Winning by Losing:
Evidence on Overbidding in Mergers
Ulrike Malmendier
Enrico Moretti
Florian Peters
UC Berkeley and NBER
UC Berkeley and NBER
U Amsterdam and DSF
March 2011
Abstract
Do acquiring companies profit from acquisitions, or do acquiring CEOs overbid and destroy
shareholder value? Answering this question empirically is difficult since the hypothetical
counterfactual is hard to determine: A negative stock reaction of the acquiror to a merger
announcement is consistent with value-destroying mergers but also with overvaluation of the
acquiror. We argue that bidding contests where at least two bidders have a significant chance
of winning and acquiring the target help to address the identification issue: the post-merger
performance of the loser allows to calculate the counterfactual performance of the winner
without the merger. We construct a novel data set of all mergers with overlapping bids between
1983 and 2009. We find that the stock returns of bidders are not significantly different before
the merger contest, but diverge post-merger. In the full sample, winners underperform losers
over a three-year horizon, but there is large heterogeneity and the effect is not significant. In
the subsample of deals where at least two bidders have a significant chance at winning losers
outperform winners, while the opposite is true in cases with a predictable winner.
Keywords: Mergers; Acquisitions; Misvaluation; Hypothetical Counterfactual
JEL classification: G34; G14; D03
1
Introduction
Do acquiring companies profit from acquisitions? Or do acquiring CEOs, on average, overbid and
destroy shareholder value? The large payments for acquisitions and negative announcement effects
observed for a large set of acquirors have attracted a lot of attention to these questions. Moeller,
Schlingemann, and Stulz (2005) calculate that, during the last two decades, U.S. acquirors lost
in excess of $220 billion at the announcement of merger bids alone. Such findings have been
interpreted as empire building of acquiring CEOs (Jensen (1986)), other misaligned personal
objectives of CEOs (Morck, Shleifer, and Vishny (1990)), or the result of CEO overconfidence
about their proposed mergers (Roll (1986), Malmendier and Tate (2008). However, the evaluation
of the causes and consequences of mergers has been hampered by the empirical difficulty in
assessing the value created (or destroyed) in mergers. Using the announcement effect as a proxy,
one may underestimate the value creation of a merger due to price pressure around mergers
(Mitchell, Pulvino, and Stafford (2004)), information revealed in the merger bid (Asquith, Bruner,
and Mullins (1987)), or failure of the efficient markets hypothesis (Loughran and Vijh (1997)).
To the extent that the returns to mergers are revealed only over time and the announcement
effect is insufficient, it is hard to measure what portion of the returns can be attributed to a merger
decision rather than other corporate events or market movements. Consider, for example, the
argument of Shleifer and Vishny (2003) and ? that CEOs undertake mergers when their own firms
are overvalued. Under this scenario, they purchase assets (i.e. targets) that are less overvalued
at a cheap price by using their overvalued stock. The announcement effect of mergers is negative
because bids reveal the acquirors’ overvaluation and because the targets are less highly valued.
However, in the long-run such mergers might still be in the best interest of current shareholders:
Had the CEOs not used the overvalued stock for acquisitions, the stock price would have fallen
even more. Moreover, long-term returns may also be affected by slowly declining overvaluation.
In other words, it is hard to evaluate the returns to mergers empirically due to the lack of a clear
counterfactual.
This paper studies bidding contests to address the identification issue and, in particular, the
1
concern about prior run-ups in stock prices. Our research design exploits the concurrent bidding
of two or more companies for the same target. We identify cases in which at least two bidders had
ex ante, i.e., at the time they made their offer, a significant chance at winning the contest and
acquiring the target. We show that, in these cases, bidders show very similar stock performance
prior to the merger contest and argue that the losing bidder’s post-merger performance helps
to calculate the hypothetical post-merger performance of the winning bidder had the merger
not taken place. The basic idea is that participation in such a bidding contest provides an
additional matching criterion, beyond the usual market-, industry-, and firm level controls. To
the extent that overvaluation affects the propensity to acquire, participation in a bidding fight
should account for that. It is also likely to capture strategic considerations that affect the decision
to attempt a takeover, which are hard to control for with the set of standard financial variables.
More generally, our empirical methodology allows us to include controls beyond time-varying firm
characteristics and beyond unspecified time-invariant firm characteristics (i. e. beyond firm fixed
effects). In particular, we are able to control for unspecified merger case effects. The ability
to include an additional set of case fixed effects and to test the validity of our hypothetical
benchmark directly, by comparing price paths and other characteristics of bidders prior to the
merger fights, provides a methodological improvement relative to prior empirical approaches to
measuring the returns to mergers for the acquiring company. While our findings are specific
to the set of contested mergers, the discrepancy between our results and those generated with
standard approaches of measuring returns to mergers suggest that the well-known biases in prior
approaches are economically important.
We construct a novel data set of all mergers with overlapping bids of at least two potential
acquirors between 1983 and 2009. We then compare adjusted abnormal returns of all candidates
both before and after a merger fight. Analyzing the price paths of bidders before the merger
fight allows us to check directly the validity of our key identifying assumption - that losing
bidders are a valid counterfactual for the winner, after employing the usual controls and matching
criteria. We find that stock returns of bidders are not significantly different before the merger
fight, but diverge significantly after one bidder has completed the merger. On average, winners
2
underperform losers over a three-year horizon post-merger, but there is large heterogeneity and
the effect is not significant. We then identify the subsample of cases where both the (ultimate)
winner and the (ultimate) losers of a given merger contest appear to have, ex ante, a significant
chance at winning the merger contest. Specifically, we exploit contest duration and press reports
to identify cases of actual ”bidding wars.” In short-duration merger contests (e.g., the lowest
quartile of two- to four-month merger contests), there is typically a clear candidate for winning,
while in long-duration contest (e.g., the highest quartile of one-year and longer contests), the
back and forth between bidders indicates that at least two bidders have a significant chance at
winning the contest. Splitting the sample into quartiles, we find that winners outperform in the
bottom quartile of contest duration but underperform in the top quartile. Each effect is at least
marginally significant, and the difference is strongly significant in all specifications. The results
exploiting press reports show the same pattern.
These results are robust to various sample selection criteria and controls. In particular, we
address the alternative explanation that winners undergo a change in risk profile due to the
merger. We test for significant differences in winner- versus loser pre- and post-merger fight
betas to address out this hypothesis. Moreover, a risk- shift explanation would require a jump in
price level of winners at merger time relative to the losers, which we do not observe.
Our results suggest that, for the subset of mergers that involve at least two bidders either one
of whom could be winning the contest, making the acquisition destroys shareholder value of the
acquiror, on average. The leading alternative hypothesis, acquiror overvaluation, would require
winning bidders to be more overvalued than losing bidders. The closely matched price paths of
winners and losers prior to the merger contest suggest that this is not the case. In addition, the
use of firm and contest dummies addresses this concern.
The research design in this paper is motivated by Greenstone, Hornbeck, and Moretti (2011).
There, the authors analyze the local consequences of attracting a million-dollar plant to a county,
including the effects on labor earnings, public finances, and property values. Compared to the
county-level analysis in their paper, mergers allow for considerably more exhaustive controls
of heterogeneity among bidders. Moreover, stock prices of bidding companies incorporate the
3
markets expectations about future cash flow. At a minimum we view this methodology as an
improvement over previous matching procedures as sole the basis for alternative (counterfactual)
returns.
The paper proceeds as follows. Section 2 describes the data sources and presents some summary statistics. Section 3 explains the econometric model. Section 4 describes the results while
Section 5 concludes.
2
2.1
Data
Sample Construction
Our initial dataset contains publicly submitted bids in merger contests between January 1983
and December 2009 recorded in the SDC Mergers and Acquisitions database.
The takeover process typically begins long before a bid is publicly placed, and can be divided
into a private and a public stage. This process has been described in detail by Boone and Mulherin (2007). It usually starts with an investment bank soliciting interest of potential acquirors,
after which interested parties sign confidentiality agreements and obtain access to non-public information about the target. Following this, several non-binding rounds of bidding are conducted
in order to identify a small group of seriously interested parties. A final auction among these
bidders then leads to a binding offer, and the prevailing bid becomes public. Any competing bid
placed from then on, is public and binding. It is the public bids, i.e. the prevailing bid of the
final private auction and any rival bid placed after that, that SDC records. In the spirit of our
identification strategy, we focus on these binding and public bids, because the respective bidders
are most seriously interested in acquiring the target and thus more likely to be ex ante similar.
For each bid we collect the SDC deal number, the acquiror’s SDC assigned company identifier
(CIDGEN), six-digit CUSIP, ticker, nation, company type, and the SIC and NAICS codes. We
also collect the following bid characteristics: announcement date, effective or withdrawal date,
the percentage of the transaction value offered in cash, stock or other means, the deal attitude
(friendly or hostile), and the acquisition method (tender offer or merger).
4
We restrict the initial sample of bids in the following ways:
• Deal status: We eliminate merger contests that are not completed by December 31, 2009.
• Acquiror nation: We restrict our analysis to U.S. acquirors.
• Acquiror type: We consider bids by public companies only, and exclude privately held
and government-owned firms, investor groups, joint ventures, mutually owned companies,
subsidiaries, and firms whose status SDC cannot reliably identify.
• Parent/subsidiary relationship: We exclude contests in which the acquiror is the ultimate
parent of the target company.
• White knights: Bids by companies that enter as white knights are excluded.
We define bidders as contestants in the same merger fight if their bids are for the same target
and the time periods in which the bids are effective overlap. SDC contains a variable indicating
whether a particular bid was contested, and provides the bid number(s) of the competing bids.
We code two bids as competing if, for a given target, the first bid is recorded as a competing
bid of the second bid and vice versa, or if both bids have a common third competing bid. We
then check that all bids classified as contested in the first step are placed before the recorded
completion date. Companies that succeed in completing the merger are classified as winners, all
other bidders participating in the same contest are coded as losers. In our initial dataset, there
are three contests to which SDC erroneously assigns two winners. We hand-check these cases and
identify the unique winner by a news wire search.
The beginning of a merger fight is defined as the announcement date of the earliest bid. The
end of a merger fight defined as the date of completion, or the last available withdrawal date
if there is no winner. We create an event time variable, t, which counts the months relative to
the contest period. We set t equal to 0 for the end of the month preceding the start date of the
merger contest, and equal to +1 for the end of the month in which the merger fight ends. Figure
1 illustrates the variable construction in a stylized example.
5
[Figure 1 approximately here]
Next, we merge the SDC data with financial and accounting information from CRSP and
COMPUSTAT using each company’s CRSP permanent company and security identifiers (PERMCO
and PERMNO). To obtain the PERMCO pertaining to each SDC bidder, we match the 6-digit
CUSIP provided by SDC with the first six digits of CRSP’s historical CUSIP (NCUSIP). In
general, the CUSIP of a particular firm can change over time as CUSIPs are sometimes reassigned. Thus, the 6-digit CUSIP uniquely identifies a firm only at a given point in time. Since
reassignment of CUSIPs is particularly common following a merger, we are careful to take this
into account when determining bidder PERMCOs. Specifically, we match, for any given bid,
SDC’s 6-digit bidder CUSIP with CRSP’s 6-digit NCUSIP for the month end preceding the announcement date, and extract the respective PERMCO. This procedure ensures that we obtain
the correct PERMCO for any given bidder. In a second step, we manually check that, for the
sample of matched CUSIPs, SDC company names correspond to CRSP company names. This
is indeed the case for all matched bidders. Another complication arises from the fact that firms
often have multiple equity securities outstanding. In these cases we use (1) the common stock
if common and other types of stock are traded; (2) Class A shares if the company has Class A
and Class B outstanding; (3) the stock with the longest available time series of data if there are
multiple common stocks traded.
We extract the following data for 5-year periods around the start and end date of each contest
and bidder:
• CRSP Monthly Stock Database: holding period stock return (RET), distribution event code
(DISTCD), delisting code (DLSTCD), and delisting return (DLRET).
• CRSP-COMPUSTAT Fundamental Annual Database: Annual accounting data such as total assets, book and market value of equity, operating income, and property, plants and
equipment.
6
• Market data: monthly CRSP value-weighted index returns, T-bill yields, and Fama-French
factor returns.
Using the monthly CRSP stock return series, we then construct the time series of monthly
bidder stock prices for an initial window of 5 years around the merger fight (t= -59 to t=60). We
normalize the price series to 100 at t=0. We emphasize that the CRSP holding period return is
adjusted for stock splits, exchanges, and cash distributions, and thus properly accounts for such
events which are particularly common around mergers.
We then construct the time series of target stock prices in the same way as we do for bidders.
We use this time series to compute the offer premium as the run-up in the target stock price
from one month preceding the start of the contest (t = −1) until completion of the merger. We
compute the offer premium both in percent of the target equity value, and in percent of the
acquiror equity value.
Our initial sample contains 193 takeover contests representing 416 bids by 402 bidders of which
154 are winners and 248 are losers. We first drop repeated bids by the same bidder, and keep only
the final bid. This eliminates 14 bids. We then drop 45 contests that have not been completed
until December 31, 2009. Next, we drop 2 contests for which either the winner or the loser could
not the matched to a CRSP PERMNO. We then delete contests where the winner is the ultimate
parent company of the target (10 contests) since ultimate parents are unlikely to provide a good
comparison for other bidders. When we require both the winner and the loser to have non-missing
stock price data for at least periods 0 (the month preceding the start of the contest) to period
1 (the month following the end of the contest), another 4 contest drop out of the sample. We
also eliminate seven bids where the bidding firm has extreme stock price volatility over the event
window, with the standard deviation of the price exceeding 200,1 since these firms appear to be
be influenced by idiosyncratic factors and are, ex ante, a poor benchmark for their respective
contestants. (If we keep these firms in our sample, our qualitative results remain unchanged, but
1
The volatility is calculated using the full event window of +/- three years. Four of these firms are in the High
Tech sector: CTS Corp, Yahoo!, Liquid Audio Inc, and QWest Communications. Two are in the Healthcare sector:
Inamed Corp and Hyseq Pharmaceuticals. One firm, Cannon Group, operates in the Service sector. All of these
firms show 10 to 20-fold increases and reversals in their stock market valuations, mostly occurring in the pre-merger
period.
7
the confidence bounds in the pre-merger period increase substantially.) For our baseline sample
we further require non-missing stock price data for at least periods −35 to +36 (i.e., 3 years
before and after the contest). This reduces the number of contests to 114. Finally, we keep only
those contests for which we data for both the winner and the loser. This reduces the sample by
another 20 contests. The final sample contains 82 merger contests, representing 172 bids by 82
winners and 90 losers. Table 1 summarizes the construction of our dataset, Figure 2 illustrates
the frequency distribution of the contests over the sample period.
[Table 1 approximately here]
[Figure 2 approximately here]
In the 3-year period following a merger contest, many bidder stocks disappear from CRSP
due to delisting. We are careful to account for delisting events and their implications for stock
holders using all available delisting information provided by CRSP. CRSP’s delisting code (DLSTCD) classifies delists broadly into mergers, exchanges for other stock, liquidations, and several
categories of dropped firms. In addition, CRSP provides delisting returns and distribution information.2 We track the performance of a delisted firm as if shareholders were buy-and-hold
investors whenever they receive payments in stock, mirroring the underlying assumption when
tracking performance of listed firms. Specifically, we assume that stock payments in takeovers are
held in the stock of the acquiring firm; exchanges for other stock are held in the new stock. When
shareholders receive payments in cash (in mergers, liquidations, and bankruptcies), or CRSP
cannot identify or does not cover the security in which payments are made, we track performance
as if all proceeds were invested in the market portfolio. We use the value-weighted CRSP index
as a proxy for the market portfolio.
2
Delisting returns are defined as shareholder returns from the last day the stock was traded to the earliest
post-delisting date for which CRSP could ascertain the stock’s value. Distribution data contains information about
whether and to what extent shareholders of a takeover target were paid in cash or stock
8
2.2
Descriptive Statistics
In this section, we provide descriptive statistics of bidder and deal characteristics. Panel A of
Table 2 contains the bidder characteristics, Panel B contains the bid characteristics. In Panel A,
we report the statistics separately for winners and losers. The two rightmost columns report the
winner-loser differences in means and medians. The variables are computed from yearly balance
sheet and income data, and refer to the fiscal year end preceding the beginning of the contest.
The first two rows of Panel A indicate that both winning and losing firms in contested bids are
very large compared to the average Compustat firm. This is due to the fact that we require
firms to be public, but also that SDC is more likely to record bids of large firms. In terms of
firm size, winners tend to be larger than losers. However, the size difference is much smaller
than that between the average acquiring and non-acquiring firm. Winners also have a somewhat
higher Tobin’s Q than losers. Profitability and leverage are virtually identical between winners
and losers.
The tests for differences in means and medians reveal that none of these differences in characteristics is statistically significant. This is a first indication that our identifying assumption that
losers are a valid counterfactual for the winners is indeed supported by the data.
Panel B shows that transaction values in contested mergers are quite large compared to bidder
size. The average transaction value is about one quarter of the losers’ market capitalization and
about 16 percent of the winner’s market capitalization. Another interesting aspect is that most
contests in SDC are between two bidders, while higher numbers of contestants are very rare. Also
notable is the fact the final offer premium in our sample of contested bids is greater than that of
non-contested bids. Betton, Eckbo, and Thorburn (2008) find that the average offer premium in
a sample of 4,889 control bids for US targets during 1980-2002 to be 48 percent. The final offer
premium in our sample is about 58 percent. This may be an indication of some degree of winner’s
curse brought about by the presence of competing offers. Below we explore this possibility in more
detail.
3
Maybe the most important difference between contested and non-contested acquisitions
3
Offer premia expressed as a percentage of the acquiror equity value are smaller since acquirors tend to be
significantly larger than targets.
9
is contest duration. While the average time to completion in single-bidder mergers is about 65
trading days (see Betton et al. (2008)), merger contests take, on average, 9.5 months - and thus
about three times as long - to close. This seems to be a defining aspect of merger contests. In
Section 4.3 we therefore explore the correlates of contest duration in more detail. One plausible
hypothesis is that contest duration is a proxy for bidder similarity. Single-bidder acquisitions
may fail to elicit competing bids - and thus have short completion times - precisely because other
potential acquirors differ too much in terms of the synergies they could generate. Competing
bids are only launched if these synergies are similar enough for at least two potential acquirors.
Finally, a contest of multiple competing bidders is likely to take longer the more similar the
synergies are.
[Table 2 approximately here]
3
Econometric Model
A simple estimator of the effect of mergers on firm performance can be obtained by regressing
stock market price on a dummy for whether a firm successfully completes a merger, controlling
for observable characteristics of the firm. Alternatively, a matching estimator can be obtained
by comparing the stock market price of firms that successfully complete a merger to the stock
market price of the average firm in the market with a similar set of observable characteristics.
The consistency of both types of estimators crucially depends on the assumption that in the
absence of the merger the stock market price of the acquiring firm would have evolved like the
stock market price of the average firm with similar observable characteristics. In other words,
both the regression and the matching estimator are based on the assumption that the acquiring
firm and the average firm in the market have identical unobserved determinants of stock prices,
conditional on covariates.
10
Of course, this assumption could be violated in reality. The acquiring firm is likely to be
different along many unobserved dimensions from the average firm in the market. Both positive
and negative selection, in terms of future stock price performance, is a priori possible. Positive
selection would occur if firms that successfully acquire other firms have better unobservable
characteristics. This would be the case if, for example, firms that are outperforming other firms
in the same industry tend to grow by mergers and acquisitions. Negative selection would occur if
firms that successfully acquire other firms have worse unobservables. This would be the case if,
for example, firms in industries that are experiencing declines in demand tend to consolidate and
merge with other firms. In terms of the naiı̀ve regression or the matching estimator described
above, positive selection would lead to an overestimate of the true effect of a merger on firm
performance. Negative selection would lead to an underestimate of the true effect of a merger on
firm performance.
One important contribution of this paper is the use of contested mergers to eliminate - or at
least reduce - the scope of this type of omitted variable bias. The idea is simple: in mergers where
there is only one potential acquiring firm, any evaluation of future long-run returns is necessarily
relative to other firms with similar observable characteristics. In contested mergers, however, two
firms fight to acquire the same target firm. Our key assumption is that the losing firm provides
a valid counterfactual for what would have happened to the winning firm in the absence of the
merger. Even if this assumption is not true exactly, it is likely that winners are more similar to
losers than to the average firm in the market, or even to non-acquiring firms that have similar
observable characteristics.
Using this identification strategy, our goal is to estimate the causal effect of prevailing in the
contested merger on stock market valuation. Here we describe the econometric model used to
estimate this effect. We use a panel of firms observed for 3 years before and after the merger. We
fit the following regression equation:
Pi,j,τ =
T
X
τ 0 =T
0
τ
πτ 0 Wi,j,τ
+
T
X
0
τ
δτ 0 Ci,j,τ
+ γ 0 Xi,j,t + ηj + ξt + εi,j,τ
τ 0 =T
11
(1)
In this equation, i references firms, j indicates a bidding contest, and τ indexes the month in
event time. As explained above, event time is defined such that τ = 0 indicates the month preceding the start of the merger contest, while τ = 1 refers to the month immediately following the
conclusion of the contest. Observations during the merger contest are not used in this regression.
The outcome variable in equation (1), Pi,j,τ , is the stock price of firm i in month τ relative to
the contest period of contest j. All stock prices are end-of-month values, normalized to 1 in the
month preceding the beginning of the contest, i.e at τ = 0. The vector ηj is a full set of case
fixed effects that adjusts for permanent case-specific differences in the intercept of the outcome
variable. These dummies account for all fixed characteristics of each pair or group of contestants. ξt is a vector of calendar month fixed effects, capturing any calendar time-specific effects
on winner or loser stock prices. These indicators essentially control for aggregate market-driven
fluctuations of bidder prices. Xi,j,t is a vector of time-varying observable firm characteristics,
εi,j,τ is a stochastic error term.
0
0
0
τ
t
τ
The key variables are the Wi,j,τ
and the Ci,j,t
indicators. The Ci,j,τ
variables are simply a
0
τ
set of dummies indicating event time, i.e. event time fixed effects. That is, Ci,j,τ
is equal to 1
- whether firm i is a winner or loser - if the event period is equal to the running index τ 0 , i.e.
0
0
τ
τ
Ci,j,τ
= 1(τ = τ 0 ). The Wi,j,τ variables are a set of event time-winner indicators. That is, Wi,j,τ
takes the value of 1 if firm i is a winner in contest j and the observation pertains to event period
0
τ
τ 0 . Formally, Wi,j,τ
= 1(τ = τ 0 and firm i wins contest j). Given these two sets of indicator
variables, the coefficients δτ 0 measure the average loser price in period τ 0 relative to the contest
period, while the coefficients πτ 0 estimate the average price difference between the winner and the
loser in event period τ 0 . In this way, the effect of winner or loser status is allowed to vary with
event time. For example, for τ 0 = 3, δτ 0 is the conditional mean of the loser price 3 months after
the end of the bidding contest, and πτ 0 is the conditional mean difference between the winning
and losing firms’ stock prices 3 months after the completion of the merger.
A few details about the identification of the π coefficients deserve highlighting. First, and most
importantly, including case fixed effects guarantees that the π-series is identified from comparisons
within a winner-loser pair. Including them allows us to retain the intuitive appeal of pairwise
12
differencing in a regression framework. Second, it is possible to separately identify the π’s, and
calendar time effects because the merger announcements occur in multiple years. Third, some
firms are winners and/or losers two times or more, and any observation from these firms will
simultaneously identify multiple π’s.
For more refined statistical tests, we specify a regression that estimates a piecewise-linear
approximation of the period-specific π-coefficients of equation (1):
Pi,j,τ = α0 + α1 Wi,j,τ + α2 τ + α3 τ × Wi,j,τ + α4 P osti,j,τ + α5 P osti,j,τ × Wi,j,τ
+ α6 τ × P osti,j,τ + α7 τ × P osti,j,τ × Wi,j,τ + ηj + ξt + δ 0 Xi,j,τ + εi,j,τ
(2)
This specification allows for linear, and different pre-contest and post-contest trends in the winner
and loser prices as well a level shift of the winner-loser price difference at the time of the merger
contest. Our statistical tests focus on the following parameters: To establish validity of our
identifying assumption that losers are a valid counterfactual, we are interested in the parameters
• α1 : The average level difference between winner and loser price before the merger contest.
• α3 : The trend difference between winner and loser price before the merger contest.
A test of [α1 +# of pre-merger periods·α3 ] = 0 is informative about whether winner and loser
stock prices diverge significantly before the beginning of the contest. An insignificant t-statistic
supports our identifying assumption.
For the causal effect of the merger we are interested in
• α5 : A differential jump in stock prices for winners relative to losers around the merger.
• α7 : A shift in the trend difference between winner and loser price after the merger contest.
• α3 : In case the identifying assumption is not rejected, α3 + α7 measure the total divergence
(not just the shift in divergence around the merger) of winners and losers post-merger.
A test of [α5 + # of post-merger periods · (α3 + α7 )] = 0 is informative about whether winner
and loser stock prices diverge significantly during and following the contest. A significant t13
statistic suggests a causal effect of the merger on the winning firm. The parameter measuring
the pre-merger trend, α3 , is included in the test equation, since our aim is to measure the total
slope of the post-merger trend, not just the incremental trend shift, given that the identifying
assumption is never rejected in our data.
To illustrate our estimation method, Figure A-1 plots the series of π-coefficients against event
time τ , along with 95% confidence bands and the piecewise-linear approximation obtained from
0
0
τ
τ
regression (2). As a starting point, we run regressions (1) and (2) with the sets of Ci,j,τ
and Wi,j,τ
dummies and case fixed effects only, i.e. without additional controls. For illustrative purposes,
we use a symmetric 3-year window around the contest period. The point estimates of the π’s
represent the period-specific mean difference between winner and loser stock prices around merger
contests. From this graph, it is evident that the winning and losing firms have very similar price
trends during the 3 years before the start of the contest. The average trend in the winner-loser
price difference, as estimated by the solid linear line in the left half of the graph, is statistically
indistinguishable from zero. This is quite important. Because stock market prices reflect both
current economic conditions as well as future expectations, the similarity in trends in the years
before the announcement lends credibility to our identifying assumption that losing firms provide
a valid counterfactual.
In addition, Figure A-1 shows that winners experience a price drop during the contest period
and in the month immediately following merger completion. From then on, the winners and
losers exhibit essentially identical price paths - the winner-loser difference remains flat. Though
the price drop of winners relative to losers is not statistically significant, below we turn to a more
detailed analysis of the various factors driving the winner-loser price divergence following merger
completion.
[Figure A-1 approximately here]
In principle, the parameters in equations (1) and (2) can alternatively be estimated using ”differenced” data. For example, the OLS estimate of the π-vector in regression (1) is approximately
14
equal to the estimate of the π-vector of the following regression:
∆Pj,τ =
T
X
0
τ
π τ 0 Cj,τ
+ β 0 ∆Xj,t + ξt + ζj,τ
(3)
τ 0 =T
Here, the dependent variable is the period-specific winner-loser price difference within a contest.
Similarly, the ∆Xj,t -vector is the vector of period-specific winner-loser differences in the observable
firm characteristics. Because, in this specification, the regression is run on the within-case winner0
τ , directly estimate the average
loser differences, the coefficients of the event time dummies, Cj,τ
0
τ , thus drops
period-specific winner-loser differences. The series of period-winner indicators, Wi,j,τ
out, as do the case fixed effects. It can be shown that, on a balanced sample with only one loser
per contest, the OLS estimates of π and π are numerically identical. However, these estimates
generally differ on unbalanced samples and on samples that contain contests with multiple losers.
In essence, the ”level” specification of equations (1) and (2) makes better use of multiple losers,
and it is therefore our specifications of choice.
4
4.1
Results
Testing the Identifying Assumption
We start by analyzing statistically the validity of our identifying assumption that the loser in
a merger contest is a valid counterfactual for the winner. The task is to establish similarity of
winner-loser pairs both in observables and unobservables. To do this, we use the pre-merger stock
price paths of each winner-loser pair. We first estimate the model
rijt − rf t = αij + βij (rmt − rf t ) + εijt
(4)
on the pre-merger period, separately for the winner and loser in each contest. In this equation, i
indicates the bidder (either winner or loser), j references the contest, and t denotes the month.
The above decomposition of stock returns allows us to distinguish observable from unobserv-
15
able determinants of bidder prices. The component βij (rmt −rf t ) of the bidder return is explained
by the exposure to market risk and the return of the market portfolio. In contrast, αij and εijt
are due to factors unobserved by the econometrician: αij is the average excess return, i.e. the
part of the price trend that cannot be explained by market risk. εijt , on the other hand, is the
monthly unexplained residual return.
If our identifying assumption indeed holds, we expect that both observable and unobservable
determinants of stock returns are similar within a winner-loser pair before the merger. This would
imply that winner and loser alphas, betas and residuals are correlated. To test this assumption,
we regress the winner alpha on the loser alpha, the winner beta on the loser beta, and the winner
residuals on the loser residuals. If winners and losers have a common determinant that is omitted
from equation (4), alphas and residuals will be correlated through this omitted factor. Such
a determinant could be observable but simply omitted. For instance, winners and losers may
load similarly on some risk factor which is observable but omitted from equation (4). In contrast,
winners and losers may also share a common determinant which is unobservable or unknown. Such
an unobservable factor could be the essential source of endogeneity in the decision to acquire. By
including the standard observable risk factors in this equation we reduce the scope of observables
driving winner-loser correlation in alphas and residuals, and we shift it towards unobservables.
Hence, while we cannot exclude the possibility that the winner-loser correlation is in part also
driven by omitted, observable factors, it is likely to be mostly due to common, unobservable
factors.
The results are shown in Table 3. We report regressions both for the pre-merger (column 1) as
well as for the post-merger period (column 2). While we are mostly interested in the pre-merger
relationships, comparing these with the post-merger results is informative about which aspects
of similarity change through the merger.
[Table 3 approximately here]
Consistent with our assumption, Panel A of Table 3 shows that the pre-merger alphas of
16
winners and losers are highly correlated. This means that winning bidders who experience runups during the three years preceding the merger are typically challenged by rival bidders that
experience a similar run-up during that period. In the context of mergers, this is an important
aspect of similarity. Contestants with markedly different pre-merger price trends may vary significantly in their motives for and prospects of acquisitions. For example, acquirors motivated
by overvaluation of their own stock - possibly following an excessive run-up - are very different
from acquirors that have experienced negative abnormal returns for an extended period. The
pre-merger winner-loser correlation in run-ups is further illustrated in Figure 3. The four panels
of the figure plot the average cumulative abnormal returns of winners and losers for different
subsamples sorted by the pre-merger abnormal return of the winning bidder. Hence quartile 1
contains the poorest-performing, and quartile 4 the best-performing winners. Consistent with
the regression results, losers exhibit abnormal performance very similar to winners in the years
preceding the merger.
[Figure 3 approximately here]
The second column of Panel A shows that the winner-loser correlation in alphas drops to one
half after the merger and remains only marginally significant. Moreover, R-squared drops from
15.5 to 3.6 percent, i.e. to about one fourth, from the pre- to the post-merger period. This drop
in trend correlation is indicative of a causal effect of the merger.
Panel B of Table 3 reports regressions of winner betas on loser betas. Here, again, we expect,
and find, a high pre-merger correlation. Comparing the pre- and post-merger period, we do not
observe a significant change in beta correlation. The coefficient drops only marginally from 0.366
to 0.329 and the R-squared drops from 12.3 to 11.3 percent. Thus, in our sample, mergers do not
lead to significant changes in exposure to market risk.
Finally, Panel C tabulates regressions of winner on loser market model residuals. The coefficient of the residual regression is identical to the coefficient, γ, of the loser return of an extended
17
market model, where the loser return is added as an explanatory variable of the winner return:4
W
L
W
rjt
− rf t = αjW + βjW (rmt − rf t ) + γjW (rjt
− rf t ) + νjt
(5)
Thus, a positive and significant coefficient means that the loser return has explanatory power for
the winner return over and above its exposure to market risk. The results shown in Panel C of
Table 3 confirm that this is indeed the case.
Taken together, the similarity of winner-loser pairs in pre-merger trends, market exposures
and market model residuals strongly supports the credibility of the identifying assumption that
the losers form a valid counterfactual for the winners.
4.2
Main Results
We now turn to the analysis of the causal effects of mergers using our econometric method
described in Section 3. We start by presenting the results for the final sample of 82 merger
contests, i.e. the balanced and matched sample. Figure A-1 illustrates the basic findings, i.e. the
estimates from regressions (1) and (2) without controls. In this section, we focus on regression
(2), the piecewise-linear approximation of the period-specific winner-loser differences. Table 4
reports six different specifications of that regression. The first column corresponds to Figure
0
0
τ
τ
A-1, and uses no additional controls besides the Ci,j,τ
and Wi,j,τ
dummies and case fixed effects.
Columns two to five add increasing numbers of control variables.
The bottom two rows report the test statistics of interest. The second-to-last row reports
the test of the identifying assumption that price paths of winners and losers are statistically
indistinguishable before the contest. The last row reports a test of whether the price paths
diverge significantly during the contest period and following the completion of the merger. If the
identifying assumption is correct, then the latter is a test of the causal effect of the merger on
the acquiring firm’s stock performance.
4
See the Frisch-Waugh-Lovell Theorem
18
[Table 4 approximately here]
As Table 4 shows, the identifying assumption of equality of winner and loser price paths
before the merger cannot be rejected in any of the specifications. The statistical test reported in
the second-to-last row formalizes the evidence illustrated in Figure A-1, in particular the wide
confidence bounds of the period-specific winner-loser differences. We also do not find statistical
significance of the causal effect of the merger, i.e we do not find a significant jump and trend
shift following the merger. Again, this result is expected given the wide confidence bands of the
period-specific coefficients.
We find the same qualitative results when we run the parameter tests using less restrictive
sample selection criteria: when the sample is balanced but not matched, when the sample is
matched but not balanced, or when the sample is neither balanced nor matched.
4.3
Subsample Analysis
The last section has shown that, on average, winners of merger contests do not perform significantly worse than losers in the years following merger completion. We now turn to sample splits
to investigate the reasons for variation in post-merger performance.
One reason why a merger can be value-destroying for stockholders of the acquiring firm is
overbidding. Overbidding can be due to an inflated perception of the target’s standalone value or
to an overvaluation of potential synergies. If the market realizes and prices overbidding quickly,
the acquiror’s stock price will experience a drop immediately following the bid announcement
and the completion of the deal. Given the way we code event time in this study, such a drop
would occur between period zero (the month end before the first bid announcement) and period 1
(the month end following completion of the transaction). In contrast, if the market incorporates
overbidding only gradually into prices, one should see a decline of the winner’s performance in the
periods following deal completion.Another reason for poor post-merger performance of winning
firms could be that winning a merger contest could be correlated with managerial characteristics
19
that lead to weak performance. For instance, CEO overconfidence implies a higher probability of
winning the merger contest and may also lead to worse post-merger performance.
The descriptive statistics in Section 2.2 show that contest duration is an aspect that seems to
set multi-bidder takeover contests apart from single-bidder acquisitions. Time to completion is
more than three times longer in contested bids than in single-bidder acquisitions. Reiterating the
arguments made above, it is plausible to expect that longer contest durations are associated with
more similarity of winners and losers in takeover battles. Single-bidder takeovers may attract
only one contestant - and thus be completed quickly - because other potential bidders are too
different from the actual bidder, and the synergies they can generate are significantly smaller. In
contrast, multi-bidder contests may be particularly fiercely fought - and hence take a long time
to complete - if bidders, and the synergies they can obtain with the target, are very similar. This
may also explain the higher takeover premia that we observe in our sample of contested bids.
We now investigate the effect of contest duration in more detail. We first analyze the relationship between contest duration and stock price performance. We split our sample of contested
mergers into duration quartiles. The quartiles (from first to fourth) contain merger contests that
last, respectively, two to four months, five to seven months, eight to twelve months, and more
than a year (up to 43 months). The contest duration in quartile one roughly corresponds to the
average duration of non-contested mergers, while all other quartiles contain significantly longer
fight durations.
To illustrate our findings, the bottom panels of Figure 4 plot the average winner and loser
prices, rather than price differences, along with the normalized industry index of the winner for
each quartile of contest duration. We add the industry index of the winner to better understand
the nature of abnormal performance, i.e. whether performance is strong or poor only relative to
the loser or also compared to the industry.5
The figures show that the effect of the merger on the acquiring firm’s stock performance varies
monotonically and significantly with the duration of the merger contest. In fact, the acquirors with
5
We
define
industries
according
to
the
Fama-French
12
industry
classification,
and
we
obtain
the
corresponding
performance
data
from
Ken
French’s
website
(http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html)
20
the shortest contest duration perform very well relative to the loser following merger completion.
While winner-loser price differences are virtually zero in the three years leading to the merger
contest, there is a sharp trend break in price paths following in the contest period. Winners
outperform losers by 36 percentage points in the three years following the merger. Despite the
small sample (21 contests), the outperformance is statistically significant at the 10 percent level.
In contrast, in the subsample of the longest contest durations, winners fare much worse than
losers. Here, the cumulative under performance of winners over three years is about 50 percentage
points and also statistically significant at the 10 percent level. Thus the interquartile range of
underperformance is 86 percentage points. We obtain the statistical tests for the winner-loser
differences by re-estimating regressions (1) and (2), separately for each contest duration quartile
of the sample. The respective difference plots are shown in Panels B to E of Figure A-2.
Interestingly, Figure 4 also shows that winners never outperform their industry significantly.
In the shortest contests outperformance is only significant relative to the loser, not relative to the
industry. In contrast, the winners of the longest merger fights, underperform both the loser and
their industry.
Table 6 reports the corresponding regression estimates of equation (2) for each of the four
quartiles of contest duration, and a statistical test of the Q4-Q1 difference of the causal impact
of the merger. The Q4-Q1 difference in the long-term value effect of the merger is economically
large and statistically significant at the 2 percent level.
[Figure A-2 approximately here]
[Table 6 approximately here]
4.4
Economic Mechanism
The results so far show that post-merger performance differs substantially with the duration of
the contest. Losing is better than winning for the subsample of long-lasting bidding contest. But
for mergers with a short duration, e.g., the lowest quartile, the result reverses. In fact, under any
econometric specification, the results for the lowest and highest quartile are always significantly
21
different.
What explains these differences across fight duration quartiles? A first possible explanation
is that longer-lasting bidding wars increase the premium paid to target shareholders, and higher
premia may explain the worse performance of acquirors. However, our prior results indicate, for
the fourth quartile, an underperformance trend over the three years following merger completion
and, for the first quartile, an overperformance trend over the following three years. This long-term
trend is hard to explain with a one-time payment.
Nevertheless we investigate a possible causal role of differences in offer premia empirically. For
this analysis we use the target price run-up prior to the contest resolution to construct the offer
premium as described in Section 2.1. Therefore, we need to restrict the sample to the 66 winning
bids for which the target stock price is available. The top panel of Figure 5 plots the offer premium
against the duration of the bidding contest (in months). We observe a weak positive correlation.
A simple linear regression of the offer premium on the duration of the merger contest, shown in
Table 8, Panel A, reveals a weakly significant effect (p-value< 0.1): one additional month implies
a premium that is 1.74 percentage points higher. As the scatter plot also shows, however, the
positive correlation is driven by a few outliers with extreme fight durations.
[Figure 5 approximately here]
[Table 8 approximately here]
In order to gauge the potential impact of overbidding on acquiror stock prices, we also estimate
the effect of contest duration on the offer premium expressed as a percentage of the acquiror’s
market valuation. This allows us to directly evaluate how much of the over- or underperformance
of the winner can be explained by contest-duration-induced additional payments. Recall that
we normalize bidder stock prices to 100 in the month prior to the beginning of the contest,
so that winner-loser differences in performance are effectively percentage differences in bidder
valuations. The bottom panel of Figure 5 shows a scatter plot of this relationship, and Panel B
of Table 8 reports the respective regression estimates. These findings show that, when expressing
the offer premium as a percentage of the acquiror rather than as a percentage of the target,
22
the correlation with contest duration becomes an order of magnitude smaller, and statistically
insignificant. Therefore, duration-induced overbidding cannot explain the underperformance of
long merger contests.
A second possible explanation of the large differences between bidder returns in the shortduration and the long-duration subsamples relates closely to our initial motivation: Our analysis
aims to exploit bidding contests to construct a hypothetical counterfactual for acquiror performance had the merger not taken place. In the case of bidding contest of short duration, however,
it is likely that the ultimate winner entered the contest as the likely winner, e.g., due to higher
synergies. In other words, winners and losers in short-duration contests of two to four months
may be significantly different along merger-relevant dimensions while winners and losers in longduration fights, which may last a year or more, are more similar. In that case, return differences
in the short-duration sample are not a good measure of the hypothetical counterfactual while the
long-duration estimates provide the correct estimate of the hypothetical counterfactual.
Measuring differences in synergies or, more broadly, differences in characteristics that affect
the returns to mergers, is difficult. Instead, we attempt to test for such differences by comparing
observable characteristics. First, we compare, one-by-one, the differences in characteristics between winners and losers, separately for the samples of long-duration and short-duration contests.
The results, reported in Table 9, Panel A, indicate no particular pattern in similarity across the
duration quartiles. Size differences, whether measured in terms of book assets, market capitalization or sales, are large in both long and short-lasting contests while small in the middle range
of contest duration. There is, however, evidence that bidders in long-duration contests tend to
be in the same industry.
Next, we aggregate these differences into a scalar distance measure. We compute the distance
between the multivariate characteristics of two bidders as the vector norm, D = x0 V x, where
x = xW − xL is the vector of differences between winner and loser characteristics, and V is a
weighting matrix. We use a diagonal matrix for V , with the inverse of the variance as diagonal
elements. [TO BE DONE.]
We also examine whether characteristics of the completed deal, i.e. that of the winning bid,
23
exhibit differences across contest duration. Panel B of Table 9 shows a range of deal characteristics
for the four quartiles of contest duration. One characteristic stands out. First, long-duration
contests are far more likely to be concluded with a stock-financed deal. The average percentage
of stock used increases monotonically from 9.5 percent in the short contest to 62 percent in the long
contests. This pattern is in part due to the more extensive documentation and filing necessary for
stock offerings. Given these differences, it could be the case that the means of financing explains
the variation of performance across contest duration. Loughran and Vijh (1997) find that stock
mergers tend to have negative abnormal returns in the five years following the merger. However,
as we show below in Section 4.6, stock mergers do not perform significantly worse than cash
mergers or mergers with both stock and cash payments in our sample of contested bids. Hence,
the duration result cannot be driven by means of payment.
[Table 9 approximately here]
4.5
Comparison with Announcement Returns
An interesting question is whether the long-run divergence of winner and loser performance is
consistent with the sign and magnitude of short-run announcement returns. Because of the
difficulty of identifying a valid benchmark for long-run performance, announcement returns are
commonly viewed as a good market-based measure of the causal effect of merger.
Table 10 reports basic descriptive statistics of 3-day cumulative abnormal announcement returns (CARs) of the winning bid. Panel A shows that the winners’ CARs of our sample of
contested bids are negative and economically large. The average CAR is -3.9 percent (median:
-2.3 percent). The announcement reaction is much more negative than for non-contested acquisitions where CARs are typically slightly negative but greater than -1 percent and not significant.
Interestingly, Panel B shows that announcement returns do not vary systematically with the
length of the contest duration, as long-run winner-loser performance differences do.
In Table 11 we regress the long-run winner-loser performance difference on the winning bid’s
announcement return in order to explore whether the market is, on average, correct in its as24
sessment of the value effects of acquisitions. Surprisingly, we do not find a significant relation,
and the sign of the coefficient is even opposite of what would be expected if markets were on
average correct. These result hold whether the raw (column 1) or the risk-adjusted performance
difference (column 2) is used.
4.6
Robustness
In this section, we explore several alternative explanations for the heterogeneity observed in our
data. Mergers vary on a wide range of aspects, from bidder and target properties to deal characteristics, and prior literature has shown - for single-bidder takeovers - that a number of characteristics are significantly associated with long-term post-merger performance. In this section, we
explore whether these characteristics also correlate with long-term performance in our sample of
contested deals. If this were the case, our result that losers do better than winner in long-lasting
contest could be driven by these characteristics, given that some of these characteristics also vary
with contest duration.
Cash vs stock. We test whether the winner-loser comparison yields systematically different
results if we subsample by means of payment: cash, stock, and mixed payments. For example,
Loughran and Vijh (1997) find that stock mergers have negative abnormal returns relative to
size and market-to-book matched firm portfolios while cash mergers outperform the matched
firms in the five year period following deal completion. We run regressions (1) and (2) separately
for cash, mixed, and stock deals. The results are shown in Figure A-3. Interestingly, postmerger underperformance is least pronounced for stock mergers, though the differences are not
statistically significant. This has two interesting implications. First, it shows that our duration
result cannot be explained by the fact that long-lasting contests tend to result in poorly performing
stock deals. Second, it suggests that losers may be a better counterfactual to winners than
characteristics-matched firms. Shleifer and Vishny (2003) argue that overvalued firms may use
their stock as cheap currency to buy less overvalued targets. Such mergers would tend to perform
poorly due to a mean reversion of the acquiror’s stand-alone valuation, not because the merger
is value-destroying. Our analysis reveals that stock mergers perform better than cash or mixed
25
mergers, when benchmarked against the losing contestant.
Hostile vs friendly. Since hostile bids are more prone to overbidding, we might expect the
underperformance of winners relative to losers to be more pronounced in the subsample of hostile
bids than among friendly takeover attempts. Thus, we run regressions (1) and (2) separately for
contests whose winning bids were hostile and for those that were friendly. Hostile bids tend to be
quite rare, as our resulting subsample of only of eight cases shows. The top two panels of Figure A4 illustrate the results for the two subsamples. As expected, hostile mergers exhibit a steep drop in
the winner’s stock price relative to loser’s around the contest period, and continue to underperform
in the long run. Despite the very small sample, this underperformance is statistically significant
at the 10 percent level. In contrast, friendly deals are followed by virtually zero performance
difference post-merger. Given the underperformance of hostile acquirors in our sample, it is
important to note that the finding of contest duration dependence of performance cannot be
driven by hostility. Hostile bids are more common in short contest rather than in long ones,
as Table 9 shows. So, the worse performance of hostile takeovers tends to weaken our duration
finding.
Acquiror Q. Prior research shows that high-Q acquirors underperform in the long run relative
to characteristics-matched firm portfolio (Rau and Vermaelen, 1998). To see whether such a
pattern is present in our data, we rank contests by the Tobin’s Q of the winning firm at the fiscal
year end preceding the beginning of the contest, and estimate equations (1) and (2) separately
for each quartile of acquiror Q. The results, illustrated in Figure A-5, show that in our data,
and using our winner-loser methodology, we find no significant performance differences across
acquiror’s Q. This result suggest that previous findings need to be interpreted with caution when
a proper counterfactual is not available. As mentioned above, Shleifer and Vishny (2003) suggest
that high-Q acquirors may be overvalued firms seeking to soften the reversal in valuation by
means of acquisitions. The poor post-merger performance of such firms may hence occur not
because of but despite the acquisition. When benchmarked against a valid counterfactual, such
mergers should not show any negative performance.
26
Firm size. Harford (2005) provides evidence of poor post-acquisition performance of large
acquirors. Since acquirors tend to be somewhat larger in long-duration contests (results not
reported), size effects could potentially explain our duration result. We thus examine sample splits
based on the market capitalization of the ultimate acquiror. Figure A-6 illustrates the results.
Again, we observe no significant differences in post-merger performance across size quartiles.
The graphs are, at best, suggestive of more negative announcement return for larger acquirors,
but there is no long-term underperformance. Thus, size effects do not explain why winners in
long-lasting contest underperform.
Relative deal size. We also use relative deal size (transaction value relative to the acquiror’s
market capitalization) as a sorting variable. This is to check whether acquisitions have a significant effect only when they are large relative to the acquiror. Figure B-5 confirms that, even for
the large acquisitions in quartile three and four, winners do not perform significantly worse than
losers.
Number of Bidders. Another explanation is that acquirors do not account for the winner’s
curse, leading to greater overbidding if more bidders participate in the contest. Splitting the
sample into contests with two and contests with more than two bidders, however, suggests no
such pattern, see Figure B-6.
Diversification. We also analyze separately diversifying and non-diversifying mergers. A
merger is coded as diversifying if the winning bidder has a Fama-French 12-industry classification
that is different from the target’s. The corresponding graphs, however, suggest no such pattern,
see Figure B-7.
5
Conclusion
We argue that bidding contests help to address the identification issue caused by the missing
counterfactual in corporate acquisitions: In contests where at least two bidders have a significant
27
chance of winning, the post-merger performance of the loser allows to calculate the counterfactual
performance the winner would have had without the merger. We construct a novel data set of all
mergers with overlapping bids between 1983 and 2009. We find that the stock returns of bidders
are not significantly different before the merger contest, but diverge post-merger. In the full
sample, winners underperform losers over a three-year horizon, but there is large heterogeneity
and the effect is not significant. We then identify the subsample of deals where at least two
bidders have a significant chance at winning, exploiting long contest duration and a bidder’s
intermediate stock price reaction to the competing bid. We show that, for cases where either
bidder was ex ante likely to win the contest, losers outperform winners, while the opposite is true
in cases with a predictable winner.
28
Completion of bid by company B
First bid of company B
First bid of company A
t=ï3
t=ï2
t=ï1
Withdrawal of bid by company A
t=0
t=1
t=2
t=3
Event time
(a) Stylized example.
Loser: Westcott Communications; announced: 7 Apr 1995; withdrawn: 4 Jan 1996
Winner: Automatic Data Processing; announced: 5 June 1995; completed: 4 Jan 1996
01/95 02/95 03/95 04/95 05/95 06/95 07/95 08/95 09/95 10/95 11/95 12/95 01/96 02/96 03/96 04/96
t=-2
t=-1
t=0
t=1
t=2
t=3
(b) Example from our dataset.
Figure 1: Illustration of Event Time in a Merger Contest This figure illustrates the construction of event
time for merger contests. The top figure shows a stylized example, the bottom figure a concrete example from our
dataset.
29
0
2
Number of contests
4
6
8
Number of contests
8
6
4
2
0
Number
1985
1990
1995
2000
2005
Year
1985
1990
1995
Year
2000
2005
Figure 2: Frequency of Merger Contests over Time This figure shows the frequency distribution of merger
contest over the sample period 1985-2006. We count the calendar year of the starting month of the merger contest
as the contest year.
30
50 Estimate, Avg. Cumulative Abnormal Return of Loser
-50
-100
100
50
-40
-20
2
0
20
40
Period
Point
Winner
50
-50
-100
-100
-50
0
0
50
100
100
50 Estimate, Avg. Cumulative Abnormal Return of Loser
-50
-100
100
50
-40
-20
2
0
20
40
Period
Point
Winner
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Cumulative Abnormal Return of Winner
Point Estimate, Avg. Cumulative Abnormal Return of Winner
Point Estimate, Avg. Cumulative Abnormal Return of Loser
Point Estimate, Avg. Cumulative Abnormal Return of Loser
(a) Pre-merger run-up of winner: 1st quartile
(b) Pre-merger run-up of winner: 2nd quartile
50
0
-50
-100
-100
-50
0
50
100
50 Estimate, Avg. Cumulative Abnormal Return of Loser
-50
-100
100
50
-40
-20
2
0
20
40
Period
Point
Winner
100
50 Estimate, Avg. Cumulative Abnormal Return of Loser
-50
-100
100
50
-40
-20
2
0
20
40
Period
Point
Winner
40
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Cumulative Abnormal Return of Winner
Point Estimate, Avg. Cumulative Abnormal Return of Winner
Point Estimate, Avg. Cumulative Abnormal Return of Loser
Point Estimate, Avg. Cumulative Abnormal Return of Loser
(c) Pre-merger run-up of winner: 3rd quartile
40
(d) Pre-merger run-up of winner: 4th quartile
Figure 3: Pre- and Post-merger Performance - by Quartile of the Winner’s Pre-merger Run-up The
figure plots the cumulative abnormal returns of winners and losers in the three years around the merger contest
by quartile of the winner’s pre-merger run-up. Quartile 1 (top left figure) contains the winners with the worst
abnormal performance in the 3-year pre-merger period. Quartile 4 (bottom right figure) contains the winners with
the strongest pre-merger performance.
31
60
80
100
120
140
160
160
140
120
100
80
60
-40
-20
2
0
20
40
Period
Point
0 Estimate, Avg. Industry
WinnerPrice
Loser
Price
Price
-40
-20
0
Period
20
40
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Loser Price
Point Estimate, Avg. Industry Price
(a) Full sample
160
140
120
100
80
60
-40
-20
2
0
20
40
Period
Point
0 Estimate, Avg. Industry
WinnerPrice
Loser
Price
Price
60
60
80
80
100
100
120
120
140
140
160
160
160
140
120
100
80
60
-40
-20
2
0
20
40
Period
Point
0 Estimate, Avg. Industry
WinnerPrice
Loser
Price
Price
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Loser Price
Point Estimate, Avg. Loser Price
Point Estimate, Avg. Industry Price
Point Estimate, Avg. Industry Price
(b) Contest duration: 1st quartile
40
(c) Contest duration: 2nd quartile
160
140
120
100
80
60
-40
-20
2
0
20
40
Period
Point
0 Estimate, Avg. Industry
WinnerPrice
Loser
Price
Price
60
60
80
80
100
100
120
120
140
140
160
160
160
140
120
100
80
60
-40
-20
2
0
20
40
Period
Point
0 Estimate, Avg. Industry
WinnerPrice
Loser
Price
Price
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Loser Price
Point Estimate, Avg. Loser Price
Point Estimate, Avg. Industry Price
Point Estimate, Avg. Industry Price
(d) Contest duration: 3rd quartile
40
(e) Contest duration: 4th quartile
Figure 4: Pre- and Post-merger Performance - in the Full Sample and by Contest Duration This figure
illustrates the pre- and post-merger stock price performance for winners, losers and the winner’s industry. The winner’s industry is defined according to the Fama-French 1232
industry classification, and the corresponding performance
data are obtained from Ken French’s website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data
library.html). The five figures correspond to the entire sample (top figure) and the different quartile of contest duration (bottom figures). Prices are normalized to 100 in the month preceding the start of the contest. The
dots connected by the solid black line correspond to the point estimates of the average winner prices. The solid
gray lines correspond to the average loser prices, the dotted lines to the industry index.
400
ï100
Offer premium [% of acquiror]
0
100
200
300
400
Offer premium [% of target]
0
100
200
300
ï100
0
10
20
30
Duration of merger contest
40
0
10
Fitted values
20
30
Duration of merger contest
40
Fitted values
Figure 5: Offer Premium versus Contest Duration The figure shows scatter plots of the offer premium
against contest duration. In the left figure, the offer premium is expressed in percentage of the target’s equity
capitalization. Is is computed as the percentage run-up in the target stock price from 1 month before the beginning
of the merger contest until completion. In the right figure, the offer premium is expressed as a percentage of the
acquiror’s equity market capitalization. Contest duration is expressed in months.
105
-100
-200
100
W-L
-.2
-.15
-.1
.-.05
0
.05
Announcement
Fitted
00 difference
values of
CAR
risk-adj.
of winner
performance at t=36
Winner-loser price difference at t=36
-200
-100
0
100
200
W-L difference of risk-adj. performance at t=36
-200
-100
0
100
105
-100
-200
200
100
Winner-loser
-.2
-.15
-.1
.-.05
0
.05
Announcement
Fitted
00 values price
CAR
difference
of winnerat t=36
-.2
-.15
-.1
-.05
Announcement CAR of winner
0
.05
-.2
Fitted values
-.15
-.1
-.05
Announcement CAR of winner
0
.05
Fitted values
Figure 6: Long-Run Winner-Loser Performance Difference versus 3-day CARs The figure shows scatter
plots of the winner-loser performance difference 3 years after the acquisition against the 3-day cumulative abnormal
announcement return (CAR) of the winning bid. In the left figure, performance is measured in terms of the raw
(normalized) price. In the right figure, performance is risk adjusted by subtracting the market-induced performance:
p̃it = p̃it−1 (1 + rit − rf t − βi (rmt − rf t ).
33
34
Initial sample
less repeated bids of same bidder for same target
less contests that have not been completed
less bidders without CRSP PERMNO
less contests where winner was parent company
less bidders with missing stock returns in periods -1 to 1
less bidders with extreme price volatility (Std>200)
less bidders that have missing price data in the baseline event window [-35,+36]: ”Balancing” criterion
less bidders in contests where contestant is missing: ”Matching” criterion
Sample selection criterion
Table 1: Sample Construction
193
193
148
146
136
134
132
114
82
Contests
416
402
310
298
280
270
263
207
172
Bids
402
402
310
298
280
270
263
207
172
Bidders
154
154
148
142
132
132
128
104
82
Winners
248
248
162
156
148
138
135
103
90
Losers
35
Transaction value [$m]
Percentage of tender offers
Percentage hostile
Percentage paid in stock
Percentage paid in cash
Number of bidders
Offer premium [% of target]
Offer premium [% of acquiror]
Contest duration [in months]
Total assets [$m]
Market capitalization [$m]
Tobin’s Q
Market leverage
Profitability
14930.58
20987.74
1.88
0.18
0.12
Mean
3326.24
4676.18
1.34
0.15
0.11
79
79
79
79
78
N
9078.59
13022.12
1.79
0.18
0.13
Mean
3436.52
0.34
0.10
36.94
43.93
2.40
57.82
9.66
9.48
344.83
0.00
0.00
8.04
30.76
2.00
42.93
6.64
7.50
12400.92
0.48
0.30
43.21
44.73
0.70
63.02
21.26
7.37
Panel B: Deal Characteristics
Mean
Median
Std
38841.35
49163.33
1.50
0.16
0.09
Winners
Median
Std
Panel A: Bidder Characteristics
Table 2: Descriptive Statistics
81
82
82
82
82
82
67
67
82
N
2440.52
2840.48
1.19
0.14
0.12
16783.93
26533.84
1.41
0.16
0.12
Losers
Median
Std
88.00
88.00
88.00
84.00
86.00
N
5851.99
7965.62
0.10
0.00
-0.01
885.72
1835.70
0.15
0.01
-0.01
Winner-loser difference
Mean
Median
Table 3: Tests of Identifying Assumption
Panel A: Alpha Regressions
Dept. variable:
CAPM Alpha - Loser
Pre-merger Period Post-merger Period
CAPM Alpha - Winner
0.364***
(0.095)
0.008***
(0.002)
82
0.154
Constant
Observations
R-squared
0.175*
(0.101)
-0.002
(0.002)
82
0.036
Panel B: Beta Regressions
Dept. variable:
CAPM Beta - Loser
Pre-merger Period Post-merger Period
CAPM Beta - Winner
0.366***
(0.109)
0.619***
(0.142)
82
0.123
Constant
Observations
R-squared
0.329***
(0.103)
0.712***
(0.126)
82
0.113
Panel C: Residual Regressions
Dept. variable:
Market model residual - Loser
Pre-merger Period Post-merger Period
Market model residual - Winner
0.169***
(0.029)
-0.000
(0.000)
2952
0.025
Constant
Observations
R-squared
36
0.227***
(0.057)
0.000
(0.000)
2952
0.058
37
α1 + 36α3
P-value of α1 + 36α3
α5 + 36(α3 + α7 )
P-value of α5 + 36(α3 + α7 )
Observations
R-squared
Number of contests
Identification:
Identification:
Merger effect:
Merger effect:
Contest fixed effects
Year fixed effects
Market leverage
Profitability
Tobin’s Q
Ln(market cap)
Winner x Post merger x Period (α7 )
Period x Post merger (α6 )
Winner x Post merger (α5 )
Post merger (α4 )
Winner x Period (α3 )
Period (α2 )
Winner (α1 )
Dependent variable
12384
0.356
82
-8.339
0.335
-5.722
0.571
X
(1)
0.123
(2.921)
0.871***
(0.140)
-0.235
(0.182)
8.248
(6.013)
-4.518
(5.437)
0.257
(0.292)
0.202
(0.317)
11672
0.429
82
-13.22
0.162
-4.146
0.696
X
(2)
-3.238
(3.805)
0.676***
(0.149)
-0.277
(0.185)
4.867
(6.266)
-3.640
(5.341)
0.567
(0.370)
0.263
(0.345)
9.278***
(1.778)
11672
0.455
82
-12.45
0.176
-3.679
0.720
X
(3)
-3.278
(4.047)
0.631***
(0.144)
-0.255
(0.172)
5.947
(6.252)
-0.762
(5.171)
0.708*
(0.357)
0.174
(0.331)
8.014***
(1.837)
11.21***
(2.279)
11487
0.460
82
-12.66
0.171
-3.092
0.764
X
(4)
-3.572
(4.176)
0.625***
(0.147)
-0.252
(0.173)
2.139
(5.527)
1.189
(5.183)
0.792**
(0.354)
0.133
(0.343)
8.627***
(1.862)
11.97***
(2.091)
-29.22
(24.68)
Price
Table 4: Regressions Estimating the Effect of a Merger
11432
0.493
82
-12.96
0.137
-3.325
0.750
(5)
-3.913
(4.307)
0.589***
(0.145)
-0.251
(0.162)
2.187
(5.264)
2.494
(5.475)
0.861**
(0.344)
0.0897
(0.303)
10.60***
(2.503)
8.135***
(2.168)
-42.32*
(21.88)
-91.66***
(22.83)
X
11432
0.535
82
-13.15
0.128
-2.408
0.818
(6)
-4.053
(4.233)
-0.407
(0.389)
-0.253
(0.160)
-6.896
(5.686)
2.811
(5.479)
1.035***
(0.340)
0.108
(0.304)
9.845***
(2.419)
7.456***
(1.963)
-39.80*
(21.24)
-85.70***
(21.46)
X
X
38
Point estimate of α1 + 36α3
P-value of α1 + 36α3
Point estimate of α1 + α5 + 36(α3 + α7 )
P-value of α1 + α5 + 36(α3 + α7 )
Observations
R-squared
Number of contests
Identification:
Identification:
Merger effect:
Merger effect:
Contest fixed effects
Year fixed effects
Market leverage
Profitability
Tobin’s Q
Ln(market cap)
Winner x Post merger x Period (α7 )
Period x Post merger (α6 )
Winner x Post merger (α5 )
Post merger (α4 )
Winner x Period (α3 )
Period (α2 )
Winner (α1 )
Dependent variable:
12384
0.217
82
-26.32
0.253
-2.619
0.690
X
(1)
-3.432
(5.648)
-0.297
(0.289)
-0.636
(0.497)
-0.321
(5.437)
0.332
(6.539)
0.241
(0.316)
0.649
(0.524)
11672
0.327
82
-31.73
0.198
-5.847
0.394
X
(2)
-6.663
(6.481)
-0.497
(0.306)
-0.696
(0.523)
-1.934
(5.679)
0.140
(6.751)
0.645
(0.476)
0.715
(0.577)
7.620***
(1.708)
11672
0.338
82
-31.17
0.203
-5.534
0.423
X
(3)
-6.692
(6.551)
-0.530*
(0.302)
-0.680
(0.516)
-1.142
(5.632)
2.249
(6.661)
0.748
(0.467)
0.650
(0.567)
6.694***
(1.761)
8.213***
(2.365)
11487
0.345
82
-31.52
0.201
-5.622
0.421
X
(4)
-7.423
(6.804)
-0.558*
(0.309)
-0.669
(0.516)
-4.476
(5.159)
4.622
(6.644)
0.812*
(0.471)
0.591
(0.576)
7.319***
(1.704)
9.372***
(2.347)
-43.31*
(22.90)
11432
0.356
82
-31.91
0.192
-6.034
0.382
(5)
-7.780
(6.988)
-0.587*
(0.311)
-0.670
(0.512)
-4.365
(4.989)
5.675
(6.879)
0.865*
(0.474)
0.561
(0.558)
8.688***
(2.153)
6.711***
(2.428)
-52.90**
(23.95)
-63.88***
(22.77)
X
Cumulative abnormal returns
Table 5: Regressions Estimating the Effect of a Merger
11432
0.396
82
-32.33
0.182
-4.517
0.513
(6)
-8.470
(7.089)
-0.724*
(0.409)
-0.663
(0.502)
-4.810
(5.513)
7.693
(7.174)
0.828*
(0.481)
0.559
(0.560)
8.723***
(2.051)
8.362***
(2.101)
-60.07***
(22.43)
-58.63***
(19.72)
X
X
39
α1 + 36α2
P-value of α1 + 36α2
α1 + α5 + 36(α3 + α7 )
P-value of α1 + α5 + 36(α3 + α7 )
Difference short minus long fight duration
P-value of long minus short fight duration
Observations
R-squared
Number of contests
Identification:
Identification:
Merger effect:
Merger effect:
Merger effect:
Merger effect:
Contest fixed effects
Winner x Post merger x Period (α7 )
Period x Post merger (α6 )
Winner x Post merger (α5 )
Post merger (α4 )
Winner x Period (α3 )
Period (α2 )
Winner (α1 )
Dept. variable:
3240
0.413
22
3.116
0.691
32.23
0.0740
Q1
2.147
(3.892)
1.086***
(0.144)
0.0269
(0.189)
-1.052
(7.830)
0.200
(8.666)
-0.612
(0.441)
0.803
(0.563)
X
Q3
1.684
(4.317)
0.995***
(0.251)
-0.152
(0.309)
7.971
(8.722)
-8.747
(7.771)
0.495
(0.644)
0.150
(0.471)
X
2808
0.411
19
3168
0.481
21
-12.83
-3.773
0.506
0.773
-0.906
-7.132
0.966
0.582
-80.200
0.004***
Q2
0.837
(3.986)
0.721**
(0.327)
-0.380
(0.459)
1.338
(7.524)
-2.383
(7.923)
0.735
(0.549)
0.398
(0.657)
X
Price
Table 6: Regressions Estimating the Merger Effect by Quartile of Contest Duration
3168
0.258
20
-23.17
0.403
-47.97
0.0440
Q4
-5.250
(9.489)
0.672*
(0.364)
-0.498
(0.513)
23.18
(18.76)
-5.147
(16.45)
0.465
(0.661)
-0.546
(0.828)
X
40
α1 + 36α2
P-value of α1 + 36α2
α1 + α5 + 36(α3 + α7 )
P-value of α1 + α5 + 36(α3 + α7 )
Difference short minus long fight duration
P-value of long minus short fight duration
Observations
R-squared
Number of contests
Identification:
Identification:
Merger effect:
Merger effect:
Merger effect:
Merger effect:
Contest fixed effects
Winner x Post merger x Period (α7 )
Period x Post merger (α6 )
Winner x Post merger (α5 )
Post merger (α4 )
Winner x Period (α3 )
Period (α2 )
Winner (α1 )
Dept. variable:
3240
0.210
22
6.863
0.534
25.68
0.0110
Q1
2.630
(3.720)
-0.00770
(0.249)
0.118
(0.321)
-7.591
(7.092)
3.087
(7.377)
-0.587
(0.369)
0.437
(0.484)
X
Q3
0.289
(4.189)
-0.239
(0.536)
-0.0937
(0.595)
7.293
(10.13)
-8.176
(7.239)
0.408
(0.602)
0.119
(0.692)
X
2808
0.336
19
3168
0.205
21
-32.73
-3.083
0.321
0.891
0.520
-6.976
0.971
0.557
-56.640
0.001***
Q2
-1.823
(5.548)
-0.153
(0.450)
-0.859
(0.770)
-0.804
(7.473)
2.781
(8.230)
0.185
(0.615)
0.846
(0.826)
X
3168
0.220
20
-84.24
0.352
-30.96
0.0470
Q4
-16.37
(22.42)
-0.749
(0.870)
-1.885
(1.846)
-0.247
(15.66)
4.296
(22.82)
0.921
(0.842)
1.361
(1.901)
X
Cumulative abnormal returns
Table 7: Regressions Estimating the Merger Effect by Quartile of Contest Duration
Table 8: Regressions of Offer Premium on Contest Duration This table reports simple regressions of the
offer premium on contests duration. In Panel A, the offer premium is expressed as a percentage of the target market
capitalization. In Panel B, the offer premium is expressed as a percentage of the acquiror’s market capitalization.
The offer premium is computed as the run-up in the target stock price from 1 month before the beginning of the
merger contest until completion. Contest duration is expressed in months.
Panel A: Offer premium as percentage of target
Dept. variable:
Offer premium as percentage of target
Duration of merger contest [months]
1.740
(0.966)*
42.021
(12.154)***
66
0.048
Constant
Observations
R-squared
Panel B: Offer premium as percentage of acquiror
Dept. variable:
Offer premium as percentage of acquiror
Duration of merger contest [months]
0.257
(0.2305)
9.011
(2.901)***
66
0.019
Constant
Observations
R-squared
41
Table 9: Descriptives Statistics of Bidder and Deal Characteristics by Contest Duration Quartile
Panel A reports the mean absolute differences of bidder characteristics between winning and losing firms. Panel B
contains average deal characteristics. These characteristics refer to the winning bid.
Panel A: Mean of Absolute Winner-Loser Difference by Contest Duration Quartile
Characteristic
Log(Total assets)
Log(Market cap)
Log(Sales)
Tobin’s Q
PP&E
Profitability
Book leverage
Market leverage
Same industry
Q1
Q2
Q3
Q4
0.52
0.48
0.79
-0.15
-0.02
-0.03
-0.03
-0.02
0.43
-0.10
-0.04
-0.09
0.19
0.04
-0.03
0.08
0.04
0.80
0.17
0.12
0.23
-0.17
-0.02
-0.04
-0.02
-0.03
0.90
0.35
0.51
0.23
0.39
-0.04
0.01
-0.01
-0.03
0.80
Panel B: Mean of Deal Characteristic by Contest Duration Quartile
Characteristic
Percentage paid in stock
Tender offer
Hostile
Number of bidders
Offer premium [% of target]
Offer premium [% of acquiror]
42
Q1
Q2
Q3
Q4
9.52
0.71
0.14
2.33
84.67
4.69
27.35
0.25
0.10
2.45
44.44
10.88
49.69
0.29
0.10
2.48
50.71
18.51
61.94
0.10
0.05
2.45
55.16
4.70
Table 10: Announcement Returns of Winning Bids in Merger Contests This table reports statistics
for cumulative abnormal returns (CAR) for the winning bid in merger contests. The CARs are computed from
residuals of the model rijt − rf t = αij + βij (rmt − rf t ) + εijt . The parameters are estimated using monthly returns
of the 3-year pre-merger period. CAR[−1, +1] is the 3-day cumulative abnormal return around the announcement
of the winning bid. Dollar CAR [scaled] is the dollar gain of the winning bid scaled by the transaction value.
Panel A: Winners’ CARs
CAR [-1,+1]
-0.0389
-0.0232
76
Mean
Median
N
Dollar CAR [scaled]
-0.2133
-0.0801
75
Panel B: Winners’ CARs by contest duration
Contest Duration
1
2
3
4
CAR [-1,+1]
-0.0339
-0.0570
-0.0320
-0.0340
Dollar CAR [scaled]
-0.3286
-0.0959
-0.1738
-0.2391
N
21
18
20
17
Table 11: Regressions of Long-Run Winner-Loser Performance Difference on 3-day CARs This table
reports regressions of the winner-loser performance difference 3 years after the merger completion, on the 3-day
cumulative abnormal announcement return of the winning bid. The winner-loser performance difference is the raw
price difference at t = 36 months (column 1), and the risk-adjusted performance difference at t = 36 (column 2).
Risk-adjusted performance is computed by first fitting the model rit − rf t = αi + βi (rmt − rf t ) + εit , for each
bidder in the pre–merger period, and then subtracting the market-induced performance from raw performance:
p̃it = p̃it−1 (1 + rit − rf t − βi (rmt − rf t ).
Dept. variable:
Announcement CAR of winner
Constant
Observations
R-squared
W-L Price Difference at t=36
W-L Difference of
Risk-adjusted Performance at t=36
-43.51
(208.2)
4.900
(11.92)
74
0.001
-104.3
(155.6)
-2.004
(8.905)
75
0.006
43
Appendix
ï30
ï20
ï10
0
10
20
A
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Figure A-1: Winner-Loser Price Differences around Merger Contests The figure plots the point estimates of the series of π-coefficients of regression (1) along with 95% confidence bounds and the piecewise-linear
approximation obtained from regression (2). The point estimates correspond to the average, period-specific price
τ0
difference between winners and losers in merger contests. Regressions (1) and (2) are run with the sets of Ci,j,τ
0
τ
and Wi,j,τ
dummies and case fixed effects only, i.e. without additional controls.
44
100
50
100
0
50
ï50
0
ï100
ï50
ï100
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Number of contests: 21
Note: Number of contests: 20
100
50
0
ï50
ï100
ï100
ï50
0
50
100
(a) Winner-loser difference, Q1 of contest duration. (b) Winner-loser difference, Q4 of contest duration.
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïIndustry Difference
Point Estimate, WinnerïIndustry Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Number of contests: 21
Note: Number of contests: 20
(c) Winner-industry difference, Q1 of contest dura- (d) Winner-industry difference, Q4 of contest duration.
tion.
Figure A-2: Effect of Merger by Contest Duration This figure illustrates the winner-loser and winnerindustry difference in pre- and post-merger performance for quartiles one and four of contest duration. The dots
connected by the solid black line correspond to the point estimates of the π-coefficients of regression (1). The dots
connected by the solid gray lines mark the respective 95% confidence intervals. The piecewise-linear approximation
given by the straight, solid line is obtained from regression (2). Neither regression includes additional controls.
45
ï60 ï40 ï20 0 20 40
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: Method of payment: Cash; Contests: 27
ï40 ï20
0
20
40
(a) Method of payment: All cash.
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: Method of payment: Cash and stock; Contests: 36
ï50
0
50
(b) Method of payment: Cash and stock.
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: Method of payment: Stock; Contests: 19
(c) Method of payment: All stock
Figure A-3: Winner-Loser Performance Difference by Method of Payment This figure illustrates the
winner-loser difference in pre- and post-merger performance for all-cash, mixed, and stock mergers. The dots
connected by the solid black line correspond to the point estimates of the π-coefficients of regression (1). The dots
connected by the solid gray lines mark the respective 95% confidence intervals. The piecewise-linear approximation
given by the straight, solid line is obtained from regression (2).
46
40
20
50
0
0
ï50
ï20
ï100
ï40
ï150
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Attitude: Hostile; Contests: 8
Note: Attitude: Friendly; Contests: 70
(a) Attitude: Hostile.
(b) Attitude: Friendly.
Figure A-4: Winner-Loser Performance Difference by Bidder Attitude and Deal Type This figure
illustrates the winner-loser difference in pre- and post-merger performance for takeover contests in which the
prevailing bid was hostile versus friendly (top panel), and where the winning bid was a tender offer versus a merger
(bottom panel). The dots connected by the solid black line correspond to the point estimates of the π-coefficients
of regression (1). The dots connected by the solid gray lines mark the respective 95% confidence intervals. The
piecewise-linear approximation given by the straight, solid line is obtained from regression (2).
47
100
40
50
20
0
0
ï50
ï20
ï40
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Quartile of acquiror Tobin’s Q = 1; Contests: 20
Note: Quartile of acquiror Tobin’s Q = 2; Contests: 20
(b) Quartile 3 of Tobin’s Q.
ï100
ï100
ï50
ï50
0
0
50
50
(a) Quartile 4 of Tobin’s Q: High Q.
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Quartile of acquiror Tobin’s Q = 3; Contests: 20
Note: Quartile of acquiror Tobin’s Q = 4; Contests: 19
(c) Quartile 2 of Tobin’s Q.
(d) Quartile 1 of Tobin’s Q: Low Q.
Figure A-5: Winner-Loser Performance Difference by Tobin’s Q of the Winning Bidder This figure
illustrates the winner-loser difference in pre- and post-merger performance for contests sorted by the Tobin’s Q of
the acquiring, i.e. the winning firm. The sorting is based on the pre-contest Tobin’s Q, based on data from the
fiscal year ending prior to the start of the bidding contest. The dots connected by the solid black line correspond
to the point estimates of the π-coefficients of regression (1). The dots connected by the solid gray lines mark the
respective 95% confidence intervals. The piecewise-linear approximation given by the straight, solid line is obtained
from regression (2).
48
50
40
20
0
0
ï20
ï50
ï40
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Quartile of acquiror market capitalization = 4; Contests: 19
Note: Quartile of acquiror market capitalization = 3; Contests: 20
(b) Quartile 3 of market capitalization.
ï40
ï100
ï20
0
ï50
20
0
40
60
50
(a) Quartile 4 of market capitalization: Large firms.
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
PiecewiseïLinear Approximation
Note: Quartile of acquiror market capitalization = 2; Contests: 20
(c) Quartile 2 of market capitalization.
40
Note: Quartile of acquiror market capitalization = 1; Contests: 20
(d) Quartile 1 of market capitalization: Small firms.
Figure A-6: Winner-Loser Performance Difference by Market Capitalization of the Winning Bidder
This figure illustrates the winner-loser difference in pre- and post-merger performance for contests sorted by the
market capitalization of the acquiring, i.e. the winning firm. The sorting is based on the pre-contest market
capitalization, based on data from the fiscal year ending prior to the start of the bidding contest. The dots
connected by the solid black line correspond to the point estimates of the π-coefficients of regression (1). The dots
connected by the solid gray lines mark the respective 95% confidence intervals. The piecewise-linear approximation
given by the straight, solid line is obtained from regression (2).
49
B
Online Appendix
50
-100 -75 -50 -25
0
25
50
75 100
-25
-50
-75
-100
100
75
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(a) Full sample
50
25
0
-100 -75 -50 -25
-100 -75 -50 -25
0
25
50
75 100
-25
-50
-75
-100
100
75
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
75 100
-25
-50
-75
-100
100
75
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(b) Contest duration: 1st quartile
40
(c) Contest duration: 2nd quartile
-25
-50
-75
-100
100
75
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-100 -75 -50 -25
-100 -75 -50 -25
0
0
25
50
25 50 75 100
75 100
-25
-50
-75
-100
100
75
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(d) Contest duration: 3rd quartile
40
(e) Contest duration: 4th quartile
Figure B-1: Risk-adjusted Performance around Merger Contests This figure illustrates risk-adjusted preand post-merger stock price performance for winners and losers. Prices are adjusted by first fitting the model
rit − rf t = αi + βi (rmt − rf t ) + εit , for each bidder and for the pre- and post-merger period separately, and then
subtracting the market-induced performance from raw performance: p̃it = p̃it−1 (1 + rit − rf t − βi (rmt − rf t ).
Finally, prices are normalized to zero in the month preceding the start of the contest. The five figures correspond
respectively to the entire sample (top figure) and the different quartiles of contest duration (bottom figures). The
dots connected by the solid black line correspond to the point estimates of the average winner prices, the solid gray
51
lines to the average loser prices.
-50
-25
0
25
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(a) Full sample
25
0
-25
-50
-50
-25
0
25
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(b) Contest duration: 1st quartile
40
(c) Contest duration: 2nd quartile
25
0
-25
-50
-50
-25
0
25
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(d) Contest duration: 3rd quartile
40
(e) Contest duration: 4th quartile
Figure B-2: Market-adjusted Pre- and Post-merger Performance This figure illustrates the pre- and
post-merger stock price performance for winners and losers adjusted by market performance. The five figures
correspond respectively to the entire sample (top figure) and the different quartiles of contest duration (bottom
figures). Winner and loser prices are normalized to zero in the month preceding the start of the contest, and are
adjusted by market performance, i.e. we plot p̃t = p̃t−1 (1 + rit − rmt ) The dots connected by the solid black line
correspond to the point estimates of the average winner prices, the solid gray lines to the average loser prices.
52
-50
-25
0
25
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(a) Full sample
25
0
-25
-50
-50
-25
0
25
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(b) Contest duration: 1st quartile
40
(c) Contest duration: 2nd quartile
25
0
-25
-50
-50
-25
0
25
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
50
-25
-50
50
25
-40
-20
2
0
20
40
Period
Point
5 Estimate, Avg. Loser
0
WinnerPrice
Price[Market-adjusted]
[Market-adjusted]
-40
-20
0
Period
20
40
-40
-20
0
Period
20
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Winner Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
Point Estimate, Avg. Loser Price [Market-adjusted]
(d) Contest duration: 3rd quartile
40
(e) Contest duration: 4th quartile
Figure B-3: Industry-adjusted Pre- and Post-merger Performance This figure illustrates the pre- and
post-merger stock price performance for winners and losers adjusted by industry performance. The five figures
correspond, respectively, to the entire sample (top figure) and the different quartiles of contest duration (bottom
figures). Winner and loser prices are normalized to zero in the month preceding the start of the contest, and are
adjusted by industry performance, i.e. we plot p̃t = p̃t−1 (1 + rit − rkt ), where k indexes the bidder’s Fama-French
(12) industry classification. The dots connected by the solid black line correspond to the point estimates of the
average winner prices, the solid gray lines to the average loser prices.
53
160
140
160
120
140
100
120
80
100
60
80
60
ï40
ï40
ï20
0
Period
20
40
ï20
0
Period
20
40
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Loser Price
Point Estimate, Avg. Industry Price
Point Estimate, Avg. Winner Price
Point Estimate, Avg. Market Price
(a) Winner vs loser
(b) Winner vs market and industry
Figure B-4: Pre- and Post-merger Stock Price Performance of Winners against Various Benchmarks
This figure illustrates pre- and post-merger stock price performance of winners in takeover contests against various
benchmarks. The left figure plots winner and loser prices. The right figure plots winner prices along with industry
and market index levels. Prices and index levels are normalized to 100 in the month preceding the start of the
contest. The dots connected by the black (gray) lines correspond to the point estimates of the average prices (index
levels).
54
50
50
0
0
ï50
ï50
ï100
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Quartile of transaction value / acquiror assets = 4; Contests: 19
Note: Quartile of transaction value / acquiror assets = 3; Contests: 18
ï60
ï40 ï20
ï40
0
ï20
20
0
40
60
20
(a) Quartile 4 of deal value to acquiror market cap- (b) Quartile 3 of deal value to acquiror market capitalization.
italization.
ï40
ï20
0
Period
20
40
ï40
ï20
0
Period
20
Point Estimate, WinnerïLoser Difference
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Upper Confidence Bound
Lower Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
40
PiecewiseïLinear Approximation
Note: Quartile of transaction value / acquiror assets = 2; Contests: 21
Note: Quartile of transaction value / acquiror assets = 1; Contests: 20
(c) Quartile 2 of deal value to acquiror market cap- (d) Quartile 1 of deal value to acquiror market capitalization.
italization.
Figure B-5: Winner-loser Performance Difference by Quartile of Relative Deal Size This figure illustrates the winner-loser difference in pre- and post-merger performance for contests sorted by the ratio of deal value
to market capitalization of the acquiror. Market capitalization of the winning bidder is computed for the fiscal
year ending prior to the start of the bidding contest. The deal value is the dollar amount of the final effective offer.
The dots connected by the solid black line correspond to the point estimates of the π-coefficients of regression
(1). The dots connected by the solid gray lines mark the respective 95% confidence intervals. The piecewise-linear
approximation given by the straight, solid line is obtained from regression (2).
55
20
0
ï20
ï40
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: Number of bidders = 2; Contests: 55
ï40
ï20
0
20
40
(a) Two bidders.
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: Number of bidders > 2; Contests: 27
(b) More than two bidders.
Figure B-6: Winner-loser Performance Difference for Contests with Two and More Bidders This figure
illustrates the winner-loser difference in pre- and post-merger performance for contests, separately for contest with
two bidders and those with more than two bidders. The dots connected by the solid black line correspond to
the point estimates of the π-coefficients of regression (1). The dots connected by the solid gray lines mark the
respective 95% confidence intervals. The piecewise-linear approximation given by the straight, solid line is obtained
from regression (2).
56
50
0
ï50
ï100
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: Diversifying Mergers; Contests: 17
ï20
ï10
0
10
20
30
(a) Diversifying mergers.
ï40
ï20
0
Period
20
40
Point Estimate, WinnerïLoser Difference
Upper Confidence Bound
Lower Confidence Bound
PiecewiseïLinear Approximation
Note: NonïDiversifying Mergers; Contests: 65
(b) Concentrating mergers.
Figure B-7: Winner-loser Performance Difference for Diversifying and Concentrating Mergers This
figure illustrates the winner-loser difference in pre- and post-merger performance for contests, separately for diversifying and concentrating mergers. A merger is classified as diversifying if the winning bidder is in a Fama-French
12-industry group that is different from the target’s, and concentrating otherwise. The dots connected by the solid
black line correspond to the point estimates of the π-coefficients of regression (1). The dots connected by the solid
gray lines mark the respective 95% confidence intervals. The piecewise-linear approximation given by the straight,
solid line is obtained from regression (2).
57
References
Asquith, P., R. F. Bruner, and D. W. J. Mullins (1987): “Merger Returns and the Form
of Financing,” Proceedings of the Seminar on the Analysis of Security Prices, 64, 115–146.
Betton, S., B. E. Eckbo, and K. Thorburn (2008): “Corporate Takeovers,” .
Boone, A. L. and J. H. Mulherin (2007): “How are Firms Sold?” The Journal of Finance,
62, 847–875.
Greenstone, M., R. Hornbeck, and E. Moretti (2011): “Identifying Agglomeration
Spillovers: Evidence from Winners and Losers of Large Plant Openings,” Journal of Political Economy.
Jensen, M. (1986): “Agency Cost of Free Cash Flow, Corporate Finance, and Takeovers,”
American Economic Review Proceedings, 76, 323–329.
Loughran, T. and A. M. Vijh (1997): “Do Long-Term Shareholders Benefit from Corporate
Acquisitions?” Journal of Finance, 52, 1765–1790.
Malmendier, U. and G. Tate (2008): “Who Makes Acquisitions? CEO Overconfidence and
the Market’s Reaction,” Journal of Financial Economics, 89, 20–43.
Mitchell, M., T. Pulvino, and E. Stafford (2004): “Price Pressure around Mergers,”
Journal of Finance, 59.
Moeller, S. B., F. P. Schlingemann, and R. M. Stulz (2005): “Wealth Destruction on a
Massive Scale? A Study of Acquiring-Firm Returns in the Recent Merger Wave,” Journal of
Finance, 60.
Morck, R., A. Shleifer, and R. Vishny (1990): “Do Managerial Objectives Drive Bad
Acquisitions,” The Journal of Finance, 45, 31–48.
Rau, P. R. and T. Vermaelen (1998): “Glamour, Value, and the Post-Acquisition Performance of Acquiring Firms,” Journal of Financial Economics, 49, 223–253.
58
Roll, R. (1986): “The Hubris Hypothesis of Corporate Takeovers,” Journal of Business, 59,
197–216.
Shleifer, A. and R. W. Vishny (2003): “Stock Market Driven Acquisitions,” Journal of
Financial Economics.
59
Cash is King
What the Market Learns about Targets through Merger Bids
PRELIMINARY AND INCOMPLETE. COMMENTS WELCOME.
Ulrike Malmendier
UC Berkeley
ulrike@econ.berkeley.edu
Marcus Matthias Opp
UC Berkeley
mopp@haas.berkeley.edu
Farzad Saidi
New York University
saidi@nyu.edu
March 22, 2011
Abstract
Returns to merger announcements are commonly used to measure the expected value created
by mergers. We provide evidence that a signi…cant portion re‡ects, instead, a revaluation
of the target. Using a sample of withdrawn deals from 1980 to 2008, we show that targets of cash o¤ers are revalued by +15% post-withdrawal. Stock bids, on the other hand,
do not seem to provide target information: targets with equity o¤ers fall back to their
pre-announcement levels after withdrawal. The results are not driven by future takeover
activity since cash targets are insigni…cantly less likely to receive future merger bids. The
results are also independent of the speci…c type of withdrawal reason. Our …ndings, as well
as the observed value changes in acquirers, are consistent with cash bids indicating target
undervaluation while stock bids signal acquirer overvaluation.
JEL classi…cation: G14, G34, D03, D82
Keywords: Mergers & Acquisitions, Private Information, Misvaluation, Cash O¤er, Stock
O¤er
We are extremely grateful to comments and suggestions by Malcolm Baker, Alberto Bisin, Slava Fos, Xavier
Gabaix, Alessandro Lizzeri, Gustavo Manso, Atif Mian, Terrance Odean, Antoinette Schoar, Andrei Shleifer,
David Sraer, Jeremy Stein, and Je¤rey Wurgler. Research assistance by Javed Ahmed and Marlena Lee on
earlier drafts of the paper was greatly appreciated.
1
1
Introduction
Much of the research on mergers and acquisitions revolves around the question: do mergers
create value? With mergers being among the most important and, possibly, most disruptive
events in a corporation’s lifetime, this question has been of foremost interest to policymakers
and researchers alike, including a recent debate about “massive wealth destruction” through
mergers in the late 90s and at the beginning of this century (Moeller, Schlingemann, and Stulz
(2005)). Empirically, the measurement of value creation is challenging: long-run studies may
confound the causal e¤ect of the merger with other return-relevant events. Short-run studies
can, under e¢ cient market assumptions, measure the expected value creation at announcement,
and are statistically most precise (see Barber and Lyon (1997) or Fama (1998)). In fact, various
studies document a small positive combined announcement return of targets and bidders, and
interpret this …nding as evidence of value creation.1
The validity of this assessment hinges on the assumption that the o¤er reveals little private
information about the stand-alone value of the two entities. In this paper, we document that
this is not the case. We provide evidence that a bid reveals economically and statistically
important information about the target. While the negative information e¤ect of stock bids
on the bidder’s stand-alone value is well understood at least since Myers and Majluf (1984),
we model, theoretically, information revelation about the target (under-)valuation and show,
empirically, that there is a large revaluation e¤ect for cash targets (+15%) but no e¤ect for
stock targets.
Our identi…cation strategy uses the sample of withdrawn deals to measure information
revelation: we compare the value of targets prior to the announcement and after withdrawal.
To see the intuition for our approach, consider the following thought experiment. Suppose
a target company is currently trading at $100 and obtains a takeover o¤er in cash for $125.
Moreover, assume that the deal fails within a short time period. After the failure of the deal, the
value implications of the takeover o¤er and the confounding e¤ect of the takeover probability
are no longer incorporated in the target stock price. If the stock price trades post-withdrawal
at $115, we can attribute $15 to the revision of beliefs about the stand-alone value of the target
as a result of the o¤er.
Our empirical analysis is motivated by a signi…cant body of existing theories that predict
that the bidder’s private information will be re‡ected in his choice of acquisition currency,
i.e., cash or stock.2 We provide a simple model that extends Shleifer and Vishny (2003) by
allowing the market to draw inferences about misvaluations revealed through takeover bids and
the choice of acquisition currency. The model predicts that the use of stock is more likely if
the (hypothetical) combined entity of the target and the bidder is overvalued, and the use of
cash if it is undervalued. A bidder willing to buy an overvalued target with stock as long as
the target is less overvalued than the bidder’s own stock. And a bidder prefers to use cash
for undervalued targets because it enables him to fully extract the long-run price appreciation.
By receiving a …xed payment, target shareholders in cash deals do not bene…t from a positive
long-run revaluation. Upon announcement of the deal and the method of exchange, the market
1
2
See Jensen and Ruback (1983), Jarrell, Brickley, and Netter (1988), or Andrade, Mitchell, and Sta¤ord (2001).
In Fishman (1989), bidders use cash o¤ers for valuable targets to preempt competing o¤ers. If the …rst bidder
signals a su¢ ciently high valuation for the target by using cash, bidder 2 does not …nd it worthwhile to start
a bidding war. In Hansen (1987) as well as the extension by Eckbo, Giammarino, and Heinkel (1990), the
bidder’s choice of the medium of exchange re‡ects private information about his own value.
2
price partially incorporates this strategic behavior of the bidder into the stock price. If a
deal fails, the information revealed through the choice of the medium of exchange is no longer
confounded with merger e¤ects, including the split up of surplus between target and acquirer
and match-speci…c synergies. This enables us to identify information revelation in the sample
of withdrawn deals.
Figure 1: Announcement and Withdrawal E¤ects
Figure 1: CARs from -25 (before announcement) to +25 (after withdrawal), normalized
0.35
0.3
0.25
Cash target CAR
0.2
Stock target CAR
0.15
Cash acquirer CAR
0.1
Stock acquirer CAR
0.05
Announcement
0
‐0.05
‐30
‐20
‐10
0
10
20
30
40
50
60
70
80
Withdrawal
‐0.1
‐0.15
Note (Figures 1-3): 95 cash and 131 stock deals.
Notes: Cumulative Abnormal Returns (CARs) from 25 trading days pre-announcement to 25
trading days post-withdrawal. The sample consists of 95 cash and 131 stock deals. See Table
1 for the construction of the sample.
Figure 2: BHARs from -25 (before announcement) to +25 (after withdrawal), normalized
0.35 1 previews our key empirical results. It plots the evolution of cumulative returns
Figure
in the sample
of withdrawn all-cash and all-stock deals between 1980 and 2008, separately
0.3
for acquirers
and targets. The pre-announcement and post-withdrawal di¤erences are striking.
0.25
Cash target BHAR
Starting 0.2
25 trading days prior to the announcement, we observe a run-up among
targets of stock
o¤ers and targets of cash o¤ers, yielding announcement e¤ects of 15% (stock)
and
25% (cash).
Stock target BHAR
0.15
At the withdrawal date, however, which is normalized to occur 50 (synthetic)
trading days later
Cash acquirer BHAR
0.1
3 the value of stock targets falls below the pre-announcement level, to which it
in the graph,
Stock acquirer BHAR
0.05 returns, while the value of cash targets remains signi…cantly higher than prior to
ultimately
Announcement
the bid: cash
targets earn 15% cumulative abnormal returns relative to the pre-announcement
0
Withdrawal
‐30 despite
‐20
‐10
0
10
20 trend
30 in
40both
50 cash
60 and70stock
80 targets post-withdrawal,
level. Hence,
the small
upward
‐0.05
stock target
returns remain more than 15% below cash target returns. In addition, we …nd that
‐0.1
stock acquirers trade at signi…cantly lower prices post-withdrawal ( 10%) while cash acquirers
‐0.15
3
For deals where the withdrawal occurs later than 50 days after announcement, we cumulated returns to match
the proportion of elapsed trading days (e.g., the …rst trading day in the normalized period is equal to the
cumulative return of the …rst two trading days for a deal withdrawn after 100 trading days). For deals where
the withdrawal occurs earlier than 50 days after announcement, we use a linear approximation to match the
proportions of elapsed trading days (e.g., the …rst two trading days in the normalized period are each equal
to half the return of the …rst trading day for a deal withdrawn after 25 trading days).
3
return to their pre-announcement level, consistent with …ndings in previous studies.4
Our results indicate that cash bids induce the market to positively revaluate the target.
One explanation for the revaluation is positive private information of the acquirer about the
target, which is revealed in the cash bid. A related explanation is limited attention, i.e., bids
drawing investors’attention to the target company and inducing them to process information
about the target that was already available. Since, in both cases, the market learns from the
bidder’s action, we will dub both interpretations information revelation in a broad sense. An
alternative explanation is that the value increase in cash targets does not re‡ect a revaluation
of the targets themselves but of them being lucrative merger opportunities relative to stock
targets. However, we …nd that failed cash deals are insigni…cantly less likely than failed stock
deals to be followed by another takeover attempt over the next two years.
We address several concerns about our identi…cation strategy. A …rst possible concern
is that transactions are not canceled randomly or, more speci…cally, that withdrawal is not
exogenous with respect to target value. However, such exogeneity is also not required. Our
identi…cation of a stock versus cash e¤ect solely requires that post-announcement news about an
entity do not di¤erentially a¤ect the likelihood of completion in the stock and cash samples. For
example, if positive news about targets makes deals more likely to be completed, the endogeneity
of completion does not a¤ect the observed di¤erences between failed stock and failed cash
deals. One important channel for di¤erential e¤ects are …nancial constraints of the acquirer:
intermediate positive news about the target may require the acquirer to adjust the bid upwards,
and this may be harder for cash than for stock deals if the acquirer is …nancially constrained.
Using several measures of …nancial constraints, we …nd no signi…cant e¤ect of the latter. While
we are able to dismiss a pure sample selection e¤ect based on the …nancial constraints measures
employed in this paper, we cannot completely rule out the …nancial constraints channel due to
the lack of availability of more precise measures. In addition, it is worth emphasizing that the
estimated revaluation e¤ects are, of course, speci…c to the sample of withdrawn mergers. Even
if there are no systematic di¤erences between the withdrawal of stock and cash bids, in terms
of their relation to target value, we still cannot generalize the speci…c revaluation estimate
beyond the sample of withdrawn mergers. Only exogenous cancelations (with respect to target
values) allow us to tackle this identi…cation issue.5 Despite these limitations, the magnitudes
of the observed valuation di¤erences recipients of cash and of stock bids indicate that merger
valuations cannot neglect the impact of information revelation on announcement returns and
make it worthwhile investigating the economic mechanism behind the sample selection.
Beyond providing new empirical facts about the informational content of merger bids, our
…ndings have important implications for the e¢ ciency of mergers and acquisitions. From a
policymaker’s perspective, it is crucial to know whether these deals quantitatively a¤ect total
surplus (allocative role), as in the Q-theory model of Jovanovic and Rousseau (2002), or whether
they merely have distributional consequences, such as in the model of Shleifer and Vishny
(2003), including value adjustment due to the revelation of information. While total value
creation might be the most relevant measure of the desirability of merger activities, the (ex-post
irrelevant) split-up of surplus in‡uences the ex-ante incentive to engage in value-maximizing
transactions. If target shareholders can extract all the surplus from a transaction, a value4
5
For example, Savor and Lu (2009) compare the stock price performance of stock bidders in the withdrawn
sample to that of stock bidders in the completed sample, and …nd that bidders in the withdrawn sample do
worse. Further evidence on stock market driven acquisitions is provided in Friedman (2006).
We are currently working on identifying a subsample of deal withdrawals unrelated to target valuations.
4
maximizing bidder would have no incentive to start a takeover attempt. This is the powerful
logic of Grossman and Hart (1980). The rationale applies regardless of the source of the
surplus, that is, also if the bidder simply has private information about the value of the target:
if a bidder’s private information about the target is fully revealed to the market and target
shareholders would, once the bidder has announced his bid, demand the full surplus, bidders
may abstain from bidding in the …rst place. An initial stake in the target company (see Shleifer
and Vishny (1986) and Hirshleifer and Titman (1990)) dampens this mechanism as the bidder
would bene…t from a revaluation of his initial stake. In summary, the division of surplus,
including a valuation increase due to information revelation, a¤ects ex-ante incentives and thus
the e¢ ciency of the takeover market mechanism.
In addition to the research cited above, our …ndings relate to several strands of the prior
literature. A large literature evaluates the returns to mergers and acquisitions. Most studies
based on announcement returns …nd that tender o¤ers generate small overall value with virtually
all the gains accruing to the target (see overview papers by Jensen and Ruback (1983), Andrade,
Mitchell, and Sta¤ord (2001), and Betton, Eckbo, and Thorburn (2008)). Using probability
scaling methods, Bhagat, Dong, Hirshleifer, and Noah (2005) …nd larger estimates of combined
value creation (7:3%) than the conventional CAR estimate (5:3%) by Bradley, Desai, and Kim
(1988). Following Travlos (1987), studies of announcement returns distinguish between cash
and stock transactions. Consistent with the pecking order model of Myers and Majluf (1984),
bidders using stock reveal negative private information about themselves, which manifests itself
in negative announcement returns for the bidder.6
Long-run post-takeover performance studies by Rau and Vermaelen (1998) and Loughran
and Vijh (1997) document strong negative abnormal returns for bidders of stock transactions
and positive abnormal returns for bidders using cash. Apart from facing statistical issues in
computing long-run abnormal returns (see Barber and Lyon (1997), Fama (1998), and Brav
(2000)), these post-takeover studies face a fundamental logical problem: if markets are e¢ cient,
even a value-destructive merger should be correctly incorporated in the price after completion
of the deal. Thus, stock bidders should not exhibit a negative CAR. In an e¢ cient market,
long-run empirical studies would solely pick up the extent of asset pricing model misspeci…cation. If stock markets are not e¢ cient, it is not clear how to use stock market data to make
surplus assessments. Malmendier, Moretti, and Peters (2010) address this identi…cation issue
by comparing the long-run returns of competing bidders in contested mergers.
Most closely related to our analysis are earlier papers by Dodd (1980), Bradley, Desai, and
Kim (1983), and Davidson, Dutia, and Cheng (1989), which examine failed acquisitions to study
information revelation about the target. For example, Davidson, Dutia, and Cheng (1989) …nd
that targets of withdrawn deals trade higher than before the announcement only because they
are more likely to become future targets, whereas targets that do not obtain a future o¤er
revert to pre-announcement levels. They interpret this result as evidence against information
revelation about the stand-alone value of the target. In contrast to our analysis, their study
does not distinguish between cash and stock transactions. This conditioning information allows
us to separate targets for which (almost) no information is revealed (stock targets) and targets
that are revalued (cash targets). Our analysis indicates that this e¤ect is not driven by cash
targets being more prone to becoming takeover targets in the future. Moreover, our results are
robust to controlling for hostile deals even though these deals are more likely to be …nanced
6
Jovanovic and Braguinsky (2004) generate bidder discounts in a Q-theory framework without resorting to the
informational content revealed through the medium of exchange.
5
with cash. Thus, the disciplinary channel of hostile bids (see Franks and Mayer (1996)) does
not drive our results either.7
The remainder of the paper is organized as follows. In Section 2, we motivate our empirical
analysis with a simple model of mergers building on the framework of Shleifer and Vishny (2003),
but allowing investors to process the information contained in merger bids. That section also
discusses the assumptions underlying our identi…cation strategy. In Section 3, we describe
the selection and composition of our data set. Section 4 contains the results of our empirical
analysis, and Section 5 concludes.
2
Theoretical Framework
2.1
Model
We consider an asymmetric information environment in which a bidder, B, possesses private
information about his own value, the value of a potential target, T , and potential synergies
between the two …rms. Our setting allows both for the possibility that stocks are (on average)
fairly valued and for systematic misvaluation. In either case, the assumption of asymmetric
information only requires that some …rms have access to information that is not fully re‡ected
by the stock price. For ease of exposition, we assume that there are no information asymmetries,
instead, between the bidder and the target. Intuitively, these assumptions could be motivated
by industry-speci…c knowledge that is shared by the bidder and the target share but is not
known to the market (yet).
Building on the modeling framework and notation of Shleifer and Vishny (2003), we study
how market misvaluations a¤ect the probability of merger bids and the form of payment, i.e.,
the choice between a cash and a stock acquisition. However, in contrast to Shleifer and Vishny
(2003), we allow market participants to learn from actions that are (potentially) motivated
by misvaluations, including the form of payment o¤ered by the bidder. This learning channel
generates testable predictions for our empirical examination of stock market reactions to cash
and stock o¤ers.
For expositional reasons, we express market valuations of …rm i, Vi , in terms of valuation
multiples, q^i , and the capital stock in place, Ki :
Vi = q^i Ki
(1)
where i 2 fB; T; M g and M refers to the merged …rm. The capital stock is observable to
the market, so that potential initial misvaluations must be caused by false market assessments
of the valuation multiple. The fair long-run multiple of the …rms is denoted by qi whereas
the initial time-0 market valuation is given by q^i0 . The true long-run synergies are given by
sKM = s (KB + KT ) whereas the market estimates synergies of s^KM .
The timeline (see Figure 2) is as follows:
7
Bhagat, Shleifer, Vishny, Jarrel, and Summers (1990) interpret the hostile takeover activity in the 1980s mostly
as a return to corporate specialization and deconglomeration.
6
Figure 2: Timeline of the Model
t
p !!!
!
t!
aa
aa
a
1 p aat
q^i (C)
!!
a=C
Players learn qi
t
Q
Q
t
p !!!
!
t!
aa
aa
a
1 p aat
q^i (S)!!
a=S
Q
Q
Q
Q
Q
Q
Q
a = N QQ
t=0
Qt
-
t=1
-
t=2
tVM = (qM + s)KM
tVi
tVM = (qM + s)KM
tVi
tVi
-
t=3
0. The bidder and the target learn the fair values of their …rms, qi , including the value of
the merged …rm.
1. The bidder can choose between three possible actions, a. The market updates the valuation multiple and synergies estimate after observing a, denoted by q^i (a) and s^ (a):
(a) All-Cash acquisition, a = C
(b) All-Stock acquisition, a = S
(c) No acquisition, a = N .
2. The deal fails with probability 1
p.
3. Market prices adjust back to long-run values (indicated by Vi ).
Since periods 1 and 2 are close events, we do not incorporate an additional learning stage
after the withdrawal. Eventually, long after the withdrawal (in period 3); market prices revert
to fundamentals. As in Shleifer and Vishny (2003) (henceforth SV), we assume that the target management is only concerned about short-run valuations whereas bidder management is
concerned about long-run valuations.8 Consequently, target shareholders will accept any o¤er
price P KT as long as P > q^T (a). Assuming that the target o¤er premium, P , is identical for
cash and stock o¤ers, a cash payment of P KT corresponds to a stock deal with an exchange
ratio such that a fraction x of the merged company is owned by target shareholders:
x=
8
P
KT
:
q^M (S) + s^ (S) KM
(2)
Our model yields the original SV model for the following parameters: p = 1, q^i (C) = q^i (S) = q^i0 , qB = qT = q,
s^ (C) = s^ (S) > 0, and s = 0.
7
Intuitively, the target share is the ratio of the takeover premium multiple, P , relative to the
combined …rm valuation multiple, s^ (S) + q^M (S), times the fraction of the capital stock that
the target contributes to the merged …rm. Note that this speci…cation is consistent with our
data (which will be discussed further in Section 3): the premia do not vary signi…cantly between the cash and stock deal samples (comprising successful and withdrawn deals).9 Furthermore, we know that the updated valuation multiple of the merged company q^M (S) is equal to
B
T
q^B (S) KBK+K
+ q^T (S) KBK+K
. Deal failure is modeled exogenously with respect to the cash
T
T
or stock choice. Section 2.2 critically discusses this important assumption.
With a slight abuse of notation, we denote the long-run value of shares owned by the
acquiring company’s shareholders as VB , also in the cases where the deal goes through:
Lemma 1 The long-run market value of shares owned by the bidder’s shareholders is:
VB (S) = (1
x) (qM + s) KM
VB (C) = (qM + s) KM
(3)
P KT
(4)
VB (N ) = qB KB :
(5)
Note that if the transaction fails, the long-run market valuation of the bidder is simply the
long-run stand-alone value, VB (N ).
Proposition 1 Given the updating functions of the market, q^M (S) and s^ (S), a bidder chooses
stock over cash if:
q^M (S) + s^ (S)
|
{z
}
>
Post-Stock O¤ er Combined Firm Valuation
A transaction is made at all as long as:
(
(P qT ) KT
sKM
qM +s
P q^M (S)+^
q T KT
s(S)
q + s:
|M{z }
(6)
Long-run Combined Firm Valuation
for
qM + s > q^M (S) + s^ (S)
otherwise
:
(7)
Proof: Since the payo¤s in case of failure (and the probabilities of failure) are the same for
stock and cash transactions, the bidder only needs to compare the valuations upon a successful
deal (see Lemma 1). The bidder chooses stock if:
1
P KT
(^
qM (S) + s^ (S)) KM
(qM + s) KM > (qM + s) KM
P KT :
(8)
Simple algebra yields the desired result.
Thus, stock is more likely to be used if both the target and the bidder are overvalued or if the
market is too optimistic about synergies, i.e., s^ (S) > s. Intuitively, a transaction is made if
either the true synergies, sKM , are high or if the target can be purchased at a discount (P < qT ).
qM +s
For stock bidders, the price P is shaded by the long-run devaluation ratio q^M (S)+^
s(S) < 1.
As long as the combined …rm is currently overvalued (relative to market expectations), i.e.,
9
Theoretically, the e¤ective value of the o¤er could be a function of the medium of exchange.
8
q^M (S) + s^ (S) > qM + s, the long-run e¤ective price of a stock o¤er is lower. Note that both
stock and cash bidders would prefer to buy the target at a discount (P < qT ). In that case,
the deal adds value for the bidder regardless of the method of payment. However, the choice
between cash and stock depends on the combined overvaluation of the bidder and the target. If
the bidder is undervalued, he would not like to let the target shareholders bene…t from his own
undervaluation. Therefore, he uses cash. Bidders in stock deals are willing to overpay targets
(P > q^T > qT ) if and only if their own overvaluation is even higher (or synergies are high).
Since the bidder takes the updating functions of the market, q^i (a) and s^ (a), as given,
these functions were treated as exogenous for the decisionmaking behavior of the bidder. If the
updating functions are rational, they must be consistent with the optimal policy function of the
bidder. As a result, if our model fully captured all payo¤-relevant dimensions for the bidder’s
decision problem, his using stock would signal that the market valuation of the combined …rm
(including synergies) is too high. This could not be consistent with an equilibrium (in the
Perfect Bayesian sense). Assuming partial updating by the market, the market will update all
terms in the same direction (but not fully) upon seeing a stock o¤er. De…ne the overvaluation
of a …rm as:
vi (a) = q^i (a) qi :
(9)
Then, the learning component of our model predicts:
Corollary 1 Partial updating implies:
vi (S)
s^ (S)
vi (C)
s
s^ (C)
(10)
s:
(11)
Note that this information e¤ect cannot be cleanly identi…ed in the sample of successful deals.
Targets that receive a takeover price P , whether in stock or cash, will simply converge to the
o¤ered takeover price (until the deal is sealed). In contrast, the sample of withdrawn deals
allows us to separately identify the information e¤ect on the stand-alone valuation multiples.
At the time of the withdrawal, match-speci…c synergies should no longer be priced, so that the
di¤erence between the post-withdrawal price and the pre-announcement price should re‡ect the
informational content revealed through the medium of exchange.
2.2
Identi…cation
The model (see Corollary 1) predicts that the expected overvaluation of a target, vT , conditional
on a stock o¤er is greater than the expected target overvaluation conditional on a cash o¤er:
E (vT (S)) > E (vT (C)) :
(12)
In order to isolate the information revealed about the target, we examine the subsample of
withdrawn takeovers (W = 1). For now, assume that deals fail for exogenous reasons (as in the
model). After a deal is withdrawn, the deal-related synergies or overpayment by this particular
bidder no longer a¤ect the target value. Our data (cf. Figure 1) are consistent with the following
conditional hypothesis:
E ( vT (S)j W = 1) > E ( vT (C)j W = 1) :
9
(13)
If deals fail for exogenous reasons (unrelated to vT ), then the conditional statement in the
withdrawn sample automatically implies the unconditional statement in the full sample.
Suppose that deals fail endogenously (related to vT ), then our results are biased if there
is a di¤ erential impact of target information about vT on the success probability of the deal
depending on whether the deal is in cash or stock. To see this, it is useful to decompose the
expected target overvaluation as:
E (vT (a)) = Pr ( W = 1j a) E ( vT (a)j W = 1)
(14)
+ Pr ( W = 0j a) E ( vT (a)j W = 0) :
Our identi…cation assumption is that the withdrawal selection does not mechanically generate
the following relationship:
E ( vT (S)j W = 0) > 0 > E ( vT (C)j W = 1) :
(15)
Put di¤erently, it would be problematic if the most undervalued targets (vT < 0) within the
cash sample (C) were more likely to be withdrawn (W = 1). Likewise, it would be problematic
if the most undervalued targets within the stock sample (S) were more likely to be completed
(W = 0). Note that it is (in theory) irrelevant whether the probability of withdrawal is di¤erent
given a cash or a stock o¤er.10
Thus, any story that threatens our identi…cation requires di¤erential e¤ects of new information on the success probability for cash vs. stock deals. One such hypothetical story would be
based on …nancial constraints of the acquirer. We will revisit our identi…cation after we present
the main empirical results.
3
Data Description
Our main sources are CRSP, Compustat, and SDC. We match CRSP market data with targets
and acquirers in the SDC database using the six-digit CUSIP provided in the SDC database.
In determining which CUSIP identi…er to use from the CRSP database, we always choose the
CUSIP with the lowest possible 7th digit (typically 1). Our underlying assumption is that earlier issuances make for greater proportions of market capitalization (note that, however, hardly
any entity in our …nal regression sample is a¤ected by this assumption). Regarding the deals
in the SDC database, we drop those for which the announcement and/or execution/withdrawal
dates are missing. Furthermore, we also drop deals which were withdrawn on the day of their
announcement, as well as deals with announcement dates after their execution/withdrawal (this
criterion is labeled as "valid deal dates" in Table 1).
Sample selection. We focus on deals that were executed/withdrawn within at least …ve
and at most 250 trading days. While economically sensible, this criterion for the deal window is
not very restrictive. Out of 12; 025 deals that ful…ll the criteria listed so far, only 657 deals are
dropped due to the deal window restriction. Note that, for e¤ective deals, targets sometimes
stop trading well before the announcement date, so we use the last trading day to calculate
the number of trading days between deal announcement and execution. Lastly, we restrict our
10
It turns out empirically that cash deals are not more likely to be withdrawn than stock deals after controlling
for other observables.
10
analysis to deals for which no competing o¤ers were outstanding. The conventional rationale
for this is to avoid capturing new deal announcements when calculating returns after an o¤er
for the same target was withdrawn.
Table 1 summarizes the sample construction outlined above, and displays the composition
of the …nal regression sample of successful and withdrawn deals.
Variable de…nitions. In our analysis, we use the following return measures:
CARit =
t
X
(rij
rmj )
(16)
j=1
1 + BHRit =
t
Y
(1 + rij )
(17)
j=1
1 + BHARit =
t
Y
(1 + rij )
j=1
t
Y
(1 + rmj )
(18)
j=1
where rij and rmj denote …rm i’s equity return and the CRSP value-weighted market return
at time j, respectively.
As can be seen in Figures 1, 3, and 4, our main …nding is robust to whether we choose
cumulative abnormal returns (CARs), buy-and-hold returns (BHRs), or buy-and-hold abnormal
returns (BHARs). Therefore, we will henceforth use cumulative abnormal returns, but can at
request provide results for the other return measures which we do not report in this paper.11
Other important variables in our analysis are the …rms’q ratios and Kaplan and Zingales
(1997) indices. The former is de…ned as the market value of equity plus assets minus the book
value of equity all over assets. As a measure of …nancial constraints, we use the four-variable
version of the KZ index given in Lamont, Polk, and Saa-Requejo (2001) and Baker, Stein, and
Wurgler (2003), namely:
KZit =
1:002
CFit
Ai;t 1
39:368
DIVit
Ai;t 1
1:315
Cit
+ 3:139LEVit
Ai;t 1
(19)
where CFit , DIVit , Cit , and LEVit denote cash ‡ows, cash dividends, cash balances, and
leverage, respectively, and Ai;t 1 is the …rm’s lagged assets.
Summary statistics. The summary statistics are in Tables 2a and 2b. We summarize the
characteristics of successful and withdrawn deals separately for the entire regression sample in
Table 2a, and then focus on the subsample of withdrawn all-cash and all-stock deals in Table
2b.
In general, successful and withdrawn deals are similar along many dimensions. Naturally,
they di¤er (and signi…cantly so at the 1% level) in their time to execution/withdrawal, the
proportion of hostile deals, and the ratio between the target’s and acquirer’s market values of
equity: deals take longer to be executed than to be withdrawn, withdrawn deals are more likely
to be hostile, and – compared to the acquirers’ market capitalization – the targets’ market
11
Figure 5 also plots the CARs for 100 days pre- and post-announcement. The results are robust.
11
values of equity are higher in withdrawn than in successful deals. Most importantly, the …rms’
q ratios and KZ indices do not vary signi…cantly between the successful and withdrawn samples.
While, at face value, withdrawn deals seem more likely to be …nanced with stock rather than
cash, the regression analysis in Table 3 will reveal that this di¤erence can be explained by deal
characteristics (most notably the log of the relative deal size).
The all-cash and all-stock subsamples add up to more than two-thirds of the total regression
sample, revealing that the majority of deals do not involve hybrid …nancing structures. As can
be seen in Table 2b, among withdrawn deals,12 all-cash deals di¤er from all-stock ones in that
the former yield higher premia (signi…cant at the 5% level), and involve acquirers and targets
with lower q ratios (signi…cant at the 1% level). Lastly, withdrawn cash deals are also more
likely to be hostile (signi…cant at the 1% level).
We conclude that the withdrawn sample is not systematically di¤erent from the sample
of successful deals, and that – conditional on other deal characteristics – stock deals are not
disproportionately more often withdrawn than cash deals, for which we will provide further
evidence in the following section.
4
Empirical Results
Before measuring the impact of cash bids on post-withdrawal returns, we …rst investigate
whether the medium of exchange has explanatory power for the withdrawal of a deal. To
this end, we estimate a linear probability model, and regress a binary withdrawal variable on
a continuous cash variable, which indicates what fraction of the total payment was o¤ered in
cash. The results are presented in Table 3. Without any controls, cash deals are less likely to
be withdrawn. However, after controlling for the relative deal size, the impact of the medium of
exchange on withdrawal becomes insigni…cant. Note furthermore that, in the last two columns,
the absolute coe¢ cient on cash drops by two-thirds once we control for announcement returns,
which backs up the idea that any signi…cance of the cash variable is likely due to other dealrelated covariates.13 The announcement return of the target, CAR (A 25; A + 1), should
control for market expectations of deal failure that are based on publicly available information
at the time of the announcement (but are unavailable to the econometrician).14 Our results are
also robust to reducing our sample to the subset of pure cash and stock deals only (cf. Table
A.3). In conjunction with the summary statistics, we can now rest assured that we may focus
on withdrawn deals in the remainder of the analysis.
Table 4 presents our main result: bids with higher cash fractions are associated with higher
post-withdrawal target CARs compared to their pre-announcement levels.15 Note that cumulative abnormal returns can be meaningfully compared across deals with di¤erent window lengths
12
13
14
15
The characteristics of successful all-cash and all-stock deals can be found in Table A.1.
Further correlates of cash deals are investigated in Table A.2.
The announcement return should approximately be: CART (a) p Premium+ (1 p) Learning(a). Since we
control for the premium and learning encoded in the choice of medium of exchange, a, variations in CAR
should capture variations in the deal probability.
In regressions unreported in this paper, we …nd that our results are robust to using industry rather than
market returns for the speci…cation of abnormal returns.
12
as long as the underlying equilibrium asset pricing model is correctly speci…ed.16 The e¤ect
is even stronger than suggested in Figure 1, which can be inferred from the estimates in the
…rst two columns without cash interaction e¤ects. If …nancial constraints of the acquirer were
driving our results, one would expect that the interaction of the medium of exchange with the
KZ index shows up signi…cantly. This is not the case. Despite a rich variety of deal- and entityspeci…c controls in Table 4, only the medium of exchange and the deal premium consistently
matter for target CARs. Intuitively, these two variables re‡ect the value of the target.
4.1
Robustness Checks
Our identi…cation strategy hinges on the assumption that there are no systematic di¤erences
between cash and stock deals in the withdrawn as opposed to the successful sample. As alluded
to above, …nancial constraints on the acquirer’s side could lead to such di¤erences as …nancially
constrained acquirers are more likely to withdraw cash rather than stock deals upon the arrival
of positive news about the target. While we …nd no such e¤ect in Table 4, it might be insightful
to decompose cash and stock deals by their withdrawal reasons. For this purpose, we collected
deal synopses as provided in the SDC database, categorized the withdrawal reasons for the
subsample of pure cash and stock deals, and report their relative frequencies in Table 5. The
…rst three categories –acquirer withdrawal, target rejection, and mutual consent –summarize
the most common reason for deal withdrawal, namely failure to …nd an agreement. Note that
acquirers may decide to withdraw deals due to (anticipated) target rejection, so the classi…cation
is not unambiguous/exclusive in these cases.
Deals that are withdrawn due to target defense are more critical for our analysis: they represent cases in which the board and/or the shareholders actively fought o¤ a merger attempt,
which suggests that the target may have believed it was undervalued. According to our discussion in Section 2.2, the inclusion of these deals would bias our estimates. On the other hand,
self-selection in the stock sample seems innocuous. Prima facie, stock deals appear more prone
to withdrawals as a reaction to market revaluations (here, news may relate to the acquirer
as well as the target as, from the theory, we know that overvaluation of the combined …rm
makes a stock deal more likely). That is, overvalued targets (and acquirers) may be relatively
overrepresented in the subsample of successful rather than withdrawn stock deals. This would
in turn imply that the di¤erence in target overvaluation between stock and cash deals that
we capture in the withdrawn sample should actually be a lower bound (in the absence of any
further self-selection in the cash sample).
Overall, only two withdrawal reasons are signi…cantly more frequent for cash rather than
stock deals, namely deals withdrawn due to target rejection and due to active target defense.
As already argued, these are potentially deals for which our identi…cation assumption may not
hold. In Table A.4, we re-run the regressions of the …fth and sixth columns of Table 4 for
di¤erent subsets of pure cash and stock deals, and …nd that our results are robust to dropping
deals that are withdrawn due to target rejection, active target defense, or both.
In Table 6, we present further robustness checks of our main …nding. We include a toehold
16
Due to the relatively short time length of our event window (see summary statistics in Table 2a), the misspeci…cation of the asset pricing model to compute "normal" returns is a second-order concern. Detailed
discussions of the statistical issues with calculating long-run returns are given by Barber and Lyon (1997),
Brav (2000), and Fama (1998).
13
variable to check whether our results are driven by deals in which the acquirer possessed an
initial stake in the company. We …nd that stock bidders with an initial toehold in the company
signal positive information about the target. This e¤ect is not present for cash bidders.17
Interestingly, our results are neither driven by the reputation of the acquirer’s investment bank
nor by within-industry (horizontal) mergers. Both variables could be reasonably related to
information asymmetries. Our …ndings are consistent with a framework in which the method
of exchange is a su¢ cient statistic for private information. If the acquisition currency perfectly
measures information, any additional variable that is correlated with information should not
matter in itself or through its interaction with cash. The results are also robust in the subsample
of pure cash and stock deals in Table A.5, where the cash e¤ect seems stronger for targets with
low q ratios. This can be interpreted as re‡ecting our theoretical prediction that the revaluation
process is more emphasized for potentially undervalued targets once cash is o¤ered for them.
As the most important robustness check, we scrutinize whether post-withdrawal CARs
are higher for cash targets merely because the latter may be more likely to become future
targets. That is, we test whether the positive cash e¤ect in Table 4 was driven by (expected)
announcement returns of future deals for cash targets. For this purpose, we consider the
sample of withdrawn deals, and regress a dummy indicating another merger bid within the
next two years (but after a grace period of half a year to avoid capturing bidding wars) on the
continuous cash variable. The results are summarized in Table 7. We …nd that cash targets
are insigni…cantly less likely to receive future merger bids. More than that, targets with low
q ratios are signi…cantly more likely to become targets in the future, which shows that we
have in part controlled for future merger bids in Table 4 by including the target’s q. For similar
reasons as in Table 3, we include the target CAR (from announcement to withdrawal) to directly
check whether the cumulative abnormal returns predict future merger activity. We cannot …nd
evidence for this. Once again, our results are also robust to considering the subset of pure cash
and stock deals only (cf. Table A.6).
5
Conclusion
In this paper, we have presented robust evidence that the medium of exchange used by the bidder
in merger transactions provides economically and statistically signi…cant information about the
stand-alone value of the target: targets of cash o¤ers trade 15% above pre-announcement levels
whereas no signi…cant information is revealed about the targets of stock o¤ers. The previous
literature has primarily focused on the information content of the method of payment related
to the bidder, and thus ignored this channel. Our analysis has important implications for the
quantitative assessment of value creation in merger transactions.
Building on the …ndings of this paper, an important next step would be the generalization of
our results to completed deals. Exploiting exogenous variation in the probability of withdrawal
in a subsample of our cases and other sources of identi…cation, it might be possible to build a
credible structural model that allows to jointly estimate the endogenous withdrawal selection,
information revelation, and value creation (or destruction) for cash and stock transactions.
Such an analysis would deepen our understanding of these important company decisions, and
would help quantify the economic bene…ts of mergers.
17
We cannot reject the hypothesis that the sum of the coe¢ cients for Toehold and Toehold
14
Cash is 0.
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15
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17
Announcement
0
‐0.05
‐30
‐20
‐10
0
10
20
30
40
50
60
70
80
Withdrawal
‐0.1
‐0.15
6 Note
Figures
(Figures 1-3): 95 cash and 131 stock deals.
Figure 3: Announcement and Withdrawal E¤ects
Figure 2: BHARs from -25 (before announcement) to +25 (after withdrawal), normalized
0.35
0.3
0.25
Cash target BHAR
0.2
Stock target BHAR
0.15
Cash acquirer BHAR
0.1
Stock acquirer BHAR
0.05
Announcement
0
‐0.05
‐30
‐20
‐10
0
10
20
30
40
50
60
70
80
Withdrawal
‐0.1
‐0.15
Notes: Buy-and-Hold Abnormal Returns (BHARs) from 25 trading days pre-announcement to
25 trading days post-withdrawal. 95 cash and 131 stock deals. The market value of equity
of the target relative to the acquirer is strictly greater than 5%. See Table 1 for the exact
construction of the sample.
18
Figure 4: Announcement and Withdrawal E¤ects
Figure 3: BHRs from -25 (before announcement) to +25 (after withdrawal), normalized
0.35
0.3
0.25
Cash target BHR
0.2
Stock target BHR
0.15
Cash acquirer BHR
0.1
Stock acquirer BHR
0.05
Announcement
0
‐0.05
‐30
‐20
‐10
0
10
20
30
40
50
60
70
80
Withdrawal
‐0.1
‐0.15
Notes: Buy-and-Hold Returns (BHRs) from 25 trading days pre-announcement to 25 trading
days post-withdrawal. 95 cash and 131 stock deals. The market value of equity of the target
relative to the acquirer is strictly greater than 5%. See Table 1 for the exact construction of
the sample.
Figure 5: Announcement and Withdrawal E¤ects
Figure 4: CARs from -100 (before announcement) to +100 (after withdrawal), normalized
0.35
0.3
0.25
Cash target CAR
0.2
Stock target CAR
0.15
Cash acquirer CAR
0.1
Stock acquirer CAR
0.05
Announcement
0
‐0.05
‐110 ‐90
‐70
‐50
‐30
‐10
10
30
50
70
90
110 130 150
Withdrawal
‐0.1
‐0.15
Note: 76 cash and 114 stock deals.
Notes: Cumulative Abnormal Returns (CARs) from 100 trading days pre-announcement to 100
trading days post-withdrawal. 76 cash and 114 stock deals. The market value of equity of the
target relative to the acquirer is strictly greater than 5%. See Table 1 for the exact construction
of the sample.
19
7
Tables
Table 1: Sample Construction
Criterion
Availability of CRSP data for acquirer and/or target
# Deals
15,502
Availability of valid deal dates
13,490
Availability of stock data
(at least 25 trading days prior to bid announcement)
12,025
Deal window of 5 to 250 trading days
(between announcement and completion/withdrawal)
11,368
No competing o¤ers
10,285
Subsamples
of which
successful withdrawn
7,071
3,214
Availability of all variables used in any regression
Graph sample of withdrawn pure cash and stock deals
Acquirer and target
Acquirer only
Target only
1,936
of which
successful withdrawn
1,716
220
236
of which
cash
stock
95
131
4
8
3
0
Notes: The availability of valid deal dates requires that announcement and withdrawal or
completion dates are not missing and consistent, e.g., completion not prior to announcement of
original bid. The graph sample contains all deals used in Figure 1, i.e., deals with the additional
requirement of being a pure cash or a pure stock deal, without competing o¤ers outstanding,
and where the target’s market value of equity is at least equal to 5% of the acquirer’s market
value of equity. Furthermore, we require CRSP data availability for at least 25 days before
announcement and at least 25 days after withdrawal.
20
21
Mean
48.76
44.47
6.77
75.85
0.12
45.57
1.86
1.39
1.11
95.27
n/a
82.05
0.35
2.50
2.06
62.30
-0.09
0.10
74.30
60.26
Successful deals
Std. dev.
Min
45.89
0
45.65
0
17.23
0
43.82
5
3.41
0
40.24
-48.30
13.53
0
4.13
0.00
17.04
0.00
16.98
1.80
n/a
n/a
38.39
0
0.87
0.00
2.39
0.51
1.71
0.50
48.48
0
1.47
-10.46
1.68
-10.05
43.71
0
48.95
0
1,716
Max
100
100
100
245
100
202.10
100
70.51
682.90
100
n/a
100
8.93
15.20
9.91
100
3.73
5.22
100
100
Mean
40.23
51.75
8.01
65.67
0.91
42.23
15.45
1.73
1.10
93.67
20.00
71.82
0.79
2.36
1.94
58.64
0.06
0.19
69.55
53.18
Withdrawn deals
Std. dev.
Min
45.96
0
46.28
0
22.97
0
51.49
5
9.51
0
45.16
-48.30
36.22
0
7.27
0.00
2.21
0.00
16.68
15.80
40.09
0
45.09
0
1.32
0.00
2.34
0.51
1.72
0.50
49.36
0
1.49
-5.94
1.58
-10.73
46.13
0
50.01
0
220
Max
100
100
100
238
100
202.10
100
77.04
21.86
100
100
100
8.93
15.20
9.91
100
3.73
4.28
100
100
p-value
0.011
0.024
0.470
0.002
0.014
0.414
0.000
0.257
0.996
0.217
n/a
0.000
0.000
0.434
0.395
0.259
0.114
0.560
0.126
0.035
Notes (Tables 2a and 2b): Time to completion/withdrawal is in trading days. The historical transaction value was converted
using Consumer Price Index (CPI) Conversion Factors. Relative deal size is the transaction value over market value of equity of the
acquirer. New deal announced within 2 years is conditional on the deal being announced at least half a year after the previous one.
Experienced acquirers appear at least …ve times in the data set. Equity ratio is de…ned as the ratio of target’s to acquirer’s market value
of equity. KZ index is the four-variable version in Lamont, Polk, and Saa-Requejo (2001). All non-deal-related variables (e.g., q ratios)
are year-end measures one year prior to the deal announcement. Premium, Equity ratio, all q and KZ variables are winsorized at the
1st and 99th percentiles. The sample is restricted to deals that include all variables used in any of the speci…cations in Tables 3-7.
Variable
Cash in %
Stock in %
Other payment in %
Time to completion/withdrawal
LBO in %
Premium (to 1 month prior) in %
Hostile deal in %
Transaction value in 2010 $bn
Relative deal size
% of target sought
New deal announced within 2 years in %
Experienced acquirer in %
Equity ratio
q of acquirer
q of target
q of acquirer > q of target in %
KZ index of acquirer
KZ index of target
Same industry (1 digit SIC) in %
Same industry (2 digits SIC) in %
N
Table 2a: Summary Statistics –All Deals
22
Variable
Time to completion/withdrawal
LBO in %
Premium (to 1 month prior) in %
Hostile deal in %
Transaction value in 2010 $bn
Relative deal size
% of target sought
New deal announced within 2 years in %
Experienced acquirer in %
Equity ratio
q of acquirer
q of target
q of acquirer > q of target in %
KZ index of acquirer
KZ index of target
Same industry (1 digit SIC) in %
Same industry (2 digits SIC) in %
N
Mean
56.27
2.82
52.97
23.94
0.66
1.09
92.67
16.90
69.01
0.77
1.84
1.51
64.79
0.12
0.21
73.24
47.89
Cash deals
Std. dev.
Min
44.97
5
16.66
0
43.78
-48.30
42.98
0
1.47
0.00
2.35
0.00
15.20
20.00
37.74
0
46.57
0
1.70
0.00
1.69
0.51
1.31
0.50
48.10
0
1.62
-5.94
1.06
-2.29
44.59
0
50.31
0
71
Max
178
100
202.10
100
8.93
11.77
100
100
100
8.93
13.72
9.91
100
3.41
2.90
100
100
Mean
62.81
0
37.57
5.68
1.56
0.84
94.44
21.59
72.73
0.78
2.89
2.51
54.55
0.00
-0.08
72.73
56.82
Stock deals
Std. dev.
Min
50.19
5
0
0
44.03
-44.20
23.28
0
6.20
0.01
1.18
0.01
17.77
19.10
41.38
0
44.79
0
1.12
0.01
2.75
0.58
2.07
0.50
50.08
0
1.43
-4.30
1.94
-10.73
44.79
0
49.82
0
88
Table 2b: Summary Statistics –Withdrawn Deals
Max
232
0
199.90
100
55.64
8.49
100
100
100
8.58
15.20
9.91
100
3.73
4.28
100
100
p-value
0.394
0.115
0.029
0.001
0.239
0.399
0.507
0.461
0.610
0.955
0.005
0.001
0.194
0.618
0.250
0.943
0.265
Table 3: Determinants of Withdrawal (All Deals)
Cash 2 [0; 1]
-0.041**
(0.02)
Log(relative deal size)
LBO
Premium to 1 month prior
Hostile
% of target sought
q of acquirer
q of target
KZ index of acquirer
KZ index of target
Experienced acquirer
Deal withdrawn
-0.017
-0.005
-0.018
(0.02)
(0.02)
(0.02)
0.027*** 0.027*** 0.023***
(0.00)
(0.00)
(0.00)
0.318
(0.31)
-0.020
(0.02)
0.333***
(0.07)
-0.001**
(0.00)
0.006*
(0.00)
-0.001
(0.00)
0.004
(0.00)
-0.000
(0.00)
-0.029*
(0.02)
Target CAR (A-25, A+1)
Industry & year FE
N
N
1,936
N
1,936
Y
1,924
Y
1,924
-0.006
(0.02)
0.020***
(0.00)
0.322
(0.31)
0.029
(0.02)
0.320***
(0.07)
-0.001*
(0.00)
0.005
(0.00)
-0.001
(0.00)
0.004
(0.00)
0.000
(0.00)
-0.031*
(0.02)
-0.109***
(0.04)
Y
1,924
Notes: The table reports marginal e¤ects of probit regressions. All non-deal-related independent
variables are year-end measures one year prior to deal announcement. Relative deal size is the
transaction value over the market value of equity of acquirer. An acquirer is experienced if the
…rm appears at least …ve times in the data set. Target CAR is measured on the [-25, 1] window
around deal announcement. Robust standard errors are in parentheses.
23
Table 4: Target Returns (Withdrawn Deals)
Cash
0.336***
(0.08)
LBO
Premium to 1 month prior
Premium
Cash
Hostile
Equity ratio
Log(relative deal size)
q of acquirer
q of target
q of target
Cash
KZ index of acquirer
KZ index of acquirer
Cash
KZ index of target
Industry & year FE
Deal sample
N
Y
All
242
Target CAR (A-25, W+25)
0.207*** 0.460*** 0.357**
0.595***
(0.08)
(0.16)
(0.16)
(0.20)
0.394
0.363
0.312
0.619
(0.31)
(0.30)
(0.42)
(0.43)
0.363*** 0.493*** 0.463*** 0.418***
(0.11)
(0.09)
(0.10)
(0.10)
-0.371
-0.319
-0.302
(0.25)
(0.24)
(0.26)
0.154**
0.141**
0.120
0.161
(0.07)
(0.06)
(0.08)
(0.10)
-0.030
-0.035
-0.044
-0.033
(0.03)
(0.03)
(0.03)
(0.04)
0.030
0.029
0.034
0.015
(0.03)
(0.03)
(0.03)
(0.03)
0.013
0.012
-0.007
0.004
(0.02)
(0.02)
(0.03)
(0.02)
-0.040
-0.030
-0.025
0.011
(0.02)
(0.03)
(0.04)
(0.03)
-0.041
-0.017
-0.101*
(0.04)
(0.04)
(0.05)
-0.059
(0.04)
0.020
(0.05)
0.030
(0.03)
Y
Y
Y
Y
All
All
All
Pure C/S
242
242
213
176
0.457**
(0.19)
0.495
(0.49)
0.321***
(0.10)
-0.185
(0.24)
0.131
(0.12)
-0.038
(0.04)
0.016
(0.04)
-0.011
(0.03)
0.033
(0.03)
-0.082
(0.05)
-0.031
(0.04)
-0.012
(0.06)
0.072**
(0.03)
Y
Pure C/S
156
Notes: OLS regressions with Target CAR from 25 days before announcement to 25 days after
withdrawal as the dependent variable. Cash is expressed as a fraction of the total payment (and
hence equal to a dummy for cash in the sample of pure cash and stock deals in the last two
columns). Equity ratio is de…ned as the ratio of target’s to acquirer’s market value of equity,
Relative deal size is equal to transaction value over market value of equity of acquirer. All
non-deal-related independent variables are year-end measures one year prior to the withdrawn
deal’s announcement. Robust standard errors are in parentheses.
24
Table 5: Synopses of Withdrawn Deals
Withdrawal reason
Acquirer withdrawal
Target rejection
Mutual consent
P
Target defense
Financing & other closing problems
Regulatory & other exogenous reasons
(Stock) market conditions
N
Cash deals
48.19%
19.28%
3.61%
71.08%
9.64%
2.41%
15.66%
1.20%
83
Stock deals
58.76%
8.25%
8.25%
75.26%
2.06%
2.06%
17.53%
3.09%
97
p-value
0.158
0.030
0.198
0.531
0.027
0.876
0.740
0.395
Notes: The sample is restricted to pure cash and stock deals as in the …fth column of Table 4. Acquirer withdrawals comprise the subset of deals that are withdrawn by the acquirer
without any mention of the target in the deal synopsis. Similarly, target rejections comprise
the subset of bids that are rejected by the target’s board and/or shareholders (this includes
cases where insu¢ cient shares are tendered). We classify deals as withdrawn by mutual consent if the deal synopsis explicitly states this (as opposed to deals withdrawn primarily due
to one party’s concerns). Deals withdrawn due to target defense include, most notably, the
adoption of a shareholder rights plan and other active defense mechanisms. By Financing &
other closing problems we denote practical reasons for deal withdrawal, e.g., shortcomings in
…nancing commitments, lacking due diligence and customary closing conditions, etc. Regulatory & other exogenous reasons include deals that were subject to regulatory approval and
sudden withdrawals triggered by events such as acquisition of the bidder (which naturally leads
to a withdrawal of the bidder’s o¤er). The last category comprises the subset of deals that are
withdrawn due to shifting market conditions (typically stock market plunges).
25
Table 6: Target Returns (Withdrawn Deals) –Robustness Checks
Cash 2 [0; 1]
LBO
Premium to 1 month prior
Premium
Cash
Hostile
Equity ratio
Log(relative deal size)
q of acquirer
q of target
q of target
Cash
KZ index of acquirer
KZ index of acquirer
Cash
KZ index of target
Toehold
Toehold
Cash
Tier-one IB
Tier-one IB
Cash
Same industry (2 digits SIC)
Same industry
Cash
Industry & year FE
N
Target CAR (A-25, W+25)
0.416**
0.359**
0.304*
0.378**
(0.17)
(0.16)
(0.16)
(0.18)
0.364
0.323
0.315
0.387
(0.43)
(0.41)
(0.44)
(0.44)
0.455*** 0.458*** 0.457*** 0.450***
(0.10)
(0.10)
(0.10)
(0.10)
-0.324
-0.320
-0.319
-0.328
(0.24)
(0.24)
(0.24)
(0.25)
0.072
0.102
0.109
0.039
(0.09)
(0.08)
(0.08)
(0.10)
-0.055
-0.046
-0.044
-0.058*
(0.03)
(0.03)
(0.03)
(0.03)
0.046
0.036
0.035
0.051*
(0.03)
(0.03)
(0.03)
(0.03)
-0.002
-0.005
-0.005
0.001
(0.03)
(0.03)
(0.03)
(0.03)
-0.023
-0.024
-0.026
-0.025
(0.03)
(0.04)
(0.04)
(0.04)
-0.021
-0.025
-0.019
-0.032
(0.05)
(0.04)
(0.05)
(0.05)
-0.063
-0.059
-0.061
-0.065
(0.04)
(0.04)
(0.04)
(0.04)
0.026
0.020
0.022
0.030
(0.05)
(0.05)
(0.05)
(0.05)
0.034
0.030
0.030
0.033
(0.03)
(0.03)
(0.03)
(0.03)
0.215**
0.224**
(0.11)
(0.11)
-0.185
-0.186
(0.14)
(0.15)
-0.046
-0.007
(0.16)
(0.16)
0.195
0.182
(0.25)
(0.26)
-0.067
-0.057
(0.09)
(0.08)
0.113
0.094
(0.15)
(0.15)
Y
Y
Y
Y
213
213
213
213
Notes: OLS regressions with Target CAR from 25 days before announcement to 25 days after
withdrawal as the dependent variable. Equity ratio is the ratio of target’s to acquirer’s market
value of equity, Relative deal size is the transaction value over the market value of equity of the
acquirer. Toehold is an indicator equal to one if the acquirer owned a share of the target before
announcement, and Tier-one IB indicates whether the acquirer was advised by either Goldman
Sachs, Morgan Stanley, Merrill Lynch, or JPMorgan. All non-deal-related independent variables
are year-end measures one year prior to the withdrawn deal’s announcement. Robust standard
errors are in parentheses.
26
Table 7: Future Takeover Attempts (Withdrawn Deals)
Cash 2 [0; 1]
Premium to 1 month prior
Hostile
Log(transaction value)
q of target
KZ index of target
Target CAR (A-25, W+25)
Industry & year FE
N
New deal announced 2 years after withdrawal
-0.665*** -0.055
-0.128
-0.125
-0.127
(0.07)
(0.14)
(0.16)
(0.16)
(0.16)
-0.221
-0.217
-0.237
(0.14)
(0.14)
(0.15)
-0.397*
-0.397* -0.404**
(0.20)
(0.20)
(0.20)
-0.011
-0.010
-0.008
(0.04)
(0.04)
(0.04)
-0.154** -0.137** -0.134**
(0.06)
(0.06)
(0.06)
0.059
0.059
(0.05)
(0.05)
0.056
(0.15)
N
Y
Y
Y
Y
562
531
531
531
531
Notes: Probit regressions of with a dummy indicating another merger bid within the next
two years. We exclude observations with merger bids within half a year after withdrawal
since their classi…cation as competing bid (in the previous takeover attempt) versus new bid is
ambiguous. Transaction value is in 2010 $bn. Target CAR (A-25, W+25) are the cumulative
abnormal returns from 25 days before announcement until 25 days after withdrawal. All nondeal-related independent variables are measured at the end of the year prior to the withdrawn
deal’s announcement. Robust standard errors are in parentheses.
27
28
Appendix Tables
Cash deals
Std. dev.
Min
37.52
5
5.55
0
36.27
-48.30
16.43
0
1.51
0.00
6.06
0.00
22.29
1.80
n/a
n/a
37.55
0
0.96
0.00
1.69
0.51
1.38
0.50
49.01
0
1.29
-6.03
1.57
-10.05
46.01
0
49.93
0
649
Max
230
100
202.10
100
17.40
137.03
100
n/a
100
8.93
15.20
9.91
100
3.73
5.22
100
100
Mean
83.32
0
46.80
0.89
1.47
0.58
96.38
n/a
78.86
0.34
3.41
2.62
64.48
-0.41
-0.28
75.13
63.41
Stock deals
Std. dev.
Min
38.64
16
0
0
45.89
-48.30
9.39
0
5.37
0.00
3.39
0.00
14.70
7.70
n/a
n/a
40.86
0
0.63
0.00
3.29
0.51
2.27
0.50
47.90
0
1.69
-10.46
1.89
-9.57
43.26
0
48.21
0
563
Max
239
0
202.10
100
70.51
79.38
100
n/a
100
8.93
15.20
9.91
100
3.01
4.70
100
100
p-value
0.000
0.188
0.774
0.016
0.001
0.662
0.000
n/a
0.063
0.258
0.000
0.000
0.117
0.002
0.016
0.034
0.000
Notes: Time to completion is in trading days. The historical transaction value was converted using Consumer Price Index
(CPI) Conversion Factors. Relative deal size is the transaction value over market value of equity of the acquirer. New deal announced
within 2 years is conditional on the deal being announced at least half a year after the previous one. Experienced acquirers appear at
least …ve times in the data set. Equity ratio is de…ned as the ratio of target’s to acquirer’s market value of equity. KZ index is the
four-variable version in Lamont, Polk, and Saa-Requejo (2001). All non-deal-related variables (e.g., q ratios) are year-end measures one
year prior to the deal announcement. Premium, Equity ratio, all q and KZ variables are winsorized at the 1st and 99th percentiles. The
sample is restricted to deals that include all variables used in any of the speci…cations in Tables 3-7.
Mean
58.34
0.31
46.13
2.77
0.70
0.70
91.96
n/a
83.05
0.29
2.09
1.85
60.09
-0.14
-0.04
69.65
53.31
Table A.1: Summary Statistics (Successful Deals)
Variable
Time to completion/withdrawal
LBO in %
Premium (to 1 month prior) in %
Hostile deal in %
Transaction value in 2010 $bn
Relative deal size
% of target sought
New deal announced within 2 years in %
Experienced acquirer in %
Equity ratio
q of acquirer
q of target
q of acquirer > q of target in %
KZ index of acquirer
KZ index of target
Same industry (1 digit SIC) in %
Same industry (2 digits SIC) in %
N
A
Table A.2: Determinants of Cash O¤ers (All Deals)
Log(relative deal size)
% of target sought
Experienced acquirer
q of acquirer
q of target
Cash 2 [0; 1]
-0.054*** -0.060***
(0.01)
(0.01)
-0.002*** -0.002**
(0.00)
(0.00)
-0.018
-0.020
(0.03)
(0.03)
-0.028***
(0.00)
-0.029***
(0.01)
LBO
Premium to 1 month prior
Hostile
KZ index of acquirer
KZ index of target
Industry & year FE
Deal sample
N
Y
All
1,932
Y
All
1,932
-0.062***
(0.01)
-0.002***
(0.00)
-0.019
(0.03)
-0.031***
(0.00)
-0.031***
(0.01)
0.365***
(0.09)
0.065***
(0.02)
0.211***
(0.05)
-0.001
(0.01)
-0.017***
(0.01)
Y
All
1,932
Cash 2 f0; 1g
-0.209*** -0.246*** -0.258***
(0.02)
(0.03)
(0.03)
-0.007***
-0.004
-0.004*
(0.00)
(0.00)
(0.00)
-0.090
-0.065
-0.087
(0.10)
(0.10)
(0.10)
-0.120*** -0.126***
(0.03)
(0.03)
-0.144*** -0.144***
(0.03)
(0.03)
Y
Pure C/S
1,400
Y
Pure C/S
1,400
0.154
(0.10)
0.933***
(0.26)
0.007
(0.03)
-0.041
(0.03)
Y
Pure C/S
1,396
Notes: OLS regressions in the …rst three columns and probit regressions in the last three
columns include acquirer and target industry controls. Cash is expressed as a fraction of the
total payment (and hence equal to a dummy for cash in the sample of pure cash and stock deals
in the last three columns). Relative deal size is equal to transaction value over market value of
the acquirer’s equity. Experienced acquirers appear at least …ve times in the data set. Robust
standard errors are in parentheses.
29
Table A.3: Determinants of Withdrawal (All Pure Cash & Stock Deals)
Cash 2 f0; 1g
-0.033*
(0.02)
Log(relative deal size)
LBO
Premium to 1 month prior
Hostile
% of target sought
q of acquirer
q of target
KZ index of acquirer
KZ index of target
Experienced acquirer
Deal withdrawn
-0.005
0.007
-0.010
(0.02)
(0.02)
(0.02)
0.032*** 0.030*** 0.025***
(0.00)
(0.00)
(0.00)
0.360
(0.31)
0.001
(0.02)
0.311***
(0.08)
-0.000
(0.00)
0.002
(0.00)
0.002
(0.01)
0.007
(0.01)
0.002
(0.00)
-0.016
(0.02)
Target CAR (A-25, A+1)
Industry & year FE
N
N
1,403
N
1,403
Y
1,396
Y
1,396
-0.003
(0.02)
0.024***
(0.00)
0.357
(0.31)
0.038
(0.03)
0.309***
(0.08)
-0.000
(0.00)
0.002
(0.00)
0.002
(0.00)
0.006
(0.01)
0.002
(0.00)
-0.018
(0.02)
-0.075*
(0.04)
Y
1,396
Notes: The table reports marginal e¤ects of probit regressions. All non-deal-related independent
variables are year-end measures one year prior to deal announcement. Relative deal size is the
transaction value over the market value of equity of acquirer. An acquirer is Experienced if the
…rm appears at least …ve times in the data set. Target CAR is measured on the [-25, 1] window
around deal announcement. Robust standard errors are in parentheses.
30
Table A.4: Target Returns (Withdrawn Pure Cash & Stock Deals) – No Target
Defense and/or Rejection
Cash 2 f0; 1g
LBO
Premium to 1 month prior
Premium
Cash
Hostile
Equity ratio
Log(relative deal size)
q of acquirer
q of target
q of target
Cash
0.794***
(0.21)
0.503
(0.53)
0.448***
(0.11)
-0.398
(0.26)
0.208*
(0.12)
-0.070*
(0.04)
0.033
(0.04)
0.013
(0.02)
-0.003
(0.03)
-0.174***
(0.06)
KZ index of acquirer
KZ index of acquirer
Cash
KZ index of target
Industry & year FE
Deal sample
N
Y
(I)
152
Target CAR (A-25, W+25)
0.683*** 0.871*** 0.680*** 0.550***
(0.20)
(0.22)
(0.21)
(0.19)
1.419*** 1.616***
0.557
1.396***
(0.40)
(0.45)
(0.49)
(0.42)
0.422*** 0.427*** 0.352*** 0.356***
(0.12)
(0.12)
(0.12)
(0.12)
-0.360
-0.410
-0.275
-0.258
(0.26)
(0.26)
(0.24)
(0.24)
0.217*
0.260*
0.162
0.238*
(0.12)
(0.15)
(0.15)
(0.13)
-0.015
-0.059
-0.080*
-0.006
(0.04)
(0.04)
(0.05)
(0.04)
0.017
0.042
0.041
0.008
(0.03)
(0.04)
(0.04)
(0.04)
-0.001
0.009
-0.007
-0.019
(0.02)
(0.02)
(0.03)
(0.03)
0.020
0.009
0.026
0.039
(0.03)
(0.03)
(0.04)
(0.04)
-0.126** -0.203*** -0.175*** -0.115**
(0.05)
(0.07)
(0.06)
(0.05)
-0.049
-0.032
(0.04)
(0.04)
0.008
-0.053
(0.06)
(0.06)
0.082**
0.062*
(0.04)
(0.03)
Y
Y
Y
Y
(II)
(III)
(I)
(II)
164
142
135
147
0.759***
(0.22)
1.454***
(0.46)
0.363***
(0.13)
-0.319
(0.25)
0.280*
(0.15)
-0.053
(0.04)
0.035
(0.04)
-0.012
(0.04)
0.033
(0.04)
-0.197***
(0.06)
-0.041
(0.04)
-0.057
(0.06)
0.071*
(0.04)
Y
(III)
128
Notes: OLS regressions with Target CAR from 25 days before announcement to 25 days after
withdrawal as the dependent variable. Deal samples are as follows: (I) no target rejection;
(II) no target defense; (III) no target rejection or defense. Equity ratio is de…ned as the ratio
of target’s to acquirer’s market value of equity, Relative deal size is equal to transaction value
over market value of equity of acquirer. All non-deal-related independent variables are year-end
measures one year prior to the withdrawn deal’s announcement. Robust standard errors are in
parentheses.
31
Table A.5: Target Returns (Withdrawn Pure Cash & Stock Deals) – Robustness
Checks
Cash 2 f0; 1g
LBO
Premium to 1 month prior
Premium
Cash
Hostile
Equity ratio
Log(relative deal size)
q of acquirer
q of target
q of target
Cash
KZ index of acquirer
KZ index of acquirer
Cash
KZ index of target
Toehold
Toehold
Cash
Tier-one IB
Tier-one IB
Cash
Same industry (2 digits SIC)
Same industry
Cash
Industry & year FE
N
Target CAR (A-25, W+25)
0.510**
0.464**
0.406**
0.482**
(0.21)
(0.19)
(0.19)
(0.22)
0.578
0.503
0.489
0.586
(0.49)
(0.47)
(0.50)
(0.47)
0.321*** 0.316*** 0.306*** 0.297***
(0.10)
(0.10)
(0.10)
(0.11)
-0.206
-0.183
-0.171
-0.193
(0.25)
(0.25)
(0.24)
(0.27)
0.112
0.084
0.141
0.075
(0.13)
(0.13)
(0.12)
(0.13)
-0.041
-0.040
-0.044
-0.053
(0.04)
(0.04)
(0.04)
(0.04)
0.019
0.018
0.021
0.030
(0.04)
(0.04)
(0.04)
(0.04)
-0.007
-0.010
-0.003
0.004
(0.03)
(0.03)
(0.03)
(0.03)
0.034
0.038
0.031
0.035
(0.03)
(0.04)
(0.03)
(0.04)
-0.081
-0.098*
-0.073
-0.088*
(0.05)
(0.05)
(0.05)
(0.05)
-0.033
-0.032
-0.030
-0.032
(0.04)
(0.04)
(0.04)
(0.04)
-0.007
-0.013
-0.012
-0.005
(0.06)
(0.06)
(0.06)
(0.06)
0.074**
0.074**
0.073**
0.077**
(0.03)
(0.03)
(0.03)
(0.03)
0.217*
0.222*
(0.12)
(0.12)
-0.240
-0.248
(0.18)
(0.18)
-0.127
-0.104
(0.23)
(0.23)
0.340
0.372
(0.35)
(0.36)
-0.096
-0.106
(0.10)
(0.11)
0.054
0.029
(0.18)
(0.19)
Y
Y
Y
Y
156
156
156
156
Notes: OLS regressions with Target CAR from 25 days before announcement to 25 days after
withdrawal as the dependent variable. Equity ratio is the ratio of target’s to acquirer’s market
value of equity, Relative deal size is the transaction value over the market value of equity of the
acquirer. Toehold is an indicator equal to one if the acquirer owned a share of the target before
announcement. Tier-one IB indicates whether the acquirer was advised by Goldman Sachs,
Morgan Stanley, Merrill Lynch, or JPMorgan. All non-deal-related independent variables are
measured at the end of the year prior to the withdrawn deal’s announcement. Robust standard
errors are in parentheses.
32
Table A.6: Future Takeover Attempts (Withdrawn Pure Cash & Stock Deals)
Cash 2 f0; 1g
Premium to 1 month prior
Hostile
Log(transaction value)
q of target
New deal announced 2 years
-0.664*** -0.040 -0.114
(0.08)
(0.16) (0.18)
-0.230
(0.17)
-0.336
(0.24)
-0.002
(0.04)
-0.131*
(0.07)
KZ index of target
Target CAR (A-25, W+25)
Industry & year FE
N
N
434
Y
411
Y
411
after withdrawal
-0.115
-0.118
(0.18)
(0.18)
-0.223
-0.271
(0.17)
(0.17)
-0.341
-0.365
(0.24)
(0.24)
0.001
0.008
(0.04)
(0.04)
-0.120* -0.116*
(0.07)
(0.07)
0.035
0.034
(0.05)
(0.05)
0.161
(0.17)
Y
Y
411
411
Notes: Probit regressions of with a dummy indicating another merger bid within the next
two years. We exclude observations with merger bids within half a year after withdrawal
since their classi…cation as competing bid (in the previous takeover attempt) versus new bid is
ambiguous. Transaction value is in 2010 $bn. Target CAR (A-25, W+25) are the cumulative
abnormal returns from 25 days before announcement until 25 days after withdrawal. All nondeal-related independent variables are measured at the end of the year prior to the withdrawn
deal’s announcement. Robust standard errors are in parentheses.
33