Q-THEORY AND ACQUISITION RETURNS

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 andsupport. I also especially thank David Robinson, Karin Thorburn, Roni Michaely, and Katrina Ellis. Commentsprovided 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 seminarparticipants at the 2008 AFA Annual Meeting, 2006 FMA Annual Meeting, the 2006 US and European FMADoctoral Seminars, the Anderson School at UCLA, UCLA Department of Economics IO Workshop, LondonBusiness 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 ResearchProgram on Takeovers, Restructuring, and Governance at the Anderson School, UCLA.

† Please direct correspondence to Kenneth R. Ahern, Ross School of Business, University of Michigan, AnnArbor 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 first-

order 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 acquir-

ers 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 impor-

tance 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 ab-

normal 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 marketreturn, 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.


costs (proxied by advisor fees) and integration costs (proxied by industry-relatedness and geo-

graphic 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 overconfi-

dence 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 re-

turns 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 changeits size through internal growth or divestitures.


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 endogene-

ity, 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 atthe announcement of the takeover.” Other recent empirical studies that refer to anticipation as a possible effecton acquirer returns include Song and Walkling (2000), Wulf (2004), Bhagat, Dong, Hirshleifer, and Noah (2005),and Song and Walkling (2008).


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 kαt 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


faces adjustment (integration) costs from the investment equal to (a/2)(i/k1)2k1, with a > 0.

The firm chooses i to maximize firm value:

max{i}

(

kα1 − i −

a

2

(

i

k1

)2

k1 +1

r[(k1 + i)α + k + i]

)

. (1)

Still following directly from Li, Livdan, and Zhang (2009), the first order condition is:

− 1 − ai

k1+

1

r

[

α(k + i)α−1 + 1]

= 0 (2)

which implies,∂r

∂i=

α(α − 1)(k1 + i)α−2

1 + a(i/k1)−

α(k1 + i)α−1a

[1 + a(i/k1)]2k1< 0 (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 thati

k1=

1

a

[

1

r

(

α(k1 + i)α−1 + 1)

− 1

]

. (4)

Taking the derivative,∂(i/k1)

∂k1=

1

arα(α − 1)(k1 + i)α−2 < 0. (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:∂i

∂k1=

a(i/k21) + (1/r)α(α − 1)(k1 + i)α−2

(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)k2

1

i · r(k1 + i)2 − α. (7)

In other words, if the adjustment costs are large enough, then the size of the investment (ac-

quisition) 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 value-

maximizing acquirer gets larger:

• Targets get larger in absolute size.

• Targets get smaller in relative size.


• Acquirer dollar gains increase.

• Acquirer percentage returns decrease.

Larger firms optimally make larger investments, but reduce integration costs by making invest-

ments 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 Ac-

quisitions 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 publictargets. However, only using completed deals may lead to a misproportional small number of hostile deals, sincehostile deals are more likely to fail (Walkling, 1985).


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 no-

acquisition 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 proto-

typical 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 de-

scription 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


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 market-

adjusted 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 an-

nouncement 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 pro-

cedure 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


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 assump-

tion 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 regres-

sions 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, Icalculate the surface distance in statute miles.


Next, I test the predictions about the relationship between firm size and returns. Econo-

metrically, 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 ap-

proach, 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. Alter-

native 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-validationis performed by minimizing the estimated prediction error in order to find the optimal bandwidth. See Hardle(1990) for more details on kernel regression estimates.


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 includ-

ing 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 non-

officer 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 mea-

sured 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 ac-

quirer, 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,


acquirer size is negatively and convexly related to acquirer CARs, consistent with the predic-

tions 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 pur-

chased 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 signifi-

cantly 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 data-

base. 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 anannouncement if other industry firms have announced acquisitions. In robustness tests I have controlled for thenumber and value of industry acquisitions in the prior year and my results are qualitatively unchanged.


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 cross-

section. 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


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 rela-

tion 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 sig-

nificantly 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 in-

crease 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


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 introduc-

tion, 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 = α1EV Fia + X1iaβ1 + c1i + uia a = 1, . . . , A (8)

EV Fia = α2CARia + X2iaβ2 + c2i + via a = 1, . . . , A (9)


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 overpaidsubstantially. Thus, due to a winner’s curse, highest bidders are most likely to succeed in an acquisition, butdestroy value. I do not think this is a large concern in my analysis. The probability I measure is the likelihoodof making a future acquisition as measured at the time of a current announcement. This incorporates both the


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 a = 1, . . . , A (11)

∆Pia = α2∆CARia + ∆X2iaβ2 + ∆via 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 relatedto 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 fittedprobabilities may not fall in the range [0, 1]. However, the advantage of a linear probability model is that nodistributional assumptions need to be made about the error term, vit. In unreported tests I compute probit andlogit models of Equation (12) and use the fitted values as proxies in Equation (11). This does not change thequalitative 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 subsequentdeal without distributional assumptions. In my analysis I directly control for both duration between acquisitionsand a firm’s acquisition intensity. Thus the gains from a hazard model are not obvious.


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, Richard-

son, 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 cor-

related 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 withhigh payout yields have less attractive investments (internal or external) than those firms that are retaining theirearnings and thus are less likely to be making external investments. The ratio of internal to total investment is alsolikely to be correlated with future acquisition activity. Large external investments may require complementaryfuture internal investments. For these to be valid instruments they also must be uncorrelated with currentCARs. Given a firm is making an acquisition, there is not a clear link between current CAR and payout yieldsor internal-to-total investment ratios.12Prior returns, public and private dummy variables, and toeholds should only be relevant for the currentacquisition since they do not predict any future activity. It is possible that public and private target dummiesproxy for relative size and hence may be correlated with the likelihood of making future acquisitions. I conductthe following analysis without these variables as instruments and find the results qualitatively unchanged.


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 characteris-

tics. 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.


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 announce-

ments 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


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 ac-

quirer 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.


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.


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 sum-

ming 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 indi-

cate 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 provi-

sions 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 headquar-

ter’s zipcode to the center of the target firm headquarter’s zipcode. Zip-

code data is from SDC.

continued on next page


Appendix - Continued


InternalTotal 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.





Outside Director

Blockholders

The number of non-officer director blockholders (5% stock ownership).

These data come from the WRDS Blockholder database with observa-

tions 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 re-

stricted 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 mar-

ket 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 an-

nouncement as reported in SDC.





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. In-

dustry 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.


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05

1015

ln(T

rans

actio

n S

ize)

−5 0 5 10 15

ln(Market Equity)

(a) Transaction Size

05

1015

Rel

ativ

e S

ize

−5 0 5 10 15

ln(Market Equity)

(b) Relative Size

−1

−.5

0.5

5−D

ay C

AR

−5 0 5 10 15

ln(Market Equity)

(c) Percentage Returns

−10

0000

−50

000

050

000

1000

005−

Day

Dol

lar

CA

R

−5 0 5 10 15

ln(Market Equity)

(d) Dollar Returns

Figure 1Nonparametric kernel regressions on acquirer ln(market equity)The figures above are generated from “leave-one-out” Nadaraya-Watson kernel regression esti-mates of E[X|ln(Acquirer Market Equity)], where X is ln(transaction size), the relative size oftarget to acquirer, CAR(−2,+2), or Dollar CAR(−2,+2). The estimates are found using a Gaussiankernel function and the bandwidth is chosen using cross-validation to minimize prediction error.The sample consists of 12,942 observations over 1981 to 2004.


CAR

Deal Number

90th

Percentile

10th

Percentile

Figure 2Anticipation theory prediction of the distribution of returns by deal numberThis figure represents a stylized representation of the anticipation theory. The dark curvesrepresent the distribution of CARs conditional on deal number. The anticipation theory positsthat the distribution of CARs becomes less dispersed at higher deal numbers. The dashedlines 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 eachpercentile.


Table 1Summary 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 inYear’ lists all recorded acquisitions for a given year in the sample. ‘Median Transaction Value’ isthe 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 SDCdatabase to be the total value of consideration paid excluding fees and expenses. Values arereported in millions of 2005 adjusted dollars.

YearSeriesStarts

MeanSeriesLength

TotalDeals

In Year

MedianTransaction

Value

TotalTransaction

Value

1981 1 2.00 1 $10.81 $111982 16 1.94 19 16.36 6421983 48 3.65 61 16.03 3, 7891984 75 3.45 101 15.50 4, 7261985 40 4.13 54 71.26 16, 3881986 56 3.46 93 50.14 13, 3951987 75 3.41 111 38.04 14, 4571988 102 3.32 139 33.88 16, 3151989 141 3.67 231 17.52 22, 6691990 128 2.92 228 12.53 13, 6711991 159 3.75 263 12.55 17, 3061992 205 3.40 398 12.81 21, 0221993 276 3.09 582 15.20 55, 0041994 398 3.22 800 15.37 64, 8321995 361 2.84 887 18.68 73, 5841996 402 3.01 1, 096 24.18 175, 7111997 501 2.53 1, 437 24.22 227, 0851998 442 2.27 1, 418 29.34 513, 3961999 378 2.29 1, 118 33.00 370, 4232000 321 2.02 980 39.13 615, 3822001 257 1.89 770 33.56 153, 8392002 173 1.72 713 24.54 88, 1932003 170 1.35 686 37.49 153, 6852004 154 1.10 756 37.31 151, 526

All 4, 879 2.65 12, 942 $25.38 $2, 787, 050

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Table 2Announcement returns by number of acquisitions and acquisition orderCumulative abnormal returns (-2,+2) in percent terms computed using an equally weighted market-adjusted model. Abnormal dollarreturns are presented in brackets. Numbers in parentheses indicate sample sizes. Statistical significance is tested with the sign testand 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

Number ofDeals in Series

1st 2nd 3rd 4th 5th >5thAll

Deals

1 3.39∗∗∗ 3.39∗∗∗

[−1.68∗∗∗] [−1.68∗∗∗](2,212) (2,212)

2 2.97∗∗∗ 1.49∗∗ 2.23∗∗∗

[7.83∗∗∗] [−19.59∗∗] [−5.88∗∗∗](1,060) (1,060) (2,120)

3 2.38∗ 2.52∗∗∗ 1.74∗∗∗ 2.21∗∗∗

[-56.88] [−2.35∗∗∗] [4.83∗∗] [−18.13∗∗∗](558) (558) (558) (1,674)

4 3.58∗∗∗ 2.12∗∗∗ 1.34 1.51 2.14∗∗∗

[13.59∗∗∗] [16.63∗∗] [−42.22] [−24.52] [−9.13∗∗∗](343) (343) (343) (343) (1,372)

5 2.73 3.11∗∗∗ 0.82 0.99 −0.09 1.51∗∗

[10.89] [2.03∗∗∗] [7.83] [−22.24] [−32.38] [−6.77∗∗](203) (203) (203) (203) (203) (1,015)

> 5 3.55∗∗∗ 2.48∗∗∗ 1.72∗∗ 1.75∗∗∗ 1.22 −0.11 1.14∗∗∗

[35.94∗∗∗] [3.24∗∗∗] [−2.12∗] [67.92∗∗∗] [68.04] [−134.54] [−41.03∗∗∗](503) (503) (503) (503) (503) (2,034) (4,549)

All 3.19∗∗∗ 2.10∗∗∗ 1.53∗∗∗ 1.52∗∗∗ 0.84 −0.11 1.98∗∗∗

[0.45∗∗∗] [−5.37∗∗∗] [−7.01∗∗∗] [20.25∗∗∗] [39.17] [−134.54] [−19.52∗∗∗](4,879) (2,667) (1,607) (1,049) (706) (2,034) (12,942)


Table 3Cross-sectional determinants of integration costsThis 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 areelasticities between an independent variable and the dependent variable. All variable definitionsare in the Appendix. Observations are over 1981–2004. Robust p−values clustered by acquirerare reported in parentheses and significance is denoted by ∗, ∗∗, and ∗∗∗ at the 10%, 5%, and 1%levels.

ln(Transaction Size) ln(Relative Value)

ln(Acquirer Size) 0.7800∗∗∗ −0.6250∗∗∗

(0.000) (0.000)

ln(Acquirer Prior Year Returns) 0.1204∗∗∗ 0.0358(0.001) (0.482)

Same Industry Dummy 0.0977 −0.2298∗∗

(0.181) (0.047)

ln(Geographic Distance) −0.0025 0.0652∗∗∗

(0.865) (0.002)

ln(Total Acquirer Fees) 0.5604∗∗∗ 0.4448∗∗∗

(0.000) (0.000)

ln(Number of Advisers) 0.2796∗ 0.2648(0.058) (0.174)

Constant 2.3206∗∗∗ 0.5238∗

(0.000) (0.083)

Observations 597 597Adjusted R2 0.7651 0.2150


Table 4Firm fixed-effects regressions of acquirer abnormal percentage returns‘All,’ ‘Governance,’ and ‘Public Target’ headings refer to the sample requirements for inclusionin the regressions. All and Governance regressions present results from first-differenced OLSregressions. 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 usingthe equally weighted CRSP index as the market. Observations are over 1981-2004. Robustp−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 Public Targets

(1) (2) (1) (2)

Acquirer Characteristics

NYSE Market equity −0.0022∗∗∗ 0.0025 0.0026 −0.0017 −0.0017(0.000) (0.137) (0.120) (0.306) (0.281)

NYSE Market equity2 0.0000∗∗ −0.0000∗ −0.0000∗ 0.0000 0.0000(0.029) (0.090) (0.075) (0.409) (0.316)

NYSE Prior returns −0.0000 −0.0000 −0.0001 −0.0000 −0.0001(0.575) (0.767) (0.680) (0.946) (0.579)

NYSE B/M 0.0001 −0.0003 −0.0003 0.0003 0.0002(0.543) (0.224) (0.295) (0.372) (0.497)

Deal number −0.0025 −0.0018 0.0009 0.0035 0.0042(0.185) (0.660) (0.826) (0.242) (0.138)

Deals/Year −0.0134∗ −0.0028 −0.0217 −0.0218 −0.0325(0.075) (0.959) (0.701) (0.521) (0.311)

Years since last 0.0020∗∗∗ 0.0013 0.0010 −0.0043∗ −0.0042∗

(0.008) (0.391) (0.522) (0.068) (0.065)

Tobin’s q −0.0009 0.0007 0.0009 0.0009 −0.0008(0.302) (0.549) (0.486) (0.600) (0.674)

Industry deals prior year −0.0002 −0.0001 −0.0001 −0.0000 −0.0000(0.132) (0.450) (0.428) (0.899) (0.943)

Wave dummy 0.0047 0.0019 0.0039 −0.0122 −0.0119(0.366) (0.850) (0.688) (0.285) (0.301)

Outside director blockholders 0.1826∗∗

(0.019)

Entrenchment index −0.0068(0.154)

Directors × Entrenchment −0.0140∗

(0.094)



Table 4 - Continued


(1) (2) (1) (2)

Target Characteristics

Public −0.0319∗∗∗ −0.0178∗ −0.0180∗

(0.000) (0.079) (0.074)

Private −0.0043 0.0008 0.0004(0.237) (0.926) (0.964)

Relative value 0.0082 −0.1077∗∗ −0.1114∗∗ −0.0504∗∗ −0.0361(0.253) (0.019) (0.017) (0.023) (0.141)

Relative value2 −0.0002 0.0515 0.0488 0.0037 0.0023(0.576) (0.116) (0.139) (0.202) (0.398)

Transaction value −0.0006 −0.0001 0.0000 −0.0005 −0.0001(0.140) (0.852) (0.975) (0.404) (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 0.0004 0.0005 0.0011 0.0012∗

(0.362) (0.701) (0.611) (0.102) (0.077)

Same industry 0.0043 0.0077 0.0076 0.0322∗∗ 0.0314∗∗

(0.313) (0.275) (0.270) (0.017) (0.021)

Deal Characteristics

Tender offer 0.0140 0.0028 0.0004 0.0239∗ 0.0262∗

(0.133) (0.852) (0.980) (0.081) (0.051)

All stock −0.0028 −0.0880∗∗∗ −0.0901∗∗∗ 0.0061 0.0057(0.828) (0.004) (0.004) (0.666) (0.687)

All cash −0.0040 −0.0124 −0.0131∗ 0.0176 0.0130(0.301) (0.122) (0.098) (0.253) (0.391)

All stock × Private 0.0157 0.0673∗∗ 0.0678∗∗

(0.231) (0.036) (0.039)

All stock × Public −0.0259∗ 0.0544∗ 0.0565∗

(0.078) (0.088) (0.082)1980–1991 −0.0141 0.1057∗∗∗ 0.1144∗∗∗

(0.273) (0.002) (0.001)1992–1999 0.0096 0.0183 0.0173 0.0638∗∗∗ 0.0692∗∗∗

(0.175) (0.146) (0.168) (0.001) (0.000)Firms 2187 320 320 217 217Observations 6420 982 982 601 601Adjusted R2 0.041 0.068 0.078 0.128 0.151


Table 5Firm fixed-effects regressions of acquirer abnormal dollar returns‘All,’ ‘Governance,’ and ‘Public Target’ headings refer to the sample requirements for inclusionin the regressions. All and Governance regressions present results from first-differenced OLSregressions. 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 CARusing the equally weighted CRSP index as the market. Observations are over 1981-2004. Robustp−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.


(1) (2) (1) (2)


NYSE Market equity 5.97∗ −8.65 −6.38 1.43 3.75(0.098) (0.823) (0.872) (0.938) (0.837)

NYSE Market equity2 −0.05 0.17 0.16 −0.02 0.06(0.388) (0.716) (0.724) (0.914) (0.790)

NYSE Prior returns 0.15 −1.14 −1.52 0.85 −0.22(0.774) (0.813) (0.749) (0.775) (0.949)

NYSE B/M −0.53 −3.70 −1.65 7.84 7.89(0.492) (0.602) (0.821) (0.325) (0.338)

Deal number 23.25 226.87 267.01 61.61 69.52(0.244) (0.204) (0.152) (0.515) (0.471)

Deals/Year −158.57∗ −4426.36 −4626.38 −276.82 −435.49(0.061) (0.164) (0.151) (0.676) (0.523)

Years since last −0.98 −69.69∗∗ −68.12∗∗ −44.97 −33.12(0.797) (0.021) (0.029) (0.262) (0.454)

Tobin’s q −22.74 −79.24 −79.29 73.22 52.20(0.394) (0.633) (0.634) (0.179) (0.418)

Industry deals prior year −1.36 −6.90 −6.30 −3.19 −3.21(0.268) (0.443) (0.482) (0.616) (0.609)

Wave dummy −58.89 −443.10 −435.79 −393.58 −370.10(0.337) (0.262) (0.277) (0.448) (0.479)

Outside director blockholders −1214.87(0.793)

Entrenchment index −53.57(0.618)

Directors × Entrenchment 391.65(0.572)



Table 5 - Continued


(1) (2) (1) (2)


Public −240.93∗∗ −870.76∗∗ −875.31∗∗

(0.012) (0.037) (0.035)

Private −57.40 −351.42 −370.69(0.250) (0.294) (0.278)

Relative value 34.81 −554.42 −699.97 88.17 751.61(0.510) (0.760) (0.707) (0.856) (0.246)

Relative value2 −1.81 490.46 540.51 −19.47 −85.20(0.472) (0.606) (0.584) (0.746) (0.226)

Transaction value −91.55∗∗ −111.49∗∗∗ −110.41∗∗∗ −151.88∗∗ −142.61∗∗

(0.037) (0.003) (0.004) (0.025) (0.044)

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 45.92 45.28 52.16 46.36(0.648) (0.400) (0.406) (0.390) (0.435)

Same industry 8.80 −43.10 −47.22 34.71 122.35(0.843) (0.861) (0.848) (0.935) (0.774)


Tender offer −40.30 −261.36 −321.16 −12.23 54.31(0.775) (0.527) (0.443) (0.977) (0.908)

All stock −174.93∗∗ −1075.01 −1099.64 110.00 32.76(0.019) (0.111) (0.105) (0.772) (0.931)

All cash −91.30∗ −665.06∗ −652.14∗ −333.05 −601.49(0.095) (0.091) (0.090) (0.441) (0.254)

All stock × Private 141.54 751.90 763.62(0.282) (0.537) (0.537)

All stock × Public 206.77 1089.92 1159.04(0.252) (0.217) (0.200)

1980–1991 216.96∗ 679.43 698.25(0.083) (0.334) (0.366)

1992–1999 219.74∗ 1156.15∗∗ 1246.21∗∗ 1377.10∗∗ 1399.79∗∗

(0.065) (0.036) (0.038) (0.022) (0.024)Firms 2187 320 320 217 217Observations 6420 982 982 601 601Adjusted R2 0.030 0.057 0.061 0.256 0.267

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Table 6Mean and median acquirer, target, and deal characteristics by deal numberFor each characteristic the mean and median values of all available observations for a particular deal number for all firms are presented, withthe mean above the median. The last two columns indicate the coefficients in the model, Variable = β0 + β1Deal 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 presentsthe 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


CAR (%) 0.032 0.021 0.015 0.015 0.008 0.000 −0.004 −0.002 0.004 −0.005 0.033∗∗∗ −0.005∗∗∗ 0.000∗∗∗

0.010 0.009 0.006 0.004 0.004 −0.001 0.000 −0.003 0.004 −0.004 0.011∗∗∗ −0.002∗∗∗ 0.000∗∗

Abnormal $ Returns (millions) −0.451 −5.371 −7.010 20.245 39.170 −31.325 38.584 29.332 41.838 −316.900 −0.009 13.045∗∗ −2.277∗∗∗

0.484 0.666 0.569 0.572 0.764 −0.754 −1.031 −2.694 0.991 −4.324 0.272∗ 0.280∗∗∗ −0.054∗∗∗

NYSE Size 23.716 29.617 34.372 39.299 44.108 47.455 49.986 53.507 55.049 59.071 19.761∗∗∗ 4.839∗∗∗ −0.072∗∗∗

15.000 20.000 30.000 35.000 45.000 45.000 50.000 50.000 55.000 60.000 8.927∗∗∗ 6.173∗∗∗ −0.100∗∗∗

NYSE Prior Returns 63.103 62.532 62.495 62.998 62.755 64.851 64.620 63.153 62.913 62.500 62.662∗∗∗ 0.153 −0.01075.000 70.000 70.000 70.000 70.000 75.000 70.000 70.000 70.000 70.000 69.950∗∗∗ 0.058 −0.008

Deals/Year 0.603 0.722 0.791 0.863 0.973 1.026 1.121 1.180 1.191 1.294 0.545∗∗∗ 0.079∗∗∗ 0.000∗∗∗

0.381 0.474 0.544 0.623 0.718 0.756 0.853 0.905 0.944 1.045 0.325∗∗∗ 0.070∗∗∗ 0.000Days Since Last 1019.436 475.703 370.887 323.063 251.666 247.159 194.022 208.041 220.864 198.397 936.432∗∗∗−124.993∗∗∗ 3.084∗∗∗

657.000 277.000 215.000 202.000 139.500 134.000 116.000 125.000 122.500 106.000 530.433∗∗∗ −72.911∗∗∗ 2.478∗∗∗

Tobin’s q 3.019 3.042 2.849 2.653 2.738 2.943 2.858 2.728 2.783 2.996 3.072∗∗∗ −0.069∗∗∗ 0.004∗∗∗

1.736 1.749 1.715 1.734 1.751 1.760 1.679 1.724 1.629 1.769 1.767∗∗∗ −0.019∗∗∗ 0.002∗∗∗

Outside Director Blockholders 0.092 0.114 0.104 0.129 0.099 0.133 0.091 0.080 0.089 0.098 0.098∗∗∗ 0.002 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Entrenchment Index 8.354 8.223 8.368 8.235 8.332 8.205 8.450 8.465 8.354 8.253 8.090∗∗∗ 0.086∗∗∗ −0.004∗∗∗

8.000 8.000 8.000 8.000 8.000 8.000 8.000 8.000 8.000 8.000 7.946∗∗∗ 0.031∗∗∗ −0.002∗∗∗


Public 0.157 0.161 0.164 0.175 0.200 0.185 0.207 0.276 0.243 0.276 0.132∗∗∗ 0.017∗∗∗ 0.000∗∗∗0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Private 0.549 0.545 0.554 0.499 0.483 0.499 0.505 0.485 0.500 0.481 0.572∗∗∗ −0.016∗∗∗ 0.001∗∗∗1.000 1.000 1.000 0.000 0.000 0.000 1.000 0.000 0.500 0.000 0.000 0.000 0.000

Subsidiary 0.294 0.294 0.281 0.326 0.317 0.316 0.288 0.239 0.257 0.244 0.297∗∗∗ 0.000 0.000∗0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Same Industry 0.603 0.612 0.638 0.648 0.647 0.676 0.674 0.672 0.680 0.667 0.590∗∗∗ 0.015∗∗∗ −0.001∗∗∗

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000∗∗∗ 0.000 0.000NYSE Size 16.271 18.138 21.214 21.164 23.198 23.176 29.737 26.769 29.778 30.610 15.306∗∗∗ 1.572∗∗∗ −0.030∗∗

5.000 10.000 15.000 15.000 10.000 15.000 20.000 15.000 20.000 20.000 7.100∗∗∗ 1.525∗∗∗ −0.038∗∗∗

NYSE Prior Returns 47.073 49.760 55.850 54.281 57.432 56.757 48.596 49.154 55.222 57.805 48.030∗∗∗ 1.011∗∗ −0.01145.000 50.000 55.000 55.000 65.000 62.500 50.000 45.000 55.000 65.000 47.220∗∗∗ 1.397∗∗ −0.003

Tobin’s q 1.756 1.999 2.343 1.839 2.283 2.006 2.299 2.100 2.033 1.907 1.873∗∗∗ 0.033 −0.0011.208 1.321 1.305 1.194 1.240 1.364 1.381 1.266 1.221 1.247 1.230∗∗∗ 0.011 0.000

Relative Value (%) 0.304 0.192 0.205 0.143 0.116 0.102 0.124 0.127 0.140 0.088 0.296∗∗∗ −0.030∗∗∗ 0.001∗∗∗

0.090 0.071 0.058 0.049 0.034 0.030 0.032 0.027 0.025 0.026 0.089∗∗∗ −0.009∗∗∗ 0.000∗∗∗

Transaction Value (millions) 100.080 134.111 168.178 178.161 292.225 335.558 594.251 589.392 821.305 686.571 −15.053 82.030∗∗∗ −1.600∗∗∗

17.696 22.174 28.807 33.051 33.637 41.000 41.944 47.773 42.315 70.319 11.816∗∗∗ 5.467∗∗∗ −0.040∗∗∗


Tender Offer 0.020 0.022 0.019 0.018 0.020 0.016 0.033 0.041 0.049 0.026 0.016∗∗∗ 0.002∗∗∗ 0.000∗∗0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

All Stock 0.264 0.257 0.241 0.237 0.256 0.262 0.242 0.276 0.301 0.308 0.253∗∗∗ 0.001 0.0000.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

All Cash 0.392 0.427 0.430 0.450 0.497 0.515 0.511 0.463 0.481 0.455 0.389∗∗∗ 0.015∗∗∗ 0.000∗∗∗

0.000 0.000 0.000 0.000 0.000 1.000 1.000 0.000 0.000 0.000 0.001∗∗∗ −0.001∗∗∗ 0.000∗∗∗

Premium 1.562 1.638 1.715 1.744 1.706 1.769 1.829 1.822 1.797 1.598 1.584∗∗∗ 0.024∗∗∗ −0.001∗

1.467 1.510 1.624 1.590 1.590 1.553 1.674 1.660 1.621 1.566 1.486∗∗∗ 0.015∗∗ 0.000


Table 7Fixed effects simultaneous equations model estimatesResults in columns 1–2 are from equation-by-equation first-differenced GMM estimations of asimultaneous equations model. Observations are over 1981-2004. Robust p−values are reportedin 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)


NYSE Market Equity 0.0027∗∗∗ −0.0019∗∗∗

(0.006) (0.001)

NYSE Prior Returns −0.0002∗∗

(0.046)

NYSE B/M −0.0007∗ 0.0003(0.080) (0.128)

Deal Number −0.1633∗∗∗ 0.0113(0.000) (0.569)

Deals/Year 0.0798∗∗ −0.0195(0.015) (0.224)

Years Since Last 0.0052∗ 0.0018(0.078) (0.186)

Tobin’s q −0.0016 0.0020(0.615) (0.270)

Prior Industry Deals −0.0002(0.430)

Wave Dummy 0.0241 0.0002(0.186) (0.977)

Net Payout Yield −0.0990∗

(0.078)

Internal/(Total investment) 0.0719∗∗∗

(0.003)



Table 7 - Continued

Pr(Future Deal) CAR(−2,+2)


Public −0.0255∗∗∗

(0.006)

Private −0.0016(0.750)

Relative Value −0.0152 0.0131(0.341) (0.144)

Transaction Value 0.0000(0.828)

Toehold −0.0002(0.798)

Same Industry 0.0081 0.0039(0.539) (0.495)


Tender Offer −0.0279 0.0103(0.438) (0.484)

All Equity −0.0221 0.0107(0.174) (0.598)

All Cash −0.0015 −0.0020(0.911) (0.722)

All Equity × Private 0.0096(0.627)

All Equity × Public −0.0557∗∗

(0.014)

1981–1991 0.1167∗∗ −0.0184(0.016) (0.446)

1992–1999 0.0982∗∗∗ 0.0027(0.003) (0.880)

Firms 1,055 1,055Observations 2,709 2,709Adjusted R2 0.1843 0.0331


Table 8Quantile regression estimatesThis table reports quantile regression coefficients with the five-day CAR as the dependent vari-able. Observations are taken from the first six deals of the subsample of acquirers who makemore than five acquisitions. All variable definitions are in the Appendix. Sample is over 1981to 2004. The F statistic from a Wald test of equality of coefficients is reported in the last threecolumns where the null hypothesis is equality. Numbers in parentheses represent p−values andsignificance is denoted by ∗, ∗∗, and ∗∗∗ at the 10%, 5%, and 1% levels.

Quantiles Wald Test - F Statistic

0.10 0.25 0.50 0.75 0.90 All Equal .25=.75 .10=.90


Deal Number −0.004∗ −0.002 −0.001 −0.005∗∗ −0.007∗∗∗ 1.550 0.890 1.330(0.097) (0.146) (0.390) (0.011) (0.004) (0.187) (0.346) (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 0.014∗∗ 0.017∗∗ 0.015∗∗ 0.008 0.380 0.010 0.000(0.421) (0.032) (0.011) (0.036) (0.477) (0.820) (0.923) (0.961)

NYSE B/M × 100 0.017 0.012 0.020∗∗∗ 0.014 0.014 0.410 0.030 0.030(0.196) (0.167) (0.008) (0.242) (0.455) (0.805) (0.852) (0.867)

Deals/Year −0.002 −0.004 −0.008∗∗ −0.002 0.000 0.900 0.110 0.050(0.749) (0.322) (0.012) (0.578) (0.960) (0.464) (0.743) (0.819)

Years Since Last 0.001 0.000 0.000 0.000 0.003 0.340 0.010 0.250(0.446) (0.737) (0.709) (0.874) (0.356) (0.854) (0.939) (0.614)

Tobin’s q 0.001 0.002 0.002∗ 0.003∗ 0.004∗∗∗ 1.300 0.690 3.190∗

(0.392) (0.107) (0.058) (0.027) (0.001) (0.269) (0.407) (0.074)

Industry Deals Prior Year 0.000∗ 0.000 0.000 0.000 0.000 1.920 1.490 2.690(0.055) (0.977) (0.317) (0.171) (0.602) (0.105) (0.222) (0.101)

Wave Dummy −0.001 0.002 −0.001 0.002 0.013 0.700 0.000 1.060(0.922) (0.696) (0.808) (0.733) (0.190) (0.591) (0.974) (0.303)


Public −0.035∗∗ −0.021∗∗ −0.024∗∗∗−0.012 −0.010 0.440 0.430 1.070(0.039) (0.038) (0.008) (0.323) (0.556) (0.779) (0.513) (0.301)

Private −0.004 −0.005 −0.008∗ 0.002 −0.003 1.280 1.290 0.010(0.576) (0.307) (0.089) (0.679) (0.700) (0.275) (0.256) (0.909)

Relative Value −0.015 −0.005 0.018∗ 0.034∗∗∗ 0.032∗ 3.460∗∗∗ 9.560∗∗∗ 5.030∗∗

(0.172) (0.633) (0.074) (0.000) (0.055) (0.008) (0.002) (0.025)

Transaction Value 0.000∗∗∗ 0.000 0.000 0.000 0.000 1.160 0.170 4.000∗

(0.098) (0.885) (0.448) (0.725) (0.257) (0.328) (0.681) (0.046)



Table 8 - Continued

Quantiles Wald Test - F Statistic

0.10 0.25 0.50 0.75 0.90 All Equal .25=.75 .10=.90

Toehold 0.000 0.000 −0.001 0.000 0.000 0.390 0.010 0.110(0.689) (0.998) (0.459) (0.919) (0.932) (0.815) (0.924) (0.745)

Same Industry −0.001 0.002 0.002 0.009 0.000 1.050 1.630 0.010(0.878) (0.718) (0.588) (0.113) (0.977) (0.378) (0.202) (0.930)


Tender Offer 0.036∗∗ 0.012 0.007 −0.014 −0.036 1.930 2.750∗ 6.350∗∗

(0.024) (0.337) (0.570) (0.338) (0.167) (0.103) (0.097) (0.012)

All Equity −0.033 0.006 0.017 0.039∗ 0.034 0.650 1.570 2.030(0.427) (0.746) (0.345) (0.095) (0.197) (0.625) (0.211) (0.154)

All Cash 0.001 0.001 0.001 −0.008 −0.009 0.560 1.650 0.650(0.860) (0.808) (0.917) (0.257) (0.343) (0.692) (0.199) (0.421)

All Equity × Private 0.035 −0.001 −0.007∗ −0.042 −0.029 1.010 2.690 1.960(0.397) (0.979) (0.725) (0.076) (0.248) (0.402) (0.101) (0.162)

All Equity × Public 0.005 −0.018 −0.035 −0.066∗∗ −0.061∗∗ 0.830 2.650 1.700(0.903) (0.387) (0.141) (0.013) (0.028) (0.507) (0.104) (0.192)

Constant −0.078 −0.011 −0.017 0.048 0.159(0.037) (0.751) (0.703) (0.436) (0.029)

Year Dummies Yes Yes Yes Yes YesIndustry Dummies Yes Yes Yes Yes YesObservations 2,470 2,470 2,470 2,470 2,470Pseudo R2 0.111 0.053 0.040 0.083 0.118

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Q-THEORY AND ACQUISITION RETURNS