One size does not fit all: Selling firms to private
equity versus strategic acquirers
Jana P. Fidrmuc∗
Peter Roosenboom†
Warwick Business School
RSM, Erasmus University
Richard Paap‡
Tim Teunissen
§
Erasmus University Rotterdam
RSM, Erasmus University
May 15, 2012
Abstract
This paper investigates the selling process of firms acquired by private equity
versus strategic buyers. In a single regression setup we show that selling firms
choose between formal auctions, controlled sales and private negotiations to fit
their firm and deal characteristics including profitability, R&D, deal initiation
and type of the eventual acquirer (private equity or strategic buyer). At
the same time, a regression model determining the buyer type shows that
private equity buyers pursue targets that have more tangible assets, lower
market-to-book ratios and lower research and development expenses relative
to targets bought by strategic buyers. To reflect possible interdependencies
between these two choices and their impact on takeover premium, as a last
step, we estimate a simultaneous model that includes the selling mechanism
choice, buyer type and premium equations. Our results show that the primary
decision within the whole selling process is the target firm’s decision concerning
whether to sell the firm in an auction, controlled sale or negotiation which then
affects the buyer type. These two decisions seem to be optimal as then they
do not impact premium.
Keywords: Private equity, Takeover premium, Auctions
JEL Classification: G32, G34
∗
Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom, Email: [email protected] (corresponding author)
†
Department of Financial Management, Rotterdam School of Management, Erasmus University,
P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands, e-mail: [email protected]
‡
Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands, e-mail: [email protected]
§
Department of Financial Management, Rotterdam School of Management, Erasmus University,
P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlands, e-mail: [email protected]
1
Introduction
In the past decade private equity firms have been a strong driving force in the market
for mergers and acquisitions (Cumming et al., 2007). The increased firepower of
private equity firms has brought even larger public companies within their reach.
Recent studies have shown that private equity bidders offer on average significantly
lower takeover premiums than corporate buyers even though, private equity firms
often manage to outbid public corporate acquirers in competitive auctions (Bargeron
et al., 2008; Officer et al., 2010; Dittmar et al., forthcoming). In this paper we show
that premium determination is just one part of a wider and complex selling process
that starts with deal initiation. Target companies design the selling process such
that it fits their specific firm situation that is reflected in their firm characteristics:
we show that different firms are sold in auctions versus controlled sales versus private
negotiations and this then impacts on whether they are sold to private equity versus
strategic (corporate) buyers. Accounting for the whole selling process that includes
the choice of the selling mechanism (auction versus controlled sale versus private
negotiation), bidder type (private equity versus strategic buyer) and premium in
a simultaneous model, we learn more about interdependencies within the system,
premium determination and sequencing of the process.
The selling process usually starts by either a prospective buyer approaching a
target or by a target management decision to offer their company for sale. In general,
the selling company management and its financial advisor could negotiate the deal
privately with an exclusive buyer or alternatively negotiate with multiple bidders
(Hansen, 2001; Povel and Singh, 2006; Boone and Mulherin, 2007). A negotiation
with multiple bidders may either be formally structured in a full-scale formal auction
or, alternatively, the selling party discretely canvasses demand from a select number
of bidders in a controlled sale (Boone and Mulherin, 2009). Boone and Mulherin
(2007) are the first to analyze in detail the private selling process that evolves prior
to the public announcement of the takeover bid. They show that about half of
targets in their sample are auctioned among multiple bidders and that the public
1
takeover activity analyzed in the literature so far reflects only the tip of the iceberg of
actual takeover competition. In their further work (Boone and Mulherin, 2009), they
distinguish controlled sales versus full-scaled auctions as two possible approaches for
competitive bidding and argue that the rule of ‘one size does not fit all’ applies to
the selling mechanism choice.
After a deal is initiated, the selling firm management has to decide on the best
way to conduct the sale taking into consideration the overall firm situation. Management has superior knowledge about the firm, its prospects and potential and
should take into account relevant firm characteristics, deal initiation, preferred potential buyer (or at least its type) and also the overall pool of potential bidders.1 In
this sense, the choice of the selling mechanism might be determined endogeneously
together with the preferred identity of the buyer and depends on the superior knowledge of the selling firm management. Therefore, we propose that the choice of the
selling mechanism reflects extra information on top of information covered by observable target and deal characteristics and its inclusion into the analysis is essential.
Choosing the preferred buyer is also an important part of the process. In fact,
Elliot Williams of Mirus Capital advises (Williams, 2007, p.1): “ [The selling company] should understand that selling to a private equity firm is not the same as
selling to a strategic buyer. Every aspect of the deal [is] affected by the type of
buyer including the negotiating process, price, tax and legal implication and most
importantly the future prospects of the company.” It is therefore important to recognize the differing nature of the private equity versus strategic buyers. Strategic
buyers are usually other firms in the industry who are likely to pay higher premium
because they redeploy the assets of the target firms close to their best use (Shleifer
and Vishny, 1992; Gorbenko and Malenko, 2009). Strategic buyers can also afford
to pay more because they buy specific assets and will benefit from synergies between
1
Target management decides about the sale design also in the case when a potential buyer
approaches the target and initiates the deal: management can either negotiate privately, approach
a limited number of bidders or organize a full-scale auction. In our data set, auctions constitute
15% of all buyer initiated deals.
2
their organization and the target firm.
In contrast, private equity buyers are usually industry outsiders who typically
cannot manage the bought targets well themselves and so face agency costs as they
have to hire specialists to run the assets for them. They fear overpaying for the target
because as outsiders they do not have the knowledge to value the assets precisely
(Shleifer and Vishny, 1992) and are expected to pay less than is the value of the
target firm’s assets in best use.2 At the same time, sale to a private equity buyer
allows the incumbent management to continue to manage and partially own the
company and profit from further growth in company value (Lehn and Poulsen, 1989;
Lehn et al., 1990; Dittmar et al., forthcoming). In contrast, strategic acquisitions
often integrate acquired assets with existing operations of the new owner.
These differences in nature between strategic versus private equity buyers highlight the importance of the buyer identity in the selling process. Moreover, these
differences may also indicate that different target firms prefer a different type of
buyer or vice versa which may result in segmented bidding where private equity and
strategic buyers do not compete for the same target.
Using a sample of 205 private equity deals of listed US targets over the period
from 1997 to 2006 matched with comparable deals by strategic (corporate) buyers
this paper makes three important contributions. First, we analyze which targets
eventually end up with private equity versus strategic buyers in a single regression
setup without analyzing the selling mechanism choice or premium determination.
The analysis shows that, everything else keeping constant, the two buyer types end
up purchasing different targets. Target initiated deals with low market to book values and high cash levels end up more frequently with private equity buyers. Targets
of strategic buyers, in contrast, have higher market to book ratios, more intangible assets and high R&D expenses. These results are in line with Gorbenko and
2
We would like to note that these general differences between private equity versus strategic
buyers do not apply universally. Some private equity firms pursue a strategy of acquiring multiple
firms in the same industry, which can lead to synergies. In the premium analysis below, we try to
control for this effect using a dummy variable for acquisitions made by portfolio firms of private
equity firms.
3
Malenko (2009) who also show that public and financial bidders’ valuations depend
on target characteristics. Strategic buyers tend to value research and development
expenses and intangible assets such as growth options. Our results also suggest that
strategic buyers are interested in targets with more specific assets that might potentially result in higher synergies whereas private equity buyers target firms with
more generally redeployable assets that they can manage more efficiently (Shleifer
and Vishny, 1992).
Our second contribution is the analysis of the selling mechanism choice, again in
a single regression setup. Our conjecture is that choosing a particular selling mechanism is an important strategic decision that fits a company situation at a given time.
To start with, our data show that the selling mechanism choice is indeed not random: more profitable firms with lower leverage are typically sold in auctions rather
than in controlled sales or private negotiations. Auctions are also associated with
private equity buyers and deals initiated by target firm’s management. Buyer initiated deals are most likely to be sold in private negotiations. Higher R&D increases
the odds of controlled sales. Moreover, higher M&A activity and lower impediments
to takeovers are associated with higher odds of auctions.
The two sets of results, however, suggest endogeneity between the selling mechanism and buyer type choices that might also eventually impact takeover premiums.
Therefore, as our third and most important contribution, we simultaneously model
the whole takeover process taking into account the selling mechanism choice, the
buyer type choice and the premium determination. The main advantage of a simultaneous model is that it takes into account potential interdependencies between the
dependent variables and empirically determines sequencing of the overall process.
We conjecture a mutual interrelation between the choice of the selling mechanism
and the choice of the buyer type and propose that these two choices then impact
the premium. However, only by estimating the system, we are able to determine the
significant feedback effects and so learn about the sequencing.
Our system results suggest the following simple sequencing of the overall selling
4
process. The starting point is the decision on how to sell a firm that best matches
the firm’s characteristics. That is, when deciding how to sell their firm, managers
take into account the firm situation: target initiated deals with better accounting
profitability are more likely to be sold in auctions. Target initiation and profitability
increase also the odds of controlled sales, but initiation impacts auctions significantly
more than controlled sales.
The selling mechanism choice then affects the buyer type. In particular, firms
sold in auctions are less likely, while those sold in controlled sales are more likely
to be sold to private equity buyers (both relative to private negotiations). Taking
into account this feedback effect between the selling mechanism and buyer type, the
reduced form parameters in the buyer type equation are all insignificant and show
that firm characteristics cease to matter for the buyer type in equilibrium. So, firm
characteristics affect the choice of buyer type only indirectly through the auction
mechanism.
Our system results also show that the effects of the selling mechanism and the
buyer type on the premium are not significant. In other words, whether a firm is
sold in an auction or negotiation, or it is sold to a private equity or strategic buyer
is irrelevant for premium determination. A significant coefficient, for example a
negative coefficient for the auction variable would indicate that, on average, firms
opting for auctions would be better off choosing private negotiations. As a result,
our insignificant coefficients indicate optimality of these choices with respect to premium given firm characteristics. Despite these optimal choices, firm characteristics
still directly impact the premium: profitable and lower market to book firms get
on average higher premiums. Interestingly, target initiation is not significant. As
exogenous factors excluded from the other equations, also poor stock performance
and analyst coverage increase premiums.
In summary, as a part of the overall selling process the firm decision whether to
sell in an auction, controlled sale or private negotiation is important as it also affects
the buyer type. Our results indicate that the choices of the selling mechanism and
5
buyer type are optimal with respect to the premium received in the deal, but firm
characteristics still do matter.
Our paper relates to several recent studies that compare takeover premiums between private equity versus public/strategic bidders. Bargeron et al. (2008) attribute
the lower takeover premiums to private equity bidders being more selective in the
price they are willing to pay for targets than public strategic bidders. They argue
that managers of public bidders have an empire-building mentality and are willing
to overpay for a target firm because they do not bear the full costs of their decisions.
At the same time, Bargeron et al. (2008) show that other observable target or transaction characteristics cannot explain the large differences in premiums paid. Officer
et al. (2010) show that target shareholders receive a lower premium in case two or
more private equity investors join forces to acquire the target firm in consortium
deals, although Boone and Mulherin (2011) do not corroborate this finding using a
sample that includes smaller private equity firms and longer event windows.
Dittmar et al. (forthcoming) analyze bidding competition faced by strategic buyers. Even though premium differences for private equity versus strategic buyers are
not the main focus of their paper, in line with previous empirical evidence they show
that premiums paid by strategic buyers following competition from financial buyers
are on average lower relative to premiums after competition with other strategic buyers. Moreover, they confirm that observable target and deal characteristics cannot
explain the difference in the premiums offered.
In this paper, we use information on the private selling process and show that
the selling mechanism choice is an important firm decision that also affects the type
of the eventual acquirer. Using a simultaneous system that empirically determines
the sequencing of the whole selling process, we make a wider contribution to the
literature beyond explaining differences in premium.
The remainder of the paper is organized as follows. Section 2 discusses our data
collection and the resulting sample. Section 3 presents our results and Section 4
concludes.
6
2
Sample
2.1
Sample selection
As the main focus of this paper is a comparison of acquisitions by private equity versus strategic buyers, our data collection starts by searching for takeovers by private
equity firms in the US. We search through all takeovers of public US targets within
the Securities Data Corporation (SDC) database over the period from January 1997
through December 2006 where acquirers seek to fully own the target company. As
a first step, we use the ‘acquirer is a leveraged buyout firm’ flag, ‘acquirer is a financial sponsor’ flag and ‘acquirer is an investor group’ flag. Then, for each of the
deals we also read the short acquirer description and deal synopsis to check that
the acquirer is indeed a private equity firm. We also require that target firms have
data available on CRSP and Compustat. This process results in a sample of 205
attempted takeovers by private equity investors of which 197 were completed and
8 were withdrawn. We include withdrawn transactions to avoid biasing our sample
in any way. 51 acquisitions involve private equity consortia with two or more private equity firms acquiring the target company. Some private equity firms pursue
a buy-and-build strategy of acquiring multiple firms in the same industry and then
merging these firms together. We identify 53 deals in which a portfolio firm that is
majority owned by a private equity investor acquired the target.
The sample of 205 private equity takeovers is then matched firm by firm with
takeovers by strategic acquirers based on the target industry, year of announcement
and deal size. Our matching procedure involves the following steps: i) For every
transaction in the private equity sample we search for a set of takeovers by strategic
buyers where the target company has the same first three SIC code digits as the
private equity target. In this list, we attempt to find a matching transaction that
was announced in the same year and comes closest in terms of transaction value,
using a +/- 25% error range. (ii) If there is no comparable transaction found in
the same year and/or with the same transaction value, the same search is applied
7
to the year before and the year after the year of announcement. (iii) If no match
is found the year before and the year after in step (ii), we widen the search to two
years before and two years after the year of announcement. (iv) If we still do not
have a match, we repeat the search in step (i) but looking for strategic buyers with
a target company within same two SIC code digits as the private equity target. (v)
In a rare occasion that this process still renders no results, we repeat the search in
steps (i)-(iv) for transactions with a +/- 50% error range. (vi) As a last resort we
repeat step (i) at the first SIC code level. Every strategic deal can be matched to
a private equity deal only once. We end up with 205 strategic takeovers that are
exclusively matched to our 205 private equity deals based on industry, deal size and
deal year. Table 1 shows the distribution of deals over time.
We consider the matching procedure to be a key feature of our research design.
Matching on industry is important due to the fact that private equity bidders are
typically interested in firms coming from particular industries with stable cash flows
and substantial fixed assets that can serve as collateral for the loans used to finance
the acquisition. Boone and Mulherin (2008b), for example, report that more than
half of the private equity takeovers occur in only four industries. Matching on size is
also important. Typically, strategic/public buyers are able to target larger companies
(Bargeron et al., 2008) and the same is the case for private equity consortium deals
(Officer et al., 2010; Boone and Mulherin, 2011). Finally, frequent observations of
tougher deal competition in later years of our sample in the popular press highlight
the importance of matching in time (Officer et al., 2010).
As a result of the matching procedure, our sample consists of 410 takeovers of
listed US targets. Table 2 Panel A shows that the mean (median) deal size of the
strategic buyers sample is $611 million ($131 million) and is comparable to the deal
size of the private equity buyers of $654 million ($139 million). The differences in
means are statistically insignificant. Target total assets are also comparable across
the two subsamples. The premium offered to the target shareholders relative to the
stock price eight weeks before the deal announcement in SDC (SDC premium) is 43%
8
versus 47% for private equity versus strategic buyers, respectively. The difference is
however, contrary to previous findings (Bargeron et al., 2008; Officer et al., 2010),
not statistically significantly different from zero. This is a direct consequence of
our matching procedure based on industry, deal size and time that results in more
comparable deals across private equity versus strategic buyers.
We have to control for possible biases stemming from the fact that auctions
take usually longer to organize, are more likely to have information leakage prior
to the formal deal announcement and therefore are more likely to be associated
with larger stock price run-ups and smaller premiums (Boone and Mulherin, 2011).
We carefully check for possible leakage of information before the announcement
reported in SDC. We follow the procedure adopted in Boone and Mulherin (2011).
First, for each deal we check the SEC documents for the date when the target
firm starts considering a sale (private date). Then, deal by deal we carefully check
Factiva for any leakage of information concerning a possible M&A deal (initial public
announcement) in the period between the private date and the SDC announcement
date. We find that targets of private equity versus strategic bidders are more likely to
leak information. On average, the initial public announcement is 122 days before the
SDC announcement date for targets eventually sold to private equity buyer relative
to 102 days for strategic buyers, a difference of 20 working days.
As a result, we benchmark the offer price against the price on the base date that
is either the date eight weeks (42 trading days) before the SDC announcement date
or one trading day before the Factiva announcement whichever is earlier. The base
date is earlier than 8 weeks before the SDC announcement date for 61 private equity
and 40 strategic bidder deals. This is our primary premium measure, which we refer
to as the adjusted premium or premium.3
3
We believe that our adjustment for leakage of information is reasonable. Alternatively, we
could benchmark the offer price against the stock price 8 weeks before the Factiva initial public
announcement date. We however feel that going too far back in time might be associated with other
biases. For example a very large lag in time might increase the probability that other important
information is communicated to the market and contaminates prices. Also, going back eight weeks
from the Factiva announcement date would mean that the time lag between the offer price at the
9
- please insert Table 2 about here For the whole sample, the average adjusted premium equals 47.2% and as expected due to leakage of information, it is slightly larger than the SDC premium.
The difference between the adjusted and SDC premium is, however, not statistically
significant. Table 2 reporting averages across private equity versus strategic buyers
shows that our adjustment increases the premium for both private equity and strategic buyer groups. Because the increase for the private equity group is slightly larger,
the overall difference between private equity versus strategic buyers after adjustment
is smaller (4.1% for the adjusted premium versus 5% for the SDC premium) and remains insignificantly different from zero. Even though we believe adjusting premium
for leakage of information and associated run-up is very important, these statistics
show a limited impact of the adjustment within private equity versus strategic buyer
groups. They also highlight that comparing more similar deals due to our matching
procedure has a more profound effect.
Another difference with the literature is that we analyze premiums directly rather
than target announcement returns. A potential drawback of cumulative abnormal
returns around the deal announcement as a measure of gains to target shareholders
is that it reflects other information that may potentially bias the measure such as
probability of the deal success and information revealed during the run-up period
(Bargeron et al., 2008; Boone and Mulherin, 2011). A potential drawback of our
direct premium measure is that it is not adjusted for a benchmark return. However,
our industry/size/time matched pairs of private equity and strategic deals should
alleviate this problem.
Table 2 further shows that private equity targets have low market to book ratio
and relatively better performance indicating that they are attractive buys with limited growth opportunities but high resale value. For clarity, all variables are defined
SDC announcement date and the benchmark price would be more than 16 weeks for some deals
but only 8 weeks for other deals resulting in larger time inconsistency. Our adjusted premium
minimizes this inconsistency while still taking into account the leakage of information.
10
in Appendix B. The high fraction of tangible assets and low R&D indicate lower
asset specificity and suitability of these firms for private equity buyers. In contrast,
strategic buyers are interested to acquire firms with better growth prospects. Despite
relatively poor past performance their targets still have high market to book ratio
and high fraction of intangible assets and R&D. Moreover, industry count is higher
for strategic buyers whereas liquidity index is larger for private equity acquirers. As
industry count measures the number of firms in the target’s industry with a value
greater than the target in the year prior to the takeover announcement, it gauges the
potential depth of the takeover market for a target (Boone and Mulherin, 2008a).
It shows that if the number of potential acquirers is larger, firms are more likely to
be acquired by strategic buyers. In turn, liquidity index is defined as the ratio of
value of corporate control transactions in the previous year to the total book value
of assets of all firms in the given industry and year (Schlingemann et al., 2002) and
measures the actual intensity of corporate control transactions. It shows that hotter
takeover markets are associated with higher odds of private equity transactions. All
these statistics indicate that the two groups of target firms are significantly different
in many aspects.
2.2
Company sale process
For all takeovers in our sample we are able to retrieve the proxy or solicitation
statements from the EDGAR database of the SEC. These filings usually contain
a ‘background to the merger’ section that describes the initiator of the takeover
(target management or an interested buyer) and whether the company was sold in a
private negotiation with one buyer or in a competitive process with multiple bidders
competing for the target. In general, competitive bidding involves either a full-scale
formal auction or a controlled sale where the selling firm negotiates with a limited
number of interested bidders.
Limiting the number of bidders in a controlled sale is a strategic decision by
the target firm (Boone and Mulherin, 2009). Auctions with a large number of bid-
11
ders increase competitive pressures among bidders and therefore increase premiums
(Bullow and Klemperer, 2009). At the same time, however, bidders in auctions collectively bear high search and evaluation costs that together with lower probability
of winning for each individual bidder may eventually result in less aggressive bidding and so lower premiums (French and McCormick, 1984). For some firms, then,
auctions might be very costly and the optimal choice might involve limited competition in form of controlled sales (Boone and Mulherin, 2009). Moreover, selling
firms might opt for controlled sales with a limited number of competing bidders in
situations when they are confident that they are able to attract the most suitable
bidders.
Usually, shortly after the decision to sell is made, the selling firm retains a financial advisor. If the firm decides for a full-scale auction the advisor serves as the
‘auctioneer’. Drawing on knowledge of the selling company, the advisor draws up a
preliminary list of potential bidders and contacts these bidders to obtain information on their interest of making a potential bid. The contacted parties who show
interest receive a very cursory description of the selling company and are offered a
more in depth information memorandum provided they sign a confidentiality agreement. Then the number of bidders is further reduced in submission of preliminary
non-binding offers (‘letters of intent’) and final sealed binding bids. The final bids
are then considered by the selling company and the best bid is chosen depending on
valuation, financing structure and future plans of the bidder.
For illustration, to highlight the distinguishing features of auctions versus controlled sales Appendix A describes two deals. The first deal involves a full-scale
auction with multiple rounds and a large number of bidders. The second involves
a controlled sale where the selling firm is explicit in listing reasons for limiting the
bidding competition.
In the whole population of deals, we have slightly more auctions (36%) and less
controlled sales (32%) and private negotiations (32%). Once we partition by buyer
type (Panel A of Table 2), the frequencies become more tilted towards auctions for
12
private equity deals (50% auctions versus 25% each in negotiation and controlled
sales) and towards private negotiations or controlled sales for strategic buyer deals
(40% and 38% negotiation and controlled sale, respectively, versus 22% auction).4
From Table 2 we further see that private equity buyers face fiercer competition
both in terms of number of bidders contacted (32 versus 12) and bidders with confidentiality agreement (14 versus 5). In addition, the fraction of private equity bidders
is remarkably high for the targets eventually bought by private equity buyers and
low for targets bought by strategic buyers.5 This fragmentation increases from 79%
(4%) of bidders contacted to 93% (3%) of bidders signing the confidentiality agreement for the private equity (strategic) group. As invitation to participate in bidding
is a decision of the target firm, but agreement signing is buyer driven, these numbers
indicate that the market segmentation is preferred by both parties.
Private equity deals are more often target rather than buyer initiated. Panel B
of Table 2 shows that the adjusted premium is larger for buyer (51%) versus target
initiated deals (44%) which suggests that it might be important to control for deal
initiation throughout our analysis (Simsir, 2008; Xie, 2010; Macias et al., 2011).
Buyer initiated deals are slightly larger and more often use negotiations whereas
target initiated deals are mostly organized in full-scaled auctions. Overall, all selling
mechanisms are relatively populated across both initiator and buyer types. The most
frequent are target initiated deals sold in auctions (116 deals). The least populated
are auctions for buyer initiated deals (30 deals).
Panel A of Table 3 suggests the importance of the sale process as a determinant
of bidding premiums for private equity versus strategic buyers. Overall, the adjusted
premium is the highest (54%) for controlled sales and the difference with respect to
premium in auctions (42%) is significant at the ten-percent level. This highlights
4
These frequencies are slightly different comparing to Boone and Mulherin (2009) whose data
favor private negotiations with 50%, followed by controlled sales and auctions with 25% each, but
they focus on 400 large corporate takeovers during the 1990s.
5
Please note that we have fewer observations for bidders contacted and bidders with confidentiality agreement (359 and 364 out of 410, respectively). The fragmentation data coverage is even
lower (239 and 255, respectively).
13
that it is important to distinguish auctions from controlled sales. In Boone and Mulherin (2007), the premium differences are not significant when comparing auctions
(including both full-scale auctions and controlled sales) versus negotiations.
When considering also the buyer type dimension, we see that the generally high
premium in controlled sales is due to a very high premium paid by strategic buyers
(61%) with private equity buyers paying significantly less (44%). In turn, strategic
buyers tend to pay relatively little in private negotiations: the average premium
is 39% and significantly smaller (at the ten-percent level) relative to 55% paid by
private equity buyers. The bottom of Panel A shows average takeover premiums
across both initiator and buyer types. Interestingly, when controlling for buyer
type, initiator identity does not matter. Overall, this part of the table indicates that
takeover premiums are affected by the selling mechanism and buyer type rather than
initiator identity.
- please insert Table 3 about here In Panel B of Table 3, we demonstrate that target firms opt for controlled sales in
situations when they are confident in identifying the right bidders. Panel B reports
the number of bidders with confidentiality agreement, the number of non-binding
bids and the number or formal final bids across auctions and controlled sales.6 We
see that auctions are more competitive according to all three measures relatively
to controlled sales. Panel B, however, also reports ratios of non-binding and final
bids to the total number of bidders signing a confidentiality agreement. These ratios
show that controlled sales are indeed more successful in identifying bidders that
are more willing to make bids. As a ratio of all bidders that sign confidentiality
agreement, 75% make a non-binding bid in controlled sales versus only 39% do so
in auctions. The difference is significant at the one-percent level. For final bids, the
corresponding ratios are 56% versus 20%.
6
They are all equal to one for private negotiations. We have fewer observations for these
variables: 231 for bidders with agreement, 237 for bidders with non-binding agreement and 254 for
bidders with final bid out of total 277 auctions and controlled sales.
14
So far, Table 3 suggests that the selling mechanism strongly affects premiums
paid for both types of buyers and therefore may provide some additional important
information that is not reflected through the other target and deal characteristics.
To check this conjecture, Panel C of Table 3 reports the characteristics provided in
Table 2 through an additional layer of selling mechanism. It shows averages of the
observable characteristics across negotiations, controlled sales and auctions, but for
each of these groups it also provides average values for the two buyer types.
The first observation is that the three selling mechanisms are associated with different firm characteristics. Target firms sold in auctions are smaller, more profitable
and have smaller market to book ratios. Firms sold in controlled sales have higher
market to book, R&D and intangible assets. Private negotiations are associated
with the largest firms, poorest profitability, smallest liquidity index and happen in
anti-takeover states. Second, we see that the buyer type matters even when we control for the selling mechanism. Across all three selling mechanisms, strategic buyers
tend to buy less profitable targets with higher market to book, more R&D, fewer
tangible assets and higher number of potential strategic bidders (industry count).
3
3.1
Results
Targets of private equity versus strategic buyers
As a first step, we estimate logistic models for a private equity buyer. Our results
in Table 4 confirm the univariate results that private equity buyers acquire targets
with characteristics that are different relative to the targets acquired by strategic
buyers. Targets of private equity buyers are more profitable, less research intensive,
have lower market to book ratio and higher tangible assets. These characteristics are
significant even when we control for the fact that private equity deals are more often
target initiated and more frequently sold in formal auctions. Private equity targets
also tend to have more cash and smaller number of larger firms in the industry
15
as potential strategic bidders.7 Overall, the results confirm predictions based on
Shleifer and Vishny (1992) that private equity buyers buy targets with more generally
redeployable assets, with tangible assets and low R&D expenses, whereas strategic
buyers are after more specific assets with high potential synergies.
- please insert Table 4 about here -
3.2
Selling mechanism choice
As a next step, we model the selling mechanism choice using a multinomial logistic
model. As private negotiation is the omitted reference category, in Table 5 we report
two sets of coefficients: for auctions and controlled sales. These coefficients show the
effect of our explanatory variables on the likelihood of being sold through an auction
or controlled sale relative to a private negotiation. We also report differences in the
two coefficients that indicate the effect of auctions relative to controlled sales.
The results in Table 5 confirm our conjecture that observable target and deal
characteristics affect the selling mechanism choice and that buyer type is also important. Firms sold to private equity buyers are more likely to be sold in auctions.
Deal initiation also matters: target initiated deals are most likely to be sold in auctions and then in controlled sales whereas buyer initiated deals are most likely to be
sold in private negotiations. Higher profitability increases the likelihood of auctions
relative to both controlled sales and private negotiations. Targets with more R&D
are more likely to be sold in controlled sales, which indicates higher interest of particular individual buyers who would like to exploit the specific assets. It also indicates
higher potential costs of full-scale auctions for these firms (Boone and Mulherin,
2009).
Higher takeover activity increases and takeover impediments decrease the odds
of both auctions and controlled sales, which highlights that competitive bidding
correlates with more liquid takeover market and lower costs of auctions. Finally,
7
Cash and industry count are later used to identify the private equation in the simultaneous
system.
16
auctions attract less levered firms.8 Taking into account that they attract also more
profitable firms, it seems that firms organize full-scale auctions when they are more
sure of their future prospects and they are able to communicate this to the bidders.
Higher leverage, as suggested by (Aktas et al., 2010) might also measure cost of
auctions. Aktas et al. (2010) argue that more levered firms might be more eager
to sell and therefore consider lengthier auctions as more costly and prefer private
negotiations.
- please insert Table 5 about here In short, these results show that the target management decision about how
to sell their company reflects each firm’s particular situation including their firm
characteristics, deal initiation, preferred buyer type and the potential buyer pool.
3.3
Premium
Table 6 shows that even though the univariate difference in premium for private
equity versus strategic buyers is insignificant, after controlling for observable target
and deal characteristics in a multivariate setting private equity buyers tend to pay
11 percentage points less. Relatively speaking, strategic buyers pay on average 25%
higher premium. The private equity premium discount increases once we control for
consortium deals and private equity portfolio firms, but neither of the two dummies
is significant. This is in line with the results of Boone and Mulherin (2011). As for
other explanatory variables, more profitable targets that are approached by a buyer
and have high market to book ratio tend to get higher takeover premium. Poor past
stock performance, smaller size and lesser firm visibility (measured through analyst
following) are also associated with larger takeover premiums.9
- please insert Table 6 about here 8
In the simultaneous model, liquidity index, anti-takeover state and leverage are used for identification of the selling process equations.
9
We use these latter three variables for identifying restrictions in the simultaneous system.
17
The negative correlation between recent stock performance and premium is puzzling and has not yet been much discussed in the literature. Bargeron et al. (2008)
show a similar relationship between deal announcement cumulative returns and target past stock performance but do not provide an explanation. Baker et al. (forthcoming) suggest that target firms engage in anchoring and show that recent stock
price highs are very significant in determining offer prices. To test for this hypothesis, we check for the minimum and maximum price within the last year ending on
the base date. We scale these prices by the price on the base date so that they could
be interpreted as relative distance from the maximum/minimum price.
Results in Panel B of Table 6 that include the minimum and maximum price
show that target shareholders get compensated in the offer price for recent stock
price declines. However, this effect disappears once we control for stock performance regardless of the period over which the stock performance is measured.10
The negative coefficient for stock performance shows that poorly performing targets
get higher premiums. Part of this effect is compensation for recent price decreases:
the effect of maximum becomes insignificant once stock performance is included.
However, as the stock performance effect is particularly strong and pertains even
when we measure stock performance over two years, it seems to capture more than
just temporary undervaluation of the target firm.
Further analysis indicates that premium is even larger for poorly performing
firms that are likely to bring along higher post-deal synergies. First, we partition
between private equity and strategic buyers. For strategic buyer deals that are more
likely to rely on post-deal synergies, the distance from minimum and maximum is
insignificant when controlling for stock performance.
Second, strategic targets with more intangible assets like growth prospects or
research and development that are more likely to bring larger post-deal synergies
10
We check stock performance over the last half year just before the base date, but also one, one
and half and two years. All are significant at the one percent level. For consistency with previous
results, Table 6 reports results with the stock performance over one and half years before the base
date.
18
are associated with higher premium: the coefficient for asset tangibility for strategic
buyers is significantly negative. Moreover, an interaction term for past stock performance with asset tangibility turns out to be positive and significant indicating that
the negative effect of stock performance is less pronounced for targets with more
tangible assets but more pronounced for targets with more intangible assets. So,
poorly performing targets get even higher premium when they have many intangible assets that potentially bring larger synergies. This in our view indicates that
the low stock performance effect reflects not only a temporary mispricing but also
longer-term underlying issues in the target firms as for example underutilization of
assets in place. The new buyer might be able to employ the assets to the best use
and therefore gain high synergies. High synergies then mean that the buyer is able
to pay a higher premium.
Similar analysis for private equity buyers in the last column of Panel B shows that
(i) the minimum price remains significant even after controlling for stock performance
indicating that the closer the price on the base date remains to a yearly minimum,
the higher the premium, (ii) the tangibility of assets does not affect premium and
(iii) its interaction term with stock performance is also not statistically significant.
Overall, the impact of stock performance on premium is less strong. It seems that
private equity bidders pay more for poorly performing targets but the correlation
between stock performance and premium is weaker and less likely to reflect synergies.
3.4
System regressions
Our results so far for modeling the buyer type, selling mechanism choice and takeover
premium indicate a certain degree of endogeneity in the system. When a company
management team decides about how to sell the firm, it considers the overall company situation and naturally it would also consider potentially interested buyers
and buyers the firm is interested in. This process might eventually also affect the
premium offered by the winning bidder. Indeed, our analysis so far suggests that
target initiated company sales tend to be organized through auctions and often are
19
acquired by private equity investors. Typically, firms sold in auctions are more profitable, less levered and have low market to book ratios, low R&D intensity and high
tangible assets. This suggests that the potential buyer type and selling mechanism
choice are interlinked and together they then affect the premium paid by the bidder.
As a result, we propose a simultaneous model that consists of a linear regression
model for premium, a binary probit model for the buyer type and a multinomial
probit model for the selling mechanism choice. The joint model incorporates a
possibility for feedback effects within the system: between the buyer type and the
selling mechanism choice and from the buyer type and selling mechanism choice to
takeover premium. To account for these feedback system effects is very important
for efficient parameter estimation and their interpretation. Our model is described
in detail in Appendix C.
3.4.1
System identification
We have to make sure that the system is properly identified. In a system of simultaneous equations, identification of the model parameters is based on exclusion
restrictions (Judge et al., 1988, Chapter 14). Loosely speaking, the variables involved in the exclusion restrictions can be seen as instruments. The role of these
variables is, however, not exactly the same as the role of instruments.
It is challenging to find good exclusion restrictions. Therefore, even though we
formally test for validity of all our exclusion restrictions, we also carefully provide
proper economic justification and intuition for validity of the exclusion restrictions
(Roberts and Whited, 2011).11 In particular, in our setting we need to justify why
the particular variables (exclusion restrictions) have a direct effect on one endogenous
variable in the system but not a direct, only indirect, effect on the other endogenous
11
We consider several candidate variables based on similar papers in the literature (Schlingemann
et al., 2002; Bebchuk et al., 2002; Boone and Mulherin, 2007, 2008a; Aktas et al., 2010). We could
not include several potential variables as they do not fit our setting with private equity buyers. For
example, we are not able to include the payment consideration, bidder size, relative size or bidder
ownership structure as all private equity deals are paid for in cash and bidder size or ownership
structure are not applicable to private equity buyers.
20
variables.
Table 7 shows the results of our statistical tests for identification. The first set of
tests corresponds to the validity (significance) of the variables involving the exclusion
restrictions in the relevant equation, while the second set of tests corresponds to the
exogeneity of these variables by adding them to the other equations. In this way,
the tests show whether all explanatory variables necessary to identify the system are
only significant in one of the equations (validity tests) but not in the other equations
(exogeneity tests). These two sets of tests account for similar properties as the test
for weak instruments and the Sargan test. For more details see Appendix C.
- please insert Table 7 about here We identify the private equity equation by excluding target cash level and industry count from the other equations. Industry count is measured as the number
of firms in a given industry that are larger than the target and therefore it proxies
for the depth of the takeover market. Intuitively, a larger pool of potential strategic
buyers increases the likelihood that the target firm is eventually sold to a strategic
buyer (Boone and Mulherin, 2008a). The validity test in Table 7 shows that industry
count is highly significant in explaining the likelihood of private equity buyer, but
the exogeneity test indicates that industry count does not contribute to explaining
the selling mechanism choice or the premium.
Aktas et al. (2010) and Boone and Mulherin (2008a) show that industry count
is positively correlated with auctions in their samples of deals by public (strategic)
buyers. The correlation matrix in Table 8 shows that, on the univariate level, industry count is also in our sample associated with higher probability of controlled sales,
which contradicts the exogeneity test in Table 7. However, this positive correlation
between industry count and controlled sales is likely to indicate that industry count
captures the depth of the strategic bidding market rather than the private equity
market. Once we pool strategic and private equity deals together and control for
R&D, liquidity index, anti-takeover state and leverage, industry count is not any
more significant in explaining the selling mechanism choice.
21
- please insert Table 8 about here Premium is not, in our data, correlated with industry count even on the univariate level. We are not aware of any academic papers that find a strong correlation between industry count and takeover premium. However, partially supporting
our assumption that industry count does not affect premium, Boone and Mulherin
(2008a) find insignificant effect of industry count on bidder returns and exclude it
from the bidder return regression when using instruments.
One of the trademarks of private equity is to keep idle cash to a minimum (Pozen,
2007) and to offer a solution to the agency costs of free cash flow (Lehn and Poulsen,
1989). Target firms with high cash levels on the balance sheet for precautionary
or other reasons are therefore seen as attractive targets for private equity buyers.
Private equity investors can have the target firm pay out the cash (Lehn and Poulsen,
1989). Moreover, more cash on the balance sheet may also be associated with higher
asset tangibility that attracts private equity bidders (Shleifer and Vishny, 1992).
The validity test for cash in Table 7 confirms these conjectures: target’s cash level
contributes significantly to explaining the buyer type.
The exogeneity test in Table 7 indicates that cash is exogenous for explaining
premium and the selling mechanism choice. This is also in line with the correlation
matrix in Table 8 that shows that cash is not correlated with premium, auctions
or controlled sales. We are not aware of any academic papers that link cash levels
to the choice of the selling mechanism. Pinkowitz (2000), however, shows that cash
levels are not correlated with takeover premium even though they predict higher
probability of takeover offers.
Concerning the selling mechanism choice, the selling firm management considers
costs and benefits of all alternatives for selling their firm. Assuming no transaction costs, selling firms should favor auctions as more bidders generaly translate to
higher premiums (Bullow and Klemperer, 2009). But auctions are costly for sellers
to organize and to search for potential bidders as well as for bidders in terms of
investigation and bid preparation costs (French and McCormick, 1984; Aktas et al.,
22
2010; Rogo, 2010). Lower auction costs are then associated with a higher probability
of auctions and higher number of bidders. We use three variables to proxy for these
costs: (i) liquidity index, (ii) a dummy for anti-takeover state and (iii) target firm
leverage.
Liquidity index captures buoyancy of takeover activity within an industry (Schlingemann et al., 2002). We conjecture that higher takeover activity is associated with
lower search costs for the selling company looking for potential bidders and therefore
increases the likelihood of auctions and controlled sales. This conjecture is confirmed
in Table 7 as the validity test for liquidity index in the selling mechanism equations
is highly significant.
Similarly, search costs might be relatively high for selling firms headquartered
in states with strong anti-takeover laws. Bidding for firms with higher takeover
impediments involves also higher costs of making bids. Comment and Schwert (1995)
argue that more stringent antitakeover law is associated with greater bargaining
power of the target. Therefore, we argue that antitakeover law lessens the likelihood
of a target being sold in an auction. Boone and Mulherin (2007) also show that
targets in anti-takeover states are more likely to choose negotiations. The importance
of high anti-takeover state in our data set is confirmed by the validity test in Table 7.
Aktas et al. (2010) use leverage as a proxy for auction costs and argue that
levered firms are more eager to sell and as auctions usually take longer to complete,
levered firms prefer private negotiations. Also, more levered target firms are more
difficult to price (Jandik and Makhija, 2005) but their value, ceteris paribus, does
not vary much across different bidders and therefore the value of the firm would not
increase with number of bidders (French and McCormick, 1984). So we conjecture
that higher leverage is associated with higher odds of negotiations, which is again
confirmed by the validity test in Table 7.
Even though high takeover activity and leverage have also been associated with
premium (Aktas et al., 2010; Bargeron et al., 2008), previous literature has not controlled for the selling process using the system of equations we use in this paper.
23
We argue that any possible correlation between takeover premium and high takeover
activity, anti-takeover provisions or leverage is only indirect through the selling process. For example, Aktas et al. (2010) argue that as leverage increases the cost
associated with organizing an auction for the selling firm, highly levered firms prefer
not to sell in auctions. As they suffer fewer outside options to sell, they also suffer
lower bargaining power. These firms prefer faster private negotiations and so are
willing to accept smaller premiums. Also in our support, Boone and Mulherin (2007)
show that even though anti-takeover state is positively correlated with negotiations,
it is not correlated with announcement returns for the selling firms.
On a univariate level, liquidity index and leverage in our sample are also correlated with private equity buyers (Table 8). However, as private equity buyers are
also highly correlated with auctions, it is likely that the relationship is indirect.
The exogeneity test in Table 7 confirms that liquidity index, anti-takeover state and
leverage do not significantly contribute to explaining the premium and the type of
buyer within our system.
Premium is likely to be associated with the bargaining power of the two parties
involved and value (synergy) created in the deal. As shown in Section 3.3, poor prior
stock performance is correlated with higher premium and this correlation is, at least
partially, associated with higher potential for synergy opportunities. Bargeron et al.
(2008) also find negative correlation between target announcement returns and past
stock performance.
It might also be argued that firms that are smaller and lack analyst coverage are
associated with higher potential synergies to acquirers. Higher analyst scrutiny and
investor visibility means that potential synergies are spotted and exploited sooner.12
Moreover, Greenwood and Schor (2009) show that smaller firms without analyst
coverage are often targeted by activist shareholders who consequently manage to
get high takeover premiums.
12
We find that the negative correlation between premium and past stock performance is more
negative for targets without analyst coverage. This indicates that the synergy effect is larger.
24
Larger firms are associated with higher institutional ownership (O’Brien and
Bhushan, 1990). This might translate into the effect of visibility just discussed above.
It might also translate into lower bargaining power of large firms with larger stakes
by institutional owners who are more inclined to sell and realize high returns. Stulz
et al. (1990) document a negative relation between target announcement abnormal
returns and institutional ownership and argue that institutional owners are willing
to accept lower premium due to their very low capital gains tax rates.13 Also, larger
firms might be more difficult to sell and therefore their bargaining power might be
lower relative to smaller firms. Betton et al. (2008) show a negative relationship
between premium and target size, while Boone and Mulherin (2011) and Bargeron
et al. (2008) show a negative correlation between target announcement returns and
target size. In fact, the validity tests in Table 7 show that stock performance,
analyst dummy and target total assets each significantly contribute to explaining
the premium. The target total assets is significant only on the ten-percent level, but
this is not a large concern as our system is overidentified.
It has been documented in the literature that private equity buyers acquire
smaller targets relative to strategic buyers (Bargeron et al., 2008). However, we
control for this effect through our matching procedure and so do not expect any
correlation between target size and buyer type. Table 2 shows that in our data set,
private equity versus strategic buyer targets are comparable in term of transaction
value and total assets.14 Analyst coverage and past stock performance are also comparable across the two buyer types. Table 7 confirms the exogeneity tests for the
private equity equation.
Concerning the relationship between premium and the selling mechanism choice,
the correlation matrix in Table 8 shows no correlation between target total assets
and auctions or controlled sales, which is further confirmed by the exogeneity test
13
Blouin et al. (2011), however, show that only a subset of institutional investors is tax sensitive.
Table 8 shows a weak correlation between log of total assets and the private equity dummy,
but this correlation is not confirmed when we add log of total assets as an additional regressor in
specifications in Table 5.
14
25
in Table 7. This is in contrast with, for example, Boone and Mulherin (2007) who
show that target size proxied by total assets is significantly negative in predicting
auctions. We believe this is because of our data collection set up where we start
with all private equity deals that are then matched with strategic deals. As private
equity deals are not particularly large, the firms in our data set are smaller. Smaller
variation in total assets then results in total asset not being significant in explaining
the selling mechanism choice.
The correlation matrix in Table 8 also indicates that lack of analyst coverage
is weakly correlated with auctions. However, the endogeneity test for the analyst
dummy in Table 7 suggest that the analyst dummy is not significant in explaining
the selling mechanism. So, the univariate correlation between analyst coverage and
auctions must be indirect: for example, sales of firms covered by analysts are less
frequently initiated by target management and target initiation is in turn strongly
associated with auctions.
In summary, we provide intuition for why our exogenous variables have a direct
effect on particular endogenous variables but no direct effect on the other endogenous variables in such a way that they identify the parameters of our simultaneous
system. Moreover, we also provide test results that show that indeed all explanatory
variables necessary to identify the system are only significant in one of the equations
(validity tests) and not in the other equations (exogeneity tests). Speaking in terms
of instruments, the relevant variables are not weak and are exogenous. As a result,
the tests confirm industry count and cash as exogenous variables in the private equity equation, liquidity index, anti-takeover state and leverage in the selling process
equation and stock performance, analyst coverage and total assets in the premium
equation.
3.4.2
System results
Table 9 reports the results. The individual equations for the premium, buyer type
(private equity buyer) and selling mechanism (auction and controlled sale) are re-
26
ported in corresponding columns. For each equation, at the top we report structural
parameters that are dependent variables of the other equations. Significance of these
structural parameters shows interdependencies throughout the model and therefore
indicates sequencing of the whole selling process.
- please insert Table 9 about here For each equation, we report in separate columns both estimated and reduced
form parameters. An estimated parameter of a particular exogenous variable measures the effect of changing this variable conditional on keeping everything else constant/unchanged. However, in a system with feedback effects, this does not provide
much information as a change of one exogenous variable necessarily impacts other
dependent variables and causes adjustments throughout the system. Given the feedback effects, we are interested in an overall effect of a change in a particular variable
that is expressed through the reduced form parameter.
The reduced form parameters, however, only make sense in the equations with
significant structural parameters because only then we can talk of significant feedback effects. Also, by definition, the reduced form parameters are different from the
estimated coefficients only for the exogenous explanatory variables that are present
in more than one equation. For the exclusion restrictions, the reduced form parameters are equal to the estimated ones as they are not present elsewhere in the
model.
Considering the structural parameters, Table 9 shows that the selling mechanism
choice significantly affects the buyer type but not the other way round. Moreover,
the coefficients for the buyer type and selling mechanism are not significant in the
premium equation. So, Table 9 shows a simple triangular system with only one
significant feedback effect from the selling mechanism choice to the buyer type that
suggests sequencing of the selling process that starts with the selling mechanism
choice.
The starting point is, therefore, the decision on how to sell a firm. In the auction
and controlled sale equations, several of the estimated coefficients are significant,
27
which shows that the decision reflects firm and deal characteristics.15 Target initiated
deals with better accounting profitability are more likely to be sold in auctions.
Target initiation and profitability increase also the odds of controlled sales. Still,
the initiation coefficient is significantly higher for auctions. The effect of profitability
is slightly larger for auctions, but the difference is not significant. Industry count
that proxies for the depth of the takeover market also increases the odds of auctions
and controlled sales relative to private negotiations. So in short, when deciding on
how to sell their firm, managers take into account the firm situation.
The selling mechanism choice then affects the buyer type. In particular, firms
sold in auctions are less likely, while those sold in controlled sales are more likely,
to be sold to private equity buyers relative to private negotiations. Interestingly,
this is in contrast to the observed correlations reflected in the univariate and single
equation results (Tables 2, 4 and 8), where we see that private equity deals are more
likely in auctions. The negative structural coefficient for auctions in the private
equity equation in Table 9, however, indicates that the positive observed correlation
between private equity buyers and auctions is due to common effects of target initiation, profitability, market to book ratio and R&D in the two equations. These
variables impact the odds of a private equity buyer and the odds of auctions in
the same direction, which causes the positive observed correlation between private
equity buyers and auctions. However, once we keep these common factors constant
and observe exogenous effects through the exclusion restrictions of liquidity index,
anti-takeover state and leverage, we see that higher odds of auctions, coming from
purely exogenous factors, decrease the odds of private equity buyers.
Similarly at odds with observed correlations, the structural coefficient for controlled sales in the private equity equation is positive: firms that decide to sell
through a controlled sale rather than a private negotiation ceteris paribus also increase the likelihood of being sold to a private equity buyer. The two structural
15
We focus here on the estimated parameters rather than the reduced form parameters as the
private equity coefficient is not significant indicating that the feedback effect is zero.
28
parameters clearly show that for a buyer type determination it is important to distinguish between full-scale auctions and controlled sales with a limited number of
bidders that with exception of Boone and Mulherin (2009) has not been done in the
related literature so far.
Taking into account the significant feedback effect between the selling mechanism
and buyer type, the reduced form parameters in the private equity equation are all
insignificant and show that firm characteristics cease to matter for the buyer type in
equilibrium. So, firm characteristics affect the choice of buyer type only indirectly
through the selling mechanism.
The effect of the selling mechanism and buyer type on premium, as estimated
through the structural parameters in the premium equation, is not significant. We
see that the observed correlation between the premium and buyer type (selling process) is only due to the correlations of both premium and buyer type (premium
and selling mechanism) with the common explanatory variables of target initiation,
profitability and market to book ratio. The fully exogenous effects that control for
this common correlation are insignificant. As a result, we can say that whether a
firm is sold in an auction or negotiation, or it is sold to a private equity or strategic
buyer is irrelevant for premium determination. A significant coefficient, for example
a negative coefficient for the auction variable would indicate that, on average, firms
opting for auctions would get higher premium if they opted for a private negotiation
instead. So they would do better by making a different choice. Insignificant coefficients, therefore, indicate optimality of the choices: by changing their choices, firms
would not improve the takeover premium. The premium remains unaffected.
This also means that private equity buyers do not pay less for their target firms.
In fact, the premium differences only reflect differing situations of firms sold to the
two types of buyers. Neither do auctions and controlled sales versus negotiations
result in differing premiums. This is in line with Aktas et al. (2010) who show
that takeover premiums in private negotiations reflect potential competition and so
should not be smaller relative to auctions.
29
Despite these optimal choices, firm characteristics still directly impact the premium. Profitable and lower market to book firms get on average higher premiums.
Interestingly, target initiation is not significant.16 It enters the system primarily
through the selling mechanism choice. The link between initiation and selling mechanism choice is documented also in Aktas et al. (2010). Xie (2010) also highlights
that target initiation is a very important factor but they do not focus on the buyer
type. Also, poor stock performance and analyst coverage significantly increase premiums.
4
Conclusions
In this paper we analyze the selling process of firms eventually sold to private equity
versus strategic buyers. On a data set of 205 private equity deals of US listed targets over the period from 1997 to 2006 matched with comparable deals by strategic
buyers we show that the selling mechanism choice is an important strategic decision
that reflects their observable firm characteristics. Our data also indicate that private
equity versus strategic buyers tend to bid for different types of targets. This suggests that the selling mechanism and buyer type choices are endogenous and both
potentially affect takeover premiums. Therefore, we estimate a simultaneous system with the takeover premium modeled together with the buyer type and selling
mechanism choice. Our results show that the selling mechanism choice is a very
important corporate decision that constitutes the beginning of the selling process
and consequently also determines whether the firm is sold to a private equity or
strategic buyer. In addition, the system results show that takeover premium paid
by private equity versus strategic buyers is not significantly different. The effect of
the selling process on the premium is also insignificant.
16
Again, as the structural parameters for private equity and selling mechanism are insignificant,
we disregard their feedback effects and focus on the estimated coefficients rather than reduced form
parameters.
30
Acknowledgements
We would like to thank the editor (J. Harold Mulherin) and an anonymous referee
for very helpful comments. Our thanks also go to Antonio Macias, Mary Anne
Majadillas, Manuel Vasconcelos, and participants of the 2010 EFM Symposium on
Entrepreneurial Finance & Venture Capital Markets in Montreal, Canada and the
2010 FMA European Conference in Hamburg, Germany.
31
References
Aktas, N., E. de Bodt, and R. Roll, 2010: Negotiations under the threat of an auction.
Journal of Financial Economics, 98, 241–255.
Baker, M., X. Pan, and J. Wurgler, forthcoming: The effect of reference point prices on
mergers and acquisitions. Journal of Financial Economics.
Bargeron, L., F. P. Schlingemann, R. M. Stulz, and C. Zutter, 2008: Why do private equity
acquirers pay so little compared to public acquirers? Journal of Financial Economics,
89, 375–390.
Bebchuk, L. A., A. Cohen, and A. Ferrell, 2002: Does the evidence favor state competition
in corporate law? California Law Review, 90, 1775–1821.
Betton, S., B. E. Eckbo, and K. S. Thorburn, 2008: Corporate takeovers. Handbook of
Corporate Finance, Empirical Corporate Finance, B. E. Eckbo, Ed., Elsevier, NorthHolland, Vol. 2, 291–429.
Blouin, J. L., B. J. Bushee, and S. A. Sikes, 2011: Tax-sensitive institutional investors.
Tech. rep., American Accounting Association Annual Meeting - Tax Concurrent Sessions.
Boone, A. L. and J. H. Mulherin, 2007: How are firms sold? Journal of Finance, 62,
847–875.
Boone, A. L. and J. H. Mulherin, 2008a: Do auctions induce a winner’s curse? evidence
from the corporate takeover market. Journal of Financial Economics, 89, 1–19.
Boone, A. L. and J. H. Mulherin, 2008b: Do private equity consortiums impede takeover
competition? Working paper, University of Georgia.
Boone, A. L. and J. H. Mulherin, 2009: Is there one best way to sell a company? auctions
versus negotiations and controlled sales. Journal of Applied Corporate Finance, 21 (3),
28–38.
Boone, A. L. and J. H. Mulherin, 2011: Do private equity consortiums facilitate collusion
in takeover bidding? Journal of Corporate Finance, 17, 1475–1495.
Bullow, J. and P. Klemperer, 2009: Why do sellers (usually) prefer auctions? American
Economic Review, 99, 1544–1575.
Comment, R. and W. Schwert, 1995: Poison or placebo? evidence on the deterrence and
wealth effects of modern anti-takeover measures. Journal of Financial Economics, 39,
3–43.
Cumming, D., D. S. Siegel, and M. Wright, 2007: Private equity, leveraged buyouts and
governance. Journal of Corporate Finance, 13, 439–460.
Dittmar, A. K., D. Li, and A. Nain, forthcoming: It pays to follow the leader: Acquiring
targets picked by private equity. Journal of Financial and Quantitative Analysis.
32
French, K. R. and R. E. McCormick, 1984: Sealed bids, sunk costs, and the process of
competition. Journal of Business, 57, 417–441.
Gorbenko, A. S. and A. Malenko, 2009: Strategic and financial bidders in takeover auctions. Working paper, Stanford University.
Greenwood, R. and M. Schor, 2009: Investor activism and takeovers. Journal of Financial
Economics, 92, 362–375.
Hansen, R., 2001: Auctions of companies. Economic Enquiry, 39, 30–43.
Jandik, T. and A. K. Makhija, 2005: Leverage and the complexity of takeovers. Financial
Review, 40, 95–112.
Judge, G. G., R. C. Hill, W. E. Griffiths, H. Lutkepohl, and T.-C. Lee, 1988: Introduction
to the theory and practice of econometrics. 2d ed., John Wiley & Sons, New York.
Lehn, K., J. Netter, and A. Poulsen, 1990: Consolidating corporate control: dual-class
recapitalizations versus leveraged buyouts. Journal of Financial Economics, 27, 557–
580.
Lehn, K. and A. Poulsen, 1989: Free cash flow and stockholder gains in going private
transactions. Journal of Finance, 44, 771–787.
Macias, A., C. A. Cain, and J. M. Sanchez, 2011: Can targets benefit from negotiations?
evidence from auctions and negotiations. Working paper, Texas Christian University.
O’Brien, P. and R. Bhushan, 1990: Analyst following and institutional ownership. Journal
of Accounting Research, 28, 55–65.
Officer, M. C., O. Ozbas, and B. A. Sensoy, 2010: Club deals in leveraged buyouts. Journal
of Financial Economics, 98, 214–240.
Pinkowitz, L., 2000: The market for corporate control and corporate cash holdings. Working paper, Georgetown University.
Povel, P. and R. Singh, 2006: Takeover contests with asymmetric bidders. Review of
Financial Studies, 19, 1399–1431.
Pozen, R. C., 2007: If private equity sized up your business. Harward Business Review,
(11), 78–87.
Roberts, M. R. and T. M. Whited, 2011: Endogeneity in empirical corporate finance.
Financial research and policy working paper No.FR 11-29, The Bradley Policy Research
Center, University of Rochester.
Rogo, R., 2010: The effect of buyers’ valuation uncertainty on the seller’s choice of selling
mechanism. Working paper, University of British Columbia - Sauder School of Business.
Schlingemann, F. P., R. M. Stulz, and R. A. Walkling, 2002: Divestitures and the liquidity
of the market for corporate assets. Journal of Financial Economics, 64, 117–144.
33
Shleifer, A. and R. Vishny, 1992: Liquidation values and debt capacity: A market equilibrium approach. Journal of Finance, 47, 1343–1366.
Simsir, S. A., 2008: The information content of deal initiation in mergers and acquisitions.
Working paper, Cornell University.
Stulz, R. M., R. A. Walkling, and M. H. Song, 1990: The distribution of target ownership
and the division of gains in successful takeovers. Journal of Finance, 45, 817–833.
Williams, E., 2007: Choosing the right buyer: Private equity versus strategic acquirer.
Viewpoint, 20.
Xie, K., 2010: The deal process, asymmetric bidders and target premia. Working paper,
Olin Business School, Washington University.
34
Appendix A
Auction versus controlled sale: two
examples
PlayCore: example of a full-scale auction
In 1998, PlayCore, Inc., a manufacturer of playground equipment expressed concern
that its common stock price on the American Stock Exchange did not adequately
reflect the financial performance or prospects of the Company. Based on these
concerns the Company interviewed four international investment banking firms from
March to May 1999 to obtain their views on value enhancing strategies for the
Company, including the sale of the Company.
On September 20, 1999, the Company publicly announced the engagement of
DLJ as their financial advisor and began the process of identifying candidates that
might be interested in acquiring or making a strategic investment in the Company.
Over the next month, DLJ contacted 119 potential buyers comprised of 90 financial buyers and 29 strategic buyers. Of the parties contacted, 51 financial buyers
and 5 strategic buyers executed confidentiality agreements, 12 parties submitted
preliminary indications of interest in early November 1999.
Beginning in late November and concluding in early December 1999, 8 interested
parties attended management presentations and were provided access to a data room,
which included the Company’s material agreements and other financial and due
diligence information. These parties were asked to submit proposals by December
3, 1999.
On December 6, 1999, the Board reviewed the proposals. DLJ reported that the
values associated with the proposals were at the lower end of the value range and
recommended that four of the interested parties, including Chartwell, be selected to
continue in the process, which would include facility tours, additional due diligence
and providing comments to the draft Merger Agreement previously distributed.
Between December 6, 1999 and January 14, 2000, Chartwell and one other party
(the Other Party) conducted additional due diligence, visited the Company’s key
manufacturing facilities and reviewed the Merger Agreement. The two other parties
declined to move forward in the process. As requested by the Company, Chartwell
provided a proposed letter of intent on January 14, 2000. Pursuant to this letter
of intent, Chartwell proposed a transaction consisting of a offer at $10.00 per share
subject to satisfactory completion of its due diligence and a number of other conditions. On January 17, 2000, the Other Party provided its proposed letter of intent to
the Company. Although unclear from the language of the letter of intent, the Other
Party’s per share acquisition price was less than $10.00. On January 17 and 18,
2000, DLJ had numerous discussions with Chartwell and the Other Party regarding
their respective proposals.
On January 20, 2000, the Company’s financial advisors reviewed with the Executive Committee of the Board the proposed letters of intent and provided an update
on the progress made with these parties. DLJ indicated that the Other Party was
unwilling to commit to a fixed per share price without additional due diligence. DLJ
35
further indicated that the Other Party had completed substantially less accounting,
business and legal due diligence than had been completed by Chartwell.
Chartwell, in discussions with DLJ, had agreed to increase its offer to $10.10
per share in cash. Chartwell had also completed substantial due diligence and had
obtained committed financing. Accordingly, the Company began negotiating an
agreement with Chartwell on January 30, 2000, whereby the Company would agree
to deal exclusively with Chartwell and not solicit additional acquisition proposals
for a specified period of time (the ”no-shop” agreement).
On April 13, 2000, legal counsel to Chartwell and the Company executed the
Merger Agreement. On April 14, 2000, the Company issued a press release announcing the execution of the Merger Agreement.
Physicians Health Services: example of a controlled sale
On January 14, 1997, the Board of Directors of Physicians Health Services, Inc.
(PHS) authorized a subcommittee to explore the company’s strategic alternatives.
This subcommittee directed management to prepare analyses of the potential values
that might be achieved for stockholders through a sale of the company, through
continued implementation of the company’s strategic plan or through other possible
strategies.
In early November 1996, representatives of Foundation Health Systems, Inc.
(FHS) had advised the company on an unsolicited basis that FHS might have an
interest in acquiring the company. On February 23, 1997, FHS reiterated the interest
of acquiring the company.
Management presented an analysis of the company’s strategic alternatives to
the members of the subcommittee on February 25, 1997. Particular attention was
focused upon the results that might be obtained, alternatively, from a continued
implementation of the company’s strategic plan, and from a possible sale of the
company. The subcommittee authorized management to explore further the possibility of a sale.
The premium that the subcommittee believed the company could obtain in connection with a sale during the period of market consolidation could be jeopardized
once the New York metropolitan market became more stabilized. It was recognized
that any such exploration of a sale would have to be conducted on a discreet basis since a more extensive exploration process, such as a public sale or auction of
the company, was likely to expose the company to substantial business risks. In
this connection, the subcommittee was advised by management that if the company
were to undertake a public sale or auction process, its competitors could and would
be expected to use the process to undermine the company’s marketing efforts. Also,
insurance brokers and consultants would be reluctant to recommend the company to
current and potential clients in light of its uncertain future. Moreover, some of the
company’s competitors might profess an interest in acquiring the company in order
to draw out the exploration process, during which the company’s ability to compete
for business would be impaired.
36
At the same time, the subcommittee recognized that unless indications of interest were solicited from at least some potential acquirors, it would be difficult to
assess whether there was any interest in an acquisition at values that would warrant a departure from the company’s existing strategic plan. At the conclusion of
the meeting, the subcommittee authorized management to approach four potential
acquirors whom it believed were the most likely to have the capability and interest
to make an acquisition proposal that would be acceptable. These candidates were
selected from the possible acquirors identified by management, based on management’s familiarity with the consolidation occurring in the industry. Other possible
acquirors were judged unlikely to be able to acquire the company in the near term
because they were engaged in the process of consolidating other acquisitions into
their operations, because they were pursuing other significant opportunities or because they did not appear to have the financial resources to acquire the company at
values that might warrant consideration.
Members of senior management approached the four possible acquirors in late
February and March of 1997 to ascertain whether discussions with any of them
might prove fruitful. Of the four possible acquirors, two indicated that they did not
have any interest in making an acquisition proposal that would involve the payment
of a substantial premium. A meeting with the president of one of these potential
acquirors was eventually scheduled for May 13, 1997. A third potential acquiror did
not express any interest in pursuing a possible acquisition. None of these approaches
led to either ongoing discussions or the prospect of fruitful discussions. The fourth
potential acquiror, FHS, indicated that it might have such an interest, and, on March
3, 1997, FHS and PHS entered into a confidentiality agreement.
On March 18, 1997, representatives of PHS management advised FHS that PHS
would entertain an acquisition proposal from FHS only if the offering price equaled
or exceeded $30 per share. On April 1, 1997, FHS indicated that it was prepared to
consider an acquisition in the range of such an offering price, subject to its review
of the possible cost savings that could be generated in a combination.
On April 3, 1997, PHS retained Morgan Stanley as its financial advisor.
On May 2, 1997, FHS advised the company that it would be prepared to offer to
acquire all of the outstanding common stock of the company at a price of $30 per
share, subject to certain conditions.
An industry publication dated May 5, 1997, reported that FHS had made an
informal offer to purchase the Company for about $27.00 per share but that PHS
wanted at least $30 per share. A reporter from The Wall Street Journal advised the
company and FHS on May 5, 1997, that he had obtained a copy of FHS’s merger
proposal and intended to publish a story with respect to the proposed merger on May
6. Accordingly, FHS and PHS issued a joint press release announcing that they were
engaged in merger discussions, involving a merger consideration of ”approximately
but not more than $30 per share.”
On May 7, 1997, PHS was then presented with FHS’ firm offer for a merger
consideration of $29.25 per share. Morgan Stanley advised the Board that following
the announcement of merger negotiations at a price of ”approximately but not more
37
than $30 per share” a number of institutional shareholders of FHS had reacted
negatively to the possible price being offered, creating pressure on FHS to secure a
price as much below $30 per share as possible. The Board was further advised that
FHS was unwilling to offer more than $29.25 per share.
Morgan Stanley rendered its opinion that the merger consideration of $29.25
was fair to the holders of the company’s common stock from a financial point of
view. The full Board then met, and upon further discussion and consideration of
such factors, concluded that the merger agreement and the transactions were in the
best interests of the stockholders of the company and, by the unanimous vote of the
directors present, approved the merger agreement.
38
Appendix B
Variable definitions
Variable
Definition
Analyst dummy
Dummy variable equal to one in case the company is followed by
at least one analyst and zero otherwise.
Dummy equal to one for targets incorporated in Delaware or
states determined by Bebchuk et al. (2002) to have strong
takeover impediments (Idaho, Indiana, Maryland, Nevada, Ohio,
Pennsylvania, South Dakota, Tennessee and Wisconsin).
Dummy variable equal to one in case the company is sold in a
highly organized auction with pre-set rules and zero otherwise.
42 trading days before the SDC announcement or one trading day
before the first mention of the deal in Factiva, whichever is earlier.
Cash and marketable securities to total assets for the financial
year ending before the SDC announcement.
Dummy variable equal to one in case the private equity acquisition is made by two or more private equity investors and zero
otherwise.
Dummy variable equal to one in case the target company decides to discreetly canvass a limited number of bidders that target
management believes to have a serious interest in acquiring the
company and zero otherwise.
Price offered less price 42 trading days before the first mention of
the deal in Factiva over the price 42 trading days before the first
mention of the deal Factiva.
Natural logarithm of the number of firms in the same FF49 industry that have larger market capitalization than the target (as
of previous December) as in Boone and Mulherin (2008a).
Long-term debt to total assets for the financial year ending before
the deal announcement.
Ratio of the value of corporate control transactions in a year to the
total book values of assets of all the firms in the same industry
during that year, using the primary SIC code of each firm and
3-digit SIC codes as in Schlingemann et al. (2002).
Dummy variable equal to one in case return on assets as defined
below is negative and zero otherwise.
Target firm market capitalization plus book value of long-term
debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement.
Natural logarithm of the ratio of the maximum price over one
year ending on the base date to the price on the base date.
Natural logarithm of the ratio of the price on the base date to the
minimum price over one year ending on the base date.
Dummy variable equal to one in case the company is sold in a
privately negotiated sale and zero otherwise.
Anti-takeover state
Auction
Base date
Cash
Consortium deal
Controlled sale
Factiva premium
Industry count
Leverage
Liquidity index
Loss
Market to book
Maximum price
Minimum price
Negotiation
continued on next page
39
continued from previous page
Variable
Definition
PE portfolio firm
Dummy variable equal to one in case the target firm is acquired by
a firm that is majority owned (≥50%) by a private equity investor
and zero otherwise.
Price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case there was a leakage of information earlier than
42 trading days before the SDC announcement, we benchmark
against the price one trading day before the first mention of the
deal in Factiva.
Return on assets (net income to total assets) for the financial year
ending before the SDC deal announcement.
Dummy variable equal to one for all Fama and French (49) industries that are top 7 industries with respect to the average industry
R&D ratio (R&D expense to total assets) and zero otherwise.
Price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement.
Abnormal return over one and half years before the base date. We
also check specifications with stock performance over two years,
one year and half of a year ending on the base date.
Net plant and property to total assets for the financial year ending
before the SDC announcement.
Dummy variable equal to one in case the selling firm initiates
the sale of their company and equal to zero in case the buyer
approaches the company with a proposal to buy it.
Book value of total assets; in regressions included as a natural
logarithm.
Total value of consideration paid by the acquiror, excluding fees
and expenses. The dollar value includes the amount paid for all
common stock, common stock equivalents, preferred stock, debt,
options, assets, warrants, and stake purchases made within six
months of the announcement date of the transaction. Liabilities
assumed are included in the value if they are publicly disclosed.
Preferred stock is only included if it is being acquired as part of
a 100% acquisition. If a portion of the consideration paid by the
acquiror is common stock, the stock is valued using the closing
price on the last full trading day prior to the announcement of the
terms of the stock swap. If the exchange ratio of shares offered
changes, the stock is valued based on its closing price on the last
full trading date prior to the date of the exchange ratio change.
For public target 100% acquisitions, the number of shares at date
of announcement is used.
Premium (adjusted)
Profitability
R&D
SDC premium
Stock performance
Tangible assets
Target initiated deal
Target total assets
Transaction value
40
Appendix C
Simultaneous model specification
The system of equations consists of a linear regression model for premium (premi ),
a binary probit model for the potential buyer type (P Ei = {0, 1}) and a multinomial
probit model for the selling mechanism choice (SMi = {1, 2, 3} that represent the
three choices of a full-scale auction, controlled sale and private negotiation, respectively). The model is given by the equations
premi
pe∗i
sm∗1i
sm∗2i
=
=
=
=
0
0
α1 pe∗i + α2 sm∗1i + α3 sm∗2i + β11
Xi + β12
Zi + ε1i
∗
∗
0
0
α4 sm1i + α5 sm2i + β21 Xi + β22 Wi + ε2i
0
0
Vi + ε3i
Xi + β32
α6 pe∗i + β31
0
0
∗
α7 pei + β41 Xi + β42 Vi + ε4i
(1)
where αj (j = 1, . . . , 7) are parameters and βjk (j = 1, . . . , 4, k = 1, 2) are parameter vectors measuring the effect of the exogenous explanatory variables Xi , Zi , Wi
and Vi . P Ei is determined using the standard probit rule
1 if pe∗i > 0
P Ei =
(2)
0 if pe∗i ≤ 0
and SMi is defined using the conventional restrictions on utility differences in an
identified multinomial probit model with three outcomes (auction, controlled sale
and private negotiation)

 1 if sm∗1i ≥ sm∗2i ∧ sm∗1i > 0
2 if sm∗2i > sm∗1i ∧ sm∗2i > 0
(3)
SMi =

3 if sm∗1i ≤ 0 ∧ sm∗2i ≤ 0
The vector of error terms εi = (ε1i , ε2i , ε3i , ε4i )0 is assumed to be normally distributed with mean 0 and (4 x 4) covariance matrix Σ. For parameters identification we impose the standard identification restrictions of probit models Σ22 = 1,
Σ33 = Σ44 = 2 and Σ34 = Σ43 = 1.
To estimate the model parameters θ = (α1 , . . . , α7 , β11 , β12 , . . . , β14 , β24 , Σ) we
use the full information maximum likelihood [FIML] approach. Let’s denote yi =
(premi , pei , smi ) the data of observation i. To derive the likelihood function we use
that the joint distribution of (premi , pe∗i , sm∗1i , sm∗2i ) is multivariate normal. The
joint density can be written as the product of marginal density of premi (univariate
normal density) and the conditional density of (pe∗i , sm∗1i , sm∗2i ) given premi which
is a multivariate normal density
f (premi , pe∗i , sm∗1i , sm∗2i ; θ) = f (premi ; θ)f (pe∗i , sm∗1i , sm∗2i |premi ; θ).
Hence, the likelihood contribution of observation i is given by
f (yi ; θ) = f (premi ; θ) Pr(P Ei = pei ∧ SMi = smi |premi ; θ),
where the latter term is
ZZZ
f (pe∗i , sm∗1i , sm2 i∗ |premi ; θ)dpe∗i dsm∗1i dsm∗2i .
41
The limits of the integral depend on the restrictions (2) and (3) imposed by the
observations pei and smi . The FIML estimator is obtained by maximizing the loglikelihood function
N
X
L(y; θ) =
lnf (yi ; θ)
i=1
with respect to θ. Standard errors of the parameters can be obtained by minus the
inverse of the second-order derivative of this log-likelihood functions evaluated in
the maximum likelihood estimates.
Parameter identification, validity and exogeneity tests
As the model is a simultaneous equation model, we cannot include the same explanatory variables in all equations as this would lead to an unidentified system. To
identify all αj parameters we need to exclude explanatory variables in a proper way,
see for example, Judge et al. (1988, Chapter 14). We impose that the explanatory
variables contained in Zi , Wi and Vi are unique and are not allowed to be part of
Xi . To achieve identification the number of exogenous variables in Zi , Wi and Vi has
to be larger than the number of endogenous variables in the corresponding equation
minus 1. Note that due to the restrictions on (sm∗1i , sm∗2i ), sm∗1i and sm∗2i correspond
to one endogenous variable.
Testing for proper exclusion restrictions (identification) is only possible in case
of overidentification as we have to perform individual likelihood ratio tests and keep
the system identified when excluding the tested variable. Therefore, we take in
each equation more exclusion restrictions than strictly necessary to identify the
parameters and the number of exogenous variables in Zi , Wi and Vi is then larger
than the number of endogenous variables in the corresponding equation minus 1.
We perform two set of tests: First, we test for the validity (significance) of the
exclusion restrictions. In particular, we check whether the individual parameters
contained in β12 , β22 and (β32 , β42 ) are significant using individual likelihood ratio
tests. Second, we test for exogeneity of the exclusion restrictions. In particular, we
test whether the individual explanatory variables contained in Zi , Wi and Vi are
significant in the other equations using several likelihood ratio tests. Exogeneity
of all variables in Zi , Wi and Vi is confirmed only if all likelihood test statistics
are insignificant. Otherwise, the corresponding exclusion restriction would not be
correct. If all individual parameters in βj2 (for j = 1, . . . , 4) are significant and the
explanatory variables in Zi , Wi and Vi are not significant in the other equations the
exclusion restrictions are correct and the system is properly identified.
42
Table 1: Sample by year
This table reports the number of deals in our sample per year of the
SDC announcement date. Our sample period covers 1997-2006, but the
matching procedure results in 4 strategic deals falling into the year 2007.
Year
Private equity buyer
Strategic buyer
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
18
28
27
21
11
10
16
17
25
32
0
16
32
28
15
14
17
14
20
26
19
4
Total
205
205
43
Table 2: Summary statistics
This table presents summary statistics. Panel A partitions the sample into 205 private equity deals versus
205 matched strategic deals. Panel B shows the summary statistics for 198 buyer initiated deals versus 212
target initiated deals. Transaction value is the total value of consideration in million USD paid by the acquiror,
excluding fees and expenses. Target total assets is the book value of total assets in million USD. Premium is
the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before
the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC
announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva.
SDC premium is the price offered less price 42 trading days before the SDC announcement over the price 42
trading days before the SDC announcement. Factiva premium is the price offered less price 42 trading days
before the first mention of the deal in Factiva over the price 42 trading days before the first mention of the deal
Factiva. Profitability stands for net income to total assets for the financial year ending before the SDC deal
announcement. Loss is a dummy variable equal to one in case of positive profitability and zero otherwise. Market
to book is defined as target firm market capitalization plus book value of long-term debt over book value of total
assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. R&D is a
dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry
R&D ratio and zero otherwise. Tangible assets is the net plant and property to total assets for the financial
year ending before the SDC announcement. Cash is defined as cash and marketable securities to total assets
for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in
the same FF49 industry that have larger market capitalization than the target as of previous December. Stock
performance is the return over one and half years before the base date. Analyst dummy is a dummy variable
equal to one in case the company is followed by at least one analyst and zero otherwise. Liquidity index is the
ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms
in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one
for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota,
Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the
deal announcement. Bidders contacted stands for the number of bidders that were contacted during the private
selling process. We have information only about 178 PE versus 181 strategic deals and 183 target vs. 176 bidder
initiated deals. Bidders with agreement is the number of bidders that signed a confidentiality agreement. We have
information only about 171 PE versus 193 strategic deals and 180 target vs. 184 bidder initiated deals. Target
initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and
equal to zero otherwise. We test for differences in means using a t-test. a , b and c denotes statistical significance
at the one-, five- and ten-percent level.
Panel A
Transaction value
Target total assets
Premium (adjusted)
SDC premium
Factiva premium
Profitability
Loss
Market to book
R&D
Tangible assets
Cash
Industry count
Stock performance
Analyst dummy
Liquidity index
Anti-takeover state
Leverage
Fraction sold in
negotiation
controlled sale
auction
Bidders contacted
% PE bidders
Bidders with agreem.
% PE bidders
Target initiated deal
Private equity buyer
Strategic buyer
Difference
Mean
St.dev.
Median
Mean
St.dev.
Median
in means
654
489
45.1%
42.9%
43.2%
-0.03
30.2%
1.11
0.22
0.29
0.15
4.4
6.6%
0.65
0.20
0.69
0.21
1,917
1,089
45.4%
40.6%
41.1%
0.23
46.0%
0.79
0.42
0.24
0.19
1.0
63.7%
0.48
0.55
0.46
0.26
139
147
36.3%
35.8%
34.9%
0.03
0%
0.96
0.00
0.23
0.07
4.5
-6.2%
1.00
0.08
1.00
0.14
611
969
49.2%
47.8%
47.3%
-0.09
54.1%
1.92
0.35
0.24
0.14
5.3
1.0%
0.66
0.13
0.75
0.16
1,811
7,694
62.7%
55.6%
56.6%
0.30
49.9%
1.50
0.48
0.22
0.15
0.9
68.7%
0.47
0.16
0.43
0.22
131
94
38.4%
35.6%
36.8%
0.01
100%
1.41
0.00
0.15
0.07
5.5
-12.7%
1.00
0.07
1.00
0.06
43
-480
-4.1%
-5.0%
-4.1%
0.06b
-23.9%a
-0.81a
-0.13a
0.05b
0.02
-0.9a
5.6%
-0.01
0.08c
-0.06
0.05b
24.9%
25.4%
49.8%
32
79%
14
93%
63.4%
43.3%
43.6%
50.1%
44
27%
19
68%
48.3%
0%
0%
0%
16
100%
5
100%
100%
40.0%
38.5%
21.5%
12
4%
5
3%
40.0%
49.1%
48.8%
41.2%
21
15%
9
11%
49.1%
0%
0%
0%
2
0%
1
0%
0%
-15.1%a
-13.2%a
28.3%a
20a
75%a
9a
90%a
23.4%a
continued on next page
44
continued from previous page
Panel B
Transaction value
Target total assets
Premium (adjusted)
SDC premium
Factiva premium
Fraction sold in
negotiation
controlled sale
auction
Bidders contacted
% PE bidders
Bidders with agreem.
% PE bidders
Buyer initiated deal
Target initiated deal
Difference
Mean
St.dev.
Median
Mean
St.dev.
Median
in means
747
555
51.0%
50.1%
51.7%
2,121
1,518
53.0%
46.5%
50.4%
141
121
39.2%
40.0%
39.0%
527
891
43.6%
40.9%
39.4%
1,581
7,503
56.2%
50.3%
47.9%
133
128
35.3%
33.3%
32.7%
220
-336
7.4%
9.2%c
12.3%b
48.0%
36.9%
15.2%
9
32%
4
30%
50.1%
48.4%
35.9%
17
43%
7
44%
0%
0%
0%
1
0%
1
0%
17.9%
27.4%
54.7%
34
47%
14
55%
38.4%
44.7%
49.9%
44
41%
19
81%
0%
0%
100%
20
50%
5
50%
30.1%a
9.5%b
-39.6%a
-25a
-15%a
-10a
-25%a
45
46
55%
39%
50%
59%
34%
40%
PE deals
Strategic buyer deals
Target initiated PE deals
PE initiated deals
Target initiated strat.-buyer deals
Strategic buyer initiated deals
28
24
30
49
52
79
131
75%
56%
Non-binding bids to agreement
Final bids to agreement
Mean
3%
3%
0.60
0.16
0.06
St.dev.
Controlled sale
45%
44%
54%
65%
44%
61%
54%
4.43
2.32
1.38
21
30
17
65
51
82
133
#obs.
Controlled sale
Premium
Bidders with agreement
Bidders with non-binding bid
Bidders with final bid
Panel B:
45%
#obs.
Negotiation
Premium
All deals
Panel A:
39%
20%
21.96
5.86
1.90
Mean
3%
2%
1.92
0.41
0.11
St.dev.
81
21
35
9
102
44
146
#obs.
Auction
40%
41%
42%
68%
40%
47%
42%
Premium
Auction
neg.vs
10%
17%
8%
-27%
-36%a
-36%a
17.53a
3.54a
0.52a
auction
contr.vs
5%
2%
12%
3%
4%
14%
-8%
12%c
15%c
auction
contr.vs
3%
auction
continued on next page
5%
15%
-20%
-25%b
11%
-22%b
-9%
contr.
neg.vs
This table presents summary statistics per selling mechanism type. Premium is the price offered less price 42 trading days before the SDC announcement over the price 42
trading days before the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against
the price one trading day before the first mention of the deal in Factiva. Bidders with agreement is the number of bidders that signed a confidentiality agreement. This data
is available only for 112 controlled sales and 119 auctions. Bidders with non-binding bid is the number of bidders that submit a preliminary non-binding offer (available
only for 118 controlled sales and 119 auctions). Bidders with final bid is the number of bidders that submit a final binding bid (available only for 125 controlled sales
and 129 auctions). Non-binding (final) bids to agreement is the ratio of the number of non-binding (final) bids to the number of all bidders that signed a confidentiality
agreement. Transaction value is the total value of consideration in million USD paid by the acquiror, excluding fees and expenses. Profitability stands for net income to
total assets for the financial year ending before the SDC deal announcement. Loss is a dummy variable equal to one in case of negative profitability and zero otherwise.
Market to book is defined as target firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the
financial year ending before the SDC announcement. R&D is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average
industry R&D ratio and zero otherwise. Tangible assets is the net plant and property to total assets for the financial year ending before the SDC announcement. Cash is
defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in
the same FF49 industry that have larger market capitalization than the target as of previous December. Stock performance is the return over one and half years before
the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least one analyst and zero otherwise. Liquidity index is the ratio
of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same industry during that year using 3-digit SIC codes.
Anti-takeover state is a dummy equal to one for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and
Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement. Target initiated deal is a dummy variable equal to
one in case the selling firm initiated the sale of their company and equal to zero otherwise. We test for differences in means using a t-test. a , b and c denotes statistical
significance at the one-, five- and ten-percent level.
Table 3: Selling mechanism summary statistics
47
Transaction value
Profitability
Loss
Market to book
R&D
Tangible assets
Cash
Industry count
Stock performance
Analyst dummy
Liquidity index
Anti-takeover state
Leverage
Target initiated deal
Panel C:
761
-0.08
50%
1.57
0.23
0.30
0.13
4.86
1%
0.68
0.12
0.78
0.20
29%
Total
555
-0.06
37%
0.94
0.16
0.33
0.15
4.45
-13%
0.69
0.13
0.71
0.22
41%
PE
buyer
Negotiation
890
-0.09
59%a
1.96a
0.27a
0.27b
0.13
5.12a
10%a
0.67
0.11c
0.83a
0.18c
21%a
Strat.
buyer
716
-0.07
42%
1.64
0.38
0.23
0.15
5.02
3%
0.70
0.19
0.69
0.19
44%
Total
1,051
-0.03
31%
1.28
0.33
0.26
0.17
4.27
12%
0.71
0.28
0.69
0.26
54%
PE
buyer
Controlled sale
Total
441
-0.02
35%
1.35
0.26
0.26
0.14
4.77
7%
0.60
0.18
0.68
0.18
79%
Strat.
buyer
496a
-0.10b
49%a
1.87a
0.42c
0.21b
0.15
5.51a
-4%b
0.70
0.14a
0.70
0.15a
38%a
502
-0.01
26%
1.10
0.21
0.28
0.15
4.52
14%
0.61
0.20
0.68
0.18
79%
PE
buyer
Auction
301c
-0.06a
55%a
1.92a
0.39a
0.23b
0.13
5.34a
-7%a
0.59
0.14
0.70
0.16
80%
Strat.
buyer
45
-0.01
8%c
-0.07
-0.16a
0.06a
-0.02
-0.16
-1%
-0.03
-0.08b
0.09b
0.01
-16%a
contr.
neg.vs
320c
-0.06b
15%a
0.22b
-0.03
0.03
-0.01
0.09
-6%
0.07
-0.07c
0.10b
0.02
-51%a
auction
neg.vs
275c
-0.05c
7%
0.29b
0.12a
-0.03
0.01
0.25b
-5%
0.10b
0.01
0.01
0.02
-35%a
auction
contr.vs
continued from previous page
Table 4: Private equity versus strategic buyers
This table presents estimation results of a logit model with a dummy variable for a private equity bidder
as the dependent variable. Robust standard errors are provided in brackets. Target initiated deal is a
dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero
otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC
deal announcement. Market to book is defined as target firm market capitalization plus book value of longterm debt over book value of total assets adjusted for short-term liabilities for the financial year ending
before the SDC announcement. R&D is a dummy variable equal to one for all FF49 industries that are
top 7 industries with respect to the average industry R&D ratio and zero otherwise. Tangible assets is the
net plant and property to total assets for the financial year ending before the SDC announcement. Cash
is defined as cash and marketable securities to total assets for the financial year ending before the SDC
announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger
market capitalization than the target as of previous December. Auction is a dummy variable equal to one
in case the company is sold in a highly organized full-scale auction with pre-set rules and zero otherwise.
Controlled sale is a dummy variable equal to one in case the target company decides to discreetly canvass
a limited number of bidders that target management believes to have a serious interest in acquiring the
company and zero otherwise. a , b and c denotes statistical significance at the one-, five- and ten-percent
level.
1
Coef.
Constant
Target initiated deal
Profitability
Market to book
R&D
Tangible assets
Cash
Industry count
Auction
Controlled sale
Number of observations
χ2
2
s.e.
Coef.
c
-0.310 (0.160)
0.985 (0.207)a
0.803 (0.427)c
-0.546
3
s.e.
Coef.
a
-0.669 (0.191)
0.989 (0.206)a
0.881 (0.432)b
(0.233)b
0.774
410
32.1a
4
s.e.
0.847
0.873
1.580
-0.842
-0.163
a
(0.321)
(0.219)a
(0.575)a
(0.214)a
(0.260)
Coef.
s.e.
6.864
0.566
0.871
-1.072
-0.200
(1.105)a
(0.276)b
(0.723)
(0.291)a
(0.328)
(0.446)c
410
29.4a
48
410
43.3a
5.273 (0.999)a
-1.387 (0.193)a
1.262 (0.335)a
0.149 (0.323)
408
95.0a
49
Constant
Private equity
Target initiated deal
Profitability
Market to book
R&D
Liquidity index
Anti-takeover state
Leverage
Number of obs.
χ2
s.e.
a
Coef.
s.e.
Controlled sale
-0.374 (0.279)
0.012 (0.275)
0.720 (0.263)a
0.178 (0.447)
0.067 (0.101)
410
89.0a
-1.662 (0.346)
1.016 (0.287)a
2.172 (0.291)a
0.923 (0.454)b
0.041 (0.112)
Coef.
Auction
1
a
-1.288
1.004a
1.452a
0.744c
-0.026
diff.
Coeff.
-1.404
0.998
2.287
1.094
-0.005
0.521
1.600
-0.522
-1.052
Coef.
a
-0.332
-0.068
0.803
0.444
-0.040
0.973
1.547
-0.482
0.015
(0.373)
(0.286)
(0.278)a
(0.447)
(0.099)
(0.311)a
(0.637)b
(0.293)c
(0.543)
s.e.
Controlled sale
Coef.
406
101.5a
(0.433)
(0.301)a
(0.310)a
(0.457)b
(0.113)
(0.351)
(0.636)a
(0.319)c
(0.546)c
s.e.
Auction
2
-1.071a
1.066a
1.484a
0.650
0.035
-0.452
0.053
-0.040
-1.066c
diff.
Coef.
This table presents estimation results for a multinomial logistic regression that models the choice of the selling mechanism. Dependent
variable is the selling mechanism that could be a full-scale auction, controlled sale or private negotiation. Private negotiation is the
omitted category. Robust standard errors are provided in brackets. Private equity is a dummy variable equal to one in case a private
equity firm was the winning bidder and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm
initiated the sale of their company and equal to zero otherwise. Profitability stands for net income to total assets for the financial year
ending before the SDC deal announcement. Market to book is defined as target firm market capitalization plus book value of long-term
debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. R&D
is a dummy variable equal to one for all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and
zero otherwise. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of
all the firms in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for targets
incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is
the long-term debt to total assets for the financial year ending before the deal announcement. a , b and c denotes statistical significance
at the one-, five- and ten-percent level.
Table 5: Choice of the selling mechanism
Table 6: Premium and target characteristics
This table presents OLS estimation results. Dependent variable is the adjusted premium defined as the price
offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC
announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of the deal in Factiva. Premium
is winsorized at the 1st and 99th percentile. Robust standard errors are provided in brackets. Private equity is a
dummy variable equal to one in case a private equity firm was eventually the winning bidder and zero otherwise.
Consortium deal is a dummy variable equal to one in case the private equity acquisition is made by two or more
private equity investors and zero otherwise. PE portfolio firm is a dummy variable equal to one in case the target
firm is acquired by a firm that is majority owned by a private equity investor and zero otherwise.Target initiated
deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero
otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal
announcement. Market to book is target firm market capitalization plus book value of long-term debt over book
value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement.
Stock performance is the return over one and half years ending at the base date. Analyst dummy is a dummy
variable equal to one in case the company is followed by at least one analyst and zero otherwise. Target total
assets is log of the book value of total assets. Minimum price is the log of the ratio of the price on the base
date to the minimum price over one year ending on the base date. Maximum price is the log of the ratio of the
maximum price over one year ending on the base date to the price on the base date. a , b and c denotes statistical
significance at the one-, five- and ten-percent level.
1
Panel A
Coef.
Constant
Private equity
Consortium deal
PE portfolio firm
Target initiated deal
Profitability
Market to book
Stock performance
Analyst dummy
Target total assets
Number of observations
R2
Coef.
a
0.738 (0.073)
-0.115 (0.065)c
-0.083 (0.056)
0.105 (0.126)
-0.106 (0.021)a
0.737
-0.140
0.090
0.006
-0.080
0.112
-0.106
410
5.6
3
s.e.
Coef.
a
(0.074)
(0.070)b
(0.071)
(0.069)
(0.055)
(0.126)
(0.021)a
Coef.
1.028
-0.128
0.072
0.058
-0.105
0.296
-0.101
1.059
-0.112
0.062
0.057
-0.092
0.295
-0.080
-0.183
-0.199
-0.045
410
5.9
Pooled sample
Panel B
Constant
Private equity
Consortium deal
PE portfolio firm
Target initiated deal
Profitability
Market to book
Tangible assets
Stock performance
Stock perf. x tangible
Analyst dummy
Target total assets
Minimum price
Maximum price
Number of observations
R2
2
s.e.
s.e.
Coef.
a
(0.147)
(0.070)c
(0.070)
(0.075)
(0.054)c
(0.149)b
(0.021)a
-0.182 (0.065)a
-0.045 (0.019)b
-0.026 (0.079)
0.160 (0.094)c
410
11.9
s.e.
Coef.
0.996 (0.145)
-0.108 (0.066)c
0.059 (0.069)
0.061 (0.075)
-0.094 (0.055)c
0.346 (0.155)b
-0.079 (0.019)a
(0.055)a
-0.198 (0.066)a
-0.042 (0.020)b
0.038 (0.086)
0.069 (0.097)
410
14.5
50
(0.125)a
(0.067)c
(0.074)
(0.070)
(0.054)c
(0.140)b
(0.019)a
(0.045)a
(0.066)a
(0.018)b
409
14.2
Strategic buyer
a
-0.169
s.e.
Coef.
s.e.
a
0.934
(0.203)a
-0.068 (0.083)
0.750 (0.148)a
-0.069 (0.021)a
-0.526 (0.166)a
-0.385 (0.104)a
0.787 (0.360)b
-0.318 (0.102)a
-0.038 (0.028)
0.174 (0.117)
0.058 (0.106)
204
26.7
-0.059
-0.186
-0.106
0.092
-0.175
0.174
-0.079
-0.049
-0.150
0.017
(0.068)
(0.194)
(0.042)b
(0.125)
(0.089)c
(0.170)
(0.078)
(0.020)b
(0.089)c
(0.173)
205
17.1
1.125
s.e.
PE buyer
(0.193)
Table 7: Test results for the validity of the exclusion restrictions
The table displays individual likelihood ratio test results for the presence of the variables
in the corresponding equations. More detailed discussion of the tests is provided in Appendix C. Industry count is the log of the number of firms in the same FF49 industry
that have larger market capitalization than the target as of previous December. Cash is
defined as cash and marketable securities to total assets for the financial year ending before the SDC announcement. Liquidity index is the ratio of the value of corporate control
transactions in a year to the total book value of assets of all the firms in the same industry
during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for
target incorporated in Delaware, Idaho, Indiana, Maryland, Nevada, Ohio, Pennsylvania,
South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for
the financial year ending before the deal announcement. Stock performance is the return
over one and half years before the base date. Analyst dummy is a dummy variable equal
to one in case the company is followed by at least one analyst and zero otherwise. Target
total assets is log of the book value of total assets.
Validity test
Variable
LR
dof
Exogeneity test
p-value
Private equity equation
Industry count
Cash
44.335
75.864
1
1
0.00
0.00
Sell. mechanism equations
Liquidity index
Anti-takeover state
Leverage
22.292
8.127
14.417
2
2
2
0.00
0.02
0.00
Premium equation
Stock performance
Analyst dummy
Target total assets
22.379
6.981
3.272
1
1
1
0.00
0.01
0.07
51
LR
dof
p-value
Premium & selling
mechanism equations
0.171
0.248
3
3
0.98
0.97
Private equity
& premium equations
0.426
1.372
0.265
2
2
2
0.81
0.50
0.88
Private equity & selling
mechanism equations
1.489
4.180
2.308
3
3
3
0.68
0.24
0.51
52
Private equity
Auction
Controlled sale
Target initiated deal
Profitability
Market to book
Stock performance
Analyst dummy
Target total assets
R&D
Cash
Industry count
Liquidity index
Anti-takeover state
Leverage
-0.038
-0.064
0.091c
-0.068
0.006
-0.192a
-0.221a
-0.184a
-0.116b
-0.055
-0.026
0.027
-0.042
-0.075
-0.009
Premium
0.295a
-0.141a
0.234a
0.114b
-0.322a
0.042
-0.010
0.086c
-0.140a
0.050
-0.420a
0.097c
-0.071
0.101b
Private
equity
-0.51a
0.413a
0.095c
-0.096c
0.039
-0.088c
-0.036
-0.045
-0.005
-0.080
0.033
-0.057
-0.037
Auction
-0.102b
-0.037
0.067
-0.013
0.063
0.017
0.142a
0.042
0.094c
0.048
-0.038
0.009
Contr.
sale
-0.032
-0.139a
0.018
-0.109b
-0.027
-0.022
0.013
-0.022
-0.088c
0.016
0.068
Target
initiated
0.151a
0.284a
0.030
0.270a
-0.214a
-0.273a
-0.343a
0.068
-0.089c
0.119b
Profitability
0.260a
0.110b
-0.114b
0.214a
0.155a
0.105b
0.003
0.001
-0.005
Market
to book
-0.093c
0.025
-0.080
-0.085c
-0.166a
-0.008
0.016
0.037
Stock
perform.
0.275a
0.094c
0.006
0.055
-0.025
-0.015
0.019
Analyst
dummy
-0.166a
-0.268a
-0.304a
0.125b
0.157a
0.263a
Total
assets
0.479a
0.381a
-0.081
-0.011
-0.250a
R&D
0.344a
-0.053
0.059
-0.351a
Cash
-0.112b
-0.026
-0.211a
Industry
count
-0.015
0.102b
Liquid.
index
0.054
Antitakeover
This table presents the correlation matrix. Premium is the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before
the SDC announcement; in case there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one
trading day before the first mention of the deal in Factiva. Premium is winsorized at the 1st and 99th percentile. Standard errors are provided in brackets. Auction is
a dummy variable equal to one in case the company is sold in a highly organized full-scale auction with pre-set rules and zero otherwise. Controlled sale is a dummy
variable equal to one in case the target company decides to discreetly canvass a limited number of bidders that target management believes to have a serious interest in
acquiring the company and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal
to zero otherwise. Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is defined as target
firm market capitalization plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC
announcement. Stock performance is the return over one and half years before the base date. Analyst dummy is a dummy variable equal to one in case the company is
followed by at least one analyst and zero otherwise. Target total assets is natural logarithm of the book value of total assets. R&D is a dummy variable equal to one for
all FF49 industries that are top 7 industries with respect to the average industry R&D ratio and zero otherwise. Cash is defined as cash and marketable securities to total
assets for the financial year ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market
capitalization than the target as of previous December. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets
of all the firms in the same industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for targets incorporated in Delaware, Idaho,
Indiana, Maryland, Nevada, Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending
before the deal announcement. a , b and c denotes statistical significance at the one-, five- and ten-percent level.
Table 8: Correlation matrix
53
Structural parameters
Private equity
Auction
Controlled sale
Direct effect parameters
Constant
Target initiated deal
Profitability
Market to book
Stock performance
Analyst dummy
Target total assets
R&D
Cash
Industry count
Liquidity index
Anti-takeover state
Leverage
(0.118)
(0.191)
(0.162)
0.886 (0.169)a
0.022 (0.282)
0.360 (0.180)b
-0.070 (0.041)c
-0.186 (0.055)a
-0.095 (0.044)b
-0.041 (0.028)
0.028
-0.095
0.056
Estimation
Coef.
s.e.
0.930 (0.204)a
-0.119 (0.056)b
0.290 (0.103)a
-0.065 (0.034)b
-0.186 (0.055)a
-0.095 (0.044)b
-0.041 (0.028)
0.004 (0.067)
-0.010 (0.134)
0.002 (0.029)
-0.052 (0.127)
0.006 (0.037)
0.066 (0.136)
Reduced form
Coef.
s.e.
Premium equation
1.183
0.196
0.160
-0.189
-0.028
1.109
-0.237
0.246
-0.123
0.267
(1.014)
(0.309)c
(0.328)
(0.135)c
(0.218)c
(0.849)
(0.177)c
-0.358
1.371
-0.294
(0.078)
(0.867)
(0.182)
(0.223)
(0.095)
(0.172)
(0.908)
(0.163)
(0.160)
(0.142)
Reduced form
Coef.
s.e.
1.333
0.598
0.332
-0.228
-0.428 (0.211)b
0.678 (0.090)a
Estimation
Coef.
s.e.
Private equity equation
(0.280)
(0.670)c
(0.242)
(0.577)
1.270
-0.293
-0.696
(0.320)b
(0.228)a
(0.579)c
(0.141)
(0.544)
0.387
-0.745
1.856
0.965
-0.069
0.290
Estimation
Coef.
s.e.
0.379
0.321
-0.069
1.341
-0.329
-0.618
(0.286)a
(0.546)
(0.117)
(0.663)c
(0.245)b
(0.576)
-0.402 (0.552)
1.912 (0.237)a
1.011 (0.575)c
-0.123 (0.105)
Reduced form
Coef.
s.e.
Auction equation
(0.428)
1.250
-0.409
0.048
0.720
(0.650)b
(0.202)
(0.489)
(0.262)
-0.279 (0.266)
0.645 (0.227)a
0.410 (0.431)c
-0.051 (0.122)
-0.166
Estimation
Coef.
s.e.
0.725 (0.263)a
-0.184 (0.456)
0.039 (0.098)
1.209 (0.635)c
-0.389 (0.197)b
0.004 (0.478)
-0.476 (0.515)
0.613 (0.215)a
0.383 (0.415)
-0.019 (0.093)
Reduced form
Coef.
s.e.
Controlled sale equation
This table presents system estimation results with the adjusted premium, buyer type and selling mechanism being determined together in one system as defined in
Appendix C. Premium is the price offered less price 42 trading days before the SDC announcement over the price 42 trading days before the SDC announcement; in case
there was a leakage of information earlier than 42 trading days before the SDC announcement, we benchmark against the price one trading day before the first mention of
the deal in Factiva. Premium is winsorized at the 1st and 99th percentile. Standard errors are provided in brackets. Private equity is a dummy variable equal to one in case
a private equity firm was eventually the winning bidder and zero otherwise. Auction is a dummy variable set equal to one if the firm was sold in a full-scale formal auction
and zero otherwise. Controlled sale is a dummy variable equal to one in case the firm was sold in competitive bidding with several bidders but not is a full-scale formal
auction and zero otherwise. Target initiated deal is a dummy variable equal to one in case the selling firm initiated the sale of their company and equal to zero otherwise.
Profitability stands for net income to total assets for the financial year ending before the SDC deal announcement. Market to book is target firm market capitalization
plus book value of long-term debt over book value of total assets adjusted for short-term liabilities for the financial year ending before the SDC announcement. Stock
performance is the return over one and half years before the base date. Analyst dummy is a dummy variable equal to one in case the company is followed by at least
one analyst and zero otherwise. Target total assets is log of the book value of total assets. R&D is a dummy variable equal to one for all FF49 industries that are top
7 industries with respect to the average industry R&D ratio and zero otherwise. Cash is defined as cash and marketable securities to total assets for the financial year
ending before the SDC announcement. Industry count is the log of the number of firms in the same FF49 industry that have larger market capitalization than the target
as of previous December. Liquidity index is the ratio of the value of corporate control transactions in a year to the total book value of assets of all the firms in the same
industry during that year using 3-digit SIC codes. Anti-takeover state is a dummy equal to one for targets incorporated in Delaware, Idaho, Indiana, Maryland, Nevada,
Ohio, Pennsylvania, South Dakota, Tennessee and Wisconsin. Leverage is the long-term debt to total assets for the financial year ending before the deal announcement.
a , b and c denotes statistical significance at the one-, five- and ten-percent level.
Table 9: System estimation results