Three essays of financial analysts

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T H R E E ESSA Y S ON FIN A N C IA L A N A LY STS

By
Xi Li

Dissertation
Subm itted to the Faculty o f the
G raduate School o f V anderbilt U niversity
in partial fulfillm ent o f the requirem ents
for the degree o f

D O C TO R O F PH IL O SO PH Y
in
M anagem ent
August, 2002
Nashville, T ennessee
A pprove^:

Date:

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UMI Number: 3058711

C opyright 2002 by
Li, Xi

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C o p y rig h t © 2002 by Xi Li
A ll R ights Reserved

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To M y Parents, Jiannan Li and Z huangping Sun
and My wife, lin g M a

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A C K N O W LED G EM EN TS

This research project was successfully completed thanks to m any persons who
helped m e at various stages. I thank m y dissertation com m ittee m em bers, N ick Bollen,
Paul C haney, C raig Lewis, Hans Stoll, and especially my dissertation chairm an, Ronald
M asulis, for providing precious advice and support. I also appreciate the valuable help of
Bruce C ooil and Christoph Schenzler. I also thank the financial support of the
D issertation Enhancem ent G rant from

Vanderbilt U niversity and the 2001

AAH

A ccepted D issertation Proposal G rant o f the Financial M anagem ent A ssociation and
Am erican A ssociation of Individual Investors.
I am also grateful to the support and encouragem ent o f m y father, Jiannan Li, my
m other, Z huangping Sun, and my lovely w ife, Jing Ma, through this long effort. W ithout
them , I cannot im agine that I can finish this long adventure.

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T A B L E OF C O N T E N T S

Page
D E D IC A T IO N .......................................................................................................................................iii
A C K N O W L E D G E M E N T S ............................................................................................................... iv
LIST O F T A B L E S .............................................................................................................................. vii
LIST O F F IG U R E S ............................................................................................................................. ix
C hapter
I.

PER FO R M A N C E A N D BEH A V IO R O F IN D IV ID U A L
FIN A N C IA L A N A L Y S T S ....................................................................................................1
Introduction.......................................................................................................................... 1
Difference from Previous Literature.............................................................................3

Data........................................................................................................................................ 8
Experim ental D esign ....................................................................................................... 14
Em pirical R esults.............................................................................................................20
C onclusions....................................................................................................................... 48
R eferences......................................................................................................................... 51

II.

W ILL PA ST LEA D ER S STILL LEA D ? PER FO R M A N C E
PER SISTEN C E O F FIN A N C IA L A N A L Y S T S ........................................................... 55
Introduction....................................................................................................................... 55
Sam ples and M ethodology ............................................................................................ 61
Tw o-Period Perform ance Persistence........................................................................ 70
M ulti-Period Perform ance Persistence......................................................................88
C onclusions....................................................................................................................... 95
R eferences......................................................................................................................... 98

III.

CA REER C O N C E R N S O F EQUITY A N A LY STS: C O M PEN SA TIO N ,
T E R M IN A TIO N , A N D PE R FO R M A N C E ...................................................................101
Introduction ..................................................................................................................... 101
Related L iteratu re........................................................................................................... 108
Sam ple, R ankings, and Perform ance M easurem ent.............................................. 111
Em pirical A nalysis........................................................................................................ 123
C onclusions..................................................................................................................... 153
R eferences........................................................................................................................ 156

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Appendix
A.

C R E A T IN G EM PIRICA L F A C T O R S ..........................................................................159

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LIST O F TABLES
C hapter I
Table

Page

1.

S u m m ary Statistics of R ecom m endations...........................................................................11

2.

J e n s e n ’s A lpha o f Factor R egression For Test P ortfolios............................................... 18

3.

P erform ance o f Analysts as a G ro u p .................................................................................... 22

4.

P erform ance as a Group: O th er Factor M odels................................................................ 26

5.

Perform ance as a Group: Portfolios Rebalanced L ater Than
R ecom m endation D ate............................................................................................................. 29

6.

C ross-sectional Distribution o f Individual Analyst Perform ance.................................. 37

7.

C ross-sectional D eterm inants o f A nalyst Perform ance,
R isk T aking Behavior, and A ggressiveness....................................................................... 43

C hapter II
1.

S um m ary Statistics of R ecom m endation D atabase...........................................................63

2.

T w o -p erio d Perform ance Persistence over the W hole Sample P eriod.........................71

3.

P ersistence T est of T w o-period Perform ance by Pairs o f
C onsecutive Subperiods........................................................................................................... 75

4.

C on tin g en cy Table of W inners and Losers over the W hole Sample P eriod................78

5.

R isk-adjusted Performance o f D ecile Portfolios C reated
A ccording to Prior-period R isk A djusted Perform ance...................................................82

6.

R isk-adjusted Performance o f P ortfolios Created
A ccording to Prior-period R aw Return Perform ance...................................................... 85

7.

B uy-and-hold Returns o f P ortfolios Created
A ccording to Previous Period Raw Return Perform ance............................................... 87

8.

P ersistence T est for M ulti-period Perform ance..................................................................92

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C hapter III
1.

Sum m ary Statistics of the IBES R ecom m endation D atabase........................................ 113

2.

Sum m ary Statistics of All-star R anking..............................................................................118

3.

P redicting Institutional Investor A ll-stars........................................................................... 126

4.

Predicting Institutional Investor A ll-stars G iven Analyst Status
in the Prior Y ear.......................................................................................................................129

5.

The E ffect o f Past Performance and R isk-taking B ehavior
on the Institutional Investor A ll-A m erican S ta tu s...........................................................134

6.

Predicting W all Street Journal A ll-stars..............................................................................137

7.

Predicting W all Street Journal A ll-stars G iven A nalyst Status
in the P rior Y e a r....................................................................................................................... 140

8.

The E ffect o f Past Performance and R isk-taking Behavior
on the W all Street Journal A ll-A m erican S ta tu s .............................................................144

9.

Predicting D eparture from Analyst P rofession ..................................................................146

10. Predicting D eparture from Profession G iven A nalyst Status........................................ 149
11. The Effect o f Past Performance and R isk-taking Behavior
on Leave from Profession...................................................................................................... 152

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LIST O F FIGURES
C hapter I
Figure
1.

Page
A bnorm al Perform ance o f A nalysts around R ecom m endation date........................... 32

C hapter II

!.

CD Fs Illustrating O ne-sided Tw o-sam ple K-S T e st.......................................................90

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C H A PT E R I

PE R FO R M A N C E A N D BEHAV IO R O F IN D IV ID U A L
FIN A N C IA L ANALYSTS

Introduction
A cadem ic researchers are div id ed on the question o f w hether following
recom m endations o f analysts generates superior returns. Evidence follow ing the first
study by C ow les (1933) suggests th at analysts do not exhibit superior perform ance. Yet.
o ther researchers find both a strong event-period abnormal return w hen recom m endations
are revised and a significant post-event return drift that lasts a m onth or even longer
[Barber, Lehavy, M cN ichoIs, and T ruem an (2001), Elton, G ruber, and G rossm an (1986).
and W omack (1996)]. Post-event return drift is evidence against m arket efficiency.
Although a finding o f abnorm al returns is usually attributed to sam ple lim itations,
inaccurate perform ance m easurem ents, and insufficient risk adjustm ents, evidence in
favor of m arket efficiency is subject to the sam e problem s.1
To shed new light on the research on analyst recom m endations, this article
pursues three lines o f inquiry. It first evaluates the perfom iance o f recom m ended buy and
sell portfolios o f individual analysts. T he study of individual an aly sts’ portfolio
recom m endations is facilitated by a new source of data from Institutional Brokers
Estim ate System (IB E S ) that includes a m ore com prehensive set o f brokerage firm s and

individual financial analysts than p reviously available. W ith m ore accurate m easurem ent

1 S e e D im so n a n d M a rsh (1 9 8 4 ) an d W o m a c k (1 9 9 6 ) fo r a c o m p re h e n s iv e r e fe r e n c e o n e a rly lite ra tu re .
P ra c titio n e rs a ls o p ro v id e e v id e n c e to th is c o n tr o v e r s y . F o r e x a m p le , a re c e n t s tu d y b y R isk M e tr ic s G ro u p
s h o w s that a n a ly s ts p e r fo r m b a d ly o n a r is k - a d ju s te d b a s is (B ro w n (2 0 0 1 )).

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o f analyst perform ance an d extensive risk adjustments, I find that the equally w eighted
portfolios o f individual an aly sts’ recom m ended portfolios generate significant abnorm al
returns. T he abnorm al returns, for both buy and sell recom m endations, are insensitive to
the various factor m odels and risk adjustm ents used. Individually, about 10% o f analysts
significantly outperform benchm arks in their buy portfolios, and 6% o f analysts
significantly outperform in their sell portfolios. About 3% o f analysts significantly
underperform benchm arks for buys o r sells.
D ecom position o f the abnorm al perform ance reveals that it is generated m ainly
w ithin an event window starting at tw o trading days before the recom m endation dates
until about five trading days later, w ith no significant post-event return drift. The
disappearance o f return d rift is m ostly due to more complete risk adjustm ents. The
gradual disappearance o f the inform ation content in recom m endations also highlights
gradual inform ation release to a w ider group o f investors, a com m on industry practice.
T his practice and the strong, short-term nature o f abnormal perform ance by analysts is
related to R egulation FD w hich currently only requires synchronous inform ation release
by com pany m anagem ent to all investors. If preferred investors o f analysts such as the
firm ’s traders do obtain prio r inform ation about recom m endations and their public release
tim e and front-run less preferred clients such as individual investors, R egulation FD may
need to be extended to financial analysts.

T he second objective is to provide new evidence on the cross-sectional
determ inants o f analyst perform ance and the relationship betw een analyst perform ance
and inform ation environm ent.2 1 find that analyst characteristics can predict the

2

C le m e n t ( 19 9 9 ) a n d J a c o b , L y s , a n d N e a le ( 1 9 9 9 ) e x a m in e the d e te r m in a n ts o f a c c u r a c y o f a n a ly s t

e a r n in g s f o re c a s ts . F ra n c is a n d S o f f e r ( 1 9 9 7 ) a n d S tic k e l (1 9 9 5 ) in v e s tig a te c r o s s - s e c tio n a l d e te r m in a n ts o f

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perform ance differences o f individual analysts’ recom m ended portfolios. Individual
analyst perform ance improves significantly with the num ber o f recom m endations issued
and w ith the size o f their brokerage firm s. The num ber o f stocks covered also has a
significantly positive, but concave relationship with perform ance. T he optimal num ber of
stocks is betw een 12 and 13. A dditional evidence suggests that Institutional Investor (II)
A ll-A m erican status and the size o f the companies they cover have little power in
predicting analyst performance.
T h e third goal is to investigate the effect of analyst career concerns on their
behavior. Scharfstein and Stein (1 9 9 0 ), Prendergast and Stole (1996), and Zwiebel (1995)
all suggest that agents’ career c o n cern s should affect their behavior. They predict that
some agents will stay with the herd w hile others will be m ore aggressive. I find that AllA m erican analysts who have m ore reputation capital tend to recom m end more
conservative portfolios and deviate significantly less often from the portfolios
recom m ended by the representative analyst. Other characteristics also affect their
behavior. For exam ple, analysts co v erin g large firms or m ore stocks tend to select less
risky portfolios and analysts in larg e brokerage firms or m aking m ore frequent

recom m endations tend to recom m end more risky portfolios.

D ifference from Previous L iterature
T his article is very different from previous studies. In the first part of perform ance
evaluation, I im prove on all three aspects that are the focus o f the controversy about
analyst perform ance: Sam ple lim itation, insufficient risk adjustm ents, and inaccurate

e v e n t r e tu r n s a n d lo n g -ru n p e rfo rm a n c e o f in d iv id u a l re v isio n s a n d r e c o n f ir m a tio n s o f re c o m m e n d a tio n s
a n d e a r n in g s fo re c a s ts . S in c e th is a rtic le e x a m in e s the p e rfo rm a n c e o f p o r tf o lio s re c o m m e n d e d b y fin a n c ia l

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perform ance m easurem ents. First, I u se a comprehensive d ata set and examine the m ost
recent tim e period. The IBES database used has much m ore com prehensive coverage o f
brokerage firm s and analysts than a n y database used in the previously published research.
It includes m any more analysts from sm aller brokerage firm s than large databases such as
First C all and Zacks. It also includes im portant brokerage firm s such as Merrill Lynch,
Goldm an Sachs, and Donaldson, L u fk in , & Jenrette that are not in Zacks.
R ecom m endations from these three firm s compose about 10% o f all the
recom m endations.3 A nother advantage is that its time period is the 1990s, “The A ge o f
Analysts” . Few previous studies have exam ined this period w hile the influence and bias
of analysts have both increased trem endously during this period.
S econd, this study provides a n um ber of im provem ents to the research design for
evaluating analyst perform ance. O ne im provem ent incorporates the recent advances in
long-run perform ance evaluation literatu re [Brav, Geczy, and G om pers (2000), D aniel,
Grinblatt, T itm an, and W erm ers (1 9 9 7 ), and Eckbo et al. (2000)]. W ith more careful risk
adjustm ent, I obtain m ore accurate ev id e n c e on market efficiency as it pertains to lo n g ­

term perform ance. I also evaluate the perform ance o f both equal- and value-w eighted
analyst p o rtfo lio s.4 A second im provem ent is to employing the m ethodology in the recent
mutual fund perform ance literature to evaluate individual a n a ly sts’ recom m endations on
a daily basis, which allow s more e fficien t coefficient estim ates [Bollen and Busse (2001)
and B usse (1999)]. Daily data can a lso dem and a shorter tim e series for individual

a n a ly sts, m y e v id e n c e is c o m p le m e n ta ry to t h e p re v io u s studies.

! 10% is a c c o r d in g to th e IB E S d a ta b ase. T h is p e rc e n ta g e will be e v e n la r g e r c o m p a re d to th e Z a c k s
d a ta b a s e b e c a u s e Z a c k s d o e s n o t o ffer the r e c o m m e n d a tio n from a s m a n y s m a ll e r b ro k e ra g e firm s a s IB E S .
4 P re v io u s lite r a tu r e in v e s tig a te s the p e r fo r m a n c e o f c ith e r value- o r e q u a l- w e ig h te d p o rtfo lio s. S o m e
d is a g re e m e n t e x is ts a s to w h e th e r valu e- o r e q u a l- w e ig h te d p o rtfo lio s a rc th e b e s t c h o ic e for te s ts o f
p e rfo rm a n c e o v e r lo n g h o riz o n s . T o test fo r a b n o r m a l p e rfo rm a n ce , a n e q u a lly w e ig h te d p o rtfo lio is m o re

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analysts and thus reduce potential survivorship bias. M onthly data are also used for the
purpose o f corroboration. The third im provem ent is m ore frequent updating o f the
m atching p ortfolios and factors. W hile m ost existing research updates the factor
portfolios annually, I construct book-to-m arket and eam in g s/p rice factors or m atching
portfolios quarterly, and size, m om entum , and liquidity factors o r m atching portfolios
monthly. T his frequent updating should im prove the accuracy o f risk adjustment
benchm arks.
The fourth improvement is to m easure analyst perform ance more precisely than
existing studies that focus on long-run perform ance. B ecause analysts may revise their
recom m endations within weeks or m onths after the original recom m endation, I keep the
stocks in the analyst portfolio until analysts revise their recom m endations. Previous

studies follow s recom m endations o f analysts for an arbitrary holding period such as 6 or
12 months, usually because they lack recom m endation revision dates. This type o f
assum ption co u ld m isrepresent analyst perform ance.5 M y experim ental design is also
advantageous com pared to studies ex am in in g the event effect o f recom m endation
revisions b ecau se 1 can exam ine the post-event return drift flexibly, and com pare the
magnitude o f event-period abnorm al returns and post-event return drift. This study o f
recom m ended portfolios is also o f interest because this is how brokerage houses suggest
that custom ers use their recom m endations [Elton et al. (1986), and Jasen (2001)].
T he m o st important im provem ent in experim ental design is the study of
individual an a ly sts’ recom m ended portfolios. The existing research has exam ined

re a so n a b le . T o a s s e s s th e w ealth e ffe c t o n in v e s to r s o f fo llo w in g re c o m m e n d a tio n s , v a lu e -w e ig h te d
p o rtfo lio s a re m o r e a p p ro p ria te .

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recom m endations only at the aggregate level, or at best, at the brokerage firm level,
partly because they lack a database with com prehensive coverage o f brokerage firms and
analysts. The inability to identify good analysts significantly impairs the value o f this
research. First, it is im possible even for institutional investors to hold portfolios o f all the
stocks recom m ended by a single brokerage firm, but even individual investors can
generally trade on the recom m ended portfolios o f individual analysts at low transaction
costs. Second, as Barber et al. (2000) point out, an investm ent strategy based on
recom m endations would be m ore profitable if good perform ers could be identified so that
only their recom m endations are fo llow ed/’ 7 As is said in the mutual fund industry, “Buy
the m anagers, not the fund” [C ullen et al. (2000)]. S tudying the average perform ance of
financial interm ediaries such as brokerage firms m ay be m uch less interesting because the

valuable elem ent o f a sell-side research departm ent is its analysts, as in the m utual fund
industry. The hiring or losing o f good analysts can affect the perform ance o f brokerage
firm s. The study o f recom m ended portfolios also enables us to investigate for the first
tim e a w ide range o f interesting questions such as cross-sectional distribution o f
perform ance, determ inants o f individual analysts’ p ortfolio perform ance and behavior,
and perform ance persistence.
Lastly, I study several new questions in the first part o f this article. I exam ine
characteristics o f individual analysts and their recom m ended portfolios in detail for a
large sam ple o f analysts. I also investigate the perform ance o f frequently used factor

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S tu d ie s o f lo n g -ru n p e rfo rm a n c e c o u ld u n d e re s tim a te a n a ly s t p e r fo rm a n c e if a n a ly s ts h a v e re v is e d th e ir

r e c o m m e n d a tio n s e a r lie r o r la te r th a n th e c u t- o f f p e rio d . H o w e v e r , th e s e stu d ie s c o u ld a ls o o v e re s tim a te
a n a ly s t p e rfo r m a n c e in th o se r e c o m m e n d a tio n s m a d e u n d e r b ia s e d in c e n tiv e s for th e s a m e re a s o n .
6 A n o th e r d ir e c tio n o f re se a rc h is in v e s tig a tio n o f w h e th e r s o m e ty p e s o f re c o m m e n d a tio n s a re m o re
in f o r m a tiv e th a n th e o th e r re c o m m e n d a tio n s . L in a n d M c N ic h o ls (1 9 9 8 ) a n d M ic h a e ly a n d W o m a c k (1 9 9 9 )
a re s o m e g o o d e x a m p le s o f th is d ir e c tio n o f re se a rc h .

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m odels w ith daily data and p ro v id e the first horse race b etw een traditional factor m odels
and the m acro factor m odels u sed in Eckbo, Masulis, and N orli (2000). In addition, this
article exam ines the cross-sectional perform ance differences in analysts’ recom m ended
portfolios and the proportions o f good and bad analysts.
The advantages o f m y sam ple and experimental d esign for the perform ance

evaluation naturally extend to the second and third parts o f this article. In addition, in the
second part o f this article, I stu d y the cross-sectional determ inants o f analysts’ portfolio
perform ance instead o f event p eriod abnorm al perform ance. Portfolio perform ance
includes both event period abnorm al perform ance and any potential post-event abnorm al
perform ance generated by analyst recom m endations. It sh o u ld be a more com prehensive
m easure for overall analyst perform ance. In the third part o f this article, I investigate for
the first tim e the im pact o f c a re e r concerns on the behavior o f analysts with different
reputation. Previous literature has only exam ined the im pact o f career concerns on the
behavior o f analysts with different age or experience [C hevalier and Ellison (1998),
Hong, Kubik, and Solom on (1999), and Lam ont (1995)]. In addition, I use investm ent
recom m endations rather than earnings forecast data as in H o n g et al. (1999), the only
existing study about the im pact o f analyst career concerns on their behavior.
The article is organized as follows: Section 2 describes the sample. Section 3
discusses the econom etrics o f the factor m odels and benchm arks the perform ance o f
various factor m odels. It also g ives details about the m ethodology used to form the
analyst portfolios and their m atching portfolios. Section 4 presents the em pirical results.
Section 5 offers concluding rem arks.

A n e c e s s a ry c o n d itio n fo r id e n tify in g g o o d a n a ly s ts to b e a really p r o f ita b le s tra te g y is th a t c u r re n t g o o d
p e rfo r m e rs w ill d o w e ll in the fu tu re .
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Data
The prim ary database u sed in this paper comes from IBES. Its m ajor benefit is
that it includes recom m endations from a very broad sample o f brokerage firm s and

financial analysts. Even large d atab ases such as Zacks do not include im portant bulge
bracket firms such as M errill L ynch, G oldm an Sachs, and D onaldson, Lufkin, & Jenrette.
The IBES database includes all m ajor brokerage firms plus a large sam ple o f sm aller
brokerage firms. Analysts can a lm o st alw ays be tracked even if they sw itch brokerage
firm s. Various m arket participants, including professional investors, use this database.
IBES has collected buy and sell recom m endations from the research reports o f
financial analysts since the end o f O ctober 1993.8 The database includes both the ratings
based on the system s adopted by individual brokerage firms and a standardized IBES
rating. The form er are usually on a three- to five- level scale. T he IB E S-created ratings
are on a uniform five-level scale; character ratings of “strong b u y ,” “buy,” “hold,”
“underperform ,” and “sell” c o rresp o n d to numeric ratings from I through 5.
Recom m endations with num eric ratings o f 1 are used to form the buy portfolios o f
financial analysts, and the sell portfolios are formed using recom m endations w ith ratings
o f 4 and 5.9 The investm ent recom m endation data are from the end o f O ctober 1993 to
D ecem ber 2000. The return and accounting data are drawn from C R SP and C om pustat,
respectively.
Panels A and B o f T able 1 sum m arize the database. T here are 241,222
recom m endations by 7,308 financial analysts from 408 institutions in the five buy and

B e c a u s e the d a te s o n th e r e s e a rc h r e p o r ts u s u a lly p re c e d e the d a te s a n a ly s ts a c tu a lly d e li v e r th e re p o rts to
th e p u b lic , I th ere fo re u se “ re p o rt d a t e s ” o r “ re c o m m e n d a tio n d a te s ” f o r th e d a te s o n th e r e s e a r c h re p o rts
a n d “ p u b lic a n n o u n c e m e n t d a te s ” f o r th e a c tu a l p u b lic a n n o u n c e m e n t d a te s .
8

T h is is b ecause th e re a re m a n y f e w e r n e g a tiv e r e p o rts .

8

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sell recom m endation categories. In the empirical analysis, I exclude analysts with fewer
than 10 recom m endations. T he rem aining sample consists o f 4,383 analysts before other
restrictions are applied. Panel B indicates that favorable recom m endations are much more
prevalent, consistent with o th er databases. The ratio o f strong buys to sells for the entire
sam ple period is about 15-to-l. Panel B also suggests that both the n u m b er o f negative
recom m endations and their percentage o f all recom m endations decline over tim e, despite
the grow ing num ber of total recom m endations made each year. T his suggests that the
b u y -to -sell ratio declines co n tinuously throughout my sam ple period. T o put the changes
in the buy-to-sell ratio in a historical perspective, according to Zacks Investm ent
R esearch, the ratio of “buy” and “strong buys” to “underperform ” and “sell” is 0.9 to 1 in
1983, 4 to 1 by the end o f the 1980s, 8 to 1 in early 1990s, and 48.2 to 1 in 1998
[Laderm an (1998)). This dram atic m onotonic decline in negative recom m endations
independent o f market conditions indicates increasing distortions in financial analysts’
incentives in recent years, instead o f a m ore bullish position by analysts in the later part
o f my sam ple period.
A verage characteristics o f analysts are reported in Panel C .10 T h e m ean market
capitalization o f stocks analysts co v er increases significantly over tim e w ith a range of $3
to $13 billion (Stkcap). T he sh arp increase in the average m arket cap is m ainly a result o f
increased stock price levels during the sam ple period, as the m ean size decile o f stocks
covered by analysts is generally betw een 4 and 5 (Caprk). A nalysts m ake betw een 11 to

10

S tk c a p is th e s iz e o f the s to c k s c o v e r e d b y a n a ly s ts and is m e a su re d a s th e m e a n m a r k e t v a lu e o f c o m m o n

s to c k s o f th e firm s th at a n a ly sts c o v e r in a s p e c if ic y ear. I o b ta in the m a rk e t c a p w h e n th e a n a ly s ts issue
th e ir r e c o m m e n d a tio n s . S ec a p p e n d ix fo r d e ta ils a b o u t how th e size d e c ile s fo r C a p r k a r e c r e a te d . D u ra is
th e n u m b e r o f d a y s betw een th e f irs t a n d th e la s t re c o m m e n d a tio n in a y e a r d iv id e d b y th e to ta l n u m b e r o f
re c o m m e n d a tio n s . B rk sz, o r the s iz e o f b r o k e r a g e h o u se , is m e a su re d a s th e n u m b e r o f a n a ly s ts th at

b e lo n g s to it in th e IB E S d a ta b a s e w ith in a y e a r. I f a n aly sts sw itc h firm s w ith in a y e a r , th e y a re a ssig n e d
th e a v e ra g e s iz e o f th e tw o b r o k e ra g e firm s .

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16 recom m endations a year (N rec), and the average tim e betw een tw o recom m endations
ranges from 23 to 30 days (D ura). The statistics indicate that analysts are now m aking
few er recom m endations a year, and it takes longer for them to m ake recom m endations,
perhaps because analysts sim ply tend not to dow ngrade their previous positive
recom m endations. A nalysts on average cover about 14-15 stocks (Nstk). The average
brokerage firm em ploys betw een 30 and 50 analysts. Firm size increases over tim e
(B rksz), which could be a result of absolute increase in size or the significant
consolidation in the brokerage industry that took place during this tim e p e rio d .11
Panel D o f T able 1 show s that analysts have an average o f 6.3 stocks in their buy
portfolios and 2.2 stocks in their sell portfolios over the sam ple period. It also presents
m ean deciles of several com m on characteristics o f com panies covered by analysts,
including size, book-to-m arket ratio (BM), m om entum (M O M ), share turnover (TO ), and
earnings/price (EP) at the tim e of the recom m endation, categorized by type of
recom m endation and y e a r.1" Decile 1 (decile 10) includes stocks w ith the largest
(sm allest) m arket capitalization, highest (low est) book-to-m arket, price m om entum ,
trading volume, and share turnover. At the tim e o f recom m endation, each recom m ended
stock is placed into a specific decile. The average decile o f each characteristic is
calculated for stocks recom m ended as buys and sells each year. If all NYSE stocks were
w eighted equally, the average decile would be 5.5 for all characteristics. If the average
stocks in analyst portfolios have characteristic deciles fairly different from 5.5, this would

11

S o m e s ta tis tic s for 1993 a r e m is s in g b e ca u se th e d a ta b a s e s ta rts in O c to b e r 1993.
P le a s e se c a p p e n d ix fo r m o r e d e ta ils o f the d e fin itio n o f v a ria b le s .

10

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Table 1
Summary Statistics of Recomm endations
T a b i c 1 r e p o rts s u m m a r y sta tistic s o f r e c o m m e n d a tio n s . P a n e l A p r e s e n ts s u m m a ry s ta tis tic s re g a r d in g the
s iz e o f IB E S re c o m m e n d a tio n d a ta b a s e . P a n e l B re p o rts n u m b e r o f re c o m m e n d a tio n s in e a c h c a te g o r y as
p e r c e n ta g e s o f all re c o m m e n d a tio n s b y y e a r, e x c e p t th e la s t c o lu m n w h ic h re p o rts th e to ta l n u m b e r ol
re c o m m e n d a tio n in e a c h y e a r. P anel C re p o rts th e a v e ra g e a n a ly s t c h a r a c te ris tic s , w h ic h i n c lu d e m ark et
c a p a n d d e c ile r a n k o f m a rk e t cap o f th e s to c k s th e y c o v e rs ( S tk c a p a n d C a p r k , re s p e c tiv e ly ), th e av erag e
d u r a tio n b e tw e e n tw o re c o m m e n d a tio n s (D u ra ), th e siz e o f th e i r b r o k e ra g e firm (B rk s z ), th e n u m b e r o f
re c o m m e n d a tio n s th e y issu e (N re c), a n d th e n u m b e r o f s to c k s th e y c o v e r (N s lk ). P a n e l D s h o w s th e m ean
o f th e n u m b e r s o f s to c k s , siz e , b o o k -to -m a rk e l (B M ), m o m e n tu m (M O M ), sh a re lu rn o v e r /T O (m o n th ly
v o lu m e d iv id e d b y n u m b e r o f sh a res o u ts ta n d in g in th e p r e v io u s m o n th ), a n d e a rn in g s /p ric e ( e a r n in g s per
s h a re s ta n d a r d iz e d b y p ric e in the fisc al q u a rte r e n d in g tw o q u a r te r s b e fo re ) (E P ) d e c ile s o f th e s to c k s that
a n a ly s is r e c o m m e n d . M o m e n tu m is b u y -a n d -h o ld re tu rn o v e r th e p a s t 12 m o n th s , e x c lu d in g th e p re v io u s
m o n th . A ll th e d e c ile s a re b a se d o n N Y S E c u to ff s . D e c ile 1 ( 1 0 ) c o n ta in s th e la rg e st ( s m a lle s t) sto ck s,
s to c k s w ith h ig h e s t (lo w e s t) b o o k -to -m a rk e t, p r ic e m o m e n tu m , a n d s h a re tu rn o v e r. D e c ile s a r c re fo rm e d
m o n th ly e x c e p t f o r b o o k -lo -m a rk e t a n d e a r n in g s /p r ic e , w h ic h a re re fo rm e d q u a rte rly . In th e c o lu m n s
u n d e r th e se ll r e c o m m e n d a tio n s , I re p o rt th e r e s u lts o f th e tw o -s a m p le t- te s t o f the h y p o th e s is th a t the
m e a n c h a r a c te r is tic s o f th e b u y and se ll s a m p le s a r e n o t s ig n if ic a n tly d if fe re n t. *** a n d ** in d ic a te th a t ls ta lis tic s a re s ig n if ic a n t a t 1% and 5 % le v e ls, r e s p e c tiv e ly . A lth o u g h th e re s u lts for th e m e d ia n s a re not
r e p o rte d in th e ta b le , th e y a re very s im ila r to th a t o f m e a n s. T h e d a ta a re fro m O c to b e r 1 9 9 3 th ro u g h
D ecem ber 2000.

_____________________ P a n e l A : S u m m a ry S ta tis tic s o f IB E S R e c o m m e n d a tio n D a ta b a se
N u m b e r o f A n a ly s ts : 7 3 0 8
N u m b e r o f B ro k e rs : 4 0 8
N u m b e r o f A n a ly s ts w ith > 10 re c o m m e n d a tio n s : 4 3 8 3
N u m b e r o f A n a ly s ts w ith > 100 re c o m m e n d a tio n s : 5 6 3 _______________________________
P a n e l B : B re a k d o w n o f th e R e c o m m e n d a tio n C a te g o r ie s b y Y ear
IB E S R a tin g s

Sell

T o ta l

3%

15337

2

3

29521

36

2

3

30854

33

32

2

2

29734

31

37

29

1

2

30350

29

39

30

1

1

35445

40

28

■>

1

37318

31

40

27

1

1

32663

29

36

32

2

2

241222

S tro n g B uy

2

3

4

1993

2 6%

30%

39%

2

1 994

25

33

37

1995

27

32

1996

30

1997
1998
1999

30

2 0 0 0

A v e ra g e

%

Panel C : M e a n C h a ra c te ris tic s o f A n a ly s ts
Y ear

C a p rk

S tk c a p

1993

4 .3 9

3 .8 4

D u ra

B rk sz

N rec

N s tk

1994

4 .7 3

3 .1 0

2 3 .2 8

3 0 .7 7

16.15

1 4 .4 9

1995
1996

4 .7 3

3.24

2 5 .3 5

3 2 .7 4

14.81

15.3 4

4 .8 8

3 .7 6

2 6 .7 6

3 5 .3 6

12.79

1997

5 .0 2

1 5 .5 6

4 .4 5

2 7 .5 5

4 0 .2 4

11.67

14.71

1998

4 .7 9

5 .9 4

1999

4 .3 4

2 6 .8 2

4 5 .5 1

12.04

1 3 .8 3

8 .2 1

28.61

4 7 .4 0

1 1 .6 8

1 4 .0 2

2 0 0 0

3 .7 7

12.59

3 0 .7 9

4 6 .1 0

10.74

1 4 .3 6

A v e rag e

4 .5 8

5 .6 4

2 4 .1 5

3 7 .8 6

12.7 9

1 4 .1 7

11

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Table 1, continued
P a n e l D . M e a n D e c ile s o f C h a ra c te ris tic s o f S to c k s A n a ly s is R e c o m m e n d
B uy R e c o m m e n d a tio n s __________________________
Y ear

N stk

1993

S ize

BM

MOM

TO

EP

4 .6 0

6 .9 8

5 .2 4

3.65

5 .5 9

S e ll R e c o m m e n d a tio n s
N stk

S iz e

BM

MOM

TO

EP

4 .2 2

5 .4 4 * *

6 .3 4 * * *

3 .8 8

5 .9 0

1994

6 .9

4.81

6 .9 6

4 .8 9

3 .5 6

5 .6 8

2.5

4 .2 3

5 .8 0 * *

6 .5 1 * * *

3.91

5 .5 7

1995

6 .9

4 .7 4

7 .0 0

4 .3 5

3 .4 3

5 .9 0

2.3

4 .0 8

5 .9 7 * *

3 .6 8

5 .9 6

1996

6 .7

5 .1 6

7 .0 6

4 .4 5

3.31

6 .2 3

2.5

4 .3 3

5 .9 9 * ’

5 .8 2 * * *
5 7 3 * .,

3 .8 0

5 .7 9

1997

6.4

5 .2 7

7.11

4 .9 9

3 .3 7

6 .4 2

2 .0

4 .7 5

5 .9 4 * *

6 .6 6

***

3 .8 5

6 .2 3

1998

6 .0

4 .8 9

7 .0 3

4 .6 6

3 .4 9

6 .4 3

2 .0

4.71

5 .9 1 * *

6 .7 2 * * *

3 .7 3

6 .2 6

1999

5 .8

4.21

7 .3 6

4 .2 6

3.14

6 .5 8

2 .0

4 .7 4

6 .2 4 * *

6 .4 8 * * *

3 .4 8

6.61

2 0 0 0

5 .6

3 .5 9

7 .9 2

3.85

2 .8 6

6.9 5

2 .1

4 .2 3

6 .6 6

6 .9 2 * * *

3 .2 4

6 .2 3

A v e ra g e

6.3

4 .6 6

7 .1 8

4 .5 9

3.35

6 .2 2

2 .2

4 .4 1

3 .7 0

6 .0 7

**

5 .9 9

6 .4 0


suggest that analysts cover stocks that have very different characteristics fro m the overall
market.
T he evidence concerning size is consistent w ith previous evidence th at analysts
usually cover large-cap stocks. The firm s in the IBES database are significantly sm aller
than the sam ple in W omack (1996), w ith a m ean decile o f about 4.5. The p o ssib le reason
is that W om ack (1996) looks only at th e recom m endations m ade by the top 14 AllAm erican research departm ents ranked by //. S m aller brokerage firms, such as regional
firms, usually co v er much sm aller stocks.
Panel D o f Table 1 also indicates that analysts overall tend to cover grow th over
value stocks, w ith mean book-to-m arket decile alw ays above that of overall m arket for
both buys and sells. They also recom m end stocks w ith higher book-to-m arket ratios as
purchases. T his could reflect a tem poral trend by analysts to cover more g ro w th stocks.
Panel D also suggests that analysts recom m end stocks w ith m om entum c lo se to overall
m arket as buys and low m om entum stocks as sells. T he difference in m om entum between
buys and sells is sim ilar to what is reported in W om ack (1996). The statistics also suggest
that analysts cover more liquid stocks, w hich is to be expected because investors have
more trading interest in those stocks. M oreover, analysts also cover more sto ck s with
m edian to low earnings/price, a pattern that has becom e m ore significant in recent
years.

i?
T o assess the statistical significance o f difference betw een the characteristics of

the analyst buy and sell recom m endations, I use a tw o-sam ple t-test and a nonparam etric
rank-sum test to com pare the mean and m edian characteristics o f buys and sells. Since

13 T h e a b o v e re s u lts a b o u t the c h a r a c te ris tic s o f s to c k s c o v e r e d b y a n a ly s ts a re c o n siste n t w ith Jc g a d e e s h ,
K im , K ris c h c , a n d L e e (2 0 0 1 ).

13

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