Analyst Recommendations and Mergers:

Do Analysts Matter?‡

David A. Becher, Jennifer L. Juergens

April 2007 ________________________________________________________________________ Abstract This paper investigates the relation between investment analyst recommendations and merger completion. Unlike the new issues market, we argue analysts’ incentives are skewed to issue recommendations that ensure merger completion rather than maximize the overall deal value. Using a comprehensive sample of completed and withdrawn mergers, we observe the direction and affiliation of recommendations significantly impact the probability of merger completion. These effects are magnified for stock mergers where analysts can directly affect the acquisition currency. In the case of withdrawn deals, recommendations are related to which party is likely to terminate a merger. Overall, our results suggest analyst recommendations are linked to merger outcomes, though those recommendations appear biased to secure deal completion. JEL classification: G32; G34; G21 Keywords: Mergers; analysts; merger success; conflicts of interest

‡ We would like to thank Tom Bates, Gennaro Bernile, Audra Boone, Jarrad Harford, Michael Hertzel, Ron Hoffmeister, Laura Lindsey, Spencer Martin, Micah Officer, Ralph Walkling, Mengxin Zhao, as well as seminar participants at Arizona State University, Villanova University, and the 2005 Financial Management Association and FMA European meetings for comments and suggestions. We are indebted to Kurt Miling and Elisa Scinto for their excellent research assistance. 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: Arizona State University, W.P. Carey School of Business, Department of Finance, Tempe, AZ 85287, phone: (480) 965-6855, email: jennifer.juergens@asu.edu.

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In this paper, we examine the role of analysts throughout the merger process. Mergers

represent events where uncertainty about future valuations of acquirers, targets, and combined

entities may increase. As information intermediaries, analysts, through their recommendations,

can mitigate this uncertainty. Analysts, however, also face a complex set of incentives that may

bias their recommendations around merger events.

Numerous studies suggest analyst recommendations provide trading signals for investors

(Malmendier and Shanthikumar, 2007) and stock prices, at least in the short-run, respond in the

direction of those recommendations (Barber et al., 2001; Frankel and Li, 2004; Irvine, 2003;

Stickel, 1995; Womack, 1996). Others demonstrate analysts are plagued by banking conflicts of

interest, which optimistically bias recommendations and limit the relevancy of their opinions

(Dugar and Nathan, 1995; Lin and McNichols, 1998; Lin et al. 2005; Michaely and Womack,

1999). It is possible analyst recommendations, directly or through the pricing mechanism, affect

investors’ perception about proposed mergers and ultimately influence merger outcomes.

Further, it is possible such influences are subject to conflicts of interest.

Mergers provide an ideal framework to test how analyst recommendations affect real

outcomes. Research documents an empirical regularity of significantly positive target and non-

positive acquirer returns. If analysts only react to price movements or extant information about a

merger, changes should mirror returns and the relative informativeness of these opinions is

questionable.1 If recommendations deviate from initial price responses, it is possible analysts

provide or interpret information. Analyst compensation, partially funded by advisory fees, may

bias recommendations and these biases may have increased recently. In the 1990s, M&A fee

income increased from $3 to $12 billion while equity underwriting fee income increased from $3

1 Recent studies show that analysts respond to information in prices as well as other public signals (Conrad et al., 2006; Jegadeesh et al., 2004).

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to $7 billion. Moreover, mergers represent an arena where real outcomes may be affected by

analyst biases. Merger financing provides a natural experiment to test analysts’ ability to

influence acquisition currency or target cost via upgrades/downgrades in stock deals. Further,

investors and managers may rely on analysts when evaluating a merger’s efficacy. As specialists,

analysts should have insight into valuations of counter-parties, especially for firms without

advisors. Analysts may provide new information and/or some certification for a merger.2

In this paper, we provide tests of whether analyst recommendations affect merger

outcomes. Using analyst opinions 50 days pre-announcement through merger termination, we

examine the direction of acquirer and target recommendation revisions. If analysts follow stock

price movements, acquirer (target) recommendation changes should be negative (positive). On

the other hand, recommendation changes for acquirers and targets will be largely positive if

analysts face traditional conflicts of interest (IB, client, or brokerage pressure). We observe

analysts provide positive acquirer revisions and unfavorably rate targets (55% downgrades or

negative initiations). Although we observe the direction and intensity of recommendations

affects announcement and short-run recommendation returns, the direction of these revisions is

opposite market response. This apparent paradox questions whether analysts’ opinions matter.

We next address why this apparent contradiction exists. Potential conflicts dictate

analysts may provide biased recommendations. While analysts have incentives to issue timely

and pertinent recommendations (Hong and Kubik, 2003), a substantial part of their compensation

during our sample is derived from banking fees, some portion of which is likely attributed to

M&As.3 Under traditional conflicts of interest affiliated analysts are excessively optimistic, but

2 Rau and Rodgers (2002) observe acquirers hire top-tier advisors to ensure deal completion rather than certification; yet analysts have discretion in the firms they cover. While we expect managers to be better informed about mergers than analysts, there may be instances where analysts identify deals undertaken for non-value maximizing reasons. 3 Since 2003 (outside of our study), analyst compensation cannot come directly from any single banking deal.

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there is no indication of directionality for counterparty analysts. Several recent papers investigate

conflicts of interest around mergers, but find little evidence the same conflicts that plague

securities issuance arise in the market for corporate control. In related work, Kolasinski and

Kothari (2007) examine whether acquirer affiliated analysts are more optimistic than unaffiliated

analysts around merger announcements. Their results suggest that both acquirer- and target-

affiliated analysts face conflicts of interest in the form of client pressure as both become more

optimistic toward acquirers after the merger announcement, regardless of financing method.

We propose analysts face an alternative conflict of interest in mergers, but not other

advisory relationships. In capital market transactions, fees are derived from the value of equity

issued. In mergers, fees are usually fixed and contingent upon deal completion not value. Similar

to traditional conflicts of interest to attract investment banking business, analysts have incentives

to provide opinions that maximize completion. Analysts can adjust acquisition currency or target

cost by up/downgrading acquirers and targets and directly affects stock prices. We hypothesize

recommendations that boost acquisition currency or decrease target costs are positively related to

deal completion. To test our proposed conflict of interest, we delineate acquirer and target

recommendations into affiliated (analyst and merger advisor same) and unaffiliated opinions.

Our work differs from prior studies in several aspects. Unlike Kolasinski and Kothari’s

client pressure hypothesis, we propose the structure of M&A advisory fees alters the incentives

of analysts toward merger completion. Both acquisition currency and target cost are affected by

revised recommendations, thereby providing a more complete picture of conflicts analysts face.

Moreover, our empirical tests focus on the impact of affiliated and unaffiliated analyst revisions

on returns and real outcomes of mergers, accounting for potential conflicts of interest, rather than

testing whether affiliated analysts are more likely to be conflicted than unaffiliated analysts.

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Our results suggest neither traditional conflicts nor client pressure hypothesis drive the

direction of analyst opinions. Both acquirer- and target-affiliated analysts are optimistic on

acquirers, pessimistic on targets. Even after controlling for endogeneity, positive acquirer and

negative target recommendations (in most cases) are significantly associated with an increased

likelihood of merger completion. Acquirer recommendations by acquirer- or target-affiliated

analysts, and advisory fees, particularly paid by targets, also positively impact deal success.

While analyst opinions may affect all mergers their impact is magnified where they can directly

affect the acquisition currency. In stock-financed deals, an increase in positive acquirer opinions

(affiliated and unaffiliated) is significant and positively related to the probability of completion.

Examining successful and withdrawn mergers makes it easier to disentangle potential

conflicts from analyst information and capture the impact of analysts. Withdrawn mergers should

be functions of external influences, such as merger terms or announcement returns. If analysts

provide new information, however, management may react to recommendation changes. We find

target management is less likely to withdraw as the number of negative target recommendations

increases, but more likely as the number of negative acquirer (reduction in acquisition currency)

or positive target recommendations increase (re-evaluation of stand-alone prospects or attraction

of subsequent rival offers). Acquirers are less likely to withdraw as positive acquirer returns

increase, consistent with value reinforcement. Our results suggest analyst opinions affect who

withdraws, especially for targets, even after controlling for returns and merger characteristics.

Although our findings suggest analysts are swayed by conflicts of interest to ensure

merger completion, our withdrawal analyses counter this by providing instances where analyst

opinions prohibit the attainment of advisory fees. Instead, analyst recommendations may reflect

their true valuation. To test this prediction, we delineate consensus recommendations into above-

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and below-buy categories and investigate two years of post-merger returns to determine out-of-

sample predictive ability. In all instances, firms with positive recommendations significantly

underperform those with negative opinions, suggesting analysts face conflicts that affect their

impartiality and/or are systematically poor predictors of future performance.

The remainder of the paper is as follows. Section I details testable hypotheses. Section II

presents our sample. Section III investigates the impact of recommendations on returns. Section

IV analyzes analyst recommendations and probability of merger completion as well as the

dynamic relation between recommendations and merger success. Section V details the effect of

analysts’ opinions on the likelihood of merger termination and out-of-sample predictive ability of

analysts. Robustness issues are discussed in Section VI. Section VII concludes.

I. Hypothesis Development and Empirical Predictions

Extensive research indicates analysts exhibit an optimistic bias in their recommendations.

Most of these studies focus on specific instances, e.g. issuance of new equity (IPO or SEO) and

affiliated analysts’ conflicts. Lin and McNichols (1998) show a spillover effect to unaffiliated

analysts, since current optimism may lead to future affiliation. The fact analysts face pressure

from more than their banking division muddles the issue. Several studies suggest firms covered

by analysts exert an upward bias on recommendations in exchange for easier information access.

Further, brokerage clients and traders exert similar pressure to keep prices elevated for current

stockholders or to generate additional trade (Agrawal and Chen, 2007).

With respect to mergers, most studies examine the role of advisory relations in

takeovers.4 Other papers investigate the investment banking conflict for mergers, where

affiliated analysts provide overly optimistic recommendations for acquirers or targets.

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Traditional conflicts of interest do not appear to exist around M&A activity.5 In a study of 252

tender offers, Bradley, et al. (2007b) finds no evidence of conflicts of interest as market response

and long-run performance are identical for affiliated and unaffiliated analysts. In a related paper,

Kolasinski and Kothari (2007) model the probability of analyst optimism around M&A activity

and find that acquirer affiliated analysts, in particular, are more likely to issue optimistic

opinions. They argue client pressure rather than banking pressure or selection bias drive

affiliated analyst optimism in mergers. We propose a different type of conflict exists for analysts

around mergers.

Unlike investment banking conflicts of interest, it is not always in analysts’ best interest

to be optimistic in mergers. This is due, in part, to the fee structure. In contrast to investment

banking fees that are generally set at a fraction of the capital raised, M&A fees are usually flat

and received only at merger completion (Hunter and Jagtiani, 2003; Kisgen et al., 2007; Rau,

2000). For our sample, over 78% of M&A fees are flat. Completion, not overall merger value,

determines a bank’s compensation in mergers. The incentives to be overly optimistic on both

acquirers and targets will not necessarily manifest. This leads to our first hypothesis:

H1: Analysts will issue recommendations to ensure merger completion.

Given the market response to mergers (significantly positive target and negative acquirer

returns) and evidence analysts respond to prices (Conrad et al., 2006), one might expect analysts

to be optimistic on targets and pessimistic on acquirers. Even if analysts support the merger,

they may be optimistic on both parties. But, if analysts are driven by conflicts of interest (either

4 Allen, et al. (2004) 495 successful deals; Calomiris and Singer (2004) 52 hostile; Kale et al. (2003) 324 successful deals; Rau and Rodgers (2002) 223 tender offers; and Simonson (2002) 69 mergers with similar valuation analysis. 5 Given changes to rules and regulations regarding investment banking transactions and analysts stemming from the Global Research Analyst Settlement, Congress, and New York State court actions, recent research has attempted to identify other conflicts of interest faced by analysts, including brokerage conflicts, client pressure, and selection bias. The issue, however, is that all of these conflicts lead to similar outcomes, which is excessive optimism.

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by pressure to obtain advisory fees or by merger parties), they may issue recommendations to

ensure merger completion, particularly for stock deals where analysts can affect stock prices.

In stock financed mergers, the acquisition currency is the stock value, which can be

managed by actions of the company (Erickson and Wang, 1999; Louis, 2004) and outside

information providers, such as analysts (Clarke et al., 2006). We hypothesize recommendations

that enhance acquirer or decrease target values are positively related to deal completion, and vice

versa. These revisions serve to reduce (increase) acquirers’ cost by lowering the value of the

target even though acquiring shareholders may re-evaluate their forecasts of merger efficacy

based on analysts’ opinions. This leads to our second hypothesis:

H2: Analysts are most optimistic (pessimistic) for acquirers (targets) in stock-financed

deals where they can affect acquirer’s acquisition currency or target’s acquisition cost

through their recommendations.

Until now, we have assumed all analysts are equally affected by conflicts 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.6 In investment banking deals,

affiliated analysts are more likely to be influenced by conflicts. Both acquirer- and target-

affiliated analysts are more likely to be optimistic (pessimistic) on acquirers (targets). Acquirer

advisor analysts have incentives to reduce target value, while keeping acquirer value high.

6 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”.

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Target affiliated analysts have incentives to ensure completion (upgrade acquirers, downgrade

targets); even at the expense of lower returns or future takeover bids if a merger is withdrawn.

H3: Acquirer optimism and target pessimism is exacerbated for affiliated analysts.

Advisory fees may affect analyst recommendations. As noted, advisor merger fees are

generally fixed, creating incentives for deal completion not value maximization. Since analysts

can impact merger costs through recommendations (at least in the short-run), acquirer optimism

and target pessimism are magnified in the presence of advisory fees. This is particularly true if

targets pay a fee since it is likely they are willing participants. Table 3 provides predictions based

on affiliation, financing method, and advisory fee structure and leads to our final hypothesis:

H4: Analyst conflicts are magnified with advisory fees, particularly if paid by the target.

We also expect the amount of information production (number of acquirer or target

analysts) will impact merger completion. Branch et al. (2003) and Jennings and Mazzeo (1993)

find the number of target analysts is negatively related to deal completion, controlling for firm

size. This suggests analyst coverage is not simply a proxy for size and instead may attract rivals

by either signaling high quality targets or decreasing information acquisition costs for potential

acquirers. Luo (2005) finds as the number of acquirer analysts increases, mergers are more likely

to be announced by a definitive merger agreement, which increases completion likelihood.

We recognize analyst recommendations represent only one source of information to

merger participants and other characteristics can impact merger completion. Bates and Lemmon

(2003) show merger programs, cash tender offers, as well as cash- and stock-financed deals are

positively related to completion. Target and acquirer termination fees are positively while hostile

mergers are negatively related to merger success (Bates and Lemmon, 2003; Dong et al., 2005;

Schwert, 2000). Merger completion declines as relative size increases (Dong et al., 2005; Branch

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et al., 2003). As a result, we include relative size and binary variables for merger programs (three

or more acquisitions of public firms in five years), stock or cash financed deals, cash tender

offers (Walkling, 1985), target/acquirer termination fees, and deal attitude (hostile).

We also include a binary variable if the acquirer and target is in the same three-digit SIC

code. Past studies suggest these focused mergers are more profitable and have higher merger

returns. Although industry relation might expedite a merger and be more likely completed, some

industries may face significant antitrust concerns. We also investigate analysts’ incentives by

examining the presence and type of collar in stock-financed deals. Collars are classified into two

types: fixed payment (similar to cash) and fixed exchange (similar to a stock bid).7 Target and

acquirer pre-announcement (-30, -5) and announcement returns (-1, +1) are included. Similar to

recommendations, higher target run-up or announcement returns may cause the target to become

too expensive (thereby decreasing the probability of bid completion) while higher acquirer

returns provide certification of a merger’s worth and increases the likelihood of completion.

II. Sample Selection and Descriptive Statistics

II.A Merger Sample

To test the association between analyst recommendations and mergers, we require a set of

completed and withdrawn U.S. mergers. From 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 collect

7 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).

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merger announcement and withdrawal dates, merger advisors, and collar type.8 Financial

variables are obtained from Compustat, while return data are from CRSP.9

Our sample of mergers is collected from SDC from January 1993 to December 2000. 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 merger

outcome (only 357 have multiple acquirers). Our sample includes 2,797 completed mergers and

647 withdrawals. Of withdrawn mergers, only 22% of targets were acquired within one year

after termination. Although our sample contains 3,444 mergers, only 1,467 completed and 213

withdrawn deals have analyst coverage. For analyses specific to recommendations, we focus

exclusively on these 1,680 mergers, but we include all viable mergers in our likelihood tests.

Table 1 details merger characteristics by deal status (completion or withdrawal) and

analyst following. The average merger value is consistently smaller when analysts do not follow

acquirers or targets. Relative size is similar across analyst following, but differs significantly by

deal status. Consistent with Dong et al. (2005) and Branch et al. (2003), relatively larger targets

are less likely acquired. Firms without analyst coverage are more likely to engage in merger

programs and make cash offers, but less likely to hire advisors, particularly for completed deals.

Consistent with the selection bias hypothesis (Lin and McNichols, 1998), acquirers and targets

have better performance with analyst following (completed and withdrawn).

8 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 were collected from news stories and press releases from Factiva, Lexis-Nexis as well as proxy statements from SEC Edgar filings. 9 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-, 6-, or 9-

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II.B Analyst Recommendations and Matching Procedure

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 eventually the merger outcome.

We collect analyst recommendations from I/B/E/S (Thomson Financial) 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 collect recommendation data from 1993 to resolution of mergers

announced by year-end 2000.10 Given the decline in mergers in 2001 and 2002 due to changes in

the economy and analyst reporting standards related to actions by the New York Attorney

General, NYSE, Nasdaq, and Global Research Analyst Settlement, our sample ends in 2000.11

While strength of ratings 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. 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. Initiations of a “buy”, reiterations of buy, hold, or sell are “neutral”

month, and 1-or 2-year abnormal buy-and-hold returns for combined firms (completed) deals or acquirer and target (withdrawn). Three-day net-of-market abnormal CAR is computed around recommendation releases. 10 We were required to hand-match approximately 50% of the 6,888 acquirer and target firms identified in SDC with the I/B/E/S data due to the construction of the I/B/E/S dataset. 11 The number (value) of mergers decreased by 45% (82%) between 2000 and 2002 (source: Mergerstat Review).

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as it is not clear if they are positive or negative.12 For robustness, we also examine multi-level

changes (Section VI). We segment recommendations by direction since studies (Stickel, 1995;

Womack, 1996) find negative recommendations appear more informative and have a larger price

impact. We eliminate recommendations that do not fall into the standard ratings system.

II.C Summary Evidence on Mergers and Analyst Coverage

As mergers are substantial value-altering events for companies, we expect an increase in

analyst activity around merger announcements. Although explicit deal terms are often not

revealed until the announcement, there is substantial evidence target prices tend to drift upward

prior to the announcement (Jensen and Ruback, 1983; Bradley, et al. 1988; Schwert, 1996).13 To

investigate whether this is due partially to analyst recommendations (affiliated or unaffiliated),

we collect recommendations from –50 days pre-merger through completion or withdrawal.

Table 2, Panel A details summary data by merger type (cash and stock deals). There are

12,804 acquirer and 4,678 target recommendations for mergers with analyst coverage. We find

significantly more upgrades (6,945) than downgrades (5,859) for acquirers (p-value = 0.00),

particularly for stock deals. The average acquirer recommendation is roughly a “2” (a buy),

consistent with the average in IBES. We do not find a significant change in the average level

from before to after the merger announcement. In contrast, there are significantly more negative

recommendations for targets (3,017) than positive (1,661), regardless of financing (p-value =

0.00). The average target recommendation is roughly 2.50 for stock offers and nearly 3.00 for

cash deals, indicating target downgrades are not just shifts from strong buy to buy. Unlike

12 Because of the lack of sell and strong sells, it is well known “holds” are implicit sell recommendations, effectively making buy recommendations holds. In unreported tests, we document that returns to the strong buys for firms in our sample are 1.28% (t = 6.72), buys are 0.23% (t = 1.53), and holds, sells, and strong sells are –1.93% (t = 9.49). 13 Evidence suggests negotiations occur prior to the public announcement and information may be partially revealed to both analysts and the market (see Boone and Mulherin, 2007; Francis, et al., 1997; Schwert, 2000). As such, analyst recommendations may reflect this information, even if the analyst never discusses the merger outright.

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acquirers, there is a significantly negative shift in the average target recommendation from pre-

(2.19) to post-announcement (2.54) periods. We obtain similar results for medians (unreported).

To determine if mergers drive differences in recommendations, we examine a benchmark

period -100 to -51 days pre-announcement. During this period, target recommendations are

evenly split between upgrades and downgrades. Although the average acquirer recommendation

is similar to the merger period (2.00), the average target recommendation is significantly more

positive (2.12) than in the merger period (t-stat = 10.94).

Targets may generate negative recommendations in the merger period for several reasons.

Targets are relatively poor performers (average ROA one-year pre-merger is –5.07%). They are

likely to be viewed negatively by analysts and the market pre-announcement, though this does

not appear to hold when the benchmark period is examined. Alternatively, post-announcement,

target prices may increase sufficiently that valuation ceilings are reached and analysts downgrade

firms, even if they remain positive on the merger. Although target returns are generally positive,

a merger bid may not change the target’s value sufficiently to warrant an upward revision in the

expected price; thus, we expect downgrades to occur frequently around announcements as price

target boundaries are reached.14 In unreported tests, we study acquirer and target downgrades,

pre-announcement run-up, and number of days until subsequent downgrades; 38% of targets in

the highest run-up decile are downgraded within 10 days after announcement.15 This is

consistent with the merger conflict hypothesis that analysts will downgrade to attain merger

completion.

14 In unreported tests, we observe that target firms, on average, earn 6.21% cumulative abnormal returns in days -30 to -5 before the merger, and have announcement period returns of 18.62%. 15In 11% of cases where acquirers, targets, or both are downgraded the merger is withdrawn. Only 10% of upgraded acquirers are withdrawn. Nearly 20% of upgraded targets are withdrawn, usually at the target’s request; suggesting upgraded targets re-evaluate stand-alone prospects or capitalize on positive recommendations to attract future rivals.

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Figure 1 shows recommendation frequency for targets and acquirers. Pre-announcement,

4,475 upgrades and downgrades are announced for acquirers and targets (relative to 2,408 in

benchmark period), a nearly two-fold increase in analyst activity. The 10-day window from

merger announcement is the most active period, encompassing 2,607 revisions, a substantial

portion of which are target downgrades. This increase in target downgrades confirms the run-up

and conflict of interest stories. Although the average merger duration is 140 days, nearly 40% of

the recommendations occur from pre-announcement to three weeks post announcement.

II.D Analyst Affiliation and Direction of Opinions

Prior to changes in NASD and NYSE rules and the Global Research Analyst Settlement

of 2002, anecdotal evidence suggests the Chinese Wall between investment banking and research

activities was not ‘solid’ as it was common for analysts to be present for banking deals, including

M&A activity. The merger conflict of interest hypothesis suggests that in order to complete a

merger, it is appropriate for both acquirer and target advisor analysts to be excessively optimistic

(pessimistic) on acquirers (targets). Positive acquirer recommendations serve to increase the

acquisition currency, particularly for stock deals, while negative target opinions reduce the

overall cost of the target. However, if target advisor analysts believe the target would have better

prospects as a stand-alone entity or could attract another acquirer, they may downgrade the

current acquirer and upgrade the target. Analyst affiliation is constructed by matching acquirer

and target advisor firms from SDC with brokerage firms from I/B/E/S.

Panel B details own- and competing-advisor analyst recommendations, where own-

advisor analyst is affiliated with acquirer and competing (or counterparty) advisor is affiliated

with target, and vice versa. Regardless of financing, acquirer and target advisor-analysts issue

significantly more upgrades for acquirers (61%-67%) than targets (42%). No significant

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differences exist between upgrades and downgrades by own and counterparty advisors for targets

or targets. This excessive optimism for acquirers generally manifests in stock-financed offers.

These results support the hypothesis acquirer and target advisor analysts bolster the acquisition

currency. Consistent with our predictions, targets yield a preponderance of downgrades.

To test merger conflict of interest, we construct a ratio of positive to negative own- and

counterparty-advisor recommendations. Ratios greater (less) than one suggest excessive

optimism (pessimism). Table 3 details own- and counterparty-advisor predictions delineated by

deal type, timing of the report, and whether an advisory fee was paid. Panel A presents

predictions and results without advisory fees, while Panel B presents advisory fee predictions

and corresponding optimism ratios. Regardless of fees paid, both timing of reports and financing

method affect the optimism ratio. Both acquirer and target advisor analysts become excessively

optimistic on acquirers, particularly after the merger announcement. Acquirer-advisor analysts

are pessimistic on targets in both pre- and post-periods (especially for stock deals), while target-

advisor analysts are optimistic on targets pre-offer, but pessimistic post. If advisory fees are paid,

target-advisor analysts are neutral to negative on targets pre-announcement, suggesting

negotiations occur pre-merger. Our results confirm the merger conflict of interest hypothesis;

affiliated analysts are positive on acquirers and negative on targets to ensure merger completion.

Panel C compares affiliated to unaffiliated analysts. Since affiliated analysts respond

similarly to acquirers and targets (Panel B), we combine affiliated analysts and compare them to

unaffiliated ones. Affiliated analysts are more positive than unaffiliated for acquirers. In stock

mergers, nearly 65% of affiliated analyst recommendations are upgrades compared to 54% for

unaffiliated (p-value 0.00). Both affiliated and unaffiliated analysts are generally pessimistic for

targets (43% affiliated vs. 36% unaffiliated, p-value 0.09). No significant differences exist

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between affiliated and unaffiliated analysts for cash deals, indicating analysts’ incentives to use

recommendations to alter stock prices are muted when they cannot affect acquisition currency.

III. Returns to Mergers and Recommendations

In the previous section we document an increase in analyst recommendations for both

acquirers and targets around mergers. We find all analysts, particularly affiliated ones, are

optimistic on acquirers and pessimistic on targets. These results suggest affiliated analysts are

either more informed about the value of merger parties or alternatively adjust recommendations

to ensure merger completion. In this section, we investigate whether and to what degree analyst

recommendations impact announcement and recommendation returns.

III.A Merger Announcement Returns

In general, merger announcement returns are similar to past studies. For completed deals,

on average acquirers lose–0.74% (t = -3.72) while targets gain 19.67%. In contrast, acquirer

announcement returns for withdrawn deals are –1.30% (t = -4.65) but target returns only increase

by 13.18%. Although we find no statistical difference between acquirer returns by deal status,

target returns for withdrawn mergers are significantly lower (p-value 0.02).

In Table 4, multiple regression acquirer and target returns are detailed. Returns are

market-model adjusted over a 3-day window (-1 to +1). We separate acquirers and targets as

returns are substantially different between merging parties. In each regression both acquirer and

target recommendations are included since spillover between acquirers and targets is likely. We

add pre- and announcement period recommendations as both may impact merger returns. In

addition to signed recommendations, average recommendation level, and number of advisors and

analysts are included. Regressions also contain merger characteristics as control variables.

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Lastly, we include merger value, pre-merger run-up, and days to resolution. Appendix A

provides a description of these variables.

Positive announcement recommendations significantly positively impact acquirer returns

while negative acquirer or target announcement recommendations negatively affect acquirer

returns. Acquirer returns are diminished as merger size increases or for stock deals, indicating

the market may be concerned about overpayment. Target announcement returns are increasing in

the number of pre-announcement positive and negative acquirer recommendations and acquirer

announcement period upgrades. Target announcement returns are muted as the number of pre-

announcement target upgrades increases. These results indicate announcement returns are a

function of analyst recommendations. Moreover, a degree of spillover between acquirer and

targets exists as each return is affected by counterparty recommendations, particularly for targets.

III.B Recommendation Returns

In other studies that examine analyst upgrades and downgrades, there is a price effect in

the recommendation direction. However, there is a significant amount of extant information in

the market with mergers, which may render analyst recommendations redundant and dampen any

impact revisions may have. In this section, we investigate returns to upgrades and downgrades

for both acquirers and targets, and control for merger financing and analyst affiliation.

Univariate return results are presented in Table 5. Similar to past studies, acquirer returns

for upgrades and downgrades are statistically significant (1.20% and –3.47%, respectively) as are

target returns (3.32% and 3.08%, respectively). Targets, however, generate significantly positive

announcement returns, which coincide with many downgrades (Figure 1); thus, in Panel A (and

all remaining panels), we exclude the announcement period (-5 to +10). Acquirer returns are not

significantly different; target returns, particularly for downgrades, are now negative, in-line with

18

others (Barber et al., 2001; Stickel, 1995; Womack, 1996). In Panel B of Table 5, we control for

financing. For acquirers, returns around downgrades in cash offers are marginally more negative

than for downgrades in stock deals (p-value 0.07). There is generally no significant difference,

however, in either acquirer or target returns to upgrades or downgrades by method of payment.

The role of own-advisor and counterparty-advisor is examined in Panel C. For acquirers,

no significant difference exists if acquirer or target advisors downgrade the acquirer. Affiliation

appears to matter for upgrades; there is an insignificantly positive response if acquirer-advisors

upgrade the acquirer and significantly positive if target-advisors upgrade (2.67%, t = 3.26). The

difference is marginally significant (p-value 0.09). For targets, no statistical difference exists for

acquirer/target advisor downgrades; yet in absolute magnitude, acquirer advisor upgrades are

larger and significant (-3.68% vs. -1.74%). Upgrade returns are significantly different between

acquirer and target advisors. Target-advisor upgrades are significantly positive (5.41%), while

acquirer-advisor are insignificantly positive (0.46%) and the p-value of the difference is 0.03.

Panel D of Table 5 compares affiliated to unaffiliated analysts. Returns for upgrades are

statistically indistinguishable for either acquirers or targets. Examining acquirer downgrades,

returns to both affiliated and unaffiliated revisions are approximately –3.00%. Returns for

affiliated target downgrades (-3.02%), in contrast, are significantly more negative (p-value =

0.05) than unaffiliated downgrades (-0.95%). In general, returns for either subset look similar to

the reduced sample results in Panel A.16 Results suggest there is a (short-term) price response to

analyst recommendations and the direction and timing of recommendations matter for merger

returns. Moreover, we document spillover between recommendations for acquirers and targets in

that returns to both are significantly impacted by the other’s recommendations. In the next

section, we model whether analyst recommendations affect the likelihood of merger completion.

19

IV. Do Analysts Matter for Merger Completion?

In this section, we implement logistic regressions to determine the extent analysts and

their recommendations affect merger outcomes.17 In Sections II and III, we presented evidence

analysts provide both positive and negative recommendations and the market responds to these

recommendations. In our logistic analysis, we attempt to capture whether the variations in these

effects impact the resultant outcome. Appendix A provides descriptions of the variables used.

IV.A Logistic Regression Analysis

In Table 6, we present three logistic regressions that examine the link between analysts

and probability of merger completion. The first model is a simplified (reduced) model containing

only key analyst opinion variables while the second (full) also include control variables, as well

as analyst and advisor characteristic variables. The third model, discussed below in Section IV.B,

investigates the relation between analyst opinions and method of payment. Predicted signs for

the recommendation and control variables are also included.

Beginning with the reduced model, increasingly positive information production about

the acquirer significantly increases the likelihood of completion. While we find the expected sign

on negative acquirer recommendations, the coefficient is not significant. Conversely, increases in

positive information production about targets reduce the likelihood of a completed merger. The

decrease in merger completion is perhaps due to the fact the target becomes too costly to acquire

or it revaluates its’ opportunities in light of the increase in positive recommendations. Although

we limit our sample to only single-acquirer mergers, it is possible another acquirer is anticipated

since 22% of the targets are acquired within one-year following the termination. Affiliated

16 We also run multiple regressions on recommendation returns to acquirers and targets and find similar results. 17 Greene (2003) indicates differences between logit and probit models are likely if few of the Y observations equal one and/or wide variation in importance in independent variables exists. We also run all analyses using a probit model and find qualitatively similar results.

20

analysts also impact the likelihood of a completed deal. As before, we aggregate affiliated

responses and find the probability of completion is significantly positively related to affiliated

acquirer recommendations in the reduced model, but target affiliation does not appear to matter.

Examining the full model confirms results from the reduced regression; positive acquirer

recommendations (affiliated and unaffiliated) matter. Although the coefficients drop below

significance, target upgrades reduce completion probability, while downgrades increase the

probability, consistent with our predictions.18 The number of target advisors is positively related

to merger completion, but the number of acquirer advisors is not. An increase in the number of

acquirer analysts significantly increases the probability of completion; however, similar to

others, an increase in the number of target analysts is (insignificantly) negatively related

Acquirer advisory fees are insignificantly related to probability of completion, but target

advisory fees are highly significantly related to completion. We observe that if a target pays an

advisory fee the merger is likely to go through. In conjunction with positive acquirer

recommendations (affiliated and unaffiliated) and the consistency with predictions for positive

and negative target revisions, these results support the hypothesis of a merger conflicts.

The number of days to completion, pure cash and stock financed deals are all positively

related to the likelihood of merger completion.19 Fixed-payment collar bids are positively,

although insignificantly, related to the probability of completion, while fixed-exchange collars

have an insignificant impact. Lastly, we observe the larger the target run-up (leakage about

impending mergers) the more likely the merger will be completed.

18 If we eliminate advisory fees, the coefficient on target downgrades loads significantly. 19 Pure stock offers may have an increased likelihood of completion because acquirer managements may not be subject to potential cash constraints and may be able to sweeten offer terms more easily.

21

IV.B Method of Payment and Analyst Opinions

The models above include all payment methods. However, analyst recommendations may

play a larger role for stock deals, as they are better able to influence firm value. Similar to our

full model, an increase in positive acquirer recommendations significantly increases the

probability of completion for stock deals only. Although target upgrades and downgrades have

the correct predicted signs, significance is weakened with target advisory fees, which remains

significant. Acquirer affiliated recommendations continue to be significantly positively related

to completion. The merger program indicator loads significantly for stock deals. The remaining

control variables are similar in direction and significance to those presented above.

Our evidence suggests analyst recommendations are related to the probability of a

completed merger and method of payment matters. As analysts provide recommendations that

bolster acquisition currency (positive acquirer opinions) and keep acquisition cost low (negative

target opinions), the probability of completion rises. Increased interest in the target (number of

positive recommendations) may attract competing acquirers or cause the target to re-evaluate its

prospects. This decreases the probability of completion (as shown in the reduced model), but

acquirer-affiliated analysts can somewhat counteract this effect by providing acquirer upgrades.

Finally, if the target pays an advisory fee, the probability of completion increases. Coupled with

the direction of recommendations in Section II and regressions in Table 6, this suggests analysts

issue upgrades for acquirers and downgrades for targets to ensure merger completion.

IV.C Endogeneity

The prior sections contain evidence on the effect of analyst recommendations on the

probability of merger completion. However, as analysts may serve as both processors and

producers of information about valuation of acquirers and targets, it is not clear whether analysts

22

provide new valuation information or simply respond to information provided by the merger

agreement or market reaction of the announced merger. As a result, analyst recommendations

and the likelihood of merger completion appear endogenous as both will respond to new

information released from the time of the merger announcement. Although we observe positive

associations between positive (negative) acquirer (target) recommendations and the probability

of merger completion, it is tenuous to determine any level of causality. The essential issue is that

stand-alone logistic regressions may provide biased estimates when dependent and independent

variables are endogenous.

To address endogeneity we follow methodology in Greene (2003) and simultaneously

estimate a two-stage model where the first stage is a probit model predicting recommendation

revisions using all exogenous variables. The fitted value from the probit is then used in the

estimation of a linearized second-stage logistic equation, which is a regression of the probability

of merger completion.20,21 We cannot test two-way directionality between recommendations and

completion since probability of success is known after all other information is available; we can

only determine one-way directionality from recommendations to likelihood of completion.

Separate acquirer and target estimations are performed in the first stage, where individual

recommendation revisions are the dependent variable. Instruments in the first stage include the

average industry recommendation, which proxies for herding among analysts (Kim and

Pantzalis, 2003; Welch, 2000) and average number of industry recommendation changes as

analysts cluster their revisions in time and across firms (Boni and Womack, 2006). Although

industry variables may be uniquely identified with acquirer and target recommendation changes,

we have do not expect industry recommendations will be correlated with merger completion.

20 For robustness, we repeat all the analysis in Section IV.C using the level of the recommendation instead of revisions. The results are qualitatively similar.

23

The dependent variable in the second stage is an indicator for merger completion. The

instruments utilized to identify the second stage include acquirer and target termination fees plus

hostile takeover indicators (Bates and Lemmon, 2003; Officer, 2003). These studies suggest

termination fees are positively related to merger completion in that firms employing these

measures are less likely to end a deal and they experience significantly higher returns. Hostile

offers, alternatively, are less likely to be completed. Neither termination fees nor hostile mergers

should necessarily alter analyst revisions. Results for the first and second stage estimations are

presented in Table 7. The validity of our instruments is assessed empirically with partial R-

square tests for the relevance of instruments (p-values 0.00) and Hansen J-statistic for

overidentifying restrictions. We obtain Hansen p-values greater than 0.30, which suggests we

cannot reject the validity of the instruments.

Analyst recommendations are significantly related to the probability of completion. Due

to the I/B/E/S ratings scale (“1” strong buy; “5” strong sell), an increase in the predicted acquirer

recommendation revisions are significantly negatively related to the likelihood of completion.

The more positive analysts are on acquirers (lower values), on average, the more likely the

merger will be successful. Conversely, the predicted target recommendation change is

insignificantly positively related to the probability of success. We find similar results for stock

mergers. It appears analyst recommendations on acquirers and targets are a significant

contributor to merger completion, controlling for determinants of changes in recommendations.

These results also suggest analyst recommendations are related to merger terminations. The next

section examines analysts’ role in withdrawn deals, focusing on the effect of analyst

recommendations on the party that terminates a merger.

21 See Amemiya (1974) on simultaneous equation models with truncated dependent variables.

24

V. Analysts, Withdrawals, and Post Merger Analyses

V.A. Do Analysts Affect Withdrawal Decisions?

Several studies have examined the rationale for merger withdrawal and subsequent

returns (Bradley, 1980; Bradley, et al. 1983; Sullivan et al. 1994) and find target gains are

significantly positive if the target cancels the deal, but effectively zero if an acquirer withdraws.

Acquirers break even or experience negative returns following a cancelled deal depending on

whether the target receives a rival bid. The reversion of target returns to pre-merger levels when

the acquirer withdraws indicates the target does not represent a profitable opportunity. Positive

returns to target cancellations suggests they are profitable and thus target-cancelled deals are not

necessarily evidence of target management acting against their shareholders best interests.

Given our analyses show analyst recommendations are associated with merger outcomes,

it is possible analysts’ recommendations will also effect merger terminations. If managers are

cognizant of analysts’ recommendations and react accordingly, we would expect the greatest

impact when management cancels a deal. We examine 213 withdrawn deals with analyst

coverage using Lexis-Nexis, Factiva, and Moody’s to determine merger termination. While many

reasons are cited (deal terms, fit, financial health, etc.), there are too few observations to

designate so specifically. Instead, we categorize observations into three types: target related,

acquirer related, or other (mutual, regulatory, unspecified, etc.). Within these categories, we

combined shareholder-activated, management-initiated, and unspecified general terminations.

Since we have a nominal response variable, we implement a generalized logit where the

response level for other is the control to compare acquirer- and target-related responses (Table

8). In Model A, targets are less likely to withdraw as negative target recommendations increase

or target pre-announcement returns increase but more likely to withdraw as post-announcement

25

recommendations (proxy for information production on acquirers and targets) increase.

Acquirers are likely to withdraw as target announcement returns increase, indicating the target

may become too costly. We examine withdrawal reason for stock deals in Model C; acquirers

are less likely to withdrawal as positive opinions on themselves increase. Similar to Model A, the

likelihood of target withdrawal decreases as their pre-announcement returns increase.

In Model B, we also examine whether different stakeholders are more likely to be

affected by analyst recommendations, focusing on management. Withdrawals fall into five

categories: acquirer or target management, acquirer or target shareholder, and other (unspecified

acquirer (target) withdrawals, mutual terminations, regulatory withdrawals, and all others). Due

to small samples for acquirer and target shareholder reasons, we combine these with “other”.

Target management is more likely to withdraw as the number of positive target or

negative acquirer recommendations increase. Target management is less likely to withdrawal as

the amount of information production in pre- and post-announcement periods increase, similar to

Table 6. We do not find any significance to suggest when the acquirer management would

terminate a merger. This is likely due to sample size: 100 target versus 27 acquirer management

withdrawals. Our results suggest acquirer and target managements are cognizant of analyst

recommendations and may use them to evaluate a merger’s efficacy. Target management with

increasing positive recommendations may terminate in order to remain independent or attract

other suitors. Poorly performing targets with negative recommendations recognize limited

opportunities and are therefore less likely to withdrawal from a merger.

V.B. Predicting Post-Resolution Returns

In the previous sections, we show that there is an increase in analyst activity around

mergers that impact returns, that recommendations are related to the likelihood of merger

26

completion (even controlling for potential endogeneity), and the direction of recommendations

affects withdrawals. Many of our results suggest that analysts face a new bias in their

recommendations around mergers, one that leads to excessive optimism on the acquirer stock,

and excessive pessimism for the target. Perhaps, instead analysts are issuing unbiased

recommendations that reflect their true valuations of the merger parties. If that is the case, then

recommendations should be able to predict post-resolution returns for the combined firm in

completed mergers, or the individual firms in instances of withdrawal.

In Table 9, we find analysts are poor in predicting out-of-sample returns based on the

consensus recommendation level from announcement through resolution. For completed deals,

those with pre-completion consensus recommendations above buy have marginally significantly

negative post-completion returns; those below have positive, albeit insignificant, returns. The

differences between the samples are statistically significantly different at 9-month, 1-year, and 2-

year horizons. We find similar patterns for withdrawn acquirer returns, though the difference is

not significant post-withdrawal. For withdrawn targets, 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-month through 2-year horizons.

These results are stronger if medians are examined (unreported). Overall, these results suggest

analysts are persistently bad forecasters for mergers and/or are swayed by conflicts of interest.

VI. Robustness Issues

In unreported tests, we examine other variables that might impact the relation between

analysts’ actions and the likelihood of merger completion. We investigate control variables

aimed to capture financial health of acquirers and targets; potential arbitrage activity through

27

speculation spreads; and the relation between analyst I/B/E/S comments and opinions presented

in public news forums. The results from these specifications are available upon request.

We include controls such as target and acquirer return on assets (ROA) and relative size,

a measure of target bargaining power. Acquirer ROA is marginally positively related to the

probability of completion while target ROA is unrelated. The coefficient on relative size is

negative and significantly related. The probability of merger conclusion decreases as the relative

size of the target increases (Schwert, 2000; Moeller et al. 2005). As noted, the number of target

analysts is negatively related to merger completion. If analysts are more likely to cover large

firms (Bhushan, 1989) and increased relative size decreases the likelihood of a merger, then

these results are complementary. While we find evidence consistent with prior research, size and

profitability are not in our main analyses since many mergers with analyst recommendations are

missing Compustat data.22 Results are unchanged with the inclusion of these variables.

Our main variables of interest are acquirer/target upgrades and downgrades. Jegadeesh et

al. (2004) indicates while recommendation levels have some predictive power, it is the revision

direction to upgrade or downgrade that matters for returns. However, we recognize a simple

upgrade/downgrade indicator loses information. In unreported tests, we include multiple-rank

changes to acquirer or target recommendation levels (i.e. "hold" to a "strong buy"). We find

including these variables does not qualitatively change our results and only acquirer multiple-

step upgrades are significant (375 observations out of 12,804). As the number of multiple-step

upgrades for acquirers increases, so does the likelihood that the merger will be completed.

Once a merger is announced, it is probable risk arbitrageurs play a prominent role in the

likelihood of merger completion. Jindra and Walkling (2004) examine speculation spreads in

28

cash tender offers, where speculation spread is holding period return from the day after the

announcement to the offer price (assuming no revisions). Similar to Jindra and Walkling, we find

average speculation spread is roughly 30%, but we find less evidence of negative spreads

(averaging 9%). Supporting earlier results, analysts downgrade targets as they approach price

ceilings; average target recommendation falls from 2.09 in benchmark period to 2.47 pre-

announcement to resolution (p-value = 0.01). When the speculation spread is negative, average

acquirer opinion (2.07 vs. 2.04) is unchanged. We find no evidence merger completion is driven

by speculation spread in conjunction with analyst recommendations in any of our regressions.

Finally, we examine how publicly made statements by analysts impact merger

completion. For each merger, we hand collect news articles from Factiva (including press wires,

major newspapers, and business periodicals) from the first date of the merger to resolution. In

total, we collected 1,409 news announcements for 829 firms, and find analysts are predominantly

positive on these mergers (87% of the time). However, we find only 270 analyst opinions that

can be matched to their recommendations, of which 27% of those news articles contradict the

provided recommendation. While we find analysts’ recommendations affect the probability of

merger completion, we do not find similar evidence from their opinions stated in news articles.

VII. Conclusions

Analysts have discretion over the recommendations they provide to the market especially

around corporate control transactions. While many factors may determine whether a merger is

completed, analysts and the recommendations they issue are likely to influence a merger

outcome. However, similar to other advisory relations that peripherally engage analysts, such as

22 Although we find Compustat data for 2,800+ observations in our sample, if we include ROA, ROE, or relative size for both targets and acquirers, our sample is reduced significantly for the logistic regressions.

29

investment banking, analysts and their revisions are subject to considerable pressure from M&A

advisors. Unlike capital market transactions, analysts skew their recommendations to ensure

merger completion as opposed to value maximization.

In this paper we examine whether recommendations are associated with merger outcomes

and find the number, direction, and affiliation of recommendations are related to the likelihood

of a merger’s completion. Positive acquirer (affiliated and unaffiliated) and negative target

recommendations (in some specifications) positively impact a merger’s completion, while we

find some evidence target upgrades reduce the likelihood of completion. These results are

stronger when we examine stock mergers where analysts have a greater ability to impact a firm’s

acquisition currency and deal value, consistent with our proposed merger conflict of interest.

As the probability of merger completion and analyst opinions are likely to be jointly

determined by information that occurs during the pre-announcement period through the

resolution of the outcome, we attempt to control for potential endogeneity. Using a simultaneous

equations system estimated via two-stage least squares, we demonstrate recommendations are

significantly related to likelihood of deal completion, even after controlling for endogeneity.

We also find some evidence recommendations, particularly positive recommendations on

either the acquirer or target, may reduce the likelihood of acquirers terminating the merger.

While we find positive target recommendations reduce the likelihood of merger completion, it

suggests evidence against causality since the target loses the premium. However, just over 20%

of these targets are subsequently acquired within one year following a merger withdrawal

suggesting other opportunities may be available in the long run. Overall, the relation between

analyst recommendations and merger completions suggests a firm’s management is cognizant of

analyst recommendations and may use these recommendations to evaluate a merger’s efficacy.

30

Finally, in out of sample tests, we find little evidence that analysts can predict future

returns to acquirers or targets. In general, these results suggest analyst recommendations are

linked to the outcome of mergers, even if conflicts of interest affect the direction of analyst

opinions. However, results from withdrawn mergers imply analysts act as information

intermediaries for both shareholders and managers, and future studies of corporate outcomes

should more carefully consider the role of analysts, both affiliated and unaffiliated.

31

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34

Appendix A Descriptions of Variables Used in Analyses

Variable Description Merger Program Dummy variable = 1 if acquirer makes three or more public acquisitions

over a five year window Acquirer (Target) Advisory Fee Dummy variable = 1 if acquirer (target) paid an advisory fee Same 3-digit SIC Dummy variable = 1 if target & acquirer have the same 3-digit SIC code Merger Withdrawn Dummy variable = 1 if merger is withdrawn after announcement Pure Cash Offer Dummy variable = 1 if merger is a pure cash deal Pure Stock Offer Dummy variable = 1 if merger is a pure stock deal Cash Tender Offer Dummy variable = 1 if merger is a cash tender offer Fixed Payment Collar Dummy variable = 1 if merger has a fixed payment collar Fixed Exchange Collar Dummy variable = 1 if merger has a fixed exchange collar Days to Resolution Number of days from merger announcement to completion or withdrawal LN(Merger Value) Natural log of value of the merger Average Acquirer Rec. Level Average acquirer recommendation Average Target Rec. Level Average target recommendation Same Analyst for Acquirer and Target Dummy variable = 1 if an analyst makes a recommendation on both

acquirer and target on same day Number of Acquirer Advisors Number of Target Advisors Number of Acquirer Analysts Number of Target Analysts Number of Recs. in Pre-Ann. Number of opinions in the pre-announcement period (-50 to 0) Number of Recs. from Ann. Number of opinions from day +1 to resolution Positive Acquirer Recs.* Number of positive acquirer recommendations Positive Target Recs.* Number of positive target recommendations Negative Acquirer Recs.* Number of negative acquirer recommendations Negative Target Recs.* Number of negative target recommendations Positive Affiliated Recs Dummy variable = 1 if positive recommendation by affiliated advisors Negative Affiliated Recs Dummy variable = 1 if negative recommendation by affiliated advisors Acquirer Recs. by Acq/Tgt Advisor Number of acquirer recommendations made by the affiliated advisors Target Recs. by Acq/Tgt Advisor Number of target recommendations made by the affiliated advisors Acquirer Run-up Pre-announcement returns for the acquirer (-30 days to –5 days) Acquirer Announcement Return Announcement returns for the acquirer (-1 day to +1 days) Target Run-up Pre-announcement returns for the target (-30 days to –5 days) Target Announcement Return Announcement returns for the target (-1 day to +1 days) Acquirer Termination Fee Indicator variable = 1 if acquirer has a termination fee Target Termination Fee Indicator variable = 1 if target has a termination fee Hostile Indicator variable = 1 if merger attitude is hostile/unsolicited Industry Mean Recommendation Average industry recommendation for acquirers/targets (daily) Industry Number of Recommendations Total number of industry recommendations for acquirers/targets

* Depending on regression specification, these recommendations could be total, pre-announcement, or announcement revisions.

35

Table 1 Summary Data on Merger Sample

This table provides summary data for the 3,444 mergers between 1993 and 2000 from the SDC Mergers & Acquisitions database delineated on whether the merger was completed and whether the acquirer and/or target had analyst coverage. Included are the number of mergers, the average value of the merger (in millions), relative size (target assets divided by acquirer plus target assets), whether the acquirer had a merger program, the method of payment in either pure cash or pure stock forms, the number of cash tender offers, collar bids and type of collar, number of advisors, and return on assets. P-values from differences of means tests between analyst coverage and no-analyst coverage are included.

Completed WithdrawnAnalyst No Analyst p-val Analyst No Analyst p-val

Number of Announcements 1467 1398 213 366Average Merger Value 1519 476 0.0001 1483 453 0.0001Relative Size 21% 21% 0.9111 36% 39% 0.5566Acquirer Merger Program 545 575 0.0292 64 86 0.0898Pure Cash Offer 239 260 0.1043 32 52 0.7899Cash Tender Offer 133 89 0.0067 8 10 0.5121Pure Stock Offer 800 736 0.3117 75 160 0.0426Collar Bid Exists 209 148 0.0029 12 14 0.3353Fixed Payment Collar 87 61 0.0573 5 2 0.1056Fixed Exchange Collar 103 67 0.0112 6 8 0.6453Number of Acquirer Advisors 1164 663 0.0001 111 87 0.0001Number of Target Advisors 1507 1106 0.0001 128 111 0.0001Acquirer ROA 2.34% -1.61% 0.0002 0.16% -9.21% 0.0046Target ROA -2.84% -9.61% 0.0001 -3.63% -19.05% 0.0029

36

Table 2 Summary Data on Recommendations

This table provides summary data on the number of analysts and recommendations for acquirers and targets from –50 days prior to the merger announcement date through completion or withdrawal. Included in Panel A are statistics on the number of analysts, total recommendations classified by sign (positive or negative), and the financing method of the merger. In Panel B, the relationship between the merger advisor-analyst and recommendations made for its advisee or the counterparty to the merger is presented. Panel C displays the relation between affiliated and unaffiliated analysts for acquirers and targets. Included are statistics on own advisor-analyst recommendations, as well as competing advisor-analyst recommendations. Difference of means tests are computed for differences in the percentage of positive recommendations among groups.

Panel A: Merger Type Acquirer Target

Variable Total Cash Stock Total Cash StockAverage number of analysts 5.57 5.34 5.61 3.22 2.35 3.35Total Observations 12,804 1,981 10,823 4,678 440 4,238Upgrades 6,945 983 5,962 1,661 110 1,551Downgrades 5,859 998 4,861 3,017 330 2,687Avg. Pre-Announcement Rec. 2.01 2.16 1.99 2.19 2.29 2.17Avg. Post-Announcement Rec. 2.04 2.14 2.04 2.54 2.93 2.50 Difference of Means p-value 0.74 0.82 0.18 0.00 0.00 0.00

Panel B: Advisor Relationships Acquirer Target

Variable Total Cash Stock Total Cash StockOwn-Advisor Recs 222 14 208 131 13 118 Positive 148 7 141 56 5 51 Negative 74 7 67 75 8 67 Percent Positive 66.7% 50.0% 67.8% 42.7% 38.4% 45.2%Counterparty-Advisor Recs 215 39 176 52 0 52 Positive 131 23 108 22 0 22 Negative 84 16 68 30 0 30 Percent Positive 60.9% 58.9% 61.4% 42.3% NM 42.3% Difference of Means p-values 0.21 0.58 0.19 0.96 NM 0.91

Panel C: Affiliated and Unaffiliated Analysts

Acquirer Target Variable Total Cash Stock Total Cash StockAffiliated Recs 437 53 384 183 13 170 Positive 279 30 249 78 5 73 Negative 158 23 135 105 8 97 Percent Positive 63.8% 56.6% 64.8% 42.6% 38.4% 42.9%Unaffiliated Recs 11,197 1,028 10,169 4,495 427 4,068 Positive 6,396 953 5,443 1,583 105 1,478 Negative 5,701 975 4,726 2,912 322 2,590 Percent Positive 52.9% 49.3% 53.5% 35.2% 24.6% 36.3% Difference of Means p-values 0.00 0.31 0.00 0.05 0.35 0.09

37

Table 3 Conflicts of Interest Around Mergers

This table provides empirical results testing predictions regarding the direction of analyst recommendations based on a conflict of interest hypothesis. We argue that advisory fees affect the direction of analyst recommendations since analysts may be pressured to provide recommendations to ensure the merger goes through rather than traditional investment banking conflicts, where analysts tend to be overly optimistic. Here, there may be incentives to be pessimistic to shift the acquisition currency or the target cost. We delineate advisor-issued recommendations by timing and direction of the recommendation, as well as the financing type for the merger and whether the acquirer or target paid an advisory fee. The Pos/Neg Ratio is a ratio of positive to negative affiliated recommendation changes. A ratio greater (less) than one corresponds to optimism (pessimism). Numbers in boldface correspond to values opposite of the predictions.

Panel A: Predictions if No Advisory Fee Analyst

Affiliation Analyst

Report on Deal Type

Timing of Report

Prediction under Merger Conflict

Pos/Neg Ratio

Acquirer Acquirer Cash Pre Indeterminant 0.25 Post Optimism 2.00 Stock Pre Optimism 1.60 Post Optimism 1.96

Target Acquirer Cash Pre Indeterminant 2.00 Post Optimism 1.00 Stock Pre Indeterminant 0.80 Post Optimism 2.00

Acquirer Target Cash Pre Indeterminant No Obs Post Indeterminant No Obs Stock Pre Pessimism 0.33 Post Pessimism 0.78

Target Target Cash Pre Optimism All Positive Post Pessimism All Negative Stock Pre Optimism 2.00 Post Pessimism 0.56

Panel B: Predictions if Advisory Fee Analyst

Affiliation Analyst

Report on Deal Type

Timing of Report

Prediction under Merger Conflict

Pos/Neg Ratio

Acquirer Acquirer Cash Pre Indeterminant All Negative Post Optimism 4.50 Stock Pre Optimism 2.00 Post Optimism 2.00

Target Acquirer Cash Pre Indeterminant 0.75 Post Optimism 2.40 Stock Pre Indeterminant 0.75 Post Optimism 2.50

Acquirer Target Cash Pre Indeterminant No Obs Post Indeterminant No Obs Stock Pre Pessimism 0.20 Post Pessimism 1.10

Target Target Cash Pre Optimism 1.00 Post Pessimism 0.33 Stock Pre Pessimism 0.76 Post Pessimism 0.71

38

Table 4 The Impact of Analyst Recommendations on Merger Returns

This table displays average cumulative abnormal returns around merger announcements for targets and acquirers. Market model adjusted returns are measured over the merger announcement period (-1 to +1 days around the announcement). Variables are described in Appendix A. a, b, and c represent confidence levels of 1%, 5%, and 10%, respectively.

Acquirer TargetVariable Est t-stat Est t-statIntercept 0.023 2.91a 0.067 3.71aPre-Announcement Positive Acquirer Recs 0.004 1.36 0.025 3.48aAnnouncement Positive Acquirer Recs 0.017 2.00b 0.088 4.42aPre-Announcement Negative Acquirer Recs 0.003 1.00 0.013 1.73cAnnouncement Negative Acquirer Recs -0.046 -4.69a -0.001 -0.06Pre-Announcement Positive Target Recs 0.004 0.87 -0.032 -3.19aAnnouncement Positive Target Recs 0.027 1.61 0.030 0.79Pre-Announcement Negative Target Recs 0.006 1.42 0.001 0.13Announcement Negative Target Recs -0.016 -1.76c 0.007 0.33Acquirer Rec by Acq/Tgt Advisor 0.001 0.26 -0.008 -1.00Target Rec by Acq/Tgt Advisor 0.005 0.95 0.010 0.91Number of Recs in Pre-Announcement -0.005 -1.77c 0.005 0.76Number of Recs from Announcement -0.001 -1.45 0.007 3.19aAverage Acquirer Rec Level -0.003 -1.59 -0.002 -0.44Average Target Rec Level 0.000 -0.24 0.004 1.00Number of Acquirer Advisors 0.001 0.21 -0.012 -1.51Number of Target Advisors 0.001 0.29 -0.007 -0.75Same Analyst for Acq/Tgt 0.004 1.50 0.001 0.22Number of Acquirer Analysts 0.002 1.57 -0.014 -4.90aNumber of Target Analysts 0.001 0.49 -0.004 -0.97Acquirer Advisor Fee -0.006 -1.37 -0.007 -0.70Target Advisor Fee 0.005 1.04 -0.007 -0.67Merger Progam 0.012 3.20a -0.001 -0.16Same 3-digit SIC -0.001 -0.36 -0.009 -1.07Days to Resolution 0.000 0.79 0.000 0.32LN(Merger Value) -0.008 -6.04a 0.001 0.27Pure Cash Offer 0.001 0.17 0.007 0.47Cash Tender Offer 0.019 2.24b 0.049 2.51bPure Stock Offer -0.020 -4.82a -0.011 -1.19Fixed Payment Collar 0.003 0.36 -0.002 -0.10Fixed Exchange Collar 0.012 1.63 -0.007 -0.41Merger Withdrawn -0.005 -0.93 -0.009 -0.70Acquirer Run-up -0.012 -0.97 -0.208 -7.31aTarget Run-up 0.022 4.11a 0.543 45.73aAdj R-square 0.0825 0.5589

39

Table 5 Returns to Targets and Acquirers Around Recommendations

This table displays the average cumulative abnormal returns around recommendation changes and initiations for the target and the acquirer firms. Returns are identified into completed and withdrawn mergers and the sign of the recommendation, as well as by the affiliation of the recommending analyst. In Panel A, the full sample and a sample that excludes recommendation returns immediately around the merger are presented. In Panel B, we delineate firms by financing method for the merger (pure cash vs. stock financed). Panel C compares returns by own- or counter-party affiliated analysts, while Panel D presents affiliated relative to unaffiliated analysts. Panels B, C, and D exclude recommendations immediately around the merger announcement. Net of market returns are measured around the recommendation day (-1 to +1 around the recommendation release). Corresponding t-statistics for cumulative abnormal returns (in parentheses) and p-values for difference of means tests are presented.

Panel A: CARs Around Recommendations Acquirer Target

Upgrade Downgrade p-val Upgrade Downgrade p-valFull Sample

1.20% (7.65)

-3.47% (-15.38)

0.0001 3.32% (10.05)

3.08% (9.67)

0.5873

Excluding Merger Ann

1.58% (10.13)

-3.00% (-13.07)

0.0001 1.22% (5.36)

-1.02% (-4.80)

0.0001

Diff of Means p-value 0.1562 0.2253 0.0001 0.0001

Panel B: by Financing Method Acquirer Target Upgrade Downgrade p-val Upgrade Downgrade p-valCash

1.54% (4.21)

-3.99% (-7.19)

0.0001 0.56% (0.56)

-1.39% (-1.35)

0.1774

Stock

1.48% (8.23)

-2.89% (-11.02)

0.0001 1.29% (4.56)

-0.92% (4.63)

0.0001

Diff of Means p-value 0.8931 0.0728 0.4866 0.6519

Panel C: by Own-Advisor and Counterparty-Advisor Analysts Acquirer Target Upgrade Downgrade p-val Upgrade Downgrade p-valOwn-Advisor

0.65% (0.77)

-2.19% (-2.10)

0.0364 5.41% (2.67)

-1.74% (-2.26)

0.0016

Counterparty-Advisor

2.67% (3.26)

-1.88% (-1.53)

0.0027 0.46% (0.62)

-3.68% (-2.14)

0.0342

Diff of Means p-value 0.0852 0.8455 0.0257 0.3111

Panel D: by Affiliated and Unaffiliated Analysts Acquirer Target Upgrade Downgrade p-val Upgrade Downgrade p-valAffiliated

1.16% (1.86)

-2.97% (-2.88)

0.0008 2.43% (2.12)

-3.02% (-2.92)

0.0007

Unaffiliated

1.59% (9.92)

-3.02% (-12.78)

0.0001 1.20% (4.94)

-0.95% (-4.35)

0.0001

Diff of Means p-value 0.4991 0.8237 0.3002 0.0547

40

Table 6 Logistic Regressions: Probability of Completion

This table details results from logistic regressions on the probability of completed mergers. Predicted signs for variables are included. Two logistic regressions are presented: the first isolates the impact of recommendation variables on the probability of merger completion; the second includes control variables. Except for “Number of Recs in Pre-Ann” and “Number of Recs from Ann”, the number of recommendations includes both pre-announcement and announcement to resolution recommendations. Chi-square statistics and log-likelihood ratios are reported. a, b, and c represent confidence levels of 1%, 5%, and 10%, respectively. All independent variables are defined in Appendix A.

Stock-FinancedAll Mergers Mergers

Variable Pred Est Chi-stat Est Chi-stat Est Chi-statIntercept 1.367 587.70a -1.748 40.06a -1.275 26.51aPositive Acquirer Recs + 0.189 10.76a 0.179 3.80b 0.168 3.75bNegative Acquirer Recs - -0.034 0.418 -0.075 0.75 -0.085 0.98Positive Target Recs - -0.285 18.09a -0.146 1.11 -0.193 1.96Negative Target Recs + -0.059 1.09 0.198 2.18 0.168 1.64Acquirer Recs by Acq/Tgt Advisor + 0.525 12.18a 0.397 3.17c 0.405 3.41cTarget Recs by Acq/Tgt Advisor - 0.206 1.50 0.134 0.27 0.115 0.20Number of Recs in Pre-Ann ? 0.004 0.01 -0.259 7.86a -0.241 6.74aNumber of Recs from Ann ? 0.028 0.49 -0.272 9.38a -0.250 7.96aNumber of Acquirer Advisors + 0.110 0.44 0.089 0.29Number of Target Advisors + 0.968 30.30a 0.956 29.97aSame Analyst for Acquirer and Target ? 0.436 7.96a 0.446 8.57aNumber of Acquirer Analysts + 0.388 27.51a 0.371 26.09aNumber of Target Analysts - -0.058 0.22 -0.065 0.29Average Acquirer Rec Level + 0.078 0.74 0.081 0.82Average Target Rec Level - -0.043 0.27 -0.057 0.50Acquirer Advisor Fee + 0.285 1.06 0.343 1.59Target Advisor Fee + 2.065 85.00a 2.035 85.11aMerger Program + 0.292 2.46 0.325 3.16cPure Cash Offer ? 0.738 7.74aCash Tender Offer + 0.275 0.46Pure Stock Offer + 0.744 16.38aFixed Payment Collar + 0.897 2.41 0.796 1.99Fixed Exchange Collar + 0.000 0.00 -0.077 0.04Same 3-digit SIC ? 0.034 0.04 -0.008 0.00Days to Resolution - 0.004 18.62a 0.004 16.99aLN(Merger Value) + 0.047 0.79 0.072 1.95Acquirer Run-up ? 0.244 0.24 0.344 0.48Target Run-up + 1.128 9.93a 1.118 9.86aAcquirer Announcement Return ? 0.314 0.12 0.006 0.00Target Announcement Return + -0.015 0.00 0.064 0.02

Likelihood 129.34 602.17 580.90

41

Table 7 The Robustness of Analyst Recommendations and Merger Completion

This table details results from first-stage probit regressions on determinants of recommendation changes and linearized second-stage regressions on the probability of completed mergers. The predicted recommendation change instruments in the second stage regressions are the fitted value from the first-stage estimation where an upgrade (downgrade) is the dependent variable and other variables are independent regressors (omitted for brevity here). Fitted values are averaged within each merger. First stage regressions are run separately for acquirers and targets. Two second-stage regressions are presented: the first includes all mergers, regardless of financing; the second excludes purely cash-financed transactions. t- and chi-square statistics are presented. a, b, and c represent confidence levels of 1%, 5%, and 10%, respectively. All independent variables are defined in Appendix A. First Stage Regression Second Stage Regressions

Acquirer Target All Deals Stock DealsVariable Est t-stat Est t-stat Variable Est Chi-stat Est Chi-statIntercept -0.116 -3.65a 0.011 0.19 Intercept -0.869 2.11 -0.729 1.49Pre-Ann + Recs (A or T) -0.985 -51.90a -1.092 -27.88a Predicted Acq Rec Chg -0.716 5.86b -0.795 6.04bPre-Ann - Recs (A or T) 1.143 56.41a 1.197 31.84a Predicted Tgt Rec Chg 0.101 0.29 0.130 0.47Post-Ann + Recs (A or T) -1.014 -65.37a -1.127 -32.45a Num of Acq Advisors 0.074 0.07 0.046 0.03Post-Ann - Recs (A or T) 1.152 73.65a 1.396 46.62a Num of Tgt Advisors 0.654 4.47b 0.773 4.88bPositive Affiliated Recs -0.080 -2.03b -0.040 -0.47 Same Analyst Acq/Tgt 0.359 3.48c 0.286 2.47Negative Affiliated Recs -0.180 -3.39a -0.134 -1.82c Num of Acq Analysts 0.069 2.61 0.086 3.65cNum of Acq Advisors 0.001 0.59 0.008 0.46 Num of Tgt Analysts -0.236 12.54a -0.251 13.95aNum of Tgt Advisors 0.008 0.76 -0.022 -1.23 Acq Advisor Fee 0.795 3.43c 0.523 1.31Same Analyst Acq/Tgt 0.122 2.72a -0.008 -0.18 Tgt Advisor Fee 1.848 27.94a 1.931 25.96aNum of Acq Analysts 0.004 3.72a -0.001 -0.47 Merger Program 0.314 0.95 0.395 1.39Num of Tgt Analysts 0.000 -0.13 0.007 2.49a Pure Cash Offer 0.969 1.94 Advisor Fee (A or T) -0.022 -1.55 -0.009 -0.40 Cash Tender Offer -0.651 0.58 Rec Initiation (A or T) -0.117 -9.45a -0.183 -6.98a Pure Stock Offer 0.669 3.95b Same 3-digit SIC -0.028 -2.42b 0.013 0.59 Same 3-digit SIC -0.545 3.08c -0.531 2.72cPure Cash Offer -0.017 -0.80 0.150 2.66a Days to Resolution -0.002 2.38 -0.002 2.63Cash Tender Offer 0.047 1.71c -0.048 -0.72 LN(Merger Value) 0.159 2.56 0.164 2.60Pure Stock Offer -0.048 -3.69a -0.051 -2.30b Acquirer Run-up 1.083 1.13 1.348 1.72Fixed Payment Collar 0.079 3.20a 0.044 0.78 Target Run-up -0.006 0.00 0.277 0.13Fixed Exchange Collar -0.042 -1.98b -0.017 -0.44 Acq Ann Return 1.955 1.53 1.331 0.72LN(Merger Value) -0.009 -2.52a -0.015 -2.43b Tgt Ann Return 1.732 3.39c 1.238 1.54Avg Industry Recs (A or T) 0.301 27.75a 0.225 12.13a Acq Termination Fee -0.256 0.38 0.017 0.00Num of Ind Recs (A or T) -0.002 -3.55a -0.002 -1.77c Tgt Termination Fee 0.865 6.59a 0.739 4.47b

Hostile -3.385 34.47a -2.864 20.78a

Number of Observations 10,969 4,383 784 673Adj R-square 0.8646 0.8108F-value 3177.50 854.46Partial R-square p-value 0.0001 0.0001Hansen J-statistic p-value 0.3904 0.3122Log Likelihood 247.41 198.95

42

Table 8 Generalized Logistic Regressions

This table details results from a generalized logistic regressions where the dependent variable is the rationale for a merger termination or who initiates the termination. In Models A and C, Term Party is the party that initiates the termination of the merger. In these models, acq indicates that the acquirer initiated termination and tgt indicates that the target initiated termination. Our baseline measure in Models A and C is all other termination, including mutual terminations, regulatory, and undefined. Model B breaks out delineated further to indicate which management party initiated termination. In this model, acq indicates that acquirer management initiated termination, while tgt indicates that target management initiated termination. The baseline measure for Model B is all non-management initiated terminations, including shareholder and regulatory. Models A and B include all mergers, regardless of financing, while Model C excludes cash-only mergers. Chi-square statistics and log-likelihood ratios are reported.

All Mergers Stock OnlyModel A Model B Model C

Term. Party Est.

Chi-stat

Term. Mgmt. Est.

Chi-stat

Term. Party Est.

Chi-stat

Intercept acq 0.011 0.00 acq -0.230 0.31 acq -0.317 0.95Intercept tgt -0.667 8.26a tgt 1.094 11.56a tgt -1.249 8.32aPositive Acquirer Recs. acq -0.200 1.30 acq 0.135 0.13 acq -0.845 3.44bPositive Acquirer Recs. tgt -0.233 1.52 tgt 0.501 2.07 tgt -0.566 1.50Negative Acquirer Recs. acq 0.002 0.00 acq -0.073 0.04 acq -0.156 0.18Negative Acquirer Recs. tgt -0.217 1.46 tgt 0.722 4.61b tgt -0.361 0.71Positive Target Recs. acq -0.020 0.01 acq 0.021 0.00 acq -0.443 1.07Positive Target Recs. tgt -0.312 1.52 tgt 0.875 3.56b tgt -0.128 0.07Negative Target Recs. acq -0.049 0.08 acq -0.415 1.08 acq -0.275 0.35Negative Target Recs. tgt -0.403 3.11b tgt 0.374 1.27 tgt -0.899 2.03Pre-Ann. Recs. acq 0.033 0.06 acq 0.080 0.06 acq 0.084 0.08Pre-Ann. Recs. tgt 0.134 0.73 tgt -0.524 3.22b tgt 0.325 0.97Post-Ann. Recs. acq 0.009 0.00 acq 0.102 0.14 acq 0.326 1.11Post-Ann. Recs. tgt 0.233 2.74c tgt -0.524 4.14b tgt 0.468 1.51Acquirer Run-up Return acq -0.437 0.33 acq 0.163 0.01 acq 0.728 0.47Acquirer Run-up Return tgt -0.482 0.21 tgt -0.155 0.01 tgt 0.459 0.08Target Run-up Return acq -0.604 1.26 acq -0.119 0.01 acq -1.155 1.63Target Run-up Return tgt -1.305 2.85c tgt 0.145 0.03 tgt -3.007 3.93bTarget Ann. Return acq 1.341 3.32b acq -1.336 0.60 acq 0.631 0.27Target Ann. Return tgt -0.066 0.00 tgt 1.241 0.83 tgt -0.875 0.16

Likelihood 24.960 24.601 21.555

a Significant at 1% confidence level b Significant at 5% confidence level c Significant at 10% confidence level

43

Table 9 Post-Resolution Returns and Analysts’ Predictive Ability

This table examines the ability of analysts to predict post-resolution returns through their recommendations. 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. 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.

Panel A: BHARs for Completed versus Withdrawn Mergers Rec N 3-month 6-month 9-month 1-year 2-year Acquirer Completion <2 1062

-1.38%

(-1.81) -2.11%

(-1.76) -2.82%

(-1.64) -5.54%

(-2.93) -4.42%

(-1.41) Completion ≥2 935

-0.32%

(-0.44) -0.02%

(-0.02) 1.11%

(0.74) 1.61%

(0.89) 3.39%

(1.41) p-value 0.3125 0.1848 0.0863 0.0061 0.0483 Withdrawal <2 240

-6.48%

(-3.01) -9.02%

(-3.42) -8.72%

(-2.66) -8.08%

(-1.91) -6.05%

(-0.59) Withdrawal ≥2 129

-1.63%

(-0.69) -7.08%

(-2.52) -7.85%

(-2.14) -6.93%

(-1.69) -0.55%

(-0.07) p-value 0.1291 0.6140 0.8601 0.8443 0.6666 Target Completion <2 - - - - - - Completion ≥2 - - - - - - Withdrawal <2 213

-6.22%

(-1.78) -7.97%

(-2.58) -6.63%

(-1.69) -9.32%

(-2.40) -12.29%

(-2.68) Withdrawal ≥2 135

-3.73%

(-1.16) 2.06%

(0.51) 3.30%

(0.69) 5.31%

(0.95) 9.81%

(0.99) p-value 0.6001 0.0476 0.1001 0.0272 0.0238

44

Figure 1

Recommendation Changes and Initiations Relative to the Merger Announcement Date

This figures displays the occurrence of recommendation revisions for both acquirers and targets by analysts relative to the merger announcement date (date = 0) over ten-day intervals (for example day -10 to -1, day 0 to day 9, day 10 to day 19, etc.). Positive and negative recommendations are displayed. Merger announcement dates are collected from Securities Data Corporation and recommendation announcement dates are collected from I/B/E/S.

0

500

1000

1500

2000

2500

3000

-41 -21 -1 19 39 59 79 99 139 179 219 259 299 399Time (Day 0 = Merger Announcement)

Positive Acquirers Negative AcquirersPositive Targets Negative Targets

45