sample descriptive statistics this table presents descriptive statistics

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Journal of Financial Economics 84 (2007) 713737

The impact of all-star analyst job changes on their

coverage choices and investment banking deal flow$

Jonathan Clarkea, Ajay Khoranaa,Ajay Patelb, P. Raghavendra Rauc,

aGeorgia Institute of Technology, Atlanta, GA 30332, USAbWake Forest University, Winston-Salem, NC 27109, USA

cPurdue University, West Lafayette, IN 47907, USA

Received 12 April 2004; received in revised form 8 August 2005; accepted 14 December 2005

Available online 9 February 2007

Abstract

Using a sample of all-star analysts who switch investment banks, we examine (1) whether analyst

behavior is influenced by banking relationships and (2) whether analyst behavior affects investmentbanking deal flow. Although the stock coverage decision depends on the relationship with the client

firms, we find no evidence that analysts change their optimism or recommendation levels when joining

a new firm. Investment banking deal flow is related to analyst reputation only for equity transactions.

For debt and M&A transactions, analyst reputation does not matter. There is no evidence that issuing

optimistic earnings forecasts or recommendations affects investment banking deal flow.

r 2007 Elsevier B.V. All rights reserved.

JEL classification: G24; G32

Keywords: All-star analyst; Analyst coverage; Market share; Investment banking relationships; Conflicts of

interests

ARTICLE IN PRESS

www.elsevier.com/locate/jfec

0304-405X/$ - see front matterr 2007 Elsevier B.V. All rights reserved.

doi:10.1016/j.jfineco.2005.12.010

$We would like to thank an anonymous referee, Bob Bruner, Susan Chaplinsky, Mike Cliff, James Cotter,

Irwin P. Daley, Dave Denis, Diane Denis, Paul Irvine, Laurie Krigman, John McConnell, Henri Servaes, Bill

Schwert, Jonathan Sokobin, Kent Womack, and seminar participants at Boston College, Ohio University,

Virginia Tech, the 2004 Utah Winter Finance Conference, the 2003 FMA Annual Meeting, and the 2003

European FMA Meeting for helpful comments and suggestions. We would also like to acknowledge the

contribution of I/B/E/S International Inc. for providing earnings per share forecast data, as part of a broad

academic program to encourage earnings expectations research.

Corresponding author. Tel.: +1 765 494 4488; fax: +1 765 494 9658.E-mail address: [email protected] (P.R. Rau).
http://www.elsevier.com/locate/jfechttp://dx.doi.org/10.1016/j.jfineco.2005.12.010mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.jfineco.2005.12.010http://www.elsevier.com/locate/jfec


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1. Introduction

Is investment banking deal flow affected by analyst behavior? Anecdotal evidence from

the popular press suggests that it is:

David Komansky, former chief executive of Merrill Lynch, and Dennis Kozlowski

discussed ways to improve research coverage of Tyco and hiring an analyst the company

liked, according to an e-mail introduced at the ex-Tyco chiefs trial. After Merrill hired

the analyst, Phua Young, Tyco immediately responded by awarding the investment

bank work on a $2bn bond offering, according to an e-mail sent in 1999 to Mr.

Komansky by Samuel Chapin, Merrills vice-chairman. To demonstrate the impact this

hire has on our relationship, Dennis Kozlowski called me on Phuas first day of work to

award us the lead management of a $2.1bn bond offering, Mr. Chapin wrote in the e-

mail of August 31 1999y1

Moreover, is analyst behavior influenced by investment banking relationships between

the bank and the firms the analyst covers? The popular press suggests that analysts might

be pressured to cover firms that they would not otherwise cover, as well as give favorable

coverage to firms that they would otherwise downgrade.2

In this paper, we analyze a sample of 216 cases in which an Institutional Investor All-

America Research Team analyst (all-star hereafter) moves from one investment bank to

another over the 1988 to 1999 period. We investigate two questions. First, we examine

whether the all-stars behavior changes when he switches investment banks. An all-star

who moves from Goldman Sachs to Merrill Lynch, for example, might choose to continue

covering only those stocks that are likely to generate investment banking business forMerrill. In addition, the analyst might issue more favorable reports for Merrill clients than

when at Goldman. Hence, we study whether, in the period following a job change, all-stars

choose to continue covering stocks and whether they become more optimistic about the

stocks they cover, based on the relationship between the firms being covered and the

investment bank employing the all-star. Second, we examine whether analyst reputation

and coverage affect investment banking deal flow after the all-star joins the new bank.

We investigate all-star job changes, instead of job changes across all analysts, because

prior research by Krigman, Shaw, and Womack (2001) and Dunbar (2000) documents that

firms value all-star research coverage. Specifically, Krigman, Shaw, and Womack find that

the perceived quality of coverage, as proxied by all-star coverage, is an important driver ina firms decision to change the lead underwriter in a follow-on offering. Dunbar (2000)

finds a strong positive relation between changes in an investment banks Institutional

Investor All-America Research Team ranking and subsequent changes in the banks

market share in the initial public offering market. If we find no effect on investment bank

market share when an all-star analyst moves, it is unlikely we will find an effect for non-all

stars. We examine both capital-raising (debt and equity underwriting) and corporate

control (M&A) transactions to develop a comprehensive understanding of the relations

among stock coverage, analyst reputation, investment bank reputation, and deal flow.

With respect to our first research question, our results show that an all-star analysts

decision to cover a firm is influenced by the investment banks relationship with the firm. In

ARTICLE IN PRESS

1Bowe and Silverman (2004).2See, for example, Schroeder and Smith (2002).

J. Clarke et al. / Journal of Financial Economics 84 (2007) 713737714


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particular, the all-star is significantly more likely to continue covering a stock that is being

covered at the new bank when that bank also has a prior investment banking relationship

(underwriting or M&A advisory) with the firm. We find no evidence, however, that

analysts change their optimism levels and recommendation ratings for the firms they cover

at the new bank. At the median level, all-stars do not become more optimistic after a jobchange, and the difference in their earnings forecasts, before and after the job change, is

not related to the existence of an investment banking relationship with the client firms. In

addition, recommendation levels, both before and after the analyst changes jobs, do not

suggest that analysts issue significantly more positive recommendations after changing

jobs. In a separate sample of non-all-star job changes, we obtain similar findings.

Turning to our second research question, we find that the bank hiring the all-star

significantly increases its market share in the industry covered by the analyst, relative to the

bank losing the all-star. We separately examine the determinants of relative market share

for bond and equity underwriting and corporate control transactions in a multivariate

framework wherein we control for investment bank reputation. Our results show that

proxies for all-star reputation, such as the timeliness and frequency of the all-stars

earnings forecasts, have a significantly positive impact on the relative market share of the

two banks for equity underwriting transactions, but not for debt or M&A transactions. We

find no evidence that optimism in earnings forecasts (deviation of the analysts earnings

forecast above consensus) affects relative market share for either capital-raising or

corporate control transactions. Finally, the new business is not generated by clients of the

analyst at the original bank who follow the all-star to the new bank; rather, it comes from

new firms that the all-star is significantly more likely to cover at the new investment bank.

Our paper contributes to the existing academic literature on analyst behavior in twoways. First, while the extant literature reports some evidence that analysts affiliated with

banks and other financial institutions tend to make more optimistic forecasts and

recommendations than unaffiliated analysts,3 there is no direct evidence that this difference

in behavior is due specifically to relationships between the investment bank and the firms

the analyst chooses to cover. Our analysis of changes in analyst behavior surrounding their

job changes enables us to examine whether investment bank pressure influences analyst

recommendations and forecasts. We find that it does not.

Second, there is no direct evidence in the literature on whether analysts are able to

increase deal flow (underwriting and M&A transactions) for their respective banks. We

show that analysts are instrumental in winning deal flow for equity underwriting, but notfor debt or M&A transactions. Our results are inconsistent with recent allegations in the

popular press that analysts have helped generate investment banking deal flow by issuing

overly positive recommendations. To the extent that such allegations are true, our results

suggest that they cannot be generalized across all analysts or types of transactions.

The remainder of the paper is organized as follows. Section 2 discusses the data and

describes the sample. Section 3 examines both the analyst stock coverage decision and

changes in analyst behavior in the period surrounding job changes by all-stars. Section 4

examines the relation between analyst coverage and investment banking deal flow. Finally,

Section 5 concludes.

ARTICLE IN PRESS

3See Dugar and Nathan (1995), Lin and McNichols (1998), Bradley, Jordan, and Ritter (2003), and Irvine,

Nathan, and Simko (2004). In addition, Michealy and Womack (1999) find that stocks that underwriter analysts

recommend earn lower returns than buy recommendations by unaffiliated analysts.

J. Clarke et al. / Journal of Financial Economics 84 (2007) 713737 715


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2. Data, variable construction, and sample description

2.1. Data

We examine a sample of Institutional Investor All-America Research Team analysts whochange investment banks between 1988 and 1999. Following Hong, Kubik, and Solomon

(2000), we use the I/B/E/S detail file to determine which analysts change jobs. The detail

file assigns each individual analyst a numerical code, making it possible to track earnings

forecasts across time even if the analyst switches investment banks. The I/B/E/S database

identifies each analyst and his or her employer by a unique numerical code. We use the

Broker Code Key to identify the last name and first initial of each analyst in the database

and the employers identity. This additional information allows us to identify those

analysts who were named to Institutional Investors All-America Research Team in a given

year.4 We consider only those cases in which the analyst was an all-star in the year of or the

year prior to the job change. Since we wish to examine analyst behavior (e.g., analyst

forecasts and recommendations) in the period immediately around the job change, during

which time it is not likely that the analyst obtained new information to change his or her

forecast, we eliminate cases in which the elapsed time between forecasts is greater than 100

trading days.5 We further eliminate cases in which the switch was due to the merging of

two investment banks. For example, we eliminate four cases where an all-star switched

from Kidder Peabody to Paine Webber in 1994. Finally, we eliminate six cases in which

Institutional Investor named an analyst as a star in the multi-industry, small growth

companies, or government sponsored enterprises categories. The final sample consists

of 216 cases of analyst job changes.Although many rankings of individual analysts are published each year, Institutional

Investors All-America Research Team is appropriate for our analysis because, as Hong,

Kubik, and Solomon (2000) note, that sell-side analysts generally aspire to be Institutional

Investor All-Americans. Moreover, Leone and Wu (2002) document that all-star analysts

realize better earnings forecast accuracy, better stock recommendation returns, and smaller

optimism bias than do their non-star counterparts.

Our analysis consists of three steps. First, we classify analysts into industries in which they

are rated all-stars. Second, we compare analyst behavior before and after the job change.

Third, we examine whether investment bank market share changes after the analyst moves.

The following three subsections describe the variable construction for each of these steps.

2.1.1. Analyst industry classification

We assign analysts to an industry based on the firms they follow. Firms are assigned a

Standard & Poors Global Industry Classification Standard (GICS) industry code,

ARTICLE IN PRESS

4Leone and Wu (2002) discuss the selection procedure for the all-American team. To summarize, selection to

the All-American team is based on survey data. Institutional Investor sends a questionnaire to the directors of

research and chief investment officers of money management institutions, and also to other sell-side analysts.

These individuals rank each analyst based on the following six dimensions: accessibility and responsiveness,

earnings estimates, useful and timely calls, stock selection, industry knowledge, and written reports. Scores foreach analyst are calculated by taking the number of votes awarded by each survey respondent and weighting them

by the size of the respondents firm. The results are published each year in the October issue of the magazine.5The median length of time between the analysts first forecast with his new employer and last forecast with his

previous employer is 24 trading days.



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supplied by COMPUSTAT. These industry codes classify companies into one of four

levels: ten sectors, 23 industry groups, 59 industries, and 122 subindustries. We use GICS

codes in preference to other classification schemes used in the literature, such as

Standardized Industry Classification System (SIC) codes, North American Industry

Classification System (NAICS) codes, or the Fama-French (1997) industry groupings,since Bhojraj, Lee, and Oler (2003) show that GICS classifications better explain stock

return co-movements as well as cross-sectional variations in valuation multiples, forecasted

growth rates, and key financial ratios. Moreover, Boni and Womack (2006) show that

partitions on the basis of GICS codes provide a good proxy for how analysts specialize by

industry. As a first pass, we assign each all-star analyst to one of the 59 GICS industries

based on the industry in which the analyst issued the largest fraction of forecasts in the two

years before the job change. We then manually check these GICS classifications against

industry classifications assigned by Institutional Investor and make changes if necessary.6 If

an analyst is an all-star in more than one industry, we also define a secondary GICS

industry for that analyst. In our sample, all-star analysts issue an average (median) of

72.7% (81.8%) of their forecasts in the primary GICS industry in which they are all-stars.

There is substantial cross-sectional dispersion in our sample across industries, with the

216 instances of job changes representing 44 unique industries. The industries with the

most job changes are chemicals (17 cases), health care providers and services (12 cases),

computers and peripherals (11 cases), and oil and gas (10 cases). All-stars are also likely to

stay all-stars after they change jobs: 80% remain all-stars in the year following their job

change and 70% are still classified as all-stars in the second year following their change in

jobs.

2.1.2. Measuring analyst behavior

Our measures of analyst behavior are based on both earnings forecasts, obtained from

the I/B/E/S detail files, and analyst recommendations, obtained from the recommendation

files. We measure analyst behavior along five dimensions: earnings forecast accuracy,

optimism, timeliness, frequency of coverage revisions, and recommendation levels. These

dimensions are meant to capture both analyst reputation and bias. As noted above, some

of the measures used by Institutional Investor to rank an analyst include responsiveness,

earnings estimates, and timeliness. Our measures of earnings forecast accuracy, frequency

of coverage revisions, and timeliness are proxies for analyst reputation. The other two

measures, optimism and recommendation levels, capture aspects of analyst behavior thatare likely to proxy for bias.

We measure analyst behavior along each of these five dimensions using a scoring

methodology. Scores are used because measures of analyst accuracy and bias are firm and

industry dependent. For example, as Hong, Kubik, and Solomon (2000) note, simply

comparing the average forecast error of an individual analyst to the average forecast error

of the other analysts who issue earnings estimates that year is problematic, because

earnings for some firms are more difficult to predict than others. Consequently, we follow

ARTICLE IN PRESS

6As an example, Institutional Investor and GICS codes distinguish between the automobile industry and the

automobile components industry. In terms of the number of firms, the automobile components industry(consisting of firms such as Midas, Cooper Tire and Rubber) is much larger than the automobile industry

(consisting of firms such as GM, Toyota, Nissan, Winnebago). One analyst in our sample issued the majority of

his forecasts in the automobile components GICS industry. However, Institutional Investor ranked the analyst as a

star in the automobile industry. We therefore re-classify this analyst as a star in the automobile industry.



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Hong, Kubik, and Solomon in constructing annual performance scores based on an

analysts relative earnings forecast accuracy, forecast frequency, timeliness, and optimism

bias.7

Forecast accuracy score: To examine accuracy across all stocks in the analysts portfolio,

we construct scores by defining Fi,j,t as the most recent forecast of annual earnings pershare issued before the fiscal year-end by analyst i on firm j for year t. Our measure of

analyst is accuracy for firm j in year t is the absolute difference between the earnings

forecast and the realized earnings per share of the firm, Aj,t:

Earnings forecast errori;j;t jFi;j;t Aj;tj. (1)

We sort the analysts who cover firm jin year t based on their forecast errors given by the

above equation. We then assign a rank based on this sorting, with the most accurate

analyst receiving a rank of one. In the case of ties, we assign each analyst the mean value of

the ranks that they take up.8 Since the maximum rank an analyst can receive for a firm

depends on the number of analysts who cover the firm, we scale an analysts rank by the

number of analysts who cover the firm. The formula for the forecast accuracy score is

given by

Forecast accuracy scorei;j;t 100 Accuracy ranki;j;t 1

number of analystsj;t 1

" # 100, (2)

where number of analystsj,t is the number of analysts who cover firm j in year t. The

accuracy score ranges from zero for the lowest-ranked analyst covering a firm to a score of

100 for the highest-ranked analyst.9

Optimism bias score: We define optimism bias as

Optimism biasi;j;t Fi;j;t Fi;j;t (3)

where Fi;j;t 1=nP

m2 if gFm;j;t; fig is the set of all analysts other than analyst i whoproduce an earnings per share estimate for stock j in quarter t, and n is the number of

analysts in {i}. Hence, Fi;j;t is a measure of the consensus forecast made by all other

analysts except analyst i following stock j in quarter t. We replicate the ranking

methodology for constructing the forecast accuracy score to arrive at an optimism bias

score, which ranges from zero for the least biased analyst covering a firm to a score of 100

for the most biased analyst covering the firm in a given year. Intuitively, the optimism bias

measures how optimistic an analyst is relative to the other analysts covering the stock the more optimistic the analyst, the higher his or her earnings forecast will be relative to the

consensus.

Frequency of coverage revision score: This score is calculated by ranking analysts based

on the number of times they revise their annual earnings estimates. Like the previous

measures, the frequency of coverage revision score ranges from zero for the least frequent

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7For robustness, we also compute simple measures of accuracy, frequency, timeliness, and bias using quarterly

estimates of earnings per share. For example, we measure earnings forecast accuracy as the difference between the

analysts prediction of earnings per share and the realized value, normalized by the stock price. Our results are

qualitatively unchanged using these alternative measures.8Alternative procedures for handling ties, such as employing the median or highest values of the assigned ranks,

produce similar results.9To compute the score, we impose the criterion that at least five analysts cover a security. This requirement

ensures that there will be a meaningful consensus with which to calculate scores.



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forecaster to 100 for the most frequent forecaster. The use of this variable is motivated by

Krigman, Shaw, and Womack (2001), who find that dissatisfaction with the frequency of

coverage is a major reason for switching underwriters.

Timeliness score: To compute a timeliness score, which ranges from zero for the least

timely forecaster to 100 for the analyst who issues the first earnings forecast on a particularstock in a given year, we rank analysts based on when they issue their first annual earnings

forecast for a firm in a given year. We then replicate the ranking methodology described

above. We include this score because Hong, Kubik, and Solomon (2000) argue that an

analyst who issues the first annual forecast is not likely to be herding with other analysts.

Moreover, Clement and Tse (2005) note that analysts who exhibit herd behavior have

lower ability, suggesting that timeliness is a useful proxy for reputation.

Recommendation levels: We gather data from I/B/E/S on analyst stock recommendations

from October 1993 (the first available date for I/B/E/S data) through the end of our

sample, and measure the analysts recommendation for each stock relative to the

consensus. Since a strong buy (strong sell) is coded as one (five), a negative relative

recommendation indicates an optimistic recommendation by the analyst. The abnormal

recommendation is computed as the difference between the analysts recommendation and

the prevailing consensus, which is calculated as the average recommendation across all

other analysts covering the security.

2.1.3. Measuring investment bank market share

We compile a comprehensive database of investment banking deals (capital-raising and

corporate control transactions) between 1986 and 2001 from Thompson Financial

Securities New Issues and Mergers and Acquisitions databases. From the new issuesdatabase, for every initial public offering, seasoned equity offering, and bond offering, we

obtain the issuer name and cusip, the filing and issue dates, the identity of the investment

bank retained by the issuer, and the size of the deal. From the mergers and acquisitions

database, we obtain information on the identity of the target and acquiror, the

announcement and effective dates of the transaction, and the size of the deal.

We use our database to calculate the industry market share for the bank the analyst is

switching from (original bank) and the bank the analyst is switching to (new bank).

Industry market share is calculated as the gross proceeds raised by an investment bank in a

particular industry divided by total gross proceeds of all deals completed in that particular

industry. Market shares are calculated for the two years before and the two years after the

analyst switches jobs. Industry classifications are based on the 59 GICS industry codes

from the COMPUSTAT database. For those analysts listed as all-stars in multiple

industries, we add up the gross proceeds across all industries to compute market share.

2.2. Sample description

Table 1 describes the sample. Panel A reports data on analyst turnover by year. The

number of analysts issuing earnings forecasts in the I/B/E/S database increases from 2,618

in 1988 to 4,543 in 1999. Measured as a percentage of all analysts, the number of all-staranalysts decreases from 12.4% in 1988 to 7.6% in 1999. The decrease is especially sharp

over the period 19931995 period, when the percentage drops from 15.7% to 8.3%, due to

a sharp increase in the total number of analysts and a fairly static number of all-stars.

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ARTICLE IN PRESS

Table 1

Sample descriptive statistics

This table presents descriptive statistics on analyst turnover and capital market activity over the sample period.

Panel A reports year-by-year statistics on job changes by analysts. The number of analysts is the number of

analysts submitting forecasts to the I/B/E/S database in a given year. The number of all-stars is the number of

analysts on the Institutional Investor All-America Research Team that issued forecasts in a given year. The number

of institutions is the number of investment banks that had analysts issuing forecasts in a given year. Analyst

turnover (all-star turnover) is the number of analysts (all-star analysts) who moved from one investment bank to

another in a given year. Panel B presents the number of deals between 1986 and 2001 in which an investment bank

served as an advisor. The panel reports both capital-raising transactions (initial public offerings of equity,

seasoned equity offerings, and bond offerings) and corporate control transactions (M&A deals). Deals are

compiled from Thompson Financial Securities New Issues and M&A databases.

Panel A: Analyst turnover by year

Year Number of

analysts

Number of

all-stars

Number of

institutions

Analyst

turnover

Percent

turnover

All-star

turnover

Percent all-

star turnover

1988 2,618 325 172 154 5.88 6 1.85

1989 2,841 368 183 249 8.76 23 6.25

1990 2,648 336 187 149 5.63 9 2.68

1991 2,440 331 191 151 6.19 8 2.42

1992 2,269 353 192 114 5.02 6 1.70

1993 2,479 389 221 166 6.70 16 4.11

1994 2,876 371 226 219 7.61 21 5.66

1995 3,141 262 231 248 7.90 21 8.02

1996 3,528 267 261 282 7.99 20 7.49

1997 3,997 272 308 349 8.73 27 9.93

1998 4,410 322 351 404 9.16 26 8.071999 4,543 344 329 367 8.08 33 9.59

Panel B: Capital market activity by year

Capital-raising transactions Corporate control transactions

Year IPOs SEOs Bond offerings Advising acquiror Advising target Total deals

1986 717 772 709 519 655 3,372

1987 544 504 521 529 655 2,753

1988 291 190 402 704 929 2,516

1989 252 313 376 642 923 2,506

1990 215 237 363 454 660 1,929

1991 397 567 845 315 488 2,612

1992 606 624 1,110 365 542 3,247

1993 820 903 1,447 511 750 4,431

1994 642 558 984 605 860 3,649

1995 575 686 1,295 814 1,142 4,512

1996 880 848 1,840 920 1,258 5,746

1997 637 824 2,410 1,085 1,504 6,460

1998 401 640 2,564 1,225 1,730 6,560

1999 573 550 2,011 1,161 1,754 6,049

2000 421 499 2,325 1,248 1,777 6,270

2001 154 627 2,339 900 1,363 5,383

Totals 8,125 9,342 21,541 11,997 16,990 67,995



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The number of institutions employing analysts increases from 172 in 1988 to 329 in 1999.

Turnover among all analysts increases from 5.9% (154) in 1988 to 8.1% (367) in 1999.

Turnover among all-star analysts increases from 1.9% (6) to 9.6% (33) over the same

period. While turnover in general has increased among analysts, the increase is much more

dramatic among all-star analysts.10

Panel B reports information on total deal activity when investment banks are

involved. Of the 67,995 deals in our database, the breakdown is as follows: 8,125 initial

public offerings, 9,342 seasoned equity offerings, 21,541 bond offerings, and 28,987

instances in which either the target or acquiror retained the services of an investment

bank. There is a rapid increase in capital-raising transactions (equity and debt) as

well as corporate control transactions (mergers and acquisitions) in the early part of the

1990s.

3. Analyst behavior surrounding the job change

3.1. Does the analysts portfolio of covered stocks change? If so, why?

We examine firms that the all-star chooses to continue covering, those he adds, and

those he drops after moving to the new bank. This allows us to investigate whether the all-

stars coverage decision depends on whether the bank has a relationship with the firm

under consideration. Panel A of Table 2 reports descriptive data on stock coverage and

deal flow in the two years before and after the all-star changes jobs. Panel B reports more

detailed descriptive statistics on firm, analyst, and bank characteristics for the samples of

firms for which the all-star retains, drops, and add coverage.The all-stars workload remains the same after switching investment banks (Panel A).

He covers 16 firms (at the median) prior to and 15 firms following the job change11; a

Wilcoxon rank sum test indicates no difference in the number of stocks covered before and

after the job change. He retains coverage of approximately 65% of the old portfolio at the

new investment bank. Replacing the 35% that he drops, approximately 35.5% of the

stocks covered by the all-star at the new investment bank are new firms he did not cover

previously.

A more relevant question for the purpose of our study is whether all-star stock coverage

relates positively to investment banking deal flow. Panel A shows that at the median, 99

unique firms complete deals in the stars industry in the two years before the job change, incontrast to 106 firms in the two years after the job change. Focusing only on the firms the

analyst covers, around half (seven to eight) complete deals in the two years before and after

the job change. However, the difference in deal flow between the original and new banks is

striking. At the mean level, a significantly smaller number of firms complete deals with the

original bank after the all-stars departure. Of the firms covered by the analyst after his

ARTICLE IN PRESS

10These results contrast with Groysberg and Nanda (2001), who find that in the aggregate, star analysts have

lower turnover than non-stars. Groysberg and Nanda attribute the lower turnover of star analysts not to their

stardom, but to demographic characteristics; stars tend to be older, more experienced, and have greater tenure

(i.e., they move less) than non-stars. We find that while in the earlier part of our sample period, all-stars doobserve a lower turnover rate than non-all-stars, in the latter part, turnover increases dramatically for all-stars and

increases at a slower rate for non-all-star analysts.11This contrasts with Boni and Womack (2005), who find that the average analyst in their sample covers ten

companies. All-stars cover more companies on average.



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Table 2

All-star analyst coverage before and after a change in jobs

This table examines all-star analyst coverage over the two years prior to (i.e., pre-turnover) departing the original inve

post-turnover) arriving at the new investment bank. Panel A reports data on overall stock coverage and investment bank denumber of stocks covered by the median analyst switching investment banks in the pre- and post-turnover period, respectiv

turnover is the fraction of stocks that the analyst continues to cover after arriving at the new investment bank. Proportio

dropped after arriving at the new investment bank. Proportion of new stocks addedis the fraction of new stocks that the an

number of unique firms completing deals is computed as the median number of firms that completed deals in the same indu

and after the analysts move. Panel B reports data on the median characteristics of stocks retained, added, and dropped. Th

coverage revision score are computed using a scoring methodology as in Hong, Kubik, and Solomon (2000). For both pane

The p-value for the difference is based on a two-sided Wilcoxon rank-sum test.

Panel A: Data on stock coverage and deal flow pre- and post-turnover

Pre-turnover P

Stock coverage

Number of stocks covered 16.00

(16.85)

Proportion of stocks retained

Proportion of stocks dropped

Proportion of new stocks added

Stock coverage and investment banking deal flow

Unique number of firms completing deals in all-stars industry 99.00

(124.92) Unique number of firms covered by analyst completing deals with any investment bank 7.00

(7.97)

Unique number of firms covered by analyst completing deals with the original bank 0.00

(1.24)

Unique number of firms covered by analyst completing deals with the new bank 0.00

(1.15)


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move to the new bank, a significantly larger number of firms complete deals with the new

bank as their advisor than they did before the move.

Panel B of Table 2 provides descriptive statistics on changes to the analysts port-

folio following the move to the new investment bank. Analysts may be more likely to

cover firms that generate investment banking or trading revenue for the new bank. Thesefirms are likely to be larger, have more trading volume, and complete more deals

in the stars industry before or after the all-stars move. We therefore compute the

proportion of firms covered by the all-star with market capitalization or trading volume

in the top 25% in their industry. We also compute the proportion of firms that complete

at least two deals in the two years before (following) the analysts job change (representing

the 75th percentile of the number of deals done by a firm during the respective two-

year window). The results in Panel B indicate that all-stars tend to retain coverage of

larger firms, firms with greater trading volume, and firms that complete a larger number

of deals in the two years before and after the all-stars move.12 Interestingly, firms

added to the all-stars portfolio are significantly smaller and have lower trading

volumes than stocks dropped from the portfolio. However, a significantly higher

proportion of these firms complete investment banking deals over the two years following

the job change. In other words, analysts add coverage of firms more likely to generate

investment banking deal flow. They drop coverage of smaller firms, firms with lower

trading volumes, and firms less likely to generate investment banking deal flow in the

future.

Since Jegadeesh, Kim, Krische, and Lee (2004) show that sell-side analysts generally

tend to recommend high growth, high volume, and relatively expensive glamour firms, we

also compute the proportion of glamour firms, defined as firms with market-to-book ratiosabove industry average, in the all-stars portfolio. Panel B shows that stocks retained or

added by the all-star have higher market-to-book ratios (relative to their industry) than

those dropped by the all-star, suggesting that, in general, all-stars not only prefer to retain

coverage of glamour stocks, they prefer to cover glamour firms within particular

industries.

In terms of analyst-level characteristics, analysts should be more likely to drop coverage

of firms for which they are less accurate and for which they produce less frequent reports.

Panel B compares the forecast accuracy and the frequency of coverage revision scores for

the firms the all-star retains to those for the firms he drops. The results show that while all-

stars are significantly more likely to drop firms for which they are less accurate, thefrequency of coverage revision is not significantly lower for firms the analyst decides to

drop versus those he decides to retain.

Finally, we compute the proportion of firms that have a prior relationship with

the new investment bank (firms that complete at least one deal at the new investment

bank in the two years prior to the all-stars arrival), and the proportion of firms

that are already covered at the new bank by any analyst or by an all-star. Panel B reports

that a significantly greater proportion (based on means) of firms the all-star retains

or adds (as opposed to those he drops) had a prior relation with the new bank prior

to the all-stars arrival. Firms already covered by the new bank are significantly more

likely to be retained by the analyst. A significantly smaller proportion of the firms

ARTICLE IN PRESS

12The differences (not reported in the table for brevity) are statistically significant.



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added by the analyst are covered at the new investment bank prior to his arrival. These

results reinforce our earlier findings that the coverage decision appears to be related to the

firms likelihood of generating revenue for the investment bank.

Next, we estimate a multivariate logistic regression to explain the likelihood of an

analyst retaining or adding (versus dropping) coverage of a stock subsequent to movingto the new bank. More specifically, we regress an indicator variable for the decision to

retain coverage of a stock (as opposed to adding or dropping it) against firm-specific

indicator variables, analyst/firm-specific variables, and firm/bank relationship-specific

indicator variables. The firm-specific indicator variables are dummy variables that take

a value of one if the firm has a market capitalization or trading volume in the top 25%

of its industry, or if it completes two or more deals in the two years prior to or after the

all-stars move, and zero otherwise. The analyst/firm-specific indicator variables are the

all-stars frequency of coverage revision score, the forecast accuracy score, and indicator

variables that take a value of one if the all-star has a higher frequency of coverage score or

a higher forecast accuracy score than the analyst at the new bank covering the firm prior to

the all-stars move. Finally, the firm/bank-relationship variables are indicator variables

that take a value of one if the firm is already covered at the new bank by any analyst, if

the firm is already covered at the new bank by an all-star analyst, or if the firm com-

pletes at least one deal with the new investment bank in the two years prior to the all--

stars arrival. We also include interaction terms to examine whether the impact of prior

coverage on the analysts decision to retain the stock is enhanced by a prior invest-

ment banking relationship with the firm under consideration. These results are reported in

Table 3.

Model 1 compares the decision to retain a firm against the decision to add a firm to theall-stars portfolio, while Model 2 compares the decision to retain a stock against the

decision to drop it. Model 3 examines the stock retention decision conditional on the

existence of coverage at the new bank before the all-stars arrival and Model 4 compares

the decision to add coverage of a stock against the decision to drop it.

Consistent with our results in Table 2, all-star analysts are more likely to add coverage

of smaller glamour firms with lower trading volume that have a higher potential for future

deal flow (Model 1). They are more likely to drop coverage of smaller firms that do not

contribute to future deal flow and are more likely to retain coverage of glamour stocks

with high market-to-book ratios (Model 2). All-stars are also more likely to retain

coverage of stocks in which they are active, providing accurate earnings forecasts withfrequent revisions over the two years preceding the job change (Model 2). They are more

likely to retain coverage if the stock was covered previously by an analyst at the new bank

(Model 2) and more likely to add coverage of stocks that were not previously covered

(Model 4). For firms with coverage at the new bank prior to the analysts arrival (Model 3),

the all-star is more likely to retain coverage of larger firms and those that have a prior

relationship with the new investment bank. The analyst is more likely to drop coverage

if the firm has a prior investment banking relationship with the new bank, and is

already being covered by another all-star analyst at the new bank (the interaction term in

Model 3).

Overall, our findings suggest that an all-star analyst is more likely to retain or addcoverage if (1) the stock is a large glamour stock in its industry, (2) the analyst has in the

past issued frequent and accurate earnings forecasts on the stock, and (3) the firm has a

prior investment banking relationship with the new bank.

ARTICLE IN PRESSJ. Clarke et al. / Journal of Financial Economics 84 (2007) 713737 725


14/25


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All-star has higher frequency of coverage revision score than

analyst at new bank prior to move (indicator variable)

Firm/Bank relationship-specific indicator variables

Firm is already covered at the new bank by any analyst (prior 0.66 0.16

coverage) (0.00) (0.09)

Firm is already covered at the new bank by an all-star analyst

Firm completed X1 deals with the new investment bank in the 1.08 0.71

2 years prior to the all-stars arrival (prior relationship) (0.01) (0.16)

Prior coverage at new bank Prior relationship with new bank 0.45 1.08

(0.36) (0.05)

Prior coverage by an all-star at new bank Prior relationship

with new bank

Number of observations 3,124 2,517

Percent concordant 84.50 66.30


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3.2. Do all-star analysts change their behavior after they move?

An analyst who is pressured by an investment bank to provide favorable coverage of a

client firm might change the optimism of his or her earnings forecasts and/or stock

recommendation rating after moving to a new bank that has a different set of clientrelationships. In this section, we investigate changes in analyst optimism bias,

recommendation ratings, and earnings forecast accuracy for different types of firms

classified on the basis of whether they have a prior investment banking relationship with

the new and/or original investment banks.

We compute scores for optimism bias, forecast accuracy, timeliness, and frequency of

coverage revision for the all-stars relative to the universe of equity analysts covering the

firms. These scores are computed using annual data for firms the all-star retains, for the

year before and year after the all-star changes jobs. We classify these firms into separate

categories on the basis of whether, in the two years before the job change, the firms have

investment banking relationships with either the new or the original bank, with neither

bank, or with both banks. We also compute changes in analyst stock recommendations

surrounding the analysts job change by comparing the analysts last recommendation on a

firm relative to consensus before leaving the original bank with his first recommendation

for the same firm after arriving at the new bank.

When the firm has an investment banking relationship with either bank, our results (not

reported for brevity) indicate that there is no significant change in the analysts optimism

bias scores or abnormal recommendation levels in the period surrounding the job change.

There is also no change in earnings forecast accuracy, timeliness, or frequency of coverage

revision across any of the categories.

13

The lack of any increase in analyst optimism, ineither earnings forecasts or stock recommendations, is inconsistent with the view in the

popular press that analysts may exhibit extreme optimism in an attempt to win investment

banking deal flow.

While analysts maintain their opinions on stocks that they previously followed, it is

plausible that they are more optimistic about stocks they are covering for the first time and

that have investment banking potential. This may be where we are most likely to find

cheating behavior. We therefore examine newly covered stocks, and separate them into

two categories based on whether two or more investment banking deals occur in the two

years following the analysts arrival at the new bank. Again, we find no evidence to suggest

that analysts are significantly more optimistic for stocks that have high future deal flow.One explanation for our results is that the reputational concerns of all-star analysts

make them less likely to succumb to pressure from their investment bank to alter their

earnings forecasts and recommendations to increase deal flow. That is, it may be the non-

star analysts who are more likely to issue optimistic recommendations or earnings

forecasts in an effort to win investment banking deal flow. To test this possibility, we

compile a sample of 1,056 non-star analysts who switch investment banks between 1988

and 1999 but continue to cover stocks in the same GICS industry and repeat the above

analysis.

ARTICLE IN PRESS

13We also obtain similar results in multivariate regressions of the change in analyst bias and reputation scores

against dummy variables proxying for prior relationships with the original and new banks. We find no evidence to

suggest that changes in analyst earnings forecasts and recommendations following the switch in jobs are related to

a prior relationship between the firm and the investment bank.



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Similar to the case of all-star analysts, we find no evidence that non-stars issue more

optimistic earnings forecasts or recommendations, change timeliness, or experience any

significant change in their earnings forecast errors after they move to the new bank. These

results suggest that analysts without strong reputational concerns do not change their

behavior either, in an attempt to win investment banking deal flow.One explanation for the inconsistency between our findings and the conflict-of-interest

arguments alleged by regulators and the press is that the incentives of both the new and

original investment banks are similar. Therefore, we should not see a change in behavior

on average. Since our data do not include any examples of analysts moving from

investment banks to pure research houses that do not underwrite deals, our analysis

cannot fully reveal the conflicts of interest that might exist. The analysis does suggest,

however, that the egregious cases of analyst bias alleged in the popular press cannot be

generalized across all analysts.

4. Analyst job changes and investment banking deal flow

4.1. Do the new banks experience increased deal flow following all-star analyst turnover?

Table 4 examines the changes in industry market share across the original and new

banks in the two years before and after the all-star switches jobs.14 The deals are classified

into two sub-categories: capital-raising transactions (initial and seasoned equity under-

writing and bond underwriting) and corporate control transactions (M&A). To get a

broad sense of how deal flow changes surrounding analyst job changes, we do not

condition on stocks being retained or dropped by the analyst or on client relationships at

the original and new investment banks. Industry market share is calculated as the gross

proceeds raised in an industry by the analysts investment bank, divided by the total gross

proceeds of all deals completed in that industry.

Following the analysts arrival at the new bank, the difference in market share between

the two banks widens significantly for both capital-raising and corporate control

transactions. Across all capital-raising transactions, for example, before the analyst

moves to the new investment bank, the market share for the median bank in the sample of

new investment banks is 2.09% as opposed to a market share of 0.86% for the median

bank in the sample of original banks. The median (mean) difference in relative marketshare is 0.82% (1.35%), significant at the 5% level. After the analyst arrives at the new

bank, the median market share at the sample of original investment banks decreases to

0.57%, while it increases to 2.35% at the new investment banks. The median (mean)

difference in market share is 2.28% (2.27%), significant at the 1% level. Similar increases

can be seen for corporate control transactions. Note, however, that the increase in relative

market share for capital-raising transactions occurs only for equity underwriting deals. For

bond deals, the zero median market share both before and after an analyst job change for

both the original and new bank is driven by a high concentration of deals done at a small

ARTICLE IN PRESS

14

We focus on all-star analysts instead of all analysts, since non-all-star analysts do not generate significantinvestment banking deal flow. All-star analysts, accounting for only 10% of all sell-side analysts, are involved in

63% of target advisory deals, 64% of SEO deals, 57% of acquiror advisory deals, 76% of bond deals, and 48% of

IPO deals. Non-all-star analysts who switch investment banks are also involved in fewer deals relative to all-stars

who switch investment banks.



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subset of investment banks. During our sample period, 38% of debt underwritingtransactions are handled by Merrill Lynch, Goldman Sachs, and Lehman Brothers.

Our results so far suggest that relative deal flow at the new investment bank increases

following the arrival of the all-star analyst. However, this analysis leaves several questions

unanswered. Is the increase due to the all-star? If so, what characteristics of the all-star are

important in determining deal flow? Alternatively, is it the case that both deal flow and the

all-star are drawn to the new investment bank because of the reputation of the investment

bank? In other words, one cannot examine the impact of the all-star analyst on investment

banking deal flow without controlling for the reputation of the investment bank.

4.2. Is the increased deal flow following the all-star analyst job change due to the analyst?

We use a multivariate framework to examine whether analyst reputation factors and/or

analyst bias measures affect deal flow after controlling for the investment banks

ARTICLE IN PRESS

Table 4

Relative industry market shares before and after the all-star analyst job change

This table presents the median (mean) market share for the bank the analyst switches to (the new bank) and the

bank the analyst switches from (the original bank) for deals reported in the all-stars industry. Industry market

share is calculated as the gross proceeds raised by an investment bank in the all-stars industry divided by total

gross proceeds for all deals completed in that particular industry. Both pre-turnover and post-turnover market

shares are calculated using two years of data. Industry classifications are based on the 59 GICS industry codes

from the COMPUSTAT database. Capital-raising transactions include seasoned equity offerings (SEOs), initial

public offerings of equity (IPOs), and bond offerings. The p-value for the difference in relative industry market

share is based on a two-sided Wilcoxon rank-sum test.

Original bank (%) New bank (%) p-value for difference

Panel A: Capital-raising transactions

Pre-turnover 0.86 2.09 0.04

(4.42) (5.77)

Post-turnover 0.57 2.35 0.00

(4.11) (5.98)

SEO and IPO deals


(3.88) (4.30)


(4.25) (5.44)

Bond offerings


(4.43) (7.06)


(4.55) (6.47)

Panel B: Corporate control transactions (M&A Deals)


(3.18) (4.61)


(3.92) (4.75)



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reputation. Our measures for analyst reputation include indicator variables that

take the value of one if the all-star has been an all-star in each of the three years

prior to turnover (repeat all-star15), or if the analyst issues earnings forecasts in the

75th percentile of all analysts in the sample when ranking the analysts separately

on accuracy, timeliness, and frequency of revision of forecasts.16 We also includeanalyst forecast optimism as a measure of analyst bias, computed similar to the measures

discussed above.

We use two measures of bank reputation. First, we use the relative market share

of the two investment banks in the two years before the analysts job change. The

relative market share is defined as the difference in market share between the two

banks in the all-stars industry. Banks with higher relative market share are likely

to be more reputable since they advise more underwriting and M&A transactions.

This measure is used by Megginson and Weiss (1991) and more recently by Ljungqvist,

Marston, and Wilhelm (2006). We also include a dummy variable to control for

the trend in relative market shares. This dummy takes the value of one if the relative

market share difference widens from two years to one year before the job change and zero

otherwise.17

Our second proxy for bank reputation is the difference in the total number of all-star

analysts at the new and original banks in the year before the analyst changes jobs. Dunbar

(2000) finds a strong relation between the change in an investment banks Institutional

Investor All-America Research Team ranking and subsequent changes in their share of

initial public offerings. Increases in the reputation of an investment banks analysts have a

positive effect on market share changes.

The dependent variables in the regressions are the relative market shares for the twobanks computed separately for the three types of deals (equity issues, bond issues, and

M&A transactions) over the two years after the analysts job change.

Our results are reported in Table 5. As expected, the banks reputation is important. The

relative market share following the all-stars arrival at the new bank is significantly

positively related to relative market share before the move for bond and M&A deals. In

addition, for M&A deals, we find that the relative market share following the move is

weakly negatively related to the trend in relative market share over the two years before the

analyst moves, possibly due to mean reversion. For all three types of deals, the difference

in the number of all-star analysts between the new bank and the original bank is

significantly positively related to relative market share following an analyst job change.Our results indicate that more reputable investment banks gain a larger market share

following the arrival of an all-star analyst.

ARTICLE IN PRESS

15Of the 216 instances of all-star analyst turnover in our sample, 72 are nonrepeat all-star turnovers.16Specifically, to construct the earnings forecast accuracy indicator variable, we first compute the relative

earnings forecast accuracy score for each stock followed by the all-star. Then, for the portfolio of stocks followed

by the all-star, we count the proportion of stocks with a score greater than 50. Finally, we assign a value of one to

earnings forecast accuracy if the proportion of stocks in the analysts portfolio is greater than the 75th percentile

of all analysts in our sample, and zero otherwise. Earnings forecast timeliness, earnings forecast frequency, andearnings forecast optimism variables are computed in a similar manner. The rationale for choosing this approach

is to determine if extremely accurate, optimistic, and timely all-star analysts affect deal flow. Our results are

qualitatively unchanged using alternative cut-offs.17The average value of the trend dummy is 0.36.



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Controlling for bank reputation, does analyst reputation influence deal flow? For debt

underwriting or M&A transactions, the answer is no. None of our proxies are significant inexplaining the relative market share for the two banks. For equity transactions, the

analysts earnings forecast timeliness and frequency of coverage revision are significantly

positively related to the relative market share of the new bank after the job change. Finally,

ARTICLE IN PRESS

Table 5

Explaining the difference in market share between the new and the original bank following an all-star analyst job

change

This table investigates the determinants of the difference in market share between the investment bank gaining

the all-star analyst (the new bank) and the investment bank losing the all-star analyst (the original bank) following

the all-stars job change. The dependent variable is the difference in market share between the new bank and the

original bank in the two years following the job change. Relative market share before turnover is the difference in

market share between the new bank and the original bank in the two years before the job change. Trend in relative

market share is an indicator variable that takes the value of one if the market share at the new bank increases

relative to that at the original bank between year -2 and year -1, and zero otherwise. The difference in number of

all-stars is computed as the total number of all-stars at the new investment bank minus the number of all-stars at

the original investment bank in the year preceding turnover. Repeat all-star is a dummy variable that takes the

value of one if the all-star has been an all-star in each of the three years prior to turnover, and zero otherwise. To

construct the earnings forecast accuracy indicator variable, we first compute the forecast accuracy score for each

stock. For the portfolio of stocks followed by the all-star, we count the proportion of stocks with a score greater

than 50. Finally, we assign a value of one to earnings forecast accuracy if the proportion of stocks in the analysts

portfolio is greater than the 75th percentile of all analysts in our sample, and zero otherwise. Earnings forecasttimeliness, frequency of coverage revision, and earnings forecast optimism indicator variables are computed in a

similar manner. p-values are reported in parentheses.

Equity Bond M&A

Intercept 3.44 2.81 2.46

(0.22) (0.42) (0.30)

Bank-specific variables

Relative market share before turnover 0.11 0.41 0.24

(0.16) (0.00) (0.00)

Trend in relative market share 1.66 2.04 2.44

(0.34) (0.36) (0.08)

Difference in number of all-stars 0.20 0.15 0.16

(0.00) (0.02) (0.00)

Analyst reputation-specific indicator variables

Repeat all-star 1.58 0.11 1.81

(0.38) (0.96) (0.22)

Earnings forecast accuracy 0.20 0.36 0.92

(0.92) (0.89) (0.57)

Earnings forecast timeliness 4.75 1.99 0.22

(0.01) (0.40) (0.89)

Frequency of coverage revision 3.33 1.51 2.22

(0.08) (0.51) (0.16)

Analyst bias-specific indicator variables

Earnings forecast optimism 0.03 0.05 0.02

(0.57) (0.42) (0.70)

Number of observations 208 198 210

Adjusted-R2 (%) 13.23 25.23 15.82



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contrary to reports in the popular press, we find no evidence that earnings forecast

optimism influences either capital-raising or corporate control transactions.18,19 In

additional tests not reported in the paper, we examine whether the rank of the analyst

(Institutional Investor 1st team, 2nd team, 3rd team, or runner-up) influences deal flow and

find no evidence that this is the case.In summary, our results suggest that all-stars are more influential in equity underwriting

deals than in debt or M&A transactions. In equity deals, analyst reputation (as measured

by forecast timeliness and frequency of coverage revision) helps increase investment

banking deal flow. In contrast, in debt underwriting and M&A transactions, investment

banking deal flow is driven by investment bank reputation rather than analyst reputation.

Our results contrast with Ljungqvist, Marston, and Wilhelm (2006), who find that analysts

affect debt, but not equity, deal flow. The evidence in Table 5 is also inconsistent with

recent reports in the media and the press that investment banks use optimistic forecasts

and recommendations by analysts to win investment banking business.

4.3. Does the increased deal flow at the new investment bank come from clients at the original

bank who follow the analyst to the new bank?

Table 4 reports that the market share for capital-raising transactions increases

(decreases) at the new (original) investment bank following an all-star job change. In

this section we investigate whether this change in deal flow is driven by firms departing the

original investment bank after the all-star leaves.

Of the 12,632 firms that carry out a transaction in the two years after the analyst moved,

only 148 are clients of the original investment bank. After the analysts move, of these 148firms, 82 stayed with the original investment bank, four firms carried out transactions with

the new investment bank, and the remainder used a third bank. These numbers do not

suggest that analysts are bringing their old clients with them to the new bank when they

switch jobs.

To analyze the movement of clients more formally, Panel A of Table 6 reports the results

of a logistic regression that models the probability of losing the client when an all-star

leaves. We consider only those firms that do an investment banking deal at the departing

all-stars bank in the two years before the all-star leaves and then complete another deal in

the two years after the all-stars arrival at the new bank. The dependent variable in this

regression takes the value of one if the individual firm switches investment banks andzero if the firm keeps the same bank in the post-move period. We regress this against

ARTICLE IN PRESS

18In an alternative specification, we examine whether recommendation bias influences investment bank relative

market share. To construct recommendation bias, we compute the proportion of stocks in the analysts portfolio

with abnormal recommendations less than zero (i.e., recommendation is more positive than the consensus). Then,

we create a dummy variable equal to one if this measure is in the bottom 25th percentile of all analysts in our

sample, and zero otherwise. This variable is not significant. Since the sample size is reduced using recommendation

data (available only after October 1993), we do not report the specification.19At the same time that analysts are moving from one bank to another, it is possible that investment bankers are

also moving, bringing their client contacts and business with them. Thus, the increase in relative market share

might be driven by the concurrent movement of investment bankers, rather than analysts. We therefore track keybankers for both the bank gaining the all-star and the bank losing the all-star. We obtain our sample of banker

movements from Investment Dealers Digest and focus on movements by bankers at the rank of managing

director and above. We find very few cases of departures by such bankers around the departure of our all-star

analysts. Controlling for these departures does not affect our results.



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ARTICLE IN PRESS

Table 6

Determinants of increase in the relative market share after all-star analyst turnover

Panel A examines a sample of firms covered by the all-star analyst prior to departure that do an investment

banking deal at the departing stars bank in the pre-turnover period, and that return to complete a deal in the

post-turnover period. The dependent variable takes the value of one if the individual firm switches investment

banks and zero if the firm keeps the same bank in the post-turnover period. Repeat all-star is a dummy variable

that takes a value of one if the all-star has been an all-star in each of the three years prior to turnover, and zero

otherwise. Forecast accuracy, optimism bias, timeliness, and frequency of coverage revision scores are computed

using a methodology used in Hong, Kubik, and Solomon (2000). Number of all-stars at original bank is the

number of stars at the original bank who also issued at least one forecast in the same GICS industry. Continued

coverage at original bank is a dummy variable taking a value of one if the original bank continues coverage after

the all-stars departure. Difference in number of all-stars is the difference in the number of all-stars within an

industry at the new investment bank versus at the original investment bank in the year preceding turnover. Panel

B reports median values of firm-specific, bank-specific, and analyst-specific variables for new client firms who do

an investment banking deal for the first time at either the original or the new bank in the two years following all-

star analyst turnover. Market capitalization is the market value of equity measured in millions. The market-to-

book ratio is the ratio of a firms market value of equity to its book value of equity measured in the year prior toturnover. Deal size is measured as gross proceeds for IPOs, SEOs, and bond offerings, and as the size of the

transaction for mergers (in $millions). Percentage of cases in which switching star covers new firm and percentage of

new firms with all-star coverage are computed in the year following turnover. In Panel A, p-values are reported in

parentheses. In Panel B, means are reported in parentheses. The p-value for the difference in Panel B is based on a

two-sided Wilcoxon rank-sum test.

Panel A: Determinants of decision by firms to switch investment banks following the all-star job change

Intercept 1.66

(0.18)

Firm-specific indicator variables

Market capitalization in top 25% of industry 0.28(0.55)

Market-to-book ratio above industry average 0.46

(0.34)

Analyst-specific variables

Forecast accuracy score 0.005

(0.54)

Optimism bias score 0.006

(0.42)

Timeliness score 0.008

(0.27)

Frequency of coverage revision score 0.009

(0.31)Repeat all-star 0.18

(0.68)

Firm/Bank-specific variables

Number of all-stars at original bank 0.03

(0.30)

Number of deals completed at original bank 0.28

(0.18)

Continued coverage at original bank 1.43

(0.00)

Difference in number of all-stars 0.006

(0.74)

Number of observations 117

Percent Concordant (%) 73.80



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analyst-specific and firm-specific control variables that might be expected to influence the

firms decision to switch investment banks.

The probability of the firm departing with the all-star is unrelated to the all-stars

reputation specific variables. It is significantly negatively related to continued coverage of

the firm at the original investment bank. This is consistent with Ljungqvist, Marston, and

Wilhelm (2006), who find that banks that have underwritten a large share of the firms pastdebt and equity offerings are significantly more likely to win future mandates.

Overall, we find no evidence that firms follow a departing all-star analyst to the new

bank. Hence, the increase in market share documented in Table 4 does not seem to be

coming from clients of the old investment bank that follow the analyst.

4.4. Is all-star analyst coverage important in winning investment banking deal flow from new

clients?

We next examine whether the increase in market share at the new investment bank is

related to the extent of all-star coverage at the new firms. For the two-year period afterthe all-star arrives at the new bank, we first examine whether new client firms who go to the

bank gaining the all-star have different firm characteristics than those who go to the

investment bank losing the all-star. Second, we investigate whether new client firms are

ARTICLE IN PRESS

Panel B: Characteristics of new business after the all-star analyst job change

Original bank New bank p-value for

difference

Firm-specific variables

Market capitalization $985.47 $1,199.52 0.13

($8,620.10) ($7,092.30)

Market-to-book ratio 2.58 2.71 0.19

(3.66) (4.23)

Deal size 123.20 148.60 0.12

(503.64) (519.05)

Percentage of firms in category

Market capitalization in top 25% of industry 68.73% 72.77% 0.18

Market-to-book ratio above industry average 28.03% 30.20% 0.47

Bank-specific variables during year prior to transaction, across all analysts

Forecast accuracy score 50.00 53.33 0.78

(52.84) (53.50)

Optimism bias score 50.00 53.85 0.30

(50.47) (53.21)

Timeliness score 50.00 52.27 0.65

(52.66) (51.24)

Frequency of coverage revision score 58.06 58.62 0.62

(57.95) (56.44)

Analyst-specific variables

Percentage of cases in which switching star covers

new firm (%)

18.58 37.37 0.00

Percentage of new firms with all-star coverage (%) 40.70 82.11 0.00

Table 6 (continued)



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influenced by the average optimism bias and earnings forecast accuracy of all analysts

employed at each of the two banks, respectively. Specifically, for each firm that carries out

a transaction for the first time with either bank in the two years subsequent to the all-star

job change, we compute the average scores for forecast accuracy, optimism bias, forecast

timeliness, and frequency of coverage revision of all the forecasts issued for the firm in theyear prior to the transaction. Third, we compute the percentage of the new business at

both the original and new banks covered by the switching all-star (and any other all-star)

in the year prior to the investment banking deal flow. These results are reported in Table 6,

Panel B.

The new investment bank attracts firms that have firm characteristicsmarket

capitalization, market-to-book ratio, and deal sizesimilar to those that provide new

business to the original bank. In addition, the scores for forecast accuracy, optimism bias,

forecast timeliness, and frequency of coverage revision are similar across both sets of firms.

The only characteristic that distinguishes the two types of firms is that of all-star analyst

coverage. The switching all-star covers 37% (19%) of the new business at the new

(original) bank in the year before the firm is awarded the deal flow. More interestingly,

following an analyst job change, the new (original) bank provides all-star analyst coverage

to 82% (41%) of the new business in the year prior to being awarded the deal.20

5. Conclusions

We examine a sample of 216 cases in which an Institutional Investor All-America

Research Team (all-star) analyst moves from one investment bank to another between

1988 and 1999 to answer the following two questions: Is analyst coverage influenced byinvestment banking relationships? Does analyst behavior, analyst reputation, and/or

investment bank reputation influence deal flow?

Using a comprehensive data set of investment banking deals (underwriting and

corporate control transactions), we find that all-star coverage choices do indeed depend on

investment banking relationships between the firm and the all-stars bank. An all-star is

more likely to retain/add coverage of larger glamour firms that have pre-existing

investment banking relationships with the bank the all-star is moving to.

However, the all-stars behavior does not change after he changes jobs. There are no

changes in optimism bias, forecast accuracy, or forecast timeliness following the job

change. The all-star is not significantly more likely to be more optimistic in hisrecommendations, that is, recommendation levels are at consensus, both before and

following job change. Our results are inconsistent with recent allegations in the popular

press that analysts have helped generate investment banking deal flow by being extremely

optimistic in their recommendations. To the extent that these allegations are true, our

results suggest that they cannot be generalized across all analysts.

Finally, even though analyst behavior does not change, the new bank does attract a

significantly larger industry market share of capital-raising and M&A deals after the

arrival of the all-star, relative to the bank the analyst leaves. Yet, after controlling for bank

reputation, all-star reputation, as measured by earnings forecast frequency and timeliness,

influences only equity underwriting transactions. Variables measuring the extent to which

ARTICLE IN PRESS

20All-star coverage at the new bank refers to coverage by any all-star at the bank and not just the analyst

experiencing turnover.



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sample descriptive statistics this table presents descriptive statistics

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