Currying Favor to Win IPO Mandates
François Derrien
Rotman School of Management, University of Toronto
November 27, 2006
Please direct correspondence to François Derrien, Rotman School of Management, University
of Toronto, 105 St George street, Toronto, ON M5S 3E6, email:
[email protected]. I thank Jay Ritter for providing access to IPO
underwriter rankings. I am also grateful to François Degeorge, Ambrus Kekscés, Jan MahrtSmith, and seminar participants at Université Paris IX Dauphine, York University, University
of Geneva, Vanderbilt University, and HEC for useful comments and suggestions. Ambrus
Kecskés provided able research assistance. I thank the Social Sciences and Humanities
Research Council of Canada for financial support. All errors are my own.
Currying Favor to Win IPO Mandates
Abstract
This paper investigates whether a bank can win IPO mandates by issuing flattering analyst
recommendations to recent IPOs. We find that security analysts increase their bank’s chance of
comanaging an IPO when they issue generous recommendations to recent IPOs managed by
the IPO’s lead manager. However, this result holds only for prestigious banks. Less prestigious
banks, whose recommendations are less influential, do not obtain such rewards from lead
managers. We also find that security analysts increase their bank’s chance of managing future
IPOs when they issue generous recommendations to recent IPOs managed by their own bank.
This result, however, holds only for non-prestigious banks. These banks, which are not the
issuers’ first choice, appear to advertize their services by issuing generous analyst coverage to
their recent offerings.
1. Introduction
A large and growing body of evidence shows that security analysts are subject to
serious conflicts of interest that cause them to issue biased earnings forecasts and
recommendations.1 However, the consequences of these analysts’ biases are unclear. Their
impact on stock prices seems limited; Michaely and Womack (1999) and Agrawal and Chen
(2005) show that investors are somewhat aware of conflicts of interest and discount biased
recommendations appropriately. However, generous analyst recommendations may have
another consequence; namely, they may generate business for the bank that employs the
analyst. This article explores this possibility in the context of initial public offerings (IPOs).
More precisely, we ask whether issuing positive recommendations to recent IPOs increases a
bank’s chances of obtaining lead and comanagement mandates in future IPOs.
Banks may earn rewards for providing positive analyst coverage to recent IPOs through
two channels. First, analysts may issue favorable recommendations to recent IPOs and hope
that this will increase their bank’s chances of being selected to comanage the next IPO
managed by the same underwriter. Bradley, Jordan, and Ritter (2005) and Bradley, Clarke, and
Cooney (2006) refer to this possibility as the “currying favor” hypothesis. Recent research
(e.g., Krigman, Shaw, and Womack 2001; Cliff and Denis 2004; Cook, Kieschnick, and Van
Ness 2006) shows that issuers perceive (presumably positive) analyst coverage, and more
generally promotion by their underwriters, as important. If analyst coverage is important to
1
See Dugar and Nathan (1995), Rajan and Servaes (1997), Lin and McNichols (1998), Michaely and Womack
(1999), Hong, Kubik, and Solomon (2000), Hong and Kubik (2003), Bradshaw, Richardson, and Sloan (2003),
Madureira (2004), Bradley, Jordan, and Ritter (2005), Degeorge, Derrien, and Womack (2006), Lin, McNichols,
and O’Brien (2005), and Malmendier and Shanthikumar (2005). On the one hand, analysts issue biased earnings
forecasts and stock recommendations to help their banks obtain investment banking business. On the other hand, it
is in their best interest to protect their reputation for accuracy. Hong, Kubik, and Solomon (2000), Hong and
Kubik (2003), and Ljungqvist, Marston, Starks, Wei, and Yan (2005) show that security analysts are aware of
these conflicting incentives and balance them when it comes to issuing earnings forecasts or stock
recommendations.
1
issuers, it also should be important to underwriters, who may be willing to reward banks that
provide generous analyst coverage to their recent IPOs. One way to reward these banks is to
offer them comanagement mandates in future IPOs, a plausible scenario if lead managers
influence the choice of IPO comanagers.2 Corwin and Schultz (2005) claim they do and find
that one of the main determinants of a bank’s participation in an IPO is its previous
relationship with the lead underwriter.
We find that providing generous analyst coverage to an underwriter’s recent IPOs
increases a bank’s chances of comanaging the next IPO managed by the same underwriter, but
only if both banks are prestigious. This suggests that analyst recommendations matter only
when they come from prestigious banks, and that prestigious banks comanage only IPOs with
prestigious lead managers. The results also vary depending on past relationships between
banks. Banks that have comanaged recent IPOs by an underwriter are rewarded for supporting
these IPOs, not the lead manager’s recent IPOs in which they did not participate. When the two
banks had no such relationships, supporting recent IPOs done by the lead underwriter increases
a bank’s chances of comanaging the next IPO managed by the same underwriter, provided that
both banks are prestigious.
Second, a bank may use analyst coverage to increase its IPO business by issuing
positive analyst recommendations to its own recent IPOs. Lin and McNichols (1998), Michaely
and Womack (1999), and others show that analysts affiliated with IPO lead underwriters offer
the most generous analyst coverage and are the most inclined to issue “booster shots,” or
positive recommendations following disappointing stock performance (James and Karceski
2006). Although these recommendations may hurt a bank’s reputation, they also can provide
2
Another (less subtle) way to reward flattering banks is to pay them for their service. Some of the banks involved
in the 2002 “Global Settlement” were charged with making such “quiet payments” to other banks in exchange for
analyst coverage of their recent IPOs.
2
some benefits in the form of a greater probability of obtaining lead management mandates in
future IPOs.
In sharp contrast with our previous findings, we find that only the least prestigious
banks receive rewards from future issuers when they provide generous analyst coverage to
their own recent IPOs. Prestigious banks may not need to advertise their services with potential
issuers, who are naturally inclined to choose prestigious underwriters to do their IPO (Ellis,
Michaely, and O’Hara 2005). However, competition for lead management mandates is tougher
among less prestigious underwriters, and the results suggest that analyst coverage represents an
important dimension of this competition.
Our results complement the findings of Ljungqvist, Marston, and Wilhelm (2005)
(hereafter LMW (2005)) and Ljungqvist, Marston, and Wilhelm (2006) (hereafter LMW
(2006)). Using a sample of debt and seasoned equity offerings (SEOs), LMW (2006) find that
the lead managers of a firm’s offering do not improve their chances of being appointed lead
managers in the firm’s next offering by issuing more generous analyst coverage to the
company. LMW (2005) find that generous analyst coverage of the firm can help banks obtain
comanagement mandates in the firm’s next offering.
Although this study also focuses on whether analysts can increase their bank’s business
through flattering recommendations, it differs from LMW (2005, 2006) in several important
respects. First, whereas LMW study how relationships between a bank and a firm, potentially
established through generous analyst coverage, affect the bank’s future business with that firm,
we focus on how relationships between banks may influence future underwriter selection.
Moreover, we analyze the impact of ingratiating recommendations to past IPOs on underwriter
selection in deals with different firms. This approach eliminates any potential concerns that a
3
bank with a favorable opinion of a firm (as indicated by positive analyst recommendations)
serves as a natural candidate for the firm’s next equity offering syndicate.
Second, LWM consider only prestigious banks, whereas we include less prestigious
(typically smaller) banks as well and show that the rewards they obtain from flattering
recommendations to recent IPOs contrast with those obtained by their larger, more prestigious
counterparts.
Third, whereas LMW study SEOs and debt issues, we focus on IPOs. Because IPO
issuers are typically new to the equity issue market, they may be more likely to choose lead
underwriters on the basis of simple, observable attributes such as analyst coverage. In addition,
IPOs are a natural research context because even though they are not the main activity of most
banks, they are very lucrative. The typical 7% IPO fee is higher than the fees generated by
most follow-on offerings (Chen and Ritter 2000), and the discretion IPO underwriters enjoy in
terms of price setting and share allocation enables them to generate indirect profits beyond
those IPO fees (e.g., Nimalendran, Ritter, and Zhang 2006). Therefore, analysts’ conflicts of
interest may be most severe in the context of IPOs, which makes IPOs a natural field for
exploring the consequences of such conflicts.
This paper also contributes to the growing literature on syndicate formation. Pichler and
Wilhelm (2001) use moral hazard in team production to explain the formation of underwriting
syndicates. Fernando, Gatchev, and Spindt (2005) analyze issuer–underwriter matches and
show that the main matching determinant is the quality of each. Corwin and Schultz (2005)
analyze empirically the role of underwriting syndicates in equity offerings and the determinants
of a bank’s probability of obtaining lead or comanagement mandates. Finally, Ellis, Michaely,
and O’Hara (2005) explore competition among underwriters on the SEO market and show that
the underwriter’s prestige and hope for better analyst coverage determine a firm’s decision to
4
change underwriters between its IPO and SEO. Our investigation confirms that analyst
coverage is central to the construction of an IPO syndicate, at both the lead and the
comanagement levels.
The rest of this article proceeds as follows: Section 2 describes the data. Section 3
presents the empirical questions and discusses the methodology used to address these
questions. Section 4 presents the empirical results. In Section 5, we summarize our findings
and conclude.
2. Data
2.1. Data set construction
To explore the effect of analyst behavior on the probability that underwriters win lead
or comanagement mandates in future IPOs, we focus on the American IPO market during
January 1997-December 2002. Our start date reflects Corwin and Schultz’s (2005) claim that
information about IPO syndicates is incomplete in SDC prior to January 1997.3 Because we
want to study analyst behavior in the year preceding the IPOs contained in the sample, we use
SDC to build an initial sample of IPOs completed between January 1996 and December 2002.
In line with prior research, we eliminate unit offers, closed-end funds, REITs, ADRs, limited
partnerships, firms with IPO prices below $5, financial companies (SIC codes between 60 and
69), and firms not listed on the NYSE, AMEX, or Nasdaq. This first filter yields 2,112 IPOs.
We then merge the data set with I/B/E/S to obtain analyst recommendations for the
sample IPOs. Of the initial sample, 199 IPOs could not be identified in I/B/E/S on the basis of
their official ticker or CUSIP number, so we eliminate them. Thus, the final sample contains
3
LMW’s (2005) sample period ends in June 2002. They argue that regulatory changes may have changed the
practices of sell-side security analysts thereafter. Excluding IPOs completed after June 2002 from our sample
leaves our results virtually unaffected.
5
1,913 IPOs. However, the tests presented subsequently include only the 1,384 IPOs completed
after January 1997, those completed in 1996 being used only to build analyst recommendations
variables for that year. Data pertaining to individual recommendations issued by security
analysts within a year of the IPO, as well as the identity of the analysts who issued them, come
from the I/B/E/S analyst-by-analyst database.4
We collect information about the lead and comanagers of the sample IPOs from SDC,
which lists all underwriters involved and assigns one of six roles to them: book runner,
comanager, global coordinator, joint book runner, joint lead manager, or syndicate member.
We follow Corwin and Schultz’s (2005) methodology to group the first five types of
underwriters into two categories: lead and comanager. We ignore syndicate members because
their role in IPO syndicates and the fraction of the IPO fees they receive are less important than
those of lead and comanagers. Thus we identify 328 banks that participated in the sample IPOs
as lead or comanagers.
In the subsequent tests, we assume that each bank is a candidate for IPO mandates for
all IPOs completed between January 1997 and December 2002, provided that the bank operates
at the time of the IPO, i.e., that the IPO date is within the bank’s first and last appearance in the
SDC database. Many bank mergers occurred during the sample period. We collect the dates of
these mergers, the names of the targets and acquirers, and the name of the new entity following
the merger from Corwin and Schultz (2005). Following a merger, we use the new entity to
replace the two premerger banks in the data set.
To determine which banks issued recommendations, we merge the sample of banks
from SDC with the broker names that appear in the I/B/E/S database. We successfully merge
253 (77%) of the 328 bank names from SDC with I/B/E/S broker names. Furthermore, of the
4
We focus on conflicts of interests at the bank level and therefore consider only the identity of the bank. We use
the terms “broker,” “bank,” and “analyst” interchangeably.
6
5,876 lead and comanager/IPO pairs, we can match 5,262 (90%) with I/B/E/S broker names.
This suggests that only the smallest banks do not appear in the I/B/E/S data. We eliminate
these banks from all subsequent tests.
2.2. Summary statistics
In this section, we discuss the summary statistics of the sample. First, Table 1 provides
statistics about the IPO syndicates.
[Insert Table 1 about here.]
Panel A of Table 1 shows that the number of lead managers increases during the study,
from an average of 1.01 per IPO in 1996 to about 1.50 per IPO in 2001 and 2002. A similar
pattern appears for comanagers; the number of comanagers doubles between 1996 and 2002
(from 1.50 to 3.10 per IPO on average). This trend is consistent with the findings of previous
research.
Panel B of Table 1 presents statistics about the IPO participation of the 328 banks of
the sample. The average bank participated in 6.43 IPOs as the lead manager and 11.49 IPOs as
the comanager. However, the median numbers (1 IPO as lead manager, 2 IPOs as comanager)
suggest these distributions are highly skewed.
This is confirmed in Panel C where the sample is split by underwriter prestige. Since
Carter and Manaster’s (1990) work, the consensus in the literature has been that rankings based
on the bank’s position in tombstone announcements of recent IPOs offer good summary
statistics of an underwriter’s prestige. Therefore, we measure bank prestige using Carter-
7
Manaster ranks of banks during 1992-2000.5 The most prestigious banks (ranks 8 and 9) are far
more active than lower-ranking banks. On average, banks with a rank of 9 (8) participate in
38.69 (12.08) IPOs as lead managers and 33.31 (25.52) IPOs as comanagers between January
1996 and December 2002. Other banks are much less involved, in line with Loughran and
Ritter (2004), who show that the IPO market share of prestigious underwriters was greater than
60% in the 1990s and 80% during the “bubble” years of 1999 and 2000.
For the 1,913 IPOs in the sample, Panel D of Table 1 presents the pairings between lead
and comanagers according to underwriter prestige. For each pair (x,y), we count the number of
cases in which the IPO’s lead manager is of rank x and the comanager is of rank y (when an
IPO has several lead managers, we take the highest rank among all lead managers). Lead and
comanagers typically have the same rank or fall within one rank of each other. For example,
77% of the comanagers in IPOs managed by banks of rank 3 are of ranks 2-4. Similarly, 80%
of the comanagers in IPOs managed by banks of rank 8 are of ranks 7-9. Firms that are unable
to attract prestigious lead managers presumably cannot attract prestigious comanagers.
Moreover, low-prestige lead managers may be reluctant to hire high-prestige comanagers,
because doing so could undermine their position in the syndicate and their relationship with the
issuer. This concern is presumably less important for prestigious lead managers.
3. Empirical questions and methodology
We attempt to determine whether positive recommendations of previous IPOs help
banks earn lead or comanager status in future IPO syndicates.
3.1. Empirical questions
5
We obtain these rankings from Jay Ritter’s website (http://bear.cba.ufl.edu/ritter/rank.xls). Because 21 of our
328 banks do not appear in the rankings data, we eliminate them from our subsequent tests.
8
First, we analyze the consequences of currying favor between banks. Assuming (1) that
lead underwriters of previous IPOs value positive analyst coverage, and (2) that lead
underwriters influence the selection of comanagers, we investigate whether lead managers
reward banks that issued positive recommendations to their previous IPOs by offering them
comanagement mandates in their future IPOs. We assume that for each IPO in the sample, each
non-lead manager is a candidate for a comanagement mandate. We run a probit regression in
which the dependent dummy variable equals 1 if the candidate bank is chosen to comanage the
IPO and 0 otherwise. The explanatory variables are variables that have been shown to
influence a bank’s probability of winning comanagement mandates in equity issues, and
variables that measure whether candidate banks have curried favor with the lead manager of
the IPO under consideration by issuing positive recommendations to this lead manager’s
previous IPOs.
Previous studies show that existing relationships between banks affect the probability
of being chosen to comanage an IPO. Moreover, a bank’s incentive to curry favor with an
IPO’s lead manager may vary depending on the prior relationship between the banks, as well as
the lead manager’s reaction to previous ingratiating analyst recommendations. Therefore, we
separately analyze the consequences of currying favor for banks with existing relationships
with the lead underwriter of the IPO (i.e., banks that participated in IPOs managed by the lead
underwriter in the past), and for banks with no such relationships.
Second, we analyze whether currying favor with its own recent IPOs increases a bank’s
probability of managing the next IPO. Krigman, Shaw, and Womack (2001) and Ellis,
Michaely, and O’Hara (2005) analyze issuers’ decisions to switch underwriters between an
IPO and subsequent offerings and show that a major determinant of this decision is the quality
of analyst coverage provided by the bank. We therefore consider whether the quality of a
9
bank’s coverage of its previous IPOs induces future IPO issuers to choose it as the lead
underwriter. To answer this question, we assume that all banks in the sample are candidates for
lead management mandates for all IPOs in the sample. We again run probit regressions in
which the dependent dummy variable equals 1 if the bank is chosen to manage the IPO and 0
otherwise.
Many banks participate in the IPO market, but their prestige and market shares differ
significantly. If the most prestigious underwriters are those that best screen potential IPO
candidates and promote the firms they take public, high-quality issuers will want to signal their
quality to the market by choosing prestigious underwriters, and prestigious underwriters will
want to preserve their reputation by taking only high-quality firms public (Fernando, Gatchev,
and Spindt 2005). If the promotional value of analyst coverage is important to issuers and
prestigious banks are better at promoting companies because their analysts are more credible
(Stickel 1995), the effect of flattering analyst recommendations should be greater for
prestigious banks. However, these banks may be less inclined to curry favor so they may
preserve their reputation. Which of these effects dominates remains an open question that we
address empirically by investigating whether the answers to our research questions depend on
bank prestige.
Finally, generous analyst coverage of a recent IPO probably is more valuable to a firm
(and its underwriters) when it suffers disappointing post-IPO stock performance. Previous
studies refer to positive recommendations issued following poor stock performance as “booster
shots” (Michaely and Womack 1999; James and Karceski 2006). The incentives behind these
shots may differ from those that prompt positive recommendations after good stock
performance. In addition, the impact of booster shots on selection probabilities for future IPO
syndicates may differ from the impact of positive recommendations following good stock
10
performance. Therefore, we analyze the effect of positive recommendations and booster shots
separately.
3.2. Empirical design and description of explanatory variables
The first variables of interest are the currying favor variables. These variables are
counts of the number of positive recommendations and booster shots issued by a bank to recent
IPOs managed by the lead manager of the IPO (in comanagement tests),6 and to recent IPOs
managed by the bank itself (in lead-management tests).7 For our purposes, positive
recommendations are those classified by I/B/E/S as 1 (“Strong buy”) or 2 (“Buy”). Booster
shots refer to positive recommendations to firms whose stock performance ranks in the first
third of stock performance of all IPOs in the same month after the offering. We define stock
performance as the buy-and-hold abnormal return since the IPO. We adjust the returns of IPO
firms using the returns of 5x5 size/book-to-market portfolios of companies that have been
trading for at least five years. If positive recommendations help banks win IPO mandates,
negative recommendations (classified by I/B/E/S as 3 (“Hold”), 4 (“Underperform”), or 5
(“Sell”)) should have the opposite effect; that is, they should reduce a bank’s probability of
winning future IPO mandates. Therefore, we subtract the number of negative recommendations
from the number of positive recommendations and booster shots to construct the currying favor
variables. To minimize the impact of outliers on our results we take the natural logarithm of 1
6
When the IPO has more than one lead manager, we count all recommendations issued by the candidate bank to
all recent IPOs by any of the lead managers of this offering.
7
Both LMW (2005) and LMW (2006) use relative recommendations to determine whether an analyst curries
favor with a company. We assume that the most generous analysts are those that issue the greatest number of
positive recommendations. This is justified by the fact that unlike LMW (2005, 2006), we do not consider the
recommendations issued to a specific company, but rather all recommendations issued by a bank to IPOs done by
other banks or to its own IPOs. This approach is also advantageous in that we do not need to model endogenous
participation and to predict relative recommendations for firms that did not issue any recommendations. Our count
variables are simply equal to 0 in this situation.
11
plus the obtained variables to construct the variables Log(No. of positive recs+1) and Log(No.
of booster shots+1), which we use in subsequent tests.8
Because IPO syndicates typically are established a few months before the offering,
when a bank is a candidate for a comanagement (lead management) mandate, we count the
number of positive recommendations and booster shots this bank issued in the period one year
to one month prior to the focal IPO to other IPOs managed by the same underwriter (by the
candidate bank itself).9
[Insert Table 2 about here.]
Table 2 presents the statistics pertaining to the currying favor variables. First, we
consider banks that are not lead managers of the IPOs and therefore compete for
comanagement mandates. Panel A offers statistics regarding the number of positive
recommendations and booster shots issued by these banks to recent IPOs managed by the lead
manager of the IPO under consideration between one year and one month prior to the IPO. In
this context, affiliated (unaffiliated) recommendations are those issued to IPOs that the banks
did (did not) comanage. Candidate banks issued an average of 0.69 positive unaffiliated
recommendations, 0.18 unaffiliated booster shots, 2.03 positive affiliated recommendations,
and 0.64 positive affiliated booster shots to recent IPOs managed by the lead manager of the
focal IPO. However these numbers vary considerably according to the Carter-Manaster rank of
8
If the number of negative recommendations for a given bank-IPO pair is greater than the number of positive
recommendations or booster shots, Log(No. of positive recs+1) and Log(No. of booster shots+1) equal 0.
9
We obtain the same qualitative results if we count the number of positive recommendations and booster shots
issued between one year and three months prior to the IPO.
12
the lead and candidate banks, probably because prestigious banks tend to be bigger than nonprestigious banks, and therefore have more research capacities.10
In Table 2, Panel B, we consider all banks and assume they compete for lead
management mandates in all IPOs. In the period one year to one month before the IPO, these
banks issued on average 8 positive recommendations and 2.33 booster shots to their own recent
IPOs. Prestigious banks issue significantly more such recommendations than their less
prestigious counterparts simply because they manage more IPOs.
The currying favor variables are potentially endogenous in the comanager and lead
manager choice models, because they can be influenced by unobserved factors that may affect
the probability of being selected as a lead or comanager in the IPO. Therefore, they can be
correlated with the residuals of the models, which may lead to biased regression coefficients.
To solve this problem, we follow LMW (2005, 2006) and instrument the currying favor
variables with instruments that likely are correlated with these variables but otherwise are
exogenous to the empirical models.
We use five instrumental variables. The first four measure the pressure that a bank puts
on its analysts to issue biased recommendations. This pressure depends on how well the bank
has fared in the IPO market recently, as well as how well it expects to perform in the future,
given recent market conditions. The first two pressure instruments therefore measure the recent
performance of the bank on the IPO market: Bad lead year (candidate bank) equals 1 if the
number of IPOs managed by the bank in the year preceding the IPO is less than the average
since the bank first appeared in the SDC database, and Bad comanagement year (candidate
10
Alternatively, I/B/E/S might fail to report as many recommendations from low-prestige banks as it does from
high-prestige banks. If so, some results that use the whole sample of banks may be biased, but the analyses in
which we split the sample by bank prestige should not be affected.
13
bank) equals 1 if the number of IPOs comanaged by the bank in the year preceding the IPO is
less than the average since the bank first appeared in SDC.
The next two pressure instruments proxy for recent market conditions. Recent
underpricing is the average underpricing (equal to the difference between the IPO price and the
market price at the end of the first trading day, measured as a percentage of the IPO price) in
the year prior to the IPO. Recent S&P500 return measures the return of the S&P500 index in
the year preceding the IPO. Previous research (e.g., Ritter 1984; Lowry and Schwert 2003)
suggests that higher recent underpricing and market return indicate a better outlook of the IPO
market and less pressure on the bank’s analysts. The effect of these four pressure variables on
analyst recommendations is ambiguous, in that it depends on the result of a trade-off between
the bank’s desire to win future IPO mandates and its goal of preserving its reputation for
issuing unbiased analyst recommendations.
The last variable we use to instrument the currying favor variables is Lead bank’s
average IPO size, which equals the natural logarithm of the average market capitalization of
IPOs managed by the lead bank since 1993.11 Its effect on the incentive of a bank’s analysts to
issue generous recommendations to recent IPOs is less ambiguous than the effect of the four
previous instruments. The larger the size of prior IPOs managed by a bank, the higher are the
IPO revenues it generates for itself and its comanagers. Therefore, candidate banks should
issue more positive recommendations and booster shots to a lead manager’s recent IPOs when
this bank has managed larger IPOs in the past.
In subsequent tests, we run two-stage probit regressions. The first stage explains the
currying favor variables using the preceding instruments. In the second stage, the predicted
values from the first-stage regression replace the currying favor variables, and are used to
11
This variable is relevant only in the comanagement tests, in which we consider recommendations issued to
recent IPOs managed by the IPO’s lead manager.
14
explain the selection of a candidate bank to comanage (manage) the IPO. In both stages, we
also use control variables that may affect lead and comanagement selection.
The first set of control variables is related to the characteristics of the offering in which
the candidate bank tries to win a mandate and the IPO market. For proxies of IPO market
characteristics, we use log(No. of recent IPOs+1), the natural logarithm of the number of IPOs
in the year preceding the IPO, plus 1. Two variables describe IPO characteristics: Log(No. of
comanagers+1) is the natural logarithm of the number of comanagers of the IPO (assumed to
be exogenous and depend on factors such as the size of the offering), plus 1, and recent lead
management activity (lead bank) is the fraction of the previous year’s IPOs managed by the
lead manager of the focal offering. Candidate banks probably are more likely to be selected to
comanage IPOs that employ more comanagers, and the number of positive recommendations to
an underwriter’s recent IPOs should be higher when it has managed more IPOs recently, even
if the candidate bank is not trying to curry favor with this underwriter.
Second, we use bank prestige variables. Candidate bank’s rank ≥ 8 (lead bank’s rank ≥
8) equals 1 if the Carter-Manaster rank of the candidate bank (lead manager) during 1992-2000
is 8 or 9.12 Candidate bank’s rank ≥ 8 x lead bank’s rank ≥ 8 is the interaction of these two
prestige variables.
Third, we consider variables that measure the aggressiveness of the candidate bank in
terms of analyst coverage in general (as opposed to currying favor variables, which measure
aggressiveness specific to certain IPOs). Log(recent recs+1) is the natural logarithm of the
number of recommendations issued by the candidate bank in the year preceding the IPO to
listed companies that are not recent IPOs (i.e., listed for more than a year at the time of the
12
This variable is admittedly endogenous. Our subsequent results are unaffected if we use rankings during 19851991 instead, but using rankings over this period reduces the number of observations because some banks in our
sample did not operate then.
15
recommendation), plus 1. It measures the activity of the candidate bank’s analysts. In addition,
recent positive recs to IPOs measures how aggressive the candidate bank is regarding recent
IPOs in terms of analyst coverage, and equals the number of positive recommendations issued
by the candidate bank to recent IPOs (but not to IPOs managed by the lead manager) in the
year prior to the IPO, divided by the number of IPOs in that period.
Fourth, we measure the candidate bank’s recent activity on the IPO market and the
relationship between the candidate bank and the lead manager of the IPO (in comanagement
tests only). Candidate bank’s network is the number of banks with which the candidate bank
participated in at least one IPO syndicate between October 1993 and January 1 of the IPO year,
divided by the number of underwriters that operated on January 1 of the IPO year. The larger
the network of the candidate bank, the less it needs to curry favor with other banks to obtain
comanagement mandates. This variable may also be another proxy for the prestige of the bank.
Recent comanagement activity (candidate bank) is the fraction of last year’s IPOs comanaged
by the candidate bank, and recent lead management activity (candidate bank) is the fraction of
last year’s IPOs managed by the candidate bank. Participation in recent IPOs managed by the
lead bank is the fraction of IPOs managed by the lead bank in which the candidate bank was a
comanager in the year preceding the IPO. Finally, Log(reciprocal participation in recent
IPOs+1) measures the reciprocal relationship as the logarithm of the number of IPOs managed
by the candidate bank and comanaged by the lead bank in the year preceding the IPO, plus 1.
Corwin and Schultz (2005) show these last four variables are strong determinants of the
probability of obtaining lead and comanagement mandates. We also include IPO year
dummies. The description of all the variables appears in the Appendix.
4. Results
16
Does issuing favorable analyst recommendations and booster shots to an underwriter’s
recent IPOs help a bank comanage the next IPO managed by that underwriter? We address this
question separately for banks with and without prior relationships with the lead underwriter. A
candidate bank is assumed to have a recent relationship with the lead underwriter if it has
comanaged at least one IPO with the same lead underwriter in the year preceding the IPO.
4.1. Currying favor to win future comanagement mandates: Banks with relationships with the
lead manager
Banks with recent relationships with the IPO’s lead manager can curry favor with the
lead bank in two different ways. First, they can issue positive recommendations and booster
shots to IPOs in which they served as comanagers. We call such recommendations affiliated
recommendations. Loughran and Ritter (2004) claim that one of the main roles of comanagers
in IPOs is to provide (presumably favorable) analyst coverage to the issuer. Therefore, a
candidate bank with recent relationships with the IPO’s lead manager should issue positive
affiliated recommendations, and these recommendations should influence the probability the
bank will be selected to comanage the lead manager’s next IPO. Second, candidate banks with
recent relationships with an IPO’s lead manager may be rewarded for issuing ingratiating
recommendations to IPOs in which they did not participate, or unaffiliated recommendations.
The literature offers no guidance about what to expect in terms of the number of these
recommendations and their impact on the probability of obtaining future comanagement
mandates.
For each IPO, we suppose that all banks with recent relationships with the IPO’s lead
manager (or with any of the IPO’s lead managers if there are several lead managers) that
themselves are not lead managers of the IPO are candidates for a comanagement mandate. We
17
use the two-stage methodology described in Section 3.2 to determine whether lead managers
reward banks that issue more positive recommendations to their recent IPOs by offering them
comanagement mandates. In the first stage, we instrument the number of affiliated and
unaffiliated positive recommendations issued by the candidate bank to IPOs managed by the
lead bank in the year preceding the IPO. In the second stage, we replace these variables by
their predicted values from the first stage and explain the selection of the candidate bank to
comanage the offering by these instrumented variables and the control variables described in
Section 3.2.
[Insert Table 3 about here.]
In the first column of Table 3, Panel A, we observe that candidate banks issue less
positive unaffiliated recommendations to IPOs managed by the IPO’s lead manager when they
suffer a bad lead or comanagement year, which suggests that reputational concerns are more
important than bank pressure in the decision to curry favor with the lead bank following a
disappointing performance. In contrast, the negative coefficient of the recent underpricing
variable suggests that banks become more aggressive when the outlook of the IPO market is
poor. As expected, analysts issue more recommendations to IPOs managed by the lead
underwriter of the IPO when the total number of recent IPOs is greater and when the lead
manager managed more of these recent IPOs. In addition, prestigious banks tend to issue less
positive unaffiliated recommendations, but the coefficient of the interaction of the two bank
prestige variables indicates that prestigious candidate banks issue more positive unaffiliated
recommendations to recent IPOs managed by prestigious underwriters. Moreover, aggressive
banks, in terms of total number of recommendations and positive recommendations to recent
IPOs, are associated with more unaffiliated recommendations to recent IPOs managed by the
18
IPO’s lead manager. Finally, candidate banks are more aggressive in flattering recent IPOs of
the lead manager when their recent comanagement activity is higher, and less aggressive when
their recent lead management activity is higher and when they managed more IPOs in which
the lead bank served as a comanager recently. These last two results suggest that less flattery is
necessary when the candidate bank’s status or the relationship between the two banks is
stronger.
In the second column of Table 3, Panel A, we repeat the same analysis for positive
affiliated recommendations. The results are similar to the results for unaffiliated
recommendations, with a few notable differences: First, when the bank is less aggressive in
terms of analyst coverage, it issues more positive affiliated recommendations to recent IPOs of
the lead manager, possibly because banks that comanaged many of the lead manager’s recent
IPOs, as shown by the large number of affiliated positive recommendations they issue, choose
to become less aggressive overall to improve their reputation. Second, a larger candidate
bank’s network and more participation in the lead bank’s recent IPOs by the candidate bank
both are associated with more positive affiliated recommendations.
The last column of Table 3, Panel A, provides the results of the second-stage probit
regression. The dependent dummy variable equals 1 if the candidate bank becomes a
comanager of the IPO. Only the coefficient for the number of unaffiliated positive
recommendations is significantly positive (at the 5% level). The other results in the third
column are as expected: Candidate banks are more likely to comanage an IPO when there are
more comanagers and when the lead bank has participated in more recent IPOs managed by the
candidate bank. Unlike Corwin and Schultz (2005), we do not find that participating in more
recent IPOs managed by the lead bank significantly increases the candidate bank’s chances of
comanaging the IPO, perhaps because all the candidate banks we consider have existing
19
relationships with the lead underwriter. Therefore, having a stronger relationship with the lead
managers does not appear to benefit these candidate banks. Interestingly, the candidate bank is
more likely to be selected as a comanager when it has comanaged more recent IPOs and less
likely when it has managed more IPOs. Some banks therefore appear to specialize in
comanagement, whereas others specialize in lead management. Neither of the analyst
aggressiveness variables has a significant impact on the probability of comanaging the IPO,
which indicates that being aggressive in and of itself does not increase a bank’s chances of
comanaging an IPO.
In Table 3, Panel B, we repeat these tests but consider booster shots, which should be
more valuable to the issuer and therefore to the lead bank but also are more costly in terms of
reputation than positive recommendations. The results of first-stage regressions are very
similar to those of Table 3, Panel A. In the second-stage probit regression (column 3), the
number of affiliated booster shots has a positive impact (significant at the 5% level) on the
probability of being chosen to comanage the IPO. The impacts of other variables are very
similar to what they are in Table 3, Panel A. Note that the Rivers and Vuong (1988) test rejects
the null hypothesis that the instrumented variables are exogenous at the 5% level for positive
recommendations (Table 3, Panel A) and at the 1% level for booster shots (Table 3, Panel B),13
which indicates that the two-stage methodology we use is appropriate.
Overall, these results provide weak support to the currying favor hypothesis. Next, we
explore whether the results vary with bank prestige. Positive recommendations by more
prestigious banks presumably are more credible and valuable. However, prestigious banks also
have more reputation capital and issuing generous recommendations could be more detrimental
to their reputation. Our previous results suggest, ceteris paribus, that prestigious banks issue
13
For a discussion of this test, see Wooldridge (2002), p.474.
20
more favorable recommendations to recent IPOs by prestigious lead managers. In Table 4, we
repeat our analysis for different levels of bank prestige, and report the results of the secondstage regressions.
[Insert Table 4 about here.]
Positive recommendations affect a candidate bank’s probability of comanaging an IPO
only when both the candidate bank and the lead manager are prestigious. In addition, only
affiliated recommendations and booster shots matter. In the first column of Table 4, Panel A, in
which we consider prestigious candidate and lead banks, the coefficient of the number of
positive affiliated recommendations is positive and significant at the 1% level. Interestingly,
the coefficient of recent participation by the candidate bank in IPOs managed by the lead bank
is significantly negative at the 10% level. In unreported probit regressions in which we exclude
currying favor variables, this variable becomes significantly positive at the 0.1% level. This
indicates that recent relationships with an IPO’s lead manager do not help the candidate bank
obtain a comanagement mandate unless the candidate bank has issued positive
recommendations to the IPOs it has comanaged. This finding confirms Loughran and Ritter’s
(2004) conjecture that “comanagers are included in a syndicate almost exclusively to provide
research coverage […].” Other coefficients are consistent with those reported in Table 3.
In the second column of Table 4, Panel A, the lead bank is prestigious but the candidate
bank is not. In this regression, only the number of comanagers of the IPO and recent
comanagement activity by the candidate bank have a significant impact on the probability of
candidate bank selection. The recommendations of less prestigious banks are less influential,
which may explain why they are not rewarded for supporting past IPOs even though they were
comanagers in these IPOs.
21
Finally, in the last column of Table 4, Panel A, we consider IPOs in which the lead
bank is not prestigious. For these IPOs, currying favor does not help to be chosen as a
comanager. None of the explanatory variables is significant at conventional levels in this
regression, possibly because most banks are not candidates to comanage IPOs that are not
managed by prestigious banks.
The results we report in Panel B of Table 4, which refers to booster shots instead of
positive recommendations, confirm those of Panel A. When the two banks are prestigious (first
column), the coefficient of the number of affiliated booster shots is positive and significant at
the 0.1% level. The other coefficients also are similar to those reported in Panel A.
4.2. Currying favor to win future comanagement mandates: Banks with no relationships with
the lead manager
In Tables 3 and 4, we address candidate banks that had existing relationships with the
IPO’s lead manager through recent participation in IPOs managed by this bank. In this section,
we repeat the analysis for candidate banks that did not participate in recent IPOs managed by
the lead underwriter of the offering, and for which all recommendations are therefore
unaffiliated.
[Insert Table 5 about here.]
Table 5 presents the results of the analysis. Columns 1 and 3 report the results of firststage regressions in which the dependent variables are the numbers of positive
recommendations (column 1) and booster shots (column 3) issued by the candidate bank to the
lead bank’s recent IPOs. In line with our previous findings, candidate banks issue more
positive recommendations and booster shots to the lead bank’s recent IPOs when both banks
22
are prestigious, when the number of recent IPOs is greater and when the lead manager has
managed more of these recent IPOs. They issue fewer such recommendations and booster shots
when their recent lead management activity has been larger and when the lead bank has
participated in more IPOs managed by the candidate bank in the recent past. In contrast with
Table 3 however, candidate banks are more aggressive when they suffered a bad
comanagement year, which indicates that when the candidate bank has no recent relationship
with the IPO’s lead manager, its incentive to issue more (potentially biased) positive
recommendations outweighs the reputational costs associated with this behavior.
Columns 2 and 4 of Table 5 report second-stage estimates in which the dependent
dummy variable equals 1 if the bank comanages the IPO. The main result is that positive
recommendations do not affect the probability of comanaging the IPO in a statistically
significant way. Most of the other coefficients are consistent with the results in Table 3 and
previous literature. Candidate banks are more likely to comanage the IPO when they are
prestigious and the lead bank is also prestigious, as well as when they comanaged more IPOs
recently or belong to a larger network. They are less likely to comanage the IPO when they
have managed more IPOs recently.
As in our previous tests, we repeat the analysis depicted in Table 5 for various levels of
bank prestige and present the results of the second-stage regressions in Table 6.
[Insert Table 6 about here.]
Consistent with our previous findings, the results reported in Panel A of Table 6 show
that banks that issue more positive recommendations to the lead manager’s recent IPOs are
more likely to comanage its next IPO only when both banks are prestigious. The coefficient of
the number of positive recommendations in the first column of Table 6, Panel A, is
23
significantly positive at the 0.1% level. In contrast, when the lead manager of the IPO is a
prestigious bank and the candidate bank is not, positive recommendations to recent IPOs
managed by the lead manager decrease a bank’s probability of comanaging the IPO; the
coefficient of the number of positive recommendations is significantly negative at the 1% level
in the second column. This finding confirms that lead managers perceive positive
recommendations as valuable only when prestigious banks issue them. As in Table 4, when the
IPO’s lead manager is not prestigious (column 3), analyst recommendations from the candidate
banks do not play any significant role in the selection of comanagers.
In Panel B of Table 6, we repeat the same tests with booster shots instead of positive
recommendations and emerge with results similar to those obtained in Panel A. Booster shots
to recent IPOs managed by prestigious lead managers significantly increase (decrease) the
chances for a prestigious (non-prestigious) candidate bank to comanage the lead manager’s
next IPO.
These results are in line with LMW (2005), who find that currying favor with a firm
increases a bank’s chances of comanaging the firm’s next equity issue. Our results show that
currying favor with recent IPO lead managers can have the same effect, provided that the
candidate bank is prestigious.14 In contrast, non-prestigious banks are not chosen to comanage
IPOs on the basis of analyst coverage. These banks, whose recommendations are presumably
less influential than those issued by more reputable institutions, harm their reputation when
they curry favor and fail to generate sufficient benefits for the banks they flatter.
4.3. Currying favor with own recent IPOs to win lead management mandates
14
LMW (2005) do not document any effect for non-prestigious banks, which are not in their sample.
24
Thus far, we have focused on banks currying favor with other banks to win
comanagement mandates. In this section, we examine whether banks that issue generous
analyst recommendations to their own recent IPOs are more likely to manage future IPOs.
Thus, we shift our focus from how lead managers perceive positive recommendations issued
by other banks to their recent IPOs to how future issuers perceive positive recommendations
that lead managers provide to their own recent IPOs. If issuers value analyst coverage, as
previous literature suggests they do, they might take analyst coverage into consideration when
choosing their lead manager.
In this section, we consider only banks that were lead underwriters for at least one IPO
in the previous year. We repeat the two-stage methodology used in the previous tests. For each
IPO, the variables of interest include the number of positive recommendations and booster
shots that lead managers issued to their own IPOs during the period one year to one month
prior to the focal IPO in the first stage, and a dummy variable equal to 1 if the bank manages
the IPO in the second stage. As in previous tests, we first consider all banks. The results appear
in Table 7.
[Insert Table 7 about here.]
Columns 1 and 3 of Table 7 report the results of the regressions of the currying favor
variables on the set of instruments and explanatory variables. Lead managers issue fewer
positive recommendations and booster shots to their own IPOs when they have managed fewer
IPOs in the year preceding the IPO than they did in previous years. The significantly negative
coefficients of the recent underpricing and recent S&P500 return variables indicate that they
are more aggressive when the outlook of the IPO market is poor. Not surprisingly, they issue
more positive recommendations to their own IPOs when there are more IPOs and when they
25
have captured a larger fraction of the IPO market in the previous year. More prestigious banks
and banks with larger networks also issue more positive recommendations and booster shots to
their recent IPOs.
Columns 2 and 4 of Table 7 report the results of the second-stage regressions. Banks
that issue more positive recommendations or booster shots to their recent IPOs are less likely to
manage the next IPO. (The coefficients of the number of positive recommendations and
booster shots are statistically negative at the 5% level.) The following variables have a
statistically significant impact on the probability of managing the IPO: bank prestige, size of
the candidate bank’s network, and recent lead management activity of the candidate bank.
[Insert Table 8 about here.]
Next we consider high- and low-prestige banks separately. Second-stage regression
results appear in Table 8. Again, these results vary with bank prestige. Prestigious banks do not
increase their chances of managing future IPOs when they issue more positive
recommendations to their recent IPOs; if anything, this decreases their chances of winning lead
management mandates (the coefficients of the number of positive recommendations and
booster shots are significantly negative at the 10% and 1% levels in columns 1 and 3,
respectively). In contrast, the more positive recommendations and booster shots low-prestige
banks provide to their recent IPOs, the more likely they are to manage the next IPO (the
coefficients on the number of positive recommendations and booster shots are significantly
positive at the 0.1% and 1% levels in columns 2 and 4, respectively). We also note that though
the coefficients of recent lead management activity of the bank are significantly positive when
the bank is prestigious, they are significantly negative when the bank is not. In unreported
probit tests, we repeat these tests for low-prestige banks and exclude the currying favor
26
variables and find that the coefficient of recent lead management activity of the bank becomes
statistically positive at the 1% level in both tests. Therefore, more lead management activity
does not increase a low-prestige bank’s chances of managing future deals, unless it provides
support to the firms it took to the market in the past through generous analyst coverage.
These results contrast sharply with those we reported previously. They indicate that
different mechanisms are at play when issuers choose lead managers than when lead managers
choose comanagers. The previous literature shows that visibility in the aftermarket is a primary
concern of issuers. The best way to attain aftermarket visibility is to use a prestigious
underwriter to take the firm public. But as Fernando, Gatchev, and Spindt (2005) argue, only
high-quality issuers can use prestigious underwriters, whereas others must choose a lead
manager from a large pool of less prestigious banks. These banks advertize their services by
offering aftermarket visibility to the firms they take public through generous analyst coverage.
These results are also worth comparing with those of LMW (2006), who find that lead
managers of equity offerings are not more likely to retain lead manager positions in future
equity deals of the same issuers when they issue positive recommendations to these issuers.
But these authors focus on large banks whose prestige guarantees visibility to the issuer. Our
results suggest that providing generous analyst coverage to their recent IPOs helps only less
prestigious banks attract future issuers.
5. Summary and conclusion
This study explores the consequences of currying favor behavior by security analysts in
the IPO market. Security analysts can curry favor with recent IPOs managed by other
underwriters and with their own recent IPOs by issuing positive recommendations or booster
shots to these IPOs in order to win lead or comanagement mandates.
27
Whether positive recommendations and booster shots increase a bank’s chances of
being selected for a subsequent IPO by the same lead manager depends on recent relationships
between the two banks and their prestige. Lead managers reward banks that issue generous
analyst recommendations to their recent IPOs only when both the lead manager and the
candidate bank are prestigious. That is, analyst recommendations matter only when they are
issued by prestigious banks, which are both more visible and more credible and therefore more
likely to influence investors’ decisions. When the banks have engaged in recent relationships,
the candidate bank receives a reward for issuing generous analyst coverage to the IPOs that it
comanaged but not to other IPOs. Banks that have no recent relationships with the IPO’s lead
underwriter also increase their chances of comanaging this bank’s next IPO when they issue
generous analyst recommendations to recent IPOs managed by the same bank, provided that
both the lead manager and the candidate bank are prestigious.
We also explore the benefits accruing to a bank that issues generous analyst
recommendations to its own IPOs. This behavior increases only low-prestige banks’ chances of
managing future IPOs. Therefore, issuers appear to use prestigious lead underwriters when they
can; otherwise, they select less prestigious underwriters on the basis of the analyst coverage
they provided to their recent IPOs.
These results extend our knowledge of how IPO syndicates form and confirm that
analyst coverage serves as a key determinant of which banks appear within a syndicate. These
results also suggest that recent relationships between banks, which influence syndicate
formation, go beyond simple participation in each other’s recent syndicates. They also show
that banks’ incentives differ depending on their position in the investment banking hierarchy,
and that the most prestigious banks are not always better off protecting their reputation by
issuing unbiased recommendations.
28
Finally, we show that conflicts of interest may be severe for security analysts, even
when their bank is not involved in an offering. This confirms Bradley, Jordan and Ritter’s
(2005) conjecture that “the conflict of interest faced by an affiliated analyst may actually be
less severe than that faced by an unaffiliated analyst, because the affiliated analyst has the
incumbent’s advantage in competing for future underwriting mandates.” Whether investors can
identify currying favor behaviors by unaffiliated analysts remains an open question.
29
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Appendix: List of Variables
Analyst behavior variables
-
-
-
-
Log(No. of positive unaffiliated recs+1): natural logarithm of the number of positive
unaffiliated recommendations minus the number of negative unaffiliated recommendations
issued by the candidate bank to IPOs managed by the lead bank between one year and one
month prior to the IPO, plus 1.
Log(No. of positive affiliated recs+1): natural logarithm of the number of positive affiliated
recommendations minus the number of negative affiliated recommendations issued by the
candidate bank to IPOs managed by the lead bank between one year and one month prior to
the IPO, plus 1.
Log(No. of unaffiliated booster shots+1): natural logarithm of the number of unaffiliated
booster shots minus the number of negative unaffiliated recommendations issued by the
candidate bank to IPOs managed by the lead bank between one year and one month prior to
the IPO, plus 1.
Log(No. of affiliated booster shots +1): natural logarithm of the number of affiliated
booster shots minus the number of negative affiliated recommendations issued by the
candidate bank to IPOs managed by the lead bank between one year and one month prior to
the IPO, plus 1.
Positive recommendations are recommendations classified by I/B/E/S as “strong buy” and “buy.”
Negative recommendations are recommendations classified by I/B/E/S as “hold,” “underperform,”
or “sell.” Booster shots are positive recommendations to firms whose stock performance is in the
first third of stock performance of all IPOs in the same month after the offering. Stock performance
is the buy-and-hold abnormal return since the IPO. Returns of IPO firms are adjusted using the
returns of 5x5 size/book-to-market portfolios of companies that have been trading for at least five
years. Affiliated (unaffiliated) recommendations are recommendations issued to IPOs that the
candidate bank did (did not) comanage.
Instruments for the analyst behavior variables
-
Bad lead year (candidate bank): dummy variable equal to 1 if the candidate bank obtained
fewer lead management mandates in the previous year than it has obtained on average since
it appeared in SDC.
Bad comanagement year (candidate bank): dummy variable equal to 1 if the candidate
bank obtained fewer comanagement mandates in the previous year than it has obtained on
average since it appeared in SDC.
Recent underpricing: average IPO underpricing in the year before the IPO.
Recent S&P500 return: return of the S&P500 index in the year before the IPO.
Lead bank’s average IPO size: natural logarithm of the average market capitalization of
IPOs managed by the lead bank since 1993.
IPO and IPO market characteristics
-
Log(No. of recent IPOs+1): natural logarithm of the number of IPOs in the previous year,
plus 1.
Log(No. of comanagers+1): natural logarithm of the number of comanagers of the IPO,
plus 1.
33
-
Recent lead management activity (lead bank): number of IPOs managed by the lead bank in
the previous year divided by the total number of IPOs in the previous year.
Bank prestige
-
Candidate bank’s rank ≥ 8: dummy variable equal to 1 if the Carter-Manaster rank of the
candidate bank in the 1992-2000 period is 8 or 9.
Lead bank’s rank ≥ 8: dummy variable equal to 1 if the Carter-Manaster rank of the IPO’s
lead manager in the 1992-2000 period is 8 or 9.
Candidate bank’s rank ≥ 8 x lead bank’s rank ≥ 8: interaction of candidate bank’s rank ≥ 8
and lead bank’s rank ≥ 8.
Candidate bank’s aggressiveness
-
Recent positive recs to IPOs: number of positive recommendations issued by the candidate
bank to recent IPOs in the previous year divided by the number of IPOs in the previous
year.
Log(recent recs+1): natural logarithm of the number of recommendations issued by the
candidate bank in the previous year to companies listed for more than a year at the time of
the recommendation, plus 1.
Candidate bank’s recent activity and relationships with the lead manager of the IPO
-
-
Candidate bank’s network: number of banks that operate as of January 1 of the IPO year
and with which the candidate bank participated in at least one IPO syndicate between
October 1993 and January 1 of the IPO year, divided by the number of underwriters that
operated on January 1 of the IPO year.
Recent comanagement activity (candidate bank): number of IPOs the candidate bank
comanaged in the previous year divided by the total number of IPOs in the previous year.
Recent lead management activity (candidate bank): number of IPOs the candidate bank
managed in the previous year divided by the total number of IPOs in the previous year.
Participation in recent IPOs managed by the lead bank: number of IPOs managed by the
lead bank and comanaged by the candidate bank in the previous year divided by the
number of IPOs managed by the lead bank in the previous year.
Log(reciprocal participation in recent IPOs+1): natural logarithm of the number of IPOs
managed by the candidate bank and comanaged by the lead bank in the previous year, plus
1.
34
Table 1
IPO syndicates
The sample consists of 1,913 IPOs completed between January 1996 and December 2002.
Panel A presents statistics on the number of IPOs and characteristics of IPO syndicates by year. For each of the 328 sample
banks, Panel B reports statistics on participation in IPO syndicates as lead manager and comanager over the period. Panel C
reports the same statistics by underwriter prestige (using Carter-Manaster ranks over the 1992-2000 period). IQR is the
interquartile range. For each pair (x,y), Panel D counts the number of cases in which the lead manager is of rank x and the
comanager is of rank y in all IPOs of the sample.
Panel A: Number of IPOs, lead managers, and comanagers per year
Number of IPOs
Number of lead
managers
Number of
comanagers
1996
529
1.01
1
1.50
1
Mean
Median
Mean
Median
1997
356
1.02
1
1.55
1
Year
1999
399
1.12
1
2.40
2
1998
204
1.05
1
1.98
2
Panel B: Underwriter participation in IPO syndicates
Mean
Number of IPOs as lead manager between January
Median
1996 and December 2002
IQR
Mean
Number of IPOs as comanager between January
Median
1996 and December 2002
IQR
2000
307
1.21
1
2.43
2
2001
66
1.53
2
2.32
2
2002
52
1.48
1
3.10
2.5
6.43
1
3
11.49
2
10
Panel C: Underwriter participation in IPO syndicates by underwriter prestige
Underwriter’s rank
1
2
3
4
5
6
(low rank)
Number of underwriters
5
16
36
34
50
32
No. of IPOs as lead
Mean
1.60
1.87
1.42
0.47
1.9
2.25
manager between Jan.
Median
2
1
1
0
1
0.5
1996 and Dec. 2002
IQR
1
3
1
1
2
3
No. of IPOs as comanager Mean
4
1.69
1.25
1.79
5.62
8
between Jan. 1996 and
Median
1
0
1
1
3
3
Dec. 2002
IQR
1
2.5
1
1
6
4.5
7
8
58
3.88
1
5
15.79
7.5
20
50
12.06
2
16
25.52
8.5
44
9
(high rank)
26
38.69
9.5
62
33.31
14.5
61
Panel D: Lead manager’s prestige/comanager’s prestige pairs
Lead manager’s rank
Comanager’s
rank
1
(low rank)
2
3
4
5
6
7
8
9
(high rank)
1
(low rank)
2
3
4
5
6
7
8
9
(high rank)
0
2
0
0
0
0
2
7
9
0
0
1
2
3
0
0
2
8
2
2
0
0
0
2
18
7
8
0
0
0
2
3
1
5
0
2
2
3
5
15
26
13
22
8
0
6
3
15
11
36
14
2
1
8
47
47
96
80
11
1
10
86
84
309
365
5
3
14
90
98
451
807
0
0
0
1
0
7
16
127
715
35
Table 2
Currying favor variables
For each bank-IPO pair, this table presents statistics on positive recommendations and booster shots issued to the IPO’s
lead manager (Panel A) or the bank’s own IPOs (Panel B) in the year leading up to the focal IPO. Banks whose
recommendations do not appear in I/B/E/S, that do not operate at the time of the IPO, that did not participate in any IPO
syndicate as lead or comanager between 1993 and the IPO date, or that issued fewer than 10 recommendations to any listed
firms in the year preceding the IPO are excluded from the list of banks. Those IPOs for which none of the lead managers
engaged in any other IPO as a lead manager in the year preceding the IPO are excluded from the list of IPOs.
Positive recommendations to an IPO are recommendations classified by I/B/E/S as 1 (“Strong buy”) or 2 (“Buy”) and
issued within one year of the IPO. Booster shots are positive recommendations to firms whose stock performance is in the
first third of stock performance of all IPOs in the same month after the offering. Stock performance is the buy-and-hold
abnormal return since the IPO. Returns of IPO firms are adjusted using the returns of 5x5 size/book-to-market portfolios of
companies that have been trading for at least five years.
Panel A presents statistics on the number of positive recommendations and booster shots per candidate bank-IPO pair to
previous IPOs with the same lead manager between one year and one month prior to the IPO date. Affiliated (unaffiliated)
recommendations are recommendations issued to IPOs that the candidate bank did (did not) comanage.
Panel B presents statistics on the number of positive recommendations and booster shots per candidate bank-IPO pair to
previous IPOs in which the candidate bank was the lead manager between one year and one month prior to the IPO date.
Panel A: Variables used in comanagement tests
All banks
No. of positive unaffiliated
recs to lead manager’s
recent IPOs
No. of unaffiliated booster
shots to lead manager’s
recent IPOs
No. of positive affiliated
recs to lead manager’s
recent IPOs
No. of affiliated booster
shots to lead manager’s
recent IPOs
Mean
Median
Std deviation
Mean
Median
Std deviation
Mean
Median
Std deviation
Mean
Median
Std deviation
0.69
0
1.70
0.18
0
0.64
2.03
1
2.69
0.64
0
1.11
Candidate bank’s
rank ≥ 8 and lead
bank’s rank ≥ 8
1.19
0
2.23
0.31
0
0.86
2.64
2
3.11
0.85
0
1.29
Candidate bank’s
rank < 8 and lead
bank’s rank ≥ 8
0.64
0
1.58
0.18
0
0.60
1.27
1
1.70
0.36
0
0.73
Lead bank’s
rank < 8
0.05
0
0.30
0.01
0
0.15
0.54
0
0.85
0.21
0
0.48
Panel B: Variables used in lead management tests
No. of positive recs to own
recent IPOs
No. of booster shots to own
recent IPOs
Mean
Median
Std deviation
Mean
Median
Std deviation
All banks
8.00
3
11.16
2.33
1
3.10
36
Bank’s rank ≥ 8
14.74
10
13.43
4.05
3
3.56
Bank’s rank < 8
2.44
2
3.20
0.90
0
1.60
Table 3
Does issuing positive recommendations to recent IPOs managed by an underwriter increase the probability of
comanaging its next IPO? Banks with recent relationships with the underwriter
For each IPO with at least one comanager between January 1997 and December 2002, this table considers all pairs formed
by the lead managers of the IPO and all other banks. For each IPO, all the banks that are not lead managers and that had
recent relationships with the lead manager (i.e., that comanaged at least one IPO managed by the lead bank in the year
preceding the IPO) are assumed to be candidates for a comanagement mandate. These banks are referred to as candidate
banks. Banks whose recommendations do not appear in I/B/E/S, that do not operate at the time of the IPO, that did not
participate in any IPO syndicate as lead or comanager between 1993 and the IPO date, that issued fewer than 10
recommendations to any listed firms in the year preceding the IPO are excluded from the list of candidates banks. Those
IPOs for which none of the lead managers engaged in any other IPO as lead manager in the year preceding the IPO are
excluded from the list of IPOs. Positive recommendations to an IPO are recommendations classified by I/B/E/S as 1
(“Strong buy”) or 2 (“Buy”) and issued within a year of the IPO. Negative recommendations are recommendations
classified by I/B/E/S as 3 (“Hold”), 4 (“Underperform”), or 5 (“Sell”). Affiliated (unaffiliated) recommendations are
recommendations issued to IPOs that the candidate bank did (did not) comanage.
Panel A presents instrumental probit regressions. In the first stage, ordinary least square (OLS) regressions explain the
natural logarithm of number of positive unaffiliated recommendations minus number of negative unaffiliated
recommendations issued between one year and one month prior to the IPO by the candidate bank to IPOs managed by the
lead bank, plus 1 (column 1), and number of positive affiliated recommendations minus number of negative affiliated
recommendations, plus 1 (column 2). The second-stage regression is a probit regression (column 3). The dependent
variable is comanagement selection, equal to 1 if the candidate bank comanages the IPO and 0 otherwise. The explanatory
variables are the instrumented variables predicted from the first-stage regressions and other explanatory variables of the
first stage. The independent variables used in the regressions are described in the Appendix.
Panel B presents the same instrumental probit regressions, except for the analyst behavior variables: the number of
unaffiliated booster shots and affiliated booster shots replace the number of unaffiliated positive recommendations and
affiliated positive recommendations, respectively. Booster shots are positive recommendations to firms whose stock
performance is in the first third of stock performance of all IPOs in the same month after the offering. Stock performance is
the Buy-and-Hold Abnormal Return since the IPO. Returns of IPO firms are adjusted using the returns of 5x5 size/book-tomarket portfolios of companies that have been trading for at least five years.
IPO year dummies are included in all regressions, but their coefficients are not reported. z-statistics are in parenthesis.
†
Significance at the 10% level. *Significance at the 5% level. **Significance at the 1% level. ***Significance at the 0.1%
level.
37
Panel A: Instrumental variable probit regressions – Positive recommendations
First Stage
First Stage
Dependent variable:
Dependent variable:
Explanatory variables
Log(No. of positive
Log(No. of positive
unaffiliated recs+1)
affiliated recs+1)
Predicted Log(No. of positive
--unaffiliated recs+1)
Predicted Log(No. of positive affiliated
--recs+1)
Instruments
-0.093***
-0.030*
Bad lead year (candidate bank)
(-7.33)
(-2.32)
Bad comanagement year (candidate
-0.066***
-0.128***
bank)
(-4.10)
(-7.76)
-0.918***
-0.698***
Recent underpricing
(-6.22)
(-4.65)
0.071
-0.212*
Recent S&P500 return
(0.85)
(-2.51)
0.207***
0.016
Lead bank’s average IPO size
(15.63)
(1.22)
IPO and IPO market characteristics
0.591***
0.555***
Log(No. of recent IPOs+1)
(13.86)
(12.80)
0.057***
0.071***
Log(No. of comanagers+1)
(5.00)
(6.08)
Recent lead management activity (lead
4.597***
5.297***
bank)
(39.24)
(44.43)
Bank prestige
-0.305***
0.003
Candidate bank’s rank ≥ 8
(-6.37)
(0.07)
0.070*
0.368***
Lead bank’s rank ≥ 8
(2.11)
(10.89)
Candidate bank’s rank ≥ 8 x lead
0.115*
0.204***
bank’s rank ≥ 8
(2.40)
(4.17)
Candidate bank’s aggressiveness
1.340***
-0.464***
Recent positive recs to IPOs
(18.90)
(-6.43)
0.183***
-0.058***
Log(recent recs+1)
(19.05)
(-5.96)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
-0.017
0.873***
Candidate bank’s network
(-0.18)
(9.31)
Recent comanagement activity
1.399***
1.273***
(candidate bank)
(9.00)
(8.04)
Recent lead management activity
-0.792***
-0.182
(candidate bank)
(-3.60)
(-0.81)
Participation in recent IPOs managed
0.095†
1.924***
by the lead bank
(1.78)
(35.40)
Log(reciprocal participation in recent
-0.062***
-0.009
IPOs+1)
(-5.66)
(-0.85)
Constant
-7.130
-3.557
R2
0.349
0.328
Wald test of exogeneity (χ2)
8.52*
No. of observations
13,942
38
Second Stage
Dependent variable:
comanagement
selection
0.489*
(2.28)
0.185
(0.42)
------0.299
(-1.61)
0.544***
(11.56)
-3.415†
(-1.71)
-0.088
(-0.51)
-0.425*
(-2.26)
0.338†
(1.88)
-0.045
(-0.10)
-0.071
(-1.06)
0.289
(0.55)
4.083***
(5.58)
-1.959**
(-2.77)
0.607
(0.72)
0.077*
(0.35)
0.104
--
Panel B: Instrumental variable probit regressions – Booster shots
First Stage
First Stage
Dependent variable:
Dependent variable:
Explanatory variables
Log(No. of unaffiliated
Log(No. of affiliated
booster shots+1)
booster shots+1)
Predicted Log(No. of unaffiliated
--booster shots+1)
Predicted Log(No. of affiliated booster
--shots+1)
Instruments
-0.013†
-0.066***
Bad lead year (candidate bank)
(-1.77)
(-6.92)
Bad comanagement year (candidate
-0.020*
-0.044***
bank)
(-2.08)
(-3.64)
-0.236**
-0.271*
Recent underpricing
(-2.73)
(-2.42)
0.010
0.007
Recent S&P500 return
(0.21)
(0.11)
0.039***
-0.012
Lead bank’s average IPO size
(5.01)
(-1.24)
IPO and IPO market characteristics
0.239***
0.158***
Log(No. of recent IPOs+1)
(9.60)
(4.90)
0.019**
0.034***
Log(No. of comanagers+1)
(2.87)
(3.93)
Recent lead management activity (lead
1.279***
2.369***
bank)
(18.67)
(26.74)
Bank prestige
-0.075**
0.023
Candidate bank’s rank ≥ 8
(-2.68)
(0.64)
0.032†
0.131***
Lead bank’s rank ≥ 8
(1.67)
(5.21)
Candidate bank’s rank ≥ 8 x lead
0.034
0.107**
bank’s rank ≥ 8
(1.22)
(2.94)
Candidate bank’s aggressiveness
0.647***
-0.225***
Recent positive recs to IPOs
(15.60)
(-4.19)
0.013*
-0.014*
Log(recent recs+1)
(2.40)
(-1.96)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
0.118*
0.468***
Candidate bank’s network
(2.18)
(6.71)
Recent comanagement activity
0.480***
0.619***
(candidate bank)
(5.28)
(5.26)
Recent lead management activity
-0.718***
-1.474***
(candidate bank)
(-5.58)
(-8.85)
Participation in recent IPOs managed
0.051
0.875***
by the lead bank
(1.62)
(21.66)
Log(reciprocal participation in recent
-0.001
0.014†
IPOs+1)
(-0.15)
(1.70)
Constant
-1.956
-1.018
R2
0.142
0.141
Wald test of exogeneity (χ2)
12.37**
No. of observations
13,942
39
Second Stage
Dependent variable:
comanagement
selection
1.570
(1.46)
1.446*
(2.18)
------0.496*
(-2.28)
0.513***
(10.56)
-5.392***
(-3.20)
-0.148
(-0.77)
-0.535***
(-3.66)
0.220
(1.18)
-0.184
(-0.24)
0.012
(0.27)
-0.440
(-0.93)
3.209***
(4.09)
0.970
(0.77)
-0.325
(-0.56)
0.014
(0.35)
1.247
--
Table 4
Positive recommendations to recent IPOs managed by an underwriter and probability of comanaging its next
IPO. Does prestige matter? Banks with recent relationships with the underwriter
This table repeats the tests in Table 3 for various levels of prestige of the candidate bank for a comanagement mandate
and the lead manager of the IPO. For candidate banks with recent relationships with the lead bank of a given IPO (that is,
banks that comanaged at least one of the IPOs managed by the lead bank in the year preceding the IPO), it explores the
link between the number of recent recommendations issued by the candidate bank to IPOs managed by the lead bank and
the probability of being selected to comanage the IPO. Banks whose recommendations do not appear in I/B/E/S, that do
not operate at the time of the IPO, that did not participate in any IPO syndicate as lead or comanager between 1993 and
the IPO date, that issued fewer than 10 recommendations to any listed firms in the year preceding the IPO are excluded
from the list of candidate banks. Those IPOs for which none of the lead managers engaged in any other IPO as lead
manager in the year preceding the IPO are excluded from the list of IPOs. Positive recommendations to an IPO are
recommendations classified by I/B/E/S as 1 (“Strong buy”) or 2 (“Buy”) and issued within a year of the IPO. Negative
recommendations are recommendations classified by I/B/E/S as 3 (“Hold”), 4 (“Underperform”), or 5 (“Sell”). Affiliated
(unaffiliated) recommendations are recommendations issued by the candidate bank to IPOs in which it did (did not)
participate as a comanager.
Panel A presents instrumental probit regressions similar to those that appear in Table 3, Panel A. In the first stage, OLS
regressions explain the natural logarithm of number of unaffiliated positive recommendations minus number of
unaffiliated negative recommendations issued between one year and one month prior to the IPO by the candidate bank to
IPOs managed by the lead bank, plus 1, and of number of affiliated positive recommendations minus number of affiliated
negative recommendations, plus 1. The second-stage regression is a probit regression. The dependent variable is
comanagement selection, equal to 1 if the candidate bank is chosen to comanage the IPO and 0 otherwise. The
explanatory variables are the instrumented variables predicted from the first-stage regressions and other explanatory
variables of the first stage. The instruments and explanatory variables are described in the Appendix. Results of secondstage regressions only are presented. Column 1 includes pairs of candidate banks and lead banks with Carter-Manaster
ranks of 8 or 9 during 1992-2000. Column 2 contains pairs of candidate banks with ranks below 8 and lead banks with
ranks 8 or 9. Column 3 contains pairs of candidate banks with any rank and lead banks with ranks below 8.
Panel B presents the same instrumental probit regressions as Panel A, except for the analyst behavior variables: the
number of unaffiliated booster shots and affiliated booster shots replace the number of unaffiliated positive
recommendations and affiliated positive recommendations, respectively. Booster shots are positive recommendations to
firms whose stock performance is in the first third of stock performance of all IPOs in the same month after the offering.
Stock performance is the Buy-and-Hold Abnormal Return since the IPO. Returns of IPO firms are adjusted using the
returns of 5x5 size/book-to-market portfolios of companies that have been trading for at least five years.
IPO year dummies are included in all regressions, but their coefficients are not reported. z-statistics are in parenthesis.
†
Significance at the 10% level. *Significance at the 5% level. **Significance at the 1% level. ***Significance at the
0.1% level.
40
Panel A: Instrumental variable probit regressions (second stage) – Positive recommendations to recent IPOs
managed by an underwriter and probability of comanaging its next IPO, depending on the ranks of the two banks
Dependent Variable: Comanagement Selection
Candidate bank’s rank ≥ 8 Candidate bank’s rank < 8
Lead bank’s rank < 8
Explanatory variables
and lead bank’s rank ≥ 8
and lead bank’s rank ≥ 8
Predicted Log(No. of unaffiliated
-0.056
-0.191
-6.766
positive recs+1)
(-0.22)
(-0.53)
(-1.13)
Predicted Log(No. of affiliated positive
0.941**
0.278
-0.639
recs+1)
(2.95)
(0.54)
(-0.27)
IPO and IPO market characteristics
-0.428*
-0.300
0.943
Log(No. of recent IPOs+1)
(-2.51)
(-1.12)
(0.75)
0.653***
0.265**
0.086
Log(No. of comanagers+1)
(12.32)
(2.97)
(0.24)
Recent lead management activity (lead
-5.424***
-1.745
5.103
bank)
(-3.18)
(-0.63)
(0.44)
Candidate bank’s aggressiveness
1.253*
-0.770
-1.357
Recent positive recs to IPOs
(2.41)
(-0.68)
(-0.27)
-0.078
0.087
0.036
Log(recent recs+1)
(-1.39)
(0.97)
(0.10)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
-0.407
-0.135
3.515
Candidate bank’s network
(-0.86)
(-0.14)
(1.34)
Recent comanagement activity
2.068*
9.875*
3.695
(candidate bank)
(2.43)
(2.48)
(0.57)
Recent lead management activity
1.579
-1.320
-4.906
(candidate bank)
(1.22)
(-0.21)
(-0.68)
Participation in recent IPOs managed
-1.473†
0.519
1.196
by the lead bank
(-1.75)
(1.06)
(0.91)
Log(reciprocal participation in recent
-0.014
0.005
-0.048
IPOs+1)
(-0.26)
(0.02)
(-0.13)
Constant
0.532
-1.347
-12.241
Wald test of exogeneity (χ2)
10.64**
1.42
0.20
No. of observations
8,045
5,306
591
41
Panel B: Instrumental variable probit regressions (second stage) – Booster shots to recent IPOs managed by an
underwriter and probability of comanaging its next IPO, depending on the ranks of the two banks
Dependent variable: Comanagement Selection
Candidate bank’s rank ≥ 8 Candidate bank’s rank < 8
Lead bank’s rank < 8
Explanatory variables
and lead bank’s rank ≥ 8
and lead bank’s rank ≥ 8
Predicted Log(No. of unaffiliated
1.187
0.346
-7.992
booster shots+1)
(1.28)
(0.33)
(-0.99)
Predicted Log(No. of affiliated booster
1.414***
0.974
-0.588
shots+1)
(3.75)
(1.14)
(-0.33)
IPO and IPO market characteristics
-0.398†
-0.416
1.087
Log(No. of recent IPOs+1)
(-1.71)
(-1.48)
(0.96)
0.631***
0.251**
0.124
Log(No. of comanagers+1)
(10.35)
(3.04)
(0.42)
Recent lead management activity (lead
-4.969**
-3.369
5.064
bank)
(-2.78)
(-1.39)
(0.70)
Candidate bank’s aggressiveness
0.212
-0.934
-0.534
Recent positive recs to IPOs
(0.29)
(-0.89)
(-0.26)
0.059
0.041
-0.091†
Log(recent recs+1)
(-1.95)
(0.73)
(0.19)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
-0.770
-0.161
3.624†
Candidate bank’s network
(-1.32)
(-0.20)
(1.77)
Recent comanagement activity
2.553***
7.322†
2.591
(candidate bank)
(3.54)
(1.72)
(0.71)
Recent lead management activity
2.945*
3.440
-4.751
(candidate bank)
(2.22)
(0.41)
(-0.87)
Participation in recent IPOs managed
-0.714
0.318
1.026†
by the lead bank
(-1.34)
(0.56)
(1.69)
Log(reciprocal participation in recent
0.004
-0.016
-0.018
IPOs+1)
(0.10)
(-0.08)
(-0.05)
Constant
0.793
-0.253
-12.018
Wald test of exogeneity (χ2)
15.00***
1.44
0.33
No. of observations
8,045
5,306
591
42
Table 5
Does issuing positive recommendations and booster shots to recent IPOs managed by an underwriter increase the
probability of comanaging its next IPO? Banks with no recent relationship with the underwriter
For each IPO with at least one comanager between January 1997 and December 2002, this table considers all pairs formed
by the lead managers of the IPO and all other banks. All banks that are not lead managers and had no recent relationship
with the lead manager (i.e., that comanaged none of the IPOs managed by the lead bank in the year preceding the IPO) are
assumed to be candidates for a comanagement mandate. These banks are referred to as candidate banks. Banks whose
recommendations do not appear in I/B/E/S, that do not operate at the time of the IPO, that did not participate in any IPO
syndicate as lead or comanager between 1993 and the IPO date, that issued fewer than 10 recommendations to any listed
firms in the year preceding the IPO are excluded from the list of candidate banks. Those IPOs for which none of the lead
managers did any other IPO as lead manager in the year preceding the IPO are excluded from the list of IPOs. Positive
recommendations to an IPO are recommendations classified by I/B/E/S as 1 (“Strong buy”) or 2 (“Buy”) and issued within
a year of the IPO. Negative recommendations are recommendations classified by I/B/E/S as 3 (“Hold”), 4
(“Underperform”), or 5 (“Sell”). Booster shots are positive recommendations to firms whose stock performance is in the
first third of stock performance of all IPOs in the same month after the offering. Stock performance is the Buy-and-Hold
Abnormal Return since the IPO. Returns of IPO firms are adjusted using the returns of 5x5 size/book-to-market portfolios
of companies that have been trading for at least five years.
The table presents two instrumental probit regressions. In the first stage, OLS regressions explain the natural logarithm of
number of positive recommendations minus number of negative recommendations issued between one year and one month
prior to the IPO by the candidate bank to IPOs managed by the lead bank, plus 1 (column 1), and the natural logarithm of
number of booster shots minus number of negative recommendations, plus 1 (column 3). The second-stage regressions are
probit regressions. The dependent variable is comanagement selection, equal to 1 if the candidate bank is chosen to
comanage the IPO and 0 otherwise. The explanatory variables are the instrumented variables predicted from the first-stage
regressions (Log(No. of positive recs+1) in column 2, Log(No. of booster shots+1) in column 4), and the other explanatory
variables of the first stage. The instrumental variables and other explanatory variables are described in the Appendix.
IPO year dummies are used in all regressions, but their coefficients are not reported. z-statistics are in parenthesis.
†
Significance at the 10% level. *Significance at the 5% level. **Significance at the 1% level. ***Significance at the 0.1%
level.
43
Explanatory variables
Predicted Log(No. of positive recs+1)
Predicted Log(No. of booster shots+1)
Positive Recommendations
First stage
Second stage
Dependent
Dependent
variable:
variable:
comanagement
Log(No. of
positive recs+1)
selection
0.827
-(0.98)
--
--
Booster Shots
First stage
Second stage
Dependent
Dependent
variable:
variable:
Log(No. of
comanagement
booster shots+1)
selection
-1.202
(0.62)
--
Instruments
Bad lead year (candidate bank)
Bad comanagement year (candidate
bank)
Recent underpricing
Recent S&P500 return
Lead bank’s average IPO size
-0.043***
(-10.27)
0.025***
(5.85)
0.026
(0.50)
0.082**
(2.78)
0.011**
(2.56)
------
-0.016***
(-6.98)
0.015***
(6.09)
-0.059*
(-1.99)
0.022
(1.37)
0.004†
(1.82)
------
IPO and IPO market characteristics
0.135***
(8.93)
0.020***
(4.30)
3.353***
(68.29)
-0.099
(-0.65)
0.391***
(8.35)
-5.217†
(-1.82)
0.062***
(7.37)
0.009***
(3.47)
1.174***
(42.89)
-0.039
(-0.29)
0.398***
(8.57)
-3.820
(-1.64)
-0.062***
(-6.02)
-0.009
(-1.39)
0.073***
(6.89)
-0.335***
(-3.31)
-0.148*
(-2.48)
0.411***
(3.85)
-0.003
(-0.55)
-0.011**
(-2.81)
0.023***
(3.94)
-0.378***
(-4.25)
-0.138*
(-2.27)
0.440***
(4.47)
1.152***
-0.657
0.478***
(25.44)
(-0.65)
(18.93)
0.033***
0.099*
-0.003†
Log(recent recs+1)
(11.07)
(2.46)
(-1.69)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
-0.047
2.449***
-0.077***
Candidate bank’s network
(-1.23)
(7.12)
(-3.64)
Recent comanagement activity
0.089
7.453***
0.210***
(candidate bank)
(0.80)
(11.41)
(3.40)
Recent lead management activity
-1.388***
-6.954***
-0.637***
(candidate bank)
(-9.20)
(-4.97)
(-7.57)
Log(reciprocal participation in recent
-0.033***
-0.018
-0.016***
IPOs+1)
(-3.81)
(-0.28)
(-3.45)
Constant
-1.339
-2.207
-0.446
R2
0.213
-0.108
Wald test of exogeneity (χ2)
0.80
No. of observations
40,676
-0.291
(-0.30)
0.129***
(4.00)
Log(No. of recent IPOs+1)
Log(No. of comanagers+1)
Recent lead management activity (lead
bank)
Bank prestige
Candidate bank’s rank ≥ 8
Lead bank’s rank ≥ 8
Candidate bank’s rank ≥ 8 x lead
bank’s rank ≥ 8
Candidate bank’s aggressiveness
Recent positive recs to IPOs
44
2.500***
(6.61)
7.278***
(10.01)
-7.268***
(-5.02)
-0.025
(-0.38)
-2.838
-0.37
Table 6
Positive recommendations and booster shots to recent IPOs managed by an underwriter and probability of
comanaging its next IPO. Does prestige matter? Banks with no recent relationship with the underwriter
This table repeats the tests presented in Table 5 for various levels of prestige of the candidate bank for a comanagement
mandate and the lead bank of the IPO. For candidate banks with no recent relationship with the lead bank of a given IPO (that
is, banks that comanaged none of the IPOs managed by the lead bank in the year preceding the IPO), it explores the link
between the number of recent recommendations issued by the candidate bank to IPOs managed by the lead bank and the
probability of comanaging the IPO. Banks whose recommendations do not appear in I/B/E/S, that do not operate at the time
of the IPO, that did not participate in any IPO syndicate as lead or comanager between 1993 and the IPO date, that issued
fewer than 10 recommendations to any listed firms in the year preceding the IPO are excluded from the list of candidate
banks. Those IPOs for which none of the lead managers did any other IPO as lead manager in the year preceding the IPO are
excluded from the list of IPOs. Positive recommendations to an IPO are recommendations classified by I/B/E/S as 1 (“Strong
buy”) or 2 (“Buy”) and issued within a year of the IPO. Negative recommendations are recommendations classified by
I/B/E/S as 3 (“Hold”), 4 (“Underperform”), or 5 (“Sell”).
Panel A presents instrumental probit regressions similar to those in Table 5. In the first stage, OLS regressions explain the
natural logarithm of number of positive recommendations minus number of negative recommendations issued between one
year and one month prior to the IPO by the candidate bank to IPOs managed by the lead bank, plus 1. The second-stage
regression is a probit regression. The dependent variable is comanagement selection, equal to 1 if the candidate bank is
chosen to comanage the IPO and 0 otherwise. The explanatory variables are the instrumented variables predicted from the
first-stage regressions and other explanatory variables of the first stage. The instruments and explanatory variables are
described in the Appendix. Results of second-stage regressions only are presented. Column 1 contains pairs of candidate
banks and lead banks with Carter-Manaster ranks of 8 or 9 during 1992-2000. Column 2 contains pairs of candidate banks
with ranks below 8 and lead banks with ranks 8 or 9. Column 3 contains pairs of candidate banks with any rank and lead
banks with ranks below 8.
Panel B presents the same instrumental probit regressions as Panel A, except for the analyst behavior variables: the number of
booster shots replaces the number of positive recommendations. Booster shots are positive recommendations to firms whose
stock performance is in the first third of stock performance of all IPOs in the same month after the offering. Stock
performance is the Buy-and-Hold Abnormal Return since the IPO. Returns of IPO firms are adjusted using the returns of 5x5
size/book-to-market portfolios of companies that have been trading for at least five years.
IPO year dummies are used in all regressions, but their coefficients are not reported. z-statistics are in parenthesis.
†
Significance at the 10% level. *Significance at the 5% level. **Significance at the 1% level. ***Significance at the 0.1%
level.
45
Panel A: Instrumental variable probit regressions (second stage) – Positive recommendations to recent IPOs
managed by an underwriter and probability of comanaging its next IPO, depending on the ranks of the two banks
Dependent Variable: Comanagement Selection
Candidate bank’s rank ≥ 8 Candidate bank’s rank < 8
Lead bank’s rank < 8
Explanatory variables
and lead bank’s rank ≥ 8
and lead bank’s rank ≥ 8
2.825***
-2.753**
-2.994
Predicted Log(No. of positive recs+1)
(3.28)
(-3.10)
(-0.54)
IPO and IPO market characteristics
-0.371
0.328
0.101
Log(No. of recent IPOs+1)
(-1.35)
(1.56)
(0.37)
0.513***
0.278***
0.620***
Log(No. of comanagers+1)
(5.06)
(3.86)
(5.32)
Recent lead management activity (lead
-12.743***
6.467*
-9.080*
bank)
(-3.42)
(2.14)
(-2.13)
Candidate bank’s aggressiveness
-2.535*
6.573**
0.323
Recent positive recs to IPOs
(-2.30)
(2.74)
(0.20)
-0.135
0.191***
0.108
Log(recent recs+1)
(-1.45)
(3.65)
(1.64)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
1.743*
2.148***
0.510
Candidate bank’s network
(2.39)
(3.46)
(0.59)
Recent comanagement activity
8.505***
10.860***
6.907***
(candidate bank)
(6.00)
(7.01)
(4.11)
Recent lead management activity
-3.556†
-6.002
-9.118***
(candidate bank)
(-1.96)
(-0.98)
(-4.10)
Log(reciprocal participation in recent
0.058
0.053
0.334*
IPOs+1)
(0.54)
(0.18)
(2.13)
Constant
1.015
-6.835
-3.145
Wald test of exogeneity (χ2)
13.37***
10.92***
0.38
No. of observations
5,589
27,014
8,073
46
Panel B: Instrumental variable probit regressions (second stage) – Booster shots to recent IPOs managed by an
underwriter and probability of comanaging its next IPO, depending on the ranks of the two banks
Dependent Variable: Comanagement Selection
Candidate bank’s rank ≥ 8 Candidate bank’s rank < 8
Lead bank’s rank < 8
Explanatory variables
and lead bank’s rank ≥ 8
and lead bank’s rank ≥ 8
7.326***
-6.119**
7.256
Predicted Log(No. of booster shots+1)
(3.38)
(-2.72)
(0.71)
IPO and IPO market characteristics
-0.164
0.228
0.174
Log(No. of recent IPOs+1)
(-0.66)
(1.12)
(0.65)
0.521***
0.273***
0.621***
Log(No. of comanagers+1)
(4.85)
(3.67)
(5.33)
Recent lead management activity (lead
-11.626***
4.432†
-8.816*
bank)
(-3.48)
(1.66)
(-2.07)
Candidate bank’s aggressiveness
-3.175*
6.837*
-1.215
Recent positive recs to IPOs
(-2.50)
(2.40)
(-0.94)
0.229*
0.078
0.100
Log(recent recs+1)
(2.12)
(1.64)
(1.55)
Candidate bank’s recent activity and recent relationships with the lead manager of the IPO
1.718*
1.607*
1.258
Candidate bank’s network
(2.18)
(2.08)
(1.30)
Recent comanagement activity
8.608***
11.664***
7.653***
(candidate bank)
(5.67)
(6.70)
(4.32)
Recent lead management activity
-5.142*
-4.516
-9.422***
(candidate bank)
(-2.53)
(-0.73)
(-4.27)
Log(reciprocal participation in recent
0.116
-0.182
0.160
IPOs+1)
(0.94)
(-0.61)
(0.80)
Constant
-1.904
-5.384
-3.344
Wald test of exogeneity (χ2)
16.55***
8.05**
0.45
No. of observations
5,589
27,014
8,073
47
Table 7
Lead bank behavior and probability of managing future deals
This table explores the relation between the behavior of banks that have been lead managers in recent IPOs and their
probability of being selected to manage the next IPO. For each IPO in the sample, all banks that have managed at least one
IPO in the previous year are assumed to be candidates to manage the IPO. Banks whose recommendations do not appear in
I/B/E/S, that do not operate at the time of the IPO, or that issued fewer than 10 recommendations to any listed firms in the
year preceding the IPO are excluded from the list of candidate banks. Positive recommendations to an IPO are
recommendations classified by I/B/E/S as 1 (“Strong buy”) or 2 (“Buy”) and issued within a year of the IPO. Negative
recommendations are recommendations classified by I/B/E/S as 3 (“Hold”), 4 (“Underperform”), or 5 (“Sell”). Booster
shots are positive recommendations to firms whose stock performance is in the first third of stock performance of all IPOs
in the same month after the offering. Stock performance is the buy-and-hold abnormal return since the IPO. Returns of IPO
firms are adjusted using the returns of 5x5 size/book-to-market portfolios of companies that have been trading for at least
five years.
The table presents two instrumental probit regressions. In the first stage, OLS regressions explain the natural logarithm of
number of positive recommendations minus number of negative recommendations issued between one year and one month
prior to the IPO by the bank to IPOs it managed, plus 1 (column 1), and the natural logarithm of number of booster shots
minus number of negative recommendations, plus 1 (column 3). The second-stage regression is a probit regression. The
dependent variable is lead management selection, equal to 1 if the candidate bank is chosen to manage the IPO and 0
otherwise. The explanatory variables are the instrumented variables predicted from the first-stage regressions (Log(No. of
positive recs+1) in column 2, Log(No. of booster shots+1) in column 4) and other explanatory variables of the first stage.
The instrumental variables and other explanatory variables are described in the Appendix.
IPO year dummies are used in all regressions, but their coefficients are not reported. z-statistics are in parenthesis.
†
Significance at the 10% level. *Significance at the 5% level. **Significance at the 1% level. ***Significance at the 0.1%
level.
48
Explanatory variables
Predicted Log(No. of positive recs+1)
Predicted Log(No. of booster shots+1)
Positive Recommendations
First stage
Second stage
Dependent
Dependent
variable:
variable: lead
management
Log(No. of
positive recs+1)
selection
-0.453*
-(-2.06)
--
Booster Shots
First stage
Second stage
Dependent
Dependent
variable:
variable: lead
Log(No. of
management
booster shots+1)
selection
--
--
--
--
-0.602*
(-2.30)
Instruments
Bad lead year
Bad comanagement year
Recent underpricing
Recent S&P500 return
-0.234***
(-21.85)
0.048***
(4.68)
-0.457***
(-4.34)
-0.182***
(-3.20)
-0.154***
(-17.13)
-0.091***
(-10.54)
-0.784***
(-8.89)
-0.190***
(-3.97)
-----
-----
IPO and IPO market characteristics
Log(No. of recent IPOs+1)
0.340***
(14.62)
0.192
(1.57)
0.530***
(21.00)
0.292*
(2.02)
0.519***
(44.09)
0.737***
(5.44)
0.169***
(17.09)
0.605***
(7.79)
-0.442***
(-7.55)
-0.083***
(-11.78)
-0.074
(-0.36)
0.104**
(2.57)
-0.012
(-0.24)
-0.122***
(20.53)
0.134
(0.71)
0.069
(1.41)
4.934***
(67.21)
-3.517***
(-27.55)
10.764***
(64.09)
-2.138
0.593
3.541**
(3.11)
0.500
(0.54)
8.021**
(3.15)
-4.841
--
4.205***
(68.26)
-1.363***
(-12.73)
3.544***
(25.16)
-2.369
0.388
3.947***
(3.27)
1.493**
(2.87)
4.845***
(4.58)
-5.324
--
Bank prestige
Bank’s rank ≥ 8
Bank’s aggressiveness
Recent positive recs to IPOs
Log(recent recs+1)
Candidate bank’s recent activity
Candidate bank’s network
Recent comanagement activity
Recent lead management activity
Constant
R2
Wald test of exogeneity (χ2)
No. of observations
5.15*
4.98*
35,638
49
Table 8
Bank prestige, lead bank behavior and probability of managing future deals
This table repeats the tests presented in Table 7 for various levels of prestige of the candidate bank for managing the IPO. It
explores the relation between the behavior of banks that have been lead managers in recent IPOs and their probability of
being selected to manage the next IPO. For each IPO in the sample, all banks that have managed at least one IPO in the
previous year are assumed to be candidates to manage the IPO. Banks whose recommendations do not appear in I/B/E/S, that
do not operate at the time of the IPO, or that issued fewer than 10 recommendations to any listed firms in the year preceding
the IPO are excluded from the list of candidate banks. Positive recommendations to an IPO are recommendations classified
by I/B/E/S as 1 (“Strong buy”) or 2 (“Buy”) and issued within a year of the IPO. Negative recommendations are
recommendations classified by I/B/E/S as 3 (“Hold”), 4 (“Underperform”), or 5 (“Sell”). Booster shots are positive
recommendations to firms whose stock performance is in the first third of stock performance of all IPOs in the same month
after the offering. Stock performance is the Buy-and-Hold Abnormal Return since the IPO. Returns of IPO firms are adjusted
using the returns of 5x5 size/book-to-market portfolios of companies that have been trading for at least five years.
The table presents instrumental probit regressions similar to those in Table 7. In the first stage, OLS regressions explain the
natural logarithm of number of positive recommendations minus number of negative recommendations issued between one
year and one month prior to the IPO by the bank to IPOs it managed, plus 1, and the natural logarithm of number of booster
shots minus number of negative recommendations, plus 1. The second-stage regression is a probit regression. The dependent
variable is lead management selection, equal to 1 if the candidate bank manages the IPO and 0 otherwise. The explanatory
variables are the instrumented variables predicted from the first-stage regressions (using predicted number of positive
recommendations in columns 1 and 2, predicted number of booster shots in columns 3 and 4) and other explanatory variables
of the first stage. The instrumental variables and other explanatory variables are described in the Appendix. Results of
second-stage regressions only are presented. In columns 1 and 3 (2 and 4), we consider banks with Carter-Manaster ranks of
8 or 9 (less than 8) during 1992-2000.
IPO year dummies are used in all regressions, but their coefficients are not reported. z-statistics are in parenthesis.
†
Significance at the 10% level. *Significance at the 5% level. **Significance at the 1% level. ***Significance at the 0.1%
level.
Explanatory variables
Predicted Log(No. of positive recs+1)
Predicted Log(No. of booster shots+1)
Positive Recommendations
Bank’s rank ≥ 8
Bank’s rank < 8
-0.309†
2.292***
(-1.64)
(3.61)
Booster Shots
Bank’s rank ≥ 8
Bank’s rank < 8
--
--
--
--
-0.455**
(-3.02)
2.750**
(2.57)
0.221
(1.30)
-0.102
(-0.33)
0.316*
(2.22)
-0.453
(-1.09)
-0.055
(-0.27)
0.117**
(2.93)
3.849**
(2.69)
0.443**
(3.04)
0.153
(0.75)
0.081†
(1.91)
3.854**
(2.63)
0.553**
(2.65)
2.758*
(2.44)
0.800
(0.99)
6.184***
(3.36)
-4.202
3.45†
16,073
-3.958*
(-2.14)
11.448***
(3.23)
-142.579***
(-3.20)
-3.683
13.32***
19,565
3.828***
(3.75)
1.500***
(3.25)
3.858***
(6.25)
-4.864
8.35**
16,073
-2.318
(-1.27)
2.899
(1.24)
-108.015*
(-2.24)
-2.453
6.13*
19,565
IPO and IPO market characteristics
Log(No. of recent IPOs+1)
Bank’s aggressiveness
Recent positive recs to IPOs
Log(recent recs+1)
Candidate bank’s recent activity
Candidate bank’s network
Recent comanagement activity
Recent lead management activity
Constant
Wald test of exogeneity (χ2)
No. of observations
50