general ability is about triple (half)that of the firm-specific measure. When we put all three ability measures inthe same equation, the firm-specific and broad general measures maintaintheir magnitudes and significance levels, but the magnitude of the narrow gen-eral measure drops about 85 percent and its coefficient becomes insignificant.

The notion that analysts have a general ability to predict earnings isintuitively appealing. Firms are dynamic, changing rapidly over time, sofirm-specific ability cannot completely capture an analyst’s ability to predictthose complexities that pertain to other firms but have not yet pertained toa specific firm, j. Different firms experience different complexities at differ-ent times, so analysts possessing general ability are better able to under-stand and incorporate complexities into their earnings forecasts, making itmore likely they will make superior firm-specific forecasts.

Our results are important to researchers, practitioners, and investors.The extant literature focuses on firm-specific ability, ignoring general ability.Our findings suggest that researchers who include firm-specific ability intheir predictive ability or stock price reaction models should also include abroad measure of general ability in their models. Practitioners and investorsseek accurate earnings forecasts. Our results suggest that their modelsshould incorporate a broad measure of general ability.

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inferior ⁄ superior with inferior ⁄ inferior. When we combined firm-specific ability with

broad general ability we obtained an F-value of 4.83 (p-value < 0.01). In contrast, when

we combined firm-specific ability with narrow general ability we obtained an F-value of

0.62 (p-value = 0.5988).



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is analyst earnings forecast ability only firm specific?

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