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.

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 remainingerrors remain our own.†Adam Smith Business School, University of Glasgow, Glasgow, G12 8QQ. Tel: +44(0)141 330 7229. Email:

leonidas.barbopoulos@glasgow.ac.uk.‡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: asaunder@stern.nyu.edu.

1. Introduction

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

(a) acquiring-firm fixed effects, (b) acquirer-advisor fixed effects, (c) target-advisor fixed effects,

(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)).

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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.

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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-

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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):

E[rj,t|Xt] = (1− β̂i)Rf,t + β̂iE(Rm,t) + β̂smbi E(SMBt) + β̂hml

i E(HMLt) + β̂umdi E(UMDt) (2)

The parameters β̂i, β̂smbi , β̂hml

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

0.73% significantly higher gains relative to advised non-earnout counterparts. Similarly, Panels

E and F show that acquirers in advised (on the target side or on both acquirer- and target-sides)

earnout-settled M&As enjoy 0.79% and 1.37% higher gains relative to advised non-earnout deals.

On the contrary, in the absence of advisors differentials of acquirer risk-adjusted returns between

earnout and non-earnout deals remain marginally statistically significant in Panels C and I (0.24%

and 0.21%, respectively), and insignificant in Panel F (0.20%). These findings provide the first

indication that the earnout-effect seems to persists, and also be much stronger, in the presence

of external advisors in either or both sides of the merger.

Disentangling the advisor-effect from the earnout-effect in acquirer gains is an important task

in our analysis. Results from the univariate analysis are reported in Panels D, G and I. In par-

ticular, Panel D shows that acquirers in advised (on the acquirer-side) relative to non-advised

earnout-settled M&As enjoy in general 0.82% higher gains, and from 0.73% to 1.74% higher

gains depending on the target firm’s listing status. The same pattern holds when we examine

the impact of target financial advisor, or the impact of joint-presence of acquirer and target fi-

nancial advisors, on the acquirer gains (Panels G and J). These results offer great support to our

third hypothesis predicting the existence of an implicit complementarity-effect between advisors

and earnouts in valuation-complex M&As. This offers novel evidence supporting the view that

financial advisors in earnout-settled M&As add significant value to acquirers and hence confirm

earlier studies uncovering the important role of financial advisors in M&As (McLaughlin (1992),

Servaes and Zenner (1996) and Bao and Edmans (2011)).

Other results reported in Table 4 uncover that acquirers of private or subsidiary target firms

enjoy significant gains as in Chang (1998) and Fuller et al. (2002), while acquirers of listed targets

experience significant losses, especially when the currency of financing is common equity (as

Travlos (1987) and Fuller et al. (2002)). Moreover, we find that acquirers of private targets that

are financed with common equity enjoy significant gains (Chang (1998) and Fuller et al. (2002)).

Lastly, acquirers of divested or subsidiary target enjoy significant gains regardless of the method

of payment used to finance the deal (Fuller et al. (2002)).

21

In Table 5 the analysis is executed on samples involving only one advisor-side (e.g. acquirer-

side) by excluding deals involving advisors on the opposite side of the transaction (e.g. the target-

side). This analysis aims identify the extent to which any variation of the acquirer gains that is

explicitly driven by the impact of a particular acquirer-side. Put simply, in Panels A to D (E to H) we

analyze only deals with acquirer (target) financial advisor while all deals involving target (acquirer)

external advisors are excluded from the analysis. Similarly, in Panes I to L we analyze only deals

involving jointly acquirer and target external advisors while deals involving only acquirer or only

target external advisor, independently, are excluded from the analysis.

Our results remain robust and consistent with the view that the acquirer- or target-side exter-

nal advisors, independently or jointly, add significant value to the acquiring firm in earnout-settled

M&As in the range of 0.64% to 2.00%. Among other interesting results recorded in Table 5, the

highest acquirer performance is observed in earnout-settled M&As in which advisors are in both

the acquirer- and target-sides and the control group contains no-advisors at all (Panels I to L).

Specifically in deals in which both the acquirer and the target are consulted by external advi-

sors, relative to M&As in which neither acquirer- nor target-side financial advisors are present,

acquirers enjoy 1.11% in all mergers, 1.11% in private target mergers, and 1.91% in subsidiary

target ones (Panel L). This provides further evidence on the existence of a complementarity-effect

between the presence of advisors (i.e. advisor-effect) and earnouts (i.e. earnout-effect) in M&As

with high information asymmetry or target valuation risk, consistent with our third hypothesis.

We examine this channel in detail later in the paper and we confirm the earnout-settled M&As

enhance firm value more in the presence of advisors who help in designing and facilitating them

more effectively. Therefore, in deals that appeal to earnout payments, financial advisors are likely

to enhance the efficient design of earnouts aiming to tie the dichotomous pre-merger expectations

to the realized monetary synergies. Hence, any frictions involved when structuring and negoti-

ating the earnout terms are likely to be efficiently managed due to advisors’ ability to address

valuation complexities and contracting costs (Servaes and Zenner (1996)).

(Insert Table 5 about here)

Overall, our findings from the univariate analysis (Tables 4 and 5) suggest that the earnout-

effect is enhanced and also persists in the presence of external advisors. Therefore, the risk-

mitigating properties and applicability of earnouts in valuation-complex M&As appear to be com-

plemented by the presence of advisors which, as we show later in the paper, tend to improve the

earnout-contract design and enhance the likelihood of full delivery of earnout payments. Later

22

in the paper we accommodate self-selection bias concerns with regards the endogenous choice of

advisor and/or earnout, as well as the source of valuation effects originated from advisors.

5.2. Addressing selection-bias

5.2.1. Propensity Score Matching and Rosenbaum-bound methods

In drawing inferences about the causal impact of each treatment, i.e. the decisions to (a) imple-

ment earnout, (b) consult an advisor, or (c) include both advisor and earnout in a deal, on our

firm performance variable (i.e. CARj , (T1, Tn)), considerable selection-bias issues arise. To deal

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

significantly higher acquirer risk-adjusted returns.

In particular, we find that earnout-settled M&As that have more stock in the initial nd deferred

stage payment are associated with with higher acquirer risk-adjusted returns (coefficients of 5.313

and 1.213, respectively) relative to deals involving cash in the deferred payment stage (coefficient

of -1.010), consistent with Barbopoulos et al. (2018b). This suggest that merger valuation risk

is reduced much more efficiently in the presence of stock in both initial and deferred payment

stages given the dual risk-reduction properties embedded in stock and the deferred stage payment

(Hansen (1987) and Kohers and Ang (2000)). More evidence suggest that advised earnout-settled

M&As that condition the delivery of deferred payment to the target firm’s profit before tax per-

formance measure, as well as the combination of target firm’s performance measures such as

(a) profit before tax, (b) future profitability, (c) revenue, and (d) EBITDA, are associated with sig-

nificantly higher acquirer risk-adjusted returns. This is novel evidence uncovering the impact

of earnout-contract design in general, and more specifically the impact of external advisors in

influencing the contract-design that offers great support to an earnout-structure skilled-advise

hypothesis that is vital for the success of earnout-settled M&As.

(Insert Table 9 about here)

A very interesting stylized fact recorded in Table 2 (Panel C) confirms that earnout-settled

M&As appear more likely (approx. 4% higher) to be successful in the presence of external advi-

28

sors counseling the target firm. In conjunction with this stylized fact, we find that acquirers enjoy

significantly higher risk-adjusted returns at the announcement period of the merger. This suggest

that, ceteris paribus, the presence of target financial advisors in an earnout-settled M&A sends a

very strong signal in the market regarding both the high-yield merger and the high-likelihood of its

success. Moreover, we find that acquirers enjoy significant gains from smaller earnout contract

sizes relative to the total deal consideration (i.e. smaller relative earnout value), which appears

to be correlated with presence of target financial advisors in the deal (as also depicted from Table

2, Panel C). As a result, earnout-settled M&As are more likely to be successful when the de-

ferred payment is relatively small (relative to the total transaction value) and hence, more likely

to be settled. This implicitly suggests the important role of target financial advisors in negotiat-

ing favorable earnout-specs and hence a complementarity effect between earnouts and external

advisors. Lastly, in general we find insignificant results regarding the impact of (a) the number

of deferred payments, and (b) length of earnout contract period (measured with the number of

deferred months in the earnout-contract) in affecting acquirer risk-adjusted returns.

The above 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 favorable earnout contract terms, which offers great support to

the skilled-advice hypothesis of Bao and Edmans (2011).

6. Conclusion

This paper presents new insights into the joint impact of financial advisors and earnouts (i.e.

advised earnout-settled Mergers and Acquisitions (M&As)) on firm value. Our analysis is based

on a sample of 8,909 M&As originating from the U.K. We show that financial advisors present a

unique channel of value creation in earnout-settled M&As due to their valuable input in struc-

turing and facilitating earnout contracts more adequately. This, as a result, 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. acquirer gains) for earnout than non-earnout settled M&As in which financial advi-

sors are in either or both sides of the deal. Based on a quasi-experimental design via which the

impact of earnout or advisor is evaluated in isolation we find that advised treated, i.e. earnout-

settled M&As, yield the highest acquirer gains relative to control deals. Such outcomes are also

derived based on a model embracing the Inverse Mills Ratio on a full or matched sample. Our

29

results support an earnout-structure skilled-advise hypothesis which is vital for the success of

earnout-settled M&As.

Therefore, in line with the predictions of our Hypotheses 1 to 3, the results confirm the superior

performance of earnout-settled M&As, financial advisor presence, and establish that earnouts

generally enhance firm value only when acquirers choose to consult a external advisor that help

them to improve and drafting the earnout agreement. This suggests that the contingent nature

of earnout can help firms to address potential adverse section and moral hazard issues when

is more likely to be structured efficiently. To this end, the uncertainty-resolution properties of

earnout help maximize the likelihood of the deal’s success. We also find that the tangible benefits

offered via advised earnout-settled M&As are higher where the target firm is unlisted and when

the deal is a diversifying one.

To reduce the exposure of the derived conclusions to potential self-selection bias issues, we

adopt a quasi-experimental research design via the Propensity Score Matching (PSM) method

augmented with the Rosenbaum-bounds (RB) sensitivity analysis through which the advisor-

and earnout-effects, both jointly and independently, are evaluated in isolation. The PSM analy-

sis offers direct evidence of the superiority of earnouts over non-earnouts (generating on average

more than 2 percentage-points higher announcement-period risk-adjusted returns) in deals also

consulted by financial advisors. In contrast, the superior performance of earnouts disappears

in deals in which the acquirer is not consulted by financial advisors. As the RB sensitivity anal-

ysis guides us that our conclusions are exposed to hidden- or omitted variable-bias, we extend

our analysis based on Heckman two-stage models that include the Inverse Mills Ratio (IMR) in

the second stage. Our conclusions remain robust in favor the hypothesis that there exists a

complementarity-effect between earnouts and advisors.

Overall, we offer a thorough examination of the impact of earnout payment mechanism in

M&As on the acquiring firm’s value when the acquiring or the target firm, both independently

and jointly, are counseling external advisors. Undoubtedly, advised earnout-settled M&As appear

to offer a major value enhancing opportunity for the acquiring firm.

30

References

Agrawal, A., Cooper, T., Lian, Q., Wang, Q., 2013. Common advisers in mergers and acquisitions:

Determinants and consequences. Journal of Law and Economics 56, 691–740.

Allen, L., Jagtiani, J., Peristiani, S., Saunders, A., 2004. The role of bank advisors in mergers

and acquisitions. Journal of Money, Credit, and Banking 36, 197–224.

Andriosopoulos, D., Barbopoulos, L. G., 2017. Relative equity market valuation conditions and

acquirers’ gains. Review of Quantitative Finance and Accounting 49, 855–884.

Asquith, P., Bruner, R. F., Mullins, D. W., 1983. The gains to bidding firms from merger. Journal

of Financial Economics 11, 121–139.

Bao, J., Edmans, A., 2011. Do investment banks matter for m&a returns? Review of Financial

Studies 24, 2286–2315.

Barbopoulos, L. G., 2019. Incentive-compatible contracts in merger negotiations: The role of

acquirer idiosyncratic stock return volatility. Financial Markets, Institutions and Instruments

Forthcoming, 1–38.

Barbopoulos, L. G., Adra, S., 2016. The earnout structure matters: Takeover premia and acquirers

gains in earnout financed M&As. International Review of Financial Analysis 45, 283–294.

Barbopoulos, L. G., Danbolt, J., Alexakis, D., 2018a. The role of earnout financing on the valu-

ation effects of global diversification. Journal of International Business Studies Forthcoming,

1–38.

Barbopoulos, L. G., Molyneux, P., Wilson, J. O., 2016. Earnout financing in the financial services

industry. International Review of Financial Analysis 47, 119 – 132.

Barbopoulos, L. G., Paudyal, K., Pescetto, G., 2012. Legal systems and gains from cross-border

acquisitions. Journal of Business Research 65, 1301–1312.

Barbopoulos, L. G., Paudyal, K., Sudarsanam, S., 2018b. Earnout deals: Method of initial pay-

ment and acquirers’ gains. European Financial Management 0000, 1–43.

Barbopoulos, L. G., Sudarsanam, S., 2012. Determinants of earnout as acquisition payment

currency and bidder’s value gains. Journal of Banking & Finance 36, 678–694.

31

Bates, T. W., Neyland, J. B., Wang, Y. Y., 2018. Financing acquisitions with earnouts. Journal of

Accounting and Economics 66, 374–395.

Behr, A., Heid, F., 2011. The success of bank mergers revisited. an assessment based on a match-

ing strategy. Journal of Empirical Finance 18, 117–135.

Bowers, H. M., Miller, R. E., 1990. Choice of investment banker and shareholders’ wealth of firms

involved in acquisitions. Financial Management 19, 34–44.

Cain, M. D., Denis, D. J., Denis, D. K., 2011. Earnouts: A study of financial contracting in

acquisition agreements. Journal of Accounting and Economics 51, 151–170.

Carhart, M. M., 1997. On persistence in mutual fund performance. The Journal of Finance 52,

57–82.

Chang, S., 1998. Takeovers of privately held targets, methods of payment, and bidder returns.

Journal of Finance 53, 773–784.

Datar, S., Frankel, R., Wolfson, M., 2001. Earnouts: The effects of adverse selection and agency

costs on acquisition techniques. The Journal of Law, Economic, & Organisation 17, 201–238.

Dehejia, R. H., Wahba, S., 2002. Propensity score-matching methods for nonexperimental casual

studies. Review of Economics and Statistics 84, 151–161.

Denis, D. J., Denis, D. K., Yost, K., 2002. Global diversification, industrial diversification, and

firm value. Journal of Finance 57, 1951–1979.

Draper, P., Paudyal, K., 2006. Acquisitions: Private versus public. European Financial Manage-

ment 12, 57–80.

Eckbo, B. E., Makaew, T., Thorburn, K. S., 2016. Are stock-financed takeovers opportunistic?

Journal of Financial Economics 128, 443–465.

Eckbo, E. B., Giammarino, R. M., Heinkel, R. L., 1990. Asymmetric information and the medium

of exchange in takeovers: Theory and tests. The Review of Financial Studies 3, 651–675.

Faccio, M., Masulis, R. W., 2005. The choice of payment method in european mergers and acqui-

sitions. Journal of Finance 60, 1345–1388.

Faccio, M., McConnell, J. J., Stolin, D., 2006. Returns to acquirers of listed and unlisted targets.

Journal of Financial and Quantitative Analysis 41, 197–220.

32

Fuller, K., Netter, J., Stegemoller, M., 2002. What do returns to acquiring firms tell us? evidence

from firms that make many acquisitions. The Journal of Finance 57, 1,763–1,793.

Golubov, A., Petmezas, D., Travlos, N. G., 2012. When it pays to pay your investment banker: New

evidence on the role of financial advisors in m&as. Journal of Finance 67, 271–311.

Golubov, A., Yawson, A., Zhang, H., 2015. Extraordinary acquirers. Journal of Financial Eco-

nomics 116, 314–330.

Gu, X. S., Rosenbaum, P. R., 1993. Comparison of multivariate matching methods: Structures,

distances, and algorithms. Journal of Computational and Graphical Statistics 2, 405–420.

Hansen, R. G., 1987. A theory for the choice of exchange medium in mergers and acquisitions.

Journal of Business 60, 75–95.

Heckman, J. J., 1979. Sample selection bias as a specification error. Econometrica 47, 153–161.

Heckman, J. J., Robb, R. J., 1985. Alternative methods for evaluating the impact of intervantions:

An overview. Journal of Econometrics 30, 239–267.

Hunter, W. C., Jagtiani, J., 2003. An analysis of advisor choice, fees, and effort in mergers and

acquisitions. Review of Financial Economics 12, 65–81.

Hunter, W. C., Walker, M. B., 1990. An empirical examination of investment banking merger fee

contracts. Southern Economic Journal 56, 1117–1130.

Kale, J. R., Kini, O., Ryan, J. H. E., 2003. Financial advisors and shareholder wealth gains in

corporate takeovers. Journal of Financial and Quantitative Analysis 38, 475–501.

Kohers, N., Ang, J., 2000. Earnouts in mergers: Agreeing to disagree and agreeing to stay. Journal

of Business 73, 445–476.

Krishnan, C., Masulis, R. W., 2013. Law firm expertise and merger and acquisition outcomes.

Journal of Law and Economics 56, 189–226.

Lukas, E., Reuer, J. J., Welling, A., 2012. Earnouts in mergers and acquisitions: A game-theoretic

option pricing approach. European Journal of Operational Research 223, 41–57.

Mantecon, T., 2009. Mitigating risks in cross-border acquisitions. Journal of Banking & Finance

33, 640—651.

33

Martin, K. J., 1996. The method of payment in corporate acquisitions, investment opportunities,

and management ownership. Journal of Finance 51, 1227 – 1246.

McLaughlin, R. M., 1990. Investment-banking contracts in tender offers: An empirical analysis.

Journal of Financial Economics 28, 209–232.

McLaughlin, R. M., 1992. Does the form of compensation matter?: Investment banker fee con-

tracts in tender offers. Journal of Financial Economics 32, 223–260.

Michel, A., Shaked, I., Lee, Y.-T., 1991. An evaluation of investment banker acquisition advice:

The shareholders’ perspective. Financial Management 20, 40–49.

Moeller, B. S., Schlingemann, F. P., 2005. Global diversification and bidder gains: A comparison

between cross-border and domestic acquisitions. Journal of Banking & Finance 29, 533–564.

Rhodes-Kropf, M., Robinson, D. T., Viswanathan, S., 2005. Valuation waves and merger activity:

The empirical evidence. Journal of Financial Economics 77, 561–603.

Rosenbaum, P. R., 2002. Observational Studies. Springer.

Rosenbaum, P. R., 2009. Design of Observational Studies. Springer.

Rosenbaum, P. R., Rubin, D. B., 1985. Constructing a control group using multivariate matched

sampling methods that incorporate the propensity score. American Statistician 39, 33–38.

Rubin, D. B., Thomas, N., 1992. Characterizing the effect of matching using linear propensity

score methods with normal distributions. Biometrika 79, 797–809.

Servaes, H., Zenner, M., 1996. The role of investment banks in acquisitions. Review of Financial

Studies 9, 787–815.

Shleifer, A., Vishny, R. W., 2003. Stock market driven acquisitions. Journal of Financial Eco-

nomics 70, 295–311.

Sudarsanam, S., 1995. The Essence of Mergers and Acquisitions, London, Prentice Hall.

Travlos, N. G., 1987. Corporate takeover bids, methods of payment, and bidding firms’ stock

returns. The Journal of Finance 42, 943–963.

Zhang, F. X., 2006. Information uncertainty and stock returns. Journal of Finance 61, 105–137.

34

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

All CBA DIV AFA TFA

AFA TFA Priv. Pub. Sub. Cash Stock

Mix.

All NEA

DV MV

All EA AFA TFA

AFA TFA Priv. Pub. Sub.

REAV (%)

1986 44 18 23 28 24 20 21 11 12 22 12 8 42 180 1,019 2 2 1 1 1 0 1 38 1987 135 29 76 48 41 36 70 29 36 56 34 25 115 98 859 20 1 1 0 17 0 3 44 1988 342 78 215 71 57 37 213 48 81 154 30 63 247 58 578 95 14 6 3 79 6 10 44 1989 418 112 249 95 67 42 231 46 141 192 40 86 318 63 619 100 17 7 2 79 3 18 45 1990 252 73 153 81 46 29 122 21 109 129 17 48 194 41 605 58 10 4 2 45 1 12 48 1991 165 37 100 64 28 20 73 22 70 72 18 40 130 52 1,135 35 11 1 1 26 2 7 35 1992 190 48 126 58 30 19 97 7 86 74 18 62 154 19 709 36 9 1 1 24 1 11 37 1993 259 67 151 94 62 37 125 17 117 120 18 82 220 55 616 39 14 7 4 25 1 13 48 1994 324 80 170 101 60 34 177 22 125 154 25 89 268 47 648 56 13 5 1 48 1 7 35 1995 329 100 197 87 79 46 194 21 114 130 19 101 250 103 894 79 12 12 3 66 1 12 38 1996 379 110 207 107 96 50 245 27 107 165 26 101 292 64 717 87 21 17 7 79 0 8 35 1997 469 148 256 121 110 65 294 34 141 186 32 145 363 95 1,056 106 23 20 11 87 0 19 38 1998 499 162 232 172 143 99 275 50 174 244 24 133 401 152 1,091 98 22 14 7 80 1 17 39 1999 522 177 220 225 163 114 285 75 162 221 35 153 409 654 1,734 113 38 21 5 90 1 22 40 2000 521 193 238 208 189 129 309 53 159 177 53 154 384 313 1,913 137 31 28 15 116 0 21 44 2001 374 135 169 150 104 67 238 25 111 123 23 102 248 47 956 126 40 22 10 111 0 15 45 2002 279 71 124 88 78 50 183 17 79 139 16 45 200 91 1,018 79 23 16 9 63 1 15 44 2003 226 76 82 65 75 37 130 24 72 103 16 52 171 68 1,845 55 14 10 5 46 0 9 39 2004 285 94 125 80 76 42 188 19 78 113 20 71 204 75 1,822 81 9 12 2 71 0 10 35 2005 368 120 147 106 119 68 261 33 74 150 23 73 246 138 2,666 122 24 28 8 115 1 6 40 2006 380 139 154 101 98 56 263 30 87 154 16 80 250 87 2,975 130 18 22 6 112 0 18 40 2007 425 150 166 122 134 68 307 34 84 162 18 100 280 163 3,260 145 28 32 12 128 0 17 37 2008 233 96 95 49 51 24 173 12 48 105 12 35 152 116 2,988 81 13 13 2 75 0 6 39 2009 126 47 50 42 36 22 77 20 29 51 17 19 87 91 4,059 39 10 5 3 36 0 3 44 2010 209 84 97 67 64 37 131 17 61 112 9 24 145 132 3,399 64 15 8 2 56 0 8 40 2011 191 88 93 61 53 29 109 14 68 90 12 24 126 70 2,207 65 13 9 3 52 0 13 43 2012 161 67 66 47 53 24 107 12 42 89 12 12 113 113 4,592 48 11 10 4 44 0 4 38 2013 165 56 58 53 40 16 118 6 41 77 5 29 111 55 2,833 54 18 13 4 46 0 8 36 2014 218 75 108 75 69 39 150 9 59 109 11 37 157 167 1,584 61 24 26 11 53 0 8 37 2015 237 92 133 78 69 34 159 8 70 45 3 130 178 132 2,915 59 18 17 6 49 1 9 33 2016 184 45 101 57 36 13 131 3 50 7 1 130 138 58 1,155 46 16 8 2 40 0 6 36 Total 8,909 2,867 4,381 2,801 2,350 1,403 5,456 766 2,687 3,725 615 2,253 6,593 - - 2,316 532 396 152 1,959 21 336 -

% of Total - 32 49 31 26 16 61 9 30 42 7 25 74 - - - 23 17 7 84 1 15 - Average - - - - - - - - - - - - 116 1,757 - - - - - - 40

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

Mean MV 1,666 1,972 792 1,192 2,699 3,116 923 930 3,718 4,041 2,121 1,202 4,148 4,397 2,095 N 8,909 6,593 2,316 6,108 2,801 2,269 532 6,559 2,350 1,954 396 7,506 1,403 1,251 152

Private Mean DV 30 34 23 17 73 87 46 17 94 107 68 20 168 193 107 Mean MV 922 1,089 623 818 1,264 1,552 698 675 2,156 2,360 1,744 799 2,584 2,808 2,031

N 5,456 3,497 1,959 4,184 1,272 844 428 4,548 908 607 301 5,081 375 267 108

Public Mean DV 1,012 1,037 124 122 1,140 1,157 199 147 1,181 1,190 338 171 1,241 1,250 375 Mean MV 5,140 5,266 665 7,166 4,850 4,931 414 868 5,973 6,021 1,615 4,660 5,271 5,317 701

N 766 745 21 96 670 658 12 125 641 634 7 164 602 596 6

Subsidiary Mean DV 104 111 60 32 258 269 163 34 271 283 168 40 447 458 333

Mean MV 2,185 2,242 1,787 1,733 3,146 3,279 2,037 1,549 3,683 3,711 3,450 1,855 3,937 4,078 2,498 N 2,687 2,351 336 1,828 859 767 92 1,886 801 713 88 2,261 426 388 38

Panel B Acquirer- and Deal- specific features

N DV MV RS (in %) N MTBV N Cash/TA N Debt/Eq. N EAV REAV All 8,909 137 1,666 31 8,909 1.95 8,212 13 8,209 42 2,316 8.63 40

Domestic 6,042 79 852 31 6,042 1.54 5,472 12 5,470 38 1,561 5.74 41 CBA 2,867 258 3,380 29 2,867 2.82 2,740 15 2,739 51 7,55 14.60 39

Focused 4,528 198 1,971 27 4,528 2.12 4,211 14 4,207 47 1,189 9.98 40 Diversifying 4,381 74 1,350 34 4,381 1.77 4,001 13 4,002 38 1,127 7.20 40

Private target 5,456 30 922 31 5,456 2.10 5,035 14 5,032 39 1,959 7.92 41 Public target 766 1,012 5,140 52 766 1.54 690 14 689 114 21 16.68 36

Subsidiary target 2,687 104 2,185 25 2,687 1.76 2,487 12 2,488 30 336 12.29 35 Cash 3,725 90 2,627 16 3,725 1.72 3,432 11 3,430 38 - - -

Stock 615 395 941 119 615 1.06 511 20 510 43 - - - Mixed 2,253 255 1,172 37 2,253 2.12 2,097 13 2,096 58 - - -

Non-Earnout 6,593 175 1,972 33 6,593 1.80 6,040 13 6,036 46 - - - Earnout 2,316 30 792 25 2,316 2.38 2,172 15 2,173 33 2,316 8.63 40

AFA 2,801 385 2,699 54 2,801 1.97 2,592 14 2,587 52 532 14.62 34 Non-AFA 6,108 23 1,192 20 6,108 1.94 5,620 13 5,622 38 1,784 6.84 42

Earnout AFA 532 69 923 48 532 2.27 499 15 499 20 532 14.62 34 Earnout Non-AFA 1,784 18 753 18 1,784 2.42 1,673 15 1,674 37 1,784 6.84 42 Non-Earnout AFA 2,269 459 3,116 56 2,269 1.90 2,093 13 2,088 60 - - -

Non-Earnout Non-AFA 4,324 26 1,372 20 4,324 1.74 3,947 12 3,948 38 - - - TFA 2,350 451 3,718 41 2,350 2.12 2,197 13 2,195 81 396 19.48 30

Non-FTA 6,559 24 930 27 6,559 1.89 6,015 13 6,014 28 1,920 6.39 42 Earnout TFA 396 95 2,121 34 396 3.06 382 15 381 56 396 19.48 30

Earnout Non-TFA 1,920 16 518 23 1,920 2.24 1,790 15 1,792 29 1,920 6.39 42 Non-Earnout TFA 1,954 523 4,041 42 1,954 1.93 1,815 13 1,814 86 - - -

Non-Earnout Non-TFA 4,639 28 1,101 28 4,639 1.74 4,225 13 4,222 28 - - - AFA_TFA 1,403 714 4,148 54 1,403 2.04 1,305 13 1,303 90 152 27.35 25

Non-AFA_TFA 7,506 29 1,202 26 7,506 1.93 6,907 13 6,906 33 2,164 7.32 41 Earnout AFA_TFA 152 174 2,095 59 152 2.82 144 15 143 52 152 27.35 25

Earnout Non-AFA_TFA 2,164 19 701 23 2,164 2.35 2,028 15 2,030 32 2,164 7.32 41 Non-Earnout AFA_TFA 1,251 779 4,397 54 1,251 1.95 1,161 13 1,160 95 - - -

Non-Earnout Non-AFA_TFA 5,342 33 1,404 28 5,342 1.76 4,879 13 4,876 34 - - -

Continued

39

Continued (Table 2)

Panel C Mean Deal Value and Acquirer Market Capitalization

All No AFA AFA AFA vs. No AFA No TFA TFA TFA vs. No TFA Cash fraction 0.532 (660) 0.529 (495) 0.542 (165) 0.012 0.511 (520) 0.614 (140) 0.103***

Shares fraction 0.064 (660) 0.063 (495) 0.067 (165) 0.004 0.068 (520) 0.048 (140) -0.021* Cash in deferred payment 0.726 (661) 0.725 (496) 0.727 (165) 0.001 0.702 (521) 0.814 (140) 0.112***

Stock in deferred payment 0.066 (661) 0.072 (496) 0.048 (165) -0.024 0.078 (521) 0.021 (140) -0.057*** Target EBITDA perf. measure 0.152 (657) 0.134 (492) 0.206 (165) 0.072* 0.146 (518) 0.172 (139) 0.026 Target revenue perf. measure 0.098 (657) 0.103 (492) 0.084 (165) -0.019 0.102 (518) 0.086 (139) -0.016

Target PBT perf. measure 0.076 (657) 0.089 (492) 0.036 (165) -0.053** 0.092 (518) 0.014 (139) -0.078*** Target combination of perf. measure 0.099 (656) 0.101 (491) 0.090 (165) -0.011 0.104 (518) 0.079 (138) -0.025

Target other perf. measure 0.120 (657) 0.115 (492) 0.133 (165) 0.017 0.115 (518) 0.136 (139) 0.021 Target future profitability perf. measure 0.599 (661) 0.572 (496) 0.678 (165) 0.106*** 0.606 (521) 0.571 (140) -0.035 Number of payments (# of installments) 2.377 (127) 2.400 (85) 2.333 (42) -0.067 2.422 (109) 2.111 (18) -0.311 Number of months (i.e. contract length) 25.97 (561) 26.31 (421) 24.92 (140) -1.390 25.96 (452) 25.98 (109) 0.013

Failed earnout 0.030 (661) 0.030 (496) 0.030 (165) 0.000 0.032 (521) 0.021 (140) -0.011 Partial successful earnout 0.093 (661) 0.094 (496) 0.090 (165) -0.004 0.095 (521) 0.085 (140) -0.010

Successful earnout 0.232 (661) 0.233 (496) 0.230 (165) -0.004 0.224 (521) 0.264 (140) 0.040** Earnout value 13.10 (661) 10.54 (496) 20.82 (165) 10.281*** 8.880 (521) 28.83 (140) 19.953***

Relative earnout value 0.381 (661) 0.385 (496) 0.369 (165) -0.015 0.400 (521) 0.310 (140) -0.090***

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.

41

Table 3 Correlation matrix of main variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25)

Acq. CAR (1) Deal Value (2) -0.02

Acq. MV (3) -0.03 0.29 Deal Rel. Size (4) 0.00 0.02 -0.02

Acq. MTBV (5) -0.01 0.00 0.00 0.00 Acq. Debt-to-Equity (6) 0.00 0.02 0.02 0.00 0.00 Cash & Equiv.-to-TA (7) 0.03 -0.01 -0.01 0.11 -0.01 0.00

Acq. Age (8) -0.03 0.02 0.18 -0.05 -0.02 0.03 -0.18 Acq. Sigma (9) 0.00 0.00 0.00 0.00 0.09 0.00 0.00 -0.03

Priv. Target dum. (10) 0.02 -0.05 -0.11 0.00 0.01 -0.01 0.05 -0.11 0.00 Sub. Target dum. (11) 0.03 -0.01 0.04 -0.02 -0.01 -0.01 -0.06 0.09 0.01 -0.83 Pub. Target dum. (12) -0.08 0.11 0.13 0.03 -0.01 0.03 0.01 0.05 -0.01 -0.39 -0.20

Foreign Target dum. (13) -0.01 0.03 0.14 -0.01 -0.01 0.01 0.09 0.15 0.02 -0.02 0.02 0.00 Diversifying Deal dum. (14) -0.02 -0.02 -0.04 0.02 -0.01 -0.01 -0.04 0.14 0.01 0.00 0.00 0.00 -0.04

Diff-to-Value dum. (15) 0.00 0.02 0.05 0.00 0.01 -0.01 0.15 -0.17 -0.01 0.04 -0.05 0.02 0.00 -0.13 Earnout dum. (16) 0.03 -0.03 -0.06 -0.02 -0.01 -0.01 0.06 -0.11 0.00 0.28 -0.20 -0.16 0.01 -0.01 0.07

Relative Earnout Ratio (17) -0.02 -0.09 -0.04 -0.02 0.01 -0.04 0.10 -0.13 -0.02 0.07 -0.07 -0.02 -0.03 0.02 0.10 . AFA (18) 0.04 0.07 0.09 0.08 0.02 0.01 0.02 0.05 -0.01 -0.22 0.01 0.37 0.02 -0.02 0.02 -0.11 -0.11 TFA (19) 0.00 0.08 0.15 0.03 0.01 0.03 -0.01 0.12 0.00 -0.28 0.05 0.40 0.02 -0.03 0.02 -0.12 -0.16 0.36

AFA_TFA (20) -0.01 0.10 0.13 0.05 0.01 0.03 0.00 0.10 -0.01 -0.31 0.00 0.53 0.03 -0.03 0.00 -0.15 -0.14 0.64 0.72 Earnout × AFA (21) 0.05 -0.01 -0.02 0.02 0.00 -0.01 0.03 -0.05 0.00 0.10 -0.07 -0.06 0.01 -0.01 0.04 0.43 -0.11 0.37 0.01 0.09 Earnout × TFA (22) 0.03 0.00 0.01 0.00 0.00 0.00 0.02 0.00 0.02 0.07 -0.04 -0.05 0.00 -0.01 0.03 0.36 -0.16 0.03 0.36 0.13 0.30

Earnout × AFA_TFA (23) 0.03 0.00 0.01 0.02 0.00 0.00 0.02 0.00 0.00 0.03 -0.01 -0.02 0.01 -0.02 0.00 0.22 -0.14 0.19 0.22 0.30 0.52 0.61 Cash-financed dum. (24) -0.03 -0.02 0.10 -0.07 -0.02 0.00 -0.11 0.18 -0.02 -0.25 0.27 0.00 0.09 -0.01 -0.05 -0.50 . -0.03 0.03 0.01 -0.21 -0.18 -0.11

Stock-financed dum. (25) -0.01 0.03 -0.02 0.13 0.02 0.00 0.11 -0.09 0.00 -0.08 -0.07 0.26 -0.05 0.01 0.03 -0.16 . 0.13 0.11 0.17 -0.07 -0.06 -0.04 -0.23

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

Panel J AFA × TFA (Panel H) vs. Non-AFA_TFA (Panel I)

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

Subsidiary Mean 0.821*** 2.002** 0.652** 0.844*** -0.173 0.277 TFA = 1 & AFA = 0

ONLY deals with TFA are included All deals involving AFA are excluded

PANEL E All M&As

All deals

Mean 0.972*** 1.137*** 0.904*** 0.783*** 1.226*** 1.061*** 0.233** 0.354*** -0.089 0.076 N 6,108 1,784 4,324 2,613 284 1,427

Private target deals

Mean 0.973*** 1.152*** 0.870*** 0.737*** 0.916** 1.039*** 0.281** 0.415*** 0.235 0.112 N 4,184 1,531 2,653 1,403 192 1,058

Public target deals

Mean 0.243 2.675 -0.009 -0.169 -0.304 0.486 2.684 2.844 2.980 2.189 N 96 9 87 38 23 26

Subsidiary target deals

Mean 1.008*** 0.990*** 1.011*** 0.870*** 2.598*** 1.174*** -0.021 0.121 -1.607** -0.184 N 1,828 244 1,584 1,172 69 343

PANEL F M&As in which Financial Advisors are consulting the Targets (TFA)

All deals

Mean 1.212*** 1.433*** 1.136*** 1.032*** 1.365 1.307*** 0.298 0.401 0.068 0.126 N 947 244 703 445 36 222

Private target deals

Mean 1.118*** 1.347*** 0.988*** 0.943*** 1.162 1.024*** 0.358 0.403 0.185 0.322 N 533 193 340 180 17 143

Public target deals

Mean -0.258 -3.534 -0.172 -0.900 0.032 0.853 -3.362 -2.634 -3.566 -4.387 N 39 1 38 18 9 11

Subsidiary target deals

Mean 1.500*** 1.868** 1.443*** 1.237*** 2.910 1.974*** 0.425 0.630 -1.042 -0.107 N 375 50 325 247 10 68

PANEL G M&As in which at no Financial Advisor is consulting the Target (Non-TFA)

All deals

Mean 0.928*** 1.090*** 0.859*** 0.732*** 1.206*** 1.016*** 0.231* 0.358*** -0.115 0.074 N 5,161 1,540 3,621 2,168 248 1,205

Private target deals

Mean 0.952*** 1.123*** 0.853*** 0.707*** 0.892** 1.041*** 0.270* 0.417*** 0.231 0.082 N 3,651 1,338 2,313 1,223 175 915

Public target deals

Mean 0.585 3.452* 0.117 0.489 -0.521 0.217 3.334 2.962 3.972* 3.235 N 57 8 49 20 14 15

Subsidiary target deals

Mean 0.881*** 0.764*** 0.899*** 0.772*** 2.545*** 0.976*** -0.135 -0.007 -1.781** -0.212 N 1,453 194 1,259 925 59 275

PANEL H TFA (Panel F) vs. Non-TFA (Panel G)

All Mean 0.284* 0.343 0.277* 0.300 0.159 0.291 Private Mean 0.166 0.223 0.135 0.236 0.269 -0.017 Public Mean -0.843 -6.985 -0.289 -1.389 0.553 0.636

Subsidiary Mean 0.619** 1.104 0.544** 0.466* 0.365 0.999*

Continued

45

Continued (Table 5)

All Earnout (1)

Non-Earnout (2)

Cash (3)

Stock (4)

Mixed (5) (1) – (2) (1) – (3) (1) – (4) (1) – (5)

AFA = 1 & TFA = 1 ONLY deals with both AFA & TFA are included

All deals without AFA or TFA are excluded Panel I

All M&As All

deals Mean 0.916*** 1.162*** 0.831*** 0.827*** 0.389 0.969*** 0.331*** 0.335** 0.773*** 0.193

N 6,564 1,692 4,872 2,766 482 1,624

Private target deals

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)

Panel A: Matching Ratio 1:1 Treated 1.329*** 1.373*** 1.089*** 0.998*** 1.960*** 1.908*** 1.747*** 1.433*** 2.223*** 2.223*** N 2,316 2,801 2,350 1,403 532 380 396 244 152 152 Control 0.880*** 0.945*** 0.911*** 1.270*** 1.109*** 0.760*** 1.006*** 0.649** 0.704* 0.912** N 1,628 1,870 1,313 983 492 350 377 238 145 144 ATT 0.449*** 0.428*** 0.178 -0.272 0.851*** 1.148*** 0.741** 0.784** 1.519*** 1.311** t-statistic 2.87 2.85 1.06 -1.24 2.74 2.85 2.04 2.04 2.69 2.08

Panel B: Matching Ratio 3:1 Treated 1.329*** 1.373*** 1.089*** 0.998*** 1.960*** 1.908*** 1.747*** 1.433*** 2.223*** 2.223*** N 2,316 2,801 2,350 1,403 532 380 396 244 152 152 Control 1.101*** 1.009*** 1.040*** 1.264*** 1.284*** 1.194*** 1.279*** 1.254*** 1.265*** 1.286*** N 3,381 3,825 2,697 2,216 1,322 926 1,056 685 428 398 ATT 0.228** 0.364*** 0.049 -0.266 0.676*** 0.714** 0.468* 0.179 0.958* 0.936** t- statistic 2.03 2.92 0.34 -1.45 -2.69 2.15 1.79 0.56 1.84 2.04

Panel C: Matching Ratio 5:1 Treated 1.329*** 1.373*** 1.089*** 0.998*** 1.960*** 1.908*** 1.747*** 1.433*** 2.223*** 2.223*** N 2,316 2,801 2,350 1,403 532 380 396 244 152 152 Control 1.133*** 0.984*** 1.043*** 1.357*** 1.384*** 1.241*** 1.324*** 1.210*** 1.215*** 1.044*** N 4,290 4,807 3,454 3,017 2,003 1,418 1,660 1,103 673 618 ATT 0.196* 0.389*** 0.046 -0.359* 0.576** 0.667** 0.423 0.223 1.008** 1.179*** t- statistic 1.76 3.32 0.35 -1.84 2.24 2.09 1.51 0.73 2.05 2.77

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

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14

OLS Treat. OLS Treat. OLS Treat. OLS Treat. OLS Treat. OLS Treat. OLS Treat.

Full sample

Full sample

Full sample

Full sample

Delete if TFA = 1 (only M&As in which AFA is present)

Delete if TFA = 1 (only M&As in which AFA is present)

Full sample

Full sample

Delete if AFA = 1 (only M&As in which TFA is present)

Delete if AFA = 1 (only M&As in which TFA is present)

Full sample

Full sample

Delete if (AFA=1 or TFA=1) & AFA×TFA=0 (only M&As with both AFA & TFA)

Delete if (AFA=1 or TFA=1) & AFA×TFA=0 (only M&As with both AFA & TFA)

Panel A: Multivariate analysis (naive)

Earnout 0.146 0.113 λ (Earnout) -1.044** AFA 0.125 0.116 0.479*** 0.485*** λ (AFA) 2.624* -4.058 TFA -0.243** -0.030 0.128 0.351** λ (TFA) 0.507*** 0.775*** AFA × TFA -0.465*** -0.433** -0.200 -0.128 λ (AFA × TFA) -1.948** -2.399*** Deal relative size 0.280*** 0.290*** 0.269*** 0.306*** 0.321*** -0.488 0.295*** 0.354*** 0.291*** 0.334*** 0.311*** -0.122 0.236*** -0.418* Acquirer MTBV 0.009 0.007 0.009 0.010 0.011 0.012 0.011 0.015 0.005 0.009 0.012 0.012 -0.003 -0.002 Debt / Equity (Acq.) -0.024 -0.024 -0.025 -0.024 -0.114** -0.112** -0.024 -0.023 -0.050* -0.049* -0.024 -0.024 0.002 0.002 Cash & Equiv. / TA (Acq.) 0.372 0.277 0.378 0.471 -0.021 0.039 0.373 0.310 0.754* 0.659* 0.378 0.442 0.263 0.373 Acquirer Age -0.037 -0.020 -0.044 -0.052 0.018 0.022 -0.029 0.011 -0.053 0.001 -0.022 -0.056 -0.060 -0.099* Acquirer Sigma -0.045* -0.041* -0.044* -0.043* -0.043 -0.040 -0.045* -0.045 -0.032 -0.031 -0.046* -0.046* -0.068** -0.067** Private target (dummy=1) 0.143 -0.573* 0.201** -0.914 0.053 0.688 0.122 -0.125 -0.108 -0.334*** 0.083 1.587** 0.183* 2.188*** CBA (dummy=1) -0.076 -0.107 -0.081 0.036 -0.028 -0.582 -0.069 -0.064 -0.001 -0.040 -0.057 -0.390** 0.045 -0.471** Diversified (dummy=1) -0.064 -0.082 -0.061 -0.129 -0.081 -0.177 -0.067 -0.092 -0.144 -0.188* -0.069 0.010 -0.041 0.062 Diff-to-Value (dummy=1) -0.239** -0.362*** -0.235** -0.126 -0.057 -0.049 -0.229** -0.227** -0.107 -0.115 -0.233** -0.230** -0.250** -0.252** Intercept 1.193*** 3.042*** 1.182*** -1.087 1.130*** 4.581* 1.245*** 0.357 1.594*** -0.053 1.274*** 3.113*** 1.174*** 2.677***

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 15.32*** 14.66*** 15.29*** 14.58*** 14.19*** 13.27*** 15.52*** 15.58*** 11.82*** 12.60*** 15.98*** 15.09*** 10.12*** 10.07*** R-square 2.03% 2.11% 2.03% 2.09% 2.55% 2.58% 2.06% 2.23% 2.28% 2.63% 2.12% 2.17% 1.83% 1.97% N 8,875 8,875 8,875 8,875 6,533 6,533 8,875 8,875 6,082 6,082 8,875 8,875 6,538 6,538

Panel B: Matching ratio = 1:1

Earnout 0.336** 0.338** λ (Earnout) -5.020

AFA 0.258* 0.239* 0.956*** 0.993*** Lambda(AFA) 5.559* 13.102**

TFA 0.123 0.187 0.311 0.360 λ (TFA) 1.211** 0.462

AFA × TFA -0.350 -0.341 0.972* 0.795* λ (AFA × TFA) -5.855 -11.774***

Intercept 1.779*** 5.178*** 0.370 -2.908* -0.249 -1.725 0.503 -0.463 1.720* 1.402 0.517 4.163 0.069 -0.092

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 8.62*** 8.03*** 8.83*** 8.51*** 4.59*** 4.55*** 7.70*** 7.44*** 4.62*** 4.28*** 5.87*** 5.45*** 4.25*** 4.40*** R-square 2.58% 2.60% 2.23% 2.33% 3.24% 3.48% 2.48% 2.59% 4.63% 4.65% 2.89% 2.91% 3.34% 3.73% N 3,925 3,925 4,652 4,652 1,657 1,657 3,654 3,654 1,155 1,155 2,376 2,376 1,490 1,490

Panel C: Matching ratio = 3:1

Earnout 0.100 0.094 λ (Earnout) -1.447

AFA 0.124 0.110 0.666*** 0.670*** λ (AFA) 3.882** 6.421**

TFA -0.046 0.054 -0.232 -0.071 λ (TFA) 0.767** 0.744*

AFA × TFA -0.392** -0.375** 0.670* 0.446 λ (AFA × TFA) -4.476 -6.205***

Intercept 1.485*** 3.000*** 1.212** -1.868 0.340 -1.123 0.413 -0.472 2.125** 1.259 0.194 3.965 0.402 0.354

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 11.69*** 10.88*** 11.65*** 11.28*** 5.53*** 5.47*** 9.79*** 9.55*** 4.09*** 5.84*** 5.92*** 5.63*** 4.85*** 5.28*** R-square 2.42% 2.44% 2.08% 2.18% 3.05% 3.25% 2.29% 2.41% 4.55% 4.72% 1.94% 2.00% 3.42% 4.01% N 5,672 5,672 6,599 6,599 2,127 2,127 5,033 5,033 1,547 1,547 3,607 3,607 1,658 1,658

Panel D: Matching ratio = 5:1

Earnout 0.058 0.045 λ (Earnout) -1.448**

AFA 0.125 0.115 0.657*** 0.657*** λ (AFA) 3.276** 5.868**

TFA -0.066 0.035 -0.092 0.095 λ (TFA) 0.539** 0.742**

AFA × TFA -0.510*** -0.481*** 0.860** 0.663** λ (AFA × TFA) -4.847** -5.718***

Intercept 1.612*** 3.406*** 1.036** -1.735 0.728 -1.044 0.624 -0.079 1.751** 0.708 0.581 5.003*** 0.312 0.185

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 13.37*** 12.57*** 12.69*** 12.21*** 6.90*** 6.69*** 9.78*** 9.44*** 7.07*** 6.88*** 7.20*** 7.05*** 4.88*** 5.47*** R-square 2.38% 2.43% 1.97% 2.06% 3.18% 3.33% 1.99% 2.08% 4.26% 4.49% 1.93% 2.05% 3.16% 3.82% N 6,579 6,579 7,577 7,577 2,537 2,537 5,784 5,784 1,917 1,917 4,406 4,406 1,807 1,807

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

1 2 3 4 5 6 7 8 9 10 11 12

Panel A: Multivariate analysis (naive)

Variables OLS Treat. OLS Treat. OLS Treat. OLS Treat. OLS Treat. OLS Treat.

Earnout × AFA 0.533* 0.542* -0.034 -0.025 λ (Earnout × AFA) -1.415* -2.807*** Earnout × TFA 0.636** 0.650** 0.075 0.017 λ (Earnout × TFA) -1.557** 1.019*** Earnout × AFA_TFA 1.181*** 1.129*** 1.108*** 0.987** λ (Earnout × AFA_TFA) 0.535** 0.909*** Earnout 0.025 -0.003 0.037 -0.010 0.012 -0.019 0.065 0.096 0.024 0.036 0.047 0.087 AFA 0.014 0.003 0.490*** 0.481*** TFA -0.365*** -0.386*** 0.109 0.336* AFA_TFA -0.610*** -0.437** -0.343* 0.021 Deal relative size 0.269*** 0.033 0.320*** -0.200 0.297*** 0.203*** 0.289*** 0.360*** 0.312*** 0.385*** 0.237*** 0.372*** Acquirer MTBV 0.009 0.008 0.011 0.009 0.011 0.009 0.004 0.009 0.012 0.015 -0.003 0.002 Acquirer Debt-to-Equity -0.024 -0.024 -0.113** -0.112** -0.023 -0.023 -0.049* -0.048* -0.023 -0.022 0.003 0.006 Acq. Cash & Equivalent-to-TA 0.370 0.303 -0.022 -0.099 0.370 0.285 0.750* 0.714* 0.372 0.378 0.256 0.264 Acquirer Age -0.039 -0.033 0.018 0.029 -0.025 -0.017 -0.052 -0.010 -0.019 0.001 -0.055 -0.024 Acquirer Sigma -0.045* -0.042 -0.043 -0.039 -0.050* -0.047* -0.033 -0.033 -0.047* -0.048* -0.068** -0.074** Private target (dummy=1) 0.148 -0.477 0.047 -0.899** 0.074 -0.336 -0.125 0.097 0.038 0.218* 0.118 0.520*** CBA (dummy=1) -0.083 -0.270* -0.029 -0.400** -0.066 -0.113 -0.002 -0.047 -0.059 -0.004 0.044 0.127 Diversified (dummy=1) -0.064 -0.084 -0.082 -0.165 -0.069 -0.040 -0.145 -0.157 -0.069 -0.096 -0.041 -0.087 Difficult-to-Value (dummy=1) -0.239** -0.148 -0.058 0.085 -0.231** -0.125 -0.110 -0.184* -0.230** -0.284*** -0.248** -0.350*** Intercept 1.185*** 4.147*** 1.121** 6.683*** 1.263*** 5.080*** 1.581*** -1.372* 1.279*** -0.366 1.181*** -1.583*

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 13.51*** 12.85*** 12.16*** 11.83*** 13.73*** 13.14*** 10.15*** 10.67*** 14.32*** 13.78*** 9.27*** 9.55*** R-square 2.09% 2.13% 2.55% 2.65% 2.12% 2.18% 2.29% 2.57% 2.21% 2.28% 1.95% 2.15% N 8,875 8,875 6,533 6,533 8,875 8,875 6,082 6,082 8,875 8,875 6,538 6,538

Panel B: Multivariate analysis (self-selection addressed) - Mahalanobis matching 1:1

Earnout × AFA 0.745** 0.742** 0.995*** 0.962*** λ (Earnout × AFA) -1.183 -36.452

Earnout × TFA 0.759** 0.762** 0.602 0.621 λ (Earnout × TFA) 1.332 0.199

Earnout × AFA_TFA 1.579*** 1.587*** 1.190** 1.195** λ (Earnout × AFA_TFA) 3.079 0.600

Intercept 1.336 3.803 0.968 25.550 2.248 1.194 1.611 1.328 -1.055 -2.915 1.636 0.075

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 2.67*** 2.48*** 2.35*** 2.31*** 2.41*** 2.23*** 1.66* 1.53* 2.19** 2.03** 2.03** 1.89** R-square 3.09% 3.11% 3.81% 4.03% 3.69% 3.69% 4.09% 4.09% 8.53% 8.57% 7.97% 8.04% N 1,018 1,018 727 727 769 769 479 479 295 295 295 295

Panel C: Multivariate analysis (self-selection addressed) - Mahalanobis matching 3:1

Earnout × AFA 0.606** 0.600** 0.597** 0.557* λ (Earnout × AFA) -1.066 -41.118***

Earnout × TFA 0.406 0.388 -0.084 0.023 λ (Earnout × TFA) -4.632 0.693

Earnout × AFA_TFA 0.951* 0.956* 0.823* 0.844* λ (Earnout × AFA_TFA) 1.137 0.843

Intercept 1.106 3.323 0.461 44.251*** 1.226 6.997 1.946* 0.568 -0.118 -0.506 1.581 -0.575

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 4.50*** 4.18*** 3.02*** 3.30*** 2.43*** 2.35*** 3.39*** 3.23*** 2.95*** 2.72*** 2.74*** 2.59*** R-square 2.86% 2.88% 2.74% 3.23% 1.99% 2.09% 4.27% 4.40% 5.91% 5.91% 5.79% 5.93% N 1,846 1,846 1,300 1,300 1,444 1,444 926 926 577 577 548 548

Panel D: Multivariate analysis (self-selection addressed) - Mahalanobis matching 5:1

Earnout × AFA 0.480* 0.469* 0.476* 0.440 λ (Earnout × AFA) -1.992 -20.001***

Earnout × TFA 0.346 0.330 -0.043 0.085 λ (Earnout × TFA) -3.441 0.782*

Earnout × AFA_TFA 0.964** 0.976** 1.078** 1.114** λ (Earnout × AFA_TFA) 2.726 0.940

Intercept 0.833 4.976 0.875 25.699*** 0.804 5.861 1.316 -0.442 -0.347 -4.329 1.876 -0.500

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes F-stat 5.05*** 4.79*** 4.58*** 4.81*** 4.24*** 4.01*** 4.67*** 4.51*** 2.52*** 2.34*** 3.66*** 3.49*** R-square 2.35% 2.42% 3.00% 3.40% 2.44% 2.50% 4.04% 4.22% 3.61% 3.64% 5.50% 5.68% N 2,525 2,525 1,791 1,791 2,045 2,045 1,343 1,343 820 820 767 767

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

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Cash fraction -0.173 Shares fraction 5.313*** Cash as deferred payment -1.010*** Stock as deferred payment 1.213* EBITDA performance -0.121 Revenue performance 0.079 PBT performance 0.508* Future profitability perf. 0.404 Other performance -0.454 Combination of perf. measures 0.525** Log(# of payments) -0.706 Log(# of deferred months) 0.281 Fail 0.238 Partial success 0.329 Success 1.063*** Log(Earnout Value) -0.223* Relative Earnout Value -1.352* Intercept 2.648 1.827 2.885* 2.453 2.733 2.760 2.586 2.595 2.788 2.606 6.091 2.690 2.535 2.453 2.054 2.931* 3.274*

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

F-stat 5.15*** 8.17*** 6.05*** 5.39*** 5.11*** 5.10*** 5.32*** 5.17*** 5.20*** 5.59*** 4.19*** 4.52*** 5.16*** 5.19*** 6.13*** 5.57*** 5.68*** R-square 4.52 6.98 5.26 4.71 4.51 4.50 4.68 4.53 4.58 4.92 17.43 4.66 4.52 4.55 5.32 4.86 4.96 N 660 660 661 661 657 657 657 661 657 656 126 561 661 661 661 661 661

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

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