Q-THEORY AND ACQUISITION RETURNS
KENNETH R. AHERN†
UNIVERSITY OF MICHIGAN — ROSS SCHOOL OF BUSINESS
Abstract
This paper applies the q−theory of investment to corporate acquisitions to explain target choice and
acquirer returns. The theory predicts that larger acquirers optimally choose larger targets, but of smaller
relative size. Dollar gains increase, but percentage returns decrease as acquirers get larger. Since later
deals are made by larger acquirers, returns appear to decline with experience. Using a panel dataset
of repeat acquirers, empirical tests support the predictions of q−theory. In contrast, I find only weak
support for an agency explanation and no support for a hubris story. I also reject the theory that
declining returns result from market anticipation of later deals.
This Version: 7 April 2010
JEL Classification: G30, G32, G34
Keywords: Mergers and acquisitions, repeat acquirers, q−theory, agency, hubris
⋆
I am extremely grateful to Antonio Bernardo, Jean-Laurent Rosenthal, and J. Fred Weston for advice and
support. I also especially thank David Robinson, Karin Thorburn, Roni Michaely, and Katrina Ellis. Comments
provided by Amy Dittmar, Ran Duchin, Raffaella Giacomini, Erica Li, Marc Martos-Vila, MP Narayanan,
Amiyatosh Purnanandam, Geoffrey Tate, Uday Rajan, Mike Stegemoller, Liu Yang, Lu Zhang, and seminar
participants at the 2008 AFA Annual Meeting, 2006 FMA Annual Meeting, the 2006 US and European FMA
Doctoral Seminars, the Anderson School at UCLA, UCLA Department of Economics IO Workshop, London
Business School, Penn State, the University of British Columbia, Virginia Tech, Michigan, Purdue, Maryland,
and Vanderbilt improved this paper significantly. I gratefully acknowledge the financial support from the Research
Program on Takeovers, Restructuring, and Governance at the Anderson School, UCLA.
† Please direct correspondence to Kenneth R. Ahern, Ross School of Business, University of Michigan, Ann
Arbor MI 48109. Telephone: (734) 764-3196. Fax: (734) 936-8715. E-mail: [email protected].
q−Theory and Acquisition Returns
Abstract
This paper applies the q−theory of investment to corporate acquisitions to explain target choice and
acquirer returns. The theory predicts that larger acquirers optimally choose larger targets, but of smaller
relative size. Dollar gains increase, but percentage returns decrease as acquirers get larger. Since later
deals are made by larger acquirers, returns appear to decline with experience. Using a panel dataset
of repeat acquirers, empirical tests support the predictions of q−theory. In contrast, I find only weak
support for an agency explanation and no support for a hubris story. I also reject the theory that
declining returns result from market anticipation of later deals.
JEL Classification: G30, G32, G34
Keywords: Mergers and acquisitions, repeat acquirers, q−theory, agency, hubris
Q-THEORY AND ACQUISITION RETURNS
1
This paper applies the q−theory of investment to mergers to explain how acquirer returns and
the size of targets are directly related to the size of the acquirer. This approach provides rational
explanations for two unexplained empirical facts reported in prior research: 1) the negative size
effect of acquirer announcement returns in Moeller, Schlingemann, and Stulz (2004) and 2) the
pattern of declining acquirer announcement returns from first to later deals reported in Fuller,
Netter, and Stegemoller (2002) and Aktas, de Bodt, and Roll (2009a). Understanding these two
facts is important since the majority of overall M&A activity involves large repeat acquirers.
In fact, in a sample of 12,942 mergers from 1980 to 2004, I find that only 38% of deals are made
by first-time acquirers and that the most acquisitive 10% of the firms account for 35% of all
deals and are also the largest firms in the sample.
Since acquisitions are simply external investments, q−theory provides a logical framework to
understand M&As. First, the incremental nature of investments through acquisitions fits well
with the theory’s focus on marginal q, rather than average q. This means that the widely-cited
measurement problems that arise when q is measured using book values of yearly investment
are largely avoided when investments are measured using acquisitions.
Second, two key assumptions made in q−theory are directly applicable to mergers. The first
assumption is that firms exhibit decreasing returns to scale. In mergers the same assumption
is plausible: as the target size increases, the potential synergy gains increase, though at a
decreasing rate due to the greater costs of coordination in larger firms (Lucas Jr., 1978). The
second central assumption of q−theory is that investments incur adjustment costs. These are
the costs of installation, costly learning by labor, or the irreversibility of investment caused
by a lack of secondary markets for new capital (Cooper and Haltiwanger, 2006). In mergers,
the analogue to adjustment costs are typically referred to as integration costs, or the difficulty
of merging two firms’ operations. Though there is not much academic research on integration
costs, consulting firms and the business press have emphasized the role of integration as a firstorder determinant of merger success (Harding and Rovit, 2004). For example, poor integration
led to high profile failures in the ATT-NCR and Daimler-Chrysler mergers, even though the
economic motivations for the deals were clear.
2
Q-THEORY AND ACQUISITION RETURNS
In a simple q−theory model, I show that under these assumptions, value-maximizing acquirers choose a target firm based on both its absolute size and on its size relative to the acquirer,
just as in traditional q−theory. In particular, as a value-maximizing acquirer gets larger, it
will optimally choose targets that are smaller in relative size, but larger in absolute size. The
intuition behind this result is that acquirers trade off greater synergy gains against integration
costs. Diminishing returns to scale and decreasing integration costs lead to smaller percentage
gains from mergers, but larger dollar gains as acquirers get larger. This highlights the importance of accounting for both the absolute dollar gains as well as the percentage gains in mergers.
Since acquirers get larger over an acquisition history, these size effects determine the pattern of
returns to repeat acquirers. Thus a simple q−theory model is able to explain the two empirical
facts that motivate this paper.
Next, I report empirical evidence in support of the q−theory hypothesis in mergers. First,
the data reveal that later deals involve larger acquirers and larger targets than in earlier deals.
However, the relative size of the target to the acquirer diminishes from the first to later deals
in a deal sequence. Likewise, non-parametric kernel regressions reveal a positive relationship
between the size of the acquirer and the size of the target, but a negative relationship between
the relative size of the target and the absolute size of the acquirer, as predicted.
Turning to announcement returns, I find that acquirer size is negatively related to the abnormal percentage announcement returns, consistent with the size effect reported in Moeller,
Schlingemann, and Stulz (2004). However, the abnormal dollar returns at the announcement
are increasing as acquirers get larger, consistent with the q−theory predictions.1 In addition,
returns decline over a firm’s history of mergers, as in Fuller, Netter, and Stegemoller (2002), but
multivariate tests reveal that the decline is due to the increase in acquirer size, not experience.
Moreover, dollar returns increase over a repeat acquirer’s sequence of deals. For completeness, I
also verify the validity of the assumptions underlying q−theory. I show that direct transaction
1Abnormal dollar gains are defined as the excess change in acquirer market equity accounting for the market
return, following Moeller, Schlingemann, and Stulz (2004). Though Moeller, Schlingemann, and Stulz (2004)
report a negative size effect on percentage returns and provide univariate evidence on abnormal dollar returns,
they do not run multivariate regressions on abnormal dollar returns as I do in this paper.
Q-THEORY AND ACQUISITION RETURNS
3
costs (proxied by advisor fees) and integration costs (proxied by industry-relatedness and geographic distance) are positively related to both the absolute size of the target and the relative
size of the target.
Since other theories may explain the observed patterns of acquirer returns, I next test the
predictions of q−theory directly against two alternative explanations: agency and hubris. The
agency hypothesis predicts that management interests become less aligned with shareholder
interests as a firm matures. Thus later deals or deals made by larger firms may be made to
generate private managerial benefits, not shareholder wealth gains (Moeller, Schlingemann, and
Stulz, 2004). The hubris hypothesis predicts that early success leads to managerial overconfidence and thus overbidding in later deals (Aktas, de Bodt, and Roll, 2009b). Both hypotheses
predict lower percentage and dollar returns as acquirers get larger.
I test these hypotheses by first identifying the cross-sectional determinants of abnormal returns for a fixed deal number in a firm’s acquisition history. Then I determine if these factors
are changing systematically over a deal sequence. Both conditions are necessary to explain both
the size effect and the significant decline in announcement returns over a firm’s deal history.
I measure agency costs using the Gompers, Ishii, and Metrick (2003) g−index of managerial
entrenchment and outside monitoring using the existence of independent blockholders. Hubris
is measured by the premium paid by the acquirer.
I find only weak support for the agency theory, and none for hubris. Agency variables affect
cross-sectional returns, but vary only slightly across deal sequences. In contrast, premiums
change substantially over deal sequences, but do not affect returns in the cross-section. However,
after controlling for various factors, I still find that dollar gains increase as acquirers get larger
and percentage gains decline, consistent with the predictions of q−theory, but not with agency
or hubris. Certainly, some merger returns can be explained by agency problems, but the results
of this paper suggest that q−theory has greater explanatory power for the average merger.
The above results rely on the idea that each deal is independent. Hence the current returns
to an acquisition depend only on the size of the acquirer and target.2 In contrast, the dynamic
process of market anticipation of future deals at the announcement of earlier deals could explain
2There is a dynamic relation between an acquirer’s size and its prior acquisitions, but the firm may also change
its size through internal growth or divestitures.
4
Q-THEORY AND ACQUISITION RETURNS
declining returns to repeat acquirers as well: when later deals are announced there is no stock
price effect because the value of the deal has already been capitalized. Though anticipation
is widely cited3, prior direct tests find mixed results, suffer from small samples, and do not
account for the dynamic endogeneity between the likelihood of future deals and current returns
(Schipper and Thompson, 1983; Asquith, Bruner, and Mullins, Jr., 1983).
To verify the robustness of my results to an anticipation effect, I conduct a series of novel
empirical tests designed to overcome the limitations of prior studies. First, to address endogeneity, I estimate a simultaneous equations model of the interaction between current M&A returns
and the likelihood of future deals. I find that markets do not capitalize the expected value of
later deals at the announcements of earlier acquisitions. Though repeat acquirers have higher
first announcement returns than firms that do not make subsequent acquisitions, these higher
returns are not related to the likelihood of future acquisitions. Second, in a new econometric
approach, I use quantile regression to identify the effect that deal order has on information
revealed by an announcement. If markets anticipate future mergers, less information will be
revealed at the announcement of later deals compared to earlier deals. I find that information,
as measured by the dispersion in returns for a cross section of acquisitions, controlling for other
factors, is constant for the first six deals in a sequence, contrary to the anticipation theory and
the assumptions made in prior studies. These results are robust to restricting the analysis to
cases where anticipation is most likely, namely samples of large transactions and of the most
frequent acquirers. Thus I find no evidence supporting anticipation using two independent and
unique empirical tests. These results are relevant in their own right, but also validate my main
results.
The main contribution of this paper is to apply q−theory to mergers in order to explain the
effect of acquirer size and experience on acquisition returns and target size. Though there is an
inherent similarity between investments and corporate acquisitions, there is little research that
connects the investment literature with the merger literature. Jovanovic and Rousseau (2002)
3Fuller, Netter, and Stegemoller (2002, p. 1764) assume that markets anticipate mergers for repeat acquirers,
allowing them to “control for much of the information about bidder characteristics contained in the returns at
the announcement of the takeover.” Other recent empirical studies that refer to anticipation as a possible effect
on acquirer returns include Song and Walkling (2000), Wulf (2004), Bhagat, Dong, Hirshleifer, and Noah (2005),
and Song and Walkling (2008).
Q-THEORY AND ACQUISITION RETURNS
5
investigates the relationship between q and aggregate merger activity, but does not analyze
returns from acquisitions, as I do in this paper. A series of earlier papers investigates the relation
between the Tobin’s q of acquirers and targets and acquisition returns (Servaes, 1991; Lang,
Stulz, and Walkling, 1989, 1991). These papers find that high q firms that takeover low q
firms earn higher announcement returns than vice versa. My paper is different because I use
q−theory to explain how acquirer size is related to the choice of target size and the subsequent
acquirer percentage and dollar returns from acquisitions. In addition, my results contribute
to a growing body of research that is concerned with corporate decisions in a dynamic, rather
than static setting. See for example Leary and Roberts (2005) on dynamic capital structure,
Helwege, Pirinsky, and Stulz (2007) on the evolution of insider ownership, and DeMarzo and
Fishman (2007) on the dynamic interaction between agency conflicts and investment.
The remainder of the paper is organized as follows. Section 1 presents a simple illustration
of the application of q−theory to mergers. The data are described in Section 2. Empirical tests
of q−theory and alternative theories are described in Section 3. Section 4 presents robustness
tests of market anticipation. Section 5 concludes.
1. A Simple Illustration of q−Theory in Mergers
To illustrate how target size affects returns I present an extremely simple illustration using the
essence of q−theory models. The goal of this exercise is not to improve upon existing rigorous
models, but merely to demonstrate the relation between the costs and benefits of target size in
mergers using the existing investment literature. The predictions presented below are identical
to the predictions of the rigorous models of Lucas Jr. (1967), Abel (1983), Cochrane (1991),
and Zhang (2005).
I start from the simple two-period q−theory example in Li, Livdan, and Zhang (2009) where
firm value increases through investment. The firm has a production function of ktα where
0 < α < 1 implies decreasing returns to scale. For simplicity, assume there is no depreciation,
so the firm’s capital at period 2 is k2 = k1 + i, where i is the investment. For the case of a
merger, i is simply the size of the target firm, k1 is the pre-merger size of the acquirer, and k2
is the post-merger size. The investment return is r. Following the q−theory literature, the firm
6
Q-THEORY AND ACQUISITION RETURNS
faces adjustment (integration) costs from the investment equal to (a/2)(i/k1 )2 k1 , with a > 0.
The firm chooses i to maximize firm value:
a
max k1α − i −
2
{i}
i
k1
2
!
1
k1 + [(k1 + i)α + k + i] .
r
(1)
Still following directly from Li, Livdan, and Zhang (2009), the first order condition is:
−1−a
i
1
+
α(k + i)α−1 + 1 = 0
k1
r
(2)
which implies,
α(α − 1)(k1 + i)α−2
α(k1 + i)α−1 a
∂r
=
−
<0
∂i
1 + a(i/k1 )
[1 + a(i/k1 )]2 k1
(3)
This means that the return is decreasing in the size of the investment. Taking this analysis
beyond what is presented in the simple example of Li, Livdan, and Zhang (2009), the first order
condition also says that
Taking the derivative,
1 1
i
α−1
=
α(k1 + i)
+1 −1 .
k1
a r
∂(i/k1 )
1
= α(α − 1)(k1 + i)α−2 < 0.
∂k1
ar
(4)
(5)
In the case of mergers, this means that as acquirers get larger, they optimally choose targets of
smaller relative size. Finally, implicitly differentiating the first order condition with respect to
k1 yields:
a(i/k12 ) + (1/r)α(α − 1)(k1 + i)α−2
∂i
=
.
∂k1
(a/k1 ) − (1/r)α(α − 1)(k1 + i)α−2
(6)
The denominator is positive because 0 < α < 1. This means that (∂i/∂k1 ) is positive if
a>
α(1 − α)k12
.
i · r(k1 + i)2 − α
(7)
In other words, if the adjustment costs are large enough, then the size of the investment (acquisition) is increasing in the size of the acquirer – large acquirers buy large targets.
Consistent with the q−theory of investment literature, these results state that as a valuemaximizing acquirer gets larger:
• Targets get larger in absolute size.
• Targets get smaller in relative size.
Q-THEORY AND ACQUISITION RETURNS
7
• Acquirer dollar gains increase.
• Acquirer percentage returns decrease.
Larger firms optimally make larger investments, but reduce integration costs by making investments that are smaller in relative size. Hence, dollar returns increase, but percentage returns
decrease as an acquirer gets larger. For the case of repeat acquirers, since acquirers get larger
through acquisitions, there is a one-to-one mapping from acquirer size to acquisition experience.
2. Data and Methodology
Since I wish to explain the pattern of returns to repeat acquirers, I must account for acquisition
experience. It would be ideal to have returns data and complete acquisition histories of all
acquiring firms. However, comprehensive merger data begins in 1980 and returns data are only
available for public firms. Thus to produce the most complete acquisition histories I limit my
sample to firms that publicly list after 1980. This may produce two types of bias. First, firms
may have extensive acquisition histories as private firms that would not be captured in my
data. However, it is likely that acquisitive private firms also will be acquisitive public firms
and this bias will affect all firms equally. Second, the post-1980 listing restriction may bias my
sample toward firms in certain industries. I address this problem below and find little bias.
The following presents a detailed description of the data.
The sample data are taken from Securities Data Corporations’s (SDC) U.S. Mergers and Acquisitions database. Only acquisitions worth at least $1 million announced between 01/01/1980
and 12/23/2004 that were completed within 1,000 days are included in the sample.4 Because
repeat acquirers may be more likely to acquire many small firms, rather than fewer large firms,
no restriction is placed on the relative value of the target to the acquirer as is commonly done
in prior studies. Also, acquirers have to own less than 50% of the target before the acquisition,
and 100% after the acquisition. This prevents the inclusion of repeat partial acquisitions of the
same target. Acquirers have to be public firms with data available on the Center for Research
in Security Prices (CRSP) and CompuStat databases. Targets are restricted to public, private,
4I restrict attention to completed deals because data on incomplete deals will likely be biased toward public
targets. However, only using completed deals may lead to a misproportional small number of hostile deals, since
hostile deals are more likely to fail (Walkling, 1985).
8
Q-THEORY AND ACQUISITION RETURNS
or subsidiaries of a public or private firm. Also, multiple acquisition announcements by the
same firm within five days of each other are excluded.
Finally, as noted above, to ensure acquisition deal histories are correctly measured, I exclude
all acquirers that were listed on CRSP before 01/01/1980. This exclusion is not typically
done in prior research on multiple acquirers but provides a solid benchmark from which to
order acquisitions. Of course acquisition histories are still likely to be incomplete as pre-IPO
firms make acquisitions. However, if no benchmark is used, acquisition data limitations will
lead to a downward bias in the measurement of acquisition experience for older firms. Using
this restriction also avoids defining the beginning of a merger program by an arbitrary noacquisition hiatus of between two and eight years, as has been done in prior studies (Loderer
and Martin, 1990).
This sampling procedure produces 12,942 acquisitions made by 4,879 acquirers. The prototypical repeat acquirer, Cisco Systems, completed 50 acquisitions, the largest number in the
sample, though the average firm completed 2.7 deals over the sample 25-year period. If a 1%
relative value restriction had been placed on the sample, Cisco would only have 10 deals in the
sample. A 5% cutoff would have left only one deal in the sample for Cisco. Thus, imposing
relative value restrictions may alter the sample significantly. Table 1 presents a summary description of the sample by year. Total deals peaked in 1997 with 1,437 announcements, though
total transaction value peaked in 2000 with $615,382 million. The median transaction value for
all years is $25.38 million, considerably less than the average value of $571 million, reflecting
the positive skewness of the distribution of transaction values.
Though I limit the sample to firms not listed before 1980, the distribution of deals by industry
shifts only slightly toward high-technology industries. In a sample where acquirers are not
restricted to being listed after 1980, using the 49 Fama French Industry Classifications,5 banking
accounts for the largest number of deals without restricting acquirer listing dates (13.9% of all
deals). Computer software (9.9%), business services (6.9%), electronic equipment (5.8%), and
communication (5.5%) round out the top five industries which together account for 42% of all
deals in the unrestricted sample. The top five industries for the sample used in this paper, where
5Generously provided on Kenneth French’s Web site.
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html
Q-THEORY AND ACQUISITION RETURNS
9
acquirers must be first listed after 1980, are software (13.9%), banking (10.8%), business services
(8.6%), communication (6.5%), and electronic equipment (6.2%), totalling 46% of all deals.
Thus the industry clustering in merger activity reported in prior work is confirmed here, and
relatively unchanged by my sample restrictions (Mitchell and Mulherin, 1996; Harford, 2005).
This suggests that the 1980 listing requirement will not produce extensive bias in my results.
Because prior acquisitions may affect any event study prediction method which estimates
abnormal returns using firm historical returns, I calculate abnormal returns using a marketadjusted model with the equally weighted CRSP index as a market proxy. For each day in
the event period, market returns are subtracted from firm returns (Brown and Warner, 1985).
Cumulative abnormal returns (CARs) are computed over the five days surrounding the announcement because the announcement dates listed on SDC are not always accurate, especially
for the small deals in my sample. I also compute dollar abnormal returns following the procedure of Malatesta (1983) and Moeller, Schlingemann, and Stulz (2004). Significance tests of
CARs are conducted with a sign test (Corrado and Zivney, 1992).
Table 2 reports percentage CARs and dollar CARs grouped by total number of deals in a
firm’s series and by acquisition order in the series. There are 2,212 firms that made only one
acquisition in the sample period, while there are 503 with over five acquisitions. These 503
firms account for 10% of all firms in the sample, but complete 35% of all the deals. The average
CAR for all firms and all deals is a significant 1.98%. Positive average returns are consistent
with Moeller, Schlingemann, and Stulz (2004) and result from including private and subsidiary
targets, in contrast to the negative average returns reported in older studies that were limited to
acquisitions of public targets. Also consistent with prior studies, CARs are declining with deal
order. For all firms, CARs are 3.19% on average for the first deal and decline to an insignificant
−0.11% for sixth and later deals.
Also consistent with prior studies is a size effect where the average dollar CARs are −$19.5
million. Dollar CARs are much noisier than percentage CARs and so do not display such an
orderly pattern across deal sequences. However, there is an increase between the significant
dollar CARs of earlier versus later acquisitions. In particular, the dollar CARs on the first
acquisition for all acquirers is $0.45 million on average, compared to $20.25 million for the
10
Q-THEORY AND ACQUISITION RETURNS
fourth deal in a series. Restricting attention to acquirers that make more than five deals, first
acquisitions generate dollar CARs of $35.94 million on average, compared to $67.92 million for
the fourth deal in their deal series. The overall negative average dollar returns are driven by a
few very large deals, consistent with Moeller, Schlingemann, and Stulz (2005), but the overall
pattern of dollar CARs is increasing over a deal sequence.
3. Empirical Tests of the q−Theory of Acquisitions
To test the q−theory in acquisitions, I first empirically examine the plausibility of the assumption that adjustment costs are increasing in the size of the target. I investigate both transaction
costs and integration costs. To measure transaction costs I retrieve the total acquirer financial
advisor fees and the number of acquirer advisors per deal from SDC. Larger deals are predicted
to have larger transaction costs. To proxy for integration costs, I record whether the target
and bidder are in the same Fama French 49 industry classification. Second, I calculate the
geographic distance between the location of bidder and target headquarters measured at the
zipcode level.6 I hypothesize that targets that are in different industries and located farther
away from the acquirer will have greater frictions and thus higher integration costs.
To test the relationship between these cost measures and target size, I run log-log regressions to estimate elasticities between the variables. These results are presented in Table 3.
This analysis should not be interpreted as causal evidence. Instead the results record whether
larger transactions are associated with higher costs, controlling for other factors. First, a 1%
increase in acquirer size is associated with a 0.78% increase in the transaction size and a 0.63%
decrease in the relative size of target to acquirer. This result is consistent with the q−theory
predictions. Second, larger deals are associated with larger transaction costs measured both by
total fees and by the number of advisers. Higher fees are also associated with deals of larger
relative values. Finally, the proxies for integration costs are positively related to relative value.
Higher relative values are associated with higher integration costs as measured by distance and
industry-relatedness. These results provide credibility to the assumption that integration costs
are related to target size.
6The zipcode is taken from SDC. Using the US Census Bureau’s database of zipcode longitudes and latitudes, I
calculate the surface distance in statute miles.
Q-THEORY AND ACQUISITION RETURNS
11
Next, I test the predictions about the relationship between firm size and returns. Econometrically, I want to estimate E(X | Acquirer Size), where X is either target size, relative size,
percentage abnormal returns, or abnormal dollar returns. Since q−theory makes distinctly
non-linear predictions, I do not impose a functional form on this expectation, but instead use
nonparametric kernel regression to plot the relationships.7 These estimated expectations are
plotted in Figure 1 along with scatterplots of the data.
The kernel regression estimates closely follow the theory’s predictions. In particular, both
percentage returns and relative size decrease towards zero as acquirer size increases. Transaction
size is also increasing in acquirer size, as predicted. This evidence shows that returns are related
to acquirer and target sizes. Thus, if acquirers are getting larger with subsequent deals, than
returns will decline over deal sequences. The plot of dollar returns in Panel (D) is too noisy
to allow much inference. The dollar returns blow up when the acquirer is large. On average,
the negative dollar returns for the very large firms are smaller than the positive dollar gains,
leading to a negative relationship for the very largest firms. This helps explain the average
negative size effect reported in Moeller, Schlingemann, and Stulz (2004), but it also shows that
the effect is dominated by a few extreme observations.
Though the nonparametric estimations provide evidence in support of the q−theory approach, they do not control for other factors that may explain declining returns. In particular,
the hypothesis assumes firms are maximizing profits by choosing an optimal target size. Alternative theories of M&As include agency and hubris, where this is not the case. The next set of
tests explicitly controls for a host of variables and investigates these alternative theories.
3.1. Cross-Sectional Tests of Q−Theory Versus Agency and Hubris Hypotheses
To test the alternative theories, I first identify the factors that significantly affect returns in the
cross-section and then test whether these factors are changing over deal sequences. Only factors
that both explain cross-sectional variation and that vary systematically over a deal sequence
can explain the pattern of declining returns.
7In particular I use the “leave-one-out” Nadaraya-Watson estimator with a Gaussian kernel. Cross-validation
is performed by minimizing the estimated prediction error in order to find the optimal bandwidth. See Härdle
(1990) for more details on kernel regression estimates.
12
Q-THEORY AND ACQUISITION RETURNS
In contrast to the efficiency-based size effect in my model, Moeller, Schlingemann, and Stulz
(2004) hypothesize that the size effect reported in their study is likely due to agency problems
of larger firms, though they provide no formal tests. I test this hypothesis directly by including measures of internal monitoring and managerial entrenchment/antitakeover provisions in
regressions on acquirer returns. As a measure of internal monitoring I use the number of nonofficer directors that are blockholders in the firm. These data on 1,913 firms over 1996-2001
come from the Blockholders database maintained by Wharton Research Data Services (WRDS)
and described in Dlugosz, Fahlenbrach, Gompers, and Metrick (2006). Entrenchment is measured using the Gompers-Ishii-Metrick (GIM) governance index of the data in the RiskMetrics
Governance database. This dataset provides information on 24 antitakeover provisions, such as
staggered boards, poison pills, and others, for a sample of predominately large firms for selected
years starting in 1990. For further information see Gompers, Ishii, and Metrick (2003). The
agency theory hypothesizes that more non-officer director blockholders will be associated with
higher returns and more antitakeover provisions will be associated with lower returns. Since
internal monitoring and the market for corporate control may be substitutes, I also look at the
interaction between the two.
To investigate hubris, I look at premiums paid by the acquirer. Premiums are defined as the
transaction value from SDC divided by the market value of the target 50 trading days before
the announcement date. The relation between premiums and CARs is not well defined. The
learning model of Aktas, de Bodt, and Roll (2009b) states that higher premiums drive down
abnormal returns from acquisitions made later in a deal sequence. However, in contrast to
this theory, Betton, Eckbo, and Thorburn (2008) find that premiums are positively related to
acquirer returns possibly due to higher synergies between bidder and target. I also include
target size, Tobin’s q, and prior year returns as these may affect the value of the investment.
Table 4 presents firm fixed effect regressions designed to test the q−theory hypothesis against
the alternative explanations. The first column regresses the five-day percentage CAR on acquirer, target, and deal characteristics, controlling for unobserved firm heterogeneity and time
effects. First, deal number is not significantly related to abnormal returns. This means that
other determinants of returns must be changing over time to explain declining returns. Second,
Q-THEORY AND ACQUISITION RETURNS
13
acquirer size is negatively and convexly related to acquirer CARs, consistent with the predictions of q−theory, but also with hubris and agency. In addition deals/year is also negatively
related to CARs, though time elapsed since the prior deal is positively related. Firms that make
many acquisitions quickly have lower CARs than firms that do not. Song and Walkling (2008)
use this as evidence of market anticipation of later deals. However, a short duration between
deals may instead indicate that integration between the target and bidder is hampered by a
subsequent acquisition. Moreover, in various explicit tests reported in Section 4, I do not find
support for anticipation of future deals at the announcement of a current deal as a determinant
of returns.8 Third, the results in Table 4 show that public targets and particularly those purchased with stock, generate significantly lower returns, consistent with the liquidity premium
shown in Officer (2007). All of these secondary results are consistent with prior studies (Fuller,
Netter, and Stegemoller, 2002; Moeller, Schlingemann, and Stulz, 2004).
Next, I include the variables measuring agency costs in column (2) under the ‘Governance’
heading in Table 4. Outside director blockholders is significant and positive as hypothesized,
the entrenchment index is negative, but not significant, and the interaction term is significantly negative. The negative sign of the interaction term indicates that the benefit of internal
monitoring is eroded with more entrenchment provisions. These results are consistent with
Masulis, Wang, and Xie (2007) who show greater shareholder control is positively related to
acquirer returns. Also of note is that the inclusion of these agency variables does not change
the insignificant acquirer size effect between regressions (1) and (2). This does not support an
agency explanation of the size effect as suggested in Moeller, Schlingemann, and Stulz (2004),
but neither is it convincing evidence against this hypothesis, since the firms with observed
agency variables tend to be much larger than those firms omitted from the RiskMetrics database. Next, I test the hubris story, where I restrict my sample to acquisitions of public targets
in order to calculate premiums. The results in column (2) under the ‘Public Targets’ heading
in Table 4 suggest that there is no relationship between premiums and CARs, contrary to the
hubris hypothesis.
8Song and Walkling (2008) investigate a different sort of anticipation where investors correctly anticipate an
announcement if other industry firms have announced acquisitions. In robustness tests I have controlled for the
number and value of industry acquisitions in the prior year and my results are qualitatively unchanged.
14
Q-THEORY AND ACQUISITION RETURNS
In Table 5, I repeat the above regressions using the acquirer’s abnormal dollar returns as the
dependent variable instead of the percentage returns. First, using the largest sample available,
abnormal dollar returns are positively and significantly related to the size of the acquirer. This
is consistent with the univariate results in Table 2. Increasing dollar returns with acquirer size
is strong evidence in support of the q−theory and contradicts the agency hypothesis. Most of
the other variables in all specifications are insignificant due to the noisiness of abnormal dollar
returns, though target public status and payment method are still significant. Also of note
is that transaction size is negative and significant. This is also consistent with the q−theory.
For a given acquirer size, there is an optimal target size. Increasing the target size for a fixed
acquirer size will move the firm away from optimal. However, this is also consistent with an
agency story since it implies that managers may be making transactions that are larger than is
optimal.
In summary, the above results are consistent with the predictions from q−theory: larger
acquirers have lower percentage returns, but larger dollar gains. Though no evidence is found
to support the hubris hypothesis, the above results also show that both target size and more
managerial entrenchment with less oversight significantly reduces acquirer returns in the crosssection. However, to explain declining acquirer returns, it is not enough that a variable affects
CARs in the cross-section alone. It also must be the case that the level of the variable changes
systematically over deal sequences.
3.2. Time-Series Tests of Q−Theory Versus Agency and Hubris Hypotheses
To determine which of these variables are consistently changing over deal number, I calculate
means and medians of firm and deal characteristics by deal number for all firms in the sample
as well as slope coefficients for both a linear and squared term similar to the procedure in
Aktas, de Bodt, and Roll (2009a). These results, presented in Table 6, provide more evidence in
support of the q−theory approach. The average acquirer size grows over subsequent acquisitions
and the average relative size of the target declines at a declining rate over deal sequences as
predicted by q−theory. Thus later deals are dominated by acquisitions of large targets, though
Q-THEORY AND ACQUISITION RETURNS
15
of a small relative size. Dollar returns are increasing and percentage returns are decreasing.
This again provides evidence consistent with the predictions from q−theory.
Returning to the results in Table 6, agency problems appear to have a weak negative relation to declining acquisition returns. First, though the number of outside director blockholders
is significantly related to CARs, they are unchanging over deal sequences, a surprising result
considering the large increase in the average acquirer size. Second, though managers are significantly more entrenched in later deals than in earlier deals in a statistical sense, the actual
change in the average number of antitakeover provisions over the first ten deals is very small.
Since these entrenchment changes only affect returns significantly in the interaction with the
outside director monitoring variable, the final effect of increased entrenchment on CARs is
very small. For robustness, other measures of agency might have been used, but they would
likely suffer from the same time invariance. For example, inside ownership may affect merger
returns, but both Zhou (2001) and McConnell, Servaes, and Lins (2008) report that inside
ownership changes are extremely small over time within the same firm. Finally, premiums increase over deal sequences, but since they are not significantly related to acquirer returns in
the cross-section they can not explain the pattern of declining returns.
Sample attrition may explain the deal-series variation if the firms completing later deals are
significantly different than those completing earlier deals. To account for this potential bias, in
unreported tests I examine deal-series variation using only observations from the 503 acquirers
with more than five deals in the sample. The results are unchanged using this smaller sample.
In addition, I control for firm fixed effects by looking at within-firm changes in variables over
deal numbers and find results that are qualitatively the same as those presented above, thus
the q−theory holds under these various robustness checks.
4. Robustness Tests of Market Anticipation of Mergers
Though the above results are consistent with the q−theory of investment, if investors anticipate
later deals at the announcement of earlier deals, the empirical patterns of the returns to repeat
acquirers could also be the consequence of an entirely different effect which would not be
detected in the above analyses. Schipper and Thompson (1983) propose a capitalization theory
16
Q-THEORY AND ACQUISITION RETURNS
where markets reflect the entire benefit of an acquisition sequence in the first announcement
of the program. Later acquisition returns only reflect surprises, which are zero on average. A
related signaling theory proposed in Asquith, Bruner, and Mullins, Jr. (1983) suggests that
each acquisition announcement provides less information to the market about the true value
of the firm than the preceding announcement. Since the signaling theory is equivalent to the
capitalization theory with uncertainty, I group them together in a theory called the anticipation
theory. This theory predicts that acquisition returns will be declining as uncertainty is resolved,
and later deals will reflect less new information.
Since the dynamic effect of anticipation could distort any cross-sectional theory explaining
declining returns, it is crucial that we determine its effect, if any. As mentioned in the introduction, these theories have yet to be tested rigorously, though they are often cited. Therefore, I
test this theory below using two completely different methods: simultaneous equations models
and quantile regression tests.
4.1. Simultaneous Equations Model
There is a possible endogenous relationship between current returns and future expected returns.
A large return on a repeat acquirer’s first deal may simply reflect a survival bias, where a
successful firm will continue to make acquisitions, rather than reflect the present value of future
deals, as suggested by the anticipation theory. To explicitly control for this endogeneity problem,
I use a simultaneous equations framework with panel data which allows me to control for the
likelihood of future acquisition activity at the current deal.
I define the following simultaneous equations model,
CARia = α1 EV Fia + X1ia β1 + c1i + uia
a = 1, . . . , A
(8)
EV Fia = α2 CARia + X2ia β2 + c2i + via
a = 1, . . . , A
(9)
Q-THEORY AND ACQUISITION RETURNS
17
where
EV F = Expected Value of Future Deals
a = Order number of acquisition.
This model allows for a simultaneous relationship between the present CAR and the expected
value of future acquisitions. The c1i and c2i terms capture assumed time-invariant unobserved
firm heterogeneity that may affect returns and the value of future deals. This would include
such attributes as corporate culture and organizational ability. The variables in the X’s reflect
other explanatory variables in the equations including size, valuation, deal number, and time
elapsed between deals.
To estimate the expected value of future deals (EV F ) I must account for both the probability
of completing more deals and the value of the deals. First, even after controlling for numerous
factors, cross-sectional studies of returns usually report R2 measures of less than 10%, indicating
that much of the variance in returns is unexplained. Thus, to reduce noise, I assume all firms
would realize a common gain if they carried out a future deal. Second, the probability of making
a subsequent deal is much higher than the probability of making ten more deals. Compounding
probabilities implies that the likelihood of the immediately subsequent deal captures the greatest
portion of the uncertainty of future M&A activity. Thus the uncertainty of the value and
likelihood of future deals motivates the following simplifying assumption,
EV Fia = Pia · Va+1
(10)
where the value of the future deal, Va+1 , is common to all firms, but the probability of making a
subsequent deal, Pia , varies by the firm and deal characteristics of the current deal, a. According
to the CARs presented above, Va+1 is non-negative on average, and so there should exist a
positive relationship between EV Fia and CARia in Equations (8) and (9).9
9One could argue that the likelihood of a successful deal is inversely related to the value of the deal. Hietala,
Kaplan, and Robinson (2003) show that Viacom won the takeover battle for Paramount in 1994, but overpaid
substantially. Thus, due to a winner’s curse, highest bidders are most likely to succeed in an acquisition, but
destroy value. I do not think this is a large concern in my analysis. The probability I measure is the likelihood
of making a future acquisition as measured at the time of a current announcement. This incorporates both the
18
Q-THEORY AND ACQUISITION RETURNS
I first-difference the panel data to cancel unobserved time-invariant firm heterogeneity. Thus
the equations to be estimated are,
∆CARia = α1 ∆Pia + ∆X1iaβ1 + ∆uia
∆Pia = α2 ∆CARia + ∆X2ia β2 + ∆via
a = 1, . . . , A
(11)
a = 1, . . . , A
(12)
where
∆Zia = Zia − Zi,a−1
where Z is any variable in Equation (11) or (12)
Pia = Probability of completing a subsequent deal for firm i at deal a
To estimate these equations I use equation-by-equation generalized method of moments (GMM)
which permits heteroskedasticity and serial correlation. I use a linear probability specification
to estimate Equation 12.10
To estimate the probability model I record for each acquisition announcement whether a
subsequent deal is made. In order to prevent biasing these numbers downward due to upper
year restrictions on the sample, i.e., only deals announced by the end of 2004 are included, or
from sample attrition, I only record no subsequent deal if the firm had enough time to complete
another deal at the 90% level. For each deal number I find the 90th percentile of trading days
until the next announcement across all firms that made a subsequent deal. If a firm does not
complete a subsequent deal, but is listed on CRSP for this number of days after its terminal
deal, I record this as not making a deal. If the firm is not listed this many days or the sample
period ends before the number of days has elapsed I record the observation as missing. I use
this dummy variable as the dependent variable Pia in Equation (12).
likelihood of making an offer and the likelihood of success. Only the second likelihood might be negatively related
to deal value and it is arguable less important than the fundamental decision to make an acquisition or not.
10Linear probability models, as opposed to probit or logit models, have the unappealing quality that fitted
probabilities may not fall in the range [0, 1]. However, the advantage of a linear probability model is that no
distributional assumptions need to be made about the error term, vit . In unreported tests I compute probit and
logit models of Equation (12) and use the fitted values as proxies in Equation (11). This does not change the
qualitative results. A non-linear hazard model also could be estimated as in Whited (2006) and Meyer (1990).
The main advantage of this model is that it controls for the effect of time on the likelihood of making a subsequent
deal without distributional assumptions. In my analysis I directly control for both duration between acquisitions
and a firm’s acquisition intensity. Thus the gains from a hazard model are not obvious.
Q-THEORY AND ACQUISITION RETURNS
19
I use Net Payout Yield and Internal/(Total Investment) to instrument for Pia in Equation
(11). Net payout yield is a simplified measure of the one used in Boudoukh, Michaely, Richardson, and Roberts (2007), and is defined as dividends plus net purchases of common stock
normalized by market equity. Internal to total investment is defined as net capital expenditures
divided by net capital and acquisition expenses. I assume these variables are correlated with
the probability of completing a future deal, but not with the CAR of the current deal.11
To instrument for CARia in the probability model (Equation (12)), I use NYSE percentile
prior returns, public and private target dummies, transaction value, toehold, and interaction
terms between equity and public and private target dummies. These are assumed to be correlated with the CAR of the current deal but not with the probability of completing a future
deal.12
The results of the simultaneous equations model are presented in Table 7. Neither endogenous
variable, CAR(−2,+2) or Pr(Future Deal), is significant, contradicting the capitalization theory.
This implies that the endogenous relationship between CARs and future acquisition activity
has no explanatory power. In particular, Pr(Future Deal) is not significantly related to the
current CAR. Furthermore, deal number is not a significant determinate of abnormal returns,
in contrast to the indication of the univariate results. Also, the time since the last deal and
the acquisition rate of the acquirer are controlled for in the analysis and are insignificant in
the regression on CAR. Instead, the significant determinants of current deal CARs are acquirer
size, prior returns, the public status of the target firm, and the form of payment used in the
transaction.
11The relation between payout yield to the probability of future acquisitions is intuitive. On average, firms with
high payout yields have less attractive investments (internal or external) than those firms that are retaining their
earnings and thus are less likely to be making external investments. The ratio of internal to total investment is also
likely to be correlated with future acquisition activity. Large external investments may require complementary
future internal investments. For these to be valid instruments they also must be uncorrelated with current
CARs. Given a firm is making an acquisition, there is not a clear link between current CAR and payout yields
or internal-to-total investment ratios.
12Prior returns, public and private dummy variables, and toeholds should only be relevant for the current
acquisition since they do not predict any future activity. It is possible that public and private target dummies
proxy for relative size and hence may be correlated with the likelihood of making future acquisitions. I conduct
the following analysis without these variables as instruments and find the results qualitatively unchanged.
20
Q-THEORY AND ACQUISITION RETURNS
4.2. Quantile Regression Tests
The signaling theory of Asquith, Bruner, and Mullins, Jr. (1983) posits that each subsequent
deal conveys less information than prior deals. In other words, if a firm has already made
multiple acquisitions, a new announcement will only be marginally informative. For a given
deal number, assuming individual deals in the cross section have heterogeneous and unique true
values, a widely dispersed distribution of abnormal returns reflects more information is being
revealed, whereas less dispersion would be associated with less information. Dispersion in this
case is not noise because each deal does not have a common true value. Thus the signaling
theory predicts that the dispersion of returns is decreasing with deal number.
To test this theory I use quantile regression to check for heteroskedasticity in returns over deal
number.13 If the slopes of the quantile regression estimates of CAR on deal number at different
quantiles are unequal, then the returns are heteroskedastic, since the dispersion of returns
is not constant. Moreover, quantile regression allows us to determine how heteroskedasticity
changes as independent variables change. The signaling hypothesis suggests that the difference
between the deal number slope of an upper tail quantile and a lower tail quantile is negative,
implying dispersion is decreasing in deal number. A stylized representation of this is presented in
Figure 2, where the slope of the 90th percentile is smaller than the slope of the 10th percentile.
Quantile regression is an ideal method to test dispersion for financial returns because it is
robust to outliers, independent of any Gaussian assumption, and confounding factors can be
controlled.
Table 8 presents the results of quantile regressions controlling for firm and deal characteristics. The estimated upper quantile slopes are not significantly different than the lower quantile
slopes. This contradicts the signaling hypothesis and indicates that information dispersion does
not significantly change over deal number, at least for the first six deals.
The finding against the signaling hypothesis is consistent with the findings above against a
capitalization hypothesis. New information is revealed with each announcement, regardless of
its order in a deal sequence. Markets are unable to anticipate this new information, and the
returns generated by each deal are deal specific and do not reflect future acquisition activity.
13See Buchinsky (1998) for details on quantile regression.
Q-THEORY AND ACQUISITION RETURNS
21
Acquisitions are judged on a deal-by-deal basis by the characteristics of the bidder, the target,
the deal structure, and the interaction between the three. This provides validation of the main
empirical findings presented in Section 3.
4.3. Further Robustness Checks
The above results provide evidence that stock price changes from current acquisition announcements do not reflect the anticipated value of future deals. In this section, I check the validity
of these results under different criteria of relative value and definitions of acquisitiveness. First,
the relative size of the target to acquirer in a current deal may affect how much information is
revealed about the likelihood of making future deals. Moreover, if markets do anticipate future
deals, larger relative size deals are more likely to be reflected in current stock price changes. I
create sub-samples where transaction values are restricted to be larger than 1%, 5%, and 10% of
the market equity of the acquirer (11,145 deals, 7,104 deals, and 4,882 deals, respectively). Firm
acquisition histories are recalculated under each criterion, and the simultaneous equations and
quantile regression models are estimated. The results are qualitatively unchanged; no evidence
of anticipation is found.
In the preceding sections, a firm’s acquisition history includes all deals a firm has made since
first listing on CRSP. Though I account for the number of deals per year in the regression
analyses, to further check robustness I exclude all observations from firms with more than 500
trading days between any consecutive acquisitions. Moreover, I also create subsamples of the
most active acquirers by only including those deals where the acquirer completes at least 0.667
deals per year (50th percentile of all deals) and a more stringent criteria of 1.16 deals per
year (75th percentile). These samples produce 4,030 and 2,016 deals respectively. Acquisition
histories are then recalculated with these sub-samples. Using these samples does not change
the results presented above. As a stronger test I combine the above robustness criteria to create
a subsample of deals of large relative size made by those firms that are the most acquisitive
and still do not find any evidence of market anticipation.
Finally, since a new CEO may make it more difficult to predict future acquisition activity, I
include a dummy variable which indicates if the current deal was made by a new CEO, with
22
Q-THEORY AND ACQUISITION RETURNS
data taken from the Compustat Execucomp database. I find that CEO changes do not change
any of the qualitative results reported above on market anticipation.
5. Conclusion
Using a simple version of q−theory, I generate predictions about the relationship between acquirer and target size and returns. Firms optimally choose a target size that maximizes profits,
though the ratio of profits to acquirer size is diminishing as acquirers get larger, thus percentage
returns decline, but dollar returns increase. This implies that value-maximization leads to lower
returns for larger firms in acquisitions. Empirical tests provide support for these predictions
and also for the assumptions underlying q−theory. Kernel regressions find patterns of returns
and target size consistent with the predictions from q−theory. In multivariate regressions, I find
that abnormal dollar returns increase and percentage returns decline as acquirers get larger.
Finally, the longitudinal decline in targets’ relative value and acquirer percentage returns, and
increase in the absolute sizes of the target, acquirer, and the acquirer dollar returns support
the predictions of the theory.
I also test two alternative hypotheses to explain the pattern of declining returns. First,
controlling for deal number, more managerial monitoring increases acquirer returns. However,
the level of monitoring is constant over a firm’s deal sequence and entrenchment levels are
only slightly increasing. These results provide weak evidence that agency costs may also lead
to decreasing abnormal returns for repeat acquirers. I find no evidence to support a hubris
explanation of decreasing returns.
For robustness, I test the widely cited theory that returns decline because markets anticipate
later deals at the announcement of earlier ones. Controlling for the endogenous relationship
between current M&A returns and the likelihood of future acquisitions, I find no evidence to
support the predictions of market anticipation. In particular, announcement returns reflect
only the estimated value change from the current acquisition, not future acquisitions, and the
informativeness of this signal does not diminish as acquirers make subsequent deals. This
implies that announcement returns are deal-specific and the empirical results on the q−theory
are robust.
Q-THEORY AND ACQUISITION RETURNS
23
The validity of the q−theory approach suggests that more research on the integration costs of
acquisitions may be warranted since they likely help to explain M&A decisions. In particular the
theoretical models of Jovanovic and Rousseau (2002) and Yang (2008), assume M&A activity
incurs a substantial fixed cost to the acquirer which affects their decision-making process. In
addition, mergers present an unexplored area for further tests of the q−theory of investment.
In contrast to the standard application of q−theory to the size of firm investment, the many
observable characteristics of mergers provide greater detail to extend q−theory to the analysis
of different types of investment.
24
Q-THEORY AND ACQUISITION RETURNS
Appendix
Variable
Description
Abnormal $ Returns
The abnormal changes (from the market adjusted returns) in market
equity from two days before to two days after the deal announcement.
All Cash
=1 if only cash was used as payment, according to SDC, 0 otherwise.
All Stock
=1 if only stock was used as payment, according to SDC, 0 otherwise.
CAR(−2,+2)
Cumulative abnormal return over event days (-2,+2) computed by summing over five days the difference between the CRSP equal-weighted
index from the firm return for each day.
Deal Number
The ordered acquisition number for a firm in a series of acquisitions.
Deals/Year
The number of trading days between the listing date and the current
announcement, divided by 250.
Days Since Listing
The number of trading days from first listing on CRSP
Debt/Equity
Long-term debt (Compustat item 9)/Common Equity (item 60)
Entrenchment Index
The Gompers-Ishii-Metrick index of 24 antitakeover provisions recorded
in the RiskMetrics database of primarily large firms. Higher values indicate more antitakeover provisions. Data is recorded in 1990, 1993, 1995,
1998, 2000, 2002, and 2004. Following Gompers, Ishii, and Metrick
(2003), I fill each missing year with the most recent governance provisions available. Also firms with dual class common stock are omitted.
Free Cash Flow
[Operating income before depreciation (Compustat item 13) - interest
income (item 15) - income taxes (item 16) - capital expenditures (item
128)]/[Total assets (item 6)]
Geographic Distance The number of statute miles from the center of the acquirer headquarter’s zipcode to the center of the target firm headquarter’s zipcode. Zipcode data is from SDC.
continued on next page
Q-THEORY AND ACQUISITION RETURNS
25
Appendix - Continued
Variable
Description
Internal
Total investment
[Capital Expenditures (Compustat item 128) - Sale of Property, Plant,
& Equipment (PPE) (item 107)]/[Capital Expenditures - Sale of PPE
+ Acquisitions (item 129)]
Leverage
[Debt in current liabilities (Compustat item 34) + Long term debt (item
9)]/[Total assets (item 6) - Common equity (item 60) + Market equity
(item 24 × 25)]
Market Equity
Price times shares outstanding at the end of the most recent month.
Net Payout Yield
[Dividends (Compustat item 21) + Common Stock purchases (item 115)
- Common Stock sales (item 108)]/Market Equity (item 24 × item 25)
Number of Advisers
Total number of financial advisers to acquirer as reported on SDC
NYSE B/M
NYSE vigintile of book-to-market (B/M). B/M is calculated for each
firm for each year as accounting book value over market value where
book value is total assets (Compustat item 6) - liabilities (item 181) +
balance sheet deferred taxes and investment credits (item 35) - preferred
stock liquidating value (item 10) or preferred stock redemption value
(item 56) or carrying value (item 35), in this order. Market equity is
price times shares outstanding at the end of December. If the fiscal
year-end of a company is between January and May, the book equity
from the prior year is matched against the market equity of December.
NYSE Prior Returns NYSE vigintile of the buy-and-hold return over the prior 12 months.
Vigintiles are 1/20ths of unity.
NYSE Size
Market equity vigintile of NYSE market equities. Market equity is price
times shares outstanding. Vigintiles are 1/20ths of unity.
continued on next page
26
Q-THEORY AND ACQUISITION RETURNS
Appendix - Continued
Variable
Description
Outside Director
The number of non-officer director blockholders (5% stock ownership).
Blockholders
These data come from the WRDS Blockholder database with observations from 1996 to 2001. For observations past 2001, I use 2001 values.
See Dlugosz, Fahlenbrach, Gompers, and Metrick (2006).
Premium
Transaction value recorded by SDC divided by the market value of the
target 50 trading days before the announcement. Premiums are restricted to range between 0 and 3. Only available for public firms.
Prior Industry Deals
Total number of completed acquisitions above $1 million in the acquirer’s
Fama-French 49 Industry classification
Prior Year Returns
Buy-and-hold return over the 12 months that concludes at the most
recent month-end.
Private
=1 if the target firm is private as recorded on SDC, 0 otherwise.
Public
=1 if the target firm is public as recorded on SDC, 0 otherwise.
Relative Value
The transaction value as recorded by SDC, divided by the acquirer market equity
Same Industry
=1 if the target and bidder are in the same Fama French 49 industry
classification
Subsidiary
=1 if the target firm is a subsidiary as recorded on SDC, 0 otherwise.
Tender Offer
=1 if the offer is a tender offer, 0 otherwise.
Tobin’s q
Total assets (Compustat item 6) - common equity(item 60) + market
equity (item 25)× (item 24)/ Total assets (item 6)
Toehold
The percentage of the target firm held by the bidder prior to the announcement as reported in SDC.
continued on next page
Q-THEORY AND ACQUISITION RETURNS
27
Appendix - Continued
Variable
Description
Total Acquirer Fees
The dollar amount of all fees paid to acquirer advisers, as reported in
SDC.
Transaction Value
The value of all consideration paid in a deal minus the costs and fees as
reported by SDC. Values are reported in $2005 adjusted millions.
Wave Dummy
=1 if the deal is classified as an industry merger wave, 0 otherwise. Industry merger waves are identified using the technique of Harford (2005),
with the only exception that I restrict to $1 million deals or greater and
I only count industry deals based on acquirer industry, rather than a
combination of bidder and target as in Harford.
Years Since Last
The number of trading days since the last acquisition announcement or
the listing date if the acquisition is the first, divided by 250.
28
Q-THEORY AND ACQUISITION RETURNS
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30
Q-THEORY AND ACQUISITION RETURNS
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15
10
0
−5
0
5
10
15
−5
ln(Market Equity)
0
5
10
15
ln(Market Equity)
(b) Relative Size
0
5
10
ln(Market Equity)
(c) Percentage Returns
15
50000
0
−50000
5−Day Dollar CAR
0
−.5
−1
−5
−100000
.5
100000
(a) Transaction Size
5−Day CAR
31
5
Relative Size
10
5
0
ln(Transaction Size)
15
Q-THEORY AND ACQUISITION RETURNS
−5
0
5
10
ln(Market Equity)
(d) Dollar Returns
Figure 1
Nonparametric kernel regressions on acquirer ln(market equity)
The figures above are generated from “leave-one-out” Nadaraya-Watson kernel regression estimates of E[X|ln(Acquirer Market Equity)], where X is ln(transaction size), the relative size of
target to acquirer, CAR(−2,+2) , or Dollar CAR(−2,+2) . The estimates are found using a Gaussian
kernel function and the bandwidth is chosen using cross-validation to minimize prediction error.
The sample consists of 12,942 observations over 1981 to 2004.
15
32
Q-THEORY AND ACQUISITION RETURNS
CAR
90th Percentile
Deal Number
10th Percentile
Figure 2
Anticipation theory prediction of the distribution of returns by deal number
This figure represents a stylized representation of the anticipation theory. The dark curves
represent the distribution of CARs conditional on deal number. The anticipation theory posits
that the distribution of CARs becomes less dispersed at higher deal numbers. The dashed
lines represent the conditional percentiles of the distributions, for the 90th and 10th percentiles.
These fitted lines correspond to the quantile regression estimates of CAR on deal number at each
percentile.
Q-THEORY AND ACQUISITION RETURNS
33
Table 1
Summary of acquisition activity by year
‘Series Starts’ reports first-time acquisition announcements in a given year. ‘Mean Series Length’
reports the mean number of deals of all acquisition series begun in a given year. ‘Total Deals in
Year’ lists all recorded acquisitions for a given year in the sample. ‘Median Transaction Value’ is
the median transaction value for all deals announced in a given year. ‘Total Transaction Value’
is the aggregate transaction value for a given year. Transaction value is defined by the SDC
database to be the total value of consideration paid excluding fees and expenses. Values are
reported in millions of 2005 adjusted dollars.
Year
Series
Starts
Mean
Series
Length
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
1
16
48
75
40
56
75
102
141
128
159
205
276
398
361
402
501
442
378
321
257
173
170
154
2.00
1.94
3.65
3.45
4.13
3.46
3.41
3.32
3.67
2.92
3.75
3.40
3.09
3.22
2.84
3.01
2.53
2.27
2.29
2.02
1.89
1.72
1.35
1.10
All
4, 879
2.65
Total
Deals
In Year
Median
Transaction
Value
Total
Transaction
Value
1
19
61
101
54
93
111
139
231
228
263
398
582
800
887
1, 096
1, 437
1, 418
1, 118
980
770
713
686
756
$10.81
16.36
16.03
15.50
71.26
50.14
38.04
33.88
17.52
12.53
12.55
12.81
15.20
15.37
18.68
24.18
24.22
29.34
33.00
39.13
33.56
24.54
37.49
37.31
$11
642
3, 789
4, 726
16, 388
13, 395
14, 457
16, 315
22, 669
13, 671
17, 306
21, 022
55, 004
64, 832
73, 584
175, 711
227, 085
513, 396
370, 423
615, 382
153, 839
88, 193
153, 685
151, 526
12, 942
$25.38
$2, 787, 050
Table 2
Announcement returns by number of acquisitions and acquisition order
Cumulative abnormal returns (-2,+2) in percent terms computed using an equally weighted market-adjusted model. Abnormal dollar
returns are presented in brackets. Numbers in parentheses indicate sample sizes. Statistical significance is tested with the sign test
and significance is denoted by ∗ , ∗∗ , and ∗∗∗ at the 10%, 5%, and 1% levels Sample is over 1981 to 2004. Total sample size is 12,942.
Acquisition Number in Series
34
Q-THEORY AND ACQUISITION RETURNS
Number of
Deals in Series
1st
2nd
3rd
4th
5th
>5th
All
Deals
1
3.39∗∗∗
[−1.68∗∗∗ ]
(2,212)
3.39∗∗∗
[−1.68∗∗∗ ]
(2,212)
2
2.97∗∗∗
[7.83∗∗∗]
(1,060)
1.49∗∗
[−19.59∗∗]
(1,060)
3
2.38∗
[-56.88]
(558)
2.52∗∗∗
[−2.35∗∗∗]
(558)
1.74∗∗∗
[4.83∗∗]
(558)
4
3.58∗∗∗
[13.59∗∗∗]
(343)
2.12∗∗∗
[16.63∗∗]
(343)
1.34
[−42.22]
(343)
1.51
[−24.52]
(343)
5
2.73
[10.89]
(203)
3.11∗∗∗
[2.03∗∗∗ ]
(203)
0.82
[7.83]
(203)
0.99
[−22.24]
(203)
−0.09
[−32.38]
(203)
>5
3.55∗∗∗
[35.94∗∗∗]
(503)
2.48∗∗∗
[3.24∗∗∗ ]
(503)
1.72∗∗
[−2.12∗ ]
(503)
1.75∗∗∗
[67.92∗∗∗]
(503)
1.22
[68.04]
(503)
−0.11
[−134.54]
(2,034)
1.14∗∗∗
[−41.03∗∗∗]
(4,549)
All
3.19∗∗∗
[0.45∗∗∗]
(4,879)
2.10∗∗∗
[−5.37∗∗∗]
(2,667)
1.53∗∗∗
[−7.01∗∗∗ ]
(1,607)
1.52∗∗∗
[20.25∗∗∗]
(1,049)
0.84
[39.17]
(706)
−0.11
[−134.54]
(2,034)
1.98∗∗∗
[−19.52∗∗∗]
(12,942)
2.23∗∗∗
[−5.88∗∗∗ ]
(2,120)
2.21∗∗∗
[−18.13∗∗∗]
(1,674)
2.14∗∗∗
[−9.13∗∗∗ ]
(1,372)
1.51∗∗
[−6.77∗∗ ]
(1,015)
Q-THEORY AND ACQUISITION RETURNS
35
Table 3
Cross-sectional determinants of integration costs
This table presents log-log OLS regressions of the determinants of transaction size (2005 dollars)
and relative value (transaction size divided by acquirer market value). Thus, coefficients are
elasticities between an independent variable and the dependent variable. All variable definitions
are in the Appendix. Observations are over 1981–2004. Robust p−values clustered by acquirer
are reported in parentheses and significance is denoted by ∗ , ∗∗ , and ∗∗∗ at the 10%, 5%, and 1%
levels.
ln(Transaction Size)
ln(Acquirer Size)
0.7800
(0.000)
ln(Acquirer Prior Year Returns)
0.1204∗∗∗
(0.001)
Same Industry Dummy
0.0977
(0.181)
ln(Geographic Distance)
−0.0025
(0.865)
∗∗∗
ln(Relative Value)
−0.6250∗∗∗
(0.000)
0.0358
(0.482)
−0.2298∗∗
(0.047)
0.0652∗∗∗
(0.002)
ln(Total Acquirer Fees)
0.5604∗∗∗
(0.000)
0.4448∗∗∗
(0.000)
ln(Number of Advisers)
0.2796∗
(0.058)
0.2648
(0.174)
Constant
2.3206∗∗∗
(0.000)
0.5238∗
(0.083)
Observations
Adjusted R2
597
0.7651
597
0.2150
36
Q-THEORY AND ACQUISITION RETURNS
Table 4
Firm fixed-effects regressions of acquirer abnormal percentage returns
‘All,’ ‘Governance,’ and ‘Public Target’ headings refer to the sample requirements for inclusion
in the regressions. All and Governance regressions present results from first-differenced OLS
regressions. Public Target regressions presents results from a firm fixed-effect (mean deviation)
regression. The dependent variable in all regressions is the five-day market adjusted CAR using
the equally weighted CRSP index as the market. Observations are over 1981-2004. Robust
p−values are reported in parentheses, clustered at the firm level and significance is denoted by
∗ ∗∗
, , and ∗∗∗ at the 10%, 5%, and 1% levels. All variable definitions are in the Appendix.
All
Governance
(1)
(2)
Public Targets
(1)
(2)
Acquirer Characteristics
NYSE Market equity
−0.0022∗∗∗
(0.000)
0.0025
(0.137)
0.0026
(0.120)
−0.0017
(0.306)
−0.0017
(0.281)
NYSE Market equity2
0.0000∗∗
(0.029)
−0.0000∗
(0.090)
−0.0000∗
(0.075)
0.0000
(0.409)
0.0000
(0.316)
−0.0000
(0.575)
−0.0000
(0.767)
−0.0001
(0.680)
−0.0000
(0.946)
−0.0001
(0.579)
NYSE B/M
0.0001
(0.543)
−0.0003
(0.224)
−0.0003
(0.295)
0.0003
(0.372)
0.0002
(0.497)
Deal number
−0.0025
(0.185)
−0.0018
(0.660)
0.0009
(0.826)
0.0035
(0.242)
0.0042
(0.138)
Deals/Year
−0.0134∗
(0.075)
−0.0028
(0.959)
−0.0217
(0.701)
−0.0218
(0.521)
−0.0325
(0.311)
0.0013
(0.391)
0.0010
(0.522)
−0.0043∗
(0.068)
−0.0042∗
(0.065)
NYSE Prior returns
Years since last
0.0020∗∗∗
(0.008)
Tobin’s q
−0.0009
(0.302)
0.0007
(0.549)
0.0009
(0.486)
0.0009
(0.600)
−0.0008
(0.674)
Industry deals prior year
−0.0002
(0.132)
−0.0001
(0.450)
−0.0001
(0.428)
−0.0000
(0.899)
−0.0000
(0.943)
0.0047
(0.366)
0.0019
(0.850)
0.0039
(0.688)
−0.0122
(0.285)
−0.0119
(0.301)
Wave dummy
0.1826∗∗
(0.019)
Outside director blockholders
Entrenchment index
−0.0068
(0.154)
Directors × Entrenchment
−0.0140∗
(0.094)
continued on next page
Q-THEORY AND ACQUISITION RETURNS
37
Table 4 - Continued
All
Governance
(1)
(2)
−0.0178∗
(0.079)
−0.0180∗
(0.074)
Public Targets
(1)
(2)
Target Characteristics
Public
−0.0319∗∗∗
(0.000)
Private
−0.0043
(0.237)
0.0008
(0.926)
0.0004
(0.964)
Relative value
0.0082
(0.253)
−0.1077∗∗
(0.019)
−0.1114∗∗
(0.017)
−0.0504∗∗
(0.023)
−0.0361
(0.141)
Relative value2
−0.0002
(0.576)
0.0515
(0.116)
0.0488
(0.139)
0.0037
(0.202)
0.0023
(0.398)
Transaction value
−0.0006
(0.140)
−0.0001
(0.852)
0.0000
(0.975)
−0.0005
(0.404)
−0.0001
(0.836)
Premium
0.0069
(0.408)
NYSE Market equity
−0.0006
(0.109)
NYSE Prior returns
0.0001
(0.441)
Tobin’s q
0.0056∗∗
(0.042)
Toehold
0.0003
(0.362)
0.0004
(0.701)
0.0005
(0.611)
0.0011
(0.102)
0.0012∗
(0.077)
Same industry
0.0043
(0.313)
0.0077
(0.275)
0.0076
(0.270)
0.0322∗∗
(0.017)
0.0314∗∗
(0.021)
0.0140
(0.133)
0.0028
(0.852)
0.0004
(0.980)
0.0239∗
(0.081)
0.0262∗
(0.051)
Deal Characteristics
Tender offer
All stock
−0.0028
(0.828)
−0.0880∗∗∗
(0.004)
−0.0901∗∗∗
(0.004)
0.0061
(0.666)
0.0057
(0.687)
All cash
−0.0040
(0.301)
−0.0124
(0.122)
−0.0131∗
(0.098)
0.0176
(0.253)
0.0130
(0.391)
0.1057∗∗∗
(0.002)
0.0638∗∗∗
(0.001)
217
601
0.128
0.1144∗∗∗
(0.001)
0.0692∗∗∗
(0.000)
217
601
0.151
All stock × Private
0.0157
(0.231)
0.0673∗∗
(0.036)
0.0678∗∗
(0.039)
All stock × Public
−0.0259∗
(0.078)
−0.0141
(0.273)
0.0096
(0.175)
2187
6420
0.041
0.0544∗
(0.088)
0.0565∗
(0.082)
1980–1991
1992–1999
Firms
Observations
Adjusted R2
0.0183
(0.146)
320
982
0.068
0.0173
(0.168)
320
982
0.078
38
Q-THEORY AND ACQUISITION RETURNS
Table 5
Firm fixed-effects regressions of acquirer abnormal dollar returns
‘All,’ ‘Governance,’ and ‘Public Target’ headings refer to the sample requirements for inclusion
in the regressions. All and Governance regressions present results from first-differenced OLS
regressions. Public Target regressions presents results from a firm fixed-effect (mean deviation)
regression. The dependent variable in all regressions is the five-day market adjusted dollar CAR
using the equally weighted CRSP index as the market. Observations are over 1981-2004. Robust
p−values are reported in parentheses, clustered at the firm level and significance is denoted by
∗ ∗∗
, , and ∗∗∗ at the 10%, 5%, and 1% levels. All variable definitions are in the Appendix.
All
Governance
(1)
(2)
Public Targets
(1)
(2)
Acquirer Characteristics
NYSE Market equity
5.97∗
(0.098)
−8.65
(0.823)
−6.38
(0.872)
1.43
(0.938)
3.75
(0.837)
NYSE Market equity2
−0.05
(0.388)
0.17
(0.716)
0.16
(0.724)
−0.02
(0.914)
0.06
(0.790)
0.15
(0.774)
−1.14
(0.813)
−1.52
(0.749)
0.85
(0.775)
−0.22
(0.949)
NYSE B/M
−0.53
(0.492)
−3.70
(0.602)
−1.65
(0.821)
7.84
(0.325)
7.89
(0.338)
Deal number
23.25
(0.244)
226.87
(0.204)
267.01
(0.152)
61.61
(0.515)
69.52
(0.471)
−4426.36
−4626.38
(0.164)
(0.151)
−276.82
(0.676)
−435.49
(0.523)
NYSE Prior returns
Deals/Year
Years since last
Tobin’s q
Industry deals prior year
Wave dummy
Outside director blockholders
−158.57∗
(0.061)
−0.98
(0.797)
−69.69∗∗
(0.021)
−68.12∗∗
(0.029)
−44.97
(0.262)
−33.12
(0.454)
−22.74
(0.394)
−79.24
(0.633)
−79.29
(0.634)
73.22
(0.179)
52.20
(0.418)
−1.36
(0.268)
−6.90
(0.443)
−6.30
(0.482)
−3.19
(0.616)
−3.21
(0.609)
−58.89
(0.337)
−443.10
(0.262)
−435.79
(0.277)
−393.58
(0.448)
−370.10
(0.479)
−1214.87
(0.793)
Entrenchment index
−53.57
(0.618)
Directors × Entrenchment
391.65
(0.572)
continued on next page
Q-THEORY AND ACQUISITION RETURNS
39
Table 5 - Continued
All
Governance
(1)
(2)
Public Targets
(1)
(2)
Target Characteristics
Public
−240.93∗∗
(0.012)
−870.76∗∗
(0.037)
−875.31∗∗
(0.035)
Private
−57.40
(0.250)
−351.42
(0.294)
−370.69
(0.278)
Relative value
34.81
(0.510)
−554.42
(0.760)
−699.97
(0.707)
88.17
(0.856)
751.61
(0.246)
Relative value2
−1.81
(0.472)
490.46
(0.606)
540.51
(0.584)
−19.47
(0.746)
−85.20
(0.226)
−91.55∗∗
(0.037)
−111.49∗∗∗
(0.003)
−110.41∗∗∗
(0.004)
−151.88∗∗
(0.025)
−142.61∗∗
(0.044)
Transaction value
Premium
−199.60
(0.415)
NYSE Market equity
−19.83
(0.262)
NYSE Prior returns
−0.41
(0.920)
Tobin’s q
107.02
(0.477)
Toehold
7.93
(0.648)
45.92
(0.400)
45.28
(0.406)
52.16
(0.390)
46.36
(0.435)
Same industry
8.80
(0.843)
−43.10
(0.861)
−47.22
(0.848)
34.71
(0.935)
122.35
(0.774)
−40.30
(0.775)
−261.36
(0.527)
−321.16
(0.443)
−12.23
(0.977)
54.31
(0.908)
−1075.01
−1099.64
(0.111)
(0.105)
110.00
(0.772)
32.76
(0.931)
−333.05
(0.441)
−601.49
(0.254)
679.43
(0.334)
1377.10∗∗
(0.022)
217
601
0.256
698.25
(0.366)
1399.79∗∗
(0.024)
217
601
0.267
Deal Characteristics
Tender offer
All stock
−174.93∗∗
(0.019)
All cash
−91.30∗
(0.095)
−665.06∗
(0.091)
−652.14∗
(0.090)
All stock × Private
141.54
(0.282)
206.77
(0.252)
216.96∗
(0.083)
219.74∗
(0.065)
2187
6420
0.030
751.90
(0.537)
1089.92
(0.217)
763.62
(0.537)
1159.04
(0.200)
All stock × Public
1980–1991
1992–1999
Firms
Observations
Adjusted R2
1156.15∗∗
(0.036)
320
982
0.057
1246.21∗∗
(0.038)
320
982
0.061
Table 6
Mean and median acquirer, target, and deal characteristics by deal number
For each characteristic the mean and median values of all available observations for a particular deal number for all firms are presented, with
the mean above the median. The last two columns indicate the coefficients in the model, Variable = β0 + β1 Deal Number + β2 (Deal Number)2 ,
where observations are not restricted to the first ten deals. The first row of each variable presents the OLS estimate, and the second row presents
the Least Absolute Deviation estimate. Significance is tested with a robust t−statistic, not reported, though significance is denoted by ∗ , ∗∗ , and
∗∗∗
at the 10%, 5%, and 1% levels. All variable definitions are in the Appendix. Sample period is 1981 to 2004. Total observations equal 12,942.
Deal Number
1
2
3
4
5
6
7
8
9
10
β0
β1
β2
Acquirer Characteristics
0.021
0.009
−5.371
0.666
29.617
20.000
62.532
70.000
0.722
0.474
475.703
277.000
3.042
1.749
0.114
0.000
8.223
8.000
0.015
0.006
−7.010
0.569
34.372
30.000
62.495
70.000
0.791
0.544
370.887
215.000
2.849
1.715
0.104
0.000
8.368
8.000
0.015
0.004
20.245
0.572
39.299
35.000
62.998
70.000
0.863
0.623
323.063
202.000
2.653
1.734
0.129
0.000
8.235
8.000
0.008
0.000
0.004 −0.001
39.170 −31.325
0.764 −0.754
44.108
47.455
45.000
45.000
62.755
64.851
70.000
75.000
0.973
1.026
0.718
0.756
251.666 247.159
139.500 134.000
2.738
2.943
1.751
1.760
0.099
0.133
0.000
0.000
8.332
8.205
8.000
8.000
−0.004
0.000
38.584
−1.031
49.986
50.000
64.620
70.000
1.121
0.853
194.022
116.000
2.858
1.679
0.091
0.000
8.450
8.000
−0.002
−0.003
29.332
−2.694
53.507
50.000
63.153
70.000
1.180
0.905
208.041
125.000
2.728
1.724
0.080
0.000
8.465
8.000
0.004 −0.005
0.004 −0.004
41.838 −316.900
0.991 −4.324
55.049
59.071
55.000
60.000
62.913
62.500
70.000
70.000
1.191
1.294
0.944
1.045
220.864 198.397
122.500 106.000
2.783
2.996
1.629
1.769
0.089
0.098
0.000
0.000
8.354
8.253
8.000
8.000
0.033∗∗∗ −0.005∗∗∗
0.011∗∗∗ −0.002∗∗∗
−0.009
13.045∗∗
0.272∗
0.280∗∗∗
∗∗∗
19.761
4.839∗∗∗
∗∗∗
8.927
6.173∗∗∗
62.662∗∗∗
0.153
69.950∗∗∗
0.058
0.545∗∗∗
0.079∗∗∗
0.325∗∗∗
0.070∗∗∗
936.432∗∗∗ −124.993∗∗∗
530.433∗∗∗ −72.911∗∗∗
3.072∗∗∗ −0.069∗∗∗
1.767∗∗∗ −0.019∗∗∗
0.098∗∗∗
0.002
0.000
0.000
8.090∗∗∗
0.086∗∗∗
7.946∗∗∗
0.031∗∗∗
0.000∗∗∗
0.000∗∗
−2.277∗∗∗
−0.054∗∗∗
−0.072∗∗∗
−0.100∗∗∗
−0.010
−0.008
0.000∗∗∗
0.000
3.084∗∗∗
2.478∗∗∗
0.004∗∗∗
0.002∗∗∗
0.000
0.000
−0.004∗∗∗
−0.002∗∗∗
0.157
0.000
0.549
1.000
0.294
0.000
0.603
1.000
16.271
5.000
47.073
45.000
1.756
1.208
0.304
0.090
100.080
17.696
0.161
0.000
0.545
1.000
0.294
0.000
0.612
1.000
18.138
10.000
49.760
50.000
1.999
1.321
0.192
0.071
134.111
22.174
0.164
0.000
0.554
1.000
0.281
0.000
0.638
1.000
21.214
15.000
55.850
55.000
2.343
1.305
0.205
0.058
168.178
28.807
0.175
0.000
0.499
0.000
0.326
0.000
0.648
1.000
21.164
15.000
54.281
55.000
1.839
1.194
0.143
0.049
178.161
33.051
0.200
0.000
0.483
0.000
0.317
0.000
0.647
1.000
23.198
10.000
57.432
65.000
2.283
1.240
0.116
0.034
292.225
33.637
0.185
0.000
0.499
0.000
0.316
0.000
0.676
1.000
23.176
15.000
56.757
62.500
2.006
1.364
0.102
0.030
335.558
41.000
0.207
0.000
0.505
1.000
0.288
0.000
0.674
1.000
29.737
20.000
48.596
50.000
2.299
1.381
0.124
0.032
594.251
41.944
0.276
0.000
0.485
0.000
0.239
0.000
0.672
1.000
26.769
15.000
49.154
45.000
2.100
1.266
0.127
0.027
589.392
47.773
0.243
0.000
0.500
0.500
0.257
0.000
0.680
1.000
29.778
20.000
55.222
55.000
2.033
1.221
0.140
0.025
821.305
42.315
0.276
0.000
0.481
0.000
0.244
0.000
0.667
1.000
30.610
20.000
57.805
65.000
1.907
1.247
0.088
0.026
686.571
70.319
0.132∗∗∗
0.000
0.572∗∗∗
0.000
0.297∗∗∗
0.000
0.590∗∗∗
1.000∗∗∗
15.306∗∗∗
7.100∗∗∗
48.030∗∗∗
47.220∗∗∗
1.873∗∗∗
1.230∗∗∗
0.296∗∗∗
0.089∗∗∗
−15.053
11.816∗∗∗
0.017∗∗∗
0.000
−0.016∗∗∗
0.000
0.000
0.000
0.015∗∗∗
0.000
1.572∗∗∗
1.525∗∗∗
1.011∗∗
1.397∗∗
0.033
0.011
−0.030∗∗∗
−0.009∗∗∗
82.030∗∗∗
5.467∗∗∗
0.000∗∗∗
0.000
0.001∗∗∗
0.000
0.000∗
0.000
−0.001∗∗∗
0.000
−0.030∗∗
−0.038∗∗∗
−0.011
−0.003
−0.001
0.000
0.001∗∗∗
0.000∗∗∗
−1.600∗∗∗
−0.040∗∗∗
0.020
0.000
0.264
0.000
0.392
0.000
1.562
1.467
0.022
0.000
0.257
0.000
0.427
0.000
1.638
1.510
0.019
0.000
0.241
0.000
0.430
0.000
1.715
1.624
0.018
0.000
0.237
0.000
0.450
0.000
1.744
1.590
0.020
0.000
0.256
0.000
0.497
0.000
1.706
1.590
0.016
0.000
0.262
0.000
0.515
1.000
1.769
1.553
0.033
0.000
0.242
0.000
0.511
1.000
1.829
1.674
0.041
0.000
0.276
0.000
0.463
0.000
1.822
1.660
0.049
0.000
0.301
0.000
0.481
0.000
1.797
1.621
0.026
0.000
0.308
0.000
0.455
0.000
1.598
1.566
0.016∗∗∗
0.000
0.253∗∗∗
0.000
0.389∗∗∗
0.001∗∗∗
1.584∗∗∗
1.486∗∗∗
0.002∗∗∗
0.000
0.001
0.000
0.015∗∗∗
−0.001∗∗∗
0.024∗∗∗
0.015∗∗
0.000∗∗
0.000
0.000
0.000
0.000∗∗∗
0.000∗∗∗
−0.001∗
0.000
Q-THEORY AND ACQUISITION RETURNS
CAR (%)
0.032
0.010
Abnormal $ Returns (millions) −0.451
0.484
NYSE Size
23.716
15.000
NYSE Prior Returns
63.103
75.000
Deals/Year
0.603
0.381
Days Since Last
1019.436
657.000
Tobin’s q
3.019
1.736
Outside Director Blockholders
0.092
0.000
Entrenchment Index
8.354
8.000
Target Characteristics
Public
Private
Subsidiary
Same Industry
NYSE Size
NYSE Prior Returns
Tobin’s q
Relative Value (%)
Transaction Value (millions)
Deal Characteristics
Tender Offer
40
All Stock
All Cash
Premium
Q-THEORY AND ACQUISITION RETURNS
Table 7
Fixed effects simultaneous equations model estimates
Results in columns 1–2 are from equation-by-equation first-differenced GMM estimations of a
simultaneous equations model. Observations are over 1981-2004. Robust p−values are reported
in parentheses and significance is denoted by ∗ , ∗∗ , and ∗∗∗ at the 10%, 5%, and 1% levels.
Variable definitions are in the Appendix.
Pr(Future Deal)
CAR(−2,+2)
Endogenous Variables
CAR(−2,+2)
−0.1258
(0.611)
Pr(Future Deal)
0.0803
(0.501)
Acquirer Characteristics
NYSE Market Equity
0.0027∗∗∗
(0.006)
−0.0019∗∗∗
(0.001)
−0.0002∗∗
(0.046)
NYSE Prior Returns
NYSE B/M
−0.0007∗
(0.080)
0.0003
(0.128)
Deal Number
−0.1633∗∗∗
(0.000)
0.0113
(0.569)
Deals/Year
0.0798∗∗
(0.015)
−0.0195
(0.224)
Years Since Last
0.0052∗
(0.078)
0.0018
(0.186)
Tobin’s q
−0.0016
(0.615)
Prior Industry Deals
Wave Dummy
−0.0002
(0.430)
0.0241
(0.186)
−0.0990∗
(0.078)
Net Payout Yield
Internal/(Total investment)
0.0020
(0.270)
0.0719∗∗∗
(0.003)
continued on next page
0.0002
(0.977)
41
42
Q-THEORY AND ACQUISITION RETURNS
Table 7 - Continued
Pr(Future Deal)
CAR(−2,+2)
Target Characteristics
Public
−0.0255∗∗∗
(0.006)
Private
−0.0016
(0.750)
Relative Value
−0.0152
(0.341)
Transaction Value
0.0000
(0.828)
Toehold
Same Industry
0.0131
(0.144)
−0.0002
(0.798)
0.0081
(0.539)
0.0039
(0.495)
Tender Offer
−0.0279
(0.438)
0.0103
(0.484)
All Equity
−0.0221
(0.174)
0.0107
(0.598)
All Cash
−0.0015
(0.911)
−0.0020
(0.722)
Deal Characteristics
All Equity × Private
0.0096
(0.627)
All Equity × Public
−0.0557∗∗
(0.014)
1981–1991
0.1167∗∗
(0.016)
−0.0184
(0.446)
1992–1999
0.0982∗∗∗
(0.003)
0.0027
(0.880)
Firms
Observations
Adjusted R2
1,055
2,709
0.1843
1,055
2,709
0.0331
Q-THEORY AND ACQUISITION RETURNS
43
Table 8
Quantile regression estimates
This table reports quantile regression coefficients with the five-day CAR as the dependent variable. Observations are taken from the first six deals of the subsample of acquirers who make
more than five acquisitions. All variable definitions are in the Appendix. Sample is over 1981
to 2004. The F statistic from a Wald test of equality of coefficients is reported in the last three
columns where the null hypothesis is equality. Numbers in parentheses represent p−values and
significance is denoted by ∗ , ∗∗ , and ∗∗∗ at the 10%, 5%, and 1% levels.
Quantiles
0.10
0.25
0.50
Wald Test - F Statistic
0.75
0.90
All Equal
.25=.75
.10=.90
Acquirer Characteristics
Deal Number
−0.004∗ −0.002 −0.001 −0.005∗∗ −0.007∗∗∗ 1.550
(0.097) (0.146) (0.390) (0.011) (0.004)
(0.187)
0.890
(0.346)
1.330
(0.248)
NYSE Market Equity × 100
0.001 −0.009 −0.015∗ −0.043∗∗∗ −0.079∗∗∗ 6.260∗∗∗ 8.670∗∗∗ 18.060∗∗∗
(0.933) (0.347) (0.077) (0.000) (0.000)
(0.000)
(0.003) (0.000)
NYSE Prior Returns × 100
0.008
(0.421)
0.014∗∗ 0.017∗∗ 0.015∗∗ 0.008
(0.032) (0.011) (0.036) (0.477)
0.380
(0.820)
0.010
(0.923)
0.000
(0.961)
NYSE B/M × 100
0.017
(0.196)
0.012
(0.167)
0.020∗∗∗ 0.014
(0.008) (0.242)
0.014
(0.455)
0.410
(0.805)
0.030
(0.852)
0.030
(0.867)
−0.002 −0.004 −0.008∗∗ −0.002
(0.749) (0.322) (0.012) (0.578)
0.000
(0.960)
0.900
(0.464)
0.110
(0.743)
0.050
(0.819)
Deals/Year
Years Since Last
0.001
(0.446)
0.000
(0.737)
0.000
(0.709)
0.000
(0.874)
0.003
(0.356)
0.340
(0.854)
0.010
(0.939)
0.250
(0.614)
Tobin’s q
0.001
(0.392)
0.002
(0.107)
0.002∗
(0.058)
0.003∗
(0.027)
0.004∗∗∗ 1.300
(0.001)
(0.269)
0.690
(0.407)
3.190∗
(0.074)
Industry Deals Prior Year
0.000∗
(0.055)
0.000
(0.977)
0.000
(0.317)
0.000
(0.171)
0.000
(0.602)
1.920
(0.105)
1.490
(0.222)
2.690
(0.101)
0.002 −0.001
(0.696) (0.808)
0.002
(0.733)
0.013
(0.190)
0.700
(0.591)
0.000
(0.974)
1.060
(0.303)
Public
−0.035∗∗ −0.021∗∗ −0.024∗∗∗ −0.012 −0.010
(0.039) (0.038) (0.008) (0.323) (0.556)
0.440
(0.779)
0.430
(0.513)
1.070
(0.301)
Private
−0.004 −0.005 −0.008∗
(0.576) (0.307) (0.089)
0.002 −0.003
(0.679) (0.700)
1.280
(0.275)
1.290
(0.256)
0.010
(0.909)
Relative Value
−0.015 −0.005
(0.172) (0.633)
0.018∗
(0.074)
0.034∗∗∗ 0.032∗
(0.000) (0.055)
3.460∗∗∗ 9.560∗∗∗ 5.030∗∗
(0.008)
(0.002) (0.025)
0.000∗∗∗ 0.000
(0.098) (0.885)
0.000
(0.448)
0.000
(0.725)
1.160
(0.328)
Wave Dummy
−0.001
(0.922)
Target Characteristics
Transaction Value
continued on next page
0.000
(0.257)
0.170
(0.681)
4.000∗
(0.046)
44
Q-THEORY AND ACQUISITION RETURNS
Table 8 - Continued
Quantiles
0.10
Toehold
Same Industry
0.000
(0.689)
−0.001
(0.878)
0.25
0.50
Wald Test - F Statistic
0.75
0.90
0.000 −0.001
(0.998) (0.459)
0.000
(0.919)
0.000
(0.932)
0.390
(0.815)
0.010
(0.924)
0.110
(0.745)
0.002
(0.718)
0.009
(0.113)
0.000
(0.977)
1.050
(0.378)
1.630
(0.202)
0.010
(0.930)
0.007 −0.014 −0.036
(0.570) (0.338) (0.167)
1.930
(0.103)
2.750∗
(0.097)
6.350∗∗
(0.012)
0.039∗
(0.095)
0.002
(0.588)
All Equal
.25=.75
.10=.90
Deal Characteristics
Tender Offer
All Equity
0.036∗∗ 0.012
(0.024) (0.337)
−0.033
(0.427)
0.006
(0.746)
0.017
(0.345)
0.034
(0.197)
0.650
(0.625)
1.570
(0.211)
2.030
(0.154)
All Cash
0.001
(0.860)
0.001
(0.808)
0.001 −0.008 −0.009
(0.917) (0.257) (0.343)
0.560
(0.692)
1.650
(0.199)
0.650
(0.421)
All Equity × Private
0.035 −0.001 −0.007∗ −0.042 −0.029
(0.397) (0.979) (0.725) (0.076) (0.248)
1.010
(0.402)
2.690
(0.101)
1.960
(0.162)
All Equity × Public
0.005 −0.018 −0.035 −0.066∗∗ −0.061∗∗
(0.903) (0.387) (0.141) (0.013) (0.028)
0.830
(0.507)
2.650
(0.104)
1.700
(0.192)
Constant
Year Dummies
Industry Dummies
Observations
Pseudo R2
−0.078 −0.011 −0.017
(0.037) (0.751) (0.703)
Yes
Yes
2,470
0.111
Yes
Yes
2,470
0.053
Yes
Yes
2,470
0.040
0.048
(0.436)
0.159
(0.029)
Yes
Yes
2,470
0.083
Yes
Yes
2,470
0.118