Analyst Impartiality and Investment Banking Relationships ...

Analyst Impartiality and Investment Banking Relationships

Hsiou–wei Lin Department of International Business

National Taiwan University Taipei, Taiwan

(011)-886-2-2363-0231 ext. 3657 Fax: 011-886-2-2363-8399 [email protected]

Maureen F. McNichols

Graduate School of Business Stanford University Stanford, CA 94305

(650) 723-0833 [email protected]

Patricia C. O’Brien

School of Accountancy University of Waterloo

200 University Ave. West Waterloo, Ontario Canada N2L 3G1

(519) 888-4567 x5423 [email protected]

Comments welcome May 2003

The authors gratefully acknowledge the financial support of National Taiwan University, Stanford Graduate School of Business, the University of Waterloo, and the Social Sciences and Humanities Research Council of Canada. We are grateful to First Call for the use of recommendation data, and to Qintao Fan and Yinnan Wang for research assistance. We have benefited from the comments of an anonymous reviewer, seminar participants at the Universities of Waterloo and British Columbia, and particularly Jerry Lawless.

Analyst Impartiality and Investment Banking Relationships

1. Introduction

The financial press and several regulatory bodies have raised concerns that analysts’

objectivity has been compromised by investment banking ties between analysts’ employers and

the companies that analysts cover.1 Morgenson (2002), in an article titled “Requiem for an

Honorable Profession,” contends that the research culture within Wall Street banks shifted, to the

point that analysts “had become salesmen and saleswomen for their investment banking

departments in their routine communications.” New York Attorney General Eliot Spitzer voiced

a similar concern in his remarks to participants at the Institutional Investor Awards Dinner in

November 2002:

For at least the last several years, analysts have labored in a corporate structure that placed undue or improper pressure on them. Too often, they were asked to tailor their investment advice to further investment banking interests, even if that was in conflict with their obligation to provide honest, objective advice…But to be frank about it, the advice provided to investors was often dishonest. It was dishonest because small investors were advised to buy stocks that the analyst believed they never should have owned, and told to hold stocks that they long ago should have sold. [Spitzer, 2002]

This was also the perspective of numerous Congressional hearings, exemplified by the Senate

hearings on analyst coverage of Enron titled “The Watchdogs That Didn’t Bark.”

While the anecdotal evidence is compelling that investment banking ties have influenced

analysts’ research, the pervasiveness and nature of its influence are less clear. In this paper, we

provide systematic evidence of the influence of investment banking ties on analysts’ research,

and document that these ties influence the timeliness with which analysts convey negative news

during the 1994-2001 period.

1 These articles include Siconolfi (1992), Siconolfi (1995a), Siconolfi (1995b), and more recently, Feldman and Caplin (2002), Byrne (2002a), Byrne (2002b), Gasparino (2002) and Morgenson (2002).

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Prior research documents differences in the reports issued by analysts with and without

investment banking ties (hereafter, affiliated and unaffiliated analysts, respectively).[Dugar and

Nathan (1995), Lin and McNichols (1998), Dechow, Hutton and Sloan (2000) and Michaely and

Womack (2000)] Collectively, these studies find that affiliated analysts issue more optimistic

earnings growth forecasts and more favorable recommendations. However, as several of these

studies acknowledge, one cannot conclude from this evidence that the investment banking ties

influenced the analysts because the direction of causation is unclear: banking ties may influence

analysts’ research or analysts’ research may influence managers’ selections of which banks to

hire as underwriters.

The aim of this study is to examine the evidence concerning the speed with which

affiliated analysts convey unfavorable news through downgrades of recommendations. We

hypothesize that affiliated analysts have incentives to respond promptly to good news, but prefer

not to issue bad news about client companies. We therefore examine the length of time before an

analyst updates his or her recommendations for evidence that affiliated analysts delay bad news.

This design directly examines a behavior alleged in numerous legal proceedings against

investment banks and analysts, that their investment banking ties caused analysts to avoid

downgrading previously recommended companies as investors incurred losses.

An advantage of our design is that it helps resolve the ambiguous causality in prior tests

that focus on ana lysts’ relative optimism at a single point in time. If our tests show that affiliated

analysts are no slower than unaffiliated to downgrade, this refutes the idea that analysts’ banking

ties influence their behavior in a way that disadvantages investors. Our tests in fact show that

affiliated analysts downgrade more slowly, supporting the claims of conflict of interest. By

examining a different dimension of analyst behavior, we render the counter-causality argument

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less credible. To incorporate our evidence into the underwriter selection story, one must assume

that managers choose underwriters both on their observable optimism and on the unobservable

strength of their beliefs.

We provide three types of evidence. First, we provide descriptive evidence of affiliated

and unaffiliated analyst coverage of companies issuing offerings, to characterize their potential

influence on investors and to lay a foundation for our statistical analysis. Second, we provide

descriptive evidence on the favorableness of affiliated and unaffiliated analysts’

recommendations, to assess how our sample period differs from those examined in prior

research. Third, we use a duration model to examine the time pattern of analysts’ revision of

recommendations.

We provide comparisons along two dimensions. First, we compare affiliated analysts to

unaffiliated analysts covering the same companies at the same time. This provides a control for

differences among companies that could potentially confound the tests. We then compare

investment bank (hereafter, bank) analysts’ behavior toward their employers’ clients with the

same analysts’ behavior toward non-clients. This second comparison provides a control for

differences among analysts, because we study the same individuals in two different settings. In

addition, we repeat both comparisons, studying upgrades rather than downgrades, to examine

whether the delay behavior occurs only in bad news cases, as we conjecture, or whether it is

more general.

This paper contributes to the literature on analysts in several ways. First, we document

that affiliated analysts issue recommendations sooner following an offering and in greater

numbers than unaffiliated analysts. Consequently, investors had access to proportionately less

unaffiliated research in the months immediately following an offering, particularly for IPOs.

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Second, we provide evidence that selection is a major force in analysts’ coverage of companies

issuing offerings, and that unaffiliated analysts are more likely than affiliated analysts to drop

coverage following the year of an offering. Third, we provide evidence that affiliation influences

analysts’ timeliness in downgrading their recommendations, consistent with anecdotal evidence.

Fourth, our study examines the 1994-2001 period, which anecdotal evidence suggests is one in

which analysts’ conflicts of interest vis-à-vis investment banking were exacerbated, and which to

our knowledge has not been systematically studied.

The findings are relevant to understanding analysts’ potential role in corporate

governance. Greenspan (2002) among others, suggests that analysts should play an important

role in corporate governance, as they are potentially more independent than corporate board

members, who have “limited incentives to safeguard shareholder interests” and outside auditors

who are “generally chosen by the CEO or by an audit committee of CEO-chosen directors.”

Furthermore, analysts have the unique role of interpreting financial statements and evaluating the

investment potential of a corporation. To the extent analysts fail to report their information on a

timely basis, particularly unfavorable information about which management may be less

forthcoming, they do not serve investors’ interests. Our study provides strong evidence that

investment banking ties increase analysts’ reluctance to reveal negative news.

The findings are also relevant to informing potential reform efforts of analysts’ research.

The New York Attorney General, the NASD and the NYSE have agreed to a $1.4 billion

settlement with major investment banks, requiring banks to insulate research analysts from

investment banking pressure and obliging banks to furnish independent research to retail

investors. Our finding that affiliated analysts issue downgrades on a less timely basis than

unaffiliated analysts provides systematic evidence that banking ties diminish analysts’ incentives

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to convey unfavorable news about client companies, consistent with the motivation for the

settlement. However, we also document that unaffiliated analysts provided no recommendations

in the year following an offering for 39.7% of companies issuing seasoned equity offerings and

51.7% of companies issuing initial public offerings. Furthermore, unaffiliated analysts are more

likely than affiliated analysts to drop coverage after initially providing recommendations. These

findings suggest that the incentives of unaffiliated analysts to provide coverage must also be

considered.

Finally, our findings are relevant to academics and others interested in the role analysts

play in providing information to investors, and the influence of investment banking ties over the

last several years. Our findings suggest that banking ties had substantial effects, and suggest that

future research should assess their potential role in inhibiting market efficiency.

In the next section, we review related literature and develop our hypotheses. In section 3,

we discuss our sample selection our statistical methods, and provide descriptive information

about our sample. Section 4 contains our results and discussion, and we conclude in section 5.

2. Related Literature and Hypothesis Development

Several studies have examined whether affiliated analysts issue more favorable research

reports than unaffiliated analysts. Dugar and Nathan (1995) examine a sample of

recommendations issued from 1983 through 1988, and document that affiliated analysts issue

more favorable earnings forecasts and recommendations than unaffiliated analysts. Lin and

McNichols (1998) examine seasoned equity offerings issued during 1988-1994, and find that

affiliated analysts issue more favorable long-term growth forecasts and recommendations than

unaffiliated analysts, though their near-term forecasts are indistinguishable. They also find that

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investors react similarly to affiliated and unaffiliated analysts’ Buy recommendations, but they

react more negatively to affiliated analysts’ Hold recommendations. This suggests that investors

at least partially discounted over-optimism in affiliated analysts hold ratings. Dechow, Hutton

and Sloan (2000) examine a sample of companies issuing offerings from 1981-1990, and

document that analysts’ long-term growth forecasts are significantly negatively associated with

post-offering under performance in stock returns. Michaely and Womack (2000) examine a

sample of IPO companies in 1991-1992 with coverage by First Call, and document that lead

underwriter recommendations are more favorable than other analysts’ recommendations, and that

stock returns are significantly greater following buy recommendations of unaffiliated analysts.

The findings of Dechow, Hutton and Sloan and of Michaely and Womack suggest that investors

did not fully discount analysts’ over-optimism.

As several of these studies note, two potential explanations for affiliated analysts’ greater

optimism are (1) banking ties create a conflict of interest that affects analysts’ behavior, and (2)

managers select banks with favorable views to underwrite their firms’ securities. The conflict of

interest and the selection arguments generate the same prediction, that affiliated analysts’

research reports are more favorable than those of unaffiliated analysts.

In this study, we conjecture that analysts prefer not to issue bad news about client

companies. It may be the case that all analysts are reluctant to issue bad news, for example to

help them retain access to management. The idea that we wish to test, however, that banking ties

create a conflict of interest, implies that this reluctance will be strongest when those ties are

present. These conjectures lead us to the following hypotheses (stated in alternative form):

H1a: Affiliated analysts downgrade their recommendations more slowly than unaffiliated

analysts.

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H2a: Analysts downgrade their recommendations more slowly for client companies than

for non-clients.

Hypotheses H1 and H2 differ by the conditioning information, or basis for comparison. H1a

states that affiliated analysts will downgrade more slowly than unaffiliated, so to test it we

condition on the issuing company, and compare different analysts for a given issuer. H2a states

that analysts will downgrade more slowly for client than for non-client companies, so here we

condition on the analyst and compare different issuers for a given analyst.2 We test these

hypotheses using a hazard model of the time until a downgrade, as described in the next section.

3. Sample and Statistical Models

Our sample is U.S. companies that issued common stock in an underwritten public equity

offering between 1994 and 2001. We choose public offerings as a starting point because the

financing event allows us to distinguish affiliated from unaffiliated analysts, and client from non-

client companies. We use the Securities Data Corporation (SDC) database, which includes only

“firm commitment” offerings, omitting “best efforts” offerings, so our sample is likewise limited

to firm commitment offerings. We expect that fees will be larger and incentives stronger for

firm commitment than for best efforts offerings, because the investment bank has less at risk in

the latter case. We include both initial public offerings (IPOs) and seasoned equity offerings

(SEOs) in our sample, which allows us to explore whether the effects differ between the two.

As Table 1 Panel A shows, from the 7,992 offerings of common stock in 1994 through

2001, we omit 544 issues where the issuer was a fund, or where the shares were ADRs. We

2 We use First Call data, and do not have access to the identities of individual analysts, so analysts are observationally equivalent to their employers. Since our hypotheses concern incentives created by banking relationships, i.e. incentives at the employer level, we believe this distinction is not material. When we refer to analysts, we mean equivalently the analysts’ employers.

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believe analyst coverage of funds and foreign entities may be qualitatively different from

coverage of domestic companies. We also omit 944 offerings where one or more of the

underwriters was not included in the First Call database, because we cannot observe the behavior

of brokers that are not included in that database. In cases where a company makes more than

one public issue of common stock during the sample period, we use only the first issue to avoid

having multiple events for a given company, reducing our sample by a further 1,893 issues. We

are left with 4,611 equity issues. Panel B of Table 1 shows that 1995 through 1997 had the

largest number of equity offerings in our sample period, and 2001 had substantially fewer than

earlier years, but no time-clustering is evident overall.

We obtain our analyst recommendations from the First Call database. By hand, we match

SDC underwriter names to First Call broker names to link the two databases. We define

affiliated analysts as those serving as either lead or co-underwriter for a given equity offering; all

others are unaffiliated.3

We examine analyst recommendations in a one- or two-year window following the equity

issue. To implement the hazard model, we define the duration of interest as the period starting

with the equity issue, and ending at the earlier of a downgrade by the analyst or the end of the

window. The general form of the hazard model is:

).()()(ln tXtth ijij Β+= α (1)

In this model, hij(t) represents the hazard, or instantaneous risk of downgrade, at time t for

analyst i and company j, conditional on survival to t; a(t) is the baseline hazard; B is a vector of

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coefficients; and Xij(t) is a matrix of observations on explanatory variables, some of which may

vary with time. We estimate the model by the method of partial likelihood developed by Cox

[(1972), hereafter Cox regression]. An advantage of this method is that we can obtain unbiased

and asymptotically normal estimates of the coefficients B, without specifying the functional form

of the baseline hazard a(t). The partial likelihood estimates are not fully efficient, relative to

estimates that employ the correct baseline hazard model. In most cases, including this one, the

true baseline hazard model is unknown, so full efficiency is probably not achievable.

Processes that prevent observation at some times t, called censoring and truncation, are

important considerations in employing hazard models. Our design has right censoring and left

truncation, and analyst data has an inherent type of random censoring. We discuss these design

issues, and how we address them, below.

Right censoring occurs because we fix the end of our observation window at one or two

years after the equity offering, so we do not observe downgrades that occur beyond the close of

the window. The likelihood function, therefore, depends only on downgrades that occur within

our window. We set a fixed window to increase the plausibility of the “affiliated” and

“unaffiliated” labels, which we determine at the time of the offering. The longer our window

extends after the equity offering, the more likely that our static labels will become obsolete.

Examining two different window lengths allows us to assess how sensitive our results are to this

choice. If the incentives associated with affiliation weakened in the second year, we expect our

results would likewise be weaker in the longer window. Our conclusions are stronger in the

longer window.

3 Specifically, SDC defines the following relations: book manager, co-manager, joint book manager and joint lead underwriter. We classify the book manager, joint book manager and joint lead underwriter as the lead underwriters, and treat the lead and co-underwriters as affiliated, and all other analyst firms as unaffiliated.

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Our design has truncation from below, or left truncation, because we begin our

measurements with the equity offering to give the model a common “event time” for each

issuing company. Analysts will enter the hazard calculation at different times relative to the

offering, depending on when they make their first post-offering recommendation. We employ a

standard method to control for left truncation, which is to define the hazard for analyst i and

company j to be zero until i’s first recommendation following j’s equity issue, and estimate

positive hazard only afterward. In essence, no risk of downgrade exists in our model until we

observe the analyst’s initial position. 4

The third form of censoring arises in analyst data because analysts may drop coverage of

a company. This introduces “informative random censoring” – “random” because it is not part

of our design, and “informative” because we believe that dropping coverage may be a substitute

for making an explicit downgrade.5 The statistics literature has no clear-cut method for dealing

with informative random censoring. One proposal is to perform sensitivity analysis, treating the

randomly censored observations first as survivors and then as “failures,” i.e. downgrades in our

context.6 In terms of our hypotheses, if affiliated analysts are more likely than unaffiliated to

drop, then treating drops as survivors will bias our tests in favor of finding an affiliation effect.

If affiliated analysts are less likely to drop, then the bias reverses. We observe in our data that

4 We considered an alternative design, in which we defined downgrades relative to recommendations that were outstanding at the time of the offering. We ran some preliminary tests in our data using this design, and found the data strongly rejected the null hypothesis that affiliated analysts downgrade no more slowly than unaffiliated. We decided against using this design because it effectively limited our sample to SEO firms, as First Call rarely lists analyst coverage prior to IPOs. 5 McNichols and O’Brien (1997) demonstrate that analysts tend to drop coverage in circumstances of unexpected bad earnings news. 6 Allison (1995) pp. 249-252. One obstacle to performing the proposed sensitivity analysis is that, to treat these observations as downgrades, we need drop dates. First Call does not provide clean drop dates. We are exploring other ways to learn when an analyst drops coverage. In our current tests, we treat dropped coverage as survivors, i.e. non-downgrades.

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unaffiliated analysts are more likely to drop coverage, so we suspect that this censoring biases

against our finding an effect of banking relationships.7

As we discussed above, our hypotheses H1 and H2 differ by the conditioning

information, or basis for comparison. H1a conditions on the issuing company, while H2a

conditions on the analyst. We effect this conditioning using the fixed-effects partial likelihood

(FEPL) method.8 The basic model (1) becomes,

for H1: ),()()(ln tXtth ijjij Β+= α (2)

and for H2: ).()()(ln tXtth ijiij Β+= α (3)

Essentially, this method absorbs an overall issuer effect or an overall analyst effect into the

baseline hazard function. We can then compare affiliated versus unaffiliated analysts within

issuing company for H1; and client versus non-client companies within analyst for H2.9

The advantage of the FEPL method for H1 is that each issuer acts as its own control:

earnings announcements or other public news events do not differ across the two groups of

analysts for a given issuer, and therefore are irrelevant for the comparison. The cost of this

strong control is that we cannot test any issuer-related hypotheses, such as whether affiliated

analyst behavior differs for IPOs versus SEOs, or for high-fee versus low-fee issuers, along with

H1.

Tests of H2, which are within-analyst across issuers, allow us to examine issuer-related

hypotheses, but also provide a less precise control. Our inferences from these tests rely on the

7 We find that 16% of affiliated and 25% of unaffiliated analysts with recommendations in the first year after the issue have no further recommendations within 3 years after the is sue. Although we cannot observe the date of the drop, we interpret two years without further recommendations as dropped coverage. 8 Chamberlain (1985) proposes this method for controlling for unobserved heterogeneity across individuals. 9 To ensure that our comparisons are meaningful, when we estimate equation (2) we restrict the sample to companies with at least two brokers covering them, and when we estimate equation (3) we restrict the sample to brokers that cover at least two companies. These restrictions have a minimal effect on our sample. Of the 3425 issuers with

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fact that all firms in the sample are equity issuers: we compare client versus non-client within

each analyst, but the non-clients of one analyst are clients of another. We presume that the

aggregate client versus non-client effect will capture only analysts’ systematic behavior, not

issuer effects. This provides us with an opportunity, however, to test for issuer-related

differences in behavior, as we discuss further below.

The remainder of our model consists of the matrix Xij of explanatory and test variables.

Our main test variable is an indicator variable, Affilij, which takes the value 1 if SDC listed

investment bank i as part of the underwriting syndicate for company j’s equity offering, and 0

otherwise. We hypothesize that affiliated analysts will delay downgrades. Equivalently, we

hypothesize a lower hazard of downgrade at any time t for affiliated than unaffiliated analysts.

We therefore expect a negative coefficient on Affil.

We condition on the level of the initial recommendation, coded 1 through 5 in First Call,

with 1 indicating “Strong Buy.” We include indicator variables for each of the top three

recommendation categories (1-3). We omit observations where the analyst’s initial

recommendation was “Strong Sell” (5) from the downgrade regressions, because no downgrade

is possible from this category. 10 We use the “Sell” category (4) as the baseline.11 We expect that

analysts will find downgrading less problematic, the more favorable the initial recommendation.

We therefore expect positive coefficients on our indicators, and we further expect the

coefficients to be larger for more favorable recommendations.

sufficient data on other variables to estimate the Cox regression, 275 had coverage by only one analyst. Of the 128 analysts with sufficient data, 1 covered only one of our sample comp anies. 10 Symmetrically, when we examine analysts’ upgrade behavior, we omit observations where the analyst’s initial recommendation was “Strong Buy” (1). 11 We discovered, after our initial attempt to estimate the model, that only one analyst downgraded from an initial Hold recommendation within our one-year window. This is not surprising, because analysts rarely issue Sell recommendations. The effect on our model, however, was that we could not obtain reliable estimates for this category in the one-year window. We therefore combine the Hold and Sell categories (3-4) as the baseline for the one-year window.

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We investigate the influence of underwriting fees on the analyst’s behavior in our tests of

H2.12 As Chen and Ritter (2000) demonstrate, investment banks in the U.S. rarely compete on

fees, and a large majority of deals have spreads of precisely seven percent of gross proceeds.

Banks may prefer larger deals, in these circumstances, if there are economies of scale in the

underwriter’s costs. We define an indicator, HiFee, which takes the value one if the fees from

the offering were greater than $10 million, approximately the 75th percentile of fees in our

sample. We interact this with the affiliation variable, Affil. If affiliated analysts’ incentives are

stronger in more lucrative deals, then we will find a significant negative coefficient on the

interaction HiFee*Affil.

We also allow for differences between IPOs and SEOs in our tests of H2, by including an

indicator variable that takes the value 1 if the offering is an IPO, and 0 if it is an SEO.13 We do

not have a conjecture for the direction of this effect.

4. Results

Table 2 provides descriptive statistics on sample companies and sample analyst firms

included in First Call. We define coverage by an analyst as the presence of a recommendation in

the First Call database within one year following the offering. The 3,553 companies with

coverage by an analyst are covered on average by 3.87 analysts, with a median of 3 analysts, a

minimum of 1 and a maximum of 28. The average number of lead analysts is 0.74, reflecting the

fact that lead underwriters cover the majority of companies whose offerings they underwrite.

The average number of co-underwriter analysts providing recommendations is greater than that

12 The fee is a characteristic of the equity offering, and we include only one offering per company in our sample, so the fee is a constant for each company in our sample. As we discussed earlier, because we test H1 by removing a company-specific fixed effect for each issue, we cannot test for fee effects in the model for H1.

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of lead analysts, with a mean of 1.09 and a median of 1.00, reflecting the fact that many deals

have multiple co-underwriters, but most have only one lead underwriter. The average (median)

company raised $103.55 million in proceeds and paid an average (median) gross spread to the

underwriters of $6.03 ($3.23) million. The average (median) percentage gross spread is 6.81%

(7%), consistent with the previously discussed findings of Chen and Ritter (2000).

Panel B of Table 2 provides descriptive statistics on the 129 analyst firms and their

involvement with the sample offerings. The average (median) analyst firm underwrote 50 (7)

offerings. The discrepancy between the mean and median indicates that some banks underwrite

substantially more offerings than others. The minimum of zero reflects the fact that some

research firms underwrote no offerings but issued recommendations that were included in the

First Call database. The banks participated in 86.18 offerings on average, with a median of 39

offerings and a range from 0 to 498 offerings. The average proceeds in deals a bank led were

$19.853 billion, with a range from $6 million to $162.235 billion. We estimated the gross spread

earned by a lead bank as 100% of the gross spread if the bank was the sole underwriter and as

60% of the gross spread with the remaining 40% shared equally by co-underwriters for deals

with multiple underwriters.14 Using this approach, we estimate the average bank earned $219.78

million from sample offerings, the median bank earned $14.63 million, and the range was from

$0 to $3.269 billion. The average gross spread per bank was 6.58%. The average bank issued

154.02 recommendations in the first year after an offering on sample companies, with a

minimum of 1 and a maximum of 1049. The average recommendation was 1.76, reflecting a

mix of recommendations less favorable than “Strong buy” (coded as 1) and more favorable than

“Buy” (coded as 2).

13 Similar to fees, the IPO/SEO status is a characteristic of the offering, and therefore we cannot estimate it in tests of H1.

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Table 3 provides descriptive evidence on the coverage and recommendations issued for

sample companies, by affiliation. Panel A documents that affiliated analysts play a major role in

covering sample companies, issuing recommendations on 62.04% of SEO issuers and 75.05% of

IPO issuers. Unaffiliated analysts cover 60.3% of SEO companies, but only 48.3% of IPO

companies. The tendency of affiliated analysts to cover a substantial percentage of their

offerings and the fact that unaffiliated analysts are more selective results in 14.85% of SEO

companies and 29.72% of IPO companies receiving only affiliated coverage. Relatedly, 39.7%

of SEO companies and 51.7% of IPO companies received either no coverage or only affiliated

coverage.

Figure 1 presents the number of recommendations issued by affiliated and unaffiliated

analysts in the year before and the three years following an SEO. The chart indicates that

although affiliated analysts issued approximately half as many recommendations per month as

unaffiliated in the year prior to the SEO, at between 100 and 200 recommendations per month,

they issued approximately 919 recommendations in the month of the SEO, or approximately

20% more than the number issued by unaffiliated analysts.

Figure 2 presents an even more striking pattern for IPO companies. Affiliated analysts

issued 2,985 recommendations in the month following the IPO, as compared to 171

recommendations issued by unaffiliated analysts. In fact, the cumulative number of affiliated

recommendations issued for IPO firms exceeds the number of unaffiliated recommendations

throughout the first year. A key point to note is that affiliated analysts initiate coverage sooner

for IPO companies, which, as we described earlier, our duration analysis explicitly takes into

account.

14 The proportions for splitting fees are based on evidence in Chen and Ritter (2000).

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Panel B presents the distribution of recommendations in the first year after an offering,

by affiliation. The data indicate that lead underwriter analysts issue the highest proportion of

recommendations in the “Strong Buy” category, at 48.77% of the recommendations they issued,

co-underwriter analysts issued the next highest, at 43.01%, and unaffiliated analysts issued the

lowest, at 39.25%. Correspondingly, lead underwriter analysts issued the smallest proportion of

“Hold”, “Sell” and “Strong Sell” recommendations at 12.46%, co-underwriter analysts issued the

next largest proportion at 15.06%, and unaffiliated analysts issued the greatest proportion, at

24.45%

Panel C documents that the average recommendation by lead bank analysts is 1.64, as

compared to the average for co-underwriter analysts, at 1.73, and unaffiliated analysts at 1.87.

Panel D documents that lead analyst recommendations are significantly more favorable than

those of co-underwriter analysts (t-statistic=6.42) but that co-underwriter analyst

recommendations are significantly more favorable than those of unaffiliated recommendations (t-

statistic=12.08). Given the substantial role that co-underwriter analysts play in providing

coverage on sample companies, and the finding that their recommendations are significantly

more favorable than those of unaffiliated analysts, we classify co-underwriter analysts as

affiliated.15

Figures 3 and 4 show the average value of recommendations issued by affiliated and

unaffiliated analysts each month for the year before and three years after an SEO and for the

three years following an IPO. Consistent with the evidence in Table 3, affiliated

recommendations are more favorable on average in the year prior to the SEO and for the first

year after the SEO. Affiliated recommendations are initially more favorable for IPO companies

15 As a sensitivity check, we estimated our duration models for only lead underwriter analysts and unaffiliated analysts, omitting co-underwriters, and find our conclusions are not sensitive to this classification.

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in the first 5 months of the year following an IPO but become less favorable later in the first

year. Recall from Figure 2 however, that a vast majority of affiliated recommendations are

issued in the first two months following the IPO.

Table 4 presents descriptive statistics on the days between ratings, for each transition

from a prior to subsequent recommendation, and by affiliation. The data include

recommendations made in the first two years following an offering. Panel A presents the median

days between ratings by unaffiliated analysts and reveals two patterns. First, the number of days

between ratings for reiterations, shown as the shaded diagonal, tends to decline as the rating

becomes less favorable. Second, the number of days between ratings above the shaded diagonal,

that is, the number of days between downgrades, tends to be larger than the number of days

between ratings below the shaded diagonal, that is, for upgrades.16

Panel B presents the median days between ratings by affiliated analysts. The data

indicate a similar pattern to Panel A: the median days between recommendations for reiterations

decline with the favorableness of the recommendation, and the median days between a

recommendation and a subsequent downgrade are greater than the median days between a

recommendation and a subsequent upgrade. In addition, the median days between

recommendations for downgrades by affiliated ana lysts (above the shaded diagonal) are each

greater than for the comparable transition by unaffiliated analysts, providing preliminary

evidence that affiliated analysts take longer to downgrade than unaffiliated analysts.

Panel C provides statistical evidence consistent with these observations. The average

time between a recommendation and a subsequent downgrade by the full sample of affiliated and

unaffiliated analysts is 187.5 days , whereas the mean time between a recommendation and a

16 These characteristics are generally consistent with those reported by McNichols and O'Brien (1997) for the period 1987-1994. McNichols and O'Brien (1997) did not separate affiliated from unaffiliated coverage.

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subsequent upgrade is 125.2. This univariate difference is highly significant, as indicated by the

t-statistic of 25.37. Panel C also documents that affiliated analysts take more than 50 days longer

than unaffiliated analysts to downgrade. This difference is also highly significant, as indicated

by a t-statistic of 16.12. Lastly, Panel C indicates that affiliated upgrades are slightly less timely,

taking approximately 10 days longer than unaffiliated upgrades.

The univariate results in Panel C of Table 4 suggest that affiliated analysts downgrade

more slowly than unaffiliated, but these statistical tests likely overstate both the size and the

significance of differences. The Table 4 results do not control for the fact that different analysts,

by choice, begin coverage at different times – the phenomenon described above as left

truncation. Unaffiliated analysts’ initial recommendations are on average 366 days (median 364

days) after the issue, while affiliated analysts’ initial recommendations follow the issue on

average by only 91 days (median 29 days). Our hazard model, discussed below, will control for

the date of initial recommendation. In addition, in Table 4 the underlying assumption of

independent observations is unlikely to be met. For example, we expect that different brokers

covering the same company will have correlated durations, if they respond to the same

information at similar times. We control for correlated durations by estimating separate hazards

for each company in our test of H1.

In Table 5, we report the results of our hazard models of time to downgrade within one-

and two-year windows. Panel A contains the results of our tests of H1, that affiliated analysts

downgrade more slowly than unaffiliated for a given issuing company. Panel B contains the

results for H2, that a given analyst downgrades more slowly for client companies than for non-

clients. In all cases, the models have highly significant Likelihood Ratio and Wald statistics (not

tabulated), relative to a global null hypothesis that all the coefficients are zero.

19

Our main test variable is Affil. We expect a negative coefficient if affiliated analysts

downgrade more slowly. For H1, where we compare affiliated versus unaffiliated analysts for a

given issuer, we find significant nega tive coefficients on Affil in both the one- and two-year

windows, and therefore reject the null.17 The hazard ratios provide a convenient way to interpret

these results. Based on our one-year results, at any point in time, an affiliated analyst is 88% as

likely to downgrade as an unaffiliated analyst for the same company. Based on our two-year

results, the affiliated analyst is 91% as likely to downgrade.

For H2, where we compare client versus non-client issuers for a given analyst, we reject

the null in the two-year window, but not in the one-year window. Based on the two-year results,

analysts are 93% as likely to downgrade a client as a non-client at any point in time. In the one-

year window, we find no difference in analysts’ time to downgrade clients versus non-clients.

Because the one-year window is a proper subset of the two-year window, we interpret the

inconsistency across windows as indicating that the one-year window did not contain enough

instances of analysts who downgraded both clients and non-clients to generate a measurable

difference.

Consistent with the argument that banking fees influence analysts’ downgrade behavior,

we find that high fees increase affiliated analysts’ propensity to delay downgrades. As Panel B

indicates, the coefficients on the interaction Affil*HiFee are significantly negative in both the

one- and the two-year windows. The hazard ratios indicate that an affiliated analyst is about

90% as likely to downgrade a high fee client as a lower fee client.

The other variables in our model generally behave as expected. Analysts are vastly more

likely to downgrade from an initial rating of StrBuy or Buy than from a Sell rating. All hazard

17 The chi-squared test is non-directional, so it is implicitly two-tailed. For all one-tailed hypotheses (that is, for all variables except IPO), we report one-tailed p-values, computed as 0.5*two-tailed p-value.

20

ratios for these indicators exceed 6, meaning that analysts are more than 6 times more likely to

downgrade from these favorable categories. In all cases, the coefficients and hazard ratios for

StrBuy, Buy, and Hold decline monotonically as the initial recommendation becomes less

favorable, as expected. Initial Hold recommendations, which we are able to estimate separately

in the two-year window, are indistinguishable from the baseline Sell categories. Thus, the

conventional wisdom that Hold recommendations are equivalent to Sells appears to hold true in

the dimension of duration to downgrade.

These ancillary results on the initial recommendation category confirm our expectations,

and refute an alternative view of the interaction between ratings levels and the strength of

analysts’ beliefs. In this alternative view, more favorable ratings represent more strongly felt

beliefs, and therefore the timing of revisions might not represent a truly different dimension of

analyst behavior from the ratings themselves. If this were the case, then we should expect

downgrades from Strong Buy to be slower than downgrades from less favorable categories. We

find the reverse.

We find different results for IPOs versus SEOs in the one-year, but not the two-year

window. We can estimate this effect only in our tests of H2, because our tests of H1 cond ition

on issuer, and the IPO indicator is perfectly collinear with a set of issuer indicators in our

sample. In the one-year window, we find analysts are significantly less likely to downgrade after

an IPO than after an SEO, with a hazard ratio of 82%. In contrast, in the two-year window, we

find no difference between IPOs and SEOs.

To explore the possibility that affiliated analysts may simply be slower to change ratings

regardless of direction, in Table 6 we repeat the Cox regression tests, using upgrades rather than

downgrades as the event of interest. If affiliated analysts have better access to information, for

21

example because of their due diligence activities or favorable treatment by management, then we

would expect them to be able to upgrade at least as quickly as unaffiliated analysts. On the other

hand, if they take more care with their analyses, this could cause all their ratings changes to be

slower than those of unaffiliated analysts.

In the upgrade models, we exclude observations where the analyst’s initial rating was

Strong Buy, because no upgrade is possible from this category. This is symmetric with our

exclusion of Strong Sells when we estimate the downgrade model. The baseline category in

Table 6 is the combined Sell and Strong Sell group.

We find mixed results in Table 6. Panel A presents the results for affiliated versus

unaffiliated analysts within issuer, while Panel B presents our results for client versus non-client

issuers within analyst, analogous to Table 5. In Panel A, we find that affiliated analysts upgrade

significantly more slowly when we use a one-year window, while in the two-year window we

find no difference in time to upgrade. In Panel B, in the one-year window we find analysts

upgrade client companies more quickly, while in the two-year window we find no difference.

Thus, all cases except the 1-year results in Panel A support the view that affiliated analysts

upgrade no more slowly than unaffiliated, therefore suggesting that their delays in downgrades

are deliberate and caused by the investment banking affiliation.

We find the inconsistency in the Table 6 Panel A results somewhat puzzling, both

because the one-year window results for H1’ and H2’ support contradictory views, and because

the one-year results show statistical significance, while the two-year do not. Because the one-

year window observations are a proper subset of the two-year, the longer window should have

more statistical power. We continue to investigate these differences.

22

The indicators for the category of the initial recommendation in Table 6 show that at any

moment, analysts are significantly less likely to upgrade from a Buy recommendation than from

a Sell, with estimated hazard ratios ranging from 56 to 66%. In contrast to the Table 5 results

that showed Hold recommendations no different from Sells, in Table 6 we find analysts upgrade

significantly less quickly from Hold than from Sell or Strong Sell in three of the four cases

studied. A possible explanation for the difference is the presence of Strong Sells in the baseline

for Table 6. In Table 5 we excluded Strong Sells because there is no risk of downgrade from this

category.

Taken as a whole, the univariate and duration model results provide some support for the

conjecture that affiliated analysts delay downgrades for client companies, relative to unaffiliated

analysts and relative to non-client companies. The two-year duration model results in Tables 5

and 6 uniformly confirm that these delays are not due to differences in the companies followed,

differences in behavior across analys ts, differences in the level or timing of the initial

recommendation, or generally longer revision times by affiliated analysts. The one-year results

are less uniform, and we continue to investigate the apparent inconsistencies. We also find

mixed evidence on the influence of high fees.

5. Summary and conclusions

This paper examines analysts’ recommendations for a sample of 3,553 companies issuing

initial public offerings or seasoned equity offerings during 1994-2001 to test for evidence that

analysts’ impartiality has been compromised by investment banking ties. Specifically, we test

the hypothesis that analysts delay the release of bad news about their employers’ underwriting

clients.

23

We find that investors have access to proportionately less unaffiliated research in the

months immediately following an offering, particularly for IPOs, because analysts affiliated with

lead and co-underwriter banks issue recommendations sooner following an offering and in

substantially greater numbers than unaffiliated analysts. We also find that unaffiliated analysts

are more likely than affiliated analysts to drop coverage following the year of an offering. We

find strong evidence that analysts issue upgrades more quickly than downgrades, with a median

of 55 days less for upgrades.

In tests of our main hypotheses, we find that affiliation influences analysts’ timeliness in

downgrading their recommendations, consistent with the anecdotal evidence we cite in the

introduction. The mean and median days between a recommendation and a subsequent

downgrade are 54 days greater for affiliated analysts than unaffiliated analysts. Our duration

model confirms these findings are not due to differences in recommendations, failure to control

for company-specific or bank-specific differences, or failure to account for unaffiliated analysts’

later initial recommendations. Using a two-year window following the equity issue, we find

strong support for the view that affiliated analysts significantly delay downgrades of client

companies, whether measured within issuer or within analyst, and that they do not similarly

delay upgrades. We find mixed evidence when we use a one-year window, and mixed evidence

on the incentive effect of high fees.

The findings raise several issues relevant to understanding analysts’ potential role in

corporate governance. Because analysts play an important role in interpreting financial

statements and evaluating the investment potential of a corporation, delays in the reporting of

their analysis can be very costly to investors, employees and other corporate stakeholders. Our

study provides evidence that analysts are slower to downgrade companies than to upgrade them.

24

Furthermore, our study provides evidence that investment banking ties increase analysts’

reluctance to reveal negative news.

These findings are also relevant to informing potential reform efforts of analysts’

research and underscore the findings of McNichols and O’Brien (1997) that selection plays a

major role in analysts’ coverage decisions. The goal of the reform efforts is to increase the

availability of objective research about companies. We find that unaffiliated analysts provided

no recommendations in the year following an offering for 39.7% of companies issuing seasoned

equity offerings and 51.7% of companies issuing initial public offerings. We also find

unaffiliated analysts are more likely than affiliated analysts to drop coverage after initially

providing recommendations. If coverage decisions themselves are informative, then mandating

coverage by unaffiliated analysts could eliminate a potentially informative signal.

Lastly, our findings are relevant for understanding analysts’ role in providing information

to investors, particularly in the initial public offering market. Our findings indicate that the

effects of banking ties were substantial both in motivating analysts to cover newly public

companies and in motivating them to delay in downgrading their recommendations. Although

there is evidence that investors discount the recommendations of affiliated analysts, an important

question for future research is how well investors and other analysts understood the nature of the

biases of affiliated analysts and whether investors adjusted for them appropriately.

25

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Table 1

Sample Information

Panel A: The effect of selection criteria on the number of companies included in the sample. Equity offerings from Jan 1, 1994 through Dec 31, 2001, per Securities Data Corporation:

7992 Less offerings by REIT’S, closed end funds and ADR’S: (544) Subtotal 7448 Less offerings underwritten by broker not included in FIRST Call: (944) Subtotal 6504 Less subsequent offerings by same issuer during sample period: (1893) Number of companies in sample 4611

Panel B: Offerings included in sample, by year and type of offering. IPO indicates an initial public offering and SEO indicates a seasoned equity offering.

Available offerings from Securities Data Corporation Database

Offerings included in sample, after eliminating subsequent offerings by the same company

Year SEO IPO Total SEO IPO Total 1994 318 340 658 295 340 635 1995 489 415 904 308 415 723 1996 601 652 1253 300 651 951 1997 533 452 985 218 450 668 1998 368 295 663 120 292 412 1999 399 471 870 109 471 580 2000 371 361 732 91 358 449 2001 354 85 439 108 85 193 Total 3433 3071 6504 1549 3062 4611

29

Table 2 Descriptive statistics on sample companies and sample banks.

Panel A: Descriptive statistics on the 3,553 sample companies with coverage by any analyst. Variable N Mean Std Dev Median Minimum Maximum Number of analysts covering company 3553 3.87 3.07 3.00 1.00 28.00Number of lead underwriter analysts covering company 3553 0.74 0.54 1.00 0.00 4.00Number of co-underwriter analysts covering company 3553 1.09 0.99 1.00 0.00 10.00Total offering proceeds (in $ millions) 3509 103.55 252.47 48.00 3.75 7322.00Gross spread (in $ millions) 3487 6.03 11.22 3.23 0.19 237.18Gross spread as percent of proceeds 3485 6.81% 2.00% 7.00% 0.16% 60.71% Panel B: Descriptive statistics on the 129 investment banks underwriting offerings in 1994-2001 and included in the First Call recommendations database. Variable N Mean Std Dev Median Minimum Maximum Number of offerings bank served as lead underwriter 129 50.26 103.58 7.00 0.00 498.00Number of offerings bank served as co-underwriter 129 86.18 115.62 39.00 0.00 543.00Total offering proceeds (in $ millions) 129 19853 35349 5830 6 162235Gross spread (in $ millions) 129 219.78 616.40 14.63 0.00 3269.00Gross spread as percent of proceeds 129 6.58% 1.08% 6.57% 2.31% 11.80%Number of recommendations issued on sample companies 129 154.02 231.19 65.00 1.00 1049.00Average recommendation 129 1.76 0.33 1.75 1.00 3.00

30

Table 3

Analyst coverage and recommendations on sample companies in the first year following an offering, by affiliation with an underwriting bank.

Panel A: Analyst coverage of sample companies, by affiliation. Coverage is defined as the presence of a recommendation within the first year following the offering. Number

of SEO companies

% of SEO

companies

Number of IPO

companies

% of IPO

companies 1. Companies with coverage by affiliated (lead

or co-underwriter) analysts 961 62.04% 2298 75.05% 2. Companies with coverage by unaffiliated

analysts 934 60.30% 1479 48.30% 3. Companies with coverage only by affiliated

analysts 230 14.85% 910 29.72% 4. Companies with no coverage 385 24.85% 673 21.98% Total of companies issuing offerings (sum of 2.

– 4.) 1549 100.00% 3062 100.00%

Panel B: Distribution of recommendations issued in the first year following an IPO or SEO, by analyst affiliation with lead underwriter, co-underwriter or no affiliation. In each of the five Recommendations columns, the table reports the frequency, with the percent of the row total below. For example, analysts in the role of lead underwriter issued 2,273 strong buy recommendations, which comprised 48.77% of recommendations from lead underwriter analysts. The column labeled Total reports the total frequency for the row, and the row’s percent of the overall total below.

Recommendations Role of the bank

1 (Strong buy)

2 (Buy)

3 (Hold)

4 (Sell)

5 (Strong sell) Total

Lead 2273 1807 566 13 2 4661 48.77 38.77 12.14 0.28 0.04 20.43 Co- 3010 2935 1013 30 11 6999 Manager 43.01 41.93 14.47 0.43 0.16 30.67 No 4379 4051 2532 138 58 11158 affiliation 39.25 36.31 22.69 1.24 0.52 48.90 Total 9662 8793 4111 181 71 22818 42.34 38.54 18.02 0.79 0.31 100.00

31

Table 3 (continued)

Panel C: Descriptive statistics on recommendations, by affiliation of analyst. 1=Strong Buy; 5=Strong Sell. Variable N Mean Std Dev Median Minimum Maximum Analyst with lead bank 4661 1.64 0.70 2.0 1 5 Analyst with co-manager bank 6999 1.73 0.73 2.0 1 5 Analyst with unaffiliated bank 11158 1.87 0.84 2.0 1 5 Panel D: Statistical tests of differences in mean and median recommendations, by affiliation of analyst.

Comparisons t-statistic Wilcoxon z-statistic

Lead recommendations more favorable than co-underwriter recommendations 6.42 6.40 Co-underwriter recommendations more favorable than unaffiliated recommendations 12.08 10.57 Affiliated (lead and co-underwriter) recommendations more favorable than unaffiliated recommendations 17.61 15.57

32

Table 4

Days between recommendations, partitioned by affiliation and by previous and subsequent recommendation.

Panel A: Days between ratings of unaffiliated analysts. The sample includes all recommendations issued in the two years following an offering, for sample offerings occurring between January 1, 1994 and May 5, 2000. The panel shows the median days between recommendations in bold type, by prior and subsequent recommendation, with the frequency and percent of observations with the same prior recommendation below. For example, the median days between a strong buy (=1) rating and reiterating that rating are 137.0, based on 1665 recommendations, which comprises 18.31% of the subsequent recommendations following a strong buy recommendation. Subsequent recommendation Prior recommendation

No subsequent recommendation 1 2 3 4 5 Total

137.0 125.0 144.0 93.0 98.5 1 4034 1665 2078 1259 35 24 9095 44.35 18.31 22.85 13.84 0.38 0.26 36.3 78.0 114.0 105.0 99.0 131.0

2 3529 1892 1330 1810 51 17 8629 40.9 21.93 15.41 20.98 0.59 0.2 34.44 82.5 81.0 119.0 88.0 70.5

3 3428 732 1171 1286 137 46 6800 50.41 10.76 17.22 18.91 2.01 0.68 27.14 50.5 83.5 60.0 74.0 30.0

4 126 28 28 114 50 13 359 35.1 7.8 7.8 31.75 13.93 3.62 1.43 20.0 42.0 48.5 39.0 43.5

5 71 7 8 36 11 42 175 40.57 4 4.57 20.57 6.29 24 0.70

Total 11188 4324 4615 4505 284 142 25058 44.65 17.26 18.42 17.98 1.13 0.57 100.00

33

Panel B: Days between ratings of affiliated analysts. The panel shows the median days between recommendations in bold type, by prior and subsequent recommendation, with the frequency and percent of observations with the same prior recommendation below. For example, the median days between a strong buy (=1) rating and reiterating that rating are 155.0, based on 1693 recommendations, which comprises 25.42% of the subsequent recommendations following a strong buy recommendation. Subsequent recommendation Prior recommendation

No subsequent recommendation 1 2 3 4 5 Total

155.0 164.0 224.5 144.0 172.0 1 2186 1693 1800 946 19 15 6659 32.83 25.42 27.03 14.21 0.29 0.23 41.54 93.0 143.0 151.0 144.5 231.0

2 1895 1545 1291 1242 20 6 5999 31.59 25.75 21.52 20.7 0.33 0.1 37.42 78.0 88.5 126.0 96.0 104.0

3 1570 346 630 601 41 14 3202 49.03 10.81 19.68 18.77 1.28 0.44 19.97 57.5 209.0 51.5.0 104.0 114.0

4 47 8 5 36 20 4 120 39.17 6.67 4.17 30 16.67 3.33 0.75 49.0 17.0 23.5 14.0 52.0

5 18 8 3 10 4 9 52 34.62 15.38 5.77 19.23 7.69 17.31 0.32

Total 5716 3600 3729 2835 104 48 16032 35.65 22.46 23.26 17.68 0.65 0.3 100 Panel C: Statistical tests of differences in days between ratings. Comparison

N

Mean days Median days

t-statistic Wilcoxon z-statistic

All downgrades 187.5 138.0 25.37 27.39 All upgrades 125.2 83.0 Affiliated downgrades 218.6 173.0 16.12 14.78 Unaffiliated downgrades 164.2 119.0 Affiliated upgrades 131.8 90.0 2.90 2.15 Unaffiliated upgrades 121.4 78.0

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Table 5 Cox regression models of time from issue until downgrade

Panel A: Model to test H1 – Affiliated analysts downgrade more slowly than unaffiliated analysts. The estimates are stratified by issuer, and we restrict the sample to issuers with at least two analysts. Affil = 1 if the analyst was in the issuer’s underwriting syndicate, and 0 otherwise. StrBuy, Buy and Hold = 1 if the analyst’s first post-issue recommendation for the issuer was Strong Buy, Buy, or Hold respectively, and 0 otherwise.

1-year window Covariate Coefficient Chi-sq p-value Hazard Ratio N=15,241 Affil -0.13 4.43 0.0177 0.88

StrBuy 2.45 183.58 <.0001 11.61 Buy 1.88 108.39 <.0001 6. 57

2-year window Covariate Coef. Chi-sq p-value Hazard Ratio N=2,679 Affil -0.09 5.50 0.0096 0.91

StrBuy 2.79 472.81 <.0001 16.35 Buy 2.27 310.93 <.0001 9.64 Hold -0.50 0.91 0.1699 0.60

Panel B: Model to test H2 - analysts downgrade more slowly for client companies than for non-clients. The estimates are stratified by analyst, and we restrict the sample to analysts covering at least two issuers. Affil = 1 if the analyst was in the issuer's underwriting syndicate, and 0 otherwise. StrBuy, Buy and Hold = 1 if the analyst's first post-issue recommendation for the issuer was Strong Buy, Buy, or Hold respectively, and 0 otherwise. HiFee = 1 if the gross underwriting spread for the issue exceeds $10 million, and 0 otherwise. IPO = 1 if the issue was an initial public offering, and 0 otherwise.

1-year window Covariate Coef. Chi-sq p-value Hazard Ratio N=15,143 Affil 0.06 1.51 0.1096 1.06

StrBuy 2.55 205.33 <.0001 12.83 Buy 2.04 128.71 <.0001 7.67 Affil*HiFee -0.09 1.56 0.1062 0.91 IPO -0.19 18.34 <.0001 0.82


StrBuy 2.89 546.29 <.0001 17.92 Buy 2.44 385.54 <.0001 11.46 Hold -0.57 1.20 0.1363 0.57 Affil*HiFee -0.10 3.11 0.0390 0.91 IPO 0.02 0.31 0.2877 1.02

35

Table 6 Cox regression models of time from issue until upgrade

Panel A: Model to test H1’ – Affiliated analysts upgrade more slowly than unaffiliated analysts. The estimates are stratified by issuer, and we restrict the sample to issuers with at least two analysts. Affil = 1 if the analyst was in the issuer’s underwriting syndicate, and 0 otherwise. StrBuy, Buy and Hold = 1 if the analyst’s first post-issue recommendation for the issuer was Strong Buy, Buy, or Hold respectively, and 0 otherwise.

1-year window Covariate Coefficient Chi-sq p-value Hazard Ratio N=8,392 Affil -0.15 2.95 0.0430 0.86

Buy -0.58 40.41 <.0001 0.56 Hold -0.62 3.43 0.0320 0.54


Buy -0.55 80.67 <.0001 0.58 Hold -0.39 3.58 0.0293 0.68

Panel B: Model to test H2’ – analysts downgrade more slowly for client companies than for non-clients. The estimates are stratified by analyst, and we restrict the sample to analysts covering at least two issuers. Affil = 1 if the analyst was in the issuer’s underwriting syndicate, and 0 otherwise. StrBuy, Buy and Hold = 1 if the analyst’s first post-issue recommendation for the issuer was Strong Buy, Buy, or Hold respectively, and 0 otherwise. HiFee = 1 if the gross underwriting spread for the issue exceeds $10 million, and 0 otherwise. IPO = 1 if the issue was an initial public offering, and 0 otherwise.


Buy -0.57 76.70 <.0001 0.56 Hold -0.25 1.10 0.1472 0.78 Affil*HiFee -0.14 2.13 0.0721 0.87 IPO 0.10 2.61 0.0530 1.10


Buy -0.42 82.28 <.0001 0.66 Hold -0.25 2.74 0.0490 0.78 Affil*HiFee -0.08 0.97 0.1622 0.92 IPO 0.00 0.01 0.4639 1.00

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Figure 1Number of recommendations issued by affiliated and unaffiliated analysts, in

the year before and three years following an SEO

affiliated

unaffiliated

0

200

400

600

800

1000

-11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Event month, where month 0 is month of SEO

Figure 2Number of recommendations issued by affiliated and unaffiliated analysts, in

the three years following an IPO

affiliated

unaffiliated

0500

100015002000250030003500

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

Event month, where month 0 is month of IPO

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Figure 3Average value of recommendations issued by affiliated and unaffiliated

analysts in the year before and three years after an SEO

unaffiliated

affiliated

1

1.5

2

2.5

-11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Event month where month 0 is the month of the SEO

Figure 4Average value of recommendations issued by affiliated and unaffiliated

analysts in the three years following an IPO

unaffiliated

affiliated

1

1.5

2

2.5

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32Event month where month 0 is the month of the IPO

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