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Advertising, Attention, and Acquisition Returns∗
Eliezer M. Fich LeBow College of Business
Drexel University Philadelphia, PA 19104
(215) 895-2304 emf35@drexel.edu
Laura T. Starks McCombs School of Business
University of Texas Austin, TX 78712-1179
(512) 471-5899 lstarks@mail.utexas.edu
Anh L. Tran Cass Business School
City University London London, EC1Y 8TZ, UK
+44-207-040-5109 anh.tran@city.ac.uk
October 4, 2015
Abstract
We examine the hypothesis that advertising allows a takeover target’s management to increase the firm’s profile and their own negotiating power, leading to higher subsequent takeover premiums. Our evidence from 7,095 merger bids supports this hypothesis. Moreover, we find an economically significant decrease in the acquirer’s market capitalization during the announcement period. To consider the possibility of codetermination of target advertising and takeover premiums, we employ instrumental variable tests as well as propensity matching methods and our results hold. Further, we find targets that advertise are more likely to be pursued by multiple bidders and receive revised increased bids.
∗ We appreciate the helpful comments from seminar participants at the University of Texas, Mississippi State University and Kansas State University.
1
Firms, particularly those that are smaller and less well known, often struggle with gaining
recognition from investors. As shown theoretically by Merton (1987), this lack of recognition
can have stock-valuation consequences. Indeed, public companies with limited visibility, (i.e.,
less investor awareness), often have higher costs of capital and lower values. Empirical evidence
supporting this theory suggests that a firm that successfully increases its investor recognition
should achieve a related increase in firm value.1
This lack of recognition would be particularly problematic for firm management if they are
interested in selling the firm or concerned that they will be subject to a hostile takeover with
what they consider to be insufficient valuation. These managers have limited strategies for
gaining investor recognition. Typically they could advertise or try to use the media to draw
attention to their firms’ products, accomplishments and expected performance. In fact, recent
studies suggest that managers opportunistically use advertising to not only attract customers, but
to also attract investor recognition and influence their firms’ stock prices.2 However, researchers
have argued that attention-grabbing activities by firms, such as advertising or press releases,
result in short-term increases in stock prices, but the activity by itself may not generate a
sustained increase in equity valuations.
However, in the event of opportunistic advertising just before a desired corporate action,
such as an IPO, SEO or takeover bid, the effect of the advertising does not need to be long-lived.
1 See, for example, Kadlec and McConnell (1994), Foerster and Karolyi (1999), Gervais, Kaniel, and Mingelgrin (2003), Lehavy and Sloan (2011), and Kaniel, Ozoguz, and Starks (2012) among others. Other theoretical and empirical literature has also focused on the effects of investor attention on financial markets and firm value. See, for example, Hirshleifer and Teoh (2003), Barber and Odean (2008). 2 For example, studies have found that firms with a greater level of advertising exhibit significantly lower bid-ask spreads (Grullon, Kanatas, and Weston, 2004); firms that signal their higher valuations by increasing product-market advertising prior to the IPO have lower underpricing (Chemmanur and Yan, 2009), and firms’ short-term stock returns are susceptible to adjustments in advertising expenditures (Lou, 2014). These results are also consistent with Stein’s (1996) argument that in an inefficient market, short-horizon managers interested in maximizing their firm’s short-term stock price can exploit investors’ misperceptions by catering to time-varying investor sentiment.
2
It needs simply to be sufficient to affect the firm’s valuation by investors at a specific point in
time. We examine this hypothesis in the current paper. Specifically, we examine whether
advertising by eventual takeover targets during the year prior to receiving an acquisition bid
affects the gains to the shareholders in those firms (including managers) as well as the gains to
the bidders. We hypothesize that, everything else equal, managers in firms with an interest in (or
concern about) becoming a takeover target will increase their advertising in order to not only
increase customer awareness of their products, making them a more attractive takeover target,
but to also increase investor awareness, allowing the firm’s management and shareholders to
capture a larger share of the rents from such a takeover. Supportive of this hypothesis, evidence
exists that those on the other side of these transactions (the acquirers) have used advertising or
the media to affect acquisition gains. For example, Lou (2014) documents a sharp increase in
advertising spending before stock-financed merger deals that essentially “pumps up” the
acquirer’s stock price. In addition, Ahern and Sosyura (2013) find that, by originating more news
during private merger negotiations, acquirers generate a short-lived run-up in their stock prices
during the period when the stock exchange ratio is determined
To test our hypothesis we analyze a sample of 7,095 (completed and withdrawn) M&A bids
submitted for U.S. publicly traded targets and announced during the 1986-2011 period. Our
empirical analyses indicate that a management strategy of advertising prior to a takeover attempt
benefits their shareholders. We find that increasing advertising by a single standard deviation
(about $1.72 million) is associated with a one percentage point increase in the premium paid to
target shareholders. This higher premium represents an increase of $10.65 million in terms of
deal value for the average target in our sample.
3
Given our central hypothesis, there are further implications as well. If increased advertising
by the target is indeed important to investor awareness and the future merger negotiations, then it
follows that the target firm’s advertising should affect not only the target’s stock price and
returns, but also the returns accruing to their acquirers. That is, while the target advertising
increases the merger gains for the target, it should reduce the gains to the bidder. Consistent with
this argument, we find that a single standard deviation increase in target firm advertising is
related to a decline of 45 basis points in their acquirer’s merger announcement return. Such a
drop has significant economic consequences as it implies a decline of $45.36 million in terms of
market capitalization for the average bidder in our sample.
Moreover, if our contention is true that managers of less known firms can increase their
firm’s recognition in financial markets through their advertising, then it follows that the
advertising itself helps make the target more attractive. As a result, it should increase the
probability of interest by more than one bidder, which will also cause the initial merger bid to be
revised upwards. These conjectures are supported by our data. We find that the targets with
increased advertising are sought by more than one bidder and are more likely to have initial
takeover bids revised upwards.3
Using the method in Comment and Schwert (1995) we find that target advertising adds value
unconditionally by increasing the combination of the premium, conditional on a takeover, and
the probability with which such a deal occurs. Given this result, we also evaluate the net impact
of advertising on target shareholder wealth by considering its joint effect on premiums and the
probability of deal completion. We find that a one standard deviation increase in advertising
3 Louis and Sun (2010) find that investors sometimes exhibit inattention during merger announcements. Importantly, we are not arguing that investors are inattentive during merger transactions. We argue that advertising increases investor attention during acquisitions and that such increase has a material effect on the premiums offered to takeover targets.
4
spending is associated with a higher probability of deal completion: from 81% to 84%. This
result, combined with the increase in deal value documented in our premium tests, indicates that
on average, target shareholder net gains increase by 5% (or $43 million) with a one standard
deviation increase in advertising. Overall, our empirical evidence suggests that increased
advertising heightens the target firm’s stock market value as well as management’s position in
the merger negotiations, which translates to higher premiums paid for firms that become
takeover targets.
A natural concern with these results and our interpretation is the problem of endogeneity in
the relation between target firm advertising and subsequent takeover premiums. To consider this
possibility, we employ two alternative methodologies, an instrumental variables approach and a
propensity score matching procedure. With respect to the first approach, given that this method
requires an instrument that is correlated with a firm’s advertising expenditure but uncorrelated
with the residuals in the premium regression, a possible instrument would be the Average
Competitor Advertising Spending during the year prior to the acquisition bid.4 The suitability of
this instrument is based on the notion (which our first stage tests confirm) that the target firm,
independent of its own characteristics, will likely spend more for advertising in a given period
whenever its competitors advertise more intensely during the same period.
Consistent with our earlier analyses, the instrumental variable tests also document a positive
association between advertising spending and merger premiums. However, we cannot test the
exclusion restriction and it could be the case that advertising by competitors may correlate with
the residuals in the second stage premium regressions. For example, takeover premiums in some
industries with higher advertising could be higher. Consequently, we also perform a propensity
4 Gurun and Butler (2012) use a similar instrument.
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score matching analysis in which we match target firms that advertise with control firms that do
not. This approach circumvents the problem that firms’ advertising choices are a function of their
characteristics. Therefore, except for the choice to advertise, both groups exhibit similar
attributes on a variety of dimensions. Results from our propensity score matching analysis
suggest that increased advertising by target firms causes an increase in the takeover premiums
these companies receive. We also conduct a number of robustness tests and find consistent
results.
Our paper contributes to several different strands of the literature. First, our findings
documenting that advertising by takeover target companies affects the wealth of both target and
acquirer shareholders contribute new evidence to the vast literature on mergers and acquisitions,
particularly papers examining the role of investor attention in the process [e.g.: Ahern and
Sosyura (2013)].5 Second, our results add to a growing body of work linking product market
advertising and firm value [Grullon, Kanatas, and Weston (2004), Fehle, Tsyplakov and
Zdorovtsov (2005), Chemmanur and Yan (2009), Fee, Hadlock, and Pierce (2009), Gurun and
Butler (2012), and Lou (2014)] and the results add to the extensive literature on the economics of
advertising [Telser (1964), Nelson (1974), Bagwell and Ramey (1994), Grossman and Shapiro
(1984), Kihlstrom and Riordan (1984), Milgrom and Roberts (1986), and Becker and Murphy
(1993)]. Finally, our study advances the literature on investor attention [Gervais, Kaniel, and
Mingelgrin (2001), Seasholes and Wu (2007), Hou, Peng, and Xiong (2009), Barber and Odean
(2008), Yuan (2008), and Da, Engelberg, and Gao (2011)] and on investor recognition [Kadlec
and McConnell (1994), Forester and Karolyi (1999), Gervais, Kaniel, and Mingelgrin (2003), 5 Ahern and Sosyura (2013) conclude that the division of gains during completed mergers that are financed with the bidder’s stock is positively related to news origination. We have a different sample (both completed and withdrawn deals and both cash and stock-financed transactions). More importantly, we have a different focus in terms of which party to the merger takes actions in order to draw attention and also on the type of actions taken. That is, whereas Ahern and Sosyura examine press releases by the acquirers we study advertising expenditures by the targets.
6
Chen, Noronha and Singal (2004), Hou and Moskowitz (2005), Bodnaruk and Ostberg (2009),
Lehavy and Sloan (2011), Kim and Meschke (2011), and Kaniel, Ozoguz, and Starks (2012)].
The rest of our paper is organized as follows. Section I describes our data. Section II contains
our main empirical tests and Section III describes a number of additional analyses. Section IV
provides our conclusions.
I. Data and Variable Definitions
This section details the sample of M&A bids we analyze as well as the proxies we use to
track the product market advertising expenditure by the target firms we study.
A. Sample Overview
We begin with all M&A offers of at least $1 million in value submitted for publicly traded
U.S. companies from 1986-2011 reported in the Securities Data Company (SDC) database. We
retain transactions involving target companies for which stock market and accounting data are
available from the Center for Research in Security Prices (CRSP) and Compustat, respectively.
Because we want major bids without tertiary issues, we implement a sample selection procedure
similar to that used by Bargeron, Schlingemann, Stulz, and Zutter (2008). Specifically, we
exclude observations involving spinoffs, recapitalizations, exchange offers, repurchases, self-
tenders, privatizations, acquisitions of remaining interest, and partial interests or assets. This
process yields 8,616 transactions announced during our sample period. From this set, we drop
1,521 bids because we cannot obtain acquisition premium data from SDC, SEC filings or trade
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publications (such as Mergers & Acquisitions or Investment Dealers’ Digest). These criteria
yield our final sample of 7,095 deals.
Panel A of Table 1 reports the temporal distribution of the targets in our sample. We note that
the annual frequency of the transactions we study is consistent with the conjecture in Shleifer
and Vishny (2003) that stock market health drives merger activity. For example, we find that in
periods of economic expansion and higher stock market valuations such as 1998-2001, the
number of transactions is greater. In contrast, during periods of economic contraction, such as
the beginning of our sample or the 2008-2009 period, the number of bids declines.
Panel A of Table 1 also reports the industrial distribution of our sample targets based on the
Fama and French (1997) classification. The distribution across industries is wide spread with
some concentration in the business services sector (which includes software) at 13.5% and the
banking sector at 14.3%.
In Panel B of Table 1 we provide summary statistics for particular key characteristics related
to the sample deals. (We provide more detailed definitions of these characteristics and other
variables in the Appendix.) In comparing the key characteristics provided in the table to other
studies on mergers and acquisitions we find similar magnitudes. For example, transactions in our
sample are completed 81% of the time and tender offers account for 24% of the sample. Both the
target and the bidder operate in the same industry in 53% of the transactions. These statistics are
comparable to those in Officer (2003). He reports a completion rate of 83%, a tender offer
proportion of 20%, and a same industry incidence of 52% in his merger sample during 1988-
2000. Similarly, 50% of our bids being all cash transactions is close to the 46% in the Masulis,
Wang and Xie (2007) study of mergers during 1990-2003. At 44.67%, the average relative size
ratio (target/acquirer) in our sample is comparable to that of 44.2% reported by Hartzell, Ofek
8
and Yermack (2004) in their sample of deals occurring between 1995 and 1997. We find that
over 89% of transactions in our sample consist of friendly acquisitions, which is close to the 93%
in Moeller’s (2005) study of mergers during 1990-1999. Finally, our sample targets exhibit an
average market value of equity of $601 million, Tobin’s Q of 1.64, and leverage of almost 23%.
For the same characteristics, Bates and Lemmon (2003) report a mean market value of equity of
$592 million, Q of 1.63 and leverage of 23% for the targets they study. Overall, in a number of
important dimensions, our sample resembles those used in previous studies in the M&A
literature.
B. Product Market Advertising
In Panel C of Table 1 we report the summary statistics of our sample target firm’s advertising
spending. We report statistics for four different advertising proxies, each of which is based on
the raw dollars of advertising spending (in million US$). Annual data on advertising
expenditures (Compustat data item 45) are measured at the fiscal year end before the merger
announcement date. The advertising proxies are: (1) ln(Advertising spending), defined as the
natural logarithm of (1 + advertising spending), (2) Scaled advertising spending, calculated as
advertising spending scaled by total assets, (3) Advertising intensity, computed as advertising
spending divided by the firm’s total sales and (4) Advertising growth, estimated as the
percentage change in advertising during the two fiscal years immediately preceding the initial
bid.6
According to the information reported in Panel C of Table 1, the mean advertising intensity
of targets in our sample is 0.94, which is close to the ratio for the Gurun and Butler (2012) 6 We note that (3) and (4) are set to zero for firms that do not spend on advertising.
9
sample of Compustat firms during 2002-2006. These measurements are calculated across firms
regardless of whether they advertise. We also report the results when the sample is restricted to
the 2,377 target firms (about 34% of our sample) that have positive advertising spending. The
average intensity for the subset of firms that advertise is close to 3%.7
II. Empirical Analyses
Provided that firms can call attention to themselves by heightening the advertising of their
products, it is possible that the increased attention could translate into higher premiums and more
bids if these companies become takeover targets. In this section, we perform several empirical
tests in order to shed light on these issues.
A. Probability of Becoming a Takeover Target
Before examining whether advertising by takeover targets affects the merger offers these
firms receive, we note that firms are unlikely to receive an acquisition bid randomly. Indeed,
existing studies [e.g., Comment and Schwert (1995) and Palepu (1986)] show that the likelihood
of receiving a merger offer has its own determinants. Therefore, in a sample of 140,839 firm-
year observations (with complete data in CRSP and Compustat) over the 1985-2011 period, we
estimate four probit regressions of the probability of becoming a target. Our explanatory
variables are similar to the ones used in those papers. Specifically, in the four regressions
reported in Panel A of Table 2, the dependent variable is equal to one if the firm becomes a
takeover target and equal to zero otherwise. Unlike previous work, however, our determinants of
7 Comparably, about 35% of the firms analyzed by Lou (2014) spend on advertising during 1974-2010 and have a mean advertising intensity of 4%.
10
the probability of becoming a target also include the firm’s advertising expenditures, which have
the potential of affecting investor attention. Specifically, the main independent variable in these
tests are the four proxies for advertising: the natural logarithm of advertising spending (in model
(1)), the scaled advertising (in model (2)), the advertising intensity (in model (3)) and the
advertising growth (in model (4)).
Parameter estimates in Panel A of Table 2 indicate that all of our advertising proxies attain
positive and significant coefficients.8 The marginal effect we estimate in model (1) indicates that
a single standard deviation increase in advertising spending augments the likelihood of becoming
a target by 0.8 percentage points.9 To put this result into context, the unconditional probability of
becoming a target in the sample analyzed in Panel A of Table 2 is 4.4%.
B. Deal Initiation
The estimates in Panel A of Table 2 indicate that firms that advertise are more likely to
become acquisition targets. One question about this result is whether targets who are engaging in
increased advertising to garner attention for a potential takeover bid would also capitalize on this
attention by initiating their own sale through a takeover. Thus, from our original sample of 7,095
merger and acquisition bids announced during 1986-2011, we determine those bid contests in
which one or several bidders bid for a single target and where we can find the deal background
from the merger proxies filed by either the target or the acquirer with the SEC (S-4, DEFM 14,
8 With respect to the other control variables, we note that firm size is the only variable that attains a statistically significant coefficient. This finding conforms to the arguments in Schwert (2000, p.2620). He reviews several papers that estimate takeover probability regressions and concludes that the only consistent predictor in the literature is size. 9 The marginal effects are computed by first calculating the probability of becoming a target using the sample means for all continuous independent variables and zeroes for all (0,1) indicator explanatory variables (the base predicted probability). The probability of becoming a target is then re-calculated by changing each independent variable (in turn) by adding one standard deviation to the mean of continuous variables (or using a one for each indicator variable). We use the same procedure to compute marginal effects for all binary response models in this paper.
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SC 14D9, SC TO, DEF 14, 8-K). We use information from the first bid in the contest to identify
the party (target or acquirer) who initiates the M&A transaction. In Panel B of Table 2, we run
logit regressions of deal initiation probability similar to those in Aktas, de Bodt and Roll (2010).
The dependent variable equals one if the deal is first initiated by the target. The main
independent variables are again the four advertising proxies. We note that 39.38% of transactions
in our sample are initiated by the target which is comparable to the 42% reported in Aktas, de
Bodt, and Roll (2010). The results in Panel B indicate that targets who increase their advertising
expenditures are more likely to also initiate their own takeover. A one standard deviation
increase in advertising spending (in Model (1)) increases the likelihood of a target initiating a
deal by 3.78%.
C. Takeover Premiums
The results in Panels A and B of Table 2 are consistent in showing that increasing advertising
spending raises the probability of becoming a takeover target. In our setting, an implication of
the Merton’s (1987) theory is that increased advertising by target firms should increase investor
recognition, which should benefit their shareholders. However, Aktas et al. (2008) find that
targets that initiate their own sale get lower premiums. They argue that this occurs because, by
initiating the transaction, these firms give up considerable bargaining power. Consequently, we
next examine the merger premiums offered for our sample targets to consider these issues.
In Panel C of Table 2, we report four premium regressions in which the four-week final
premium reported by SDC is the dependent variable.10 Our target premium tests closely follow
10 In order to mitigate problems with outliers, we limit the premium to values between 0 and 2 (or 200%) as does Officer (2003).
12
those in Bargeron, Schlingemann, Stulz, and Zutter (2008). We expand their basic specification
by using our four advertising proxies as the respective key independent variables in each of the
four premium regressions. These tests also include year- and industry-fixed effects. In addition,
because firms do not randomly become acquisition targets, we use the Heckman (1979)
methodology to address issues related to self-selection. Therefore, we estimate an inverse Mill’s
ratio from each of the four models in Panel A of Table 2 and respectively use them as additional
controls in the regressions reported in Panel C.
Other studies estimate premium regressions similar to ours and we note that several control
variables in Panel C generate estimates that are in agreement with those in prior work. As in
Gaspar, Massa and Matos (2005), we find premiums to be higher when there are competing bids
or when the transaction is classified as a tender offer. We also document acquisition premiums to
be increasing in the targets’ leverage (Cai and Sevilir, 2012). Premiums are also higher when the
deal includes a target termination fee (Officer, 2003), when the bid is hostile (Bargeron,
Schlingemann, Stulz, and Zutter, 2008) and when the transaction is structured as a cash-only deal
(Aktas, de Bodt and Roll, 2010). Conversely, takeover premiums are inversely related to the size
of the target firm (Bargeron, Schlingemann, Stulz, and Zutter, 2008) and also drop in deals
characterized as a merger of equals (Wulf, 2004, and Wang and Xie, 2009).
More importantly, the coefficients for our advertising variables are statistically significant in
all of the premium regressions reported in Panel C of Table 2. These tests document an
economically important positive association between each of our advertising proxies and the
takeover premiums. According to the estimates in model (1), increasing advertising spending by
one standard deviation translates into a premium increase of 1 percentage point. For the average
transaction in our sample, this increase implies an additional $10.65 million in terms of deal
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value for the target shareholders. Put differently, for the average deal in our sample, an extra
dollar of advertising is related to an increase in deal value of $6.19.
C.1. Endogeneity of Target Premiums and Advertising: Instrumental Variables Approach
One potential concern about our results is the possibility of simultaneity bias, that is, that our
variables of interest (takeover premiums and advertising) are jointly determined. Additionally,
the possibility exists that our premium tests are susceptible to an omitted variables bias. This
could happen if the documented association between the premiums and the advertising
explanatory variables partly reflect omitted factors related to both variables. To address these
issues, we estimate separate two-stage least squares (2SLS) systems in each of which we
instrument for one of the target advertising proxies. To properly specify the systems, we need
instruments correlated with a target’s advertising spending in the first stage regressions (the
relevance condition) but not with the residuals in the second stage premium regressions (the
exclusion restriction).
Gurun and Butler (2012) conjecture that if a firm’s industry peers advertise more
aggressively during a given period, then the firm, regardless of its own characteristics, will be
inclined to do the same during that period. Following a similar logic, our instruments are based
on the Average Competitor Advertising Spending during the year prior to the takeover offer. That
is, we estimate four separate first stage regressions to respectively instrument for the Average
Competitor Advertising Dollar Spending, for the Average Competitor Scaled Advertising, for the
Average Competitor Advertising Intensity and for the Average Competitor Advertising Growth.
For each sample target, we compute these variables for all of its competitors during the year
before the target receives a public takeover bid. We define competitors as companies (with data
14
available in CRSP and Compustat) operating in the same Fama and French (1997) industry as a
target firm.
Table 3 reports our 2SLS tests. We note that our first stage regressions, reported as models
(1), (3), (5), and (7) indicate that the relevance condition appears to be satisfied.11 In each of
these tests, the average advertising by competitors variables are positively associated with the
dependent variables measuring advertising spending by the target firms. The second stage
regressions, reported as models (2), (4), (6) and (8) show that the fitted values from their
respective first stage tests exhibit positive coefficients. The results are economically meaningful.
For instance, according to the fitted parameter estimate for advertising spending in model (2),
increasing advertising by one standard deviation leads to a premium increase of 1.09%. Overall,
the results from our instrumental variables tests suggest that increased advertising by targets
during the year before a takeover causes an increase in the takeover premiums these firms obtain.
C.2. Endogeneity of Target Premiums and Advertising: Propensity Score Matching Approach
Our 2SLS analyses document a positive association between advertising spending and
premiums. However, because the exclusion restriction cannot be tested we cannot rule out that
advertising by competitors may correlate with the residuals in the second stage premium
regressions. To alleviate this issue, in Table 4 we use a propensity score matching procedure to
estimate an average treatment effect (ATE) of target advertising on acquisition premiums. An
attractive feature of the propensity score matching technique is that it enables us to make causal
11 Our estimations are efficient since the first-stage R2 values in Table 3 are large [22.71% in model (1), 17.75% in model (3), and 11.97% in model (5)]. Furthermore, in these regressions the F-statistics for Total Advertising Spending, for Scaled Advertising Spending, and for Advertising Intensity are 25.76, 18.92, and 11.93, respectively. Therefore, the F-statistics on the instruments are above critical values according to a Stock and Yogo (2005) weak identification test.
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inferences from the analysis because it sidesteps the fact that firms’ advertising preferences are a
function of their own characteristics.12
The first column of Panel A in Table 4 reports a logit model of the probability of being in the
treatment group (i.e., of advertising) as a function of observable characteristics. From this model,
we use the estimated ex ante probability of advertising to form matched samples of treatment and
control target firms where both groups display a similar estimated ex ante probability of being in
the treatment group but different ex post realizations of the treatment. In other words, our
method estimates the counterfactual outcomes of target firms by using the outcomes from a
subsample of matched target firms from the other group (treatment or control). Following Abadie
and Imbens (2008), we obtain confidence intervals using a matching estimator that uses a
Gaussian kernel with 500 bootstrap repetitions. Since we are matching jointly on multiple
variables, treatment and control samples may not have the same size or similar characteristics in
all matched dimensions. Nevertheless, our results do not change if (a) we employ different
subsets of these matching characteristics, or (b) we use neighborhood matching instead of
Gaussian kernel.
The last three columns in Panel A compare the treatment and the control group and document
no significant differences in the mean values related to several characteristics that determine
advertising spending. The ATE reported in Panel B of Table 4 shows that in deals in which the
target firm spends on advertising target shareholders are offered a takeover premium that is about
3.3 percentage points higher. As with the findings in Tables 2 and 3, those from our propensity
12 Rosenbaum and Rubin (1983) define treatment assignment to be strongly ignorable if two conditions are met. The first (also known as unconfoundness) states that treatment assignment is independent of the potential outcomes conditional on the observed baseline covariates. The second condition (also known as overlap) requires every subject to have a nonzero probability to receive either treatment. Rosenbaum and Rubin (1983) show that if treatment assignment is strongly ignorable, then conditioning on the propensity score leads to unbiased estimates of the ATE.
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matching procedure suggest that advertising by target firms causes an increase in the takeover
premiums these companies obtain.
D. Unconditional Premiums
In Table 5 we report four unconditional premium regressions, which we estimate in a sample
of 140,839 firm-years with data available from CRSP and Compustat during 1985-2011. Using
the method in Comment and Schwert (1995), in all tests the dependent variable is equal to zero
in nontakeover firm-years. Otherwise, this variable is equal to the actual takeover premium as
recorded in SDC if there is a takeover associated with the firm-year. The key explanatory
variables in the four regressions in Table 5 are our four advertising spending proxies,
respectively.
The estimates related to all of our key independent variables in Table 5 indicate that
unconditional premiums increase in advertising. According to the coefficient in model (1), a one
standard deviation increase in advertising is related to an unconditional premium increase of 6
basis points. Since the unconditional takeover premium combines the effects of a conditional
takeover premium and the likelihood with which a takeover bid occurs, this result suggests that
advertising by the target firm adds value unconditionally by increasing some combination of the
premium conditional on a takeover (as in Panel B of Table 2) and the probability with which
such a deal occurs (as in Panel A of Table 2). Moreover, the beneficial effect of advertising
during takeovers (documented in Tables 2, 3, and 4) is probably understated since, as the tests in
Table 5 suggest, advertising increases the unconditional value of the firm.
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E. Acquirer Returns
So far, our results show that during M&A deals premiums increase in the target firm’s pre-
takeover advertising spending. This finding appears consistent with the idea that increased
advertising raises the attention levied upon the target firm. Thus, while target shareholders
benefit from their firm’s advertising it is unclear whether (and how) advertising by the target will
affect the shareholders of the acquirer. To illuminate this issue, in Table 6, we estimate
regressions explaining the three-day merger announcement cumulative abnormal return (CAR)
for the 3,036 publicly traded 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 the merger announcement.
The four acquirer return regressions reported in Table 6 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 augment the specification in those studies by including our four target advertising
spending proxies as the respective key independent variables. The results indicate that acquirer
returns decrease in the targets’ advertising spending. According to the parameter estimates in
model (1), a one standard deviation increase in total advertising spending by the target is
associated with a 45 basis points decrease in the return to the acquirer. This drop implies a value
decline of over $45.36 million for shareholders in the average bidder in our sample. In other
words, a single dollar increase in total advertising by the target is related to a drop in market
capitalization of nearly $26.37 for the average acquirer in our sample.
We observe that the control variables in Table 6 yield results similar to those by other
authors. For instance, as in Moeller (2004) the coefficient for the bidder’s leverage is positive
and the estimate for the targets’s industry liquidity index is negative. Similar to Wang and Xie
18
(2009) our tests also indicate that all-cash transactions are associated with higher bidder CARs.
Like Officer (2003) and Bates and Lemmon (2003) we find that tender offers are greeted with
more enthusiastic market reactions.
F. Deal Completion
It is possible that managers invest more in advertising to increase the odds of being acquired
and receiving a larger premium. Yet, even if managers are not interested in selling their
companies, receiving a takeover bid could help increase investors’ attention toward their firms
and, in turn, increase the value of their firms. Indeed, Malmendier, Opp, and Saidi (2012) find
that firms experience a permanent revaluation of up to 15% (based on their pre-bid market value)
when they receive a cash merger offer that is subsequently withdrawn.
Moreover, given that the unconditional takeover premium combines the effects of a
conditional takeover premium and the probability of selling the firm, we need to study the effect
of advertising on the probability of deal completion to estimate the net effect of advertising on
the wealth of target shareholders.
In Table 7 we report the estimation of four logit models in which the dependent variable
equals one if the target is sold and zero if it is not. The results for the control variables in Table 7
are consistent with those in the existing M&A literature. For example, transactions are more
likely to materialize if there is a target termination fee (Officer, 2003). As in Bates and Lemmon
(2003), deals are less likely to be completed if there is prior bidding. In addition, deals classified
as hostile are less likely to be completed (Schwert, 2000).
19
As for our key advertising explanatory variables in Table 7, we note that all of their
parameter estimates are positive and statistically significant. The marginal effect related to the
coefficients in model (1) imply that increasing advertising spending by a one standard deviation
raises the probability of merger completion by 3.25 percentage points. This effect is
economically important since the unconditional probability of deal completion in our sample is
81.09%.
This result of increased probability of merger completion, combined with the deal value
increase of $10.65 million associated with a one standard deviation increase in advertising
spending (Table 2) indicates that the wealth of shareholders increases from $860 million
(81.09% X $1.06 Bn) to $903 million (84.34% X $1.07 Bn). Therefore, the average effect of
raising advertising by one standard deviation is a net gain to target shareholders of $43 million or
about 5%.
III. Additional Analyses
In this section we perform further tests in order (i) to explore whether target advertising
affects other facets of the acquisition process and (ii) to probe the robustness of the preceding
findings.
A. Bid Competition
A further implication of increased advertising resulting in increased investor attention
towards target firms is that the increased attention could attract additional bidders. Thus, we
examine the hypothesis that target companies with increased advertising are more likely to be
20
pursued by multiple bidders. In Table 8 we estimate logit regressions in which the dependent
variable is set to one for targets that receive a public takeover offer from more than one bidder,
and set to zero otherwise. In these tests we examine a subsample of 6,502 bid contests.13 Our
four target advertising proxies are the respective key explanatory variables in the four models
reported in Table 8. Aside from these, all other controls are similar to those in Officer (2003).
The parameter estimates in Table 8 suggest that advertising by the target is associated with
competing bids in takeovers: the coefficient estimates for our advertising proxies are
significantly positive in all models. According to model (1), the marginal economic impact
related to a one standard deviation increase in advertising spending implies a 7.13 percentage
point increase in the probability that more than one bidder submits a public offer for the target.
This is quite a considerable effect when benchmarked against the 7.4% incidence of bid
competition for the transactions in our sample.
The results in Table 8 suggest that investor attention (generated by product market
advertising) triggers additional interest in acquiring the targets, promoting competition to buy
these firms. The increased competition prompted by the increased attention could explain the
higher takeover premiums paid to firms with more advertising and the lower merger
announcement returns earned by their acquirers.
B. Offer Revisions
Given that advertising by target firms generates interest by multiple bidders to buy these
firms, we now study whether the bidders are more likely revise their bids upwards in order to 13 As in Eckbo (2010), the contest may be single-bid (first offer is accepted or rejected with no further observed bids) or multiple-bid (several bids and/or bid revisions are observed). The initial bidder may win, a rival bidder may win, or all bids may be rejected (no bidder wins).
21
acquire the targets. We define a bid revision as the percent difference between the initial and
final bid premium offered for the target firm as recorded by SDC.14 We note that 829 (or
11.68%) of the bids in our sample experience a bid revision. This frequency compares favorably
to that of 10.32% in Bates, Lemmon and Linck (2006).
In Table 9 we estimate four bid revision logit regressions. In these tests, the dependent
variable is set to one if the bid is revised upward and set to zero otherwise. The variables used to
control for target advertising are similar to those we use in the deal completion tests.
Our bid revision regressions indicate that all of our advertising proxies are associated with
increases in the bid premium offered to the target firms. According to the marginal effect we
estimate in model (1), a one standard deviation increase in advertising spending raises the
probability of an upward bid premium revision by 3.62 percentage points. Together, with the
findings from our bid competition tests, those in Table 9 suggest that increased investor attention
resulting from increased advertising by target companies, all but prompts a bidding war to
acquire these firms.
C. Alternative Metrics of Premiums and Acquirer Returns
The regressions presented in Panel C of Table 2 use the four-week premium reported by SDC
as the dependent variable. We re-estimate the same regressions using two different premium
measures as dependent variables. The first is the target’s CAR during the window (-20, +1)
relative to the announcement date as in Jarrell and Poulsen (1989). Our second measure follows
Schwert (1996) and uses the target’s CAR during the window (-42, +126). To conserve space, in
14 We cannot observe any bid revisions that are privately negotiated before the initial bid is publicly announced.
22
Panel A of Table 10, we report the regression results related to our four advertising proxies when
these premium alternatives are used. As with our earlier tests, the estimates for all four target
advertising spending variables are positive and significant.
We also estimate alternative bidder return regressions similar to those reported in Table 6. In
these tests we follow the procedure in Masulis Wang, and Xie (2007) and replace the acquirer’s
return (-1, +1) with the CAR accruing to the bidder on deal announcement during the (-2, +2)
and (-5, +5) windows. In Panel B of Table 10, we note that the coefficients for our target
advertising spending variables are still negatively related to the acquirer’s return as measured
during these alternative windows.
We retain the residuals from the four advertising tests in the first-stage regressions (models
(1), (3), (5) and (7)) of Table 3. These residuals (which measure the abnormal level of our
advertising spending proxies) serve as the respective key independent variables in four premium
regressions, which are specified similar to those in Panel C of Table 2. We report the estimates
for the abnormal level of advertising in Panel C of Table 10. These coefficients capture the effect
of advertising that is purged from the effect of the performance or size of the target firm. We find
that the abnormal advertising spending estimates are positive and significantly associated with
the bid premium. These findings (together with those from the endogeneity tests in Tables 3 and
4) lessen the concern that our results are due to the fact that better performing or larger target
firms are better able to advertise.
We also conduct three falsification tests. In the first test we build our advertising proxies with
advertising expense data from three years before the deal (rather than from the fiscal year before
the M&A deal as in our earlier tests). This test allows us to determine whether current
advertising matters more than past advertising. In the second test we use R&D expense rather
23
than the advertising proxies, which could result if we are picking up growth with the advertising
variable. In the third test we use excess cash instead of advertising spending proxies under the
premise that the bidder is trying to buy the target’s excess cash balance. The results of these tests
are shown in Panels D, E and F of Table 10. In all three of the falsification tests we find no
significant relationships between these alternative variables and either the takeover premium or
the abnormal return to the bidder at the announcement of the acquisition.
D. Target Firms with Consumer Products
It is possible that target firms with products or services sold to consumers (instead of to other
businesses) may extract more benefits from advertising. If this pattern is pervasive in our data, it
is possible that target’s with business to consumer (B2C) products could be driving our results.
To explore this possibility, in Panel G of Table 10 we estimate acquisition premium regressions
for subsamples of targets that belong to B2C industries and of those in other industries. Targets
are classified as B2C if they operate in consumer-oriented industries which we identify following
the taxonomy in Sharpe (1982). The key independent variables in the premium regressions are
our four proxies to measure target advertising.
For both B2C and non-B2C targets, the results in Panel G indicate a positive and significant
association between our advertising proxies and premiums. This evidence mitigates the concern
that B2C targets drive our results. Still, we note that for three of our proxies, differences in
parameter estimates show that advertising is related to higher premiums for B2C targets. Based
on the advertising spending tests, a one standard deviation increase in advertising is related to a
premium increase of 1.91% for B2C targets and only 65 basis points for non-B2C targets.
24
E. Managerial Incentives
We argue that managers can draw attention to their firms’ products, accomplishments and
expected performance through advertising. Moreover, academic work by Grullon, Kanatas, and
Weston, 2004, Chemmanur and Yan, 2009, and Lou, 2014 (among others) suggest that managers
deliberately use advertising to attract investor recognition and influence their firms’ stock prices.
Two questions that follow from the above evidence are (1) whether managers with stronger
incentives are more likely to advertise and (2) whether advertising is associated with larger
valuation effects when stronger incentives are present. To address these issues, we examine a
subsample of 2,777 M&A transactions with available target CEO ownership data from either the
Execucomp database or the Thomson Financial Insider database.
First, in an untabulated logit regression similar to that in the first column of Panel A of
Table 4, we find that raising ownership by a single standard deviation (16.80%) is associated
with a 13.84 percentage point increase in the probability of advertising. This result is
economically important given that the unconditional probability of advertising in the subsample
is about 33% (close to that of 33.50% in the full sample of 7,095 deals). We also re-estimate our
four premium regressions interacting target CEO ownership with each of the four respective
advertising proxies. The results, reported in Panel H of Table 10, show that the positive
association between target advertising and the premiums paid to these firms increases in target
CEO ownership. According to the first regression in Panel H, increasing advertising spending by
one standard deviation is associated with a premium increase of 1%. However, a similar increase
in advertising spending produces a premium increase of 2.11% when accompanied by a standard
deviation increase in target CEO ownership.
25
IV. Conclusions
We hypothesize that managers interested in (or concerned about) being taken over will
employ advertising in order to not only raise customer and investor awareness, but to also
increase their negotiating position in the case of a merger. We test this hypothesis through
examining a sample of 7,095 (completed and withdrawn) M&A bids for U.S. publicly traded
targets announced during 1986-2011.
Consistent with our hypotheses, we find that the relation between a firm’s increased
advertising and its takeover premium is strongly and significantly positive. Specifically, we find
that a $1 increase in a target firm’s advertising expenditure is associated with a $6.19 deal value
increase, on average. Moreover, this premium increase tends to be paid for out of the acquirer’s
share of takeover gains as the $1 increase in the target firm’s advertising is also associated with a
$26.40 decrease in the acquirer’s market capitalization during the announcement period. Our
other empirical results are also consistent with our hypothesis in that targets that advertise are
more likely to be pursued by multiple bidders and these bidders are more likely to revise their
bids upwards. Thus, our evidence suggests that increased advertising heightens not only
customer attention, but also investor attention and manager negotiation positions, which
translates to higher premiums paid for firms that become takeover targets.
Our empirical evidence suggests that managers have considerable ability to materially affect
their firm’s profile in the eyes of investors through their advertising. Moreover, our evidence is
consistent with managers having ownership incentives to engage in such attention-gathering
activity – we find that the relation between advertising and firm value is heightened in firms with
greater managerial ownership.
26
References
Abadie, A., and Imbens, G. W., 2008. On the failure of the bootstrap for matching estimators.
Econometrica 76, 1537-1557.
Ahern, K. R., and Harford, J., 2013. The importance of industry links in merger waves. Journal of Finance 69, forthcoming.
Ahern, K. R., and Sosyura, D., 2013. Who writes the news? Corporate press releases during merger negotiations. Journal of Finance 69, 241-291.
Aktas, N., de Bodt, E., and Roll, R., 2010. Negotiations under the threat of an auction. Journal of Financial Economics 98, 241-255.
Bagwell, K., and Ramey, G., 1994. Coordination economies, advertising and search behavior in retail markets. American Economic Review 84, 498-517.
Barber, B. M., and Odean, T., 2008. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies 21, 785-818.
Bargeron, L., Schlingemann, F., Stulz, R., and Zutter, C., 2008. Why do private acquirers pay so little compared to public acquirers? Journal of Financial Economics 89, 375-390.
Bates, T., and Lemmon, M., 2003. Breaking up is hard to do? An analysis of termination fee provisions and merger outcomes. Journal of Financial Economics 69, 469-504.
Bates, T., Lemmon, M., and Linck, J., 2006. Shareholder wealth effects and bid negotiation in freeze-out deals: Are minority shareholders left out in the cold? Journal of Financial Economics 81, 681-708.
Becker, G. S., and Murphy, K. M., 1993. A simple theory of advertising as a good or bad. Quarterly Journal of Economics 108, 941-964.
Bodnaruk, A., and Ostberg, P., 2009. Does investor recognition predict returns? Journal of Financial Economics 91, 208–226.
Cai, Y., and Sevilir, M., 2012. Board connections and M&A transactions. Journal of Financial Economics 103, 327-349.
Chen, H., Noronha, G., and Singal, V., 2004. The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Journal of Finance 59, 1901–1930.
Chemmanur, T., and Yan, A., 2009. Product market advertising and new equity issues. Journal of Financial Economics 92, 40-65.
Comment, R., and Schwert G.W., 1995. Poison or placebo? Evidence on the deterrence and wealth effects of modern antitakeover measures. Journal of Financial Economics 39, 3-43.
27
Da, Z., Engelberg, J., and Gao, P., 2011. In search of attention. Journal of Finance 66, 1461-1499.
Eckbo, B. E., (Ed.). 2010. Corporate Takeovers: Modern Empirical Developments. Academic Press.
Fama, E., and French, K., 1997. Industry costs of equity. Journal of Financial Economics 43, 153-193.
Fee, C., Hadlock, C., and Pierce, J., 2009. Investment, financing constraints, and internal capital markets: Evidence from the advertising expenditures of multinational firms. Review of Financial Studies 22, 2361-2392.
Fehle, F., Tsyplakov, S., Zdorovtsov, V., 2005. Can companies influence investor behavior through advertising? Super bowl commercials and stock returns. European Financial Management 11, 625–647.
Foerster, S., Karolyi, G. A., 1999. The effect of market segmentation and investor recognition on asset prices: Evidence from foreign stock listing in the U.S. Journal of Finance 54, 981–1013.
Gaspar, J., Massa, M., and Matos, P., 2005. Shareholder investment horizons and the market for corporate control. Journal of Financial Economics 76, 135-165.
Gervais, S., Kaniel, R., and Mingelgrin, D. H., 2001. The high-‐volume return premium. Journal of Finance 56, 877-919.
Grossman, G. M., and Shapiro, C., 1984. Informative advertising with differentiated products. Review of Economic Studies 51, 63-81.
Grullon, G., Kanatas, G., and Weston, J., 2004. Advertising, breadth of ownership, and liquidity. Review of Financial Studies 17, 439-461.
Gurun, U. G., and Butler, A. W., 2012. Don't believe the hype: local media slant, local advertising, and firm value. Journal of Finance 67, 561-598.
Hartzell, J., Ofek, E., and Yermack, D., 2004. What’s in it for me? CEOs whose firms are acquired. Review of Financial Studies 17, 37-61.
Heckman, J., 1979. Sample selection bias as a specification error. Econometrica 47, 153-161.
Hirshleifer, D. and S. Teoh, 2003. Limited attention, information disclosure, and financial reporting, Journal of Accounting and Economics 26, 337-386.
Hou, K., and Moskowitz, T., 2005. Market frictions, price delay, and the cross-section of expected returns. Review of Financial Studies 18, 981-1020.
Hou, K., Peng, L., and Xiong W., 2009. A tale of two anomalies: The implications of investor attention for price and earnings momentum. Working paper, Ohio State University.
28
Jarrell, G., and Poulsen, A., 1989. Stock trading before the announcement of tender offers: insider trading or market anticipation? Journal of Law, Economics and Organizations 5, 223-248.
Kadlec, G., McConnell, J., 1994. The effect of market segmentation and illiquidity on asset prices: Evidence from exchange listings. Journal of Finance 49, 611–636.
Kaniel, R., Ozoguz, A. and L. Starks, 2012. The high volume return premium: Cross-country evidence, Journal of Financial Economics 103, 255-279. .
Kihlstrom, R. E., and Riordan, M. H., 1984. Advertising as a signal. Journal of Political Economy 92, 427.
Kim, Y., and Meschke, F., 2011. CEO interviews on CNBC. Working paper, University of Kansas.
Lehavy R. and R. Sloan, 2008. Investor recognition and stock returns, Review of Accounting Studies 13, 327-361.
Lou, D., 2014. Attracting investor attention through advertising. Review of Financial Studies, forthcoming.
Loughran, T., and Ritter, J., 2004. Why has IPO underpricing changed over time? Financial Management 33, 5-37.
Louis, H., and Sun, A., 2010. Investor inattention and the market reaction to merger announcements. Management Science 56, 1781-1793.
Malmendier, U., Opp, M., and Saidi, F., 2012. Cash is king: Revaluation of targets after merger bids. Working paper, University of California at Berkeley.
Masulis, R., Wang, C., and Xie, F., 2007. Corporate governance and acquirer returns. Journal of Finance 62, 1851-1889.
Merton, R. C., 1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510.
Milgrom, P., and Roberts, J., 1995. Complementarities and fit strategy, structure, and organizational change in manufacturing. Journal of Accounting and Economics 19, 179-208.
Moeller, S., Schlingemann, F., and Stulz, R., 2004. Firm size and the gains from acquisitions. Journal of Financial Economics 73, 201-228.
Moeller, T., 2005. Let's make a deal! How shareholder control impacts merger payoffs. Journal of Financial Economics 76, 167-190.
Nelson, P., 1974. Information and consumer behavior. Journal of Political Economy 81, 729-754.
Officer, M., 2003. Termination fees in mergers and acquisitions. Journal of Financial Economics 69, 431-467.
29
Palepu, K., 1986. Predicting takeover targets: A methodological and empirical analysis. Journal of Accounting and Economics 8, 3-35.
Rosenbaum, P. R., and Rubin, D. B., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
Schlingemann, F., Stulz, R., and Walkling, R., 2002. Divestitures and the liquidity of the market for corporate assets. Journal of Financial Economics 64, 117–144.
Schwert, G.W., 1996. Markup pricing in mergers and acquisitions. Journal of Financial Economics 41, 153-192.
Schwert, G.W., 2000. Hostility in takeovers: In the eyes of the beholder? Journal of Finance 55, 2599-2640.
Seasholes, M. S., and Wu, G., 2007. Predictable behavior, profits, and attention. Journal of Empirical Finance 14, 590-610.
Sharpe, W. F., 1982. Factors in New York Stock Exchange security returns, 1931-1979, Journal of Portfolio Management 8, 5-19.
Shleifer, A., and Vishny, R., 1986. Large shareholders and corporate control. Journal of Political Economy 94, 461-488.
Stein, J. C., 1996. Rational capital budgeting in an irrational world. Journal of Business 69, 429-455.
Stock J., and Yogo M., 2005. Testing for weak instruments in linear IV regression. Andrews DWK Identification and Inference for Econometric Models. Cambridge University Press, 80-108.
Telser, L. G., 1964. Advertising and competition. Journal of Political Economy 72, 457-466.
Wang, C., and Xie, F., 2009. Corporate governance transfer and synergistic gains from mergers and acquisitions. Review of Financial Studies 22, 829-858.
Wulf, J., 2004. Do CEOs in mergers trade power for premium? Evidence from mergers of equals. Journal of Law, Economics, and Organization 20, 60-101.
Yuan, Y., 2008. Attention and trading. Working Paper, University of Pennsylvania.
30
Table 1: Sample characteristics This table describes our sample which consists of 7,095 U.S. merger and acquisition bids for public targets announced during 1986-2011 and tracked in the Securities Data Company’s (SDC) merger and acquisition database. We screen deals from SDC following the criteria in Bargeron, Schlingemann, Stulz, and Zutter (2008). In addition, we require that target firms have stock market and accounting data available from the Center for Research in Security Prices and Compustat, respectively. In Panel A we report the temporal and Fama and French 48 industrial distribution of the sample targets. In Panel B we report deal status, mode of acquisition, method of payment, deal attitude, deal value, and target financial characteristics. In Panel C, we report summary statistics for four advertising spending measures for the entire sample of 7,095 targets and for the sub-sample of 2,377 targets with positive advertising spending. For the ln(1+Advertising spending) and ln(Adv spending) – ln(Adv spending prior year) variables, we report the actual value of spending (in $US million) and advertising spending growth rate, respectively, which we estimate with the standard eX – 1 transformation. All financial variables are measured at the end of the fiscal year before the merger public announcement date and inflation-adjusted to the end of 2011. Panel A: Temporal and industrial distribution
Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 Deal count 228 261 349 259 123 103 83 137 197 Percent 3.21 3.68 4.92 3.65 1.73 1.45 1.17 1.93 2.78
Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 Deal count 367 353 488 514 563 476 352 201 251 Percent 5.17 4.98 6.88 7.24 7.94 6.71 4.96 2.83 3.54
Year 2004 2005 2006 2007 2008 2009 2010 2011 Total Deal count 220 247 297 318 204 129 219 156 7,095 Percent 3.1 3.48 4.19 4.48 2.88 1.82 3.09 2.2 100
Industry Count % Industry Count % Agriculture 20 0.28 Shipbuilding, Railroad Equipment 15 0.21 Food Products 89 1.25 Defense 6 0.08 Candy & Soda 7 0.10 Precious Metals 17 0.24 Beer & Liquor 20 0.28 Industrial Metal Mining 12 0.17 Tobacco Products 2 0.03 Coal 7 0.10 Recreation 45 0.63 Petroleum and Natural Gas 206 2.90 Entertainment 125 1.76 Utilities 202 2.85 Printing and Publishing 50 0.70 Communication 206 2.90 Consumer Goods 99 1.40 Personal Services 58 0.82 Apparel 65 0.92 Business Services 958 13.50 Healthcare 181 2.55 Computer Hardware 319 4.50 Medical Equipment 237 3.34 Computer Software 328 4.62 Pharmaceutical Products 247 3.48 Measuring and Control Equipment 155 2.18 Chemicals 78 1.10 Business Supplies 83 1.17 Rubber and Plastic Products 87 1.23 Shipping Containers 18 0.25 Textiles 44 0.62 Transportation 171 2.41 Construction Materials 130 1.83 Wholesale 213 3.00 Construction 51 0.72 Retail 317 4.47 Steel work 88 1.24 Restaurants, Hotels, Motels 149 2.10 Fabricated Products 24 0.34 Banking 1017 14.33 Machinery 188 2.65 Insurance 224 3.16 Electrical Equipment 57 0.80 Real Estate 51 0.72 Automobiles and Trucks 59 0.83 Trading 316 4.45 Aircraft 25 0.35 Others 29 0.41 Total 7,095 100.00
31
Panel B: Deal and firm characteristics Proportion of sample Mean Median Deal characteristics Completed 0.8109 Tender offer 0.2389 All cash payment 0.5080 Friendly attitude 0.8937 Same industry 0.5333 Deal value (US$ billion) 1.0652 0.1812 Relative size (Target/Acquirer) 0.4267 0.1506 Target characteristics Market value of equity (US$ billion) 0.6012 0.0897 Q 1.6356 1.2042 Leverage 0.2298 0.1800
Panel C: Target’s advertising spending
Mean Q1 Median Q3 Σ (1) Advertising spending: ln(1+Advertising spending) 0.6135 0.0000 0.0000 0.4780 1.7281 (2) Scaled advertising spending 0.0095 0.0000 0.0000 0.0015 0.0283 (3) Advertising intensity 0.0094 0.0000 0.0000 0.0083 0.0256 (4) Advertising growth: ln(Adv spending) – ln(Adv spending prior year)
-0.0659 0.0000 0.0000 0.0000 2.2339
Conditional on positive advertising spending (5) Advertising spending: ln(1+Advertising spending) 3.1666 0.4690 1.7999 8.3512 2.6161 (6) Scaled advertising spending 0.0283 0.0015 0.0112 0.0365 0.0430 (7) Advertising intensity 0.0279 0.0082 0.0143 0.0309 0.0379 (8) Advertising growth: ln(Adv spending) – ln(Adv spending prior year)
0.1010 -0.1051 0.1005 0.4289 1.2803
32
Table 2: Target’s advertising spending and acquisition premiums The sample consists of 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1. Panel A presents first stage regressions of the probability of becoming a takeover target using 140,839 firm-years with data from CRSP and Compustat during fiscal years 1985-2011. These tests are similar to those in Palepu (1986) and Comment and Schwert (1995). Standard errors are clustered at the firm level. In Panel B, from the original sample, we examine 2,326 bid contests in which one or several bidders bid for a single target and where we can find the deal background from the merger proxies filed by either the target or the acquirer with the SEC (S-4, DEFM 14, SC 14D9, SC TO, DEF 14, 8-K). We use information from the first bid in the contest. We run logit regressions of deal initiation probability similar to those in Aktas, de Bodt and Roll (2010). The dependent variable equals one if the deal is first initiated by the target. In Panel C we estimate OLS regressions of merger premiums similar to those in Bargeron, Schlingemann, Stulz, and Zutter (2008). The dependent variable is the final offer premium reported by SDC. The main independent variables in all three panels are advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). We include the inverse Mill’s ratio obtained from the corresponding first stage tests in Panel A to control for target self-selection (Heckman, 1979). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Panel A: Probability of becoming a target Dependent variable = Target (0,1) Model (1) Model (2) Model (3) Model (4) Advertising spending 0.0494** (0.0169) Scaled advertising spending 1.8292** (0.0136) Advertising intensity 1.7021** (0.0283) Advertising growth 0.0149** (0.0119) Size -0.4361*** -0.4031*** -0.4110*** -0.4204*** (0.0001) (0.0001) (0.0001) (0.0001) Q 0.0006 0.0005 0.0005 0.0004 (0.6084) (0.6485) (0.6420) (0.6788) Leverage 0.0067 0.0162 0.0149 0.0156 (0.5881) (0.1672) (0.2055) (0.1850) OCF -0.0741 -0.0794 -0.0765 -0.0755 (0.4177) (0.3856) (0.4025) (0.4090) Prior year market adjusted return -0.0043 -0.0047 -0.0044 -0.0047 (0.6327) (0.6012) (0.6273) (0.5983) Industry Herfindahl-Hirschman Index 0.8251 0.8223 0.8294 0.8534 (0.1857) (0.1867) (0.1829) (0.1715) Industry liquidity index 0.3497 0.3551 0.3450 0.3497 (0.1825) (0.1752) (0.1884) (0.1824) One year macroeconomic change 0.0099 0.0098 0.0099 0.0109 (0.5772) (0.5782) (0.5742) (0.5397) Constant -27.4937 -23.8407 -24.2939 -21.1273 (0.5746) (0.6109) (0.6048) (0.6392) Year and industry fixed effects Yes Yes Yes Yes N 140,839 140,839 140,839 140,839 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
33
Panel B Target’s advertising spending and deal initiation
Dependent variable = Target initiated (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1601*** (0.0005) Scaled advertising spending 5.6441** (0.0185) Advertising intensity 7.8485*** (0.0008) Advertising growth 0.1213** (0.0415) Target characteristics Size -0.1741*** -0.1410*** -0.1441*** -0.1451*** (0.0001) (0.0001) (0.0001) (0.0001) Q -0.0132 -0.0212 -0.0239 -0.0196 (0.6500) (0.4711) (0.4214) (0.5004) Leverage 0.3011 0.3101 0.3108 0.3105 (0.1774) (0.1654) (0.1642) (0.1655) OCF -0.3256 -0.3141 -0.2355 -0.3429 (0.1682) (0.1830) (0.3177) (0.1480) Prior year market adjusted returns -0.0234 -0.0301 -0.0254 -0.0296 (0.7193) (0.6444) (0.6976) (0.6490) Deal and market characteristics Private acquirer (0,1) 0.3920* 0.3998* 0.3974* 0.3974* (0.0575) (0.0523) (0.0539) (0.0545) Cash only payment (0,1) 0.2328* 0.2196* 0.2244* 0.2269* (0.0519) (0.0663) (0.0609) (0.0578) Tender offer (0,1) -0.3481** -0.3454** -0.3593** -0.3302** (0.0149) (0.0158) (0.0123) (0.0205) Hostile deal (0,1) -1.6835*** -1.6582*** -1.6534*** -1.6493*** (0.0006) (0.0007) (0.0007) (0.0007) Competed deal (0,1) -0.2301 -0.2258 -0.2073 -0.2316 (0.2765) (0.2829) (0.3244) (0.2716) Toehold -0.0099 -0.0099 -0.0094 -0.0089 (0.3225) (0.3203) (0.3449) (0.3725) Target termination fee (0,1) 0.0381 0.0344 0.0364 0.0383 (0.7275) (0.7530) (0.7396) (0.7261) Lockup (0,1) -0.2161 -0.2478 -0.2393 -0.2440 (0.4600) (0.3950) (0.4125) (0.4018) Same industry (0,1) 0.0968 0.0917 0.0923 0.0817 (0.3586) (0.3838) (0.3811) (0.4373) Merger of equals (0,1) 0.3911 0.3589 0.3643 0.3697 (0.2508) (0.2864) (0.2804) (0.2718) Target industry liquidity index 0.4450* 0.4406* 0.4326* 0.4219 (0.0887) (0.0915) (0.0974) (0.1058) One year macroeconomic change 0.0568 0.0530 0.0547 0.0558 (0.1413) (0.1694) (0.1574) (0.1485) Constant 0.4427 0.2840 0.3037 0.3794 (0.5699) (0.7159) (0.6970) (0.6279) Year and industry fixed effects Yes Yes Yes Yes N 2,326 2,326 2,326 2,326 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
34
Panel C: Target’s advertising spending and acquisition premiums Dependent variable = Acquisition premium Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.0100** (0.0386) Scaled advertising spending 0.4390*** (0.0079) Advertising intensity 0.4899*** (0.0054) Advertising growth 0.0083** (0.0237) Target characteristics Size -0.0343*** -0.0322*** -0.0325*** -0.0329*** (0.0001) (0.0001) (0.0001) (0.0001) Q -0.0034 -0.0040 -0.0041 -0.0050* (0.2418) (0.1760) (0.1669) (0.0862) Leverage 0.2016*** 0.2042*** 0.2031*** 0.2015*** (0.0001) (0.0001) (0.0001) (0.0001) OCF -0.0471** -0.0483** -0.0475** -0.0136 (0.0398) (0.0349) (0.0380) (0.1526) Prior year market adjusted returns -0.0306*** -0.0306*** -0.0304*** -0.0308*** (0.0001) (0.0001) (0.0001) (0.0001) Deal and market characteristics Private acquirer (0,1) -0.0676*** -0.0669*** -0.0668*** -0.0649*** (0.0001) (0.0001) (0.0001) (0.0001) Cash only payment (0,1) 0.0225** 0.0223** 0.0224** 0.0221** (0.0327) (0.0345) (0.0337) (0.0365) Tender offer (0,1) 0.0666*** 0.0663*** 0.0663*** 0.0637*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) 0.0500** 0.0506** 0.0505** 0.0589*** (0.0177) (0.0165) (0.0167) (0.0059) Competed deal (0,1) 0.0935*** 0.0931*** 0.0934*** 0.0927*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold -0.0013** -0.0013** -0.0013** -0.0013** (0.0245) (0.0228) (0.0234) (0.0231) Target termination fee (0,1) 0.0208** 0.0204** 0.0203** 0.0189* (0.0435) (0.0473) (0.0487) (0.0699) Lockup (0,1) -0.0704** -0.0722** -0.0717** -0.0747** (0.0398) (0.0349) (0.0362) (0.0290) Same industry (0,1) 0.0104 0.0104 0.0102 0.0088 (0.2799) (0.2821) (0.2872) (0.3623) Merger of equals (0,1) -0.1770*** -0.1772*** -0.1770*** -0.1757*** (0.0001) (0.0001) (0.0001) (0.0001) Target Herfindahl-Hirschman Index -0.3430** -0.3435** -0.3385** -0.3456** (0.0211) (0.0209) (0.0229) (0.0202) Target industry liquidity index -0.0147 -0.0137 -0.0141 -0.0221 (0.5144) (0.5434) (0.5306) (0.3229) One year macroeconomic change -0.0056* -0.0056* -0.0057* -0.0060* (0.0932) (0.0952) (0.0902) (0.0740) Constant 0.5424*** 0.5317*** 0.5338*** 0.5496*** (0.0001) (0.0001) (0.0001) (0.0001) Heckman self-selectivity correction Yes Yes Yes Yes Year and industry fixed effects Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
35
Table 3: Endogeneity of advertising spending and acquisition premiums This table addresses the endogeneity of advertising spending and acquisition premiums with a two stage approach using 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1. Models (1), (3), (5), and (7) present the first stage regression of the determinant of advertising spending. Models (2), (4), (6), and (8) present the second stage regression of the acquisition premium on the advertising spending instruments obtained from the first stage. All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Dependent variable = Advertising spending
Acquisition premium
Scaled adv spending
Acquisition premium
Advertising intensity
Acquisition premium
Advertising growth
Acquisition premium
1st stage 2nd stage IV 1st stage 2nd stage IV 1st stage 2nd stage IV 1st stage 2nd stage IV Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Target’s advertising spending measures Fitted advertising spending 0.0109*** (0.0014) Fitted scaled advertising spending 0.4410*** (0.0003) Fitted advertising intensity 0.6843*** (0.0001) Fitted advertising growth 0.0420** (0.0363) Instruments Average competitor advertising spending 0.2101*** (0.0001) Average competitor scaled adv. spending 0.8045*** (0.0001) Average competitor advertising intensity 0.8521*** (0.0001) Average competitor advertising growth 0.1300*** (0.0001) Target characteristics Size 0.1628*** -0.0491*** 0.0005* -0.0432*** 0.0001 -0.0440*** 0.0422*** -0.0451*** (0.0001) (0.0001) (0.0916) (0.0001) (0.6878) (0.0001) (0.0001) (0.0001) Q -0.0004 0.0001 0.0000 0.0001 0.0000 0.0001 0.0001 0.0001 (0.3155) (0.4904) (0.5234) (0.5728) (0.7592) (0.5803) (0.8150) (0.5374) Leverage -0.2735*** 0.0505** -0.0097*** 0.0696*** -0.0059*** 0.0714*** 0.0623 0.0606*** (0.0001) (0.0325) (0.0001) (0.0030) (0.0006) (0.0023) (0.4531) (0.0099) OCF 0.0924* 0.0319** 0.0071*** 0.0195 0.0050*** 0.0222 0.0199 0.0144 (0.0992) (0.0483) (0.0001) (0.2377) (0.0010) (0.1728) (0.5203) (0.1339) Prior year market adjusted return -0.0377* -0.0715*** -0.0007 -0.0718*** -0.0006 -0.0718*** -0.0021 -0.0734*** (0.0696) (0.0001) (0.2456) (0.0001) (0.3256) (0.0001) (0.9379) (0.0001)
36
Target Herfindahl-Hirschman Index 0.0120 -0.1903** 0.0005 -0.2067** -0.0007 -0.2093** 0.7794 -0.1451* (0.9741) (0.0215) (0.9650) (0.0130) (0.9477) (0.0117) (0.1089) (0.0775) Target industry liquidity index -0.2098*** 0.0356** -0.0061*** 0.0376** -0.0045*** 0.0370** 0.0203 0.0517*** (0.0002) (0.0294) (0.0001) (0.0212) (0.0032) (0.0236) (0.7802) (0.0008) One year macroeconomic change -0.0205** -0.0079*** -0.0005** -0.0082*** -0.0003 -0.0078*** -0.0149 -0.0084*** (0.0135) (0.0001) (0.0469) (0.0001) (0.2579) (0.0001) (0.1699) (0.0001) Deal characteristics Private acquirer (0,1) -0.0817*** -0.0821*** -0.0819*** -0.0773*** (0.0001) (0.0001) (0.0001) (0.0001) Cash only payment (0,1) 0.0088 0.0086 0.0095 0.0104 (0.3882) (0.3988) (0.3487) (0.3087) Tender offer (0,1) 0.0951*** 0.0943*** 0.0942*** 0.0985*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) 0.0584*** 0.0585*** 0.0593*** 0.0612*** (0.0056) (0.0055) (0.0049) (0.0037) Competed deal (0,1) 0.0960*** 0.0961*** 0.0962*** 0.0995*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold -0.0014** -0.0014** -0.0014** -0.0013** (0.0183) (0.0176) (0.0152) (0.0246) Target termination fee (0,1) 0.0338*** 0.0338*** 0.0340*** 0.0318*** (0.0004) (0.0004) (0.0004) (0.0010) Lockup (0,1) -0.0448 -0.0452 -0.0446 -0.0501 (0.1909) (0.1863) (0.1927) (0.1438) Same industry (0,1) 0.0061 0.0064 0.0055 0.0037 (0.5168) (0.4954) (0.5613) (0.6914) Merger of equals (0,1) -0.1754*** -0.1765*** -0.1749*** -0.1811*** (0.0001) (0.0001) (0.0001) (0.0001) Constant -0.7037*** 0.6250*** 0.0125*** 0.5969*** 0.0052 0.5931*** -0.2182 0.6128*** (0.0001) (0.0001) (0.0005) (0.0001) (0.1235) (0.0001) (0.1806) (0.0001) Year and industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
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Table 4: Propensity score matching estimates for advertising spending on premiums In Panel A, we report the results of the propensity score matching estimates, the sample means of the treatment and control samples, and the p-values of the difference in means. In Panel B, we report the average treatment effects on premiums where the treatment is defined as “Advertising spending > 0”. Matching estimates use the Gaussian kernel with a fixed bandwidth of 0.10. We report the p-value of the treatment effects using 500 bootstrap repetitions in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Propensity score model estimates for advertising spending
Dependent variable = 1 if advertising spending > 0
Treatment sample mean
Control sample mean
p-value for difference
Size 0.0416** 5.4888 5.4530 0.4712 Q 0.0208 1.6050 1.6874 0.1074 Leverage -0.2197 0.1748 0.1618 0.0126 Operating cash flow 0.0399 0.0639 0.0679 0.5568 Prior year market adjusted return 0.0001 -0.0034 0.0044 0.7156 Private acquirer (0,1) -0.1107 0.2049 0.1926 0.2880 Cash payment (0,1) 0.0270 0.5301 0.5359 0.6882 Tender offer (0,1) 0.1597** 0.2507 0.2449 0.6435 Hostile deal (0,1) -0.0481 0.0501 0.0477 0.7083 Competed deal (0,1) 0.1142 0.1342 0.1254 0.3684 Toehold 0.0009 2.0801 1.9861 0.6654 Target termination fee (0,1) -0.0533 0.5002 0.4852 0.3018 Lockup (0,1) 0.1776 0.0172 0.0203 0.4431 Same industry (0,1) 0.0643 0.5313 0.5300 0.9253 Merger of equals (0,1) 0.2064 0.0126 0.0122 0.9074 Target Herfindahl-Hirschman Index -1.2862 0.0590 0.0612 0.2131 Target industry liquidity index -0.5527*** 0.3457 0.3654 0.0282 One year macroeconomic change -0.0447** 1.8638 1.8987 0.7182 Intercept -1.7973*** Year and industry fixed effects Yes N (treated observations) 2,377 N (untreated observations) 2,368
Panel B: Average treatment effect on premiums for advertising spending
Average treatment effect (p-value)
Premiums (Advertising spending > 0 vs. = 0) 0.0330*** (0.0022)
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Table 5: Advertising spending and unconditional premiums This table presents unconditional premium regressions similar to those in Comment and Schwert (1995). The dependent variable is the final offer premium reported by SDC. The premium is set to zero in non-takeover firm-years. All models use 140,839 firm-years with data available from CRSP and Compustat during fiscal year 1985-2011. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. Standard errors are clustered at the firm level. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Acquisition premium Model (1) Model (2) Model (3) Model (4) Advertising spending 0.0007** (0.0334) Scaled advertising spending 0.0422*** (0.0016) Advertising intensity 0.0366*** (0.0018) Advertising growth 0.0002* (0.0537) Size -0.0006*** -0.0005*** -0.0005*** -0.0005*** (0.0001) (0.0001) (0.0001) (0.0001) Q 0.0000 0.0000 0.0000 0.0000 (0.9730) (0.9777) (0.9768) (0.9796) Leverage -0.0005 -0.0001 -0.0001 -0.0004 (0.7262) (0.9428) (0.9261) (0.7900) OCF -0.0012* -0.0012* -0.0012* -0.0012* (0.0726) (0.0693) (0.0705) (0.0712) Prior year market adjusted return 0.0000 0.0000 0.0000 0.0000 (0.9744) (0.9676) (0.9711) (0.9968) Herfindahl-Hirschman Index -0.0137* -0.0142* -0.0139* -0.0133* (0.0742) (0.0631) (0.0696) (0.0816) Industry liquidity index 0.0010 0.0010 0.0009 0.0008 (0.7661) (0.7721) (0.8020) (0.8256) One year macroeconomic change 0.0000 0.0000 0.0000 0.0000 (0.6512) (0.6222) (0.6313) (0.5940) Constant 0.0004 0.0000 0.0000 0.0002 (0.7504) (0.9743) (0.9908) (0.8911) Year and industry fixed effects Yes Yes Yes Yes N 140,839 140,839 140,839 140,839 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
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Table 6: Target’s advertising spending and acquirer returns From the original sample of 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1, we examine 3036 deals in which the acquirer is publicly traded. We run OLS regressions of acquirer announcement returns similar to those in Moeller, Schlingemann, and Stulz (2004) and Masulis, Wang and Xie (2007). The dependent variable is the acquirer’s cumulative abnormal return (CAR) over three days around the merger announcement date. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Acquirer CAR [-1,+1] Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending -0.0045*** (0.0001) Scaled advertising spending -0.1432*** (0.0022) Advertising intensity -0.1531*** (0.0014) Advertising growth -0.0016*** (0.0013) Acquirer characteristics Size -0.0002 -0.0007 -0.0006 -0.0008 (0.7156) (0.2885) (0.3428) (0.1678) Q 0.0000 0.0000 0.0000 0.0000 (0.8475) (0.7734) (0.7928) (0.7419) Leverage 0.0169** 0.0166** 0.0167** 0.0164** (0.0222) (0.0247) (0.0236) (0.0268) OCF -0.0111 -0.0108 -0.0107 0.0054 (0.1502) (0.1649) (0.1669) (0.3605) Prior year market adjusted return 0.0095*** 0.0093*** 0.0093*** 0.0094*** (0.0001) (0.0001) (0.0001) (0.0001) Target characteristics Q 0.0000 0.0000 0.0000 0.0000 (0.8229) (0.8981) (0.8883) (0.9360) Leverage -0.0020 -0.0033 -0.0034 -0.0029 (0.7311) (0.5737) (0.5590) (0.6248) OCF 0.0062 0.0063 0.0060 0.0014 (0.3684) (0.3619) (0.3876) (0.4171) Prior year market adjusted return 0.0037** 0.0038** 0.0037** 0.0039** (0.0184) (0.0163) (0.0193) (0.0138) Deal and market characteristics Relative size (Target / Acquirer) -0.0001 -0.0001 -0.0001 -0.0001 (0.8405) (0.7671) (0.8348) (0.8133) Cash only payment (0,1) 0.0246*** 0.0255*** 0.0253*** 0.0251*** (0.0001) (0.0001) (0.0001) (0.0001) Tender offer (0,1) 0.0059* 0.0058* 0.0061* 0.0056 (0.0862) (0.0918) (0.0800) (0.1065) Hostile deal (0,1) -0.0075 -0.0092 -0.0091 -0.0086 (0.2086) (0.1199) (0.1242) (0.1454) Competed deal (0,1) -0.0027 -0.0032 -0.0033 -0.0031 (0.5231) (0.4507) (0.4344) (0.4722) Toehold 0.0004 0.0004 0.0004 0.0004 (0.1025) (0.1146) (0.1149) (0.1134) Merger of equals (0,1) 0.0269** 0.0258** 0.0259** 0.0272**
40
(0.0204) (0.0263) (0.0256) (0.0191) Same industry (0,1) 0.0022 0.0018 0.0019 0.0015 (0.4841) (0.5629) (0.5340) (0.6410) Competitive industry (0,1) 0.0045 0.0042 0.0043 0.0043 (0.1426) (0.1630) (0.1609) (0.1592) Unique industry (0,1) 0.0018 0.0020 0.0022 0.0011 (0.5942) (0.5694) (0.5334) (0.7505) High tech industry (0,1) -0.0053 -0.0057 -0.0057 -0.0047 (0.2560) (0.2257) (0.2211) (0.3177) Target Herfindahl-Hirschman Index 0.0619 0.0616 0.0605 0.0603 (0.2068) (0.2096) (0.2180) (0.2198) Target industry liquidity index -0.0107* -0.0109* -0.0109* -0.0110* (0.0980) (0.0925) (0.0948) (0.0886) One year macroeconomic change -0.0005 -0.0005 -0.0004 -0.0004 (0.5517) (0.5971) (0.6203) (0.6041) Constant -0.0103 -0.0067 -0.0069 -0.0065 (0.4971) (0.6576) (0.6456) (0.6642) Heckman self-selectivity correction Yes Yes Yes Yes Year and industry fixed effects Yes Yes Yes Yes N 3,036 3,036 3,036 3,036 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
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Table 7: Target’s advertising spending and deal completion
The sample consists of 7,095 merger and acquisition bids announced during 1986-2011 described in Table 1. We run logit regressions of merger completion probability similar to those in Bates and Lemmon (2003) and Officer (2003). The dependent variable equals one if the proposed bid is completed. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Deal completion (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1537*** (0.0004) Scaled advertising spending 3.8094** (0.0181) Advertising intensity 4.6809** (0.0107) Advertising growth 0.1150*** (0.0001) Target characteristics Size -0.0622** -0.0374 -0.0389 -0.0429* (0.0153) (0.1326) (0.1170) (0.0853) Q 0.0202 0.0156 0.0145 0.0136 (0.5137) (0.6115) (0.6392) (0.6592) Leverage -0.3433 -0.3331 -0.3383 -0.3331 (0.1218) (0.1330) (0.1270) (0.1338) OCF -0.1117 -0.1138 -0.1109 -0.2231 (0.5722) (0.5643) (0.5741) (0.2639) Prior year market adjusted returns 0.2662*** 0.2668*** 0.2664*** 0.2659*** (0.0009) (0.0009) (0.0009) (0.0009) Deal and market characteristics Private acquirer (0,1) -0.8083*** -0.8034*** -0.8020*** -0.8077*** (0.0001) (0.0001) (0.0001) (0.0001) Cash only payment (0,1) 0.1402 0.1403 0.1406 0.1420 (0.1178) (0.1174) (0.1164) (0.1138) Tender offer (0,1) 1.7871*** 1.8016*** 1.8024*** 1.8047*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) -2.4226*** -2.4169*** -2.4192*** -2.4124*** (0.0001) (0.0001) (0.0001) (0.0001) Competed deal (0,1) -2.0470*** -2.0464*** -2.0454*** -2.0662*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold 0.0089* 0.0086* 0.0086* 0.0086* (0.0549) (0.0618) (0.0618) (0.0628) Target termination fee (0,1) 1.8601*** 1.8556*** 1.8563*** 1.8543*** (0.0001) (0.0001) (0.0001) (0.0001) Lockup (0,1) 0.5790 0.5543 0.5584 0.5386 (0.1220) (0.1375) (0.1346) (0.1495) Same industry (0,1) 0.4323*** 0.4300*** 0.4317*** 0.4260*** (0.0001) (0.0001) (0.0001) (0.0001) Merger of equals (0,1) -0.3182 -0.3225 -0.3207 -0.3290 (0.3529) (0.3461) (0.3487) (0.3364) Target industry liquidity index 0.0390 0.0278 0.0261 -0.0056 (0.8355) (0.8822) (0.8892) (0.9761) One year macroeconomic change 0.0503* 0.0486* 0.0475* 0.0484* (0.0759) (0.0862) (0.0931) (0.0875) Constant 0.2589 0.1759 0.1911 0.2793 (0.5698) (0.6997) (0.6751) (0.5415) Year and industry fixed effects Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
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Table 8: Target’s advertising spending and bid competition From the original sample of 7,095 merger and acquisition bids announced during 1986-2011 described in Table 1, we examine 6,502 bid contests in which one or several bidders bid for a single target. We run logit regressions of bid competition probability similar to those in Officer (2003). The dependent variable equals one if the contest involves multiple bidders. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Multiple bidders (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1267** (0.0110) Scaled advertising spending 3.7071** (0.0213) Advertising intensity 5.0197** (0.0122) Advertising growth 0.2544*** (0.0037) Target characteristics Size 0.1997*** 0.2286*** 0.2252*** 0.2226*** (0.0001) (0.0001) (0.0001) (0.0001) Q -0.2838*** -0.2993*** -0.2960*** -0.2736*** (0.0003) (0.0002) (0.0002) (0.0004) Leverage -0.5489* -0.5336* -0.5537* -0.5684* (0.0860) (0.0952) (0.0830) (0.0758) OCF 0.2937 0.2822 0.2774 0.1761 (0.3226) (0.3428) (0.3519) (0.5486) Prior year market adjusted returns 0.3168*** 0.3178*** 0.3155*** 0.3105*** (0.0015) (0.0014) (0.0015) (0.0019) Deal and market characteristics Private acquirer (0,1) 0.4454*** 0.4536*** 0.4577*** 0.4554*** (0.0008) (0.0006) (0.0006) (0.0006) Cash only payment (0,1) 0.2016 0.1957 0.1956 0.1888 (0.1102) (0.1206) (0.1210) (0.1353) Tender offer (0,1) 0.5053*** 0.5108*** 0.5114*** 0.5189*** (0.0001) (0.0001) (0.0001) (0.0001) Hostile deal (0,1) 0.8337*** 0.8355*** 0.8375*** 0.8460*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold -0.0122* -0.0124* -0.0124* -0.0124* (0.0706) (0.0652) (0.0666) (0.0670) Target termination fee (0,1) -0.0010 -0.0009 -0.0011 0.0019 (0.9935) (0.9942) (0.9932) (0.9880) Lockup (0,1) -0.5627 -0.6142 -0.6018 -0.6051 (0.3013) (0.2603) (0.2695) (0.2676) Same industry (0,1) 0.1589 0.1605 0.1607 0.1691 (0.1624) (0.1581) (0.1577) (0.1369) Merger of equals (0,1) 0.2294 0.2212 0.2207 0.2306 (0.6450) (0.6562) (0.6570) (0.6430) Target industry liquidity index -0.0076 -0.0032 -0.0085 -0.0034 (0.9767) (0.9902) (0.9740) (0.9896) One year macroeconomic change -0.0396 -0.0406 -0.0419 -0.0411 (0.3646) (0.3527) (0.3372) (0.3483) Constant -4.4930*** -4.6285*** -4.6004*** -4.5905*** (0.0001) (0.0001) (0.0001) (0.0001) Year and industry fixed effects Yes Yes Yes Yes N 6,502 6,502 6,502 6,502 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
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Table 9: Target’s advertising spending and bid revision The sample consists of 7,095 mergers and acquisitions announced during 1986-2011 described in Table 1. We run logit regressions of bid revision probability similar to those in Bates, Lemmon, and Linck (2006). The dependent variable equals one if there is an upward bid revision. The main independent variable is advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Dependent variable = Upward bid revision (0,1) Model (1) Model (2) Model (3) Model (4) Target’s advertising spending measures Advertising spending 0.1878*** (0.0001) Scaled advertising spending 6.3718*** (0.0001) Advertising intensity 4.6054*** (0.0068) Advertising growth 0.2595*** (0.0032) Target characteristics Size 0.1169*** 0.1613*** 0.1552*** 0.1506*** (0.0005) (0.0001) (0.0001) (0.0001) Q -0.1170** -0.1159** -0.1109* -0.0680 (0.0452) (0.0465) (0.0525) (0.1894) Leverage -0.2511 -0.2169 -0.2184 -0.1648 (0.3824) (0.4495) (0.4452) (0.5645) OCF 0.5178** 0.4890** 0.5141** 0.0091 (0.0254) (0.0353) (0.0268) (0.9579) Prior year market adjusted returns -0.0827 -0.0751 -0.0789 -0.0915 (0.4382) (0.4808) (0.4581) (0.3919) Deal and market characteristics Private acquirer (0,1) 0.3322*** 0.3380*** 0.3419*** 0.3627*** (0.0096) (0.0084) (0.0075) (0.0047) Cash only payment (0,1) 0.0244 0.0233 0.0272 0.0388 (0.8383) (0.8450) (0.8195) (0.7453) Tender offer (0,1) 0.4615*** 0.4654*** 0.4746*** 0.4973*** (0.0002) (0.0002) (0.0001) (0.0001) Hostile deal (0,1) 2.9311*** 2.9271*** 2.9172*** 2.9265*** (0.0001) (0.0001) (0.0001) (0.0001) Competed deal (0,1) 1.6621*** 1.6536*** 1.6530*** 1.6248*** (0.0001) (0.0001) (0.0001) (0.0001) Toehold 1.1028*** 1.0945*** 1.0885*** 1.1016*** (0.0001) (0.0001) (0.0001) (0.0001) Target termination fee (0,1) -0.3771*** -0.3770*** -0.3778*** -0.3637*** (0.0010) (0.0010) (0.0010) (0.0015) Lockup (0,1) -1.1868 -1.2163* -1.1686 -1.1916 (0.1072) (0.0995) (0.1116) (0.1041) Same industry (0,1) 0.1843 0.1797 0.1769 0.1852* (0.1012) (0.1098) (0.1151) (0.0996) Merger of equals (0,1) -1.4681* -1.4497* -1.4430* -1.4226* (0.0642) (0.0686) (0.0696) (0.0734) Target industry liquidity index -0.0397 -0.0343 -0.0522 0.0364 (0.8682) (0.8859) (0.8271) (0.8770) One year macroeconomic change -0.0105 -0.0095 -0.0133 -0.0113 (0.7572) (0.7809) (0.6953) (0.7398) ln (Initial offer premium) -0.2470*** -0.2469*** -0.2437*** -0.2482*** (0.0001) (0.0001) (0.0001) (0.0001) Constant -4.7748*** -4.9344*** -4.8981*** -4.9855*** (0.0001) (0.0001) (0.0001) (0.0001) Year and industry fixed effects Yes Yes Yes Yes N 7,095 7,095 7,095 7,095 Regression’s p-value 0.0001 0.0001 0.0001 0.0001
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Table 10: Additional analyses In Panel A we estimate target return regressions similar to those in Panel B of Table 2. In Panel B we estimate OLS regressions of acquirer announcement returns similar to those in Table 6. In Panel C, we estimate acquisition premium regressions similar to those in Panel B of Table 2. In Panel D, we estimate acquisition premium and acquirer return regressions using advertising data three fiscal years before the merger announcement date. In Panel D and Panel E, we estimate acquisition premium and acquirer return regressions with each advertising spending proxy interacted with above median R&D spending (0,1) and above median excess cash (0,1), respectively. The main independent variable in Panel C is the residuals from advertising spending in Model (1), scaled advertising spending in Model (2), advertising intensity in Model (3), and advertising growth in Model (4). The residuals are estimated from the corresponding first stage regressions in Table 3 Models (1), (3), (5), and (7). In Panel G we estimate acquisition premium regressions with each advertising spending proxy for subsamples of targets that belong to B2C industries and of those in other industries. B2C industries are consumer-oriented ones following the classification by Sharpe (1982). In Panel H, we estimate acquisition premium regressions with each advertising spending proxy interacted with the target’s managerial (CEO) ownership using a subsample of 2,777 deals with ownership data available from Execucomp and Thomson Financial Insider databases. To save space, we do not report the control variables in the regressions. All variables are defined in the appendix. We report p-values in parentheses. The symbols *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. Panel A: Target’s advertising spending and target return alternatives Dependent variable = Target return alternatives CAR [-20,+1] CAR [-42,+126] Advertising spending 0.0061** 0.0194*** (0.0324) (0.0004) Scaled advertising spending 0.3032*** 0.6332*** (0.0098) (0.0008) Advertising intensity 0.4210*** 0.7852*** (0.0008) (0.0001) Advertising growth 0.0048* 0.0075** (0.0540) (0.0349)
Panel B: Target’s advertising spending and acquirer return alternatives Dependent variable = Acquirer return alternatives CAR [-2,+2] CAR [-5,+5] Advertising spending -0.0057*** -0.0077*** (0.0001) (0.0001) Scaled advertising spending -0.1308** -0.0846** (0.0272) (0.0474) Advertising intensity -0.1757*** -0.1560** (0.0036) (0.0363) Advertising growth -0.0012* -0.0023*** (0.0653) (0.0032)
Panel C: Abnormal advertising spending and acquisition premiums Dependent variable = Acquisition premium Model (1) Model (2) Model (3) Model (4) Advertising spending residual 0.0131*** (0.0073) Scaled advertising spending residual 0.3574** (0.0316) Advertising intensity residual 0.4413** (0.0129) Advertising growth residual 0.0084** (0.0208)
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Panel D: Falsification tests using advertising spending 3 years before merger announcement Dependent variable = Acquisition premium Acquirer CAR [-1,+1]
Advertising spending 0.0056 -0.0015 (0.2066) (0.1949) Scaled advertising spending 0.2193 -0.0566 (0.2804) (0.2301) Advertising intensity 0.2630 -0.0232 (0.2428) (0.6095) Advertising growth 0.0025 0.0000 (0.3411) (0.7567)
Panel E: Alternative explanations: R&D Dependent variable = Acquisition premium Acquirer CAR [-1,+1]
Advertising spending × High R&D (0,1) 0.0058 -0.0010 (0.5185) (0.5981) Scaled advertising spending × High R&D (0,1) 0.0990 -0.0248 (0.7703) (0.8210) Advertising intensity × High R&D (0,1) 0.0407 -0.0478 (0.9159) (0.6915) Advertising growth × High R&D (0,1) 0.0049 -0.0029 (0.5043) (0.1656)
Panel F: Alternative explanations: Excess cash Dependent variable = Acquirer return alternatives Acquisition premium Acquirer CAR [-1,+1] Advertising spending × High excess cash (0,1) 0.0012 -0.0000 (0.8871) (0.9863) Scaled advertising spending × High excess cash (0,1) -0.3521 0.0518 (0.2578) (0.5928) Advertising intensity × High excess cash (0,1) -0.1317 0.0707 (0.7157) (0.5158) Advertising growth × High excess cash (0,1) -0.0038 -0.0004 (0.6126) (0.7650)
Panel G: Advertising spending, B2C industries and acquisition premiums Dependent variable Advertising = Acquisition premium proxy =
Advertising spending
Scaled adv. spending
Advertising intensity
Advertising growth
Coefficient of advertising proxy for 0.0191*** 0.6461*** 0.7163*** 0.0092** targets in B2C industries (1) (0.0078) (0.0036) (0.0056) (0.0171) Coefficient of advertising proxy for 0.0065** 0.3278* 0.3835** 0.0080* targets in other industries (2) (0.0455) (0.0697) (0.0468) (0.0653) Difference (1) – (2) 0.0126* 0.3183* 0.3328* 0.0012 (0.0696) (0.0756) (0.0604) (0.6080)
Panel H: Advertising spending, managerial ownership and acquisition premiums Dependent variable Advertising = Acquisition premium proxy =
Advertising spending
Scaled adv. spending
Advertising intensity
Advertising growth
Advertising proxy 0.0220*** 0.5993** 0.8286*** 0.0097** (0.0068) (0.0167) (0.0045) (0.0100) Managerial ownership -0.0750 -0.0629 -0.0504 -0.0034 (0.1337) (0.1875) (0.2821) (0.9345) Advertising proxy × Managerial ownership 0.0659** 1.2315** 0.9798* 0.0011* (0.0216) (0.0210) (0.0665) (0.0979)
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Appendix: Variable definitions Advertising spending proxies Advertising spending the natural logarithm of (1+advertising spending in million $US) Scaled advertising spending advertising spending scaled by total assets Advertising intensity advertising spending scaled by sales Advertising growth the log difference of advertising spending Deal characteristics Acquisition premium the offer price divided by the target’s stock price four weeks before the
merger announcement date, as reported by SDC and limited between 0% and 200%
Target CAR the target’s cumulative abnormal return over the window around the merger announcement date, calculated as the residual from the market model estimated during the one year window ending four weeks prior to the merger announcement
Acquirer CAR the acquirer’s cumulative abnormal return over the window around the merger announcement date, calculated as the residual from the market model estimated during the one year window ending four weeks prior to the merger announcement
Completion (0,1) one if the announced deal is completed Upward revision (0,1) one if the offer price is revised upward Multiple bidders (0,1) one if the deal involves multiple bidders Private acquirer (0,1) one if the bidder is not publicly traded Cash payment (0,1) one if the deal is paid entirely in cash Tender offer (0,1) one if the form of the deal is tender offer Hostile deal (0,1) one if the deal is classified hostile by SDC Competed deal (0,1) one if the deal has a competed offer identified by SDC Toehold fraction of the target’s shares owned by the bidder Target termination fee (0,1) one if the target has a termination fee provision in the merger contract Lockup (0,1) one if the deal includes a lockup of target or acquirer shares Merger of equals (0,1) one if the deal is classified by SDC as a merger of equals Same industry (0,1) one if both the target and the acquirer belong to the same Fama and
French (1997) 48 industrial classification group Market characteristics Target Herfindahl-Hirschman index the competitiveness of the target industry. An industry’s Herfindahl
index is computed as the sum of squared market shares of all firms in the industry using data on sales, as in Masulis, Wang and Xie (2007).
Target industry liquidity the liquidity of the market for corporate control for the target firm’s industry. This variable is defined as the value of all corporate control transactions for US$1 million or more reported by SDC for each year and industry divided by the total book value of assets of all Compustat firms in the same industry and year, as in Schlingemann, Stulz and Walkling (2002)
One year macroeconomic change the difference in the industrial production index over one year period before the merger
Competitive industry (0,1) one if the bidder’s industry is in the bottom quartile of all industries sorted annually by the Herfindahl index. An industry’s Herfindahl index is computed as the sum of squared market shares of all firms
47
in the industry using data on sales (as in Masulis, Wang and Xie, 2007)
Unique industry (0,1) one if the bidder’s industry is in the top quartile of all industries sorted annually by industry-median product uniqueness. Product uniqueness is defined as selling spending scaled by sales (as in Masulis, Wang and Xie, 2007)
High tech industry (0,1) one if bidder and target are both from high tech industries defined by Loughran and Ritter (2004)
Financial characteristics Size the natural logarithm of the market value of assets Q the market value of assets divided by the book value of assets Leverage the book value of debt divided by the sum of book value of debt and
market value of equity. OCF the cash flow from operations scaled by the value of assets Prior year market adjusted return the cumulative abnormal return during the one year window ending
four weeks prior to the merger announcement, calculated as the residual from the market model estimated during the year before
R&D the research and development expenditure scaled by the value of assets Excess cash the residual from Fresard and Salva (2010) model: ln(Cash) =
β1ln(Assets) + β2 Cash Flow + β3 Net Working Capital + β4 Q + β5Capex + β6 Leverage + β7 R&D + β8 Dividend + firm, industry, and time fixed effects
Managerial ownership the equity ownership by the CEO as a proportion of the number of shares outstanding
Instruments Average competitor advertising
spending the natural logarithm of the industry average advertising spending in
million $US, excluding the firm’s contribution to the industry Average competitor scaled
advertising spending industry average advertising spending scaled by total assets, excluding
the firm’s contribution to the industry Average competitor advertising
intensity industry average advertising spending scaled by sales, excluding the
firm’s contribution to the industry Average competitor advertising
growth industry average advertising growth in log difference of advertising
spending, excluding the firm’s contribution to the industry Other variables Heckman self-selectivity the Heckman (1979) lambda in a two stage process. In the first-stage,
we estimate the probability of becoming a target. In the second stage, the inverse Mill's ratio from the first stage model is included in the estimation as a variable to control for self-selection.
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