How Do Representations and Warranties Matter? Risk

How Do Representations and Warranties Matter? Risk Allocation in Acquisition Agreements

Omri Even-Tov*

University of California at Berkeley

James Ryans† London Business School

Steven Davidoff Solomon**

University of California at Berkeley

August 2020

ABSTRACT We obtain a proprietary sample of claims for breaches of representations and warranties in acquisition agreements related to 1,690 acquisitions comprising $470 billion in enterprise value. We analyze this claims data to study how and when representations and warranties (“reps”) matter. Parties appear to negotiate reps consistent with the risk allocation necessary in an agreement. They utilize reps to reduce information asymmetry and create value depending upon the industry, firm volatility, and governing law. Our findings also show that more experienced acquirers such as private equity appear to utilize reps more efficiently to create value. These results highlight the importance of reps in risk allocation, reducing information asymmetry and creating value, but they also highlight the role of investor skill in creating value from reps. JEL Codes: D82, G22, G24, G34, K12, K22, K41, L14, M41. Keywords: mergers and acquisitions, representations and warranties, indemnification, insurance, valuation uncertainty, material misstatements, moral hazard. We are grateful to the insurance company that provided us with the data used in this study and to several individuals in the company’s M&A insurance group for valuable comments. We also appreciate the comments of David Aboody, Matthew Cain, Keith Crocker, Kimmie George, Sean Griffith, Michelle Hanlon, John Hughes, Lukasz Langer, Jennifer Maxwell, Panos Patatoukas, Brett Trueman, Young Yoon, and seminar participants at Cass Business School. James Ryans acknowledges the support of the London Business School Research and Materials Development fund.

* University of California at Berkeley, 545 Student Services #1900, Berkeley, CA 94720; Tel: +1 510 642 0192; Email: [email protected]. † London Business School, Regent’s Park, London, NW1 4SA, United Kingdom; Tel: +44 020 7000 7000; Email: [email protected]. ** University of California at Berkeley, 693 Simon Hall, Berkeley, CA 94720; Tel: +1 510 642 1769; Email: [email protected]

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1. Introduction

When buyers and sellers negotiate an acquisition they do so through an acquisition

agreement. Significant transactional costs are incurred as attorneys negotiate a lengthy agreement

which contains representations and warranties (collectively referred to as “reps”). In an acquisition

agreement, sellers promise—represent and warrant—that a set of facts about the target are true.

The reps usually cover a variety of issues, such as accuracy of financial statements, ownership of

intellectual property and real estate assets, and compliance with regulations (Coates, 2015; Freund,

1975; Kling, Simon, & Goldman, 1997).

Reps perform a number of economic functions. They allocate risk among the parties by

assigning the value of the rep to a party. For example, a rep by the target that it “has no litigation

pending” may obligate the target to indemnify the buyer for damages if the buyer post-completion

discovers litigation did exist. By allocating risk, reps reduce information asymmetry – the buyer

will have reduced concerns about due diligence on the target and failure to discover this issue.

Reps thus also create value by engendering valuation certainty – the buyer can pay a higher price

since it can assume that it is paying for a target with no litigation.

The theoretical role of these reps for risk allocation, reduction of information asymmetry

and valuation certainty has been theorized (e.g., Gallozi & Phillips, 2002; Griffith, 2020; Hill,

Quinn, & Davidoff Solomon, 2016). However, because information about breaches in the reps are

rarely, if ever, publicly disclosed, the literature has heretofore been unable to study the economic

magnitude of the reps. Several studies examine the determinants and effects of earnout provisions,

contractual mechanisms which allocate post-transaction performance risk and create value in M&A

(Bates, Neyland, & Wang, 2018; Cadman et al., 2014; Cain et al., 2011; Datar et al., 2001; Kohers

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& Ang, 2000). Other studies of risk allocation in acquisitions include those on material adverse

change or event clauses (Denis & Macias, 2013; Gilson & Schwartz, 2005; Talley, 2009). But all

of these studies are limited to specified contractual features which are not as common or as

fundamental to the acquisition process as reps. In other words, there has yet to be a study of

whether, and to what extent, reps actually function in addressing risk allocation, reducing

information asymmetry, and creating value in an acquisition transaction.

To reduce costs associated with seller indemnities, insurance companies created

representations and warranties insurance (RWI) products to transfer indemnification risk to a third-

party insurer for an up-front premium payment.1 Buyers and sellers purchase this product thereby

transferring the risk of a breach of the rep to an insurer. We obtain a proprietary sample of

representations and warranties policies issued worldwide by a global insurance company for

acquisitions of non-public targets between January 2011 and December 2016. This sample

represents 1,690 acquisitions and $470 billion in enterprise value acquired.2 Our sample reflects

4.6% of the total number of deals in the SDC population.

The RWI data we obtain contains extensive information on the underwriting process as

well as whether subsequent claims are made, the type of rep breached, and the ultimate payout (or

estimate of the total payout if the claim has not yet been closed). We use this novel dataset to ask

a number of questions about how reps allocate risk and thereby mitigate problems associated with

valuation uncertainty and information asymmetry. First, we assess the demand for reps by parties

to the acquisition agreement, and their ability to correctly assess the risk allocation of reps at the

1 Outside the U.S., RWI is commonly known as warranties and indemnities (W&I) insurance. 2 Betton, Eckbo, and Thorburn (2007) estimate that non-public targets represent more than 63% of all acquisition targets. RWI is more commonly utilized in acquisitions of private companies rather than public company acquisitions. This is primarily due to the fact that the reps in public deals typically do not provide for post-completion indemnification for breaches.

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time of execution of the agreement. Second, we utilize the third party underwriting process of

RWI to proxy for the value creation of reps. Third, we examine RWI claims data to ask which

factors predict breaches of reps and, in particular, which reps matter in the allocation of risk,

addressing information asymmetry and value creation. Finally, we examine the role of law in the

allocation of risk and addressing information asymmetry in reps.

We begin with extensive descriptive statistics, which provide the first significant evidence

that reps are used to allocate risk and address valuation issues. We observe that buyers demand

seller indemnities that represent a material fraction of deal value—approximately 23% of

enterprise value—and that the rate of breaches in the reps, which we observe for about 20% of

acquisitions, reflects the important role reps play in risk allocation. We find that in 18.6% of deals

there is a claim for breach. However, claims for breach are lagged in our data as we find that

26.6% of all deals in 2011 resulted in a claim for breach by September 2017.3 The four largest

categories of breach account for 65.2% of all breaches: financial statement errors (21.3%), material

contract issues (15.1%), tax issues (14.4%), and regulatory compliance issues (14.4%). We

examine the region and industry of the deals in our dataset and find that these deals, and the

accompanying RWI, are spread across all geographies and all industries. These descriptive

statistics highlight the varying role that reps play in assuring contractual certainty in valuation.

We then turn to our first question—how parties to an acquisition agreement assess the risk

allocation of reps at the time of execution of the agreement—by examining the demand for RWI

by buyers and targets. We find evidence that demand for RWI is related to proxies of target risk,

such as returns volatility and lower-quality financial reporting. Demand is also influenced by

3 Whereas the RWI data ends in December 2016, we observe claims reported as of September 2017.

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whether the buyer or seller is linked to high institutional costs of indemnities, as in the case of

private equity (PE) firms. We also find that demand for RWI is significantly higher if the target

is in a common law regime (as opposed to civil or other legal regimes), consistent with greater

expected indemnity contracting costs in common law legal environments. These findings indicate

that buyers and targets utilize reps (and by proxy RWI) to allocate risk and contract around

valuation uncertainty and information asymmetries.

We then turn to our second question of how the third party process of RWI acts to

independently assess the value of reps. To answer this question, we look at the RWI policy

premium, which is based on the insurance company’s expectation of losses due to breaches, plus

administrative overhead and profits. Our analysis of policy premiums reveals that the insurance

company effectively classifies target risk by charging significantly higher premiums for deals with

greater realized future claims and losses for breaches. Moreover, the insurance companies appear

to anticipate breaches for particular types of representations and warranties, charging the most

significant amounts for future claims associated with financial statements. Overall, the policy

premium reflects that the insurance company, as a third party assessor of reps, maintains a superior

ability to price for deal-specific losses and proxies of perceived uncertainty and other contracting

costs relating to seller indemnities. These results imply that while targets and acquirers utilize reps

to allocate risk, they may not do so optimally to create value.

Next, we examine our third question: which factors predict breaches of reps and, by

implication, which reps matter in reducing information asymmetry for different types of acquirers,

industries and regions? We find that when compared to strategic buyers, private equity buyers are

on the whole less likely to have a claim for breach of reps related to those that are more easily

subject to due diligence (compliance with laws, intellectual property, and fundamental matters)

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versus those which cannot (tax and financial statements). These findings are consistent with the

greater acquisition experience of private equity firms generally. We also find that reps appear to

be used, and have value, differently depending upon the industry and region of the acquisition.

For example, claims made for breaches in IP reps are significantly more likely to occur in

acquisitions in the technology industry and in the U.S., claims for breaches in compliance with

laws are significantly more likely in the consumer industry and in the U.S., and claims for breaches

on financial statements reps are significant in all regions and in all industries. Overall, our findings

show that acquirers and buyers appear to focus on reps to reduce information asymmetry in

acquisitions, that sophistication and experience matters in the utilization of reps and that,

accordingly and by implication, reps are utilized in a manner designed to create value certainty.

We conclude by examining the role of law in reps claims and information asymmetry. We

find that claims are more likely under common law for breaches of reps associated with law

enforcement (such as compliance with laws), consistent with the more complex legal regimes in

common law jurisdictions. The overall positive association we find between breaches and

common law is also consistent with these regions’ higher levels and costs of litigation (e.g., La

Porta et al., 2008), which affect both expected losses and contracting costs, and are thus associated

with increased frequency of breaches in the reps (Ramseyer and Rasmusen, 2010; Browne, Chung,

and Frees, 2000). Last, we find that claims are more common in Europe and North America than

in Asia, which is consistent with the common law enforcement story. These findings also highlight

the role of reps in reducing information asymmetry in particular areas associate with the law.

There is a rich literature on valuation uncertainty in acquisitions (Shleifer and Vishny

(2003), Rhodes-Kropf and Viswanathan (2004), and Officer (2004)) as well as on the role of

contracting in reducing valuation costs (Cain et al., 2011). Our study contributes to and extends

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this literature by showing the importance of contractual reps in risk allocation and the reduction of

information asymmetry and valuation uncertainty. Parties appear to negotiate reps consistent with

the risk allocation necessary in an agreement. They utilize reps to reduce information asymmetry

and create value depending upon the industry, firm volatility, and governing law. However, our

findings also show that more experienced acquirers such as private equity appear to utilize reps

more efficiently. This conclusion is reinforced by our finding that third party insurers better assess

the value of reps than parties to acquisition agreements. Our findings highlight the importance of

reps in risk allocation, reducing information asymmetry and creating value, but they also highlight

the role of skill in acquisitions in utilizing reps (Chung et al. 2012).

2. Background

2.1 Representations and Warranties Insurance

Representations and warranties policies compensate the insurance holder for losses

incurred due to breaches in the reps of an acquisition, thus transferring the risk of these losses to a

third-party insurer. RWI is issued either as a “buy-side” or a “sell-side” policy, depending on

whether the policyholder is the acquirer or the seller, respectively. In a buy-side policy, the value

of any breach is claimed directly from the insurance company, so the seller indemnity is no longer

required. In a sell-side policy, the indemnity provision is retained, and the RWI policy serves to

compensate the seller for any subsequent indemnity claims made by the buyer against the seller.

In recent years there has been rapid growth in the use of RWI. Although this insurance

was first introduced in the 1990s, developments in the underwriting process, coverage, and the

prevalence of private equity (PE) participants in the acquisition market have dramatically

expanded their usage over the past ten years (Griffith, 2020). Industry reports estimate that the

U.S. market for RWI was less than 100 policies in 2011, growing to more than 2,500 policies

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bound in 2018.4 It is now presumed that RWI insurance is obtained in most significant private

deals.

2.2 RWI policy terms

There are four key terms in an RWI policy: liability limit, retention, policy period, and

premium. The liability limit is the maximum amount that the insurance company will pay for all

claims made under the policy. The limit usually, but does not always, match the indemnification

amount included in the merger agreement; indeed, the limits are sometimes synthetic, in that the

policy provides coverage even in the absence of a contractual seller indemnity. The retention

represents the initial amount of losses that the insured must pay before insurance coverage applies.

Retentions mitigate moral hazard by guaranteeing that the insured bears some of the economic

cost of losses. In addition, since the insurer incurs significant administrative overhead, broker

commissions, and premium taxes, it is not efficient to insure small or frequent losses. With the

retention, these costs are eliminated because the insured pays small claims directly (e.g., Grossman

& Hart, 1983; Holmstrom, 1979; Mirrlees, 1999). The policy period begins at the acquisition’s

closing and ends at an agreed upon expiration date. In order for a claim to be paid, the insurance

company must be notified of the loss during the policy period. The premium is the amount paid

to the insurer for providing coverage when the policy is bound, generally prior to the deal’s close.

3. Sample and data description

3.1 M&A transaction sample

We examine a proprietary sample of 1,690 RWI policies issued worldwide by a global

insurance company for acquisitions of non-public targets during the period between January 2011

4 Risk and Insurance Magazine (https://riskandinsurance.com/the-value-of-reps-warranties/). Retrieved November 2019.

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and December 2016. This sample represents $470 billion in enterprise value acquired. The data

does not include the acquisition of public targets, but it does include the acquisition of subsidiaries

or units of public companies. For these RWI policies, we observe liability limits, duration,

retention, and premium, and for the acquisition parties, we observe deal size (enterprise value),

buyer and target ownership type, target’s region, and target’s industry.5 We also observe claims

reported for each policy as of September 2017. We draw from the work of Datar et al. (2001) and

Cain et al. (2011), who examine the role of earnouts in valuation uncertainty in private firms and

proxy for target valuation uncertainty and growth expectations using several measures based on

the target’s industry membership.

To understand how RWI acquisition parties differ from those involved in other deals, we

compare our sample to similar acquisitions from the same time period in the Securities Data

Corporation’s (SDC) Mergers and Acquisitions database. In order to align the SDC sample with

our own, we exclude those cases where: (1) the acquirer did not purchase full ownership in the

target, (2) the target is a public company, (3) the acquirer and the target are the same firm, (4) the

transaction value is missing from the SDC database, or (5) the target company is located in Central

America. We remove Central American targets because our RWI sample does not include any

policies written in this region. After applying these filters, the SDC sample consists of 36,769

completed acquisitions.

(Insert Table 1 about here)

In Panel A of Table 1 we report the number of deals with RWI coverage by year and

compare them to the SDC population. As depicted in this panel, the 1,690 acquisitions in our

5 Due to the lack of variation in policy duration, we do use this term in our empirical analysis.

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sample reflect 4.6% of the total number of deals in the SDC population. Over the course of our

sample period, the percentage of RWI policies in proportion to the SDC sample increases from

1.9% in 2011 to 7.7% in 2016. Panel B of Table 1 reports the industry classification for targets

in our sample and compares them to the SDC population. In Appendix B we show the mapping

between the insurance company’s industry classification and that of the Securities Data

Corporation. Although the target companies in our RWI sample represent a range of industries,

they exhibit some differences relative to the SDC population. Of the total RWI sample, 37.3% of

the industries possess large amounts of intangible assets, drawn from the consumer (15.6%),

technology (12.5%), and healthcare (9.2%) industries. In comparison, the same three industries

combined account for only 18.1% of the SDC population.

Panel C of Table 1 reports the region classification for targets in our sample and compares

them to the SDC population. Again, we note a key difference with respect to regional

representation. In our sample, 73.4% of the targets are in either the United Sates (46.4%), the

United Kingdom (14.7%), or Australia (12.3%). In comparison, in the SDC population, these

same regions combined account for only 45.3% of that sample. Relative to SDC, the descriptive

statistics in Panel D of Table 1 indicate that RWI policies are more likely to be issued on larger

deals and to involve a private equity acquirer. The mean deal size of our RWI sample is $278

million, as compared to $175 million for the overall SDC population. These univariate differences

are consistent with minimum fixed underwriting costs, which discourage the acquisition of RWI

for smaller deals. With respect to the acquirer type, we find that PE firms represent 40.9% of the

acquirers in our sample, as compared to only 20.1% in the SDC sample, consistent with PE firms

mitigating the risk of future conflict when they retain target managers in the acquired company

(Cornelli & Karakas, 2013).

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3.2 Descriptive statistics of RWI policies and coverage

Panels A and B of Table 2 provide descriptive statistics of the deals and RWI policies in

our sample, respectively. Panel A shows that mean (median) deal size is $278.3 ($120.0) million.

Panel A highlights the diversity of deal characteristics in our sample. 40.9% of deals have a private

equity acquirer while 22.9% have a seller who is an individual. 74.37% of deals are negotiated

under common law.

Panel B shows that there is a wide range of demand for policy limits relative to deal size.

The mean (median) limit of coverage is $22.6 million ($19.2 million), corresponding to 22.7%

(13.3%) of the mean (median) deal size, respectively. The mean (median) retention to deal size is

2.5% (1.2%). The mean (median) premium is $0.54 million ($0.38 million), corresponding to

2.6% (2.5%) of the mean (median) limit. Panel C of Table 2 presents Spearman and Pearson

correlations among key variables. Deal size is positively associated with limit levels and

negatively associated with the ratio of limit to deal size, indicating that relatively lower limits are

demanded as the deal size increases. Premiums and the ratio of premium to limits are positively

correlated with deal size, as the losses due to breaches are expected to increase in proportion to

deal size.


3.3 Descriptive statistics of claims for breach of reps

Figure 1 illustrates the characteristics of claims for breach of reps filed for the policies in

our sample as of September 2017. Panel A of Figure 1 reports the frequency of claims for breach,

specifying the number of policies issued during each year of our sample period, the number of

claims made on these polices as of September 2017, and the percentage of policies issued that

incurred a claim. Each policy represents a specific acquisition or deal. As shown in this figure,

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26.6% of all policies issued (i.e., acquisitions) in 2011 resulted in a breach of reps claim by

September 2017. The number of breach of reps claims as a percentage of policies issued in 2012,

2013, 2014, 2015, and 2016 was 24.2%, 23.5%, 20.1%, 16.2%, and 11.6%, respectively. These

numbers highlight the significant number of alleged breaches of reps which occur in acquisitions

as well as evidence the utility of the reps. While most breach of reps claims are generally reported

within two years of the acquisition, some claims, such as those that are tax-related, may take six

or more years to be filed after the applicable tax audit window expires. In our regression analysis,

we add year fixed effects to account for this claims development pattern. Panel B of Figure 1

indicates that approximately 95.9% of total claims for breach of reps are submitted within 24

months, meaning that discovery of the breach occurs during this time period, although the value

of the claim may not be known for some time following the breach’s discovery.

In Panel C of Figure 1 we illustrate the distribution of breach types. The four largest

categories of breaches account for 65.2% of all breaches: financial statement errors (21.3%),

material contract issues (15.1%), tax issues (14.4%), and regulatory compliance issues (14.4%).6

The remaining breaches stem from intellectual property, employment, operational, litigation, and

environmental issues. In Panel D of Figure 1, we present the frequency of claims by type for the

four industries with the greatest number of observations in our data: manufacturing, healthcare and

pharmaceuticals, technology, and financial services. Target firms in the healthcare and

pharmaceutical industries are most likely to experience breaches due to legal compliance and

regulation issues, whereas intellectual property breaches are most common in the technology

industry. In untabulated results, we find that tax is the biggest driver of reported incidents in

6 Of the 448 claims submitted, 143 claims have yet to be fully investigated and categorized, thus we omit these when tabulating the distribution of claim types.

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Europe (25%), which reflects the greater frequency of cross-border deals among European

acquisitions and the complexity of its multi-jurisdictional tax system (e.g., Erel, Liao, & Weisbach,

2012). Overall, these descriptive statistics illustrate that breaches in the reps occur frequently in

acquisitions, and that they vary by industry and region.

4. Risk Allocation and Demand for Reps

We begin our analysis by examining whether parties to the acquisition agreement correctly

assess the risk allocation of reps at the time of execution of the agreement. We do so by looking

at the determinants of the decision to purchase RWI and the level of limit of the insurance. These

both function as proxies for the parties’ assessment of risk and their demand for reps to contract

around areas of valuation uncertainty and information asymmetries.

4.1 The Demand for Reps

We expect that the role of reps will vary based upon the parties’ risk assessment as it applies

to the target’s assets and liabilities and other considerations covered by the reps. In particular, reps

will be more likely to be desirable for risk allocation in situations of higher information asymmetry,

reduced ability to conduct due diligence, and greater target risk due to industry factors. We first

examine these issues by exploiting the decision to purchase RWI. We hypothesize that RWI

purchases are specifically driven by risk aversion. As such, demand for reps is a proxy for the

areas where reps function to create contracting certainty by allocating risk and creating valuation

certainty. We explore three measures to assess the demand for reps and the target’s assessment

thereof.

Our first measure of demand is the identity of the acquirer, since the identity of the acquirer

may give rise to a different assessment of the risk of the target and need for reps. While our sample

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includes an indicator variable if the seller is an individual, the SDC database does not record this

category. Therefore, our purchase decision analysis is limited to examining PE and non-PE deal

participants, wherein the non-PE participants consist of both individuals and corporate entities.7

To examine the relationship between PE firm involvement and RWI purchase, we include indicator

variables equal to one if the acquirer is a PE firm (acquirer PE) and if the seller is a PE firm (seller

PE).

Our second measure of demand is the relationship between the legal environment and

demand for RWI. We create an indicator variable (common law) that takes the value of one if the

target is in a predominantly common law jurisdiction, and zero otherwise (La Porta et al., 1997).

We predict that common law will be associated with increased demand for RWI, because the more

certain legal environment is associated with a greater ability to assess the value of reps and

correspondingly purchase RWI. Since our RWI data lists the specific target country for the US

and the UK and region for targets located in other countries, we consider a region to be common

law if the majority of its acquisition activity by deal volume occurs in common law countries,

using the SDC sample as a guide. In Appendix C, we provide further detail about each region’s

common law classification.

The third set of demand measures are related to industry metrics and follow Datar et al.

(2001) and Cain et al. (2011), who examine the role of earnouts in valuation uncertainty in private

firms. The first of these is the industry ratio of R&D to sales (target industry R&D to Sales). We

use an indicator variable equal to one if the target’s industry ratio has an above-median ratio, which

reflects both uncertainty about future prospects (Officer et al., 2009) and the extent to which there

7 In the RWI sample, the seller type is 47.8% strategic (corporate) entities, 29.3% PE, and 22.9% individuals.

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are unrecorded assets on the balance sheet (Lev & Sougiannis, 1996). Since the reps signal the

value of a firm’s recorded assets and liabilities, and most internal research-related assets are not

recorded on financial statements, the financial statement reps are potentially less likely to be

breached for R&D intensive firms. The second is an indicator variable equal to one if the target’s

industry has above-median standard deviation of daily returns over the prior year (target industry

STD returns). The third proxy reflects the target’s growth prospects using the target industry

median Tobin’s Q ratio (target industry Tobin’s Q). In addition to the industry-based proxies for

valuation uncertainty, we proxy for financial reporting quality using the target’s industry mean

rate of internal control weakness prevalence (target industry ICW). Because financial statement

errors are the largest source of breaches, inferences about financial reporting quality likely signal

other aspects of general management and target quality. Internal control deficiencies have been

correlated with information uncertainty, higher audit fees, lower financial reporting quality, and

fraud (e.g., Amiram et al., 2018; Beneish, Billings, & Hodder, 2008; Hogan & Wilkins, 2008;

Hoitash, Hoitash, & Bedard, 2009).

To examine the effects of these factors on RWI demand and the risk allocation of reps, we

use the following logit regression specification where the dependent variable is an indicator

variable if RWI has been acquired:

!(#$%&ℎ()*), = ./ + 12345(6*(7)9:*), + 1;<&=$9%*%#>, + 1?@*77*%#>,

+ 1AB4CC4D7(E, + 1FG(%5*H9DI$)H%JG4K9DL)M,

+ 1NG(%5*H9DI$)H%J@G6%*H$%D), + 1OG(%5*H9DI$)H%JP&6H4@(7*),

+ 1RG(%5*H!DI$)H%J!BS, + T*(%U> + *,.(1)


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Table 3 presents the results of the regression described in Equation 1. For this test, we

merge the SDC acquisition data used in Table 1 with our RWI sample, and each observation, i, is

an acquisition in the combined sample. While some of the acquisitions in the SDC sample may

be covered by an RWI policy, all of the observations in our proprietary sample purchase the

insurance, and so measurement error introduced by assuming that the SDC sample does not have

RWI coverage will only serve to reduce our coefficient estimates. As shown in Table 3, the

positive and significant coefficient on log deal size is consistent with the fact that larger deals may

generate more breach of reps claims due to their complexity and the difficulty of conducting

adequate due diligence during the acquisition timeline. We find that PE acquirers are more likely

to purchase insurance (acquirer PE), with a marginal effect of a 2.5% greater likelihood as

compared to strategic acquirers. This result is consistent with the theory of greater skill in

investing due to the repeat nature of private equity; they are more likely to expect and assess the

need for reps accordingly to compensate for any areas which cannot be subject to full due

diligence. Moreover, the positive and significant coefficient on seller PE indicates a 27.3% higher

chance of purchasing the insurance. This finding is consistent with theories of RWI purchase in

order to mitigate risk to the PE fund of a future payout (Griffith, 2020).

Deals involving targets in common law countries also have a greater propensity to include

RWI, consistent with the higher propensity for litigated breaches in these jurisdictions (as well as

the greater ability to recover and rely on breaches of reps). Firms in industries with more R&D

assets and future growth opportunities, labeled by industry Tobin’s Q, have a lower likelihood of

purchasing RWI, consistent with the fact that the value of high-growth firms is predicated more

on future prospects than on existing assets and liabilities, and so reps are less important for risk

allocation. The coefficient on R&D intensity (target industry R&D to Sales) is not significantly

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different from zero, indicating that any valuation uncertainty associated with R&D-related reps

(and the need for risk allocation) may be offset by the fact that many internally developed

intangibles are generally not reflected in the financial statement reps. Lastly, financial reporting

risk, represented by target industry ICW, is positively and significantly associated with RWI

purchase. The marginal effect of a one percentage point rise in industry ICW rates increases RWI

purchase likelihood by 2.9 percent. Overall, we find that the demand for RWI is consistent with

the following factors: information uncertainty about the reps, expected cost of litigation, financial

reporting risk, and institutional costs for indemnities that are mitigated by the RWI purchase.

4.2 Party Assessment of the Value of Reps

Whereas the determinants of RWI purchase reflect the demand for reps, they also represent

in our sample demand for RWI itself. While the demand for each is theoretically coterminous, the

demand for RWI is multi-dimensional. In order to tease out the difference between the two and

demand for reps themselves we further examine the demand for policy limits, which signals the

insurance buyer’s ability to ex-ante assess the value of reps and the potential for breaches. More

specifically, the limits represent the buyer or target’s expectation of the potential maximum value

of breaches, and their subsequent transfer of that risk to the insurer. This assessment is thus a

second measure of demand for reps and their role in risk allocation, which accounts for the separate

purchase of RWI rather than direct indemnification against a seller.

As revealed in Panel A of Table 1, there is a wide variation in the amount of the limit

relative to deal size. On average, the limit is equal to 22.7% of the mean deal size, but a shift from

the 25th to the 75th percentile changes the limit from 8.2% to 25.3% of the deal value. If the RWI

limits reflect the indemnity that a buyer would demand in RWI’s absence, then we observe that

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indemnity amounts are a material fraction of deal value, and thus they indicate a significant amount

of valuation risk retained by the seller in acquisitions.

While we recognize that premiums and policy terms are jointly determined, we follow Core

(2000) and exclude policy premiums from our demand regressions: this omission does not affect

our inferences because the marginal benefit to the insured of purchasing a higher limit is less than

the marginal cost of the extra premium, and therefore it is unnecessary to control for the

insurance’s marginal cost. To examine the factors that impact the liability limit amount, we regress

the RWI coverage limit on the characteristics of the buyer and seller, the litigation environment,

and proxies for the target’s valuation uncertainty:

345(39C9H), = ./ + 12345(I*(7)9:*), + 1;1$J*%#479&J, + 1?<&=$9%*%#>, +

1A@*77*%#>, + 1F@*77*%!DI9X9I$(7, + 1NB4CC4D3(E, +

1OG(%5*H!DI$)H%JG4K9DL)M, + 1RG(%5*H!DI$)H%J@G6P*H$%D), +

1YG(%5*H!DI$)H%JP&6H4@(7*), + 12/G(%5*H!DI$)H%J!BS, + T*(%U> +

*,(2)


Table 4 reports the results of estimating Equation (2) where the dependent variable is the

logarithm of the insurance policy limit demand by buyers and sellers. The requested limit is

increasing in deal size, corresponding to greater perceived risk of higher losses in larger

acquisitions. The coefficient on acquirer policy is positive and significant, consistent with

acquirers facing more valuation uncertainty about the target than sellers do. The negative and

significant coefficient on acquirer PE may be explained by PE acquirers’ greater average

acquisition experience and due diligence skills, resulting in less perceived valuation uncertainty,

18

as compared to strategic buyers. We do not find that the coefficients on target industry Tobin’s

Q, target industry R&D to Sales, and target industry ICW are statistically significant, indicating

that insurance buyers do not consider these factors to be significant drivers of expected breach

values. The coefficient on target industry STD returns is marginally significant (p < 0.10),

reflecting increased valuation uncertainty for targets in industries with greater market volatility.8

Taken together, these findings indicate that buyers and targets utilize reps (and by proxy RWI) to

allocate risk and contract around areas of greater valuation uncertainty and information

asymmetries.

5. Third party assessment of potential breaches of representations and warranties

In this section, we examine our second question of how the third-party process of RWI acts

to independently assess the value of reps. To answer this question, we look at the RWI policy

premium, which is based on the insurance company’s expectation of losses due to breaches, plus

administrative overhead and profits.

We expect premiums to increase relative to target valuation uncertainty and factors

associated with the cost of breaches, e.g., deal size, given the greater risk of breaches with larger

and more complicated acquisitions; valuation uncertainty, due to higher target industry STD of

returns; lower financial reporting quality, reflected in target industry ICW; and the cost of

breaches, associated with common law jurisdictions. Because target industry R&D to Sales

involves both valuation uncertainty, which might be associated with higher risk of breaches, and

lesser representation of value reflected in the financial statement reps, given that many R&D assets

are often not recorded, we cannot predict the impact of R&D intensity on premiums. We also

8 Results remain unchanged when this analysis is limited to buy-side policies, where there is no contractual indemnity, to ensure that contracting cost issues are absent.

19

expect that premiums will be priced to mitigate adverse selection, proxied by abnormal limits,

since insured parties who suspect greater chances of losses will demand higher than expected

limits.

To assess the impact of these factors on the insurer’s pricing of breach costs, we estimate

the following regression:

345(#%*C9$C),

= ./ + 12345(I*(7)9:*), + B;<KD4%C(739C9H, + 1?1$J*%#479&J,

+ 1A<&=$9%*%#>, + 1F@*77*%#>, + 1N@*77*%!DI9X9I$(7,

+ 1OB4CC4D3(E, + 1RG(%5*H!DI$)H%JG4K9DL)M,

+ 1YG(%5*H!DI$)H%J@G6P*H$%D), + 12/G(%5*H!DI$)H%JP&6H4@(7*),

+ 122G(%5*H!DI$)H%J!BS, + T*(%U> + *,(3)


Table 5 reports the results of estimating Equation 3 where the dependent variable is the

logarithm of the policy premium. The coefficient on abnormal limit is positive and significant,

indicating that the insurer recognizes and charges increased premiums when the insured demands

abnormally high limits, thereby countering potential adverse selection. The coefficient on

acquirer policy is positive and significant, illustrating that the insurer perceives heightened risk of

losses for buy-side policies, consistent with the buyer’s information asymmetry and the exclusion

of fraud claims if the seller is the policyholder. The positive and significant coefficients on seller

PE and seller individual, relative to a strategic seller, are consistent with the policy’s value to the

policyholder based on the reduction in buyers’ contracting costs. The coefficient on common law

is positive and significant, consistent with these regions’ higher levels and costs of litigation, which

20

affects both expected losses and RWI contracting costs. The positive coefficient on target industry

R&D to Sales indicates that the insurance company expects R&D-intensive firms to have a greater

risk of future claims. Although these assets may not be recorded on the balance sheet or covered

by the financial statement reps, R&D risks, e.g., ownership of patents and trademarks and issues

related to trade secrets, do appear to be priced by the insurer. This inference is supported by Figure

1, Panel D, which illustrates that IP-related claims represent a major source of RWI losses,

accounting for more than 20% of claims for targets in the technology industry. The coefficient on

target industry ICW is positive and significantly associated with premiums, indicating that

industries with poor financial statement quality are perceived to have a greater risk of loss.

If the insurance company observes additional deal-specific information that predicts

breaches and is able to use this information to estimate future claims, then the premiums should

be significantly associated with future claims. Prices that are significantly associated with future

claims illustrate that the insurer is countering adverse selection by classifying premiums according

to the risk of losses.

To examine whether the insurance company can predict different breaches of reps, we

regress each type of breach on the premium-to-limit, reflecting the policy’s price, while controlling

for industry and legal environment proxies:

GJ]*4^K%*(&ℎ, = ./ + 12#%*C9$C)H439C9H), + 1;<&=$9%*%#>, + 1?@*77*%#>, +

1AB4CC4D3(E, + 1FG(%5*H!DI$)H%JG4K9DL)M, +

1NG(%5*H!DI$)H%J@G6P*H$%D), + 1OG(%5*H!DI$)H%JP&6H4@(7*), +

1RG(%5*H!DI$)H%J!BS, + YearFE + *,(4)


21

Table 6 reports the results of estimating Equation (4) where the dependent variable is one

of nine different types of claim: financial statements, compliance with laws, tax, intellectual

property, employment, fundamental, environment, litigation, and operations. We find that claims

are generally more likely when the insurance company charges higher premiums. Specifically,

breaches of financial statements, compliance with law, tax, IP, fundamental, and litigation are

significantly more likely to occur when the insurance company charges higher premiums.

Overall, we conclude that the insurance company is able to independently assess the risk

of loss of the breaches through adjusting premiums based on the specific idiosyncratic and

industry-wide risk of the reps. This conclusion provides evidence of the value of reps in specific

situations as well as the ability of an industry participant to reduce adverse selection and regularly

assess the value of reps.

6. Breaches of reps

In this section, we build on our prior analysis of the demand and value of reps to examine

our third question as to which factors predict breaches of reps and, in particular, which reps matter

in reducing information asymmetry for different types of acquirers, industries, regions, and legal

environment. To examine the factors that impact the different breaches of reps, we regress each

type of breach on the characteristics of the buyer and seller:

GJ]*4^K%*(&ℎ, = ./ + 12<&=$9%*%#>, + 1;@*77*%#>, + 1?@*77*%!DI9X9I$(7, +

YearFE + *,(5)


Table 7 reports the results of estimating Equation (5). We find that when compared to

strategic buyers, private equity buyers are on the whole less likely to have a claim for breach of

22

reps related to those that can be more easily diligenced (compliance with laws, intellectual

property, and fundamental matters) versus those which cannot (tax and financial statements).

These findings are consistent with the greater acquisition experience of private equity firms

generally.

To examine the factors that impact the different breaches of reps, we regress each type of

breach on indicators for the industry of the target. Intercepts are suppressed because the industry

dummy variables fully span the sample.

GJ]*4^K%*(&ℎ, = 12B4D)$C*%, + 1;U9D(D&9(7, + 1?g*(7Hℎ&(%*, +

1Ah(D$^(&H$%9D5, + 1Fh*I9(, + 1Ni(H$%(7P*)4$%&*), + 1OjHℎ*%, +

1RP*(7>)H(H*, + 1YP*H(97, + 12/@*%X9&*), + 122G*&ℎD4745J, + YearFE + *,(6)


Table 8 reports the results of estimating Equation (6). We find that reps appear to be

utilized and have greater value depending upon the industry of the acquisition. For example,

claims made for breaches in IP reps are significantly more likely to occur in acquisitions in the

technology industry, claims for breaches in compliance with laws are significantly more likely in

the consumer industry, and claims for breaches on financial statements reps are significant in

nearly all industries, save for Services.

To examine the factors that impact the different breaches of reps, we regress each type of

breach on regions of the target. Intercepts are suppressed because the region dummy variables

fully span the sample.

GJ]*4^K%*(&ℎ, = 12<)9(, + 1;<$)H%(79(, + 1?B(D(I(, + 1A>$%4]*, + 1FjHℎ*%, +

1Nlm, + 1Ol@, + YearFE + *,(7)

23


Table 9 reports the results of estimating Equation (7). We find that reps appear to be

utilized and have greater efficacy depending upon the region of the acquisition. For example,

claims made for breaches in compliance with laws reps, IP reps, and fundamental reps are

significantly more likely to occur in acquisitions in the U.S., whereas claims for breaches in

litigation are more likely in Europe. Claims for breaches in Tax are more likely in the U.K. and

the U.S. and claims for breaches in employment are more likely in Australia, Europe, U.K., and

the U.S. Last, claims for breaches on financial statements reps are significant in all regions.

Overall, our findings show that acquirers and buyers appear to focus on reps to reduce information

asymmetry in acquisitions, that sophistication and experience matters in the utilization of reps and

that, accordingly, reps are utilized in a manner designed to create value certainty.

For our final question, we examine the role of law in reps claims and information

asymmetry. To examine this question, we regress each type of breach on the legal environment of

the target:

GJ]*4^K%*(&ℎ, = ./ + 12B4CC4D3(E, + YearFE + *,(8)


Table 10 reports the results of estimating Equation (8). We find that claims are more likely

under common law for breaches of reps associated with law enforcement (such as compliance with

laws), consistent with the more complex legal regimes in common law jurisdictions. The overall

positive association we find between breaches and common law is also consistent with these

regions’ higher levels and costs of litigation (e.g., La Porta et al., 2008), which affects both

expected losses and contracting costs and are thus more associated with frequency of breaches in

24

the reps (Ramseyer and Rasmusen, 2010; Browne, Chung, and Frees, 2000). Last, we find that

claims are more common in Europe and North America than in Asia, which is consistent with the

common law enforcement story. These findings also highlight the role of reps in reducing

information asymmetry in particular areas associated with the common law (or not).

7. Conclusion

Drawing from a large and unique sample of RWI policies, we confirm prior theory that

reps play an important role in risk allocation, reduction of information asymmetry, and valuation

certainty. We find that buyers and targets utilize reps (and by proxy RWI) to allocate risk and

contract around areas of valuation uncertainty and information asymmetries. However, we also

find evidence that while targets and acquirers utilize reps to allocate risk they may not do so

optimally to create value. We find that when compared to strategic buyers, private equity buyers

are on the whole less likely to have a claim for breach of reps which are more easily subject to due

diligence (compliance with laws, intellectual property, and fundamental matters) versus those

which cannot (tax and financial statements). These findings are consistent with the greater

acquisition experience of private equity firms generally. We also find that reps appear to be have

more utility depending upon the industry and region of the acquisition. Overall, our findings show

that acquirers and buyers appear to focus on reps to reduce information asymmetry in acquisitions,

that sophistication and experience matters in the utilization of reps and that, accordingly, reps are

utilized in a manner designed to create value certainty. We conclude by examining the role of law

in reps claims and information asymmetry. We find that claims are more likely under common

law for breaches of reps associated with law enforcement (such as compliance with laws),

consistent with the more complex legal regimes in common law jurisdictions.

25

The acquisition industry is a multi-trillion dollar one. Our results inform the value and

need for reps contributing and extending the theoretical literature by showing the importance of

contractual reps in risk allocation and reducing information asymmetry and valuation uncertainty.

Ultimately, our results extend prior work on the role of contracting and risk allocation to reduce

information asymmetry and create valuation certainty in a live setting.

26

REFERENCES

Amiram, D., Bozanic, Z., Cox, J., Dupont, Q., Karpoff, J., and Sloan, R., 2018. Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature. Review of Accounting Studies, 23(2): 732-783

Bates, T., Neyland, J., Wang, Y., 2018. Financing acquisitions with earnouts. Journal of Accounting and Economics, 66: 374-395.

Beneish, M., Billings, M., Hodder, L., 2008. Internal control weaknesses and information uncertainty. The Accounting Review, 83(3): 665-703.

Betton, S., Eckbo, B.E., Thorburn, K., 2007. Corporate takeovers. Handbook of Empirical Corporate Finance, Vol. 2, B.E. Eckbo Editor, Elsevier.

Browne, M.J., Chung, J., Frees, E.W., 2000. International property-liability insurance consumption. Journal of Risk and Insurance, 67(1): 73-90.

Cadman, B., Carrizosa, R., Faurel, L., 2014. Economic determinants and information environment effects of earnouts: New insights from SFAS 141(R). Journal of Accounting Research, 52(1): 37-74.

Cain, M.D., Denis, D.J., Denis, D.K., 2011. Earnouts: A study of financial contracting in acquisition agreements. Journal of Accounting and Economics, 51(1): 151-170.

Chung, Ji-Woong, Sensoy, B., Stern, L. & Wesibach, M., 2012. "Pay for Performance from Future Fund Flows: The Case of Private Equity," Review of Financial Studies, 25(11): 3259-3304.

Coates, J., 2015. M&A contracts: purposes, types, regulation, and patterns of practice. In Research Handbook on Mergers and Acquisitions (Davidoff Solomon, S. & C. Hill eds.).

Core, J., 2000. The directors’ and officers’ insurance premium: an outside assessment of the quality of corporate governance. Journal of Law Economics and Organization 16: 449–477.

Cornelli, F., Karakas, O., 2013. CEO turnover in LBOs: The role of boards. Working paper, London Business School.

Datar, S., Frankel, R., Wolfson, M., 2001. Earnouts: The effects of adverse selection and agency costs on acquisition techniques. Journal of Law, Economics, & Organization, 17(1): 201-238.

Denis, D., Macias, A., 2013. Material adverse change clauses and acquisition dynamics. Journal of Financial and Quantitative Analysis, 48(3): 819–847.

27

Erel, I., Liao, R., Weisbach, M., 2012. Determinants of cross-border mergers and acquisitions. Journal of Finance. 67(3): 1045-1082.

Freund, J., 1975. Anatomy of a merger: strategies and techniques for negotiating corporate acquisitions, Law Journal Press, New York.

Gallozi, M.s., Phillips, E. 2002. Representation and warranties insurance. Environmental Claims Journal, 14(4): 455-467.

Gilson, R., Schwartz, A., 2005. Understanding MACs: Moral hazard in acquisitions. Journal of Law, Economics, and Organization, 21: 330-358.

Griffith, S.J., 2020. Deal Insurance: Representation and warranty insurance in M&A contracting. Minnesota Law Review, 104(4): 1839-1920.

Grossman, S.J., Hart, O. 1983. An analysis of the principal-agent problem. Econometrica, 51(1): 7-45.

Hill, C., Quinn, B., Davidoff Solomon, S., 2016. Mergers and acquisitions: Law theory, ad practice. St Paul, MN: West Academic Publishing.

Hogan, C., Wilkins, M., 2008. Evidence on the audit risk model: Do auditors increase audit fees in the presence of internal control deficiencies? Contemporary Accounting Research, 25(1): 219-242

Hoitash, U., Hoitash, R., Bedard, J., 2009. Corporate governance and internal control over financial reporting: A comparison of regulatory regimes. The Accounting Review, 84(3): 839-867.

Holmstrom, B., 1979. Moral hazard and observability. The Bell Journal of Economics, 10(1): 74-91.

Kling, L., Simon, E.N., Goldman, M., 1997. Summary of acquisition agreements. University of Miami Law Review, 51: 779-812.

Kohers, N., Ang, J., 2000. Earnouts in mergers: Agreeing to disagree and agreeing to stay. Journal of Business 73(3): 445-476.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R.W., 1997. Legal determinants of external finance. Journal of Finance, pp.1131-1150.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2008. The economic consequences of legal origins. Journal of Economic Literature. 46(2): 285-332.

28

Lev, B., Sougiannis, T., 1996. The capitalization, amortization, and value-relevance of R&D. Journal of Accounting and Economics, 21: 107-138.

Mirrlees, J., 1999. The theory of moral hazard and unobservable behavior: Part I. Review of Economic Studies, 66(1): 3-21.

Officer, M., 2004. Collars and renegotiation in mergers and acquisitions. Journal of Finance, 59: 2719-2743.

Officer, M., Poulsen, A., Stegemoller, M., 2009. Target-firm information asymmetry and acquirer returns. Review of Finance 13: 467-493.

Ramseyer, J., Rasmusen, E., 2010. Comparative litigation rates. Working Paper, Harvard Law School.

Rhodes-Kropf, M., & Viswanathan, S., 2004. Market Valuation and Merger Waves. The Journal of Finance, 59(6): 2685-2718.

Shleifer, A., & Vishny, R. W., 2003. Stock market driven acquisitions. Journal of Financial Economics, 70(3): 295-311.

Talley, E.L., 2009. On Uncertainty, Ambiguity, and Contractual Conditions. Delaware Journal of Corporate Law, 34: 755-812.

29

APPENDIX A: Variable definitions

Variable Definition

Deal Size Total enterprise value of the acquisition in US dollars;

Acquirer policy

An indicator variable equal to one if the acquirer is the named insured (a buy-side policy) and zero if the seller is the named insured (a sell-side policy);

Acquirer PE

An indicator variable equal to one if the acquirer is a private equity firm and zero otherwise;

Seller PE An indicator variable equal to one if the seller is a private equity firm and zero otherwise;

Seller Individual

An indicator variable equal to one if the seller is an individual and zero otherwise;

Common Law

An indicator variable equal to one if the target is located in a predominantly common law jurisdiction (US, UK, Canada, Australia, Africa, and the Middle East) and zero otherwise;

Target industry Tobin’s Q

An indicator equal to one if the target firm’s industry is above median in the ratio of the firm’s market value, measured as book value of total assets less book value of equity plus market value of equity, to the book value of its total assets for firms in the same industry as the target;

Target industry STD of returns

An indicator equal to 1 if the target firm’s industry is above median in the industry’s median value of the standard deviation of daily returns for firms in the same industry as the target;

Target industry R&D to Sales

An indicator equal to 1 if the target firm’s industry is above median in the median value of R&D to sales for firms in the same industry as the target;

Target industry ICW

The mean fraction of firms in the same industry as the target firm and fiscal year equal to the deal year, with reported material weaknesses in internal control (IC_IS_EFFECTIVE not equal to ‘Y’; Audit Analytics);

Limit The aggregate amount, in US dollars (USD), that the policy will pay to the insured for one or more losses incurred;

Abnormal Limit

The fitted residuals from the regression specified in Equation 2, reported in Table 4.

Retention The amount, in USD, which must be borne by the insured before the insurance policy will begin to pay claims;

Premium The amount, in USD, paid by the insured for coverage under the representations and warranties insurance policy;

30

Variable Definition

Claim An indicator variable equal to one if a policy has received any claims for losses made by the insured and zero otherwise;

31

APPENDIX B: Industry classification

The table below reports our mapping between the 13 industry classifications in our sample and the Fama-French 48 industry classifications.

Sample Classification Fama-French 48 Industry Classifications

1 Consumer 2 Food – food products; 3 Soda – soda & candy; 4 Beer – beer & liquor; 5

Smoke – tobacco products; 6 Toys – recreation; 9 Household – consumer goods; 10 Clothes – apparel; 43 Meals – restaurants, hotels, & motels.

2 Energy and natural resources

27 Gold – precious metal; 28 Mines – non-metallic & industrial metal mining; 29 Coal; 30 Oil – petroleum & natural gas.

3 Financial 44 Banking; 45 Insurance; 46 Finance - trading

4 Healthcare 11 Healthcare; 12 Medical equipment; 13 Drugs – pharmaceutical products

5 Manufacturing 15 Rubber – rubber & plastic products; 16 Txtls – textiles; 17 BldMt – construction materials; 19 Steel – steel works etc; 20 FabPr – fabricated products; 21 Machinery; 22 Electrical equipment; 23 Autos - automobiles & trucks; 24 Aero – aircraft; 25 Ships – ship building & railroad equipment; 26 Guns – defense; 31 Util – utilities; 37 Labeq – measuring & control equipment; 38 Paper – business supplies; 39 Boxes – shipping containers

6 Media 7 Fun – entertainment; 8 Books – printing & publishing.

7 Real estate 46 RlEst – real estate

8 Retail 41 Whlsl – wholesale; Rtail - retail

9 Services 33 PerSv – personal services; 34 BusSv – business services

10 Technology 35 Comps – computers; 36 Chips – electronic equipment

11 Telecomm 32 Telcm – communication

12 Transportation 40 Trans – transportation

13 Other 14 Chems – chemical; 18 Cnstr – construction; 48 Other

32

APPENDIX C: Legal system

The table below reports our mapping between the sample’s region coding, an indicator for common law legal status, and SDC’s region coding. We code an SDC region as common law when the majority of deals occur in countries having a common law legal system.

Region Common Law

Region’s countries Legal system

Group Weighting in SDC Region

US 1 Group 1: US Common 100%

UK 1 Group 1: UK Common 100%

Canada 1 Group 1: Canada Common 100%

Asia 0 Group 1: China, Japan, South Korea, Indonesia, Taiwan, Vietnam, Philippines

Civil 78%

Group 2: India, Thailand, Hong Kong, Malaysia, Singapore, Pakistan, Sri Lanka

Common 22%

Europe 0 Group 1: Russia, French, Germany, Spain, Italy, Sweden, Poland, Netherlands, Norway, Turkey, Denmark, Switzerland, Finland, Belgium, Portugal, Austria, Greece.

Civil 89%

Group 2: Ireland Common 3%

Australia 1 Group 1: Australia; New Zealand Common 98%

Africa 0 Group 1: Nigeria, Kenya, Namibia, Zimbabwe, South Africa, Tanzania,

Common 71%

Group 2: Ethiopia, Ivory Coast, Mauritius Civil 3%

Group 3: Mozambique, Morocco, Tunisia, Ghana, Zambia,

Mixed 10%

Middle East

0 Group 1: Israel Common 38%

Group 2: Jordan; Lebanon Civil 5%

Group 3: UAE, Kuwait, Oman, Saudi Arabia, Qatar Mixed 52%

South America

0 Group 1: Falkland Islands, Guyana Common 1%

Group 2: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela

Civil 99%

Source: Central Intelligence Agency. 2018. The World Factbook. Legal System. Retrieved 2018-04-02. https://www.cia.gov/library/publications/the-world-factbook/fields/2100.html.

33

Figure 1: Breach of Representations and Warranties: frequency and type

The following figures provide information on the frequency and types of claims for breach of representations and warranties in our sample. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. Panel A shows the number of deals by year and the number of deal with a claim of breach made by September, 2017. Panel B shows the number of claims by type. Panel C provides the distribution of claims by type and Panel D provides the frequency by type and industry.

Panel A: Claim for breach of representations and warranties: frequency by year

Panel B: Distribution of average time to claim for breach of representations and warranties

128157

217

333

563

292

34 38 51 6791

34

26.6%24.2% 23.5%

20.1%

16.2%

11.6%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

0

100

200

300

400

500

600

2011 2012 2013 2014 2015 2016

Number of policies issued Number of policies issued that resulted in a claim

% of policies that resulted in a claim

34

*Measured from time of RWI policy inception date.

Panel C: Frequency of representation and warranty claims by type

39.4%

24.1%21.3%

11.1%

4.1%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

<6 months >6-12 months >12-18 months >18-24 months >24 months

3.6%4.6% 5.2% 5.9%

7.5% 7.9%

14.4% 14.4% 15.1%

21.3%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Envir

onmenta

l

Litiga

tion

Operat

ions

Fundam

ental

Employm

ent

Intelle

ctual

Property

Complia

nce w

ith la

wsTa

x

Mat

erial

contra

ct

Finan

cial S

tate

men

ts

35

Panel D: Frequency of representation and warranty claims by type and industry

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Envir

onmenta

l

Litiga

tion

Operat

ions

Fundam

ental

Employm

ent

Intelle

ctual

Property

Complia

nce w

ith la

wsTa

x

Mat

erial

contra

ct

Finan

cial S

tate

men

ts

Manufacturing Health & Pharma Technology Financial services

36

TABLE 1: Comparison between Sample and SDC Population

This table reports descriptive statistics for our sample of 1,690 mergers and acquisitions (“RWI Sample”) between January 1st, 2011 and December 31st, 2016 and compares them to the SDC population. Panel A presents an annual time profile for our sample and for the SDC sample. Panels B and C present target industry and region, respectively, for our sample and for the SDC universe. Panel D presents the deal size and the percentage of acquisitions where the acquirer is a private equity firm in our sample and compares them to the SDC universe. Reported p-values are from two-sided t-tests (for means) or Wilcoxon signed-rank test (for medians).

Panel A: Time profile

Year RWI Sample SDC Sample RWI / SDC

mergers (%) N % N % 2011 128 7.6% 6,590 17.9% 1.9% 2012 157 9.3% 6,369 17.3% 2.5% 2013 217 12.8% 6,035 16.4% 3.6% 2014 333 19.7% 7,097 19.3% 4.7% 2015 563 33.3% 6,879 18.7% 8.2% 2016 292 17.3% 3,799 10.3% 7.7% Total 1,690 100.0% 36,769 100.0% 4.6%

Panel B: Target industry

Year RWI Sample SDC Sample N % N % Manufacturing 273 16.2% 4,583 12.5% Consumer 263 15.6% 3,187 8.7% Technology 212 12.5% 1,155 3.1% Real Estate 199 11.8% 5,551 15.1% Healthcare 155 9.2% 2,318 6.3% Financial Services 122 7.2% 3,303 9.0% Natural Resources 90 5.3% 3,068 8.3% Transportation 57 3.4% 1,000 2.7% Media 52 3.1% 753 2.1% Retail 41 2.4% 2,290 6.2% Telecomm 39 2.3% 761 2.1% Services 30 1.8% 6,454 17.6% Other 157 9.3% 2,346 6.4% Total 1,690 100.0% 36,769 100.0%

37

Panel C: Target region

Variable RWI Sample All mergers N % N % Asia 73 4.3% 9,091 24.7% US 784 46.4% 11,296 30.7% Europe 326 19.3% 6,765 18.4% UK 248 14.7% 3,316 9.0% Canada 18 1.1% 2,438 6.6% Australia 207 12.3% 2,061 5.6% South America 4 0.2% 963 2.6% Africa 17 1.0% 546 1.5% Middle East 13 0.8% 293 0.8% Total 1,690 100.0% 36,769 100.0%

Panel D: Deal characteristics

Mean (median), $ in thousands

Sample All mergers

Difference p-value

Deal size 278,332 (120,000) 174,990 (22,594) 0.000 (0.000)

Acquirer type N % N %

Private equity 691 40.9% 7,390 20.1% 0.000

38

TABLE 2: Deal and RWI characteristics and pairwise correlations

This table reports descriptive statistics of the deals contained in our sample. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. Deal size reflects the value amount paid by the acquirer for a target. An insurance limit represents the maximum amount an insurance company will pay for a covered loss. Similar to a deductible, insurance retention refers to the amount of money an insured company is responsible for in the event of a claim. Gross written premium is the total payment the buyer issues for an RWI policy. Buyer-side policy is a dummy variable equal to 1 if the policy was purchased by the buyer and 0 otherwise. Policy duration is the number of years covered by the policy. Panel B provides the pairwise correlations among these variables, with Spearman correlations above the diagonal, and Pearson correlations below the diagonal. Numbers in bold are significant at the 10% level or better.

Panel A: Deal descriptive statistics

Variable Mean Median Standard Deviation

25th percentile

75th percentile

Deal Size ($000’s) 278,332 120,000 493,317 47,672 292,757 Acquirer PE 40.9% 0.0% 49.2% 0.0% 100.0% Seller PE 29.3% 0.0% 45.6% 0.0% 100.0% Seller individual 22.9% 0.0% 42.0% 0.0% 0.0% Common law 74.37% 100% 43.66% 0.0 100% Target industry Tobin’s Q 1.40 1.34 0.41 1.14 1.56 Target industry STD returns 235% 225% 65.5% 187% 267% Target Industry R&D to sales 6.1% 2. 0% 11.0% 0.0% 10.0% Target Industry ICW 16.6% 16.8% 6.1% 11.9% 20.9%

Panel B: Policy descriptive statistics

Variable Mean Median Standard Deviation

25th percentile

75th percentile

Limit ($000’s) 22,638 19,196 18,912 10,000 30,000 Limit to deal size 22.7% 13.3% 24.4% 8.2% 25.3% Retention ($000’s) 6,076 1,500 16,525 518 4,400 Retention to deal size 2.5% 1.2% 4.3% 1.0% 2.0% Premium ($000’s) 545 380 555 208 730 Premium to limit 2.6% 2.5% 1.2% 1.4% 3.6% Buyer-side policy 87.2% 100.0% 33.4% 100.0% 100.0% Claim incidence 18.6% 0.0% 38.9% 0.0% 0.0%

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Panel C: Pairwise correlations

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) Deal Size - 0.43 -0.34 0.45 -0.05 0.44 0.09 0.03 0.06 (2) Limit 0.70 - -0.04 0.21 -0.01 0.52 -0.17 0.09 0.08 (3) Limit to deal size (%) -0.80 -0.18 - -0.20 0.13 -0.24 -0.33 -0.05 -0.02 (4) Retention 0.79 0.58 -0.62 - 0.47 0.16 -0.05 0.04 0.06 (5) Retention to deal size ((%)

-0.03 -0.03 0.02 0.52 - -0.05 -0.07 0.00 0.02 (6) Premium 0.72 0.75 -0.40 0.66 0.12 - 0.39 0.07 0.12 (7) Premium to limit (%) 0.17 -0.17 -0.39 0.23 0.22 0.47 - 0.01 0.06 (8) Buyer-side policy 0.10 0.13 -0.04 0.13 0.08 0.13 0.02 - 0.19 (9) Policy duration 0.14 0.20 -0.02 0.07 -0.10 0.09 -0.16 0.19 -

40

TABLE 3: Determinants of RWI Sample

This table presents the estimated coefficients for the following logit regression !(#$%&ℎ()*), = ./ + 12345(6*(7)9:*), + 1;<&=$9%*%#>, + 1?@*77*%#>, + 1AB4CC4D7(E, +1FG(%5*H9DI$)H%JTobinLsM, + 1NG(%5*H9DI$)H%J@G6%*H$%D), +1OG(%5*H9DI$)H%JP&6H4@(7*), + 1RG(%5*H!DI$)H%J!BS, + T*(%U> + *,.in, where each observation is an acquisition in the combined RWI and SDC acquisition sample, and the dependent variable is an indicator variable equal to 1 when if the deal is in the RWI Sample is purchased, and 0 otherwise. All other variables are defined in Appendix A. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016 and 33,442 acquisitions in SDC for the comparable period. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively.

Dependent Variable: I(Purchase)

Coefficient t-statistic Marg. Prob.

Log(Deal Size) 0.381*** 26.146 0.8%

Acquirer PE 0.886*** 14.925 2.5%

Seller PE 3.013*** 37.138 27.3%

Common Law 1.173*** 18.232 2.6%

Target Industry Tobin’s Q -0.261*** -3.479 -0.6%

Target Industry STD Returns 0.398*** 4.929 0.9%

Target Industry R&D to Sales 0.109 1.446 0.2%

Target Industry ICW 1.334** 2.520 2.9%

Observations 35,132

Pseudo R2 0.242

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TABLE 4: Demand for RWI limits

This table presents the estimated coefficients for the following OLS regression: 345(39C9H), = ./ + 12345(I*(7)9:*), + 1;1$J*%#479&J, + 1?<&=$9%*%#>, + 1A@*77*%#>, +1F@*77*%!DI9X9I$(7, + 1NB4CC4D3(E, + 1OG(%5*H!DI$)H%JG4K9DL)M, +1RG(%5*H!DI$)H%J@G6P*H$%D), + 1YG(%5*H!DI$)H%JP&6H4@(7*), +12/G(%5*H!DI$)H%J!BS, + T*(%U> + *,, where each observation is an RWI insurance policy in our sample and the dependent variable is the logarithm of the limit of liability on the policy. All other variables are defined in Appendix A. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. t-statistics are adjusted for heteroscedasticity. **, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively.

Dependent Variable: Log(Limit)

Coefficient t-statistic

Log(Deal Size) 0.379*** 27.27

Acquirer policy 0.208*** 4.06

Acquirer PE -0.169*** -5.52

Seller PE 0.006 0.17

Seller individual -0.007 -0.19

Common Law 0.018 0.46

Target Industry Tobin’s Q 0.001 0.04

Target Industry STD Returns 0.068* 1.73

Target Industry R&D to Sales -0.033 -0.91

Target Industry ICW -0.314 -0.96

Observations 1,690

Adjusted R2 0.435

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TABLE 5: Third party assessment of potential breaches of representations and warranties

This table presents the estimated coefficients for the following OLS regression 345(#%*C9$C), = ./ + 12345(I*(7)9:*), + B;<KD4%C(739C9H, + 1?1$J*%#479&J, +1A<&=$9%*%#>, + 1F@*77*%#>, + 1N@*77*%!DI9X9I$(7, + 1OB4CC4D3(E, +1RG(%5*H!DI$)H%JG4K9DL)M, + 1YG(%5*H!DI$)H%J@G6P*H$%D), +12/G(%5*H!DI$)H%JP&6H4@(7*), + 122G(%5*H!DI$)H%J!BS, + T*(%U> + *,, where each observation is deal in our RWI sample and the dependent variable is the logarithm of the policy premium for the RWI for that deal. All other variables are defined in Appendix A. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. t-statistics adjusted for heteroscedasticity are shown underneath the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively.

Dependent Variable: Log(Premium)

Coefficient t-statistic

Log(Deal Size) 0.426*** 39.80

Abnormal limit 0.561*** 11.44

Acquirer policy 0.096** 2.45

Acquirer PE 0.027 1.05

Seller PE 0.107*** 3.46

Seller individual 0.159*** 5.24

Common Law 0.249*** 8.58

Target Industry Tobin’s Q -0.045 -1.56

Target Industry STD Returns 0.048 1.51

Target Industry R&D to Sales 0.085*** 2.97

Target Industry ICW 1.133*** 4.13

Observations 1,690

Adjusted R2 0.692

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Table 6: Third party assessment of potential breaches of representations and warranties

This table presents the estimated coefficients for the following OLS regression: !"#$&'()$*+ℎ- = /0 + 234)$567589&:65698- + 2;<+=76)$)4>- + 2?@$AA$)4>- + 2BC&55&D:*E- +2F!*)G$9HDI789)"!&(6DJ8K- + 2L!*)G$9HDI789)"@!MN$97)D8- + 2O!*)G$9HDI789)"N&M9&@*A$8- + 2Q!*)G$9HDI789)"HCR- +YearFE + $-, where each observation is a deal in our sample and the dependent variables are the various reasons for breach. All other variables are defined in Appendix A. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. t-statistics adjusted for heteroscedasticity are shown underneath the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Dependent Variable:

Financial Statements

Compliance with Laws

Tax Intellectual Property

Employment Fundamental Environmental

Litigation Operations

(1) (2) (3) (4) (5) (6) (7) (8) (9) Premium-to-Limit 0.012*** 0.010*** 0.010*** 0.005** 0.003 0.006*** 0.002 0.004** 0.001 (2.80) (2.76) (2.77) (2.06) (1.15) (2.70) (1.08) (2.23) (0.281) Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 Adjusted R2 0.025 0.035 0.018 0.017 0.011 0.010 0.009 0.007 0.004

44

Table 7: Type of claimant

This table presents the estimated coefficients for the following OLS regression: !"#$&'()$*+ℎ- = /0 + 23<+=76)$)4>- + 2;@$AA$)4>- + 2?@$AA$)HDI6Y6I7*A- + YearFE + $-, where each observation is a deal in our sample and the dependent variables are the various reasons for breach. All other variables are defined in Appendix A. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. t-statistics adjusted for heteroscedasticity are shown underneath the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Dependent Variable:






(1) (2) (3) (4) (5) (6) (7) (8) (9) Acquirer PE -0.012 -0.026*** 0.002 -0.012** -0.003 -0.012** -0.005 -0.007 0.000 (-1.19) (-3.11) (0.25) (-1.99) (-0.48) (-2.29) (-1.13) (-1.47) (0.02) Seller PE 0.014 0.032*** 0.011 0.008 0.016** 0.016*** 0.008 0.003 0.014*** (1.22) (3.36) (1.18) (1.12) (2.30) (2.63) (1.58) (0.51) (2.35) Seller Individual 0.007 0.004 0.010 0.011 0.023*** 0.010 0.003 0.000 0.007 (0.53) (0.39) (1.02) (1.45) (3.13) (1.44) (0.53) (0.02) (1.15) Observations 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 Adjusted R2 0.019 0.025 0.014 0.001 0.011 0.006 0.007 0.005 0.006

45

Table 8: Type of claim for breach of representations and warranties on industries

This table presents the estimated coefficients for the OLS regression: !"#$&'()$*+ℎ- = 23C&D875$)- + 2;Z6D*D+6*A- + 2?[$*A9ℎ+*)$- + 2B\*D7'*+97)6DG- + 2F\$I6*- + 2L]*97)*AN$8&7)+$8- +2O^9ℎ$)- + 2QN$*A>89*9$- + 2_N$9*6A- + 230@$)Y6+$8- + 233!$+ℎD&A&G"- + YearFE +$-, where each observation is a deal in our sample and the dependent variables are the various reasons for breach. All other variables are defined in Appendix A. Intercepts are suppressed because the industry dummy variables fully span the sample. The sample consists of deals in 1,690 RWI policies written between January 1st, 2011 and December 31st, 2016. t-statistics adjusted for heteroscedasticity are shown underneath the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Dependent Variable:






(1) (2) (3) (4) (5) (6) (7) (8) (9) Consumer 0.076*** 0.042** 0.023 0.001 0.053*** 0.009 0.003 0.020* 0.000 (3.282) (2.171) (1.187) (0.061) (3.727) (0.684) (0.272) (1.802) (-0.002) Financial 0.137*** 0.039* 0.025 -0.001 0.065*** 0.018 0.016 0.009 0.013 (5.023) (1.736) (1.092) (-0.084) (3.909) (1.227) (1.367) (0.688) (0.941) Healthcare 0.082*** 0.037* 0.038* -0.008 0.031** 0.015 0.006 0.016 0.004 (3.201) (1.750) (1.773) (-0.523) (2.018) (1.059) (0.566) (1.339) (0.329) Manufacturing 0.106*** 0.023 0.040** -0.001 0.054*** 0.009 0.017* 0.017 0.019

(4.343) (1.160) (1.998) (-0.074) (3.638) (0.680) (1.704) (1.453) (1.568) Media 0.091*** 0.000 0.083*** 0.026 0.051** 0.038** 0.019 0.028* 0.015 (2.676) (0.015) (2.932) (1.273) (2.503) (2.089) (1.358) (1.711) (0.871) Natural Resources

0.079*** 0.014 0.011 -0.012 0.028 0.012 -0.003 0.017 -0.006 (2.730) (0.598) (0.472) (-0.703) (1.602) (0.748) (-0.272) (1.227) (-0.442)

Other 0.098*** 0.007 0.045*** 0.001 0.025** 0.007 -0.002 0.015* 0.010 (5.448) (0.498) (3.001) (0.125) (2.252) (0.672) (-0.301) (1.745) (1.067) Real Estate 0.071*** 0.017 0.030 -0.004 0.034** 0.000 0.001 0.011 -0.001 (2.763) (0.811) (1.390) (-0.250) (2.169) (0.018) (0.111) (0.915) (-0.112) Retail 0.083** -0.004 0.064** 0.015 0.032 0.025 -0.001 0.011 0.022 (2.264) (-0.117) (2.103) (0.681) (1.427) (1.262) (-0.046) (0.632) (1.200) Services 0.059 0.002 0.015 -0.008 0.034 0.002 0.001 0.011 -0.001 (1.462) (0.061) (0.431) (-0.330) (1.362) (0.077) (0.067) (0.583) (-0.060)

46

Technology 0.091*** 0.027 0.050** 0.052*** 0.052*** 0.025* 0.005 0.029*** 0.002 (3.592) (1.292) (2.377) (3.321) (3.370) (1.780) (0.433) (2.427) (0.170) Telecomm 0.111*** 0.000 0.010 -0.008 0.033 0.002 0.000 0.009 -0.002 (2.977) (0.000) (0.337) (-0.342) (1.445) (0.103) (-0.019) (0.531) (-0.124) Transportation 0.082** 0.015 0.021 -0.013 0.031 0.000 -0.001 0.008 0.012

(2.515) (0.563) (0.769) (-0.663) (1.557) (-0.007) (-0.053) (0.493) (0.741) Observations 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 Adjusted R2 0.058 0.041 0.042 0.039 0.022 0.011 0.011 0.014 0.010

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Table 9: Type of claim for breach of representations and warranties on region of target

This table presents the estimated coefficients for the following OLS regression: !"#$&'()$*+ℎ- = 23<86*- + 2;<789)*A6*- + 2?C*D*I*- + 2B>7)&#$- + 2F^9ℎ$)- + 2L`a- + 2O`@- + YearFE +$-, where each observation is a deal in our sample and the dependent variables are the various reasons for breach. All other variables are defined in Appendix A. Intercepts are suppressed because the industry dummy variables fully span the sample. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. t-statistics adjusted for heteroscedasticity are shown underneath the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Dependent Variable:






(1) (2) (3) (4) (5) (6) (7) (8) (9) Asia 0.089*** 0.012 0.023 -0.002 0.027 0.007 -0.002 0.007 0.002 (3.174) (0.500) (0.987) (-0.118) (1.602) (0.468) (-0.197) (0.515) (0.166) Australia 0.111*** 0.014 0.033 0.005 0.037*** 0.007 -0.004 0.019 0.009 (5.552) (0.871) (1.955) (0.427) (3.064) (0.608) (-0.480) (1.948) (0.877) Canada 0.150*** 0.027 0.034 0.003 0.035 0.010 0.003 0.011 0.007 (3.103) (0.682) (0.848) (0.100) (1.191) (0.378) (0.125) (0.482) (0.300) Europe 0.097*** 0.023 0.052 -0.001 0.028*** 0.010 0.004 0.022*** 0.012

(5.158) (1.488) (3.292) (-0.062) (2.461) (1.029) (0.487) (2.396) (1.201) Other 0.08** 0.017 0.027 0.001 0.030 0.009 -0.001 0.009 0.004 (2.204) (0.558) (0.880) (0.022) (1.341) (0.452) (-0.039) (0.484) (0.227) UK 0.075*** 0.008 0.033** -0.004 0.030*** 0.006 -0.001 0.007 0.003

(3.863) (0.518) (2.011) (-0.312) (2.518) (0.564) (-0.067) (0.790) (0.323) US 0.135*** 0.063*** 0.061*** 0.029** 0.053*** 0.032*** 0.009 0.016* 0.017

(6.926) (3.876) (3.776) (2.379) (4.500) (3.065) (1.057) (1.685) (1.719) Observations 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 Adjusted R2 0.066 0.057 0.043 0.031 0.024 0.020 0.013 0.012 0.012

48

Table 10: Type of claim for breach of representations and warranties on common law

This table presents the estimated coefficients for the following OLS regression: !"#$&'()$*+ℎ- = /0 + 23C&55&D:*E- + YearFE +$-, where each observation is a deal in our sample and the dependent variables are the various reasons for breach. All other variables are defined in Appendix A. Our sample is taken from 1,690 mergers and acquisitions represented in RWI policies written from between January 1st, 2011 and December 31st, 2016. t-statistics adjusted for heteroscedasticity are shown underneath the coefficient estimates. ***, **, and * denote significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Dependent Variable:






(1) (2) (3) (4) (5) (6) (7) (8) (9) Common Law 0.024** 0.021** 0.005 0.018*** 0.017 *** 0.012** 0.002 -0.004 0.003 (2.236) (2.412) (0.562) (2.756) (2.640) (2.096) (0.436) (-0.719) (0.586) Observations 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 1,690 Adjusted R2 0.021 0.019 0.015 0.011 0.009 0.003 0.007 0.005 0.004

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How Do Representations and Warranties Matter? Risk