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THE RETURNS TO REPEAT ACQUIRERS⋆

KENNETH R. AHERN†

UNIVERSITY OF MICHIGAN — ROSS SCHOOL OF BUSINESS

Abstract

Why are repeat acquirers’ early abnormal returns higher than those from later acquisitions? This

paper argues that acquirers minimize integration and transaction costs by choosing an optimal target size.

As acquirers get larger, they optimally choose targets of larger absolute size, but smaller relative size,

leading to declining returns. Using a panel dataset of repeat acquirers over two decades, empirical tests

support this argument. In contrast, I find no support for alternative explanations of declining returns

based on hubris and diminishing opportunity sets, and only weak support for agency explanations. In

addition, using new econometric techniques, I reject the popular theory that declining returns result

from market anticipation of later deals.

This Version: 14 September 2008

JEL Classification: G30, G32, G34

Keywords: Mergers and acquisitions, repeat acquirers, corporate governance, agency, hubris

⋆ This paper is an extension of chapter two of my doctoral dissertation completed at UCLA. I am extremelygrateful to Antonio Bernardo, Jean-Laurent Rosenthal, and J. Fred Weston for advice and support. I alsoespecially thank David Robinson, Karin Thorburn, Roni Michaely, and Katrina Ellis. Comments provided byRaffaella Giacomini, Marc Martos-Vila, MP Narayanan, Han Kim, Geoffrey Tate, Uday Rajan, Mike Stegemoller,Liu Yang, and seminar participants at the 2008 AFA Annual Meeting, 2006 FMA Annual Meeting, the 2006 USand European FMA Doctoral Seminars, the Anderson School at UCLA, UCLA Department of Economics IOWorkshop, 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 financialsupport 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, AnnArbor MI 48109. Telephone: (734) 764-3196. Fax: (734) 936-8715. E-mail: [email protected].

THE RETURNS TO REPEAT ACQUIRERS

Abstract

Why are repeat acquirers’ early abnormal returns higher than those from later acquisitions? This

paper argues that acquirers minimize integration and transaction costs by choosing an optimal target size.

As acquirers get larger, they optimally choose targets of larger absolute size, but smaller relative size,

leading to declining returns. Using a panel dataset of repeat acquirers over two decades, empirical tests

support this argument. In contrast, I find no support for alternative explanations of declining returns

based on hubris and diminishing opportunity sets, and only weak support for agency explanations. In

addition, using new econometric techniques, I reject the popular theory that declining returns result

from market anticipation of later deals.

JEL Classification: G30, G32, G34

Keywords: Mergers and acquisitions, repeat acquirers, corporate governance, agency, hubris

1. Introduction

The vast majority of research on mergers and acquisitions assumes M&As are singular firm

events. Yet in a sample of 12,942 mergers from 1980 to 2004, I find that only 38% of deals

are made by first-time acquirers. Furthermore, the most active ten percent of the firms in the

sample account for 35% of all deals. Repeat acquirers are the norm, not the exception. The

few studies that account for repeat acquisitions find that a firm’s announcement returns decline

substantially over subsequent deals (Fuller, Netter, and Stegemoller, 2002; Aktas, de Bodt, and

Roll, 2007b). In my sample, acquirers have an average three-day abnormal announcement return

of 3.19% on the first deal, declining to -0.11% for fifth and later deals. This paper empirically

addresses the question raised by this pattern: Why do repeat acquirers have declining returns?

Whereas academic studies have concentrated on strategic fit and over-payment as key deter-

minants of merger success, consulting firms and the business press have emphasized the role

of target size on the costs of due diligence and integration strategies. For example, the 2002

merger of equals between Hewlett-Packard and Compaq was widely presumed to be doomed

to failure at its announcement. However, 16 months after the merger, costs had been reduced

by $3.5 billion, roughly 5%. By 2005, HP’s stock had risen 46% compared to rival Dell’s rise

of 2% and IBM’s decline of 23%. Compaq’s CFO, Jeff Clarke, attributed the merger’s success

to careful integration, stating, “We believe this was the most thoroughly planned merger in

history” (Harris, 2003, 2006). In contrast, 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. Though research has found that firm size affects acquirer returns, it is still not

understood what drives this result (Moeller, Schlingemann, and Stulz, 2004).

In this paper, I argue that returns decline by deal order because the costs of M&As increase

as acquirers get larger. I develop a simple model to illustrate my hypothesis. I assume that

transaction costs depend upon target size and integration costs are determined by relative size.

Acquirers choose an optimal target to maximize profits taking these costs into consideration.

Because larger targets incur greater integration costs, acquirers have an incentive to buy small

targets. However, targets that are too small do not provide enough value to offset the search

and transaction costs of the acquisition. Thus both the absolute and relative size of a target1

2 THE RETURNS TO REPEAT ACQUIRERS

influence the acquirer’s outcome. The model predicts that larger firms will optimally choose

larger targets, though both relative size and returns exhibit a hump-shaped pattern. Above a

critical size, as acquirers get larger they optimally buy targets of decreasing relative size, though

increasing absolute size, leading to declining returns. Since firms grow through acquisitions,

firms later in a deal sequence are larger than firms earlier in a sequence. Thus the model predicts

a pattern of declining returns to repeat acquirers even when firms are maximizing profit.

My empirical tests support this argument. Nonparametric kernel regressions reveal a positive

relationship between the absolute sizes of the acquirer and the target as well as a hump-shaped

pattern of both returns and relative size as a function of acquirer size. The data confirm that

acquirers get larger over deal sequences and since most first-time acquirers are large enough to be

on the declining side of the hump-shaped pattern of returns, later deals generate lower average

returns than do earlier deals. In addition, by analyzing the relationship between transaction

size and advisor fees, I find evidence supporting the assumption that transaction costs increase

with the size of the target. I also find that the relative size of target to acquirer is positively

related to integration costs, proxied by industry-relatedness and geographic distance between

bidder and target.

Next, I use a large panel dataset of repeat acquirers to test the predictions of the cost

minimization hypothesis directly against three alternative explanations: agency, hubris, and

diminishing opportunity sets. The agency hypothesis predicts that management interests be-

come less aligned with shareholder interests as a firm matures. Thus later deals 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 over-

confidence and thus overbidding in later deals (Aktas, de Bodt, and Roll, 2007a). Finally, the

opportunity set hypothesis predicts that the best targets are acquired first and worse targets

later (Klasa and Stegemoller, 2007).

I test these hypotheses by first identifying the cross-sectional determinants of abnormal re-

turns for a fixed deal number. Then I determine if these factors are changing systematically over

a deal sequence. Both conditions are necessary to explain significant declines in returns. I mea-

sure agency using the Gompers, Ishii, and Metrick (2003) g−index of managerial entrenchment

THE RETURNS TO REPEAT ACQUIRERS 3

and outside monitoring by independent blockholders. Hubris is measured by premiums paid,

and opportunity sets are measured by an acquirer’s market-to-book ratio, Tobin’s q, and prior

year returns. Regression analysis supports the cost minimization hypothesis. Controlling for a

host of factors, the absolute and relative sizes of the target affect returns in the cross-section

and also change systematically over deal sequences. In contrast, I find only weak support for the

agency theory, and none for hubris and opportunity set explanations. Agency variables affect

cross-sectional returns, but vary only slightly across deal sequences. In contrast, premiums and

growth option proxies change substantially over deal sequences, but do not affect returns in the

cross-section.

The above results rely on independent cross-sectional and longitudinal tests. In contrast, the

dynamic process of market anticipation of future deals at the announcement of earlier deals

could explain declining returns 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

cited1, 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 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,

1Fuller, 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).


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.

Prior research has investigated other issues concerned with repeat acquirers. Loderer and

Martin (1990) is perhaps the earliest paper to recognize that firms make multiple acquisitions.

More recently, Fuller, Netter, and Stegemoller (2002) examines repeat acquirers to investigate

how the public status of the target affects acquirer returns. Moeller, Schlingemann, and Stulz

(2005) notes that large loss deals are usually made by a repeat acquirer following a series of

successful deals, and Song and Walkling (2008) investigates the anticipation of future acquisi-

tions following industry shocks. To the best of my knowledge, the only other paper to attempt

to directly understand why returns decline is Aktas, de Bodt, and Roll (2007a). They posit

that a self-interested manager learns through repeat acquisitions to increase premiums in or-

der to capture larger private benefits, which leads to declining returns. More generally, 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 2 presents the theoretical model

of M&A cost minimization. The data are described in Section 3. Empirical tests of the cost

minimization hypothesis and alternative theories are described in Section 4. Section 5 presents

tests of market anticipation. Section 6 concludes.


2. The cost minimization hypothesis

In this section I present a simple model where acquirers maximize profit by choosing an

optimal target size given transaction and integration costs. The model generates empirically

testable predictions about the relations between acquirer and target size and returns. First I

discuss how integration and transaction costs are related to target size.

Though acquiring larger targets might generate more synergies, transaction costs are certainly

higher. Larger targets require greater costs of due diligence, valuation analysis, and more com-

plex financing. Consistent with this argument, a positive relation between advisory fees and

transaction size has been documented by McLaughlin (1990, 1992) and Ma (2006). This means

that bidders have an incentive to purchase small firms to reduce costs. However, targets that

are very small may not provide enough value to offset the search and transaction costs of the ac-

quisition. Anecdotal evidence supports this idea. In the 1984 Berkshire Hathaway ‘Chairman’s

Letter to Shareholders,’ Warren Buffet details the qualities of an attractive takeover candidate.

The first requirement is “(1) large purchases (at least $5 million of after-tax earnings).” In 1985,

the size requirement was listed as $10 million in earnings (Buffet, 1984; Buffet, 1985).

Clearly the actual size of a ‘large’ acquisition depends on the size of the acquirer. This

implies that the relative size of target to acquirer is an important decision criteria as well.

In particular, integration is likely to be more costly when the target is large relative to the

acquirer. Though there is no direct empirical evidence on integration costs, it is commonly

accepted that mergers-of-equals produce greater post-merger frictions, compared to mergers

where the acquirer dominates the target. These relations between target size and costs imply

that even if there is a strong economic motivation for a deal, the successful realization of net

gains will depend upon target size.

I next present a simple model of M&As where acquirers trade off these types of costs and

choose targets based on size in order to maximize their profits. I assume that benefits from a

merger are linearly increasing in target size, T , at constant rate β. Benefits here are the synergies

from the deal net of the purchase price. If the marginal benefits were decreasing in target size,

as might be expected, the qualitative results of this model would remain, so linearity is assumed

for convenience. Transaction costs are denoted g(T ) and integration costs are denoted h(T/A),


where A is the acquirer size. Thus transaction costs are a function of absolute target size, and

integration costs are a function of relative size as argued above. Search costs are fixed and

denoted F . It is plausible that search costs decrease with target size, since smaller firms are

less visible. However, this assumption would strengthen the qualitative results of the analysis,

so fixed search costs are assumed for simplicity.

The acquiring firm chooses target size T = T ∗ to maximize absolute profit:

π(T (A), A) = βT − F − g(T ) − h(T/A) (1)

I assume that costs increase monotonically, g′(T ) > 0 and h′(T/A) > 0. The returns are

defined as profit scaled by acquirer size, R(T (A), A) = πA . The following two propositions define

how the shape of the cost curves lead to changes in the optimal target size and relative size

derived from maximizing the firm’s objective function in Equation 1.

Proposition 1. If −TA · h′′(T/A)

h′(T/A) < 1, then T ∗′(A) > 0.

Proposition 2. If g′′(T ) > h′(T/A)A , then T ∗

A

′

(A) < 0.

All proofs are in Appendix A. Proposition 1 states that if integration costs are not too

concave, than the optimal target size will increase monotonically as acquirer size gets larger.

Thus larger acquirers optimally choose larger targets.2 Proposition 2 states that the optimal

size of the target grows at a slower rate than acquirer size when the change in the marginal

transaction cost is greater than the marginal integration cost. Intuitively, as acquirers get larger,

the optimal relative size of target to acquirer will decrease if transaction costs are increasing

faster than integration costs. For a fixed target size, integration costs will fall less for a large

acquirer than for a small acquirer for the same increase in acquirer size. For size increases in

small acquirers, the optimal relative value of target to acquirer may actually increase until the

transaction costs outweigh the integration costs. Proposition 2 also implies that transaction

costs must be strictly convex for relative size to decrease with acquirer size because h′(T/A) > 0

2One can interpret the necessary degree of convexity by noting that − TA·

h′′(T/A)h′(T/A)

is the same form as the relative

risk aversion coefficient for utility functions. For values of this coefficient between zero and one, costs are concave,but the maximization problem will still lead to increasing target size as acquirers get larger. Thus the shape ofthe integration cost curve is not restricted to convexity for this result.


by assumption. This restriction is plausible given that convex transaction costs have been

documented for activities less complex than mergers, notably the issuance of new securities

(Altinkilic and Hansen, 2000).

The third proposition gives the necessary condition for returns to decrease as a function of

acquirer size.

Proposition 3. If TA · h′(T/A)

A < πA = R(A), then R′(A) < 0.

This proposition states that when the marginal cost of integration scaled by the relative size

of target to acquirer is less than the return, then returns are declining in A. In other words, an

increase in acquirer size will lead to a less than proportional increase in profits when the savings

from reduced integration costs are not large. Thus, as in Proposition 2, the same increase in size

for a small acquirer, will lead to higher returns than for a large acquirer, because the marginal

reduction in integration costs is larger for the small firm. Conversely, since the marginal benefit

of reduced integration costs to large firms is small, returns will be lower for larger acquirers.

To illustrate the implications of the general model I solve for the optimal target size, relative

value, and returns using the following specification:

π(T,A) = βT − F − 0.5δT 2 − 0.5γ (T/A)2 (2)

where the parameters β, δ, and γ measure the intensity of net synergy, transaction costs, and

integration costs, respectively. The profit function is concave and the first-order condition yields

an optimal target size T ∗ and relative size T ∗

A as follows:

T ∗ =A2β

A2δ + γ(3)

T ∗

A=

Aβ

A2δ + γ(4)

The conditions of Proposition 1 and 2 are satisfied because both cost functions are strictly

convex. Thus the optimal target size, T ∗, is increasing in the acquirer size A for all A, but the

optimal relative size decreases as acquirers get larger only after acquirers reach a certain critical


size where increases in transaction costs outweigh increases in integration costs. Substituting

the optimal T ∗ in the profit equation yields,

π(T ∗(A), A) =A2β2

2(A2δ + γ)− F (5)

and the returns from the acquisition are,

R(T ∗(A), A) =π

A=

Aβ2

2(A2δ + γ)−

F

A. (6)

To illustrate the results of this model, I plot the optimal target size, relative size, and return

as functions of acquirer size in Figure 1. Parameter values are β = 1, F = 0.5, δ = 0.1,

and γ = 5. Since γ measures the integration cost based on relative size and δ measures the

transaction cost based on the absolute size, γ is much larger than δ.

Figure 1 shows that as acquirers get larger they optimally choose larger targets though of

decreasing relative size. The relative size and returns depend upon the size of the acquirer in

similar ways. When acquirers are small, the integration costs are large relative to the transaction

costs. As the acquirer size increases, the savings from the integration costs more than offset the

additional transaction costs and so returns and relative size increase. At a critical point, the

transaction costs overcome the integration costs and increasing acquirer size leads to decreasing

returns and relative size.

In summary, under the following assumptions,

1. Acquirers maximize profits by choosing an optimal target size.

2. Transaction costs are increasing in the absolute size of the target.

3. Integration costs are increasing in relative size and are not too concave.

the model generates the following empirically testable predictions,

1. Target size grows monotonically with acquirer size.

2. Relative size and returns follow a hump-shaped pattern, increasing for small acquirers and

decreasing for acquirers larger than a critical size.

The implication for repeat acquirers is that if there is a positive relationship between deal order

and acquirer size, as would be expected, this model predicts that we will observe that returns


decline over a deal sequence for large enough firms even when firms are maximizing profit from

acquisitions.

3. Data and methodology

To test theories of repeat acquirers 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.3 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,

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

3I 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).


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 is commonly done in prior studies (Loderer and

Martin, 1990; Conn, Cosh, Guest, and Hughes, 2004).

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 15-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,4 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

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.

4Generously provided on Kenneth French’s Web site.http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html


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. Significance tests of CARs are conducted with a sign test

(Corrado and Zivney, 1992). Table 2 reports CARs grouped by total number of deals in a firm’s

series, acquisition order in series, and target organizational status.

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%.

Subsidiary targets yield higher returns (3.09%) than do private targets (2.30%), which yield

higher returns than public targets, which are statistically negative (-0.86%). Consistent with

prior studies, CARs are declining with deal order, regardless of target status. Public targets

yield an insignificant 0.35% for first deals declining to a significant −2.06% for sixth and later

deals. First acquisitions of private targets generate 3.39%, declining to 0.72% for sixth and

later deals.5

The sample sizes reported for each CAR in Table 2 also reflect that the type of target is

changing with deal order. For the first through third deals, roughly 16% are public targets,

55% are private, and 29% are subsidiaries. For fourth and higher deals, public targets become

more prevalent, increasing to 25% of the sample for the sixth or later deals, while private

targets decrease to 48%. Subsidiary targets first increase to 33% then decline to 27% for the

later deals. This supports the idea that changing firm characteristics may explain part of the

decline in abnormal returns with deal order.

5In unreported calculations, average (median) abnormal dollar returns are −$19.5 million ($0.4 million) in 2005dollars, consistent with the same calculations in Moeller, Schlingemann, and Stulz (2004). However, the abnormaldollar returns do not exhibit a strong pattern of decline over deals, as do the equally weighted abnormal returns.Nevertheless, I replaced CARs with dollar returns in all of the following analyses as a robustness check and foundno qualitative difference in the results.


4. Empirical tests of the cost minimization hypothesis

To test the cost minimization hypothesis, I first empirically examine the plausibility of its

assumptions and then compare the model predictions to the data. The model assumes that

transaction costs affect the absolute size of the target, whereas integration costs affect the

relative size. 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 model prediction 1. 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 model assumptions 2 and 3 on the relationships between

costs and target sizes. Assumption 1 states that firms maximize profits. The plausibility of this

assumption is directly tested against other alternative motives for M&As in later analyses.

Next, I estimate the model predictions about the relationship between firm size and returns.

Econometrically, I want to estimate E(X | Acquirer Size), where X is either returns, target

size, or relative size. Since the model makes distinctly non-linear predictions, I do not impose

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.


a functional form on this expectation, but instead use nonparametric kernel regression to plot

the relationships.7 These estimated expectations are plotted in Figure 2 along with scatterplots

of the data.

The kernel regression estimates closely follow the model’s predictions. This evidence is par-

ticularly strong because the unique hump-shaped pattern predicted by the model is replicated

in the data. In particular, both returns and relative size increase over a range of small acquirer

values and then decrease toward zero. Moreover, consistent with the model, the marginal change

in relative value is much larger than the marginal change in returns for increases in acquirer

size. Finally, 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.

Though the nonparametric estimations are strong evidence in support of the cost minimiza-

tion hypothesis, they do not control for other factors that may explain declining returns. In

particular, the cost minimization hypothesis assumes firms are maximizing profits by choosing

an optimal target size. Alternative theories of M&As include agency, hubris, and diminishing

opportunity sets. The next set of tests explicitly controls for a host of variables and investigates

these alternative theories.

4.1. Tests of the alternative theories

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.

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 in-

cluding measures of internal monitoring and managerial entrenchment/antitakeover provisions

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 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 measured using the Gompers-Ishii-Metrick (GIM) governance index of the data in the In-

vestor Responsibility Research Center’s (IRRC) Governance database. This data set provides

data 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 (2007a) states that higher premiums drive down

abnormal returns from acquisitions made later in a deal sequence. However, in contrast to this

theory, it is possible that high premiums may indicate the possibility of high synergies between

bidder and target, which could lead to higher abnormal returns.

Finally, to test the diminishing opportunity set theory, I would like to be able to measure the

number and quality of remaining targets for a given acquirer. However, I am unaware of any

direct measure of these factors. Instead, I use acquirer book-to-market, Tobin’s q, and prior

year returns as proxies of opportunity sets. These variables have been used in other studies

to measure growth options, and I assume that a component of these growth option measures

includes acquisition options (Billett, King, and Mauer, 2007).

Table 4 presents firm fixed effect regressions designed to test the cost minimization hypothesis

against the alternative explanations. The first column regresses the five-day 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. Moreover,

experience does not appear to influence merger returns. Second, acquirer size is negatively

and significantly related to CARs, consistent with the cost minimization hypothesis, 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. A short duration between deals may instead be related to the

indigestion hypothesis of Conn, Cosh, Guest, and Hughes (2004), where integration between the

target and bidder is hampered by a subsequent acquisition. Moreover, in various explicit tests

reported in Section 5, I do not find support for anticipation as a determinant of returns. 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 these results are consistent with prior studies (Fuller, Netter, and Stegemoller,

2002; Moeller, Schlingemann, and Stulz, 2004). Finally, note that relative value is positive and

significant at the 10.4% level, though transaction value is insignificant.

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 moni-

toring 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 in a static cross-section analysis. 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, Schlinge-

mann, 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 IRRC database. This size constraint on the sample also affects the estimated effect of the

relative value on the CARs. Since larger firms have lower CARs, larger relative sizes decrease


the returns, as opposed to the positive relationship found in the full sample where returns are

higher on average.

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. However, target

size has a significant negative relation to acquirer CARs, consistent with the cost minimization

hypothesis. In unreported tests, for robustness I also include the number of bidders in a

takeover contest and find no qualitative differences in the results. Finally, none of the acquirer

opportunity set measures are significant in any specification in Table 4. This suggests that

diminishing opportunity sets do not explain declining acquirer returns either.

In summary, though no evidence is found to support the hubris or opportunity set hypotheses,

the above results show that both target size and more managerial entrenchment with less

oversight significantly reduces acquirer returns in the cross-section. Other factors also explain

announcement returns. Public targets generate lower returns, as does using all stock financing.

Both relative value and the number of years since the last acquisition have a positive effect

in the large sample, but a negative effect in the public target samples. 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.

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 (2007b). These results, presented in Table 5, provide more evidence

in support of the cost minimization hypothesis. Average acquirer size continues to grow over

subsequent acquisitions, but the average relative size of the target declines at a declining rate

over deal sequences. Thus later deals are dominated by acquisitions of large targets, though of a

small relative size. Because relatively smaller targets create smaller abnormal returns, acquirer

returns are decreasing over deal sequences. In summary, the empirical relation between costs

and sizes, the nonlinear kernel regression results, and the longitudinal decline in relative value


and increase in target absolute size support the basic assumptions and predictions of the cost

minimization hypothesis.

Returning to the results in Table 5, 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 (2007) report that inside ownership

changes are extremely small over time within the same firm.

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.

In summary, returns decline because acquirers get larger and the relative size of targets to

acquirers gets smaller. This pattern is shown to be consistent with a firm’s objective to minimize

the costs of M&As, after controlling for variables related to agency, hubris, and opportunity set

theories.

5. Theory and prior evidence of market anticipation of mergers

Though the above results are consistent with the cost minimization hypothesis, 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 announce-

ment 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.

5.1. Capitalization theory

There is an endogenous relationship between current returns and future expected returns. A

high first deal return of a repeat acquirer 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 (7)

EV Fia = α2CARia + X2iaβ2 + c2i + via a = 1, . . . , A (8)

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 (9)

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 (7) and (8).8

8One 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 thelikelihood 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.


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 (10)

∆Pia = α2∆CARia + ∆X2iaβ2 + ∆via a = 1, . . . , A (11)

where

∆Zia = Zia − Zi,a−1 where Z is any variable in Equation (10) or (11)

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 heteroskedastcity and serial correlation. I use a linear probability specification

to estimate Equation 11.9

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 (11).

9Linear 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 (11) and use the fitted values as proxies in Equation (10). 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

(10). 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.10

To instrument for CARia in the probability model (Equation (11)), 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.11

The results of the simultaneous equations model are presented in Table 6. 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.

10The 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.11Prior 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.


5.2. Signaling theory

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.12 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 3, 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 7 presents quantile regressions controlling for firm and deal characteristics. The es-

timated 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.

12See 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 4.

5.3. Further robustness checks

The above results provide evidence that stock price changes from current acquisition an-

nouncements 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 acquisitive-

ness. 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 simul-

taneous 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.

6. Conclusion

This paper investigates the determinants of the pattern of declining event returns to repeat

acquirers. Using a simple model based on firms’ desires to balance post-completion costs of

integration and pre-completion costs of search and transaction, 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, thus

returns decline. Empirical tests provide validation for the assumptions of the relationships

between target size and M&A costs. Kernel regressions find patterns of returns and target size

consistent with the model predictions. Finally, the longitudinal decline in relative value and

increase in absolute target size support the predictions of the model.

I also test three alternative hypotheses to explain the pattern of declining returns. First,

controlling for deal number, more managerial monitoring and a smaller target size increase

returns, though public targets decrease returns. Comparing these factors in earlier to later

deals, monitoring is unchanging, though managers are more entrenched, and targets are getting

larger, more likely public, and of smaller relative sizes. I reject hubris and opportunity set

explanations for declining returns, since I show that premiums and measures of investment

opportunities do not affect abnormal returns.

Next, 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 infor-

mativeness 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 cost minimization

hypothesis are robust.


The validity of the cost minimization model suggests that more research on the costs of

acquisitions may be warranted since they may help 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.

Measurement of other costs of M&As, such as integration costs would also provide new insights

into the success or failure of merger strategies.


Appendix A

Proof of proposition 1

The firm objective function is:

π(T,A) = βT − F − g(T ) − h(T/A) (A.1)

The first-order condition is:

∂π(T,A)

∂T= β − g′(T ) − h′(TA−1) · A−1 = 0 (A.2)

and the second-order condition is assumed to be satisfied:

∂2π(T,A)

∂T 2= −g′′(T ) · A2 − h′′(TA−1) < 0 (A.3)

Now solve for T ′(A) by taking the derivative of both sides of Equation A.2 with respect to A:

∂

∂A

∂π(T,A)

∂T: − g′′(T )T ′(A)−[

h′′(TA−1)(

T ′(A)A−1 + T (A)(−A−2))

A−1 + h′(TA−1)(−A−2)]

= 0(A.4)

Then rearrange and solve for T ′(A):

∂T (A)

∂A= A−1

[

Ah′(TA−1) + h′′(TA−1)T (A)

A2g′′(T ) + h′′(TA−1)

]

(A.5)

We want to find conditions such that ∂T (A)∂A > 0. First note that the denominator in

Equation A.5 is the negative of the second order condition (A.3). By the assumption of con-cavity we can multiply Equation A.5 by its denominator and then solve the inequality,

Ah′(TA−1) + h′′(TA−1)T (A) > 0

Because h′(TA−1) > 0 by assumption,

−T

A·h′′(T/A)

h′(T/A)< 1 (A.6)

�


Solve for the change in relative size as a function of A:

∂

∂A

(

T (A)

A

)

= T ′(A)A−1 − T (A)A−2

= A−1[

T ′(A) − T (A)A−1]

Substituting in Equation A.5 for T ′(A),

= A−1

[

Ah′(TA−1) + h′′(TA−1)T (A)

A2g′′(T ) + h′′(TA−1)−

T (A)

A

]


Rearranging terms,

∂

∂A

(

T (A)

A

)

= A−1

[

h′(TA−1) − Ag′′(T )T (A)

h′′(TA−1) + A2g′′(T )

]

(A.7)

We want to find conditions such that Equation A.7< 0. By the assumption of concavity inthe second-order condition (A.3) we can multiply Equation A.7 by its denominator and thensolve the inequality,

h′(TA−1) − Ag′′(T )T (A) < 0

h′(TA−1) < Ag′′(T )T (A)

A−1h′(TA−1) < g′′(T )T (A)

By assumption,

0 < A−1h′(TA−1) < g′′(T )T (A)

�


Returns are profit divided by acquirer size,

R(A) = A−1βT (A) − A−1F − A−1g(T ) − A−1h(TA−1) (A.8)

Taking the derivate of R with respect to A and setting it less than zero,

∂R

∂A=β

[

− A−2T + A−1T ′(A)]

+ A−2F −[

− A−2g(T ) + A−1g′(T )T ′(A)]

−

−[

− A−2h(TA−1) + A−1h′(TA−1)[

T ′(A)A−1 − T (A)A−2]

]

< 0

= A−3

[

T ′(A)[

A2β − A2g′(T ) − Ah′(TA−1)]

+

+ T (A)[

−Aβ + h′(TA−1)]

+ A[

g(T ) + h(TA−1) + F]

]

< 0

The term in the first small brackets is the first-order condition of the profit function (Equation A.2)scaled by A2 and so is zero. Rearranging terms yields,

T (A)h′(TA−1) < A[

βT − g(T ) − h(TA−1) − F]

T (A)A−1h′(TA−1) < π(T (A), A)

T (A)

A·h′(TA−1)

A<

π(T (A), A)

A�


Appendix B

Variable Description

Abnormal $ Returns The abnormal changes (from the market adjusted returns) in marketequity 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-weightedindex 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 currentannouncement, 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 recordedin the Investor Responsibility Research Center (IRRC) database of pri-marily large firms. Higher values indicate more antitakeover provisions.Data is recorded in 1990, 1993, 1995, 1998, 2000, 2002, and 2004. Fol-lowing Gompers, Ishii, and Metrick (2003), I fill each missing year withthe most recent governance provisions available. Also firms with dualclass common stock are omitted.

Free Cash Flow [Operating income before depreciation (Compustat item 13) - interestincome (item 15) - income taxes (item 16) - capital expenditures (item128)]/[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.

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 (item9)]/[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

continued on next page


Appendix B - Continued


NYSE B/M NYSE vigintile of book-to-market (B/M). B/M is calculated for eachfirm for each year as accounting book value over market value wherebook value is total assets (Compustat item 6) - liabilities (item 181) +balance sheet deferred taxes and investment credits (item 35) - preferredstock liquidating value (item 10) or preferred stock redemption value(item 56) or carrying value (item 35), in this order. Market equity isprice times shares outstanding at the end of December. If the fiscalyear-end of a company is between January and May, the book equityfrom 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 pricetimes shares outstanding. Vigintiles are 1/20ths of unity.

Outside DirectorBlockholders

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 thetarget 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’sFama-French 49 Industry classification

Prior Year Returns Buy-and-hold return over the 12 months that concludes at the mostrecent 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 industryclassification

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) + marketequity (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 inSDC.



Appendix B - Continued


Transaction Value The value of all consideration paid in a deal minus the costs and fees asreported 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 andI only count industry deals based on acquirer industry, rather than acombination of bidder and target as in Harford.

Years Since Last The number of trading days since the last acquisition announcement orthe listing date if the acquisition is the first, divided by 250.


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Acquirer Size

10 20 30 40

Rel

ati

ve

Siz

e

0.2

0.4

0.6

0.8

1.0

Targ

etSize

2

4

6

8

10Optimal Target Size

Optimal Relative Size

Return

Figure 1

M&A cost minimization hypothesis example resultsThis figure represents the solutions to the acquiring firm’s profit maximization problem for differ-ent acquirer size. The parameterized objective function is π(T, A) = T −0.5−0.5 ·0.1 ·T 2−0.5 ·5 ·

(T/A)2, where T is target size and A is acquirer size. In the figure, acquirer size is measured on

the horizontal axis. The left vertical axis measures relative size of target to acquirer and profit.The right vertical axis measures the target size. The dashed line is the optimal T = T ∗ as afunction of A. The dotted line is the optimal relative size T ∗/A, as a function of A, and the solidline is the return using the optimal T ∗, as a function of A.


05

1015

ln(T

rans

actio

n S

ize)

−5 0 5 10 15

ln(Market Equity)

05

1015

Rel

ativ

e S

ize

−5 0 5 10 15

ln(Market Equity)

−1

−.5

0.5

5−D

ay C

AR

−5 0 5 10 15

ln(Market Equity)

Figure 2

Nonparametric 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), CAR(−2,+2), or therelative size of target to acquirer. The estimates are found using a Gaussian kernel function andthe bandwidth is chosen using cross-validation to minimize prediction error. The sample consistsof 12,942 observations over 1981 to 2004.


CAR

Deal Number

90th

Percentile

10th

Percentile

Figure 3

Anticipation 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 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 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


Table 2

Announcement returns by number of acquisitions, acquisition order, and target organizationalstatusCumulative abnormal returns (-2,+2) in percent terms computed using an equally weightedmarket-adjusted model. Numbers in parentheses indicate sample sizes. Statistical significance istested with the sign test. 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

Panel A: All Targets

1 3.39∗∗∗ 3.39∗∗∗

(2,212) (2,212)

2 2.97∗∗∗ 1.49∗∗ 2.23∗∗∗

(1,060) (1,060) (2,120)

3 2.38∗ 2.52∗∗∗ 1.74∗∗∗ 2.21∗∗∗

(558) (558) (558) (1,674)

4 3.58∗∗∗ 2.12∗∗∗ 1.34 1.51 2.14∗∗∗

(343) (343) (343) (343) (1,372)

5 2.73 3.11∗∗∗ 0.82 0.99 −0.09 1.51∗∗

(203) (203) (203) (203) (203) (1,015)

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

(503) (503) (503) (503) (503) (2,034) (4,549)

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

(4,879) (2,667) (1,607) (1,049) (706) (2,034) (12,942)

Panel B: Public Targets

1 1.13 1.13(378) (378)

2 −0.66 −1.08 −0.88(157) (171) (328)

3 −2.02 −1.68 1.43 −0.62(79) (74) (93) (246)

4 1.24 −0.60 −0.54 −2.34 −0.58(53) (61) (61) (56) (231)

5 2.73 0.68 −4.53 −4.08∗∗ −3.01 −1.55(33) (38) (26) (36) (39) (172)

> 5 0.81 −0.65 −3.13∗∗ −0.19 −1.30 −2.06∗∗∗ −1.67∗∗∗

(66) (86) (84) (92) (102) (504) (934)

All 0.35 −0.88 −1.06 −1.61∗ −1.78 −2.06∗∗∗ −0.86∗∗∗

(766) (430) (264) (184) (141) (504) (2,289)



Table 2 - Continued

Acquisition Number in Series

Number ofDeals in Series

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

Deals

Panel C: Private Targets

1 3.43 3.43(1,165) (1,165)

2 3.36∗∗∗ 0.88 2.13∗∗∗

(594) (586) (1,180)

3 3.37∗∗∗ 1.74∗∗ 1.24∗ 2.13∗∗∗

(315) (314) (301) (930)

4 3.38∗∗∗ 3.31∗∗∗ 1.52 2.28 2.63∗∗∗

(205) (178) (196) (170) (749)

5 3.20 3.67∗∗∗ 1.13 2.14 1.44 2.32∗∗

(109) (103) (118) (97) (86) (513)

> 5 3.37∗∗∗ 3.03∗∗∗ 2.26∗∗ 2.38∗∗∗ 1.70 0.72∗∗ 1.79∗∗∗

(290) (272) (276) (256) (255) (980) (2,329)

All 3.39∗∗∗ 1.96∗∗∗ 1.60∗∗∗ 2.30∗∗∗ 1.63 0.72∗∗ 2.30∗∗∗

(2,678) (1,453) (891) (523) (341) (980) (6,866)

Panel D: Subsidiary Targets

1 4.59∗∗∗ 4.59∗∗∗

(669) (669)

2 4.06∗∗∗ 4.12∗∗∗ 4.09∗∗∗

(309) (303) (612)

3 2.62 5.78∗∗∗ 2.83∗ 3.77∗∗∗

(164) (170) (164) (498)

4 5.53∗∗∗ 1.67 2.27 2.24 2.81∗∗

(85) (104) (86) (117) (392)

5 1.88 3.67 2.56∗ 2.01 −0.31 1.85∗

(61) (62) (59) (70) (78) (330)

> 5 5.88∗∗∗ 3.32∗∗∗ 3.51∗∗∗ 1.87∗∗∗ 2.13∗∗ 0.19 1.98∗∗∗

(147) (145) (143) (155) (146) (550) (1,286)

All 4.32∗∗∗ 3.97∗∗∗ 2.90∗∗∗ 2.02∗∗∗ 1.28∗∗ 0.19 3.09∗∗∗

(1,435) (784) (452) (342) (224) (550) (3,787)

∗∗∗ Statistical significance at the 1% level.∗∗ Statistical significance at the 5% level.∗ Statistical significance at the 10% level.


Table 3

Cross-sectional regressions on target size and relative sizeThis 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 Appendix B. Observations are over 1981–2004. Robust p−values clustered by acquirer arereported in parentheses.

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 597R2 0.7651 0.2150


Table 4

Firm fixed-effects regressions of returns on governance and public target characteristics‘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. All variable definitions are in Appendix B.

All Governance Public Targets

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

Acquirer Characteristics

NYSE Market Equity −0.0012∗∗∗ −0.0002 −0.0003 −0.0003 −0.0002(0.000) (0.717) (0.667) (0.451) (0.746)

NYSE Prior Returns 0.0000 −0.0001 −0.0001 0.0000 −0.0001(0.505) (0.642) (0.564) (0.789) (0.491)

NYSE B/M 0.0001 −0.0003 −0.0003 0.0004 0.0003(0.447) (0.257) (0.332) (0.266) (0.386)

Deal Number −0.0026 −0.0022 0.0004 0.0038 0.0047(0.171) (0.588) (0.921) (0.202) (0.108)

Deals/Year −0.0135∗ −0.0077 −0.0260 −0.0226 −0.0336(0.071) (0.888) (0.649) (0.521) (0.309)

Years Since Last 0.0020∗∗∗ 0.0014 0.0011 −0.0046∗ −0.0045∗

(0.008) (0.347) (0.466) (0.062) (0.063)

Tobin’s q −0.0007 0.0004 0.0005 0.0012 −0.0004(0.430) (0.750) (0.681) (0.563) (0.845)

Industry Deals Prior Year −0.0002 −0.0002 −0.0002 −0.0001 0.0000(0.155) (0.259) (0.235) (0.684) (0.739)

Wave Dummy 0.0049 0.0007 0.0028 −0.0095 −0.0092(0.351) (0.946) (0.773) (0.359) (0.381)

Outside Director Blockholders 0.1911∗∗∗

(0.010)

Entrenchment Index −0.0062(0.204)

Directors × Entrenchment −0.0150∗

(0.065)

Target Characteristics

Public −0.0318∗∗∗ −0.0194∗ −0.0195∗

(0.000) (0.060) (0.056)

Private −0.0042 0.0011 0.0006(0.237) (0.893) (0.937)

Relative Value 0.0061 −0.0442∗ −0.0508∗∗ −0.0345∗∗ −0.0259∗

(0.104) (0.076) (0.044) (0.011) (0.068)



Table 4 - Continued

All Governance Public Targets

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

Transaction Value 0.0000 0.0000 0.0000 0.0000 0.0000(0.175) (0.603) (0.771) (0.349) (0.817)

Premium 0.0069(0.425)

NYSE Market Equity −0.0006∗

(0.091)

NYSE Prior Returns 0.0001(0.486)

Tobin’s q 0.0057∗∗

(0.012)

Toehold 0.0003 0.0005 0.0006 0.0011∗ 0.0011∗

(0.362) (0.588) (0.499) (0.054) (0.053)

Same Industry 0.0044 0.0065 0.0064 0.0330∗∗ 0.0324∗∗

(0.297) (0.353) (0.348) (0.014) (0.017)

Deal Characteristics

Tender Offer 0.0135 0.0039 0.0017 0.0250∗ 0.0272∗∗

(0.149) (0.794) (0.908) (0.051) (0.029)

All Stock −0.0027 −0.0801∗∗∗ −0.0819∗∗∗ 0.0080 0.0070(0.835) (0.008) (0.008) (0.567) (0.625)

All Cash −0.0044 −0.0091 −0.0100 0.0207 0.0149(0.259) (0.264) (0.214) (0.152) (0.329)

All Stock × Private 0.0155 0.0637∗∗ 0.0636∗

(0.238) (0.049) (0.053)

All Stock × Public −0.0257∗ 0.0462 0.0478(0.078) (0.152) (0.143)

1980–1991 −0.0146 0.1013∗∗∗ 0.1117∗∗∗

(0.260) (0.005) (0.002)

1992–1999 0.0088 0.0198 0.0191 0.0625∗∗∗ 0.0682∗∗∗

(0.213) (0.119) (0.130) (0.002) (0.000)

Firms 2,187 320 320 217 217Observations 6,420 982 982 601 601R2 0.0400 0.0601 0.0700 0.1219 0.1460


TH

ER

ET

UR

NS

TO

RE

PE

AT

AC

QU

IRE

RS

43

Table 5

Mean 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. All variable definitions are in Appendix B.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 0.000Private 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 0.000Subsidiary 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 0.000Same 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∗∗−0.011

45.000 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 0.000All 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 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000All 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 6

Fixed 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. Variable definitions are in Appendix B.

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 6 - 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 2,709R2 0.1843 0.0331



Table 7

Quantile 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 Appendix B. Sample is over 1981 to2004. 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.

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)

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)



Table 7 - Continued

Quantiles Wald Test - F Statistic

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


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


the returns to repeat acquirers - michigan rosswebuser.bus.umich.edu/kenahern/ahern.rra.pdf ·...

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