AARHUS UNIVERSITY, SCHHOL OF BUSINESS & SOCIAL SCIENCES

MASTER THESIS

How to design effective earnouts in M&A deals?

Empirical evidence on an option-pricing model on earnout design

November 1, 2015

Author

Johann Arne Spanuth

201302396

MSc in Finance & International Business

Academic Supervisor

Stefan Hirth

Associate Professor

Department of Economics and Business Economics

Number of characters (excl. spaces): 149,379

Abstract

Literature on Mergers & Acquisitions (M&As) shows growing interest in earnouts as a

payment form that mitigates valuation risks from information asymmetry. Theory argues

that earnouts present a solution to problems of adverse selection by serving as a signalling

tool for high quality targets and to agency problems by serving as an incentive tool

towards the target’s management post-closing. (E.g. Kohers & Ang, 2000; Ragozzino &

Reuer, 2009) Indeed, research finds strong evidence that earnouts are more likely used in

deals facing high information asymmetry and report positive market reactions for the

acquiring firm. (E.g. Datar, Frankel & Wolfson, 2001; Cain, Denis & Denis, 2011) To

the contrary, little is known about how to design earnouts to ensure their effectiveness.

The thesis contributes to the limited research on earnout design by investigating both

theoretically and empirically what factors determine the design of earnouts in M&A deals.

First, the thesis develops a game-theoretic option pricing model on earnout design. The

effectiveness of earnouts is expected to depend on the likelihood that the target will

receive an earnout premium. A higher likelihood is associated with less efforts by the

target and with even low-quality firms tending to accept the earnout. The model suggests

two ways for the acquirer to react to an increased likelihood. On the one hand by

increasing the contingent part of the acquisition price, i.e. the earnout ratio, in order to

stronger incentivize the target for post-closing cooperation and to motivate only high

quality targets to accept the deal and on the other hand by shaping the earnout parameters

such that the likelihood is controlled for. Based on the option-like characteristics of

earnouts, the model argues that a higher uncertainty about the target’s future performance,

longer earnout periods and a lower performance goal all increase the likelihood of an

earnout pay-out in the end. Given a degree of uncertainty, the acquirer is therefore

expected to adjust the remaining earnout parameters accordingly.

The empirical analysis by means of regression models based on a UK sample of 377

earnout deals finds only weak evidence for the hypothesized dynamics. However,

previous research reports empirical results in favour of the theoretical model. These

studies document higher earnout ratios and shorter earnout periods in case of high

uncertainty. Consequently, there is strong evidence that the earnout ratio is used as the

primary “control lever” if the acquirer faces the need to design strong signalling and

incentive tools and also some evidence that the earnout period is decreased to control for

the likelihood of an earnout payment. Due to data limitations the determinants of the

performance goal could not be empirically examined.

After all, the thesis to a large extent fails to find evidence for the theoretical model on

earnout design, but the available literature shows some, yet limited, support. Further

research on the option-based approach that overcomes the thesis’s data limitations is

strongly encouraged to pave the way towards the definition of optimal earnout design.

Keywords: Earnout Design; Determinants; Uncertainty; Information Asymmetry; Option

Pricing Methodology

i

Table of contents

List of tables iii

List of figures iii

1 Introduction 1

1.1 Problem statement 2

1.2 Approach and structure 3

1.3 Scope and delimitation 3

1.4 Evaluation of sources 4

2 Literature review on earnouts in Mergers & Acquisitions 5

2.1 Motives to use earnouts 5

2.1.1 Theoretical hypotheses 5

2.1.1.1 The information asymmetry hypothesis 5

2.1.1.2 The uncertainty hypothesis 8

2.1.2 Insights from empirical studies 9

2.1.2.1 Evidence for the information asymmetry hypothesis 9

2.1.2.2 Evidence for the uncertainty hypothesis 15

2.1.3 Summary 17

2.2 Design of earnouts 18

2.2.1 The earnout parameters 18

2.2.2 Current state of research 19

3 Theoretical model on the design of earnouts in Mergers & Acquisitions 22

3.1 The option-like characteristics of earnouts 23

3.1.1 Earnout premium payment profiles 25

3.1.2 Mapping the earnout parameters onto a financial call option 29

3.2 A game-theoretic option pricing model on design of earnouts 31

3.2.1 Original model by Lukas, Reuer & Welling (2012) 31

3.2.2 Own advancements to the model 34

4 Hypotheses 37

5 Data sample creation 38

5.1 Deal search 38

5.2 Collecting data on earnout parameters 39

5.3 Generating the explanatory variable “uncertainty” 40

5.4 Final data sample 42

ii

6 Methodology 43

6.1 Control variables for information asymmetry 43

6.2 Regression models on earnout parameters 44

6.2.1 Tobit regression model of the earnout ratio 45

6.2.2 OLS regression model of the earnout period 46

6.2.3 Binary choice model of the performance measure 46

7 Empirical results 48

7.1 Descriptive statistics 48

7.2 Results from the regression models 51

7.2.1 Determinants of the earnout ratio 52

7.2.2 Determinants of the earnout period 55

7.2.3 Determinants of the performance measure 57

8 Evaluation and avenues for further research 62

8.1 Discussion of the empirical results 62

8.2 Limitations to the study 64

8.3 Further research 66

9 Conclusion 67

References 70

Appendices 74

Appendix 1: Earnout premium payment profiles 74

Appendix 2: Data selection process 75

Appendix 3: Methodology 80

iii

List of tables

Table 1 Main findings of empirical studies on the choice to use earnouts 11

Table 2 Main findings of empirical studies on wealth effects of earnouts 15

Table 3 Definitions of control variables for information asymmetry 44

Table 4 Descriptive statistics on data sample 49

Table 5 Results from Tobit model on determinants of earnout ratio 53

Table 6 Results from OLS model on determinants of earnout period 56

Table 7 Results from logit model on determinants of sales measure 58

Table 8 Results from logit model on determinants of income measure 59

Table 9 Results from logit model on determinants of non-financial measure 61

List of figures

Figure 1 Payment profile and implications of earnout like a call option 26

Figure 2 Payment profile and implications of earnout like a binary option 27

Figure 3 Payment profile and implications of earnout like a call option with cap 28

Figure 4 Mapping an earnout onto a financial call option 30

Figure 5 Dynamics of model on earnout design by Lukas, Reuer & Welling

(2012)

33

Figure 6 Dynamics of advanced model on earnout design 36

1

1 Introduction

“In an uncertain economic climate, there may be more willingness to take the wait-and-

see option offered by an earnout-structured acquisition.”

Craig & Smith (2003, p. 46)

There is an extensive body of literature examining the question of whether Mergers &

Acquisitions (M&As) are value creating or value destroying events for the acquirer’s and

the target’s shareholders. By now, research agrees that on average M&A deals

significantly increase target’s shareholder value while the acquirer’s shareholders

experience a value decreasing effect. (For a comprehensive review of studies see Eckbo,

2009) Around these basic results, literature especially focuses on the firm and deal

specific characteristics that drive the negative effect for the bidders. Studies report that

next to the size of the bidder (e.g. Asquith, Bruner & Mullins, 1983; Moeller,

Schlingemann & Stulz, 2004), and the target’s status as being public or private (e.g.

Bargeron et al., 2008), also the method of payment determines the bidder’s value gains

from a transaction (e.g. Chang, 1998, Fuller, Netter & Stegemoller, 2002). Most of the

studies concerning the method of payment in M&A deals surround the value enhancing

effects of cash deals as compared to stock deals.

However, recently a new stream of research developed that examines not only the

payment currency (cash versus stock) but focuses on the form how the takeover price is

settled by the acquirer. More specifically, research shows an increasing interest in the

form of contingent payments such as the earnout. Earnouts define a part of the overall

acquisition price to be contingent on the target’s ability to meet a prespecified

performance goal within a certain time frame post-closing of the deal. (Reuer, Shenkar &

Ragozzino, 2004, p. 20) Research argues that this type of payment form secures the buyer

against adverse selection and agency problems and thereby mitigates valuation risks.

Indeed, studies find strong evidence for a positive impact from the choice to use earnouts

on acquirer’s shareholders value gains. (E.g. Kohers & Ang, 2000; Mantecon, 2009)

Knowing that bidding companies’ shareholders on average lose value from M&A events,

these findings indicate that the research on earnouts is a valuable supplement to existing

M&A literature. Moreover, as Lukas, Reuer & Welling (2012, p. 257) conclude the

research on earnouts, in contrast to other areas of the M&A literature, is still in its infancy.

2

Most of these studies have focused on the motives of using earnouts in M&A deals and

reached consensus on the deal and firm characteristics that best suits the choice to use an

earnout. (E.g. Barbopoulos & Sudarsanam, 2012) Yet only a few studies are concerned

with the design of these contracts and there is still a lack of theory and empirical insights

on the determinants of appropriate earnout design. Consequently, the question of how to

shape the parameters to make earnouts a valuable tool has yet to be answered.

Therefore, this revenue stream presents a unique and exciting opportunity for a master

thesis to contribute to a promising new research avenue. As an additional personal

motivation, the author of this thesis was concerned with the use of earnouts in an M&A

context in practical life. Throughout the process of writing this thesis, the issue of how to

specifically design an earnout according to the deal’s circumstances therefore also had

practical value for the author.

1.1 Problem statement

This thesis aims to contribute to the yet limited research on earnout design. While

earnouts are complex contractual arrangements tailored to the requirements of each

particular deal, they all comprise common parameters that together constitute the earnout

mechanism. (Cain, Denis & Denis, 2011, p. 152) The contract defines the contingent part

of the overall acquisition price (the earnout ratio), the time frame during which the target’s

performance is monitored (the earnout period), the performance measure and the exact

performance goal that the target has to reach in order to receive the earnout payment. The

thesis therefore seeks both to contribute to theory that explains the determinants of each

single parameter and also to provide empirical evidence that tests the constituted theory.

Consequently, the overall research question is:

What factors determine the design of earnouts?

The following sub research questions systemize the overall problem statement along the

common earnout parameters:

(a) What factors determine the earnout ratio?

(b) What factors determine the earnout period?

(c) What factors determine the performance measure?

(d) What factors determine the performance goal?

3

1.2 Approach and structure

The problem statement requires to set up a model on earnout design from which testable

hypotheses are derived. The thesis therefore follows a deductive approach. By applying

standard regression model methodology these hypotheses are subsequently empirically

tested. Consequently, the thesis is both a theoretical and an empirical study of the stated

problem statement with the following structure.

Chapter 2 starts with a review of available earnout literature. First, theory and empirical

findings regarding the motives to use earnouts in M&A deals are examined. Second, the

common earnout mechanism and its parameters are defined and results from studies on

earnout design are presented.

Chapter 3 develops a theoretical model on effective earnout design based on previous

work by Lukas, Reuer & Welling (2012). Referring to the similarities between earnouts

and financial options, an option pricing model on earnout design is set up. This part is the

theoretical focus of the thesis.

Chapter 4 states the testable hypotheses on effective earnout design. Chapter 5 describes

the data collection process and chapter 6 briefly outlines the empirical methodology used

to test the hypotheses.

Chapter 7 presents descriptive results on the data sample and the results of the hypotheses

testing. Chapter 8 evaluates the evidence in the context of previous studies, discusses

implications for the theoretical model and points out limitations to this thesis and further

avenues for research.

Chapter 9 finally concludes on the thesis’ results and its main theoretical and empirical

contributions to research.

1.3 Scope and delimitation

Since the body of literature on earnouts is still limited, the thesis can present an exhaustive

overview of findings from relevant literature. However, the extensive body on M&A

literature in general is not tackled as the introduction already classifies the earnout

research stream into the overall context of M&A literature.

The theory on earnouts requires insights from information asymmetry theory, game

theory and option pricing methodology. Those assumptions and rationales essential to

4

explain theory and the model on earnout design are introduced but these different

economic schools are not discusses in detail.

Theory and models in literature to explain the use of earnouts are mostly qualitative rather

than mathematical derivations. The theoretical model developed in chapter 3 is

consequently also only derived in a qualitative manner.

The collection of earnout specific data requires in depth analysis of primary sources such

as the public deal announcement of every single deal. No reliable databases are yet

available that comprehensively summarize this information. Therefore, the thesis could

only consider a limited data sample size. Thus, the data sample only includes deals from

UK-based acquirers in the period between 01.01.2006 and 30.06.2015. (See chapter 5 for

details)

The empirical analysis is limited to standard cross-sectional regression models as learned

within the master studies and as dominantly used in earnout research. Since the focus is

on identifying determinants of earnout design, the statistical details of these

methodologies are not part of the thesis.

1.4 Evaluation of sources

The literature referred to in this thesis was retrieved through a broad search in literature

databases made available by Aarhus University and through the bibliography of earnout

related studies. Since the earnout research is still new and its literature is limited, the

literature review in chapter 2 is able to present an exhaustive overview. The downside of

this fact is that many articles must be heavily referred to along the analysis. Also, no

selection criteria regarding the quality of publishing journals could be used in order to

remain a meaningful bibliography size for this thesis.

The empirical data is retrieved from different databases. Through the Bureau van Dijk

database Zephyr a sample of earnout deals was searched. The Bureau van Dijk database

Orbis is used in order to identify proxy companies for the deals’ targets. (See chapter 5

for details) Thomson Reuters Datastream is used for all required stock market data.

Finally, Investegate, a database of public deal announcements of UK acquirers, was used

as the primary source for detailed data on the earnout parameters. The search for deal

announcements was unproblematic, however the announcements itself differ in their

5

information content regarding earnout specific data what consequently reduced the

sample size.

2 Literature review on earnouts in Mergers & Acquisitions

This chapter seeks to give an exhaustive overview of the theory and empirical evidence

of earnout literature so far. In order to examine the problem statement of how to design

effective earnouts, the purposes of using earnouts in M&A deals must be cleared first.

Research related to the motives of using earnouts in fact has dominated earnout literature

so far. Section 2.1 covers the relevant theory and empirical evidence. As the next step,

section 2.2 defines the basic structure of an earnout mechanism and covers the yet limited

literature on earnout design.

2.1 Motives to use earnouts

Literature agrees that an earnout contract is a valuable instrument to mitigate the valuation

risk an acquirer faces in an M&A transaction. Research has identified two hypotheses that

explain how earnouts mitigate this problem. The first and most dominant hypothesis is

that earnouts are able to serve as a solution to problems of information asymmetry

between the acquirer and the target in a deal. The second and less studied hypothesis is

that earnouts also mitigate pre-contractual uncertainty about the target’s future

performance that is present in a deal even in the absence of information asymmetry. Both

hypotheses are described in terms of economic theory first. Subsequently, empirical

evidence for both rationales is summarized. In this way, chapter 2.1 aims at answering

the question of why earnouts should be used in M&A deals in the first place.

2.1.1 Theoretical hypotheses

2.1.1.1 The information asymmetry hypothesis

One of the pivotal parts of negotiating an M&A deal is the agreement upon the purchase

price. While the acquirer faces the risk of overpayment, the seller at the same time might

be concerned with underpayment. This type of risk is especially distinctive in situations

of information asymmetries. (E.g. Travlos, 1987; Chang, 1998) If the bidder of a deal is

less informed about the true value of the target firm than the seller, the two parties might

face a disagreement about the fair valuation of the target resulting in a valuation gap. The

acquirer’s lack of information ultimately leads to problems of adverse selection. (Lukas

6

& Heimann, 2014, p. 482) The rationale how earnout agreements mitigate these problems

is therefore referred to as the adverse selection hypothesis. Moreover, the acquirer might

overpay for a target if key human capital with informational advantages leave the new

combined firm post-closing. In this case the acquirer faces an agency problem. (Beard,

2004) The rationale of how earnouts can mitigate these risks from post-contractual

information asymmetry is referred to as the agency problem hypothesis. Together, the

rationales of how earnouts mitigate risks from information asymmetries present the

dominant hypothesis in research. (See Kohers & Ang 2000; Ragozzino & Reuer 2009;

Cain, Denis & Denis 2011)

The basic assumption underlying the adverse selection hypothesis is information

asymmetry between the bidder and the target that leads to suboptimal outcomes of the

negotiations. While the seller knows about the true value of the target firm, the bidder has

limited access to this information and therefore faces an informational disadvantage.

(Myers & Majluf, 1984) Since the acquirer has to base his valuation of the target firm on

limited information, he will bear a greater risk of misvaluation and overpayment. In face

of this risks, the bidder would only be willing to offer a relatively low acquisition price.

(Beard, 2004, p.26) At the same, the seller would be unable to command an attractive

sales price. Thus, the parties have to deal with a valuation gap. This situation on the M&A

market resembles the problems of a “market for lemons” as famously introduced by

Akerlof (1970). Due to its informational disadvantage, the bidder cannot distinguish

between high-quality and low-quality targets and therefore only offers a price to which

the high-quality sellers are unwilling to close the deal. As a consequence, the high-quality

targets will leave the market and the bidder is left with low-quality targets. This dynamic

is defined as the adverse selection problem. (Akerlof, 1970, p. 493) In the extreme case,

the market might collapse. In any case, the information asymmetry causes costs since the

parties to the deal are forced to accept suboptimal outcomes in order to agree on a

purchase price at all. (Akerlof, 1970, p. 495)

As Krishnamurti & Vishwanath (2008, p. 134) point out, the motive of bridging a

valuation gap between bidders and targets is most commonly discussed in earnout

literature. To overcome this problem caused by information asymmetry game theory

suggests that the seller needs a tool to credible signal the quality of the target firm to the

bidder. (See e.g. Spence, 1973) In fact, the literature on earnouts agrees that an earnout

mechanism can serve as such a signal: “By deferring the valuation decision until post-

7

closing, sellers can create credible information signals and thereby resolve potential

adverse selection problems.” (Quinn, 2012, p. 138-139) By accepting that a part of the

acquisition price is contingent on future performance of the target, the target reveals his

confidence of meeting the prespecified performance benchmark. For low quality firms,

this signal would be too costly to replicate since they are not confident to reach the

demanded performance goals of the earnout. (Beard, 2004, p. 27)

By allowing high-quality sellers to convey hidden information about their unobservable

quality to the bidder, the earnout helps separating the high quality sellers from the low

quality sellers. In this way, a contingent payment such as the earnout functions as a so

called separating equilibrium. (See Spence, 1973, p. 358) Ragozzino & Reuer (2009)

suggest that earnouts mitigate the adverse selection problem by transferring the risk of

overpayment from the acquirer to the bidder. The bidder adheres its initial valuation and

pays a corresponding price upfront while any earnout premium paid at a later point in

time would only reflect that the target outperformed the bidder’s expectations. (Beard,

2004, p.26) At the same time, the seller is compensated for this performance and in total

receives a purchase price closer to his original valuation. In this way, both bidder and

seller potentially benefit from the features of a contingent earnout. All in all, earnouts

helps to manage risks associated with the adverse selection problem, and thereby

ultimately might bridge the valuation gap.

The agency problem hypothesis assumes that the value of a target firm strongly depends

on key human capital that has an informational advantage as compared to the acquirer.

Kohers & Ang (2000) argue that a loss of key management post-closing would

consequently diminish the target’s value. These agency costs, if significantly high,

potentially jeopardize the entire future success of the business and ultimately result in

overpayment by the acquirer. (Lukas & Heimann, 2014, p. 482) Research agrees that

earnouts help to mitigate these agency problems due to its incentivizing structure. The

earnout agreement defines a part of the overall acquisition price to be contingent on future

performance. Consequently, in order to cash-in the maximal purchase price, the key

management, respectively the target’s shareholders, is incentivized to remain with the

target post-transaction and to reach the agreed performance goal of the earnout

mechanism. (Datar, Frankel & Wolfson, 2001) Thus, the earnout can benefit the M&A

deal by retaining the key management. In a more general sense, an earnout thereby helps

to align the interest of the key agents with the interest of the shareholders of the acquiring

8

firm. (Beard, 2004, p. 27) Even in the case that the target’s value is not to a large part

dependent on key human capital and the management is replaced after closing of the deal,

the earnout still functions as an incentive tool to the agent in place.

To sum it up, in theory earnouts carry the value to mitigate problems of information

asymmetry in M&A. Potentially, these contingent payment agreements ultimately

diminish the risk of overpayment for the acquirer by bridging valuation gaps and retaining

key management that would have otherwise jeopardized or even prevented the deal from

closing. Kohers & Ang (2000, p. 445) conveniently wrap up the two potential benefits of

earnouts as “agreeing to disagree” (bridging the valuation gap) and “agreeing to stay”

(management retention).

2.1.1.2 The uncertainty hypothesis

The uncertainty hypothesis offers an opposing explanation for the value of utilizing

earnouts in M&A. It argues that earnouts still help to overcome diverging expectations

and valuations even in the absence of information asymmetries. While acknowledged by

several scholars, the explicit formulation of this hypothesis is only to be found in a recent

empirical study by Quinn (2012). The author assumes acquirer and target to have

symmetric information pre-closing of the deal. However, in order to agree on a fair value

estimation of the target, the bidder and seller are required to have joint expectations about

the future. Their expectations in turn are intimately tied to their risk preferences. Less

risk-averse parties would be more optimistic about the future, while more risk-averse

parties would be more pessimistic. If buyers and sellers do not share the same preference

for risk, the expectations differ and would consequently result in a valuation gap. In this

case, the valuation gap emerges from uncertainty about future states rather than from

information asymmetry. The phrase “uncertainty” in this thesis therefore is always related

to pre-contractual uncertainty about future states and does not refer to the problems

arising from information asymmetry.

Following Quinn (2012), an earnout contract would still be able to overcome this

problem. If the parties can agree on a transaction structure that distributes the probability

of an adverse event to the party with the larger risk-preference, then the parties are finally

able to create uniform assumptions and to generate an efficient price for the seller.

Earnouts shift uncertainty to the seller, which is commonly expected to be more risk-

seeking, not because he is better informed about future states but because the seller agrees

9

to bears the costs of being wrong due to its higher risk preference. Thereby, earnouts

allow the buyers in turn to reduce their exposure to uncertainty. In this way, the bidder

reduces its risk of overpaying if the target turns out to be less successful as ex ante

predicted by the seller. Earnouts therefore undertake an important function of distributing

pre-contractual uncertainty. (Lukas & Heimann, 2014, p. 484)

So, even in the absence of information asymmetry, an earnout contract serves as an

effective risk reducing tool by hedging against the risk of misvaluing targets and thereby

ultimately closing the valuation gap. (Kohers & Ang, 2000)

2.1.2 Insights from empirical studies

2.1.2.1 Evidence for the information asymmetry hypothesis

Empirical research reports strong evidence for the adverse selection hypothesis. The

studies follow two approaches to prove the potential benefits of earnouts. On the one

hand, most of the studies examine if earnouts are used more likely in situations that

indicate information asymmetry. (Kohers & Ang, 2000; Datar, Frankel & Wolfson, 2001;

Ragozzino & Reuer, 2009; Cain, Denis & Denis, 2011) Their findings are presented first.

On the other hand, some studies test if the hypothesized benefits of using earnouts is

reflected in wealth effects for the acquirer’s shareholders. (Lukas & Heimann, 2014;

Barbopoulos & Sudarsanam, 2012; Kohers & Ang, 2000) These studies are briefly

summarized as the second step. At the end of each section, the main empirical findings

are summarized in a table.

The choice to use earnouts

The ground-breaking empirical study on earnout use was conducted by Kohers & Ang

(2000). Their results report strong evidence that these contingent payments are more

likely to be used for deals that face a high degree of information asymmetry as indicated

by certain deal and firm characteristics. For a sample of 938 earnout deals in the US

between 1984 and 1996, the authors try to predict the use of earnouts by means of a

logistic regression on indicators of information asymmetry and the importance of human

capital. High-tech firms are considered to imply information asymmetry since their value

primarily depends on difficultly verifiable growth opportunities. Firms from the service

industry imply information asymmetries as they usually carry low tangible, easily

valuable assets and heavily rely on intellectual property and key human capital. Privately

10

held firms are considered problematic since they lack previously disclosed information.

Also, acquisitions in unfamiliar territories such as cross-industry deals are considered to

indicate information asymmetries due to a gap of market-specific knowledge between

acquirer and target. Finally, bidders that can least afford to absorb the risk of

overpayment, i.e. small bidders, should tend to use earnouts more likely.

The reported results strongly support the expectations and suggest that the use of an

earnout is significantly more likely for acquisitions that carry these deal and firm

characteristics. Further US-based research carried out by Data, Frankel & Wolfson

(2001), Ragozzino & Reuer (2009) and Cain, Denis & Denis (2011) consistently confirms

these basic findings for more recent deal samples. Barbopoulos & Sudarsanam (2012)

prove these results to be robust also for a sample of UK-based acquirers. Consequently,

there is wide consensus among scholars that earnouts are chosen as a payment form in

situations where information asymmetry problems are expected.

Apart from evidence regarding the choice to use earnouts, Kohers & Ang (2000) further

examine the retention rate of target management in the post-acquisition phase by a long-

term analysis. Statistics suggest that the majority of the tracked managers remain with the

firm after the earnout period. The authors therefore conclude that earnouts serve the two

main functions hypothesized from theory: As a mechanism to shift mispricing risk from

the acquirer to the seller, and as a retention incentive for valuable target human capital.

(Kohers & Ang, 2000, p. 475) Similar conclusions are drawn by Ragozzino & Reuer

(2009) who find that earnouts as contractual agreements substitutes for other ways by

which a bidding firm might otherwise deal with information asymmetries such as equity

partnerships. This result implies that using earnouts is not only a choice of the payment

method, but also a strategic decision.

Another focus of research surrounds cross-country deals in particular. Kohers & Ang

(2000) expect a high degree of information asymmetry to be present in these deals,

however their empirical evidence shows that earnouts are not significantly more likely

opted for in these circumstances. Datar, Frankel & Wolfson (2001) and Barbopoulos &

Sudarsanam (2012) underline this exception by reporting evidence that for cross-country

deals per se earnouts are even less likely to be used. The studies explain this result by the

fact that the difficulties of enforcing an earnout contract in a foreign country under a

different legal system may offset the benefits. In fact, a more differentiated analysis

documents that earnouts are more likely used if acquirer and target of a cross-country deal

11

operate under similar legal system and less likely used if these legal system are not

considered congruent. (Kohers & Ang, 2000) These insights shed light on the operational

requirements for an earnout to serve as an effective tool.

Finally, empirical research also investigate certain acquirer and industry characteristics

that make the use of earnouts less valuable and therefore less likely. Datar, Frankel &

Wolfson (2001) empirically prove that earnouts are less likely used in industries with an

active M&A market. The authors argue that in an active market, the acquirer is provided

with more reference points to base its valuation decision on and consequently face less

need to secure against valuation risk. Barbopoulos & Sudarsanam (2012) argue that older

acquirers cope better with valuation risks due to more experience and that larger acquirers

are less exposed to overpayment risk and therefore have less need for an earnout contract.

Indeed, their empirical analysis reports that earnouts are less likely chosen by older and

larger acquirers. Furthermore, there is evidence that larger deals favour the use of

earnouts. The authors conclude that an increasing deal size increases the absolute

valuation risk and accordingly increases the value to use an earnout.

All in all, there is strong empirical support in literature for the information asymmetry

hypothesis. For deal characteristics that imply adverse selection and agency problems to

be severe, earnouts are more likely used as the payment form. These results strongly

indicate that acquirers rely on earnouts to serve as effective signalling and incentive

instruments. Table 1 summarizes the evidence from research and shows the deal and firm

characteristics that are considered as indicators for information asymmetry, their effect

on the valuation risk and their impact on the probability than an earnout is used.

Table 1: Main findings of empirical studies on the choice to use earnouts

Deal characteristic Effect on

valuation risk

Probability to

use earnouts Study

High-tech target + + Kohers & Ang (2000)

Datar, Frankel & Wolfson (2001)

Barbopoulos & Sudarsanam (2012)

Service-industry target + + Kohers & Ang (2000)

Datar, Frankel & Wolfson (2001)

Barbopoulos & Sudarsanam (2012)

Private target + + Kohers & Ang (2000)

Datar, Frankel & Wolfson (2001)

Cain, Denis & Denis (2011)

Barbopoulos & Sudarsanam (2012)

Small target + + Datar, Frankel & Wolfson (2001)

Young target + + Ragozzino & Reuer (2009)

12

Cross-industry deal +

+

Kohers & Ang (2000)

Datar, Frankel & Wolfson (2001)

Ragozzino & Reuer (2009)

Cain, Denis & Denis (2011)

Barbopoulos & Sudarsanam (2012)

Cross-country deal + ~

-

Kohers & Ang (2000)

Datar, Frankel & Wolfson (2001)

Barbopoulos & Sudarsanam (2012)

if different legal systems - Kohers & Ang (2000)

if similar legal system + Kohers & Ang (2000)

Active M&A market - - Datar, Frankel & Wolfson (2001)

Acquirer’s size - - Kohers & Ang (2000)

Barbopoulos & Sudarsanam (2012)

Acquirer’s age - - Barbopoulos & Sudarsanam (2012)

Source: Author’s table

The wealth effect of earnouts

The second stream of research presented here follows the logic that the hypothesized

benefits of earnouts should ultimately be reflected in terms of value gains to the

shareholders. As commonly defined in M&A literature, these wealth effects are measured

in terms of stock market reactions around the deal announcement date.

In connection with their findings that information asymmetry triggers the use of earnouts,

Kohers & Ang (2000) also conducted the first event-study of stock market reactions to

earnout transactions. The authors find that acquiring firms using earnouts per se show

positive and significantly larger abnormal returns than bidders using different method of

payments. The empirical study reports abnormal returns for bidders in earnout deals of

1.35% on announcement date as compared to 0.9% for bidders in cash or stock deals.

Beard (2004) and Mantecon (2009) confirm these results for US-based acquirers,

Barbopoulos & Sudarsanam (2012) report the same evidence for UK-based acquirers and

Lukas & Heimann (2014) are finally able to document the same positive effect for a

sample of German acquirers.

Moreover, these studies reveal that market reactions are larger the higher the degree of

information asymmetry present in a deal. Consistently, studies show that for deals facing

high information asymmetry bidders that utilize earnouts enjoy significantly larger

returns than bidders that opt for other methods of payment. (Kohers & Ang, 2000; Beard,

2004; Barbopoulos & Sudarsanam, 2012) The same deal and firm characteristics that

trigger the use of earnouts also positively impact the market reaction, i.e. for private

13

targets, targets from high-tech or service industries and in the case of cross-industry deals.

These results imply that especially in cases where earnouts are the appropriate choice, the

bidding firm is better off using an earnout as compared to other payment agreements.

Also, the takeover premium paid to target shareholders is significantly higher for earnout

deals than for comparable deals using simple stock or cash offers indicating that both

parties benefit from the earnout’s features. Consequently, this evidence suggests that

contingent payments are perceived by the market to mitigate adverse selection and agency

problems. (Barbopoulos & Sudarsanam, 2012, p. 693)

Again, scholars explicitly focus on market reactions to cross-country earnout deals.

Mantecon (2009) finds no evidence, that buyers benefit from using earnouts in cross-

country deals. To the contrary, buyers gained from earnouts in domestic transactions. In

a more differentiated approach, Lukas & Heimann (2014) show that earnouts in cross-

country deals does not add value to the acquirer per se, while a positive effect is examined

if the legal systems are considered similar. In reference to Kohers & Ang (2000) these

findings are in line with the argument that the costs of enforcing and monitoring earnout

contracts in countries with differing legal systems and accounting standards outweigh the

benefits.

Furthermore, research on the market reactions identifies several acquirer characteristics

that tend to positively impact the wealth effect for the shareholders. Lukas & Heimann

(2014) argue that earnouts are more likely used in transactions involving Research &

Development (R&D)-intensive targets that studies often classify as targets from the high-

tech industries. However, the researchers point out that earnouts are not by definition an

effective tool to mitigate R&D-induced information asymmetry but that the effectiveness

depends on the “absorption capacity” of the acquirer. This capacity is defined as “the

ability to identify, process and interpret encoded knowledge” (Lukas & Heimann, 2014,

p. 486). For acquirers that carry a large stock of R&D related knowledge themselves this

capacity should be more established. The conducted event study confirms this hypothesis

and reports significant positive abnormal returns for acquirers with large absorption

capacity. The study also shows that larger acquirers tend to profit more from the use of

earnouts, which might indicate that these firms have better access to information on the

target and therefore more appropriately use earnouts in the M&A context. (Lukas &

Heimann, 2014, p. 486).

14

Finally, Barbopoulos & Sudarsanam (2012) accumulate the results of empirical research

on earnouts in their model of optimal earnout choice. The main innovative finding of the

paper is, that not only the simple use of earnouts is value enhancing or the use in case of

appropriate deal and firm characteristics, but that the wealth effect is also significantly

higher when it is optimal to use earnouts on the industry level. The authors identify those

target industries in which the information asymmetries between acquirer and target are

particularly high and show that the market reaction is significantly more positive if

acquirers use earnouts in these circumstances. The industries deemed optimal for earnout

use are characterized to carry large intangible assets such as intellectual property and to

be R&D-intensive. Clearly, the earnout’s benefits of retaining human capital and shifting

risks due to uncertain project outcomes to the target are more valuable for these target.

The only contradictory results are reported in a long-term study on the performance of

earnouts from an accounting perspective by Quinn (2012). To answer the question if

earnouts successfully address the problem of adverse selection, the author analyses the

actual fair value estimates of the earnouts in the acquirers’ balance sheets. Though the

author finds that earnouts are more prevalent in circumstances where one might expect

adverse selection to be a potential problem, there is little evidence to suggest that earnouts

actually function as a solution to the adverse selection problem. The researcher argues

that in order to function as a credible signalling tool for high quality targets, then post-

closing fair value estimates would have to increase as the acquiring firms confirm the

target’s quality. However, the comparison of fair value estimates of earnouts at

acquisition date and during the earnout period reported no significant differences. From

these results, the author concludes that earnouts fail to sort high quality from low quality

targets and therefore fail as a valid signalling tool. A limiting factor to this study is a small

sample of 140 transactions only. Furthermore, the author himself points towards the issue

that accounting data might be biased by management decisions. Especially, firms have an

incentive to overestimate the likelihood of an earnout payment and thereby its fair value

at acquisition date due to conservative accounting practices. Consequently, most of the

earnout fair values would decline over time as the acquirer adjusts its estimation of the

likelihood to pay an earnout to more realistic levels. Therefore, on average no upwards

adjustment of the fair value of earnouts would be observable over time.

After all, these results are not confirmed by further research studies and should not change

the overall acceptance of the information asymmetry hypothesis. The main findings from

15

the presented studies are summarized in table 2, reporting the focus of the studies and the

observed value effect to the acquirer’s shareholders around the deal announcement date.

Table 2: Main findings of empirical studies on wealth effects of earnouts

Focus of study Value effect

to acquirer Study

Earnout use in general + Kohers & Ang (2000)

Beard (2004)

Mantecon (2009)

Barbopoulos & Sudarsanam (2012)

Lukas & Heimann (2014)

For US acquirers Kohers & Ang (2000)

Beard (2004)

Mantecon (2009)

For UK acquirers + Barbopoulos & Sudarsanam (2012)

For German acquirers + Lukas & Heimann (2014)

Earnout use with high information asymm. + Kohers & Ang (2000)

Beard (2004)

Barbopoulos & Sudarsanam (2012)

Private target + Kohers & Ang (2000)

Beard (2004)

Barbopoulos & Sudarsanam (2012)

Lukas & Heimann (2014)

High-tech target + Kohers & Ang (2000)

Beard (2004)

Barbopoulos & Sudarsanam (2012)

Service-industry target + Kohers & Ang (2000)

Beard (2004)

Barbopoulos & Sudarsanam (2012)

Cross-industry deal + Kohers & Ang (2000)

Barbopoulos & Sudarsanam (2012)

Cross-country deal - Mantecon (2009)

Barbopoulos & Sudarsanam (2012)

If similar legal system + Lukas & Heimann (2014)

If different legal system - Lukas & Heimann (2014)

Earnout use of large acquirers + Lukas & Heimann (2014)

Earnout use of R&D-intensive acquirers + Lukas & Heimann (2014)

Earnout use of experienced acquirers + Beard (2004)

Deal size + Barbopoulos & Sudarsanam (2012)

Source: Author’s table

2.1.2.2 Evidence for the uncertainty hypothesis

The alternative uncertainty hypothesis is far less studied in academia than the information

asymmetry hypothesis presented before. In fact, Quinn (2012, p. 163) points out that the

information asymmetry hypothesis is dominant among scholars while the author claims

16

that the uncertainty hypothesis is common amongst practitioners. Consequently, the

review of literature could only identify two studies that explicitly examine the role of pre-

contractual uncertainty on earnouts. (Reuer, Shenkar & Ragozzino, 2004; Lukas &

Heimann, 2014)

Both studies use the volatility or standard deviation in the target’s industry as a measure

of uncertainty. Indeed, this type of uncertainty is not related to situations of information

asymmetry, rather it presents an indicator for uncertainty about the future performance of

a target that both the acquirer and the target face symmetrically. To this extent,

uncertainty creates valuation risk that cannot be reduced by the buyer by due diligence,

screening or other selection mechanisms. According to the uncertainty hypothesis, this

type of uncertainty evokes valuation gaps as well, simply because the buyer and the seller

might have different risk preferences and therefore estimate different future scenarios.

Reuer, Shenkar & Ragozzino (2004) test if earnouts are used for risk-sharing purposes

only rather than to mitigate information asymmetry problems. The uncertainty measure

is specified as the volatility of net sales in a particular industry. However, results from

their regression models indicate that uncertainty is not significant in explaining the use of

earnouts. Consequently, their test rejects the uncertainty hypothesis.

Lukas & Heimann (2014) follow the alternative approach and test if market reactions to

the use of earnouts are related to the target’s uncertainty. The authors argue that in the

case of higher volatility in the target’s cash flows, the valuation risk is more severe since

the prediction of future cash flows is problematic. Consequently, the study states the

hypothesis that with increasing uncertainty in the target’s performance, the use of

earnouts becomes more favourable. The authors define the target’s uncertainty as the

standard deviation of daily returns from comparable target companies. Indeed, their

empirical results report a positive and significant impact of the uncertainty measure on

the bidder’s abnormal return. (Lukas & Heimann, 2014, p. 491) Thus, this study finds

evidence that earnouts are viewed by the market as an appropriate instrument to manage

risks from pre-contractual uncertainty in a deal.

To sum it up, research offers limited and contradictory evidence for the uncertainty

hypothesis.

17

2.1.3 Summary

The motives of using earnouts in M&A deals is the focus of research so far. Theory

suggests that such contingent payments serve as a tool to mitigate valuation risk for the

acquirer and offers two competing hypotheses as explanations.

The information asymmetry hypothesis states that the risk of misvaluing a target firm

stems from problems of adverse selection and agency problems. This research stream

presents earnouts as a solution to this two problems. By shifting a part of the valuation

risk away from the acquirer towards the seller, the earnout contract serves as a signalling

tool for high-quality targets and thereby reduces problems of adverse selection.

Furthermore, the contingent payment functions as an incentive tool for strong post-

closing performance and helps to retain target’s key management. Empirical studies

report strong evidence for this rationale from two perspectives. First, they show that

earnouts are more likely to be used in situations where information asymmetries and

consequently adverse selection and agency problems can be expected. Second, they report

positive market reactions to the use of earnouts, especially in situations of high

information asymmetries. These results are consistent for samples of different time

frames, ranging from 1984 to 2008, and for samples of acquirers from the US, the UK

and Germany.

The uncertainty hypothesis explains that earnouts are valuable to hedge against valuation

risk even in the absence of information asymmetry. Theory argues that diverging target

valuations may simply emerge from the different risk preferences of the acquirer and the

target. By declaring a part of the overall acquisition price contingent on future

performance, an earnout therefore can close this gap by shifting the valuation risk towards

the party with the higher preference, i.e. usually the target. However, literature so far

offers only very limited and inconsistent empirical evidence for this alternative

hypothesis.

We can conclude that the concept of information asymmetry has high power to explain

why earnouts are used in M&A deals in the first place and when it is appropriate.

Logically, now the research question of this thesis arises of how to design earnouts to

make it an effective and valuable tool which is tackled in the next chapter.

18

2.2 Design of earnouts

After having explained the choice to use earnouts in M&A deals. This section now turns

towards the research question of how earnouts have to be designed in order to serve the

purpose of mitigating valuation risk. The concrete form of an earnout contract can vary

widely. Usually, the structure of an earnout is tailored to the specific characteristics of a

deal and is subject to detailed negotiations of the acquirer and the target. However, there

are some variables that are common to all earnout agreements. These earnout parameters

are introduced and defined in 2.2.1. Subsequently, chapter 2.2.2 summarizes theory and

evidence on the determinants shaping the parameters.

2.2.1 The earnout parameters

Reuer, Shenkar & Ragozzino (2004, p. 20) define earnouts as “deferred variable payments

tied to the target’s ability to meet prespecified performance goals within a certain time

frame after the deal has been consummated”. From this definition and in reference to

further attempts (e.g. Lukas, Reuer &Welling, 2012), we can conclude on the common

parameters of earnouts that together constitute the earnout mechanism:

Earnout premium:

The amount paid to the seller that is dependent on

the target’s performance. This parameter is also

referred to as the contingent payment, the deferred

variable payment or simply the earnout payment.

Earnout ratio: The earnout premium in proportion to the overall

maximal acquisition price (fixed upfront payment at

acquisition plus earnout premium).

Earnout period: The prespecified time frame over which the target’s

performance is measured.

Performance measure: The prespecified measure of the target’s

performance.

Performance goal: The prespecified goal in terms of the performance

measure that the target has to reach in order to

receive the earnout premium.

19

Required performance increase: The increase in the performance measure compared

to pre-closing performance that is required in order

to reach the performance goal.

Despite considerable heterogeneity in the specific details, the basic functionality of an

earnout mechanism is common to each contract. (Krishnamurti & Vishwanath, 2008, p.

138) At the time of the acquisition, the acquirer pays a fixed upfront payment to the seller.

During and until the end of the earnout period, the performance of the target is monitored

according to the prespecified performance measure. At the end of the earnout period, it is

determined if the target reached the prespecified performance goal. If so, the acquirer

pays the earnout premium to the seller. In case of a performance below the performance

goal, the acquirer does not pay the earnout premium.

In practice, earnouts are more complex and unique than the simplified mechanism

described above. Empirical studies show that all earnout parameters exhibit substantial

heterogeneity within a chosen sample of earnout deals. (E.g. Kohers & Ang, 2000; Beard,

2004; Cain, Denis & Denis, 2011 as referred to in the descriptive statistics in chapter 7)

The reported heterogeneity in earnout parameters indicates that earnout mechanisms are

actively designed instruments rather than standardized “off-the-shelf” agreements. This

conclusion in turn implies that there might be some identifiable characteristics that

determine the shape of each earnout parameter. The current state of research on the

determinants of earnout design is therefore examined in the next section.

2.2.2 Current state of research

Theory on the appropriate design of earnouts is limited. Some studies analyse single

earnout parameters but offer little theoretical rationale for their findings. Only Cain, Denis

& Denis (2011) examine the determinants of all relevant earnout parameters

simultaneously. Nevertheless, all these study consistently seek to explain the parameter

design by the same indicators of information asymmetry that determined the use of

earnouts in the first place. The findings are summarized for each parameter separately in

the following paragraphs.

Determinants of the earnout ratio

The earnout ratio attracted the most academic interest. Kohers & Ang (2000), Beard

(2004) and Cain, Denis & Denis (2011) find that the same characteristics that affect the

20

likelihood that earnouts are used also determine the size of the earnout ratio. For private

targets, cross-industry deals and in case of high-tech targets and service-industry targets

the earnout ratio tends to increase. Kohers & Ang (2000) argue that if problems from

information asymmetry are more severe, it is favourable to shift a larger part of the

valuation risk towards the seller. Beard (2004) further concludes that a higher earnout

ratio allows the target to even stronger signal its quality to the acquirer. Cain, Denis &

Denis (2011) add the insight that the earnout ratio is also driven by the importance of

target’s human capital. Thus, the contingent payment is raised the more the acquirer faces

the need to incentivize key management to stay. Furthermore, the earnout ratio is also

reported to be higher for targets from more volatile industries, suggesting that a higher

degree of uncertainty shifted to the target is preferable in these environments. (Cain,

Denis & Denis, 2011) Event-study analyses show that the market reacts more favourably

to larger earnout ratios. (Beard, 2004; Barbopoulos & Sudarsanam, 2012) Again, this

value enhancing effect for the acquirer might stem from the more favourable risk

distribution in case of larger earnout ratios.

All in all, there is evidence for the earnout ratio to be driven by indicators for adverse

selection and agency problems. The fact that the value of an earnout increases with the

degree of information asymmetry and uncertainty is therefore mirrored in the same

positive relationship for the earnout ratio. This result is intuitive, since increased valuation

risks not only favours the simple use of earnouts but also favours to shift more of the risk

to the target by larger contingent payments.

Determinants of the earnout period

Barbopoulos & Sudarsanam (2012, p. 693) further examine the effect of different lengths

of the earnout period on the acquirer’s abnormal returns. Their analysis reveals that the

earnout period, in contrast to the earnout ratio, has insignificant power to explain the gains

to the acquirer’s shareholders. A contradictory result is reported by Lukas & Heimann

(2014) who show that buyers benefit from shorter earnout periods. They refer to contract

theory and argue that longer earnout periods give rise to an increased probability that

results of the performance measure are manipulated or negotiated performance goals are

questioned. Ultimately, this might put the successful post-closing cooperation between

acquirer and target at risk or triggers lawsuits that create additional costs for the acquirer.

Cain, Denis & Denis (2011) find earnout periods to be longer when the information

asymmetry is likely to be resolved over time like for targets that heavily rely on the

21

successful outcome of R&D projects. Interestingly, this positive impact is offset in

situations of high uncertainty in terms of industry volatility which tends to decrease the

length of the earnout period. (Cain, Denis & Denis, 2011, p.161) The authors argue that

stronger fluctuations in the performance measure over a longer period of time would

complicate to measure the performance that is actually controllable by the target. Shorter

earnout periods are therefore favourable in case of increased uncertainty in an industry.

All in all, the empirical results are inconsistent. Even more importantly, the economic

theories referred to as explanations of the design of earnout periods are inconsistent as

well. While Barbopoulos & Sudarsanam (2012) lack any discussion of their results, Cain,

Denis & Denis (2011) acknowledge reverse effects of information asymmetry and

uncertainty. Consequently, research misses a comprehensive approach to explain the

determinants of the earnout period.

Determinants of the performance measure

Cain, Denis & Denis (2011) finally report the choice of a performance measure to be

related to proxies indicating the amount of information revealed by that measure and to

the verifiability of that measure. More specifically, if there is high information asymmetry

between the buyer and the target or if the target’s industry is highly uncertain the measure

is more likely to be sales rather than profits. This might be due to the fact that sales are

more easily verifiable. (Cain, Denis & Denis, 2011, p.162) Consequently, for less

uncertain industries and if information asymmetry is not severe, income is more likely

used as performance measure. Moreover, the authors argue and find empirical evidence

that for targets whose value is primarily derived from future growth-opportunities, i.e.

typically younger firms, the performance measure is more likely to be a non-financial

measures. Since this type of measures is often related to milestones in growth projects,

more relevant information is conveyed than it would be the case with sales or profit

measures. In contrast to the earnout period, information asymmetry indicators and a

measure of uncertainty show complementary effects on the performance measure.

Summary

To sum it up, the information asymmetry theory is referenced as the dominant theory

explaining the shape of the earnout parameters. However, except for the earnout ratio

studies so far reported inconsistent results and offer no model that integrates all of the

parameters that constitutes an earnout mechanism. Especially, no study so far comprised

22

the performance goal and consequently the required performance increase in its analysis.

Research therefore still lacks a comprehensive and complete approach to answer the

question of how earnouts should be designed.

However, only recently Lukas, Reuer & Welling (2012) formulated an alternative model

of earnout design. Its strength is that it directly follows from the well-studied and widely

accepted information asymmetry hypothesis and that it comprises the parameters of

earnout ratio, earnout period and required performance increase simultaneously. Its

novelty stems from the fact that the model dynamics are based on the option-like

characteristics of earnout mechanisms. Although it is a recurring concept in earnout

research to describe earnouts as options on a target’s fair value (e.g. Bruner & Stiegler,

2001) this approach is not deeply examined and also the model by Lukas, Reuer &

Welling (2012) lacks an empirical testing. Therefore, their model offers a promising yet

unexplored new stream in earnout literature to overcome the inconsistencies and the lack

of a comprehensive theory on earnout design so far. The model and the option-like

characteristics are analysed in detail in the next chapter.

3 Theoretical model on the design of earnouts in Mergers &

Acquisitions

A systematic approach to model the appropriate design of an earnout should inevitably

be linked to the motives of using earnouts in the first place. In order to receive the full

potential benefits of an earnout, the structure of the earnout mechanism must ensure to

serve the purposes of mitigating adverse selection and agency problems. Indeed, literature

refers to information asymmetry concepts to explain earnout design. However, the

presented studies so far miss to identify one single factor that determines the earnouts

effectiveness in solving these problems and which at the same time is directly affected by

the earnout parameters.

Lukas, Reuer & Welling (2012) identify this missing factor to be the likelihood that an

earnout payment is paid at the end of the earnout period. Clearly, in order to serve as a

powerful incentive tool, the acquirer should control for this likelihood. The more likely

the target will finally receive the earnout premium, the less efforts it will invest in the

post-closing phase. Moreover, if the likelihood for an earnout payment is high, even

relatively low quality targets are willing to accept this contract and the separating

23

equilibrium is violated. As a consequence, the earnout’s power to mitigate agency and

adverse selection problems is strongly dependent on its likelihood to result in an earnout

payment.

Therefore, the likelihood of the earnout premium to be paid has to be an important

reference point for the effective design of earnouts. Lukas, Reuer & Welling (2012)

develop a model on earnout design that is based on this very rationale. The authors state

that the likelihood is determined by the earnout parameters through dynamics that are

derived from option pricing methodology. They justify the use of option pricing

techniques with similarities between an earnout mechanism and a financial option. These

option-like dynamics in combination with the information asymmetry assumptions

outlined in the previous paragraph constitute the authors’ game-theoretic option pricing

model on earnout design.

The information asymmetry related assumptions were already introduced in detail in

chapter 2. The option-like characteristics of earnouts, however, are novel. As this

assumption is essential, chapter 3 starts with an analysis of the similarities between

earnouts and financial options. After that, the model by Lukas, Reuer & Welling (2012)

can justifiably be introduced in 3.2.

3.1 The option-like characteristics of earnouts

Several researchers point out that earnouts have option-like characteristics. (Craig &

Smith, 2003; Caselli, Gatti & Visconti, 2006; Bruner & Stiegler, 2001; Krishnamurti &

Vishwanath, 2008) More specifically, Krishnamurti & Vishwanath (2008, p. 137)

describe an earnout contract as a call option on the target’s future performance.

The upfront payment in an M&A deal would in fact only incorporate an estimation of the

target’s future performance that both acquirer and seller can agree on. The earnout

premium in turn would be based on any additional performance that the target generates

during the earnout period, at least if the performance goal is reached. Achieving the

performance goal therefore implies that the target performs stronger than initially

expected by the acquirer. In this case the target’s fair value would be adjusted upwards.

Since the acquirer then has to pay the earnout premium to the seller, both parties would

eventually benefit from this increase in value. Consequently, an earnout grants both

parties of the deal the right to benefit from this upside potential in the target’s future

performance. Caselli, Gatti & Visconti (2006) specify that the seller therefore holds a

24

long position in the call option since they potentially cash in the earnout premium at the

end of the earnout period. Since the earnout premium usually does not comprise the entire

fair value increase of the target, however, the acquirer would also benefit from a stronger

than expected performance.

This logic is straightforward and allows for the conclusion that earnouts resemble a real

option. According to Real Option Theory (ROT), a real option is the right to undertake a

particular business decision such as an investment in a company. (Berk & DeMarzo,

2014, p. 774) More specifically, earnout contracts resemble the type of a real option to

wait. The option to wait gives rise to two additional sources of value for the acquirer. (For

the general case of investment opportunities as real options see e.g. McDonald & Siegel,

1986; Trigeorgis, 1991; Luehrmann, 1998)

First, like any real option to wait an earnout allows the acquirer to defer the contingent

payment until the end of the earnout period and to enjoy the time value of money. (Del

Roccili & Fuhr, 2001) Second, the earnout includes the option to defer the final purchase

price determination until new and better information becomes available. Since earnouts

are especially used in situations of high information asymmetry, the option to wait is

valuable for the acquirer as valuation risk due to an imperfect information basis is reduced

over time. The acquirer’s informational disadvantage regarding the target’s performance

is mitigated over the length of the earnout period as the target reveals its true performance

power. Consequently, the additional information gained allows the acquirer for a more

appropriate fair value estimation.

Following Kohers & Ang (2000), we can conclude that an earnout just like a common

financial option ensures the acquirer’s ability to participate in the upside potential of the

target while at the same time it insulates him from poor performance. If the target’s fair

value goes up, the acquirer pays the earnout premium and benefits from the additional

value himself too. If the target’s fair value goes down, the acquirer does not pay the

earnout premium. From the acquirer’s perspective, this outcome is also fine. By utilizing

an earnout and waiting with additional investments until the end of the earnout period, he

avoids to overpay for the target what would have been the case if he would have followed

the more optimistic estimations of the seller upfront. (Luehrmann, 1998, p. 53)

Although the similarities between earnouts and a call option seem strong at the first

glance, there are some limitations to this approach. In general, both a financial and a real

25

option give the holder the right but not the obligation to make a certain business decision.

(Berk & DeMarzo, 2014) In case of earnouts, however, both the acquirer and the seller

enjoy the right to participate in upside potential but the acquirer also faces the obligation

to pay the additional contingent payment if the performance goal is triggered. The earnout

contract which is agreed and fixed at closing of the deal therefore also fixes all duties for

both parties. In contrast to a perfect real option situation the acquirer cannot chose the

most attractive alternative after new information on the target is available. (Berk &

DeMarzo, 2014, p. 774) To the contrary, the earnout already determines at closing date

the acquirer’s business decision at the end of the earnout period, i.e. its obligation to pay

an earnout premium. Neither can the buyer adjust his investment decision at the end of

the earnout period, nor can he choose the point in time.

Nevertheless, the earnout can be considered at least a “restricted” real option. Although

the contract restricts discretion, it offers in a simplified manner two possible outcomes.

Either the valuation of the target was understated in the beginning and both parties benefit

from the upside potential or the pessimistic view of the acquirer was appropriate and

misevaluation costs were avoided. Therefore the contingent payment still adds more

flexibility value to an investment project than any purchase agreement that only includes

a simple upfront payment. To further underline the option-like structure of earnout

mechanism, the subsequent chapter describes different earnout payment profiles with the

help of illustrative examples.

3.1.1 Earnout premium payment profiles

Describing an earnout in terms of a call option also eases the understanding of the possible

states in which the earnout might be due. Just like a financial call option, an earnout at

the end of the earnout period can expire in three states:

(1) Out of the money (OTM), if the performance goal is not reached and no premium is

paid

(2) At the money (ATM), if the target’s performance is just below the performance goal

and no premium has to be paid

(3) In the money (ITM), if the performance goal is reached or exceeded and a premium

has to be paid

26

To illustrate the option-like possible outcomes of an earnout, examples A to C present the

value of an earnout in different future scenarios. Although all earnout mechanisms

comprise the same parameters, the formulas that determine the exact earnout premium

can take many forms, varying widely from single lump sum payments to complex

formulas based on the degree to which the performance goals are exceeded. (Del Roccili

& Fuhr, 2001)

However, the following examples A to C illustrate the most common types of earnout

mechanisms. Each example is accompanied by a chart showing the earnout payment

profile with the earnout premium (y-axis) dependent on the performance achieved by the

target at the end of the earnout period (x-axis). Furthermore, each chart shows the

implications of different performance scenarios for the target’s estimated fair value (FV),

the earnout premium to be paid, the status in which the earnout expires and ultimately the

acquirer’s mispricing of the target as the difference between his valuation of the target at

closing and the true fair value of the target at the end of the earnout period. A negative

mispricing implies a loss in the target’s FV while a positive mispricing indicates a gain.

All figures are in million Euros.

Example A: Earnout premium like a call option

At closing of the deal, the target’s estimated fair value and therefore the initial payment

to the target's shareholders is EUR 10mn. Additionally, the target’s shareholders receive

50% of the amount that the Year One EBIT exceeds EUR 5mn and nothing below that

level. Figure 1 shows the payment profile and further implications.

Figure 1: Payment profile and implications of earnout like a call option

Source: Author’s figure

27

Example A describes an earnout premium that is a percentage of the amount to which the

EBIT in year one exceeds the performance goal of EUR 5mn. Clearly, the payment profile

is similar to a financial call option with the strike price EUR 5mn. Once the year one

EBIT exceeds this threshold, the target’s shareholders will cash in, at least partly, the

target’s increased fair value (FV). The accompanying table shows further implication of

this earnout mechanism. For an EBIT ≤ EUR 5mn, the call option expires OTM or at best

ATM and no earnout is paid to the target’s shareholders. However, for an EBIT of EUR

6mn the option is ITM, so target’s shareholders receive an earnout payment of EUR

0.5mn. Assuming an EBIT-multiple of 2, the target’s fair value is initially estimated as

being EUR 10mn, which is paid to the target’s shareholders as the initial consideration.

An EBIT of EUR 5mn in year one post-closing would confirm the expectations on which

the initial estimation was based. However, for an EBIT of EUR 4mn, the estimated fair

value would decrease to EUR 8mn, while an EBIT of EUR 6mn would increase the

estimation to EUR 12mn. So, the valuation decision changes as new information on the

target’s performance is available at the end of the earnout period. Obviously, the

acquirer’s downside risk of mispricing is limited to the initial consideration, while both

parties participate in the upside potential of stronger performance than expected.

Example B: Earnout premium like a binary option

At closing of the deal, the target’s estimated fair value and therefore the initial payment

to the target's shareholders is EUR 10mn. Additionally, the target’s shareholders receive

1mn if the Year One EBIT exceeds EUR 5mn and nothing below that level. Figure 2

shows the payment profile and further implications.

Figure 2: Payment profile and implications of earnout like a binary option

Source: Author’s figure

28

This example illustrates the simplest case of an earnout as a single lump sum payment if

the option expires ITM. The payment profile in this case is similar to a binary option. The

implications for the target’s estimated fair value and regarding the acquirer’s downside

risk of mispricing remain the same as in example A. It should be noticed, however, that

this earnout mechanism includes a cap of maximal payment of EUR 1mn. This implies,

that from the point on that the target’s performance generates a higher additional FV than

EUR 1mn, all the surplus accrues to the buyer.

Example C: Earnout premium like a call option with cap

At closing of the deal, the target’s estimated fair value and therefore the initial payment

to the target's shareholders is EUR 10mn. Additionally, the target’s shareholders receive

50% of the amount that the Year One EBIT exceeds EUR 5mn and nothing below that

level. The maximal earnout payment is capped at EUR 1mn. Figure 3 shows the payment

profile and further implications.

Figure 3: Payment profile and implications of earnout like a call option with cap

Source: Author’s figure

This case extends example A by adding a cap to the earnout mechanism. The payment

profile resembles a combination of a call and a put option. If the call option ends up ITM,

the target’s shareholders cash in a part of the additional generated FV such as EUR 0.5mn

for an EBIT of EUR 6mn. However, the earnout is capped at a maximum of EUR 1mn.

The put option therefore has a strike price of EUR 7mn, that is higher than the call option’s

strike price. Similar to the binary option, this mechanism limits the degree to which the

target’s shareholders participate in the upside potential and also the acquirer’s obligation

to pay if the target’s business performs outstandingly.

29

A more elaborate example of an earnout including a floor and a cap simultaneously is

shown in figure A1 in appendix 1.

All in all, these examples underline the similarity of earnouts and financial options. The

analysis carried out supports the view of researchers that options should therefore be

treated and valued as options. (Krishnamurti & Vishwanath, 2008; Bruner & Stiegler,

2001; Caselli, Gatti & Visconti, 2006) This result is extremely useful as we can justifiably

transfer knowledge about the dynamics and value of financial options to the case of

earnouts. Therefore and as a conclusion of the analytics carried out in this chapter, the

subsequent section matches earnout parameters to parameters of financial options.

3.1.2 Mapping the earnout parameters onto a financial call option

Theory commonly relies on the Black-Scholes Option Pricing Model in order to value

financial call options. (Berk & DeMarzo, 2014, p. 747) This valuation approach only

requires five input parameters to value an option: the stock price of the underlying (S),

the exercise price (X), the time to expiration (T), the uncertainty of the stock (𝜎), and the

risk-free interest rate (i). (Berk & DeMarzo, 2014, p. 748) If these parameters are also

available for a real option like an earnout, the same option pricing technique could be

transferred to the case of earnouts. In fact, practitioners point out the usefulness of option

pricing approaches such as the Black Scholes Model to value earnouts. (E.g. Thompson

& Schnorbus, 2010; American Appraisal, 2015)

Since earnouts resemble real options, the thesis maps an earnout parameters onto the

variables of a financial call option following the same approach how ROT maps

investment opportunities in general onto financial options. (See Luehrman, 1998, p. 52;

Berk & DeMarzo, 2014, p. 778 and) The match is illustrated in figure 4 and explained for

each parameter hereafter.

The current stock price of the underlying (S) is the difference between the acquirer’s

estimation of the target’s fair value at closing and the true fair value of the target at the

end of the earnout period. As the examples A-C illustrate, the actual performance of the

target during the earnout period can cause a positive or negative change in the target’s

estimated fair value. This deviation from the initial fair value estimation by the acquirer

is therefore labelled as “target’s unconsidered fair value”.

30

Figure 4: Mapping an earnout onto a financial call option

Source: Author's chart based on Luehrman (1998, p. 52)

The exercise price or strike price of the option (X) corresponds to the performance goal

written in the earnout contract. As we have seen from the payment profiles, only if the

performance goal is exceeded, the earnout ends up ITM. In this case the target’s true fair

value is higher than initially estimated and both parties share the additional value. The

degree to which the seller participates in a positive unconsidered fair value, i.e. the

earnout premium to be paid, is determined by formulas of varying complexity (e.g. an

EBIT-multiple as in examples A-C).

The time to expiration (T) perfectly corresponds to the earnout period. The uncertainty in

the target’s future performance (𝛔) corresponds to the standard deviation of returns on

the stock of the financial option. It is important to notice that this measure of uncertainty

is not related to information asymmetries between the acquirer and the target but to the

volatility in an industry. Finally, the time value of money (i) resembles the risk-free rate

of return of a financial option.

To sum it up, the earnout parameters are convincingly matched with the variables of a

common financial call option. On basis of this detailed analytical derivation, the thesis

31

now introduces the theoretical model on earnout design by Lukas, Reuer & Welling

(2012) from which hypotheses are derived subsequently.

3.2 A game-theoretic option pricing model on design of earnouts

3.2.1 Original model by Lukas, Reuer & Welling (2012)

In their model on earnouts in M&A deals, Lukas, Reuer & Welling (2012) examine the

optimal timing of M&A deals utilizing earnouts on the one hand and the design of the

earnout parameters on the other hand. Since this thesis aims at insights on the optimal

design of earnouts, the second stream of the model developed by Lukas, Reuer & Welling

(2012) is the focus of interest. The authors derive their model mathematically by means

of dynamic programming. However, for the purpose of describing the determinants of

earnout design, it is sufficient to explain the dynamics and consequences without referring

to its mathematical derivation.

First, this chapter describes how the model’s assumptions follow directly from the

information asymmetry hypothesis. Second, the chapter outlines how the presented model

incorporates implications from option pricing techniques. Finally, the chapter introduces

the game-theoretic dynamics of the model that lead straight to the hypotheses on earnout

design stated by Lukas, Reuer & Welling (2012). The following paragraphs refer to the

model dynamics as illustrated in figure 5.

Information asymmetry assumptions

Consistent with the information asymmetry hypothesis, Lukas, Reuer & Welling (2012)

describe earnouts as a useful tool to mitigate problems of information asymmetry. More

specifically, the authors outline an M&A deal in which the acquirer faces the necessity to

retain the target’s human capital. This is due to maintain market knowledge and

relationships with key customers that otherwise are at risk if the target’s key management

leaves post-closing. Furthermore, in order generate a value-enhancing effect from the

deal, the acquirer desires to create synergies (ϴ) in the post-closing phase. Synergies,

however, critically depend on the target’s cooperation (C) in the post-takeover phase.

Consequently, the acquirer faces two agency problems. On the one hand the success of

the business critically depends on the retention of management, on the other hand it

depends on not perfectly observable cooperation efforts by the target resulting in a moral

hazard problem. (Lukas, Reuer & Welling, 2012, p. 258) We can refer to chapter 2 and

32

conclude that an earnout serves as a solution to both of these problems. By making part

of the overall acquisition price contingent on future performance, the target is incentivized

to retain its key human capital. Moreover, the target is incentivized to cooperate during

the earnout period in order to more likely meet the performance goal.

Clearly, the potential benefits of an earnout agreement therefore critically depend on its

power to incentivize. Most importantly and as a novel aspect to the earnout literature the

authors point out, that this power in turn is varying with the likelihood (N) that the earnout

ends up ITM. Since the similarities of earnouts and financial options were adequately

analytically derived, we can turn towards implications from option pricing methodology

to identify determinants of the likelihood (N).

Implications from option pricing methodology

Following financial option pricing methodology, there are three determinants of the

probability of a financial option to end up ‘in the money’: (Berk & DeMarzo, 2014, p.

720; Hull, 2012, p. 215)

1. The higher the uncertainty of the underlying (σ), the higher the probability

2. The longer the time to expiration (T), the higher the probability

3. The lower the strike price (X), the higher the probability

As the thesis analytically derived before, each of these option parameters has its

counterpart in an earnout mechanism. Consequently, the model by Lukas, Reuer &

Welling (2012) transfers these implications from financial option methodology to the

likelihood (N) that an earnout ends up in the money.

1. The higher the uncertainty in the target’s future performance (𝛔), the higher is N

2. The longer the earnout period (T), the higher is N

3. The lower the required performance increase (𝛙) to reach the performance goal

(X), the higher is N

Similar research that uses option pricing methodology to value earnouts follow the same

logic. (E.g. Bruner & Stiegler, 2001; Caselli, Gatti & Visconti, 2006) Consequently, the

option pricing methodology allows to draw conclusions on how some of the earnout

parameters ultimately affect in which state the earnout is likely to expire.

33

Figure 5: Dynamics of model on earnout design by Lukas, Reuer & Welling (2012)

Source: Author's figure based on Lukas, Reuer & Welling (2012, p. 260)

Game-theoretic dynamics and hypotheses

The essential game-theoretic assumption that the authors state is that the acquirer and the

target firm have different interests post-closing. The acquirer seeks to create synergies

(ϴ) in order to make the deal value enhancing and is therefore dependent on the target’s

cooperation (C) in the post-closing phase. The more the acquirer faces agency problems

and consequently the necessity to incentivize the target, the larger he would determine

the contingent part of the acquisition price, i.e. the earnout ratio (EOR). For the target,

cooperation requires efforts and the target is only willing to invest these efforts as long as

it serves his goal to cash-in the earnout premium. Following game-theory, the higher the

likelihood (N) for an earnout premium to be paid, the less cooperation efforts the target

would invest. Lukas, Reuer & Welling (2012, p. 261) draw the conclusion that with

increasing N, the only way for the acquirer to secure cooperation and ultimately the

desired synergies is by increasing the incentive for cooperation by raising the earnout

ratio. (EOR)

However, as option pricing methodology suggests, the earnout parameters impact N.

More specifically, higher uncertainty (𝛔), longer earnout periods (T) and a lower required

performance increase (𝛙) all increase N and therefore in turn also determine an increase

of EOR. Based on these essential interrelationships, the authors formulate the following

34

hypotheses regarding the appropriate design of earnouts in order to secure their power to

incentivize:

H1: Ceteris paribus, the higher the uncertainty of the target’s future performance, the

higher the earnout ratio.

H2: Ceteris paribus, the higher the earnout period, the higher the earnout ratio.

H3: Ceteris paribus, the lower the required performance increase in the target’s

performance, the higher the earnout ratio.

All in all, the authors convincingly combine insights from the information asymmetry

hypothesis with option pricing methodology and game-theoretic assumptions to arrive at

a model that explains the determinants of the earnout ratio. However, this model lacks the

determinants of the earnout period and the required performance increase. This thesis

therefore advances the model in order to explain all three earnout parameters in an

integrated approach.

3.2.2 Own advancements to the model

Lukas, Reuer & Welling (2012) explicitly state retaining management and mitigating

post-closing moral hazards as the reason for which the acquirer needs to incentivize the

target. The information asymmetry hypothesis further argues that mitigating adverse

selection problems is a purpose of using earnouts. However, the authors do not consider

this benefit within their model.

This thesis suggests that the value of earnouts as a signalling tool can perfectly be

integrated into the model on earnout design by Lukas, Reuer & Welling (2012). For an

earnout to serve as a credible signal for high quality targets, the acquirer should indeed

control for the likelihood that the contingent payment has to be paid in the end. If the

design of an earnout is such that the earnout premium is likely to be paid also lower

quality targets would tend to accept it and the assumption of a separating equilibrium is

violated. Consequently, this thesis suggests that only earnout design that controls for the

likelihood to expire ITM ensures the contract’s power as an effective signalling tool. With

this adjustment, all aspects of the question why an earnout is used in the first place would

also be reflected in deciding how to design an earnout in order to be effective.

The original model needs further adjustments since it does not comprise the determinants

of the earnout period and the required performance increase. Since the thesis is aiming at

35

describing the design of these parameters as well, it is necessary to adjust the model in

this regards. In fact, analysing the original model’s dynamics reveal two major disputable

issues.

First, the original model assumes that the acquirer faces an increase in N by increasing

the incentive for the target to cooperate through a higher EOR. To the contrary, the

acquirer could also face the increased likelihood caused by one of the independent

variables (T, 𝝈, or 𝝍) by shaping the remaining earnout parameters to offset the increased

likelihood. For instance, a higher uncertainty might be matched with a shorter earnout or

a higher required performance increase respectively. Consequently, the acquirer has two

options to react to an increased likelihood. Either the earnout ratio is increased to

encourage cooperation, or the likelihood of paying an earnout premium is decreased by

designing the remaining earnout parameters accordingly. Both ways would serve the goal

of mitigating the increased agency and adverse selection problems.

Second, the model by Lukas, Reuer & Welling (2012) lacks a systematic distinction

between exogenous and endogenous variables. Clearly, 𝝈 is an exogenous variable that

cannot be influenced by the parties of the deal. To the contrary EOR, T and 𝝍 are

endogenous variables of an earnout mechanism and are therefore designed actively.

This distinction allows for an important adjustment of the model dynamics, since all the

earnout parameters considered here should therefore be designed given a certain degree

of uncertainty in the M&A deal. Consequently, the empirical study presented in this paper

will be based on an advancement of the original model as depicted in figure 6.

36

Figure 6: Dynamics of advanced model on earnout design

Source: Author's figure

Additionally to the dynamic of choosing EOR (dynamic A), the acquirer has the option

to control N by choosing T (dynamic B1) and by choosing 𝜓 (dynamic B2). Given a high

degree of uncertainty, the acquirer could either raise the earnout ratio, decrease the

earnout period, or raise the required performance increase. These choices are not mutually

exclusive. As we have to think of the design of an earnout as a process of negotiation

between the buyer and the seller, it is more likely that the acquirer needs to adjust through

all three dynamics rather than shaping only one as he demands. Also, as Lukas, Reuer &

Welling (2012, p. 258) point out accountants and lawyers working on an M&A deal are

paid per rata of the earnout premium. Facing an increasing N solely by raising EOR

would therefore also increase the transaction costs for the buyer. From this perspective,

dynamic B1 and dynamic B2 present attractive alternatives. Consequently, we would

expect the exogenous variable of uncertainty to affect the earnout ratio, the earnout period

and the required performance increase at the same time.

All in all, this advanced model prescribes the acquirer to design the earnout contract

dependent on the degree of uncertainty within the M&A deal. The great upside of this

advanced model is that hypotheses can be derived stating the determinants for each

earnout parameter.

37

4 Hypotheses

The testable hypotheses H1-H3 regarding the earnout parameters are directly derived

from the advanced model on earnout design in 3.2.2. Accordingly, uncertainty about the

target’s future performance is the pivotal explanatory variable for each parameter.

H1: The higher the uncertainty of the target’s future performance, the higher the earnout

ratio.

H2: The higher the uncertainty of the target’s future performance, the shorter the earnout

period.

H3: The higher the uncertainty of the target’s future performance, the higher the

required performance increase.

The choice of the performance measure, however, is not directly linked to the game-

theoretic option pricing model. In order to test this remaining earnout parameter, a

hypothesis is therefore derived from empirical results available so far. Again, research

suggests that uncertainty is one key determinant for this choice.

As introduced before, Cain, Denis & Denis (2011) refer to the “informativeness principle”

of Holmström (1979) to explain the appropriate use of performance measures. They

suggest that in case of moral hazard problems an incentive tool such as the earnout should

be tied to observable and verifiable measures of the target’s efforts. In case of adverse

selection problems, the performance measure should be an observable and verifiable

signal of the target’s true value. Accordingly, Cain, Denis & Denis (2011, p. 162)

hypothesize that for targets with high growth opportunities but low profitability today, a

non-financial or a measure of sales are most appropriate as these measures present drivers

of future profitability. Indeed, their empirical results indicate that for targets with high

growth opportunities the performance measure is more likely to be sales or a non-financial

measure rather than income. Furthermore, they find that for targets from highly volatile

industries a measure of sales is more likely to be used. The authors argue that sales in

uncertain environments are easier to verify than income measures which might be

affected by arbitrary cost allocation.

Since this logic just like the hypotheses 1-3 follows directly from information asymmetry

problems and since uncertainty again seems to be an important determinant of the earnout

38

parameter performance measure, this thesis will test the results reported by Cain, Denis

& Denis (2011) through the following hypotheses:

H4a: For high uncertainty in the target’s future performance, the performance measure

for the earnout agreement is more likely to be sales.

H4b: For high uncertainty in the target’s future performance, the performance measure

for the earnout agreement is less likely to be a measure of income.

H4c: For targets with high growth opportunities, the performance measure for the earnout

agreement is more likely to be sales or a non-financial measure.

5 Data sample creation

This chapter describes the three step data selection process carried out to create a sample

of data required to test the hypotheses H1-H4. First, deals that have used an earnout

agreement are identified through the Zephyr database. As the second step, information on

the earnout parameters (the dependent variables) is retrieved from Zephyr and

Investegate, a database of company announcements. Third, two alternative uncertainty

measures (the independent variables) are generated for each deal through the use of Orbis

and Datastream. Only those deals for which both independent and at least one dependent

variable could be retrieved remain in the final data sample that is briefly presented at the

end of this chapter. The data sample creation and how each selection step reduces the

sample size can be tracked in table A1 in appendix 2.

5.1 Deal search

The initial sample of M&A deals employing earnouts is created by a customized search

in the Bureau van Dijk database Zephyr. As the first step, a general search strategy

including 8 search criteria was set up. (1) As deal types only mergers and acquisitions

were considered that (2) were completed at the time of the deal search. (3) The acquirer

must be a listed company since these firms are expected to provide more detailed

disclosure of earnout specific information than unlisted firms. (4) Most importantly, the

method of payment has to include an earnout. (5) To ensure a significant principal-agent

relationship between acquirer and target, the final stake owned by the acquirer has to be

at least 50%. (6) To ensure significant potential valuation risk in the deal, the deal value

has to be at least EUR 10mn while the deal value is capped at a maximum of EUR 1bn to

39

exclude extremely dominant large deals. (7) The deal has to be completed between

01.01.2000 and 30.06.2015. (8) Finally, like in previous studies the acquirers had to

originate from the same territory. The search strategy was then run for acquirers from

Germany, Scandinavia, UK and US separately. The individual search strategy results are

shown in figures A2-A5 in appendix 2.

For acquirers from Germany, Zephyr reported only 38 deals while for Scandinavia 73

deals were identified. Both sample sizes do not allow a meaningful empirical analysis and

were therefore not considered. For the UK, Zephyr reported 550 deals while for the US

883 deals were identified. Since potentially each single deal has to be analysed for earnout

specific information, the US sample was considered as too large to handle in the scope of

this thesis. Therefore, the UK sample was chosen as the data basis.

5.2 Collecting data on earnout parameters

Information on the earnout parameters was retrieved from the Zephyr database and the

Investegate database. Investegate is an online database to search for announcements by

UK listed companies, including announcements on M&A events. Therefore, for each deal

identified in Zephyr, the corresponding announcement in Investegate was retrieved and

analysed for parameter-specific information. (See table A2 in appendix 1 for an example)

In order to systemize the search for data on the earnout parameters, the exact definition

of each parameter had to be standardized.

The earnout ratio expresses the portion of the overall deal value that in the databases was

classified to be contingent on performance and paid out as an earnout in the databases

used. The measure is therefore expressed in percentages.

The earnout period was defined to be the overall length of time in which the performance

of the target determines some part of the contingent payment. Consequently, the earnout

period is measured in years. While some earnouts are paid for instance based on the

target’s performance of the last 3 years, other earnouts are paid in three instalments at the

end of each of this three years according to sub-performance goals. Still, in this thesis

both earnouts are considered to have an earnout period of three years since the final

purchase price is ultimately determined at the end of the third year. Moreover, this

simplification was necessary as the information from Zephyr and Investegate did not

allow for a systematic separation of these cases as frequently only the start and end date

of the earnout period were reported.

40

The required performance increase was defined as the required increase in the target’s

performance of the last fiscal year prior to the acquisition in order to reach the

performance goal. This measure is expressed in percentage and is usually expected to be

positive. In order to generate this measure, data on the target’s pre-closing performance

and the performance goal is required. However, the analysis in Zephyr and Investegate

could not reveal this type of information to a large extent. In fact, only for 17 deals data

on the specific performance goal could be retrieved. The creation of a large enough data

sample would therefore had required further analysis in additional databases. Due to time

limitations, this task could not be fulfilled in the scope of this thesis. Consequently, H3

cannot be empirically tested.

The hypotheses regarding the performance measure (H4a-c) required a classification of

performance measures as ‘sales’, ‘income’ and ‘non-financial measures’. This distinction

is also common in previous studies. (Kohers & Ang, 2000; Cain, Denis & Denis, 2011)

In general, measures that are deemed to be easily verifiable and not diluted by decisions

on accounting regulations were determined to be sales-like measure, such as revenues or

share price. To the contrary, measures that are to be found lower at the income statement

or depend on a deeper valuation analysis were determined to be income-like measures,

such as EBITDA or asset valuation. Non-financial measures turned out to be easily

identifiable, such as milestones in R&D projects or operational conditions. The

classification of all measures reported in the deals is shown in table A3 in appendix 2.

As a result of collecting data on the earnout parameters, a total of 40 deals had to be

excluded. For 34 of these deals the search yielded no information on earnout parameters

at all. Further 6 of these deals in fact utilized a different payment method than earnouts

and were therefore wrongly listed in the Zephyr database.

5.3 Generating the explanatory variable “uncertainty”

As the next step, the remaining 510 deals in the data sample were analysed for the

availability of an uncertainty measure. Research agrees to use the target’s standard

deviation in daily returns as a proxy measure for uncertainty in the target’s future

performance. (E.g. Cain, Denis & Denis, 2011; Lukas & Heimann, 2014) The analysis of

the target sample, however, reveals that in 508 deals the target was unlisted, while in 2

deals the target was delisted from the stock after the transaction. Therefore, no data on

the target’s daily standard return on the stock market is available. Consequently, a proxy

41

measure is required. Since the quality of the independent variable is essential to the

empirical analysis, two alternative measures of uncertainty as defined hereafter were

generated to serve as a robustness check. It is a strict requirement that both uncertainty

measures are available for a particular M&A deal in order to remain in the data sample.

Uncertainty of a single proxy target

As a first uncertainty measure, this study utilize the standard deviation of daily returns

for the median firm operating in the same industry as the target, thereby following

previous research. (Cain, Denis & Denis, 2011, p. 158; Lukas & Heimann, 2014, p.486)

The standard deviation of daily returns was calculated over a one year period prior to the

announcement date of each particular deal. Consistent with prior studies, the US SIC

industry codes are used as the industry classification. (E.g. Barbopoulos & Sudarsanam,

2012; Lukas & Heimann, 2014) In the Bureau von Dijk database Orbis, UK-listed

companies with the same SIC code as the target were searched. In order to identify the

median firm, the reported list of companies were ranked according to their cash-flows in

the fiscal year prior to the respective deal. Cash-flows are considered to appropriately

reflect a firm’s business activity.1 However, standardized and comparable financial

figures are only available through Orbis from 2005 onwards. Consequently, only for deals

that were announced in 2006 or later the firms in the same industry could be ranked

according to their prior year’s cash-flows. For all deals announced in 2005 or earlier the

required data was not available. Consequently, 103 deals between 2000 and 2005 had to

be excluded from the sample. Furthermore, 28 deals had to be excluded since no listed

proxy target could be identified in the respective industry.

For the remaining 379 deals, the standard deviation of daily returns for the proxy company

(median firm) in the one year prior to the announcement of the acquisition was calculated.

The required data on daily returns was retrieved through Datastream and the standard

deviation was calculated subsequently. (For detailed formula see appendix 2) In the

subsequent empirical analysis, this measure of uncertainty is referred to as uncert_proxy.

Uncertainty of the proxy target’s industry

An alternative measure of uncertainty was derived in order to carry out a robustness

check. Instead of using the uncertainty of a single proxy target, this alternative approach

1 As an exception, for the insurance industry profit was chosen as the criteria since cash flows are not available.

42

derives an uncertainty measure for the entire industry this proxy target operates in.

Thomson Reuters Datastream offers industry indices for UK based companies.

Consequently, for each proxy target daily return data was retrieved for its particular

industry index one year prior to the respective deal announcement. Again, the uncertainty

measure was finally calculated as the standard deviation in daily returns of these indices.

(For detailed formula see appendix 2) In the subsequent empirical analysis, this measure

of uncertainty is referred to as uncert_ind.

After all, 2 more deals had to be excluded from the sample since the acquirer was listed

primarily at a US stock exchange which was revealed in a further data check. This final

data clean-up reduced the sample to 377 deals.

5.4 Final data sample

As a result of the different data collecting steps described above, the final total sample

consists of 377 earnout deals. For all of these deals both measures of uncertainty are

available. However, not every deal provides information regarding all earnout parameters

simultaneously.

The earnout ratio is known for 353 deals, while 279 deals show information on the earnout

period, and 220 deals for the performance measure. These sample sizes allow for powerful

empirical analysis of H1, H2 and H4. The performance goal, however, could only be

retrieved for 17 deals as described before. Consequently, this sample size does not allow

for a meaningful empirical analysis of the required performance increase and H3 has to

be dropped. Finally, 172 deals comprise data on earnout ratio, earnout period and

performance measure simultaneously. This subsample therefore is labelled the full

information sample. As a further robustness check, all hypotheses are tested on both the

total sample and the full information sample. The accompanying CD to this thesis

includes an overview of both samples with all required deal specific data.

43

6 Methodology

6.1 Control variables for information asymmetry

The theoretical model at the centre of this thesis describes the earnout as a solution to

problems of information asymmetry. Furthermore, this approach models earnouts as real

options and consequently derives hypotheses on the design of its parameters according to

option pricing methodology. The variable of uncertainty therefore is the pivotal

determinant of earnout design since all earnout parameters are expected to be adjusted

according to the degree of uncertainty in the deal.

However, from the review of theory and empirical evidence on earnout design in chapter

2 we know that alternative explanations of the earnout design exist. Although lacking

consent, most of the scholars argue that not only the choice to use earnouts in the first

place but also the design of its parameters is determined by indicators of information

asymmetry. (E.g. Kohers & Ang, 2000; Barbopoulos & Sudarsanam, 2012) Therefore, it

is of great interest to not only test the hypotheses H1-H4 as derived from the option

pricing model. Rather, we would like to control for the opposing view that information

asymmetry indicators in fact directly determine the earnout design, thereby ignoring the

‘likelihood-concept’.

Therefore, the regression models developed to test H1-H4 as described in the subsequent

chapters are supplemented by common indicators of information asymmetry. Referring

back to chapter 2, research agrees that private targets, targets from the high-tech and

service industry, cross-industry deals and cross-country deals are indicators of high

information asymmetry between the acquirer and the target. For simplicity reasons, a

target is considered private if Zephyr reports it as being unlisted. Furthermore, research

argues that larger and older acquirers better cope with information asymmetries in M&A

deals and that the deal size in turn affects the magnitude of the valuation risk present in a

transaction. Thus, also acquirer’s size, acquirer’s age and deal size act as control

variables.

Table 3 below shows the control variables, the mnemonic used in the regression models,

the variable type, the exact definition and the database from which it is sourced.

44

Table 3: Definition of control variables for information asymmetry

Variable Mnemonic Type Definition Database

Private target private Dummy 1 = unlisted;

0 = listed

Zephyr

Cross-industry

deal

cross_ind Dummy 1 = different two-digit SIC

code for acquirer and target

0 = same two-digit SIC code

Zephyr

Cross-country

deal

cross_count Dummy 1 = different country code

for acquirer and target

0 = same country code

Zephyr

High-tech target high_tech Dummy 1 = SIC code matches high-

tech classification2

0 = no match

Zephyr

Service industry

target

serv_ind Dummy 1 = SIC code matches

service industry

classification3

0 = no match

Zephyr

Acquirer’s age acqu_age Continous Number of years between

acquirer’s incorporation and

deal’s announcement year

Zephyr

Acquirer’s size acqu_size Continous Acquirer’s market value of

equity 4 weeks prior to deal

announcement in GBP mn

Datastream

Deal size deal_size Continous In GBP mn Zephyr

Source: Author’s table

6.2 Regression models on earnout parameters

This chapter explains and explicitly states the regression models used to analyse the

hypotheses H1, H2 and H4a-c. Consistent to previous research, the earnout ratio is

examined by means of a Tobit regression, the earnout period by means of an OLS

regression and the choice of the performance measure is structured as a logit model. The

regression models and test statistics are all run in the econometric software Eviews. As

mentioned in the chapter on data, the methodology includes two robustness checks.

First, each model is run for the two uncertainty measures (uncert_proxy and uncert_ind)

separately. Since the hypotheses state uncertainty to be the central determinant of the

earnout design, ensuring the validity of this measure is of high importance. The regression

models stated below for simplicity reasons only show the case for the uncertainty measure

uncert_proxy. Second, each hypothesis is tested on the total sample and the full

information sample. As the latter might possess higher data quality compared to deals for

which the earnout parameters are only partly known.

2 High-tech industry classification by SIC codes according to a meta-study on optimal high-tech classifications by Kile & Phillips (2009). See table A4 in appendix 3 for details. 3 Own classification according to SIC code logic. See table A5 in appendix 3 for details.

45

The significance of the estimated coefficients of the explanatory variables will be tested

by simple t-tests. As both the quality of the regression models to provide effective

estimators and the power of the t-tests depend on normality assumptions, the common

Gauss-Markov assumptions (A1-A4) are tested to hold: (Verbeek, 2012, p. 18)

A1: Error terms (𝜀𝑖) have mean zero

A2: All error terms are independent of all explanatory variables

A3: All error terms have the same variance (homoscedasticity)

A4: The error terms are mutually uncorrelated (no autocorrelation)

As described along with the empirical results, the models and test statistics are adjusted

if one assumption is found to be violated. For details on the t-test see appendix 3.

6.2.1 Tobit regression model of the earnout ratio

Following previous studies, a Tobit regression is used to examine the determinants of the

earnout ratio (EOR) by means of maximum likelihood estimation. (Kohers & Ang, 2000;

Cain, Denis & Denis, 2011) The use of a Tobit model accounts for the fact that the earnout

ratio is a continuous variable but constrained to a range from 0 to 1. (Verbeek, 2012, p.

238) Consequently, all values below 0 and above 1 are mapped to 0.

The Tobit model tries to explain the earnout ratio for each deal i (𝐸𝑂𝑅𝑖) by a regression

on a constant (𝛼), the uncertainty measure (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) and the control variables.

For the purpose of an easier interpretation of the resulting coefficients, the continuous

variables 𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖, 𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖, 𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖 and 𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖 are transformed to

logarithmic variables. The model therefore indicates the absolute change in the earnout

ratio caused by a ceteris paribus percentage change of these log variables. (Verbeek, 2012,

p. 75) The regression model to be run in Eviews therefore is:

𝐸𝑂𝑅𝑖 = 𝛼 + 𝛽1 ∗ log (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) + 𝛽2 ∗ 𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑖 + 𝛽3 ∗ 𝑐𝑟𝑜𝑠𝑠_𝑖𝑛𝑑𝑖 + 𝛽4

∗ 𝑐𝑟𝑜𝑠𝑠_𝑐𝑜𝑢𝑛𝑡𝑖 + 𝛽5 ∗ ℎ𝑖𝑔ℎ_𝑡𝑒𝑐ℎ𝑖 + 𝛽6 ∗ 𝑠𝑒𝑟𝑣_𝑖𝑛𝑑𝑖 + 𝛽7

∗ log (𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖) + 𝛽8 ∗ log (𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖) + 𝛽9 ∗ log (𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖) + 𝜀𝑖

with 𝐸𝑂𝑅𝑖 = 0 for all 𝐸𝑂𝑅𝑖 ≤ 0 and ≥ 1

46

6.2.2 OLS regression model of the earnout period

The determinants of the earnout period (EOPer) are examined by means of a cross-

sectional linear regression employing the method of ordinary least squares (OLS) since

this continuous variable is not subject to any upper limit and cannot be negative. The OLS

regression model tries to explain the earnout period for each deal i (𝐸𝑂𝑃𝑒𝑟𝑖) by a

regression on a constant (𝛼), the uncertainty measure (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) and the control

variables. Again, for the purpose of an easier interpretation of the resulting coefficients,

the continuous variables 𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖, 𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖, 𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖 and 𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖 are

transformed to logarithmic variables. The regression model to be run in Eviews therefore

is:

𝐸𝑂𝑃𝑒𝑟𝑖 = 𝛼 + 𝛽1 ∗ log (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) + 𝛽2 ∗ 𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑖 + 𝛽3 ∗ 𝑐𝑟𝑜𝑠𝑠_𝑖𝑛𝑑𝑖 + 𝛽4

∗ 𝑐𝑟𝑜𝑠𝑠_𝑐𝑜𝑢𝑛𝑡𝑖 + 𝛽5 ∗ ℎ𝑖𝑔ℎ_𝑡𝑒𝑐ℎ𝑖 + 𝛽6 ∗ 𝑠𝑒𝑟𝑣_𝑖𝑛𝑑𝑖 + 𝛽7

∗ log (𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖) + 𝛽8 ∗ log (𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖) + 𝛽9 ∗ log (𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖) + 𝜀𝑖

6.2.3 Binary choice model of the performance measure

H4 a-c seek to explain the probability of the performance measure to be either a measure

of sales, a measure of income, or a non-financial measure. The pivotal explanatory

variables again are the measures of uncertainty. The dependent variable, i.e. the use of

the performance measure in question, is therefore limited to the two possible outcomes

“yes” or “no”, i.e. two discrete alternatives. Therefore, a binary choice model is required

to model these dependencies. Probit and logit models are most common in studies to

examine these types of relationships. (Verbeek, 2012, p. 208) While the probit model

assumes a standard normal distribution, the logit model assumes a standard logistic

distribution of the residuals. Therefore, in case the probit model results report non-

normality characteristics such as a high skewness in its residuals, the logit model is the

more appropriate approach. (Verbeek 2012, p. 208) As described later in the chapter on

empirical results and as shown on the accompanying CD, the probit model reports high

skewness and therefore a logit model was chosen for H4a-c as depicted below.

To test H4a, the dependent binary variable is defined as 𝑠𝑎𝑙𝑒𝑠𝑖 with the two discrete

alternative outcomes:

47

𝑠𝑎𝑙𝑒𝑠𝑖 = 1 , if the performance measure is a measure of sales

𝑠𝑎𝑙𝑒𝑠𝑖 = 0 , if the performance measure is not a measure of sales

The logit model tries to explain the probability of 𝑠𝑎𝑙𝑒𝑠𝑖 to be the performance measure

in a deal by a constant (𝛼), the uncertainty measure (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) and the control

variables. The model to be run in Eviews therefore is:

logit(𝑠𝑎𝑙𝑒𝑠𝑖 = 1) = 𝐿(𝛼 + 𝛽1 ∗ log (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) + 𝛽2 ∗ 𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑖 + 𝛽3 ∗

𝑐𝑟𝑜𝑠𝑠_𝑖𝑛𝑑𝑖 + 𝛽4 ∗ 𝑐𝑟𝑜𝑠𝑠_𝑐𝑜𝑢𝑛𝑡𝑖 + 𝛽5 ∗ ℎ𝑖𝑔ℎ_𝑡𝑒𝑐ℎ𝑖 + 𝛽6 ∗

𝑠𝑒𝑟𝑣_𝑖𝑛𝑑𝑖 + 𝛽7 ∗ log (𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖) + 𝛽8 ∗ log (𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖) +

𝛽9 ∗ log (𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖) + 𝜀𝑖)

with L being the standard logistic distribution function

To test H4b, the dependent binary variable is defined as 𝑖𝑛𝑐𝑜𝑚𝑒𝑖 with the two discrete

alternative outcomes:

𝑖𝑛𝑐𝑜𝑚𝑒𝑖 = 1 , if the performance measure is a measure of income

𝑖𝑛𝑐𝑜𝑚𝑒𝑖 = 0 , if the performance measure is not a measure of income

The logit model tries to explain the probability of 𝑖𝑛𝑐𝑜𝑚𝑒𝑖 to be the performance measure

in a deal by a constant (𝛼), the uncertainty measure (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) and the control

variables. The model to be run in Eviews therefore is:

logit(𝑖𝑛𝑐𝑜𝑚𝑒𝑖 = 1) = 𝐿(𝛼 + 𝛽1 ∗ log (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) + 𝛽2 ∗ 𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑖 + 𝛽3 ∗

𝑐𝑟𝑜𝑠𝑠_𝑖𝑛𝑑𝑖 + 𝛽4 ∗ 𝑐𝑟𝑜𝑠𝑠_𝑐𝑜𝑢𝑛𝑡𝑖 + 𝛽5 ∗ ℎ𝑖𝑔ℎ_𝑡𝑒𝑐ℎ𝑖 + 𝛽6 ∗

𝑠𝑒𝑟𝑣_𝑖𝑛𝑑𝑖 + 𝛽7 ∗ log (𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖) + 𝛽8 ∗ log (𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖) +

𝛽9 ∗ log (𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖) + 𝜀𝑖)

with L being the standard logistic distribution function

To test H4c, the dependent binary variable is defined as 𝑛𝑜𝑛_𝑓𝑖𝑛𝑖. (The determinants of

a measure of sales are already tested in H4a) The two discrete alternative outcomes for

𝑛𝑜𝑛_𝑓𝑖𝑛𝑖 are:

𝑛𝑜𝑛_𝑓𝑖𝑛𝑖 = 1 , if the performance measure is a non-financial measure

𝑛𝑜𝑛_𝑓𝑖𝑛𝑖= 0 , if the performance measure is not a non-financial measure

48

The logit model tries to explain the probability of 𝑛𝑜𝑛_𝑓𝑖𝑛𝑖 to be the performance

measure in a deal by a constant (𝛼), the uncertainty measure (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) and the

control variables. The model to be run in Eviews therefore is:

logit(𝑖𝑛𝑐𝑜𝑚𝑒𝑖 = 1) = 𝐿(𝛼 + 𝛽1 ∗ log (𝑢𝑛𝑐𝑒𝑟𝑡_𝑝𝑟𝑜𝑥𝑦𝑖) + 𝛽2 ∗ 𝑝𝑟𝑖𝑣𝑎𝑡𝑒𝑖 + 𝛽3 ∗

𝑐𝑟𝑜𝑠𝑠_𝑖𝑛𝑑𝑖 + 𝛽4 ∗ 𝑐𝑟𝑜𝑠𝑠_𝑐𝑜𝑢𝑛𝑡𝑖 + 𝛽5 ∗ ℎ𝑖𝑔ℎ_𝑡𝑒𝑐ℎ𝑖 + 𝛽6 ∗

𝑠𝑒𝑟𝑣_𝑖𝑛𝑑𝑖 + 𝛽7 ∗ log (𝑎𝑐𝑞𝑢_𝑎𝑔𝑒𝑖) + 𝛽8 ∗ log (𝑎𝑐𝑞𝑢_𝑠𝑖𝑧𝑒𝑖) +

𝛽9 ∗ log (𝑑𝑒𝑎𝑙_𝑠𝑖𝑧𝑒𝑖) + 𝜀𝑖)

with L being the standard logistic distribution function

7 Empirical results

This chapter presents the results of the empirical analysis. First, the most interesting

descriptive statistics on the data sample are presented. In the second part of the chapter,

the results from the regression models on the determinants of the earnout parameters are

presented in detail.

7.1 Descriptive statistics

This section presents descriptive statistics on the final data samples on which the

hypotheses were tested. It thereby serves as a reference point to conclude on differences

between the total sample and the full information sample which is a robustness check.

Also, descriptive statistics allow to compare the data sample of this thesis with previous

empirical studies on earnouts. This helps to conclude on consistency with previous

research or on possible limitations to which degree the results of the thesis can be

integrated into the current state of research.

Table 4 below compiles descriptive statistics on the total sample as compared to the full

information sample for which all relevant earnout parameters are known. The observed

variables are presented as their mnemonic and clustered into the categories “uncertainty

measures”, “earnout parameter”, “deal characteristics” and “acquirer characteristics”.

49

Table 4: Descriptive statistics on data sample

Variables Total sample Full information sample

Number of deals:

377

172

Un

certa

inty

mea

sure

s

uncert_proxy: min 0.0024 0.0024

Ø 0.0251 0.0251

max 0.1050 0.1050

rel. dispersion 59% 67%

uncert_ind: min 0.0049 0.0049

Ø 0.0131 0.0129

max 0.0424 0.0373

rel. dispersion 37% 38%

Ea

rno

ut

pa

ram

eter

s

EOR (in %): min 1% 2%

Ø 38% 40%

max 100% 100%

rel. dispersion 60% 55%

EOPer (in years): min 0.25 0.25

Ø 2.5 2.6

max 8 8

rel. dispersion 53% 55%

Performance measure

sales N 45 34

income N 146 123

non-fin N 48 28

Dea

l ch

ara

cter

isti

cs deal_size: min 6.8 6.8

(in GBP mn) Ø 44.4 47.2

max 750.5 679.4

cross_ind N / (in % of total) 130 (34%) 67 (39%)

cross_count N / (in % of total) 183 (49%) 88 (51%)

private N / (in % of total) 375 (99%) 171 (99%)

high_tech N / (in % of total) 129 (34%) 55 (32%)

serv_ind N / (in % of total) 198 (53%) 93 (54%)

Acq

uir

er c

ha

ract

eris

tics

acqu_age (in years)

min 1 1

Ø 29 30

max 169 169

acqu_size (in GBP mn)

min 2 2

Ø 1,661 821

max 84,597 10,989

Source: Author’s analysis

50

For the total sample, the statistics report a significant difference between the two

alternative uncertainty measures. The relative dispersion coefficient shows that the proxy

target’s standard deviation is significantly stronger dispersed around its mean than the

measure of the target’s industry in relative terms.4 Clearly, this result reflects that the

uncertainty of an industry is calculated on a larger sample of companies what counteracts

more extreme measures that might be found if solely looking at single proxy targets.

Furthermore, the sample includes earnout ratios across the entire possible range of values

from 1% to 100% and reports a mean of 38%. This result is perfectly in line with a recent

study from the UK (38%) by Barbopoulos & Sudarsanam (2012, p. 685) and close to

figures for the US (33%) reported by Cain, Denis & Denis (2011, p. 155). Consequently,

there is evidence that the earnout premium on average represents a significant part of the

overall maximum acquisition price.

The earnout period ranges from 0.25 years to a maximum of 8 years, reporting a mean of

2.5 years. This finding is consistent with previous studies from the US and UK that also

reported an earnout period of around 2.5 years on average. (See Barbopoulos &

Sudarsanam, 2012, p. 683; Cain, Denis & Denis, 2011, p. 157) The relative dispersion

proves considerable variability for the earnout ratio and the earnout period within both

samples. We can therefore conclude that the design of the two parameters is strongly

adjusted to each deal’s requirements.

Income is by far the most frequently used performance measure, while sales and non-

financial measures occur with almost equal frequency. The study by Cain, Denis & Denis

(2011) supports that income measures are most often used, however they report a higher

usage of sales than non-financial measure indicating a difference between US and UK

based samples.

The deal characteristics reveal a rather small average deal size of GBP 44mn. This finding

is to some extent backed by the study of Kohers & Ang (2000) for US earnout deals that

reports an average deal size of USD 44mn. Relatively small deals are not surprising taking

into account that 99% of the target companies in the deal sample are private targets.

Consequently, we will exclude the variable private from the subsequent regression

4 The relative dispersion coefficient shows the extent of variability in relation to the mean of the population and is defined as the ratio of standard deviation to the mean (σ/µ). It is a measure of dispersion in a sample that is independent from the sample’s scale and allows for comparison across different scaled samples.

51

analysis as it describes the almost entire sample of observations. Similarly, in their

study of UK deals using earnouts, Barbopoulos & Sudarsanam (2012, p. 683) report that

99% of the deals involve non-public targets and same evidence is available for the US by

Cain, Denis & Denis (2011).5 Consequently, we find strong support for the dominant

hypothesis in literature that earnouts are best suited for private targets.

Also, the high percentage of cross-industry deals (34%), cross-country deals (49%) and

targets from the high-tech (34%) and service industry (53%) gives support to the

argument that earnouts are favourably chosen in situations that indicate high information

asymmetry. Finally, the acquirer characteristics report that earnouts are used by

established (av. age of 29 years) and rather large acquirers (av. GBP 1.6bn market

capitalization).

For the full information sample, the statistics do not have to be reviewed in detail since

they strongly resemble the ones from the total sample. As an exception, the acquirer’s

size differs significantly. The statistic reports acquirers in the full info sample that are on

average only half as large in terms of market capitalization than in the total sample (GBP

1.6bn vs. GBP 0.8bn). Taking a closer look at the data sample reveals that these results

are driven by the top 7 deals in terms of deal size in the total sample that are all not

included in the full info sample due to missing information on the earnout parameters.

Exclusive of these 7 deals, the average acquirer’s size of the total sample is GBP 1.06bn

and therefore much closer to the mean of GBO 0.8bn in the full info sample.

Most importantly, we can conclude that the statistics regarding the earnout parameters

are in line with previous studies. The data collection and the parameter definitions

therefore seem consistent with research so far. Furthermore, the comparison of the two

samples considered here reveals high similarity. The full information sample therefore is

representative for the total sample and should serve as a good robustness check.

7.2 Results from the regression models

This chapter finally presents and interprets the empirical results from the regression

models. This section therefore aims to identify the determinants of the earnout ratio (H1),

the earnout period (H2) and the performance measure (H4a-c). As described before, each

5 The authors differentiate non-public targets as private targets or subsidiaries, however both studies are consistent with only finding 1% of public targets.

52

hypotheses is tested on both the total sample and the full information sample as a

robustness check. Furthermore, for each hypothesis a regression model is run including

the uncert_proxy variable and another model is run including the uncert_ind variable

separately. The first is referred to as Model 1 while the latter is referred to as Model 2.

Both models include all control variables as defined before. Since the two uncertainty

measures can be expected to be highly correlated, a separate analysis avoids the problem

of heteroscedasticity in the explanatory variables to this extent. Also, a separate

examination allows for a better comparison of the explanatory power of both models.

Detailed results and analysis steps to test the normality assumptions can be found on the

accompanying CD.

7.2.1 Determinants of the earnout ratio

Before interpreting the results of the regression model, the Gauss-Markov assumptions

A1-A4 were tested for both models in both samples. Autocorrelation is not expected to

be an issue since no time series data is included in the model. Also, there is no reason to

expect that the error terms are dependent on the explanatory variables. However,

heteroscedasticity can be expected to be present in the data sample throughout the entire

analysis in several ways.

First, the high-tech and service industry variable are overlapping in terms of SIC codes

and therefore show a correlation. Second, the acquirer’s age and the acquirer’s size in

terms of market capitalization can be expected to correlate as well. Third, we can expect

larger acquirers to close deals of larger size. In general, heteroscedasticity is frequently

occurring in cross-sectional regressions including many dummy variables, as it is the case

here. (Verbeek, 2012, p. 98) In order to deal with this issue, the Tobit model is adjusted

for heteroskedastic-consistent White standard errors. (White, 1980)

Finally, the sample was analysed for normally distributed residuals. For a sample to be

normally distributed its error terms should be mean zero. For a continuously observed

variable, such as the earnout ratio in our case, testing for normality should also include

the check for skewness and excess kurtosis. (Verbeek, 2012, p. 202) The statistics given

at the bottom of table 5 prove that the residuals are very close to mean zero for both

models in both samples. Also, the Kurtosis measure is close to 3 indicating a close to

normal distribution. (Verbeek, 2012, p. 202) The skewness measure is slightly positive,

implying that the residuals are slightly right-skewed and not perfectly symmetrical

53

distributed around zero. However, all in all we accept that the residuals are close to normal

distributed. Also, relying on the law of large numbers the distribution is considered to be

asymptotically normal distributed. (Verbeek, 2012, p. 34) The regression model and the

t-statistic are therefore considered to have high power.

Table 5 presents the results of the OLS regression for H1 for both model 1 and 2 based

on the total sample and the full information sample separately. For each explanatory

variable the respective coefficient and t-ratio is reported. Coefficients that are significant

at the common 1%, 5% or 10% levels are marked by asterisk (***), (**) or (*)

respectively. The levels indicate the marginal significance level for which the null

hypothesis can be rejected. (Verbeek, 2012, p. 31)

Table 5: Results from Tobit model on determinants of earnout ratio

Source: Author’s analysis

First of all, we should compare the explanatory power of the different models to conclude,

which one reports the most reliable and valid results. There is no consensus in academia

on a goodness of fit measure for Tobit models like the R² measure for OLS regressions.

(Veall & Zimmermann, 1994) However, we can rely on the Akaike information criterion

(AIC) and the Schwartz information criterion (BIC) as means for model selection. Models

with a lower AIC or BIC are preferable. (Verbeek, 2012, p. 66) Both measures report

lower values for the total sample and therefore consistently suggest that the models of the

total samples are better specified than those in the full information sample. More

54

specifically, model 2 in the total sample reports the most negative values that implies the

best available specification. We therefore tend to rely the most on the reported

coefficients therein.

From H1, we expect a positive effect of the uncertainty measures on the earnout ratio. In

the total sample, the uncert_proxy coefficient shows an unpredicted slightly negative

effect on the earnout ratio. In contrast to that, in model 2 the alternative measure of

uncertainty uncert_ind shows the predicted positive coefficient. Clearly, these results

fundamentally differ. However, since model 2 reports preferable AIC and BIC values and

since the t-ratio for uncert_ind implies a considerably higher significance than

uncert_proxy we should really rely more on the uncert_ind measure. Consequently, as

predicted in H1 an increasing uncertainty would also increase the earnout ratio. A 10%

larger uncertainty, ceteris paribus, would therefore raise the earnout ratio by 0.004 points.

Besides this increase being very low, the coefficient for uncert_ind is still not significant

for standard significance levels. The insignificance of the uncertainty measures is

consistent also across samples. All in all, we cannot find strong evidence for uncertainty

to be a significant determinant of the earnout ratio and therefore have to reject H1.

The indicators of information asymmetry cross_ind, cross_count and serv_ind all report

positive coefficients for model 2 in the total sample while only serv_ind is highly

significant at a 1% level. Ceteris paribus, the target being from the service industry

therefore increases the earnout ratio by 0.08 points. Basically, the earnout ratio is

therefore found to be higher in situations that imply a high degree of information

asymmetry between acquirer and target. This result suggests that in situations that imply

adverse selection and agency problems to be severe, the contingent portion is increased

to provide a stronger signalling tool on the one hand and a stronger management retention

incentive on the other hand. Surprisingly, the high-tech variable shows a negative

coefficient which runs counter this argumentation. Still, its effect is highly insignificant

and does not change the overall conclusion.

The coefficients acqu_age, acqu_size and deal_size all show slightly negative

coefficients. Only the coefficient for the acquirer’s size is close to a 10% significance

level, suggesting that larger acquirers tend to use smaller earnout ratios. This might be

due to the fact that larger acquirers are less exposed to the valuation risk and therefore

face less need to shift the risk towards the target.

55

A cross-sample comparison reveals that both models in both samples yield consistent

results in terms of the sign and to a large extent in terms of the significance of the

coefficients. Therefore, the results from the Tobit regression model are robust across

samples and indicate an unbiased data selection process.

7.2.2 Determinants of the earnout period

With regards to test the normality assumptions, autocorrelation and the independence of

the error terms was not considered an issue. However, the Breusch-Pagan-Godfrey test

for heteroscedasticity reported heteroscedasticity for both models in both samples. This

issue was dealt with by adjusting for heteroskedastic-consistent White standard errors. In

contrast to the model for H1, the residuals for the total sample this time showed excess

Kurtosis and were significantly right-skewed. The data sample therefore was checked for

outliers. In order to arrive at approximate normal distribution properties to ensure high

power of the test statistic and consistency of the OLS estimators, the six most positive

outlying observations were removed from the sample. To ensure consistency between

the two samples, the same outliers were removed from the full information sample as

well. Detailed results of the normality tests are shown on the accompanying CD.

Table 6 presents the coefficients and t-ratios for each explanatory variable of the

regression. Significant coefficients are market by asterisk.

The explanatory power of the models set up to test H2 differs significantly, with a

considerably higher adjusted R² measures for the full information sample and model 2 in

particular. We therefore tend to rely more on the results from this regression, noting,

however, that the fit of the linear regressions to model the dependent variable is still low.

56

Table 6: Results from OLS model on determinants of earnout period

Source: Author’s analysis

According to H2, we would expect a negative coefficient for the uncertainty measures

within the regression results. Model 1 for the full information sample returns the expected

result for uncert_proxy, however the coefficient is highly insignificant. Model 2 in the

sample and both regressions for the total sample to the contrary report a positive

coefficient for the respective uncertainty measure. Due to these inconsistent results and

since the coefficient of uncert_proxy changes its sign between the two samples, a negative

correlation between earnout period and uncertainty remains highly questionable. The

most significant result presents uncert_ind for the total sample, but still it cannot be

considered a valid result in terms of common significance levels. Again, uncert_ind yields

the more significant coefficients implying a better specification to measure uncertainty

than uncert_proxy. After all, we cannot find strong evidence that would support a negative

impact of uncertainty on the earnout period and consequently have to reject H2.

According to the information asymmetry theory, a longer earnout period might resolve

the information asymmetries over time which is expected valuable especially in case of

targets with high growth opportunities. However, the results for the high_tech variable,

which is supposed to indicate high-growth opportunities, runs counter this rationale since

its coefficient suggests a negative but insignificant effect for the full information sample.

To the contrary, there is strong evidence for the variable cross_count to be a significant

57

and strongly positive determinant of the earnout period. While in the total sample it is

significant on a 5% level, the more powerful full information sample reports a

significance level of 1%. The coefficient shows that in case of a cross-country deal, the

earnout period increases by 0.6 years. Positive coefficients are also found for cross_ind

and serv_ind that, however, lack significance. We might still conclude in terms of

information asymmetry theory, that acquirers desire longer earnout periods in these

situations to resolve cultural differences in cross-country deals, asymmetries in market

knowledge in cross-industry deals or simply to incentivize target’s key human capital to

remain longer with the firm such as for target from the service industry that typically

heavily rely on human capital.

Furthermore, there is some evidence that the acquirer’s size has a negative impact on the

earnout period. No study so far offers a possible explanation to this finding. However,

larger acquirers are expected to cope better with information asymmetries due to their

longer experience in M&A deals and better access to information on the target.

Consequently, they might have less need for additional information that is revealed during

the earnout period in order to mitigate valuation risks. Still, this rationale is speculative.

7.2.3 Determinants of the performance measure

As the first step, the distribution of the residuals in the H4a-c models was tested in order

to conclude on the fit of the probit and logit model respectively. Running the binary

choice models reported non-normal statistics of the residuals. (See CD) Therefore, the

logit model was opted for as the estimation model with better fit to test H4a-H4c. Also,

since heteroscedasticity among the explanatory variables should be expected, the logit

model was adjusted by White standard errors. In general, the coefficients from logit

models can only be interpreted as how they impact the probability of choosing the

performance measure in question by looking at their signs and significance levels. A

positive coefficient increases this probability, while a negative one lowers it accordingly.

However, the exact size of the coefficient cannot be interpreted meaningfully. (Verbeek,

2012, p. 208)

Determinants for the choice to use sales as performance measure

Table 7 presents the coefficients and t-ratios that result from examining the probability

that sales is used as the performance measure in a logit model:

58

Table 7: Results from logit model on determinants of sales measure

Source: Author’s analysis

First of all, the explanatory power of the models is compared by means of Mc Fadden R²,

which is a goodness-of-fit measure especially adjusted to binary choice models.

(Verbeek, 2012, p. 212) The models of the total sample report the highest explanatory

power, implying that up to 9% of the variation in the performance measure sample is

explained by the model. The subsequent analysis is therefore focused on the results from

the total sample.

From H4a, uncertainty is expected to show a positive impact on the probability that sales

is used as the performance measure. Both uncertainty coefficients show an unpredicted

negative impact on the probability of choosing sales as the performance measure. Still,

there is no evidence for a significant impact of uncertainty on the probability that sales is

chosen as the performance measure. This finding is also consistent across both samples

and both models. Therefore, both the signs of the coefficients and their significance levels

tell us to reject H4a.

There is strong evidence that in deals with high-tech targets the performance measure is

more likely sales as indicated by the significant positive coefficient for high_tech. This

finding is consistent across models and samples. For these firms that typically carry high

growth opportunities, a measure of sales might serve best as a driver measure of future

profitability and thereby reveals the most valuable information for targets of this kind.

Consequently, we cannot reject H4c in this regard.

59

To the contrary, the coefficients for cross_ind and serv_ind suggest a negative impact on

the probability of the performance measure being sales. Although their coefficients are

not significant up to a 10% level in any of the reported models, these results run counter

the general assumption that in case of high information asymmetry the performance

measure is more likely to be an easier verifiable sales measure.

Moreover, there is some evidence for acqu_size and deal_size being significant

determinants of the choice for sales. While the acquirer’s size is negatively related, the

size of the deal increases the probability. Literature suggests that larger acquirers better

cope with information asymmetry what might indicate that these firms not have to rely

on easily verifiable measure such as sales. Regarding the deal size, however, the theory

on information asymmetry does not offer a possible explanation.

Determinants for the choice to use income as performance measure

Table 8 presents the coefficients and t-ratios that result from examining the probability

that income is used as the performance measure in a logit model:

Table 8: Results from logit model on determinants of income measure

Source: Author’s analysis

Again, the models within the total sample carry higher explanatory power as indicated by

Mc Fadden R², while model 1 and model 2 perform almost equally.

According to H4b, uncertainty is expected to negatively affect the probability that income

is used as a performance measure. The coefficients for uncert_proxy and uncert_ind for

60

the total sample reveal a positive relationship between uncertainty and the choice of

income as the performance measure. This finding runs counter the expectations that

increasing uncertainty would make it less probable for the performance measure to be

income. Also, the results are at odds with previous empirical evidence that income is

chosen for targets from less volatile industries. (Cain, Denis & Denis, 2011) Again, this

inconsistency to previous studies might indicate a misspecification in the uncertainty

measures. All in all, we strongly have to reject H4b.

The coefficients for cross_count and high_tech both suggest a negative impact on the

probability that income is used. In case of cross-country deals this result is plausible as a

measure of income is potentially affected by unfamiliar accounting standard in other

countries and not easily verifiable for the acquirer. In case of high-tech targets this result

is intuitive as we have shown that for high growth-targets a driver measure of future

profitability such as sales is favourable.

The coefficients for cross_ind and serv_ind run counter the argumentation that income is

less likely a measure for deals with high information asymmetry. However, their

coefficients are insignificant and therefore do not change the overall conclusion.

Finally, larger acquirers tend not only to use less likely sales but also less likely income.

This result contradicts the possible explanation given for H4a and does not allow for a

consistent conclusion.

Determinants for the choice to use a non-financial performance measure

Table 9 presents the coefficients and t-ratios that result from regressing the probability

that a non-financial performance measure is used in a logit model.

The models of both samples report very similar explanatory power in terms of Mc Fadden

R². To be consistent throughout the analysis of H4a-c, the total sample is focused on.

Again, the uncertainty measures have no significant impact. However, the focal point in

the analysis of the results is the question whether for targets with high growth

opportunities the performance measure is more likely to be non-financial.

61

Table 9: Results from logit model on determinants of non-financial measure

Source: Author’s analysis

As predicted in H4c, the indicator for targets with high growth opportunities high_tech

shows a positive coefficient. Consequently, the results suggest that for targets that heavily

rely on reaching milestones in development projects and that require high investments for

future profits, a non-financial measure is the best driver measure for future profit and

therefore most appropriate. Still, the coefficient is not very significant and therefore we

have to reject H4c in this regard. As shown before, there is significant evidence though

that for high-tech targets a measure of sales is more likely to be used. Consequently, the

empirical results do suggest that for these targets an easy verifiable measure like sales or

a non-financial driver measure is the appropriate choice since profitability measures

would only hinder current growth targets.

Furthermore, for cross-country deals, cross-industry deals and targets from the service-

industry the probability that non-financial measures are opted for increases. Although

none of these coefficients is significant, it indicates that in situations of high information

asymmetry the non-financial measure conveys more valuable information.

62

8 Evaluation and avenues for further research

This chapter first evaluates the most interesting empirical findings in the context of the

theoretical model and its assumptions and compares the results to previous studies.

Furthermore, it points out the limitations the thesis faces in terms of literature, data and

methodology. Finally, the last subchapter is devoted to possible avenues for further

research.

8.1 Discussion of the empirical results

In order to discuss the empirical results of this thesis, we should refer back to the advanced

model on earnout design. The quality of the model can be concluded on by discussing the

implications of the findings for the model’s assumptions.

Basically, the advanced model on earnout design expects two possible dynamics if the

likelihood for an earnout premium to be paid increases due to increased uncertainty. To

ensure the effectiveness of the earnout contract as an incentive and signalling tool, the

acquirer could on the one hand choose to raise the earnout ratio in order to stronger

incentivize the target for cooperative behaviour and sort low from high quality targets.

Referring back to figure 6, this was labelled dynamic A. On the other hand, the acquirer

could also choose to shorten the earnout period (B1) or to increase the performance goal

(B2) in order to reduce the increased likelihood. While results for the earnout ratio

basically test dynamic A, the results for the earnout period and the required performance

increase would consequently test for the advanced model’s dynamics B1 and B2.

For the earnout ratio, the empirical analysis reports the predicted positive signs in the

coefficients for the better specified uncertainty measure. However, its impact on the

earnout ratio seems insignificant. Cain, Denis & Denis (2011) report uncertainty to be a

strong and significant driver of the earnout ratio. Therefore, in context of recent literature

there is indeed some evidence that acquirers tend to raise the earnout ratio as a reaction

to increased agency and adverse selection problems caused by an increased likelihood of

the earnout to end up in the money. This conclusion is in support of the dynamic A that

was developed in the original model by Lukas, Reuer & Welling (2012). Furthermore,

the control variables in the thesis’ analysis show strong evidence that the earnout ratio is

higher in situations of severe information asymmetry. Especially for targets from the

service industry the ratio tends to increase while cross-industry and cross-country deals

63

report the same tendency. Similar to the case of an increased likelihood, higher earnout

ratios in case of high information asymmetry reflect the acquirer’s need for a strong

signalling and incentive tool to mitigate related problems. In so far, the thesis contributes

strong support to theory and previous empirical results. (Kohers & Ang, 2000; Beard,

2004; Cain, Denis & Denis, 2011)

Regression results suggest that the length of the earnout period is not determined by the

uncertainty about the target’s future performance. Consequently, we would have to deny

that acquirers seek to control the likelihood of an earnout to be paid through the shape of

this parameter. To the contrary, Cain, Denis & Denis (2011) report the predicted

significant negative relationship between uncertainty and earnout periods. Moreover,

Lukas & Heimann (2014) in their event-study show that acquirers benefit from shorter

periods. The authors argue that a shorter time frame reduces the impact of “noise” in the

performance measure which is in line with the option-model logic that longer earnout

periods would simply increase the likelihood of the performance gaol to be reached. Their

results therefore give some evidence for the theory that acquirers consider the likelihood

when designing the earnout period (B1) although these studies were not referring to the

theoretic model developed here. After all, the thesis fails to replicate these findings.

Our findings regarding the control variables of information asymmetry remain unclear.

Firms with high growth opportunities do not show a positive impact on the length of the

earnout contract. To the very contrary, other indicators of information asymmetry such

as cross-country and cross-industry deals are highly significant and positive drivers of the

earnout period. We might argue that longer earnout periods in these cases are favourable

to resolve the information asymmetry over time. This argumentation, however, runs

counter to Cain, Denis & Denis (2011) who report longer earnout periods only for high

growth firms. The authors, in line with Lukas & Heimann (2014), to the very contrary

suggest that longer earnout periods in general are unfavourable since they increase the

risk of disputes about the performance measure and the performance goal between the

parties. The empirical results presented here cannot clarify the contradictions regarding

the impact of information asymmetry.

Obviously, the dynamic regarding the required performance increase (B2) could not be

tested due to data limitations. A substantial part of the advanced model on earnout design

therefore remains untested.

64

Although not directly linked to the theoretical model, the thesis additionally examined

the choice of the performance measure. Uncertainty is not found to be a significant

determinant of this choice. To the contrary, indicators of information asymmetry are

reported to drive this decision instead. For targets with high growth opportunities, there

is strong evidence that the performance measure is more likely to be sales or a non-

financial measure rather than income which confirms findings by Cain, Denis & Denis

(2011). However, the empirical findings do not allow to extend this argumentation to all

types of information asymmetry as the remaining indicators show inconsistent results.

Still, the results allow the conclusion that in situations of higher information asymmetry,

income is not the appropriate choice as the performance measure and thereby confirms

the overall conclusion of Cain, Denis & Denis (2011).

To sum it all up, the thesis in combination with previous research finds evidence that the

earnout ratio is an important control lever to the acquirer to face increased adverse

selection and agency problems. The rationale that the acquirer could instead also control

for the likelihood of an earnout premium to be paid finds some promising prove in

previous studies at least for the case of earnout periods. However, this thesis fails to yield

supportive empirical prove. This might be due to some limitations of this study as outlined

in the next subchapter. It remains unknown if the same dynamics apply to the required

performance increase, i.e. the performance goal. Taking all into consideration, the current

state of research, although offering encouraging results, does not allow for a final

conclusion on the value of the option-based model presented in this thesis. Further

research as outlined in 8.3 is strongly encouraged.

8.2 Limitations to the study

The thesis faces limitations with regards to the body of literature available, the data

sample and the theoretical model that should be taken into account in the evaluation.

First, there is only a very limited body of literature available regarding the design of

earnouts to source from. The research that can serve as reference points to evaluate the

empirical results is actually limited to five relevant studies and heavily relies on the most

comprehensive one by Cain, Denis & Denis (2011). Consequently, research is not yet

offering reliable and robust results and lacks consensus. We can therefore only conclude

that previous studies offer promising results for the option-based model on earnout

design, but we can for sure not take the limited body of literature as a proof for its

65

adequateness. The theoretical option pricing model itself can rely on several scholars that

identified the similarities between earnouts and options. However, only the work by

Lukas, Reuer & Welling (2012) transfers this approach into a systematic model. This in

turn, to the author’s knowledge, lacked empirical testing. The advanced model presented

in this thesis and its empirical test are therefore a novel work and should be considered to

some extent explorative.

Second, the earnout specific data selection is a challenging task. Especially the disclosure

of targets’ performance pre-closing is not directly observable. As the most obvious

limitation to this study, hypotheses 3 regarding the required performance increase had

consequently to be dropped. Unfortunately, one parameter that is considered to influence

the likelihood of an earnout payment thereby remains unobserved. Also, since collecting

data on the earnout parameters requires a time-consuming, in-depth analysis of primary

sources such as public deal announcements only a limited sample size could be handled

in this thesis. Consequently, this leads to a loss in representativeness and power of test

statistics. In order to standardize data for empirical analysis several simplifications were

required. The earnout deals in the data sample were assigned a single measure of the

earnout period although they were comprising possible earnout payments in several

instalments over the years. Also, no distinction was made between earnouts with lump

sum payments or more complex formulas determining the exact earnout premium.

Instead, only the maximal possible earnout payment was considered to be able to state an

earnout ratio. These simplifications possibly causes a lack of accuracy.

Third and maybe most importantly, the quality of the empirical test of the theoretical

model is essentially dependent on the quality of its measure of uncertainty. As the

empirical analysis indicates, the results are sensitive to the exact definition of this

measure. The definition via a single proxy target based on the criteria ‘cash-flows’ seems

oversimplified and misspecified. Mostly, this measure yielded highly insignificant

coefficients. The measure for the target’s industry instead can be considered to provide a

reliable measure of inter-industry differences in volatility. Throughout the empirical

analysis, this alternative uncertainty proxy yielded more significant coefficients.

However, a more accurate multi-dimensional matching of the actual target with a

comparable listed proxy would deliver more accurate results than the simple industry

average. Since information on the mostly privately-held targets is not easy to access, this

matching would again be a complex and time-consuming task. In contrast to the

66

uncertainty measure, the dependent variables earnout ratio, earnout period and

performance measure are less likely misspecified since the descriptive statistics proved a

high similarity as compared to data samples of previous studies.

Apart from the definition of uncertainty, the model heavily relies on the assumption that

earnout parameters determine the likelihood of an earnout pay-out. While the hypotheses

tested here are based on this assumption, no analysis was carried out to examine if a

certain design of earnout parameters de facto results in more frequent earnout payments.

Such an analytical approach is so far missing throughout literature.

Consequently, there are several possible avenues for further research on earnout design

that could overcome these limitations.

8.3 Further research

After all, the model on earnout design based on option pricing methodology is still

considered a comprehensive and promising theory. Further research is therefore

encouraged to run additional empirical analyses that overcome the limitations of this

thesis as outlined before. Priority should be given to find a most appropriate and accurate

measure of uncertainty and to collect data on the required performance increase as this

parameter remains the most under-studied part of an earnout contract. Furthermore, it

would be meaningful to test the model on a deal sample only comprising single lump sum

earnouts and exclude any contracts with several instalments paid over the years since

these are suspicious to dilute the option pricing analogy of earnouts.

Another approach to test, if the likelihood of earnouts to end up in the money is correctly

modelled by reference to option pricing techniques, is a long-run study. Data should be

collected on the actual outcome of earnout contracts, i.e. if an earnout had to be paid at

the end of the earnout period. Scholars then could examine if those earnouts that

according to option pricing methodology were designed to more likely end up in the

money actually resulted in more frequent earnout payments.

If future research finds more evidence for the option-based hypotheses to be valid, these

hypotheses at the same time could serve as “rules” for the optimal earnout design. Those

deals that apply them in their contract design would be labelled as optimal and a larger

wealth effect for the acquirer would be expected from these deal announcements. This

thesis did not proceed with this analysis since the determinants of the earnout parameters

67

so far remain uncertain. However, the appendix 3 includes a possible methodological

approach to this problem.

9 Conclusion

This thesis investigates the research question of what factors determine the design of

earnouts in M&A deals. Thereby in contributes to the yet limited research on earnout

design both theoretically and empirically. In order to arrange the empirical results into

the broader context it is therefore worth to revisit the theoretical contributions in brief.

Through a review of earnout literature the two main motives to use earnouts in M&A are

identified. First, to mitigate adverse selection problems by serving as a signalling tool for

high quality targets and second, to mitigate agency problems by serving as an incentive

tool to target’s management in the post-closing phase.

As the main theoretic contribution to research, the thesis develops a model on earnout

design that explains how the common earnout parameters are shaped to ensure the

earnout’s effectiveness as a signalling and incentive tool. The basic assumption of this

model states, that the effectiveness of an earnout depends on the likelihood that the target

will receive an earnout premium in the end. In reference to game-theory, we expect that

a higher likelihood is associated with less efforts by the target in the post-closing phase

and with even lower quality firms tending to accept the earnout. Consequently, as the

likelihood for an earnout payment increases, the earnout is expected to lose its power as

a signalling and incentive tool. The model suggests two ways for the acquirer to react to

an increased likelihood for an earnout premium to be paid. On the one hand by increasing

the earnout ratio, i.e. the contingent part of the overall acquisition price, in order to

stronger incentivize the target for post-closing cooperation and to motivate only high

quality targets to accept the deal and on the other hand by shaping the earnout parameters

such that the likelihood is controlled for.

Based on an in-depth analysis of the similarities between earnouts and financial call

options, the thesis derives that the same forces that determine a financial option to expire

in the money also determine the likelihood of an earnout agreement to result in an earnout

premium paid to the target. In reliance on option-pricing techniques a higher uncertainty

about the target’s future performance, a longer earnout period and a lower performance

goal are all expected to increase the likelihood for the earnout to expire in the money.

68

Consequently, the hypotheses are derived that in case of high uncertainty, i.e. increased

likelihood, the acquirer reacts with an increased earnout ratio, shorter earnout periods or

higher performance goals. Thereby, the thesis successful answers the states research

question from a theoretical perspective.

The thesis further contributes to research by testing these hypotheses empirically on a

sample of 377 earnout deals from UK acquirers between 2006 and 2015. Results from

tested regression models document weak evidence that the acquirer choose higher earnout

ratios in case of high uncertainty, while previous studies even show strong support for

this relationship. Further, this thesis reports strong evidence that in case of high

information asymmetry between acquirer and target, like in cross-country and cross-

industry deals and for targets from the service industry, the earnout ratio increases. Again,

this result is in line with previous studies. Consequently, there is strong evidence that the

earnout ratio is used as the primary “control lever” if the acquirer faces the need to design

strong signalling and incentive tools in case of high uncertainty and information

asymmetry.

In contrast, the regression models regarding the earnout period yield inconsistent and

insignificant results. Previous research, however, finds that acquirers indeed choose

shorter earnout periods if uncertainty and consequently the likelihood is high. In context

of this yet limited literature, the rationale that acquirers shape the earnout period in order

to offset increased likelihood due to high uncertainty at least remains reasonable.

Unfortunately, due to data limitations the determinants of the performance goal could not

be empirically examined. A substantial part of the advanced model on earnout design

therefore remains untested and no previous study offers supplementary evidence.

Thereby, the thesis to some extent fails to successfully answer the research question from

an empirical perspective.

Though not related to the theoretical model, the thesis also provides empirical evidence,

that the appropriate performance measure in situations of high information asymmetry is

not income but rather sales or non-financial, while uncertainty is no significant

determinant.

After all, as the main contribution to earnout research the thesis develops a model that

explains that not only the contingent part of the overall acquisition price, i.e. the earnout

ratio, but also the remaining earnout parameters play a role in ensuring an earnout’s power

69

to serve its purpose of solving information asymmetry problems. The real option view on

earnouts proves to be a useful framework to understand the implications of different

designs on the earnout’s value as a risk-reducing instrument. While empirical evidence is

only partly available through this thesis and previous studies, this avenue of research is

promising. Future research that overcomes some of the data limitations of this thesis, that

utilizes more elaborate measures for uncertainty in the target’s future performance and

that conducts a long-run analysis of the likelihood of earnout premiums to be paid

eventually is strongly encouraged to pave the way towards the definition of optimal

earnout design.

70

References

Akerlof, G.A. 1970, ‘The market for “lemons”: Quality uncertainty and the market

mechanism’, Quarterly Journal of Economics, vol. 84, no. 3, pp. 488-500.

American Appraisal 2015, Earn-Outs and Contingent Consideration: Valuation,

viewed 07 May 2015, <http://www.americanappraisal.com/US/Library/Articles/

EarnOutsandContingentConsideration.htm?PrintPage=yes&FB_Values=&&&#>.

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

merger’, Journal of Financial Economics, vol. 11, no. 1, pp. 121-139.

Barbopoulos, L. & Sudarsanam, S. 2012, ‘Determinants of earnout as acquisition

payment currency and bidder’s value gains’, Journal of Banking & Finance, vol. 36, no.

3, pp. 678-694.

Bargeron, L.L., Schlingemann, F.P., Stulz, R.M. & Zutter, C.J. 2008, ‘Why do private

acquirers pay so little compared to public acquirers?’, Journal of Financial Economics,

vol. 89, no. 3, pp. 375-390.

Beard, D.R. 2004, ‘The mitigation of asymmetric information through the use of

earnouts’, Dissertation, Louisiana State University, Louisiana.

Berk, J. & DeMarzo, P. 2014, Corporate Finance – Global Edition, 3rd edn, Pearson,

Harlow.

Bruner, R.F., Stiegler, S. 2001, ‘Technical note on structuring and valuing incentive

payments in M&A: Earnouts and other contingent payments to the seller‘, Working Paper

(Excerpt), Darden Graduate School of Business Administration, University of Virginia.

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

in acquisition agreements’, Journal of Accounting and Economics, vol. 51, pp. 151-170.

Caselli, S., Gatti, S. & Visconti, M. 2006, ‘Managing M&A risk with collars, earn-outs,

and CVRs’, Journal of Applied Corporate Finance, vol. 18, no. 4, pp. 91-104.

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

returns’, The Journal of Finance, vol. 53, no. 2, pp. 773-784.

71

Craig, B. & Smith, A. 2003, ‘The art of earnouts’, Strategic Finance, vol. 84, no.12, pp.

44-47.

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

costs on acquisition techniques’, Journal of Law, Economics, & Organization, vol. 17,

no. 1, pp. 201-238.

Del Roccili, J.A. & Fuhr, J.P. 2001, ‘The pros and cons of earnouts‘, Journal of Financial

Service Professionals, vol. 55, no. 6, pp. 88-93.

Eckbo, B.E. 2009, ‘Bidding strategies and takeover premiums: A review’, Journal of

Corporate Finance, vol. 15, no. 1, pp. 149-178.

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, vol. 57,

no. 4, pp. 1763-1793.

Holmström, B. 1979, ‘Moral hazard and observability’, The Bell Journal of Economics,

vol. 10, no. 1, pp. 74-91.

Hull, J.C. 2012, Options, Futures, and other Derivatives – Global Edition, 8th edn,

Pearson, Harlow.

Kile, C.O. & Phillips, M.E. 2009, ‘Using industry classification codes to sample high-

technology firms: Analysis and recommendations’, Journal of Accounting, Auditing &

Finance, vol. 24, no. 1, pp. 35-58.

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

stay’, Journal of Business, vol. 73, no. 3, pp. 445-476.

Krishnamurti, C. & Vishwanath, S.R. 2008, ‘Real options analysis in mergers and

acquisitions’, in Krishnamurti C. & Vishwanath S.R. (ed.), Mergers, acquisitions and

corporate restructuring, SAGE Publications India, New Delhi, pp. 115-145.

Luehrman, T.A. 1998, ‘Investment opportunities as real options: Getting started on the

numbers’, Harvard Business Review, vol. 76, no. 4, pp. 51-67.

Lukas, E. & Heimann, C. 2014, ‘Technological-induced information asymmetry, M&As

and earnouts: Stock market evidence from Germany’, Applied Financial Economics, vol.

24, no. 7, pp. 481-193.

72

Lukas, E., Reuer, J.J. & Welling, A. 2012, ‘Earnouts in mergers and acquisitions: A

game-theoretic option pricing approach’, European Journal of Operational Research,

vol. 223, pp. 256-263.

Mantecon, T. 2009, ‘Mitigating risks in cross-border acquisitions’, Journal of Banking

& Finance, vol. 33, no. 4, pp. 640-651.

McDonald, R. & Siegel, D. 1986, ‘The value of waiting to invest’, The Quarterly Journal

of Economics, vol. 101, no. 4, pp. 707-728.

Moeller, S.B., Schlingemann, F.P. & Stulz, R.M. 2004, ‘Firm size and the gains from

acquisitions’, Journal of Financial Economics, vol. 73, no. 2, pp. 201-228.

Myers, S.C. & Majluf, N.S. 1984, ‘Corporate financing and investment decisions when

firms have information that investors do not have’, Journal of Financial Economics, vol.

13, no. 2, pp. 187-221.

Quinn, B.J.M. 2012, ‘Putting your money where your mouth is: The performance of

earnouts in corporate acquisitions’, University of Cincinnati Law Review, vol. 81, pp.

127-172.

Ragozzino, R. & Reuer, J.J. 2009, ‘Contingent earnouts in acquisitions of privately held

targets’, Journal of Management, vol. 35, no. 4, pp. 857-879.

Reuer, J.J., Shenkar, O. & Ragozzino, R. 2004, ‘Mitigating risk in international mergers

and acquisitions: The role of contingent payouts’, Journal of International Business

Studies, vol. 35, no. 1, pp. 19-32.

Spence, M. 1973, ‘Job market signaling’, Quarterly Journal of Economics, vol. 87, no.

3, pp. 355-375.

Thompson, C.J. & Schnorbus L.A. 2010, M&A facilitators: The value of earnouts,

viewed 19 May 2015, <http://www.srr.com/article/ma-facilitators-value-earnouts>.

Travlos, N.G. 1987, ‘Corporate Takeover Bids, Methods of Payment, and Bidding Firms'

Stock Returns’, Journal of Finance, vol. 42, no. 4, pp. 943-963.

Trigeorgis, L. 1991, ‘Anticipated competitive entry and early preemptive investment in

deferrable projects’, Journal of Economics and Business, vol. 43, no. 2, pp. 143-156.

73

Veall, M.R. & Zimmermann, K.F. 1994, ‘Goodness of fit measures in the Tobit model’,

Oxford Bulletin of Economics and Statistics, vol. 56, no. 4, pp. 485-499.

Verbeek, M. 2012, A guide to modern econometrics, 4th edn, John Wiley & Sons,

Chichester.

White, H. 1980, ‘A heteroscedasticity-consistent covariance matrix estimator and a direct

test for heteroscedasticity’, Econometrica, vol. 48, no. 4, pp. 817-838.