Signaling and risk allocation in merger agreements
Antonio J. Macias and Thomas Moeller*
Abstract
Acquirers and targets allocate interim risk in merger agreements through the Material Adverse
Change (MAC) clause and its exclusions. While virtually all acquisitions have a MAC clause,
there is large cross-sectional variation in the numbers and types of MAC exclusions. MAC
exclusions can address firm-specific or market-wide adverse changes. Fewer MAC exclusions
imply broader abandonment options for acquirers. Using comprehensive hand-collected data, we
find that both acquirer and target announcement returns are higher with broader firm-specific
abandonment options. Broad firm-specific abandonment options are credible signals for higher
target quality and are more prevalent when information asymmetries are likely high.
Keywords: Mergers, acquisitions, signaling, material adverse change clauses and exclusions
JEL classification: D86, G14, G32, G34
This draft: September 2013
* We thank Sandy Klasa, Elena Simintzi, and seminar participants at Texas Christian University and the
2013 SFS Finance Cavalcade for helpful comments. Both authors wish to thank the Luther King Capital
Management Center for Financial Studies in the Neeley School of Business at TCU for its financial
support for this research. All errors are our own.
Contact information: Antonio J. Macias, Texas Christian University, TCU Box 298530, Fort Worth, TX
76129, [email protected], 817.257.5962. Thomas Moeller, Texas Christian University, TCU Box
298530, Fort Worth, TX 76129, [email protected], 817.760.0050.
Signaling and risk allocation in merger agreements
Abstract
Acquirers and targets allocate interim risk in merger agreements through the Material Adverse
Change (MAC) clause and its exclusions. While virtually all acquisitions have a MAC clause,
there is large cross-sectional variation in the numbers and types of MAC exclusions. MAC
exclusions can address firm-specific or market-wide adverse changes. Fewer MAC exclusions
imply broader abandonment options for acquirers. Using comprehensive hand-collected data, we
find that both acquirer and target announcement returns are higher with broader firm-specific
abandonment options. Broad firm-specific abandonment options are credible signals for higher
target quality and are more prevalent when information asymmetries are likely high.
More than half of acquirers experience negative announcement returns (Andrade, Mitchell,
and Stafford, 2001) and some acquisitions are associated with large wealth destruction (Moeller,
Schlingemann, and Stulz, 2005). These poor outcomes for acquirers are surprising because,
through the abandonment option provided by the Material Adverse Change (MAC) clause,
acquirers can protect themselves from being forced to consummate acquisitions of targets that
experience material reductions in value. The occurrence or revelation of a MAC between the
announcement and completion of an acquisition is not uncommon and has significant effects.
About 9% of targets in our sample experience MACs and offer prices are reduced by 15%, on
average, in such cases.1
While virtually all acquisitions have a MAC clause, there is substantial variation in the
MAC exclusions. MAC exclusions limit the scope of the acquirer’s abandonment option. A
broader abandonment option reduces the acquirer’s downside risk and thereby increases the
expected value of the acquisition. A limited abandonment option increases the probability that the
acquisition will be completed as originally negotiated even if the target turns out to be flawed.
Using comprehensive hand-collected data, we exploit the variability in MAC exclusions to
study what determines the scopes of the acquirers’ abandonment options and how they affect
acquirers’ and targets’ acquisition gains. Our paper contributes to the literature on acquisitions and
contract provisions by examining the signaling and risk allocation roles of abandonment options
that MAC clauses provide. In general, the empirical evidence on the relations among contract
provisions, information asymmetries, signaling, and risk allocation in takeovers is sparse.
Although MAC clauses are more prevalent (99% of all the acquisitions in our sample) than most
other contractual provisions in takeovers, e.g., termination fees (63%), lockup options (38%),
1
Denis and Macias (2013) show how the structure of the MAC clause affects the probability of deal renegotiation
and termination.
1
reverse termination fees (27%), collars (20%), and earnouts (1%), there are only few rigorous
academic studies about MAC clauses, arguably because of a lack of readily available data. MAC
clauses have been almost exclusively studied by practitioners and legal scholars.2 While Choi and
Triantis (2010) provide a legal and economic framework for the benefits of customizing risk
allocation through MAC clauses, Talley (2009) examines the association between contracting and
ambiguity aversion, and Gilson and Schwartz (2005) present a theoretical model and descriptive
statistics proposing a moral hazard explanation for the use of MAC exclusions, we do not know of
any multivariate analysis that explores the benefits of signaling through financial contracting in
acquisitions. We also add to the more general literature on financial contracting.3
Deals can have broader abandonment options for at least three reasons. First, acquirers can
offer higher takeover premiums in return for broader abandonment options. If the broader
abandonment option is fairly priced, there should be no relation between the abandonment option’s
scope and acquirer or target announcement returns. Acquirers and targets simply trade off higher
promised takeover premiums for lower deal completion probabilities. The scope of the
abandonment option would reflect the result of zero-sum bargaining.
Second, acquirers with strong bargaining power may “force” targets to accept broader
abandonment options, at the target expense. If broad abandonment options are associated with
strong acquirer bargaining power, the scope of abandonment options should be positively related
to acquirer gains, negatively related to target gains, and unrelated to combined acquirer and target
gains. Again, the scope of the abandonment option would reflect the result of zero-sum bargaining.
2
For example, Davidoff (2007 and 2008), Davidoff and Baiardi (2008), Listokin (2005), Sikora (2006), Cicarella
(2007), Toub (2003), Grech (2003), Klein and Cooper (2007), Alexander (2005), and Adams (2004, 2006, 2013).
3
A couple of loosely related papers focus on lender-borrower relationships. Roberts and Sufi (2009) find that the
structure of the initial debt contract affects whether either party can renegotiate, in particular when contingencies
occur. Garlenau and Zwiebel (2009) find that information asymmetry influences how debt covenants are negotiated.
2
Third, targets can signal their higher quality and value by agreeing to broader abandonment
options for the acquirer. Broader abandonment options can credibly signal higher target value,
including greater potential synergies, because the signal is less costly for better targets. Better
targets face lower probabilities of adverse changes that lower their value to the acquirer, thereby
making the abandonment option less valuable for the acquirer and less costly for the target.
Because it reduces adverse selection concerns, signaling with broader abandonment options should
be associated with more valuable deals and result in higher combined acquirer and target
announcement gains. Many other factors, like bargaining skills, hubris, and agency conflicts, affect
the split of the higher announcement gains between target and acquirer, causing us to treat the split
largely as an empirical question here.
To better isolate signaling incentives, we distinguish between firm-specific and market-wide
MAC exclusions. Firm-specific MAC exclusions address worries about asymmetric information
and adverse selection where the target knows more about itself than the acquirer does. Although
some firm-specific MAC exclusions can cover exogenous risks (e.g., the accidental death of the
Chief Executive Officer), most focus on issues that are under the control of, or at least known by,
the target (e.g., pending earnings restatements). Market-wide MAC exclusions allocate systematic
risks that should neither be known to, nor under the control of, targets or acquirers. Examples are
changes in economic, market, industry, or regulatory conditions. Acquirers should be better able
to shoulder market-wide risks than targets because they tend to be larger and more diversified.
Implementing precautionary measures, like hedging, to minimize the effects of these exogenous
market-wide risks should also be easier for acquirers (Gilson and Schwartz, 2005). Appendix A
presents two examples of MAC clauses and exclusions.
3
Consistent with targets signaling their higher values or greater synergies, we find that broader
firm-specific abandonment options are associated with higher acquirer and target announcement
returns4 and higher combined acquirer and target announcement gains. Instead of representing the
outcome of zero-sum bargaining between acquirer and target, our results suggest that broader firmspecific abandonment options are credible signals of reduced downside risk. Indeed, targets in
deals with broader firm-specific abandonment options seem to be of higher quality. For example,
they have better prior operating performance. Consistent with the mitigation of adverse selection
concerns, broader firm-specific abandonment options are more prevalent when acquirer and target
are in different industries and information asymmetries are likely larger.
We calculate combined gains using long-term cumulative abnormal dollar returns of acquirer
and target (e.g., Moeller, Schlingemann and Stulz, 2004). Following Schwert (2000), our event
window extends from two months before to four months after the announcement date (or to the
acquisition termination or completion date, if it occurs earlier). The combined gains, as a
percentage of the combined acquirer and target market value of equity, are on average 2.9%
(median 4.2%) higher in deals that have broad firm-specific abandonment options (i.e., below
median numbers of firm-specific MAC exclusions), and both acquirers and targets seem to benefit
from broader firm-specific abandonment options.
Our arguments build on the role of contract terms as viable signals of private information as
established in the literature, e.g., Aghion and Hermalin (1990). More specifically, Spier (1992)
states that “an individual may refrain from including a particular clause in a contract in order to
signal his type.” Choi and Triantis (2008) examine the incentives that parties have during the
contracting stage driven by the verification cost of contractual signals and Coates (2012) posits
4
Our results are similar when we analyze takeover premiums instead of target announcement returns.
4
that providing broader contractual warranties signals higher quality, in particular under conditions
of uncertainty and information asymmetry. Moreover, Talley (2009) argues that under high
uncertainty, individuals become more risk averse and aim to reduce exogenous risk by negotiating
more “expansive” provisions.
An analysis of acquisitions in which the public disclosure of the merger agreements is delayed
provides evidence that our results are driven by broader abandonment options. We split our sample
based on whether the merger agreement was publicly available within two days of the
announcement date. Acquirer announcement returns, target announcement returns, and combined
acquirer and target announcement gains are significantly related to the scopes of the firm-specific
abandonment options only in the subsample with the contemporaneously publicly available merger
agreements, suggesting that investors react to the information contained in the merger agreements.
Our results are robust to measuring the abandonment option scope with continuous or indicator
variables. We control for bargaining power, exogenous risks, and other deal characteristics, such
as relative size, method of payment, legal adviser quality, industry concentration, competition,
acquirer’s prior acquisition experience, economic conditions, and the use of non-MAC contract
provisions. We use Heckman models (Heckman, 1979; Li and Prabhala, 2007; Guo and Fraser,
2010; Wooldridge, 2010) to address potential simultaneity and omitted-variable biases. The
Heckman analysis suggests that targets self-select to signal their quality through broader firmspecific abandonment options but leaves our main results intact. When we examine the principal
components of the abandonment option, we find that adverse selection concerns seem to matter
more than moral hazard concerns.
In contrast to the positive effect of broader firm-specific abandonment options, the scope of
the market-wide abandonment options does not affect acquirer, target, or combined gains.
5
Interestingly, acquirers tend to assume the largely exogenous, market-wide interim risk in mergers
in regulated industries that also have longer expected completion periods. Information
asymmetries should be relatively low in these industries because, due to regulation, much
information is publicly available and many firm-specific risks are mitigated. For example, public
utility companies in many U.S. states can price their services to earn a “guaranteed” return on
equity. With fewer information asymmetries, the dominant motivation for limited market-wide
abandonment options should be the allocation of exogenous risk to acquirers.
1.
Overview of MAC clauses and exclusions
Because of the relatively long period between the announcement and the completion of an
acquisition (4.5 months on average in our sample), there is a nontrivial probability of adverse
events occurring or being revealed. Practitioners regard a carefully drafted acquisition contract as
an important mechanism to protect the acquirer against adverse selection and preserve the value
of the target until the completion of the acquisition.5 Before the takeover announcement, the
acquirer and the target usually settle on the acquisition terms, including the MAC clause, that
address protection in case the value of the target declines (Figure 1).
MAC exclusions, which limit the scope of the acquirer’s abandonment option, meaningfully
affect the probability of renegotiations, terminations, and price changes in case a MAC occurs. For
example, MACs are the underlying cause of 69% of acquisition terminations and 80% of
renegotiations (Denis and Macias, 2013). Gilson and Schwartz (2005, p. 330) posit that “[t]he term
[MAC clause] today engenders substantial litigation and occupies center stage in the negotiation
5
See anecdotal evidence in Boston Consulting Group’s Cools, Gell, and Ross (2006), McKinsey’s Christofferson,
McNish, and Sias (2004), Accenture’s Chanmugam, Shill, Mann, and Fivery (2005), Economist (2001a, 2001b,
2005), Knowledge@Wharton (2006), New York Times’ Davidoff (2008), and Romanek, Davidoff, Grossbaeur,
Laster, Lord, and Winokur (2008).
6
of merger agreements,” and Davidoff (2009, p. 56) states that “[t]he MAC clause is thus one of
the most important provisions of the [merger] agreement.” Indeed, claims of MAC clauses played
key roles in several recent acquisitions.6
All else equal, targets should prefer to limit the acquirer’s flexibility to terminate the
acquisition while acquirers should prefer broader walk-away rights in case of a MAC. The main
elements of a MAC clause are the number and type of MAC exclusions because through them the
acquirer and the target allocate the interim risk (Gilson and Schwartz, 2005).
2. Hypothesis development
We examine two explanations for the determinants and economic impact of the scope of
acquirers’ abandonment options, namely, zero-sum bargaining and signaling. Although the
hypotheses are not all mutually exclusive, they allow us to test which explanation dominates.
The takeover premium and abandonment option scope result from negotiations. The acquirer
can “pay” for a broader abandonment option with a higher promised takeover premium. If the
“price” for the broader abandonment option is fair, there should be no effect on acquirer or target
announcement returns.7
Hypothesis 1: Acquirers and targets negotiate fair tradeoffs between abandonment option scope
and takeover premium. Consequently, the abandonment option scope has no effect on acquirer and
target announcement returns.
6
For example, Cerberus Capital Management-Chatham Lodging Trust v. Innkeepers USA Trust (2011), United
Rentals, Inc. v. Ram Holdings, Inc. (2007), and of Genesco, Inc. v. The Finish Line, Inc, (2007). See Davidoff
(2009, 2011) and Wolff and Moore (2007) for details.
7
Denis and Macias (2013) find that the number of MAC exclusions is negatively related to the offered premium.
They do not examine long-term announcement returns and are unable to distinguish whether the MAC exclusions
create value or solely allocate risk without any valuation implications.
7
Alternatively, the abandonment option scope can reflect the acquirer’s relative bargaining power.
Powerful acquirers can “force” weak targets to accept broad abandonment options without fair
compensation.
Hypothesis 2: Strong acquirers negotiate broad abandonment. Therefore, strong abandonment
options are associated with high acquirer and low target announcement returns. The abandonment
option scope has no effect on the combined acquirer and target gains.
Both hypotheses 1 and 2 imply zero-sum bargaining. Yet, broad abandonment options can also
serve as signals of high target quality and value that alleviate adverse selection concerns and
increase the overall value of the acquisition. Incomplete contracts that enable ex-post efficient
renegotiations can enhance the contractual efficiency in acquisitions and provide an explanation
why such signaling can be credible. Under both the frameworks of Hart and Moore’s (1988)
incomplete contracts and Crocker and Masten’s (1991) flexibility to renegotiate, the MAC clause,
as an enforceable contingent contract provision, provides the option to terminate or renegotiate an
acquisition if the acquirer finds hidden problems in the target. Having a broader abandonment
option makes it more costly for the target to fight an acquirer who claims a MAC, for example,
because of higher expected litigation costs (Choi and Triantis, 2008). The higher costs for the
target in turn increase the acquirer’s leverage to renegotiate the terms of the acquisition (Robinson
and Stuart, 2007). Talley (2009) argues that higher uncertainty increases the ambiguity aversion
of the bidders and can exacerbate the discount for targets with significant private information.
Figure 2 summarizes the relation between abandonment option scope and expected litigation costs.
The target likely knows its own condition and capabilities best and has an incentive to increase
its perceived value, creating an adverse selection problem (Akerlof, 1970). Aghion and Hermalin
(1990) show that a firm can signal information through the terms it negotiates in a contract. To
8
separate themselves from low-quality targets, high-quality targets can send credible signals of their
quality by agreeing to broad abandonment options for the acquirer. Broad abandonment options
can credibly signal higher target value and should result in higher combined announcement gains.
Because of the reduction of the downside risk of acquiring a flawed target, the acquirer
announcement return should be higher when the acquirer has more leverage to terminate or
renegotiate the acquisition. The target can also benefit from a smaller adverse selection (“lemon”)
discount. Because the split of merger gains is, at least in part, determined by the relative bargaining
power of the acquirer and the target, it is an empirical question how higher combined merger gains
are allocated between acquirer and target.
Hypothesis 3: Broad abandonment options for acquirers are credible signals of high target quality
and value and should be associated with high combined acquirer and target announcement gains.
Hypothesis 4: Broad abandonment options for acquirers are associated with high-quality targets.
Signaling with a broad abandonment option is more important when the information
asymmetry between acquirer and target is large because the lemon discount (Akerloff, 1970)
should be large. When the information asymmetry is small, signaling should be unimportant and
unnecessary. An additional cost of a broad abandonment option is that the acquirer can take
advantage of it and force a renegotiation of the merger deal even when no significant MAC has
occurred. Defending against such abandonment option abuse is costly for the target, e.g., due to
litigation expenses and managerial distraction. Therefore, with few benefits when information
asymmetries are low, signaling with a broad abandonment option should only be efficient when
information asymmetries are sufficiently high.
9
Hypothesis 5: Signaling is costly and has more benefits when the information asymmetries
between the acquirer and the target are high. Therefore, targets only signal with broad
abandonment options when information asymmetries are sufficiently high.
Our first five hypotheses are about firm-specific abandonment options that we measure with
firm-specific MAC exclusions. Our final hypothesis addresses market-wide MAC exclusions.
Excluding exogenous and market-wide risks does not serve as a signal because neither the acquirer,
nor the target has control over these events. Yet, acquirers should be better able to handle and
hedge market-wide risk (Gilson and Schwartz, 2005). Since a limited market-wide abandonment
option, i.e., a MAC clause with many market-wide MAC exclusions, can potentially be construed
to address firm-specific shortcomings, the benefits from a limited market-wide abandonment
option should be higher when information asymmetries are low and the expected time to
completion is long. Regulated industries generally have relatively low information asymmetries
and long deal completion times.8
Hypothesis 6: Shifting exogenous risk to the acquirer through limited market-wide abandonment
options should be more prevalent in regulated industries where market-wide risks dominate firmspecific information asymmetries.
3. Sample characteristics
We describe the sample selection, data collection, and summary statistics.
8
Gilson and Schwartz (2005) argue that changes in laws and regulations, classified as market-wide MAC exclusions
here, should be of greater significance in older, more regulated industries.
10
3.1 Sample selection
Our sample begins with the universe of acquisitions announced by U.S. public acquirers for
U.S. public targets from 1998 to 2005 that are in the Thomson Reuters’ Securities Data Company
(SDC) Mergers & Acquisitions database. We require data in the Center for Research in Securities
Prices (CRSP) and Compustat databases and that the size of the target is at least 1% the size of the
acquirer and with a minimum target size of $2.5 million. We measure size as the book value of
debt plus the market value of equity as of the quarter prior to the acquisition announcement. Details
of our variable definitions are in appendix B. We hand-collect merger agreement information,
including the MAC clauses and exclusions, from the proxies that U.S. public acquirers and U.S.
public targets must file with the Securities and Exchange Commission (SEC). Acquirers need to
seek control of the targets (more than 50% of ownership sought in the transaction) because only
for such acquisitions does the SEC require the filing of merger agreements. We also confirm
selected information from the SEC filings with the LivEdgar M&A database. We exclude
unsolicited and hostile deals because their merger agreements, if any, are likely negotiated under
unusual pressure. We include both completed and terminated acquisitions although our results hold
if we use only completed acquisitions. Our final sample has 751 acquisitions.
About 6%, or 45, of our sample acquisitions were terminated and 50 renegotiated, of which 41
were eventually completed. Nearly 9% of the acquisitions in our sample experience the occurrence
or revelation of a material adverse change between the announcement and completion of the
acquisition. These MACs trigger the vast majority of the terminations and renegotiations.
Consistent with Denis and Macias (2013) we find in our sample that offer prices are reduced, on
average, by 15% when targets experience MACs.
11
3.2 Summary statistics
Panel A of Table 1 describes the main contract provisions of merger agreements that allocate
risks associated with the target’s value declining between the signing of the merger agreement and
the completion of the merger. More than 99% of merger agreements include a MAC clause and
75.6% have at least one MAC exclusion. There is substantial cross-sectional variation in numbers
and types of MAC exclusions. In Panel B of Table 1, we classify the MAC exclusions into 15
categories (Denis and Macias, 2013) and group them into firm-specific or market-wide.9 Merger
agreements include, on average, fewer firm-specific than market-wide MAC exclusions (1.8 vs.
2.2). Untabulated analyses show that the individual MAC exclusions have significant, yet
relatively small, correlations. Two notable exceptions are the high correlations between Implied
disproportionate economic condition and Implied disproportionate industry condition of 0.74 and
between Change in accounting and Change in law or regulations of 0.74.
Panel B shows that the average number of MAC exclusions increased substantially between
1998 and 2005, consistent with Gilson and Schwartz (2005) who show an increase in the presence
of MAC exclusions in MAC clauses from 18% of acquisitions in 1993 to 83% in 2000. Two
examples illustrate how MAC exclusions evolve after significant events. After the IBP, Inc. v.
Tyson Foods, Inc. (2001) and Frontier Oil Corp. v. Holly Corp. (2005) seminal MAC court
decisions in which projections played critical roles, the incidence of Failure to meet projections
exclusions increased. After the bursting of the NASDAQ bubble in 2001, we find more exclusions
related to declines in stock prices.
We control for the growing trend in the number of MAC exclusions by adjusting the number
of MAC exclusions with their annual medians. For expositional convenience, we define
9
See Nixon-Peabody (2007, 2010) and American Bar Association (2006) for more detailed legal explanations of the
categories. Some contracts refer to “Material Adverse Change” as “Material Adverse Event.”
12
Abandonment option scope to have higher values when the option is broader. For firm-specific
(FS) options, the definition is:
Abandonment option scope FS = 10 – (number firm-specific MAC exclusions
– annual median number firm-specific MAC exclusions)
De-medianized firm-specific MAC exclusions range from -3 to 6, for a range of ten distinct
outcomes. Adding 10, or any value larger than 6, ensures that all realizations of Abandonment
option scope FS are positive. For a deal with 5 firm-specific MAC exclusions during a year when
the median number of firm-specific MAC exclusions is 2, Abandonment option scope FS equals
10 – (5 – 2) = 7. With 3 MAC exclusions, i.e., a broader abandonment option, Abandonment option
scope FS would equal 10 – (3 – 2) = 9. For market-wide (MW) abandonment options,
Abandonment option scope MW is defined in the same way. Again, these conversions are mainly
for expositional ease and results are similar with unadjusted numbers. We also construct two
indicator variables Broad abandonment options FS and Broad abandonment options MW for firmspecific and market-wide MAC exclusions being below their annual medians.
Finally, we check for the presence of three additional contract provisions, namely, reverse
termination fees, collars, and earnouts (see Figure 3). MACs are far more prevalent as only 27%
of merger agreements have reverse termination fees, 20% have collars, and 1% have earnouts.10
4. Acquisition returns and MAC exclusions
We test hypotheses 1 through 3 by examining whether the scope of firm-specific abandonment
options affects announcement returns. We measure Acquirer CAR with the cumulative return in
10
Bates and Lemmon (2003) report a frequency of 25% for reverse termination fees (called “bidder termination
fees” in their paper). About 18% of deals in Officer (2003, 2004) have collars, which are mainly used in stock deals.
Earnouts are mainly used in acquisitions of private and subsidiary targets (Cain, Denis, and Denis, 2011).
13
excess of the CRSP equal-weighted index from two months before to the earlier of either four
months after the announcement date or the completion or termination date. Target CAR is defined
in the same manner. Results are similar when we adjust returns with the Fama-French three-factor
model. We use long-term returns to get a more comprehensive estimate of the value created in the
acquisition (Schwert, 1996). Additionally, arbitrage spreads can affect short-term returns, mainly
for the target, as the probability of deal completion is related to the MAC clause and its exclusions
(Denis and Macias, 2013).
4.1 Univariate analysis
Table 2 splits our sample into deals with broad and limited firm-specific abandonment options,
i.e., with the number of firm-specific MAC exclusions being below or at and above the annual
median, respectively. Mean and median Acquirer CAR is higher (5.5% vs. 1.7% and 6.0% vs. 0.4%, respectively) in deals with broad abandonment options. Targets also benefit from broad
abandonment options with significantly higher Target CAR. Target CAR with broad abandonment
options is 6.1% (mean) and 8.3% (median) higher. With Acquirer CAR and Target CAR higher for
deals with broad abandonment options, it is not surprising that the acquirer’s and target’s
Combined gain is also significantly higher with broad abandonment options. We calculate
Combined gain as the sum of the acquirer’s and target’s dollar gains divided by the sum of the
acquirer’s and target’s market value of equity (Moeller, Schlingemann and Stulz, 2004). The
acquirer (target) dollar gain equals Acquirer (Target) CAR multiplied by acquirer (target) market
value of equity (Bates, Lemmon, and Linck 2006). We measure the market values of equity four
weeks before the announcement date.
The insignificant differences in Target relative gain show that targets receive similar fractions
of Combined gain regardless of the scopes of the abandonment options. We compute Target
14
relative gain as the difference between the target and acquirer dollar gains divided by the sum of
target and acquirer market value of equity (Ahern, 2012; Cai and Sevilir, 2012). Ideally, we would
like to estimate the target’s relative gain as the difference between the target and acquirer dollar
gains divided by Combined gain. However, such a variable has large outliers when Combined gain
is small and nonsensical values when Combined gain is negative. Overall, the univariate results
support our signaling hypothesis as both acquirers and targets seem to benefit from broader
abandonment options. Next, we conduct multivariate analyses with control variables to ensure that
our results reflect the effects of the scopes of the abandonment options instead of other previously
known relations.
4.2 Motivation and description of control variables
Table 3 describes the control variables. To reduce the influence of outliers, we winsorize all
variables at the 0.01 and 0.99 percentiles. We use three proxies for information asymmetry
between the acquirer and the target: Diversifying deal, Stdev target net income, and Stdev target
stock return. Diversifying deal is an indicator that equals one if the acquirer and the target are in
different 4-digit SIC industries.11 Thomas (2002) finds evidence on the association between
corporate diversification and asymmetric information while Coates (2012) posits that the target’s
informational advantage is larger when the acquirer and the target are in different industries. The
acquirer and the target are in different industries in 21% of our sample. Stdev target net income is
the target’s net income volatility, calculated as the standard deviation of the quarterly net income
during the three years prior to the acquisition announcement (Krishnaswami and Subramaniam,
1999). The average of Stdev target net income is 0.262 with large cross-sectional variation. Stdev
11
Results are similar if we use a measure of the overlap in the acquirer and target industries instead of the
diversifying indicator based on the main industry of the acquirer and target. Results are also robust to using the 12 or
48 Fama-French industry classifications.
15
target stock return is the target’s idiosyncratic return volatility (Officer, Poulsen and Stegemoller,
2009), calculated as the standard deviation of the monthly abnormal stock returns using the market
model during the year prior to the acquisition announcement. The average of Stdev target stock
return is 0.233.
We use two measures of firm performance to distinguish between higher and lower quality
targets (Morse, Nanda and Seru, 2011). First, Target ROA is the industry-adjusted (based on 48
Fama-French industries) return on assets, calculated as the ratio of earnings before interest, taxes,
depreciation, and amortization to total assets.12 Second, Target CAR prior year is the target’s
abnormal return using the market model based on the CRSP value-weighted index over the one
year prior to the acquisition announcement. The mean and median of Target CAR prior year are
negative at -5.0% and -1.4%, respectively. While the main reason for signaling should be the
unobservable part of the target’s quality, we assume at least some positive correlation between the
observable and unobservable components of target quality.
The target is on average 27.8% of the size of the acquirer in terms of market capitalization..
We control for the potential impact of targets’ financial constraints on the bargaining power of the
targets with Cash / assets (Majluf and Myers, 1984; Erel, Jang, and Weisbach, 2013).
Industry characteristics are related to information asymmetry and exogenous risks. Technology
firms are arguably more difficult to value and should have higher information asymmetries (Bates
and Lemmon, 2003). Gilson and Schwartz (2005) argue that, to reduce moral hazard, the alignment
of incentives through MAC exclusions is important for targets in technology industries. We set
Target in technology industry equal to one if the target’s main industry is medical equipment,
pharmaceutical products, machinery, electrical equipment, defense, computers, electronic
12
Our results are qualitatively similar if we use unadjusted ROA.
16
equipment, or measuring and control equipment, based on the Fama-French 48 industries. In our
sample, 21.3% of the targets are in a technology industry.
We follow Barclay and Smith (1995) and Boone and Mulherin (2008) to classify a target as
regulated (Target in regulated industry) if the target’s main industry (based on the Fama-French
48 industries) is defense, petroleum and natural gas, utilities, communication, banking, insurance
or trading. Takeovers in regulated industries tend to have longer completion times due to greater
need of regulatory approvals (e.g., Bates and Lemmon, 2003; Gilson and Schwartz, 2005). The
longer completion times, coupled with more stringent regulatory requirements, increase the
exogenous risk for deals with regulated targets. In our sample, 37.9% of targets are in regulated
industries.
Transaction characteristics can also affect the market’s assessment of a takeover. While 14.8%
of the takeovers in our sample are tender offers, 60.6% of the payment for the target is on average
in acquirer stock. About half of all deals are stock-only and 28.5% are cash-only. The method of
payment, in particular the use of acquirer stock, can mitigate adverse selection risk (Hansen, 1987;
Officer, Poulsen, and Stegemoller, 2009). Therefore, it is an important control variable.
We use the Chicago Board Options Exchange Market Volatility Index (VIX), i.e., the implied
volatility of S&P 500 index options, as our proxy for market uncertainty (Talley, 2009). Volatility
index is the average of the daily VIX for the six months prior to the announcement date. Table 3
also reports descriptive statistics for additional control variables related to target characteristics,
size, legal advisers, market environment, and the year of a seminal legal case related to MACs.
We discuss these additional control variables as we describe the multivariate analyses in which
they appear.
17
4.3 Multivariate analysis of acquirer gains
We test hypotheses 1 through 3 in a multivariate setting in models 1 (Acquirer CAR), 2 (Target
CAR), and 3 (Combined gain) of Table 4. Although we do not have any prior expectations on the
relative distributions of the value generated in the acquisition, we assess the economic impact of
the abandonment options’ scopes on Target relative gain in model 4.
Throughout the paper, we use a fairly large number of control variables, many with frequently
insignificant coefficients. To alleviate concerns that the multitude of control variable
inappropriately affects our results, we also run (untabulated) regressions with reduced sets of
controls and obtain similar results. We estimate the p-values using Eicker-Huber-White-Sandwich
heteroskedastic-robust standard errors clustered by acquirer industry. Our results are similar when
we cluster the standard errors by target industry or by announcement year, as well as when we
include year and industry fixed effects.
The main variable of interest is Abandonment option scope FS. Its coefficient is significantly
positive in the first three models of Panel A. Consistent with signaling, acquisitions with broader
abandonment options have both higher Acquirer CAR and Target CAR. The results for Combined
gain also support signaling. Broader firm-specific abandonment options are associated with more
value creation in the acquisitions. High-quality targets can signal with broader firm-specific
abandonment options and this potentially costly signal seems to benefit targets. As in the univariate
results, the scope of the firm-specific abandonment options does not affect Target relative gain.
Assessing the economic effects of information asymmetry, Diversifying deal is insignificant
in all the models. The standard deviation of the target’s net income (Stdev target net income) and
the standard deviation of the target’s stock return (Stdev target stock return) have significantly
positive relations with Acquirer CAR, Target CAR, and Combined gain, suggesting that more good
18
news is revealed in the announcements of acquisitions of targets with high information
asymmetries. In contrast, the Target relative gain estimation indicates that targets with higher
information asymmetries, based on Stdev target net income, receive smaller portions of the value
generated in the acquisitions. The smaller Target relative gain may reflect a reluctance of acquirers
to acquire targets with highly uncertain values.
Among the measures of prior target performance, Target ROA and Target CAR prior year have
positive associations with Acquirer CAR and Combined gain, but a negative relation with Target
relative gain. Overall, better prior target performance is associated with larger acquisitions gains.
There are no consistently significant relations for the other control variables.
In Panel B, we repeat the analysis of Panel A but replace Abandonment option scope FS with
the indicator Broad abandonment option FS. The results are largely the same. Both Panels A and
B show that acquirer and target announcement returns are higher if the acquirer has more flexibility
to walk away from the acquisition. Consequently, the combined value generated in the acquisition
is also higher. Having a broader firm-specific abandonment option does not translate in a different
distribution of such gains between acquirer and target. Overall, Table 4 supports our signaling
hypothesis.
4.4 Alternative estimates of acquirer and target announcement returns
Table 5 replicates models 1 and 2 of Table 4, now using alternative estimates of Acquirer CAR
(Panel A) and Target CAR (Panel B). The specifications are the same as in Table 4 but we only
report the coefficients of our main explanatory variable. In models 1 and 2, we adjust
announcement returns with the three Fama-French factors over the windows from 60 days before
to 120 days after and 30 days before to 120 days after the announcement date, respectively. Again,
we use a shorter window in case an acquisition is terminated or completed in less than 120 days.
19
In model 3, we estimate short-term announcement returns in excess of the CRSP equal-weighted
index over the window from two days before to two days after the announcement date, Acquirer
ST CAR and Target ST CAR.
Panel A shows that with the two long-term Fama-French adjusted Acquirer CAR,
Abandonment option scope FS is significant at the 0.01 level. In Panel B, with the two estimates
of long-term Fama-French adjusted Target CAR, Abandonment option scope FS is significant at
the 0.01 level. However, Abandonment option scope FS, is positive yet insignificant when
Acquirer ST CAR or Target ST CAR are the dependent variables. The relations between
Abandonment option scope FS and Acquirer ST CAR or Target ST CAR are likely noisy because
of arbitrage spreads, as documented by Denis and Macias (2013). Having more limited
abandonment options is associated with smaller arbitrage spreads, that is, Target ST CAR is higher
since the target price moves closer to the offered takeover premium. Although targets that signal
with broader firm-specific abandonment options should have higher expected announcement
returns, broader firm-specific abandonment options also cause larger arbitrage spreads due to a
higher probability of termination and therefore reduce the target announcement return. Overall,
our results are robust to different ways of estimating Acquirer CAR and Target CAR, but are
weaker with the short-term announcement return window.
4.5 Effect of public disclosure of the MAC structure
The positive association between announcement returns and the firm-specific abandonment
option scope suggests that the target signal decreases the probability that the target is materially
flawed. Yet, it is possible that announcement returns and the firm-specific abandonment option
scope are both driven by other underlying, unobserved phenomena. For example, perhaps acquirers
and targets choose to have broader firm-specific abandonment options in acquisitions that a priori
20
have lower risks of material flaws. If investors react positively to these lower risk acquisitions, we
should observe our empirical results although no signaling is taking place.
To address this issue, we note that investors can only assess the relevance of a signal of higher
target quality given by different MAC structures if they have access to the details of the MAC
structure in the merger agreement. In our sample, about 40% of the merger agreements are filed
with the SEC within three days after the takeover announcement.13 Thus, if the positive association
between short-term announcement returns and firm-specific abandonment option scope is due to
investors inferring the target’s higher quality from the structure of the MAC clause, the positive
association should only be present in those transactions in which the merger agreement has been
filed within the short-term announcement return window. In contrast, if announcement returns and
the firm-specific abandonment option scope are both driven by an unobserved factor, we do not
expect the date on which the merger agreement is filed to affect the positive association between
short-term announcement returns and firm-specific abandonment option scope.
Panel A of Table 6 indicates higher short-term returns when the merger agreement contains a
broad abandonment option and when the merger agreement is public within two days after the
announcement date. Panel B of Table 6 presents ordinary least squares (OLS) regressions of
Acquirer CAR, Target CAR, Combined gain, and Target relative gain after splitting the sample by
public disclosure of the MAC clauses. As of day two following the initial acquisition
announcement, merger agreements have been filed in only 263 of the 626 acquisitions with
available data. In the subsample of these 263 observations, Abandonment option scope FS has
significantly positive associations with Acquirer ST CAR, Target ST CAR, Combined ST gain, and
Target relative ST gain. In contrast, these relations are insignificant when the merger agreement
13
Our results are robust to using 3-day, 5-day and 20-day windows although the number of acquisitions for which
merger agreements have not been made public decreases with the longer windows.
21
has not been filed as of day two. Returns only being related to the scopes of firm-specific
abandonment options when such information is public suggests that investors use information
contained in the merger agreement – specifically the firm-specific abandonment option scope – to
update their priors on value creation and adverse selection concerns related to the acquisition.
5. Determinants of abandonment option scope
We test hypotheses 4 and 5 by assessing whether acquisitions of targets with better prior
performance or with higher information asymmetries have broader firm-specific abandonment
options. We expect that signaling is most beneficial for precisely these types of targets.
5.1 Univariate analysis
Table 7 splits our sample along two dimensions, higher versus lower information asymmetry
acquisitions and above versus below the annual median industry-adjusted Target ROA. We
contend that acquirers face greater information asymmetries in diversifying acquisitions and when
targets’ net incomes and stock returns are more volatile. Therefore, signaling by targets of higher
quality should be particularly effective in acquisitions with greater information asymmetries.
Indeed, both the continuous variable (Abandonment option scope FS) in Panel A and the indicator
(Broad abandonment option FS) in Panels B, C, and D show that acquisitions of targets with better
prior performance have significantly broader abandonment options, especially in acquisitions with
greater information asymmetries. Consistent with hypothesis 4, higher quality targets seem to
signal their better quality by giving the acquirer broader abandonment options. The results are also
consistent with hypothesis 5, and with Coates (2012) who finds that diversifying acquisitions are
more likely to allocate risk to targets. Coates (2012) examines risk allocation through indemnities,
earnouts, price adjustment clauses, seller financing, holdbacks, and escrows.
22
5.2 Multivariate analysis
To analyze the determinants of firm-specific abandonment option scope in a multivariate
framework, Table 8 presents tobit models on the firm-specific abandonment option scope and a
logit model on the indicator of broad firm-specific abandonment options. We include two
additional control variables, Acquirer- and Target top legal adviser. The experience of the legal
advisers can affect the merger agreement (Coates, 2012). We estimate the quality of the legal
advisers for the acquirer and the target based on the SDC ranking tables for mergers and
acquisitions legal advisers (Boone and Mulherin, 2008). Using the average rank-level in the three
years before the acquisition announcement, we group the legal adviser into three levels: level 1
identifies the top one to five legal advisers, level 2 the top six to 20, and level 3 the remaining legal
advisers. We then classify as a top legal adviser those advisers in level 1. Our results are robust to
using either top legal adviser (0,1) or legal adviser ranking (1, 2, 3). As reported in Table 3, the
acquirers’ legal advisers have slightly higher average rankings than the targets’.
Consistent with hypotheses 4 and 5, and the univariate results in Table 8, models 1, 2, and 4
show that merger agreements have broader firm-specific abandonment options when information
asymmetries are likely larger (Diversifying deal and higher Stdev target net income) and when
targets have better prior operating (Target ROA) and stock price (Target CAR prior year)
performance. The insignificant coefficients for Stdev target stock return in models 3 and 4 suggest
that market uncertainty is not as important as the other determinants of abandonment option scope.
The Diversifying deal and Stdev target net income coefficients remains significant when Broad
abandonment option FS is the dependent variable in model 5 while Target ROA and Target CAR
prior year lose their significance in model 5.
23
Models 1 and 2 show that the presence of a reverse termination fee, to be paid when the acquirer
walks away from the deal, is unrelated to the firm-specific abandonment option scope. A reverse
termination fee, just like more MAC exclusions, limits the acquirer’s abandonment option. Yet,
only 27% of the acquisitions in our sample have reverse termination fees, suggesting that MAC
exclusions play a more important role in merger agreements than reverse termination fees. Collars,
another provision in about 20% of merger agreements and used mainly in stock deals, are not
significantly related to the Abandonment option scope FS. Taken together, the results from Table
4 and Table 8 indicate that out of the three contract provisions that address value reductions of the
target, MAC exclusions appear to be more relevant than reverse termination fees and collars. A
potential explanation is the higher customization of risk allocation attainable through MAC
exclusions compared to the all-or-nothing feature of reverse termination fees and collars.
All models indicate that acquisitions of larger targets (Log target market capitalization) have
more limited abandonment options, consistent with smaller information asymmetry concerns with
larger firms (Barry and Brown, 1984) that presumably have more publicly available information
and are subject to more intense scrutiny, e.g., from analysts. In contrast, transactions with targets
that are large relative to their acquirers (Target/ acquirer market cap) have broader firm-specific
abandonment options. One explanation for this result is that relatively larger targets represent more
significant additions to acquirers, and large “bad” acquisitions can cause large problems for
acquirer. Therefore, acquirers seem to protect themselves from “big” mistakes through broader
abandonment options. Alternatively, signaling through broader abandonment options can be
particularly valuable when the target is relatively large.
Targets with more cash on hand (i.e., higher Target cash/ assets) have more limited
abandonment options, consistent with smaller information asymmetry concerns when the target is
24
not financially constrained. Consistent with the acquirer’s motivation to use stock as method of
payment to reduce the effects of information asymmetries (Hansen, 1987; Officer, Poulsen and
Stegemoller, 2009), models 1 to 4 suggest a substitution effect between using a smaller percentage
of stock and having a broader abandonment option.
Consistent with the acquirer’s motivation to negotiate broader abandonment options, the
Acquirer top legal adviser coefficient is positive in all models. In contrast, the Target top legal
adviser coefficient is always negative, but only significant in model 5, suggesting that the targets’
legal advisers aim to negotiate more limited acquirers’ abandonment options to increase the
probability of acquisition completion. Essentially, good lawyers seem to do what they are paid for,
i.e., ensuring broad and limited abandonment options for their acquirer and target clients,
respectively.
With the exception of Target in regulated industry, Volatility Index, and After 2001, none of
the remaining variables in models 1 to 4, i.e., Target in technology industry, and Tender offer, are
significant. Overall, Tables 7 and 8 support hypotheses 4 and 5. Targets with greater information
asymmetries and better prior operating performance seem to signal their higher quality with
broader abandonment options.
6. Heckman models and endogeneity
Several decisions are made simultaneously during merger negotiations. For example, terms,
including MAC clause and exclusions, and takeover price are negotiated at the same time.
Simultaneously, targets decide whether to signal with broader abandonment options or to take
advantage of their private information in other ways. An endogeneity problem can occur if we do
not control for omitted variables or simultaneity in the decision process (Wooldridge, 2010). In
our study, omitted variables should be related to asymmetric information and endogeneity to self25
selection, especially if targets proactively negotiate broader abandonment options to signal their
better types.
We run Heckman regressions to account for the endogeneity of the target’s private information
potentially explaining the signaling decision and ultimately affecting the acquisition pricing and
announcement returns. Although instrumental variables in two- or three-stage-least-squares
models can control for endogeneity in the form of simultaneity bias (Baum, 2006), these
techniques do not address self-selection biases (Guo and Fraser, 2010), tend to suffer from omitted
variables (Li and Prabhala, 2007), and have a bias towards finding insignificant results (Ettner,
2007). Even though the targets do not choose the abandonment option scope (it is the outcome of
negotiations), treatment effect regressions should help assess the impact of the abandonment
option scope, the chosen treatment in our setup. Our Heckman-style specification should address
the lack of randomness in the sample (Heckman, 1979; Li and Prabhala, 2007; Guo and Fraser,
2010).14
For identification purposes, we include Acquirer top legal adviser and Target top legal adviser.
As shown in Table 8, they are significantly related to the scope of the firm-specific abandonment
option and we can reasonably expect that their influence on announcement returns is much smaller
than on specific merger agreement terms. To further reduce the reliance on the non-linear
specification of the treatment effect model for identification, we follow Li and Prabhala’s (2007)
suggestion of including the actual magnitude of the selection variable, in our case, the Broad
abandonment option FS, as an independent source of variation in the selection correction term.
Because the model is identified by nonlinearity, exclusion restrictions are helpful, but not critical
14
Other examples of treatment effect regressions in corporate finance are in Campa and Kedia (2002), Song, (2004),
Fang (2005), Bris, Welch, and Zhu (2006), and Jiang, Li and Wang (2012).
26
in the Heckman selection procedure (Li and Prabhala, 2007; Golubov, Petmezas, and Travlos,
2012).
Table 9 presents the Heckman regressions in which the dependent variables in the second stage
models are Acquirer CAR, Target CAR, Combined gain, and Target relative gain. The dependent
variable in the first stage model is the indicator variable Broad abandonment option FS. In
treatment effect nomenclature, a value of one in this indicator variable represents the “treatment.”
Our results are robust to solving the models with maximum likelihood instead of using a two-step
approach. Our discussion focuses on the coefficients of our main variable of interest, Abandonment
option scope FS.
The first stage estimation in column 1 confirms the findings from Table 8. Consistent with
signaling hypotheses 4 and 5, acquisitions of targets with higher information asymmetry
(Diversifying deal and higher Stdev target net income) have broader firm-specific abandonment
options. We also confirm the negative coefficients for Log target market capitalization and Target
cash/ assets and the positive coefficients for Acquirer top legal adviser and Target/ acquirer
market cap. The Target top legal adviser indicator becomes insignificant.
Supporting hypothesis 3, and consistent with the results in prior tables, models 2, 3, and 4 show
that acquisitions with broader firm-specific abandonment options have higher acquirer and target
announcement returns and higher combined gains. The associations between Abandonment option
scope FS and Target relative gain is insignificant. Hence, by signaling their higher quality through
broader firm-specific abandonment options targets are not worse off in the distribution of the
higher combined gain. These results are consistent with an equilibrium where only high quality
targets engage in costly signaling, especially when information asymmetries are large.
27
The firm-specific abandonment option scope seems to cause our results. Results using the
alternative event windows discussed in Table 5 are similar to the ones tabulated in Table 9. The
results from splitting the sample based on public disclosure (Panel B of Table 6) are also robust to
using Heckman models on short-term announcement returns.
Overall, accounting for potential simultaneity and omitted variables biases suggests that the
decision to include broader firm-specific abandonment options is not random. As expected, in
equilibrium, primarily high quality targets engage in costly signaling. It confirms our results that
the choice of broader firm-specific abandonment options is positively associated with higher
Acquirer CAR, Target CAR, and higher Combined gain.
7. Robustness analyses and extensions
We examine clusters of firm-specific abandonment options, robustness to additional control
variables, and the role of market-wide abandonment options.
7.1 Clustering of firm-specific abandonment options
To alleviate potential concerns of using only a count and an indicator variable, we cluster the
individual MAC exclusions into three distinct categories, Adverse Selection, Moral hazard, and
Other. The Adverse Selection category is comprised of the following firm-specific MAC
exclusions: Litigation, Stock price, Implied disproportionate industry condition, and Implied
disproportionate economic condition. The Moral hazard category is comprised of the Loss of
customers, suppliers, employees and Failure to meet projections MAC exclusions. The Other
category is comprised of the rest of the firm-specific MAC exclusions: Previously contended or
existent MAC, Changes due to agreement or transaction announcement, and Miscellaneous. We
measure each firm-specific cluster by adding the number of MAC exclusions for each category
28
and then adjusting by the annual median number of exclusions for such cluster. Finally, similar to
our main variable, we convert the variable to measure the scope of the abandonment option of each
cluster.
Panel A of Table 10 confirms a large heterogeneity even within the clusters. Most important,
Panel B presents OLS models on Acquirer CAR, Target CAR, Combined gain, and Target relative
gain using the same controls as in Table 4. The main result is that the Adverse Selection and Other
clusters are positively related to the returns. In contrast, we find that the Moral hazard cluster
shows a negative impact on the returns. Results for the Adverse Selection and Other become
stronger when using each cluster separately. In contrast, when using only the Moral hazard cluster
separately, the results become weaker. The alleviation of adverse selection, as opposed to moral
hazard concerns, seems to be of first order importance when signaling with broader abandonment
options. Classifying individual MAC exclusions into adverse selection and moral hazard concerns
is difficult. For example, Failure to meet projections can relate to both types of information
asymmetries. Therefore, our clustering analysis provides more of an illustration of the effects of
the different sub-categories of firm-specific MAC exclusions than definitive tests. An untabulated
principal component analysis indicates that, if we replicate Panel B of Table 10, the top two
principal components have positive and significant effects on Acquirer CAR, Target CAR, and
Combined gain.
7.2 Robustness to additional control variables
We further assess the robustness of our results to omitted variables that may drive the positive
relation between announcement returns and abandonment option scope by examining additional
control variables related to information asymmetry, misevaluation of the target, competition, and
acquirer takeover experience. Results are untabulated to conserve space.
29
When we replicate models 1 (Acquirer CAR), 2 (Target CAR), and 3 (Combined gain) of Table
4, we include an additional proxy for information asymmetry based on the difficulty of valuing the
target (Officer et al., 2009), namely, the ratio of research and development expenses to sales (Total
R&D/ sales). To proxy for potential misvaluation of the target, we follow Rhodes-Kropf,
Robinson, and Vishwanathan (2005). They decompose the market-to-book ratio into three
components: firm-specific value deviation from short-run industry value, sector-wide, short-run
deviations from firms’ long-run value, and long-run value-to-book. We divide each component by
the book value of assets and add the firm-specific and the industry sector-specific errors to estimate
the total misvaluation error (Total RRV misvaluation error/ assets).
We also include four measures that proxy for competition and acquirer experience. Our first
competition proxy is the Herfindahl index of the target’s main industry based on the sales reported
by Compustat (Industry concentration). Our second competition proxy is the number of serious
bidders for the target during the pre-announcement takeover period (Number of serious bidders).
Number of serious bidders is defined as bidders that continue on after the initial “contacted
potential bidders” and “requested information memorandum” stages of the takeover discussions.
We collect Number of serious bidders from the “background of the merger” sections in SEC
filings. The average deal has 1.5 serious bidders. We focus on serious bidders instead of potential
bidders that the target contacts or that sign confidentiality agreements because only the serious
bidders enter into legal negotiations that involve MAC clauses and exclusions. Our third
competition proxy is the number of acquisitions announced in the U.S. during the three months
prior to the announcement date (Acquisitions in prior 3 months; Aktas, de Bodt, and Roll, 2010).
Finally, our measure of the acquirer’s acquisition experience is the number of the acquirer’s prior
acquisitions of public, private, and subsidiary U.S. targets during the five years prior to the
30
announcement date (Acquisitions by acquirer in prior 5 years, Aktas, de Bodt, and Roll, 2013).
Acquirers make on average 11.5 acquisitions during that period.
After adding these additional controls, Abandonment option scope FS remains significantly
positive at the 0.01 level in affecting Acquirer CAR, Target CAR, and Combined gain. These
results are robust to splitting the sample by stock-only or cash-only bids and to using Heckman
models on short-term announcement returns similar to those in Table 9.
When we examine these additional controls in untabulated tobit models of determinants of
firm-specific abandonment option scope, all but Industry concentration are insignificant. Higher
industry concentration is related to broader firm-specific abandonment options. A possible
explanation is that in highly concentrated industries acquirers and targets have substantial
information about each other which makes signaling less valuable and abandonment options less
necessary. Overall, the positive association between announcement returns and broader firmspecific abandonment options is robust.
7.3 Effects of market-wide abandonment options
Gilson and Schwartz (2005) claim that the acquirer, as the future long-term owner of the
combined firm, can bear market-wide risk more efficiently because it can better hedge against such
risk. Furthermore, acquirers are usually larger than targets and frequently more diversified, giving
them a better ability to shoulder market-wide risks. Panel A of Table 11 shows the firm-specific
and market-wide abandonment option scopes separately for targets in non-regulated and regulated
industries. We expect market-wide risks to play a more dominant role in regulated industries
because the time to completion of acquisitions is longer in regulated industries. In contrast,
asymmetric information should matter less for regulated targets because of extensive regulation
and disclosure requirements. Supporting our hypothesis 5, limited market-wide abandonment
31
options are significantly more prevalent in regulated industries. For firm-specific abandonment
options, the difference between non-regulated and regulated industries is only significant with the
indicator variable Broad abandonment option FS. This evidence suggests that market-wide risk
tends to get allocated to the acquirer when market-wide risks are likely more important than firmspecific risks.
In an untabulated tobit regression with Abandonment options scope MW and a logit regression
with Broad abandonment option MW, we find that, except for the significantly positive coefficient
for Stdev target net income, the coefficients for information asymmetry proxies (Diversifying deal
and Stdev target stock return) and target performance proxies (Target ROA and Target CAR prior
year) are insignificant. More importantly and consistent with hypothesis 5, Regulated industry is
negatively related to Abandonment option scope MW at the 1% confidence level. While the
coefficient for the acquirer legal adviser is insignificant, the target legal adviser has a significantly
negative association with Abandonment option scope MW. Percent stock is insignificantly
associated with Abandonment option scope MW. Additional untabulated models indicate that the
Volatility index, our proxy for market-wide risk, is significantly negatively related to Abandonment
option scope MW. As expected, market-wide MAC exclusions are particularly important when
market-wide risks are large.
Panels B and C of Table 11 present OLS regressions (similar to Table 4) of Acquirer CAR,
Target CAR, Combined gain, and Target relative gain. The results confirm the significantly
positive coefficients for Abandonment option scope FS and Broad abandonment option FS. In
contrast, the coefficients for Abandonment option scope MW and Broad abandonment option MW
are statistically insignificant. Results are robust to only including the market-wide without the
firm-specific abandonment option variables. Although Panel A suggests that exogenous market-
32
wide risk is allocated to the acquirer, we do not find evidence that this allocation is priced in the
market reaction, nor that it affects the distribution of the acquisition gains.
8. Conclusion
The empirical evidence on the relations among contract provisions, information asymmetries,
and signaling in takeovers is sparse. Acquirers and targets allocate interim risks through the MAC
clause and its exclusions. While virtually all acquisitions contain an abandonment option via the
MAC clause, there is large cross-sectional variation in the numbers and types of MAC exclusions.
Using comprehensive hand-collected data, we find that targets signal their higher quality with
broader abandonment options. The signaling seems to alleviate adverse selection concerns and
such risk allocation has positive effects on acquirer announcement returns, target announcement
returns, and combined merger gains. Broader firm-specific abandonment options are credible
signals because they are more costly for lower quality targets. Indeed, targets that signal with
broader abandonment options appear to be of higher quality. Moreover, the signaling with broader
abandonment options occurs when information asymmetries are likely high, i.e., in cases where
signaling is particularly beneficial. Overall, signaling and risk allocation with contract provisions
seem to benefit both acquirers and targets.
33
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39
Appendix A: Examples of MAC structure and negotiations in merger agreements
Our count and classification of MAC exclusions is in brackets.
A.1- Broader acquirer’s abandonment option with fewer MAC exclusions:
Richey Electronics, DEF14A, December 4, 1998
Agreement and Plan of Merger, dated as of September 30, 1998, by and among Arrow
Electronics, Inc., a New York corporation ("Arrow"), Lear Acquisition Corp., a Delaware
corporation ("Acquisition Corp.") and Richey Electronics
Conditions to Completion of the Merger
The obligations of Parent and Sub to effect the Merger are subject to the satisfaction of the
following conditions, unless waived by Parent and Sub:
(a) REPRESENTATIONS AND WARRANTIES; PERFORMANCE OF OBLIGATIONS.
Except as otherwise contemplated or permitted by this Agreement,
(i) the representations and warranties of the Company contained in this Agreement or in any
certificate or document delivered to Parent pursuant hereto shall as of the Closing Date, (x) to the
extent qualified by Company Material Adverse Effect, be true in all respects
(e) MATERIAL ADVERSE CHANGE. Since the date hereof, there shall not have been any
events, changes or occurrences which have had, or are reasonably likely to have, individually or
in the aggregate, a Company Material Adverse Effect.
For the purposes of this Agreement, "Company Material Adverse Effect" shall mean a Material
Adverse Effect on the financial condition, assets, liabilities (contingent or otherwise), results of
operation, business or business prospects of the Company and its Subsidiaries, if any, taken as a
whole. For purposes of this Agreement, a Company Material Adverse Effect shall not include a
Material Adverse Effect on the financial condition, assets, liabilities (contingent or otherwise),
results of operation, business or business prospects of the Company as a result of
(i)
(ii)
changes in the conditions or prospects of the Company and its Subsidiaries taken as a
whole which are consistent with general economic conditions [1. Economic changes] or
general changes affecting the electronic component distribution or electronics assembly
industries, [ 2. Industry changes]
40
A.2- More limited acquirer’s abandonment option with more MAC exclusions:
NUI Corp, DEF14A, August 13, 2004
Agreement and Plan of Merger that we (NUI Corp) have entered into with AGL Resources Inc.
"Company Material Adverse Effect" shall mean a material adverse effect on the business,
properties, financial condition or results of operations or prospects of the Company and its
Subsidiaries taken as a whole. Material Adverse Effect shall exclude any effects, consequences
or conditions attributable to
(i)
any change in U.S. or global economic conditions or [1. Economic changes]
(ii)
U.S. or global financial markets or condition, or [2. Financial Markets changes]
(iii) GAAP; [3. Accounting changes]
(iv)
any change relating to the industries in which the Company or any of its Subsidiaries
operates or [4. Industry]
(v)
in any generally applicable law, regulation or order, in each case that does not
specifically relate to the Company and that does not affect the Company in a materially,
disproportionate manner relative to other participants in the industries in which the
Company operates, [5. Law/regulations changes]
(vi)
any failure by the Company to meet any revenue or earnings predictions of equity
analysts for any period, [6. Failure to meet Projections]
(vii) any change in the market share price or trading volume, [7. Price/ volume changes]
(viii) any shareholder class action, derivative or similar litigation arising primarily from
allegations of breach of fiduciary duty relating to this Agreement, [8. Litigation]
Background of the merger
On April 13, 2004, our board held a joint meeting, also attended by certain members of
management and our legal and financial advisors. At this meeting, our financial advisors
discussed with our board the status of the NUI sales process … On April 26, 2004, Ms. Rosput
sent Mr. Matthews an issues list relating to the draft merger agreement delivered by our financial
advisors. …
On June 30, 2004, our board held a meeting, which was also attended by certain members of
management and our legal and financial advisors. Our board discussed the status of the sales
process and the significant issues remaining in the proposed merger agreement with AGL. Our
board determined that key issues were price, certainty of closing and time required to close. Our
financial advisors also discussed the financial aspects of AGL’s offer with the board. At the June
30 meeting, the board determined, after consulting with its financial and legal advisors, that the
terms of AGL’s offer of $14.00 per share did not provide adequate certainty of closing due to the
nature and breadth of the termination rights that AGL required at that offer price. …
From June 30, 2004 to July 7, 2004, White & Case and LeBoeuf Lamb exchanged several
revised drafts of the merger agreement. On July 7, 2004, AGL informed us that its final bid price
would be reduced to $13.70 and the merger agreement would be revised to provide greater
certainty with regard to AGL’s obligation to complete the merger.
On July 7, 2004, our board held a meeting, which was also attended by certain members of
management and our legal and financial advisors. The board discussed the occurrence of certain
events that would allow AGL to terminate the proposed merger agreement, as well as AGL’s
revised offer of $13.70 per share. …. On July 13, 2004, AGL’s board approved the merger.
41
Appendix B: Selected variable definitions
Variable
MACs contract provisions
MAC clause
Description
Source
Material-Adverse-Change clause for target included in
the merger agreement; also called Material-AdverseEvent
SEC filings
Market-wide MAC exclusions
Number of market-wide MAC exclusions minus
annual median number of market-wide MAC
exclusions
SEC filings
Firm-specific MAC exclusions
Number of firm-specific MAC exclusions minus
annual median number of firm-specific MAC
exclusions
Market-wide + firm-specific MAC exclusions
SEC filings
Total MAC exclusions
SEC filings
Abandonment option scope
(firm-specific, market-wide)
10 – (number MAC exclusions – annual median
number MAC exclusions); calculated separately for
firm-specific (FS) and market-wide (MW) MAC
exclusions
SEC filings
Broad abandonment option (FS, MW)
Indicator for number of MAC exclusions < annual
median number of MAC exclusions
SEC filings
Operating income before depreciation/ book value of
assets, adjusted with industry median ROA, calculated
as of quarter before announcement
Compustat
Cumulative abnormal return in excess of market model
based on CRSP value-weighted index from 425 to 61
days before the announcement date
CRSP
Target and acquirer in different industry, using FamaFrench 12 industry classification
Standard deviation of net income/ book value of assets
in prior year calculated as of quarter before the
announcement
Standard deviation of target’s monthly abnormal return
in excess of market model based on CRSP valueweighted index from 425 to 61 days before the
announcement date
SDC, Compustat
Prior performance
Target ROA
Target CAR prior year
Information asymmetry proxies
Diversifying deal
Stdev target net income
Stdev target stock return
Compustat
CRSP
Target characteristics
R&D/ sales
Research and development expenses/ sales
Compustat
Target cash/ assets
Cash and cash equivalents / book value of assets
Compustat
Total RRV misvaluation error/ assets
Total misvaluation error calculated following RhodesKropf, Robinson, and Viswanathan (2005)
Compustat
Book value assets - book value common equity +
common shares outstanding * share price (as of quarter
end prior to acquisition announcement); log of the
variable is the natural logarithm
Target / acquirer market capitalization
Compustat
Size
Acquirer and Target market
capitalization
Target/ acquirer market cap
42
Compustat
Transaction attributes
Target in technology industry
Target in regulated industry
Tender offer
Percent stock
Acquirer and target legal adviser
ranking
Main industry of target is classified as medical
equipment, pharmaceutical products, machinery,
electrical equipment, defense, computers, electronic
equipment, or measuring and control equipment in
Fama-French 48 industries
Compustat
Main industry of target is classified as defense,
petroleum and natural gas, utilities, communication,
banking, insurance or trading in Fama-French 48
industries
Indicator variable = 1 if tender offer
Compustat
Percentage of takeover price that is paid with acquirer
stock
1 = top 1 to 5; 2 = ranks 6 to 20; 3 = below 20 or not
available; using the average of prior three years, as
reported in the SDC's league tables
SDC
SDC
SDC, LivEdgar
M&A database
Acquirer and target top legal adviser
1 = legal adviser ranking equal to one; 0 = otherwise
Number of serious bidders
Number of bidders that continue in the sale process
after the initial "contacted potential bidders" and
"requested information memorandum" stages
SEC filings
After 2001
Indicator variable that takes the value of one if the
announcement date is after year 2001
SDC, SEC filings
Reverse termination fee
Indicator variable that takes the value of one if the
merger agreement includes a reverse termination fee
SDC, SEC filings
Collar
Indicator variable that takes the value of one if the
merger agreement includes a collar
SDC, SEC filings
Earnout
Indicator variable that takes the value of one if the
merger agreement includes an earnout
SDC, SEC filings
VIX, the Chicago Board Options Exchange Market
Volatility Index, measured over the six months prior to
acquisition announcement
CRSP
Acquisitions by acquirer in prior 5
years
Number of acquisitions of U.S. targets by the acquirer
during the prior five years
SDC
Acquisitions in prior 3 months
Number of acquisitions of U.S. targets in the U.S.
market during the prior three months
SDC
Industry concentration
Herfindahl index (divided by 100) based on the
industry sales as of the prior year, using the FamaFrench 48 industries classification
Compustat
Market-wide and experience
characteristics
Volatility index
43
Table 1. Summary statistics for MAC clauses and exclusions
The sample includes 751 acquisitions of U.S. public targets announced by U.S. public acquirers between 1998 and 2005. We obtain the MAC clauses and
exclusions directly from the merger agreements filed with the SEC. MAC clause present and MAC exclusions present are indicator variables that equal one
when the merger agreement has the respective clause or exclusion. The numbers of MAC exclusions are adjusted by the annual median number of exclusions.
Unadjusted numbers are indicated by (raw). Panel A reports descriptive statistics for the entire sample period while Panel B adds the annual averages for each
sample year. Firm-specific and market-wide MAC exclusions are defined in Panel B based on Denis and Macias (2013). All other variables are defined in
Appendix B. StDev refers to the standard deviation, Min to the minimum value, P25 to the 0.25 percentile, P75 to the 0.75 percentile, and Max to the maximum
value.
Panel A: Summary statistics
Mean
Stdev
Min
P25
Median
P75
Max
Firm-specific MAC exclusions (raw)
Firm-specific MAC exclusions
Abandonment option scope FS
Broad abandonment option FS
1.836
0.142
9.840
0.414
1.657
1.609
1.609
0
-3
4
0
-1
9
2
0
10
3
1
11
8
6
13
Market-wide MAC exclusions (raw)
Market-wide MAC exclusions
Abandonment options scope MW
Broad abandonment option MW
2.225
0.531
10.840
0.406
1.9
1.742
1.742
0
-3
4
0
-1
9
2
1
10
3
2
12
9
7
14
MAC clauses
Other contract provisions (%)
Reverse termination fee
Collar
Earnout
26.9
20.1
1.1
44
Panel B: MAC exclusions over time
Announcement year
All
1998
1999
2000
2001
2002
2003
2004
2005
Number of announced acquisitions
751
157
161
118
96
44
61
70
44
MAC clause present (%)
99.3
100
98.8
99.2
97.9
100
100
100
100
MAC exclusion present (%)
75.6
60.4
61.1
75.6
85.4
90.9
93.4
91.4
97.7
Total MAC exclusions (raw)
4.1
2.4
2.4
3.5
4.2
5.1
5
5.8
6.5
Firm-specific MAC exclusions (raw)
1.8
1.2
1.2
1.6
1.9
2.5
2.1
2.2
2.6
Market-wide MAC exclusions (raw)
2.2
1.2
1.2
1.8
2.2
2.6
2.9
3.6
3.9
Stock price
18.3
4.4
8.6
18.5
24
31.8
27.9
31.4
43.2
Loss of customers, suppliers, employees
11.5
6.9
7.4
10.9
15.6
15.9
16.4
17.1
15.9
Failure to meet projections
7.3
0.6
3.1
6.7
8.3
6.8
9.8
17.1
27.3
Litigation / breach of fiduciary duty due to acquisition
5.6
1.3
4.9
5.0
7.3
13.6
9.8
5.7
6.8
Previously contended or existent MAC (before signing)
2.0
0.6
1.2
0.1
2.1
9.1
4.9
1.4
4.5
Changes due to agreement or transaction announcement
54.8
34.6
43.2
55.5
61.5
79.5
72.1
70.0
81.8
Implied disproportionate economic condition
31.4
37.1
28.4
37.8
32.3
36.4
21.3
28.6
15.9
Implied disproportionate industry condition
36.4
34.6
28.4
36.1
35.4
54.5
44.3
40.0
40.9
Miscellaneous
9.7
8.2
5.6
5.9
11.5
11.4
6.6
12.9
34.1
Any economic condition
59.2
38.4
41.4
62.2
68.8
72.7
83.6
77.1
95.5
Target industry conditions
56.4
41.5
38.3
59.7
67.7
75
65.6
80.0
75.0
Change in accounting
27.4
20.8
22.2
21.8
31.3
31.8
34.4
38.6
45.5
Change in law or regulations
28.7
22.6
19.1
22.7
28.1
38.6
39.3
42.9
56.8
Any capital markets condition
23.6
10.7
6.2
26.1
28.1
29.5
44.3
47.1
45.5
War or terrorism
9.7
3.8
1.2
1.7
6.3
13.6
16.4
28.6
47.7
Average number of
Types of exclusions present (%)
Firm-specific
Market-wide
45
Table 2. Univariate analysis of announcement returns
Panel A reports descriptive statistics of Acquirer CAR, Target CAR, Combined gains, and Target relative
gain for the entire sample. Panel B shows means and medians separately for transactions with broad (i.e.,
below annual median firm-specific MAC exclusions) or limited (at or above annual median firm-specific
MAC exclusions) Abandonment option scope FS. We estimate Acquirer CAR and Target CAR with the
cumulative abnormal return in excess of the CRSP equal-weighted index from 60 days before to 120 days
after the announcement date or to the completion or termination date, whatever occurs earlier. Combined
gain equals the combined dollar gain (the sum of Acquirer CAR and Target CAR, each multiplied by their
respective market values) divided by the sum of the acquirer and target market values measured 60 days
before the announcement date. Target relative gain is the difference of the target and acquirer dollar gains
(Acquirer CAR minus Target CAR, each multiplied by their respective market values) divided by the sum
of the acquirer and target market values measured 60 days before the announcement date. The last column
of Panel B shows the difference between the broad and limited firm-specific abandonment option
subsamples. All variables are winsorized at the 0.01 and 0.99 percentiles. The table uses our entire sample
of 751 observations. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Significance levels in the “Broad” and “Limited” columns refer to t-tests on differences of the means
from zero. In the “Broad – Limited” column, they are based on t-tests for the differences in means and on
a nonparametric K-sample test (chi-squared test statistic) for the differences in medians.
Panel A. Descriptive Statistics
[%]
Acquirer CAR
Fraction of Acquirer CAR > 0
Target CAR
Fraction of Target CAR > 0
Combined gain
Fraction Combined gain > 0
Target relative gain
Mean
3.32***
53.50
36.37***
87.11
4.77***
61.18
1.77*
Stdev
P10
P25 Median
31.63 -33.33 -15.99
1.41
P75
20.99
P90
42.21
42.89
-6.46
12.00
31.05
56.01
81.51
16.97 -14.57
-5.17
3.45
13.29
25.19
14.03 -14.09
-6.01
2.22
9.95
18.56
Panel B. Univariate Analysis
[%]
Acquirer CAR
Target CAR
Combined gain
Target relative gain
mean
median
mean
median
mean
median
mean
median
46
Abandonment option scope FS
Broad
Limited
Broad – Limited
5.53***
1.72
-3.81 *
5.96
-0.36
6.32 *
39.95***
33.81***
6.14 **
36.43
28.15
8.28 ***
6.44***
3.55***
2.89 **
5.8
1.58
4.22 ***
1.65*
1.86***
-0.21
2.16
2.43
-0.27
Table 3. Selected sample statistics
The table shows selected statistics for our sample of 751 observations. Acquirer and Target legal adviser
ranking ranges from 1 (best) to 3 (worst). All variables are defined in Appendix B. The firm-specific
variables are winsorized at the 0.01 and 0.99 percentiles. StDev refers to the standard deviation, Min to
the minimum value, P25 to the 0.25 percentile, P75 to the 0.75 percentile, and Max to the maximum
value.
Mean
Information asymmetry proxies
Diversifying deal
Stdev target net income
Stdev target stock return
Prior performance
Target ROA
Target CAR prior year
Stdev
p25
0.004
-0.050
0.042
0.770
Size
Acquirer market capitalization ($millions)
Target market capitalization ($millions)
Target/ acquirer market cap
9,915
1,211
0.278
24,405
3,496
0.425
528
80
0.048
1,942
255
0.141
8,136
780
0.371
Industry, transaction, and market-wide
characteristics
Target in technology industry
Target in regulated industry
Tender offer
Cash-only bid
Volatility index (divided by 100)
After 2001
Acquirer top legal adviser
Target top legal adviser
Acquirer legal adviser ranking
Target legal adviser ranking
0.213
0.379
0.148
0.285
0.235
0.287
0.266
0.247
2.269
2.343
0.004
0.214
0.234
0.257
0.854
0.849
1
2
3
3
3
3
0.409
0.956
0.190
47
0.049 0.188
0.001
-0.014
p75
0.021 0.102
-0.003
-0.374
0.213
0.262
0.233
Median
0.154
0.302
0.021
0.306
Table 4. Multivariate analysis of announcement returns
Acquirer CAR is the dependent variable in column 1, Target CAR in column 2, Combined gain in column
3, and Target relative gain in column 4. Abandonment option scope FS is the main independent variable
in Panel A while the indicator variable Broad abandonment option FS is the main independent variable in
Panel B. All models include intercepts. All variables are defined prior tables or in Appendix B. All
control variables are winsorized at the 0.01 and 0.99 percentiles. All regressions use our entire sample of
751 observations but missing data reduce the usable number of observations. p-values based on
heteroskedasticity-robust standard errors clustered by acquirer industry are in parentheses. ***, **, and *
denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Panel A: Abandonment option scope FS
Dependent variable
Abandonment option scope FS
Diversifying deal
Stdev target net income
Stdev target stock return
Target ROA
Target CAR prior year
Reverse termination fee
Collar
Log target market capitalization
Target/ acquirer market cap
Target cash/ assets
Target in technology industry
Target in regulated industry
Tender offer
Percent stock
Volatility index
After 2001
Intercept
Adjusted R2
Number of observations
(1)
Acquirer
CAR
1.573***
(0.000)
-0.542
(0.826)
4.188***
(0.004)
21.865***
(0.005)
0.981**
(0.041)
6.474***
(0.000)
1.989
(0.217)
-1.729
(0.548)
-0.190
(0.810)
-1.444
(0.727)
2.782
(0.462)
1.819
(0.326)
0.781
(0.837)
-6.136
(0.284)
0.020
(0.787)
0.112
(0.749)
0.712
(0.833)
Yes
0.045
608
48
(2)
Target CAR
2.429***
(0.000)
1.861
(0.408)
7.227***
(0.000)
144.500***
(0.000)
1.540***
(0.001)
9.044***
(0.000)
3.426
(0.230)
1.538
(0.621)
-0.169
(0.775)
-5.064*
(0.062)
-8.021
(0.389)
-3.486
(0.449)
5.268
(0.218)
-2.581
(0.300)
-0.061**
(0.031)
0.443
(0.314)
-0.343
(0.895)
Yes
0.399
627
(3)
Combined
gain
1.073***
(0.000)
-0.927
(0.549)
2.187***
(0.002)
21.300***
(0.000)
0.467**
(0.044)
3.639***
(0.000)
2.058*
(0.060)
-0.585
(0.728)
0.135
(0.716)
4.683*
(0.065)
-2.115
(0.117)
0.224
(0.790)
-0.029
(0.988)
-3.925
(0.132)
-0.005
(0.899)
0.110
(0.579)
-0.159
(0.922)
Yes
0.089
608
(4)
Target
relative gain
-0.141
(0.598)
-0.737
(0.310)
-1.327*
(0.074)
1.558
(0.778)
-0.413**
(0.016)
-2.063***
(0.001)
0.608
(0.525)
0.719
(0.339)
0.244
(0.593)
4.970**
(0.015)
-4.307**
(0.040)
-1.642*
(0.061)
-0.922
(0.580)
1.559
(0.586)
-0.025
(0.394)
0.011
(0.936)
-1.020
(0.502)
Yes
0.062
608
Panel B: Broad abandonment option FS
(1)
(2)
(3)
(4)
Acquirer
CAR
Target
CAR
Combined
gain
Target
relative
gain
4.382***
(0.003)
6.552***
(0.004)
2.962***
(0.000)
-0.465
(0.671)
-0.711
(0.765)
1.684
(0.440)
-1.040
(0.486)
-0.715
(0.295)
Stdev target net income
4.251***
(0.006)
7.382***
(0.000)
2.232***
(0.002)
-1.328*
(0.076)
Stdev target stock return
21.835***
(0.005)
144.109***
(0.000)
21.276***
(0.000)
1.552
(0.779)
Target ROA
0.991**
(0.045)
1.556***
(0.000)
0.474**
(0.049)
-0.413**
(0.016)
Target CAR prior year
6.634***
(0.000)
9.325***
(0.000)
3.749***
(0.000)
-2.077***
(0.001)
Reverse termination fee
1.998
(0.195)
3.397
(0.264)
2.063*
(0.060)
0.605
(0.524)
Collar
-1.599
(0.584)
1.756
(0.572)
-0.497
(0.770)
0.707
(0.348)
Log target market capitalization
-0.216
(0.778)
-0.211
(0.733)
0.116
(0.750)
0.245
(0.590)
Target/ acquirer market cap
-1.362
(0.740)
-4.916*
(0.067)
4.741*
(0.062)
4.968**
(0.015)
Target cash/ assets
2.203
(0.532)
-9.150
(0.308)
-2.517**
(0.043)
-4.275**
(0.035)
Target in technology industry
1.628
(0.392)
-3.720
(0.402)
0.094
(0.436)
-1.624
(0.195)
Target in regulated industry
1.117
(0.773)
5.825
(0.195)
0.196
(0.920)
-0.961
(0.573)
Tender offer
-5.869
(0.304)
-2.134
(0.394)
-3.742
(0.148)
1.537
(0.591)
Percent stock
0.021
(0.782)
-0.062**
(0.032)
-0.005
(0.904)
-0.025
(0.389)
Volatility index
0.107
(0.754)
0.428
(0.319)
0.107
(0.582)
0.011
(0.935)
After 2001
1.311
(0.696)
-7.753
0.486
(0.861)
-6.368
0.247
(0.880)
-5.79
-1.081
(0.452)
1.861
0.044
608
0.397
627
0.087
608
0.062
608
Dependent variable
Broad abandonment option FS
Diversifying deal
Intercept
Adjusted R2
Number of observations
49
Table 5. Alternative estimates of Acquirer CAR and Target CAR
This table presents ordinary least squares estimations for alternative estimates of Acquirer CAR in Panel
A and Target CAR in Panel B. In columns 1 and 2, we adjust Acquirer and Target CAR with the FamaFrench three-factor model and vary the estimation window between 60 days (column 1) and 30 days
(column 2) before to 120 days after the announcement date. Column 3 has a short window from two days
before to two days after the announcement date and uses the return of the CRSP equal-weighted index as
the market benchmark. We use the same control variables as in Table 4. All estimations have intercepts.
All variables are defined in prior tables or in Appendix B. All control variables are winsorized at the 0.01
and 0.99 percentiles. All columns use our entire sample of 751 observations but missing data reduces the
usable number of observations. p-values based on heteroskedasticity-robust standard errors clustered by
acquirer industry are in parentheses. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels,
respectively.
Panel A: Alternative estimates of Acquirer CAR
Fama-French
Acquirer CAR adjustment
[-60, +120]
(1)
Abandonment option scope FS
2.110***
(0.002)
Control variables as in Table 4
Yes
2
Adjusted R
0.024
Number of observations
604
Fama-French
[-30, +120]
(2)
1.735***
(0.007)
Yes
0.030
604
Market
[-2, +2]
(3)
0.239
(0.295)
Yes
0.122
626
Fama-French
[-30, +120]
(2)
2.102***
(0.000)
Yes
0.393
627
Market
[-2, +2]
(3)
0.334
(0.489)
Yes
0.355
627
Panel B: Alternative estimates of Target CAR
Target CAR adjustment
Abandonment option scope FS
Control variables as in Table 4
Adjusted R2
Number of observations
Fama-French
[-60, +120]
(1)
2.615***
(0.000)
Yes
0.387
627
50
Table 6. Effect of public disclosure on short-term announcement returns
Panel A presents univariate analyses and Panel B ordinary least squares models for subsamples based on whether
the scope of the firm-specific abandonment option, i.e., the MAC clause and its exclusions, is publicly disclosed
within three days after the announcement date. The last column in Panel A shows the difference between the
broad and limited firm-specific abandonment option subsamples. Significance levels in the “All”, “Broad” and
“Limited” columns refer to t-tests on differences of the means from zero. In the last column, they are based on ttests for the differences in means. In Panel B, the dependent variables are Acquirer ST CAR (columns 1 and 2),
Target ST CAR (columns 3 and 4), Combined ST gain (columns 5 and 6), and Target relative ST gain (columns 7
and 8). “ST” denotes short-term announcement returns estimated from three days before to three days after the
announcement date. We use the return of the CRSP equal-weighted index as the market benchmark. We use the
same control variables as in Table 4. All estimations have intercepts. All variables are defined in prior tables or
in Appendix B. All control variables are winsorized at the 0.01 and 0.99 percentiles. The last row in panel B
reports the p-value and statistical significance based on a t-test of the difference in the coefficients between the
subsamples with and without public disclosure of the MAC clause. The table uses our entire sample of 751
observations. p-values based on heteroskedasticity-robust standard errors clustered by acquirer industry are in
parentheses. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Panel A. Univariate analysis after splitting by public disclosure
Abandonment option scope FS
All
Broad Limited Broad – Limited
Public disclosure?
[N=728] [N=302]]
[N=426]
Yes [N=295]
No [N=433]
-2.33
-1.61
-2.82
1.22 *
-1.66
-1.18
-2.01
0.82
Target ST CAR
Yes
No
24.43
22.86
25.02
24.17
24.01
21.92
1.01
2.26
Combined ST gain
Yes
No
1.39
1.05
2.24
1.63
0.80
0.63
1.44 **
1.00 **
Target relative ST gain
Yes
No
3.46
2.57
3.77
2.70
3.24
2.49
0.53
0.21
Acquirer ST CAR
Panel B. Multivariate analysis after splitting by public disclosure
Acquirer
Target
Dependent variable
ST CAR
ST CAR
Public disclosure of MAC
Yes
No
Yes
No
(1)
(2)
(3)
(4)
Abandonment option scope FS 0.326** 0.155
1.201* 0.006
(0.011) (0.670)
(0.083) (0.296)
Control variables as in Table 4
Adjusted R2
Number of observations
p-value and significance of
difference Yes-No
Yes
0.36
263
Yes
0.075
363
Yes
0.533
263
(0.089) *
Yes
0.242
364
(0.038) **
51
Combined
ST gain
Yes
No
(5)
(6)
0.561*** 0.106
(0.007) (0.566)
Yes
0.236
263
Yes
0.143
363
(0.036) **
Target relative
ST gain
Yes
No
(7)
(8)
0.350** 0.026
(0.037) (0.893)
Yes
0.307
263
Yes
0.12
363
(0.025) **
Table 7. Determinants of firm-specific abandonment option scope, univariate analysis
Panel A shows average values of Abandonment option scope FS for the entire sample and for doublesorted subsamples. The first column shows the averages for all observations while the next two columns
show them for subsamples based on Target ROA being above or below its median. The last column shows
the difference between the above and below median Target ROA subsamples. The second and third rows
show two subsamples based on whether the acquirer and target are in different industries (Diversifying
deal). Panel B repeats the analysis of Panel A but examines the Broad abandonment option FS. Panels C
and D repeat the analysis of Panel B but now splitting the sample based on whether Stdev target net
income and Stdev target stock return are above or below their medians. All variables are defined in
Appendix B. The table uses our entire sample of 751 observations. ***, **, and * denote significance at
the 0.01, 0.05, and 0.10 levels, respectively. Significance levels are based on t-tests for the differences in
means.
Panel A: Abandonment option scope FS
Target ROA
All
Diversifying deal? Yes
Diversifying deal? No
Yes - No
All
Above median
9.87
9.89
9.86
0.031
9.96
10.15
9.90
0.251
Below Above median below
9.77
0.20 *
9.50
0.65 **
9.82
0.07
-0.324
0.662
Panel B: Proportion of acquisitions with Broad abandonment option FS as a function of diversifying deal
Target ROA
All
Diversifying deal? Yes
Diversifying deal? No
Yes - No
All
Above median
0.41
0.42
0.41
0.012
0.45
0.48
0.44
0.038
Below Above median below
0.37
0.09 **
0.33
0.16 *
0.38
0.07
-0.050
0.216
Panel C: Proportion of acquisitions with Broad abandonment option FS as a function of the standard
deviation of target’s net income
Target ROA
Above median
All
Stdev target net income: Above median
Stdev target net income: Below median
Above - Below
Below Above median below
0.45
0.42
0.49
0.37
0.33
0.41
-0.068
-0.080
0.08 *
0.09 **
0.08
Panel D: Proportion of acquisitions with Broad abandonment option FS as a function of the standard
deviation of target’s monthly abnormal return
Target ROA
Above median
All
Stdev target stock return: Above median
Stdev target stock return: Below median
Above - Below
0.46
0.48
0.43
0.056
52
Below Above median below
0.37
0.40
0.34
0.060
0.09 **
0.09 *
0.09 *
Table 8. Determinants of abandonment option scope, multivariate analysis
Abandonment option scope FS is the dependent variable in columns 1, 2, 3, and 4, and Broad
abandonment option FS is the dependent variable in column 5. Columns 1, 2, 3, and 4 use tobit estimations
while column 5 uses logit. All estimations have intercepts. All variables are defined in prior tables or in
Appendix B. All control variables are winsorized at the 0.01 and 0.99 percentiles. All columns use our
entire sample of 751 observations but missing data reduce the usable number of observations. p-values
based on heteroskedasticity-robust standard errors clustered by acquirer industry are in parentheses. ***,
**, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Dependent variable
Estimation
Diversifying deal
Abandonment option scope FS
Tobit
(1)
0.109**
(0.010)
Stdev target net income
Tobit
(2)
0.265*
(0.067)
Stdev target stock return
Target ROA
Target CAR prior year
Reverse termination fee
Collar
Log target market capitalization
Target/ acquirer market cap
Target cash/ assets
Target in technology industry
Target in regulated industry
Tender offer
Percent stock
Volatility index
After 2001
Acquirer top legal adviser
Target top legal adviser
Correctly classified
Maximum likelihood R2
Number of observations
Tobit
(3)
0.010*
(0.085)
0.141*
(0.072)
-0.109
(0.308)
0.109
(0.532)
-0.074*
(0.088)
0.298**
(0.020)
-1.116***
(0.005)
-0.002
(0.993)
-0.079*
(0.060)
0.212
(0.297)
-0.001**
(0.011)
-0.012**
(0.011)
0.117
(0.352)
0.241**
(0.049)
-0.039
(0.854)
0.018*
(0.086)
0.142*
(0.073)
-0.144
(0.152)
0.109
(0.535)
-0.080**
(0.045)
0.303**
(0.020)
-1.260***
(0.000)
-0.030
(0.894)
-0.050*
(0.056)
0.222
(0.274)
-0.001**
(0.013)
-0.014**
(0.025)
0.093
(0.466)
0.256**
(0.042)
-0.039
(0.852)
-0.544
(0.179)
0.008*
(0.094)
0.123*
(0.091)
-0.105
(0.320)
0.101
(0.556)
-0.092**
(0.040)
0.284**
(0.024)
-1.122***
(0.003)
0.015
(0.949)
-0.104*
(0.058)
0.227
(0.265)
-0.001**
(0.013)
-0.011**
(0.030)
0.090
(0.472)
0.248**
(0.050)
-0.023
(0.906)
(0.050)
627
(0.055)
627
(0.053)
627
53
Tobit
(4)
0.142**
(0.043)
0.279*
(0.053)
-0.538
(0.192)
0.015*
(0.084)
0.122*
(0.092)
-0.126
(0.283)
0.093
(0.295)
-0.089***
(0.000)
0.291*
(0.064)
-1.234***
(0.000)
-0.033
(0.882)
-0.067*
(0.073)
0.233
(0.423)
-0.001**
(0.011)
-0.013***
(0.000)
0.056
(0.515)
0.258**
(0.012)
-0.030
(0.854)
(0.060)
627
Broad abandonment
option FS
Logit
(5)
0.356***
(0.000)
0.343*
(0.061)
-0.588
(0.188)
0.012
(0.135)
0.016
(0.884)
-0.198
(0.142)
0.012
(0.955)
-0.099**
(0.049)
0.457*
(0.100)
-1.225***
(0.000)
0.097
(0.412)
-0.499**
(0.042)
0.080
(0.845)
-0.002
(0.524)
-0.011
(0.576)
-0.450*
(0.053)
0.437***
(0.009)
-0.211*
(0.061)
61.94%
(0.057)
627
Table 9. Heckman models
This table presents self-selection models using Heckman (1979) estimations. The dependent variables in
the second stage are Acquirer CAR, Target CAR, Combined gain, and Target relative gain. The dependent
variable in the first stage is Broad abandonment option FS. All estimations have intercepts. All variables
are defined in prior tables or in Appendix B. All control variables are winsorized at the 0.01 and 0.99
percentiles. p-values based on heteroskedasticity-robust standard errors clustered by acquirer industry are
in parentheses. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels.
Dependent variable
Stage 1
Broad
abandonment
option FS
(1)
Abandonment option scope FS
Diversifying deal
Stdev target net income
Stdev target stock return
Target ROA
Target CAR prior year
Reverse termination fee
Collar
Log target market capitalization
Target/ acquirer market cap
Target cash/ assets
Target in technology industry
Target in regulated industry
Tender offer
Percent stock
Volatility index
After 2001
Acquirer top legal adviser
Target top legal adviser
Lambda
p-value (Χ2)
Number of observations
Stage 2
Acquirer
CAR
Target
CAR
Combined
gain
(2)
(3)
(4)
4.792**
(0.022)
-1.787
(0.730)
7.172
(0.179)
21.845
(0.197)
0.908**
(0.045)
9.775***
(0.000)
-2.824
(0.434)
-7.123
(0.420)
-0.415
(0.852)
4.337
(0.583)
-24.421***
(0.009)
8.180
(0.104)
-9.227**
(0.013)
-8.511
(0.208)
0.041
(0.515)
-0.437
(0.207)
-9.509
(0.195)
0.227*
(0.078)
0.190*
(0.082)
-0.333
(0.281)
0.010
(0.159)
0.006
(0.936)
-0.128
(0.309)
-0.016
(0.905)
-0.057*
(0.085)
0.285*
(0.067)
-0.750**
(0.020)
0.068
(0.644)
-0.306**
(0.026)
0.050
(0.773)
-0.001
(0.363)
-0.005
(0.694)
-0.263**
(0.038)
0.273**
(0.014)
-0.140
(0.321)
Yes
(0.005)
634
(0.000)
54
12.898***
3.103**
(0.000)
(0.025)
4.428
-1.223
(0.341)
(0.709)
8.636***
3.187
(0.008)
(0.144)
132.558*** 27.564***
(0.000)
(0.003)
1.775***
0.377*
(0.006)
(0..065)
1.601
5.561***
(0.651)
(0.000)
1.605
0.318
(0.759)
(0.880)
-2.818
-3.227
(0.636)
(0.471)
-0.228
0.258
(0.861)
(0.792)
-5.960**
6.098
(0.022)
(0.143)
-39.755** -18.333***
(0.014)
(0.000)
6.428*
3.918
(0.087)
(0.145)
0.578
-5.042**
(0.915)
(0.013)
-14.160*** -5.357**
(0.000)
(0.044)
-0.078
0.005
(0.125)
(0.899)
0.698
-0.072
(0.238)
(0.706)
-6.136
-5.376
(0.380)
(0.161)
Yes
(0.001)
634
Yes
(0.063)
631
Target
relative
gain
(5)
-0.815
(0.679)
0.599
(0.852)
-2.367
(0.352)
10.368
(0.148)
-0.338
(0.304)
-2.823
(0.118)
3.658
(0.174)
3.490
(0.207)
0.579
(0.461)
1.492
(0.554)
0.334
(0.972)
-4.453
(0.134)
2.239
(0.581)
1.721
(0.614)
-0.041
(0.201)
0.314
(0.281)
3.857
(0.271)
Yes
(0.120)
631
Table 10. Abandonment option scope FS clusters
Panel A reports descriptive statistics for the three main clusters of the firm-specific abandonment options.
The Adverse selection cluster is comprised of the following MAC exclusions: Litigation, Stock price,
Implied disproportionate industry condition, and Implied disproportionate economic condition. The Moral
hazard cluster is comprised of the Loss of customers, suppliers, employees and Failure to meet projections
MAC exclusions. The Other cluster is comprised of the rest of the firm-specific MAC exclusions:
Previously contended or existent MAC, Changes due to agreement or transaction announcement, and
Miscellaneous. We measure each cluster by adding the number of MAC exclusions for each category and
then we adjust by the annual median number of exclusions for such cluster. Similar to our main variable,
we convert the variable to measure the scope of the abandonment option of each cluster. StDev refers to
the standard deviation, Min to the minimum value, P25 to the 0.25 percentile, Max to the maximum value.
Panel B presents ordinary least squares models. The dependent variables are Acquirer CAR, Target CAR,
Combined gain, and Target relative gain. All estimations have intercepts and the same control variables as
in Table 4. All variables are defined in prior tables or in Appendix B. All control variables are winsorized
at the 0.01 and 0.99 percentiles. All columns use our entire sample of 751 observations but missing data
reduce the usable number of observations. p-values based on heteroskedasticity-robust standard errors
clustered by acquirer industry are in parentheses. ***, **, and * denote significance at the 0.01, 0.05, and
0.10 levels, respectively.
Panel A: Description of firm-specific abandonment option scope clusters
Mean
Stdev Min
P25 Median
P75
Max
6
3
4
8
3
5
Abandonment option scope FS clusters
Adverse selection
Moral hazard
Other
5.78
2.81
3.94
0.97
0.44
0.67
3
1
2
Panel B: Ordinary least squares models with announcement returns
Acquirer
Target
Dependent variable
CAR
CAR
(1)
(2)
Adverse selection
1.377*
3.692***
(0.100)
(0.003)
Moral hazard
-3.561**
-7.735***
(0.043)
(0.010)
Other
7.145**
7.443**
(0.019)
(0.016)
Same control variables as in Table 4
Yes
Yes
Adjusted R2
Number of observations
0.043
609
55
0.061
628
5
3
4
6
3
4
Combined
gain
(3)
0.965*
(0.056)
-1.822*
(0.076)
Target relative
gain
(4)
0.315
0.491
1.874**
(0.048)
4.304**
(0.016)
Yes
-1.476*
(0.093)
Yes
0.055
609
0.063
609
Table 11. Regulated industries and market-wide abandonment option scope
Panel A shows the firm-specific and market-wide abandonment option scopes for all observations and
regulated and unregulated target industries. The last column shows the difference between the regulated and
non-regulated industry subsamples. Significance levels in Panel A are based on t-tests for the differences in
means. Panel B and C presents ordinary least squares models on the continuous and binary abandonment
option variables, respectively. The dependent variables are Acquirer CAR, Target CAR, Combined gain, and
Target relative gain. All estimations have intercepts and the same control variables as in Table 4. All control
variables are winsorized at the 0.01 and 0.99 percentiles. p-values based on heteroskedasticity-robust
standard error clustered by acquirer industry are in parentheses. Both panels use our entire sample of 751
observations but missing data reduce the usable number of observations. All variables are defined in prior
tables or in Appendix B. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Panel A: Abandonment option scope and regulated industry, univariate analysis
Regulated industry
All
Yes
No
Yes - no
Abandonment option scope FS
6.84
6.78
6.87
-0.09
Broad abandonment option FS
0.41
0.33
0.47
-0.14 ***
Abandonment option scope MW
7.91
7.51
8.16
-0.66 ***
Broad abandonment option MW
0.41
0.27
0.49
-0.22 ***
56
Panel B: Economic impact of market-wide abandonment option scope
(1)
(2)
Acquirer
Target
Dependent variable
CAR
CAR
Abandonment option scope FS
1.280***
2.889***
(0.000)
(0.000)
Abandonment option scope MW
0.602
-0.960
(0.554)
(0.341)
Diversifying deal
-0.595
1.931
(0.808)
(0.403)
Stdev target stock return
4.097***
7.390***
(0.002)
(0.000)
Stdev target net income
21.465***
145.172***
(0.009)
(0.000)
Target ROA
0.982**
1.537***
(0.044)
(0.001)
Target CAR prior year
6.505***
8.997***
(0.000)
(0.000)
Reverse termination fee
1.992
3.410
(0.214)
(0.236)
Collar
-1.781
1.626
(0.531)
(0.590)
Log target market capitalization
-0.084
-0.340
(0.920)
(0.574)
Target cash/ assets
3.075
-8.501
(0.444)
(0.341)
Target/ acquirer market cap
-1.525
-4.924*
(0.714)
(0.066)
Target in technology industry
1.771
-3.422
(0.345)
(0.450)
Target in regulated industry
0.929
4.991
(0.815)
(0.225)
Tender offer
-6.348
-2.245
(0.261)
(0.361)
Percent stock
0.018
-0.057**
(0.811)
(0.020)
Volatility index
0.116
0.436
(0.740)
(0.319)
After 2001
0.852
-0.542
(0.798)
(0.839)
Intercept
Yes
Yes
Adjusted R2
0.044
0.399
Number of observations
608
627
57
(3)
Combined
gain
0.990***
(0.003)
0.170
(0.747)
-0.942
(0.542)
2.162***
(0.001)
21.187***
(0.000)
0.468**
(0.045)
3.648***
(0.000)
2.059*
(0.060)
-0.600
(0.719)
0.165
(0.687)
-2.032
(0.167)
4.660*
(0.068)
0.211
(0.806)
0.012
(0.995)
-3.984
(0.123)
-0.005
(0.890)
0.111
(0.572)
-0.119
(0.941)
Yes
0.088
608
(4)
Target
relative gain
0.067
(0.823)
-0.428
(0.196)
-0.699
(0.328)
-1.263
(0.154)
1.843
(0.743)
-0.414**
(0.018)
-2.085***
(0.001)
0.606
(0.516)
0.756
(0.309)
0.169
(0.706)
-4.515**
(0.036)
5.027**
(0.012)
-1.608*
(0.072)
-1.027
(0.555)
1.709
(0.545)
-0.024
(0.427)
0.008
(0.952)
-1.119
(0.450)
Yes
0.062
608
Panel C: Economic impact of broad market-wide abandonment option
(1)
(2)
Acquirer
Target
Dependent variable
CAR
CAR
(3)
Combined
gain
(4)
Target relative
gain
Broad abandonment option FS
3.556**
(0.011)
6.240**
(0.047)
2.797***
(0.003)
0.222
(0.874)
Broad abandonment option MW
1.078
(0.749)
Yes
0.026
609
(3.477)
(0.314)
Yes
0.047
628
0.003
(0.999)
Yes
0.034
609
(1.194)
(0.304)
Yes
0.059
609
Controls and intercept as in panel B
Adjusted R2
Number of observations
58
I. Pre-Announcement (Private) Takeover Process
Merger Agreement negotiation
(Contractual mechanisms: MACs?)
Lawyers
start merger
agreement
negotiation
First draft of
merger
agreement
Lawyers
finish
merger
agreement
negotiation
Announcement
Date
First idea
of the
merger
First talk of
this specific
deal
Approval by Board of Directors
and (maybe some days later)
official signing of the merger
agreement
II. Post-Announcement (Public)Takeover Process
Re-negotiation?
Announcement
Date
Completion
Date
Termination?
Material Adverse Event?
Figure 1: Timeline of the takeover process
The announcement date divides the takeover process in the pre-announcement and the post-announcement takeover processes. Acquirers and
targets report specific information about the pre-announcement takeover process in SEC filings after the announcement date.
59
Abandonment option scope
Broad
Number of MAC exclusions
Few
Many
Signal of better target type?
Yes
No
Target’s chance of MAC litigation success
Low
High
Target’s MAC litigation cost
High
Low
Limited
Figure 2: Effects of abandonment option scope according to signaling hypothesis
This figure summarizes the relations between abandonment option scope, number of MAC exclusions, the target’s chances of winning MACrelated litigation, and the expected target’s cost of such litigation.
60
Figure 3: Expected value of the target and the role of contract provisions
The figure shows the distribution of the expected value of the target and how various contract provisions address different risks. The distribution is
estimated at the moment of the acquisition announcement. The expected value of the target includes expected synergies. The figure depicts two
potential scenarios that could arise between the announcement date and the completion of the acquisition. First, in case of a Material-AdverseChange occurs before completion, the acquirer can decide to renegotiate or terminate the acquisition if the expected value of the target falls below
a material threshold, VT-. Second, in case the value of the firm increases above a material threshold, VT+, or a third-party thinks the higher value
can be obtained, the acquisition can be challenged and the required price to complete the transaction could increase. Before signing the merger
agreement, firms can negotiate contract provisions that allocate the interim risks to provide the desired completion certainty or flexibility after the
announcement of the acquisition.
61