Conflicts of Interest within Investment Banks:
Analysts and Proprietary Traders
David Haushalter
Penn State University
E-mail: gdh12@psu.edu
Phone: (814) 865-7969
Michelle Lowry
Penn State University
E-mail: mlowry@psu.edu
Phone: (814) 865-1483
March 21, 2008
Abstract:
We examine the interaction between an investment bank’s trading, analyst recommendations,
and advising activities around mergers. Banks advising an acquirer provide higher analyst
recommendations to acquirers than non-advising banks around the time of the merger. On
average the trading of an acquirer by the advisor bank following the announcement of an
acquisition is in line with the recommendations made by the advisor’s analyst. However,
additional tests show that this relation only holds for banks that do not rely heavily on investment
banking as a source of revenue. The results are consistent with arguments that the conflicts faced
by analysts differ predictably across firms. Traders within an investment bank are cognizant of
the incentives of the in-house analysts, and knowledge of such incentives significantly affects the
extent to which they rely on their recommendations when making trades.

We thank Lubomir Petrasek for excellent research assistance.
1. Introduction
The potential for conflicts of interest is prevalent within investment banks. Nearly all
large investment banks offer underwriting services, provide analyst coverage, and have
proprietary trading desks, and a wide body of evidence suggests that these divisions do not act
independently. In fact, Leaven and Levine’s (2007) finding of a diversification discount for
financial conglomerates and DeLong’s (2001) event study analysis of investment bank mergers
suggests a valuation penalty for offering too many services under one roof. An understanding of
these conflicts is important to institutions, to companies buying services from the banks, and also
to the banks themselves.
To study the conflicts between investment banking activities and other aspects of a
bank’s operations, we focus on mergers. Although conflicts of interest can be ongoing, they are
arguably particularly large when a bank is advising a merger. First, analysts can be important in
both enabling the bank to land a merger deal and in increasing the probability that the deal will
be completed (see, e.g., Becher and Juergens, 2005). Mergers are a large source of revenues for
investment banks. For example, in 2006 alone, the top 20 investment banks earned almost $35
billion in fees from underwriting mergers and acquisitions.1 This is about half of the total fees
that they earned from all investment banking activities. Second, the insights of analysts into the
expected costs and/or synergies of the merger can make their resulting recommendations
particularly valuable. The value of companies can change dramatically around mergers, and the
ability of analysts within the investment banks to forecast these changes can have dramatic
1
See “The Good Times Roll,” Bloomberg Magazine, April, 2007.
1
effects on their personal reputations.2
The first portion of the paper explores the ways in which analysts support the M&A
advisory business. Specifically, we examine changes in analyst recommendations prior to,
around the time of, and following merger announcements and completions. We find that
analysts of the advisor firm issue significantly more optimistic recommendations around the
merger, compared to non-affiliated analysts. This finding is consistent with the findings of
Michaely and Womack (1999) and Lin and McNichols (1998), among, others, who find that
affiliated analysts are more optimistic than non-affiliated analysts around equity offerings. Both
of these studies conclude that the affiliated analysts are being overly optimistic to support the
investment banking business. However, the possibility that affiliated analysts truly have a rosier
outlook regarding the prospects of the acquirer company cannot entirely be ruled out. It would
not be surprising if acquiring companies were more likely to choose an advisor that thought well
of the merger in question, as opposed to one that had a more negative outlook.
The second portion of the paper attempts to distinguish between the potential reasons that
affiliated analysts are more optimistic than non-affiliated analysts, i.e., to determine if advisor
firm analysts are being overly optimistic in an effort to support the investment banking business
or if the advisor firm analysts truly have more positive expectations regarding the merger. To
shed light on this issue, we contrast the actions of the advisor firm analysts with those of the
advisor firm traders. It seems reasonable to assume that traders within a given institution would
have more information regarding the incentives and pressures that analysts within their own bank
face, compared to analysts at other banks. If the bank’s traders perceive that their firm’s analysts
act independently from their investment banking arm, then we would expect that its traders
2
As shown by Moeller, Schlingemann, and Stulz (2005), returns to acquirers around the announcement of mergers
at the 5% and 95% level range from -6% to 7% between 1980 to 1997 and -19% to 13% between 1998 and 2001.
2
would buy more shares of upgraded stocks. In contrast, if the bank’s traders perceive analysts to
upgrade acquirer stocks in an effort to win or support the investment banking business, we
expect that its traders would ignore the recommendations of these affiliated analysts. Our
comparisons focus on the recommendations of and trading in the acquirer firm.
We find no evidence that the institutional traders of the advisor firm trade in line with
their analysts’ recommendations (on the acquirer firm) prior to the merger. However this
association changes markedly after the merger: there is a significant positive relation between an
advisor’s analyst recommendations and its trading following the merger. Mergers in which an
advising firm’s analyst upgrades the acquirer’s stock are associated with significantly larger
increases in share ownership of the acquirer by the advisor bank.
The finding that traders’ buys and sells are more closely tied to analyst recommendations
following the merger is consistent with at least two different scenarios. First, advisor firm
analysts may provide more accurate recommendations following the merger, perhaps due to
better information. The in-house traders would be more likely to act on the analyst upgrades or
downgrades if they were perceived as higher quality, resulting in a stronger positive relation
between recommendations and trades in the quarters following the merger. Alternatively, both
the analysts and the traders of the advisor bank may face a greater conflict of interest following
the merger. Suppose both analysts and traders are pressured to support the investment banking
business, for example by issuing positive recommendations and by purchasing the stock. It is
possible that pressure from the investment banking business causes analyst upgrades and inhouse purchases of the stock at the same time, thereby producing a positive relation between the
two in the quarters following the merger.
To distinguish between these alternative explanations, we attempt to classify investment
3
banks into those for which the conflict of interest from investment banking is likely to be more
or less severe.
Specifically, we classify investment banks into those that rely heavily on
investment banking revenues versus those for which investment banking is less important. If
there exists some expectation that the traders and analysts both support the investment banking
arm, then this expectation should be greater in those firms for which investment banking is a
relatively more important source of revenue. That is, we would expect the post-merger relation
between analyst recommendations and in-house trading to be strongest in investment banks that
rely most heavily on investment banking as a source of revenue. Alternatively, if traders
perceive the analysts to have more accurate forecasts following the merger (due to more
available information), then we would expect this increase in accuracy to be greatest among
those banks where the conflict of interest from investment banking is particularly low. In this
case, we would expect the post-merger relation between analyst recommendations and in-house
trading to be strongest in those investment banks that rely least on investment banking as a
source of revenue.
We find that the positive relation between analyst recommendation changes and in-house
trading is only significant among those investment banks that rely least on investment banking as
a source of revenue, i.e., in those banks where analysts are likely to face the lowest conflicts of
interest. Within these banks, traders’ actions indicate that they perceive analysts’ post-merger
recommendations to be particularly valuable. In contrast, in banks where investment banking
revenue is an important source of revenue, i.e., where the conflict of interest faced by analysts is
likely to be quite high, the traders show no significant tendency to buy and sell in line with
analyst recommendations. The in-house traders are evidently aware of the conflicts faced by
analysts, and as a result they disregard their recommendations.
4
We also find that the relation between analyst recommendations and in-house trading
varies according to the percentage of investment bank revenue that comes from trading activities.
Among those banks that rely most heavily on trading as a source of revenue, there is a
significantly stronger relation between analyst recommendation changes and in-house trading.
These findings are consistent with an interpretation that analysts’ objective functions differ in
predictable ways across institutions. Within investment banks that rely most heavily on trading,
analysts are expected to provide the most accurate recommendations, and consequently traders
within these institutions are much more likely to act on these recommendations. In contrast,
within banks that rely most heavily on investment banking as a source of revenue, analysts are
expected to support the investment banking business, and consequently the in-house traders pay
little heed to their recommendations.
Our findings contribute to several streams of literature. First, our study relates to the
debate regarding analyst incentives and the extent to which conflicts of interest cause analysts to
issue overly forecasts and recommendations. Michaely and Womack (1999), Dugar and Nathan
(1995), and Lin and McNichols (1998), among others, find that analysts employed by
underwriters of security offerings tend to be more optimistic than other analysts. However,
Cowen, Groysber, and Healy (2006), Jacob, Rock and Weber (2003), Clarke, Khorana, Patel,
and Rau (2004), and Agrawal and Chen (2005) find no evidence that conflicts of interest from
investment banking make analysts more optimistic or less precise. We take a new approach to
this problem, by considering the ways in which analyst incentives are likely to vary both over
time and across investment banks.
Second, although there are a number of papers on analyst actions around equity issues,
there is relatively little evidence on analyst actions around mergers and acquisitions. M&A
5
activity is a substantial source of revenues for many investment banks, and our study increases
our understanding on the ways in which investment banks potentially compete for this business.
Our paper proceeds as follows. Section 2 reviews prior literature on conflicts of interest
within investment banking. Section 3 outlines the data. Section 4 describes analyst
recommendations and institutional ownership around the merger. Section 5 includes empirical
tests on the relation between analyst recommendations and institutional trades, by the advisor
investment bank. Section 6 investigates how the relation between analyst recommendations and
institutional trades varies depending on the likely magnitude of the conflict of interest faced by
analysts. Finally, Section 7 concludes.
2. Related Literature
A substantial body of literature has examined the value of analyst recommendations.
Givoly and Lakonishok (1979), Stickel (1991), Womack (1996), Barber, Lehavy, McNichols and
Trueman (2001), Jegadeesh, Kim, Krische, and Lee (2004), Loh and Mian (2005), and Busse and
Green (2002) all show that analysts’ earning forecasts and stock recommendations have
investment value. Consistent with analyst recommendations being value relevant, research by
Jackson (2005), Hong and Kubik (2003), and Mikhail, Walther, and Willis (1999) shows that
analysts are motivated to increase their reputations by issuing the most informative forecasts and
recommendations.3 In the merger framework, Becher and Juergens (2005) find that analysts
have insight into the value of a merger, and as a result they can impact the outcome of a merger.
Although considerable evidence suggests that analyst recommendations have value, there
is also a large literature on the conflicts of interest that analysts face. Specifically, as discussed
3
Ljungqvist, Malloy and Marston (2006) find that the importance of accuracy for career outcomes has become more
limited in recent years.
6
in detail by Mehran and Stulz (2007), analysts face pressure from within their firm to issue
overly optimistic forecasts and recommendations to support the investment banking business.
While there is broad consensus that analysts face conflicts of interest, the effects of such
conflicts are disputed. Lin and McNichols (1998), and James and Karceski (2006), among
others, find that analysts employed by underwriters of security offerings tend to be more
optimistic than other analysts. Findings of Michaely and Womack (1999), Aggarwal,
Purnanandam, and Wu (2005), Barber, Lehavy, and Trueman (2007) suggest that this optimism
contributes to inflated stock prices.
The findings of Dugar and Nathan (1995) and McNichols, O’Brien, and Pamukcu (2006)
similarly show that affiliated analysts are more optimistic. However, they find that the market
discounts the affiliated analysts’ recommendations. Agrawal and Chen (2005) and Bradley,
Jordan and Ritter (2006) reach similar conclusions.
Finally, papers by Cowen, Groysber, and Healy (2006) and Jacob, Rock and Weber
(2003) find no evidence that a conflict of interest from investment banking causes analysts to
issue overly optimistic or less precise forecasts. Similarly, Agrawal and Chen (2004) find no
evidence that accuracy or bias in earnings forecasts are related to the importance of investment
banking as a source of revenue to the financial institution.4
Part of the inconsistency in these streams of prior literature is potentially related to the
fact that not all analysts face the same conflicts at all times with respect to all stocks. Ljungqvist,
Marston, Starks, Wei, and Yan attempt to address this issue by separating stocks by the level of
Note that Agrawal and Chen’s (2004) examination of earnings forecasts shows no evidence of a conflict of interest
from investment banking resulting in overly optimistic forecasts. In contrast, Agrawal and Chen’s (2005) study of
analyst recommendations yields the opposite conclusion. This difference is consistent with Mehran and Stulz’s
observation that more evidence exists suggesting that recommendations are biased and less evidence suggesting that
earnings forecasts are biased. This potentially reflects the fact that earnings forecasts are more verifiable and
potentially have greater effects on an analyst’s reputation.
4
7
institutional ownership. Analysts’ career paths are largely influenced by the All-Star rankings,
which are based on institutional investor feedback. Consequently, it follows that an analyst’s
incentives to provide unbiased, accurate recommendations are highest in those stocks with the
highest institutional ownership. Consistent with this conjecture, the authors find that
recommendations relative to consensus are positively related to investment banking relationships
and negatively related to ownership by institutional investors.
Although all of the above studies examine analyst conflicts and are therefore obviously
related to our research question, there are only a few prior papers that link analyst
recommendations with institutional trading, as we do. Chen and Cheng (2002) find that
quarterly institutional trades are correlated with consensus stock recommendations. However,
they compare all institutional trading with consensus recommendations, rather than matching
institutions with their own recommendations, as we do.
The paper potentially most closely related to our own is Chan, Chang, and Wang (2005).
Similar to us, they match quarterly trades of financial firms with in-house recommendations.
However, their specification as well as the focus differs considerably from ours. The primary
objective of our study is to examine conflicts of interest within investment banking. As
discussed previously, we believe that quarters around mergers provide an ideal setting to
examine such issues. In contrast, Chan et al focus more solely on the value relevance of
recommendations in a more general setting, and correspondingly they examine all
recommendations, not just those around a corporate event. Consistent with the analyst
recommendations having value, they find that in-house trade is more positive around upgrades
than downgrades. Differences between their findings and our own are discussed in more detail
later.
8
3. Data
Our data consists of mergers and acquisitions between 1995 and 2004, as obtained from
the Securities Data Company (SDC) database. To ensure that the merger is a material event for
the acquiring firm, we require the market value of the target to be at least 5% of the combined
market capitalization of the bidder and the target. Both targets and acquirers are public firms
traded in the U.S., and the acquirer must be publicly traded for at least three years prior to the
merger announcement. We require each bidder firm to be followed by analysts, as listed on the
IBES recommendation database, and to have institutional ownership, as listed in the Spectrum
13(f) filings, one year prior to the announcement of the acquisition.
Our analysis necessitates merging the SDC merger data, the IBES recommendation data,
and the Spectrum institutional holdings data. For each merger, we identify the advisory
investment bank from SDC. We match by hand the identity of this bank with the IBES broker
code and with the Spectrum institutional name. In matching the institutions between the SDC,
IBES, and Spectrum databases, we are careful to account for both mergers between investment
banks and for banks reporting under different names (e.g., Smith Barney Inc. and Smith Barney
& Co). We attempt to match every investment bank that served as an advisor in at least 10 deals
over our sample period. The only banks that were not matched were those such as Houlihan,
Lokey, Howard & Zukin and Greenhill & Co, LLC, neither of which have either a trading desk
or analysts. Mergers in which the advisor either did not have an advisory arm (i.e., wasn’t listed
in IBES), didn’t have a trading arm (i.e., wasn’t listed in Spectrum), or served as an advisor in
less than ten deals are omitted from our sample.
For our analysis of analyst recommendations, we obtain advisor firm recommendations
9
and consensus recommendations from IBES. The summary data are available monthly, on the
15th day of each month, and they represent the average of all outstanding and new
recommendations made during the previous month.5 For the independent analyses of analyst
recommendations, we use the analyst recommendations 1, 4, 7, 10, and 13 months prior to the
merger announcement, and the recommendations 1, 4, 7, 10, and 13 months following the
merger completion. In addition, to assess the effects of the merger completion, we compare
analyst data one month following merger completion to one month preceding merger
completion.
Institutional holdings data are reported in Spectrum quarterly, on March 31st, June 30th,
September 30th, and December 31st of each year. We calculate total shares held by each advisor
institution and each non-advisor institution one through five quarters prior to merger
announcement and one through five quarters following merger completion.
Our interest in analysts’ response to the merger and also institutional traders’ response to
the analysts dictates the merging of the three datasets. Specifically, we match each quarterly
institutional reporting date to the prior analyst consensus. For example, the December 31st
institutional data would be matched to the December 15th analyst recommendation data. Thus,
for a merger on either October 12th or December 8th, quarter +1 relative to the merger
announcement would represent the December 31st institutional data, and we would use the
corresponding December 15th analyst recommendation data.
Although these examples pose no serious difficulties, there are other cases for which
comparing analyst recommendation data and institutional trading data around the merger proves
to be especially problematic. Not surprisingly, these difficulties are driven primarily by the
Consistent with IBES’ calculations of the consensus estimates, we match an advisor analyst’s recommendation (as
obtained from the detail database) with the subsequent consensus recommendation (as obtained from the summary
database).
5
10
infrequent interval at which the institutional trading data are available. For example consider a
merger on September 23rd. Using the measurement intervals described above, quarter +1 would
correspond to the September 30th institutional trading data. However, the preceding analyst
consensus data would be from September 15th, a date that actually precedes the merger
announcement. There is an obvious difficulty in capturing analyst recommendations to the
merger and the corresponding trades of the institutions in such cases. We therefore eliminate any
case where the date of the merger announcement falls in between the first available institutional
reporting data and the corresponding (i.e., most recent) analyst consensus recommendation data
As shown in Table 1, these requirements result in a sample of 726 mergers, of which 403
are stock acquisitions, 96 are cash, and 227 are mixed. Many of the mergers have more than one
advisor. Due to our interest in conflicts of interest at the investment bank level, many of our
analyses focus on advisor-level recommendations and trading. Our sample includes 816 advisorlevel observations. The sample is spread over time, with the largest number of transactions
occurring in the late 1990s. This concentration is consistent with the finding in prior literature
that M&A activity tends to be particularly high when the stock market is strong. Looking at the
industry distribution, the largest number of mergers is in the business equipment and finance
industries.
Table 2 provides descriptive statistics on the full sample, the sample divided by the
presence or absence of analyst coverage by the advisory firm, and the sample divided by the
presence or absence of institutional ownership by the advisory firm. Median market
capitalization is measured one month prior to the announcement of the merger, and all other
financial data reflect medians measured at the end of the fiscal year preceding the announcement
of the merger. Relative merger size represents the market capitalization of the target firm
11
divided by the sum of the market capitalizations of the target and acquirer firm, where all market
capitalizations are measured one month prior to the announcement of the merger.
The median market capitalization of the acquirer firm is approximately $1.8 billion. We
find that the market capitalization is significantly larger for firms in which the advisor firm
provides analyst coverage and in which the advisor firm owns shares. This likely reflects the
fact that both analyst coverage and institutional ownership are greater in larger firms, as shown
by Gompers and Metrick (2001) and Barth, Kasznik, and McNichols (2001). Similar inferences
can be made based on the total assets and the sales of the acquirer firms. Market-to-book is
significantly higher and working capital as a fraction of total assets is significantly lower for
firms in which the advisory firm owns shares. The significant differences in market-to-book are
consistent with Barth et al’s (2001) finding that analysts are more likely to cover growth firms.
Finally, relative merger size is significantly lower for companies in which the advisor firm issues
recommendations. This is potentially driven by differences in firm size – companies in which
the advisor firm issues recommendations are significantly larger, meaning a given target size will
be relatively smaller.
4. Analyst Recommendations and Institutional ownership around the merger
As discussed in section 2, prior literature provides contradictory evidence on the extent of
analyst optimism due to conflicts from investment banking. The majority of this prior literature
has focused on analyst forecasts and recommendations either around equity offerings or across
all firms. However, little is known about analyst recommendations around mergers. Notably,
the magnitude of fees from advising M&A banks likely make it one of the most competitive
12
areas of investment banking.6 Therefore, M&A provides a setting in which the pressures from
investment banking on a bank’s other activities are potentially the greatest. Tables 3 and 4
examine the extent to which analyst recommendations appear related to either expected or recent
M&A advisory business by the investment bank.
The first column of Table 3 shows the percent of all 816 advisors (across the 726
mergers) that issue recommendations in the acquirer company, from five quarters prior to the
announcement of the merger to five quarters following the completion of the merger. That is, the
analysis is at the advisor level rather than the deal level, meaning a deal in which there are two
advisors would be represent two observations. We assume that the investment bank has little idea
of an upcoming merger on which they could potentially advise five quarters ahead of time. In
contrast, one quarter prior to the merger, an investment bank might know that a merger is likely
and consequently decide to initiate coverage in the hopes of increasing the chances of winning
the advisory business.
Results show that the percent of advisors issuing recommendations on the acquirer
company increase substantially over time, from 52% of advisors five quarters prior to the
merger, to 68% one quarter prior to the merger, 73% one quarter after the completion of the
merger, and 82% five quarters after the completion of the merger. Column 2 shows similar
increases in the total number of recommendations per company. Moreover, the last column
shows that the median market capitalization of the acquirer companies is also increasing
substantially over time, suggesting that at least a portion of the increase in coverage is driven by
increases in firm size.
In an effort to isolate the portion of increases in advisor coverage that is driven by efforts
to win M&A advisory business, column 3 shows the percent of total recommendations that
6
For example, as discussed above, fees from advising M&A in 2006 exceeded $35 billion.
13
represent recommendations by the advisor firm. Interestingly, this percentage increases
substantially from five quarters prior to the announcement to one quarter following the
completion. The substantial increases around the time of the merger are consistent with efforts
to win and/or support the M&A advisory business.
Columns 4, 5, and 6 of Table 3 show similar statistics for advisor firm ownership in the
acquirer company. Similar to inferences from analyst coverage, we find increases in the
percentage of advisor institutions that own shares, from 58% five quarters prior to announcement
to 69% five quarters following completion. However, there is no evidence that increases in the
incidence of the advisor firm owning shares in the acquirer company are related to the merger.
In fact, advisors as a percent of all institutions that own shares in the acquirer company actually
decrease around the time of the merger, indicating that non-advisor institutions are investing in
the acquirer firms for the first time faster than advisor institutions. The observed increases in
advisor firm ownership are likely driven by increases in firm size.
Finally, column 7 of Table 3 shows the percent of advisors that both issue
recommendations and own shares in the acquirer company. Consistent with the other statistics,
this percentage increases markedly over time, from 27% five quarters prior to the announcement
to 58% five quarters following completion.
Table 4 looks more specifically at the dynamics of analyst recommendations and
institutional ownership in the period immediately surrounding the merger. Panel A focuses on
the analysts’ recommendations, Panel B on institutional ownership across all institutions, and
Panel C on institutional ownership across institutions that have an investment banking arm.
Looking first at Panel A, we see that non-advisor analysts are more likely to both upgrade
and downgrade the acquirer stock following announcement of the acquisition. One potential
14
reason for the greater activity in both directions among non-advisor analysts is that the
announcement of the merger was more of a surprise to them. However, it is nevertheless
surprising to observe a significantly greater portion of non-advisor analysts upgrading the stock.
It is possible that the higher average rating among advisor analysts prior to the merger
announcement explains part of this difference. (Moreover, the first two columns of Table 5
show that the average advisor rating continues to be higher following the merger.) More
consistent with our expectations is the difference in the percentage of analysts downgrading the
acquirer stock following the merger announcement, with 4% of non-advisor analysts
downgrading, compared to only 1% of advisor analysts.
The third row of Panel A shows the average recommendations as well as upgrades and
downgrades following merger completion, where upgrades and downgrades are relative to the
quarter prior to completion (but by definition subsequent to the merger announcement).
Consistent with predictions, we find that more advisor analysts upgrade the acquirer stock
following merger completion; however the difference is not significant. The percentage of
analysts downgrading the stock is similar between advisors and non-advisors.
Panel B examines similar issues as pertaining to the institutional ownership by the
advisor versus non-advisor firms. Columns 2 and 3 of Panel B of Table 4 suggest that the
advisor firm institutions are more active traders than non-advisor institutions. They are more
likely to both purchase and sell shares in the acquirer company in the quarter prior to and
following announcement.
Panel C suggests that at least a portion of this difference reflects differences between
investment bank versus non-investment bank institutions. It is possible that institutions behave
differently if they have an investment banking business. Such institutions may be pressured to
15
support the investment banking business. Alternatively, the traders associated with investment
banks may hold different positions for other reasons, for example lower trading costs, better
information on more firms, etc. To examine the extent to which such issues affect our analysis,
we restrict the sample of institutions to those that have an investment banking business.
Specifically, we restrict the sample of institutions to those who are in our M&A sample, i.e.,
those that served as advisors in at least ten deals over our sample period and had both analysts
and a trading desk.
Once we restrict the sample of institutions to those with a significant investment banking
business (i.e., Panel C), we find that institutions of the advisor firm are significantly more likely
to buy shares in the acquirer company, but there is no difference in the probability of selling
shares after the merger announcement.
Finally, both panels B and C indicate that non-advisor institutions are more likely to
purchase shares and less likely to sell shares following completion of the merger. This is exactly
opposite our prediction. In an attempt to understand this result, we re-calculate these statistics
based solely on cash mergers. Within the cash subsample, we find no evidence that non-advisor
firms are more likely to buy shares following completion. This leads us to believe that this
apparently puzzling result in the full sample reflects the exchange of shares in stock mergers, in
particular the institutions that had held shares in the both the target and acquirer company now
owning more shares of the combined entity.
In sum, Tables 3 and 4 provide substantial evidence of both analysts and institutional
traders behaving differently when they belong to the advisor versus non-advisor investment
bank. The next section examines this conclusion in more depth, by investigating the relation
between analyst recommendations and institutional trades.
16
5. Relation between analyst recommendations and institutional trades
As a first step towards understanding the relation between analyst recommendations and
institutional trades, Table 5 provides some descriptive evidence. Quarters -3 through -1, as
labeled in the top row of each panel, represent the quarters prior to the merger announcement,
and quarters +1 through +5 represent quarters following merger completion. For example,
consider a merger announced on April 12th, 1998 and completed on August 20th, 1998. In this
case, the quarter -1 institutional trading data would be measured on March 31st, 1998 and the
quarter -1 analyst recommendation data would be measured on March 15th, 1998. For quarter -2,
institutional trading data would be measured on December 31st, 1997 and analyst
recommendation data would be measured on December 15th, 1997. Analogously, quarter +1
institutional trading data would be measured on September 30th, 1998 and quarter +1 analyst
recommendation data would be measured on September 15th, 1998. Upgrades and downgrades
are measured relative to the analyst’ recommendation one quarter prior.
Looking at the first row of Panel A, we see that there are 18 instances of advisor firm
analyst upgrades three quarters prior to merger announcement. Of these 18 cases, 44% are
associated with decreases in institutional holdings by the advisor firm, relative to the previous
quarter, and 50% are associated with increases in institutional holdings by the advisor firm. To
the extent that analyst upgrades are viewed as positive information, we would expect them to be
associated with a greater percentage of affiliated institutions buying shares, versus selling shares.
However, we see little evidence of this at quarter -3.
Evidence of traders following affiliated analysts recommendation changes is strongest in
quarters -1 and +2. (Note that inferences on quarter +1 are difficult because the institutions are
17
so often net buyers of shares in stock acquisitions.) For example, upon an affiliated analyst
upgrade in quarter -1, 67% of in-house institutions (i.e., institutions within the same investment
bank) buy shares compared to only 27% selling. Similarly, in quarter +2 after the merger, an
analyst upgrade is associated with 68% of in-house institutions buying compared to only 32%
selling. However, little if any relation is seen in the quarters farther from the merger.
Inferences are similar based on downgrades. To the extent that downgrades contain
negative information, we would expect a greater frequency of selling by affiliated institutions,
compared to buying. Consistent with this prediction, an affiliated analyst downgrade in quarter
+2 is associated with 65% of in-house traders selling, compared to only 23% buying. Statistics
are similar for quarters 3 and 4, albeit weaker. However, there is no evidence of any relation in
other quarters.
In sum, Table 5 shows a substantially stronger relation between analyst
upgrades/downgrades and in-house trades immediately around the merger, and little if any
relation at other times. Our evidence is somewhat inconsistent with the findings of Chan, Cheng,
and Wang (2005) who find a significant relation between analyst recommendations and in-house
trading throughout time. It is possible that their larger sample size (which they obtain by looking
at all firms across a ten-year sample period) gives them more power to find significant
differences. Nevertheless, it remains the case that our analysis suggests a stronger relation
around the merger event.
Tables 6 and 7 examine this relation between analyst recommendations and changes in
institutional holdings in a regression framework. The matching of the merger data, institutional
holdings data, and analyst recommendations data is as described above. The dependent variable
is the change in advisor shares held from quarter t-1 to t, divided by the quarter t-1 holdings. For
18
each firm, regression observations include t-4 to t-1 (relative to announcement) and t+1 to t+5
(relative to completion). We omit the period of time between the announcement and completion
because the time varies so substantially across firms, from 0 days (in cases where the
announcement and completion date are the same) to over 18 months. In each regression, the
independent variable of interest is the change in advisor analyst recommendation, defined as the
advisor recommendation outstanding immediately prior to the quarter t holdings date minus the
advisor recommendation outstanding immediately prior to the quarter t-1 holdings date. This
change is then multiplied by -1, so that a positive recommendation change can be interpreted as
an upgrade and a negative recommendation change as a downgrade.
Control variables include dummies for the level of the advisor recommendation at the end
of quarter t-1, to account for the fact that an analyst with a strong buy cannot upgrade. We only
include dummies for strong buy, buy, and hold, because there are so few observations with lower
recommendations (sells and strong sells). Finally, we also include the change in market
capitalization of the acquirer firm, to account for the strong positive relation between
institutional ownership and firm size. Institutions are arguably more likely to increase
shareholdings when firm size is increasing.
Looking first at Column 1, we see a significant positive relation between changes in
advisor institution shareholdings and changes in advisor analyst recommendations. It appears
that advisor firm institutions are trading consistent with advisor firm analyst recommendations.
However, column 2 shows that this relation is actually only significant for the quarters following
merger completion. The interaction term, change in advisor analyst recommendation * post
merger dummy, is significantly positive, with a t-statistic of 3.07. In contrast, the interaction
term advisor analyst recommendation * pre merger dummy is not significant. Results suggest
19
that traders buy and sell in line with analyst recommendation changes following a merger, but
not before.
Finally, column 3 compares the effects of advisor firm versus non-advisor firm analysts
on advisor firm institutional trading. In the post merger period, advisor firm traders appear to
pay close attention to their own analysts, but disregard non-advisor analysts. In contrast, in the
pre merger period, the advisor firm traders pay significant attention to the non-advisor consensus
analyst estimate, but disregard their own firm’s analysts.
Regressions in Table 7 are specified similarly to those in Table 6, except for the fact that
the sample is restricted to the quarters following the completion of the merger, the only period
where we find evidence of a relation between analyst recommendations and institutional trades
by the advisor firm. Table 7 includes various control variables that might affect institutional
trading, such as method of payment (cash versus stock), relative size of the merger, and market
capitalization of the acquirer firm. Inferences are all similar, showing a significant positive
relation between changes in advisor analyst recommendations and changes in advisor
institutional holdings.
Results in tables 6 and 7 are consistent with advisor firm analysts having particularly
valuable information on the acquirer firm following a merger, and consequently the in-house
traders acting on their recommendations. In contrast, prior to the merger the advisor firm
analysts have no better information than analysts at other banks, and consequently the traders are
more likely to look at consensus recommendations. While this interpretation fits our results, it
does not incorporate any conflicts of interest, which analysts are likely to face. We investigate in
more detail the role of analyst information versus analyst conflicts of interest in the next section.
20
6. Information sharing versus conflict of interest
Results from tables 5, 6, and 7 indicate that advisor firm institutions trade in line with
advisor firm analyst recommendation changes in the quarters immediately following the merger.
To the extent that the advisor firm analysts have particularly valuable information, this is exactly
what we would expect. However, as discussed earlier, there is also substantial evidence that
affiliated analysts face serious conflicts of interest – the investment bank advising the acquirer
firm might encourage its analysts to upgrade the acquirer stock. In this section, we investigate in
more detail the effects of such conflicts of interest.
To examine the conflict of interest motivation for advisor analyst upgrades, we classify
investment banks into various categories based on their source of revenue. We posit that
analysts working for institutions in which investment banking is a more important source of
revenue will face greater conflicts of interest, for example stronger pressures to upgrade stocks
of companies for which the bank has recently served as advisor on an acquisition
For each advisor investment bank, we download the income statement from the bank’s
10K. Data limitations restrict this sample to those investment banks that are publicly traded.
This limits us to 25 of the investment banks. However, these 25 banks served as advisors in the
vast majority of our acquisitions. Investment banks are required to describe the source of their
revenues, and the banks generally break down the revenues into those from investment banking,
those from proprietary trading, and also those from various other activities on which we are not
focusing. Thus, for each bank and each year, we are able to determine the percent of revenues
from investment banking versus proprietary trading. For each year, we classify firms with
above-median percent of revenues from investment banking as high investment banking firms.
Similarly, firms with above-median percent of revenues from trading are classified as high
21
trading firms.
If the analysts from the high investment banking firms face more serious conflicts of
interest, then we would expect these analysts to be more optimistic regarding the acquirer firms,
particularly in the period following the merger. Table 8 provides evidence consistent with this
prediction. Similar to previous analyses, all recommendations are for the acquirer company and
they are by the analysts working for the acquirer’s advisor. The first row of the table shows that
the average recommendation by the analysts working at low IB banks (i.e., banks where
investment banking is a less important source of revenue) is 2.00, compared to 1.87 for analysts
working at high IB banks. Recalling that lower numbers represent more optimistic
recommendations, this indicates that the analysts working for banks where the conflict of interest
from investment banking is likely to be the greatest offer more optimistic recommendations. The
difference of 0.12 is significant at the 1% level.
Rows 2 and 3 of Table 8 indicate that the difference between the recommendations of
analysts at high IB versus low IB banks is only significant over the period following the merger.
Prior to the merger, the recommendations of the two groups of analysts do not differ
significantly. However, following the merger, the average recommendation of the low IB banks
is 2.04, compared to 1.86 for the high IB banks. The difference of 0.17 is significant at the 1%
level.
Results in Table 8 suggest that analysts at the high IB banks are more optimistic
regarding the acquirer, possibly in an effort to support the investment banking business. If this is
the case, then we would expect traders at these banks (i.e., the high IB banks) to be less likely to
act on their analysts’ recommendation changes. Table 9 examines this proposition.
Similar to Table 7, Table 9 shows regressions of the changes in advisor firm institutional
22
holdings on changes in advisor firm analyst recommendations in the five quarters following the
merger. In fact, column 1 of Table 9 is nearly identical to Column 1 of Table 7, the only
difference being that it is based on the smaller sample size used throughout Table 9, i.e., on those
mergers for which we have sources of revenues for the advisor investment bank. Similar to prior
findings, we find a significant positive relation between changes in advisor analyst
recommendations and changes in advisor institutional shareholdings.
In column 2, we replace the change in advisor analyst recommendation with two
interaction terms, the change in advisor analyst recommendation times the high investment bank
dummy and the change in advisor analyst recommendation times the low investment bank
dummy. (Recall that the high (low) investment bank dummy equals 1 for those advisor firms for
which revenue from investment banking is above (below) the median, 0 otherwise). Results
suggest that the significantly positive relation between advisor firm recommendations and
trading only exists among those banks that receive a relatively low portion of their revenue from
investment banking. It seems that the investment bank traders avoid following the in-house
analyst recommendations around mergers in those cases where the conflicts of interest from
investment banking are likely to be the greatest, i.e., in those banks that generate the largest
portions of their revenues from investment banking.
The regression in column 3 is similar to that in column 2, except that it divides the
recommendation changes according to the importance of trading revenue for the bank.
Interestingly, results indicate that the positive relation between advisor analyst recommendation
changes and in-house trading is concentrated among those firms that rely most heavily on trading
as a source of revenue. Perhaps banks that rely most on trading revenues provide different
incentives to their analysts. Results suggest that such banks encourage their analysts to make the
23
most informative recommendations, presumably to help the traders, rather than the most
optimistic recommendations that might aid the investment banking business.
Finally, the regression in column 4 shows that inferences on the importance of investment
banking revenue and of trading revenue are robust to including interaction terms for both within
a single regression.
7. Conclusion
Conflicts of interest are pervasive within investment banks. Prior literature has not
reached a consensus on the extent to which such conflicts affect analyst recommendations. We
take a new approach to this problem, by examining the interaction between analyst
recommendations and in-house trading. In addition, we broaden the examination of conflicts of
interest, by looking at the extent to which such conflicts affect both the analysts and the
proprietary traders.
Because we are interested in conflicts of interest, we choose a setting in which such
conflicts are likely to be severe: mergers and acquisitions. We find that advisor firm analysts
tend to be significantly more optimistic about the acquirer firms than non-advisor analysts.
Moreover, the advisor-firm trading desk buys and sells on advisor firm analyst recommendations
around the time of the merger. However, findings suggest that this positive relation is restricted
to those settings where the conflict faced by analysts is likely to be less severe. In the banks that
rely most on investment banking business, traders are significantly less likely to listen to their
analysts. In contrast, in banks that rely most heavily on trading as a source of revenue, traders
rely significantly more on their analysts’ recommendations, suggesting that these analysts’
recommendations are less biased.
24
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28
Table 1: Descriptive Statistics on M&A Sample
The sample consists of 726 mergers over the 1995 to 2004 period. For a merger to be included in the sample, the
acquirer firm must be followed by at least one analyst, as listed in the IBES database, and be owned by at least one
institutional investor, as listed in the Spectrum database. In addition, the target market capitalization must be at least
5% of the combined market capitalization of the target plus acquirer, where all market capitalizations are measured
one month prior to the merger announcement. A merger with two advisors is treated as two advisor-level
observations; there are 816 advisor observations across the 726 mergers. Mergers are classified into industries
based on the Fama-French 12 industry groupings.
Relative Size > 5%
Number of advisor observations
816
Number of unique mergers
726
Stock
403
Cash
96
Mixed
227
Year
# Mergers
Industry
# Mergers
1995
71
Consumer Nondurables
13
1996
72
Consumer Durables
8
1997
120
Manufacturing
65
1998
126
Oil, gas, coal extraction
40
1999
85
Chemicals and allied products
14
2000
80
Business Equipment
149
2001
47
Telephone & TV transmission
19
2002
34
Utilities
22
2003
56
Wholesale, Retail
55
2004
35
Healthcare, Med. Eqpt, Drugs
60
Finance
221
Other
60
29
Table 2: Descriptive Statistics – 5% sample – Medians
Descriptive statistics are provided for the sample of 726 mergers over the 1995 – 2004 time period. All variables, with the exception of relative merger size,
refer to the acquirer firm, and all statistics represent medians. Market capitalization (in millions) is measured one month prior to the announcement of the
merger. All other financial variables are measured at the fiscal year end preceding the merger announcement. Market-to-book equals the equity market
capitalization divided by the book value of equity. Book leverage equals the sum of short-term and long-term debt, divided by total assets. Market leverage
equals the sum of short-term and long-term debt divided by the total firm market value, where total firm market value equals total assets plus market value of
equity minus the book value of equity. Total assets, sales, sales/TA, EBIT/TA, and WC/TA are computed using the relevant Compustat data items. Relative
merger size equals the target market capitalization divided by the combined market capitalization of the target plus acquirer, where all market capitalizations are
measured one month prior to the merger announcement. Statistics are computed for the whole sample, conditional on whether or not the advisor bank to the
acquirer firm has an analyst issuing recommendations on the acquirer, as listed on IBES, and conditional on whether or not the advisor bank to the acquirer firm
owns shares in the acquirer firm, as reported on Spectrum. Asterisks denote whether the advisor analyst vs. no advisor analyst statistics are significantly
different, and similarly whether then advisor institutional ownership vs. no advisor institutional ownership are significantly different (*, **, *** represent the 10,
5, and 1% levels of significance).
Whole Sample
Advisor
Analyst
Following
(n=473)
No Advisor
Analyst
Following
(n=253)
Advisor
Institutional
Ownership
(n=443)
No Advisor
Institutional
Ownership
N=283)
Market Cap (mil)
1,855
2,274
1,201***
3,007
906***
Total Assets (mil)
1905
1974
1806*
2832
1,141***
Sales (mil)
865
1,007
1,329
496***
Sales / TA
0.64
0.67
0.57
0.65
0.64
MB
2.52
2.57
2.36
2.64
2.20***
Book leverage
0.20
0.21
0.18
0.21
0.18
Market leverage
0.13
0.13
0.12
0.13
0.13
EBIT / TA
0.08
0.08
0.07*
0.08
0.07
WC / TA
0.21
0.22
0.21
0.18
0.24**
Relative Merger Size
28%
27%
32%**
29%
646***
27%
30
Table 3: Incidence of advisor recommendations and share ownership in the acquirer companies
This table provides information on the incidence of advisor recommendations and advisor institutional ownership in the acquirer company, from five quarters
prior to the announcement of the merger to five quarters following the completion of the merger. Percent of advisors represents the percentage of the 816
advisor-level observations in which the advisor bank to the acquirer had an analyst following the acquirer. Average number of recs per company represents the
average number of analysts covering each acquirer firm. Percent of total recs by advisor equals the number of advisors covering each firm divided by the total
number of recs in each firm, averaged across the 726 mergers. Percent of advisors that own shares represents the percentage of the 816 advisor-level
observations in which the advisor bank to the acquirer owned shares in the acquirer. Average # insts invested in co equals the total number of institutions
invested in each acquirer firm, averaged across all mergers. Advisors as a % of total equals the number of advisors owning shares in each firm divided by the
total number of institutions owning shares in each firm, averaged across the 726 mergers. Percent of advisors that issue recs and own shares equals the percent
of the 816 advisors to the acquirer firms that both have an analyst following the acquirer and own shares in the acquirer. Company mkt cap equals the median
market capitalization of the acquirer firm.
Issuance of Recommendations
Ownership of Shares
% of
Advisors
that Issue
Recs and
own
Shares
% of
Advisors
Avg #
Recs per
Company
% of Total
Recs that
are by
Advisor
% of
Advisors
that Own
Shares
Avg #
Insts
invested
in co
Advisors
as % of
total insts
5 qtrs pre- ann’t
4 qtrs pre- ann’t
3 qtrs pre- ann’t
2 qtrs pre- ann’t
1 qtrs pre- ann’t
52%
57%
60%
64%
68%
10.3
10.5
10.8
11.2
11.4
7.8%
8.3%
8.9%
9.1%
9.4%
58%
59%
59%
61%
62%
173.9
180.1
187.2
195.3
202.6
0.58%
0.55%
0.52%
0.52%
0.48%
27%
34%
37%
39%
43%
$1,537
$1,637
$1,747
$1,800
$1,995
1 qtr post-completion
2 qtrs post-completion
3 qtrs post-completion
4 qtrs post-completion
5 qtrs post-completion
73%
76%
79%
81%
82%
11.5
12.1
12.5
12.7
13.0
10.3%
10.3%
10.2%
9.9%
9.7%
66%
68%
69%
70%
69%
241.9
247.1
250.2
251.3
250.3
0.43%
0.43%
0.45%
0.46%
0.47%
51%
53%
55%
58%
58%
$2,495
$2,571
$2,602
$2,592
$2,698
Company
Mkt Cap
($mil)
31
Table 4: Changes in the characteristics of advisor recommendations and share ownership in the acquirer companies
In Panel A, statistics are computed one month prior to the merger announcement, one month after the merger announcement, and one month following the
merger completion. For each time period, analyst upgrades and downgrades are relative to the prior month (i.e., for the one month after completion row,
upgrades are relative to the month preceding merger completion). An analyst upgrade represents a case where an analyst changed its recommendation on the
acquirer company to be more positive (e.g., from a 2 to 1, which represents a buy to a strong buy). In Panel B, statistics are computed one quarter prior to the
merger announcement, one quarter after the merger announcement, and one quarter following the merger completion. For each time period, the percent of
institutions that purchase additional shares or sell shares is measured relative to the previous quarter, similar to Panel A. Asterisks represent significant
differences between the advisor and non-advisors for each catetgory different (*, **, *** represent the 10, 5, and 1% levels of significance). Statistics in Panel C
are similar to those in Panel B, except that non-advisors only include investment banks in our sample (as opposed to all institutions).
Panel A: Advisor vs. Non-Advisor Analyst recommendations – All Analysts
Average Recommendations
1 mth prior to ann’t
1 mth after ann’t
1 mth after completion
Advisor
Non-Advisor
1.88
1.88
1.85
2.04***
2.01***
1.99***
% Analysts with upgrades
NonAdvisor
Advisor
1.1%
2.3%**
1.7%
5.5%***
3.9%
2.6%
% Analysts with downgrades
Advisor
Non-Advisor
1.8%
1.4%
2.5%
2.3%
3.9%***
2.3%
Panel B: Advisor vs. Non-Advisor Institutional Ownership – All Institutions
Average Ownership
by:
1 qtr prior to ann’t
1 qtr after ann’t
1 qtr after completion
Total
Institutional
Ownership
59.0%
60.9%
60.5%
Advisor
0.47%
0.47%
0.46%
NonAdvisor
0.47
0.45
0.37**
% Inst’s that purchased
additional shares
% Inst’s that sold
shares
Advisor
NonAdvisor
Advisor
NonAdvisor
55%
54%
63%
39%***
39%***
66%
41%
42%
37%
38%*
38%*
27%***
32
Table 4 (cont.)
Panel C: Advisor vs. Non-Advisor Institutional Ownership – Investment Bank Institutions Only
Average Ownership
by:
1 qtr prior to ann’t
1 qtr after ann’t
1 qtr after completion
Total
Institutional
Ownership
57.3%
59.1%
59.1%
Advisor
0.47%
0.47%
0.46%
NonAdvisor
0.43%
0.43%
0.36%
% Inst’s that purchased
additional shares
% Inst’s that sold
shares
Advisor
NonAdvisor
Advisor
NonAdvisor
55%
54%
63%
44%***
46%***
67%***
41%
42%
37%
42%
42%
29%***
33
Table 5: Relation between advisors’ recommendations and institutional holdings
For Panel A, each quarter, from three quarters prior to the merger announcement to five quarters following the merger completion, we compute the total number
of advisor analyst upgrades (i.e., upgrades to the acquirer stock by the investment bank that is advising the acquirer) relative to the end of the previous quarter.
For each of these upgrades, we compute the percentage of cases in which the advisor firm sold shares in the acquirer stock vs. bought shares in the acquirer stock
during that same quarter. Panel B shows analogous statistics for advisor analyst downgrades.
Panel A: Changes in Advisors’ institutional holdings when Advisor analysts Upgrade the acquirer
Quarter
-3
-2
-1
1
2
3
4
5
Sell Shares
44%
40%
27%
27%
32%
53%
47%
37%
Buy Shares
50%
60%
67%
73%
68%
47%
53%
63%
18
20
15
37
28
17
19
16
N Obs
Panel B: Changes in Advisors’ institutional holdings when Advisor analysts Downgrade the acquirer
Quarter
-3
-2
-1
1
2
3
4
5
Sell Shares
47%
31%
18%
38%
65%
50%
67%
46%
Buy Shares
47%
63%
76%
62%
23%
40%
28%
46%
15
16
17
29
26
20
40
35
N Obs
34
Table 6: Determinants of change in acquirer shares held by the advisor investment bank
This table shows fixed effects regressions, where the dependent variable is the change in shares owned by the
advisor investment bank in the acquirer company. Regressions are estimated over the 816 advisor-level
observations, for five quarters prior to the merger announcement to five quarters following the merger completion,
excluding those quarters in between the announcement and completion. The first independent variable is the change
in the advisor analyst recommendation of the acquirer company (measured over the same quarter but observed prior
to the measurement of institutional ownership). This recommendation is interacted with both a pre-merger dummy
and a post-merger dummy (equal in 1 in the quarters prior to and following the merger completion, respectively, 0
otherwise). Dummies for the level of the advisor recommendation at the beginning of the quarter are included
(strong buy, buy, and hold). The change in market capitalization represents the change in the market capitalization
of the acquirer company over the quarter. The last column also includes the change in the consensus
recommendation of the non-advisor analysts over the quarter and the level of the consensus recommendation among
non-advisor analysts at the beginning of the quarter. All recommendation variables are multiplied by negative 1,
such that higher recommendations and increases in recommendations can be interpreted as more positive
recommendations. T-statistics are shown in parentheses.
Model 1
Model 2
Model 3
Rec * Pre- Merger dummy (Advisor)
0.30
(0.78)
0.34
(0.87)
Rec * Post Merger dummy (Advisor)
0.75***
(3.07)
0.63**
(2.52)
Rec (Advisor)
***
0.57
(2.75)
Rec * Pre Merger dummy .(Non-Adv)
1.80**
(2.56)
Rec * Post Merger dummy (Non-Adv)
-0.02
(-0.05)
Strong Buy Dummy (Advisor)
Buy Dummy (Advisor)
Hold Dummy (Advisor)
0.63
(0.68)
0.39
(0.44)
0.04
(0.05)
0.64
(0.70)
0.43
(0.49)
0.11
(0.12)
Rec level non-advisor
Mkt Cap
R-Squared
N Obs
0.02*
(1.85)
12%
3876
0.02*
(1.85)
12%
3876
0.19
(0.20)
0.10
(0.12)
-0.06
(-0.07)
0.92***
(2.61)
0.02*
(1.72)
13%
3846
35
Table 7: Determinants of change in acquirer shares held by the advisor investment bank,
Quarters following the merger completion
This table shows fixed effects regressions, where the dependent variable is the change in shares owned by the
advisor investment bank in the acquirer company. Regressions are estimated over the 816 advisor-level
observations, over the first five quarters following the merger completion. The first independent variable is the
change in the advisor analyst recommendation of the acquirer company (measured over the same quarter but
observed prior to the measurement of institutional ownership. Dummies for the level of the advisor
recommendation at the beginning of the quarter are included (strong buy, buy, and hold). Rec non-advisor equals
the change in the consensus recommendation of the non-advisor analysts over the quarter, and Rec level non-advisor
equals the level of the consensus recommendation among non-advisor analysts at the beginning of the quarter. The
change in market capitalization represents the change in the market capitalization of the acquirer company over the
quarter, and the level of market capitalization equals the acquirer company market capitalization measured at the
beginning of the quarter. The stock dummy equals 1 if the merger was paid for with stock, 0 otherwise. The cash
dummy is measured analogously. Relative merger size equals the target market capitalization divided by the
combined market capitalization of the target plus acquirer, where all market capitalizations are measured one month
prior to the merger announcement. All recommendation variables are multiplied by negative 1, such that higher
recommendations and increases in recommendations can be interpreted as more positive recommendations. Tstatistics are shown in parentheses.
Rec (Advisor)
Strong Buy Dummy (Advisor)
Buy Dummy (Advisor)
Hold Dummy (Advisor)
Model 1
Model 2
0.76**
(2.48)
0.51
(0.35)
1.19
(0.88)
0.22
(0.17)
0.78**
(2.52)
0.57
(0.39)
1.23
(0.91)
0.23
(0.18)
0.02*
(1.82)
0.03**
(2.03)
-0.02
(-0.93)
2.67
(0.16)
2.45
(0.13)
-4.28
(-0.15)
Rec non-advisor
Rec level non-advisor
Mkt Cap
Mkt Cap
Stock dummy
Cash dummy
Relative merger size
R-Squared
N Obs
20%
2112
20%
2112
Model 3
0.59*
(1.88)
-0.36
(-0.25)
0.53
(0.39)
-0.11
(-0.08)
0.61
(1.06)
2.64***
(3.94)
0.02*
(1.75)
21%
2100
36
Table 8: Analyst Recommendations, conditional on source of investment bank revenue
Average analyst rankings of the acquirer company by the advisor investment bank are compared across categories of
investment banks. Low (high) IB banks represents those banks whose investment banking revenues as a percent of
total revenues fall below (above) the median (when ranked across all investment banks in that year). Average
rankings are reported over the entire event period (five quarters prior to the merger announcement through five
quarters after the merger completion), the pre-merger period (the five quarters prior to the merger announcement),
and the post-merger period (the five quarters following the merger completion). The last column reports the
difference in recommendation level, between low IB banks and high IB banks. Asterisks report the significance of
the difference (*, **, *** represent the 10, 5, and 1% levels of significance)..
Low IB Banks
High IB Banks
Difference
All Quarters
2.00
1.87
0.12***
Pre-Merger
1.96
1.90
0.06
Post-Merger
2.04
1.86
0.17***
37
Table 9: Determinants of change in acquirer shares held by the advisor investment bank,
Conditional on sources of advisor investment bank revenue
This table shows fixed effects regressions, where the dependent variable is the change in shares owned by the
advisor investment bank in the acquirer company. Regressions are estimated over the 816 advisor-level
observations for which we are able to obtain sources of revenue data for the advisor investment bank, over the first
five quarters following the merger completion. The first independent variable is the change in the advisor analyst
recommendation of the acquirer company (measured over the same quarter but observed prior to the measurement of
institutional ownership). The recommendation change is interacted with a high IB dummy, equal to one if the
advisor bank’s investment banking revenues as a fraction of total revenues fell above the median (when ranked
across all investment banks in that year) for the year of the merger, and zero otherwise. The recommendation
change is also interacted with a high Trade dummy, equal to one if the advisor bank’s trading revenues as a fraction
of total revenues fell above the median (when ranked across all investment banks in that year) for the year of the
merger, and zero otherwise. Similarly, recommendation changes are also interacted with low IB dummies and low
trade dummies. Dummies for the level of the advisor recommendation at the beginning of the quarter are included
(strong buy, buy, and hold). The change in market capitalization represents the change in the market capitalization
of the acquirer company over the quarter. T-statistics are shown in parentheses.
Model 1
Rec
(Advisor)
Model 2
Model 3
0.55*
(1.90)
Model 4
-0.87*
(-1.67)
Rec * Low IB
(Advisor)
0.94**
(2.50)
Rec * High IB
(Advisor)
0.14
(0.37)
1.58***
(2.82)
Rec * Low Trade
(Advisor)
0.24
(0.69)
Rec * High Trade
(Advisor)
1.06**
(2.49)
1.63***
(2.81)
Strong Buy Dummy
(Advisor)
0.01
(0.01)
-0.01
(-0.01)
-0.08
(-0.06)
-0.22
(-0.17)
Buy Dummy
(Advisor)
1.04
(0.89)
1.06
(0.91)
0.93
(0.80)
0.88
(0.76)
Hold Dummy
(Advisor)
0.07
(0.06)
0.12
(0.10)
-0.11
(-0.09)
-0.18
(-0.16)
Mkt Cap
0.02
(1.48)
0.02
(1.60)
0.02
(1.34)
0.02
(1.45)
R-Squared
N Obs
20%
1388
20%
1388
20%
1388
21%
1388
38