Analyst coverage and acquisition returns: Evidence from natural experiments*
Eliezer M. Fich
LeBow College of Business
Drexel University
Philadelphia, PA 19104, USA
+1-215-895-2304
efich@drexel.edu
Jennifer L. Juergens
LeBow College of Business
Drexel University
Philadelphia, PA 19104, USA
+1-215-895-2308
jlj54@drexel.edu
Micah S. Officer
College of Business Administration
Loyola Marymount University
Los Angeles, CA 90045, USA
+1-310-338-7658
micah.officer@lmu.edu
This draft: March 9, 2015
Abstract
Takeover target firms covered by more equity analysts are sold for higher premiums while their acquirers
earn lower merger announcement returns. We confirm these results using exogenous shocks to coverage
arising from brokerage-house mergers or closures (i.e., quasi-natural experiments) as instruments for the
loss of analyst coverage. In general, our findings indicate that target coverage by equity analysts materially
affects the wealth of all shareholders during acquisitions. Our empirical evidence supports Jensen and
Meckling’s (1976) theory that security analysts perform an external monitoring role that mitigates
managerial agency problems thereby enhancing the wealth of shareholders.
JEL classification: G24; G34
Keywords: Acquisitions; Agency Costs; Analyst Coverage; Natural Experiments
*
We thank Ernst Maug, Zacharias Sautner, Günter Strobl, Karin Thorburn, and seminar participants at City University
London (Cass), Drexel University, Frankfurt School of Management and Finance, Norwegian School of Economics,
University of Kentucky, University of Mannheim, University of Texas at Austin, University of Waterloo, and
Villanova University for helpful comments and suggestions. We thank Rachel Gordon for research assistance. All
errors are our responsibility.
Jensen and Meckling (1976) argue that agency costs related to the separation of ownership and control,
which manifest in non-value maximizing managerial behavior, are curtailed by monitoring from external
financial analysts. Based on this argument, Jensen and Meckling (1976, pg. 355) state: “…we expect the
major benefits of the security analysis activity to be reflected in the higher capitalized value of the
ownership claims to corporations…” Indeed, because financial analysts review disclosures and track
corporate activities to inform the stock market about a firm’s current and future prospects, they may
facilitate the monitoring of managerial actions that might not be in the shareholders’ best interests.
In this paper, we empirically test Jensen and Meckling’s monitoring hypothesis in the context of
acquisitions. This is an ideal setting to examine this hypothesis because acquisitions are transactions highly
vulnerable to agency problems in which the interests of managers and shareholders are not always aligned.
In support of this idea, academic studies suggest that managers of acquisition targets, for example, appear
to trade merger premiums for personal benefits (e.g., Hartzell, Ofek, and Yermack, 2004). We examine
whether sell-side analyst coverage of takeover targets affects the premiums paid for these firms and the
returns to their acquirers using a sample of over 1,000 completed M&A deals between 1993 and 2008.
There is empirical support Jensen and Meckling’s theory that equity analysts perform an external
monitoring role that improves corporate governance. Yu (2008), for example, shows that firms with more
analysts manage their earnings less and Irani and Oesch (2013) find and inverse association between analyst
coverage and the level of information provided in corporate disclosures. Despite this evidence, we
recognize that acquisitions are different than the settings considered by Yu (2008) and by Irani and Oesch
(2013) because, after deal completion, targets cease to exist as standalone firms and target CEOs often lose
their jobs. As a result, top managers of takeover target firms could resist the discipline imposed by external
monitoring by putting their personal interests ahead of their shareholders’.1
1
Moreover, the relation between managers and analysts may affect monitoring by external analysts. Several studies
argue that analysts often pressure managers to meet or beat short-term earnings targets at the expense of long-term
value maximization, and this may compromise the potential monitoring role exerted by equity analysts on
management. See Yu (2008), for a comprehensive discussion of the literature that documents this pressure.
1
This suggests that it is not obvious ex-ante that, under the oversight of equity analysts, the incentives
of managers of takeover targets are aligned with the incentives of target shareholders. Evidence of effective
external monitoring by financial analysts, therefore, should come in the form of improved bargaining by
target managers on behalf of their stockholders in the acquisition context. Put differently, under the
monitoring hypothesis, target managers should exercise and exhibit bargaining power to negotiate better
acquisition terms for their shareholders.
The baseline empirical evidence we present supports the monitoring hypothesis: under analysts’
monitoring, target managers appear to bargain for more favorable terms for their shareholders. Specifically,
our results indicate that takeover premiums increase with the number of analysts covering the target firm.
This result is economically important: adding one analyst to cover the average sample target is associated
with an increase of 0.8 to 1.3 percentage points in the takeover premium. Consistent with these higher
premiums paid to target shareholders, acquirers of targets with greater analyst coverage experience lower
merger announcement returns. Moreover, using the method proposed by Ahern (2012) we find that, relative
to their acquirers, targets covered by more analysts capture a higher share of the acquisition gains.
Relatedly, Aktas, de Bodt, and Roll (2010) show that target companies that initiate their own
acquisitions receive lower premiums. We find that the higher the number of analysts covering the target the
less likely the target is to initiate its own sale. We also find that merger agreements in deals involving targets
covered by more analysts are less likely to include target termination fee provisions, allowing target
managers the flexibility to pursue superior acquisition offers for their shareholders.
The lower acquirer returns for bidders (and the higher premiums for their targets) could result from
increased (latent or actual) competition to buy targets covered by more analysts. While it is difficult to
measure latent competition accurately (e.g., Aktas, et al. 2010), we can measure the number of potential
acquirers that submit public takeover offers. Our results indicate that a one standard deviation increase in
the number of analysts covering the target is associated with an increase of 1.3 to 2.0 percentage points in
the probability of observing a competing bid. This finding is noteworthy since only 4.7% of the deals in
2
our sample involve a competing acquisition bid. The increased competition we uncover is also consistent
with our finding of a lower incidence of target termination fee usage by targets covered by more analysts.
We cannot make causal inferences about our baseline results without tests for endogeneity. In our
setting, it is conceivable that analysts look to cover firms that are more likely to earn higher premiums upon
becoming takeover targets (i.e., have the greatest potential for value improvement under alternative
ownership). Under this possibility, causality would run in the opposite direction. Our results could also be
biased from unobservable firm heterogeneity correlated with both corporate policies and analyst coverage.
Moreover, although our baseline tests show that targets with more analysts receive higher premiums it is
unclear whether losing coverage is detrimental for these firms.
To address these issues, we employ natural experiments involving the mergers and closings of
brokerage firms (Hong and Kacperczyk, 2010, and Kelly and Ljungqvist, 2012), which produce exogenous
variation in analyst coverage. Specifically, we use mergers and closures of brokerage houses to study the
probability of losing analyst coverage. In 2SLS analyses, we use the instrumental variable (IV) of coverage
loss as the key independent variable in both target premium regressions and acquirer CAR regressions. The
results of these tests show that the loss of analyst coverage causes takeover premiums to decrease and
acquirer returns to increase. These findings, which are consistent with the evidence from our baseline
analyses, are qualitatively unaffected when brokerage-house closures and mergers are used independently
to instrument for the coverage loss. Overall, the results from our IV tests imply that target analyst coverage
prior to M&A deals has a causal effect on the wealth of both acquirer and target shareholders.
In sum, our results suggest that with greater monitoring by external equity analysts target managers
negotiate better acquisition terms for their shareholders. Targets covered by a greater number of analysts
are sold for higher takeover premiums, are more likely to receive takeover offers from more than one
acquirer, are less likely to initiate their own takeover, and are less likely to commit to pay deal termination
fees. Similarly, targets that lose coverage due to exogenous shocks are paid lower premiums while their
acquirers earn higher merger announcement returns. These findings, which withstand a battery of
3
robustness tests, are consistent with the view that analyst scrutiny acts as a monitoring device that
incentivizes targets managers to bargain for deal terms that are in the best interests of their shareholders.
This paper delivers contributions to different strands of the literature. First, our empirical evidence
supports the predictions in Jensen and Meckling (1976) that security analysis leads to a higher value of the
ownership claims in corporations. Our results also support the prediction in Fama (1990) that monitoring
will be conducted not only by a firm’s residual claimants (equity and debt holders), but also by their agents
(e.g., analysts and auditors).2
Second, our results also advance the M&A literature by documenting the effect of analyst coverage
during acquisitions. To our knowledge, most studies in this literature focus on acquirers but not on targets
(as we do here). In this vein, our paper complements the findings in contemporaneous work showing that
acquiring firms that experience a reduction in analyst coverage are more likely to make value-destroying
acquisitions (Chen, Harford, and Lin, 2014), documenting that the dispersion in analysts’ forecasts for
acquiring firms increases during merger waves (Duchin and Schmidt, 2013), and finding that banks change
their stockholdings in the acquirer after a merger announcement when the advising banks’ analysts change
their recommendations about the acquirer (Haushalter and Lowry, 2011). More generally, our study is
related to the work considering the link between sell-side analysts and firm value (see, for example,
Womack, 1996, Barber, Lehavy, McNichols, and Trueman, 2001, Jegadeesh, Kim, Krische, and Lee, 2004,
Loh and Stulz, 2011, and Derrien and Kecskes, 2013).
Third, our paper also contributes to the growing body of research that uses brokerage-house mergers
and/or closures as natural experiments that produce an exogenous variation in analyst coverage. Studies in
this literature use this exogenous variation to examine whether analyst coverage induces reporting bias
(Hong and Kacperczyk, 2010), affects credit ratings (Fong, Hong, Kacperczyk, and Kubik, 2012),
2
The evidence in Dyck, Morse, and Zingales (2010), suggesting that in some circumstances analysts are more effective
in detecting corporate fraud than financial market regulators, is also consistent with the view that analysts perform an
external monitoring role.
4
influences firm valuation (Kelly and Ljungqvist, 2012), improves external monitoring (Irani and Oesch,
2013; Chen, et al. 2014), and deters innovation (He and Tian, 2013).
The paper continues as follows. Section 1 describes our data. Section 2 presents the main empirical
analyses. Section 3 provides a number of robustness tests. Section 4 contains our conclusions. The
Appendix defines the key variables we use in this study.
1. Data
We begin with all M&A transactions announced and completed between 1993 and 2008 in the
Thomson/Reuters Securities Data Corporation (SDC) M&A database, in which both the target and the
acquirer are publicly traded U.S. firms. Due to incomplete SDC data, we supplement information on deal
announcement and completion from SEC filings, trade publications (such as Mergers & Acquisitions or
Investment Dealers Digest), and searches on Lexis-Nexis, Factiva, and Dow Jones Newswire. Following
Moeller, Schlingemann, and Stulz (2004) and Masulis, Wang, and Xie (2007), we exclude spinoffs,
recapitalizations, exchange offers, repurchases, self-tenders, privatizations, acquisitions of remaining
interest, partial interests or assets, divestitures, leveraged buyouts, liquidations, unit trusts, and deals valued
at less than $1 million. We filter out cases without complete data on deal status, transaction value,
consideration offered, deal attitude, or deal premium. We keep transactions for which acquirers and targets
have stock return, accounting, and institutional ownership data available from the Center for Research in
Securities Prices (CRSP), Compustat, and the Thomson-Reuters Institutional Holdings 13F database,
respectively. This process yields a final sample of 1,098 completed deals. Our focus on completed deals
circumvents the issue that investor reactions may reflect the market’s expectations that the transaction will
be completed. In this regard, we follow recent studies in the M&A literature that also analyze completed
deals (e.g.: Masulis, et al. 2007; Savor and Lu, 2009; Gorton, Kahl, and Rosen, 2009; Lin, Officer, and Zou,
2011; and Chen, et al. 2014).
Panel A of Table 1 provides the temporal and (Fama-French 12) industrial distribution of our sample.
The number of transactions increases during periods of economic expansion, peaking around 1998. We
5
observe a decline in deals during the recessions in the early (and late) 2000s. Shleifer and Vishny (2003)
argue that stock market health drives merger activity. The temporal distribution of our sample is consistent
with their conjecture. Panel A in Table 1 shows that our target companies appear well distributed across
several industries, with some clustering in the Business and the Healthcare sectors (29% and 15% of the
sample, respectively).
Panel B of Table 1 reports summary statistics for key characteristics of our sample. The Appendix
contains the definition of all variables used in the paper. The relative size of the target is approximately
14% of the combined entity. Comparably, Lin, et al. (2011) report a relative size of 15% for the deals they
study. Target firms in the deals we examine obtain an average premium of almost 46%, close to the average
premium of 47.8% in Officer (2003).
We read the S-4, DEFM14A, SC-TO, and DEF14A proxies filed with the SEC by the target and/or
acquiring firm to obtain information on the party that initiates the deal. In our sample, targets initiate their
own sale approximately 39% of the time. By comparison, Fich, Cai, and Tran (2011) find that target firms
initiate the takeover in approximately 38% of the cases they study. Approximately 15% of the deals we
analyze are tender offers. A competing acquirer makes an alternate, public offer for the target firm in 4.7%
of our transactions, on average, and nearly 3% of the deals are characterized as hostile. Roughly one-third
of all deals are financed with 100% cash, which is comparable to the 33% of the sample that are all-cash
deals in Gorton, et al. (2009). In many dimensions, the descriptive statistics in our sample appear consistent
with those reported in the extant M&A literature.
Since our study is focused on the impact of target analyst coverage on M&A deal terms, we match our
sample to Thomson’s Institutional Brokers’ Estimate System (I/B/E/S) summary files. Analyst coverage is
defined as the number of analysts providing earnings estimates each month, as in Diether, Malloy, and
Scherbina (2002). For our purposes, we collect the maximum number of sell-side equity analysts providing
research coverage on the target firm in any month in the six months prior to the deal announcement month
to identify the amount of analyst coverage on the target firms. According to the last two rows in Panel B of
Table 1, approximately 68% (or 746) target firms have at least one analyst providing research coverage
6
during the six months prior to an M&A deal announcement and the median number of analysts covering
targets in our sample is two. Comparably, for a sample of over 6,000 firms with a mean value of close to
that of our target firms (around $1 billion), Hong, et al. (2000) report that just over 63% are covered by at
least one financial analyst and that the median number of analysts in that sample is also two.
2. Empirical analyses
In this section we investigate whether analyst coverage of target firms affects target premiums,
announcement returns for the acquirer, target termination fees, bid competition, and the identity of the deal
initiator. We begin by addressing the selection bias related to analyst coverage.
2.1. Identification
Existing studies suggest that a selection bias in coverage exists because analysts tend to cover larger
companies (Bhushan, 1989) and those about which they have favorable opinions of future prospects
(McNichols and O’Brien, 1997). These issues imply that analysts may favor coverage of larger target firms
or those that are poised to earn higher premiums during takeovers. Therefore, without devoting proper
attention to the endogeneity of analyst coverage our results will be difficult to interpret. To circumvent this
issue, we use natural experiments related to the mergers of brokerage houses (Hong and Kacperczyk, 2010)
and closures of analyst firms (Kelly and Ljungqvist, 2012).
We note that the variation in analyst coverage related to mergers and closures of brokerage houses is
almost surely orthogonal to the gains accruing to our target firms, at the very least because the broker
mergers/closures (that affect targets in our sample) occur with no less than a six month lag prior to their
acquisition announcement dates. Given this, these experiments provide a reliable source of identification in
our setting.
7
Panel A in Table 2 reports the mergers and closures of brokerage houses during our sample period.
Panel A also reports the distribution of the 5,515 companies that lose coverage as a result of these events.3
From this group, we identify 383 instances in which a firm that loses coverage is one of the target firms in
our sample. In all, 165 unique targets in our sample lose at least one analyst due to a brokerage closure or
merger. Again, because we ensure that the brokerage event (merger or closure) occurs at least six months
prior to the acquisition announcement, the timing mitigates the concern that the takeover event itself triggers
the reduction in coverage.
We sort the 746 targets in our sample with analyst coverage during the six-month period prior to the
merger announcement by whether they lose coverage as a result of a brokerage merger or closure. We
compare mean and median values for key target characteristics in the two subsamples and report the results
in Panel B of Table 2. This analysis reveals that, in all of the dimensions we consider, targets that lose
coverage are similar to those that do not lose analysts. This evidence suggests that coverage loss due to
brokerage closings or mergers is indeed random and that the reduction in the number of analysts is truly
exogenous.
In Panel C of Table 2, we use a panel of 47,881 firm-year observations with complete data in CRSP,
Compustat, and I/B/E/S, to estimate four logistic regressions in which the dependent variable is set to 1 if
the firm experiences a loss in analyst coverage during the calendar year. The dependent variable is otherwise
set to 0. We compute this variable for each company in every calendar year in which the firm is still active
as of the December 31st of the calendar year. Firms that drop from the sample at any time before this date
are removed from the analysis.
The regressions in Panel C of Table 2, control for explanatory variables similar to those in Mola, et al.
(2013) and Yu (2008). The tests in Panel C include two additional control variables: an indicator for Brokers
Closed and an indicator for Brokers Acquired. If a brokerage firm that covers firm “i” closes during the
3
Kelly and Ljungqvist (2012) note that Lehman is not suitable to be used as a source of identification since Barclays
took over Lehman’s entire U.S. research department in order to establish its own U.S. equities business. Consequently,
we exclude Lehman from our sample.
8
calendar year, the indicator is set to 1 in that calendar year for company “i”. Otherwise, the indicator is set
to 0. The Broker Acquired indicator is coded in an analogous manner. Hong and Kacperczyk (2010) and
Kelly and Ljungqvist (2012) respectively show that these events reduce the number of sell-side analysts
that cover a given firm. These results are borne out in our data as well. The coefficient estimates for Brokers
Acquired and Brokers Closed exhibit positive and significant estimates indicating that these events trigger
a meaningful decline in analyst coverage. The marginal effects derived from model (4) in Panel C of Table
2 indicate that the probability of experiencing a coverage loss increases by 6.19 percentage points when
brokers merge and by 7 percentage points when brokers close.4
In the remainder of the paper, we employ 2SLS methods to use mergers and closure of brokerage houses
to instrument the analyst coverage loss experienced by the target firms.5 By doing so, we address
endogeneity concerns related to co-determination of coverage and premiums and also to unobservable firm
heterogeneity correlated with corporate decisions and analyst coverage.
2.2. Analysts coverage and takeover premiums
In Table 3 we report the results from eight regressions in which the dependent variable is the acquisition
premium (conditional on an acquisition occurring). In models (1), (2), (5) and (6), the dependent variable
is the 4-week acquisition premium reported by SDC, while in the other regressions the dependent variable
is the target’s cumulative abnormal return (CAR) during the window (-42, +126) where day 0 is the merger
announcement day (as in Schwert, 1996). In the first four tests in Table 3, the key explanatory variable is
4
The marginal effects are computed by first calculating the probability of losing coverage using the sample means for
all continuous independent variables and zeroes for all indicator independent variables (the base predicted probability).
The probability of competition is then re-computed by changing each independent variable (in turn) by adding one
standard deviation to the mean of continuous variables (or using a 1 for each indicator variable). We use the same
procedure to compute marginal effects for all logit models in this paper.
5
A naïve two-stage approach would fit a logit model for the coverage loss in a first stage and then, in a second stage
model, use the fitted coverage loss probability from the first stage as the main independent control variable. This
approach, however, delivers inconsistent estimators and suffers from the forbidden regression problem described by
Wooldridge (2002, p. 236 and p. 478). To avoid this issue and following Wooldridge’s suggestion, we fit the second
stage regressions using 2SLS by instrumenting coverage loss with the fitted coverage loss probability from a first
stage OLS regression. In addition, in untabulated 2SLS analyses, we estimate the first stage regression to instrument
for coverage loss using fiscal years (instead of calendar years). The results of the second stage tests using this
alternative instrumentation produce results that are qualitatively similar to those reported.
9
the number of sell-side analysts covering the target firm. This variable is defined as the maximum number
of sell-side equity analysts providing research coverage on the target firm during the six months prior to
the announcement of the deal. The key explanatory variable in models (5)-(8) is the fitted probability of
coverage loss from a 2SLS system in which the first stage regression is a linear model similar to model (4)
in Panel C of Table 2.6 This IV approach helps us (i) address the possibility of co-determination of analyst
coverage and takeover premiums, and (ii) examine the effect of coverage loss on takeover premiums.
Aside from our independent variables related to analyst coverage, the control variables in our premium
regressions (defined in the Appendix) are similar to those used in the extant M&A literature. Importantly,
in order to properly specify our second stage regressions, additional controls (some of which are not
reported) include those used in model (1) of Panel C of Table 2. In addition, to account for the influence of
industry and time trends on M&A premiums, the odd-numbered regressions in Table 3 include year and
industry (Fama-French 12 categories) fixed effects while the even-numbered regressions are estimated with
standard errors clustered by industry and year. Controlling for the influence of time trends using different
econometric techniques in our tests is particularly important given the time variation in regulatory
provisions and other events that affect (a) the flow of information between publicly traded firms and sellside analysts and (b) the population of firms with analyst coverage.7 Likewise, using different econometric
6
The standard errors in the second stage regression are adjusted for the fact that the instrumental variable for analyst
coverage is estimated. See Roberts and Whited (2012) for a discussion of this issue. In addition, to assess the strength
of our instruments, we perform tests following Stock and Yogo (2005) and report the associated p-value for the F
statistic in all of our second stage regressions. We also estimate Anderson’s (1984) likelihood-ratio test of the null
hypothesis that the test statistic is distributed χ2, so that it may be calculated even for an exactly identified equation.
All of our second stage tests report the associated p-values for this test.
7
On August 15, 2000, the Securities and Exchange Commission (SEC) adopted Regulation Fair Disclosure (FD).
Regulation FD provides that when a firm discloses material nonpublic information to certain individuals or entities
(such as stock analysts or holders of the firm's securities who could potentially trade on the basis of the information)
the firm must simultaneously make public disclosure of that information. In addition, The Global Settlement was an
enforcement agreement reached on April 28, 2003 between the SEC, NASD, NYSE, and ten of the United States’
largest investment firms to address issues of conflict of interest within their businesses. Its purpose was to curb
conflicts of interest that affected analysts’ research by substantially curbing links between research and investment
banking departments. The new rules also established rigorous disclosure requirements aimed at making research
output more meaningful. According to Kadan, Madureira, Wang, and Zach, (2009) analyst coverage declines
following the Global Settlement agreement.
10
methods to account for the influence of the targets’ industry affiliation is important given that some sectors
(i.e.: Business and Healthcare) appear to be slightly overrepresented in our sample (Table 1).
The coefficient on the number of analysts covering the target is positive and statistically significant in
models (1)-(4) of Table 3. Based on the estimates from the standard OLS regressions, on average, premiums
increase by 0.8 to 1.3 percentage points with the addition of one analyst. The estimates for the instrumented
analyst variable in our second-stage premium regressions [Models (5)-(8)] are negative and statistically
significant, indicating that target firms that lose coverage experience a large drop in premiums (ranging
from 1.9% to 10.7%). These findings suggest that analyst coverage has a causal effect on the premiums
paid to firms that are sold. In general, the results from our merger premium tests are consistent with the
hypothesis that monitoring by sell-side equity analysts incentivizes managers to bargain for higher merger
premiums when their firms become takeover targets.8
We observe that several of the control variables in Table 3 yield coefficient estimates that are similar
to those reported in other studies. For example, we find acquisition premiums to be higher in deals
characterized as tender offers (Bates, Lemmon, and Linck, 2006). In contrast, acquisition premiums are
significantly lower in deals initiated by the target (Aktas, et al. 2010) and also inversely related to the size
of the target firm (Bargeron, Schlingemann, Stulz, and Zutter, 2008). This last result (of larger targets
earning lower premiums) is noteworthy because larger firms are less likely to lose coverage (see Panel C
of Table 2). Nevertheless, the positive association between analyst coverage and premiums is robust to the
inclusion of the target’s size variable in the regressions reported in Table 3.
The positive association between analyst coverage and premiums suggests that coverage adds value (at
least conditional on an acquisition). It is possible that firms in general and potential takeover targets in
particular are aware of such value and, therefore, seek to buy coverage. While investment banks provide
most research coverage with no explicit costs to firms, there are some independent research firms that
We have experimented with including the dispersion of analysts’ forecasts in these regressions. Analyst forecast
dispersion does not load significantly in any of the specifications, and the coefficients on the number of analysts
covering the target firm (our key independent variable) are unaffected. The same is true for an indicator variable for
whether the same analyst covers both the acquiring and target firm.
8
11
provide coverage for a fee to companies. The evidence in Kirk (2011) suggests that coverage is indeed
valuable. He estimates positive average CARs (1.68%) for firms that buy coverage and shows that those
with greater uncertainty, weaker information environments, and low turnover are more likely to buy
coverage as they have the most to gain from analyst coverage but are unlikely to attract sell-side analysts.
In our sample, only six target companies buy coverage. Removing these observations from the analyses
does not alter our results.
2.2.1. Unconditional premiums
Comment and Schwert (1995) estimate regressions explaining unconditional takeover premiums. These
regressions (see Table 4 of their paper) use panel data of all firm-years in their sample period, where the
dependent variable (unconditional takeover premium) is set equal to 0 in non-takeover firm-years. The
dependent variable is equal to the actual takeover premium if there is a takeover associated with the firmyear. The control variables in their regressions include lagged (relative to the year in question) firm-specific
measures of abnormal returns, sales growth, liquidity, debt-to-equity, market-to-book, price-to-earnings,
and company size.
In untabulated analyses, we estimate unconditional premium regressions using all firm-years with
CRSP, Compustat, SDC, and analyst coverage data in our sample period. Specifically, using 31,517 firmyear observations for which the full set of control variables employed in Comment and Schwert (1995) is
available, we run basic OLS regressions of unconditional takeover premiums on the number of analysts
covering the target firm.
We find that the effect of analyst coverage on unconditional takeover premiums is positive and
significant. Given that the unconditional takeover premium blends the effects of a conditional takeover
premium and the probability with which a takeover offer occurs, our results suggest that the number of
analysts covering the target firm adds value unconditionally by increasing some combination of the
premium conditional on a takeover (as in Table 3) and the probability that such a deal occurs.
12
2.2.2. Natural experiments: Potential issues
In the preceding analyses, we address the selection bias in analyst coverage by exploiting natural
experiments related to mergers of brokerage houses (Hong and Kacperczyk, 2010) and to closures of analyst
firms (Kelly and Ljungqvist, 2012). Specifically, we use these events (which produce an exogenous
variation in coverage) as instruments for the loss of analyst coverage because they are (i) likely correlated
with such loss (satisfying the relevance condition); and (ii) unlikely correlated with the residual in the target
premium regressions (satisfying the exclusion condition).
Nevertheless, because the exclusion restriction cannot be tested it is plausible that the events leading to
a consolidation in the Brokerage Industry could have affected some targets in our sample (particularly those
operating in the financial industry). We address this concern in two ways. First, we repeat all our tests
excluding the 86 target firms in our sample that operate in the Money and Finance industry. The results,
excluding these firms, lead to inferences similar to those arising from the tabulated analyses. In our second
approach, we re-estimate (but do not tabulate) all of the second-stage tests using the closure of brokerage
houses as the sole instrument for coverage loss. The results of these tests are similar to our tabulated
findings. The coefficient in the SDC premium regression implies that losing coverage is associated with a
deal premium decline.
We also note that the closings of brokerage houses are temporally clustered. This raises the potential
issue that many transactions in our sample could be more susceptible to these events than others. To
alleviate this concern, we re-estimate all of our second stage regressions using the merger of brokerage
houses independently to instrument for the coverage loss. The estimates from these untabulated analyses
are consistent with those reported in Table 3. Moreover, in the robustness tests presented in Section 3 we
further address endogeneity concerns by using the identification method proposed by Yu (2008).
2.2.4. Premiums and ex-ante coverage
Crawford, Roulstone, and So (2012) indicate that analysts initiating coverage on firms without existing
coverage produce industry- and market-wide information. Conversely, they argue that analysts initiating
13
coverage on firms with existing coverage produce firm-specific information. Their study suggests that
analysts have different roles, and effects, in firms that are covered ex-ante versus those that are not. In our
context, this implies that the relation between acquisition premiums and the number of analysts covering a
target firm might have a discontinuity around zero (i.e., might be non-linear, and different going from zero
to one analyst as opposed to going from one to two analysts).
To investigate this in greater detail, in untabulated analyses we re-estimate our premium regressions
for the subsample of 746 deals in which the target firm is covered by at least one analyst (similar in spirit
to the regressions in Panel B of Table 3). The results indicate that, for the average transaction, increasing
coverage by one analyst (from at least one analyst ex-ante) is associated with a significant increase in the
SDC premium of 0.8% to 1.2%. A similar increase in the number of analysts is associated with an increase
of almost 1% in the Schwert (1996) premium. Thus, for covered firms the association between the number
of analysts and realized takeover premiums is larger in magnitude.
2.3. Acquirer returns
In Table 4, we estimate four regressions explaining the three-day merger announcement cumulative
abnormal announcement return (CAR) for the acquirers in our sample. This CAR is centered on the
acquisition announcement day, and is calculated as the cumulated residuals from a market model estimated
during the one-year window ending four weeks prior to merger announcement. We control for variables
similar to those in the acquirer return tests performed by Moeller, et al. (2004) and by Masulis, et al. (2007),
except that we expand the specifications in those studies by our target analyst coverage proxies.9
Specifically, models (1) and (2) report coefficients from OLS regressions in which the explanatory variable
of interest is the number of analysts covering the target company. Models (3) and (4) provide estimates
from second-stage regressions in which the key independent variable is the coverage-loss instrument
obtained from a first stage regression (similar to the procedure used in our premium IV tests).
9
To correctly specify our second stage models, additional controls include all of the independent variables used in
model (1) of Panel C in Table 2. The estimates for some (but not all) of these controls are reported in Table 4.
14
The results in models (1)-(2) of Table 4 indicate that acquirer returns decrease in the number of analysts
covering the target. According to the OLS coefficient estimates, a one standard deviation increase in the
number of analysts covering the target is associated with a 66 basis points decrease in the return to the
acquirer. Coefficients from our second-stage regressions of acquirer returns in Models (3)-(4) indicate that
acquirer CARs are 4.9 to 5.9 percentage points higher when targets lose coverage. These results also hold
when we use closures and mergers of brokerage houses independently to instrument for coverage loss (not
tabulated). The coefficient estimates for these untabulated second stage tests are 0.067 (p-value = 0.000;
when brokerage mergers is the instrument) and 0.060 (p-value = 0.000; when brokerage closure is the
instrument), similar to those reported in Table 4.
Looking at the control variables in Table 4, we note that several produce results that conform to the
existing literature. For example, as in Masulis, et al. (2007) and Cai and Sevilir (2012), the relative size
variable yields negative coefficient estimates. In addition, similar to many papers in the literature (including
Malmendier, Opp, and Saidi, 2012) acquirer returns upon the announcement of the deal are higher when
cash is used to buy the target firm.
Together with the findings from our bid premium regressions, our acquirer return tests suggest that
analyst coverage serves as an external monitoring device that increases the bargaining incentives of the
target managers. Indeed, shareholders in targets covered by more analysts appear to capture more of the
gains from an acquisition (higher premiums and lower bidder returns) than do targets with lesser analyst
coverage. The opposite, however, occurs when the target’s analyst coverage is lost. In the later scenario,
acquirers pay lower premiums and earn higher merger announcement returns.
2.4. Target-payable termination fees
The existing literature (Bates and Lemmon, 2003; Officer, 2003) considers one particular contractual
provision in merger agreements (target-payable termination fees) as an efficient solution to the problem of
bargaining with potential acquirers. Specifically, to induce a bidder to make a public offer to acquire the
target firm at a premium, the target in many cases must offer to pay a termination fee to the acquirer if the
15
target later reneges on the agreed deal. If the incentives created by monitoring from financial analysts
increase the bargaining power of some targets (as argued in this paper), those targets with higher analyst
coverage may be able to negotiate with potential acquirers without having to agree to pay a termination fee.
In other words, if monitoring by analysts increases target bargaining power we may observe M&A deals in
which the target firm receives a high premium (as in Table 3) but does not have to offer to pay a termination
fee to the acquirer, thereby increasing the target’s flexibility to pursue superior acquisition offers for their
shareholders. This is what we find.
In Table 5 we report logit (and OLS) regressions explaining the incidence (or size) of target-payable
termination fees in M&A agreements. As can be seen from the coefficients in the first row of the table, the
number of analysts covering the target firm is significantly negatively associated with both the incidence
and size of target-payable termination fees (although the coefficient in column 4 is not significant at
conventional levels). According to the estimates from the logit models, increasing target coverage by a
single analyst is associated with a 9 to 12 percentage point drop in the probability that the deal contains a
target termination fee provision.
To put this result in perspective, about 86% of the deals in our sample include a target payable
termination fee. We interpret this as further evidence consistent with the notion that greater analyst coverage
enhances the monitoring and, in turn, the bargaining power of the target: targets with greater analyst
coverage appear to get higher premiums in M&A deals (Table 3) and do so without having to promise a
termination fee to the acquirer. An alternative (but entirely consistent) explanation is that some bidders
want break-up fees in opaque firms because doing due diligence in these targets might be more costly.
Coverage makes targets less opaque thereby lowering the need for the break-up fees. Under this possibility,
the break-up or termination fee results are also consistent with the Merton’s (1987) investor recognition
theory predicting that investors would be more comfortable buying stocks with which they are familiar.
2.5. Competing bids
16
Our evidence in Table 5 suggests that targets with greater analyst coverage might be more likely to
experience competing bids because, at the margin, the lack of a termination fee in a deal is more conducive
to bid competition. Given this, we explore whether analysts coverage of the targets affects the level of
competition to buy these firms.
Table 6 presents two logit models of the determinants of bid competition. The dependent variable in
these tests is equal to 1 for targets that receive a public takeover offer from more than one bidder, and set
to 0 otherwise. The specification in Table 6 augments that in Officer (2003) to address whether the number
of analysts covering the targets deters or invites competing bidders.
The estimates in Table 6 suggest that analyst coverage of the target is associated with competing bids
in takeovers: the coefficient estimates for the number of analysts’ variable are significantly positive. The
marginal economic impact related to a one standard deviation increase in analyst coverage implies a 1.3 to
2 percentage points increase in the probability that an alternate offer for the target emerges. This is quite a
substantial effect when benchmarked against the 4.7% incidence of bid competition for the transactions in
our sample (Table 1).
The results in Table 6 suggest that analyst coverage generates additional interest in acquiring the target
firm, encouraging competition to buy these companies. The increased competition triggered by the
increased coverage could explain the higher takeover premiums paid to firms with more coverage and the
lower merger announcement returns earned by their acquirers. Moreover, it is possible that the increased
competition to buy the target is due, at least in part, due to the target managers’ ability to structure deals
without committing to pay a merger termination fee (Table 5).
2.6. Deal initiation
Aktas, et al. (2010) argue that target firms that initiate their own acquisition signal a clear willingness
to sell, thereby weakening their bargaining power during merger negotiations. However, if monitoring by
analysts provides managers with powerful bargaining incentives, some of them may want to initiate the
acquisition of their firms. This possibility could explain the result in Table 6 showing increased competition
17
to acquire targets covered by more analysts. On the other hand, if the increased monitoring that comes with
high analyst coverage alleviates the need for targets offer themselves up for sale, analyst coverage of the
target firm may be negatively associated with the decision to initiate the acquisition.
To examine these issues, in Table 7 we estimate two logit models similar to those in Aktas, et al. (2010)
in which the dependent variable is set to 1 for target initiated deals, and set to 0 otherwise. We extend their
specification by including the number of analysts covering the target firm as the key explanatory variable
in the logit tests. The marginal effect associated with our (statistically significant) analyst coverage
variables in Table 7 implies that a one standard deviation increase in the number of analysts reduces the
probability that the target initiates the deal by 5.3 percentage points. The magnitude of this effect is
economically important since targets initiate their own acquisition in about 39% of the transactions we
study (Table 1).
This result is consistent with at least two non-mutually exclusive explanations. To the extent that
financial analysts perform a monitoring role that improves firm operations and value, the results in Table 7
are consistent with the idea that well-monitored companies are less likely to succumb to the strategic
alternative of offering themselves up for sale (which, presumably, is a last resort for many firms following
periods of poor performance). Alternatively, the results in Table 7 support the notion that the firms with
greater analyst coverage have enhanced investor recognition (Merton, 1987) and, therefore, are more likely
to attract unsolicited takeover offers from potential suitors (as opposed to needing to invite such offers).
3. Robustness tests
In this section we perform different tests in order to address some issues of potential concern and
examine the robustness of our findings.
3.1. Acquirers’ analysts
Our results indicate that analyst coverage enhances the wealth of shareholders of firms that are sold.
Correspondingly, it is possible that analyst coverage also improves the wealth of shareholders of the
18
acquirer firm. To evaluate this possibility, we re-run our target premium and acquirer return regressions
(similar to those reported in Tables 3 and 4, respectively) adding a variable to control for the number of
analysts covering the acquirer firm. This variable is defined as the maximum number of analysts covering
the acquirer company during the six months prior to the merger announcement. We present the results of
these analyses in Panel A of Table 8. To conserve space, we only report the parameter estimates for the
number of analysts covering the target and also for the number of analysts covering the acquirer.
The results in Panel A of Table 8 show that our main findings are robust to the inclusion of the acquirer
analysts’ variable. We continue to find that the number of analysts covering the target is positively
associated with the premiums paid to these firms and negatively related to the acquirers’ merger
announcement returns. Interestingly, we note that the number of acquirer analysts’ variable has a negative
and significant coefficient in the SDC premium tests. These results appear generally consistent with the
findings in Chen, et al. (2014) that analyst monitoring lowers the probability that acquirer firms make valuedestroying acquisitions. However, the results in our acquirer CAR regressions are not supportive.
In untabulated tests, we also evaluate whether the number of analysts covering the acquirer changes
(increases or decreases) after the acquisition and whether such change is related to the number of analysts
covering the acquired target company. The results indicate that, after the merger, acquirers gain one new
analyst for every two analysts covering their acquired target. To the extent that acquirers pay higher
premiums to essentially “buy” more coverage it would be reasonable to assume that the additional coverage
is valuable to the merged firm in the long run. To test this conjecture, we regress the acquirers’ buy-andhold abnormal return (BHAR) during the year after the acquisition using the number of analysts covering
the target firm as the key explanatory variable. We find that a one standard deviation increase in target
analysts is associated with BHAR increases of 2.3% to 2.9% for the merged firm.
3.2. Anticipation bias
It is possible that stock prices might reflect the anticipation of a takeover premium if analyst coverage
reveals that a takeover is more likely. To consider this possibility, we employ a methodology similar to that
19
in Comment and Schwert (1995) and decompose the number of analyst covering the target into variables
related to the anticipated and surprise components of analyst coverage. These components are estimates
from the target analyst coverage regression reported in model (4) of Panel C of Table 2. The predictable
component is the fitted value of target analyst coverage from that regression and the surprise component is
the residual.
In Panel B of Table 8, we report abbreviated premium regressions (similar to those reported in Table
3) in which the main explanatory variables are the anticipated and surprise components of the number of
analysts covering the target. The coefficients for both variables are positive and statistically significant.
These results, which suggest that the benefits of analyst coverage are not fully imputed in the value of the
firm before the takeover, mitigate the concern that investors are able to anticipate takeovers of firms with
greater analyst coverage. Moreover, since the residual measures abnormal or excess analyst coverage which
is purged from its determinants, the positive coefficient on the surprise component variables indicates that
more analyst coverage is associated with substantially higher premiums.
3.3. Alternative identification
Yu (2008) finds that firms followed by more analysts manage their earnings less. His findings are also
consistent with the hypothesis that analysts perform an external monitoring role. To tackle endogeneity, Yu
uses the change in brokerage size as an instrumental variable for the number of analysts covering the firm.
To rationalize this choice, Yu argues that the size of brokerage houses changes over time because it depends
on changes in profits and revenues. As an example, Yu mentions that Lehman Brothers reported a $966
million net operating loss in 1990, which triggered a reduction in the number of firms that Lehman’s
analysts covered. Yu argues that such drop created an exogenous shock to coverage. Yet, since Lehman
continued covering some firms, it is possible that the firms that lost coverage were not randomly dropped.
Under this possibility, the change in analyst coverage triggered by brokerage size changes might not be
really exogenous. With this caveat in mind and following Yu (2008), we employ 2SLS methods and use
the change in size of brokerage houses during the year before the merger announcement as an instrument
20
to fit the number of analysts covering targets in our sample. We then use this fitted variable in second stage
tests of target premium and acquirer announcement returns, respectively.10 The abbreviated results appear
in Panel C of Table 8.
Using the alternative instrumentation proposed in Yu (2008) to fit analyst coverage for our target firms
does not alter our results. The estimates in Panel C of Table 8 also document that the fitted variable for the
number of target analysts is positively related to premiums and negatively associated with the acquirer
announcement returns.
3.4. Division of gains
Our bid premium regressions in tandem with our acquirer return tests indicate that targets covered by
more analysts seem to get bigger “piece of the acquisition pie” for their shareholders. To evaluate this issue
in more detail, we follow the procedure in Ahern (2012). Specifically, in Panel D of Table 8 we estimate
two regressions in which the dependent variable is the target’s gain relative to the acquirer’s gain. To
construct this variable we first estimate the target $CAR and the acquirer $CAR as the cumulative abnormal
return earned over three days surrounding the merger announcement adjusted by the equally weighted
Center for Research in Security Prices index and then multiplied by market equity of the firm. Next, we
compute the target’s $CAR minus the acquirer’s $CAR. We then divide this difference by the sum of
acquirer and target market values 50 trading days before the merger announcement to obtain our relative
gain dependent variable. All of the control variables in Panel D of Table 8 are similar to those in Table 4.
However, to conserve space, we only report coefficient estimates for the variable tracking the number of
analysts.
10
Using the change in size of brokerage houses during the year before the merger announcement as an instrument to
fit coverage loss produces results similar to those tabulated in Table 3 (for premiums) and in Table 4 (for acquirer
M&A announcement CARs).
21
The regressions in Panel D show that targets with more coverage get a relatively higher share of the
gains. The economic effect related to the coefficients implies a 33 basis points increase in the relative gain
of the target vs. the acquirer per dollar of total market value of the target and the acquirer.
3.5. Log transformation and lagged coverage
To further evaluate the robustness of our results, we use several alternate ways to examine analyst
coverage. For example, following Hong, et al. (2000) we compute the natural log of (1+ number of analysts
covering the target). In (untabulated) analyses, we re-estimate our target premium and acquirer return tests
using this transformation as the key explanatory variable. The inferences arising from these tests are very
similar to those tabulated. The coefficient estimates for the log-transformed variable imply that doubling
the number of analysts is associated with a 3.5% to 5.4% percentage point increase in the 4-week SDC
premium. Likewise, adding one more analyst to cover a target that is already covered by two other analysts
is related to an SDC premium increase of 3.2%. We also find that increasing the number of analysts by one
standard deviation is related to a 0.8% to 1.0% decline in the CAR meeting the acquirer firms upon deal
announcement.
We also repeat our premium and acquirer return tests using a variable that lags analysts’ coverage by
two years. The results using the number of analysts covering the targets two years before deal
announcement are consistent with those tabulated. The lagged coverage variable attains positive and
significant coefficients (0.020, p-value = 0.009 and 0.016, p-value = 0.057) in the SDC premium
regressions. Likewise, the estimates are positive and significant (0.019, p-value = 0.000 and 0.020, p-value
= 0.000) in the Schwert (1996) premium regressions. In contrast, the coefficient for lagged coverage are
significant and negative in the acquirer CAR regressions (-0.001, p-value = 0.013). These results reduce
concerns that coverage is augmented or initiated by analysts that anticipate acquisitions.
3.6. Agreement amongst analysts
22
Jegadeesh, Kim, Krische, and Lee (2004) find that for stocks with unfavorable quantitative
characteristics, higher consensus recommendations are associated with worse subsequent returns. They
argue that the level of the consensus recommendations among sell-side analysts adds value only among
stocks with favorable quantitative characteristics (i.e., value stocks and positive momentum stocks). Given
the results in Jegadeesh, et al. (2004) it is possible that agreement amongst analysts (or the lack thereof)
affects our reported findings. To address this issue, we re-estimate our premium and acquirer return
regressions in the subsample of 746 transactions in which the target has analyst coverage while controlling
for the degree of agreement among the covering analysts.
Specifically, we define an indicator variable equal to 1 when more than half of the analysts covering
the target issue a “sell” recommendation during the six-month period prior to the acquisition announcement
(and 0 otherwise). All of our results prove robust to the inclusion of this control variable.11 Parameter
estimates for the number of analysts’ variable continue to be positive and significant in the SDC (0.014 and
0.008) and Schwert (0.009 and 0.008) premium regressions. The analysts’ agreement variable, however,
does not achieve statistical significance in any of the specifications. Likewise, the coefficient for the number
of analysts is negative and significant in our acquirer return regressions (-0.003, p-value = 0.000). In
contrast, in the same tests the coefficient on the analysts’ agreement control variable is not statistically
significant.
3.7. Analysts from investment banks
SDC regularly reports league tables (see, for example, Rau 2000) ranking investment advisors in
completed M&A deals. We use this information to evaluate whether our results are robust to controls for
(i) the rank (quality) of the investment advisor and (ii) the affiliation of a target analyst to an investment
bank advising the target firm.
11
In the premium tests the analyst agreement control variable does not attain statistically significant estimates.
However, it attains significantly positive estimates in the acquirer return regressions. Coefficients range between
0.009 and 0.015 and p-values are significant at the 1% level.
23
Following a procedure similar to that in Rau (2000), we create an indicator which we set to 1 if the
advisor is ranked as a top 10 advisor (on the basis of the value of transactions advised) by SDC. The
indicator is set to 0 for all other advisors. Advisors are ranked in purchases of at least 50% of the target
company, repurchases, self-tender offers, exchange offers for equity and/or securities convertible into
equity, and leveraged recapitalizations. They are not ranked in partial acquisitions of less than 50% of the
target, in ownership interests valued at less than $1 million, or in splitoffs. Advisors receive full credit for
each transaction in which they advise either the target or the acquirer firm.
We also construct an indicator variable equal to 1 if an analyst covering the target belongs to an
investment bank that is identified as the target’s lead advisor in the transaction. Otherwise, the indicator
takes the value of 0. SDC classifies a firm as a financial advisor if it acts as dealer manager, serves as lead
or junior underwriter, provides financial advice, issues a fairness opinion, initiates the deal or represents
shareholders, board of directors, seller, major holder or claimants. Firms that act as equity participants or
arrange deal financing are not classified as advisors.
The results from untabulated empirical analyses indicate that including the analyst rank and the
affiliated-analyst as additional control variables to our target merger premium and acquirer announcement
return regressions does not alter the significance or inferences related to the number of target analysts’
independent variables.
4. Conclusions
In this paper, we examine whether financial analysts play a monitoring role during acquisitions. Our
results indicate that firms covered by more analysts are sold for larger premiums, are less likely to pay
termination fees, are more likely to be targeted by more than one acquirer, and are less likely to initiate
their own takeover. In addition, we also find that acquirer returns upon the announcement of a deal decrease
in the number of analysts covering the target firms. In general, we find that targets covered by more analysts
obtain a larger share of the gains in a takeover.
24
From an econometric perspective, the potential for coverage selection bias and omitted variables raise
valid concerns with our baseline empirical tests. Moreover, our baseline results indicate that being covered
by more analysts enhances the wealth of shareholders in firms that become takeover targets. However, our
baseline tests do not directly reveal whether losing such coverage is detrimental. To address these issues,
we use natural experiments involving brokerage houses that merge or close to instrument for coverage loss
in the context of 2SLS analyses. Specifically, we use these models to evaluate the causal relationship
between analyst coverage and acquisition gains of the parties to the transactions. Our results related to these
natural experiments as the source for identification indicate that, in deals involving target firms that lose
analyst coverage, target premiums decrease and acquirer merger announcement returns increase. Aside
from withstanding concerns related to endogeneity and selection, we note that our findings are also robust
to a battery of supplementary tests that include numerous additional controls, and an alternative source of
identification.
Overall, the empirical evidence supports our monitoring hypothesis that, under the oversight from
financial analysts, managers of takeover targets have the incentive to bargain for better deal terms which
create substantial value for their shareholders. In this vein, our findings suggest that analysts perform an
external monitoring role that enhances the wealth of shareholders of firms that become takeover targets.
25
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28
Table 1: Sample characteristics
This table describes our sample from the Securities Data Company’s (SDC) merger and acquisition database. The sample consists of 1,098 completed merger and
acquisitions by U.S. bidders for U.S. targets during 1993-2008 in. We screen deals from SDC following the criteria in Moeller, Schlingemann, and Stulz (2004)
and Masulis, Wang, and Xie (2007). In addition, we require that both acquirer and target firms have stock market, accounting, and analyst coverage data available
from the Center for Research in Security Prices (CRSP), Compustat, and I/B/E/S, respectively. In Panel A we report the temporal and Fama and French 12 industrial
distribution of the targets. In Panel B we report present summary statistics for key characteristics for the sample of 1,098 targets. The Appendix provides definitions
for all variables used in this paper.
Panel A - Temporal and industrial distribution
1993 1994
1995 1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total
% of
Total
Non-Durables
1
1
2
0
5
3
4
11
4
2
3
4
0
4
3
0
47
4.28%
Durables
0
0
1
0
0
2
1
0
1
1
0
0
1
0
1
0
8
0.73%
Manufacturing
0
1
11
8
7
16
15
15
7
5
1
5
6
4
5
1
107
9.74%
Energy
0
0
1
5
6
7
3
6
10
4
2
4
5
4
1
1
59
5.37%
Chemicals
0
0
2
1
1
2
3
2
2
1
0
1
1
0
1
1
18
1.64%
Business Equipment
3
4
15
23
26
27
30
25
29
14
22
19
24
24
19
15
319
29.05%
Telecommunications
1
2
4
5
2
8
6
1
0
1
1
1
1
2
2
0
37
3.37%
Utilities
0
1
1
4
5
8
11
7
1
0
0
1
2
1
0
1
43
3.92%
Shops
0
2
5
14
6
13
10
2
3
2
2
2
4
3
5
3
76
6.92%
Healthcare
5
8
10
13
16
19
12
4
11
8
7
11
14
9
13
12
172
15.66%
Money and Finance
1
1
6
7
13
6
9
5
6
1
7
7
4
4
5
4
86
7.83%
Other
2
0
6
8
16
21
15
13
10
3
9
9
2
5
4
4
127
11.57%
Total
13
20
64
88
102
132
119
91
84
42
54
64
64
60
59
42
1,098
29
100.00%
Panel B: Deal characteristics
Mean
Median
Standard deviation
Q1
Q3
Relative Size
14.42%
7.89%
16.63%
2.05%
21.76%
Target Initiates
39.31%
0.00%
48.87%
0.00%
100.00%
SDC Premium
Target-Payable Termination Fee (0,1)
Termination Fee (% of Target MVE)
100% Cash
45.90%
86%
5%
32.85%
39.01%
100%
4%
0.00%
42.42%
35%
4%
46.99%
21.08%
100%
3%
0.00%
60.75%
100%
6%
100.00%
100% Stock
30.30%
0.00%
45.97%
0.00%
100.00%
Tender Offer
24.02%
0.00%
42.74%
0.00%
0.00%
Same 3-digit SIC
40.86%
0.00%
49.18%
0.00%
100.00%
Competing Acquirer
4.73%
0.00%
21.24%
0.00%
0.00%
Hostile
2.55%
0.00%
15.76%
0.00%
0.00%
Target 1-year BHARs
10.67%
5.83%
63.46%
-19.17%
34.59%
Targets’ Analyst Coverage
67.97%
100.00%
46.68%
0.00%
100.00%
2.62
2.00
3.31
0.00
4.00
Number of Analysts Covering the Target
30
Table 2: Analyst coverage
Panel A identifies brokerage firms that either close or are acquired during our sample period (1993-2008), and the date on which the event occurs. Panel A also reports the number
of firms that lose analyst coverage due to the event (Coverage Loss) and the number of target firms in our sample that lose coverage (Sample Loss). Brokerage mergers are identified
in Hong and Kacperczyk (2010) and brokerage closures are identified in Kelly and Ljungqvist (2012). Panel B provides sample statistics of the 746 covered target firms sorted by
whether they lose coverage due to the events reported in Panel A at least six months prior to the acquisition announcement. Panel C provides year and industry fixed-effects logistic
regression estimates of the probability of losing coverage. In these tests we analyze 47,881 firm-year observations during our sample period with stock market, accounting, and
analyst data from CRSP, Compustat, and I/B/E/S, respectively. The dependent variable is set to one if a firm coverage decreases during the calendar year and set to zero otherwise.
We compute this variable for each company in every calendar year in which the firm is still active as of the December 31 st of the calendar year. Firms that drop from the sample at
any time before this date are removed from the analysis. The Appendix provides the definition for all control variables. We report p-values in parenthesis.
Panel A: Brokerage Mergers and Closures
Date
Event
Coverage
Loss
259
196
164
113
16
Sample
Loss
45
29
16
7
2
Date
Event
Closed/Target Firm
Nov-01
Jan-02
Apr-02
Jul-02
Jul-02
Closure
M&A
Closure
Closure
Closure
Hoak, Breedlove, Wesneski
Sutro & Co
ABN AMRO
Frost Securities
Robertson Stephens
Dain Rauscher
113
9
Aug-02
Closure
Closed/Target Firm
Acquiring Firm
PaineWebber
Morgan Stanley
Smith Barney
EVEREN Capital
D.A. Davidson
Coverage
Loss
36
13
480
77
456
Sample
Loss
3
3
39
6
26
Vestigo-Fidelity
31
0
Acquiring Firm
Dec-94
May-97
Nov-97
Jan-98
Feb-98
M&A
M&A
M&A
M&A
M&A
Apr-98
M&A
Oct-99
M&A
Kidder Peabody
Dean Witter Reynolds
Salomon Brothers
Principal Financial Securities
Jensen Securities
Wessels Arnold &
Henderson
EVEREN Capital
First Union
107
6
Apr-03
Closure
Commerce Capital Markets
49
2
M&A
M&A
M&A
Closure
Closure
Schroder Wertheim
Wit Capital
J.C. Bradford
Brown Brothers Harriman
George K. Baum
Salomon Smith Barney
SoundView
PaineWebber
151
12
189
163
83
11
2
8
13
8
Jul-03
Feb-04
Oct-04
Mar-05
Mar-05
Closure
Closure
M&A
Closure
M&A
The Chapman Company
Montauk Capital Markets
Schwab Soundview
Traditional Asiel Securities
Parker/Hunter
13
15
167
53
6
0
0
10
0
0
Oct-00
M&A
Donaldson Lufkin & Jenrette
Credit Suisse First Boston
391
21
Jun-05
Closure
IRG Research
92
4
Dec-00
M&A
PaineWebber
UBS Warburg Dillon Reed
480
17
Aug-05
Closure
Wells Fargo Securities
175
9
Dec-00
Dec-00
Jan-01
Feb-01
May-01
Jun-01
Jul-01
M&A
M&A
M&A
M&A
M&A
M&A
Closure
Chase Manhattan
R.J. Steichen
Hambrecht & Quist
Wasserstein Perella
ING Financial Markets
Epoch Partners
Emerald Research
J.P. Morgan
Miller Johnson & Kuehn
J.P. Morgan Chase
Dresdner Bank
79
14
93
19
77
30
30
7
1
6
16
25
0
2
Dec-05
Dec-05
Sep-06
Dec-06
Jan-07
Mar-07
Apr-07
M&A
M&A
Closure
M&A
M&A
M&A
Closure
Advest
Legg Mason Wood Walker
Moors & Cabot
Petrie Parkman
Ryan Beck & Co
J.B. Hanauer
Cohen Brothers
13
24
23
34
39
58
65
2
4
1
1
1
0
0
Aug-01
M&A
Robinson-Humphrey
Suntrust Equitable Securities
14
5
Jun-07
Closure
Prudential Equity Group
309
0
Sep-01
Oct-01
Oct-01
M&A
M&A
Closure
Josephthal Lyon & Ross
Wachovia Securities
Conning & Co
Fahnestock
First Union
39
36
82
4
0
1
Sep-07
Oct-07
Nov-07
M&A
M&A
Closure
Cochran, Caronia Securities
A.G. Edwards & Sons
Nollenberger
Fox-Pitt Kelton
Wachovia Securities
31
163
85
0
7
0
Nov-01
M&A
Tucker Anthony Sutro
RBC Dain Rauscher
32
3
Jan-08
M&A
CIBC World Markets
Fahnestock
26
1
Apr-00
May-00
Jun-00
Jun-00
Oct-00
Goldman Sachs
31
RBC Dain Rauscher
UBS
Janney Montgomery Scott
Merrill Lynch
Citigroup
Merrill Lynch
Stifel Financial
RBC Dain Rauscher
Panel B: Characteristics of target firms
No Loss of Coverage
Loss of Coverage
p-values for
(N = 578)
(N = 168)
Difference Tests
Mean
Median
Mean
Median
t-test
Wilcoxon
ROA
-1.91%
3.48%
-3.64%
3.46%
0.593
0.908
Leverage
48.45%
48.68%
49.29%
51.52%
0.158
0.129
$1,449M
$434M
$1,293M
$629M
0.445
0.018
1.61%
0.43%
1.45%
0.58%
0.029
0.772
2.44
1.73
2.63
1.62
0.550
0.415
46.19%
13.27%
34.99%
14.71%
0.230
0.377
16.1
10.8
16.3
9.8
0.233
0.455
Earnings Per Share
$0.57
$0.63
$0.46
$0.62
0.840
0.219
Dividends Per Share
$0.26
$0.00
$0.26
$0.00
0.472
0.514
2.16
2.00
2.14
2.00
0.294
0.346
Market Value
Cash Ratio
Tobin’s Q
Sales Growth
Years Since IPO
Mean
Recommendation
Panel C: Determinants of coverage loss
(1)
(2)
Broker Acquired
(3)
0.278
0.264
(0.000)
Broker Closed
ROA
Leverage
Log Mkt Value
Sales Growth (%)
Cash Ratio
Tobin’s Q
Institutional Ownership
Prior 1yr BHAR
Merger Wave Indicator
(4)
(0.000)
0.324
0.303
(0.000)
(0.000)
0.133
0.134
0.134
0.135
(0.000)
(0.000)
(0.000)
(0.000)
0.143
0.144
0.145
0.146
(0.000)
(0.000)
(0.000)
(0.000)
-0.218
-0.222
-0.221
-0.225
(0.000)
(0.000)
(0.000)
(0.000)
-0.000
-0.000
-0.000
-0.000
(0.548)
(0.549)
(0.549)
(0.550)
-0.007
-0.008
-0.008
-0.008
(0.016)
(0.015)
(0.015)
(0.014)
-0.009
-0.010
-0.009
-0.010
(0.003)
(0.002)
(0.002)
(0.002)
-0.464
-0.486
-0.479
-0.498
(0.000)
(0.000)
(0.000)
(0.000)
-0.173
-0.172
-0.170
-0.169
(0.000)
(0.000)
(0.000)
(0.000)
-0.056
-0.057
-0.057
-0.058
32
Constant
Year FE
(0.021)
(0.019)
(0.020)
(0.017)
8.110
8.163
8.148
8.197
(0.000)
(0.000)
(0.000)
(0.000)
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
N
Pseudo- R2
47,881
0.1069
47,881
0.1073
47,881
0.1072
47,881
0.1076
33
Table 3: Merger premiums
This table reports regression estimates of merger premiums. The dependent variable in Models (1), (2), (5), and (6) is
the premium reported by SDC. The dependent variable in the remaining models is the premium computed as in
Schwert (1996). The main independent variable is the number of analysts covering the target firm. Models (5)-(8)
report 2nd stage regressions in which coverage loss is instrumented using a 1 st stage OLS regression similar to model
(4) in Panel C of Table 2. Additional controls include all of the independent variables used in model (1) of Panel C in
Table 2. The estimates from some (but not all) of these additional variables are tabulated. We report p-values in
parenthesis.
SDC 4-week
Premium
Target Analysts/Coverage Loss
Target Initiates
Cash
Acquirer Log Mkt Value
Target Log Mkt Value
Tender
Hostile
Same SIC
Acquirer Q
Target Q
Years from IPO
Institutional Ownership
Schwert (1996)
Premium
2nd Stage – IV
SDC
Premium
2nd Stage – IV
Schwert
Premium
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.013
0.008
0.009
0.008
-0.061
-0.107
-0.019
-0.029
(0.000)
(0.000)
(0.000)
(0.000)
(0.062)
(0.001)
(0.059)
(0.004)
-0.064
-0.063
-0.006
-0.006
-0.063
-0.065
-0.009
-0.010
(0.000)
(0.000)
(0.066)
(0.062)
(0.000)
(0.000)
(0.007)
(0.006)
-0.040
-0.079
0.017
0.010
-0.042
-0.075
0.013
0.006
(0.005)
(0.000)
(0.000)
(0.009)
(0.004)
(0.000)
(0.003)
(0.115)
0.042
0.044
-0.004
-0.003
0.045
0.046
-0.003
-0.002
(0.000)
(0.000)
(0.000)
(0.004)
(0.000)
(0.000)
(0.021)
(0.180)
-0.100
-0.089
-0.013
-0.013
-0.080
-0.079
-0.003
-0.004
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.061)
(0.034)
0.110
0.147
0.023
0.031
0.113
0.153
0.022
0.028
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
0.016
0.031
0.012
0.017
-0.007
0.018
0.021
0.023
(0.678)
(0.441)
(0.257)
(0.118)
(0.870)
(0.675)
(0.089)
(0.052)
0.021
0.021
-0.002
-0.003
0.019
0.020
0.002
0.001
(0.059)
(0.060)
(0.622)
(0.430)
(0.095)
(0.083)
(0.546)
(0.713)
0.002
0.005
0.002
0.003
0.002
0.004
0.001
0.002
(0.340)
(0.055)
(0.016)
(0.000)
(0.446)
(0.095)
(0.807)
(0.001)
-0.002
-0.006
0.001
0.001
-0.002
-0.004
0.000
0.000
(0.569)
(0.083)
(0.463)
(0.469)
(0.544)
(0.206)
(0.961)
(0.692)
-0.001
-0.001
-0.000
-0.000
-0.002
-0.001
-0.001
-0.001
(0.009)
(0.025)
(0.006)
(0.019)
(0.001)
(0.003)
(0.000)
(0.000)
0.035
-0.006
0.008
0.002
0.035
-0.005
0.014
0.008
(0.209)
(0.803)
(0.346)
(0.823)
(0.238)
(0.844)
(0.114)
(0.350)
0.578
0.529
0.086
0.075
0.628
0.525
0.049
0.055
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.002)
(0.000)
Additional Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year FE
Yes
No
Yes
No
Yes
No
Yes
No
Industry FE
Yes
No
Yes
No
Yes
No
Yes
No
2-way Clustered SE
No
Yes
No
Yes
No
Yes
No
Yes
Constant
34
N
1,098
1,098
1,098
1,098
1,098
1,098
1,098
1,098
Adjusted- R2
Anderson LM χ2-stat p-value
0.1850
0.1410
0.2008
0.1711
0.1799
0.1394
0.1529
0.1243
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
Stock-Yogo F-stat p-value
35
Table 4: Acquirer announcement returns
Regressions of the acquirer’s cumulative abnormal return (CAR) over three days around the merger announcement
date. Our tests are specified as in Moeller, Schlingemann, and Stulz (2004) and Masulis, Wang, and Xie (2007). The
main independent variable is the number of analysts covering the target firm. In columns (3) and (4) we report acquirer
CAR 2nd stage regressions in which the coverage loss is instrumented using a 1st stage OLS regression similar to model
(4) in Panel C of Table 2. Additional controls include all of the independent variables used in model (1) of Panel C in
Table 2. The estimates from some (but not all) of these additional variables are tabulated. We report p-values in
parenthesis.
OLS
OLS
2nd Stage – IV
2nd Stage – IV
(1)
(2)
(3)
(4)
-0.002
-0.002
0.049
0.059
(0.000)
(0.000)
(0.002)
(0.000)
0.004
0.002
0.004
0.003
(0.046)
(0.274)
(0.039)
(0.215)
0.001
-0.000
0.000
-0.001
(0.487)
(0.881)
(0.986)
(0.304)
0.007
0.007
0.008
0.007
(0.000)
(0.000)
(0.000)
(0.001)
0.002
0.000
0.004
0.002
(0.378)
(0.862)
(0.170)
(0.399)
0.019
0.017
0.024
0.020
(0.008)
(0.014)
(0.002)
(0.010)
0.025
0.023
0.027
0.026
(0.000)
(0.000)
(0.000)
(0.000)
-0.000
-0.002
0.000
-0.002
(0.887)
(0.552)
(0.997)
(0.558)
-0.065
-0.073
-0.064
-0.068
(0.000)
(0.000)
(0.000)
(0.000)
-0.001
-0.001
-0.000
-0.001
(0.161)
(0.020)
(0.756)
(0.261)
0.026
0.024
0.022
0.018
(0.000)
(0.000)
(0.001)
(0.005)
0.031
0.040
0.034
0.050
(0.003)
(0.000)
(0.002)
(0.000)
0.001
0.000
0.001
0.000
(0.000)
(0.000)
(0.000)
(0.000)
0.009
0.011
0.004
0.008
(0.043)
(0.007)
(0.442)
(0.065)
-0.037
-0.042
-0.085
-0.066
(0.219)
(0.000)
(0.001)
(0.000)
Additional Controls
Yes
Yes
Yes
Yes
Year FE
Yes
No
Yes
No
Industry FE
Yes
No
Yes
No
Target Analysts/Coverage Loss
Target Initiates
Acquirer Log Mkt Value
Same SIC
Tender
Hostile
Cash
Stock
Relative Size
Acquirer Q
Acquirer Debt to Assets
Acquirer OpCF to Assets
Years from IPO
Institutional Ownership
Constant
36
2-way Clustered SE
No
Yes
No
Yes
N
1,098
1,098
1,098
1,098
Adjusted- R2
Anderson LM χ2-stat p-value
0.1524
0.0968
0.1499
0.0928
0.0001
0.0001
0.0001
0.0001
Stock-Yogo F-stat p-value
37
Table 5: Target-payable termination fees
In this table we report regressions explaining the probability of a merger agreement containing a target-payable
termination fee. The coefficients reported in columns 1 and 2 are from logit regressions where the dependent variable
is an indicator variable equal to one if the acquisition involves a target-payable termination fee, and zero otherwise.
Columns 3 and 4 report OLS regressions of the target termination fee percentage (termination fee scaled by the target
market value). The regressions are specified as in Officer (2003). The key explanatory variable is the number of sellside analysts covering the target firm. All variables are defined in the Appendix. We report p-values in parentheses.
Target Analysts
Target Initiates
Premium
Cash
Acquirer Log MVE
Target Log MVE
Tender Offer
Hostile
Same SIC
Years from IPO
Institutional Ownership
Constant
Year FE
Industry FE
2-way Clustered SE
N
Pseudo- R2 or Adjusted- R2
Logit
Logit
OLS
OLS
(1)
-0.067
(0.000)
-0.121
(0.127)
-0.000
(0.739)
-0.423
(0.000)
-0.005
(0.845)
0.303
(0.000)
0.491
(0.000)
-1.246
(0.000)
-0.001
(0.991)
0.004
(0.825)
-0.000
(0.926)
0.728
(0.000)
(2)
-0.037
(0.028)
-0.188
(0.009)
-0.002
(0.002)
-0.152
(0.620)
-0.008
(0.730)
0.132
(0.000)
0.178
(0.062)
-1.428
(0.000)
0.105
(0.155)
0.011
(0.227)
-0.002
(0.538)
1.600
(0.000)
(3)
-0.054
(0.001)
0.125
(0.139)
0.025
(0.000)
-0.348
(0.001)
-0.107
(0.000)
(4)
-0.026
(0.122)
0.141
(0.106)
0.024
(0.000)
-0.150
(0.125)
-0.076
(0.003)
0.710
(0.000)
-0.367
(0.241)
-0.037
(0.666)
-0.006
(0.066)
-0.298
(0.088)
4.625
(0.000)
0.715
(0.000)
-0.860
(0.007)
0.098
(0.260)
-0.008
(0.007)
-0.194
(0.239)
4.275
(0.000)
Yes
Yes
No
1,098
0.2012
No
No
Yes
1,098
0.1814
Yes
Yes
No
1,098
0.1594
No
No
Yes
1,098
0.0905
38
Table 6: Probability of a competing bid
In this table we report logit regressions of bid competition probability. The dependent variable equals one if the target
receives an offer from more than one bidder, and zero otherwise. The regressions are specified following Officer
(2003). The key independent variable is the number of sell-side analysts covering the target firm. All variables are
defined in the Appendix. We report p-values in parentheses.
Target Analysts
Target Initiates
Premium
Cash
Acquirer Log MVE
Target Log MVE
Tender Offer
Hostile
Same SIC
Years from IPO
Institutional Ownership
Constant
Year FE
Industry FE
2-way Clustered SE
N
Pseudo- R2
39
(1)
0.655
(0.000)
-0.156
(0.711)
0.004
(0.392)
0.590
(0.242)
-0.738
(0.000)
-0.839
(0.001)
1.241
(0.007)
-0.795
(0.377)
0.543
(0.169)
0.004
(0.825)
1.547
(0.153)
0.345
(0.916)
(2)
0.530
(0.000)
-0.193
(0.508)
0.004
(0.243)
0.212
(0.620)
-0.662
(0.002)
-0.711
(0.002)
1.278
(0.017)
-0.003
(0.997)
0.321
(0.413)
0.011
(0.227)
1.429
(0.065)
2.061
(0.065)
Yes
Yes
No
1,098
0.4533
No
No
Yes
1,098
0.3733
Table 7: Target initiates deal
The dependent variable in the logit regressions reported in this table equals one if the target firm initiates its own sale.
Otherwise, the variable is set to zero. The key explanatory variable is the number of sell-side analysts covering the
target firm. All other control variables are similar to those used in an analogous test by Aktas, de Bodt, and Roll
(2010). We report p-values in parentheses.
Target Analysts
Target Log Mkt Value
Target Q
Target Sales Growth
Target ROA
Years from IPO
Institutional Ownership
Constant
Year FE
Industry FE
2-way Clustered SE
N
Pseudo- R2
40
(1)
-0.077
(0.000)
0.011
(0.682)
-0.025
(0.051)
-0.000
(0.642)
-0.484
(0.000)
0.000
(0.137)
-0.193
(0.669)
-2.038
(0.001)
(2)
-0.069
(0.000)
0.041
(0.102)
-0.021
(0.082)
-0.000
(0.570)
-0.423
(0.000)
-0.002
(0.358)
-0.085
(0.450)
-0.393
(0.000)
Yes
Yes
No
No
No
Yes
1,098
0.0673
1,098
0.0234
Table 8: Additional analyses
In Panel A, we re-estimate the premium and acquirer return regressions to control for the number of analysts covering
the acquirer firms during the six month before the merger announcement. In Panel B, we decompose the number of
analysts covering the target firm into predicted and surprise components and use these as the key explanatory variables
specified similar to those in Table 3. In Panel C we use a different fitted variable of target analysts’ coverage to rerun second stage regressions of target premiums and acquirer merger announcement returns, respectively. The first
stage OLS regression analyzes the same sample in Panel C of Table 4 except that we replace the brokerage closure
and brokerage acquired variables in that regression with a change in brokerage size variable and the dependent variable
is now the maximum number of analysts covering the firm during the calendar year. In Panel D we study the division
of gains of the target relative to the acquirer firm using the method proposed by Ahern (2012).
Panel A: Controlling for analyst coverage of acquirer firms
Year and industry fixed-effects
and all other control variables
Estimate
p-value
SDC Premium
Target Analysts
0.017
0.000
Acquirer Analysts
-0.005
0.000
2
0.1875
R
Schwert (1996) Premium
Target Analysts
0.005
0.000
Acquirer Analysts
0.006
0.000
2
0.2502
R
Acquirers’ Announcement CAR
Target Analysts
-0.003
0.000
Acquirer Analysts
0.000
0.774
0.1515
R2
Panel B: Predicted/surprise analyst coverage
Year and industry fixed-effects
and all other control variables
Estimate
p-value
SDC Premium
Predicted Target Coverage
0.010
0.006
Residual Target Coverage
0.006
0.002
2
0.1844
R
Schwert (1996) Premium
Predicted Target Coverage
0.004
0.000
Residual Target Coverage
0.002
0.000
2
0.2357
R
41
Clustered Standard Errors
and all other control variables
Estimate
p-value
0.013
-0.007
0.1465
0.000
0.000
0.005
0.005
0.2066
0.000
0.000
-0.002
0.000
0.0918
0.000
0.862
Clustered Standard Errors
and all other control variables
Estimate
p-value
0.017
0.004
0.1514
0.001
0.014
0.005
0.002
0.2088
0.000
0.000
Panel C: Alternative identification using size changes in brokerage houses
Year and industry fixed-effects
Clustered Standard Errors
and all other control variables
and all other control variables
Second stage regressions
Estimate
p-value
Estimate
p-value
SDC Premium
Fitted target Analysts
0.048
0.000
0.032
0.000
2
0.1379
0.1265
R
Schwert (1996) Premium
Fitted Target Analysts
0.025
0.000
0.017
0.000
2
0.0769
0.0588
R
Acquirers’
Announcement
CAR
Fitted Target Analysts
-0.006
0.000
-0.005
0.001
2
0.1095
0.0695
R
Panel D: Relative Gain (target vs. acquirer)
Year and industry fixed-effects
and all other control variables
OLS Regressions
Estimate
p-value
Relative Gains
Target Analysts
0.001
0.004
0.2351
R2
42
Clustered Standard Errors
and all other control variables
Estimate
p-value
0.001
0.1857
0.017
Appendix
Variable
Acquirer 3-day CAR
Acquirer Debt to Assets
Acquirer OpCF to Assets
Description
Acquirer CAR from days -1 to +1 around the deal announcement
Total long-term debt/total assets
Acquirer EBITDA/total assets
Cash
An indicator equal to 1 if the deal is a pure cash transaction, 0 otherwise
Cash Ratio
Cash Tender Offer
Cash and equivalents scaled by current liabilities
An indicator equal to 1 if the deal is a pure cash tender offer, 0 otherwise
If a brokerage firm that covers firm “i” is acquired during the calendar year, the
indicator is set to 1 in that calendar year for company “i.” Otherwise, the
indicator is set to 0.
If a brokerage firm that covers firm “i” closes during the calendar year, the
indicator is set to 1 in that calendar year for company “i.” Otherwise, the
indicator is set to 0.
Broker Acquired (0,1)
Broker Closed (0,1)
DPS (dividends per share)
EPS (earnings per share)
Cash dividends scaled by common shares outstanding
Net income scaled by common shares outstanding
Hostile
An indicator equal to 1 if the deal attitude is noted as hostile in SDC, 0 otherwise
Institutional Ownership
Cumulative percentage of institutional ownership in the quarter prior to the deal
announcement (sum of institutional ownership/shares outstanding)
Mean Recommendation
Average of all outstanding investment recommendations in the month prior to
the merger announcement (IBES summary recommendations file)
Relative Size
Target market value of equity/(Acquirer + Target market value of equity)
Same SIC
An indicator equal to 1 if the acquirer and target are in the same 3-digit SIC code
SDC Premium
Target cumulative abnormal return measured 42 days from before the deal
announcement through day -1
Target 4-week deal premium collected from SDC
Stock
An indicator equal to 1 if the deal is a pure stock transaction, 0 otherwise
Schwert 42-day Premium
Target (or Acquirer) Log MVE
Target Analysts
Market value of equity (shares outstanding times stock price) one month prior to
the deal announcement
Maximum number of analysts providing target research coverage in the six
months preceding the deal announcement.
Target Initiates
An indicator equal to 1 if the target initiates the transaction, 0 otherwise
Target ROA
Target Net Income/Total Assets
Target Sales Growth
Target one-year sales growth measured as (sales/salest-1) -1
Tender Offer
An indicator equal to 1 if the deal is a tender offer, 0 otherwise
Tobin's Q
Years from IPO
(Total assets + market value of equity - book value of equity + deferred
taxes)/total assets
(Deal Announcement Date – First CRSP Date)/365 for target firms
43