The impact of all-star analyst job changes on their coverage choices and investment banking deal flow

Jonathan Clarke Georgia Institute of Technology

Ajay Khorana Georgia Institute of Technology

Ajay Patel Wake Forest University

P. Raghavendra Rau† Purdue University

Journal of Financial Economics, forthcoming

December 2005 †Corresponding author Department of Finance, Krannert Graduate School of Management Purdue University, MGMT, KRAN, 403 West State Street West Lafayette, IN 47907-2056, U.S.A. Tel: 1 (765) 494 4488 Fax: 1 (765) 494 9658 Email: raghu@purdue.edu 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.

The impact of all-star analyst job changes on their coverage choices and investment banking deal flow

Abstract

Using a sample of all-star analysts who switch investment banks between 1988 and 1999, we examine (1) whether analyst behavior is influenced by investment banking relationships and (2) whether analyst behavior affects investment banking deal flow (debt and equity underwriting and corporate control transactions). Although the stock coverage decision is dependent on the investment banking 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 underwriting transactions. For debt underwriting and M&A transactions, after controlling for bank reputation, analyst reputation does not matter. There is no evidence that issuing optimistic earnings forecasts or recommendations affects investment banking deal flow. Keywords: All-star analyst; analyst coverage; market share; investment banking relationships; conflicts of interests JEL Classification: G24, G32

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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 chief’s 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, Merrill’s vice-chairman. ‘To demonstrate the impact this hire has on our relationship, Dennis Kozlowski called me on Phua’s 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 1999…1

In turn, 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 cover otherwise, as well as to 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 1988–1999 period. We investigate two questions. First, we examine whether the

all-star’s 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 stocks

that are likely to generate investment banking business for Merrill. In addition, the analyst might

issue more favorable reports for Merrill clients than when at Goldman. Hence, we study whether,

in the period following their 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

1 Bowe, Christopher and Gary Silverman, “Merrill rewarded after hiring analyst Tyco favoured,” Financial Times, February 3, 2004, p. 1. 2 See, for example, Schroeder, Michael and Randall Smith, “CSFB analysts felt pressured on stock reports,” Wall Street Journal, September 6, 2002, p. C1.

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

perceived quality of coverage, as proxied by all-star coverage, as an important driver in the

decision to change the lead underwriter for a follow-on offering. Dunbar (2000) finds a strong

positive relation between changes in an investment bank’s Institutional Investor All-America

Research Team ranking and subsequent changes in the bank’s 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 get a comprehensive

understanding of the relation between stock coverage, analyst reputation, investment bank

reputation, and deal flow.

Our results show that an all-star analyst’s decision to cover a firm is influenced by the

investment bank’s relationship with the firm. 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 job change, 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: non-all-stars likewise do not change behavior when joining a new bank.

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 where we control for investment bank reputation. Our

results show that proxies for all-star reputation, such as the timeliness and frequency of the all-

star’s 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 analyst’s 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

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bank who follow the all-star to the new bank. Instead, 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 two ways.

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 has been no direct evidence that this difference in behavior is

specifically due 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 not for 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.

If these 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. In Section 2, we discuss the data and

describe the sample. Section 3 examines 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. We conclude in Section 5.

2. Data, variable construction, and sample description

2.1. Data

We examine a sample of Institutional Investor All-America Research Team analysts who

change 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

3 See 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" recommendation by unaffiliated analysts.

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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 employer’s identity. This

additional information allows us to identify those analysts who were named to Institutional

Investor’s All-America Research Team in a given year.4 We consider only those cases where 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, where it is not likely that the analyst obtained new

information to change his or her forecast, we eliminate cases where the elapsed time between

forecasts is greater than 100 trading days.5 We further eliminate cases where 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 where

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, the choice of

Institutional Investor’s All-America Research Team is appropriate for our analysis. Hong,

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

Investor All-Americans. Leone and Wu (2002) document that these all-star analysts have better

earnings forecast accuracy, better stock recommendation returns, and smaller optimism bias than

their non-star counterparts.

In our analysis, we (1) classify analysts into industries in which they are rated all-stars, (2)

compare analyst behavior before and after the job change, and (3) examine whether investment

bank market share changes after the analyst moves. The following three subsections describe the

variable construction for each of these steps.

4 Leone and Wu (2002) discuss the selection procedure for the all-American team. To summarize the procedure, selection to the All-American team is based on survey data. II sends out a questionnaire to the directors of research and chief investment officers of money management institutions and also to other sell-side analysts. They rank each analyst based on the following six dimensions: accessibility and responsiveness, earnings estimates, useful & timely calls, stock selection, industry knowledge, and written reports. Scores for each analyst are calculated by taking the number of votes awarded by each survey respondent and weighting them by the size of the respondent’s firm. The results are published each year in the October issue of the magazine. 5 The median length of time between the analyst’s first forecast with his new employer and the last forecast with his previous employer is 24 trading days.

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2.1.1. Analyst industry classification

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

Standard & Poor’s Global Industry Classification Standard (GICS) industry code, supplied by

COMPUSTAT. These industry codes classify companies into four levels, to one of ten sectors,

23 industry groups, 59 industries, and 122 sub-industries. 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 are significantly better at explaining stock return co-movements as well as cross-

sectional variations in valuation multiples, forecasted growth rates, and key financial ratios.

Moreover, Boni and Womack (2005) 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. The 216

instances of job changes are from 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.

6 As 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 etc) is much larger than the automobile industry (consisting of firms such as GM, Toyota, Nissan, Winnebago, etc). 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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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 that are likely to proxy for bias.

Along each of these five dimensions we measure analyst behavior 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 produce earnings estimates that year is problematic, because earnings for some

firms are more difficult to predict than others. Consequently, we follow Hong, Kubik, and

Solomon in constructing annual performance scores based on an analyst’s relative earnings

forecast accuracy, forecast frequency, timeliness, and optimism bias.7

Forecast accuracy score: To examine accuracy across all stocks in the analyst’s portfolio, we

construct scores by defining Fi,j,t as the most recent forecast of annual earnings-per-share issued

before the fiscal year end by analyst i on firm j for year t. Our measure of analyst i’s 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:

tjtjitji AFerrorforecastEarnings ,,,,, −= (1)

We sort the analysts who cover firm j in 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, each analyst is assigned the mean value of the ranks

7 For 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 analyst’s prediction of earnings per share and the realized value, normalized by the stock price. Our results are qualitatively unchanged using these alternative measures.

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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 analyst’s rank by the number of analysts who

cover the firm. The formula for the forecast accuracy score is given by:

1001

1100

,

,,,, ×

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−

−−=

tj

tjitji analystsofnumber

rankAccuracyscoreaccuracyForecast (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:

tjitjitji FFbias Optimism ,,,,,, −−= (3)

Where { }∑ −∈− =

im tjmtji Fn

F ,,,,1 , where {–i} is the set of all analysts other than analyst i who

produce an earnings per share estimate for stock j in quarter t, and n is the number of analysts in

{–i}. Hence, tjiF ,,− 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 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.

8 Alternative procedures for handling ties, such as median values of the ranks analysts are assigned, or the highest value of the ranks they are assigned, produce similar results. 9 To compute the score, we impose the criterion that at least five analysts must be covering a security. This is done so that there will be a meaningful consensus with which to calculate scores.

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Timeliness score: Analysts are ranked 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

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 particular stock in a given year. 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 (2004) 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

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

sample, and measure the analyst’s recommendation for each stock relative to the consensus.

Since a strong buy (strong sell) is coded as 1 (5), a negative relative recommendation indicates

an optimistic recommendation by the analyst. The abnormal recommendation is computed as the

difference between the analyst’s 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 issues database, for every

initial public offering, seasoned equity offering, and bond offering, we obtain the issuer name

and cusip, the filing and issue date, 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

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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-star analysts decreases

from 12.4% in 1988 to 7.6% in 1999. The decrease is especially sharp over the period 1993-

1995, 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.

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. There

are a total of 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 analyst’s portfolio of covered stocks change and 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-star’s coverage

decision depends on whether the bank has a relationship with firm under consideration. Panel A 10 These 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 move less than non-stars. Established stars are less likely than new stars to switch jobs. In the earlier part of our sample period, all-stars indeed have a lower turnover rate than non-all-stars. In the latter part, the turnover increases dramatically for all-stars and increases at a slower rate for non-all-star analysts.

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of Table 2 reports descriptive data on stock coverage and deal flow in the two years before and

after the all-star changes jobs while Panel B reports more detailed descriptive statistics on firm,

analyst and bank characteristics for the sample of firms for which the all-star retains coverage,

those he drops and those he adds.

The all-star’s workload remains the same after switching investment banks (Panel A.1). He

covers 16 firms (at the median level) prior to and 15 firms following the job change.11 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.2 shows that at the median, 99 unique

firms complete deals in the star’s industry in the two years before the job change, in contrast to

106 firms in the two years after the job change. Focusing only on the firms the analyst covers,

around half (7-8) 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-star’s

departure. Of the firms covered by the analyst after his 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 analyst’s portfolio

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. These firms are likely to be

larger, have more trading volume, and complete more deals in the star’s industry before or after

the all-star’s 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

analyst’s 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.1 indicate that compared with the

11 This contrasts with Boni and Womack (2005) who find that the average analyst in their sample covers 10 companies. All-stars cover more companies on average.

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universe of stocks pre-switch, 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-star’s move.12 Interestingly, firms added to the all-star’s 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 ratios above industry

average, in the all-star’s portfolio. Panel B.1 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 where they are less accurate and where they produce less frequent reports. Panel B.2

compares the forecast accuracy and the frequency of coverage revision scores for the firms the

all-star retains to those he drops. The results show that while all-stars are significantly more

likely to drop firms where they are less accurate, the frequency 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-star’s arrival), and the proportion of firms that are already covered at the

new bank by any analyst or by an all-star. Panel B.3 reports that a significantly greater

proportion (based on means and distributions) 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-star’s arrival. Firms already

being covered by the new bank are significantly more likely to be retained by the analyst. A

significantly smaller proportion of the firms added by the analyst are covered at the new

investment bank prior to his arrival. These results reinforce our earlier findings that the coverage

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

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decision appears to be related to the firm’s 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 moving to 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 on a value of one if the firm has a market

capitalization or trading volume in the top 25% of its industry, or if it completed two or more

deals in the two years prior to or after the all-star’s move, and zero otherwise. The analyst/firm-

specific indicator variables are the all-star’s 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-star’s 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

completes at least one deal with the new investment bank in the two years prior to the all-star’s

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

analyst’s decision to retain the stock is enhanced by a prior investment 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 the all-

star’s 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-star’s 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 M/B ratios (Model

2). All-stars are also more likely to retain coverage of stocks where they are active, providing

accurate earnings forecasts with frequent 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

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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 analyst’s

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 add coverage if

(1) the stock is a large glamour stock in its industry, (2) the analyst has in the past issued

frequent and more accurate earnings forecasts on the stock, and (3) the firm has a prior

investment banking relationship with the new bank.

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 client relationships. 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 had 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 had 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 analyst’s job change by

comparing the analyst’s last recommendation on a firm relative to consensus, before leaving the

original bank, with his or her 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 analyst’s 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

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across any of the categories.13 The lack of any increase in analyst optimism, in either 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 stocks being newly covered, and then separate them into two categories

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

analyst’s 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. 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.

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 conflicts-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

13 We 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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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 underwriting 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 analyst’s

investment bank, divided by the total gross proceeds of all deals completed in that industry.

Following the analyst’s 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 market share 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 subset

of investment banks. During our sample period, 38% of debt underwriting transactions are

handled by Merrill Lynch, Goldman Sachs, and Lehman Brothers. 14 We focus on all-star analysts instead of all analysts, since non-all-star analysts do not generate significant investment banking deal flow. All-star analysts, accounting for only 10 percent of all sell-side analysts, are involved in 63 percent of target advisory deals, 64 percent of SEO deals, 57 percent of acquiror advisory deals, 76 percent of bond deals and 48 percent 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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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 bank’s reputation. Our

measures for analyst reputation include indicator variables that take on 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

include analyst 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 analyst’s job change. The relative market share is

defined as the difference in market share between the two banks in the all-star’s 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 (2004). 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

15 Of the 216 instances of all-star analyst turnover in our sample, 72 are non-repeat all-star turnovers. 16 Specifically, 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 analyst’s portfolio is greater than the 75th percentile of all analysts in our sample, and zero otherwise. Earnings forecast timeliness, earnings forecast frequency, and earnings 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.

- Page 17 -

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 bank’s Institutional Investor All-America

Research Team ranking and subsequent changes in their share of initial public offerings.

Increases in the reputation of an investment bank’s analysts have a positive effect on market

share changes.

The dependent variables in the regressions are the relative market shares for the two banks

computed separately for the three types of deals (equity issues, bond issues, and M&A

transactions), over the two years after the analyst’s job change.

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

relative market share following the all-star’s 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.

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 in

explaining the relative market share for the two banks. For equity transactions, the analyst’s

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, 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

17 The average value of the trend dummy is 0.36. 18 In an alternative specification, we examine if recommendation bias influences investment bank relative market share. To construct recommendation bias, we compute the proportion of stocks in the analyst’s portfolio with abnormal recommendations less than zero (i.e., recommendation is more positive than the consensus). Then, we create a dummy variable set 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.

- Page 18 -

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 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 (2004) 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 carried out a transaction in the two years after the analyst moved,

only 148 were clients of the original investment bank. After the analyst’s move, of these 148

firms, 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-star’s bank in

the two years before the all-star leaves and then complete another deal in the two years after the 19 At 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 key bankers 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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all-star’s arrival at the new bank. The dependent variable in this regression takes the value of one

if the individual firm switches investment banks and zero if the firm keeps the same bank in the

post-move period. We regress this against analyst-specific and firm-specific control variables

that might be expected to influence the firm’s decision to switch investment banks.

The probability of the firm departing with the all-star is unrelated to the all-star’s 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 (2004) who

find that banks that have underwritten a large share of the firm’s past debt 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 after the 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 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 the year 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 characteristics—market capitalization,

market-to-book ratios, and deal sizes—similar 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

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distinguishes the two types of firms is 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. Conclusion

We examine a sample of 216 cases where 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 questions: Is analyst coverage influenced by investment banking

relationships, and do analyst behavior, analyst reputation, and/or investment bank reputation

influence deal flow?

Using a comprehensive dataset 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-star’s 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-star’s 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 his or her recommendations—

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. If 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, measured by earnings forecast frequency and timeliness, influences only equity

underwriting transactions. Variables measuring the extent to which analysts make optimistic

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

- Page 21 -

earnings forecasts or recommendations do not influence deal flow. The new business does not

come from client firms at the old bank who follow the all-star to the new bank. It seems to come

from firms that the all-star is more likely to cover than at his original bank. In other words, our

results suggest that coverage is more important than the degree of optimism of that coverage.

References

Bhojraj, Sanjeev, Charles M. C. Lee, and Derek Oler, 2003, What’s my line? A comparison of

industry classification schemes for capital market research, Journal of Accounting Research 14,

745-774.

Boni, Leslie, and Kent L. Womack, 2005, Analysts, industries, and price momentum, Journal of

Financial and Quantitative Analysis, forthcoming.

Bradley, Daniel J., Bradford D. Jordan, and Jay R. Ritter, 2003, The quiet period goes out with a

bang, Journal of Finance 58, 1-36.

Clement, Michael B., and Senyo Y. Tse, 2005, Financial analyst characteristics and herding

behavior in forecasting, Journal of Finance 60, 307-341.

Dugar, Amitabh, and Siva Nathan, 1995, The effects of investment banking relationships on

financial analysts’ earnings forecasts and investment recommendations, Contemporary

Accounting Research 12, 131-160.

Dunbar, Craig, 2000, Factors affecting investment bank initial public offering market share,

Journal of Financial Economics 55, 3-41.

Groysberg, Boris, and Ashish Nanda, 2001, Does stardom affect job mobility? Evidence from

analyst turnover in investment banks, unpublished working paper, Harvard Business School.

Hong, Harrison, Jeffrey D. Kubik, and Amit Solomon, 2000, Security analysts’ career concerns

and herding of earnings forecasts, Rand Journal of Economics 31, 121-144.

Irvine, Paul J. A., Siva Nathan, and Paul J. Simko, 2004, Asset management and affiliated

analysts' forecasts, Financial Analysts Journal 60, 67-78.

Jegadeesh, Narasimhan, Joonghyuk Kim, Susan D. Krische, and Charles M C. Lee, 2004,

Analyzing the analysts: When do recommendations add value? Journal of Finance 59, 1083-

1124.

Krigman, Laurie, Wayne H. Shaw, and Kent L. Womack, 2001, Why do firms switch

underwriters?, Journal of Financial Economics 60, 245-284.

Leone, Andrew, and Joanna Shuang Wu, 2002, What does it take to become a superstar?

Evidence from institutional investor rankings of financial analysts, unpublished working paper,

University of Rochester.

Ljungqvist, Alexander P., Felicia Marston, and Willam J. Wilhelm, Jr., 2004, Competing for

securities underwriting mandates: banking relationships and analyst recommendations, Journal

of Finance forthcoming.

Lin, Hsiou-wei, and Maureen F. McNichols, 1998, Underwriting relationships, analysts’ earning

forecasts and investment recommendations, Journal of Accounting and Economics 25, 101-127.

Megginson, William and Kathleen Weiss, 1991, Venture capitalist certification in initial public

offerings, Journal of Finance 46, 879-903.

Michaely, Roni, and Kent L. Womack, 1999, Conflict of interest and the credibility of

underwriter analyst recommendations, Review of Financial Studies 12, 653-686.

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 where 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 8.07 1999 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

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 investment bank and the two years after (i.e., post-turnover) arriving at the new investment bank. Panel A reports data on overall stock coverage and investment bank deal flow. The number of stocks covered is the number of stocks covered by the median analyst switching investment banks in the pre- and post-turnover period, respectively. The proportion of stocks retained after turnover is the fraction of stocks that the analyst continues to cover after arriving at the new investment bank. Proportion of stocks dropped is the fraction of stocks dropped after arriving at the new investment bank. Proportion of new stocks added is the fraction of new stocks that the analyst begins to cover at the new bank. The number of unique firms completing deals is computed as the median number of firms that completed deals in the same industry as the all-star in the two years prior to and after the analyst’s move. Panel B reports data on the median characteristics of stocks retained, added, and dropped. The forecast accuracy score and frequency of coverage revision score are computed using a scoring methodology as in Hong, Kubik, and Solomon (2000). For both panels, mean values are reported in parentheses. 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 Post-turnover P-value for difference

A.1 Stock coverage Number of stocks covered 16.00 15.00 0.24 (16.85) (16.76) Proportion of stocks retained 64.71% (61.48%) Proportion of stocks dropped 35.29% (38.52%) Proportion of new stocks added 35.50% (36.39%) A.2 Stock coverage and investment banking deal flow

99.00 106.00 0.00 Unique number of firms completing deals in all-star’s industry (124.92) (136.05)

7.00 8.00 0.00 Unique number of firms covered by analyst completing deals with any investment bank (7.97) (9.40)

0.00 0.00 0.02 Unique number of firms covered by analyst completing deals with the original bank (1.24) (0.98)

0.00 1.00 0.00 Unique number of firms covered by analyst completing deals with the new bank (1.15) (1.84)

Panel B: Descriptive statistics on stocks retained, added and dropped

Universe of stocks

covered pre-switch

Stocks retained

Stocks added

Stocks dropped

P-value for difference(retained

vs. added)

P-value for difference

(retained vs. dropped)

P-value for difference (added vs. dropped)

B.1 Firm-specific variables Proportion of firms with market cap in top 25% of industry 78.57 90.00 50.00 66.67 0.00 0.00 0.01 (76.22) (84.24) (48.41) (59.65) with trading volume in top 25% of industry 75.00 83.97 12.50 66.67 0.00 0.00 0.00 (74.37) (80.54) (19.30) (59.56) that completed ≥2 deals in the 2 years prior to all-star’s move 30.00 33.33 20.00 22.22 0.00 0.00 0.25 (33.23) (33.83) (24.44) (27.90) that complete ≥2 deals in the 2 years after the all-star’s move 30.56 34.31 33.33 18.75 0.77 0.00 0.00 (32.45) (34.91) (34.48) (25.96) with market-to-book ratios above industry average 20.00 21.98 12.50 7.69 0.00 0.00 0.01 (24.94) (28.16) (19.30) (21.86) B.2 Analyst-specific variables Forecast accuracy score 59.11 61.76 55.48 0.03 (58.65) (60.93) (55.12) Frequency of coverage revision score 54.95 56.90 52.30 0.25 (54.11) (55.65) (51.64) B.3 Relationship-specific variables Proportion of firms that complete ≥1 deals with the new investment bank in the 0.00 0.00 0.00 0.00 0.32 0.03 0.00 2 years prior to the all-star’s arrival (6.08) (7.44) (8.85) (4.54)

are already covered at the new bank by any analyst 46.67 57.74 14.29 33.33 0.00 0.00 0.01 (46.95) (51.23) (23.28) (34.50) are already covered at the new bank by an all-star 0.00 0.00 0.00 0.00 0.50 0.34 0.72 (16.47) (16.55) (12.95) (13.42)

Table 3 Determinants of stock retention and addition

This table reports the results of logistic regressions of the all-star analyst’s decision to retain, add or drop a stock on firm-specific indicator variables, analyst/firm-specific variables and firm/bank relationship-specific indicator variables. The forecast accuracy score and frequency of coverage revision score are computed using a scoring methodology as in Hong, Kubik, and Solomon (2000). P-values are reported in parentheses.

Retained vs. added Retained vs. dropped

Added vs. dropped

All firms All firms Firms with

prior coverage All firms (1) (2) (3) (4) Intercept -1.43 -0.84 -0.09 1.016 (0.00) (0.00) (0.73) (0.00) Firm-specific indicator variables Market capitalization in top 25% of industry 1.10 0.84 1.06 -0.41 (0.00) (0.00) (0.00) (0.00) Trading volume in top 25% of industry 2.22 0.18 -0.04 -1.80 (0.00) (0.13) (0.89) (0.00) Firm completed ≥ 2 deals with any investment bank in the 2 0.27 0.01 -0.05 -0.28 years prior to all-star’s move (0.02) (0.94) (0.79) (0.03) Firm completes ≥ 2 deals with any investment bank in the 2 -0.25 0.29 0.13 0.59 years after all-star’s move (0.02) (0.01) (0.43) (0.00) Market-to-book ratio above industry average -1.47 0.25 0.19 1.58 (0.00) (0.02) (0.28) (0.00)

Analyst/Firm-specific variables Forecast accuracy score 0.003 (0.07) Frequency of coverage revision score 0.01 (0.00) All-star has higher forecast accuracy score than analyst 0.05 at new bank prior to move (indicator variable) (0.75)

All-star has higher frequency of coverage revision score than 0.17 analyst at new bank prior to move (indicator variable) (0.26)

Firm/Bank relationship-specific indicator variables Firm is already covered at the new bank by any analyst (prior 0.66 0.16 -0.46 coverage) (0.00) (0.09) (0.00)

Firm is already covered at the new bank by an all-star analyst -0.24 (0.15) Firm completed ≥ 1 deals with the new investment bank in the -1.08 -0.71 0.82 0.85 2 years prior to the all-star’s arrival (prior relationship) (0.01) (0.16) (0.03) (0.05)

Prior coverage at new bank × Prior relationship with new bank 0.45 1.08 -0.12 (0.36) (0.05) (0.81)

Prior coverage by an all-star at new bank × Prior relationship -0.94 with new bank (0.07) Number of observations 3,124 2,517 941 2,366 Percent Concordant 84.50 66.30 59.7 76.7

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-star’s industry. Industry market share is calculated as the gross proceeds raised by an investment bank in the all-star’s 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)

A1. SEO and IPO deals Pre-turnover 0.55 0.61 0.30 (3.88) (4.30) Post-turnover 0.00 1.51 0.00 (4.25) (5.44)

A2. Bond offerings Pre-turnover 0.00 0.00 0.03 (4.43) (7.06) Post-turnover 0.00 0.00 0.07 (4.55) (6.47)

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

Pre-turnover 0.26 0.77 0.03 (3.18) (4.61) Post-turnover 0.40 1.75 0.00 (3.92) (4.75)

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-star’s 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 on 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 analyst’s portfolio is greater than the 75th percentile of all analysts in our sample, and zero otherwise. Earnings forecast timeliness, 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

Table 6 Determinants of increase in the relative market share after all-star analyst turnover

Panel A examines a sample of firms that are covered by the all-star analyst prior to departure, that do an investment banking deal at the departing star’s 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 on 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 on a value of one if the original bank continues coverage after the all-star’s 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 firm’s market value of equity to its book value of equity measured in the year prior to turnover. 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 where 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

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

Original bank New bank P-value fordifference

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 where 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