Can serial acquirers be profiled?

75 Pages·2016·1.48 MB·English

by  Antonio Macias

Can serial acquirers be profiled?

ANTONIO J. MACIAS
Baylor University

P. RAGHAVENDRA RAU
University of Cambridge

ARIS STOURAITIS
Hong Kong Baptist University

July 2016

Abstract

We show that the characteristics of serial acquirers are very different from those studied in prior research.
Specifically, we find four major types of acquirers common in the data – loners, occasional acquirers,
sprinters, and marathoners. Importantly, these acquirers can be distinguished on an ex ante basis.
Marathoners are efficient acquirers that acquire external growth and learn from prior acquisitions. Industry
and overvaluation effects matter somewhat for sprinters who acquire targets in short intervals. Path
dependency typically does not seem to matter for serial acquirers.

Keywords: Serial acquirers; Mergers; Acquisitions; M&A; Firm characteristics; Overvaluation;
Efficiency; Industry Effects; Path dependency; Learning

JEL Classification: G14; G34; G35

Macias: Hankamer School of Business, Baylor University, USA (e-mail: [email protected]); Rau:
University of Cambridge, Judge Business School, Trumpington Street, Cambridge CB2 1AG, UK. (email:
[email protected]); Stouraitis: School of Business, Hong Kong Baptist University, Renfrew Road,
Kowloon Tong, Hong Kong, People's Republic of China ([email protected]). We would like to thank
Pramuan Bunkanwanicha, Eric de Bodt, Andrey Golubov, Mike Stegemoller, and seminar participants at
Baylor University, ESSEC, the Judge Business School, the University of Bristol, the Université Paris-
Dauphine and the Western Finance Association 2016 for helpful comments
Can serial acquirers be profiled?

Abstract

We show that the characteristics of serial acquirers are very different from those studied in prior research.
Specifically, we find four major types of acquirers common in the data – loners, occasional acquirers,
sprinters, and marathoners. Importantly, these acquirers can be distinguished on an ex ante basis.
Marathoners are efficient acquirers that acquire external growth and learn from prior acquisitions. Industry
and overvaluation effects matter somewhat for sprinters who acquire targets in short intervals. Path
dependency typically does not seem to matter for serial acquirers.

Keywords: Serial acquirers; Mergers; Acquisitions; M&A; Firm characteristics; Overvaluation;
Efficiency; Industry Effects; Path dependency; Learning

JEL Classification: G14; G34; G35
“I can make you faster, but I can’t make you fast.” 1
“Most champions’ biographies would indicate they were always the fastest kid in their neighborhood,
even before they did any formal training or received any coaching... [F]rom Helen Stephens, a 1936
Olympian, to Usain Bolt, there were no exceptions.” 2
Symposium attendees agreed that there is no single identifiable cause or factor that leads to the
development of a serial killer. Rather, there are a multitude of factors that contribute to their
development. The most significant factor is the serial killer’s personal decision in choosing to pursue
their crimes. 3

1. Introduction
Most of the literature on mergers and acquisitions examines the cross-sectional determinants of
acquirer activity, typically treating each acquisition as an independent observation. However, acquirers are
very different in their propensity to acquire. While a majority of acquirers make but one or two acquisitions,
a sizeable minority of serial acquirers undertake an extremely large number of acquisitions (Fuller, Netter,
and Stegemoller, 2002, and Aktas, de Bodt, and Roll, 2013).
In this paper, we first document that serial acquirers are much more complex beasts than the previous
literature has recognized. We show that acquirers can be classified into four distinct types that we denote as
loners, occasional acquirers, sprinters, and marathoners. Our sample contains approximately 56,000
acquisitions of U.S. public, private and subsidiary targets by more than 9,000 U.S. publicly listed acquirers
during 1984-2013. Of these acquirers, about 44% make one or two acquisitions over the entire period
(comprising 10% of all the acquisitions in the sample), while 11% of the acquirers make close to half (49%)
of all the acquisitions in the sample. We analyze what characterizes these serial acquirers on an ex ante basis,
i.e. we predict which acquirers will eventually become serial acquirers at the start of their acquisition activity.

1 Jerry Baltes, Head Coach, Grand Valley State University cross-country and track and field.
2 Lombardo, Michael and Deaner, Robert, 2014, “Practice does not make perfect: Elite sprinters destroy myth that
athletes made, not born”, Genetic Literacy Project.
3 Serial Murder, Multi-disciplinary perspectives for investigators, report published by the Behavioral Analysis Unit-2,
National Center for the Analysis of Violent Crime, Critical Incident Response Group, Federal Bureau of Investigation,
2005.
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The consistent ex ante differences that we document across the different types show that serial acquirers are
not all alike. The quotes above suggest that there are intrinsic differences among runners in that some runners
are innately faster than others, while serial killers do not appear to be innately different from other violent
offenders. We show that, similar to runners but unlike serial killers, acquirers are intrinsically and predictably
different in their characteristics and their propensities to acquire.
Documenting the characteristics of serial acquirers is important for two reasons. First, even though
serial acquirers undertake a huge number of acquisitions, there is no consistent definition for what is a serial
acquirer. For example, Fuller, Netter, and Stegemoller (2002) and Karolyi, Liao, and Loureiro (2015) define
serial acquirers as companies that acquired more than five targets over their sample period. In contrast, Billett
and Qian (2008) define serial acquirers to be those that make more than two acquisitions over the entire
sample period or over a three- or five-year rolling window. Second, implicit in these definitions is the notion
that, as long as they satisfy the necessary threshold, all serial acquirers represent a homogeneous class. Third,
these definitions of serial acquirers are all based on ex post information that the market does not have when
the serial acquirer begins acquiring. This has important consequences for the conclusions drawn from prior
research. For example, a number of papers argue that these serial acquirers appear to perform worse as they
continue acquiring (Fuller, Netter, and Stegemoller, 2002, Billett and Qian, 2008, Boubakri, Chan, and Kooli,
2012). The main competing explanations for this decreasing performance are that the market anticipates the
next acquisition from a serial acquirer (Schipper and Thompson, 1983, Malatesta and Thompson, 1985, and
Loderer and Martin, 1990), that bidders become overconfident from successful prior acquisitions (Billett and
Qian, 2008) or that they make bids for agency reasons (Jensen, 2005). However, some acquirers in our sample
make over a hundred acquisitions over a period of more than 20 years across a range of industries. It is
difficult to imagine for example, that managers at these firms are so entrenched that they will be freely able
to conduct this enormous number of acquisitions without shareholder intervention. Similarly, it is plausible
that some rational potential serial acquirers stop acquiring when they realize that the market is anticipating
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their next bid, driving the target price too high for them to justify a bid, implying that there is a selection bias
in the ex post analysis of serial acquirers.
Since there is no theoretical basis for defining a serial acquirer, we impose some common sense
criteria. Specifically, we define a serial acquirer as one that is likely to undertake a large number of
acquisitions, either over relatively continuous periods or in bursts of acquisitions (what we term acquisition
blocks). This gives us three dimensions – the total number of acquisitions undertaken, the number of
acquisition blocks over which the activity is undertaken, and the acquisition intensity, defined as the
maximum number of acquisitions within an acquisition block divided by the duration of the block in days, at
the acquirer level. These dimensions appear to reliably distinguish acquirers on the basis of their timing of
acquisitions. Although the unconditional average time between subsequent acquisitions is 376 days (1 year)
over the whole sample, within acquisition blocks, subsequent transactions occur over relatively short horizons
(152 days on average). The average time between the end of an acquisition block and the start of a subsequent
acquisition block (if any) is 3.1 years. Although the average acquirer makes 9.6 acquisitions over the sample
period, within a given acquisition block, the average number of acquisitions is 3 (regardless of the number of
prior acquisition blocks the acquirer has already conducted). We note that these figures imply that prior ad
hoc definitions of serial acquirers are not consistent with the data.
We next use cluster analysis to classify acquirers into categories based on these three attributes.
Acquirers appear to be classified into four distinct types that we denote as loners, occasional acquirers,
sprinters, and marathoners. Loners (roughly a third of the sample of unique acquirers) enter the takeover
market once or twice, making up 7% of the total number of acquisitions in our sample. Occasional acquirers
(constituting another third) enter the market occasionally with a mean of 3.5 acquisitions. Sprinters (around
a quarter of the sample) run various longer acquisition streaks, conducting an average of 10 acquisitions per
acquirer over an average of 4 acquisition blocks, adding up to 36% of the total number of acquisitions in our
sample. The 577 marathoners (6.3% of all the unique acquirers) acquire targets almost continuously as part
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of a few long and continuous acquisition blocks. They conduct an average of 35 acquisitions per acquirer
over an average of 5 acquisition blocks, adding up to 36% of the total number of acquisitions in our sample.
In the second part of our analysis, we analyze whether there are innate differences in the
characteristics of these different types of acquirers. We classify acquirer characteristics as related to one of
five non-mutually exclusive hypotheses that have been examined in previous studies of serial acquirers: the
efficiency, learning, industry effects, overvaluation, and path-dependent hypotheses. These hypotheses
inform our choice of acquirer characteristics. Our aim is not to test the hypotheses per se but to examine
whether their proxies have differential impact across the different types of acquirers.
The efficiency hypothesis says that acquirers acquire for strategic reasons (neoclassical efficiency
reasons) (Gort, 1969). For example, they might consistently choose to acquire targets that give them external
growth opportunities, or have cost or revenue synergies. The efficiency hypothesis also relates to the public
status of the target (private targets are illiquid and have less bargaining power) (Officer, 2007) and the number
of available targets in the industry. They will stop acquiring when these types of targets disappear. The
learning hypothesis says that serial acquirers learn how to make acquisitions and hence increase the speed
with which they acquire over time. We follow Aktas, de Bodt, and Roll (2013) in examining whether the time
since last acquisition and the acquisition index number (AIN) within an acquisition block explains the time
to the next. The industry effects hypothesis says that acquirers choose to acquire because of industry-wide
factors, such as deregulation or a merger wave (Andrade, Mitchell, and Stafford, 2001). The overvaluation
hypothesis says that serial acquirers acquire to take advantage of their own or industry overvaluation (Shleifer
and Vishny, 2003). An acquirer will continue pursuing targets as long as the acquirer’s overvaluation enables
taking advantage of its overvalued stock, likely over a short window of opportunity. We follow Rhodes-
Kropf, Robinson, and Vishwanathan (2004, 2005) in decomposing the firm’s Tobin’s Q into firm- and
industry-specific components to examine the role of both sources of potential misvaluation. Finally, the path
dependent hypothesis says that a serial acquirer becomes a serial acquirer because of positive market or
operating feedback and performance from the prior acquisition. In this hypothesis, all acquirers are potential
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serial acquirers. The ones that do well in their prior acquisition(s) continue to acquire, the ones that experience
poor results stop acquiring. Hence the probability of a subsequent acquisition depends on the market reaction
in the prior acquisition and on changes in operating performance since the prior acquisition. We use the
announcement period returns to the acquirer in their prior acquisitions and the change in operating
performance since their last acquisition as characteristics related to path dependence.
We examine the characteristics of the acquirers at the border of each acquirer type and we also use
duration analysis to examine the determinants of continuing to another acquisition. More specifically, we
conduct four sets of tests. We first run a set of univariate analyses on the acquirer’s decision to continue to a
subsequent acquisition. We next focus on the acquirer’s decision to continue to a subsequent acquisition at
the border of each acquirer type (from loners to occasional acquirers, from occasional to sprinters and finally
from sprinters to marathoners) using both univariate analyses and multivariate logistic regressions. In our
third set of tests, we use sequential logits to examine the probability that the acquirer will go beyond the
transition of each acquirer type using the information available at the first acquisition of each acquirer. In our
final set of tests, we use hazard analysis to examine the likelihood and timing of another acquisition by each
acquirer type.
All four sets of tests yield remarkably consistent results. In particular, characteristics related to
efficiency and learning appear to be the strongest determinants of innate differences among the four acquirer
types. For instance, size and operating performance are both monotonically positively related to the number
of acquisitions an acquirer conducts. This conclusion holds when we compare only the first acquisition for
each acquirer, when we examine the entire sample of acquirers and acquisitions, and when we compare only
the first acquisition in each acquisition block. This is consistent with the hypothesis that the most efficient
acquirers are the ones that continue on to become serial acquirers. Interestingly while marathoners have
significantly higher operating performance over the entire sample than the other types of acquirers,
marathoners have significantly lower R&D expenditures and growth opportunities when they make their first
acquisition. Our results shed new light on the results of Arikan and Stulz (2016), who find that mature
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acquirers (those likely classified as marathoners in our study) perform well and have good investment
opportunities. Consistent with the learning hypothesis, the time to the next acquisition declines monotonically
as we move from occasional acquirers to marathoners.
We find weaker evidence for industry effects and overvaluation proxies. As we move from loners to
marathoners, acquirers conduct more acquisitions in different industries than their own, during M&A waves,
earn monotonically higher returns in the prior quarter, and have larger firm-specific errors. Sprinters appear
to take advantage of market timing, conducting more acquisitions during the dot-com bubble and exhibiting
higher stock-returns volatility. Finally, we find little evidence for path dependence. Specifically, as we move
from loners to marathoners, the acquirers earn lower short-term returns in the current and previous
acquisitions, and display smaller changes in sales growth since their prior deal. Overall, the analysis suggests
that marathoners are operationally efficient acquirers that have a policy of consistently buying external
growth. In contrast, sprinters seem to be more likely to acquire with some degree of timing, either conducting
more acquisitions during M&A waves or when they are misvalued.
The final part of our analysis examines whether serial acquirers can be profiled using an out-of-
sample test. We split the sample into two periods, an estimation and a prediction window lasting 15 years
each. Starting with a cluster analysis in the estimation window, we classify firms into one of the four types.
Then we compute regression coefficients in the estimation window to get parameter estimates of the
likelihood of becoming a serial acquirer at the first acquisition and at various borders beyond the initial
acquisition. In the prediction window, for both the first acquisition and at each border, we use the parameters
derived from the estimation window to predict the eventual acquirer type. Finally, we compare actual
outcomes (based on an ex post cluster classification) with predicted acquirer types. Using only information
at the first acquisition by the acquirer, the model correctly predicts between 57% and 80% of the transitions.
The predicted precision is even higher if we just look at the marathoners. Specifically, the model correctly
predicts that 78% of the actual marathoners will indeed eventually make multiple serial acquisitions based on
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their firm characteristics at their first acquisition (as opposed to correctly predicting 51% of the acquirers who
make a few occasional acquisitions).
Overall, our paper shows that serial acquirers are considerably more complex beasts than
hypothesized in the extant literature. Large firms with high operating performance, low internal R&D, and a
habit of making frequent acquisitions continue to make serial acquisitions to acquire external growth. They
appear to ignore stock market announcement returns to their acquisitions when acquiring. These factors help
us predict who will become a serial acquirer.
Our paper contributes to the literature in three ways. First, with the exception of Fuller, Netter, and
Stegemoller (2002), Klasa and Stegemoller (2007), Aktas, de Bodt, and Roll (2013), Karolyi, Liao, and
Loureiro (2015), and Golubov, Yawson and Zhang (2015), few papers examine the behavior of serial
acquirers, who conduct three-quarters of all U.S. acquisitions. However, unlike the previous studies, we show
that the serial acquirers follow distinct patterns in their acquisition series. Our results complement Aktas, de
Bodt, and Roll (2013) who argue that knowledge gleaned from previous acquisitions within an acquisition
block may confer valuation expertise and other benefits. They document that acquirers gain by making
repetitive acquisitions. We show that acquisition experience also relates to prior acquisition blocks.
Second, we provide evidence consistent with the existence of extraordinary acquirers and factors that
affect the acquirer’s decision to continue acquiring at key moments in the firm's life cycle. We complement
the evidence on the persistence of short term excess returns for extraordinary acquirers (Gobulov, Yawson,
and Zhang, 2015) and on how acquisition patterns change over the firm life cycle (Arikan and Stulz, 2016).
Third, we provide new evidence on the misvaluation hypothesis by explicitly tracking the same
acquirers over time, rather than treating their acquisitions as independent observations as the previous
literature has done (Shleifer and Vishny, 2003; Rhodes-Kropf, Robinson, and Viswanathan (RRV), 2005).
We show that the misvaluation hypothesis typically does not hold for non-serial loner acquirers who do not
visit the market multiple times and hence may have little opportunity to detect whether their shares are
overvalued. Sprinters, in particular, seem to take advantage of firm-specific and industry-specific
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misvaluations. For the sprinters and the marathoners, the primary determinant is efficiency, in particular,
operating performance. Market reactions to their acquisitions do not appear to influence the acquisition
decision for these acquirers.
2. Classifying serial acquirers
2.1 Data and descriptive statistics
We obtain our sample of acquisitions of U.S. targets (public, private, and subsidiary firms) announced
by U.S. public acquirers during 1984-2013 from the Thomson One (SDC) database. We require that the bidder
seeks to acquire more than 50% ownership of the target, and that the Center for Research in Security Prices
(CRSP) and COMPUSTAT provide information for the acquirer. We obtain stock return and accounting data
for the universe of U.S. publicly listed firms from CRSP and COMPUSTAT as of the prior quarter before the
announcement date. To alleviate truncation concerns for acquirers at the start and end of our sampling
window, we require that (i) the acquirer has not conducted any acquisition in the 3 years prior to the start of
our sample period, i.e., 1981-1983; and (ii) that the acquirer conducts its first acquisition at least by the year
2008. Our initial sample consists of 56,095 merger and acquisitions transactions conducted by 9,166 unique
acquirers.
Figure 1 depicts the histogram of the total number of acquisitions by unique acquirers as a function
of the total number of unique acquirers. 2,572 acquirers (28% of all the acquirers in our sample) conduct just
one acquisition between 1984 and 2013. Another 1,500 unique acquirers conduct a total of two acquisitions
each. In contrast, 1,317 unique acquirers (14% of all the acquirers) conduct more than ten acquisitions each.
We find 81 acquirers that conduct more than fifty acquisitions each, including twelve acquirers with over a
hundred acquisitions each.4 However, these acquisitions do not always occur evenly over time. In untabulated

4 The twelve are Arthur J. Gallagher & Co (262), Airgas Inc (214), Brown & Brown, Inc. (170), Cisco Systems (166),
IBM (148), HILB Rogal & Hobbs (139), Microsoft (127), Illinois Tools Works (120), Parker-Hinnifin (118), Fiserv
(111), Omnicare (107), and Oracle (101). Internet Appendix D Table D.1 lists the most active serial acquirers in the
sample.
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