Do financial advisors provide tangible benefits to acquirers in earnout-settled M&As? * Leonidas G. Barbopoulos † Anthony Saunders ‡ January, 2019 Abstract We show that financial advisors present a unique channel of value creation in earnout-settled Mergers and Acquisitions (M&As) due to their valuable input in structuring and facilitating earnout agreements more accurately. This helps merging firms to maximize the merger payoff and the acquirer to share the merger valuation-risk with the more informed target. The outcome of this relation is reflected in the higher acquirer risk-adjusted returns (i.e. gains) for earnout than non-earnout settled M&As in which financial advisors are in either or both sides of the deal. A quasi-experimental design based on which the impact of earnout or advisor, independently or jointly, is evaluated in isolation, confirms that advised earnout-settled M&As yield the highest acquirer gains relative to counterfactual deals. Such outcomes are further confirmed based on a model embracing the Inverse Mills Ratio on full or matched samples. Overall, we argue that our results support an earnout-structure skilled-advise hypothesis which is vital for the success of earnout-settled M&As. Keywords: Earnouts; Financial advisors; Merger valuation risk; Risk-adjusted returns; Heck- man two-stage procedure; Propensity Score Matching (PSM); Inverse Mills Ratio (IMR). JEL Classification Numbers: G12; G13; G14; G34. * We are grateful to comments and suggestions offered from Ahmed Elnahas, Christophe Godlewski, William Procasky, Vadym Volosovych, and other participants of the 2013 (11th ) INFINITI Conference, Aix-en-Provence, France, the 2014 (41st ) Southwestern Finance Association (SWFA) Conference, Dallas (TX), USA, and the 2017 (15th ) INFINITI Conference, Valencia, Spain. We are also grateful to comments and suggestions offered from James Ang, Martin Brown, Louis T.W. Cheng, Gjergji Cici, Theodoros M. Diasakos, Douglas Foster, Ulrich Geilinger, Andrew Marshall, Daniel Quint, Raghaven- dra Rau, Luca Savorelli, Sudi Sudarsanam, Nickolaos G. Travlos, Lenos Trigeorgis, and Josef Zechner. All remaining errors remain our own. † Adam Smith Business School, University of Glasgow, Glasgow, G12 8QQ. Tel: +44(0)141 330 7229. Email: [email protected]. ‡ Department of Finance, Kaufman Management Center, Stern School of Business, New York University, New York, N.Y. 10012. Tel: +1(0)212 998 0711. Email: [email protected].
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Do financial advisors provide tangible benefits toacquirers in earnout-settled M&As?∗
Leonidas G. Barbopoulos† Anthony Saunders‡
January, 2019
Abstract
We show that financial advisors present a unique channel of value creation in earnout-settledMergers and Acquisitions (M&As) due to their valuable input in structuring and facilitatingearnout agreements more accurately. This helps merging firms to maximize the merger payoffand the acquirer to share the merger valuation-risk with the more informed target. The outcomeof this relation is reflected in the higher acquirer risk-adjusted returns (i.e. gains) for earnoutthan non-earnout settled M&As in which financial advisors are in either or both sides of the deal.A quasi-experimental design based on which the impact of earnout or advisor, independently orjointly, is evaluated in isolation, confirms that advised earnout-settled M&As yield the highestacquirer gains relative to counterfactual deals. Such outcomes are further confirmed based on amodel embracing the Inverse Mills Ratio on full or matched samples. Overall, we argue that ourresults support an earnout-structure skilled-advise hypothesis which is vital for the success ofearnout-settled M&As.
∗We are grateful to comments and suggestions offered from Ahmed Elnahas, Christophe Godlewski, William Procasky,Vadym Volosovych, and other participants of the 2013 (11th ) INFINITI Conference, Aix-en-Provence, France, the 2014(41st ) Southwestern Finance Association (SWFA) Conference, Dallas (TX), USA, and the 2017 (15th ) INFINITI Conference,Valencia, Spain. We are also grateful to comments and suggestions offered from James Ang, Martin Brown, Louis T.W.Cheng, Gjergji Cici, Theodoros M. Diasakos, Douglas Foster, Ulrich Geilinger, Andrew Marshall, Daniel Quint, Raghaven-dra Rau, Luca Savorelli, Sudi Sudarsanam, Nickolaos G. Travlos, Lenos Trigeorgis, and Josef Zechner. All remainingerrors remain our own.†Adam Smith Business School, University of Glasgow, Glasgow, G12 8QQ. Tel: +44(0)141 330 7229. Email:
[email protected].‡Department of Finance, Kaufman Management Center, Stern School of Business, New York University, New York, N.Y.
The earnout provision (also known as contingent consideration or contingent payment mecha-
nism) in Mergers and Acquisitions (henceforth M&As) has gained significant popularity during
the most recent decades. Among the most earnout-active markets for corporate control, the U.K.
one has maintained its leading position in terms of both absolute and relative earnout-activity.
To provide just a glimpse of the data, more than one-in-four (all years average), and recently
more than one-in-three M&As involving U.K. domiciled acquirers are settled in earnouts.1 In an
earnout-settled M&A the contingent consideration is deliverable to the target firm’s owners via
a multi-stage contingent payment structure: an up-front payment in the form of cash, stock, or
mixture of cash and stock, and one or more than one future payment(s), commonly referred to as
earnouts, often in the form of cash, the delivery of which depends on the target firm achieving
pre-agreed performance-related goals within pre-specified periods (Barbopoulos, Paudyal, and
Sudarsanam (2018b)).
A great deal of effort has recently gone into the study earnouts in an attempt to explain both
the determinants of their involvement in merger negotiations (often with substantial information
asymmetry) and their valuation effects on acquirer risk-adjusted returns (Kohers and Ang (2000),
Barbopoulos and Sudarsanam (2012), Barbopoulos, Danbolt, and Alexakis (2018a) and Bates,
Neyland, and Wang (2018)). Extant studies suggest that in general such mergers are adding higher
value to acquirers (i.e. the earnout-effect) relative to deals that are settled in conventional single
up-front payments in cash, stock or mixed. However, relatively little attention has been given
to the important role of the earnout structure and in particular, to the conditions under which
the earnout-effect persists.2 This phenomenon, we believe to be at least as important as several
others that have received noticeable attention in the M&A literature when it comes to explaining
the choice of the M&A payment method and its valuation effects on the acquirer risk-adjusted
returns (see for example Travlos (1987), Martin (1996), Faccio and Masulis (2005) and Eckbo,
Makaew, and Thorburn (2016)). In this paper we seek to fill this gap by estimating a model of
earnout contracts between firms which involve outside advisors that help them structuring and
facilitating earnouts more accurately.1The U.K. market for corporate control offers an excellent laboratory to gain insights into the workings of earnouts
given that 26% of all M&As announced by U.K. domiciled acquirers are settled in earnouts.2In a Middle Market Review report published in 2016 by Axial, which is titled as ‘Should You Take an Earnout?’, Kenneth
Sanginario, Founder of Corporate Value Metrics, claims that ‘When properly structured, earnouts can work – and whenthey work, they work really well...’ Kenneth also added in the same report ‘But earnouts can also turn nightmarish inthe case of misaligned expectations, unfriendly terms, and hidden stipulations...’ (https://www.axial.net/forum/should-you-take-an-earnout/).
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We were led to this problem by current interest in the question of what is the fundamental role of
the earnout payment mechanism in merger negotiations with substantial asymmetric information?
In general, earnout contracts present significant heterogeneity in their structures, i.e. the fraction
of deferred payment relative to the total transaction value, the length of the period(s) between the
multi-payments, the choice of performance metrics (Cain, Denis, and Denis (2011)). While these
terms are endogenously determined and are likely to reflect trade-offs among one another, and
possibly with other terms of the merger, they may significantly affect merger outcomes as they
affect incentives. Moreover, our feasibility of writing earnout agreements presents a necessary
condition to observing them in practice (Kohers and Ang (2000)). A direct implication of these
empirical facts suggests that the outcomes of earnout-settled M&As should be correlated with
a technical expertise in structuring and facilitating them accurately and hence, an acquiring-
firm fixed effect (that accounts for unobservable firm-specific trials) should be able to explain
a large fraction of the variation of acquirer risk-adjusted returns within earnout-settled M&As
(see Golubov, Yawson, and Zhang (2015) for a similar interpretation). However, as established
in earlier research (and also detailed in the descriptive statistics of our sample), acquirers in
earnout-settled M&As are relatively small firms with their CEOs or top-management teams being
unlikely to making M&A decisions regularly. This, in turn, in addition to limiting their acquisition
experience it narrows their access to the appropriate technology and expertise in structuring and
executing earnouts adequately. As a result, they typically seek counseling from investment banks
or external advisory firms who help them to facilitate earnouts adequately and hence, signal the
higher merger valuation effects that appear to be derived from the input of earnout in enhancing
the merger success and endorsed by external advisors (see Bowers and Miller (1990), Servaes and
Zenner (1996) and Bao and Edmans (2011)).3
The additional value creation that may arise from external advisors is due to their ability to
assist the merging firms to evaluate the deal from economic, strategic and financial perspectives,
in addition to recommend the financing method, and negotiate the terms of the transaction and
the offer price, while in earnout-settled M&As, to properly accommodate the complexities involved
in structuring and facilitating earnouts more accurately. As a result, they are expected, ceteris
paribus, to improve the structure and drafting of earnout agreements and hence, the combination
of earnouts and advisors in difficult merger negotiations to lead to stronger predictions about the
merger payoff (i.e. stimulate higher future performance or merger synergies) relative to similar3As portrayed by earlier studies that the efficient design of earnout presents a major determinant of the earnout-settled
M&A success, we focus solely on financial advisors who participate in the financing and valuation process of the deal,rather than legal advisors who ensure its regulatory/legal compliance (Krishnan and Masulis (2013)).
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mergers in which earnouts are employed without advisors.4 Concretely, then, rationality suggests
that advice-seeking firms (e.g. newer, smaller, involved in industry diversifying deals, merge with
targets operating in highly intangible rich sectors, merge with high information asymmetry) that
are engaged in earnout-settled M&As to extract economic rents from the costly financial advice, for
which they are willing to pay. Despite the list of hypothesized benefits, existing research generally
fails to show such a relation within earnout-settled M&As and hence, the underlying mechanism
allocating any tangible benefits to merging firms via the channel of costly financial advise awaits
to be identified. Put forward, we set out to quantify how much of the observed variation in acquirer
risk-adjusted returns from engaging in earnout-settled M&As comes from costly financial advise.
We document that U.K. domiciled acquirers receive tangible benefits (i.e. higher acquirer gains)
from external counseling that arises in the form of earnout-structure related advice, provided by
(independent) advisors counseling either or both firms in the merger. Our analysis based on 8,909
M&As announced between 1986 and 2016 (inclusive) by U.K. based acquirers of both U.K. and
non-U.K. domiciled target firms. We employ standard event-study methodology to measure the
impact of each merger announcement, in which earnout and advisor is used, on the acquiring
firm’s risk-adjusted returns and we addresses selection-bias concerns with regard to the endo-
geneity of the decision to employ earnout and/or advisors in the deal based on the Propensity
Score Matching (PSM) and the Inverse Mills Ratio (IMR). Our results are as follows.
First, the analysis of a hand-collected dataset highlighting many and distinctive features of 917
(after screening over 1,400) earnout agreements from 2007 to 2016 (inclusive), shows that advised
earnout-settled M&As, relative to non-advised counterparts, are associated with: (a) significantly
more (less) cash (stock) in both the initial and deferred stage payments, (b) significantly more
contingencies linked to the target firm’s EBITDA and also the target firm’s future profitability
(when only acquirer advisor is involved), (c) significantly less contingencies linked to the target
firm’s PBT, (d) larger earnout sizes, (e) significant lower relative earnout size (ratio of earnout value
to total deal consideration), and (f) fewer earnout payments. These earnout-contract features, in
conjunction with evidence uncovering the significantly higher success rates (i.e. full delivery
of (all) earnout payments) of earnout-settled M&As in the presence of advisors, as opposite to
partial or failed earnout-settled M&As in the absence of advisors, suggest that external advisors
can help firms negotiate favorable and achievable earnout terms, which ultimately help firms4The financial press also portrays advisors as able to ‘assess relative intangibles such as corporate culture, management
retention, technological compatibilities and the likelihood that potential synergies can be realized’ (WSJ, 1997, ‘After-Merger Advice Busies The Consultants’. Source: Factiva). More recently, a ‘tilt towards seeking advice from specialistM&A advisors’ is identified as ‘advice and human capital have become a more wanted quantity’ (FT, 2014, ‘Small provesbeautiful at boutique banks’. Source: Factiva).
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to successfully complete high-yield deals. Our subsequent analysis further confirms that these
features are correlated with significant valuation effects for the acquiring firm, which implicitly
suggests the important role of financial advisors in negotiating favorable (earnout) specs for the
merging firms and hence a complementarity effect between earnouts and external advisors.
Second, our preliminary univariate and baseline multivariate regression results suggest that
acquirers enjoy higher risk-adjusted returns from earnout- than non-earnout-settled M&As only
when external advisors are involved in either (i.e. acquirer or target), or both (i.e. both ac-
quirer and target), sides of the merger. We include in the model a set of fixed effects, such as
(d) target-industry fixed effects, and (e) year fixed effects. Interestingly, we find that while both the
acquiring-firm and year fixed effects explain a significant fraction of the variation of acquirer risk-
adjusted returns across all mergers (as in Golubov et al. (2015)), in earnout-settled mergers alone,
the acquirer fixed effects do not add to the explanatory power of the model. However, acquirer-
advisor fixed effects explain a significant fraction of the variation in the acquirer risk-adjusted
returns in earnout-settled M&As as in Bao and Edmans (2011). We analyze the determinants
of such (i.e. acquirer-advisor specific) valuation effects by presenting interesting empirical facts
about the working of earnouts. Importantly, our results show that the aforementioned tangible
benefits to acquirers are accrued from valuable earnout-structure advise that is conditioning the
earnout payment(s) to the target firm’s future profitability, EBITDA, motivate more cash in the
initial and deferred-stage payment(s), and keep the overall deferred payment relative to the total
consideration at low levels, which is ultimately linked to higher earnout-settled merger success.
In particular, we find that earnout-settled M&As that have more (less) stock (cash) in the initial
and deferred stage payments, more profit before tax as the performance metric, successful deliv-
ery of the full earnout consideration, and smaller earnout size and also small ratio of earnout size
to total deal size, are associated with higher acquirer gains. This reflects the ability of advisors
to deliver tangible benefits to merging firms by helping them to structure the earnout contract
more efficiently, following their advise towards identifying higher synergies and negotiate favor-
able earnout contract terms, which offers great support to the skilled-advice hypothesis of Bao
and Edmans (2011). We further show that our results are more pronounced in deals in which
acquirers face distributions that can cause large and harder-to-measure merger valuation risk.
An important consideration that emerges when interpreting our results is the issue of self-
selection regarding (a) the merging firms’ endogenous choice of earnout (or not), and (b) the ex-
ternal advisor’s endogenous choice to be involved in a deal (or not) who is subsequently choosing
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the earnout (or not).5 While the issue of self-selection is well recognized in both the earnout (see
Barbopoulos et al. (2018a)) and the financial advisors literatures (see Allen, Jagtiani, Peristiani,
and Saunders (2004)), the self-selection issue is far more complicated in advised earnout-settled
M&As. In particular, we are not only dealing with a firm’s choice to use earnout (or not), or to
hire an external advisor (or not), but also, in addition to the choice of using earnout or hiring
external financial advisors, with the decision to hire external advisors or to use earnout, respec-
tively. We therefore take into account the self-selection concerns and examine the robustness of
our results by employing two (well-established) methods: first, we rely on a quasi-experimental
design through which the impact of each treatment, either individually (i.e. earnout and advisor)
or jointly (i.e. advisor-earnout-effect) is evaluated in isolation via the Propensity Score Matching
(PSM) method (Dehejia and Wahba (2002)), which is accompanied with the Rosenbaum-bounds
(RB) sensitivity analysis that aims to quantify the sensitivity of the treatment-effect to hidden-
or omitted variable-bias. Second, guided by results obtained from the RB analysis, we rely on
the Heckman two-stage procedure via which the inclusion of the Inverse Mills Ratio (IMR) in the
second stage, applied in the matched sample, accounts for potential hidden- or omitted variable-
bias (please refer to Section (3.2.2) for a more detailed discussion of this approach). Therefore,
the combination of PSM (including the RB) and Heckman two-stage methods are more likely to
lead to a least-biased estimator of each treatment’s effect.
Our results, after addressing self-selection issues, remain robust in favor of the hypothesis
that external advisors contribute significantly to the structuring and facilitation of earnout agree-
ments, and hence, to the acquiring firm value. We show that acquirers enjoy significantly higher
risk-adjusted returns from earnouts only when (a) acquirers or targets, independently, hire exter-
nal financial advisors, and (b) both acquirers and target use external financial advisors simulta-
neously (where the highest risk-adjusted returns of acquirers are captured). Moreover, acquirers
enjoy significantly higher abnormal returns from earnout-settled M&As only when advisors are
involved in counseling the acquirer (i.e., advised-earnout-settled M&As), relative to (a) M&As set-
tled in earnouts without the presence of financial advisors or, (b) M&As settled in non-earnouts
irrespective of the presence of advisors. We, once again, argue that the documented benefits
arise from valuable earnout-structure advice that helps acquirers secure the anticipated merger
payoff and share the merger valuation-risk with the more informed target. Our results are in5As discussed by earlier studies and also detailed in our descriptive statistics (Section 4.2), (a) advised deals relative
to non-advised ones, and (b) earnout-settled M&As (in which financial advisors may appear in either or both sides ofthe transaction) relative to non-earnout M&As (despite the presence of financial advisors in the deal), are significantlydifferent in terms of several deal- firm-specific characteristics.
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support of both the ‘Better Merger’ and the ‘Skilled-Advice’ hypotheses developed by Bowers and
Miller (1990) and Bao and Edmans (2011), respectively, which suggest that advisors are able to
identify firms with which an acquisition would result in greater economic synergies, advise the
merging firms towards the execution of the deal, and in our context, contribute to the structure of
the earnout contract. Therefore, consistent with our predictions, advisors offer tangible benefits
to acquirers in earnout-settled M&As (adding approximately as much as 2.00 percentage points
higher risk-adjusted returns relative to their absence).
Our results are related to two parallel-growing, independent literatures that concentrate on
whether earnouts or financial advisors offer tangible benefits to acquirers. First, the results add
insights around a source of the variation in acquirer risk-adjusted returns in earnout-settled
M&As, which is pined down to the advise offered by external advisors. Put simply, earnouts are
to a large extent value enhancing for the acquirer only when advisors are involved in the deal.
In the absence of advisors, nevertheless, the earnout-effect becomes negligible or even flips sign.
This suggests the presence of a complementarity-effect between earnouts and advisors in M&As
with significant information asymmetry.
Second, we add to evidence supporting the view that advisors add value in predominately ac-
quirers of listed-targets. Our results show that the involvement of financial advisors in unlisted
target M&As explains a large fraction of the variation in acquirer risk-adjusted returns.6 Specif-
ically, in earnout-settled M&As with high information asymmetry there is scope for negotiation
and hence the advice offered to the merging firm appears to be very valuable. Lastly, consistent
with Golubov et al. (2012) we find no evidence in support of the hypothesis that advisor reputation
matters to firm value in unlisted-target M&As.7
Overall, the paper provides evidence that improves our understanding on the extent to which
earnouts accommodate valuation risk relative to conventional single up-front payments in cash or
stock and whether advisors help in structuring and facilitating earnout contracts more accurately.
Overall, the paper resolves the long-standing puzzle of earnout-settled M&As—an important cor-
porate finance issue.
The paper proceeds as follows: Section 2 presents the salient literature on earnouts and ad-6Bowers and Miller (1990) shows that the presence of advisors imposes substantial wealth implications while no dis-
tinction is made between listed and unlisted target M&As, as is the case in Hunter and Walker (1990), Servaes and Zenner(1996) and Michel, Shaked, and Lee (1991). Moreover, McLaughlin (1992), Hunter and Jagtiani (2003), and Allen et al.(2004) focus only on M&As involving listed targets. Golubov, Petmezas, and Travlos (2012) illustrates that advisors rep-utation does not seem to significantly influence acquirer risk-adjusted returns in unlisted target deals, while Agrawal,Cooper, Lian, and Wang (2013) study, among others, the effect of common advisors in subsidiary target deals.
7Golubov et al. (2012) find ‘no effect of financial advisor reputation on bidder returns in acquisitions of unlisted firms’(p. 273). In unreported results, the presence of top-tier advisors in earnout-settled mergers, the vast majority of whichinvolves unlisted targets, yields insignificant acquirer risk-adjusted returns.
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visors (independently) in M&As, and develops our testable hypotheses. Section 3 outlines the
methodology used to conduct the empirical analysis. Section 4 provides a description of the
dataset and Section 5 discussed the empirical findings. Lastly, Section 6 offers a conclusion.
2. Related Literature and Development of Hypotheses
2.1. Rationales for earnout contracts in M&As and their valuation effects
Information asymmetry between the merging firms in merger negotiations often leads to sub-
stantial disagreements over the merger payoff and to significant merger valuation-risk (Hansen
(1987) and Eckbo, Giammarino, and Heinkel (1990)). This is more pronounced in deals involving
unlisted (i.e. private or subsidiary) and often young target firms that operate in intangible-rich
sectors such as the hi-tech, service, and pharmaceutical ones. For such firms, in addition to the
limited availability of information in the market (i.e. the case of adverse selection), the extent of
intangibility of their assets can cause larger and harder-to-estimate valuation-risk. Moreover, in
mergers of such target firms the value of the combined entity is highly sensitive to the committed
efforts and creativity of only a few personnel in the target firm whose retention in the combined en-
tity during the integration period of the merger presents an important source of value creation (i.e.
the case of moral hazard). Therefore, the managers of merging firms may have access to superior
information about the valuations of the firms they manage, which gives rise to adverse selection,
while the (unobserved) efforts of the target firms’ managers towards the objective of maximizing
the merger outcome gives rise to moral hazard. As a result, the earnout payment mechanism is
regularly employed aiming to offer a solution and ‘bridge the gap’ in the implied merger outcomes
dissacords by motivating the delivery of earnout payments, and also an additional premium to
the target owners relative to mergers settled in single up-front payments, all conditional on the
target firm achieving pre-specified performance-related goals (Kohers and Ang (2000) and Bar-
bopoulos and Adra (2016)).8 Ultimately, this payment mechanism allows the merging firms that
initially disagree over the merger outcome to reach an agreement and continue in the merger by
motivating the more-informed target with high expectations to accept the contingent part of the
payment, and the less-informed acquirer to shift a large part of the merger valuation-risk to the
target, hence achieving an efficient risk sharing between the two (Kohers and Ang (2000)).8Such performance goals often relate to cash flows, sales, pre-tax income, gross profits, and net income. The deferred
payment, which accounts for approximately 35% of the total deal consideration, is delivered over the time period between0.5 to 5 years (Cain et al. (2011); Barbopoulos et al. (2018b)).
7
Previous studies show that earnouts are beneficial for the acquirer. In particular, Kohers and
Ang (2000) and Barbopoulos and Sudarsanam (2012) show that earnout-settled M&As, especially
those exposed to the highest merger valuation-risk, yield greater acquirer risk-adjusted returns
compared to those financed with conventional single up-front payments in cash or stock.9 Bar-
bopoulos and Sudarsanam (2012) further show that ‘optimally’ classified earnout occurrences,
based on a likelihood model that predicts the correct earnout choice in a deal, yield even higher
acquirer risk-adjusted returns. Mantecon (2009) examines alternative methods of valuation un-
certainty avoidance in foreign target deals and shows that the use of earnout predominantly
benefits acquirers of domestic rather than foreign targets.10 However, Barbopoulos et al. (2018a)
show that the earnout valuation effects in foreign target M&As are inversely related to the extent
of the acquiring firm’s existing degree of global diversification, i.e. they add more value to acquir-
ers without or with limited global diversification at the time of M&A announcement. Moreover,
Barbopoulos et al. (2018b) show that acquirer risk-adjusted returns in earnout-settled M&As are
sensitive to the choice of the payment method (i.e. cash, stock, combo of cash and stock, or mixed)
in the initial and deferred payment stages. Lastly, Barbopoulos (2019) show that the earnout-
effect appears elusive due to the presence of a significant acquirer information dissemination
effect in the majority of earnout-settled M&As. Overall, the positive earnout-effect depicted by
earlier studies appears to be derived from the ability of earnout to motivate information shar-
ing between the merging firms, which contributes to the reduction of both adverse selection and
moral hazard issues and hence to the higher likelihood of merger success.
Put simply, the involvement of earnout in a merger transaction is by itself a signal regarding the
quality of the target firm that is prepared to accept the earnout terms and signals the committed
efforts of its managers (often owners) to maximize the performance of the combined entity during
the earnout period. We therefore set our first (H1) hypothesis: Earnout-settled M&As yield higher
acquirer risk-adjusted returns relative to M&As settled in conventional single up-front payments in
cash, stock, or mixed.
2.2. Rationales for financial advisors in M&As and their valuation effects
The role of financial advisors in the market for corporate control has received a fair amount of
attention in the literature. The involvement of advisors in the M&A process is shown to improve9Kohers and Ang (2000) report a 2.20% 2-day cumulative abnormal return for earnout acquirers compared to 1.80%
for cash and 1.13% for stock acquirers.10Datar, Frankel, and Wolfson (2001) show that due to several differences in accounting practices and corporate gover-
nance mechanisms worldwide, foreign bidders of U.S. targets are less likely to use earnout than domestic (U.S.) bidders.
8
the merger payoff given the advisors ability to identify and extract significant synergies from
the merger. These studies, however, concentrate on M&As settled in single up-front payments
whose contractual design avoids contingency considerations about future payments, among other
important aspects.
Bowers and Miller (1990) show that the choice of investment banker as an advisor, and par-
ticularly a top-tier one due to its better expertise, is able to identify firms with which an acquisi-
tion would result in greater economic synergies, supporting the ‘Better Merger’ and ‘Bargaining
power’ hypotheses. Hunter and Walker (1990) further argue that advisors may possess special-
ized knowledge about firms with particular characteristics including information on financial or
product market potential, which would-be acquirers may not have. The authors argue that advi-
sors may also provide efficiency gains in relation to information costs regarding a deal, as well as
the timing of the search for potential targets. Sudarsanam (1995) argues that the usefulness of
advisor inclusion comes from providing a ‘fair value’ for the target firm, devising the appropriate
financing structure and advising the bidding firm on negotiating tactics and strategies for both
friendly and hostile bids. Along these lines, Servaes and Zenner (1996) argue that transaction
costs and, in part, contracting costs and information asymmetry are related to the choice to hire
an advisor. Specifically, an investment bank is more likely to be consulted when the acquisition
is more complex, when acquirers have less takeover experience as well as when targets operate
in an unrelated industry in relation to the acquirer. More recently, Bao and Edmans (2011) show
that the presence of investment banks influences takeover outcomes. The authors establish the
‘skilled-advice’ hypothesis indicating that investment banks, acting as financial advisors, are ca-
pable of identifying higher synergy gains in target firms. This consulting superiority of financial
advisors results in a significant investment bank fixed effect in the announcement returns of M&A
deals.
Within the same strand of literature, several scholars have investigated the extent to which
the advisor reputation can explain the variation of acquirer risk-adjusted returns through the
channels of ‘better’ and more ‘experienced’ advice. Kale, Kini, and Ryan (2003) focus on a measure
of the relative reputation of the merging parties’ advisors and show that the absolute wealth gain,
as well as the share of the total takeover wealth gain accruing to the acquirer increase as the
reputation of the acquirer’s advisor increases relative to that of the target’s one. Hunter and
Jagtiani (2003) also indicate that advisor quality and the number of advisors that are employed
in a given transaction, are important in determining the probability of completing a deal, as
well as the time required for its completion. More recently, Golubov et al. (2012) show that top-
9
tier advisors, the majority of which are investment banks, deliver higher acquirer risk-adjusted
returns than their non top-tier counterparts, but in public target acquisitions only (i.e. not private
and subsidiary).
These studies illustrate the ability of advisors to deal with uncertainty over the merger outcome
and identify substantial synergies in M&As. We therefore set our second (H2) hypothesis: Advised
M&As yield higher acquirer risk-adjusted returns compared to M&As without the involvement of
financial advisors.
2.3. Why might financial advisors benefit acquirers in earnout-settled M&As?
Earnouts, however, are not free of problems. Their structure appears very complex and highly
sensitive to the challenges involved in the valuation of the (mainly unlisted) target firm and hence
the calculations of the merger’s expected payoff (Kohers and Ang (2000)). Cain et al. (2011), us-
ing Monte Carlo simulation methods, show that expected earnout payments are strongly related
to various proxies for valuation uncertainty, as well as the characteristic properties of earnout
in mitigating valuation uncertainty in a deal.11 The authors argue that ‘[...] earnouts are com-
plex, multidimensional contracts exhibiting substantial heterogeneity in the size of the potential
earnout payment, the performance measure on which the earnout is based, the interval over
which performance is measured, the performance thresholds that must be achieved in order to
receive the earnout payment and the form of the earnout payment [...]’ (p. 152). Similarly,
Lukas, Reuer, and Welling (2012) argue that earnouts constitute intricate payments with sub-
stantial heterogeneity in their terms and structure among different deals. These empirical facts
suggest that the efficient risk sharing properties offered via the earnout can be largely attributed
to the efficient design of earnout-payments (i.e. size of earnout payments), the time interval(s) be-
tween them, as well as the choice of performance metrics. Hence, failure to adequately structure
earnouts by incorporating all relevant valuation-uncertainty parameters in the contract may lead
to significant legal disputes and to value destroying M&As. Therefore, intense negotiations and
certainly an earnout-structure technical expertise is required that may serve the efficient design
of earnouts.
Along these lines, Bao and Edmans (2011) (p. 2287) argue that ‘As CEOs make M&A decisions
rarely, they typically lack experience and seek counsel from investment banks [...]’. This should
be even more pronounced for CEOs of firms engaged in earnout-settled M&As provided the addi-11Such proxies include the type of performance measure on which the deterred payment is contingent, the size of the
deterred payment, its mode of payment, as well as the time period over which the performance measure is estimated.
10
tional technology required to design earnouts and, given the relatively small size of merging firms,
their even more limited acquisition experience and constrained financial resources. However, the
absence of evidence regarding the role of external financial advisors on the variation of acquirer
risk-adjusted returns renders this relationship to be at best neutral, hence casting doubts on
both the quality of services provided by financial advisors (especially given the small size and
restricted financial resources), as well as the fundamental role of earnouts in resolving valuation
uncertainty in M&As.
Moreover, the involvement of financial advisors is illustrated to be positively related to the
riskiness of the merger (Servaes and Zenner (1996)), which is also one of the major determinants
of the earnout choice in the payment process of the deal. As a result, it becomes an empirical
question as to whether financial advisors offer tangible benefits to acquirers in earnout-settled
M&As. Put simply, the documented properties of earnout in dealing with disagreements over
the intrinsic value of the deal and motivating both parties towards the realization of the implied
synergies, along with the usefulness of advisor involvement in dealing with contracting costs and
valuation uncertainty during the deal process, should simultaneously lead to higher acquirer
gains or an complementarity-effect.
We therefore argue that in the presence of financial advisors, many of the earnouts benefits
arrive from the valuable earnout-structure advise that helps the merging firms identify syner-
gies, secure or even maximize the expected merger payoff, and the acquirer to share the merger
valuation-risk with the more informed target. We hypothesize that the earnout-effect is largely
affected by the variation of specs in the earnout contract. We therefore set our third (H3) hypoth-
esis: M&As including earnout payments and financial advisors yield higher acquirer risk-adjusted
returns relative to deals involving (a) earnout without financial advisor and, (b) single up-front pay-
ments (i.e., non-earnout) regardless of the advisor presence.
3. Empirical Methodology
We first present the methods we employ to estimate the acquirer risk-adjusted returns. The
discussion is then turned to the univariate and multivariate parts of the analysis, wherein the
variables entering the latter are also discussed. Subsequently, the discussion is turned into the
tests we use to address self-selection bias concerns with regards to endogeneity of merging firms
to employ earnout (or not) in the financing process of the merger, and advisor(s) (or not) in either
or both sides of the deal.
11
3.1. Estimation of acquirer risk-adjusted returns
The estimation of acquirer abnormal (i.e. risk-adjusted) returns is obtained as follows:
ARj,t = rj,t − E[rj,t|Xt] (1)
Where: ARj,t, is the risk-adjusted return of acquirer j at a given day t, Rj,t is the actual realized
return of acquirer j at a given day t, E[rj,t|Xt] is the expected return of the acquiring firm j at
a given day t, with Xt denoting the information set at time t. We consider a number of alterna-
tive specifications for the estimation of E[rj,t|Xt]. We estimate the expected return based on the
Carhart (1997) 4 factor model (4-FM) as shown in the Equation (2):
i , and β̂umdi estimated over the window t− 250 to t− 20, where t = 0 is
the announcement day of the M&A by the acquiring firm j, as shown in the Equation (3):
(Rj −Rf )t = α+ βi(Rm −Rf )t + βsmbi SMBt + βhml
i HMLt + βumdi UMDt + εj,t (3)
The announcement period cumulative abnormal return (CAR) for acquirer j is the sum of the
risk-adjusted returns from day T1 (days before the announcement day t) to day Tn (days after the
announcement day t), where t = 0 is the M&A announcement day, as shown in Equation (4):
CARj , (T1, Tn) =
Tn∑t=T1
ARj,t (4)
For robustness, and in line with numerous studies with similar sample characteristics (see for
example Fuller, Netter, and Stegemoller (2002) and Faccio, McConnell, and Stolin (2006)), the
announcement period risk-adjusted returns for an acquiring firm j are also estimated using the
Fama-French 3-factor model (3-FFM), the capital asset pricing model (CAPM), the market model
(MM), and the market-adjusted model (MAM). In unreported results (available upon request from
the author) we find that the correlations between the CAR obtained from (a) the 4-FM, (b) the
3-FFM, (c) the CAPM, (d) the MM, and (e) the MAM, are in excess of 95%. All results using CAR
obtained from (a), (b), (c), (d) and (e) are qualitatively similar, and our conclusions hold regardless
of which model we use to compute the ARj,t in the event study.
12
3.2. Univariate and multivariate analysis
At first, the acquiring firm’s value, measured with the acquirer CARj , (T1, Tn), is analyzed by the
deal’s delivery payment mechanism (earnout and non-earnout), the deal’s method of payment
(cash, stock, mixed, and earnout) and the target firm’s listing status (private, public, and sub-
sidiary). The same analysis is repeated for deals in which financial advisors are consulting only
the acquirer or only the target or both merging firms. Differentials of the acquirer CARj , (T1, Tn)
between portfolios comprised by (a) earnout M&As and non-earnout, cash, stock or mixed M&As
(respectively), for deals of different target listing statuses and advisor influence, are calculated and
reported on the rightmost columns of the corresponding table, and (b) advised and non-advised
M&As, are calculated and reported on the bottom raws of each panel. To assess the comparative
performance of different groups of M&As, the difference in means is tested using the t-test.
The impact of expensive financial advise on acquirers’ risk-adjusted returns engaged in earnout-
vs. non-earnout- settled deals is further examined within a multivariate framework where the
effects of several other factors known to shape acquirers’ risk-adjusted returns are simultaneously
controlled. Accordingly, the following Equation (5) is estimated in a nested regression form:
CARj , (T1, Tn) = α + β1 · Earnout Dummyj
+ β2 · Financial Advisor Dummyj
+ β3 · (Earnout Dummy × Financial Advisor Dummy)j
+
k∑i=4
βiXji +
k∑i=1
γiZji + d̃t + εj j = 1...N (5)
where j corresponds to the deal index. The intercept α accounts for the average risk-adjusted
returns accrued to acquirers’ shareholders after accounting for the effects of all the explanatory
variables that enter the information sets Xj and Zj. The dependent variable, CARj , (T1, Tn), is
the five-day announcement period acquirer (Equation 4). β1 captures the impact of earnout on
acquirer risk-adjusted returns in non-advised M&As. β2 captures the impact of financial advisor
on acquirer risk-adjusted returns in non-earnout settled M&As. β3 captures the joint effect of
earnout and advisor. (β1+β3) captures the additional effect of advisor in earnout-settled mergers
on acquirer risk-adjusted returns. Lastly, (β2 + β3) captures the additional effect of earnout in
advised mergers on acquirer risk-adjusted returns. The matrices of explanatory variables, Xj
and Zj, includes several deal- and firm-specific factors, respectively, that are likely to affect the
acquiring firm’s value, the impact of each is measured and stacked in the vectors βj and γj,
13
respectively. These variables are discussed in detail in Section 3.2.1. d̃t represents year fixed
effects.
3.2.1. Variables
Previous studies show that the acquiring firm’s value is sensitive to the choice of payment delivery
mechanism (Kohers and Ang (2000); Barbopoulos et al. (2018b)). Therefore, to account for the
implications of the payment mechanism on firm value, a dummy variable (=Earnout) is included
in Equation (5), which is assigned the value of 1 if the earnout payment mechanism is included
in the deal, and 0 otherwise. Previous research also shows that the merger outcome is sensitive
to the presence of financial advisor(s) in the deal (McLaughlin (1992); Kale et al. (2003); Bao and
Edmans (2011); Golubov et al. (2012)). Therefore, to account for the impact of counseling offered
by financial advisors on the merger outcome, a dummy variable is included in Equation (5), which
is assigned the value of 1 if financial advisor(s) advise the acquirer (=AFA) or the target (=TFA)
or both acquirer and target (=AFA TFA), according to the model specification, and 0 otherwise.
As argued earlier in the paper, we expect that the involvement of advisors in earnout-settled
M&As to affect the acquirer risk-adjusted returns given the influence of advisors in improving
the structure of the earnout contract and help on its facilitation in order to maximize the merger
payoff. Therefore, to account for this effect in Equation (5) the interaction effect of (=‘Earnout ×
AFA’ or ‘Earnout × TFA’ or ‘Earnout × AFA TFA’) is added in Equation (5), according to the model
specification.
The M&A literature provides ample evidence on the influence of the target firm’s listing status
(i.e. unlisted vs. listed) on the acquiring firm’s value (Chang (1998); Fuller et al. (2002); Faccio
et al. (2006)). A dummy variable (=Private target) assigned the value of 1 if the target is a private
firm, and 0 otherwise is therefore included in Equation (5). Extant literature (Denis, Denis, and
Yost (2002); Barbopoulos and Sudarsanam (2012)) point to the impact of industrial diversification
on firm value. Therefore, to control for the potential effect of industrial diversification, a dummy
variable (=Diversified) assigned the value of 1 for cross-industry deals (i.e. where the target and
acquirer do not share the same primary two-digit SIC code), and 0 otherwise, is included in Equa-
tion (5). Along these lines, the merger-valuation risk and post-acquisition integration challenges
for the acquirer increases with the level of intangible assets of the target. To account for such
challenges, in Equation (5) we include a dummy variable (=Diff-to-Value) that is assigned the
value of 1 if the target is operating within Media, Retail, High Technology, Healthcare, or Telecom-
14
munication sectors, and 0 otherwise, as in Barbopoulos et al. (2018b). To account for the impact
of foreign acquisitions on acquirer value, we include in Equation (5) a dummy variable (=CBA)
assigned the value of 1 if the target is foreign, and 0 otherwise, as in Moeller and Schlingemann
(2005).
Extant literature (e.g., Fuller et al. (2002); Barbopoulos, Molyneux, and Wilson (2016)) shows
that acquirer value is positively related to the relative size of the deal (measured as the ratio of the
deal value to the market value of the acquirer 20 trading days prior to the M&A announcement).
Therefore, the relative size of the deal (=Deal relative size) is included in Equation (5). Further,
information asymmetry between merging firms can influence firm value. Zhang (2006) suggest
that investors tend to have more information on firms with a longer trading history, which results
in lower information asymmetry. Therefore, the age of the acquirer (=Acquirer Age), measured by
the log of the number of days between the announcement day and the first record of the company
in Datastream, is included in Equation (5).
Key financial ratios of the acquiring firm, such as its market-to-book value (=Acquirer MTBV),
its cash-and-equivalent relative to its total assets (=Acquirer Cash & Equivalent-to-TA), and its
debt to equity ratio (=Acquirer Debt-to-Equity) that record information about the acquirer’s growth
opportunities and profitability are also included in Equation (5), as in Barbopoulos et al. (2018a).
Lastly, to account for potential unobserved time-variant characteristics that are related to a given
year in which an M&A deal is announced, year fixed effects (=Year Fixed Effects) are included in
Equation (5). A detailed presentation of all variables used in this paper can be found in Appendix
A.
3.2.2. Self-selection
As also stated in the Introduction part of the paper (Section (1)), an important consideration
in our analysis is the issue of self-selection regarding (a) the merging firms’ endogenous choice
of earnout (or not), and (b) the external advisor’s endogenous choice to be involved in the deal
(or not) who is subsequently choosing the earnout (or not). We recognize that the issue of self-
selection is far more complicated in advised earnout-settled M&As due to (a) the firm’s dual-choice
of earnout and advisor or vice-versa, or (b) the advisor’s dual choice to participate in the deal and
use earnout.
To address such issues in our analysis and enhance the robustness of our findings we first
rely on a quasi-experimental design through which the impact of each treatment, either individ-
15
ually (i.e. earnout and advisor) or jointly (i.e. advisor-earnout-effect) is evaluated in isolation via
the Propensity Score Matching (PSM) method (Dehejia and Wahba (2002)), which is accompanied
with the Rosenbaum-bounds (RB) sensitivity analysis. The PSM is performed on 1:1, 3:1 and 5:1
matching ratios and 0.001 caliper. We also choose the nearest neighbor on the large-effect vari-
ables, defined by the Mahalanobis distance, among all units within say 0.25 standard deviations
(also known as ‘calipers’) of the propensity score computed from all variables (Gu and Rosenbaum
(1993); Rubin and Thomas (1992)). Put simply, to measure the contribution of each treatment
on acquirer risk-adjusted returns, we match treated deals to untreated ones (that do not include
the treatment) yet they exhibit ‘similar’ probability of including the treatment. As a result, the
treatment effect(s) is less likely to be driven by deal- or firm-specific features (based on which
self-selection issues arise) but rather from the treatment(s) itself.12 To reduce our exposure to
hidden-bias concerns due to omitted covariates in our propensity score estimator, which are likely
to directly affect the outcome of our PSM exercise, we employ the RB sensitivity analysis that aims
to quantify the sensitivity of the treatment-effect to omitted- or hidden variable-bias (Rosenbaum
(2002)). As in most occasions the RB method suggests that hidden- or omitted variable-bias re-
mains an important issue in the analysis, we secondly rely on the Heckman two-stage procedure
via which the inclusion of the Inverse Mills Ratio (IMR) in the model, which is applied in the
matched sample, accounts for potential hidden- or omitted variable-bias.13
4. Data and Stylized Facts
4.1. The sample
The sample consists M&As announced by U.K. listed firms between 01/01/1986 and 31/12/2016
(inclusive) and recorded by the Security Data Corporation (SDC).14 SDC records 82,575 M&A
announcements in the sample period. For a deal to remain in the sample, it must meet the
following criteria: (a) the acquirer is a U.K. listed firm in the London Stock Exchange with its
market value being in excess of $1m, measured four weeks prior to the announcement of the12In addition, to measuring the contribution of each of the two treatments, or the joint effect of both treatments, on
the acquirer gains we also decompose the advisor-effect or the earnout-effect from the joint advisor-earnout-effect bymatching to advised earnout-settled M&As with (a) only deals that are settled in earnouts without the advisor (hence thedifference is the advisor-effect), (b) only deals that are counseled by advisors without the earnout (hence the differenceis the earnout-effect) and, (c) only deals that are jointly settled in non-earnout and not-counseled by advisors (hence thedifference is the pure advisor-earnout-effect).
13The selection equation in the Heckman two-stage procedure relies on the same specification that is used in the PSManalysis.
14The choice of the starting date in the sample is guided by the period SDC has started recording M&A announcementscomprehensively, in addition to the year earnouts have became available.
16
deal, (b) to avoid the noisy effects of tiny deals, only M&As with deal value in excess of $1m,
excluding fees, remain in the sample, (c) to ensure that the acquirer gains control over the target
firm, only M&As in which the acquirer owns at the announcement period less then 5%, and
aims to control at least 50%, of the target firm’s assets or equity after the deal’s completion, are
included in the sample, (d) we include in the sample only M&As in which the target is a listed,
private or a subsidiary firm, both U.K. and non-U.K. domiciled, and (e) to avoid the confounding
effects of multiple deals, all M&As announced within 5-days surrounding another M&A by the
same acquirer are excluded from the sample.15 Buy-backs, recapitalizations, spin-offs, exchange
offers, and repurchases are excluded from the sample. M&As in which either the acquiring or
the target firm are government organizations, as well as withdrawn deals, are also excluded from
the sample. Lastly, we keep only M&As for which the daily stock price and market value of equity
of the acquirer are available from the Datastream. The above criteria leave us with 8,909 M&As.
2,316 M&As are settled in earnout (=26%). 2,801 (531) M&As involve financial advisors (and also
include earnouts) on the acquirer side, 2,350 (396) M&As involve financial advisors (and also
include earnouts) on the target side, and 1,403 (152) M&As involve financial advisors (and also
include earnouts) on both sides of the transaction.
4.2. Sample characteristics
Table 1, Panel A, records the annual distribution of all M&As in our sample according to several
deal- and firm-specific features (e.g. foreign target deal, diversifying deal, advisor involvement,
target listing status, deal value). Panel B repeats the same exercise on exclusively earnout-settled
M&As according to only a selection of deal- and firm-specific features of Panel A. Overall, M&As
follow a pro-cyclical pattern with their highest activity realized during the years 1989, 1999, 2000
and 2007. On the contrary, the lowest merger activities are experienced after the 2008 financial
crisis. Figure 1 further depicts the correlation of M&As activities with the overall stock market
index (consistent with Shleifer and Vishny (2003) and Rhodes-Kropf, Robinson, and Viswanathan
(2005) for the U.S. market and Andriosopoulos and Barbopoulos (2017) for the U.K. market).
The volume of M&As increased significantly during the 1990s and subsequently dropped, in the
aftermath of the dot-com bubble. It once again increased, during the years 2004 to 2007, only
to start dropping during the credit crunch and financial crisis of 2008. Similar patterns are
observed across all characteristics in both Panels A and B. Figure 2 also shows that the ratio15While other event-study windows are employed in the paper, the 5-days window is the most commonly used.
17
of earnout M&As to all M&As has increased significantly after 1999 to maintain its high levels
throughout the remaining sample period.
(Insert Table 1 and Figure 1 about here)
Table 1 indicates that 32% of the M&As are with non-U.K. target firms (consistent with Bar-
bopoulos, Paudyal, and Pescetto (2012)) and 49% of the M&As are industry diversifying (consistent
with Barbopoulos and Sudarsanam (2012)). Considering the advisors involvement, 2,801 (2,350)
advisors are involved in the acquirer (target) side of the M&A, which correspond to 31% (26%)
of the full sample. Advisors appear in both sides of the transaction in only 16% of the sample.
Consistent with Faccio and Masulis (2005) and Draper and Paudyal (2006), the vast majority
of M&As announced by U.K. domiciled acquirers involve unlisted target firms (private and sub-
sidiary target M&As represent 61% and 30% of the sample, respectively), while cash and mixed
payments dominate the acquisitions’ financing currencies (42% and 25%, respectively). Lastly,
the largest deals in our sample took place in the years 1998, 1999 and 2014, while the largest
acquirers were observed in the years 2007-2009 and 2015.
Earnout-settled M&As account for 26% of our sample, consistent with the only recent U.K.
study of Barbopoulos et al. (2012).16 More statistics reveal that the use of earnouts has increased
substantially since the late 80’s reaching 35% of total M&As activity in the year 2008 compared
to only 5% in 1986 (see also Figure 2). The vast majority of earnout-settled M&As involve unlisted
target firms, mainly private ones (84%), followed by subsidiary firms (15%). We also find that
23% and 17% in our earnout-settled M&As are advised by financial advisors in the acquirer or
the target side, respectively. In only 7% of the earnout-settled M&As advisors appear in both sides
of the transaction. Lastly, our statistics show that on average 40% of the deal value is deferred at
a future time (i.e. relative earnout value) while it varies from from 33% to 48% across the years.
Table 2, Panel A, presents earnout vs. non-earnout summary statistics of the deal value and
acquirer market capitalization for the full sample, as well as sample-groupings according to the
advisor presence on either or both sides of the deal, and the target firm’s listing status. The
statistics indicate that, (a) the deal size and (b) the acquirer size, are on average much larger
in M&As that are settled in single up-front payments (i.e. NEA) than those settled in earnouts16Similar statistics are reported in earlier studies, such as 26.1% in Barbopoulos and Sudarsanam (2012) and 25.1%
in Barbopoulos et al. (2018a). Moreover, the earnout-activity in M&As announced by the U.K. acquirers is much higherthan the 3.9% in Cain et al. (2011), 4.1% in Datar et al. (2001), 5.6% in Kohers and Ang (2000), 6.0% in Barbopoulos et al.(2018b), and 9.4% in Barbopoulos et al. (2018a), which are all U.S. based. Similarly, Barbopoulos et al. (2018a) report7.0% (10.2%) of M&As announced by Canadian (Australian) acquirers are settled in earnouts. The relatively high earnout-activity in the U.K. is due to, among others potential reasons, the 80% of the total M&A activity involving private targets(Draper and Paudyal (2006)), or more than 90% of the total M&A activity involving unlisted (i.e. private and subsidiary)targets (Faccio and Masulis (2005)).
18
(means of $175m vs. $30m and $1,972 vs. $792, respectively), consistent with Kohers and Ang
(2000). The same pastern holds regardless of the presence of advisors in either or both sides of
the deal, and the target firm’s listing status. Consistent with McLaughlin (1990), the summary
statistics further show that both the deal value and the acquirer market capitalization are larger
in advised than non-advised deals (regardless on the type of advisor involvement).
(Insert Table 2 about here)
Table 2, Panel B, shows that foreign and focused deals (independently) appear larger, involve
larger acquirers, involve acquirers with higher MTBV and higher cash and leverage ratios, relative
to their domestic and diversifying counterparts. However, the relative deal size is much higher
for domestic and diversifying deals. Moreover, M&As of listed targets are much larger in size,
announced by much larger acquirers, have higher relative size ratio, and involve much more
leveraged acquirers, relative to M&As of private or subsidiary target firms. Noticeably, in domestic
and private target deals (independently) a larger fraction of the deal value is delivered via earnout
payments (REAV of 41% in both groups). This may suggest that merger valuation risk appears
to be larger in both private and domestic target deals, as it is correlated with much higher REAV
ratio (Cain et al. (2011)), relative to non-private and foreign target deals.
Our summary statistics also depict some very interesting aspects regarding the impact of
advisors in negotiating favorable earnout terms. Panel B shows that while advised earnout deals
are on average riskier than their non-advised earnout counterparts, based on the higher mean
relative deal size of the former (48% vs. 18%), they tend to be associated with much lower relative
earnout value (34% vs. 42%) and hence with a possibly higher likelihood of full delivery of the
earnout payment(s).17 In fact, our hand gathered earnout-contract information suggests that
advised earnout-settled M&As tend to be associated with significantly higher success rates.18
These statistics indicate that the increased exposure of acquirers to merger valuation risk is
addressed via the simultaneous involvement of earnouts and advisors in the deal.
Our summary statistics also depict some very interesting aspects regarding the earnout-
contract structure in the presence vs. absent of external advisors. Panel C shows that advised
rather than non-advised earnout-settled M&As have (a) significantly more (less) cash (stock) in17This difference is also noticeable in advised earnout M&As when advisors are in both the acquirer- and target-side
relative to non-advised earnout M&As (59% vs. 23%). Along similar lines, Servaes and Zenner (1996) argue that riskierM&As are more likely to involve advisors.
18Our subsequent analysis also depicts an inverse relationship between the relative earnout value and the acquiringfirm’s risk-adjusted returns. Therefore, advisors, while frequently involved in riskier deals, the earnout contract designthat they possibly recommend tends to be associated with significantly higher merger success.
19
both the initial and deferred stage payments, (b) significantly more contingencies linked to the tar-
get firm’s EBITDA and also the target firm’s future profitability (when only acquirer advisor is in-
volved), (c) significantly less contingencies linked to the target firm’s PBT, (d) larger earnout sizes,
(e) significant lower relative earnout size (ratio of earnout value to total deal consideration), (f)
fewer earnout payments, and (g) significantly higher success rates. These earnout-contract char-
acteristics suggest that advisors can influence significantly the structure of the earnout-contract
and negotiate favorable and achievable earnout specs that is more likely to lead to higher merger
outcomes.
Table 3 records the correlations between the variables in the analysis. In general, the cor-
relation coefficients do not raise any concerns regarding multicollinearity that may impede the
assessment of the effect of the independent variables in multiple regressions.
(Insert Table 3 about here)
5. Results
5.1. Univariate analysis of acquirer abnormal returns
Table 4 presents our findings from the univariate analysis of the acquirer gains (i.e. cumulative
risk-adjusted returns computed as in Equation (4)) according to the payment mechanism (i.e.
earnout and non-earnout), currency of financing in the non-earnout category (i.e. cash, stock,
or mixed), and the target firm’s listing status for all deals (Panel A), deals under acquirer-advisor
presence or absence (Panels B and C), deals under target-advisor presence or absence (Panels
E and F), and deals under the joint acquirer- and target-advisor presence or their joint absence
(Panel H and I). Differentials of acquirer gains between M&As that are settled in earnouts vs. (a)
non-earnout (in general), (b) cash, (c) stock, and (d) mixed currencies are recorded in the rightmost
columns of each Panel. Lastly, Panels D, G, and J record differentials of acquirer gains between
deals under external-advisor presence vs. absence.
Consistent with earlier studies (see Kohers and Ang (2000), Barbopoulos and Sudarsanam
(2012), and Barbopoulos et al. (2018a)), Panel A illustrates that acquirers in deals settled in
earnouts enjoy 0.31% higher gains relative to acquirers in deals that settled in single up-front
payments (i.e. non-earnout). In all deals, as well as in deals of private target firms, earnout-
settled M&As significantly outperforming their cash-settled counterparts (differentials of 0.38%
and 0.47%, respectively, both significant at 1% level). These results support our first hypothesis
20
predicting higher gains to acquirers in the presence of earnout in the M&A payment process.
(Insert Table 4 about here)
Panels B, E and H further show that the higher acquirer gains in earnout-settled M&As is
shaped by deals involving external advisors in either or both sides of the merger. Specifically,
Panel B uncovers that acquirers in advised, on the acquirer side, earnout-settled M&As enjoy
with self-selection issues in the univariate analysis (and also in the multivariate analysis later
on), we employ the Propensity Score Matching (PSM) method, which is accompanied with the RB
method (see Section (3.2.2) for more information the construction of the test).19 The PSM allows
for a bias-reduced causal inference by pairing treated deals with control ones, based on a propen-
sity score that is estimated at deal level via a likelihood model using observable features, similar
to those discussed in Section (3.2.1). The RB sensitivity analysis is also employed that aims to
quantify the sensitivity of the treatment-effect to omitted- or hidden variable-bias (Rosenbaum
(2002)).
The propensity scores of the firm’s choice of earnout vs. non-earnout or advisor-presence vs.
advisor-absence are estimated within different samples depending on whether one or both treat-
ments are used simultaneously. Specifically, the PSM is employed in four matching-exercises:
(a) in the full sample in which we model the earnout endogenous choice or the external advi-
sor endogenous choice, (b) in only earnout-settled M&As including acquirer financial advisor (i.e.
AFA) in the full sample and separately in a sample that excludes those deals including also target
financial advisor (i.e. TFA), (c) in only earnout-settled M&As including TFA in the full sample and
separately in a sample that excludes those deals including also AFA, and (d) in only earnout-
settled M&As including AFA and TFA jointly in the full sample and separately in a sample that
excludes those deals including AFA or TFA independently. Each exercise allows us to disentangle
the impact of earnout or advisor (independently and jointly) versus non-earnout or non-advisor
from the impact of other deal- and firm-specific characteristics. Through this quasi-experimental
research design which is based on the PSM, (a) the earnout-effect, (b) the advisor-effect, (c) mul-
tiple combinations of earnout- and advisor-effects, are evaluated in isolation.19See Dehejia and Wahba (2002) for an application of the PSM methodology in non-experimental settings. Moreover,
Behr and Heid (2011), among others, provide a thorough discussion of the PSM methodology along with its applicationin evaluating the success of German bank mergers in the period 1995 – 2000.
23
We first evaluate the results from the estimation of propensity scores, as well as the balance
of covariates between the treated and control portfolios (to conserve space these results are un-
reported but available upon request from the authors). Results are consistent with previous
earnout and financial advisor studies regarding their involvement in a deal (see Barbopoulos and
Sudarsanam (2012) and Bao and Edmans (2011)). Moreover, as Section (3.2.2) outlines, the PSM
method aims to identify a counter-factual sample units that do not receive the treatment, yet, they
exhibit the same probability to receive the treatment as the treated sampled units. The identifi-
cation of the counter-factual sample unit is conditional on a propensity score that is determined
by all covariates included in the propensity score estimator (logit model), and not on a single
ex-ante characteristic, or covariate. Consequently, an important robustness check in each of our
matching sequences involves the comparison of the distributions of each of the models’ covariates
between the treated and control groups. Rosenbaum and Rubin (1985) and Rosenbaum (2009)
illustrate that the two-sample t-test for comparing the distributions of covariates’ means is appro-
priate. The test results (available from the author upon request) confirm that the distributions
of the logistic model covariates across all three matching exercises between treated and control
groups, while they are significantly different before the matching, are not statistically different
after the matching. Therefore, effective matching between the treated and untreated samples is
achieved.
(Insert Table 6 about here)
Based on each matching-exercise, we compare firm value (i.e. CARj , (T1, Tn)) between treated
and control portfolios. The comparative performance of treated vs. control portfolios in the uni-
variate analysis is assessed based on a t-test of equality of means. Results are reported in Table
6. Column (1) indicates that acquirers in earnout-settled M&As (i.e. treated) significantly out-
perform their control ones, which exhibit similar probability in including earnout. Specifically,
results show that acquirers in earnout-settled M&As enjoy 0.449% higher gains (Panel A) relative
to the control deals, while similar conclusions are drawn from results recorded in Panels B and
C. These findings are consistent with previous earnout studies uncovering the valuation effects
of the earnout payment mechanism in M&As (see Kohers and Ang (2000) and Barbopoulos et al.
(2018a)). Results also suggest that the involvement of external advisors add significant value to
acquirers (Columns 2 to 4). Put forward, advisors on the acquiring firm’s side add significant
value to acquirers (Column 2), yet Columns (3) and (4) depict the marginally significant valuation
effects of the presence of advisor on the target side or the joint presence of acquirer and target
24
financial advisor in the deal. Consistent with our main predictions in this paper, the remain-
ing results in Table 6 (Columns 5 to 10) suggest that acquirers enjoy the highest gains from the
joint presence of earnout and external advisors in a deal. In particular, Column (9), Panel (A),
shows that earnout-settled M&As in which both acquirers and targets are advised by external
advisors enjoy 1.519% higher gains relative to control deals, significant at the 1% level. This of-
fers great support to our third hypothesis that predicts the presence of a complementarity-effect
between earnout and external advisors that leads to the highest acquirer gains. We come to this
conclusion by identifying the crucial role of (a) advisor in enhancing the likelihood of success of
earnout-settled M&As and (b) of earnout in enhancing the likelihood of success of advised deals.
5.3. Multiple regression analysis of acquirer risk-adjusted returns
The univariate analysis in Tables 4 and 5 (including the PSM univariate analysis in Table 6) sug-
gest that acquiring firms involved in advised earnout-settled M&As enjoy positive, significant, and
higher gains compared to acquiring firms engaged in either non-advised earnout-settled M&As
or non-earnout M&As regardless of whether they use external advisors. Table 6 confirms these
findings after addressing self-selection concerns. The multivariate analysis that follows further
examines the channels of the additional valuation and draws conclusions based on the theoreti-
cal arguments outlined in Section (2). In particular, this allows us to assess the valuation effects
of earnouts and financial advisors, independently, on the acquirer risk-adjusted returns, while
accounting simultaneously for known factors influencing the acquirer value.
Our regression model follows the Equation (5) and is based on the full sample of M&As. It uses
an earnout indicator variable to distinguish between earnout and non-earnout deals, as well as a
financial advisor indicator variable to distinguish between advised (on either or both sides of the
merger) and non-advised deals. The dependent variable is the cumulative acquirer risk-adjusted
return over the five-day event window (CARj , (T1, Tn)). Table 7 reports the results of our multiple
regression analysis.
(Insert Table 7 about here)
Right-hand side control variables in Equation (5) are discussed in Section (4) and also defined
in the Appendix A. As in univariate tests, Model (1) shows that earnout-settled M&As tend to add
significant value to acquirers, yet this is only obtained in the matched sample based on the PSM
(Panel B). This finding is consistent with previous studies such as Barbopoulos and Sudarsanam
(2012). We also find that relatively large deals (relative to the acquirer size) add consistently higher
25
value to acquirers, consistent with Asquith, Bruner, and Mullins (1983) and Fuller et al. (2002)
while deals with targets operating in the group of intangible-rich sectors destroy value. Deals of
unlisted targets add value to acquirer shareholders (Model 3), consistent with Chang (1998) and
Draper and Paudyal (2006). Other controls are generally insignificant.
We consider how elements affecting the merging firms’ endogenous choice of earnout (or not)
can impact our results. Since firm-level factors influencing the likelihood of an earnout (or finan-
cial advisor) transaction could also be linked to the resultant valuation of the M&A, as suggested
by the matched sample comparisons, Heckman treatment effect models (Heckman (1979) and
Heckman and Robb (1985)) are estimated. We are also guided to control for the impact of hidden-
or unobserved-bias in our multivariate analysis from the low value of RB sensitivity test, which
suggest that the outcome of the matching exercises is sensitive to hidden-bias. The treatment
model consists of a two-step procedure using in the first-stage probit regression (available upon
request) to model the earnout propensity to be used in a M&A, rather than a single up-front pay-
ment. To accommodate the possibility of the likelihood affecting the results of the main model,
we include in the second-stage equation a selectivity correction variable, the inverse Mills ratio
(Lambda), calculated from the probit estimates. Model 2 shows the results of the Heckman treat-
ment model. The coefficient on the earnout indicator (0.113) remains statistically insignificant
as in Model 1 (Panel A), while Lambda is negative and also statistically significant (-1.044). The
initial model, therefore, could be underestimating the earnout-effect, since factors influencing
the earnout use appear negatively associated with acquirer risk-adjusted returns. In Panel B,
however, the Lambda is negative and statistically insignificant (-5.020). The initial model, there-
fore, is likely to provide an accurate estimation of the earnout-effect, since factors influencing the
earnout use appear not to be associated with acquirer returns. Similar results are offered in the
Panels C and D.
Consistent with our univariate analysis, the multivariate analysis suggests that external ad-
visors add in general value to acquirers. At first, the positive and highly significant coefficient of
AFA in Models (5) and (6) across all panels suggest that external advisors on the acquirer side pro-
vide tangible benefits to acquirers, consistent with Bao and Edmans (2011). Similar conclusions
are drawn from results recorded in Model (10) when we model the impact of external advisor on
the target firm’s side. Lastly, the presence of advisors on both sides of the merger (acquirer and
target) further suggests that positive valuation effects originated from financial advisors.
Next we focus on the joint-impact of both treatments (i.e. earnout and advisors) on acquirer
risk-adjusted returns. Table 8 reports findings from the naive regressions (Panel A), regressions on
26
the matched sample based on the PSM matching ratio 1:1 (Panel B), regressions on the matched
sample based on the PSM matching ratio 3:1 (Panel C), and regressions on the matched sample
based on the PSM matching ratio 5:1 (Panel D). The results appear very strong on suggesting the
presence of a complementarity-effect between earnouts and advisors. In particular, Models (1) to
(4) focus on the impact of earnout and external advisor on the acquirer-side. Models (1) and (2)
that are executed on the full sample suggest that advised (on the acquirer side) earnout-settled
M&As add significantly higher value to acquirers relative to other deals. While in Models (3) and
(4) the interaction of advisor and earnout remains insignificant in Panel (A), in Panels (B) to (D)
this effect is positive, strong and robust in favor of our third hypothesis.
(Insert Table 8 about here)
Models (5) to (8) focus on the impact of earnout and external advisor on the target-side. Models
(3) and (4), which are executed on the full sample, suggest that advised (on the target side) earnout-
settled M&As add significantly higher value to acquirers relative to other deals. This finding also
holds in Panel (B) but in general the remaining finds in Models (5) to (8) in the remaining Panels
appear insignificant.
As expected, and consistent with our univariate analysis, Models (9) to (12) record very strong
evidence regarding the impact of earnout and external advisor on the both the acquirer- and
target-sides. Models (9) and (10), across all Panels, show that advised earnout-settled M&As add
significantly higher value to acquirers relative to other deals. Models (11) and (12), which are
executed on a sample excluding the independent presence of acquirer- or target-side advisors, i.e.
keep only deals in which both acquirer- and target-advisors are involved, confirm the significant
valuation effects that are originating from earnouts and both-side advisors. The positive and
strong impact of this relation is also captured in matched samples across Panels (B) to (D). Lastly,
only in the analysis executed in the full sample (Panel A) suggest that the Lambda (IMR) is positive
and significant in both treatment models (10 and 12) suggesting that the coefficient of ‘Earnout ×
AFA TFA’ in the initial models (9 and 11) is potentially overestimating the treatment effect, since
factors influencing the treatment’s choice appear positively associated with acquirer risk-adjusted
returns.
Overall, our multiple regression analysis provides compelling evidence suggesting that the
joint presence of advisors and earnouts constitutes an important channel of value creation. We
argue that the effects tracing from the ability of advisors to address valuation complexities more
efficiently and also contribute in the efficient design and facilitation of the earnout contract. To
27
this end, the positive implications of advisor in earnout-settled M&As become more pronounced
in deals in which acquirers face distributions that can cause large and harder-to-measure merger
valuation risk. As a result, our results further augment our complementarity argument regarding
the impact of the joint presence of advisors and earnouts on the likelihood of success of small,
yet risky M&As, consistent with our third hypothesis.
5.4. Multiple regression analysis of the impact of earnout contract design
In Table 9 we present our findings from our multivariate analysis of the impact of earnout contract-
design on acquirer risk-adjusted returns. We document that the aforementioned tangible benefits
offered to acquirers from external advisors are accrued from valuable earnout-structure advise.
Put simply, earnout contracts with (a) more stock in the initial and deferred payment stages, (b)
more contingencies related the target firm’s profit before tax, future profitability, and EBITDA
(combined within the ‘Combination of perf. measures’), and (c) low overall deferred payment
and also low ratio of deferred payment to the total consideration, appear to be associated with
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35
Appendix A Variable Definitions
Variable Type/Name Description Source All Refers to the entire sample analysed in this paper. SDC
Acquirer Age (Age) Number of days between the acquirer’s first recorded day on Datastream and the deal’s announcement day.
Datastream
Cross-border M&A (CBA) Dummy = 1 when the target is a non-UK based firm, and = 0 when both acquirer and target are UK institutions (=Domestic).
SDC
Cash-financed M&A Dummy = 1 when payment is 100% cash, and = 0 otherwise. SDC
Cash ratio Acquirer's total cash and cash equivalents to its total assets during the quarter prior to the deal’s announcement.
Datastream
Cash fraction Cash fraction refers to the fraction of cash financing in the initial payment. Target firm’s annual reports
Cash as deferred payment Cash in deferred payment refers to the fraction of cash financing in the deferred payment.
Target firm’s annual reports
Diversifying deal Dummy = 1 when acquirer and target do not share the same two-digit SIC code and = 0 otherwise.
SDC
Deal Value Deal’s transaction value, in million dollars. SDC
Domestic Dummy = 1 when acquirer and target are UK based, and = 0 when target is not a UK company.
SDC
Debt-to-Common Equity ratio Acquirer's total debt to common equity during the quarter prior to the deal’s announcement.
Datastream
Earnout Value (EAV) Value of earnout, in million dollars (proxy for size of earnout). SDC
Earnout-financed deals Dummy = 1 when payment includes earnout in addition to cash, stock, or mixed, and = 0 otherwise (=Non-Earnout).
SDC
Advised Earnout-Financed deals Dummy = 1 when there exists at least one financial advisor counseling the acquirer and the transaction includes an earnout provision, and = 0 otherwise.
SDC
Non-Advised Earnout-Financed deals Dummy = 1 when there does not exists a financial advisor counseling the acquirer and the transaction includes an earnout provision, and = 0 otherwise.
SDC
Acquirer Financial Advisor (AFA) Dummy = 1 when there exists at least one financial advisor counseling the acquirer, and = 0 otherwise.
SDC
Target in intangible sector Dummy = 1 when target belongs to an intangible-rich industry (Media and Entertainment, Consumer Products and Services, High Technology), and = 0 otherwise.
SDC
Market Value Acquirer’s market value of equity four weeks prior to deal’s announcement, in million dollars.
Datastream
Market-to-Book Value ratio Ratio of the acquirer’s market value four weeks prior to the deal’s announcement over the acquirer’s book value at the end of the last quarter prior to the deal’s announcement.
Datastream
Mixed-financed M&A Dummy = 1 when the payment is a mixture of cash, stock and/or other methods of payment, excluding earnout, and = 0 otherwise.
SDC
Non-Earnout Dummy = 1 when full-cash, or full-stock, or mixed payments without earnouts are used, and = 0 otherwise.
SDC
Non-AFA deals Dummy = 1 when there does not exist a financial advisor counseling the acquirer, and = 0 otherwise.
SDC
Number of payments (# of installments) Number of payments refers to the number of payments the earnout component or earnout value will be settled.
Target firm’s annual reports
Number of months (i.e. contract length) Number of deferred months refers to the months the full deferred consideration will be settled.
Target firm’s annual reports
Advised non-Earnout Financed deals Dummy = 1 when there exists at least one financial advisor counselling the acquirer and the transaction does not include earnout, and = 0 otherwise.
SDC
Non-Advised Non-Earnout Financed deals Dummy = 1 when there does not exist a financial advisor counselling the acquirer and the transaction does not include earnout, and = 0 otherwise.
SDC
Private target Dummy = 1 if target is private, and = 0 otherwise. SDC
Public target Dummy = 1 if target is publicly traded, and = 0 otherwise. SDC
Relative Size Ratio of DV to MV. SDC & Datastream
Relative Earnout Value Ratio of EAV to DV SDC
Focused deal Dummy = 1 when acquirer and target share the same two-digit SIC code and = 0 otherwise.
SDC
Stock-financed M&A Dummy = 1 when payment is 100% stock exchange, and = 0 otherwise. SDC
Shares fraction Shares fraction refers to the fraction of stock financing in the initial payment. Target firm’s annual reports
Stock as deferred payment Stock in deferred payment refers to the fraction of stock financing in the deferred payment.
Target firm’s annual reports
Subsidiary target Dummy = 1 if target is a subsidiary institution, and = 0 otherwise. SDC
Target in high tech sector Dummy = 1 if target is belonging to the High-Tec industry, and = 0 otherwise. SDC
Continued
36
Continued (Appendix A)
Variable Type/Name Description Source
Acquirer sigma Acquirer’s idiosyncratic stock return volatility measured as the standard deviation of the residuals in the CAPM and estimated over the period from 𝑡 −250 to 𝑡 − 20, where 𝑡 = 0 is the M&A announcement day.
Datastream
Target consumer product and services sector Dummy = 1 if target is belonging to the Consumer Products and Services industry, and = 0 otherwise.
SDC
Unlisted target Dummy = 1 if target is not a listed firm, and = 0 otherwise. SDC
Target Financial Advisor (TFA) Dummy = 1 when there exists at least one financial advisor counseling the target, and = 0 otherwise.
SDC
Acquirer and Target Financial Advisor (AFA_TFA) Dummy = 1 when there exists at least one financial advisor counseling each of the merging firms, and = 0 otherwise.
SDC
Target EBITDA perf. measure EBITDA performance is the fraction of target ‘EBITDA’ to ‘target total assets’ in which the earnout payment is conditional upon.
Target firm’s annual reports
Target revenue perf. measure Revenue performance is the fraction of ‘target revenue’ to ‘target total assets’ in which the earnout payment is conditional upon.
Target firm’s annual reports
Target PBT perf. measure PBT performance is the fraction of ‘target Profit-Before-Tax’ to ‘target total assets’ in which the earnout payment is conditional upon.
Target firm’s annual reports
Target combination of perf. measure Combination of performance measures is the fraction of target ‘EBITDA + Revenue + PBT’ to ‘target total assets’ in which the earnout payment is conditional upon.
Target firm’s annual reports
Target other perf. measure Other performance measures are the fraction of target ‘Other’ performance measures to ‘target total assets’ in which the earnout payment is conditional upon.
Target firm’s annual reports
Target future profitability perf. measure Future profitability performance is the fraction of ‘target future profitability’ to ‘target total assets’ in which the earnout payment is conditional upon.
Target firm’s annual reports
Failed earnout Fail refers to cases in which the earnout component is not delivered. Target firm’s annual reports
Partial successful earnout Partial success refers to cases in which the earnout component is partly delivered.
Target firm’s annual reports
Successful earnout Successful refers to cases in which the earnout component is fully delivered. Target firm’s annual reports
The table defines the variables used in the empirical analysis and indicates the data source used. SDC denotes the Thomson-Reuters SDC ONE Banker database.
37
Table 1 Annual distribution of all sampled M&A activities
Panel A: All M&As Panel B: Only Earnout-Settled M&As
Panel A refers to all M&As in our sample; Panel B refers to only M&As that are settled in earnouts. All refers to the entire M&A activity within each group; CBA refers to cross-border deals in which the acquirer (domiciled in the U.K.) and target is domiciled in a different country (non-U.K.); DIV refers to diversifying deals in which acquirer and target operate in different industries, i.e. they do not share the same two-digit SIC code; AFA refers to deals in which at least one financial advisor is counselling the acquiring firm; TFA refers to deals in which at least one financial advisor is counselling the target firm; AFA_TFA refers to deals in which at least one financial advisor is counselling the acquiring and one financial advisor is counselling the target firm; Priv. refers to deals in which the target is a private firm; Pub. refers to deals in which the target is a public firm; Sub. refers to deals in which the target is a subsidiary firm; Cash refers to deals fully financed with cash; Stock refers to deals fully financed with stock; Mix. refers to deals financed with a combination of cash, stock and/or other payments excluding earnout; NEA refers to all deals in the Cash, Stock and Mix. groups; DV refers to the transaction value; MV refers to the acquirer market capitalization 20 days prior to the deal’s announcement date; in Panel B only, EA refers to M&As that are settled in earnout; REAV refers to the ratio of ‘Earnout Value’ to the ‘Deal Value’. More information on the definition of each variable can be found in Appendix A.
38
Table 2 Summary Statistics
Panel A Mean Deal Value and Acquirer Market Capitalization
All NEA Earnout Non AFA AFA
NEA AFA
Earnout AFA
Non TFA TFA
NEA TFA
Earnout TFA
Non AFA_TFA AFA_TFA
NEA FTA_TFA
Earnout EFA_FTA
All Mean DV 137 175 30 23 385 459 69 24 451 523 95 29 714 779 174
Panel A presents the mean transaction-value of M&A deals and the mean acquirer market-value by different deal-characteristics and listing status groupings of the target firm; All refers to the entire M&A activity in the sample or within each group of deals; NEA refers to non-earnout-financed deals whose financing method consists of single payments in cash, stock, or mixed payments in cash, stock and/or other payments excluding earnout provisions; Earnout refers to deals financed with earnout; AFA refers to deals in which at least one financial advisor is counselling the acquiring firm; TFA refers to deals in which at least one financial advisor is counselling the target firm; AFA_TFA refers to deals in which at least one financial advisor is counselling the acquiring and one financial advisor is counselling the target firm; N refers to the number of observations. Panel B presents mean values of the transaction-value, acquirers’ market-value (measured by the company’s market capitalization 20 days prior to the M&A announcement), relative deal size (=deal value/acquirer’s market value), market-to-book ratio (measured by the acquirer’s market value 20 days prior to the deal’s announcement over the acquirer’s book value at the end of the last quarter prior to the deal’s announcement), cash-ratio ratio (measured by the acquirer’s ratio of cash and cash & equivalents to total assets at the end of the last quarter prior to the deal’s announcement) and debt-to-equity ratio (measured by the acquirer’s ratio of total debt to common equity at the end of the last quarter prior to the deal’s announcement), as well as the mean value of earnout value (EAV) and the relative earnout value (REAV) (=earnout value/deal value). All refers to the entire M&A activity in the sample or within each group of deals. Domestic (CBA) refers deals in which the acquirer and the target are domiciled same (different) countries; Focused (Diversifying) refers to deals in which acquirer and target operate in the same (different) industries, i.e. they do (do not) share the same two-digit SIC code; Private target refers to deals in which the target is a private firm; Public target refers to deals in which the target is a public firm; Subsidiary target refers to deals in which the target is a subsidiary firm; Cash refers to deals fully financed with cash; Stock refers to deals fully financed with stock; Mix. refers to deals financed with a combination of cash, stock and/or other payments excluding earnout; NEA refers to all deals in the Cash, Stock and Mix. groups; Earnout refers to M&As that are settled in earnout; AFA refers to deals in which at least one financial advisor is counselling the acquiring firm; TFA refers to deals in which at least one financial advisor is counselling the target firm; AFA_TFA refers to deals in which at least one financial advisor is counselling the acquiring and one financial advisor is counselling the target firm; N refers to the number of observations. Panel C presents the mean of each contract characteristic (N in parentheses refers to the number of observations); AFA refers to deals in which at least one financial advisor is counselling the acquiring firm; TFA refers to deals in which at least one financial advisor is counselling the target firm; AFA_TFA refers to deals in which at least one financial advisor is counselling the acquiring and one financial advisor is counselling the target firm; Cash fraction refers to the fraction of cash financing in the initial payment; Shares fraction refers to the fraction of stock financing in the initial payment; Cash in deferred payment refers to the fraction of cash financing in the deferred payment; Stock in deferred payment refers to the fraction of stock financing in the deferred payment; EBITDA
40
performance is the fraction of target ‘EBITDA’ to ‘target total assets’ in which the earnout payment is conditional upon; Revenue performance is the fraction of ‘target revenue’ to ‘target total assets’ in which the earnout payment is conditional upon; PBT performance is the fraction of ‘target Profit-Before-Tax’ to ‘target total assets’ in which the earnout payment is conditional upon; Combination of performance measures is the fraction of target ‘EBITDA + Revenue + PBT’ to ‘target total assets’ in which the earnout payment is conditional upon; Other performance measures is the fraction of target ‘Other’ performance measures to ‘target total assets’ in which the earnout payment is conditional upon; Future profitability performance is the fraction of ‘target future profitability’ to ‘target total assets’ in which the earnout payment is conditional upon; Number of payments refers to the number of payments the earnout component or earnout value will be settled; Number of deferred months refers to the months the full deferred consideration will be settled; Fail refers to cases in which the earnout component is not delivered; Partial success refers to cases in which the earnout component is partly delivered; Success refers to cases in which the earnout component is fully delivered; EAV refers to the size of earnout value; REAV refers to the ratio of EAV to deal or transaction value. More information on the definition of each variable can be found in Appendix A.
This table reports the Pearson pairwise-correlation coefficients for combinations between the following variables: Acq. CAR refers to the acquiring firm’s cumulative
abnormal returns over 5-days window surrounding the M&A announcement day; Deal Value reflects the deal’s transaction value (in $mil.); Acq. MV refers to the acquiring
firm’s market capitalization (measured 20 days prior to the deal’s announcement); Deal Rel. Size refers to the relative size of the deal (=deal value/acquirer’s market value
20 days prior to the deal’s announcement); Acq. MTBV refers to the acquiring firm’s market-to-book ratio (measured 20 days prior to the deal’s announcement); Acq. Debt-
to-Equity refers to the ration of acquire total debt to common equity at the end of the last quarter prior to the deal’s announcement; Cash & Equiv.-to-TA refers to the ratio
of acquirer cash and cash equivalents to total assets at the end of the last quarter prior to the deal’s announcement; Acq. Age refers to the number of years between the
acquirer’s first recorded day on Datastream and the deal’s announcement day; Acq. Sigma refers to the acquiring firm’s idiosyncratic stock return volatility (measured as in
Moeller et al., 2007); Priv. Target dum. refers to deals involving private targets; Sub. Target dum. refers to deals involving subsidiary targets; Pub. Target dum. refers to deals
involving public targets; Foreign Target dum. refers to cross-border acquisitions in which the acquirer and target are based in different countries; Diversifying Deal dum.
refers to diversifying deals in which acquirer and target operate in different industries, i.e. they do not share the same two-digit SIC code; Diff-to-Value dum. refers to deals
in which the target firm is based in a sector that is loaded with significant amounts of intangible assets such as the Media, Retail, High Technology, Financeial, Healthcare, or
Telecommunications; Earnout dum. refers to earnout-settled M&As; Relative Earnout Ratio refers to the ratio of earnout consideration or earnout value to deal or transaction
value; AFA refers to deals in which at least one financial advisor is counselling the acquiring firm; TFA refers to deals in which at least one financial advisor is counselling
the target firm; AFA_TFA refers to deals in which at least one financial advisor is counselling the acquiring and one financial advisor is counselling the target firm; Cash-
financed dum. refers to deals that are financed fully in cash; Stock-financed dum. refers to deals that are financed fully in stock. Further information on the definition of each
variable can be found in the Appendix A.
42
Table 4 Univariate Analysis of Acquirer Abnormal Returns
All Earnout (1)
Non-Earnout (2)
Cash (3)
Stock (4)
Mixed (5) (1) – (2) (1) – (3) (1) – (4) (1) – (5)
Panel A All M&As
All deals
Mean 1.099*** 1.329*** 1.019*** 0.950*** 0.841*** 1.181*** 0.310*** 0.379*** 0.488** 0.148 N 8,909 2,316 6,593 3,725 615 2,253
Private target deals
Mean 1.179*** 1.312*** 1.105*** 0.840*** 1.427*** 1.357*** 0.207* 0.472*** -0.115 -0.045 N 5,456 1,959 3,497 1,746 291 1,460
Public target deals
Mean -0.222 0.503 -0.243 0.531** -0.823* -0.834** 0.746 -0.028 1.326 1.337 N 766 21 745 321 216 208
Subsidiary target deals
Mean 1.314*** 1.480*** 1.290*** 1.146*** 2.592*** 1.458*** 0.190 0.334 -1.111** 0.022 N 2,687 336 2,351 1,658 108 585
Panel B M&As in which (at least) a Financial Advisor is consulting the Acquirer (AFA)
All deals
Mean 1.373*** 1.960*** 1.235*** 1.334*** 0.528 1.385*** 0.725*** 0.626** 1.432*** 0.576* N 2,801 532 2,269 1,112 331 826
Private target deals
Mean 1.862*** 1.879*** 1.854*** 1.274*** 2.461*** 2.199*** 0.025 0.605* -0.582 -0.320 N 1,272 428 844 343 99 402
Public target deals
Mean -0.278 -1.144 -0.262 0.629** -0.863* -1.010*** -0.882 -1.773 -0.281 -0.134 N 670 12 658 283 193 182
Subsidiary target deals
Mean 1.935*** 2.743*** 1.838*** 1.787*** 2.505** 1.833*** 0.904* 0.955* 0.238 0.910 N 859 92 767 486 39 242
Panel C M&As in which no Financial Advisor is consulting the Acquirer (Non-AFA)
All deals
Mean 0.974*** 1.141*** 0.905*** 0.786*** 1.207*** 1.063*** 0.236* 0.355*** -0.066 0.078 N 6,108 1,784 4,324 2,613 284 1,427
Private target deals
Mean 0.971*** 1.153*** 0.866*** 0.734*** 0.894** 1.037*** 0.287** 0.420*** 0.259 0.117 N 4,184 1,531 2,653 1,403 192 1,058
Public target deals
Mean 0.165 2.699 -0.097 -0.203 -0.485 0.401 2.796* 2.902 3.184 2.298 N 96 9 87 38 23 26
Subsidiary target deals
Mean 1.022*** 1.004*** 1.025*** 0.881*** 2.641*** 1.194*** -0.021 0.124 -1.637** -0.190 N 1,828 244 1,584 1,172 69 343
Panel D AFA (Panel B) vs. Non-AFA (Panel C)
All Mean 0.399*** 0.820*** 0.330** 0.548*** -0.679 0.322 Private Mean 0.891*** 0.726*** 0.988*** 0.540** 1.567* 1.162*** Public Mean -0.443 -3.843 -0.165 0.833 -0.378 -1.412
Subsidiary Mean 0.913*** 1.738*** 0.813*** 0.907*** -0.136 0.639* Panel E
M&As in which (at least) a Financial Advisor is consulting the Target (TFA)
All deals Mean 1.089*** 1.747*** 0.955*** 1.155*** -0.103 1.076*** 0.792*** 0.592** 1.850*** 0.671**
N 2,350 396 1,954 1,043 270 641
Private target deals
Mean 1.393*** 1.680*** 1.251*** 1.002*** 1.059 1.552*** 0.429 0.679* 0.621 0.128 N 908 301 607 283 55 269
Public target deals
Mean -0.343* -1.879 -0.326 0.447* -0.876* -0.895** -1.552 -2.326 -1.002 -0.984 N 641 7 634 266 188 180
Subsidiary target deals
Mean 1.889*** 2.263*** 1.843*** 1.624*** 2.915** 2.255*** 0.420 0.639 -0.652 0.008 N 801 88 713 494 27 192
Panel F M&As in which at no Financial Advisor is consulting the Target (Non-TFA)
All deals Mean 1.103*** 1.243*** 1.045*** 0.870*** 1.581*** 1.223*** 0.198 0.373*** -0.338 0.020
N 6,559 1,920 4,639 2,682 345 1,612
Private target deals
Mean 1.136*** 1.245*** 1.074*** 0.809*** 1.513*** 1.313*** 0.171 0.436*** -0.268 -0.068 N 4,548 1,658 2,890 1,463 236 1,191
Public target deals
Mean 0.398 1.694 0.234 0.934* -0.463 -0.443 1.459 0.760 2.157 2.136 N 125 14 111 55 28 28
Subsidiary target deals
Mean 1.070*** 1.202*** 1.050*** 0.944*** 2.484*** 1.069*** 0.153 0.259 -1.281* 0.133 N 1,886 248 1,638 1,164 81 393
Panel G TFA (Panel E) vs. Non-TFA (Panel F)
All Mean -0.015 0.504* -0.090 0.285* -1.684*** -0.147 Private Mean 0.257 0.435 0.177 0.193 -0.454 0.240 Public Mean -0.741* -3.573 -0.561 -0.487 -0.413 -0.452
Subsidiary Mean 0.819*** 1.060* 0.793*** 0.681*** 0.431 1.186***
Continued
43
Continued (Table 4)
All Earnout (1)
Non-Earnout (2)
Cash (3)
Stock (4)
Mixed (5) (1) – (2) (1) – (3) (1) – (4) (1) – (5)
Panel H M&As in which (at least) a Financial Advisor is consulting the Acquirer and the Target (AFA_TFA)
All deals Mean 0.998*** 2.223*** 0.849*** 1.236*** -0.325 0.952*** 1.374*** 0.987** 2.547*** 1.270**
N 1,403 152 1,251 598 234 419
Private target deals
Mean 1.791*** 2.257*** 1.603*** 1.105** 1.047 2.178*** 0.654 1.152* 1.209 0.078 N 375 108 267 103 38 126
Public target deals
Mean -0.343* -1.603 -0.330 0.550* -0.909** -1.009** -1.273 -2.153 -0.694 -0.594 N 602 6 596 248 179 169
Subsidiary target deals
Mean 2.194*** 2.730*** 2.141*** 1.979*** 2.761* 2.380*** 0.589 0.751 -0.031 0.351 N 426 38 388 247 17 124
Panel I M&As in which no Financial Advisor is consulting neither the Acquirer nor the Target (Non-AFA_TFA)
All deals Mean 1.118*** 1.266*** 1.058*** 0.895*** 1.558*** 1.233*** 0.208* 0.371*** -0.292 0.033
N 7,506 2,164 5,342 3,127 381 1,834
Private target deals
Mean 1.134*** 1.257*** 1.063*** 0.823*** 1.484*** 1.279*** 0.193 0.433*** -0.227 -0.023 N 5,081 1,851 3,230 1,643 253 1,334
Public target deals
Mean 0.220*** 1.345 0.107 0.464 -0.405 -0.077 1.239 0.881 1.750 1.423 N 164 15 149 73 37 39
Subsidiary target deals
Mean 1.148*** 1.321*** 1.122*** 1.001*** 2.560*** 1.210*** 0.199 0.320 -1.239* 0.110 N 2,261 298 1,963 1,411 91 461
All Mean -0.120 0.956** -0.209 0.341* -1.882*** -0.281 Private Mean 0.657** 1.000** 0.540* 0.281 -0.437 0.899* Public Mean -0.563 -2.948 -0.437 0.086 -0.504 -0.931
Subsidiary Mean 1.046*** 1.409* 1.019*** 0.979*** 0.201 1.169**
Panel A presents mean announcement period 5-day (t-2, t+2) cumulative abnormal returns (CAR) for all M&As within our sample as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel B presents mean announcement period 5-day CARs for only deals in which at least one financial advisor is counseling the acquirer as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel C presents mean announcement period 5-day CARs for deals in which no financial advisor is counseling the acquirer as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel D records differentials between deals that do (Panel B) and do not (Panel C) include at least one financial advisor counseling the acquirer. Panel E presents mean announcement period 5-day CARs for only deals in which at least one financial advisor is counseling the target as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel F presents mean announcement period 5-day CARs for deals in which no financial advisor is counseling the target as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel G records differentials between deals that do (Panel F) and do not (Panel G) include at least one financial advisor counseling the target. Panel H presents mean announcement period 5-day CARs for only deals in which at least one financial advisor is counseling the acquirer and the target as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel I presents mean announcement period 5-day CARs for deals in which no financial advisor is counseling the acquirer and the target as well as differentials (four right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel J records differentials between deals that do (Panel H) and do not (Panel I) include at least one financial advisor counseling the acquirer or the target. Across all Panels All refers to the entire M&A activity within each group; Earnout refers to M&As that are settled in earnout; Non-Earnout refers to all deals in the Cash, Stock and Mixed groups; Private target deals refers to deals in which the target is a private firm; Public target deals refers to deals in which the target is a public firm; Subsidiary target deals refers to deals in which the target is a subsidiary firm; N refers to the number of observations. The statistical significance of differences in returns between groups of acquirers is tested using the t-test for equality of means. ***, **, and * indicate significance at 1%, 5% and 10%, respectively. More information on the definition of each variable can be found in Appendix A.
44
Table 5 Univariate Analysis of Acquirer Abnormal Returns
All Earnout (1)
Non-Earnout (2)
Cash (3)
Stock (4)
Mixed (5) (1) – (2) (1) – (3) (1) – (4) (1) – (5)
AFA = 1 & TFA = 0 ONLY deals with AFA are included
All deals involving TFA are excluded PANEL A All M&As
All deals
Mean 1.142*** 1.285*** 1.082*** 0.891*** 1.702*** 1.268*** 0.203* 0.395*** -0.416 0.017 N 6,559 1,920 4,639 2,682 345 1,612
Private target deals
Mean 1.179*** 1.289*** 1.116*** 0.834*** 1.642*** 1.358*** 0.172 0.454*** -0.353 -0.070 N 4,548 1,658 2,890 1,463 236 1,191
Public target deals
Mean 0.437 1.776 0.268 0.934* -0.378 -0.393 1.508 0.842 2.154 2.169 N 125 14 111 55 28 28
Subsidiary target deals
Mean 1.098*** 1.235*** 1.077*** 0.960*** 2.594*** 1.113*** 0.158 0.275 -1.359** 0.122 N 1,886 248 1,638 1,164 81 393
PANEL B M&As in which (at least) a Financial Advisor is consulting the Acquirer (AFA) [NO TFA by default]
All deals
Mean 1.804*** 1.908*** 1.765*** 1.490*** 2.726*** 1.885*** 0.143 0.419 -0.818 0.023 N 1,398 380 1,018 514 97 407
Private target deals
Mean 1.953*** 1.806*** 2.034*** 1.394*** 3.486*** 2.269*** -0.227 0.413 -1.679* -0.463 N 897 320 577 240 61 276
Public target deals
Mean 0.317 -0.685 0.414 1.189 -0.177 -1.034 -1.099 -1.874 -0.508 0.349 N 68 6 62 35 14 13
Subsidiary target deals
Mean 1.731*** 2.801*** 1.578*** 1.630*** 2.468* 1.307*** 1.223* 1.171 0.333 1.494* N 433 54 379 239 22 118
PANEL C M&As in which at no Financial Advisor is consulting the Acquirer (Non-AFA) [NO TFA by default]
All deals
Mean 0.962*** 1.132*** 0.890*** 0.749*** 1.301*** 1.060*** 0.241* 0.383*** -0.169 0.072 N 5,161 1,540 3,621 2,168 248 1,205
Private target deals
Mean 0.989*** 1.165*** 0.887*** 0.725*** 0.999** 1.084*** 0.277* 0.440*** 0.165 0.081 N 3,651 1,338 2,313 1,223 175 915
Public target deals
Mean 0.580 3.622 0.084 0.489 -0.580 0.162 3.538 3.133 4.201 3.460 N 57 8 49 20 14 15
Subsidiary target deals
Mean 0.910*** 0.799*** 0.927*** 0.786*** 2.641*** 1.030*** -0.127 0.013 -1.841** -0.231 N 1,453 194 1,259 925 59 275
PANEL D AFA (Panel B) vs. Non-AFA (Panel C)
All Mean 0.842*** 0.777*** 0.875*** 0.741*** 1.426* 0.825*** Private Mean 0.963*** 0.641** 1.146*** 0.669** 2.486** 1.186*** Public Mean -0.263 -4.307 0.331 0.700 0.403 -1.196
Mean 1.001*** 1.177*** 0.902*** 0.719*** 0.831** 1.150*** 0.275* 0.458*** 0.346 0.027 N 4,026 1,446 2,580 1,326 213 1,041
Public target deals
Mean -0.299*** 1.232 -0.332* 0.528* -0.941** -0.947** 1.565 0.705 2.173 2.179 N 659 14 645 268 193 184
Subsidiary target deals
Mean 1.162*** 1.061*** 1.176*** 1.018*** 2.528*** 1.381*** -0.115 0.043 -1.466** -0.320 N 1,879 232 1,647 1,172 76 399
PANEL J M&As in which at least a Financial Advisor is consulting the Acquirer and the Target simultaneously (AFA_TFA)
All deals
Mean 0.963*** 2.169*** 0.816*** 1.214*** -0.380 0.917*** 1.353*** 0.955** 2.549*** 1.252** N 1,403 152 1,251 598 234 419
Private target deals
Mean 1.753*** 2.208*** 1.569*** 1.072** 1.003 2.145*** 0.639 1.136* 1.205 0.063 N 375 108 267 103 38 126
Public target deals
Mean -0.375* -1.627 -0.362* 0.531* -0.963** -1.036** -1.265 -2.158 -0.665 -0.591 N 602 6 596 248 179 169
Subsidiary target deals
Mean 2.158*** 2.658*** 2.109*** 1.959*** 2.666* 2.330*** 0.549 0.699 -0.008 0.328 N 426 38 388 247 17 124
PANEL K M&As in which at Financial Advisor is consulting neither the Acquirer nor Target (Non-AFA_Non-TFA)
All deals
Mean 0.904*** 1.062*** 0.836*** 0.721*** 1.115*** 0.987*** 0.226* 0.342** -0.052 0.075 N 5,161 1,540 3,621 2,168 248 1,205
Private target deals
Mean 0.924*** 1.094*** 0.825*** 0.689*** 0.794* 1.013*** 0.269* 0.405** 0.300 0.081 N 3,651 1,338 2,313 1,223 175 915
Public target deals
Mean 0.498 3.377 0.028 0.489 -0.664 0.060 3.349 2.888 4.041 3.317 N 57 8 49 20 14 15
Subsidiary target deals
Mean 0.870*** 0.748*** 0.888*** 0.767*** 2.488*** 0.953*** -0.140 -0.019 -1.739** -0.204 N 1,453 194 1,259 925 59 275
PANEL L AFA_TFA (Panel J) vs. Non-AFA_TFA (Panel K)
All Mean 0.059 1.107** -0.020 0.493** -1.494*** -0.071 Private Mean 0.829*** 1.114** 0.744** 0.383 0.209 1.132** Public Mean -0.873 -5.004 -0.390 0.042 -0.298 -1.097
Subsidiary Mean 1.288*** 1.910* 1.220*** 1.192*** 0.178 1.377***
Panel A presents mean announcement period 5-day (t-2, t+2) cumulative abnormal returns (CAR) for all M&As except
those involving target financial advisor within our sample as well as differentials (four right-most columns) between deals
financed with earnout and deals financed with non-earnout single up-front payments; Panel B presents mean
announcement period 5-day CARs for only deals in which at least one financial advisor is counseling the acquirer except
those deals involving target financial advisor as well as differentials (four right-most columns) between deals financed with
earnout and deals financed with non-earnout single up-front payments; Panel C presents mean announcement period 5-
day CARs for deals in which no financial advisor is counseling the acquirer (by default deals involving target financial
advisor are excluded) as well as differentials (four right-most columns) between deals financed with earnout and deals
financed with non-earnout single up-front payments; Panel D records differentials between deals that do (Panel B) and do
not (Panel C) include at least one financial advisor counseling the acquirer (by default deals involving target financial
advisor are excluded). Panel E presents mean announcement period 5-day (t-2, t+2) cumulative abnormal returns (CAR)
for all M&As except those involving acquirer financial advisor within our sample as well as differentials (four right-most
columns) between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel F
presents mean announcement period 5-day CARs for only deals in which at least one financial advisor is counseling the
target except those deals involving acquirer financial advisor as well as differentials (four right-most columns) between
deals financed with earnout and deals financed with non-earnout single up-front payments; Panel G presents mean
announcement period 5-day CARs for deals in which no financial advisor is counseling the target (by default deals involving
acquirer financial advisor are excluded) as well as differentials (four right-most columns) between deals financed with
earnout and deals financed with non-earnout single up-front payments; Panel H records differentials between deals that
do (Panel B) and do not (Panel C) include at least one financial advisor counseling the target (by default deals involving
acquirer financial advisor are excluded). Panel I presents mean announcement period 5-day (t-2, t+2) cumulative abnormal
returns (CAR) for all M&As except those involving acquirer or target financial advisor independently (i.e. we keep only those
deals that involve jointly acquirer and target financial advisors in addition to all remaining ones except those involving
acquirer or target financial advisor independently) within our sample as well as differentials (four right-most columns)
between deals financed with earnout and deals financed with non-earnout single up-front payments; Panel J presents mean
announcement period 5-day CARs for only deals in which financial advisor is counseling jointly the acquirer and the target
except those deals involving acquirer financial advisor as well as differentials (four right-most columns) between deals
financed with earnout and deals financed with non-earnout single up-front payments; Panel K presents mean
46
announcement period 5-day CARs for deals in which no financial advisor is counseling jointly the acquirer and the target
(by default deals involving acquirer or target financial advisor independently are excluded) as well as differentials (four
right-most columns) between deals financed with earnout and deals financed with non-earnout single up-front payments;
Panel L records differentials between deals that do (Panel J) and do not (Panel K) include financial advisor counseling
jointly the acquirer and the target (by default deals involving acquirer or target financial advisor independently are
excluded). Across all Panels All refers to the entire M&A activity within each group; Earnout refers to M&As that are settled
in earnout; Non-Earnout refers to all deals in the Cash, Stock and Mixed groups; Private target deals refers to deals in which
the target is a private firm; Public target deals refers to deals in which the target is a public firm; Subsidiary target deals
refers to deals in which the target is a subsidiary firm; N refers to the number of observations. The statistical significance
of differences in returns between groups of acquirers is tested using the t-test for equality of means. ***, **, and * indicate
significance at 1%, 5% and 10%, respectively. More information on the definition of each variable can be found in Appendix
A.
47
Table 6 ATT analysis
(1) Earnout
(2) AFA
(3) TFA
(4) AFA × TFA
(5) Earnout × AFA
(6) Earnout × AFA
(7) Earnout × TFA
(8) Earnout × TFA
(9) Earnout × AFA × TFA
(10) Earnout × AFA × TFA
Full sample Full sample Full sample Full sample Full sample
Delete if TFA = 1 (only M&As in which AFA is present)
Full sample
Delete if AFA = 1 (only M&As in which TFA is present)
Full sample
Delete if (AFA=1 or TFA=1) & AFA×TFA=0 (only M&As in which both AFA & TFA is present)
This table presents the mean announcement period, 5-day (t−2, t+2), cumulative abnormal returns (in %) of treated and control deals following matching between deals of the groups based on the Propensity Score Matching method. The treatments vary across models, which are as follows: in Model (1) the treatment is the earnout payment mechanism; in Model (2) the treatment is the presence of acquirer financial advisor in a deal; in Model (3) the treatment is the presence of target financial advisor in a deal; in Model (4) the treatment is the joint presence of acquirer and target financial advisor in a deal; in Model (5) the treatment is the joint presence of earnout and acquirer financial advisor in a deal; in Model (6) the treatment is the joint presence of earnout and acquirer financial advisor in a deal and all deals including target financial advisor are excluded from the control group; in Model (7) the treatment is the joint presence of earnout and target financial advisor in a deal; in Model (8) the treatment is the joint presence of earnout and target financial advisor in a deal and all deals including acquirer financial advisor are excluded from the control group; in Model (9) the treatment is the joint presence of earnout and both acquirer and target financial advisor in a deal; in Model (10) the treatment is the joint presence of earnout and both acquirer and acquirer financial advisor in a deal and all deals including acquirer and target financial advisor independently are excluded from the control group. The PSM technique employs 1:1 (Panel A), 3:1 (Panel B), and 5:1 (Panel C), nearest neighbor matching allowing for replacement. N refers to the number of observations in each deal portfolio. ***, **, and * indicate significance at 1%, 5% and 10%, respectively. Appendix A provides the definitions of the variables.
48
Table 7 Multivariate analysis on individual treatments
The table presentes the outputs from our multivariate analysis based on OLS and Treated models. The dependent variable consists of the announcement period 5-day (t-2,t+2) cummulative abnormal returns (CAR) of acquirers which are regressed against a set of explanatory variables. Regression outputs are estimated using Ordinary Least Squares (OLS) with the coefficients adjusted for possible heteroscedasticity using White (1980) heteroscedasticity-consistent standard errors and covariance. λ is the inverse Mills ratio. The treatments vary across models, which are as follows: in Models 1 and 2 the treatment is the earnout payment mechanism; in Models 3 and 4 the treatment is the presence of acquirer financial advisor in a deal; in Models 5 and 6 the treatment is the presence of acquirer financial advisor in a deal and all deals including target financial advisor are excluded from the sample; in Models 7 and 8 the treatment is the presence of target financial advisor in a deal; in Models 9 and 10 the treatment is the presence of target financial advisor in a deal and all deals including acquirer financial advisor are excluded from the sample; in Models 11 and 12 the treatment is the joint presence of acquirer and target financial advisor in a deal; in Models 13 and 14 the treatment is the joint presence of acquirer and target financial advisor in a deal and all deals including both acquirer and target financial advisor, independently, are excluded from the sample. The same models are estimated in matched samples that are formed based on the PSM technique that employs 1:1 (Panel B), 3:1 (Panel C), and 5:1 (Panel D), nearest neighbor matching allowing for replacement. ***, **, and * indicate significance at 1%, 5%, and 10% respectively. N stands for the number of observations. Further information on the definition of each variable can be found in the Appendix A.
49
Table 8 Multivariate analysis of interaction of treatments
The table presentes the outputs from our multivariate analysis based on OLS and Treated models. The dependent variable consists of the announcement period 5-day (t-2,t+2) cummulative abnormal returns (CAR) of acquirers which are regressed against a set of explanatory variables. Regression outputs are estimated using Ordinary Least Squares (OLS) with the coefficients adjusted for possible heteroscedasticity using White (1980) heteroscedasticity-consistent standard errors and covariance. λ is the inverse Mills ratio. The interactions of treatments, which vary across models, are as follows: in Models 1 to 4 the treatment is the joint presense earnout payment mechanism and acquirer financial advisor (Models 3 and 4 are estimated on samples excluding deals including target financial advisors); in Models 5 to 8 the treatment is the joint presense earnout payment mechanism and target financial advisor (Models 7 and 8 are estimated on samples excluding deals including acquirer financial advisors); in Models 9 to 12 the treatment is the joint presense earnout payment mechanism and both acquirer and target financial advisor (Models 11 and 12 are estimated on samples excluding deals including acquirer and target financial advisor independently). The same models are estimated in matched samples that are formed based on the PSM technique that employs 1:1 (Panel B), 3:1 (Panel C), and 5:1 (Panel D), nearest neighbor matching allowing for replacement. ***, **, and * indicate significance at 1%, 5%, and 10% respectively. N stands for the number of observations. Further information on the definition of each variable can be found in the Appendix A.
50
Table 9 Multivariate analysis on the impact of contract design
The table presentes the outputs from our multivariate analysis on the earnout contract design. The dependent variable consists of the announcement period 5-day (t-2,t+2) cummulative abnormal returns (CAR) of acquirers which are regressed against a set of explanatory variables. Regression outputs are estimated using Ordinary Least Squares (OLS) with the coefficients adjusted for possible heteroscedasticity using White (1980) heteroscedasticity-consistent standard errors and covariance. Cash fraction refers to the fraction of cash financing in the initial payment; Shares fraction refers to the fraction of stock financing in the initial payment; Cash in deferred payment refers to the fraction of cash financing in the deferred payment; Stock in deferred payment refers to the fraction of stock financing in the deferred payment; EBITDA performance is the fraction of target ‘EBITDA’ to ‘target total assets’ in which the earnout payment is conditional upon; Revenue performance is the fraction of ‘target revenue’ to ‘target total assets’ in which the earnout payment is conditional upon; PBT performance is the fraction of ‘target Profit-Before-Tax’ to ‘target total assets’ in which the earnout payment is conditional upon; Combination of performance measures is the fraction of target ‘EBITDA + Revenue + PBT’ to ‘target total assets’ in which the earnout payment is conditional upon; Other performance measures is the fraction of target ‘Other’ performance measures to ‘target total assets’ in which the earnout payment is conditional upon; Future profitability performance is the fraction of ‘target future profitability’ to ‘target total assets’ in which the earnout payment is conditional upon; Number of payments refers to the number of payments the earnout component or earnout value will be settled; Number of deferred months refers to the months the full deferred consideration will be settled; Fail refers to cases in which the earnout component is not delivered; Partial success refers to cases in which the earnout component is partly delivered; Success refers to cases in which the earnout component is fully delivered; EAV refers to the size of earnout value; REAV refers to the ratio of EAV to deal or transaction value. Controls include the Deal relative size, the Acquirer MTBV, the Acquirer Debt-to-Equity, the Acq. Cash & Equivalent-to-TA, the Acquirer Age, the Acquirer Sigma, Private target, CBA (foreign -non-U.K.- target deal), Diversified deal, Difficult-to-Value target. More information on the definition of each variable can be found in Appendix A. ***, **, and * indicate significance at 1%, 5%, and 10% respectively. N stands for the number of observations. Further information on the definition of each variable can be found in the Appendix A.
51
Figure 1 Absolute M&A Activity and Relative Earnout Activity
Figure 2 Absolute M&A Activity and Relative Earnout Activity