What Does It Take to Become a Superstar?
Evidence from Institutional Investor Rankings of
Financial Analysts
Andrew Leone* and Joanna Shuang Wu**
*
**
Smeal College of Business
Penn State University
ajl14@psu.edu
William E. Simon Graduate School of Business Administration
University of Rochester
wujo@simon.rochester.edu
May 23, 2007
We thank Ana Albuquerque, Barclay Douglas, Marty Butler, Elizabeth Demers, Zhaoyang Gu, Philip
Joos, Carolyn Levine, Cliff Smith, Charlie Wasley, Ross Watts, Tzachi Zach, Jerry Zimmerman and
workshop participants at Carnegie Mellon University and University of Rochester for helpful
comments. We thank the Simon School for financial support. Analyst earnings forecast data are
obtained from I/B/E/S and stock recommendation data are obtained from First Call.
Electronic copy available at: http://ssrn.com/abstract=313594
What Does It Take to Become a Superstar?
Evidence from Institutional Investor Rankings of
Financial Analysts
Abstract
We study the attributes of superstar financial analysts ranked in the Institutional
Investor magazine annual surveys from 1991 to 2000. In addition to documenting a strong
positive relation between the rankings and analyst performance, we also investigate whether
the performance by ranked analysts is due to luck or superior ability. Our results indicate that
ranked analysts’ performance is likely due to their superior ability because there is
performance persistence and the ranked analysts are recognized as leaders by other analysts
even before they first become ranked by Institutional Investor. We next test whether the
superior ability of ranked analysts stems from their greater experience or innate talent. Our
findings point to an “innate talent” rather than an “experience” explanation for the ranked
analysts. Collectively, the evidence supports the notion that the Institutional Investor
rankings serve the meaningful role of identifying high quality analysts in the labor market and
is inconsistent with the allegation that the rankings are ‘popularity contests’ with little
substance.
Keywords: financial analysts; rank-order tournament; superstar.
JEL classification: G10; J33; M41.
Electronic copy available at: http://ssrn.com/abstract=313594
1. Introduction
The Institutional Investor magazine rankings of financial analysts are widely viewed as a
measure of analyst reputation.1 Anecdotal evidence suggests these rankings play a significant
role in determining analysts’ pay (e.g. Stickel, 1992).2 An analysis of the mechanism behind the
rankings is therefore important for understanding the incentives facing financial analysts and for
understanding the roles these rankings play in the functioning of the financial analyst labor
market.
Each year Institutional Investor magazine surveys a large number of the consumers of
sell-side analyst research, which include portfolio managers, investment officers, and buy-side
analysts at major money management institutions worldwide. For expositional convenience we
refer to this group of voters collectively as “buy-siders.” The survey asks buy-siders to evaluate
the quality of sell-side analysts in each industry. Important performance dimensions include
industry knowledge, written reports, stock picks, earnings estimates, timely communication with
investors, and responsiveness to investor requests. The resulting rankings from the surveys are
powerful determinants of sell-side analyst reputation, although what exactly ‘reputation’
represents is still under debate three decades after the rankings were first introduced. Some view
the rankings as a measure of analyst quality; while others allege that the poll is largely a ‘beauty
contest’ with little substance.
Our understanding of the mechanism behind the Institutional Investor rankings is still
limited. Using data from the 1980’s, Stickel (1992) documents a positive relation between the
rankings and analyst performance in terms of earnings forecast accuracy, forecast frequency, and
1
Numerous academic studies use Institutional Investor rankings as a proxy for analyst reputation, for example,
Krigman, Shaw, and Womack (2001), Hong and Kubik (2003), and Cliff and Denis (2004).
2
In a 1997 article on sell-side analysts, Fortune Magazine writes “Weirdly, salaries are determined by where
analysts rate in a magazine survey—Institutional Investor’s annual ranking as determined by a poll of the buy side.”
1
Electronic copy available at: http://ssrn.com/abstract=313594
price impact of forecast revisions. However, many questions remain unexplored about the
driving forces behind the analysts that become ranked. For example, given the large payoffs to
becoming a ranked analyst do some analysts simply gamble and those who happen to get it right
become ranked? Or do the analysts that become ranked have superior ability? If the rankings
indeed identify high quality in analysts, is their superior ability due to their greater experience or
innate talent? Compared to a study that links performance with analyst reputation as in Stickel
(1992), ours is a broader inquiry about the qualities that make an analyst a superstar.
Our study addresses four research questions.
First, we extend Stickel (1992) and
investigate the link between the Institutional Investor rankings and analyst performance. We
expect the rankings to identify superior performers. If they did not, it is unlikely they would
have survived for this long or have had such an impact on analysts’ pay. After establishing the
relation between Institutional Investor rankings and performance, we explore the second question
of whether the superior performance of ranked analysts is attributable to luck or superior ability.
Third, to the extent we find evidence of superior ability for ranked analysts we test whether it is
the result of their greater experience or innate talent. Finally, investigate how Institutional
Investor rankings affect analyst career outcomes to provide validation for the rankings’ role in
analyst reward system. Below we discuss each research question in turn.
A positive relation between the Institutional Investor rankings and analyst performance
is consistent with the rankings serving an economic role of resolving information asymmetry in
the labor market rather than being ‘popularity contests.’ Our performance measures are broader
than those in Stickel (1992) and our use of a multivariate regression framework better controls
for confounding factors compared to Stickel’s univariate setting. In addition, our sample period
spans the decade of the 1990’s (versus the 80’s in Stickel) which corresponds to the rise in
2
prominence of star analysts (e.g., Krigman, Shaw, and Womack, 2001 and Cliff and Denis,
2004). Following Stickel, we expect the Institutional Investor rankings to identify superior
performers. Stickel’s performance measures are based exclusively on analyst earnings forecasts,
which constitute only a subset of analysts’ research output. Consistent with this, the Institutional
Investor rankings involve a wider range of analyst attributes than earnings forecast performance.
In this study, we also include stock recommendation performance and measures of analyst
optimism in both their earnings forecasts and stock selections.
In addition to earnings forecasts and stock recommendations, analysts provide a number
of other outputs, such as written company and industry reports and phone calls to buy-siders.
Even though the qualities of these other outputs are much more difficult to assess, buy-siders
value these outputs at least as much as stock selections and earnings forecasts.3 Because it can
be prohibitively expensive and in some cases impossible for researchers to objectively measure
the quality of these other outputs sell-side analysts provide, we proxy for them using analyst
earnings forecast frequency, stock coverage, and brokerage firm size. Frequent forecasts can
reflect more timely communication with investors, which is viewed as an important aspect of
performance by the Institutional Investor survey participants.4
Greater stock coverage can
contribute to broader industry knowledge, which is also valued by the buy-side according to the
survey.
Finally, prior studies document that larger brokerage firms employ higher quality
analysts (e.g. Clement, 1999 and Jacob, Lys, and Neale, 1999).5 Our empirical results indicate
3
For example, starting in 1998 Institutional Investor reports the order of importance of different sell-side analyst
attributes as rated by buy-siders. In the three years of 1998, 1999 and 2000 when this information is provided,
‘industry knowledge’ is consistently ranked number one and in two out of the three years ‘written reports’ is ranked
above both ‘stock selection’ and ‘earnings estimates.’
4
Stickel (1992) documents that ranked analysts issue more frequent earnings forecasts.
5
A recent study by Li and Emery (2005) argue that the Institutional Investor rankings are ‘popularity contests’
because they primarily reflect the effects of ‘recognition’, measured as employment at larger brokerage houses and
prior star status. What that study fails to recognize is the endogenous nature of the size of an analyst’s employer and
an analyst’s prior star status, both likely reflecting performance. In other words, although a larger employer and
3
that analyst performance is significantly positively associated with the likelihood of ranking by
Institutional Investor, consistent with these rankings serving the economic role of resolving
information asymmetry in the labor market.
After establishing the relation between Institutional Investor rankings and performance,
we explore the second question of whether the superior performance of ranked analysts is
attributable to a “winning gamble” or superior ability. We begin by examining analysts’ risktaking behavior. Scharfstein and Stein (1990) suggest that economic agents such as money
managers and analysts have a tendency to herd due to career concerns. Hong, Kubik, and
Solomon (2000) further find that younger analysts are more risk-averse due to even stronger
concerns about termination. However, while average performers are likely to herd to avoid
termination, superior analysts have incentive to be bold.
Trueman (1994) demonstrates
theoretically that higher ability analysts will be bolder in that they will deviate more from the
consensus forecast. The tournament-like setting created by Institutional Investor rankings also
gives better analysts strong incentive to take risk due to the large payoffs to the winners
(Nalebuff and Stiglitz, 1983).6 We argue that more talented analysts, who either have better
information or are better processors of information, will distinguish themselves by deviating
more from the consensus (being bolder). We therefore expect to observe more risk-taking
behavior among ranked analysts than non-ranked analysts.
A positive relation between boldness and the likelihood of ranking could arise because
higher ability analysts deviate more from consensus forecasts, as suggested by Trueman (1994),
or because analysts simply gamble and those who happen to “win the lottery” become ranked.
prior star status no doubt bring an analyst more ‘recognition’, it does not explain how an analyst gets into those
positions in the first place.
6
As formalized by Lazear and Rosen (1981), in a tournament setting, participants are compared on a relative basis
and the winner receives disproportionately large payoffs.
4
To distinguish between these two explanations, we examine the persistence of performance by
ranked analysts as well as how other analysts react to ranked analysts’ forecasts.
Our results suggest that ranked analysts are indeed bolder than non-ranked analysts in
that their forecasts deviate more from the consensus. Furthermore, we find that the superior
performance of ranked analysts persists after they become ranked and that their forecasts are
followed by other analysts in forecast timing. These findings support the notion that the ranked
analysts are bolder because they possess higher quality, and other analysts in the profession
recognize this higher quality. Our results therefore suggest while reputation concerns might lead
analysts to herd (Hong, Kubik, and Solomon, 2000), the rank-order tournament effect from
Institutional Investor rankings likely provides offsetting risk-taking incentives for superior
analysts to be bold. In addition, the superior performance of ranked analysts is likely attributable
to their ability rather than ‘gambling.’
We next address our third question of whether the apparent superior ability of ranked
analysts is the result of greater experience or innate talent. Prior research reports that as
analysts gain experience they become better performers (e.g. Mikhail, Willis, and Walther,
1997). If the superior ability by ranked analysts is primarily due to their greater experience, we
expect analysts who are ranked for the first time to be more experienced than the average nonranked analyst. However, to the extent ranked analysts have innate talent, analysts becoming
ranked for the first time may NOT have more experience relative to analysts who have never
been ranked. As an analogy to sporting tournaments, on the PGA golf tour, golfers improve in
ability over time but the average age of first-time winners is not higher but even lower than the
average age of touring pros that have never won a professional event. This is because talented
5
players that go on to win multiple times win early on in their careers.7 We test this “talent”
explanation versus the “experience” explanation and find evidence consistent with a “talent”
explanation for the relation between experience and rankings. Analysts ranked for the first time
have less, not more, experience than analysts who have never been ranked. However, the
average experience level of all ranked analysts (first-timers and previously ranked analysts) is
greater than the average experience of non-ranked analysts. This apparent inconsistency arises
from survivorship. As argued by MacDonald (1988), in markets with skewed compensation, less
talented participants (analysts) will drop out of the market over time and the stars will remain.
Finally, we investigate the role the Institutional Investor rankings play in affecting
analyst career outcomes. Since the rankings are viewed as an important element in analyst
reward systems, we expect them to correlate with other measures of analyst career success. We
find that ranked analysts are more likely to be promoted and less likely to be demoted or
terminated. Even though we do not directly have data on analyst compensation as is the case for
other studies in the literature, our evidence on turnover implies that analysts have strong
incentives to become ranked.
Our paper makes several contributions to the financial analyst literature. First, by tying
performance metrics with the Institutional Investor rankings, and with analyst career outcomes in
promotions, demotions, and terminations, we better understand sell-side analysts’ incentives.
Our findings suggest that analysts have strong incentives to improve their performance. Second,
we show that the superior performance of ranked analysts is not likely attributable to luck.
While analysts are bolder than non-ranked analysts, their performance persists into subsequent
periods and they are recognized as leaders by other analysts. Third, while prior literature
emphasizes that analysts have little incentive to be bold as bolder analysts are more likely to be
7
Tiger Woods is an obvious example.
6
terminated (e.g. Hong, Kubik, and Solomon, 2000), our findings suggest that analysts can also
have strong incentives to take risk because being bold increases the likelihood of being ranked
by Institutional Investor and leads to better career outcomes. Our fourth contribution is the
finding that innate talent is likely an important quality of a superstar analyst. Ranked analysts
appear to be inherently more talented than non-ranked analysts.
Collectively, our evidence supports the notion that the Institutional Investor rankings
serve the meaningful role of identifying high quality analysts in the labor market and is
inconsistent with the allegation that the rankings are ‘popularity contests’ with no substance.
Finally, in contrast to Hong and Kubik (2003), which suggests labor market benefit from issuing
optimistic forecasts, we find that analysts are worse off by being systematically optimistic
relative to other analysts, as this optimism increases the likelihood of termination and decreases
the likelihood of promotion. Our results are consistent with Zhang (2005) who argues that
analyst reputation concerns serve to constrain their optimism.
Our contribution is not limited to the analyst forecast literature, however. Even though
the studies on herding behavior of economic agents have mostly focused on the potential
penalties for being bold, we point out that being bold can greatly enhance one’s career and lead
to large payoffs. This is consistent with economic agents taking more risk when faced with a
rank-order tournament.
Therefore, the risk-taking incentives to be bold created by the
tournament may very well offset the incentives to herd for career concerns.
The remainder of the paper is organized as follows. Hypothesis development is Section
2. Section 3 describes the methodology and reports descriptive statistics. Section 4 investigates
the various characteristics of ranked analysts. Section 5 conducts multivariate analysis of the
7
Institutional Investor rankings. The effects of the rankings on analyst career outcomes are
presented in Section 6, and Section 7 concludes.
2. Hypothesis development
In this section we develop hypotheses to address each of the four research questions
raised earlier -- the relation between Institutional Investor rankings and analyst performance
(Section 2.1), the issue of luck versus skill (Section 2.2), and experience versus innate talent for
ranked analysts (Section 2.3), and the effect of the rankings on analyst career outcomes (Section
2.4).
2.1. Institutional Investor rankings and analyst performance
We expect the Institutional Investor rankings to identify superior performers. If they did
not, it is unlikely they would have survived for this long or have had such an impact on analysts’
reputation. The evidence in Stickel (1992) that ranked analysts have greater earnings forecast
accuracy, higher forecast frequency, and larger price impact from their forecast revisions
supports this notion.8 We state our first hypothesis as follows (all hypotheses are in alternative
form):
Hypothesis 1: Ceteris paribus, better performing analysts are more likely to be ranked by
Institutional Investor than other analysts.
Stickel’s performance measures are based exclusively on analyst earnings forecasts,
which constitute only a subset of analysts’ research output. In addition to earnings forecast
accuracy, we also include stock recommendation returns as a performance measure. Making
stock recommendations is one of the most important functions that analysts perform (Schipper,
1991, Womack, 1996, Francis and Philbrick, 1993). Womack (1996) confirms that analyst
8
Stickel does NOT find that analysts who are about to be ranked for the first time are more accurate than non-ranked
analysts. As discussed later, we do find such evidence.
8
recommendations have investment value by analyzing stock price movements surrounding
recommendation changes.
Supporting the proposition that stock recommendations are an
important part of a sell-side analyst’s job, buy-siders surveyed by Institutional Investor rank
stock picking ability as an important characteristic in their overall voting decisions. We expect
measures of analyst stock picking ability to be positively related to the likelihood of ranking.
We also investigate the role of analyst optimism in the rankings. Numerous studies have
documented optimistic bias by financial analysts (see Kothari, 2001, and the references therein).
Some suggest that analysts produce optimistic research to promote revenue-generating
businesses such as trading or investment banking for their employers (e.g. Cowen, Groysberg,
and Healy, 2006; Lin and McNichols, 1998). If the distortion caused by optimism reduces the
value of sell-side analyst research to buy-siders, more optimistic analysts will have a lower
likelihood of becoming ranked.
On the other hand, buy-side institutions are generally
sophisticated investors (Cowen, Groysberg, and Healy, 2006). To the extent that they are able to
understand and adjust for the optimistic bias in sell-side research, optimism may not affect their
investment and voting decisions in the Institutional Investor survey. Therefore, we view the
relation between rankings and optimism bias as an empirical question. We measure optimism in
both analyst earnings forecasts and stock recommendations.
In addition to earnings forecasts and stock recommendations, sell-side analysts also
provide other services.
For example, analysts write detailed industry-level and firm-level
reports. This information can be helpful to buy-siders who use sell-side analyst research output
as input into their own financial models.
To buy-siders, the industry and firm-specific
information conveyed in these reports and in phone conversations can be at least as important as
the sell-side analysts’ stock recommendations and earnings forecasts themselves. We note that
9
Institutional Investor is the only major ranking system of financial analysts that considers
multiple dimensions of analyst output.9 We believe this is why a voting-based ranking system
like the one employed by Institutional Investor, which considers a wide range of analyst output,
has become the industry standard in evaluating sell-side analysts.
Ideally, we would like to include measures such as the quality of analysts’ written
research reports and their explanatory conversations with buy-siders in our model along with
analyst performance in earnings estimates and stock picks. However, it would be prohibitively
expensive and in some cases impossible for researchers to objectively measure the quality of
these other outputs sell-side analysts provide. Instead we proxy for them using analyst earnings
forecast frequency, stock coverage, and brokerage firm size. Frequent forecasts can reflect more
timely communication with investors, which is viewed as an important aspect of sell-side analyst
performance by the Institutional Investor survey participants.
Greater stock coverage can
contribute to broader industry knowledge, which is also valued by the buy-side according to the
survey.
Finally, prior studies document that larger brokerage firms employ higher quality
analysts (e.g. Clement, 1999 and Jacob, Lys, and Neale, 1999), suggesting brokerage size can be
another proxy for analyst performance.
Summarizing the above discussions, we expect an analyst’s ranking likelihood by
Institutional Investor to increase with earnings forecast accuracy, stock recommendation returns,
earnings forecast frequency, breadth of stock coverage, and brokerage firm size.
We do not
have a specific prediction on the relation between the ranking likelihood and analyst optimism.
9
For example, the more recently developed analyst ranking system by the Wall Street Journal is based on a single
dimension of analyst performance: stock recommendation returns.
10
2.2. Luck versus superior ability
To the extent we establish a positive relation between the Institutional Investor rankings
and analyst performance we next investigate whether the superior performance by ranked
analysts is due to a ‘winning gamble’ or to superior ability. We begin by investigating analysts’
risk-taking behavior.
Scharfstein and Stein (1990) suggest that economic agents such as money managers and
analysts have a tendency to herd due to career concerns. Hong, Kubik, and Solomon (2000)
report that analysts (especially young ones) who make bold forecasts are more likely to be
terminated. Therefore, analysts attempt to conform to the consensus to reduce the probability of
being fired. However, rank-order tournament theory suggests that players are likely to take risk
when the payoffs to winning are disproportionably large (Nalebuff and Stiglitz, 1983), as is the
case for Institutional Investor rankings. Trueman (1994) demonstrates theoretically that higher
ability analysts will be bolder and will deviate more from the consensus forecast in order to
distinguish themselves and make better forecasts than their peers. We therefore make the
following prediction:
Hypothesis 2: Ceteris paribus, bolder analysts are more likely to be ranked by Institutional
Investor than are other analysts.
An alternative explanation for a positive relation between boldness and the likelihood of
being ranked is that ranked analysts gamble and make bold predictions that happen to be correct.
Analysts may engage in this kind of gambling behavior if the payoff to the winner is
disproportionately large, as is apparently the case for Institutional Investor rankings. That is, if
the rewards are so substantial, analysts may make extreme forecasts that are not warranted by the
information they have in the hope they will get lucky. We distinguish between our hypothesis
and this alternative explanation by examining the persistence of ranked analysts’ performance.
11
Our proposition that the Institutional Investor rankings serve the meaningful economic role of
identifying superior analysts in the analyst labor market suggests that ranked analysts’ superior
performance is likely to persist, which leads to the following hypothesis:
Hypothesis 3: There is persistence in the performance of Institutional Investor ranked analysts.
The quality of ranked analysts can also be inferred indirectly from other analysts’
reactions. To the extent that high quality analysts are recognized as such in the profession, other
analysts will follow or herd on these analysts. Following Cooper, Day, and Lewis (2001), we
construct a leader-follower ratio based on relative forecast timing. It measures the extent to
which an analyst is followed by other analysts when making earnings forecasts. We expect this
measure to be positively correlated with the Institutional Investor rankings.
Hypothesis 4: Ceteris paribus, leader analysts are more likely to be ranked by Institutional
Investor than other analysts.
2.3. Experience versus innate talent
Several studies examine analyst learning and find that analysts improve forecast accuracy
with experience (Mikhail et al.1997; Clement, 1999). If ranked analysts’ performance advantage
is primarily due to their greater experience (the “experience” explanation), one would predict
that analysts ranked for the first time by Institutional Investor have more experience than analyst
who have never been ranked.
An alternative prediction arises from a “talent” explanation. To the extent that this is a
“superstar” market such as that described by Rosen (1981) and MacDonald (1988), analysts that
are ranked for the first time may NOT be more experienced than other non-ranked analysts.
Analysts that become ranked have exceptional inherent talent, similar to a superstar professional
athlete. Therefore, while they might improve with experience, they come to the position with
12
superior innate talent and tend to be recognized early in their careers. This “talent” explanation
gives the following prediction.
Hypothesis 5: Analysts who are ranked for the first time by Institutional Investor are NOT more
experienced than non-ranked analysts.
We also expect that, over time, less talented analysts will drop out of the profession and
more talented analysts will remain (MacDonald, 1988). This survivorship leads to greater
average experience of all ranked analysts (first-timers and previously ranked ones) than other
analysts.
2.4. Institutional investor rankings and analyst career outcomes
Since data on analyst compensation are not directly available, researchers have used
analyst career outcomes in terms of job separations as a proxy (e.g. Mikhail, Willis, and Walther,
1999 and Hong and Kubik, 2003). We investigate how Institutional Investor rankings affect
analyst job separations to provide validation for the rankings’ role in analyst reward system. If
the Institutional Investor rankings are an important element in the reward system, we expect
them to positively correlate with other measures of analyst career success.
Hypothesis 6: Ceteris paribus, ranked analysts by Institutional Investor are more likely to
experience favorable career outcomes.
3. Methodology and summary statistics
3.1. Data
We collect the Institutional Investor rankings of All-American Research Team analysts
for the years of 1991 to 2000. The Institutional Investor All-American rankings are published
each year in the October issue of the magazine. The votes of more than 3,000 individuals,
13
representing approximately 90 percent of the 100 largest U.S. equity managers, as well as more
than 300 other key money management firms, were collected earlier during that year.10
Analyst earnings forecasts and actual earnings are obtained from I/B/E/S and stock
recommendations are obtained from First Call. One limitation with the First Call data is that it
identifies only brokerage houses and not individual analysts as I/B/E/S does. We make the
assumption that for each firm-year there is only one analyst issuing forecasts and
recommendations from each brokerage house.11 We then try to match each firm-year-brokerage
observation from the First Call recommendation file with the I/B/E/S annual earnings forecast
file. In places where we are able to find a match, the I/B/E/S analyst code is passed to the First
Call observation.12,13 As a sensitivity check, we also replicate our analysis using the I/B/E/S
recommendation file, and our conclusions are unaffected. We obtain stock returns information
from CRSP.
3.2. Methodology
In our analysis of the determinants of Institutional Investor rankings, we measure the
explanatory variables over the ‘pre-voting period.’ It is the one-year period ending in April of
10
Institutional Investor collects the votes by sending a questionnaire covering approximately 80 industry groups and
investment specialties to portfolio managers and buy-side analysts at major money management institutions. The
voters are asked to name up to four top analysts in an industry that s/he is familiar with. Institutional Investor does
not provide a list of candidates for the purpose of the voting. Rankings are determined by the score each analyst
receives, which is produced by taking the number of votes awarded to an individual analyst and weighting them
based on the size of the voting institution.
11
This seems to be a reasonable assumption. We find that coverage by multiple analysts from the same brokerage
house accounts for less than 10% of the firm-year-brokerage observations in I/B/E/S annual earnings forecast file.
Similar evidence is obtained by Gilson et al. (2001), who also report that multiple analyst coverage is almost
exclusively driven by analyst turnover.
12
The IBES firm-year-brokerage observations with multiple analyst followings are eliminated to remove any
ambiguity. This leads to the loss of about 10% of the firm-year-brokerage observations from I/B/E/S (see the
previous footnote).
13
Another possible source of stock recommendation data is I/B/E/S, which does identify individual analysts.
However, the I/B/E/S recommendation dataset has its own disadvantages. In our correspondence with personnel at
Thomson Financial, owner of both the First Call and I/B/E/S data, they indicated that the I/B/E/S stock
recommendations dataset is likely to have less accurate stock recommendation dates than First Call. Furthermore,
we find 267,559 observations in First Call recommendation file that have a valid cusip identifier, analyst code and
recommendation date. The corresponding number of observations in the I/B/E/S recommendation file is a much
smaller 134,329.
14
each year to ensure that information in the explanatory variables is publicly available before the
votes are cast for that year.14 A general timeline is presented in Appendix A.
Below we provide definitions for our main test variables.
More detailed variable
definitions are in Appendix B. The unit of observation in our analysis is an analyst year. For
brevity, analyst and year subscripts are suppressed. The main dependent variable, RANK, is an
indicator for an analyst’s ranking status in a particular year.
RANK: an indicator variable that is equal to one if an analyst is ranked by Institutional Investor
magazine in a year, and zero otherwise.
We measure analyst performance with their earnings forecast accuracy (ACCU), stock
recommendation returns (RET-reco), the optimism in their earnings forecasts (OPT-fcst) and
stock recommendations (OPT-reco). We also include proxies for analyst performance other than
earnings forecasts and stock selections through forecast frequency (FREQ), stock coverage
(COVER), and the size of an analyst’s employer brokerage firm (BRKR).15
ACCU: forecast accuracy is defined as the absolute value of the difference between earnings
forecasts and actual earnings. The accuracy measure is converted into rank scores of 1 to 100,
following Hong, Kubik, and Solomon (2000), for each firm and fiscal year, with higher scores
indicating higher accuracy.16 These relative scores are then averaged across firms for each
analyst and year.
RET-reco: four-day [0,+3] size-adjusted abnormal returns for buy and sell recommendations
(returns for sell recommendations are multiplied by –1), averaged across firms for each analyst
and year.17
14
The votes are cast around April or May each year (see Stickel, 1992).
For each firm and fiscal year with at least three analysts following, all forecasts issued within a 180-day period (120 days, +60 days) around the fiscal yearend are retained for calculations of earnings forecast accuracy, optimism,
forecast frequency, and boldness.
16
Because earnings forecast accuracy varies by firm and time period, to ensure greater comparability across firm
and year, we rank forecasts based on their accuracy within each firm and fiscal year (see Hong, Kubik, and
Solomon, 2000).
15
17
Stock recommendations are usually made with a five-point scale from 5 (the most favorable) to 1 (the least
favorable). We classify a recommendation as a buy if it is a 5 or 4 and a sell if it is a 1 or 2. First Call actually
classifies recommendations in the reverse order. That is, 1 is a strong buy and 5 is a strong sell. For convenience
and ease of interpretation, we transform these values so that a 5 represents a strong buy. In our sample, the
15
OPT-fcst: forecast bias is defined as actual earnings minus the forecast, also converted into rank
scores of 1 to 100 for each firm and fiscal year, with higher scores indicating greater optimism.
These relative scores are then averaged across firms for each analyst and year.
OPT-reco: relative stock recommendation optimism, which is calculated as the stock
recommendation minus the average of all other recommendations issued within the same oneyear period for the same firm. This measure is then averaged across firms for each analyst and
year.
FREQ: relative frequency is calculated as the number of forecasts issued by an analyst for a
firm-fiscal year minus the average number of forecasts issued by all other analysts for the same
firm-fiscal year. This measure is then average across firms for each analyst and year. 18
COVER: the number of stocks that an analyst issues earnings forecasts for in a given year.
BRKR: an indicator variable that takes on the value of one for analysts employed by a large
brokerage house in a given year and zero otherwise. Following Hong, Kubik, and Solomon
(2000) a brokerage house is classified as large if it employs 25 or more analysts. 19
We employ two measures for analyst risk-taking tendencies, deviation of an analyst’s
earnings forecasts from the prevailing consensus (BOLD) and the frequency by which an analyst
makes the first forecast, i.e., being the first mover, in a fiscal year for a firm (FMVR).
BOLD: forecast deviation is defined as the absolute value of the difference between an earnings
forecast and the outstanding consensus forecast, also converted into rank scores of 1 to 100 for
each firm and fiscal year, with higher scores indicating greater deviation from the consensus.
These relative scores are then averaged across firms for each analyst and year.
FMVR: for each firm-fiscal year with more than one analyst following, the analyst that issues the
first forecast is assigned a first mover score of one and all other analysts receive a score of zero.
This measure is then averaged across firms for each analyst and year.
distribution of recommendations is as follows: 31% of the observations are strong buy, 33% buy, 32% hold, 2% sell
and only 1% strong sell.
18
Defining relative frequency as the number of forecasts issued by an analyst divided by (instead of minus) the
average number of forecasts issued by all other analysts for the same firm and fiscal year does not change the
inferences.
19
Each year, about 15% of the roughly 300 brokerage houses are classified as large.
16
To measure an analyst’s impact on other analysts in the profession, we follow Copper,
Day and Lewis (2001) and construct the leader-follower-ratio (LFR) based on the relative
forecast timing among analysts.
LFR: for each forecast that is not issued within 5 days of an earnings announcement, we identify
the 5 preceding forecasts and 5 subsequent forecasts issued by other analysts. The leaderfollower-ratio for this forecast is defined as T0/T1, where T0 is the cumulative number of days by
which the preceding forecasts lead the forecast of interest and T1 is the cumulative number of
days by which the subsequent forecasts lag the forecast of interest. We calculate the average
leader-follower-ratio for each analyst and for each year.
Finally, we define analyst experience as the number of years an analyst has appeared in
the I/B/E/S annual earnings forecast database (EXPR). Since the first year I/B/E/S started to
collect annual earnings forecasts is 1981, this variable will be biased downward for analysts
issuing forecasts before that.
EXPR: the number of years an analyst has appeared in the I/B/E/S annual earnings forecast
database.
Many of our variables are based on relative scores but extreme observations can still be a
concern. As suggested by Hong, Kubik, and Solomon (2000), for analysts who cover relatively
few stocks in a particular year (especially when those stocks are also thinly followed), outliers
are likely to be problematic. For our reported results, we exclude the extreme top and bottom
1% of the observations for most of our variables with the following exceptions. For COVER,
only the top 1% is excluded as 10% of the observations have the lowest value of 1. For BRKR
and EXPR, no trimming is done because these variables are measured with high precision. Our
results are similar if we do not exclude any extreme observations.
17
3.3. Descriptive Statistics
Table 1 reports the summary statistics of our main test variables. Of the 42,014 analystyear observations in our sample, 3,099 or 7% are ranked analyst-years (RANK). The mean
(median) for both earnings forecast accuracy (ACCU) and optimism (OPT-fcst) are very close to
50 because these variables have been converted to rank scores.
The mean (median) of
recommendation returns, RET-reco, is 1.02% (0.67%). As is generally the case for returns the
distribution is right skewed. The recommendation optimism measure (OPT-reco) has a mean
(median) of 0.03 (0.00). There are far fewer observations for the variables obtained from First
Call (RET-reco, OPT-reco), largely because First Call data covers a shorter time-period (19942000).
[Insert Table 1 here]
The mean relative frequency of forecast revisions (FREQ) is -0.06 and the median is 0.09. Recall that FREQ is calculated as the relative frequency of forecasts compared to other
analysts and, consequently, negative values are common. The mean (median) number of firms
covered by an analyst (COVER) is 11.39 (10). The dummy variable for brokerage size (BRKR)
has a mean of 0.59. More than half of the analysts in the sample work for large brokerage firms,
defined as those firms with 25 or more analysts.
Like ACCU and OPT-fcst, our first measure of analyst risk-taking behavior, BOLD, is a
ranked value and has a mean (median) of 49.70 (50.00). The variable FMVR has a mean of 0.09
and median of 0.00. This value is relatively low because, by definition, only one forecast for
each firm-fiscal year can be classified as the first. The leader-follower ratio (LFR) has a mean of
2.00 and median of 1.61. This variable is right skewed because it can take on large values when
18
an analyst’s forecast is preceded by other forecasts by many days but it cannot take on a negative
value. The mean (median) years of experience (EXPR) for analysts in our sample is 5.37 (4.00).
In Table 2 we report the correlation coefficients for the main variables with Pearson
(Spearman) coefficients in the upper (lower) diagonal. Several correlations are worth noting.
ACCU and BOLD are positively correlated, supporting the contention that analysts make bold
forecasts when they have better information rather than to gamble. The significant negative
correlation between OPT-fcst and BOLD implies that on average analysts do not make bold
forecasts in order to be more optimistic. Analysts at larger brokerage firms (BRKR) issue more
accurate forecasts and have better returns for their recommendations. They also appear to be less
optimistic, consistent with larger brokerage firms employing analysts of higher quality.
[Insert Table 2 here]
4. Characteristics of ranked analysts
In this section, we investigate the characteristics of analysts who are about to be ranked
for the first time by Institutional Investor (Section 4.1), of ranked analysts in general (Section
4.2), and of ranked analysts who are about to lose their rankings (Section 4.3). We also present
evidence related to the persistence of ranked analysts’ performance (Section 4.4).
4.1. First-time ranked analysts
Table 3 reports univariate tests comparing analysts who are about to be ranked for the
first time to non-ranked analysts. The comparison is done in the pre-voting period, and for
comparison, in the ‘post-publication period’ as well. The ‘post-publication period’ is defined as
the one-year period starting in October, when the rankings are published for that year (see
Appendix A for a timeline). ‘First-timers’ are analysts who are ranked for the first time in the
current year. ‘Non-ranked’ are analysts who have not been ranked since the start of our sample
19
period. Since our Institutional Investor ranking data are from 1991 to 2000, for analysts ranked
in 1991, we cannot be sure whether that was their first ranking year or not, so we exclude 1991
from our first-timers classification. To be conservative, we also exclude 1992. As a result, we
define an analyst as a first-timer only if he/her first appears in our rankings database sometime
from 1993 to 2000 (and not in 1991 or 1992). Columns (1)-(3) correspond to the pre-voting
period, where column (1) reports the variable means for all analysts ranked for the first time and
column (2) reports the variable means for non-ranked analysts. Column (3) reports differences
in the means between these two groups.20
[Insert Table 3 here]
The first three columns of Table 3 highlight important characteristics of analysts ranked
for the first time. In the pre-voting period, the first-timers are significantly more accurate
(ACCU) forecasters than non-ranked analysts.21 Abnormal returns in days [0,+3] around the
stock recommendations (RET-reco) are higher for the first-timers by over 90% (1.77% versus
0.92%) and the difference is highly significant.22 In un-tabulated results, we investigate the
abnormal returns over the next 6 months starting from day +4 and find no significant return
differences between ranked analysts and non-ranked analysts. There is no evidence, therefore,
that the difference in returns in the short window of [0,+3] days reverses in the subsequent sixmonth period.
There is no apparent difference in forecast optimism (OPT-fcst) or
recommendation optimism (OPT-reco) in the pre-voting period. We also find that the relative
frequency of forecast (FREQ) and stock coverage (COVER) are significantly higher for first20
Un-tabulated results on nonparametric comparisons are qualitatively similar.
These results differ from Stickel (1992) who does not find the first-timers to be more accurate than non-ranked
analysts. Sample period and research design differences likely contribute to the difference in findings. For
example, Stickel (1992) matches each ranked analyst forecast with that of a non-ranked analyst issued on the same
day. This procedure leads to a loss of about 88% of the forecast observations by ranked analysts due to the lack of a
matching forecast by a non-ranked analyst.
22
We also find similar results for the 4-day [0, +3] abnormal returns for stock recommendation changes (not levels).
21
20
timers than for non-ranked analysts. Finally, first-timers are more likely to be employed at large
brokerage firms. Overall, the results suggest that first-time ranked analysts are better performers
than non-ranked analysts, which supports H1.
The results on the risk-taking measures suggest that first-timers are bolder than nonranked analysts before they first get ranked, consistent with H2. The mean of BOLD for firsttimers is significantly higher than that of non-ranked analysts. We also find that first-timers are
more likely to be the first ones to issue an earnings forecast for a firm-fiscal year (FMVR).
The leader-follower ratio (LFR) is significantly different between the two groups. The
results suggest that other analysts tend to make forecasts soon after the first-timers make their
forecasts and few forecasts precede the first-timers’ forecasts. This indicates the first-timers are
leaders among analysts even before they first become ranked by Institutional Investor and
supports the notion that first-time ranked analysts possess superior ability. These findings offer
support for H4.
Interestingly, analysts who are about to be ranked for the first time are not more
experienced than other non-ranked analysts, inconsistent with the “experience’ explanation and
supporting the “talent explanation” in H5. Average experience is 4.82 years for first-timers and
4.98 for non-ranked analysts. It appears that the superior performance of ranked analysts is not
arising from experience but from their “type.” Otherwise, we would see first-timers being more
experienced than non-ranked analysts. Overall, the results in columns (1) to (3) of Table 3
suggest that compared to non-ranked analysts, first-timers have superior performance and are
bolder. Further, the higher LFR for first-timers implies that other analysts recognize the superior
forecasting ability of first-timers and herd on them. Finally, the superiority of ranked analysts in
a large part comes from their innate talent.
21
Columns (4) to (6) in Table 3 compares the first-timers with non-ranked analysts in the
post-publication period and the results are very similar to the pre-voting period. The fact that the
first-timers continue to outperform non-ranked analysts in the post-publication period suggests
that their pre-voting superior performance is likely due to skill rather than luck. The boldness of
first-timers likely comes from superior information or information processing rather than from
simply “playing the lottery.” The finding that first-timers in the post-publication period have
more experience than the non-ranked analysts is likely due to survivorship, as the first-timers
should be less likely to drop out of the profession than non-ranked analysts.
4.2. Ranked analysts in general
In Table 4, we compare the characteristics of all ranked analysts (both first-timers and
previously ranked analysts) to non-ranked analysts in both the pre-voting and post-publication
periods. The findings for the pre-voting period reported in columns (1)-(3) of Table 4 are
qualitatively similar to those in Table 3 except for the following differences. First, ranked
analysts are less optimistic than non-ranked analysts (both forecast optimism and
recommendation optimism). This is inconsistent with conjectures that highly visible analysts
tend to be optimistic in order to attract investment banking business (Francis and Philbrick,
1993; Hong and Kubik, 2003). The other notable difference compared to the Table 3 pre-voting
period results relates to EXPR. In the Table 3 pre-voting period results, the experience of firsttime ranked analysts is similar to, and even slightly lower than that of non-ranked analysts. In
Table 4, however, the population of ranked analysts has an average experience of 8.18 years
versus 4.96 for non-ranked analysts. Combined, these findings are consistent with MacDonald
(1988), who shows that talented professionals (analysts) remain in the occupation while the less
talented drop out. This survivorship explains why the first-time ranked analysts are no more
22
experienced than non-ranked analysts while the population of all ranked analysts (including firsttimes and previously ranked ones) has significantly more experience.
[Insert Table 4 here]
In columns (4)-(6) of Table 4, we compare the performance of ranked and non-ranked
analysts in the post-publication period. The results are very similar to the pre-voting period.
The overall consistency in the findings across the pre-voting and post-publication periods
suggests that it is likely skill rather than luck that drives the rankings.
4.3. Analysts about to lose rankings
Table 5 compares characteristics of previously ranked analysts who lose their rankings in
the current year with analysts who continue to be ranked. Analysts who lose their rankings are
significantly less accurate and issue forecasts less frequently. This suggests that analysts who
lose their rankings have declining performance. We also find that these analysts have more
experience than other ranked analysts, which is consistent with some of these analysts winding
down their careers.
[Insert Table 5 here]
4.4. Persistence in ranked analyst’s performance
To further investigate the issue of the persistence of superior performance by ranked
analysts, we conduct time-series analysis and report the results in Table 6. We identify analysts
who are ranked consecutively for at least four years. The first-year is required to be after 1991,
so we can ascertain that it is indeed the beginning of a string of consecutive rankings. The
requirement of four years of consecutive rankings is somewhat arbitrary. However, we weigh
the advantages of a longer time horizon (more evidence on persistence), with the onerous data
requirements (hence significant sample size reduction) a long time horizon imposes. After we
23
identify the strings of consecutive rankings for at least four years, we keep only the first three
years, this way we make sure that the fourth year is not the last year of ranking when an analyst
may suffer declines in performance. The same number of observations is required for each of
the three years within each variable, so the comparison over time is done with a constant sample.
The number of observations varies from 45 (RET-reco) to 143 (EXPR) out of the total
number of 817 ranked analysts over our sample years. We find that over the first three years
after an analyst becomes ranked, forecast accuracy and stock recommendation returns do not
change significantly. This is consistent with our argument that the superior performance of
ranked analysts is due to their superior ability rather than luck, consistent with H3. Stock
coverage appears to increase over time. Similarly, there is a significant increase in the leaderfollower-ratio in the third year over the second year. Because experience increases
deterministically by one year over the three-year period, we do not conduct significance tests on
its time-series changes. 23
[Insert Table 6 here]
5. Multivariate analysis of Institutional Investor rankings
Table 7 reports our multivariate analysis of Institutional Investor ranking determinants.
We include all variables from our univariate analyses except for the stock recommendation
variables, because the inclusion of these variables considerably reduces the number of
23
The results in Table 6 do not completely rule out the possibility that analysts gamble by deviating from consensus
in order to get ranked because we only include analysts who continue to be ranked in subsequent years. It is
possible that analysts ranked for only one year gambled to get there and then failed to outperform in the following
year. These analysts would be excluded from our Table 6 sample. However, given the post-publication results in
Table 3 and Table 4 and the positive correlation between boldness and performance, it is likely that this kind of
behavior is the exception and not the rule.
24
observations.24 The analysis for first-time ranking by Institutional Investor is based on the
following model:
Prob [First_Timer = 1] = Probit (a0 + a1 ACCU + a2 OPT-fcst + a3 FREQ +
a4 COVER + a5 BRKR + a6 BOLD + a7 FMVR + a8 LFR + a9 EXPR)
(1)
First_Timer is an indicator variable equal to one for analysts who are ranked for the first
time in the current year, and equal to zero for analysts who have not been ranked since the start
of our sample period. The independent variables are measured in the pre-voting period.
The results are reported in column (1) of Table 7. Similar to the univariate results in
Table 3, we find that greater forecast accuracy (ACCU) increases the likelihood of being ranked
(significant at the 10% level). The coefficient on OPT-fcst is not significant in the multivariate
setting. Consistent with the univariate results, the likelihood of becoming ranked for the first
time increases in FREQ, COVER and BRKR (significant at the 1% level in all three cases).
These findings are consistent with H1 that better performance is associated with higher
likelihood of ranking by Institutional Investor.
[Insert Table 7 here]
The coefficient on BOLD is positive and significant at the 10% level, suggesting analysts
who are bolder are more likely to become ranked, consistent with H2. Furthermore, we find that
the coefficient on LFR is positive and highly significant at less than the 1% level, supporting H4.
This suggests that ranked analysts were recognized by other analysts as leaders even before they
first become ranked, consistent with the ranked analysts possessing superior ability.
Interestingly, the coefficient on EXPR is negative and significant at the 1% level,
supporting H5. Not only are the first-time ranked analysts not more experienced than nonranked analysts, they are actually less experienced after controlling for other analyst attributes.
24
When RET-reco is included in the multivariate regressions, it shows a significant positive relation with the
likelihood of ranking for both first-timers and ranked analysts in general.
25
The results do not support the “experience” explanation for the rankings, which predicts a
positive relation between first-time ranking and experience. We interpret this finding as high
quality analysts being identified relatively early in their careers because the skills required to
become ranked come in a large part from the innate talent that these analysts bring to the job.
This is not to say that analysts do not necessarily become better over time but that the ranked
analysts likely come to the job with more talent than other analysts.
We also estimate the following model for the probability of becoming ranked for both the
first-timers and previously ranked analysts.
Prob [Rank = 1] = Probit (a0 + a1 LAGRANK + a2 ACCU + a3 OPT-fcst + a4 FREQ
+ a5 COVER + a6 BRKR + a7 BOLD + a8 FMVR + a9 LFR + a10 EXPR) (2)
RANK is an indicator variable equal to one for analysts who are ranked in the current
year, and equal to zero for analysts who are not ranked in the current year. The independent
variables are measured in the pre-voting period and they are the same as those in model (1) with
the addition of LAGRANK, an indicator variable equal to one if an analyst was ranked in the
previous year, and zero otherwise.
The estimation results (reported in column (2) of Table 7) are similar to those reported in
column (1) with the important exception for EXPR. In column (2) the sign on EXPR is positive
and significant, implying that the population of all ranked analysts (first-time ranked and other
ranked analysts) is more experienced than non-ranked analysts. However, because the firsttimers are actually less experienced as reported in column (1), we attribute the positive
coefficient on EXPR in column (2) to survivorship as ranked analysts likely stay in the
profession longer (MacDonald, 1988). We do not interpret this as experience increasing the
likelihood of becoming ranked. If that were the case, the sign of the coefficient on EXPR for
26
first-timers would also be positive. The coefficient on LAGRANK is positive and significant.
Analysts are much more likely to become ranked if they were ranked in the previous year.
Finally, we estimate the following model of the probability of a ranked analyst losing
his/her ranking.
Prob [Lose_Ranking = 1] = Probit (a0 + a1 ACCU + a2 OPT-fcst + a3 FREQ +
a4 COVER + a5 BRKR + a6 BOLD + a7 FMVR + a8 LFR + a9 EXPR)
(3)
Lose_Ranking is an indicator variable equal to one for an analyst who was ranked in the
previous year but loses ranking in the current year, and equal to zero for an analyst who was
ranked in the previous year and stays ranked in the current year. The independent variables are
measured in the pre-voting period.
Column (3) of Table 7 displays the results. We find that poor forecast accuracy (ACCU)
and low values of FREQ, COVER, and BRKR significantly increase the likelihood that a ranked
analyst will lose his/her ranking. Combined with the results in column (1) and (2), it suggests
that the Institutional Investor rankings not only provide incentives for non-ranked analysts to
perform better in order to get ranked, but also serve as a disciplinary mechanism for poorly
performing ranked analysts.
6. The effects of Institutional Investor rankings on analyst career outcomes
While data on the direct pecuniary benefits to becoming ranked (added compensation) are
not publicly available, we proxy with analyst career outcomes and examine how they are
impacted by the Institutional Investor rankings with the following regression model:
Prob [Career_Outcome = 1] = Probit (a0 + a1 LAGRANK + a2 ACCU + a3 OPT-fcst + a4 FREQ
+ a5 COVER + a6 BRKR + a7 BOLD + a8 FMVR + a9 LFR + a10 EXPR
(4)
Career outcomes are measured in terms of promotion, demotion, and termination.
Promotion (Demotion) is defined as moving from a smaller (larger) brokerage house to a larger
(smaller) one. Termination is defined as an analyst disappearing from the I/B/E/S database. The
27
main variable of interest on the right hand side is LAGRANK. We also include the other
explanatory variables from the previous regression models.
Table 8 reports the regression results. We find that Institutional Investor rankings in the
previous year (LAGRANK) significantly increase the likelihood of promotion and decrease the
likelihood of demotion and termination, consistent with H6.
The results indicate that the
Institutional Investor rankings have significant impact on analyst career outcomes and as a result
analysts likely have strong incentives to become ranked.
[Insert Table 8 here]
Consistent with prior literature, we also find that analysts who are more accurate (ACCU)
are more likely to be promoted and less likely to be demoted (e.g. Mikhail, Willis, and Walther,
1999). Interestingly, there is evidence that optimism (OPT-fcst) leads to worse career outcomes
by decreasing the likelihood of promotion and increasing the likelihood of termination. This
evidence is inconsistent with Hong and Kubik (2003) who argue that optimism brings labor
market benefits, but supports the evidence in Zhang (2005).
Also worth noting are the results on BOLD, which is associated with better career
outcomes (higher likelihood of promotion and lower likelihood of demotion and termination).
Hong, Kubik, and Solomon (2000) report that bolder analysts are more likely to be terminated
and infer that analysts have little incentive to be bold. However, to the extent that being bold
increases the likelihood of being ranked by Institutional Investor (as shown in Table 7) and leads
to better career outcomes, analysts have strong incentives to take risk.
The column (1) results on EXPR suggest that more experienced analysts are more likely
to be promoted, consistent with increasing performance with experience. However, we also find
that greater experience is associated with a higher likelihood of demotion (column (2)) and
28
termination (column (3)). These seemingly contradictory findings for EXPR are likely due to
more experienced analysts consisting of two groups. One group of more experienced analysts is
at the career stage of advancement. These are strong performers who are getting promoted and
generating the significant coefficient on EXPR in the promotion model in column (1). The
second group of experienced analysts is retiring or winding down their careers, contributing to
the results in columns (2) and (3) on demotions and terminations.
7. Conclusions
We study the attributes of superstar financial analysts ranked in the Institutional Investor
magazine annual surveys from 1991 to 2000. We seek to address four research questions. First,
we investigate the link between the Institutional Investor rankings and analyst performance.
After establishing a positive relation between the rankings and performance, we explore the
second question of whether the superior performance of ranked analysts is attributable to luck or
superior ability. Third, to the extent we find evidence of superior ability for ranked analysts
whether it is the result of their greater experience or innate talent. Finally, we investigate the
role the rankings play in affecting analyst career outcomes.
We document a strong positive relation between Institutional Investor rankings and
analyst performance. Our results further indicate that ranked analysts’ performance is likely due
to their superior ability because there is performance persistence and the ranked analysts are
recognized as leaders by other analysts even before they first become ranked by Institutional
Investor. Our findings also point to an “innate talent” rather than an “experience” explanation
for the ranked analysts. Contrary to the “experience” explanation’s prediction, the first-time
ranked analysts are not more experienced than non-ranked analysts. Collectively our evidence
supports the notion that the Institutional Investor rankings serve the meaningful role of
29
identifying high quality analysts in the labor market and is inconsistent with the allegation that
the rankings are ‘popularity contests’ with no substance. Finally, we find that ranked analysts
are more likely to be promoted (move to a larger brokerage house) and less likely to be demoted
(move to a smaller one) or terminated (disappear from the I/B/E/S database). These findings
suggest that the rankings are important determinants of analyst career outcomes.
30
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Appendix A. Pre-voting versus Post-publication Periods
Part I. General Timeline: Based on forecast (recommendation) issuance time
Pre-voting period
Post-publication period
(12 months)
(12 months)
____________________________|_____________________|____________________________
April
October
(Time of voting)
(Time of rankings publication)
Part II. Exceptions
A) Pre-voting Timeline for ACCU, OPT-fcst
One particular problem with the pre-voting period for the analyses of ACCU and OPT-fcst is that
we cannot simply use all the forecasts issued one year before voting. We need to also make sure
that the actual earnings for those forecasts are announced by the time votes are cast, since the
accuracy and bias measures are calculated relative to actual earnings. For the pre-voting period,
therefore, we use all the forecasts issued from 120 days before to 60 days after the fiscal yearend,
which can be anywhere between January and December in the year before the voting takes place.
This way, by the time the votes are cast around May in the following year, even the December
yearend firms would have already announced their annual earnings. Specifically, the pre-voting
period forecasts can be issued anywhere from September of year t-2 (year t being the voting year)
for January yearend firms to February of year t for December yearend firms. However, since
most of the firms have December yearends, most forecasts are issued within the one-year period
before the voting takes place.
|
|120 days|
January
Pre-voting period
fiscal year ends
|
| 60 days |
December
|
.
April
(Time of voting)
B) Timeline for FREQ
Since forecast frequency is not a measure that corresponds to an individual forecast, we define the
pre-voting period for FREQ as we did for ACCU and OPT-fcst, where the forecasts are issued
over the 180-day window around fiscal yearends in the calendar year before voting.
33
RANK
Appendix B. Variable Definitions
an indicator variable that is equal to one if an analyst is ranked by
Institutional Investor magazine in a year, and zero otherwise.
ACCU
forecast accuracy, which is defined as the absolute value of the difference
between earnings forecasts and actual earnings. Following Hong, Kubik, and
Solomon (2000) we rank forecasts based on their accuracy within each firm
and fiscal year. Relative scores ranging from 1 to 100 are then formed based
on the rankings, with higher accuracy receiving higher scores. These relative
scores are then averaged across firms for each analyst and year.
OPT-fcst
forecast bias, which is defined as actual earnings minus the forecast.
Forecasts are ranked based on their biases within each firm and fiscal year.
Relative scores ranging from 1 to 100 are then formed based on the rankings,
with more optimistic bias receiving higher scores. These relative scores are
then averaged across firms for each analyst and year.
RET-reco
four-day [0,+3] size-adjusted abnormal returns for buy and sell
recommendations in First Call (returns for sell recommendations are
multiplied by –1), averaged across firms for each analyst and year. Stock
recommendations are classified as strong buy (5), buy (4), hold (3), sell (2)
and strong sell (1). We classify a recommendation as a buy if it is a 5 or 4
and a sell if it is a 1 or 2.
OPT-reco relative stock recommendation optimism, which is calculated as the stock
recommendation minus the average of all other recommendations issued
within the same one-year period for the same firm. This measure is then
averaged across firms for each analyst and year. Stock recommendations are
classified as strong buy (5), buy (4), hold (3), sell (2) and strong sell (1).
FREQ
relative frequency is calculated as the number of forecasts issued by an
analyst for a firm-fiscal year minus the average number of forecasts issued
by all other analysts for the same firm-fiscal year. This measure is then
averaged across firms for each analyst and year.
COVER
the number of stocks that an analyst issues earnings forecasts for in a given
year.
BRKR
an indicator variable that takes on the value of one for analysts employed by
a large brokerage house in a given year and zero otherwise. Following
Hong, Kubik, and Solomon (2000) a brokerage house is classified as large if
it employs 25 or more analysts.
34
BOLD
forecast deviation is defined as the absolute value of the difference between
an earnings forecast and the outstanding consensus forecast. Forecasts are
ranked based on the deviation measure within each firm and fiscal year.
Relative scores ranging from 1 to 100 are then formed based on the rankings,
with higher deviation receiving higher scores. These relative scores are then
averaged across firms for each analyst and year.
FMVR
for each firm-fiscal year with more than one analyst following, the analyst
that issues the first forecast is assigned a first mover score of one and all
other analysts receive a score of zero. This measure is then averaged across
firms for each analyst and year.
LFR
following Copper, Day and Lewis (2001), for each forecast that is not issued
within 5 days of an earnings announcement, we identify the 5 preceding
forecasts and 5 subsequent forecast issued by other analysts. Leaderfollower-ratio for this forecast is defined as T0/T1, where T0 is the cumulative
number of days by which the preceding forecasts lead the forecast of interest
and T1 is the cumulative number of days by which the subsequent forecasts
lag the forecast of interest. We calculate the average leader-follower-ratio for
each analyst and for each year.
EXPR
the number of years an analyst has appeared in the I/B/E/S annual earnings
forecast database.
35
Table 1. Summary statistics
Our sample is based on Institutional Investor financial analyst rankings from 1991 to 2000.
Variable definitions are in Appendix B.
N
Mean
STD
Median
RANK
42,014
0.07
0.26
0.00
ACCU
30,278
49.81
13.41
50.21
OPT-fcst
30,333
49.84
13.90
50.00
RET-reco (%)
10,483
1.02
4.84
0.67
OPT-reco
10,865
0.03
0.54
0.00
FREQ
28,897
-0.06
0.48
-0.09
COVER
35,177
11.39
9.05
10.00
BRKR
35,427
0.59
0.49
1.00
BOLD
30,364
49.70
13.79
50.00
FMVR
33,477
0.09
0.14
0.00
LFR
28,342
2.00
1.37
1.61
EXPR
34,229
5.37
4.27
4.00
36
Table 2. Correlation matrix
Our sample is based on Institutional Investor financial analyst rankings from 1991 to 2000. Variable definitions are in Appendix B. Pearson
coefficients are above the diagonal line and Spearman coefficients are below the line.
Description
ACCU
0.017 ***
ACCU
OPT-fcst
OPT-fcst
0.024 ***
RET-reco
-0.001
0.013 **
OPT-reco
FREQ
COVER
BRKR
BOLD
FMVR
LFR
EXPR
-0.029 ***
-0.036 ***
0.017 ***
0.069 ***
0.109 ***
0.029 ***
0.065 ***
0.022 ***
-0.111 ***
0.016 ***
0.002
0.005
-0.019 ***
0.016 *
0.004
0.006
0.051 ***
0.017 *
-0.047 ***
-0.019 *
0.035 ***
0.048 ***
-0.011 *
0.053 ***
0.046 ***
0.004
0.083 ***
0.050 ***
0.331 ***
0.157 ***
0.073 ***
0.082 ***
0.040 ***
0.002
0.020 ***
RET-reco
-0.003
0.023 **
OPT-reco
-0.030 ***
0.012
0.020 **
FREQ
-0.039 ***
0.010 *
0.009
-0.003
0.006
0.026 ***
-0.028 ***
0.062 ***
0.004
-0.024 **
0.024 ***
0.024 ***
0.008
COVER
0.021 ***
BRKR
0.080 ***
-0.021 ***
0.059 ***
-0.045 ***
0.052 ***
0.039 ***
BOLD
0.124 ***
-0.116 ***
0.017 ***
-0.019 ***
-0.016 ***
0.037 ***
0.096 ***
FMVR
0.035 ***
-0.013 **
0.029 ***
0.035 ***
0.085 ***
0.280 ***
0.000
0.053 ***
LFR
0.076 ***
0.000
0.037 ***
0.002
0.071 ***
0.178 ***
0.173 ***
0.104 ***
0.057 ***
EXPR
0.023 ***
0.002
0.006
0.014 **
0.412 ***
0.066 ***
0.052 ***
0.086 ***
-0.049 ***
0.026 ***
-0.008
0.086 ***
-0.007
0.038 ***
-0.002
0.038 ***
-0.004
0.006
0.000
-0.048 ***
0.077 ***
0.109 ***
***, **, * significant at 1%, 5%, and 10% level, respectively.
37
Table 3. Analysis of first-time-ranked analysts
‘First-timers’ are analysts who are ranked for the first time in the current year. ‘Non-ranked’ are analysts who have not been ranked since the start
of our sample period. Since our Institutional Investor ranking data are from 1991 to 2000, for analysts ranked in 1991, we cannot be sure whether
that was their first ranking year or not, so we exclude 1991 from our first-timers classification. To be conservative, we also exclude 1992. As a
result, we define an analyst as a first-timer only if he/her first appears in our rankings databases sometime from 1993 to 2000. As described in
Appendix A, the pre-voting period is the one-year period ending in April. The post-publication period spans the one-year period beginning in
October. Variable definitions are in Appendix B.
First-Timers
(1)
Pre-voting period
Non-Ranked
(2)
ACCU
51.39
49.87
OPT-fcst
49.52
49.88
RET-reco (%)
1.77
0.92
OPT-reco
0.02
FREQ
Difference
(1)-(2)
1.52 ***
Post-publication period
First-Timers
Non-Ranked
Difference
(4)
(5)
(4)-(5)
52.18
49.64
2.54***
49.85
49.81
0.03
0.85 ***
1.67
0.98
0.69**
0.02
0.00
0.03
0.03
0.00
0.08
-0.08
0.17 ***
0.07
-0.07
0.14***
COVER
14.69
11.10
3.59 ***
15.59
10.41
5.18***
BRKR
0.93
0.53
0.39 ***
0.95
0.57
0.38***
BOLD
51.40
49.34
2.05 ***
51.79
49.43
2.36**
FMVR
0.11
0.10
0.01 *
0.11
0.09
0.02**
LFR
2.21
1.84
0.37 ***
2.48
2.00
0.48***
EXPR
4.82
4.98
5.68
4.91
0.77***
-0.37
-0.17
The numbers of observations in columns (1) and (4) for ‘First-Timers’ range from 371 to 373, except for RET-reco and OPT-reco, which have 221 and 234 observations,
respectively.
The numbers of observations in columns (2) and (5) for ‘Non-Ranked’ range from 20,737 to 24,933, except for RET-reco and OPT-reco, which have 8,779 and 19,113
observations, respectively.
***, **, * significant at 1%, 5%, and 10% level, respectively.
38
Table 4. Analysis of ranked analysts in general
‘Ranked’ are analysts who are ranked in the current year. ‘Non-Ranked’ are analysts who are unranked in the current year. As described
in Appendix A, the pre-voting period is the one-year period ending in April. The post-publication period spans the one-year period
beginning in October. Variable definitions are in Appendix B.
Ranked
(1)
Pre-voting period
Non-Ranked
(2)
Difference
(1)-(2)
ACCU
51.76
49.85
1.92 ***
OPT-fcst
49.57
49.96
RET-reco (%)
1.29
0.92
OPT-reco
-0.01
0.02
FREQ
0.08
-0.08
COVER
17.32
BRKR
Post-publication period
Ranked
Non-Ranked Difference
(4)
(5)
(4)-(5)
51.70
49.60
49.68
49.86
1.30
0.98
0.00
0.03
0.17 ***
0.04
-0.08
0.12 ***
11.31
6.01 ***
18.01
10.78
7.23 ***
0.95
0.52
0.43 ***
0.96
0.55
0.41 ***
BOLD
52.01
49.44
2.57 ***
52.11
49.43
2.68 ***
FMVR
0.11
0.10
0.01 ***
0.10
0.09
0.01 ***
LFR
2.17
1.82
0.35 ***
2.37
1.95
0.42 ***
EXPR
8.18
4.96
3.21 ***
9.15
5.00
4.16 ***
-0.39 **
0.37 ***
-0.03 **
2.10 ***
-0.18
0.32 ***
-0.03 **
The numbers of observations in columns (1) and (4) for ‘Ranked’ range from 3,035 to 3,088, except for RET-reco and OPT-reco, which have 1,323 and 1,381
observations, respectively.
The numbers of observations in column (2) and (5) for ‘Non-Ranked’ range from 24,043 to 29,367, except for RET-reco and OPT-reco, which have 9,101 and 9,446
observations, respectively.
***, **, * significant at 1%, 5%, and 10% level, respectively.
39
Table 5. Analysis of analysts about to lose ranking
‘Lose ranking’ are analysts who were ranked in the previous year but lose ranking in the current year.
‘Other ranked analysts’ are analysts were ranked in the previous year and remain ranked in the current
year. As described in Appendix A, the pre-voting period is the one-year period ending in April. Variable
definitions are in Appendix B.
Pre-voting period
Lose ranking
ACCU
(1)
50.96
Other ranked
analysts
(2)
51.80
Difference
(1)-(2)
-0.84 **
OPT-fcst
50.08
49.69
0.39
RET-reco (%)
1.01
1.16
-0.15
OPT-reco
0.00
-0.01
0.01
FREQ
-0.02
0.08
-0.09 ***
COVER
17.70
18.17
-0.47
BRKR
0.95
0.96
-0.01
BOLD
51.89
52.16
-0.27
FMVR
0.11
0.11
0.00
LFR
2.19
2.22
-0.03
EXPR
9.68
8.92
0.76 ***
The number of observations in column (1) for analysts who lose ranking ranges from 549 to 569, except for RET-reco and OPTreco, which have 322 and 333 observations, respectively.
The number of observations in column (2) for other ranked analysts ranges from 2,172 to 2,205, except for RET-reco and OPTreco, which have 1,032 and 1,072 observations, respectively.
***, **, * significant at 1%, 5%, and 10% level, respectively.
40
Table 6. Time series analysis
This analysis includes analysts who are ranked for the first time after 1991 and who remain ranked for at
least four consecutive years. The results are measured over the three post-publication periods starting with
the first ranking year. As described in Appendix A, the post-publication period spans the one-year period
beginning in October. Variable definitions are in Appendix B.
Post-publication
period
N
Post-publication Post-publication
period
period
ACCU
140
Year 1
(1)
52.40
Year 2
(2)
51.62
Year 3
(3)
51.65
OPT-fcst
140
51.40
49.42
50.25
RET-reco (%)
45
1.66
1.29
1.27
OPT-reco
56
0.07
0.00
-0.04
FREQ
137
0.11
0.11
0.09
COVER
141
16.87
BRKR
141
0.95
0.96
0.95
BOLD
140
51.66
51.83
51.40
FMVR
138
0.11
0.11
0.11
LFR
135
2.09
2.46
2.55 ***
EXPR
143
5.91
6.91
7.91
18.24 **
19.07 ***
Significance tests reported to the right of Year 2 and Year 3 are t-tests on the following differences: Year 2 minus Year 1 (in
column 2), and Year 3 minus Year 2 (in column 3), respectively. Experience increases deterministically by one year over the
three-year period; therefore no significance test is conducted on its time-series changes.
***, **, * significant at 1%, 5%, and 10% level, respectively.
41
Table 7. Multivariate tests of rankings determinants
The analysis in column (1) is based on the following regression model:
Prob [First_Timer = 1] = Probit (a0 + a1 ACCU + a2 OPT-fcst + a3 FREQ +
a4 COVER + a5 BRKR + a6 BOLD + a7 FMVR + a8 LFR + a9 EXPR)
(1)
First-Timer = 1 for analysts who are ranked for the first time in the current year. First-Timer = 0 for
analysts who have not been ranked since the start of our sample period. Since our Institutional
Investor ranking data are from 1991 to 2000, for analysts ranked in 1991, we cannot be sure whether
that was their first ranking year or not, so we exclude 1991 from our first-timers classification. To be
conservative, we also exclude 1992. As a result, we define an analyst as a first-timer only if he/her
first appears in our rankings databases sometime from 1993 to 2000. The independent variables are
measured in the pre-voting period, which as described in Appendix A, is the one-year period ending in
April. Variable definitions are in Appendix B.
The analysis in column (2) is based on the following regression model:
Prob [Rank = 1] = Probit (a0 + a1 LAGRANK + a2 ACCU + a3 OPT-fcst + a4 FREQ
+ a5 COVER + a6 BRKR + a7 BOLD + a8 FMVR + a9 LFR + a10 EXPR)
(2)
RANK = 1 for analysts who are ranked in the current year. RANK = 0 for analysts who are not ranked
in the current year. The independent variables are measured in the pre-voting period, which as
described in Appendix A, is the one-year period ending in April. LAGRANK is an indicator variable
equal to one if an analyst was ranked in the previous year, and zero otherwise. Other variable
definitions are in Appendix B.
The analysis in column (3) is based on the following regression model:
Prob [Lose_Ranking = 1] = Probit (a0 + a1 ACCU + a2 OPT-fcst + a3 FREQ +
a4 COVER + a5 BRKR + a6 BOLD + a7 FMVR + a8 LFR + a9 EXPR)
(3)
Lose_Ranking = 1 for an analyst who was ranked in the previous year but loses ranking in the current
year. Lose_Ranking = 0 for an analyst who was ranked in the previous year and stays ranked in the
current year. The independent variables are measured in the pre-voting period, which as described in
Appendix A, is the one-year period ending in April. Variable definitions are in Appendix B.
42
Table 7. Multivariate tests of rankings determinants (continued)
Variable
INTERCEPT
First-Time
Ranking
(1)
-3.107 ***
(-15.708)
Ranking in
General
(2)
-3.072 ***
(-22.115)
Loss of Ranking
(3)
-0.266
(-0.78)
2.293 ***
(64.969)
LAGRANK
ACCU
0.003 *
(1.553)
0.006 ***
(4.045)
-0.008 **
(-2.264)
OPT-fcst
0.000
(0.034)
-0.001
(-0.807)
0.002
(0.504)
FREQ
0.258 ***
(5.003)
0.375 ***
(11.083)
-0.384 ***
(-5.065)
COVER
0.008 ***
(2.808)
0.008 ***
(4.288)
-0.008 **
(-2.097)
BRKR
0.901 ***
(12.993)
0.973 ***
(21.49)
-0.255 **
(-1.832)
BOLD
0.003 *
(1.514)
0.004 ***
(2.977)
-0.003
(-0.748)
FMVR
0.207
(1.233)
-0.107
(-0.968)
0.071
(0.332)
LFR
0.038 ***
(2.33)
0.002
(0.213)
-0.009
(-0.387)
EXPR
-0.028 ***
(-4.352)
0.006 *
(1.688)
Log Likelihood
Number of Observations
Dep=1
-1590.3
16,891
364
-4389.9
22,991
2,939
0.033
(4.35)
***
-1288.4
2,626
527
***, **, * significant at 1%, 5%, and 10% level, respectively.
43
Table 8. Effect of rankings on analyst career outcomes
The analysis in this table is based on the following regression model:
Prob [Career_Outcome = 1] = Probit (a0 + a1 LAGRANK + a2 ACCU + a3 OPT-fcst + a4 FREQ
+ a5 COVER + a6 BRKR + a7 BOLD + a8 FMVR + a9 LFR + a10 EXPR)
(4)
We measure analyst career outcome in three different ways, Promotion (column 1), Demotion (column
2), and Termination (column 3).
Promotion = 1 for analysts who were employed at a small brokerage house last year and move to a
large brokerage houses in the current year. A larger brokerage is defined as one that employs 25 or
more analysts.
Promotion = 0 for analysts who were employed at a small brokerage house last year and continue to
be employed at a small brokerage in the current year.
Demotion = 1 for analysts who were employed at a large brokerage house last year and move to a
small brokerage houses in the current year.
Demotion = 0 for analysts who were employed at a large brokerage house last year and continue to be
employed at a large broker in the current year.
Termination = 1 for analysts who disappear from the I/B/E/S database in the current year.
Termination = 0 for analysts who remain in the I/B/E/S database in the current year.
LAGRANK is an indicator variable equal to one if an analyst was ranked in the previous year, and zero
otherwise. Other variable definitions are in Appendix B. All independent variables are measured over
the previous year.
44
Table 8. Effect of rankings on analyst career outcomes (continued)
Variable
Promotion
(1)
Demotion
(2)
Termination
(3)
INTERCEPT
-1.454 ***
(-8.732)
-0.843 ***
(-4.126)
-0.553 ***
(-5.949)
LAGRANK
0.681 ***
(4.546)
-0.847 ***
(-7.211)
-0.261 ***
(-4.798)
ACCU
0.005 ***
(2.804)
-0.008 ***
(-3.201)
-0.009 ***
(-8.517)
OPT-fcst
-0.004 **
(-2.232)
0.003
(1.22)
FREQ
0.113 ***
(2.564)
0.101 **
(1.75)
-0.407 ***
(-13.896)
COVER
-0.008 ***
(-3.418)
-0.015 ***
(-4.156)
-0.039 ***
(-19.929)
BOLD
0.005 ***
(3.013)
-0.010 ***
(-3.975)
-0.001 *
(-1.361)
FMVR
-0.157
(-1.125)
-0.026
(-0.134)
-0.333 ***
(-3.415)
LFR
0.062 ***
(3.424)
-0.030 *
(-1.561)
-0.009
(-0.821)
EXPR
0.009 *
(1.719)
0.021 ***
(3.495)
0.010 ***
(2.903)
Log Likelihood
Number of Observations
DEP=1
-2521.5
6,530
869
-1298.7
8,879
319
-5388.2
19,323
1,738
0.004 ***
(4.163)
***, **, * significant at 1%, 5%, and 10% level, respectively.
45