Download Market Power, Toeholds and the Takeover Premium

Market Power, Toeholds and the Takeover Premium
August 2013
Robert G. Bowman
University of Auckland and University of Queensland
Adrian Richards
Goldman Sachs, Melbourne, Australia
Keywords: takeover premium, market power, toehold
JEL classification: G34, G14, L22
Market Power, Toeholds and the Takeover Premium
Abstract
We provide new evidence that market power is a motivation for takeovers. We develop a
proxy based on the market concentration doctrine that measures whether a takeover is likely
to cause an increase in the market power of the acquirer. In a test of the determinants of the
takeover premium we find that it is over 10% higher when the acquirer expects to gain
additional market power in the transaction. This result indicates that, contrary to the existing
literature, firms do use the market for corporate control as a tool to increase their market
power. We also consider the influence a toehold has on the takeover premium. We develop a
non-linear model that allows for a more refined analysis of this affect. Accounting for the
degree of influence a toehold provides the acquirer is important when conducting empirical
research on the takeover premium.
1.
Introduction
The mergers and acquisitions market has been a rich source of study in finance and
economics. Research has explored many aspects of this market for corporate control including
the bidders, the targets, the pre and post transaction performance, bidding strategies and the
takeover premium. We focus on two factors that influence the takeover premium – market
power and toeholds.
We provide new evidence that market power is a motivation for takeovers. In a test of the
determinants of the takeover premium we find that premiums are over ten percent higher
when the acquirer expects to gain additional market power in the transaction. This result
indicates that firms use the market for corporate control as a tool to increase their market
power. Existing literature has generally relied on a measure of abnormal returns to proxy for
the takeover premium. We use offer price data to estimate the takeover premium, enabling it
to account for private information. The results are found in a large sample of US takeovers
between 1990 and 2011.
Contrary to past results (Eckbo, 1983; Stillman 1983), this study provides evidence that
market power is a motivation for horizontal acquisitions. Acquirers are willing to compensate
targets for the additional market power they will acquire in a transaction.
We also consider the influence a toehold provides the acquirer over the target in arriving at
the takeover premium. While previous literature has documented the varying sizes of
toeholds, it has failed to directly consider the influence it has on the takeover premium. We
develop a non-linear model to capture the influence of different toehold levels. Accounting
for the degree of influence a toehold provides the acquirer is important. When the toehold
does not provide the acquirer with substantial control, the takeover premium is not
significantly lower. This is not consistent with the extant literature. However, when the
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toehold does provide the acquirer with substantial control, the takeover premium is
significantly lower and qualitatively similar to the existing literature.
1.1. Research questions
The amount offered in a takeover above the target’s market value is the takeover premium.
This should reflect the target’s share of the increased value the acquirer expects to realize
from the transaction. Holding the relative bargaining position of the deal parties constant, an
increase in acquirer value due to an increase in its market power post acquisition increases the
takeover premium. Therefore, the first research question of this study is whether acquirers
offer a higher takeover premium in transactions that are expected to increase their market
power. By extension, this question also asks whether firms use the market for corporate
control as a tool to increase their market power.
The second research question asks whether the magnitude of a toehold has a differential
impact on the takeover premium. A number of researchers have developed models showing
that the takeover premium will be smaller in the presence of toeholds. However, the empirical
evidence is mixed. We show that there is a critical toehold level, where the acquirer has
substantial influence, that is important in understanding the relationship between toeholds and
the takeover premium.
The remainder of this study is as follows: Section 2 provides an overview of the relevant
literature; Section 3 develops and presents the main models and hypotheses; Section 4
describes the sampling methodology and provides an analysis of the descriptive statistics;
Section 5 presents and analyzes the results; Section 6 provides robustness testing and Section
7 concludes.
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2.
Literature review
2.1. Industry organization and firm value
A long history of research shows positive correlation between market power and firm
profitability (Bain, 1951; Shepherd, 1972, among others). Together, these studies show
market power allows firms to earn monopoly rents through higher product prices. Firms
obtain this power through market imperfections such as concentrated seller markets and
barriers to entry. However, these studies do not provide a direct link between market power
and higher firm value.
Thomakadis (1976) presents a model linking market power to firm value. He models firm
value as the sum of the reproduction cost of existing assets and the capitalized value of future
monopoly rents. When market power exists, firm value is the sum of existing assets and
expected future excess profits from monopoly rents. He also employs his model to test a
sample of 158 firms between 1961 and 1968 (Thomakadis, 1977). The results indicate firm
value increases with market power (measured by market concentration). Furthermore, they
indicate persistence in monopoly rents. Sullivan (1977) provides empirical evidence that the
increased profitability of firms with market power is fully capitalized into their share price.
The positive relation between market power and firm value is robust to alternative
measures of market power. Lindenberg and Ross (1981) provide evidence based on Tobin’s q
that market power is associated with higher firm value. Tobin’s q can be considered a proxy
for monopoly rents as a q ratio greater than unity implies firm value greater than the
replacement cost. Consistent with Sullivan (1977), the results indicate those firms with greater
capacity to extract monopoly rents have higher q ratios, which are greater than one on
average.
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2.2. Market power as a motivation for takeovers
The findings of Eckbo (1983) do not support the acquisition of market power as a
motivation for takeovers. He argues rival firms will benefit from anticompetitive, horizontal
mergers due to the higher probability of successful collusion. This results in higher product
prices, which should then result in positive stock returns around the announcement date.
However, if an antitrust regulator challenges a takeover, rival firms should experience
negative returns. Eckbo examines 259 horizontal and vertical transactions in the mining and
manufacturing sectors and finds that while rivals have a significant and positive excess return
of 2.45% around the announcement date, they also experience significant and positive returns
of 1.78% around the time of a regulatory challenge. Furthermore, these results are statistically
independent. Therefore, he concludes that there is no support for the collusion hypothesis.
Stillman (1983) also fails to find evidence that the acquisition of market power is a motive
for takeovers. Similar to Eckbo (1983), he argues that the benefits of an anticompetitive
takeover should result in positive share returns for rivals around the announcement date. To
test this, Stillman examines 11 transactions between 1964 and 1972 that antitrust regulators
challenged. Like Eckbo (1983) he finds no evidence that additional market power is a
motivating factor in takeovers.
Eckbo (1985) was the first to use the market concentration doctrine in the context of
mergers; (higher) market concentration is directly related to (greater) market power. Using a
sample of 266 mergers (196 horizontal, 70 non-horizontal), he finds market concentration
does not increase the abnormal returns of rival firms. Therefore, he concludes that an increase
in market power is not a motive for horizontal mergers.
These results do not support the argument that firms use the market for corporate control as
a means of increasing market power.
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2.3. Toeholds as an advantage in takeovers
Toehold positions offer a number of apparent advantages in the event of a decision to
pursue a complete takeover. The larger the toehold, the less shares need to be acquired to
achieve full ownership. Also, at some level, a toehold may confer information advantages in
assessing the value of a prospective target. The existence of a toehold may deter competitive
bidding. Similarly, at some toehold level, the target management will be familiar with the
management of the toehold company. These factors may result in a lower takeover premium.
Although numerous models indicate that toeholds are value adding for bidders, 1 most studies
indicate that the level of toeholds in practice is low and declining. 2
Betton and Eckbo (2000) and Betton et al. (2008b) provide results showing a negative
association between the level of toehold and the takeover premium for takeovers that are
consummated.
Betton et al. (2009) examine a sample of over 10,000 US control bids and show that the
frequency of toeholds in takeover offers has steadily declined since the early 1980s, but when
present, the toehold is relatively large. They find the optimal toehold is either zero or greater
than a threshold, which they estimate averages 9%.
Chemmanur et al. (2009) show that private information held by the bidders and targets in a
takeover has implications for the method of payment, which in turn impacts on valuations,
abnormal returns and the likelihood of competing bids. Toeholds have the potential to create
private information, but Chemmanur et al. do not explore this issue.
1
See Grosman and Hart (1980), Chowdhry and Jegadeesh (1994), Burkart (1995), Bulow et al. (1996),
Goldman and Qian (2005) and Dodonova (2012).
2
See Bradley et al. (1988), Jennings and Mazzeo (1993), Betton and Eckbo (2000), Povel and Sertsios (2013),
and especially Betton et al. (2009).
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Eckbo (2009) provides an excellent review of takeover bidding strategies, including the
role of toeholds. He reports (p175) that “… toeholds are large when they exist and that they
occur mostly in hostile bids.”
2.4. Takeover premiums in the M&A literature
2.4.1. Measuring the takeover premium
Studies of takeover premiums predominantly use abnormal returns as the measure of the
takeover premium. For example, Schwert (1996) presents the takeover premium as the sum of
the runup and a markup, where runup is the change in the share price over a period prior to
announcement and markup is the increase in the share price on the announcement date. The
Takeover offer is not included in this calculation.
Other studies measure the takeover premium as the ratio of the offer price relative to the
target’s share price at some point prior to the announcement date (Betton and Eckbo, 2000;
Bange and Mazzeo, 2004; Betton et al., 2009). These studies use varying lengths of time prior
to the takeover announcement.
It is reasonable to assume that in any takeover transaction there is significant information
asymmetry between deal parties and the market. Acquirers have an incentive to increase the
information asymmetry between themselves and the market, resulting in share prices that are
less likely to reflect expected gains from a transaction. When the acquirer has been in
discussions with the target prior to announcement, the target will have a better understanding
of the expected gains from the transaction. This implies that information asymmetry is lower
between the deal parties than between the acquirer and the market.
We believe the difference between the market price of the target immediately before the
takeover announcement and the offer price is the best reflection of the value expected to be
gained from the transaction. This is our measure of the takeover premium.
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2.4.2. Determinants of the takeover premium
The focus of this study is the initial offer premium. Therefore, when considering the
determinants of the takeover premium it is important to ensure that information about the
determinants is likely to be known prior to announcement. 3 In this context, the impact of
target, acquirer and deal characteristics on the takeover premium are important.
The existing literature has investigated the relation between several key target
characteristics and the takeover premium. Betton et al. (2008b) were the first to show that a
runup increases the markup (and by necessity the premium). A $1 runup increases the offer
price by approximately $0.84. Acquirers do not respond fully to changes in the target’s share
price over the runup period.
Target size and book-to-market are typically included as control variables in studies of the
takeover premium. Empirical results indicate the former is negatively and the latter positively
correlated with the takeover premium (Officer, 2003; Betton et al., 2008b, 2009).
The existing literature also investigates the relation between several key acquirer
characteristics and the takeover premium. Betton and Eckbo (2000) and Betton et al. (2009)
estimate the effect of toeholds on the offer premium. They find negative correlation between
toeholds and the offer premium. However, while both studies document that toeholds range in
size, neither consider the potential implications of this on the takeover premium. We fill this
gap. Furthermore, Bargeron et al. (2008) find that public acquirers tend to offer larger
premiums compared to private acquirers. They attribute the result to over bidding by public
acquirers due to lower managerial ownership.
The existing literature also considers deal characteristics. Huang and Walkling (1987) find
evidence that higher abnormal returns characterize tender offers. In a sample of 204 control
contests abnormal returns in tender offers are 4.7% higher. However, when using a premium
3
Studies that consider post-bid information include Officer (2003, 2004) and Bates and Lemmon (2003) who
examine the role of deal protections, and Betton and Eckbo (2000) who examine the effect of bid jumps.
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based on the offer price Betton et al. (2009) find no relation between offer premiums and
tender offers. Schwert (2000) finds that hostile responses increase the takeover premium,
arguing this result is consistent with more aggressive bargaining by target managers. Gilson et
al. (1988) argue cash-offers will increase the takeover premium as the acquirer must
compensate the target’s shareholders for higher capital gains tax. However, while Eckbo and
Langohr (1989) fail to find compelling evidence to support this argument they, along with de
la Bruslerie (2013), find a positive empirical relation.
2.5. Summary
The existing literature shows that market power translates into market value. Furthermore,
while the previous literature rejects market power as a motivation for takeovers, we provide a
new approach that supports the market power dimension of takeovers. We also investigate the
impact of toeholds on the takeover premium using a non-linear model. We show that the level
of the toehold is critical in estimating the impact. Low levels of toehold do not seem to
influence the premium, but substantial levels, above 20%, do reduce the premium.
3.
Model development and hypotheses
3.1. The takeover premium
The takeover premium is our primary dependent variable. It represents the amount above
the target’s market price the acquirer is willing to pay to acquire control. We measure the
premium as the difference between the initial offer price per share made by the acquirer (t =
0) and the target’s share price one day prior to announcement (t = -1), relative to the target’s
share price one day prior to announcement. That is:
𝑃𝑟𝑒𝑚𝑖𝑢𝑚 =
𝑖𝑛𝑖𝑡𝑖𝑎𝑙 𝑜𝑓𝑓𝑒𝑟 𝑝𝑟𝑖𝑐𝑒−𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−1
𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−1
(1)
Measuring the takeover premium in this way provides two advantages: (1) it standardizes the
takeover premium measure for the target size, providing easily interpretable and comparable
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results across events; and (2) it provides a very short period from which to obtain an objective
measure of the takeover premium. We discuss this in more detail in Section 3.2.1.
3.2. Target, acquirer and deal related variables
Consistent with Betton et al. (2008a), we place each independent variable in one of three
groups; target characteristics, acquirer characteristics or deal characteristics. We provide an
explanation of the calculation of each variable, its reason for inclusion and the expected
direction of its relation with the takeover premium.
3.2.1. Target characteristics
The first target characteristic included in the model is the target’s size (Size). This variable
is measured by the market value of the target four calendar weeks prior to the announcement
of the deal on a fully diluted basis. The expected relation between Size and Premium depends
on the relative strength of two effects, the ‘bargaining power effect’ and the ‘absolute
premium effect’. The ‘bargaining power effect’ refers to the superior bargaining position large
target firms hold in deal negotiations due to their capacity to hire sophisticated legal and
financial advisors and the experience of their management and board. This power allows the
target to capture a greater proportion of the incremental value of the transaction and hence, a
higher premium. On the other hand, the ‘absolute premium effect’ refers to the greater
proportion of the acquirer’s equity value an increase in the premium represents when the
target is large. This implies that acquirers will be less willing to offer a higher premium the
higher the target’s size. A conflict exists between these two effects resulting in the expected
direction of the relation between Size and Premium being indeterminable ex ante.
The second target characteristic included in the model accounts for a target’s stock
exchange listing (Major Exchange). This binary variable is one if the target is listed on the
American Stock Exchange, the NYSE or the NASDAQ, and zero otherwise. It should be
positively correlated with the Premium variable for two reasons. First, firms listed on these
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exchanges are more likely to have sophisticated boards and management, giving them greater
bargaining power than those listed on lesser known exchanges. Second, due to the greater
exposure of these exchanges to retail investors, it is likely that the share register of firms
listed on these markets is more diffuse. This creates the potential for the free-rider issue
identified by Grossman and Hart (1980). When there is no major shareholder to discipline
management, shareholders will free-ride off the value enhancing actions of the bidder as they
know the value of control to the bidder is greater than zero. Therefore, shareholders will
demand a premium over the current market value of their shares up to the value the bidder
expects to generate by having that control. We expect the Major Exchange variable to be
positively correlated with the Premium variable.
The third target characteristic included in the model is the target’s equity book-to-market
ratio (BM). Book value is the target’s accounting equity as of the date of the most current
financial information prior to the announcement, and market value is the value of the target’s
equity four calendar weeks prior to the announcement of the deal. Low BM firms are
considered growth firms as the firm’s relatively high market value implies the market’s
expectations about the firm’s future earnings are high. In the context of contests for corporate
control we assume if a firm has more growth opportunities then the value of being able to
control these is higher, and the premium an acquirer would be willing to pay would be higher.
Therefore, as a higher BM implies that the firm has relatively fewer growth opportunities, the
relation between the BM and Premium variables should be negative.
The fourth target characteristic included in the model is the target’s runup (Runup). This
variable is calculated as the difference between the target’s stock price one day prior to
announcement and its stock price four calendar weeks prior to announcement, relative to its
stock price four weeks prior to announcement. That is:
𝑅𝑢𝑛𝑢𝑝 =
𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−1 −𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−28
𝑡𝑎𝑟𝑔𝑒𝑡 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−28
(2)
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If the offer price is set some time before the announcement date and the share price
increases over the runup period, it should not have a significant impact on the offer price, but
the takeover premium will be relatively lower. Therefore, we expect negative correlation
between this variable and the takeover premium.
3.2.2. Acquirer characteristics
The first acquirer characteristic included in the model is the toehold variable (Toehold).
We measure this variable as the percentage of the target’s outstanding shares the acquirer
owns prior to the announcement of the transaction. The expected relation between Toehold
and the Premium variable is negative. As discussed earlier, when a target has a diffuse share
register, shareholders may be able to demand a premium over the current market value of
their shares up to the value the bidder expects to generate by having control. Therefore, when
an acquirer has a toehold in the target, it reduces the degree to which they need to compensate
the free-riding shareholders (Shleifer and Vishny, 1986; Hirshleifer and Titman, 1990).
Betton and Eckbo (2000) provide empirical evidence consistent with a negative relation
between these variables.
The second acquirer characteristic identifies publicly listed acquirers (Public). This binary
variable is one for a publicly listed acquirer and zero otherwise. Consistent with the findings
of Bargeron et al. (2008), the expected relation between this variable and the offer premium is
positive. As discussed in Section 2.4.2, they find that public acquirers tend to overbid for
targets, relative to private companies. Managers’ ownership stakes in their company is low.
This tendency to overbid causes takeover premiums to be higher when the acquirer is public.
The third acquirer characteristic identifies horizontal takeovers (Horizontal3). This binary
variable is one if the acquirer has the same three-digit SIC code as the target and zero
otherwise. We expect this variable to be positively correlated with the takeover premium due
to increased potential for synergy gains and less information asymmetry between bidder and
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target. This will increase the maximum premium the acquirer is willing to pay and improve
the target’s ability to determine the synergy value. Therefore, the premium is expected to be
higher as the target’s bargaining position improves and the value available to target
shareholders increases.
3.2.3. Deal characteristics
The first deal characteristic included in the model accounts for the type of consideration
offered. This binary variable (Cash) is one if the entire consideration offered by the acquirer
is cash and zero otherwise. We expect this variable to have a positive relation with the
takeover premium. As discussed in Section 2.4.2, Gilson et al. (1988) argue that acquirers
need to compensate target shareholders for the relatively higher capital gains tax paid in cash
offers. While, Eckbo and Langohr (1989) fail to find compelling evidence for this argument
they, along with de la Bruslerie (2013), find a positive empirical relation between the two.
The second deal characteristic included in the model accounts for the form of the
transaction (Tender). This binary variable is one if the deal is a tender offer and zero
otherwise. We expect the relation between Tender and Premium to be positive. Tender offers
are a more aggressive style of takeover, which requires more aggressive bidding. This could
lead to a strategic choice by the acquirer to increase the initial offer price to reduce the
probability of a competing bid. Kohlers et al. (2007), however, show tender offers are more
likely to be all cash. Therefore, given the expectation that cash offers are associated with
higher premiums, it may be the positive effect of cash driving the result. In any case, Eckbo
and Langohr (1989), Franks and Harris (1989), and Nathan and O’Keefe (1989) find that
tender offers are correlated with higher offer premiums.
The third deal characteristic accounts for the expected target response (Hostile). This
binary variable is one if the initial response of the target to the acquirer’s offer is hostile and
zero otherwise. We expect this variable to correlate positively with the Premium variable.
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Importantly, as the acquirer decides upon its initial offer premium the target’s response is not
known. Therefore, this variable accounts for the acquirer’s expectations about the target’s
reaction. As a hostile response is likely to reduce the probability of a successful bid, an
acquirer may compensate for this by increasing the offer price.
Table 1 provides a summary of our expectations for the relations the target, acquirer and
deal characteristic variables have with the takeover premium.
-------------------------------------Insert Table 1 about here
-------------------------------------3.3. Market power related variables
We expect that market power will be important in explaining the takeover premium. A
number of variables capture elements of market power. Of interest to us is the additional
market power that the acquirer expects to gain from the transaction. This is consistent with the
nature and measure of the takeover premium as the amount offered above the target’s market
value. Measures of the existing market power of the target or acquirer are useful only as an
aid in the estimation of the additional market power generated by the transaction; they are not
directly associated with the premium.
3.3.1. Herfindahl-Hirschman Index
The Herfindahl-Hirschman Index (HHI) is a commonly used measure of market
concentration. This measure proxies industry concentration by summing the squared market
shares of each firm in an industry where market share is the firm’s proportion of the total
industry sales (by three-digit SIC code). That is:
𝑠
𝐻𝐻𝐼𝑆𝐼𝐶 = ∑𝑛𝑖=1( 𝑆𝑖 )2
(3)
where si is the total sales of firm i, and S is the total sales of the industry.
A measure close to the lower bound of zero indicates a highly competitive industry, and a
measure close to the upper bound of one indicates a highly concentrated industry.
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Importantly, HHI is a measure of existing market concentration, not a measure of the
expected change in the market power due to the transaction.
3.3.2. Top four firms
The acquirer’s existing market position may be an indicator of the potential change in
market power due to the transaction. The four-firm concentration ratio measures the level of
industry concentration based on the market share of the top four firms in a four-digit SIC code
industry. That is:
𝑠
4𝐹𝐶𝑅𝑆𝐼𝐶 = ∑4𝑖=1( 𝑆𝑖 )2
(4)
Like HHI, it is bounded by approximately zero and one. A convenient characteristic of this
measure is that if the acquirer is a top four firm in its industry and the target is in the same
industry, industry concentration, and hence market power, must increase. A binary variable
(Acquirer top 4) is one if the acquirer is a top four firm (by four-digit SIC code) and zero
otherwise. Similarly, Target top 4 is one if the target is a top four firm and zero otherwise.
The top firms in an industry are likely to be large, so we expect correlation between
Acquirer top 4 and Size. Large acquirers are likely to have superior bargaining power, so we
anticipate positive correlation with the Premium variable. Therefore, to interpret this variable
as a measure of the incremental market power expected from the transaction, we remove the
effect using an interaction variable as explained in Section 3.6.
3.3.3. Regulatory challenge
Section 7 of the Clayton Act prohibits a merger or acquisition “if in any line of commerce
or in any activity affecting commerce in any section of the country, the effect of such
acquisition may be substantially to lessen competition, or to tend to create a monopoly.”
Sections 1 and 2 of the Sherman Act, and Section 5 of the Federal Trade Commission Act,
have similar provisions to ensure that companies do not use the market to materially increase
their market power (Department of Justice and Federal Trade Commission, 2010).
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The regulatory agencies that seek to enforce these laws are the Department of Justice and
the Federal Trade Commission. They use several analytical techniques to, “identify and
challenge competitively harmful mergers while avoiding unnecessary interference with
mergers that are either competitively beneficial or neutral” (Department of Justice and the
Federal Trade Commission, 2010). These techniques include both quantitative (such as the
cross-price elasticity of products and the HHI) and qualitative measures (such as discussions
with customers and other interested parties). This implies that, when a regulator challenges a
transaction it has estimated the expected effect of the transaction on the market. Therefore,
whether a transaction is challenged by a regulator is a proxy for transactions that are expected
to lead to a material change in market power. However, using it in this way is limited by some
real world complications.
The pragmatic realities of regulation prohibit the use of a regulatory challenge as a direct
measure of the expected increase in market power. Regulators’ resources are small relative to
the resources required to consider all mergers where market power might materially change.
Also, when deciding whether to investigate a transaction, regulators often rely on interested
parties to request a challenge to the transaction (Posner, 1969). Together, these limit the use
of a regulatory challenge as a direct proxy for an increase in market power as challenges may
be based on considerations other than the expected change in market power.
3.3.4. Measuring incremental market power
Of the available options to measure the expected change in market power as a result of a
transaction, we regard whether the acquirer is one of the top firms in its sector as the best
candidate. It is an objective measure of the expected increase in market power as a result of
the transaction. However, as discussed in Section 3.3.2 the market power of the acquirer is
most likely to change when it is acquiring a firm in the same industry. Therefore, we interact
the Acquirer top 4 variable with a new binary variable, Horizontal2, that is one if the acquirer
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and target have the same two-digit SIC code and zero otherwise. This interacted variable
(Acquirer top 4*Horizontal2) proxies for the incremental market power created in a
transaction through related operations.
3.4. Toeholds
The extant literature on takeovers and market power frequently include a toehold variable.
An implicit and significant assumption in the existing treatment of toeholds is that a toehold
of any size will allow the acquirer to influence the size of the offer premium. For example,
Betton et al. (2009) find that, across their entire sample, if the acquirer increases its toehold
by 1% the initial offer premium is reduced by approximately 10%. This implies that, whether
the acquirer increases their toehold from 1% to 2% or from 10% to 11% they can expect to
reduce the offer premium by 10%. However, it seems likely that the latter increase will have
more impact on the acquirer’s influence, and hence more effect on the offer premium.
Assuming a toehold of any size will provide the acquirer with comparable influence over the
target is not likely to be correct. The existing literature potentially overstates the value of a
small toehold and understates the value of a large toehold.
To provide an objective measure of the influence of a toehold, we adopt the accounting
guidance in the Statement of Financial Accounting Standards No. 115, Investments in Debt
and Equity. The standard does not consider a toehold of less than 20% to have substantial
influence. 4 It considers companies that own more than 20% but less than 50% of another
company to have substantial influence. Companies that own 50% or more of another company
are considered to have control over the company. We exclude these from our sample. We use
these definitions to provide a non-linear measure of the importance of toehold in explaining
the takeover premium.
4
The accounting regulation uses the term ‘significant influence’. However, to avoid confusion when discussing
the statistical results, we use the term ‘substantial influence’.
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To account for toehold influence we create the variable Small Toehold. This binary
variable is one if the toehold is greater than 0% but less than 20%. This will differentiate
between toeholds based on whether they provide the acquirer with substantial influence over
the target. We expect that the previously observed value of toeholds will exist only when the
toehold is greater than 20%.
3.5
Variable transformations
The statistical properties of the variables presented above may not conform to the
assumptions of our test procedures. For each key variable, we consider standard
transformations and adjustments. We chose the form of the variable that has the least
violation of the assumptions of our methodologies. The changes made here will not alter the
directional expectations and economic reasoning presented in Section 3.
3.5.1. Premium
The Premium variable is not normally distributed – there is positive skewness and
significant excess kurtosis. We consider a logarithmic (log) transformation, which materially
improves its distributional qualities, confirmed by the improvement in the Jarque-Bera
statistic and the skewness and kurtosis measures. Therefore, the transformed variable,
lnPremium will replace Premium in our tests.
Transforming the Premium variable reduces the effect of significant outliers without their
explicit removal. This allows the information in these variables to be captured in testing
procedures, but dampens any potential undue influence they may otherwise have on the
results.
3.5.2. Target size
The distribution of Size is highly, positively skewed with a significant excess kurtosis. This
implies that, while the majority of the observations in the sample have a market value four
calendar weeks prior to announcement of between US$50 million and US$200 million, there
17
are a few very large firms that are causing the distribution to violate the normality
assumption. Consistent with much of the finance literature, we use lnSize in our tests.
3.5.3. Runup
The Jarque-Bera statistic indicates that the distribution of the Runup variable is
significantly different from a normal distribution, with positive skewness and kurtosis.
Therefore, we again test the log transformation of one plus the Runup variable. Unlike the
Premium and Size variables, the transformation process does not improve the distribution and
characteristics of the Runup variable. While the process improves the skewness (1.17 to 0.47), there is an increase in kurtosis (8.03 to 10.01) and the Jarque-Bera statistic deteriorates
from 2322 to 3759. As there is no material improvement in the statistical properties of Runup,
the transformed variable we will not adopt a transformation.
3.5.4. Toehold
It is evident from the distribution of the Toehold variable for the Small Toehold Sample
that the use of a continuous variable for toeholds when the acquirer holds less than 20% is
inappropriate. There is a small number of observations that are greater than zero. We will use
a binary variable, DToehold, that will be one when the acquirer’s toehold is greater than 0%
but less than 20% and zero otherwise.
When considering the Toehold variable in the Full Toehold Sample, it is the incremental
additions that are of concern. There are only 34 transactions where the acquirer’s toehold was
greater than 20% and less than 50%. From our analysis, it is clear that that the distribution of
the Large Toehold variable, with a Jarque-Bera statistic of 1.58, is not statistically different
from a normal distribution.
3.6. Models and hypotheses
Section 3.2 provided a description of the key variables relevant in determining the size of
the takeover premium. Section 3.3 presented proxies for transactions where the acquirer’s
18
market power is expected to increase. Section 3.4 discussed the role influence plays in
toeholds. Section 3.5 considered whether transformations are appropriate for the variables.
The variables and associated discussion in these sections form the basis of our models.
The first model estimates the relation between the additional market power an acquirer is
expected to receive in a transaction and the takeover premium. The second model builds on
the first to improve the method by which the toehold is accounted for by incorporating the
role of toehold influence. The third model provides a statistical justification for the use of the
variables used to measure market power.
3.6.1. Model 1 and hypotheses
The relative bargaining power of the deal parties must be accounted for to ensure the
market power proxy provides the incremental effect of market power on the premium.
Therefore, the Acquirer top 4 variable is included separately. This ensures that the market
power poxy Acquirer top 4*Horizontal2 represents the incremental effect of a top four firm
acquiring a firm in the same industry, absent any bargaining power effect due to acquirer size.
Furthermore, to account for the relative bargaining power of the target, we interact the
incremental market power proxy with the Size variable. This accounts for any confounding
effects caused by a large target with a strong bargaining position.
Model 1
𝑙𝑛𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = β0 + β1 𝑙𝑛𝑆𝑖𝑧𝑒 + β2 𝐵𝑀 + β3 𝑅𝑢𝑛𝑢𝑝 + β4 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + β5 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑
+ β6 𝑃𝑢𝑏𝑙𝑖𝑐 + β7 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙3 + β8 𝐶𝑎𝑠ℎ + β9 𝑇𝑒𝑛𝑑𝑒𝑟 + β10 𝐻𝑜𝑠𝑡𝑖𝑙𝑒
+ β11 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + β12 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + β13 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4
∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 ∗ 𝑙𝑛𝑆𝑖𝑧𝑒 + ε
Our first hypothesis is:
(5)
H1a: when an acquirer is a top four firm and is acquiring a target in the
same industry it will offer a higher takeover premium.
19
A significant positive relation between Premium and Acquirer top 4*Horizontal2 will confirm
this hypothesis.
As discussed in Section 3.4, it is unlikely that toeholds of less than 20% will provide the
acquirer with substantial influence over the target. Therefore, our second hypothesis is:
H1b: when the acquirer has a toehold of less than 20% it will not be able to
reduce the takeover premium.
In the context of Model 1, there will not be a significant relation between Premium and
Toehold.
3.6.2. Model 2 and hypotheses
As discussed in Section 3.4, by failing to account for the degree of influence the toehold
provides the acquirer, the benefit of having a toehold may be over or under stated. Therefore,
we develop a model that explicitly accounts for the degree of influence the toehold provides.
This model is the same as Model 1 but with an adjustment to account for toehold size.
Model 2
𝑙𝑛𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = β0 + β1 𝑙𝑛𝑆𝑖𝑧𝑒 + β2 𝐵𝑀 + β3 𝑅𝑢𝑛𝑢𝑝 + β4 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + β5 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑
+ β6 (1 − 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑) ∗ 𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + β7 𝑃𝑢𝑏𝑙𝑖𝑐 + β8 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙3 + β9 𝐶𝑎𝑠ℎ
+ β10 𝑇𝑒𝑛𝑑𝑒𝑟 + β11 𝐻𝑜𝑠𝑡𝑖𝑙𝑒 + β12 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + β13 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4
∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + β14 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 ∗ 𝑙𝑛𝑆𝑖𝑧𝑒 + ε
This model allows us to test the following hypothesis in relation to the toehold variable:
(6)
H2: only toeholds that provide the acquirer with substantial influence will
reduce the takeover premium.
To confirm this hypothesis, there will be not be a significant relation between Premium and
Small Toehold*Toehold, and there will be a significantly negative relation between Premium
and (1-Small Toehold)*Toehold.
20
3.6.3. Probability of a regulatory challenge
As discussed in Section 3.3.3, regulators judge whether a transaction will materially affect
market power. Therefore, we develop a model that estimates the key variables that explain a
regulator decision to challenge a transaction. We use a probit model to provide an indication
of the target, acquirer and deal characteristics that increase the probability of a regulatory
challenge. By extension, this indicates which variables aid in determining whether a
transaction will increase the market power of the acquirer.
Model 3
𝐶ℎ𝑎𝑙𝑙𝑒𝑛𝑔𝑒𝑑 = α0 + α1 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 + α2 𝑇𝑎𝑟𝑔𝑒𝑡 𝑡𝑜𝑝 4 + α3 𝐷𝐻𝐻𝐼 + α4 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2
+ α5 𝑆𝑡𝑎𝑡𝑒 + α6 𝐷𝐵𝑀 + α7 𝐷𝑆𝑖𝑧𝑒 + α8 𝐷𝑅𝑂𝐴 + α9 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒
+ α10 𝐷𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + α11 𝐷𝐿𝑎𝑟𝑔𝑒 𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + α12 𝑃𝑢𝑏𝑙𝑖𝑐 + α13 𝐶𝑎𝑠ℎ + α14 𝐻𝑜𝑠𝑡𝑖𝑙𝑒
+ α15 𝑇𝑒𝑛𝑑𝑒𝑟 + ε
Where new variables are:
Challenged
(7)
is 1 if the deal is challenged by the Department of Justice or the Federal
Trade Commission and 0 otherwise,
DHHI
is 1 if the HHI of the target’s three-digit SIC industry is greater than 0.2
(indicating moderate to high concentration) and 0 otherwise,
State
is 1 if the target and the acquirer are headquartered in the same state and 0
otherwise,
DBM
is 1 if the target’s book-to-market is greater than the industry average (by
three-digit SIC code) and 0 otherwise,
DSize
is 1 if the target’s market value four weeks prior to announcement is greater
than the industry average (by three-digit SIC code) and 0 otherwise,
DROA
is 1 if the target’s return on assets is greater than the industry average (by
three-digit SIC code) and 0 otherwise,
DLargeToehold is 1 if the toehold is greater than 20% but less than 50% and 0 otherwise,
21
3.7. Summary
We develop three models in this section. The first model is to determine whether an
acquirer in a corporate transaction pays a higher premium when it is expected that the
acquirer’s market power will increase due to the transaction. We do this by establishing a set
of explanatory variables based on the existing literature and positing a proxy for the expected
increase in market power due to the transaction. We then refine the model to directly account
for the degree of influence a toehold provides the acquirer. Lastly, by exploiting the
regulatory environment, we present a model that provides a more robust basis on which to
assert the appropriateness of the market power variables posited in Model 1 and Model 2.
4.
Sampling
4.1. Transaction data
We sourced the transaction data for this study from the ThomsonONE database. To be
included in our sample: (1) both the target and acquirer must be US companies, (2) the target
must be a public company, (3) the deal type must not be a recapitalization, exchange offer or
repurchase, (4) any toehold must be less than 50%, and (5) the transaction must have been
announced between January 1990 and December 2011. These restrictions ensure that: (1) no
cross border transactions are included in the sample, (2) takeovers are not included if the
bidder already has control of the target, (3)information regarding the target’s stock price and
financial position is available, and (4) transactions are included regardless of whether they are
ultimately successful.
To ensure transactions are an attempt to acquire the entire target and the target is a separate
entity to the acquirer, we remove all buybacks and acquisitions of specific assets. We exclude
all mining, financial and telecommunications companies and transactions where the equity
value of the target four calendar weeks prior to the announcement date was less than US$50
million. We also removed transactions if the initial offer price, the target’s share price one day
22
prior to the announcement date, the target’s share price four calendar weeks prior to the
announcement date or the book value of the target were missing.
To construct the market power variables described in Section 3.3, we collected data from
the Compustat database.
Finally, we removed any transaction where the takeover premium was less than 0% or
greater than 200%. An acquirer offering a target something less than its current market price
either has a dominant bargaining position or is not making a serious takeover attempt and a
target being offered three times its current market price has either a dominant bargaining
position or is substantially smaller than the acquirer and hence, the absolute value of the
premium relative to the acquirer’s assets is small. Such transactions are not representative of a
normal transaction and simply introduce unnecessary noise.
For testing Model 1, we exclude transactions where the acquirer’s toehold in the target was
greater than 20%. This is the Small Toehold Sample where we assume none of the acquirers
have substantial influence over the target. For Model 2 we include transactions where the
acquirer had a toehold greater than 20% but less than 50%. This is the Full Toehold Sample.
The sample selection process deleted observations from the sample based on three criteria:
(1) the transaction was not representative of a normal transaction; (2) data which materially
affected the calculation of key variables we missing; and (3) the transaction could not be
matched correctly across the two databases.
4.2. Description of samples
4.2.1. Small toehold sample
Panel A of Table 2 provides descriptive statistics for the Small Toehold Sample. In this
sample, the average takeover premium is 33.81% of the target’s share price one day prior to
announcement. Taking the median firm size of US$282.27 million, this represents an average
23
premium of US$95.45 million. 5 Including the share price runup, the takeover premium based
on the target’s share price four calendar weeks prior to announcement is 40.05%. Overall,
these statistics indicate that the sample of takeover premiums for this study is qualitatively
similar to those collected in the previous literature
-------------------------------------Insert Table 2 about here
-------------------------------------The target characteristics indicate the average firm in the sample is medium sized and
95.4% are listed on a major US exchange. The target book-to-market ratio of 0.5126 indicates
that the targets would not generally be considered growth firms. Lastly, the average runup
over the 27 days prior to the base period (6.23%) is consistent with some market anticipation
of the transaction. Overall, the target characteristics are consistent with medium size firms
that are past their growth phase.
The bidder characteristics indicate that toeholds uncommon in this sample with only 5.65%
of acquirers having a stake in the target. Furthermore, when a toehold is present, the average
size is 9.06%. This supports the use of a binary variable when accounting for the toehold.
Other bidder characteristics indicate that the majority of acquirers are public companies
(65.39%) and less than half (39.65%) of transactions occur between firms in the same sector.
The deal characteristics indicate that approximately half the transactions are all cash offers
and approximately a quarter are tender offers. As these variables are binary, we can interpret
their means as the percentage of the sample that exhibits the characteristic. Interestingly, only
3.38% had a hostile response from the target, a percentage that is low given previous
literature. However, the period of the study (1990 to 2011) was not marked by aggressive
takeover activity.
5
We use median firm size due to the obvious skew in the distribution of firm size.
24
4.2.2. Full toehold sample
To test Model 2 we add those transactions where the toehold is greater than 20% but less
than 50% to the sample. This resulted in a sample of 1,840 observations. Table 2 Panel B
provides a summary of the descriptive statistics and how they changed relative to the Small
Toehold Sample.
Table 2 Panel B indicates that, except for Toehold, there is no material change in the means
of the variables. This indicates that the observations where substantial influence was present
do not differ materially from the observations in the Small Toehold Sample except for the size
of their toehold. Panel B also shows that when an acquirer does have an influential toehold,
they tend to have substantially more than 20% with the average influential toehold in the
sample being 33.76%.
4.3. Summary
This section described the method we used to collect the primary samples for this study. It
has also provided an analysis of the descriptive statistics of the sample. The section
considered the statistical properties of each of the continuous variables except for BM.
Inspection of the distributions of these variables suggested that several transforms were
appropriate. This resulted in three new variables being adopted; lnPremium, lnSize and
DToehold. Finally, the models in Section 3.5 were revised to reflect these changes, none of
which resulted in a change in the economic interpretation of the results.
5.
Results
5.1. Univariate analysis
Table 4 Panel A provides correlations between variables in the Small Toehold Sample and
Panel B correlations in the Full Toehold Sample. Several observed correlations between
lnPremium and the model variables are of interest. The negative relation between lnSize and
lnPremium provides evidence that the ‘absolute premium effect’ is stronger than the
25
‘bargaining power effect’. Second, the positive relation between BM and lnPremium is
inconsistent with expectations. This may be explained by the correlation between BM and
lnSize as larger targets tend to have lower BM ratios and also tend to have lower takeover
premiums. Third, the negative relation between the Runup and lnPremium suggests that
acquirers base the offer price on the target’s share price some time before the announcement.
Several correlations among the explanatory variables are also of interest. First, public
acquirers tend to make fewer all cash offers. This may be because the target shareholders do
not find exchanging their public and liquid shares for illiquid shares in the bidder. Second, the
runup in a target’s share price is higher when the acquirer is public, a result potentially due to
higher disclosure requirements of public firms increasing the probability of a leak. Third,
acquirers who are a top four firm are likely to be publicly listed and larger firms.
5.2. Model 1
This section provides the results from Model 1 posited in Section 3.5.1. When interpreting
these results it is important to remember that the coefficient estimates are the relations with
lnPremium. When considering the relation to the takeover premium this transformation must
be reversed. For convenience, the converted estimates are provided in the results table and
referred to throughout the discussion.
5.2.1. Explanatory variables
The results from Model 1 run of the Small Toehold Sample are provided in Table 5. Before
considering the additional market power variables, we discuss the explanatory variables
described in Section 3.2.
Of the target characteristics, three of the four variables are significantly different from
zero. First, lnSize is significantly different from zero and negative. This implies that the
‘absolute premium effect’ is greater than the ‘bargaining power effect’ described in Section
3.2.1. Second, BM is significant and positive, a result that is contrary to our expectations. As
26
discussed above, a possible explanation for this is the correlation between BM and lnSize.
Target size is negatively correlated with the takeover premium and with BM. It may that the
negative relation between BM and Premium is driven by a ‘size relation’ as larger firms tend
to have lower BM, implying a positive relation between BM and Premium. Third, Runup is
negative and significant, a result consistent with expectations. This implies that acquirers do
not adjust the offer price in response to target share price movements over the runup period.
This supports the argument that acquirers set the offer price some time before the
announcement of the transaction. Lastly, the Major Exchange variable is only weakly
significantly different from zero in the Full Toehold Sample. Only five percent of targets are
not listed on a major exchange so there is little dispersion in the variable.
-------------------------------------Insert Table 4 about here
-------------------------------------The relations between the acquirer characteristics and the takeover premium are as
expected. DToehold is not significant. This is consistent with Hypothesis H1b that only
toeholds that provide the acquirer with substantial influence result in a lower takeover
premium. Secondly, consistent with expectations, Public is significantly different from zero
and positive. Public companies are more likely to offer higher premiums, perhaps due to
agency issues. Lastly, Horizontal3 is significant and positively correlated to the takeover
premium. This is consistent with the argument that targets operating in the same industry as
the acquirer have superior bargaining power due to a reduction in information asymmetry.
Some of the expectations regarding the deal characteristics were not supported. Cash is
significant and negatively correlated with Premium (-0.0256). This is inconsistent with the
discussion in Section 3.2.3 and the empirical evidence in the extant literature. A potential
explanation for this is that targets in transactions where 100% of the consideration is cash
tend to be smaller. This is consistent with the negative correlation between Cash and lnSize.
27
Secondly, the relation between Tender and Premium is significant and positive (0.0428). This
result is consistent with the expectation that tender offers attract higher offer premiums due to
their aggressive nature. Lastly, Hostile is not significantly different from zero. This may result
from only 3.38% of the transactions being hostile.
Together, these results indicate that the size of the deal parties is an important issue when
considering the drivers of the takeover premium. This provides support for the use of the
interaction variables when estimating the relation between the additional market power
expected to be generated in the transaction and the takeover premium. Furthermore, these
results provide evidence that the toehold only allows the acquirer to reduce the takeover
premium when it does not have substantial influence.
5.2.2. Market power variables
As mentioned, the results provide support for the need to control for the effect of target and
acquirer size when considering the effect of additional market power. The result for Acquirer
top 4 is mildly significant and positive (0.0246). This is as expected and is likely due to the
‘absolute premium effect’ previously discussed in relation to public acquirers. The interactive
Acquirer top 4*Horizontal2*lnSize is significant and negative (-0.0175). This is consistent
with the correlations observed and the discussion above with regards to target size.
The results provide support for Hypothesis H1a and the aim and motivation of this
research. We found the expected significant and positive relation between the expected
additional market power proxy, Acquirer top 4*Horizontal2. These results indicate that when
an acquirer is a top four firm and it is acquiring a firm in the same industry it, on average,
offers a takeover premium 11.76% higher than that offered by other acquirers who are top
four firms and are acquiring targets of a similar size. This result is not just statistically
significant; it is also economically significant. It implies that for the average target, an
acquirer is willing to pay over US$143 million above the target’s market value.
28
5.2.3. Summary
This section has presented and discussed the results of Model 1 posited in Section 3.5.1.
Overall, the results of the explanatory variables are largely as expected. Even, where the
results diverge from expectations, there are reasonable explanations for each. Furthermore,
these results indicate the role of size in determining the takeover premium. This highlights the
importance of controlling for any potentially confounding size effects when estimating the
effect of market power on the takeover premium.
The results in this section provide support for hypothesis H1a. The results indicate that a
transaction where the acquirer is a top four firm and is acquiring a firm in the same industry it
will on average have a higher takeover premium. The positive relation between Premium and
Acquirer top 4*Horizontal2 provides support.
The results in this section also provide support for hypothesis H1b. On average, having a
toehold of less than 20% in the target does not reduce the takeover premium offered by the
acquirer. This is evidenced by the insignificant relation between Premium and DToehold.
5.3. Model 2
This section presents and discusses the results from Model 2 posited in Section 3.5.2. As
discussed in Section 4.2 the Full Toehold Sample implemented does not materially differ from
the Small Toehold Sample except for the toehold variable and additional observations. Table 5
presents the results of this model.
-------------------------------------Insert Table 5 about here
-------------------------------------5.3.1. Explanatory variables (excluding Toehold)
Except for Major Exchange, the estimates of the explanatory variables between Model 1
and Model 2 are not materially changed. This result is consistent with the discussion in
Sections 4.2 regarding the differences between the two samples; they only differ on the
29
toehold variable. This indicates that it is the change in the toehold that is driving the change in
the results observed in the other variables of interest.
5.3.2. Toehold variables
The results provide evidence for Hypothesis H2. Table 5 shows that those toeholds that
provide the acquirer with substantial influence over the target are negatively correlated with
the takeover premium. This is evidenced by the negative and significant relation between (1DToehold)*Toehold and Premium. This relation implies that when an acquirer has a toehold
of between 20% and 50%, takeover premium is reduced by an average 14.15%. Furthermore,
the continued insignificance of DToehold provides further evidence that the degree of
influence a toehold provides the acquirer matters.
The change in the market power variable provides some further support for hypothesis H2.
This can be seen in the reduction in the statistical significance of the additional market power
proxy, Acquirer top 4*Horizontal2 from significance at the 5% to the 10% level. This result
implies transactions where the acquirer already has substantial control over the target are not
expected to increase the market power of the acquirer. A reasonable explanation for this is
that the substantial control allows the acquirer to capture some gains from additional market
power prior to the transaction. For example, the acquirer may be able to gain access to
otherwise confidential information regarding customers, or it may be able to better manage
supply agreements. This also suggests acquirers may have already captured other synergy
gains from a takeover which would reduce the takeover premium.
5.3.3. Summary
This section has discussed the results of Model 2. These results provide evidence for
hypotheses H2 that the degree of influence the toehold provides the acquirer is an important
distinction to make when conducting empirical research on takeover premiums. Only toeholds
30
that provide the acquirer with substantial influence reduce the premium. Toeholds that do not
provide substantial influence are not significantly correlated to the takeover premium.
5.4. Model 3
This section presents the results for Model 3 posited in Section 3.5.3. We developed the
model as potential support for the method we use to proxy the expected additional market
power acquired in a takeover. Table 6 presents the results for this model.
5.4.1. Results
The results presented in Table 6 provide support for the method used to account for the
additional market power expected to be acquired in a transaction. This is evident as the only
market power related characteristics that significantly increase the likelihood of a transaction
being challenged by an antitrust regulator are Acquirer top 4 and Horizontal2. Antitrust
regulators are more likely to challenge transactions where the acquirer is a top four firm or the
transaction is between firms in the same industry,. The significant and positive result for Size
provides further evidence of the need to account for target size in Model 1 and Model 2.
-------------------------------------Insert Table 6 about here
-------------------------------------Interestingly, both DToehold and DLargeToehold significantly reduce the probability of a
challenge. Perhaps if the acquirer already has a stake in the company it is less likely that
antitrust regulators will have an issue with an acquisition of the remaining shares. This also
provides more evidence that the degree of influence a toehold provides is important, as the
estimates for DToehold and DLargeToehold in the Full Toehold Sample decrease when the
toehold is influential. This implies that if the acquirer already has meaningful influence over
the target, the regulator is less likely to challenge a bid for the remaining shares.
The results also show that several control variables affect the probability of a challenge.
When the acquirer is public it is more likely to have its bid challenged. Public acquirers are
31
generally larger than private acquirers. As large companies are likely to have a larger
customer base, acquisitions between large acquirers would be of greater interest to antitrust
regulators, making them more likely to investigate the transaction. Second, tender offers
increase the probability of a challenge. As such offers are made directly to shareholders there
may be less involvement by the regulator prior to the announcement of the transaction. It may
be the case that some potential mergers are more likely to be stopped by the regulator in the
private discussion stage of a transaction. Lastly, the negative relation between Hostile and
Challenged implies that when a target is hostile to an acquirer’s bid, the regulator is less
likely to challenge the transaction. Hostile bids have a lower probability of success, and
therefore regulators may be less likely to devote scarce resources to a challenge. All other
control and industry variables are insignificant, a result that is not particularly disconcerting.
5.4.2. Summary
This section has provided and discussed the results of Model 3. The primary finding
supports the method used to proxy additional market power in Model 1 and Model 2.
Furthermore, it provided some interesting results regarding other factors that tend to either
increase or decrease the probability of a challenge by an antitrust regulator. In particular, the
results support the argument that influence matters when considering toeholds, as the
probability of a challenge falls when the toehold becomes influential.
5.5. Summary
This section has provided the results of the models presented in Section 3.5 and revised in
Section 4.4. They show evidence that in transactions where the acquirer is expected to
increase their market power, the takeover premium is 11.76% higher. The results show that
accounting for the degree of influence a toehold provides the acquirer is important when
conducting empirical research on the takeover premium. The results of both models indicate
that when the toehold does not provide the acquirer with substantial control, the takeover
32
premium is not significantly lower. However, when the toehold does provide the acquirer
with substantial control the takeover premium is significantly lower and qualitatively similar
to the existing literature. Furthermore, there is statistical support for the method used to
account for transactions where the market power of the acquirer is expected to increase.
6.
Robustness tests
6. 1. Self-selection bias
Self-selection bias is an issue in M&A research as firms choose, or are chosen, to be
acquirers or targets. That is, acquirer’s select targets from the population based on their
characteristics. This can result in a sample of targets that is not randomly selected from the
population, biasing results. We employ a statistical technique to account for the possibility of
self-selection bias.
6.1.1. Correcting self-selection bias
We use a two stage Heckman model to correct any self-selection bias in the results from
Section 5. This involves a first stage probit model which estimates the probability of a firm
being selected as a target. Importantly, the error term in this model (also called the Inverse
Mills Ratio or IMR) can proxy for private information (Li and Prabhala, 2007: 42-45). We
then re-run Model 1 with the IMR included. This corrects any self-selection bias as the IMR
proxies for omitted factors that might have caused inclusion of the target in the sample.
To conduct these tests, we collected a new sample which proxies the population of
potential targets. To achieve this, firms were matched on two characteristics: (1) they share
the same three-digit SIC code as the target and (2) their revenues are within +/-10% of the
target’s revenue in the year of the transaction. This matching process created a new sample of
13,547 firms. This sample is used for the first stage probit model.
𝑇𝑎𝑟𝑔𝑒𝑡 = α0 + α1 𝐷𝐵𝑀 + α2 𝐷Size + +α3 𝐷𝑅𝑂𝐴 + α4 𝐷𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + α5 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒
+ α6 𝑇𝑖𝑔ℎ𝑡𝑙𝑦 𝐻𝑒𝑙𝑑 + α7 𝐵𝑖𝑔4 + 𝜆
(8)
33
Where new variables are:
Target
is 1 if the firm is a target and 0 otherwise,
DLeverage
is 1 if the firm’s leverage is greater than the industry average (by three-digit
SIC code) and zero otherwise
Tightly Held
is 1 if there are less than 500 shareholders in the company and 0 otherwise,
Big4
is 1 if the firm’s auditor is a big four firm or Arthur Anderson and 0
otherwise, and
𝜆
is the error term which is the Inverse Mills Ratio.
The revised Model 1 with the IMR included is then:
Model 1 (IMR):
𝑃𝑟𝑒𝑚𝑖𝑢𝑚 = β0 + β1 𝑆𝑖𝑧𝑒 + β2 𝐵𝑀 + β3 𝑅𝑢𝑛𝑢𝑝 + β4 𝑀𝑎𝑗𝑜𝑟 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 + β5 𝑇𝑜𝑒ℎ𝑜𝑙𝑑 + β6 𝑃𝑢𝑏𝑙𝑖𝑐
+ β7 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙3 + β8 𝐶𝑎𝑠ℎ + β9 𝑇𝑒𝑛𝑑𝑒𝑟 + β10 𝐻𝑜𝑠𝑡𝑖𝑙𝑒 + β11 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4
+ β12 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 + β13 𝐴𝑐𝑞𝑢𝑖𝑟𝑒𝑟 𝑡𝑜𝑝 4 ∗ 𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑡𝑎𝑙2 ∗ 𝑆𝑖𝑧𝑒
6.1.2. Results
+ β14 𝐼𝑀𝑅 + ε
(9)
Table 7 presents the results of Model 1 (IMR). The results show that self-selection bias
was not materially affecting in the results in Section 5. Panel A shows that all variables in the
first stage model significantly affect the probability of a firm becoming a target. Firms are
more likely to become targets if they are underperforming growth firms, if they are larger,
more leveraged firms and are more likely to be listed on a large stock exchange.
Unexpectedly there is a positive relation between Tightly Held and Target (0.2936) and a
negative relation between Big4 and Target (-1.0718).
The results presented in Panel B indicate that self-selection bias is not significantly
affecting the results in Section 5. This is evident as the parameter estimates of Model 1 are not
materially changed after accounting for self-selection bias. Interestingly, there is a positive
34
and significant relation between IMR and lnPremium. This suggests that private information
increases the takeover premium.
--------------------------------------------Insert Table 7 about here
--------------------------------------------6.2. Excessive influence of observations
Belsley, Kuh and Welsch (1980) suggested tests for determining if any observations have
excessive influence on test results. We used the RStudent, 6 DFFITS 7 and COVRATIO 8 to
determine the most influential observations in the sample (unreported). Considering the
results of the three statistics, the undue influence is not extreme. 43 of the 50 top observations
are the largest premiums in the sample, indicating that the effect of large premiums outweigh
the effect of small premiums. This supports our decision to remove transactions where the
premium was greater than 200%. However, removal of transactions at a lower premium
threshold would reduce the variability of the data. We believe the explanatory power lost by
removing these observations outweighs the gains from removing them.
6.3. Summary
This section has provided the results of two robustness tests conducted on the results in
Section 5. Section 6.1, using a Heckman model has shown that the results of Model 1 are
robust to self-selection bias. The results indicate that private information has a significant and
positive relation with the takeover premium. Section 6.2 provided the results of an undue
influence test. These test show that although larger premiums tend to influence the results
more, the effect is not strong enough to warrant major concern.
6
The error variance in the studentized residual RStudent is estimated without the ith observation. Observations
with RStudent larger than 2 in absolute value may warrant attention.
7
The DFFITS statistic is a scaled measure of the change in the predicted value for the ith observation and is
calculated by deleting that observation. A large value indicates that the observation is very influential. A cutoff
of 2 is commonly used.
8
The COVRATIO statistic measures the change in the determinant of the covariance matrix of the estimates
by deleting the ith observation.
35
7.
Conclusions
7.1. Summary of key findings
This study analyses a large sample of takeover transactions in the US over the 1990 to
2011 period. We show that acquirers offer a higher takeover premium in transactions that
increase their market power. By extension it has also provided evidence that firms use the
market for corporate control as a tool to increase their market power. The evidence suggests
that in transactions where the acquirer expects to increase its market power the takeover
premium is 11.76% higher.
We conduct an additional test to determine if the method used to measure whether a
transaction would lead to an increase in the market power of the acquirer was appropriate. We
use a probit model of the probability of a proposed takeover being challenged by antitrust
regulators. The model provides statistical justification for the method we use to proxy the
additional market power expected to be acquired in a takeover.
The study also considers the effect of substantial toehold influence on the takeover
premium. We show that accounting for the degree of influence a toehold provides the acquirer
is important when conducting empirical research on the takeover premium. The results of two
models indicate that when the toehold does not provide the acquirer with substantial
influence, the takeover premium is not significantly lower. However, when the toehold does
provide the acquirer with substantial influence the takeover premium is significantly lower
and qualitatively similar to the existing literature.
Additionally, the results were robust to both self-selection bias and influence from extreme
variables. Interestingly, the results of the two stage Heckman model suggest a significant
positive relation between private information and the takeover premium. Regardless, the
results of this test show the additional market power proxy does not change when we account
36
for self-selection. Furthermore, the results of the extreme influence tests indicate that
transactions with large premiums have greater influence over the results.
These results will be of interest to academics, regulators and practitioners. For academics,
they provide evidence that market power is a significant motivation for horizontal acquisitions
and that empirical tests of takeover premiums should account for the degree of influence a
toehold provides the acquirer. For regulators, it indicates they may be failing to prevent
transactions that increase market power at the expense of consumers. For practitioners, it
indicates that acquirers are willing to compensate targets for the additional market power they
expect to gain from the transaction. We also show the importance that private information can
have on reducing the takeover premium.
37
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40
Table 1: Directional Expectations
T his table provides a summary of the expected relationships between the
target, acquirer and deal related variables and the takeover premium.
Variable
Expected direction
Target characteristics
Target Size
nd
Target book-to-market
+
Runup
+
Major Exchange
+
Bidder characteristics
Toehold
-
Public
+
Horizontal3
+
Deal characteristics
Cash
+
Tender
+
Hostile
+
Market power
Top 4 firm
+
Top 4 firm*Horizontal2
+
Top 4 firm*Horizontal2*Size
-
Toeholds
Small Toehold
nm
Large Toehold
-
Where 'nd' and 'nm' are not determinable and not meaningful
respectively.
41
Table 2: Descriptive Statistics
This table presents descriptive statistics for the Small Toehold Sample and the Full Toehold Sample of US transactions
between January 1990 and December 2011. Panel A provides the descriptive statistics for the Small Toehold Sample of
1,806 transactions where the acquirer’s toehold in the target was less than 20%. Panel B provides the descriptive
statistics of the Full Toehold Sample of 1,840 transactions where the acquirer’s toehold in the target was less than 50%.
Panel A: Small toehold sample
Variable
Mean
Median
Std Dev
Premium
0.3381
0.2840
0.2566
0.2788
Target Size
BM
1216.80
282.27
4475.39
737.47
0.5126
0.4512
0.4486
0.4254
Runup
0.0623
0.0384
0.1829
0.1737
Major Exchange
0.9540
Bidder characteristics
Toehold
0.0051
0.0000
0.0232
0.0000
Toehold (when > 0%)
0.0906
0.0903
0.0421
Public
0.6539
Horizontal3
0.3965
Interquartile Range
Target characteristics
Deal characteristics
Cash
0.5161
Tender
0.2442
Hostile
0.0338
Number of Observations
1806
Panel B: Full toehold sample
Variable
Mean
Median
Std Dev
Interquartile Range
Premium
0.3374
0.2834
0.2564
0.2802
Target Size
BM
1204.31
280.84
4436.24
735.80
0.5127
0.4511
0.4492
0.4253
Runup
0.0617
0.0379
0.1819
0.1732
Major Exchange
0.9451
0.0000
Target characteristics
Bidder characteristics
Small Toehold
0.0113
0.0000
0.0516
Large Toehold
Public
0.3376
0.3387
0.0856
Horizontal3
0.3913
0.6489
Deal characteristics
Cash
0.5185
Tender
0.2462
Hostile
0.0332
Number of Observations
1840
42
Table 4: Correlations
Acquirer top 4
Hostile
0.0509**
0.0189
0.0969*** -0.0918***
0.0127
0.1918*** -0.099***
0.0978***
0.0305
0.0644***
-0.0587**
0.0385
-0.0751***
0.0539**
Tender
-0.1292*** 0.134***
-0.1055***
-0.0393*
0.0236
Cash
-0.0558**
0.0713***
0.0932***
0.0032
Horizontal3
-0.0196
0.0478**
1
Runup
0.0699*** 0.1181*** -0.1718*** 0.1458***
-0.0062
Dtoehold
0.0965***
-0.0191
1
BM
Public
-0.0312
-0.1958*** -0.073***
Runup
Major Exchange
0.1087*** -0.2565***
BM
1
-0.115***
1
lnSize
lnSize
lnPremium
lnPremium
Panel A: Small Toehold Sample
1
0.0061
-0.0175
0.0263
-0.0062
0.0427*
1
Public
-0.0368
0.2198***
0.0564**
0.4016***
0.0523**
0.0152
0.0977*** -0.4063***
-0.0904*** 0.3064***
0.0571** -0.1245***
-0.0265
1
Major
Dtoehold
Exchange
0.0517**
-0.0074
0.0215
-0.1529***
1
Horizontal3
-0.0324
-0.0029
0.2255***
1
Cash
0.0687***
0.1577***
1
Tender
0.0309
1
Hostile
1
Acquirer
top 4
T his table provides the correlations between the key variables in this study. Panel A provides the correlations for the variables in Model 1 in the Small T oehold Sample of 1,806 transactions
where the acquirers toehold in the target is less than 20%. Panel B provides the correlations for the variables in Model 2 in the Full Toehold Sample of 1,840 transactions where the acquirers
toehold in the target is greater than 20% but less than 50%.
43
0.0013
0.0921***
-0.0256
-0.0547**
Acquirer top 4
Hostile
Tender
Cash
0.0465**
0.0193
-0.1038***
0.0126
-0.0578**
0.038
-0.0768***
0.0574**
0.1932*** -0.0974*** 0.0668***
0.0979***
0.0242
-0.1333*** 0.134***
0.0973*** -0.0937***
-0.0366
0.0312
0.0706***
-0.0361
-0.0182
0.0045
Horizontal3
-0.022
DLarge Toehold
0.046**
1
Runup
0.0673*** 0.1231*** -0.1723*** 0.1489***
-0.0054
DToehold
0.0851***
-0.0206
1
BM
Public
-0.0273
-0.1943*** -0.0698***
Runup
Major Exchange
0.1095*** -0.2548***
BM
1
-0.1172***
1
lnSize
lnSize
lnPremium
lnPremium
Panel B: Full Toehold Sample
44
1
-0.0332
1
DLarge
Toehold
0.0173
-0.0087
0.0214
-0.0126
-0.0353
0.2205***
0.0545**
0.0956***
-0.0389*
-0.0254
0.034
0.0353
0.0514** -0.0872*** -0.0769***
0.0677*** -0.1204*** -0.0766***
-0.2859***
-0.0146
1
Major
DToehold
Exchange
0.4037***
0.0535**
0.0107
-0.4081***
0.3098***
1
Public
0.058**
-0.0054
0.0148
-0.1567***
1
Horizontal3
-0.0347
-0.0038
0.2301***
1
Cash
0.0649***
0.1549***
1
Tender
0.0317
1
Hostile
1
Acquirer
top 4
Table 5: Results
T his table provides the results of Model 1 and Model 2. T hese models were developed in Section 3.5 and revised in Section
4.4. T hey are run on two samples. T he Small T oehold Sample consists of 1,806 transaction between January 1990 and
December 2011 where the acquirer had a toehold of less than 20%. T he Full T oehold consists of 1,840 transaction
between January 1990 and December 2011 where the acquirer had a toehold of greater than 20% but less than 50%.
Numbers in parentheses are p-values.
Small Toehold Sample
Variable
Full Toehold Sample
Expected direction lnPremium Premium lnPremium Premium
0.5662***
Intercept
0.761503
(0.0000)
0.5752***
0.7775
(0.0000)
Target characteristics
lnSize
nd
-0.013***
-0.0130
(0.0001)
BM
-
0.0413***
Runup
-
-0.2193***
0.0421
0.0414***
-0.1969
-0.2193***
-0.0286
0.0423
(0.0000)
(0.0000)
+
-0.0136
(0.0000)
(0.0000)
Major Exchange
-0.0137***
-0.1970
(0.0000)
-0.0282
(0.1272)
-0.0343*
-0.0338
(0.0533)
Bidder characteristics
Toehold_D
nm
-0.1106
-0.1047
(0.5203)
(1-Toehold_D)*Toehold
-0.0117
-0.0117
(0.5025)
-
-0.1526*
-0.1415
(0.0765)
Public
+
0.0216**
0.0218
(0.0408)
Horizontal3
+
0.0225**
0.0211**
0.0213
(0.0436)
0.0228
(0.0154)
0.023**
0.0233
(0.0127)
Deal characteristics
Cash
+
-0.026***
Tender
+
0.0419***
-0.0256
-0.0254***
0.0428
0.0432***
(0.0038)
(0.0044)
(0.0000)
Hostile
+
-0.0016
-0.0251
0.0442
(0.0000)
-0.0016
(0.941)
-0.0013
-0.0013
(0.9541)
Market Power variables
Acquirer top 4
+
0.0246*
Acquirer top 4*Horizontal2
+
0.1111**
0.0249
(0.085)
-
-0.0176**
0.0919*
0.0962
(0.0936)
-0.0175
-0.0151*
(0.0331)
(0.0652)
Number of Observations
1,806
1,840
Adjusted R 2
F-value
0.0920
0.0918
15.06
14.28
Where nd and nm mean 'not determinable' and 'not meaningful'
'***', '**' and '*' indicates significance at the 1%, 5% and 10% levels respectively
0.0250
(0.0837)
0.1176
(0.0452)
Acquirer top 4*Horizontal2*lnSize
0.0247*
-0.0150
45
Table 6: Challenge Probit Model Results
T his table provides the results of Model 3. T his model was developed in Section 3.5 and run
on two samples. T he Small T oehold Sample which consists of 1,806 transaction between
January 1990 and December 2011 where the acquirer had a toehold of less than 20% and the
Full T oehold consists of 1,840 transaction between January 1990 and December 2011 where
the acquirer had a toehold of greater than 20% but less than 50%.
Small Toehold Sample Full Toehold Sample
Variable
Challenged
Challenged
Intercept
-1.1485***
-1.1***
0.2142***
0.2254***
Target top 4
-0.0151
0.0120
DHHI
-0.0694
-0.0693
0.1952***
0.197***
-0.0428
-0.0587
Market power variables
Acquirer top 4
Horizontal2
State
Industry variables
DBM
0.1107
0.1183*
DSize
0.3947***
0.3705***
DROA
0.1033
0.0955
0.2553
0.2219
-0.5072***
-0.5006***
Control Variables
Major Exchange
DToehold
DLarge Toehold
-0.6439**
Public
0.1615*
0.1646**
Cash
0.0458
0.0237
Hostile
-0.4843**
-0.4769**
Tender
0.5255***
0.5058***
1,806
1,840
0.0620
0.0631
Number of Observations
McFadden R
2
'***', '**' and '*' indicates significance at the 1%, 5% and 10% levels respectively
46
Table 7: Self-selection bias
T his table shows the results of the two stage Heckman model. Panel A shows the results of the
probit model run on a sample of 13,457 firms. Panel B shows the results of the second stage OLS
model the same as Model 1 except for the addition of the IMR variable from the first stage
model. T his model was run on the Small Toehold Sample of 1,806 transactions. Numbers in
parentheses are p-values.
Panel A: Probit Selection Model
Variable
Target
Intercept
-1.217***
DBM
-0.0718**
DSize
0.9706***
DROA
-0.3945***
DLeverage
0.1126***
Major Exchange
1.0047***
Tightly Held
0.2936***
Big4
-1.0718***
Number of Observations
McFadden R
2
13,547
0.1957
Panel B: Second Stage OLS
Variable
IMR
lnPremium
lnPremium
0.0457***
(0.002)
0.5356***
0.5662***
(0.0000)
(0.0000)
Target Size
-0.0131***
-0.013***
(0.0001)
(0.0001)
Target book-to-market
0.0409***
0.0413***
(0.0000)
(0.0000)
-0.2179***
-0.2193***
(0.0000)
(0.0000)
-0.0291
-0.0286
(0.1203)
(0.1272)
-0.102
-0.1106
(0.5522)
(0.5203)
Public
0.0209**
0.0216**
(0.0471)
(0.0408)
Horizontal3
0.0231**
0.0225**
(0.0125)
(0.0154)
-0.0259***
-0.026***
(0.0039)
(0.0038)
0.0417***
0.0419***
(0.0000)
(0.0000)
-0.0036
-0.0016
(0.87)
(0.941)
Intercept
Target characteristics
Runup
Major Exchange
Bidder characteristics
DToehold
Deal characteristics
Cash
Tender
Hostile
47
Table 7: Self-selection bias continued
Market Power variables
Acquirer top 4
0.0259*
0.0246*
(0.0695)
(0.085)
Acquirer top 4*Horizontal2
0.1114**
0.1111**
(0.0442)
(0.0452)
Acquirer top 4*Horizontal2*lnSize
-0.0176**
-0.0176**
(0.0331)
(0.0331)
Number of Observations
1,806
1,806
Adjusted R 2
F-value
0.0963
0.0920
14.74
15.0620
'***', '**' and '*' indicates significance at the 1%, 5% and 10% levels respectively
48