M&A Advisory Fees and Analyst Conflicts of Interest
David A. Becher
Jennifer L. Juergens†
November 2009
We identify how the structure and type of fees paid to advisors in mergers and acquisitions
generates conflicts of interest. A significant percentage of fees paid to advisors are fixed and
contingent upon deal completion, thereby creating incentives for advisors and analysts to
undertake actions to ensure merger completion rather than shareholder wealth maximization. We
find that pre-merger analyst recommendations are related to the type of fee that advisors receive
and, following the merger announcement, analysts revise their opinions on acquirers and targets
in response to these fees. Further, we document that acquirer optimism and target pessimism,
coupled with the fees generated by the advisory relationship, are positively related to merger
completion. Our results suggest that analysts face a new conflict of interest in mergers and
acquisitions, one which is aimed at deal completion as opposed to enhancing shareholder returns.
JEL classification: G32; G34; G21
Keywords: Mergers; analysts; merger success; merger fees, conflicts of interest
†
David Becher: Drexel University, Department of Finance, 218 Academic Building and Fellow, Wharton Financial
Institutions Center, University of Pennsylvania, Philadelphia, PA 19104, phone: (215) 895-2274, email:
becher@drexel.edu. Jennifer Juergens: The University of Texas – Austin, The Red McCombs School of Business,
Department of Finance, Austin, TX 78712, phone: (512) 232-6841, email: jennifer.juergens@mccombs.utexas.edu.
We thank seminar participants at the University of Texas – Austin for helpful comments. The current version of this
paper combines two previous working papers, “Analyst Recommendations and Mergers: Do Analysts Matter?” and
an earlier version of a working paper under the same current title. Earlier versions benefited from comments and
discussions with Tom Bates, Gennaro Bernile, Audra Boone, Jarrad Harford, Michael Hertzel, Ron Hoffmeister,
Laura Lindsey, Spencer Martin, Tobias Muhlhofer, Micah Officer, Ralph Walkling, Mengxin Zhao, as well as
seminar participants at Arizona State University, Villanova University, the 2007 Conference on Financial
Economics and Accounting, and the 2005 Financial Management Association and FMA European Meetings. We
thank Andrew Koch, Kurt Miling, Wenjing Ouyang, and Elisa Scinto for excellent research assistance.
In the past two decades, academics, regulators, and practitioners have carefully examined
the conflicts of interest faced by securities analysts.1 At the forefront of the controversy has been
the interaction between investment banking divisions and research departments, where analysts
provided excessively optimistic investment opinions about current and potential investment
banking clients. Following the burst of the technology bubble in 2000, regulators took steps to
reduce these conflicts and curb analysts’ excessive optimism. This intervention resulted in
revisions to rules and regulations governing brokerage firms and securities analysts as well as
over $1 billion in fines to the top 12 brokerage firms in 2002 and 2003. More recent research has
explored areas beyond the banking conflict that could drive analyst biases, including trading and
brokerage commissions, access to information, and cognitive biases. One area that has been
underexplored is the relation between merger (M&A) advisory services and securities analysts.
Compensation to brokerage firms derived from M&A advisory services is nearly twice as large
as that derived from banking revenues, suggesting that analyst conflicts exist in this setting as
well. In this paper, we examine the conflicts of interest that challenge analysts around M&A
activity, and determine whether these conflicts affect the probability a merger is consummated.
Although analyst recommendations have been studied in the context of mergers, much of
the existing literature has attempted to fit existing conflicts, namely over-optimism by analysts
about firms under research coverage, to mergers.
Many of these papers examine analyst
opinions of acquirers in isolation or focus on small subsets of the overall merger market, such as
tender offers. The general conclusion is that either no conflicts exist around mergers (Bradley,
Morgan, and Wolf, 2007; Calomiris and Singer, 2004) or excessive optimism is a function of
client pressure by acquirers (Kolasinski and Kothari, 2007). We argue that because of the nature
of M&A activity and the structure of the compensation received by advisors to the deal, standard
1
Over 60 academic papers have explored analyst conflicts of interest as of November 2009.
2
conflicts of interest and the subsequent optimism generated by analysts do not apply when both
acquirers and targets are examined.
Studies of traditional conflicts of interest for analysts contend that the biases affecting
analysts are asymmetric, and that one way to influence new banking deals, trading, or access to
information is to provide optimistic recommendations for firms under research coverage, which
are related to the compensation structure for analysts.2 Both investment banking and brokerage
conflicts are directly related to analyst compensation, where analysts share in the fees garnered
by increased deals (either in the size or frequency of IPOs or SEOs) or receive a percentage of
commissions generated from trading that takes place through the brokerage firm. While client
pressure or currying favor with management does not directly affect analyst compensation,
analysts benefit from access to valuable information from managers in the form of more timely
or accurate information, allowing them to obtain All-Star rankings, promotions, or placements at
more important brokerage firms (Hong and Kubik, 2003). Although new regulations by the
NYSE, Nasdaq, and the SEC were introduced in 2002 to prevent analysts from receiving
compensation linked to specific underwriting deals, to this day analysts are compensated by a
percentage of aggregate banking fees and brokerage commissions generated.
Moreover, these
new regulations have little to say about compensation derived from other advisory services, such
as those related to M&A.
The structure of M&A advisory fees differs from underwriting fees thereby altering the
relation between analysts and the firms they follow. In general, M&A advisory fees are fixed and
contingent upon merger completion rather than based on a percentage of the transaction value.
Thus, analysts have an incentive to issue recommendations that ensure merger completion rather
2
See, Agrawal and Chen, 2007; Bradley, Clarke, and Cooney, 2007; Derrien, 2006; Dugar and Nathan, 1995; Irvine,
2003; Kolasinski and Kothari, 2007; Michaely and Womack, 1999; Rajan and Servaes, 1997; among others, for a
review of conflicts of interest for analysts.
3
than deal value maximization for either acquirer or target shareholders, leading to a new conflict
of interest for analysts. This conflict of interest, however, may not be limited to the analyst alone.
M&A fees may also affect the roll of advisors in mergers. Early work suggests contingencybased fee structures provide incentives to ensure merger completion (Hunter and Walker, 1990;
McLaughlin, 1990, 1992), but can lead to conflicts of interest with shareholders because the
advisor’s goal is also deal completion.3 Hunter and Jagtiani (2003) provide additional support to
these studies. Recent studies suggest advisors use fees derived from fairness opinions (advisorybased) to ensure deal completion and maximize their compensation (Kisgen, Qian, and Song,
2007; Makhija and Narayanan, 2007). In this paper, we explore more fully the M&A fee
structure, analyzing the relation among M&A fees, analyst recommendations, and subsequent
merger outcomes.
Using a sample of mergers between publicly traded companies announced between 1993
and 2008, we investigate how the structure (and amount) of M&A advisory fees can lead to
biased analyst recommendations around mergers. We observe that the timing and direction of
analyst revisions differs for acquirers and targets and demonstrate that these recommendations
affect the probability of merger completion. Analysts issue optimistic opinions on acquirers, but
are pessimistic on targets (particularly after the merger announcement).
We analyze four stages of the merger process. We first examine whether pre-merger
announcement recommendations impact the type and size of fees paid to M&A advisors. The
results suggest advisors are aware of “market sentiment” toward acquirers and targets as
measured by the pre-merger announcement recommendations, and they structure their fees to
take advantage of this information. In particular, if analysts are pessimistic about acquirers or
3
Rau (2000) demonstrates it is the number of completed mergers, not the overall deal value or post-merger
performance that affects M&A advisory market share. Thus, advisors’ reputations do not appear to be damaged by
promoting non-value-enhancing mergers.
4
optimistic about targets before the merger announcement, fees are more likely to be percentage
based, especially if fees are paid to the target advisor.
In the second stage, we investigate whether the fee structure influences both affiliated and
unaffiliated analysts (Lin and McNichols, 1998) to revise recommendations following the merger
announcement to increase the probability of completion.4 As advisors’ effort and incentives are
tied to the fee type generated (Hunter and Jagtiani, 2003; Hunter and Walker, 1990), effort may
be reduced if the job of “selling the merger” becomes easier. One way to sell the merger is to
increase an acquirer’s acquisition currency or decrease the target cost by changing analyst
recommendations, especially in equity-financed deals. While post-merger announcement
optimism increases for acquirers when fees are paid, acquirer pessimism increases when no fees
are paid. Thus, changes in analyst recommendations do not appear to be value driven. Targets,
on the other hand, experience a significant decrease in ratings, regardless of whether fees are
paid. However, target pessimism is significantly higher if the target pays an advisory fee relative
to observations where no fee is paid. In multiple regression analysis, target fees are positively
related to increased acquirer optimism and target pessimism after the merger is announced.
Further, if targets pay a flat contingency fee to their advisors, the probability of postannouncement target pessimism increases.
These results suggest analysts adjust their
recommendations following the merger announcement when M&A fees are at stake.
4
Throughout the paper, we generally assume all analysts (affiliated and unaffiliated) are affected by the M&A
advisory conflict of interest. This is plausible for several reasons. Optimistic acquirer recommendations may occur
because analysts agree with a merger and other conflicts such as client/brokerage pressure or selection bias may
exist. Targets may generate negative recommendations if they are poor performers or the merger elevates target
prices sufficiently to warrant a downgrade. Similarly, spillover effects (Bradley, et al., 2007a), herding (Welch,
2000), and potential tacit collusion among analyst firms can lead to correlated affiliated and unaffiliated analyst
revisions. Although difficult to detect, Alicia Ogawa of Lehman Brothers addressed the issue of possible collusion
among analysts at the 2004 NASD Education conference on “Analyst Issues and Regulatory Responsibilities”.
Additional variables related to affiliation are included in our main tests in order to examine the impact of affiliated
revisions for acquirers and targets.
5
To determine whether analysts and advisors affect the likelihood of merger completion,
we next examine the interaction among analyst recommendations, M&A fee structure, and
probability of merger completion. Early research posits fees provide incentives to increase
advisor’s effort and aligns the objectives of advisors and shareholders. We document that the
prevalence of flat contingent fees could instead lead to conflicts of interest as incentives are
skewed toward deal completion rather than deal value maximization. Therefore, we expect fees
(particularly those that are fixed and contingent) to be positively associated with the probability
of completion. If fees influence analyst revisions, then the direction of analyst recommendations
will be related to merger completion. We implement logistic regression analyses to measure how
fees and recommendations affect the probability of completion.
Even after controlling for merger characteristics, we show both fees and
recommendations affect whether a merger is consummated. As expected, flat contingent fees are
significantly positively related to merger completion. Moreover, positive acquirer and negative
target recommendations are positively associated with merger completion, while negative
acquirer and positive target ratings, as well as affiliated target recommendations, are negatively
related to deal completion. Our results suggest that both analysts and advisors face conflicts of
interest and undertake actions to ensure merger completion rather than value maximization for
both acquirers and targets. This conflict is exacerbated in stock-financed deals, where analysts
can directly affect the acquisition currency or target cost through their recommendations.
Moreover, recent regulatory changes designed to curb conflicts of interest faced by analysts and
their relationship with investment banking divisions do not appear to have stemmed potential
conflicts that exist between M&A advisory services and research departments.
6
Lastly, we investigate post-resolution return performance to determine whether analysts
and advisors are driven by conflicts of interest or provide value-enhancing advice. Returns to
acquirers that have positive pre-completion recommendations are significantly lower than for
acquirers that are more negatively recommended, suggesting that analysts are likely to be driven
by conflicts of interest or are unable to identify good and bad investment opportunities.
Acquirers also generally underperform if a fee was paid to either the acquirer or target advisor,
suggesting that advisors have an incentive to complete deals, even if this leads acquirers to
overpay and results in lower subsequent stock returns.
In withdrawn deals, targets where
advisory fees are paid outperform targets where no advisory fees are paid, indicating target
advisors may provide valuable advisory services.
The remainder of the paper is structured as follows. Section I provides a background on
conflicts of interest for analysts, fees in the merger process, and how analysts and fees are related
to merger completion. Section II describes the data and sample selection. In Section III, we
investigate how recommendations impact the fees paid to M&A advisors while Section IV
provides evidence on how analysts respond to fees after the merger is announced. Section V
examines the interaction of analyst recommendations and fee structure on the probability of
merger completion. Section VI examines post-resolution returns and Section VII concludes.
I. Analyst Recommendations, Compensation, and Conflicts of Interest
Early work on analyst conflicts stems from the investment banking relation and the
compensation derived from underwriting fees. Michaely and Womack (1999), and Ljungqvist,
Marston, and Wilhelm (2006), among others, document that analysts associated with capital
market transactions (IPOs, SEOs, debt offerings) tend to issue recommendations that are more
positive than unaffiliated analysts. These optimistic opinions are either promised in order to
7
obtain new banking business or are issued as a means of attracting future banking-related
business from new or existing clients (Lin and McNichols, 1998). Further, analysts themselves
have benefited from their conflicts in that managers have rewarded analysts for their optimism
through a continued supply of information about the company (Lin, McNichols, and O’Brien,
2005). There is some evidence that the market is cognizant of the biases of affiliated analysts, as
evidenced by lower returns to new recommendations relative to unaffiliated analysts, for
example. It is only since 2002 that regulators have curtailed analyst compensation driven by
particular banking deals, thus somewhat mitigating one conflict faced by analysts.
Banking fees, however, are not the sole driver of excessive optimism. More recent
studies have focused on brokerage commissions, the analyst-firm relationship (client pressure,
currying favor), and selection bias as conflicts faced by analysts. Each of these potentially
generates positive recommendations by analysts, regardless of banking affiliation, which helps to
explain the pervasive optimism by all analysts prior to new regulations.5
I.A Evidence on Analyst Conflicts
Although access to management does not directly provide financial remuneration to
analysts,6 both brokerage and banking conflicts are directly related to the compensation received
by analysts and hence their recommendations. Most research departments are cost centers as
opposed to direct revenue producers. One way analysts are compensated is through indirect
brokerage commissions arising from trade related to their recommendations. Agrawal and Chen
5
Ljungqvist, Malloy, and Marston (2009) observe the average I/B/E/S recommendation between 1993 and 2006 is a
buy or a “2” on the I/B/E/S standardized ratings scale, which goes from strong buy (“1”) to strong sell (“5”).
6
One could argue access to management provides analysts with better information, thereby increasing the analyst’s
probability of being ranked in one of several ranking polls, such as the Institutional Investor All-American Research
Team poll, which does have financial consequences, either in terms of bonuses or the ability to move to a more
prestigious investment bank (Clarke, et al, 2006).
8
(2007), Irvine (2003, 2004), Jackson (2005), and Beyer and Guttman (2007) suggest that buy
ratings generate more trades than sell ratings.
With sell ratings, generally only existing
stockholders can sell on a downgrade. Buy recommendations, however, can be pitched to both
existing and new clients, thereby increasing trading volume for upgrades.
Optimistic
recommendations, therefore, are more valuable than pessimistic recommendations from a trading
perspective, leading to one cause of analyst conflict.
A second source of analyst compensation is through investment banking fees.
Investment banking fees are based on a percentage of the total capital raised in initial public
offerings, as well as follow-on stock and debt offerings (Chen and Ritter, 2000). The greater the
capital raised, the larger the fees for the banks, and the larger the subsequent compensation paid
to analysts. Between 1994 and 2007, global investment banking fees rose from approximately
$3 billion to $21 billion per year.
Prior to 2002, a substantial portion of some analysts’
compensation was directly tied to investment banking fees earned by the firm. Analysts such as
Jack Grubman of Salomon Smith Barney and Henry Blodgett of Merrill Lynch became better
known for their prowess at generating banking business than for their research skills. In turn,
these analysts received large salaries (in excess of $10 million per year) specifically tied to
banking business rather than the quality of research coverage. Even after significant price
declines in 2000 and 2001, anecdotal evidence suggests analysts provided excessively optimistic
recommendations for their banking clients in return for large remuneration. Although the 2002
Global Research Analyst Settlement (GRAS) was designed to curb analyst compensation related
to specific banking deals, analysts are still compensated from the pool of banking fees today.
9
I.B M&A Advisory Fees
Much of the existing literature on analyst compensation to date has focused on capital
markets transactions instead of the market for corporate control; there has been little discussion
on how analysts are compensated in M&A transactions.7 Mergers differ from the market for new
capital in several ways. First, a substantial number of mergers take place without the use of
advisors. Second, the average size of public-to-public mergers is significantly greater than the
average amount of capital raised in either IPO or SEO transactions.8 Third, merger complexity
can be significant depending upon the deal attitude (friendly or hostile), method of payment, and
regulatory approval, especially among firms in related industries.
Although merger parties have the option of undertaking the merger without advisors,
Servaes and Zenner (1996) show that as the complexity and asymmetric information increases in
the transaction, acquirers and targets are more likely to hire M&A advisors. Moreover, the
choice of advisor is significantly related to the wealth gains by acquirers and targets (see Bowers
and Miller, 1990; Rau, 2000; Saunders and Srinivasan, 2001; Allen, et al., 2004). In our sample
of public mergers, 54% of acquirers and 72% of targets hire at least one advisor to assist in their
transaction, thereby generating M&A advisory fees for the bank.
While fees generated from investment banking increased by a factor of seven between
1994 and 2007, global M&A advisory fees increased by more than a factor of 15 from $3 billion
to over $45 billion during this same period. As noted, M&A fees are generally fixed (or flat)
when the terms of the merger are set, and thus independent of the final deal value. These fees
are further split into an advisory fee (such as fairness opinions) paid regardless of deal outcome
7
An exception is Kolasinski and Kothari (2007), who mention that fees attributed to M&A exceed that of banking.
The average transaction value in our sample is slightly over $1 billion, while the average IPO (“The Sarbox
Monster”, Wall Street Journal online, 4/26/2007) or SEO (Liu and Malatesta, 2005) capital raised was slightly over
$100 million for the same time period.
8
10
as well as an “incentive-based” component paid as a contingency fee that is only received upon
merger completion.
Hunter and Walker (1990) suggest contingency fees provide proper incentive alignment
for advisors, since compensation is dependent upon the outcome, and gains are positively related
to fee size.
McLaughlin (1990, 1992) argues that the incentive feature associated with
contingency fees can lead to conflicts of interest by advisors since their objective is not to
maximize acquirer or target shareholder returns, but to finalize the transaction regardless of the
overall value. This could lead acquirers to overpay for targets, or targets to accept sub-optimal
bids. The general conclusions of these early studies are that the contingent structure of the
advisory fees works to align incentives rather than promote conflicts of interest between
advisors, acquirers, and targets. Of note, however, is that the contingent structure of fees in these
samples is predominantly percentage based (on either shares tendered or overall transaction
value). In our sample, contingency fees, especially for acquirers, are largely fixed at the time the
advisor is engaged and therefore not dependent upon final transaction value. This finding
suggests that potential conflicts of interest have increased in recent years.
To further exacerbate the potential conflict for advisors, the contingency fee is, on
average, the largest part of total compensation received. In our sample, the average advisory fee
is approximately $1 million, while contingency fees are $3 million for acquirers and $3.5 million
for targets. Hunter and Jagtiani (2003) observe that contingency fees (in relation to total fees
paid) have a significant role in expediting deal completion, whereas advisory fees, regardless of
the fee size, do not impact deal completion. Rau (2000) further finds that contingency fee size is
directly related to the advisor’s market share, which is a function of the percentage of deals
completed. More recently, studies have shown that advisory fees, such as fairness opinions also
11
impact the probability of deal completion (Kisgen, Qian, and Song, 2007; Makhija and
Narayanan, 2007).
I.C Recommendations, Fees, and Merger Completion
The evidence thus suggests that the advisor’s incentive is not to maximize value for either
the acquirer or target shareholders, but to ensure deal completion. We argue that advisors can
affect the deal outcome through recommendations provided by analysts. If fees were based on
the overall deal value, we would expect analysts to provide optimistic ratings for acquirers and
especially for targets.9
If the compensation structure, however, is independent of the overall deal size, then
analysts will provide recommendations that lead to deal completion, as they can affect either the
acquisition currency and the target cost through their recommendations (particularly in stockfinanced deals). Prior work has shown that the higher the acquisition currency or the lower the
target cost, the more likely the merger will be completed (Rhodes-Kropf, Robinson, and
Viswanathan, 2005). We further link this to analyst recommendations, and find that
recommendations that increase acquisition currency (positive acquirer ratings) or decrease target
cost (negative target opinions) are positively related to deal completion. On the other hand,
negative ratings on acquirers and positive ratings on targets decrease the likelihood of merger
consummation. By combining these results with the structure of M&A advisory fees, we provide
evidence of a new conflict of interest faced by analysts.
9
While we anticipate that analysts should provide optimistic ratings for targets if they provided unbiased opinions,
at some point, the target valuation could increase to the point that the likelihood of completion declines when the
acquirer no longer believes that they can afford the target. Thus, a hump-shaped trade-off may exist, although
modeling this relationship is beyond the scope of this paper.
12
Acquirer optimism can occur for several reasons. Analysts can directly affect the
acquisition currency by raising or lowering their ratings. If the objective is deal completion, then
analysts will increase their ratings on acquirers around the merger. Alternatively, optimistic
acquirer opinions could simply occur because analysts are enthusiastic about the deal. Since the
acquirer is the surviving entity after the merger, banking, brokerage, and client pressure conflicts
still exist between analysts and acquirers.
Unlike acquirers, the incentive for optimism on targets is muted if deal completion is the
objective.
Although numerous studies show that targets generally have poor operating
performance in the year prior to the merger announcement, we find that acquirers and targets
have similar investment ratings (approximately a buy) –100 to –50 days prior to the merger
announcement.
After the merger is announced, target ratings become increasingly more
negative; the average target rating drops to a hold, but remains a buy for acquirers.10 If operating
performance alone drove this pessimism, then target ratings should be lower in the preannouncement period as well.
Targets, on average, have a substantial price increase at a merger announcement. Conrad
et al. (2006) show that following large price moves, upgrades are just as likely as downgrades.
Thus, if price drives recommendations, we would be equally likely to see upgrades and
downgrades to targets in the post-announcement period. If analysts are enthusiastic about the
merger and make recommendations to maximize target shareholders’ value, we would expect
optimism by analysts. Instead, target pessimism suggests analysts systematically downgrade
targets to reduce the overall target cost (especially in stock-financed deals), thereby leading to
10
Although the announcement of a merger provides some uncertainty resolution for both acquirer and target
shareholders, the announcement does not render analyst recommendations following the merger irrelevant. In
unreported tests, we examine returns to upgrades and downgrades separately and find no significant difference in
returns for either acquirers or targets from before and to after the merger announcement.
13
merger completion. Further, we see increased pessimism at the target advisory firm, indicating
the target firm is onboard with ratings changes that ensure deal completion. The increased
pessimism on targets coupled with the increased optimism on acquirers suggests that conflicts of
interest drive ratings, where this conflict appears to be driven by the structure of M&A advisory
fees rather than traditional conflicts of interest.
II. Data and Sample Selection
II.A Merger Sample
To test the association between analyst recommendations, advisory fees, and mergers, we
require a set of completed and withdrawn U.S. mergers. Our merger sample is collected from
SDC from 1993 to 2008.
Each announced merger must be resolved (either completed or
withdrawn) by December 31, 2008 to be included in our sample. We exclude all non-U.S. and
non-publicly traded acquirers or targets, divisions, divestitures, spin-offs, leveraged buyouts,
liquidations, observations where the form was not a merger (i.e. majority interest), unit trusts,
REITs, and ADRs. We limit our study to mergers with a single acquirer to reduce the problem of
confounding events on analysts’ inferences and the merger outcome.
From the Thomson/SDC Mergers & Acquisitions database we gather information on
acquirers and targets, including names and cusips, SIC codes, whether the merger was withdrawn
or a tender offer, number of days to completion, merger value, consideration offered, whether a
collar bid was made, and number of and names of merger advisors and advisory fees. Because of
potential problems with incomplete SDC data, we supplement merger advisory fees, merger
14
announcement and withdrawal dates, merger advisors, and collar type with hand-collected data.11
Financial variables are obtained from Compustat, while return data are from CRSP.12
Our sample includes 4,977 completed mergers and 811 withdrawals. Figure 1, Panel A,
displays the sample of completed and withdrawn mergers by year. The number of mergers
increases steadily from 1993 through 2001, peaking in 1998. The total number of announced
mergers declined significantly in 2002, following the technology bust and general economic
decline in 2001. Although activity increases in 2006 and 2007, possibly driven by greater
participation of private equity investors, transactions are 40% below the peak years of 19971999, before falling off sharply in 2008. For the period between 1993 and 2001, the frequency
of withdrawn mergers averaged approximately 17% of total announced mergers in a given year;
however, the frequency of withdrawals declines to 10% on average from 2002-2008.
Panel A of Table 1 details the merger characteristics. Approximately 40% of the mergers
in our sample are pure stock deals, while more than one-third of the acquirers have a dedicated
merger program (three or more deals in a five year window). The frequency of stock–financed
transactions declined significantly from 50% of all deals between 1993 and 2000 to 30%
between 2001 and 2008. Regardless of whether the merger is completed or withdrawn, the
average merger size is slightly greater than $1 billion and average premium is approximately
46%. Targets experience significant pre-merger announcement returns (-30 to -5 trading days
prior to the merger announcement), averaging 19% for completed mergers, but only 12% for
11
Dates are collected from Lexis-Nexis, Dow Jones Newswire, and others. Acquirer and target advisors are obtained
from Mergers & Acquisitions and Investment Dealers Digest. Although the collar sample was well defined in SDC,
the type was unidentified in over 50% of the cases. Information on collar types and merger advisory fees were
collected from news stories and press releases from Factiva, Lexis-Nexis as well as proxy statements from SEC
Edgar filings.
12
Return periods are: pre-announcement (–30, –5), announcement (–1, +1), resolution (+1, termination). Abnormal
returns are market-model adjusted, with an estimation period –240 to -31. Post-resolution returns are 3-, or 6-month,
and 1-or 2-year abnormal buy-and-hold returns for combined firms (completed) deals or acquirer and target
(withdrawn).
15
mergers that are eventually withdrawn. Target announcement period returns (-1 to +1) are 21%
for completed deals versus 15% for withdrawn ones. On average, there is a slightly negative
announcement period return for acquirers of approximately -1%.
II.B Analyst Recommendations
Sell-side analysts provide recommendations and earnings forecasts. Although revisions
to earnings forecasts impact stock prices and trading behavior of investors, recent research
indicates initiations of coverage (Irvine, 2003) and recommendation changes (Womack, 1996;
Barber, et al., 2001) have larger price and trading implications (Malmendier and Shanthikumar,
2007). We focus on the role of analyst opinions in the merger process and determine whether
analysts, through their revisions, impact merger returns and the outcome.
We collect analyst recommendations from I/B/E/S (Thomson Financial) from 1993 to
2008 and obtain a firm’s ticker, brokerage house, date of current and prior recommendations,
standardized current and prior recommendation codes (“1” strong buy; “5” strong sell), and type
of recommendation (upgrade, initiation, etc.). We analyze recommendations from -50 days
before the merger announcement through deal completion or withdrawal.13
Our 16-year sample period encompasses a number of potential structural shocks. Several
new regulations and rules in the second half of our sample (2001 – 2008) were designed to
change the way analysts do business and the information that analysts provide. The first of
these, Regulation FD, went into effect in October 2000 and limited the ability of analysts to gain
early access to material information from companies. The second, actions by the New York
13
Ljungqvist, Malloy, and Marston (2009) show that changes to the I/B/E/S database may affect the quality of the
recommendation data. However, because we are looking at data specific to particular events, and obtained our data
in 2003 for the sample between 1993 and 2000, and 2009 for the sample between 2001 and 2008, we do not believe
that our results will be affected by the I/B/E/S inconsistencies.
16
Attorney General, NASD, NYSE, and the U.S. Congress, under the guise of the Global Research
Analyst Settlement (GRAS), changed the way analysts are compensated, required more
consistent and coherent recommendation scales, and limited communication between investment
banking and research. This led to a massive rescaling of recommendations by brokerage firms,
indicating that comparisons between pre-GRAS and post-GRAS periods may be problematic
(Kadan et al., 2006).14 While Reg FD had a pervasive effect on analyst communications with all
companies under coverage, GRAS did not explicitly provide guidance on the M&A advisory
relationship and analysts.
Therefore, these shocks to the analyst information production
environment provide exogenous events to determine whether the regulations have worked to
mitigate potential conflicts of interest.
While rating strength may affect merger outcomes, we focus on positive and negative
recommendation changes/initiations. Jegadeesh, et al. (2004) show changes in recommendations,
not levels, have predictive power for returns. For our study, the use of changes rather than levels
is especially important, since GRAS led to widespread rescaling of recommendations, which
could affect any inferences drawn from using levels. Positive recommendations are initiations
with a strong buy or any upgrade change. Negative recommendations are initiations with a hold,
sell, or strong sell, and downgrades.15 We segment recommendations by direction since prior
research (Stickel, 1995; Womack, 1996) shows negative recommendations appear to be more
informative and have a larger price impact. We eliminate recommendations that do not fall into
the standard ratings system.
14
Although one goal of GRAS was to simplify recommendations issued by brokerage firms, I/B/E/S has not altered
their standardized scale. While the characteristics of brokerage-specific recommendations in each I/B/E/S category
may vary between the pre- and post-GRAS periods, further analysis into that issue is beyond the scope of this study.
15
Initiations with a buy recommendation and reiterations are excluded from our analysis as the direction is
ambiguous. While GRAS did somewhat shift the distribution of recommendations away from buy to hold,
recommendations on partially adjusted, and for most of our sample, buy recommendations were implicitly
considered to be hold recommendations.
17
Panel B of Table 1 details characteristics of the recommendation sample. Although our
sample contains 5,788 mergers, only 2,932 completed and 333 withdrawn deals have analyst
coverage. For analyses specific to recommendations, we focus exclusively on these 3,265
mergers, but include all viable mergers in our likelihood tests. Acquirers have significantly more
positive recommendations relative to negative ones than targets.
The ratio of positive
recommendations to negative recommendation is 0.88X for acquirers compared to 0.40X for
targets. Prior to the rescaling and significant increase in downgrades driven by GRAS, this ratio
was 1.14X for acquirers and 0.55X for targets. This relationship holds both before and after the
merger is announced, and whether the analyst is affiliated with the M&A advisor. There is a
significant increase in target pessimism following the merger announcement; the average
recommendation level declines to 2.79 from 2.33, and both affiliated and unaffiliated analysts
become increasingly pessimistic on targets post-merger announcement. There is no significant
change in the average acquirer recommendation pre- to post-merger announcement.
Panel B of Figure 1 shows the average recommendation for acquirers and targets from 50 days before the merger announcement through merger resolution by year of announcement.
In each year, the average acquirer recommendation is less than the average target
recommendation by approximately 0.4, although in some years, that difference widens
substantially. For both acquirers and targets, there is a significant increase in pessimism in 2002
and 2003 (corresponding to the GRAS settlement) and ratings stay elevated through 2008.
II.C The Structure of M&A Advisory Fees
We obtain merger advisory fees from several sources. Unlike capital market transactions,
neither advisors nor firms are required to report the amount or type of M&A advisory fees in
18
specific deals. While SDC provides some information on fee types and size for both acquirers
and targets, complete fee information for a substantial portion of our mergers is missing. We
supplement the fee data from two additional sources: SEC Edgar (proxies and S-4 filings) and
Factiva news stories. We maintain the original fee structure as presented in SDC for our
supplemental information. We segment merger advisory fees into two categories: contingent
fees and advisory fees. Contingent fees, which also include bust-up fees paid to the advisor upon
the withdrawal of a merger, are conditional upon some action by either the advisor or the firm,
such as the consummation of the merger. All other fees, including advisory, deal management,
fairness opinion, retainer, deal initiation, and seller representation fees, are categorized as
advisory fees, and are received by the advisor regardless of the outcome of the merger.16
When available, we report the dollar amount of advisory and contingency fees paid, as
well as the total paid by targets and acquirers. Since fee type may be equally as important as fee
size, we also report binary variables if the target or the acquirer pays an advisory or contingency
fee. Furthermore, SDC also reports binary variables based on whether the fee is flat or a
percentage of the total deal; thus we include these binary variables as well. Our prediction is that
flat contingent fees are more likely to generate conflicts of interest by analysts than either
advisory fees or contingent fees based on a percentage of the total deal value. We also construct
interaction variables between the fee type (advisory or contingent) and the structure of the fees
(flat or percentage-based).17
Table 1, Panel C, provides fee characteristics for acquirers and targets. On average,
advisory fees range from $850,000 to $1.22 million, although the median fees are substantially
smaller. For both acquirers and targets, contingent fees constitute the largest portion of the total
16
For every deal where the firm employed a contingency fee and/or a bust-up fee, we retain only the contingency
fee for deals that were completed and retain only the bust-up fee for those deals that were withdrawn.
17
Similar interaction variables are constructed for advisory and contingent fee amounts and the structure of the fee.
19
fee paid to advisors (54% to 62% on average; medians are 69% to 78%). Contingency fees are
slightly larger for target versus acquirer advisors ($3.55 million and $3.04 million, respectively).
Overall, flat contingency fees are the largest fees for both acquirers and targets. Unlike earlier
studies, we find that percentage-based fees are less frequently used for either acquirers or targets,
although targets are more likely to have a combination of flat and percentage fees than acquirers.
Although the average total fee is approximately $4.3 million dollars for acquirers and targets,
these fees constitute less than 1% of the total value of the consideration paid in the merger.
Figure 1, Panel C, displays the average total fees (advisory and contingency) paid to acquirer and
target advisors on an annual basis. The average fee size is larger in years where M&A activity is
in decline from the previous year, suggesting that advisors may charge higher fees in low activity
years to smooth the revenue streams driven by fees over time.
III. Do Pre-Announcement Recommendations Impact the Type of Advisor Fee?
In this section, we investigate whether analyst coverage and merger characteristics impact
the type and/or amount of fees paid to M&A advisors. Table 2 details how analyst coverage,
method of payment, and the probability of completion affects the type and size of fees.18 A
multiple regression approach is then applied in Table 3 to determine how pre-merger
announcement analyst activity (recommendations made in the 50 days prior to the
announcement) affects the type of fee paid to acquirer and target advisors, controlling for analyst
ratings, merger characteristics, and pre-merger returns.
From Panel A of Table 2, the average fee (advisory or contingency fees for either
acquirers or targets) is larger for firms with analyst coverage. This result is not surprising as
18
We cannot ex ante measure the probability that a merger will be completed at its announcement date, although
perhaps the market can to some extent. Instead, we use the ex post outcome of whether the merger is completed or
withdrawn to draw inferences about completion probabilities.
20
there is a positive correlation between analyst coverage and firm size, which could proxy for the
complexity of the deal (Servaes and Zenner, 1996). While advisory fees are slightly larger for
firms with analyst coverage, contingency fees are significantly larger in deals where there is
analyst coverage ($3.6 million and $3.9 million versus $1.8 million and $2.6 million, for
acquirers and targets, respectively). Further, the percentage of contingency to total fees is larger
for firms with analyst coverage than without.
Although we postulate that changes in analyst recommendations can influence acquisition
currency or target cost, the results for fee differences by method of payment are mixed. Pure
cash and pure stock transactions are examined in Panel B of Table 2. For targets, in particular,
there is no significant difference in fees for any of the fee amounts, regardless of the payment
method.
The exception is percentage-based fees, which are significantly larger in stock
transactions. Fees as a percentage of total value are larger for targets in cash financed deals,
suggesting deal size is smaller when the method of payment is cash rather than stock. For
acquirers, contingency fees are significantly larger in stock deals, but there is no significant
difference in the ratio of contingency fees to total fees between methods of payment. Unlike
targets, fees as a percentage of value are significantly larger in stock-financed deals for acquirers.
Panel C shows whether the probability of completion affects the size of the fee earned.
For both acquirers and targets, the advisory fee is significantly larger in withdrawn mergers than
in completed ones, while the contingency fee for targets is significantly larger in completed
deals.19 As noted in Section II.C, both contingency and bust-up fees are part of the contingentbased component of fees. The contingent fee paid to advisors in completed mergers is
significantly larger than the bust-up fee in withdrawn deals, although it should be noted that the
19
Although not statistically different, the average contingency fee in completed deals is $3.1 million whereas it is $2
million in withdrawn deals for acquirers. This difference seems economically large, and the lack of statistical
significance is likely due to the small sample size of withdrawn deals where acquirer advisory amounts are known.
21
frequency of bust-up fees is low. Similar to the full sample reported in Table 1, the portion of
the total fee driven by contingent fees ranges is 56% (65%) for acquirers (targets) while the
proportion declines to approximately 15% in withdrawn deals. In unreported results, we observe
that fee frequency is low for mergers that are subsequently withdrawn, suggesting the presence
of an advisory fee is positively related to merger completion. For those withdrawn deals where
fee data are available, fees are generally flat and advisory. This result implies advisors are
cognizant of the low probability of completion, and maximize their own compensation by using
flat, advisory fees. We explore this issue further in Section V.
The results from Table 2 imply that the type and size of fees paid to advisors is a function
of merger characteristics as well as analyst coverage. We next investigate in Table 3 the extent
that pre-merger announcement recommendations and merger characteristics affect the fee
structure for M&A advisors in a multiple regression setting. Although we believe that a causal
relation between recommendations and fees exists, we argue that this result will be more evident
in targets as the incentives to increase acquisition currency for acquirers should only manifest
after the merger is announced.
Since fees are negotiated prior to the merger announcement, our analysis of analyst
recommendations in Table 3 is restricted to those that occur prior to the merger announcement,
because they are less likely to be influenced by the advisory relationship.20 Given that acquirer
and target recommendations impact the likelihood of merger completion, both are included as
regressors for acquirer and target fees. Recommendations are delineated by direction to test our
premise that analysts make revisions (positive acquirer and negative target ratings) to ensure
merger completion. We further include a combination of own- and counterparty-advisor analysts
20
Although in some instances advisors have long-standing relationships with acquirers and targets or hire advisors
well in advance of the merger announcement date (Boone and Mulherin, 2007), often advisors are not hired until
just before or in some cases after the takeover had been announced.
22
for acquirers and targets, as affiliated analysts are optimistic on acquirers and pessimistic on
targets in general. Regressions in Table 3 also contain the number of acquirer and target analysts
and advisors (Branch, Higgins, and Wilkens, 2003; Jennings and Mazzeo, 1993; Luo, 2005), as
well as merger characteristics including method of payment and the presence of merger
programs (Bates and Lemmon, 2003), cash tender offers (Walkling, 1985), collar type21, log
value of the consideration offered, relatedness, and pre-announcement run-up returns. While we
examine each type of fee (flat, percentage, advisory, and contingent, plus the interactions of
these variables), the results presented in Table 3 are limited to the presence of a fee, flat
contingency fees, and percentage contingency fees.
For both acquirers and targets, the relation between positive acquirer and negative target
pre-announcement recommendations is negatively related to the likelihood that a fee is paid to
the advisors. If analysts are negative on targets prior to the merger announcement, the price of
the target should reflect this, and already be depressed, thereby lowering the cost of the
acquisition. Further, if analysts are optimistic on acquirers, the acquisition currency has already
increased, and therefore, the perceived benefits of hiring advisors may be low. When specific
fee types are investigated, we observe relatively little relation between recommendations and flat
contingency fees for acquirers or targets, but we find that for both acquirers and targets, the
likelihood of a percentage contingency based fee is increasing in the number of negative acquirer
recommendations.
As the number of positive pre-announcement target recommendations
increases, the likelihood that target percentage-based contingency fees are paid also increases.
Consistent with Servaes and Zenner (1996), as the size of a merger increases, the
likelihood that fees will be paid also rises. Further, when acquirers have merger programs in
21
Bruner (2004) details four separate categories, but due to data limitations and duplication of directional impact,
we categorize collars into these two categories along the lines of Officer (2006).
23
place, the likelihood they hire advisors or pay fees declines, but the presence of a merger
program increases the likelihood that targets will pay fees to their advisors. In nearly all cases,
target advisory fees are less likely to be paid in cash-financed transactions, suggesting that
method of payment is important in determining the likelihood and type of M&A advisory fees.
IV. The Effect of Fees on Analyst Recommendations Following the Merger Announcement
The results from Section III indicate that pre-announcement analyst recommendations are
related to the fee type sought by merger advisors, especially for targets. In this section, we more
closely examine whether analysts revise their recommendations after the merger is announced in
such a way to capitalize on the fee type. If the objective of the advisors and analysts is deal
completion, then the incentives to increase the acquisition currency by improving
recommendations on acquirers or lower the target cost by reducing target ratings would be
magnified with contingent fees. Increased acquirer optimism and target pessimism suggests
analysts are neither swayed by traditional conflicts nor by price patterns for acquirers and targets.
In Table 4, the effect of fees on cumulative post-announcement analyst recommendation
revisions are examined. In order to test whether analysts revise opinions when fees are paid to
advisors, we separate mergers into “fee” and “no fee” categories and analyze changes in the
frequency of positive and negative recommendations from the pre- to post-merger announcement
periods. We compute the percentage of positive recommendations in each category and conduct
difference of means tests to determine whether analysts become more optimistic or pessimistic
after the merger is announced, depending upon the existence of a fee.
When we condition on a fee (regardless of the type) being paid to either the acquirer or
the target, acquirer optimism and target pessimism increases in all samples. We first examine
24
how fees impact acquirer ratings. When acquirer fees are paid, the percentage of positive
recommendations in the post-announcement period increases from 47.5% to 50.7%, whereas
when no fees are paid, the percentage of positive recommendations declines from 49.3% to
48.7%. There is an increase in acquirer optimism when fees are paid to target advisors, and a
decrease in acquirer optimism in the no fee sample, although the difference is not statistically
significant. When target ratings are examined, there is a statistically significant decline in target
optimism when we condition on target fees in both samples, although the decrease is
significantly larger when target fees are paid. In the target fee (no fee) subset, the percentage of
positive target ratings declines from 39.6% to 17.9% (41.7% to 26.9%). When acquirer fees are
examined, there is a statistically significant increase in target pessimism in both the fee and no
fee subsets; however, the increase is pessimism is significantly larger if no acquirer fees are paid.
We observe similar results when we condition on the type of fee being paid. For
conciseness, we report only the presence of an advisory fee, flat contingency fee, and percentage
contingency fee, although our results hold for any fee category. For each of the subsamples
where acquirer fees are paid, we find acquirer optimism increases from 43%-52% positive
recommendations in the pre-announcement period to 54%-59% after the merger is announced.
When target fees are paid, we observe that acquirer optimism increases, with the exception of the
target flat contingency fee, although the difference is not statistically different from the no-fee
samples for any type of target fee.
Target pessimism increases after the merger is announced in all subsets. As noted above,
when targets pay a fee, the target optimism is statistically significantly lower after the merger is
announced than if the target does not pay a fee (17%-18% on average versus 21%-24% on
average, respectively). We observe significant declines in target optimism in both samples when
25
we condition on acquirer fees; however, the decline is significantly larger if no acquirer fee is
paid. While the target results when acquirer fees are paid appear to be inconsistent, Rau (2000)
indicates that acquirers tend to offer large premiums when acquirer advisors are used to induce
completion of the merger. Thus, analyst pessimism may be mitigated when acquirer advisors are
used. When advisors are not used, acquirers may exert pressure on the analysts that cover their
firms to reduce the target cost by issuing negative recommendations prior to completion. Our
results confirm this finding.
The univariate results presented in Table 4 suggest that analysts become pessimistic on
targets and optimistic on acquirers when either acquirers or targets retain advisory services. In
Table 5, the impact of fees on post-announcement recommendations is further analyzed. We
examine two sets of regressions, where the dependent variables are the number of positive or
negative recommendations for acquirers and targets. Our variable of interest in Panel A is an
indicator for whether a target or acquirer fee was paid. In Panel B, we delineate fees into
advisory, flat contingency, and percentage contingency for acquirers and targets.
Controls
include recommendation timing, affiliation, number of acquirer and target analysts and advisors,
the average recommendation level, and merger characteristics, including method of payment and
collar type, merger size, relatedness, merger programs, acquirer and target run-up returns, and
announcement period returns; controls are omitted from Table 5 for brevity.
Since both acquirer and target advisors may have analyst coverage, we include both
acquirer and target fees. Panel A of Table 5 shows a positive relation between the presence of a
target fee and an increase in both positive acquirer and negative target recommendations
following the merger announcement. Further, we observe that affiliated acquirer analysts are
26
more likely to provide positive acquirer recommendations, while affiliated target analysts are
less likely to upgrade and more likely to downgrade targets following the merger announcement.
When fees are segmented into type, both acquirer and target advisory fees are observed to
be positively related to increased acquirer optimism, while only target advisory fees are
positively related to increased target pessimism. If target advisors expect to receive a flat
contingency fee, it is less likely that positive recommendations are issued on the target. Method
of payment matters, as acquirer optimism increases in stock-financed transactions, but decreases
if the merger is financed by cash.
Although the results from Table 5 are slightly weaker than those presented in Table 4,
there is some evidence to suggest analysts revise their ratings in response to the acquirer and
target M&A fees. In the next section, we examine the joint effect of recommendations and
M&A fees on the probability that a merger will be completed.
V. The Effect of Analyst Recommendations and Fees on the Probability of Completion
Prior analyses suggest there is a relation between M&A advisory fees and the optimism
or pessimism in acquirer and target recommendations. The argument is that the fee type provides
adverse incentives for advisors and analysts to ensure merger completion rather than shareholder
value maximization. These actions include, but are not limited to, recommending acquirers pay
large premiums to targets (Rau, 2000) and revisions to recommendations to increase acquisition
currency (positive acquirer recommendations) or decrease target cost (negative target
recommendations). In this section, we analyze the relation among M&A fees, analyst
recommendations, and the probability of merger completion. We implement logistic regressions
augmented with the type and structure of the M&A advisory fees by including the presence of a
27
fee by acquirers and targets, whether the fee is advisory or contingent, and the interaction
between fee type (advisory and contingent) and structure (flat or percentage-based). Since we
posit and show some evidence that analysts revise their recommendations after the merger
announcement to ensure merger completion, only post-announcement recommendations are
included in the regressions. The results are presented in Table 6.
V.A Logistic Regressions
In Table 6, the dependent variable is an indicator for whether the merger was completed,
and four models are examined, which vary by the definition of fees in each of the models. The
first includes indicators for the presence of target and acquirer fees, but does not distinguish
between fee type. Model 2 separates fees into advisory or contingency fees, while Model 3
divides the contingency fee into flat or percentage-based.
In Model 4, both advisory and
contingency fees are split into fixed or percentage-based components. Other variables of interest
include the number of signed post-announcement recommendations and the number of affiliated
recommendations for acquirers and targets. We include as control variables those listed in
Tables 3 and 5, plus the days to resolution, which has been shown to be positively related to the
likelihood of merger completion.
In each model examined in Table 6, we note a significant positive (negative) relation
between analyst recommendations on acquirers (targets) and the likelihood of merger
completion, even after controlling for fees and merger characteristics. When fees are separated
by type, an increase in target pessimism is also positively related to merger completion. It is the
direction of the recommendation (not just the presence of one) that affects merger completion,
since negative acquirer and positive target recommendations are negatively related to merger
28
completion in each model. These results suggest that analysts can be instrumental in ensuring
merger completion by altering their recommendations after the merger is announced.
When fees are examined, Model 1 shows that it is the presence of the target fee, but not
an acquirer fee that is positively related to completion. In Models 2, 3, and 4, where fees are
split into various categories, we note a tension between acquirer advisory fees and acquirer
contingency fees. The likelihood of completion increases when acquirers pay flat contingency
fees. If acquirers pay advisory fees, however, the probability of completion actually declines.
This result suggests that if acquirer advisors are hired only to provide an opinion on the quality
of the merger, the merger is less likely to be consummated, perhaps because acquirers are less
likely to overpay. When flat contingency fees are charged instead, mergers are more likely to be
finalized, suggesting that a conflict of interest may exist.
For targets, both advisory and
contingency fees are significantly positively related to merger completion in Models 2 and 3.
When advisory fees are separated into fixed and percentage-based for targets in Model 4, there is
a small probability that likelihood of completion declines if a percentage-based advisory fee is
paid (p = 0.13).
The results from Table 6 suggest the recommendation type as well as type and structure
of M&A fees are significantly related to the probability of merger completion. Moreover, the
joint effect suggests that contingency fees, coupled with analyst recommendations designed to
increase acquisition currency or decrease target cost, impact whether a merger is undertaken.
Taken together with the results presented in earlier sections, it appears that fee types and
structure affect analysts’ impartiality. The combined effect is that both advisors and analysts act
to ensure merger completion rather than value maximization for acquirer and target shareholders.
29
V.B Robustness Tests
In this section, we discuss two additional issues that may affect the relation between
analysts, fees, and deal completion. The first is the effect of method of payment and the second
relates to changes in the regulatory environment designed to reduce or eliminate conflicts of
interests for analysts and investment banks.
Our results suggest that analysts can affect the probability of merger completion by
adjusting their recommendations.
While this effect may be pervasive for all mergers, those
financed by stock would more likely be affected than cash deals, since analysts can directly
affect acquisition currency or target cost through stock price adjustments. In untabulated results,
we rerun our analyses for stock-financed mergers. In the full sample (1993-2008), the results for
stock-financed deals are generally consistent with Table 6, although the coefficient on negative
target recommendations becomes statistically insignificant, as does the coefficient for acquirer
advisory fees. As noted, the use of stock to finance mergers declined significantly in the second
half of our sample. Therefore, when we restrict the sample to years where stock financing was
the predominant method of payment, the results are quantitatively similar to those presented in
Table 6.22 These results indicate that in periods where acquirers use stock to finance mergers,
analysts can have a significant impact on the outcome of mergers by changing their
recommendations to ensure merger completion.
Within our sample period, there are two exogenous shocks to the analyst information
production environment: Regulation FD, which became effective in October 2000, and the
Global Research Analyst Settlement, which began having effects as early as 2002 and was
22
In periods where the method of payment is predominantly cash, optimistic acquirer ratings are positively related
to the likelihood of merger completion, while negative acquirer opinions are negatively related to completion.
While the signs on the coefficients for targets are similar to those found for stock transactions, the statistical
significance is below traditional levels.
30
finalized in the third quarter of 2003. We investigate whether these shocks changed the relation
between analyst recommendations, fees, and merger completion. Because of the close proximity
of the events in time, and the small number of events that occur between the two, we segment the
data into three time periods: pre-Regulation FD (1993-October 2000), post-Regulation FD
(October 2000-2001) and post-GRAS (2005-2008), and the GRAS period (2002-2004).23
Although we have no reason to believe the relations between analysts, fees, and mergers changed
significantly during the GRAS period, one requirement of GRAS was a rescaling of
recommendations which artificially introduced a temporary downward bias in this time period.
We observe quantitatively similar results to Table 6 when either the pre-Regulation FD or the
post-regulation non-GRAS period is examined, although negative target recommendations are
not statistically significant. When the GRAS period is examined, as expected, we do not find
any relation between analyst recommendations and merger outcomes. While the objective of
GRAS was to reduce conflicts of interest in brokerage firms, it did not specifically address the
M&A advisory business or the M&A advisory relationship with regard to analysts. Our results
suggest the current regulatory environment has not hindered M&A advisory-analyst conflicts.
VI. Predicting Post-Resolution Returns
Many of our results presented in the preceding sections suggest that analysts face a new
bias in their recommendations around mergers, one that leads to excessive optimism for the
acquirer and excessive pessimism for the target. Perhaps, instead analysts are issuing unbiased
recommendations that reflect their true valuations of the merger parties. In this case, then
recommendations should be able to predict post-resolution returns for the combined firm in
23
Ideally, we would segment the data into pre-regulatory shock and post-regulatory shock periods; however, the
GRAS period artificially introduced new recommendations into the I/B/E/S data, thus we exclude that period from
the post-regulatory window.
31
completed mergers, or the individual firms in instances of withdrawal. Further, if we assume
that advisors are hired to obtain the best value for, as opposed to ensuring deal completion, then
acquirers and targets that hire advisors should outperform those that do not. In Tables 7 and 8,
we analyze buy-and-hold abnormal returns using methodology presented in Barber and Lyons
(1997) for three months, six months, one year and two years following the resolution (either
completion or withdrawal) of the merger.24 Table 7 presents analyst results, while Table 8
examines results to advisors based on the presence of a fee.
The results in Table 7 suggest that analysts do not appear to be able to predict postresolution returns based on the consensus recommendation level from announcement through
completion or withdrawal. For completed deals, surviving firms where analysts’ pre-completion
consensus recommendations are above buy have marginally insignificantly negative postcompletion returns [although the return is significantly negative at year 1 (-2.50%, t = -2.16)].
Surviving firms with low (≥2) pre-completion recommendations have positive, and significant,
returns at 6-month, 1-year, and 2-year horizons.
The differences between positively and
negatively recommended firms are significant at 6-month, 1-year, and 2-year horizons.
When withdrawals are examined, we find significantly negative returns for highly
recommended acquirers at 3-month, 6-month, and 1-year horizons, but insignificant returns to
poorly recommended acquirers over the same horizons. The differences between returns at any
horizon for acquirers in withdrawn mergers are insignificant.
On the other hand, highly
recommended targets generate significantly negative post-withdrawal returns, while poorly
recommended targets generate insignificant positive returns. Again, similar to the completed
sample, positive and negative recommended targets have significantly different returns at 6-
24
For mergers that were consummated or withdrawn between 2007 and 2008, we utilize as much data as available to
compute post-resolution returns.
32
month and 1-year horizons.
Overall, these results suggest analysts are persistently bad
forecasters for mergers and/or are swayed by conflicts of interest.
Table 8 provides post-resolution return results delineated by whether a fee was paid to
either the acquirer or target merger advisor. As in Table 7, we examine completed mergers and
the effects for the surviving firm, as well as separate effects for acquirers and targets in
withdrawn mergers. When fees are paid to advisors, post-resolution returns are slightly negative
at all horizons. If fees are not charged by advisors, returns to the surviving firm are generally
positive, and significantly so in years 1 and 2. Further, there exists a significant difference in
returns to firms based on whether advisors are paid fees at the 1-year and 2-year horizons.
Similar to Table 7, we find no significant difference in returns to acquirers in withdrawn
mergers based on the presence of a fee, although the returns to acquirers where no fee was paid
are significantly negative at the 3-month, 6-month, and 1-year horizons.
When targets of
subsequently withdrawn mergers pay advisory fees, they outperform withdrawn targets where no
fees are paid. Thus, the results for the advisory relationship are mixed. When fees are paid, but
the merger is subsequently withdrawn, targets do better than when no fees are paid, suggesting
that advisors maximize value for the target shareholders. In completed mergers, the surviving
firms have substantially lower performance when fees are paid, indicating advisors may
recommend acquirers overpay or targets accept sub-optimal bids – actions designed to ensure
deal completion rather than shareholder value maximization. As with analysts, we cannot reject
that advisors are driven by conflicts of interest.
33
VII. Conclusions
The existing literature provides evidence that analysts are generally optimistic about the
firms for which they supply research coverage. Many of these studies suggest conflicts of
interest related to banking and brokerage commissions and continued access to management
have driven the excessive optimism displayed by analysts. In this study, we propose analysts
face a new conflict of interest that arises as a result of the structure of M&A advisory fees.
We find M&A fee type and structure varies with pre-merger announcement
recommendation levels on acquirers and targets. When analysts are negative on acquirers or the
target is positively recommended, fees are either advisory based (not dependent upon merger
completion) or based on a percentage of the total deal. These results suggest advisors must work
harder when analyst recommendations are in opposition of those that would ensure merger
completion.
After fees become publicly known, analyst recommendations are revised to increase the
likelihood of completion, depending on whether a fee is paid and the fee type and structure.
When acquirer or targets pay fees, there is a significant increase in acquirer optimism. Target
ratings, on the other hand, become significantly more negative after the merger announcement
regardless of whether fees are paid, but the pessimism is magnified if target advisors are the ones
paying the fees, especially if the fee is fixed and contingent upon completion. These results
imply analysts revise their recommendations to guarantee receiving M&A fees, as opposed to
reflecting the true value to either acquirers or targets.
Next, we examine the joint effects of analyst recommendations and M&A fees on merger
completion. Acquirer optimism and target pessimism is significantly related to the probability
that a merger will be completed.
The probability of deal completion declines with negative
34
acquirer or positive target revisions, indicating that the sign of the recommendation matters. We
further document that the type of M&A fees matters for completion. We find the contingent
portion of the fee is related primarily to deal completion, especially for acquirers; however,
contrary to early studies, we observe the fee structure (flat or percentage-based) also matters.
When out-of-sample returns are analyzed, the results show that both analysts and advisors face
significant conflicts of interest and promote deal completion to the detriment of acquirer and
target shareholders.
Taken together, our results suggest that advisors and analysts work together to maximize
compensation earned by the M&A advisor. The relation between fees and recommendations
imply that pre-merger sentiment affects the fee type an advisor requires, while analysts then
revise recommendations post-merger announcement to ensure completion and obtain the largest
fees possible. Although regulators have recently attempted to reduce conflicts of interest faced
by analysts and investment banks, our results suggest that at least in the M&A arena, conflicts of
interest between analysts, advisors, and investors continue to exist today.
35
References
Agrawal, A. and M. Chen, 2007, Do analyst conflicts matter? Evidence from stock recommendations, Journal of
Law and Economics, forthcoming.
Allen, L., J. Jagtiani, S. Peristiani, and A. Saunders, 2004, The role of bank advisors in mergers and acquisitions,
Journal of Money, Credit, and Banking 36, 197-224.
Barber, B., R. Lehavy, M. McNichols, and B. Trueman, 2001. Can investors profit from the prophets? Consensus
analyst recommendations and stock returns, Journal of Finance 56, 531-564.
Beyer, A. and I. Guttman, 2007, The effect of trading commissions on analysts’ forecast bias, Working paper,
Stanford University.
Bowers, H. and R. Miller, 1990, Choice of investment banker and shareholders wealth of firms involved in
acquisitions, Financial Management 19, 34-44.
Branch, B., H.N. Higgins, and K. Wilkens 2003. Risk arbitrage profits and the probability of takeover success,
Working paper, Worcester Polytechnic Institute.
Bradley, D., J. Clarke, and J. Cooney, 2007, Do analysts curry favor with issuing firms?, Working paper, Georgia
Institute of Technology.
Bradley, D., A. Morgan, and J. Wolf, 2007b, Analyst behavior surrounding tender offer announcements, Journal of
Financial Research 30, 1-19.
Calomiris, C.W. and H.J. Singer, 2004. How often do “conflicts of interest” in the investment banking industry
arise during hostile takeovers?, Working paper, Columbia University.
Chen, H. and J. Ritter, 2000, The seven-percent solution, Journal of Finance 50, 1105-1131.
Clarke, J, A. Khorana, A. Patel, R. Rau, 2006, The impact of All-Star analyst job changes on their coverage choices
and investment banking deal flow, Journal of Financial Economics, forthcoming.
Conrad, J., B. Cornell, W. Landsman, and B. Roundtree, 2006. How do analyst recommendations respond to major
news?, Journal of Financial and Quantitative Analysis 41, 25-48.
Derrien, F., 2006, Currying favor to win IPO mandates, Working paper, HEC.
Dugar, A. and S. Nathan, 1995, The effect of investment banking relationships on financial analysts’ earnings
forecasts and investment recommendations, Contemporary Accounting Research 12, 131-160.
Hong, H. and J. Kubik, 2003, Analyzing the analysts: Career concerns and biased analyst forecasts, Journal of
Finance 57, 313-351
Hunter, W. and J. Jagtiani, 2003. An analysis of advisor choice, fees, and effort in M&A, Review of Financial
Economics 12, 65-82.
Hunter, W. and M. Walker, 1990, An empirical examination of investment banking merger fee contracts, Southern
Economic Journal 56, 1117-1130.
Irvine, P., 2003, The incremental impact of analyst initiation of coverage, Journal of Corporate Finance 9, 431-451.
_____, 2004, Analysts’ forecasts and brokerage-firm trading, The Accounting Review 79, 125-149.
Jackson, A., 2005, Trade generation, reputation, and sell-side analysts, Journal of Finance 60, 673-717.
Jegadeesh, N., J. Kim, S. Krische, and C. Lee, 2004, Analyzing the analysts: when do recommendations add value?,
Journal of Finance 69, 1083-1124.
Jennings, R.H. and M.A. Mazzeo, 1993. Competing bids, target management resistance, and the structure of
takeover bids, Review of Financial Studies 6, 883-909.
Kadan, O., L. Madureira, R. Wang, and T. Zach, 2006, Conflicts of interest and stock recommendations – the effects
of the Global Settlement and Related Regulations, Working paper, Washington University in St. Louis.
36
Kisgen, D., J. Qian, and W. Song, 2007, Are fairness opinions fair? The case of mergers and acquisitions, Working
paper, Boston College.
Kolasinski, A., and S. Kothari, 2007. Investment Banking and Analyst Objectivity: Evidence from Analysts
Affiliated with M&A Advisors, Journal of Financial and Quantitative Analysis, forthcoming.
Lin, H. and M. McNichols, 1998, Underwriter relationships, analysts’ earnings forecasts and investment
recommendations, Journal of Accounting and Economics 25, 101-127.
Lin, H., M. McNichols, and P. O’Brien, 2005, Analyst impartiality and investment banking relationships, Journal of
Accounting Research 43, 623-650.
Ljungqvist, A., C. Malloy, and F. Marston, 2009, Rewriting history, Journal of Finance, forthcoming.
Ljungqvist, A., F. Marston, and W. Wilhelm, 2006, Competing for securities underwriting mandates: Banking
relationships and analyst recommendations, Journal of Finance 61, 301-340.
Luo, Y. 2005. Do insiders learn from outsiders? Evidence from mergers and acquisitions, Journal of Finance 60,
1951-1982.
Makhija, A. and R. Narayanan, 2007, Fairness opinions in mergers and acquisitions, Working paper, Ohio State
University.
Malmendier, U. and D. Shanthikumar, 2007, Do security analysts speak in two tongues?, Working paper, University
of California at Berkeley.
McLauglin, R., 1990, Investment-banking contracts in tender offers: an empirical analysis, Journal of Financial
Economics 28, 209-232.
______., 1992, Does the form of compensation matter? Investment banker fee contracts in tender offers, Journal of
Financial Economics 32, 223-260.
Michaely, R. and K. Womack, 1999, Conflicts of interest and the credibility of underwrite analyst recommendations,
Review of Finance Studies 12, 653-686.
Rajan, R. and H. Servaes, 1997, Analyst following of initial public offerings, Journal of Finance 52, 507-529
Rau, P.R., 2000. Investment bank market share, contingent fee payments and the performance of acquiring firms, Journal
of Financial Economics, 56, 293-324.
Rhodes-Kropf, M., D. Robinson, and S. Viswanathan, 2005, Valuation waves and merger activity: the empirical
evidence, Journal of Financial Economics 77, 561-603.
Saunders, A. and A. Srinivasan, 2001, Investment banking relationships and merger fees, Working paper, New York
University.
Servaes, H. and M. Zenner, 1996, The role of investment banks in acquisition, Review of Financial Studies 9, 787-815.
Stickel, S., 1995. The anatomy of the performance of buy and sell recommendations, Financial Analyst Journal 51,
25-39.
Womack, K., 1996, Do brokerage analyst recommendations have investment value?, Journal of Finance 51, 137167.
37
Appendix A
Descriptions of Variables Used in Analyses
Variable
Number of Recs. in Pre-Ann.
Number of Recs. from Ann.
Positive Acquirer Recs.*
Positive Target Recs.*
Negative Acquirer Recs.*
Negative Target Recs.*
Affiliated Acquirer Recs
Affiliated Target Recs
Average Acquirer Rec. Level
Average Target Rec. Level
Same Analyst for Acquirer and Target
Number of Acquirer Advisors
Number of Target Advisors
Number of Acquirer Analysts
Number of Target Analysts
Acquirer (Target) Fee
Acquirer (Target) Flat Fee
Acquirer (Target) Percentage Fee
Acquirer (Target) Advisory Fee
Acquirer (Target) Contingency Fee
Acq (Tgt) Flat Advisory Fee
Acq (Tgt) Percentage Advisory Fee
Acq (Tgt) Flat Contingency Fee
Acq (Tgt) Percentage Contingency Fee
Merger Program
Same 3-digit SIC
Merger Withdrawn
Pure Cash Offer
Pure Stock Offer
Cash Tender Offer
Fixed Payment Collar
Fixed Exchange Collar
Days to Resolution
LN(Merger Value)
Acquirer Run-up
Acquirer Announcement Return
Target Run-up
Target Announcement Return
*
Description
Number of opinions in the pre-announcement period (-50 to 0)
Number of opinions from day +1 to resolution
Number of positive acquirer recommendations
Number of positive target recommendations
Number of negative acquirer recommendations
Number of negative target recommendations
Number of recommendations for the acquirer by acquirer or target affiliated analysts
Number of recommendations for the target by acquirer or target affiliated analysts
Average acquirer recommendation
Average target recommendation
Indicator = 1 if an analyst makes a recommendation on both acquirer and target on
same day
Number of M&A advisors to the acquirer
Number of M&A advisors to the target
Number of analysts making at least 1 acquirer recommendation in merger window
Number of analysts making at least 1 target recommendation in merger window
Indicator = 1 if any fee is paid to the acquirer (target) advisor
Indicator = 1 if the fee paid to the acquirer (target) advisor is not dependent upon the
value of the transaction
Indicator = 1 if the fee paid to the acquirer (target) is dependent upon the value of the
transaction
Indicator = 1 if the fee paid to the acquirer (target) is not dependent upon the
completion of the merger or other actions by the advisors
Indicator = 1 if the fee paid to the acquirer (target) is contingent upon the completion
of the merger or other actions by the advisors
Interaction indicator = 1if the fee paid is fixed but not contingent upon completion
Interaction indicator = 1if the fee paid is based upon the value of the transaction but
not contingent upon completion
Interaction indicator = 1 if the fee paid is fixed and contingent upon the value of the
transaction
Interaction indicator = 1 if the fee paid is contingent upon both the value of the
transaction and the completion of the transaction
Indicator = 1 if acquirer makes three or more public acquisitions over a five year
window
Indicator = 1 if target & acquirer have the same 3-digit SIC code
Indicator = 1 if merger is withdrawn after announcement
Indicator = 1 if merger is a pure cash deal
Indicator = 1 if merger is a pure stock deal
Indicator = 1 if merger is a cash tender offer
Indicator = 1 if merger has a fixed payment collar
Indicator = 1 if merger has a fixed exchange collar
Number of days from merger announcement to completion or withdrawal
Natural log of value of the merger
Pre-announcement returns for the acquirer (-30 days to –5 days)
Announcement returns for the acquirer (-1 day to +1 days)
Pre-announcement returns for the target (-30 days to –5 days)
Announcement returns for the target (-1 day to +1 days)
Depending on regression specification, these recommendations could be total, pre-announcement, or announcement revisions.
38
Table 1
Descriptive Statistics: Merger Sample, Recommendations, and Fees
The table provides descriptive data on the merger sample, the recommendations, and M&A advisory fees for all
single-bidder public mergers that were announced and resolved between 1994 and 2008. Merger variables,
including method of payment, merger program, horizontal mergers, merger window, merger value, premium, and
merger returns are presented in Panel A. Data on recommendations to acquirers and targets by affiliation, direction,
and timing of the recommendation is presented in Panel B. Summary statistics on the magnitude of fees (in
millions) paid by acquirers and targets in mergers is shown in Panel C. Data is collected from SDC, I/B/E/S, and
SEC filings for merger variables, analyst recommendation data, and fee structure, respectively.
Panel A: Merger Sample
N
Average
Number of Mergers
Cash Financed
Stock Financed
Merger Program
Same SIC Code
Days to Completion
Value of Consideration (in millions)
Acquirer Run-up
Target Run-up
Acquirer Announcement Return
Target Announcement Return
Premium
Full
5,788
1,431
2,291
2,240
1,894
130
$1,127
1.95%
18.40%
-0.89%
19.92%
45.76%
Panel B: Recommendation Sample
Acquirer
All Positive Negative
Total Recommendations
20,048
9,391 10,651
Pre-Merger Announcement
4,991
2,386
2,605
Post-Merger Announcement
13,350
6,434
6,919
Own Advisor Affiliated
412
176
236
Counterparty Affiliated
348
146
202
All
Pre
Post
Average Recommendation
2.14
2.17
2.18
Average Affiliated (Own & Counterparty)
2.05
2.19
2.09
Average Unaffiliated
2.16
2.17
2.20
Advisory
Contingency
Total
Flat Advisory
Flat Contingency
Percent Advisory
Percent Contingency
Contingency/Total Fees
Total Fees/Merger Value
Completed
4,977
1,286
1,984
2,005
1,654
132
$1,103
1.91%
19.29%
-0.83%
20.60%
45.73%
Withdrawn
811
145
307
235
240
118
$1,317
2.23%
12.22%
-1.25%
15.21%
45.98%
Target
All Positive Negative
9,018
2,582
6,436
2,107
826
1,281
5,957
1,467
4,490
242
55
187
151
40
111
All
Pre
Post
2.58
2.33
2.79
2.38
2.36
2.55
2.59
2.33
2.80
Panel C: M&A Advisory Fees ($ millions)
Acquirer
Target
N
Mean Median Std Dev
N Mean Median Std Dev
1,101
1.22
0.31
2.68
2,913
0.85
0.25
2.02
909
3.04
0.75
6.06
2,546
3.55
1.07
6.70
4.27
1.69
7.18
4.41
1.65
7.50
1,034
1.19
0.28
2.68
2,294
0.71
0.15
1.88
810
2.83
0.42
6.06
1,660
2.18
0
5.50
92
0.07
0
0.44
640
0.20
0
0.95
127
0.40
0
2.94
1,123
1.65
0
4.86
53.90% 69.23% 39.90%
62.24% 78.13% 38.14%
0.71%
0.46%
1.17%
0.92%
0.75%
0.97%
39
Table 2
The Effect of Merger Characteristics on Merger Fee Amounts
This table provides average merger fee amounts by fee type (advisory, contingency, and the interaction of fee type
with flat or percentage-based payment) by merger party (acquirer or target) and various merger characteristics
(analyst coverage, method of payment, and deal completion). Average levels of fees (in $ millions) are reported, as
well as p-values from difference of means tests to determine whether the fee amount varies by merger characteristic.
Data is collected from SDC, I/B/E/S, and SEC filings for method of payment and completion status, analyst
coverage, and fee structure, respectively.
Advisory
Contingency
Total
Flat Advisory
Flat Contingency
Percent Advisory
Percent Contingency
Contingency/Total Fees
Total Fees/Value of Merger
Advisory
Contingency
Total
Flat Advisory
Flat Contingency
Percent Advisory
Percent Contingency
Contingency/Total Fees
Total Fees/Value of Merger
Advisory
Contingency
Total
Flat Advisory
Flat Contingency
Percent Advisory
Percent Contingency
Contingency/Total Fees
Total Fees/Value of Merger
Panel A: Analyst Coverage
Acquirer
Coverage No Coverage
p-val Coverage
1.47
0.60
0.000
0.92
3.56
1.75
0.000
3.88
5.03
2.36
0.000
4.80
1.43
0.57
0.000
0.76
3.34
1.55
0.000
2.37
0.09
0.03
0.001
0.22
0.38
0.46
0.772
1.83
56.46%
47.42%
0.000
66.10%
0.63%
0.90%
0.002
0.87%
Target
No Coverage
0.64
2.61
3.29
0.58
1.63
0.10
1.15
54.92%
1.06%
p-val
0.000
0.000
0.000
0.018
0.002
0.000
0.002
0.000
0.000
Target
Cash
0.86
3.30
4.16
0.71
2.16
0.15
1.32
63.80%
1.20%
p-val
0.830
0.204
0.261
0.966
0.903
0.089
0.015
0.649
0.000
Panel C: Deal Status
Acquirer
Target
Completed Withdrawn
p-val Completed Withdrawn
1.17
2.05
0.044
0.82
1.55
3.11
1.97
0.401
3.63
1.59
4.28
4.11
0.904
4.46
3.17
1.14
1.99
0.052
0.68
1.41
2.89
1.90
0.469
2.23
0.84
0.07
0.13
0.465
0.19
0.23
0.33
1.56
0.361
1.68
0.94
56.13%
15.16%
0.000
65.20%
14.35%
0.72%
0.45%
0.002
0.94%
0.54%
p-val
0.004
0.010
0.128
0.004
0.000
0.669
0.321
0.000
0.001
Panel B: Method of Payment
Acquirer
Stock
Cash
p-val
1.24
1.02
0.469
3.15
2.24
0.057
4.40
3.32
0.054
1.21
1.02
0.330
2.93
2.14
0.100
0.07
0.05
0.559
0.44
0.12
0.004
53.96%
53.50%
0.895
1.19%
0.64%
0.001
40
Stock
0.85
3.63
4.48
0.71
2.19
0.21
1.75
63.06%
0.84%
Table 3
The Effect of Recommendations on Fee Types
This table displays the effect of recommendations on the likelihood of a particular type of M&A advisory fee being
implemented by an M&A advisor. Only pre-merger announcement recommendations are used in the logistic
regressions. Controls for affiliation, number of analysts and advisors, and the average pre-announcement
recommendation level for acquirers and targets as well as control variables (including method of payment, collar
type, merger program, value of transaction, and the probability of success as measured by whether the merger is
eventually withdrawn) to capture merger characteristics and pre-announcement returns are included in regressions;
however, those variables are suppressed in the tables presented below. χ2 p-values are reported and bold indicates
significance of at least 10%. Independent variables are defined in Appendix A.
Fee
Intercept
Positive Acquirer Recs
Negative Acquirer Recs
Positive Target Recs
Negative Target Recs
Acquirer Affiliated Recs
Target Affiliated Recs
Total Pre-Announcement Recs
Same Analyst
Number of Acquirer Analysts
Number of Target Analysts
Number of Acquirer Advisors
Number of Target Advisors
Merger Program
Same 3-digit SIC
Ln(Merger Value)
Cash
CTO
Stock
Fixed Payment Collar
Fixed Exchange Collar
Acquirer Run-up
Target Run-up
Est
-2.48
-0.16
-0.08
-0.14
-0.13
0.03
0.20
0.05
0.25
-0.06
-0.04
1.28
0.13
-0.39
0.25
0.20
-1.39
2.04
0.11
-0.18
0.02
0.34
-0.81
p
0.00
0.00
0.11
0.11
0.06
0.63
0.02
0.01
0.00
0.00
0.12
0.00
0.14
0.00
0.00
0.00
0.00
0.00
0.26
0.37
0.90
0.27
0.00
Acquirer
Flat Contingency % Contingency
Est
p
Est
p
-3.23
0.00 -4.62
0.00
-0.01
0.93
0.06
0.76
-0.03
0.63
0.30
0.04
-0.04
0.70
0.24
0.25
-0.02
0.78 -0.37
0.13
-0.02
0.76 -0.02
0.91
0.25
0.00
0.99
0.01
0.01
0.73 -0.10
0.15
0.32
0.22
0.15
0.00
-0.04
0.04 -0.19
0.00
-0.06
0.01
0.90
0.03
0.66
0.70
0.00
0.00
-0.17
0.11 -0.03
0.91
-0.65
0.25
0.00 -0.28
0.11
0.28
0.29
0.19
0.28
0.17
0.00
0.06
-2.86
0.00 -1.17
0.01
3.19
0.00
0.42
0.51
0.00
0.04
-0.20
0.43
0.15
0.75
0.07
0.70
0.08
0.86
-0.08
0.81 -1.44
0.05
-0.19
0.30
0.11
0.75
41
Fee
Est
-1.66
-0.11
0.04
-0.10
-0.19
0.08
0.02
0.03
0.11
0.00
-0.06
0.16
1.43
0.26
-0.03
0.20
-0.49
1.35
-0.03
0.54
0.38
-0.58
-0.05
p
0.00
0.03
0.38
0.30
0.01
0.31
0.87
0.19
0.12
0.80
0.04
0.05
0.00
0.00
0.74
0.00
0.00
0.00
0.80
0.02
0.04
0.06
0.74
Target
Flat Contingency % Contingency
Est
p
Est
p
-2.45
0.00 -2.33
0.00
0.04
0.38
0.05
0.32
-0.07
0.09
0.07
0.10
-0.12
0.13
0.21
0.01
-0.02
0.70 -0.05
0.48
0.10
0.03
0.68
0.07
0.04
0.63
0.07
0.36
-0.01
0.63 -0.01
0.66
0.09
0.23
0.07 -0.07
0.00
0.91
0.02
0.09
-0.03
0.18 -0.06
0.02
0.09
0.19
0.11
0.14
0.66
0.17
0.00
0.04
0.09
0.25
0.38
0.00
0.01
0.87
0.01
0.93
0.19
0.14
0.00
0.00
0.26
0.03 -0.65
0.00
0.17
0.25
0.67
0.00
-0.09
0.32
0.09
0.36
0.38
0.29
0.12
0.04
0.31
0.15
0.35
0.05
-0.40
0.17 -0.47
0.13
-0.01
0.92
0.17
0.20
Table 4
The Effect of M&A Advisory Fee Type on Recommendations
This table presents the number of acquirer and target recommendations and the effect of M&A advisor fees on preand post-merger announcement upgrades and downgrades. Recommendations are delineated by positive and
negative recommendations and by the timing of those recommendations. We examine whether the acquirer or target
pays a fee and whether that fee by either the acquirer or target is an advisory, contingent or a combination of both
fees. P-values from difference of means tests between the fee and no-fee samples are presented for the percentage
of positive to negative recommendations in the post-announcement period. Data is collected from SDC, I/B/E/S,
and SEC filings for method of payment and completion status, analyst coverage, and fee structure, respectively.
Acquirer
Target
Pre
Fee
Post
Pre
No Fee
Post
647
744
2310
2220
1739
1861
4214
4696
47.5%
50.7%
49.3%
48.7%
1557
1810
4694
4991
829
795
1740
1925
47.2%
49.1%
52.1%
49.8%
473
461
1766
1488
1913
2144
4668
5428
% Positive
52.2%
Target Advisory Fee
54.1%
47.9%
47.9%
1209
1414
3748
3968
1177
1194
2686
2945
47.0%
48.8%
50.8%
50.1%
1447
1288
1986
2176
4987
5628
% Positive
50.1%
53.8%
Target Flat Contingency Fee
48.5%
48.4%
p-val
Pre
Fee
Post
No Fee
Pre
Post
329
537
821
2184
497
744
646
2306
38.1%
21.8%
41.7%
18.7%
539
861
1051
3495
287
420
416
995
39.6%
17.9%
41.7%
26.9%
243
364
647
1427
583
917
820
3063
38.6%
24.6%
40.9%
18.0%
426
682
830
2775
400
599
637
1735
38.9%
17.7%
41.8%
23.6%
221
327
548
1217
605
954
919
3273
39.4%
23.5%
40.5%
19.0%
295
505
617
2012
531
776
850
2478
37.4%
17.4%
42.0%
22.0%
22
26
73
137
804
1255
1394
4353
46.7%
29.3%
40.1%
19.7%
241
327
334
1257
585
954
1133
3233
41.5%
18.6%
39.8%
20.7%
p-val
Acquirer Fee
Positive
Negative
% Positive
Target Fee
Positive
Negative
% Positive
0.16
0.64
0.03
0.00
Acquirer Advisory Fee
Positive
Negative
Positive
Negative
% Positive
0.00
0.34
0.00
0.00
Acquirer Flat Contingency Fee
Positive
Negative
Positive
Negative
% Positive
400
429
883
932
2535
2680
1503
1673
3899
4236
49.4%
49.1%
48.4%
49.7%
0.00
0.61
0.01
0.00
Acquirer Percentage Contingency Fee
Positive
Negative
2347
2558
6285
6808
% Positive
42.6%
59.4%
48.9%
Target Percentage Contingency Fee
49.0%
Positive
Negative
% Positive
39
47
149
108
608
691
1770
1900
1788
1914
4664
5016
46.1%
48.8%
49.6%
49.5%
0.02
0.59
42
0.03
0.19
Table 5
Regression Analysis of M&A Advisory Fees on Recommendations
This table presents results from an OLS regression on the number of positive and negative acquirer or target recommendations
following the merger announcement. Recommendations are regressed upon recommendation characteristics (timing and
affiliation, if the same analyst covered both the acquirer and target, and the number of analysts following the acquirer and target),
merger characteristics (number of acquirer and target advisors, method of payment and collar type, relatedness, and value), preannouncement and announcement period returns, and indicators for fees, advisory fees, flat contingent fees, and percentage-based
contingent fees for acquirers and targets. Panel A presents results for consolidated fee indicators, while Panel B presents results
for specific fee types. p-values from t-statistics are reported and bold indicates significance of at least 10%.
Variable
Intercept
Affiliated Rec (Acq/Tgt)
Acquirer Fee
Target Fee
Adjusted-R2
Panel A: Presence of a Fee
Positive
Negative
Acquirer
Acquirer
Est
p
Est
p
-0.16
0.19
0.06
0.67
0.08
0.02
0.66
0.05
0.07
0.33
-0.02
0.77
0.14
0.10
0.16
0.05
0.55
0.57
Positive
Target
Est
p
0.03
0.65
-0.06
0.03
0.04
0.29
-0.02
0.61
0.47
Negative
Target
Est
p
-0.18 0.03
0.13 0.00
0.03 0.53
0.18 0.00
0.75
Variable
Intercept
Affiliated Rec (Acq/Tgt)
Acquirer Advisory Fee
Acq Flat Contingency Fee
Acq Pct Contingency Fee
Target Advisory Fee
Tgt Flat Contingency Fee
Tgt Pct Contingency Fee
Adjusted-R2
Panel B: By Fee Type
Positive
Negative
Acquirer
Acquirer
Est
p
Est
p
-0.18
0.13
0.07
0.60
0.08
0.02
0.71
0.05
0.27
0.01
0.87
0.00
-0.01
0.92
-0.06
0.57
0.10
0.56
-0.17
0.37
0.12
0.03
0.62
0.06
-0.08
0.23
0.03
0.65
0.03
0.62
0.11
0.14
0.55
0.57
Positive
Target
Est
p
0.01
0.83
-0.06
0.03
0.07
0.13
-0.00
0.99
0.12
0.16
0.02
0.51
-0.06
0.10
-0.04
0.27
0.47
Negative
Target
Est
p
-0.16 0.05
0.12 0.00
-0.06 0.32
-0.00 0.95
0.15 0.19
0.13 0.00
0.05 0.30
0.06 0.16
0.75
43
Table 6
Logistic Regressions: Probability of Completion
This table presents results from logistic regressions on the probability of a merger completion. Predictors of merger
completion include recommendation and analyst characteristics, merger characteristics, and indicator variables for
different types of M&A advisory fees. χ2 p-values are reported and bold indicates significance of at least 10%.
Likelihood ratio p-values are also provided for each model. Independent variables are defined in Appendix A.
Model 1
Variable
Intercept
Positive Acq Recs
Negative Acq Recs
Positive Tgt Recs
Negative Tgt Recs
Affiliated Acq Recs
Affiliated Tgt Recs
Num Recs in Pre-Ann
Num Recs from Ann
Avg Acq Rec Level
Avg Tgt Rec Level
Same Analyst
Num Acq Analysts
Num Tgt Analysts
Num Acq Advisors
Num Tgt Advisors
Pure Cash Offer
Cash Tender Offer
Pure Stock Offer
Fixed Payment Collar
Fixed Exchange Collar
Merger Program
Same 3-digit SIC
Days to Resolution
Ln(Merger Value)
Acq Run-up
Acq Ann Return
Tgt Run-up
Tgt Ann Return
Acquirer Fee
Acq Advisory Fee
Acq Contingent Fee
Acq Flat Contingent Fee
Acq % Contingent Fee
Acq Flat Advisory Fee
Acq % Advisory Fee
Target Fee
Tgt Advisory Fee
Tgt Contingent Fee
Tgt Flat Contingent Fee
Tgt % Contingent Fee
Tgt Flat Advisory Fee
Tgt % Advisory Fee
Likelihood Ratio p-value
Model 2
Model 3
Model 4
Est
p-val
Est
p-val
Est
p-val
Est
p-val
0.86
0.11
-0.22
-0.23
0.10
0.04
-0.16
-0.02
-0.03
0.08
0.06
0.06
0.18
-0.07
0.23
0.57
0.55
-0.23
0.16
0.74
0.13
0.39
0.09
0.00
-0.27
0.49
0.74
0.57
0.07
0.26
0.00
0.08
0.00
0.01
0.14
0.77
0.21
0.56
0.42
0.26
0.34
0.55
0.00
0.16
0.06
0.00
0.00
0.45
0.31
0.11
0.67
0.01
0.50
0.66
0.00
0.26
0.30
0.02
0.80
0.20
1.12
0.15
-0.21
-0.20
0.12
0.05
-0.26
-0.03
-0.04
0.09
0.06
0.00
0.18
-0.06
0.22
0.67
0.47
-0.02
0.14
0.78
0.07
0.38
0.07
0.00
-0.32
0.56
1.07
0.54
0.08
0.00
0.03
0.00
0.02
0.09
0.69
0.05
0.46
0.27
0.17
0.34
0.97
0.00
0.22
0.08
0.00
0.02
0.94
0.35
0.09
0.79
0.01
0.58
0.77
0.00
0.20
0.15
0.02
0.79
1.07
0.15
-0.21
-0.19
0.12
0.04
-0.26
-0.03
-0.05
0.09
0.06
0.01
0.18
-0.06
0.24
0.68
0.47
-0.02
0.14
0.77
0.09
0.36
0.09
0.00
-0.30
0.52
1.11
0.54
0.09
0.00
0.03
0.00
0.02
0.09
0.76
0.05
0.41
0.21
0.19
0.34
0.90
0.00
0.26
0.05
0.00
0.01
0.95
0.35
0.09
0.76
0.01
0.52
0.73
0.00
0.23
0.14
0.03
0.76
1.08
0.15
-0.21
-0.20
0.13
0.04
-0.28
-0.03
-0.04
0.11
0.07
0.02
0.18
-0.07
0.25
0.74
0.46
0.09
0.14
0.81
0.04
0.36
0.08
0.00
-0.31
0.51
1.20
0.59
0.08
0.00
0.03
0.00
0.02
0.06
0.73
0.03
0.43
0.30
0.12
0.30
0.84
0.00
0.18
0.04
0.00
0.02
0.77
0.38
0.08
0.88
0.01
0.54
0.59
0.00
0.24
0.11
0.02
0.79
-0.50
1.24
0.03
0.00
-0.37
0.10
1.12
-0.04
0.00
0.93
1.18
-0.47
-0.41
1.07
0.00
0.43
0.07
0.24
0.80
0.00
1.24
1.88
0.00
0.00
1.25
2.23
0.64
-0.46
0.00
0.00
0.00
0.13
0.00
1.96
0.00
0.72
1.71
0.00
0.00
0.00
0.00
44
0.00
Table 7
Post-Resolution Returns and Analysts’ Predictive Ability
This table examines the ability of analysts to predict post-resolution returns through their recommendations. Postresolution returns are measured as buy-and-hold abnormal returns using the methodology presented in Barber and
Lyons (1997) for three months, six months, nine months, one year and two years following the resolution (either
completion or withdrawal) of the merger. Recommendations are averaged across analysts and are delineated into
positive and negative recommendations. Positive (negative) recommendations are those with a value less than
(greater than or equal to) two. Acquirers and targets are distinguished by whether the merger was completed or not.
T-statistics are reported in parentheses while p-values for difference of means tests between recommendation levels,
as well as difference of means tests for completed or withdrawn upgrades and downgrades are presented.
Rec
N
Acquirer
Completion
<2
2288
Completion
≥2
1462
Withdrawal
<2
373
Withdrawal
≥2
155
Target
Completion
Completion
Withdrawal
<2
≥2
<2
315
Withdrawal
≥2
176
p-value
p-value
p-value
3-month
6-month
1-year
2-year
-0.30%
(-0.53)
0.52%
(0.97)
0.2823
-0.90%
(-1.11)
1.88%
(2.58)
0.0102
-2.50%
(-2.16)
2.84%
(2.36)
0.0014
-1.40%
(-0.78)
5.45%
(3.61)
0.0038
-3.60%
(-2.18)
-0.60%
(-0.26)
0.2831
-5.80%
(-2.63)
-4.70%
(-1.72)
0.7583
-5.30%
(-1.65)
-1.80%
(-0.48)
0.4741
-1.90%
(-0.28)
1.35%
(0.23)
0.7183
-5.30%
(-1.97)
-1.40%
(-0.42)
0.3658
-6.80%
(-2.68)
4.03%
(1.09)
0.0138
-4.50%
(-1.30)
5.98%
(1.36)
0.0658
-4.70%
(-1.18)
7.17%
(0.87)
0.1936
45
Table 8
Post-Resolution Returns and Fees
This table examines whether advisors, independent of the analyst recommendations, have the ability to predict postresolution returns. Post-resolution returns are measured as buy-and-hold abnormal returns using the methodology
presented in Barber and Lyons (1997) for three months, six months, nine months, one year and two years following
the resolution (either completion or withdrawal) of the merger. To introduce some simplicity to the fee structure, an
indicator variable is constructed and equals one if either the acquirer or target paid a fee to their advisor, and zero
otherwise. Acquirers and targets are distinguished by whether the merger was completed or not. T-statistics for the
significance of returns are reported in parentheses. p-values for difference of means tests between fee and no fee
subsamples are presented for completed and withdrawn mergers.
Fee
N
Acquirer
Completion
Yes
2592
Completion
No
1158
Withdrawal
Yes
117
Withdrawal
No
411
Target
Completion
Completion
Withdrawal
Yes
No
Yes
315
Withdrawal
No
176
p-value
p-value
p-value
3-month
6-month
1-year
2-year
-0.30%
(-0.61)
0.72%
(1.04)
0.2299
-0.40%
(-0.60)
1.49%
(1.28)
0.1584
-1.90%
(-2.06)
2.97%
(1.69)
0.0138
-0.60%
(-0.38)
5.34%
(2.39)
0.0288
-1.40%
(-0.44)
-3.10%
(-2.12)
0.6274
-6.90%
(-1.70)
-5.00%
(-2.62)
0.6718
-0.90%
(-0.16)
-5.20%
(-1.90)
0.5165
-1.70%
(-0.20)
-0.80%
(-0.12)
0.9267
-0.44%
(-0.14)
-4.82%
(-1.95)
0.2804
3.45%
(0.85)
-4.69%
(-1.91)
0.0883
7.12%
(1.32)
-2.91%
(-0.92)
0.1097
14.78%
(1.33)
-4.68%
(1.19)
0.0408
46
Figure 1
Merger Statistics by Year
This figure provides an annual summary of merger characteristics over the sample period 1993 – 2008. In Panel A,
the number of completed and withdrawn mergers is shown. Panel B depicts the average recommendation for
acquirers and targets in the year the merger is announced. Panel C provides the average total fees paid to acquirer
and target merger advisors. Data is collected from SDC, I/B/E/S, and SEC filings for number and completion status
of the sample, analyst coverage, and fee structure, respectively.
Panel A: Completed and Withdrawn Mergers by Year (1993‐2008)
600
500
400
300
200
100
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Completed
Withdrawn
Panel B: Average Acquirer and Target Recommendations through Merger Completion or Withdrawal (1993‐2008) 3.5
3
2.5
2
1.5
1
0.5
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Acquirer Rec
47
Target Rec
Panel C: Acquirer and Target Total Advisory Fees, in millions (1993‐2008)
12
10
8
6
4
2
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Acquirer Fees
48
Target Fees