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Federal Reserve Bank of Chicago
Betcha can’t acquire just one:
merger programs and compensation
Richard J. Rosen
WP 2004-22
Betcha can’t acquire just one:
merger programs and compensation ∗
Richard J. Rosen
Federal Reserve Bank of Chicago,
Chicago, IL 60604
Financial Institutions Center
Wharton School
Philadelphia, PA 19104
rrosen@frbchi.org
This draft: November 2004
First draft: July 2004
Abstract
This paper examines the evolution of merger programs, that is, repeated acquisitions by the same
firm. Most acquisitions are made by firms with merger programs. Acquisitions that are part of
programs are different from one-off acquisitions both in the effect on CEO compensation and in
the reaction of the stock market. CEO compensation rises more after growth from program
acquisitions than after internal growth or growth from one-off acquisitions. During a merger
program, the increase in CEO compensation is much larger when the acquirer’s stock price is
increasing than at other times. This is not true for other types of growth. Merger programs also
show a distinct evolution. Initially, program mergers are received better by the stock market than
are one-off mergers. As a program progresses, however, the acquisitions tend to have lower
announcement reactions and long-run returns. In addition, the effect on CEO compensation is
smaller for mergers later in a program. There is evidence that some firms are predisposed to
make acquisitions. Firms that have made acquisitions in the recent past and that already pay their
CEOs well are more likely to make future acquisitions. This suggests that there may be a
managerial motivation for merger programs: firms where CEOs can expect to get large
compensation increases from acquisitions are more likely to have merger programs.
JEL classification: G34, G14
Keywords: mergers, CEO compensation, merger programs, repeat acquirers, managerial
motivations
∗
The opinions expressed do not necessarily reflect those of the Federal Reserve Bank of Chicago or its
staff. The author thanks Terry Nixon and the participants in a workshop at the Chicago Fed for their
comments.
Betcha can’t acquire just one: merger programs and compensation
I. Introduction
There was more merger activity in the last decade of the twentieth century than in any other
period. In 2000, firms comprising over one third of the value of U.S. equity markets were
involved in an acquisition.1 This marked the end of one of the two biggest merger waves in U.S.
history, the other having occurred in the 1980s. While there have been a number of attempts to
explain why and when firms merge, one aspect that has received little attention is that many firms
are repeat acquirers. A majority of the acquisitions in the 1990s were made by only 20 percent of
acquirers (six percent of the firms in the CRSP data).2 This paper uses these frequent acquirers to
illuminate possible motivations for mergers and to ask whether these acquirers are like firms that
make one-off acquisitions.
At the same time as the recent merger waves there was a rapid increase in CEO
compensation. In 1993, the average total compensation for CEOs at firms in the S&P 1500 was
$2.0 million. By 2000, this figure had risen to $6.7 million. We examine how the interplay
between mergers, shareholder value, and compensation is affected by frequent acquisitions.
As an example of the issues involved, consider the case of A. H. Belo Corporation, an owner
of newspapers and television stations. Belo announced seven acquisitions between 1994 and
2000, including six where the target was at least five percent the size of Belo. During this period,
the CEO of Belo saw his total annual compensation increase from $1.8 million to $4.7 million.
Did the acquisitions affect the compensation of Belo’s CEO, or did the desire to increase
compensation affect the decision to acquire? The answers may be complicated by the lack of a
simple relationship between compensation and merger activity. For example, the increase in
compensation for Belo’s CEO was more rapid for the early acquisitions. Compensation rose to
$3.8 million by the end of 1996, halfway through the merger program. In this paper, we explore
whether this pattern of compensation change is common. Note that shareholders were not as
happy with the acquisitions as Belo’s CEO. During this period, the return on Belo shares was 31
percent while the market as a whole went up almost 200 percent.
To capture firms that are repeat acquirers, we define a merger program as a series of two or
more major acquisitions by a single firm with a gap of no more than two years between
acquisitions. By examining how and when merger programs evolve, we get a good window into
the motivations for acquisitions. On average, a merger program has four major mergers, but the
longest program in our sample has 18 major mergers (and 18 smaller mergers). Over the length
of a program, we find that the quality of mergers decreases. The cumulative abnormal returns
1
2
We use the terms merger and acquisition interchangeably.
Asquith, et. al. (1983) find that 45 percent of acquirers in their sample made at least four acquisition
attempts in a 17-year period.
2
(CAR) to the merger announcement and the buy-and-hold returns (BHAR) in the year following an
acquisition are both lower for mergers that are later in a program. The final mergers in a program
are likely to have a negative CAR and a negative BHAR. This brings up an issue we address in this
paper: whether firms – or, more concretely, their CEOs – use programs primarily to increase
returns to shareholders or for other reasons such as to increase the private benefits of the CEOs.
A focus on merger programs allows us to add depth to discussions on the reasons for mergers,
including the role of agency problems. Academic studies and general media articles suggest
private benefits play an important role in acquisition decisions (e.g., Morck, et. al., 1990). CEOs
of firms that get larger by making acquisitions can gain big increases in compensation or bonuses,
even if the acquisitions do not benefit their shareholders (Bliss and Rosen, 2001; Grinstein and
Hribar, 2004). Consistent with this, there is evidence that the compensation committees of Board
of Directors often look to asset size for a guide in setting CEO compensation (Murphy, 1999).
This offers the private-benefit hungry CEO a potential way to increase compensation rapidly. By
engaging in a merger program, the CEO can add a significant amount of assets and argue that her
compensation should increase to match the asset growth.
A main goal of this paper is to examine how CEO compensation increases change over the
life of a merger program. This is a complicated issue, since it combines the quality of
acquisitions during a merger program with the choices of how to compensate CEOs made by
Boards of Directors. It may be more difficult to justify a large merger-related increase in
compensation for a CEO who has just had a similar increase (or increases) in recent years, even if
all the mergers increased shareholder value. We examine how the change in a CEO’s
compensation depends on asset growth and stock performance. To focus on mergers, we divide
asset growth into internal growth and growth through acquisition, and further divide acquisition
growth into that from one-off mergers and from merger programs. These variables are then
interacted with measures of performance.
We find that mergers that are part of a program contribute significantly more to compensation
then do one-off mergers or internal growth, holding stock return constant. But, this effect is not
uniform across programs or over a program. The relationship between compensation changes and
asset growth for program mergers depends on how the acquiring firm’s stock is doing relative to
the market. When stock prices are decreasing relative to the market, program mergers increase
compensation by about the same amount as internal growth does. On the other hand, when
returns are increasing, compensation increases at a much faster rate for assets acquired in
program mergers that for other asset growth. Thus, CEOs do better when their firms do better. In
addition, the sensitivity of compensation to the assets acquired in acquisitions decreases as a
merger program evolves. That is, if a firm is doing better than the market, acquiring a $1 billion
firm has a much bigger effect on a CEO’s compensation if the acquisition is the first or second in
a program rather than the fourth or fifth.
3
The finding that acquiring as part of a merger program leads to compensation increases does
not necessarily mean that the desire for increased compensation leads a CEO to make frequent
acquisitions. We use a merger prediction model to indirectly address this issue. The evidence is
consistent with acquisitions, at least in part, being made to increase private benefits. We use our
model of compensation to predict the change in CEO compensation as a function of asset growth,
stock performance, and other controls. The difference between the actual and predicted changes
could signal how likely Boards are to reward a CEO for a future acquisition. Consistent with this,
we find that firms where the difference between the actual and predicted changes in CEO
compensation is largest have a higher probability of making a future acquisition. But this result is
entirely due to firms with an ongoing merger program. This is more evidence that mergers during
programs are different than one-off mergers. It suggests that CEO compensation, and the ability
to get large compensation increases, may play a role in merger programs.
The remainder of the paper is as follows. Section II reviews the existing literature and what it
has to say about merger programs. The third section defines a merger program and discusses the
data we use. This section includes some evidence on the evolution of merger programs. Then,
Section IV analyzes the effect of mergers on CEO compensation. Section V sets up and tests the
merger prediction model to help examine the effect on compensation on mergers. Finally,
Section VI concludes.
II. Literature review
Mergers and compensation are two well-studied areas of corporate finance. The merger
literature attempts to explain, among other things, why firms make acquisitions. We examine
mergers from the point of view of the acquirers, dividing previous studies into three groups.
First, the neoclassical theory says that mergers allow firms, by combining, to take advantage of
synergies. In the second group of papers, mergers reflect agency problems between managers
and shareholders. Finally, some studies look at whether mergers result from managers or
shareholders misvaluing the benefits of two firms combining. We discuss papers from each of
these areas with an eye toward what we can learn about merger programs. Then, we briefly cover
the few studies that explicitly examine merger programs. Finally, we look to the compensation
literature to learn which factors influence CEO compensation.
The neoclassical theory of mergers implies that firms – acting in the interests of shareholders
– only make acquisitions that increase their value. Under this theory, firms may make multiple
acquisitions because a series of unrelated synergistic opportunities arise. What appears to be a
merger program could be just a group of independent acquisitions. This would imply that there is
no difference between mergers in a “program” and other mergers, and no expected difference
among mergers within a program.
4
Another possibility consistent with the neoclassical theory is that some mergers are an
efficient response to changed circumstances. There is evidence that mergers tend to cluster, with
different industries having different patterns of acquisition activity (Mitchell and Mulherin, 1996;
Andrade and Stafford, 2004). Notably, merger activity increases after economic shocks such as
deregulation and technological changes. This suggests that mergers might reflect firms adapting
to changed economic conditions. If mergers are a response to economic shocks, then merger
programs could arise naturally. For example, in the 1980s and 1990s, deregulation meant that a
banking organization could have banks in multiple states rather than just one. This led many
banking organizations to acquire a series of banks in different states, something not possible
earlier. Deregulation had a similar effect in other industries, for example, making it permissible
for media firms to expand into more markets and areas than previously. An easy way to do so is
through merger programs. Thus, merger programs are more likely to arise during periods
following shocks. Under the neoclassical hypothesis, these mergers would create value for the
acquiring firms, although there is no reason to expect mergers that are part of a program to have
different characteristics than other mergers.
Managers may not act in the interests of their shareholders when making acquisition
decisions. A number of studies propose managerial motivations for some mergers (e.g., Morck,
et. al., 1990; Gorton, et. al., 2002). Mergers offer managers an easy way to increase private
benefits such as compensation (Bliss and Rosen, 2001), including merger bonuses (Grinstein and
Hribar, 2004). So, for example, deregulation gave firms in several industries the ability to expand
rapidly. We explore whether the expansion was in the interests of the acquirers’ shareholders or
their managers. It can be difficult and costly for shareholders to stop their managers from making
acquisitions, and so managers may have the freedom to make negative NPV acquisitions as long
as the acquisitions do not harm acquiring shareholders too much.3 Consistent with this, there is
evidence that merger bonuses are related to managerial power but not to deal performance
(Grinstein and Hribar, 2004). Agency problems can lead in a straightforward manner to merger
programs: Managers that care more about private benefits relative to shareholder wealth are more
likely to make multiple acquisitions. Thus, acquisitions during programs are likely to be worse
than other acquisitions, and may harm acquiring firm value. In addition, not all managers will be
equally likely, or equally able, to benefit from acquisitions. Managers that benefit more from
acquisitions, all else equal, should be more likely to make them.
In addition to any synergies and agencies problems, mergers may result from the systematic
misvaluation of either stock prices or potential synergies. Managers may make value destroying
acquisitions because of hubris (Roll, 1986). They wrongly believe that they can create synergies.
3
Bebchuk and Fried (2003) review evidence that Boards of Directors grant CEOs great discretion in areas
such as compensation.
5
Hubris can lead to the same set of problems as managers that put a heavy weight on private
benefits.
Markets may also overvalue firms at times, giving managers an incentive to make
acquisitions, especially using overvalued stock (Shleifer and Vishny, 2003). Acquiring
shareholders can benefit from acquisitions as long as the stock of the acquirer is more overvalued
than that of the target. While the misvaluations persist, firms should keep making acquisitions.
These acquisitions should benefit shareholders, at least in the long run. 4 As with the neoclassical
theory, there is no reason to believe that merger programs affect compensation any differently
than one-off mergers.
The flip side of this is that when the market is overvaluing the synergies from mergers (a
“hot” merger market), managers have an incentive to make more acquisitions (Rosen, 2006). A
manager, especially one concerned with private benefits, may use market misvaluation, as a cover
to make a series of acquisitions. Under these circumstances, program acquisitions may look
better in the short run than other acquisitions, but will look worse in the long run.
The empirical evidence on value creation in mergers is mixed, but there are some broad
conclusions that can be drawn. A number of papers look at the change in the market value of
merger partners at the time a merger is announced. Early studies of announcement effects found
that mergers generated synergies that were split between the acquiring and target firms (e.g.,
Bradley, et. al., 1988). However, the announcement returns to acquiring firm shareholders have
declined over time (Jarrell, et. al., 1988). Mergers since the 1980s do not appear to have created
wealth for acquiring firm shareholders (Jarrell, et. al., 1988; Andrade, et. al., 2001; Moeller, et.
al., 2004). There is some evidence that the long-run returns to mergers may be negative
(Loughran and Vijh, 1997; Agrawal and Jaffe, 2002). 5 Overall, these papers do not provide much
support for the neoclassical theory. They are consistent with some aspects of agency theory and
misvaluation. We add to this literature by focusing on merger programs. This allows us to take
advantage of the fact that a single firm is making a series of acquisitions.
A small number of studies examine firms that make mult iple acquisitions. Schipper and
Thompson (1983) find that the announcement of a merger program generates a positive
cumulative abnormal return (CAR), on average. Regulatory changes that reduce the value of
(future) mergers have a negative impact on firms with programs. This indicates that the market at
least partially prices future merger activity. However, there is evidence that the market does not
4
Merger waves can also occur when targets systematically overvalue merger synergies (Rhodes-Kropf and
Viswanathan, 2003). In this case, mergers create value for acquiring firm shareholders. For the purpose of
our paper, however, there is no reason to expect merger programs to be associated with this sort of
misvaluation.
5
This conclusion, which is based on long-run stock returns, is controversial, see Mitchell and Stafford
(1998) who find no long-run drift. Studies that look at accounting data find mixed results (Healy, et. al.,
1992, find improvements while Linn and Switzer, 2001, do not).
6
fully price future mergers (Asquith, et. al., 1983; Malatesta and Thompson, 1985). Our analysis
extends and updates these previous studies although we do not explicitly address the issue of
whether the market partially prices merger programs at the time of the announcement (as in
Schipper and Thompson). Instead, as discussed in the next section, we look at merger
announcements, picking up the reaction to the part of a program that is unanticipated by markets.
One focus of our study is the evolution of merger programs. Thus, we are interested in how
mergers early in a program differ from those that occur later. The evidence on the evolution of a
program is limited and mixed. Asquith, et. al. (1983) finds no important difference among the
CARs for the first four mergers in a program while Fuller, et. al. (2002) find the first acquisition
has a higher CAR than the fifth and subsequent acquisitions. We address this issue more
explicitly than the previous studies do by examining both the CAR and the post-announcement
return.
Many studies have found that CEO compensation is influenced by both stock market
performance and firm size (see, e.g., Murphy, 1999, for a review of the literature). Several
studies examine the impact of mergers on compensation and vice versa. As discussed above,
there is evidence that as firms grow larger, CEO compensation generally rises, whether the
growth is internal or through acquisition (Bliss and Rosen, 2001). Part of this may be due to the
frequency of bonuses following mergers (Grinstein and Hribar, 2004). The likelihood of
receiving a bonus is negatively correlated with the CAR for the merger announcement, consistent
with a disconnect between bonuses and shareholder value (Grinstein and Hribar). We extend this
literature by comparing compensation growth from mergers during a program to that from oneoff mergers. We also examine the evolution of compensation changes over the life of a merger
program. This allows us to see how private benefits might offer CEOs incentives to start and stop
merger programs.
There is also evidence that compensation may affect acquisition decis ions. Firms where
managers receive a higher proportion of equity-based compensation make better acquisitions
(Datta, et. al, 2001). We ask whether compensation also affects the likelihood of acquisition, and
whether this in turn depends on whether firms have a merger program.
III. Merger programs
We analyze merger activity during the period 1992 – 2001. 6 Our first step is to define a
merger program. Many firms make multiple acquisitions as part of a broad strategic plan. We
call two mergers part of the same program if the second is announced within two years of the first
announcement and both targets are at least five percent as large as the acquirer is. A firm may
have more than one merger program during the sample period.
6
Results are similar when we extend the start date back to 1986. We use the shorter period here because of
limitations in the compensation data.
7
By defining merger programs using actual acquisition activity rather than announced program
intentions, we are able to capture a larger number of programs. For example, Boston Scientific, a
medical devices company, made a series of acquisitions in the 1990s. The only announcement
they made prior to their first merger was a statement by an executive of the company that he
“wouldn’t be surprised” if they made some acquisitions. 7 It is difficult to know, except by
looking ex post, whether this should be classified as the beginning of a merger program. In the
other direction, Anheuser Busch filed a shelf registration with the SEC in 1995, announcing that
the facility was dedicated toward acquisition. Yet they made no major acquisitions over the next
several years. 8 Should this be classified as the start of a merger program? Since we do not focus
the information contained in the announcement of a program (as Schipper and Thompson, 1983,
do), we avoid these problems by defining merger programs based on activity.
Using acquisition activity also helps us with setting an end date for a merger program. It
would be nice if firms announced that acquisition activity was ceasing, but that is rare. The end
of a program may not be the result of an explicit decision not to make any acquisitions. For
example, Lomak Petroleum engaged in a merger program in the 1990s, with their last major
acquisition in 1996. During their program, Lomak stated that they expected “growth to be driven
principally by a combination of acquisitions and development and, to a lesser extent,
exploration.” 9 In the year of their last acquisition, their strategy changed to “increase [their] asset
base, cash flow and earnings through a balanced strategy of development, exploration and
acquisition activities in core operating areas,” then to “strategic acquisitions” the next year, and
finally to “low-risk acquisition and development.”10 Even well after their last merger, Lomak still
listed acquisition (albeit low-risk acquisition) as a strategic goal. This illustrates how difficult it
can be to pin down an end date for many programs. For this reason, we say that a merger
program ends when a firm does not announce an acquisition for two years, rather than basing the
end on an announcement by a firm.
While our focus is on mergers that are announced between 1992 and 2001, we need to look
beyond these endpoints to collect information on merger programs. 11 A merger announced in
1992 might be part of a merger program begun in 1990 or earlier. We also want to identify
whether a merger in 2000 is the last in a program. For these reasons, we use mergers announced
between 1981 and 2002 to define programs (although we do not use mergers outside the sample
7
As quoted in Dow Jones News Service, February 3, 1993.
Anheuser-Busch did buy a majority interest in a Chinese Brewery in early 1995.
9
Annual report, 1995.
10
1996, 1997, and 1998 Annual Reports.
11
We use the announcement dates in the SDC database. Fuller, et. al. (2002) find that the SDC
announcement date is within two days of the announcement date found by a search of other sources for
each of the 500 mergers they examined.
8
8
period for our analysis). This helps ensure that what we call the first and last mergers in a
program actually are.12
Our merger data comes from the Securities Data Corporation (SDC) database. SDC includes
full and partial acquisitions of public firms, private firms, and subsidiaries. We define a merger
as an acquisition of equity where one firm purchases at least 50% of another and, after the
purchase, the bidder owns at least 90% of the target. Thus, we do not include gradual
acquisitions, where a bidder establishes a toehold and then slowly increases its ownership until it
takes over control of the target. However, our definition does allow the acquisition of a
subsidiary, as long as it meets the other criteria we set.
Many of the mergers in the SDC database involve a target that is much smaller than the
bidding firm. It is unlikely that such an acquisition would have a material effect on the bidder.
To concentrate on the mergers most likely to have a significant effect on the bidding firm, we
require that the target be at least five percent of the bidder’s size when we define a program. By
focusing on significant mergers only, we may miss some merger programs that involve the
frequent acquisition of firms that are very small relative to the acquirer. Still, we count all
acquisitions for purposes other than defining a program.
To measure the relative size of the target and the bidder, we calculate the ratio of the market
value of the target to the market value of the bidder. If we cannot find a market value for the
target (most targets are not publicly traded), we use the price paid in the acquisition as a proxy for
it. When we cannot find the price paid in the acquisition, we use the book values of equity for
both the target and the bidder to estimate relative size.
The merger data is matched with accounting data from Compustat, market data from CRSP,
and executive compensation data from Execucomp. The data source that most shapes the sample
is Execucomp. Execucomp has data on the 1500 largest public firms from 1992 on (although
there are few observations for 1992). 13 Thus, a sample that includes compensation data from
Execucomp is restricted to relatively large firms and to the 1990s. This leaves us with 2,222
firms.
To give some background for the discussion of mergers and compensation, we examine
mergers that are part of a program and compare them with one-off mergers. We include all
acquisitions where the target was at least five percent of the size of acquirer and that were
announced during 1993−2001 by firms that ever appear in our sample. Panel A of Table 1 gives
summary statistics for all mergers, acquisitions that are part of merger programs and, one-off
mergers. Whether the sample statistics can be interpreted to say that mergers add to the acquiring
firm’s shareholder value depends on the proxy used for the value created. Using the stock market
reaction to a merger announcement, measured using the cumulative abnormal return (CAR),
12
13
This means that there may be only one merger in a program that is part of our sample period.
We drop a small number of observations where the ownership share of the CEO is above 50%.
9
mergers increase shareholder wealth. 14 However, looking over a longer horizon to include some
post-announcement results gives a mixed picture. We measure that stock market performance
after a merger announcement using the buy-and-hold return (BHAR) for the one-year period
starting two days before the merger is announced.15 The BHAR has a positive mean, but a
negative median. This indicates that the median acquirer’s shareholders gets nothing significant
from a merger, although the average value is positive. From our perspective, however, we want
to know about differences across types of mergers. We start by comparing the over 60 percent of
mergers that are part of programs to one-off mergers.
There are significant differences between the stock market performance of firms making oneoff mergers and those acquiring as part of a program. The CAR is significantly larger for firms
with merger programs. This may occur because firms with merger programs seem stronger.
Stock market performance heading into a merger, as measured by the buy-and-hold return for the
year ending three days before merger announcement (RUNUP), is better for firms with merger
programs. On the other hand, the accounting return, as measured by the return on assets (ROA),
and the market-to-book ratio (MARKET-TO-BOOK) are lower in the year prior to the merger for
firms with merger programs.
Mergers that are part of a program are generally made by larger firms than those that make
one-off acquisitions (as measured by LGTA, the log of total assets) and more leveraged firms
(lower equity-to-asset ratio, EQ/TA). However, the summary statistics for merger programs in
Panel A include all sample mergers that are part of a program. Data for mergers that are not the
first in a program include the size increases and leverage changes attributable to the prior
mergers. This may explain why firms in programs are larger at the time of their mergers than
other acquirers. It may also explain the post-announcement stock performance results. The BHAR
is similar for one-off and program mergers. As we see, however, this masks differences across
the mergers in a program.
Since firms change over the life of a merger program, the best way to compare those firms to
firms making one-off acquisitions may be to focus on the first acquisition in a program. Panel B
of Table 1 compares one-off mergers to the first acquisition of a merger program. Here we
require a program to have at least four mergers, although the results are similar for shorter
programs. As with the findings in Panel A, the CAR for a program merger is significantly larger
than for a one-off merger. However, the difference is much larger than earlier. Also, the post14
The CAR is the return of the announcing firm minus the return on the CRSP value-weighted index for
days –2 to +2. The use of the five-day window for SDC data is discussed in Fuller, et. al. (2002). Results
are similar for other window sizes.
15
Formally, BHAR =
1 +R i ,τ
∏ 1 +R
− 1, where Ri,τ is the return on the acquiring firm’s stock, Rindex,τ is
index,τ
the return on the CRSP valued-weighted index, and τ runs from the two days before the announcement to
the end of year t. Note that the BHAR contains the announcement period in the CAR.
10
merger performance of firms with merger programs is better than that of firms with one-off
mergers. This, of course, might be because strong post-merger performance makes a subsequent
acquisition more likely.
Panel C of Table 1 shows the evolution of merger programs. For programs with at least four
mergers, we break the program into four parts: the first merger, intermediate mergers (those not
in the other categories), the penultimate merger, and the last merger.16 The market reaction to the
mergers declines over the life of an average program. The median CAR decreases from 2.83% at
the start to –0.31% at the end while the median BHAR decreases from 10.96% to –16.29%. If we
assume that the return to an acquisition (to the acquiring firm’s shareholders) can be proxied by
the BHAR, then (at least) the last two mergers in a program are bad for shareholders. Most of the
other characteristics presented in Panel C remain about the same over the life of a program, while
asset size increases as is predictable. One interesting fact is that the post-merger performance
falls by more than the pre-merger performance. One might think that pre-merger performance
and the BHAR should move somewhat in tandem, since what is after one merger is likely before
another. However, it turns out that the firms time their announcements. If their stock price falls
after a merger announcement, a firm often waits until the price starts to rise again before
announcing another acquisition. Also, if the price does not rise, then the firm is less likely to
make another acquisition. This is consistent with the very low BHAR after the last acquisition
announcement.
The univariate results suggest that mergers during a program are different from one-off
mergers and that mergers at the start of a program are different from those at the end. These
results are robust to including a larger sample of mergers. If we extend the time horizon or
include firms too small to be in the Execucomp sample, the patterns are, if anything, stronger.
IV. The effect of mergers on compensation
If the quality of mergers declines over the evolution of a program, why do firms continue
programs? There is evidence that managers may make acquisitions to benefit themselves at the
expense of their shareholders (e.g., Morck, et. al., 1990). This section examines how CEO
compensation and ownership are related to acquisition decisions and merger programs. A CEO’s
compensation and ownership may affect her decision to make an acquisition or engage in a
merger program. We extend the finding that acquisitions increase CEO compensation (Bliss and
Rosen, 2001) by exploring whether mergers that are part of a program affect compensation
differently than other mergers.
Our measure of compensation is total compensation, which includes salary, bonus, long-term
incentive payments, the value of restricted stock plus options granted during the year, and all
16
Note that not all the mergers in a program may be during the sample period. This is why, for example,
the number of observations in a program is different for the various categories.
11
other payments to the CEO (TDC1 in the Execucomp database). Total compensation is $4.92
million (2001 dollars) for the average CEO in our sample while the percentage of a firm owned
by its CEO averages 4.67%. Among CEOs with acquisitions in the prior year, compensation is
higher while ownership is lower.
We want to examine how acquisitions change CEO compensation. Compensation is affected
by both firm size and market equity values (Murphy, 1999; Bliss and Rosen, 2001). We examine
the impact of mergers by breaking down size to focus on the changes due to merger activity. The
basic model is:
Change in compensation = f(change in asset size, stock performance, controls)
(1)
The objective is to determine how compensation changes over a three-year period given the
merger activity during that period. Thus, to be in the sample in year t, a firm must have the same
CEO since at least year t-3.
To determine how much the correlation between compensation and firm size is driven by
acquisition activity, we follow Bliss and Rosen (2001) by dividing a firm’s assets in year t into
three parts: assets at the end of year t-3 (TA3), internal asset growth from years t-3 to t (INT
GROW),
and asset growth through acquisition during that period (MRG GROW). 17 So, for example,
a firm with $7 billion in assets at the end of year t-3, a $2 billion acquisition in year t, and $10
billion in assets at the end of year t would have TA3 = $7 billion, INT GROW = $1 billion, and MRG
GROW
= $2 billion.
We also control for the impact of stock performance on compensation. We use two variables
to proxy for stock-market equity value changes, primarily to distinguish between market-wide
and firm-specific movements. INDEX CHG captures that part of the return attributable to marketwide effects, proxied by the return on the value-weighted CRSP index. INDEX CHG is the equal to
the percentage return on the index from the end of year t-3 to the end of year t multiplied by the
firm’s equity value at the end of year t-3. FIRM CHG is the dollar change in the firm’s equity value
not due to changes in the index value. It is defined as the percentage change return on the firm’s
stock (including reinvested dividends) from the end of year t-3 to the end of year t multiplied by
the firm’s equity value at the end of year t-3 minus INDEX CHG, the change in value due to
movement in the index. Thus, FIRM CHG captures the change in a firm’s equity value due to
idiosyncratic, firm-specific events. To illustrate, if a firm’s equity value increases from $1 billion
to $1.5 billion during a three-year period (with no acquisitions) when the CRSP index increases
by 20%, INDEX CHG = $100 million (20% × $1 billion) and FIRM CHG = $300 million ($500
million gain − $200 million due to the index change).
We also include other controls that have been used in studies of acquisitions and
compensation. For example, CEO ownership might also affect compensation decisions through
17
We assume assets acquired in acquisitions prior to year t grow at the same rate as other assets.
12
its influence on managerial entrenchment (see, e.g., Morck, et. al., 1990; Gorton and Rosen,
1995), so we include a measure of CEO ownership. Also, accounting measures of return may
capture improvements in performance not present in the stock market returns, For this reason, we
include ROA as a control. In addition, Q-theory suggests that a firm’s investment rate (possibly
including mergers) should increase with the firm’s market-to-book ratio (Jovanovic and
Rousseau, 2002), and there is evidence that firms tend to acquire firms with lower market-to-book
ratios (Rhodes-Kropf, et. al., 2004). We include the ratio of the market value of equity to the
balance sheet value of equity as a control (Rau and Vermaelen, 1997; Harford, 1999). Capital
structure can also play a role in merger decisions since firms with high leverage have an incentive
to underinvest (Myers, 1977). This leads us to follow other studies by including EQ/TA, the
(accounting) leverage ratio, as a control. Firms with more free cash flow may be more able or
more willing to make acquisitions. We control for this using the ratio of EBITDA to sales
(Andrade and Stafford, 2004). All values are measured as of the end of year t, but we could take
the average of the three years ending in year t without affecting the qualitative results. To
simplify the presentation, we do not report the coefficients for the controls. When they are
statistically significant, the signs conform to expectations (e.g., higher return on assets implies
higher compensation). None of the key results are qualitatively affected by the inclusion or
exclusion of any of these controls.
The results from regressions estimating equation (1) are presented in Table 2. The results
indicate that both organic (internal) growth and growth through acquisition add to compensation,
holding stock return constant. The regression in the first column shows that a merger adds about
$102 in compensation per million dollars of assets acquired, virtually identical to the $103 if the
assets were the result of internal growth. Note that as in previous studies, changes in equity value
are positively correlated with compensation.
The impact of merger activity on compensation might depend on the whether acquisitions are
part of a program. Let PROG GROW be the asset growth from acquisitions that are part of a merger
program and ONE-OFF GROW be the asset growth from one-off acquisitions (those that are not part
of a merger program). This allows us to test whether program mergers have a different effect on
compensation than one-off mergers. The regression in the second column of Table 2 replaces
MRG GROW
with these two variables.
The results show that program mergers have a significant effect on compensation, but one-off
mergers do not. Adding $1 million of assets in a program merger increases compensation by
$135. This is larger than if the growth was internal, although the difference is not statistically
significant.
Compensation committees may be able to distinguish among mergers in a program. One
aspect that would seem relevant is the quality of the merger. We want to determine whether
CEOs get the same reward for increasing assets in a “good” merger versus in a “bad” merger.
13
There are a number of possible measures of merger quality, including the CAR from a merger
and the post-announcement return. It turns out that the CAR is too noisy to be a measure of
quality. There is no significant difference in the contribution to compensation from mergers with
positive and negative CARs. Using post-announcement return is better, but there is a question of
what post-announcement means for firms with multiple acquisitions. Do we want to start the
clock with the first announcement, the last announcement, or the largest merger? What if a firm
has a program in place at the end of year t-3? To avoid these issues, and to allow us to examine
whether compensation committees reward all asset growth differently based on firm performance,
we separate firms into good and bad based on the stock performance over the entire 3-year
period, t-3 to t.
We create interaction variables between our asset measures and measures of idiosyncratic
stock performance. Let PROG GROW POS be the assets acquired in program mergers during a
period when the firm has a positive firm-specific return, that is FIRM CHG > 0, and let PROG GROW
NEG be
the assets acquired in program mergers when FIRM CHG is non-positive. We define
similar variables for all the other categories of asset change, adding the suffixes POS when FIRM
CHG is
positive and NEG when FIRM CHG is non-positive.
As shown in column (3) of Table 2, stock performance affects the relationship between asset
growth and compensation only for growth from program mergers. Internal growth adds to
compensation whether stock performance is positive or not, and the coefficients on the two
internal growth variables, INT GROW POS and INT GROW NEG, are not significantly different from
each other. As earlier, one-off mergers do not increase compensation independent of stock
performance. However, there is a significant difference in the impact of asset growth from
program mergers depending on stock performance. Adding $1 million of assets adds $425 to
compensation if the acquisition occurred during a period when idiosyncratic stock performance
was positive but only $82 when it was not. The $82 gain is roughly the same as the gain from $1
million in internal asset growth. This shows that for firms with program mergers, CEO
compensation is correlated with firm performance both directly (through FIRM CHG) and
indirectly, through bigger size-related gains when performance is strong.
Having established that not all program mergers have the same impact on compensation, we
now turn to the evolution of a program. The results in the previous section show that merger
quality changes over the life of a program. It is possible that this affects the way CEOs are
compensated. Let PROG GROW i be the assets acquired in the ith merger in a program for i ∈ {1,
2, 3} and let PROG GROW 4+ be the assets acquired in all mergers after the third in a program. As
before, we add the suffix POS if the firm has a positive idiosyncratic stock return during the period
and NEG if it is non-positive. Column (4) of Table 2 introduces the new program merger
variables. Again, there are differences between the compensation increases from program
mergers depending on whether stock performance is good or not. When the idiosyncratic return
14
is non-positive, adding assets through program mergers has the same effect on compensation
independent of where the merger falls within a program. This is not true when stock performance
is strong.
For mergers during periods when idiosyncratic stock performance is positive, the position of
a merger within a program matters. It is clear that the compensation benefit from program
mergers declines over the life of a program. The first acquisition increases compensation by $680
per million dollars of assets acquired, while by the fourth merger, the increase is only $59 per
million of assets acquired.18 The results may reflect unwillingness on the part of Boards of
Directors to continue to increase the compensation of a CEO who has seen her compensation
already grow a lot.
During the sample period, aggregate compensation for CEOs in the Execucomp data more
than triples. The results in Table 2 indicate that some of this increase is due to firms getting
larger and some is due to changes in stock market values. One way to measure the relevant
importance of these is to estimate the contribution to compensation change of the different growth
and stock market factors. To do this, we evaluate the regression in column (4) of Table 2 at the
mean values for each of the independent variables. We say that the contribution of internal
growth equals 0.083 × the mean value of INT GROW POS + 0.057 × the mean value of INT GROW
NEG.
To get the share of compensation change that is due to internal growth, we divide this by
the mean level of compensation change. Variables for one-off mergers, program mergers, and
stock market valuation are defined similarly.
Table 3 presents the shares of compensation change attributable to the different factors. The
first column gives the shares when the entire sample is included. Asset growth leads to 17.73%
of the compensation change, more than the 14.61% contribution from changes in stock market
valuation. Interestingly, although the marginal contribution from program merger growth is
significantly larger than that for internal growth, internal growth contributes slightly more to the
overall change in compensation. This occurs because few firms have merger programs.
However, about two-thirds of all compensation change comes from factors other than growth and
stock return. Essentially all of this is due to the upward trend in compensation (that is, from the
constant term, which is not broken out in the table).
The picture is very different for firms with merger programs. Trend growth is less important
for these firms, perhaps because CEO compensation at these firms increases faster than at the
average firm. The rapid increase in compensation is due in large part to the assets acquired in
program mergers. Growth due to mergers contributes ten times as much to compensation as does
changes in stock market valuation. Internal growth also is more important at these firms relative
18
Separating out the fifth and subsequent mergers has no effect on the results. Mergers from number four
on all increase compensation by about the same amount.
15
to other firms, leaving open the possibility that Boards of Directors at firms with program
mergers reward CEOs for different things than do Boards at firms without them.
The differential effect on compensation of growth from program mergers and internal growth
might signal an unmodeled difference between firms with acquisitions and those without. As a
robustness check, we rerun the regression in column (3) of Table 2 first for firms with some
merger activity (MRG GROW > 0) and then for firms with program merger activity (PROG GROW >
0). The results are reported in columns (1) and (2) of Table 4. It is evident that the key results
hold for these subsamples.
There may be a difference between cash compensation and other, generally equity-based,
compensation. The ratio of cash compensation to total compensation has declined over time, and
fell significantly for the sample firms, from 63 percent in 1993 to 47 percent in 2001. This leaves
open the possibility that the extra compensation CEOs get from program mergers is from noncash compensation. To see how growth affects the different kinds of compensation, we rerun the
regression in column (3) of Table 2 using the change in cash compensation (TCC in the
Execucomp database) rather than the change in total compensation. The results, presented in
column (3) of Table 4, are consistent with those for total compensation, although the magnitudes
of the coefficients are smaller. A $1 million increase in assets through a program merger when
the firm is doing well increases total compensation by $425 (column (3) of Table 2), of which
$16 (column (3) of Table 4) is cash compensation. This suggests that the extra compensation is
disproportionately in the form of equity (e.g., stock options), since the ratio of cash to total
compensation averages 55 percent in the sample. As a robustness check, column (4) of Table 4
presents the results for cash compensation when we restrict the sample to firms with merger
activity. The results are qualitatively the same as for the full sample. Thus, program mergers add
to both cash and equity-based compensation, but the majority of the dollar gain is in equity-based
compensation.
The evidence above suggests that merger programs are an efficient way for CEOs to increase
their compensation. Assets added in merger programs give a bigger bang for the buck than other
asset additions, but only if they are accompanied by strong stock performance and, even with this,
the effect wanes as a program progresses. This pattern may explain why merger programs start
and stop if acquisition decisions are, at least in part, motivated by the private benefits they offer
to CEOs. As a program progresses, the benefits from a good acquisition fall and bad acquisitions
may be more likely. This limits a CEO’s ability to increase compensation by taking actions
against shareholder interests. Still, there is a problem of cause and effect here. We have yet to
determine whether CEOs engage in merger programs because they expect them to lead to
compensation increases.
16
V. The effects of compensation on mergers
Firms make acquisitions for a number of reasons. These may be specific to the acquiring
firm – such as how well the firm has done lately – or may be more general – such as a
technological or regulatory shock. The focus on this section is on whether, all else equal, some
firms are more likely than others to make acquisitions and how this interacts with CEO
compensation.
We ask how likely it is that a firm announces a (major) acquisition over the next year. Our
dependent variable is FUTURE MERGER, a dummy variable that takes the value one in year t if and
only if a firm announces the acquisition of a target at least 5 percent as large as the firm during
year t + 1 and the acquisition is successfully completed. As noted above, examining major
acquisitions allows us to concentrate on the mergers that are most important strategically. Table
5 provides descriptive statistics for FUTURE MERGER and the other variables we use in the
predictive model. The first set of columns includes all observations while the second set of
columns includes only those observations with a merger in the recent past. During our sample
period, 13 percent of firms made at least one acquisition in a given year. This is roughly twice as
large as for the entire SDC merger sample, possibly reflecting the fact that we have compensation
data for large firms only.
Our goal is to determine whether merger activity is generated by the desire to increase
compensation. Before doing this, however, we set out a baseline regression including
information about prior merger activity, stock returns, and other firm characteristics. Let PRIOR
MERGERS be
the number of mergers in a merger program as of the first prior merger (i.e., the
most recent merger prior to the end of year t). To ensure that outliers do not drive the results, we
cap PRIOR MERGERS at five. To the extent that some firms are predisposed to make acquisitions,
the coefficient on PRIOR MERGERS should be positive.
We introduce several additional variables to reflect recent merger activity. Merger
negotiations take management time as does completing a merger. Moreover, it may take time for
a merged firm to integrate its constituent parts. This could lead firms with recent mergers (or
merger announcements) to be less likely to make another acquisition attempt. The lack of a new
announcement, thus, may not reflect a change in strategy as much as a delay caused by
operational issues. For this reason, we drop all firms with a merger announced in the last six
months of the year and all firms that have announced a merger but not yet completed it by the end
of the year. To further control for the possibility that merger integration takes time, we use
YEARS,
which measures the number of years prior to the end of year t that the last announcement
took place.
The quality of a previous merger may affect the probability of another acquisition. We
measure merger quality as the buy-and-hold return starting two days prior to the announcement of
the most recent merger and extending to the end of year t. Let PAST NET be the BHAR for the
17
acquiring firm’s stock. The mean and median values of PAST NET are negative, although not
significantly so for the mean.19
Firms may be more likely to make acquisitions after strong stock performance. To account
for this, we control for a firm’s overall stock performance for the three years ending in year t. Let
MKT RET i
be the percentage return on its stock in year t-i. We also include year dummies, so
MKT RET i
picks up the effect of the idiosyncratic return on the probability of a future merger.
Including the market return gives us two measures of stock return post-announcement: PAST NET
and the MKT RET variables. Thus, PAST NET measures the incremental impact of stock market
return for firms with a recent announcement relative to firms with a similar overall return but
without a recent merger.20
A number of other factors are known to affect merger activity. We include the log of total
assets at the end of year t as an independent variable since the size of the acquirer has been found
to affect the probability of making an acquisition (Harford, 1999) as well as the announcement
return from a merger (Moeller, et. al., 2004). We also control for ROA, the market-to-book ratio,
the leverage ratio, and the EBITDA-to-sales ratio as in our compensation regressions. As shown in
Table 5, firms with prior acquisitions tend to be slightly larger and more leveraged. They have a
lower ROA and a lower market-to-book ratio, but a higher EBITDA-to-sales ratio. Again, to
simplify the presentation, we do not show the coefficients on these controls when we present
regression results.
The baseline model is
FUTURE MERGER
= f(merger activity, stock market return, controls, year dummies).
(2)
We run the baseline model for all firm-year observations where the CEO has been in office for at
least one year and, as noted above, there are no acquisitions announced in the last six months of
the year or announced and not yet completed.
Column (1) of Table 6 presents the results of a logistic regression using (2). The coefficient
on PRIOR MERGERS is significantly positive. The other control variables suggest that market
performance and the time needed to integrate previous acquisitions drive future merger activity.
Both measures of merger performance are significant. Better firm-specific stock return, that is,
higher values of the MKT RET variables, makes a future acquisition more likely. Having
successful post-merger returns increases the acquisition probability beyond that for firms with
similar firm-specific return but no recent acquisition. That PRIOR MERGERS is significant even
19
The difference between the mean and median values of PAST NET and the BHAR as given in Table 1 arises
because many firms with successful mergers (high BHAR) make multiple acquisitions in a three-year period.
PAST NET only reflects the last acquisition from the end of year t-3 to the end of year t.
20
Note that the time periods do not perfectly overlap since the MKT RET variables cover three years while
covers only the post-announcement period. Thus, PAST NET reflects the additional return over the
post-announcement period.
PAST NET
18
after controlling for stock return is evidence that some firms are predisposed to make
acquisitions. The next question is how this is affected by CEO compensation.
To examine how compensation affects merger decisions, we first include three measures of
compensation as independent variables. The first is COMP /TA, the ratio of compensation to total
assets as of the end of year t. This gives a measure of whether a CEO is paid well relative to her
peers. It is not a perfect measure because compensation does not increase linearly with asset size.
We also include COMP CHG/COMP , which is the percentage increase in compensation between
years t-3 and t (including only firms with the same CEO for all four years). Finally, since the
share of compensation that is equity based may affect merger quality (Datta, et. al, 2001), we
include EQ/TOT COMP , which measures this share. Compensation growth and the share of equity
compensation is higher at firms with recent mergers than at other firms (Table 5).
There are reasons why the decision to make an acquisition might also be affected by CEO
ownership. Mergers dilute the control a CEO has and may increase risk, since the merger may
not succeed. For these reasons, the probability of an acquisition may be decreasing in ownership.
On the other hand, larger firms may be more diversified, making it safer for managers (Amihud
and Lev, 1981).21 If this holds, the probability of an acquisition would be increasing in
ownership. We include OWNERSHIP , the percent of the firm owned by the CEO as a control.
The second column of Table 6 introduces the compensation and ownership variables. None
of the compensation variables are significant, but CEO ownership significantly reduces the
probability of an acquisition.
One reason that the compensation variables are not significant may be because they do not
directly address the key issue. We want to know whether a CEO anticipates that an acquisition
will increase her compensation. A CEO may be paid well (as measured by COMP /TA) or have
received an increase in compensation (as measured by COMP CHG/COMP or a higher level of
EQ/TOT COMP )
because of her strong performance. However, what may be more important is
whether the CEO is paid more than her performance warrants, since this might indicate a Board
that excessively rewards its CEO, and would continue to do so after an acquisition. To measure
the expected change in compensation, we use (1), the model from the previous section. We rerun
the regression reported in column (4) of Table 2 on a year-by-year basis, taking the residuals from
these regressions as a measure of excess compensation. Let EXCESS COMP TOP 50 be a dummy
variable that takes the value one if the residuals are above the median value for their year and
zero otherwise. Since we find that CEOs with merger programs get more compensation per
dollar of assets acquired, it is also possible that they react more to the possibility of getting
compensation. To account for this possibility, define EXCESS COMP TOP 50 PROG to be a dummy
variable that takes the value one if and only if EXCESS COMP TOP 50 equals one and a firm has a
merger program with at least two mergers by the end of year t. Similarly, define EXCESS COMP
21
Rose and Shepard (1997) find that CEO compensation increases with firm-level diversification.
19
TOP 50 NO PROG as
a dummy variable that takes the value one if and only if EXCESS COMP TOP 50
equals one and a firm does not have a merger program with at least two mergers by the end of
year t. 22
The excess compensation dummy variables are introduced into the regression reported in the
third column of Table 6. The results of this regression are consistent with excess compensation
affecting future acquisitions for firms with merger programs, but not other firms. The coefficient
on EXCESS COMP TOP 50 PROG is positive and significant while the coefficient on EXCESS COMP
TOP 50 NO PROG is
not significant. Note also that the coefficient on PRIOR MERGERS is
insignificant. This is evidence that firms are predisposed to make acquisitions only if their CEOs
get excess compensation.
We can include firms with recent merger announcements or exclude all firms that have no
acquisition activity without changing the qualitative results. Table 7 presents regressions
supporting this conclusion. The first regression drops the restriction requiring that firms have not
announced a merger in the six months prior to the end of year t. The second regression includes
only firms that have merger activity between the end of year t-3 and six months prior to the end
of year t. In both cases, the coefficient on EXCESS COMP TOP 50 PROG is positive and significant
while the coefficient on EXCESS COMP TOP 50 NO PROG is not significant, as with the main
sample.
The prediction results show that strong stock performance is associated with a higher
probability of making acquisitions. This is especially true for strong performance following an
earlier merger announcement. Increasing CEO ownership reduces the incentive to acquire. Once
we have controlled for these factors, there is a role for CEO compensation, but only at firms that
give excess compensation to their CEOs and at which there is an existing merger program. These
firms are more likely to announce future acquisitions. Thus, excess compensation is associated
with the continuation of merger programs. This is consistent with CEOs at these firms being
motivated by the ability to increase compensation by making acquisitions.
VII. Conclusions
Firms that are repeat acquirers make a majority of acquisitions in the U.S. We examine these
firms, and the merger programs they conduct, to see how they are different from other acquirers.
By focusing on merger programs, we provide support for managerial explanations of acquisition
activity.
The quality of acquisitions varies over a merger program. Initially, the acquisitions in a
program are better than one-off acquisitions, measured either by the market reaction to the merger
announcement (the CAR) or by the buy-and-hold return (BHAR) following the announcement.
22
Note that some of these firms will be at the start of a merger program, but we cannot look forward
without significantly biasing our results.
20
However, the quality of acquisitions fades as a program progresses. By the last mergers in a
program, the quality is below that of one-off mergers. Moreover, the CAR is essentially zero and
the BHAR is negative. These findings lead to the question of why firms make acquisitions that
have a negative return for their shareholders. To put it more generally, why do firms start merger
programs and why do they end them?
Changes in CEO compensation provide evidence on managerial motivations for merger
programs, and possibly on why programs end. Compensation increases after some program
mergers, but not after one-off mergers. But not all program mergers have an equal effect on
compensation. It is only when a firm is doing well overall that program mergers have a
disproportionate effect on compensation. And, even in this group, as a program progresses, the
rate that CEO compensation increases as assets are acquired decreases.
Turning to the other side of the coin, we find that some firms have a predisposition to make
acquisitions. Previous studies show that the stronger a firm is, measured in a variety of ways, the
more likely it is to make an acquisition. Our results support this, but also imply that, controlling
for firm strength, firms where CEOs get excess compensation and that have existing merger
programs are the ones more likely to make future acquisitions.
Overall, the results are consistent with managerial motivations for merger programs. The
evidence is consistent with compensation playing a role in acquisition decisions at some firms
with programs. On average, acquisitions during programs add significantly more to
compensation than other growth. Also, when CEOs get excess compensation, they are more
likely to make future acquisitions, consistent with an attempt to further increase compensation.
While the results here indicate a role for managerial motivations, it is not all bad news for
shareholders. The early mergers in a program add significant value for shareholders. In addition,
compensation changes depend on shareholder value in two ways. First, there is the direct effect,
since higher stock returns mean larger compensation increases. But, compensation also depends
on firm size, so even a bad acquisition can lead to increased compensation. However, the
compensation increases for acquisitions during a merger program are largest when the acquiring
firm is doing well. Thus, even if CEOs make acquisitions in large part to increase their
compensation, in order to get big increases, their shareholders have to do well also.
21
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performance of bidding firms,” Journal of Financial Economics 49(3), 223-253.
Rhodes-Kropf, Matthew, David T. Robinson, and S. Viswanathan, 2004, “Valuation Waves and
Merger Activity: The Empirical Evidence,” Journal of Financial Economics, forthcoming.
Rhodes-Kropf, Matthew and S. Viswanathan, 2004, “Market Valuation and Merger Waves,”
Journal of Finance, forthcoming.
Roll, Richard, 1986, “The Hubris Hypothesis Of Corporate Takeovers,” Journal of Business 59,
197-216.
Rose, Nancy L. and Andrea Shepard, 1997, “Firm Diversification and CEO Compensation:
Managerial Ability or Executive Entrenchment?” Rand Journal of Economics, 28(2), 489514.
Rosen, Richard J., 2006, “Merger Momentum and Investor Sentiment: the Stock Market Reaction
to Merger Announcements,” Journal of Business, forthcoming.
Schipper, Katherine and Rex Thompson, 1983, “The Impact of Merger-Related Regulations on
the Shareholders of Bidding Firms,” Journal of Accounting Research 21(1), 184-221.
Shleifer, Andrei and Robert W. Vishny, 2003, “Stock Market Driven Acquisitions,” Journal of
Financial Economics 70(3), 295-311.
.
Table 1. Comparison of different types of mergers.
Includes all acquisitions during 1993-2001 by firms in the Execucomp data where the target is at least 5 percent as large as the acquirer. A merger
program is a group of acquisitions by the same firm with no more than two years between any two acquisitions.
Panel A. Merger programs versus one-off mergers.
All mergers
CAR
RUNUP
BHAR
LGTA
EQ/TA
ROA
MARKET -TO-BOOK
PROGRAM TOTAL *
Mean
Median
Std dev
1.28%
14.21%
7.41%
9.234
0.393
0.038
3.019
0.59%
2.48%
-1.43%
9.141
0.388
0.038
2.212
9.41%
67.95%
61.05%
0.749
0.218
0.079
3.222
Mergers in programs with at
least two acquisitions
Mean
Median Std dev
1.60%
17.25%
7.58%
9.286
0.373
0.033
2.898
4.096
Observations
2,729
* -- Length of a merger program from start to finish.
0.71%
5.76%
-0.12%
9.214
0.375
0.032
2.220
3.000
1,748
9.86%
66.58%
61.42%
0.731
0.219
0.071
2.579
2.745
One-off mergers
Mean
Median
Std dev
0.73%
8.81%
7.09%
9.139
0.428
0.046
3.240
0.29%
-2.37%
-3.93%
9.034
0.418
0.046
2.197
8.52%
70.04%
60.42%
0.772
0.212
0.091
4.133
981
Panel B. First merger in a program versus one-off mergers.**
First merger in a program
Mean
Median Std dev
CAR
RUNUP
BHAR
LGTA
EQ/TA
ROA
MARKET -TO-BOOK
4.89%
15.90%
28.01%
9.221
0.383
0.035
3.195
2.83%
7.43%
10.96%
9.177
0.398
0.037
2.255
16.48%
50.94%
66.45%
0.673
0.232
0.065
3.337
One-off mergers
Mean
Median Std dev
0.73%
8.81%
7.09%
9.139
0.428
0.046
3.240
126
Observations
** -- Program mergers are the first in a program with at least four mergers.
0.29%
-2.37%
-3.93%
9.034
0.418
0.046
2.197
981
8.52%
70.04%
60.42%
0.772
0.212
0.091
4.133
P-value for test of
program vs. one-off
0.005
0.161
0.001
0.204
0.039
0.084
0.890
P-value for test of
program vs. one-off
0.016
0.002
0.840
0.000
0.000
0.000
0.019
24
Panel C. Evolution of merger programs.***.
CAR
RUNUP
BHAR
LGTA
EQ/TA
ROA
MARKET -TO-BOOK
Std dev
16.48%
50.94%
66.45%
0.673
0.232
0.065
3.337
Intermediate mergers
Mean
Median Std dev
1.52%
0.59%
8.63%
29.69% 12.75% 66.25%
18.01%
6.55%
68.30%
9.439
9.437
0.663
0.332
0.335
0.214
0.029
0.027
0.063
2.843
2.201
2.065
353
Penultimate merger
Mean
Median Std dev
Last merger
Mean
Median Std dev
Mean
4.89%
15.90%
28.01%
9.221
0.383
0.035
3.195
Observations
CAR
RUNUP
BHAR
LGTA
EQ/TA
ROA
MARKET -TO-BOOK
Observations
1.23%
25.42%
-3.48%
9.435
0.352
0.020
2.537
First merger
Median
2.83%
7.43%
10.96%
9.177
0.398
0.037
2.255
126
0.22%
7.30%
-9.72%
9.317
0.357
0.021
1.905
148
8.44%
94.12%
45.39%
0.716
0.205
0.070
2.135
0.70%
-0.31%
9.39%
-4.61%
-10.16% -16.29%
9.436
9.417
0.349
0.348
0.022
0.030
2.403
1.930
133
10.68%
75.51%
44.87%
0.704
0.194
0.108
1.677
P-value for test of first versus
last merger
0.016
0.414
0.000
0.012
0.209
0.235
0.017
*** -- Includes all merger programs with at least four mergers. Intermediate mergers are those not in the other categories.
Table 2. Regressions of change in compensation against growth and stock market
performance.
Regressions with change in total compensation for the end of year t-3 to the end of year t, as the
dependent variable. Firms must have the same CEO from year t-3 to year t. Annual data for
firms in the Execucomp database, 1995-2001. The dependent variable is in dollars, all other
variables are in thousands of dollars.
TA 3
INT GROW
(1)
-0.021
(0.139)
0.103
(0.004)***
(2)
-0.017
(0.243)
0.085
(0.011)**
INT GROW POS
INT GROW NEG
MRG GROW
(3)
-0.021
(0.175)
(4)
-0.019
(0.226)
0.105
(0.007)***
0.061
(0.024)**
0.083
(0.067)*
0.057
(0.037)**
-0.025
(0.556)
0.056
(0.022)**
-0.022
(0.583)
0.053
(0.033)**
0.102
(0.009)***
ONE-OFF GROW
0.044
(0.205)
ONE-OFF GROW POS
ONE-OFF GROW NEG
0.135
(0.030)**
PROG GROW
0.425
(0.000)***
PROG GROW POS
PROG GROW 1 POS
0.680
(0.000)***
0.446
(0.000)***
0.162
(0.031)**
0.059
(0.376)
PROG GROW 2 POS
PROG GROW 3 POS
PROG GROW 4+ POS
0.082
(0.000)***
PROG GROW NEG
PROG GROW 1 NEG
0.123
(0.027)**
0.051
(0.320)
0.127
(0.024)**
0.051
(0.324)
0.115
(0.041)**
0.063
(0.208)
0.078
(0.589)
0.071
(0.000)***
0.094
(0.081)*
0.081
(0.000)***
0.118
(0.038)**
0.064
(0.209)
Observations
4,517
4,517
4,517
4,517
R-squared
0.093
0.097
0.127
0.131
PROG GROW 2 NEG
PROG GROW 3 NEG
PROG GROW 4+ NEG
FIRM CHG
INDEX CHG
Robust p values in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: Controls not shown.
26
Table 3. Contribution to compensation change of growth and stock market return.
The results in this table are based on the regression in column (4) of Table 2. The contribution
from internal growth is equal to (0.083 × INT GROW POS + 0.057 × INT GROW POS) / change in
compensation. The contribution from one-off mergers is equal to (-0.022 × ONE-OFF GROW POS +
0.053 × ONE-OFF GROW POS) / change in compensation. The contribution from program mergers
is equal to (0.680 × PROG GROW 1 POS + 0.446 × PROG GROW 2 POS + 0.162 × PROG GROW 3 POS
+ 0.059 × PROG GROW 4+ POS + 0.078 × PROG GROW 1 NEG + 0.071 × PROG GROW 2 NEG + 0.094
* PROG GROW 3 NEG + 0.081 × PROG GROW 4+ NEG) / change in compensation. The contribution
from the growth in size is the sum of the contributions from internal growth, one-off mergers, and
program mergers. The contribution from stock market return is equal to (0.118 × FIRM CHG +
0.064 × INDEX CHG) / change in compensation. Trend change is the change not due to the growth
in size and stock market return (and thus due to the constant and controls in the regression).
Entire sample
Contribution from:
Asset growth
Internal growth
One-off mergers
Program mergers
Stock market return
Trend
Observations
17.73%
9.19%
0.01%
8.53%
14.61%
67.66%
4,517
Firms with program mergers
(PROG GROW > 0)
52.94%
6.64%
1.13%
45.17%
4.34%
42.73%
680
27
Table 4. Robustness checks of the relationship among the change in compensation, growth,
and stock market performance.
Regressions with change in total and cash compensation for the end of year t-3 to the end of year
t. Firms must have the same CEO from year t-3 to year t. Annual data for firms in the
Execucomp database, 1995-2001. The dependent variable is in dollars, all other variables are in
thousands of dollars. The regressions in columns (1) and (4) include only firms with some
merger activity in years t-2 − t and the regression in column (2) includes only firms with a merger
program in years t-2 − t (the regression in column (3) includes the full sample).
dependent variable:
CHANGE IN TOTAL COMPENSATION
(1)
TA 3
INT GROW POS
INT GROW NEG
ONE-OFF GROW POS
ONE-OFF GROW NEG
PROG GROW POS
PROG GROW NEG
FIRM CHG
INDEX CHG
Observations
R-squared
(2)
(3)
(4)
-0.014
(0.561)
0.123
(0.010)**
0.081
(0.245)
-0.059
(0.221)
0.041
(0.314)
0.401
(0.000)***
0.084
(0.000)***
0.206
(0.000)***
0.031
(0.810)
-0.106
(0.024)**
0.173
(0.000)***
-0.023
(0.771)
-0.019
(0.569)
0.041
(0.228)
0.449
(0.000)***
0.131
(0.000)***
0.232
(0.079)*
0.441
(0.018)**
0.002
(0.422)
0.014
(0.057)*
-0.005
(0.187)
0.003
(0.821)
-0.003
(0.567)
0.016
(0.000)***
-0.002
(0.157)
0.009
(0.161)
0.011
(0.179)
-0.002
(0.638)
0.019
(0.009)***
-0.003
(0.533)
0.001
(0.954)
0.000
(0.989)
0.014
(0.005)***
-0.001
(0.658)
0.018
(0.180)
0.029
(0.159)
2,048
0.164
680
0.524
4,517
0.079
2,048
0.121
Robust p values in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: Controls not shown.
CHANGE IN CASH COMPENSATION
28
Table 5. Summary statistics for the predictive regressions.
Annual data for firms in the Execucomp database, 1992-2000. All data are year-end except for
return variables and merger variables. Firms with an acquisition within six months of the most
recent year end are not included in the sample. Future merger is a merger in the following 12
months.
All firms
FUTURE MERGER
Mean
Median
12.77%
0.00%
Standard
deviation
33.38%
Firms with a past merger
(PRIOR MERGERS > 0)
Mean
Median
Standard
deviation
20.91%
0.00%
40.68%
PRIOR MERGERS
1.739
1.000
1.165
YEARS
0.700
0.775
0.506
-1.70%
-9.66%
55.96%
PAST NET
MKT RET 1
0.49%
-6.26%
48.71%
0.09%
-6.78%
57.06%
LGTA
9.210
9.131
0.729
9.326
9.214
0.713
EQ/TA
0.440
0.424
0.215
0.389
0.382
0.210
ROA
0.047
0.048
0.100
0.035
0.035
0.085
MARKET -TO-BOOK
3.231
2.224
3.428
3.035
2.209
2.937
EBITDA -TO-SALES
0.152
0.159
0.427
0.178
0.161
0.340
COMP/TA
0.33%
0.14%
0.86%
0.27%
0.13%
0.55%
132.87%
34.55%
1106.21%
218.68%
50.31%
1693.12%
43.52%
44.64%
27.69%
48.96%
51.80%
27.51%
4.35%
1.72%
6.94%
3.35%
1.65%
4.77%
COMP CHG/ COMP
EQ/TOT COMP
OWNERSHIP
Observations
7,280
1,339
29
Table 6. Regressions to predict future mergers.
Logistic regressions with FUTURE MERGER as the dependent variable. Annual data for firms in the
Execucomp database, 1992-2000. Firms with an acquisition within six months of the most recent
year end are dropped from the sample.
(1)
PRIOR MERGERS
PAST NET
YEARS
MKT RET 1
MKT RET 2
MKT RET 3
0.190
(0.000)***
0.354
(0.000)***
0.100
(0.388)
0.183
(0.043)**
0.167
(0.025)**
0.273
(0.002)***
COMP/TA
COMP CHG/ COMP
EQ/TOT COMP
OWNERSHIP
(2)
0.114
(0.050)**
0.368
(0.001)***
0.176
(0.237)
0.129
(0.262)
0.075
(0.532)
0.296
(0.022)**
-0.650
(0.945)
0.003
(0.247)
0.288
(0.140)
-1.837
(0.025)**
EXCESS COMP TOP50 PROG
EXCESS COMP TOP50 NO PROG
Observations
7,320
4,443
Robust p values in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: Controls and year dummies not shown.
(3)
0.009
(0.904)
0.275
(0.016)**
0.407
(0.015)**
0.284
(0.024)**
0.026
(0.862)
0.422
(0.003)***
2.192
(0.853)
0.005
(0.249)
0.194
(0.419)
-1.741
(0.046)**
0.702
(0.001)***
0.045
(0.728)
3,486
30
Table 7. Partial sample regressions to predict future mergers.
Logistic regressions with FUTURE MERGER as the dependent variable. Annual data for firms in the
Execucomp database, 1995-2000. The regression in column (1) uses all firms, including those
with an acquisition within six months of the most recent year end. The regression in column (2)
includes only firms with some merger activity between the end of year t-3 and six months prior to
the end of year t. It drops firms with an acquisition within six months of the most recent year
end.
PRIOR MERGERS
PAST NET
YEARS
MKT RET 1
MKT RET 2
MKT RET 3
COMP/TA
COMP CHG/ COMP
EQ/TOT COMP
OWNERSHIP
EXCESS COMP TOP50 PROG
EXCESS COMP TOP50 NO PROG
Observations
(1)
(2)
0.038
(0.529)
0.321
(0.003)***
0.343
(0.027)**
0.326
(0.007)***
0.062
(0.666)
0.466
(0.001)***
1.143
(0.921)
0.005
(0.284)
0.270
(0.235)
-1.399
(0.081)*
0.543
(0.004)***
0.049
(0.698)
-0.005
(0.943)
-0.092
(0.668)
0.411
(0.018)**
0.768
(0.001)***
0.670
(0.017)**
0.559
(0.028)**
2.660
(0.872)
0.008
(0.093)*
0.070
(0.853)
-0.361
(0.828)
0.607
(0.012)**
-0.170
(0.501)
3,771
1,041
Robust p values in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Note: Controls and year dummies not shown.
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WP-03-02
Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall
WP-03-03
When is Inter-Transaction Time Informative?
Craig Furfine
WP-03-04
Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods
in Chicago
Maude Toussaint-Comeau and Sherrie L.W. Rhine
WP-03-05
Distinguishing Limited Commitment from Moral Hazard in Models of
Growth with Inequality*
Anna L. Paulson and Robert Townsend
WP-03-06
Resolving Large Complex Financial Organizations
Robert R. Bliss
WP-03-07
4
Working Paper Series (continued)
The Case of the Missing Productivity Growth:
Or, Does information technology explain why productivity accelerated in the United States
but not the United Kingdom?
Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan
WP-03-08
Inside-Outside Money Competition
Ramon Marimon, Juan Pablo Nicolini and Pedro Teles
WP-03-09
The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences
William H. Greene, Sherrie L.W. Rhine and Maude Toussaint-Comeau
WP-03-10
A Structural Empirical Model of Firm Growth, Learning, and Survival
Jaap H. Abbring and Jeffrey R. Campbell
WP-03-11
Market Size Matters
Jeffrey R. Campbell and Hugo A. Hopenhayn
WP-03-12
The Cost of Business Cycles under Endogenous Growth
Gadi Barlevy
WP-03-13
The Past, Present, and Probable Future for Community Banks
Robert DeYoung, William C. Hunter and Gregory F. Udell
WP-03-14
Measuring Productivity Growth in Asia: Do Market Imperfections Matter?
John Fernald and Brent Neiman
WP-03-15
Revised Estimates of Intergenerational Income Mobility in the United States
Bhashkar Mazumder
WP-03-16
Product Market Evidence on the Employment Effects of the Minimum Wage
Daniel Aaronson and Eric French
WP-03-17
Estimating Models of On-the-Job Search using Record Statistics
Gadi Barlevy
WP-03-18
Banking Market Conditions and Deposit Interest Rates
Richard J. Rosen
WP-03-19
Creating a National State Rainy Day Fund: A Modest Proposal to Improve Future
State Fiscal Performance
Richard Mattoon
WP-03-20
Managerial Incentive and Financial Contagion
Sujit Chakravorti, Anna Llyina and Subir Lall
WP-03-21
Women and the Phillips Curve: Do Women’s and Men’s Labor Market Outcomes
Differentially Affect Real Wage Growth and Inflation?
Katharine Anderson, Lisa Barrow and Kristin F. Butcher
WP-03-22
Evaluating the Calvo Model of Sticky Prices
Martin Eichenbaum and Jonas D.M. Fisher
WP-03-23
5
Working Paper Series (continued)
The Growing Importance of Family and Community: An Analysis of Changes in the
Sibling Correlation in Earnings
Bhashkar Mazumder and David I. Levine
WP-03-24
Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining
on Older Displaced Workers
Louis Jacobson, Robert J. LaLonde and Daniel Sullivan
WP-03-25
Trade Deflection and Trade Depression
Chad P. Brown and Meredith A. Crowley
WP-03-26
China and Emerging Asia: Comrades or Competitors?
Alan G. Ahearne, John G. Fernald, Prakash Loungani and John W. Schindler
WP-03-27
International Business Cycles Under Fixed and Flexible Exchange Rate Regimes
Michael A. Kouparitsas
WP-03-28
Firing Costs and Business Cycle Fluctuations
Marcelo Veracierto
WP-03-29
Spatial Organization of Firms
Yukako Ono
WP-03-30
Government Equity and Money: John Law’s System in 1720 France
François R. Velde
WP-03-31
Deregulation and the Relationship Between Bank CEO
Compensation and Risk-Taking
Elijah Brewer III, William Curt Hunter and William E. Jackson III
WP-03-32
Compatibility and Pricing with Indirect Network Effects: Evidence from ATMs
Christopher R. Knittel and Victor Stango
WP-03-33
Self-Employment as an Alternative to Unemployment
Ellen R. Rissman
WP-03-34
Where the Headquarters are – Evidence from Large Public Companies 1990-2000
Tyler Diacon and Thomas H. Klier
WP-03-35
Standing Facilities and Interbank Borrowing: Evidence from the Federal Reserve’s
New Discount Window
Craig Furfine
WP-04-01
Netting, Financial Contracts, and Banks: The Economic Implications
William J. Bergman, Robert R. Bliss, Christian A. Johnson and George G. Kaufman
WP-04-02
Real Effects of Bank Competition
Nicola Cetorelli
WP-04-03
Finance as a Barrier To Entry: Bank Competition and Industry Structure in
Local U.S. Markets?
Nicola Cetorelli and Philip E. Strahan
WP-04-04
6
Working Paper Series (continued)
The Dynamics of Work and Debt
Jeffrey R. Campbell and Zvi Hercowitz
WP-04-05
Fiscal Policy in the Aftermath of 9/11
Jonas Fisher and Martin Eichenbaum
WP-04-06
Merger Momentum and Investor Sentiment: The Stock Market Reaction
To Merger Announcements
Richard J. Rosen
WP-04-07
Earnings Inequality and the Business Cycle
Gadi Barlevy and Daniel Tsiddon
WP-04-08
Platform Competition in Two-Sided Markets: The Case of Payment Networks
Sujit Chakravorti and Roberto Roson
WP-04-09
Nominal Debt as a Burden on Monetary Policy
Javier Díaz-Giménez, Giorgia Giovannetti, Ramon Marimon, and Pedro Teles
WP-04-10
On the Timing of Innovation in Stochastic Schumpeterian Growth Models
Gadi Barlevy
WP-04-11
Policy Externalities: How US Antidumping Affects Japanese Exports to the EU
Chad P. Bown and Meredith A. Crowley
WP-04-12
Sibling Similarities, Differences and Economic Inequality
Bhashkar Mazumder
WP-04-13
Determinants of Business Cycle Comovement: A Robust Analysis
Marianne Baxter and Michael A. Kouparitsas
WP-04-14
The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau
WP-04-15
Reading, Writing, and Raisinets1: Are School Finances Contributing to Children’s Obesity?
Patricia M. Anderson and Kristin F. Butcher
WP-04-16
Learning by Observing: Information Spillovers in the Execution and Valuation
of Commercial Bank M&As
Gayle DeLong and Robert DeYoung
WP-04-17
Prospects for Immigrant-Native Wealth Assimilation:
Evidence from Financial Market Participation
Una Okonkwo Osili and Anna Paulson
WP-04-18
Institutional Quality and Financial Market Development:
Evidence from International Migrants in the U.S.
Una Okonkwo Osili and Anna Paulson
WP-04-19
Are Technology Improvements Contractionary?
Susanto Basu, John Fernald and Miles Kimball
WP-04-20
7
Working Paper Series (continued)
The Minimum Wage and Restaurant Prices
Daniel Aaronson, Eric French and James MacDonald
WP-04-21
Betcha can’t acquire just one: merger programs and compensation
Richard J. Rosen
WP-04-22
8
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