auditor expertise in mergers and acquisitions...auditor industry specialization is an important...

Auditor expertise in Mergers and Acquisitions

Ronen Gal-Or

D'Amore-McKim School of Business Northeastern University

422 Hayden Hall 360 Huntington Avenue

Boston, MA 02115-5000 Email: [email protected] Office: 617-373-4645

Rani Hoitash

Gibbons Research Professor Department of Accountancy

Bentley University 175 Forest Street

Waltham, MA 02452-4705 Email: [email protected]

Office: 781-891-2588

Udi Hoitash Gary Gregg research fellow

D'Amore-McKim School of Business Northeastern University

404 Hayden Hall 360 Huntington Avenue

Boston, MA 02115-5000 Email: [email protected]

Office: 617-373-5839

January 2018

Auditor expertise in Mergers and Acquisitions

Abstract

We contribute to the literature on task-specific expertise by examining the role of auditor experience with mergers and acquisitions (M&A). We predict and find that clients engaging in acquisitions are more likely to switch to an M&A expert auditor. Further, we find that M&A expert auditors are associated with a lower likelihood of M&A related misstatements in the year of acquisition. Our results hold only in industries with high accounting complexity, suggesting that while auditors without M&A expertise are able to navigate complex M&A transactions in non-complex industries, clients benefit from auditor M&A expertise in complex industries. Finally, we observe that M&A experts charge higher fees, but during acquisition years they are able to pass savings that are likely due to improved cost efficiencies back to their clients. While the academic literature has mostly concentrated on the role of auditor industry expertise, our study suggests that auditors can develop other value-enhancing forms of expertise that transcend industries.

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I. INTRODUCTION

Auditor industry specialization is an important topic that has received significant attention

in research and practice. Research on auditor industry specialization generally finds that specialist

auditors improve financial reporting quality and attract clients who seek better quality (or

perceived quality) audits. Although research is often focused on industry specialization, auditors

can gain expertise across industries by auditing specific types of complex accounts, especially

during periods where audit clients experience structural changes. In this study we examine auditor

specialization in mergers and acquisitions (M&A), a complex topic in accounting.

Public Company Accounting Oversight Board (PCAOB 2007, 2015a) inspections routinely

indicate that auditors struggle with M&A accounting.1 In addition, the accounting rules

surrounding business combinations have recently been revised due to the changing and complex

nature of M&A transactions. For example, the Financial Accounting Standards Board (FASB)

revised SFAS 141 in 2007 (SFAS 141R) to expand the scope of the original accounting standard

surrounding business combinations. Recognizing the complex nature of M&A transactions, the

FASB subsequently targeted business combination accounting in its accounting simplification

initiative (FASB 2015). Given the complex nature of M&A accounting, we examine whether

clients engaging in acquisitions are more likely to switch to auditors with substantial M&A

experience. We then examine whether M&A expert auditors are associated with higher audit

1 PCAOB inspection results in 2015 (PCAOB 2015b) indicated an increase in the number of audit deficiencies associated with business combinations related to the testing of internal controls and/or substantive tests, including evaluating the accounting for M&A transactions.

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quality. Finally, we explore whether M&A expert auditors are able to extract higher rents or

whether they pass along savings from increased efficiency back to acquisitive clients.

Auditors can gain industry-specific expertise through investments in staff, education,

technology, and concentrated audit work in particular industries. Through this expertise, auditors

can improve audit processes, audit efficiency, and overall audit quality. The reputation from

specialization can help attract clients that seek expert auditors and as a result command audit fee

premiums relative to non-expert auditors. While past research predominantly focuses on auditor

industry expertise only, few studies examine expertise in other domains such as taxes (McGuire,

Omer, and Wang 2012; Christensen, Olson and Omer 2015), R&D (Godfrey and Hamilton 2005),

and reverse mergers (Mao and Scholz 2016). This research generally finds that expertise influences

fees and performance. Hence, although the investigation of auditor expertise beyond industry

specialization is important, at this juncture, other forms of expertise received little attention.

Corporate mergers and acquisitions are important company events that often carry

significant risks. One important risk pertains to the accuracy of financial reports of the joined

company. To address M&A risks, auditors need to perform significant work during the acquisition

year. For example, auditors need to verify that reserves in the form of accruals and contingencies

are accurate and are not used to increase earnings in future periods (i.e. “cookie jar” reserves).

Similarly, delaying revenue recognition through deferred revenues to future periods is another

source of concern for auditors. Importantly, auditors must ensure that the purchase price is

correctly allocated to assets and liabilities as well as to tangible, intangible, and goodwill assets.

This is especially complex because goodwill (FASB 2001, SFAS 142) and business combination

(FASB 2007, SFAS 141R) standards require (with few exceptions) the use of fair value accounting

in the valuation of all acquired assets and liabilities. The intricacies of business combination

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accounting, the complexity of these transactions, and the challenges auditors face suggest that

greater auditor experience and expertise can improve the accounting surrounding M&As.

Although auditor M&A expertise is not directly observable, a measure of expertise can be

constructed based on auditor experience. Auditors in offices with large numbers of clients

engaging in M&As acquire knowledge and experience that allows them to gain efficiency in

auditing business combination related accounts and concentrate limited audit resources on high

risk areas. As such, M&A expert auditors are expected to attract clients that engage in M&As, and

improve audit quality during acquisition years. We investigate these questions by constructing a

measure of auditor-office M&A expertise based on the number of audit clients with past M&A

activity. We concentrate on audit offices, because prior research suggests that the transfer of

knowledge often occurs between professionals in the same office rather than across offices in a

national setting (Ferguson, Francis and Stokes 2003; Reichelt and Wang 2010; Chyz, Gal-Or and

Naiker 2016). Our primary measure of M&A expertise captures whether an office has audited at

least 30 clients that have completed acquisitions over a three year period.2 As robustness, we also

measure M&A expertise as a proportion of all possible M&A clients3 in a city over the prior three

years (i.e. at least 30%). Correlation of both auditor M&A expertise variables with auditor industry

expertise is low, suggesting that the two capture different types of auditor expertise.

While M&A expertise is established across industries, we further examine whether such

expertise is more applicable and beneficial in complex industries. Past experimental research finds

that the benefits of industry specialization can vary across industries (Moroney 2007) and that

specialist knowledge is more valuable in complex industries (Moroney and Simnett 2009). Recent

2 Results are robust to alternate counts of audit clients (i.e. at least 20 and 40 M&A audit clients in the office). 3 This alternate proxy is similar to the industry specialization measure developed in Neal and Riley (2004) capturing industry expertise using a market proportion measure.

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studies use the existence of supplemental audit and accounting industry guidance to measure

industry accounting complexity. Francis and Gunn (2015) find that industry expertise contributes

to financial reporting quality only in complex industries. Together, past research suggests that

specialization is more important when accounting standards are more complex. We expect this

logic to extend to corporate acquisitions because of the difficulty in applying the already complex

purchase accounting rules to the unique accounts and transactions in industries with complex

accounting. Further, the accounting surrounding these accounts and transactions often differs

during periods of M&A activity.4 Therefore, the value of M&A expertise should be greater in

industries with complex accounting.

Using a sample over the years 2004-2014, we first investigate the association between

M&A activity and auditor selection. As mentioned earlier, navigating M&As is complicated and

clients hope to avoid unnecessary costs in the form of weak financial reporting quality, especially

given their objectives of upside benefits from an acquisition. The reputation and experience of

M&A expert auditors may be valuable to clients with M&A activity. As such, we predict that

clients that switch auditors and currently engage in M&A activity are more likely to select an

M&A expert auditor. Consistent with our predictions, results show that firms with M&A activity

in the current year are more likely to select an M&A expert auditor but are not more likely to hire

an auditor with general industry expertise. When separately examining complex and non-complex

4 For example, in complex industries such as the software and technology, firms must account for deferred revenue acquired from a target in a different manner than if the long term contract with prepayment originated with the acquirer (Lubniewski et al. 2016).

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industries we observe that results are driven by complex industries and are insignificant in non-

complex industries.

We next examine whether M&A expert auditors are associated with better audit quality.

Past research finds that specialist auditors are often associated with higher audit quality (e.g.

Krishnan 2003; Balsam, Krishnan, and Yang 2003; Romanus, Maher and Fleming 2008; Reichelt

and Wang 2010). Because M&A transactions are complex, we predict that expert M&A auditors

will improve audit quality based on their ability to leverage their expertise and focus limited audit

resources on high risk areas. We focus on financial statement misstatements because specific

misstatements can be attributed to M&A activity. Our results show that M&A experts are

associated with a lower likelihood of M&A related misstatements relative to firms audited by non

M&A expert auditors. We do not observe similar results for industry expert auditors. Once again,

our results are driven by firms in complex industries and are insignificant in non-complex

industries. Overall, we find support for our hypotheses, even after subjecting our tests to alternative

specifications, such as propensity score matched sample research design and alternative

construction techniques of the M&A expertise measure.

To supplement our tests, we examine the association between M&A expertise and audit

pricing. Past research on the association between auditor expertise and audit pricing finds mixed

results. On the one hand, expert auditors can command greater fee premium because of the demand

for their service and associated reputation. Consistently, researchers find that industry

specialization is associated with higher audit fees (e.g. Craswell, Francis, and Taylor 1995;

Mayhew and Wilkins 2003; Carson 2009; Cahan, Jeter, and Naiker 2011). However, specialist

M&A auditors can benefit from increased cost efficiencies emanating from improved processes,

focused training, and greater topic specific knowledge that can be shared across engagements.

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Auditors can then pass these savings to their clients in the form of reduced audit fees. While

research does not often find fee discounts from industry expert auditors, several studies do find

that under certain circumstances expert auditors can transfer cost efficiencies to clients (Ettredge,

Xu and Yi 2014; Bills et al. 2014). Our investigation shows that auditor M&A expertise as well as

auditor industry expertise are associated with higher audit fees. Consistent with prior research we

also find that acquisitions are associated with higher audit fees for all firms, whether or not they

are audited by an M&A expert. However, the increase in audit fees during acquisition years are

lower for firms audited by M&A expert auditors relative to the increase by firms audited by non-

expert auditors. These results suggest that M&A auditors may be better positioned to audit firms

with M&A activity and pass savings along to their audit clients. Again, we do not find that such

cost savings are passed along during acquisition years when the firm is audited by an industry

expert.

Our study makes several important contributions to the accounting literature. First, we add

to the extensive auditor industry expertise literature by examining an important form of expertise

that transcends industries. Given that not all audit firms can be industry city leaders, some auditors

can develop expertise in auditing complex transactions such as M&A accounting. A low

correlation observed in our sample between auditor M&A expertise and auditor industry expertise,

suggesting that these two types of expertise are distinct. Second, we propose a method to capture

auditor M&A expertise that is based on auditors whose clients have had more acquisitions over

the prior three years. This approach for measuring expertise around specific events (or category of

accounts) can be used by others to examine expertise in other domains. Finally, we demonstrate

clear benefits of engaging with M&A expert auditors, particularly in acquisition years, but

highlight that the benefits we observe accrue only to firms that operate in complex industries. We

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do not observe similar results with respect to industry expert auditors. This suggests that extending

the extensive auditor industry expertise literature to other expertise domains is important to our

understanding of audit quality of certain complex accounting issues. Our results also have practical

implications to acquisitive companies in complex industries. Specifically, our results suggest that

these companies should consider engaging with an M&A expert auditor that can help them obtain

greater assurance on M&A related transactions without paying an audit fee premium during the

acquisition year.

The rest of the paper is organized as follows. Section 2 provides an overview of relevant

research and motivation for the hypotheses. Sections 3 and 4 describe the methodology and present

the results. The final section discusses the major findings and their implications for research and

practice.

II. BACKGROUND AND HYPOTHESIS DEVELOPMENT

The auditor expertise literature has predominantly focused on industry expertise. Business

models and the translation of economic activities into accounting vary across industries. These

inter-industry differences require specialized industry specific capabilities and knowledge that can

be acquired through audit work on multiple clients in the same industry, investment in

technologies, hiring strategies, and personnel training. As a result, industry expertise is expected

to facilitate better audits. Indeed, research shows that auditors who specialize in certain industries

provide higher audit quality (Krishnan 2003; Balsam, Krishnan, and Yang 2003; Reichelt and

Wang 2010).

Extending the auditor industry expertise literature, studies have focused on industry

expertise in specific industries including banking (Ettredge, Xu and Yi 2014; Bratten, Causholli

and Myers 2015), municipalities (Payne and Jensen 2002), governmental entities (Lowensohn et

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al. 2007), expertise in auditing school districts (Deis and Giroux 1992; O’Keefe, King, and Gaver

1994) and expertise in the pension plan audit market (Cullinan 1998). These studies generally

conclude that auditor expertise is associated with improved audit quality.

Recent research documents that the number of industries serviced by audit offices in the

U.S. has grown by 20%, suggesting that there is a trend away from industry specialization and a

move towards diversification (Asthana 2013). This trend suggests that while general industry

knowledge can facilitate better audit quality other strategies may be needed as offices become

more diversified. Because several complex accounting issues transcend industry boundaries,

auditors may be able to improve audit quality by gaining expertise in specific complex accounting

issues.

A limited number of studies extend the auditor industry expertise literature to examine

auditor expertise in complex accounting issues. Taxes, a complex accounting area, is one example

wherein an auditor can gain expertise. Accordingly, Christensen et al. (2015) find that tax expert

auditors are able to curtail earnings management through tax accounts. Similarly, Chyz et. al.

(2016) find that offices providing high levels of tax compliance services to audit clients are

associated with reduced likelihood of accounting misstatements. Taken together, an auditor that

gains expertise in taxes can influence the accuracy, of tax accounts and overall audit quality.

Focusing on R&D, another complex accounting issue, Godfrey and Hamilton (2005) find that

intensive R&D companies across different industries are more likely to hire an auditor with R&D

expertise. Finally, Mao and Scholz (2016) examined auditor expertise in Chinese reverse mergers

and find that expert auditors can extract higher audit fees and help companies navigate regulatory

requirements for up-listing to national exchanges. Their findings suggest that expert auditors have

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influence in areas that pertain to their expertise but otherwise, their performance is similar to other

auditors.

One area of auditor expertise that was not specifically examined by prior literature is

expertise in auditing companies in periods of M&A activity. This type of expertise is particularly

important because M&A activity has increased over the past decade.5 In addition, business

combination accounting is complicated, has been recently revised, and is one of the areas the FASB

is trying to amend in its simplification project (FASB 2015). Furthermore, the PCAOB has recently

identified M&A as a major risk factor (PCAOB 2015a), and has reported an increase in the number

of audit deficiencies surrounding audits of acquisitive clients (PCAOB 2015b). While the PCAOB

acknowledges that more experienced members of the engagement team are responsible for

auditing business combinations, significant deficiencies exist nonetheless. Such deficiencies may

be attributable to lack of experience auditing M&A transactions. Therefore the PCAOB

recommends that companies should ask the following question related to the auditing of M&A

transactions: ” Does your auditor have the expertise necessary to address the audit issues that may

arise from the reporting requirements related to business combinations as well as other effects of

a business combination..?“ (PCAOB 2015a).

Auditors and M&A

Mergers and acquisitions are major transactions with significant ramifications to company

shareholders, creditors, employees, management and other important stakeholders. Therefore,

proper M&A accounting is imperative to the success of companies that engage in acquisitions.

Extant research examined the role of the auditor in facilitating M&A transactions, focusing on

5 The reasons for the significant increase in M&A activity is attributable to high cash levels, low interests rates and shareholder demand for growth.

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acquisition success. Several studies focused on the auditor of the target finding that firms of Big-

N auditors are more likely to become a target for an acquisition that will ultimately be completed

(Xie, Yi and Zhang 2013). Similarly, De Franco et al. (2011) find that private targets receive a

premium if they are audited by a Big4 auditor and Lee et al. (2015) find that acquisition returns

are higher if their auditor is an industry specialist. In contrast, Louis (2005) finds that acquirers

audited by non-Big4 auditors outperform those that are audited by Big4 auditor. Their results are

more pronounced when the target is private and where the auditor has greater likelihood to have

an advisory role. While Louis (2005) suggests that acquirers can equally benefit from having a

smaller audit firm, most studies suggest that targets and acquirers can benefit from having a larger

“brand-name” auditor, because a Big4 audit is associated with a more credible signal about the

accuracy of their information.

Recent studies have examined instances where the acquirer and the target share an auditor.

Dhaliwal et al. (2015) find that in such cases, acquisitions are associated with significantly lower

deal premiums, lower (higher) target (acquirer) returns and higher deal completion rates. Cai et al.

(2015) find similar results. Both studies suggest that a shared auditor can reduce uncertainties by

acting as an information intermediary between the acquirer and the target.

While studies described above examine the auditor’s role in facilitating better acquisitions

by serving as unofficial advisors, they do not examine the role of the auditor in performing their

main responsibility of auditing financial statements and disclosures. One exception is Cai et al.

(2015) who suggest that auditors that audit both the target and the acquirer face increased liability

from shareholders of both parties and therefore have higher incentives to limit misreporting.

Consistently, Cai et al. (2015) find that a common auditor is associated with a lower likelihood to

misstate the financials and a lower level of discretionary accruals. However the sample in Cai et

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al. (2015) is limited to acquirer and targets that are both publically traded. Since many acquisitions

involve a privately held target, this data constraint, significantly limits the scope of their sample.

Further, and more importantly to our examination, their measure of sharing an auditor between an

acquirer and the target does not speak to auditor expertise in auditing M&As.

Hypothesis development

Auditor Selection

Firms that demand higher audit quality or want to signal high financial reporting quality to

external users often choose to engage with auditors that are perceived to provide higher audit

quality. For example, prior to the issuance of debt or securities, firms are more likely to switch to

a higher quality auditor (Johnson and Lys 1990; Francis and Wilson 1988; Defond 1992).

Similarly, firms often switch to an auditor with perceived higher audit quality prior to an IPO

(Hogan 1997). A major benefit that accrues with such changes is the reduction in IPO underpricing

(Balvers, McDonald and Miller 1988; Beatty 1989) but at the cost of higher audit fees (Hogan

1997). In a different context, Godfrey and Hamilton (2005) focus on firms with high R&D and

argue that auditing R&D accounts is complex and therefore requires auditor expertise.

Consistently, they find that firms with more R&D are more likely to choose an auditor specializing

in auditing R&D related accounts. Similarly, we predict that firms engaging in acquisitions are

more likely to switch to an auditor with M&A expertise in the period of the acquisition. This

prediction leads to our first hypothesis.

H1: Firms that engage in M&A activity are more likely to switch to an M&A expert auditor during the period of acquisition compared to other firms not engaged in M&A activity.

Audit Quality

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Past research finds that auditor industry specialists provide higher audit quality as

evidenced by lower levels of absolute discretionary accruals (Krishnan 2003; Balsam, Krishnan,

and Yang 2003; Reichelt and Wang 2010), lower likelihood to meet or just beat analysts’ forecasts

(Reichelt and Wang 2010), and fewer restatements (Romanus et al. 2008). Honing in on a more

nuanced type of auditor expertise, Christensen et al. 2015 find that tax expert auditors are able to

curtail earnings management facilitated through the tax accounts. This suggests that greater auditor

expertise in auditing tax accounts or activities enable the auditor to leverage its knowledge and

improve audit quality. Similarly, Mao and Scholz (2016) find that auditors who are experts in

Chinese reverse merger can better assist companies navigate regulatory requirements for up-listing

to national exchanges. Yet, they do not find that such expertise is associated with improved

financial reporting quality.

An acquisition is a major event that has the potential to significantly affect the economics

of the acquiring firm, alter its information system, and influence the structure and value of its

assets. Thus, acquisitions are often linked to earnings management and reporting errors. Earnings

management around M&As can take several forms. For example, it is easier for firms to create

“cookie jar” reserves during an acquisition year (accruals and contingencies) and release those

reserves in future periods to increase earnings. Other techniques include undervaluing assets of the

acquired firm (e.g. inventory), reclassifying target cash outflows from operations to an investing

activity to increase cash flow from operations, restructuring the target just before the acquisition

is complete, and writing off intangible assets. Consistently, PCAOB inspections (2007; 2015a)

reveal deficiencies that relate to inadequate testing of fair value estimates of acquired assets,

inadequate allocation of the purchase price to assets and liabilities, inappropriate reliance on

management valuation, and inadequate testing of the valuation model assumptions.

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Under SFAS 142 and business combination accounting (ASC 805), auditors also verify the

value of acquired assets and liabilities and regularly test the balance of the goodwill account.

Because of the required use of fair value for business combinations and because goodwill valuation

involves substantial discretion of unverifiable information, auditing these accounts is difficult.

Further, acquisitions have significant tax implications, require the correct classification of assets,

and often require currency translation.6 Auditors with M&A experience are more likely to

understand the intricacies of M&As, know which questions to ask and perform proper procedures

in specific accounting areas where errors can materialize. As a result, relative to auditors with less

M&A experience, M&A expert auditors are likely to allocate limited audit resources to high risk

areas in a more effective manner enabling them to successfully detect material misstatements in

the accounting for a business combination. This discussion leads to our second hypothesis.

H2: Firms that are audited by an M&A expert are less likely to report an M&A related misstatement during an acquisition year.

Industry Accounting Complexity

There is reason to expect that the benefits of M&A auditor expertise are not uniform across

industries. Because M&As often influence assets, liabilities as well as revenues and expenditures,

the complexity of dealing with M&A related transactions can intensify when firms operate in

industries with complex accounting. Past research finds differences in how expert auditors perform

their work in more- and less- complex industries, finding that the industry expertise has greater

benefits when industry-specific knowledge is needed. Francis and Gunn (2015) find that industry

specialists are associated with smaller accruals and fewer restatements in industries that have

6 Misstatement text that relates to acquisitions revealed a large array of reasons for the misstatements. For example, Biolex Therapeutics reported a restatement to “correct an error in the accounting for income taxes in connection with the purchase price allocation related to the LemnaGene acquisition”. “Pricemart reported a correction of currency translation that relates to an acquisition…”

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complex accounting, but experts are not incrementally beneficial along these same dimensions in

less complex industries. Similarly, because business combination accounting depends heavily on

accounting guidance in specific industries, we expect that the associations that we predict in H1

and H2 will be more pronounced in complex industries and less so in industries that are not

complex.

III. METHOD

Sample

To construct the auditor changes sample, we begin with 58,398 firm-year observations

between the years 2004-2014 obtained from the intersection of the Compustat, Audit Analytics

and Thomson One SDC databases. We exclude banks and financial firms (SICs 6,000 through

6,999) because accruals and other control variables are fundamentally different in the financial

industry and obtain 45,864 firm-year observations for the auditor change sample. We then

eliminate 17,506 firm-year observations due to missing control variables used in the auditor

change model reducing the number of observations to 28,358. Next, we remove 18,692

observations in cases where the firm was audited by an industry or M&A expert in the previous

year. We selected this non-expert sample to examine which type of expert (if any) would be more

desirable as the successor auditor in an acquisition year, an industry or M&A expert. This final

elimination yielded a sample of 9,666 firm-year observations representing 2,572 unique firms in

757 distinct audit offices between the years 2004-2014.7

7 Our primary test variable for M&A expertise identifies whether an office has audited at least 30 clients in the current or prior two years with at least one completed acquisition. Because many audit offices fit this definition of M&A expert, the sample is greatly reduced by the requirement of being audited by a non-expert in the prior year. However, our secondary measure of M&A expertise, a dominant proportion (i.e. at least 30%) of M&A audit clients in a city over the current or prior two years, is more restrictive. Thus, when this alternative measure is used we only eliminate 13,237 (rather than 18,692) observations for a final sample of 15,121 firm-year observations (8,697 and 7,062 in complex and non-complex industries, respectively).

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The misstatement analyses utilize the same databases described above but the sample stops

in 2011 to allow sufficient time for the revelation of misstatements (Francis and Michas 2013;

Paterson and Valencia 2011). The initial sample contains 47,256 firm-year observations and after

removing financial firms drops to 37,104 firm-year observations. From this baseline sample we

concentrate on misstatements that Audit Analytics identifies as accounting rule application

failures, financial fraud, errors, irregularities, and misrepresentations and exclude restatements

reported as out-of-period-adjustments, technical restatements, and adjustments to retained

earnings to adopt SAB108 and FIN48. Our initial M&A related misstatement sample contains 298

firm-year observations. We eliminate 109 observations due to missing control variables used in

the misstatement model for a final M&A related-misstatement sample of 189 observations.

We identify firms exhibiting no misstatements over the sample period (i.e., 2004-2012) as

our control sample of 26,502 non-misstatement firm-year observations. We eliminate 11,570

observations with missing variables for a final non-misstatement sample of 14,932 firm-year

observations. Combining our M&A related misstatement (189 observations) and non-misstatement

(14,932 observations) sample, our final sample consists of 15,121 firm-year observations

representing 3,868 unique firms in 761 distinct audit offices between the years 2004-2011.

Research Design

We test H1 by analyzing whether firms switch to an M&A expert auditor during an

acquisition year. We use the following logistic regression model with standard errors clustered at

the firm level:

AUDITOR_CHG = β0 + β1 MA_EXP + β2 IND_EXP + β3 ACQ + β4 MA_EXP*ACQ + β5

IND_EXP*ACQ + β6 SIZE + β7 INV_REC + β8 LEV + β9 |DACC| + β10 CFFO + β11 ROA + β12

TENURE + β13 LOSS + β14 GCO + β15 #OFF_CLIENTS + Industry + Year + ε (1)

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We develop Model (1) based on variables from the existing auditor switching literature (e.g.

Landsman, Nelson and Rountree 2009; Skinner and Srinivasan 2012; Hennes, Leone and Miller

2014; Brown and Knechel 2016). The dependent variable, AUDITOR_CHG, is an indicator equal

to 1 if the firm switches to another audit firm in the current year, 0 if the firm does not switch

auditors.

Our M&A expertise variable, MA_EXP_GR30, is equal to 1 if the audit office has audited

at least 30 public clients in the current or prior two year with at least one completed acquisition, 0

otherwise. Our secondary M&A expertise measure, MA_EXP_PCT, is equal to 1 if the office

audits at least 30% of all possible public clients engaged in an acquisition in a city in the current

or prior two year, 0 otherwise. In addition to M&A expertise, we test the influence of industry

expertise on auditor changes. Similar to Minutti-Meza 2013 and Francis and Gunn 2015, our

industry expertise variable, IND_EXP, is equal to 1 if the auditor is the city-level market leader,

measured by audit fees in the client’s industry (using FF48 industry codes) throughout the city.

Because our auditor change sample only consists of firms audited by non-industry and non-M&A

experts in the prior year, we predict that the coefficients on MA_EXP and IND_EXP will be

positive and significant.

We are primarily interested in examining the likelihood of switching to an M&A expert in

an acquisition year. Thus, we include, ACQ, a dummy variable equal to 1 if the company acquired

50% or more of the target firm in the current year, 0 otherwise. Prior research suggests that M&A

activity can lead to an increased likelihood of changing auditors (Landsman, Nelson and Rountree

2009). Thus, we predict a positive association between ACQ and AUDITOR_CHG. We test H1

by interacting ACQ and MA_EXP and expect a positive association with AUDITOR_CHG that

would suggest that firms without an expert are more likely to switch to an M&A expert auditor

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during an acquisition year. We also interact ACQ and IND_EXP with no directional prediction.8

Essentially, by comparing the coefficients of MA_EXP*ACQ and IND_EXP*ACQ, we test whether

clients audited by non-expert auditors are more likely to switch to an M&A expert and/or an

industry expert (or neither) during an acquisition year.

The model includes control variables identified in prior auditor change studies. We include,

SIZE, measured as the natural log of total assets and expect larger firms to change auditors less

frequently, because the costs of switching auditors are higher for larger clients (DeAngelo 1981).

We include INVREC, |DACC|, TENURE, and GCO as proxies for audit risk9 (Landsman et al.

2009). INVREC is the level of inventories and receivables divided by total assets. |DACC| is the

absolute value of discretionary accruals measured as a variant of the modified Jones model as

introduced by Kothari et al. (2005). TENURE is the number of years the prior audit firm

continuously audited the client. GCO is equal to 1 if the client received a going concern opinion

in either of the prior two years, 0 otherwise. Consistent with prior studies, we predict a positive

relationship between INVREC, |DACC|, GCO and AUDITOR_CHG. Because prior studies

suggest a non-monotonic relationship between auditor tenure and audit quality (e.g. Boone,

Khurana and Raman 2008), we do not make a prediction for TENURE.

Following Johnstone and Bedard (2004) and Landsman et al (2009), we include ROA,

CFFO, LOSS, and LEV as proxies for financial risk.10 ROA is calculated as pretax book income

divided by prior year total assets, LEV is calculated as total debt divided by total equity, CFFO is

cash flow from operations, and LOSS is equal to 1 if the company had negative net income, and 0

8 Lee, Mande and Park (2015) provide evidence that stock market returns surrounding M&A announcements are higher for acquiring firms audited by industry experts. Thus, while we do not make any prediction, it is possible that acquisitive firms may have an incentive to switch to an industry expert during an acquisition year. 9 Audit risk is defined as the risk that the auditor will incorrectly provide an unqualified opinion on financial statements that are materially misstated 10 Financial risk is defined as the risk that the client’s economic condition will decline.

18

otherwise. Consistent with prior studies, we expect highly levered and loss firms to be positively

associated with the likelihood of AUDITOR_CHG. Conversely, we expect firms with high ROA

and high levels of operating cash flows to be negatively associated with AUDITOR_CHG.

Because office size may influence the relationship between our measure of M&A expertise

and auditor changes, we include #OFF_CLIENTS, a variable capturing the number of audit clients

in an office.11 Finally, we include year and industry fixed effects. All continuous control variables

in the model are winsorized at the 1st and 99th percentiles.

We test H2 by analyzing whether the clients of M&A expert auditors are less likely to

experience M&A-related misstatements during an acquisition year. We use the following logistic

regression model with standard errors clustered at the firm level:

MA_MISSTATE = β0 + β1 MA_EXP + β2 IND_EXP + β3 ACQ + β4 MA_EXP*ACQ +

β5 IND_EXP*ACQ + β6 SIZE + β7 BM + β8 NEW_FIN + β9 LOSS + β10 BIG4 +

β11 AUDITOR_CHG + β12 ROA + β13 LEV + β14 FOR + β15 #OFF_CLIENTS + Industry + Year

+ ε (2)

Consistent with prior studies that have considered the impact of auditor industry expertise on audit

quality, we measure audit quality with reported financial accounting misstatements (e.g. Chin and

Chi 2009; Romanus et al. 2008). A material restatement of originally audited financial statements

is strongly suggestive that the audit of the original misstated financial statements was of low

quality (DeFond and Zhang 2014; Francis and Michas 2013; Francis et al. 2013). Further, we are

able to identify material misstatements that were directly the result of M&A related accounting

failures.12 Accordingly, the dependent variable in model (2), MA_MISSTATE, is an indicator

11 Results are robust to the inclusion of an alternative office size variable such as the log of audit fees in the office. 12 Audit Analytics includes M&A related misstatements in a broader category of misstatements that also include disposal and re-organization accounting issues. While we are only interested in acquisition and mergers related restatements, our research design of examining the influence of M&A experts in an acquisition year increases the likelihood that we identify M&A related restatements rather than disposal and re-organization related restatements. However, we recognize that disposals and reorganizational activities may occur in the same year as an acquisition and acknowledge this limitation in the data.

19

variable equal to 1 if the company’s financial reports contained a significant MA-related

misstatement that subsequently led to a restatement, and 0 otherwise.

The primary variables of interest are identical in models (1) and (2). In model (2), we make

no prediction about the influence of MA_EXP on MA_MISSTATE, because it is unclear whether

M&A experts influence the likelihood of M&A related misstatements during non-acquisition

years. Consistent with H2, we predict that MA_EXP*ACQ will be negatively associated with

MA_MISSTATE. This suggests that during misstatement years, M&A experts provide improved

audit quality, particularly in regards to the audit work surrounding M&A accounting.

We predict a positive association between ACQ and MA_MISSTATE, because prior

literature provides evidence that misstatements are more likely during acquisition years (e.g.

Kinney et al. 2004). We continue to explore the competing influence of M&A and industry

expertise on audit quality in model (2) by including IND_EXP and IND_EXP*ACQ. The industry

expertise literature generally suggests that industry expertise improves audit quality. Thus, we

predict a negative relationship between IND_EXP and MA_MISSTATE. However, it is unclear

whether industry experts provide higher quality during acquisition years. Thus, we do not make a

prediction regarding the relation between IND_EXP*ACQ and MA_MISSTATE.

Because restating companies are more likely to be smaller and low growth, we include

SIZE and the book to market ratio (BM) (Kinney and McDaniel 1989 and DeFond and Jiambalvo

1991). We control for Debt and/or equity issuances (NEW_FIN) because firms raising external

capital have more incentives to manipulate their financial statements (Richardson, Tuna, and Wu

2002). We control for LOSS because of the financial reporting incentives associated with avoiding

losses. We include a Big 4 indicator variable (BIG4) because larger auditors were shown to

improve audit quality (DeAngelo 1981). We include an indicator variable that capture auditor

20

switches in the current or prior year (INIT) because audit failures are more likely in the earlier

years of the auditor/client relationship (Geiger and Raghunandan 2002). Because of the conflicting

evidence linking earnings quality to debt covenants (Dechow, Ge, Larson, and Sloan 2011;

DeFond and Jiambalvo 1994; Healy and Palepu 1990) and profitability (Kinney and McDaniel

1989; Summers and Sweeney 1998), we control for leverage (LEV) and return on assets (ROA) but

do not provide a directional prediction. We include the presence of foreign operations (FOR)

because prior literature suggests that complexity is negatively associated with accruals quality

(Ashbaugh-Skaife, Collins, Kinney, and LaFond 2008) which could impact restatements. Because

office size may influence the relationship between our measure of M&A expertise and the

likelihood of a misstatement, we include #OFF_CLIENTS. Finally, we include year and industry

fixed effects. All continuous control variables in the model are winsorized at the 1st and 99th

percentiles.13

IV. RESULTS

Descriptive Statistics

Table 1 – Panel A presents summary statistics for the variables examined in the auditor

change model (1). Approximately 11 percent of the sample experienced an auditor switch

(AUDITOR_CHG) over the sample period with changes peaking in 2009 and 2010 (13% and 16%,

respectively). After imposing a sample restriction eliminating firms that have an M&A

(MA_EXP_GR30) or industry (IND_EXP) expert auditor at time t-1, 2.1 percent of firms switch to

an M&A Expert14 and 3.8 percent switched to an industry expert at time t. Around 17 percent of

the firms acquired at least 50% of another company during the year (ACQ). While most firm and

13 Appendix A defines the dependent and independent variables in models (1) and (2). 14 3.7% of firms switch to an M&A expert using the alternate market based expertise measure (MA_EXP_PCT).

21

auditor level descriptive statistics are similar to prior auditor change and industry specialization

studies (e.g. Gul, Fung and Jaggi 2009; Hennes et al. 2014; Landsman et al. 2009), the sample

restriction of no auditor expertise in the prior year skews the sample towards smaller (mean

SIZE=4.381) and less profitable (mean ROA=-0.298) firms. The average audit office in the sample

consists of 12.772 audit clients (#OFF_CLIENTS).

INSERT TABLE 1 ABOUT HERE

Table 1 – Panel B presents summary statistics for the variables examined in the

misstatement model (2). MA_MISSTATE, comprise 1.2 percent of the sample or 189 firm-years.15

42.1 percent of the sample was audited by an M&A expert (MA_EXP_GR30)16 and 25.9 percent

was audited by an industry expert (IND_EXP). 24.5 percent of firms acquired at least 50% of

another company during the year. Other firm and auditor level descriptive statistics are similar to

prior restatements studies (e.g. Paterson et al. 2011). Finally, the average audit office in the sample

consists of 32.055 audit clients (#OFF_CLIENTS).

Multivariate Results – H1

In Table 2, we examine whether firms are likely to switch to an M&A expert auditor during

an acquisition year. Here and in subsequent analyses, columns 2 and 3 examine whether the

hypothesized associations differ between firms in complex and non-complex industries. Columns

4-6 mirror columns 1-3 after replacing our main M&A expertise variable, MA_EXP_GR30, with

the alternate M&A expertise variable, MA_EXP_PCT.

15 In the alternate sample comparing M&A to non-M&A misstatement years, M&A misstatements, MA_MISSTATE_ALT, make up 10.5% of total misstatements. 16 19.1% of firms were audited by an M&A expert using the alternate market based measure (MA_EXP_PCT).

22

In Table 2 – Columns 1 and 4, we find a positive and significant (p < 0.01) coefficient on

MA_EXP and IND_EXP. Because our sample is comprised of firms without an auditor expert in

the prior year, any auditor switches to an expert in the current year will induce a positive coefficient

on these variables. Thus, these results are a product of our sample construction. Consistent with

auditor change studies (Skinner and Srinivasan 2012; Hennes et al. 2014), we find that acquisition

years (ACQ) are not associated with auditor changes. In Table 2 – Columns 1 and 4, we find a

positive and significant coefficient (p < 0.05 in column 1 and p<0.10 in column 4) on MA_EXP *

ACQ. Consistent with H1, these findings suggest that clients are incrementally more likely to

switch to an M&A expert in the year in which they engage in an acquisition. The insignificant

coefficient on IND_EXP*ACQ in columns 1 and 4 suggest that firms are not more likely to switch

to an industry expert in an acquisition year. With the exception of LEV, the coefficients on the

remaining control variables are in the expected direction.


In columns 2 and 3 of Table 2, we observe that the coefficient on MA_EXP_GR30 * ACQ

is positive and significant (p<0.05) among firms in complex industries (Column 2), but not among

firms in non-complex industries (Column 3). Similar results are obtained in columns 5 and 6.

These results support our prediction that firms subject to more accounting complexity are more

likely to switch to an M&A expert auditor during an acquisition year.

Next, we repeat our analysis in Table 2, using a propensity score matched sample design

to address the potential endogeneity concern that there are omitted confounding variables

correlated with the decision to switch auditors and the decision to engage an M&A expert as the

successor auditor. The propensity score matched approach seeks to identify a subsample of firms

with a set of similar characteristics that affect the auditor switch decision but differ by MA_EXP.

23

The first stage of this approach estimates a conditional logistic regression of MA_EXP on all

control variables in auditor change model (1), and then use the resulting coefficient estimates to

determine a probability (i.e., the propensity score) of the firm engaging an M&A expert. Based on

these propensity scores, we match each observation with a value of 1 for MA_EXP to a unique

observation with a value of 0 for MA_EXP without replacement using a caliper width of 0.01.

When MA_EXP_GR30 (MA_EXP_PCT) is employed in column 1 (column 4), this procedure

results in a subsample of 1,042 (3,252) firm-year observations.17


The results from the second stage of this procedure involves repeating our analysis in Table

2 based on the propensity score matched sample. The results from this analysis, reported in Table

3 continue to reveal a positive and significant coefficient (p < 0.10 in column 1 and p<0.01 in

column 4) on MA_EXP * ACQ, confirming our earlier findings and conclusions for H1. We

perform the same propensity score match procedure within the subsample of firms in complex

industries (columns 2 and 5) and the subsample of firms in non-complex industries (columns 3 and

6). Consistent with the results in table 2, we observe that the coefficient on MA_EXP*ACQ is

positive and significant (p<0.05 in Column 2 and p<0.10 in Column 5), but insignificant in

Columns 3 and 6.

In Table 4, we assess whether M&A auditor experts are more likely than other auditors to

be selected as the successor auditor within the sample of firms switching auditors. After limiting

the sample to auditor switch firm-years, we regress the auditor expertise variables

17 Untabulated results from the covariate balance checks reveal that all control variables are insignificantly related to MA_EXP when we repeat our first stage logistic regression based on the propensity score matched sample. Additionally, we find that the mean values of all control variables are not statistically different across the subsamples of firms with a value of 1 and 0 for MA_EXP. These findings indicate that the propensity score matching procedure has been successful.

24

(MA_EXP_GR30 in columns 1-3, MA_EXP_PCT in columns 4-6, and IND_EXP in columns 7-9)

on ACQ, and the other control variables in auditor change model (1). We predict and find a positive

coefficient on ACQ among firms in complex industries (p<0.10) in Columns 2 and 5. This suggests

that, compared to other firms switching auditors, firms are more likely to switch to an M&A expert

during an acquisition year. The insignificant coefficients on ACQ in columns 7-9 suggest that

auditor switchers are not more likely to switch to an industry specialist during an acquisition year.


Multivariate Results – H2

In Table 5, we examine whether firms are less likely to experience an M&A related

misstatement when an M&A expert auditor is engaged during an acquisition year. In all columns,

we report an insignificant coefficient on MA_EXP. These results suggest that in non-acquisition

years M&A experts do not influence the likelihood of an M&A related misstatement. Although

we expect M&A related misstatements to occur during acquisition years, we find that the

coefficient on ACQ is only positive and significant in Table 5 - column 2. This suggests that

acquisition-related misstatements are only prevalent among firms in complex industries.18

In Table 5 – Columns 1, we find a negative and significant coefficient (p < 0.05) on

MA_EXP_GR30 * ACQ. Consistent with H2, this finding suggests that clients are less likely to

experience an M&A related misstatements during the acquisition year if the auditor was an M&A

expert auditor. However, these results are insignificant when applying our alternate M&A

expertise variable in column 4 and thus do not support H2. The insignificant coefficients on

IND_EXP and IND_EXP*ACQ in columns 1 and 4 suggest that industry specialists do not

18 Because Audit Analytics includes two other categories of misstatements with M&A related misstatements, we posit that the other misstatement firm-year observations are likely related to disposals and restructuring activities.

25

influence the likelihood of M&A related misstatements in both acquisition and non-acquisition

firm-years. The coefficients on the remaining control variables are in the expected direction.


In columns 2 and 3 of Table 5, we observe that the coefficient on MA_EXP_GR30 * ACQ

is negative and significant (p<0.01) among firms in complex industries (Column 2), but not among

firms in non-complex industries (Column 3). While the statistical significance is marginal

(p<0.10), similar results are obtained in columns 5 and 6. These results support our assertion that

M&A experts are more likely to provide higher audit quality and improve the financial reporting

surrounding an acquisition in the presence of higher accounting complexity.

We repeat our analysis in Table 5, using a propensity score matched procedure to address

the potential endogeneity concern that omitted confounding variables are correlated with the

likelihood of an M&A related misstatement and engagement of an M&A expert. Similar to the

auditor change analysis, we first regress MA_EXP on all control variables in misstatement model

(2), and use the resulting coefficient estimates to determine a propensity of the firm engaging an

M&A expert. Based on these propensity scores, we match each observation with a value of 1 for

MA_EXP to a unique observation with a value of 0 for MA_EXP without replacement using a

caliper width of 0.01. When MA_EXP_GR30 (MA_EXP_PCT) is employed in column 1 (column

4), this procedure results in a subsample of 9,542 (5,952) firm-year observations.19


19 Untabulated covariate balance checks and mean comparisons between M&A and non-M&A expert observations indicate that the propensity score matching procedure was successful.

26

The results using the matched sample are reported in Table 6 and reveal a negative and

significant coefficient (p < 0.01 in column 2 and p<0.10 in column 5) on MA_EXP * ACQ. These

findings support H2, but only among firms in industries with complex accounting.

In table 7, we replicate results from model (2), but with an alternate sample and

construction of the M&A-related misstatement variable. Rather than comparing M&A related

misstatements to non-misstatement firm years, we compare the likelihood to have M&A related

misstatements to the likelihood of having a non-M&A related misstatement. Thus,

MA_MISSTATE_ALT equals 1 for M&A related misstatement firm years and 0 for other non-M&A

related misstatement firm years. The full sample consists of 1,795 (1,786) misstatement firm-year

observations in column 1 (column 4). This analysis examines whether M&A experts improve

audit quality in general (suggesting no difference in the likelihood of M&A and non-M&A related

restatements) or incrementally improve audit quality surrounding acquisition accounting

(suggesting a more prominent influence on M&A related restatements than on non-M&A related

restatements).


In Table 7 – Columns 1, we find a negative and significant coefficient (p < 0.05) on

MA_EXP_GR30 * ACQ. These results are consistent with H2. Further, in columns 2 and 5 of

Table 7, we observe that the coefficient on MA_EXP*ACQ is negative and significant (p<0.01 in

column 2 and p<0.10 in column 5) among firms in industries with complex accounting. These

same results are not obtained in the sample of firms in non-complex accounting industries

(columns 3 and 6). These results further support H2, but only in the presence of higher accounting

complexity.

27

Additional Analysis – The Influence of M&A Experts on Audit Fees

Next, we explore the association between M&A expertise and audit pricing. Past research

on the association between auditor expertise and audit pricing finds mixed results. On the one

hand, expert auditors can command greater fee premiums because of the demand for their service

and associated reputation. Consistently, researchers find that industry specialization is associated

with higher audit fees (e.g. Craswell, Francis, and Taylor 1995; Mayhew and Wilkins 2003; Carson

2009; Cahan, Jeter, and Naiker 2011). However, specialist M&A auditors can benefit from

increased cost efficiencies emanating from improved processes, focused training, and greater topic

specific knowledge that can be shared across engagements. Auditors can then pass these savings

to their clients in the form of reduced audit fees. While research does not often find fee discounts

from expert auditors, several studies show that under certain circumstances, expert auditors can

provide cost efficiencies to clients (Ettredge, Xu and Yi 2014; Bills et al. 2014).

We examine the influence of M&A expertise on the log of audit fees (LOG_AUDFEE) in

a well-specified audit fee model. We include variables identified in Choi et al. (2010), DeFond et

al. (2002), Francis and Wang (2005), Ghosh and Pawlewicz (2009), and Whisenant et al. (2003)

to develop the model. These variables are defined in Appendix A. Similar to the auditor change

and the misstatement models, we also include MA_EXP, IND_EXP, ACQ, MA_EXP * ACQ, and

IND_EXP * ACQ. Because audit fees are often set lower in the first two years of an auditor’s

tenure, we eliminate firms with an auditor change at time t and t+1.


Our results in Table 8 - column 1 provide evidence that auditor M&A expertise,

MA_EXP_GR30, as well as auditor industry expertise, IND_EXP, are associated with higher audit

28

fees (p<0.01). These results are not supported using the alternate construction of M&A expertise,

MA_EXP_PCT, in column 2. Consistent with prior research, we also find that M&A activity,

ACQ, is associated with higher audit fees for all firms (p<0.01). We find a negative and significant

coefficient (p < 0.01 in column 1 and p<0.05 in column 2) on MA_EXP * ACQ. This suggests that

during acquisition years the increase in audit fees is lower among M&A expert auditors relative to

the increase by firms audited by non-M&A experts. These results suggest that M&A experts may

be better positioned to pass along savings to their audit clients during acquisition years. Because

the coefficient on IND_EXP*ACQ is insignificant, we do not find evidence that auditor industry

experts charge audit clients any more or less during an acquisition year. The coefficients on the

remaining control variables are mostly significant and in the expected direction.

V. SUMMARY AND CONCLUSIONS

The extant auditor specialization literature is predominantly focused on auditor industry

expertise. Yet, through experience and resource allocation auditors can also become experts in

complex accounting issues that transcends industries. In this paper, we focus on expertise in

auditing M&As, an inherently complex accounting topic (FASB 2015). Recently, the PCAOB

raised concerns with respect to audit deficiencies surrounding M&As. These concerns suggest that

auditors may not have sufficient expertise in auditing M&A transactions. We composed two

measures of auditor M&A expertise. The first measure is based on the number of clients with

acquisitions in each auditor-office and the second captures whether offices audit a significant

percent of the M&A transactions of local clients. Using these measures we test two hypotheses.

We first examine whether companies are more likely to switch to an auditor with M&A expertise

during an acquisition year. Second, we examine whether M&A expert auditors are associated with

29

better financial reporting quality, captured by the likelihood of an M&A related misstatement in

the acquisition year.

We find support for both hypotheses. Specifically, acquiring firms are more likely to hire

an M&A expert auditor and these expert are more likely to curtail M&A related misstatements

during the acquisition year. Our results do not apply to firm operating in less complex industries.

Rather, they are driven by firms that operate in industries with complex accounting. Notably, we

do not observe any results with respect to auditor industry expertise during the acquisition year

which suggests that these two types of auditor expertise are distinct. We also find that M&A

experts charge higher audit fees in general, but appear to pass along savings to their clients during

acquisition years. These savings are likely due to the increased efficiency M&A experts

experience when they audit clients during acquisitions years.

Our paper is among the first to focus on auditor expertise in specific accounting

transactions. We focus on auditor M&A expertise because it is timely and answers recent concerns

raised by the FASB and PCAOB. Future studies can follow the method proposed in the paper to

capture other transaction specific expertise. Our findings also have practical implications for

acquisitive companies operating in complex industries. Specifically, these companies should

consider hiring an M&A expert auditor to help them contend with the complexity of M&A

transactions.

30

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Appendix A Variable Definitions

Variable Name Variable Definition [source] Dependent Variables AUDITOR_CHG Indicator variable equal to 1 if the firm switches to another audit firm in the

current year, 0 if the firm does not switch auditors [Audit Analytics]. MA_MISSTATE Indicator variable equal to 1 if the firm experienced an M&A related

misstatement during the year, 0 if the firm experienced no reported misstatements between 2004 and 2012 [Audit Analytics].

MA_MISSTATE_ALT Indicator variable equal to 1 if the firm experienced an M&A related misstatement during the year, 0 if the firm experienced a non-M&A related misstatement during the year [Audit Analytics].

LOG_AUDFEE Log (audit fees) [Audit Analytics].

Independent Variables of Interest MA_EXP_GR30 Indicator variable equal to 1 if the audit office has audited at least 30 public

clients in the current or prior two year with at least one completed acquisition, 0 otherwise [Audit Analytics and SDC].

MA_EXP_PCT Indicator variable equal to 1 if the office audits at least 30% of all possible public clients engaged in an acquisition in a city in the current or prior two year, 0 otherwise [Audit Analytics and SDC].

ACQ Indicator variable equal to 1 if the company acquired 50% or more of another firm in the current year, 0 otherwise [SDC].

Control Variables IND_EXP Indicator variable equal to 1 if the auditor is the city-level market leader,

measured by audit fees in the client’s industry (using FF48 industry codes) throughout the city [Audit Analytics].

SIZE Log (total assets) [COMPUSTAT].

INV_REC Inventories and receivables divided by total assets [COMPUSTAT].

LEV Leverage calculated as long term debt plus debt in current liabilities divided by log of prior year total assets [COMPUSTAT].

|DACC| The absolute value of discretionary accruals measured as a variant of the modified Jones model as introduced by Kothari et al. (2005) [COMPUSTAT].

CFFO Cash Flow from Operations divided by lagged total assets [COMPUSTAT].

ROA Return on assets calculated as pretax book income [PI] divided by prior year total assets [COMPUSTAT].

TENURE The number of years the prior year audit firm continuously audited the client [Audit Analytics].

LOSS Indicator variable equal to 1 if the company had net income less than $0, and 0 otherwise [COMPUSTAT].

GCO Indicator variable equal to 1 if the client received a going concern opinion in either of the prior two years, 0 otherwise.

#OFF_CLIENTS The number of audit clients in an office [Audit Analytics].

BM Book to market ratio calculated as the book value of stockholders equity divided by the market value of stockholders equity [COMPUSTAT].

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Variable Name Variable Definition [source] Control Variables (cont) NEW_FIN Indicator variable equal to 1 if the company issued more than $10 million of

debt and equity during the fiscal year, and 0 otherwise [COMPUSTAT]. BIG4 Indicator variable equal to 1 if auditor is Ernst & Young, Deloitte & Touche,

PricewaterhouseCoopers or KPMG, 0 otherwise. [Audit Analytics] INIT Indicator variable equal to 1 if the company switched auditors in the current

or prior year, 0 otherwise. [Audit Analytics] FOR Indicator variable equal to 1 if firm reports foreign income, 0 otherwise

[COMPUSTAT]. CRATIO Current Assets divided by Current Liabilities [COMPUSTAT].

ZFC ZFC = Zmijewski’s (1984) financial condition index [Compustat].

SEGMENTS The number of reported business and geographic segments [Compustat Segment file]

EMPLOYEES Square root of the number of employees [COMPUSTAT].

REPORT_LAG The number of days between the current fiscal year end and the annual earnings announcement date [COMPUSTAT].

ICW Indicator variable equal to 1 if the company has reported a section 404 internal control material weakness in the either of the prior two years [Audit Analytics].

RESTATE Indicator variable equal to 1 if the company has restated its financial reports in the either of the prior two years [Audit Analytics].

CLIENT_IMPORT Total fees from audit clients at the engagement-level divided by total fees from all audit clients in the audit office [Audit Analytics].

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Table 1 – Panel A - Descriptive Statistics – Auditor Change Model Mean 25th Pct Median 75th Pct Std Dev AUDITOR_CHG 0.110 0.000 0.000 0.000 0.313 MA_EXP_GR30 0.021 0.000 0.000 0.000 0.145 MA_EXP_PCTǂ 0.037 0.000 0.000 0.000 0.189 IND_EXP 0.038 0.000 0.000 0.000 0.192 ACQ 0.169 0.000 0.000 0.000 0.375 SIZE 4.381 2.816 4.160 5.812 2.230 INV_REC 0.260 0.076 0.210 0.401 0.216 LEV 0.722 0.259 0.463 0.703 1.630 |DACC| 0.125 0.029 0.070 0.151 0.152 CFFO -0.102 -0.087 0.047 0.113 0.586 ROA -0.298 -0.212 0.007 0.082 1.381 TENURE 7.093 3.000 5.000 9.000 6.224 LOSS 0.477 0.000 0.000 1.000 0.499 GCO 0.160 0.000 0.000 0.000 0.367 #OFF_CLIENTS 12.772 6.000 11.000 17.000 10.233 Observations 9,666

Table 1 – Panel B - Descriptive Statistics – Misstatement Model Mean 25th Pct Median 75th Pct Std Dev MA_MISSTATE 0.012 0.000 0.000 0.000 0.110 MA_MISSTATE_ALT 0.105 0.000 0.000 0.000 0.308 MA_EXP_GR30 0.421 0.000 0.000 1.000 0.494 MA_EXP_PCT 0.191 0.000 0.000 0.000 0.393 IND_EXP 0.259 0.000 0.000 1.000 0.438 ACQ 0.245 0.000 0.000 0.000 0.430 SIZE 5.086 3.407 5.112 6.881 2.651 BM 0.285 0.172 0.383 0.672 1.720 NEW_FIN 0.300 0.000 0.000 1.000 0.458 LOSS 0.439 0.000 0.000 1.000 0.496 BIG4 0.601 0.000 1.000 1.000 0.490 INIT 0.174 0.000 0.000 0.000 0.379 ROA -0.424 -0.181 0.027 0.098 2.153 LEV 0.893 0.268 0.465 0.688 2.642 FOR 0.376 0.000 0.000 1.000 0.484 #OFF_CLIENTS 32.055 10.000 22.000 46.000 28.400 Observations 15,121

ǂ The descriptive statistics for MA_EXP_PCT is based on the alternate sample construction yielding 15,759 firm-year observations (see columns 4-6 is Table 2) All variables are defined in Appendix A.

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Table 2 - M&A Expertise and Auditor Change during Acquisition year DV = AUDITOR_CHG Hypothesized MA_EXP_GR30 MA_EXP_PCT Directional (1) (2) (3) (4) (5) (6) Expectation Full Complex Non-Complex Full Complex Non-Complex MA_EXP + 2.027*** 1.889*** 2.134*** 0.505*** 0.404 0.616** (9.66) (6.22) (7.46) (2.80) (1.60) (2.39) IND_EXP + 0.711*** 0.557* 0.884*** 0.688*** 0.703*** 0.683*** (3.73) (1.94) (3.43) (4.72) (3.37) (3.34) ACQ ? 0.048 0.083 -0.050 0.022 0.075 -0.091 (0.46) (0.65) (-0.26) (0.27) (0.73) (-0.61) MA_EXP * ACQ + 0.754** 1.233** 0.237 0.570* 0.827** 0.051 (2.09) (2.51) (0.44) (1.81) (2.12) (0.09) IND_EXP * ACQ ? -0.005 -0.441 0.396 0.250 0.135 0.484 (-0.01) (-0.72) (0.73) (0.93) (0.37) (1.20) SIZE - -0.220*** -0.208*** -0.226*** -0.225*** -0.217*** -0.230*** (-9.84) (-7.65) (-5.73) (-12.08) (-9.59) (-7.12) INV_REC + 0.362** 0.369 0.327 0.324** 0.316 0.300 (2.10) (1.55) (1.28) (2.13) (1.47) (1.38) LEV + -0.049* -0.050 -0.074 -0.056* -0.064 -0.075 (-1.65) (-1.24) (-1.42) (-1.75) (-1.58) (-1.52) |DACC| + 0.582*** 0.748** 0.365 0.528*** 0.697*** 0.302 (2.69) (2.47) (1.21) (2.74) (2.61) (1.10) CFFO + 0.219** 0.050 0.454*** 0.294*** 0.079 0.563*** (2.19) (0.42) (2.83) (2.95) (0.72) (3.83) ROA - -0.064 -0.021 -0.175** -0.084 -0.032 -0.196** (-1.34) (-0.51) (-2.09) (-1.55) (-0.76) (-2.57) TENURE ? -0.016** -0.013 -0.021** -0.012** -0.005 -0.021*** (-2.39) (-1.34) (-2.22) (-2.41) (-0.75) (-2.70) LOSS + 0.080 0.172 -0.057 0.176** 0.258*** 0.057 (0.97) (1.49) (-0.48) (2.56) (2.75) (0.56) GCO + 0.251** 0.254* 0.245* 0.193** 0.165 0.227* (2.49) (1.81) (1.67) (2.17) (1.31) (1.78) #OFF_CLIENTS ? 0.006* 0.002 0.013** -0.012*** -0.011*** -0.013*** (1.83) (0.53) (2.37) (-7.48) (-5.25) (-5.26) Constant -11.497*** -10.912*** -1.226 0.140 -0.558 -1.794 (-9.55) (-8.38) (-0.88) (0.15) (-0.50) (-1.43) Observations 9,666 5,387 4,279 15,759 8,697 7,062 Pseudo R2 0.081 0.082 0.089 0.078 0.081 0.080

Table 2 presents logistic regression results based on model (1). The sample is limited to firm-year observations where the prior year auditor was neither an M&A expert nor an industry expert. The definition of M&A expert is MA_EXP_GR30 in Columns 1-3, and MA_EXP_PCT in Columns 4-6. Columns 1 and 4 report the regression results in the full sample, Columns 2 and 5 report the results in the sample of firms in industries with supplementary FASB and/or Audit Guidance (i.e. high industry accounting complexity), and Columns 3 and 6 report the results in the sample of firms in industries without supplementary FASB or Audit Guidance (i.e. low industry accounting complexity). Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.

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Table 3 - M&A Expertise and Auditor Change during Acquisition year PSM Analysis matched by MA_EXP

DV = AUDITOR_CHG Hypothesized MA_EXP_GR30 MA_EXP_PCT Directional (1) (2) (3) (4) (5) (6) Expectation Full Complex Non-

Complex Full Complex Non-

Complex MA_EXP + -0.513 -0.348 -0.951 -0.538** 0.016 -0.526 (-1.51) (-0.88) (-1.55) (-2.24) (0.06) (-1.05) IND_EXP + 1.663*** 1.281*** 1.457*** 0.518*** 0.576** 0.685** (5.25) (2.88) (3.22) (2.65) (2.07) (2.35) ACQ ? 0.932** 0.846 1.385** 0.905*** 1.271*** 0.886*** (2.29) (1.63) (2.35) (4.49) (4.42) (3.12) MA_EXP * ACQ + 1.008* 1.778** 0.648 0.931*** 0.751* 0.409 (1.61) (2.38) (0.52) (2.61) (1.72) (0.62) IND_EXP * ACQ ? 0.217 -0.432 -0.236 0.395 -0.003 0.471 (0.28) (-0.45) (-0.45) (1.05) (-0.01) (0.73) Constant -2.019*** -1.982*** -1.893*** -3.546*** -3.864*** -2.178*** (-7.32) (-5.31) (-5.15) (-4.83) (-7.43) (-2.87) Observations 1,042 601 562 3,252 1,831 1,357 Pseudo R2 0.172 0.138 0.151 0.044 0.070 0.051

Table 3 presents the auditor change analysis using matched samples based on a multivariate propensity score, including all control variables in model (1) as determinants of auditor choice. Before matching observations, the sample is limited to firm-year observations where the prior year auditor was neither an M&A expert nor an industry expert. Firm-year observations where M&A expert=1 are matched to observations where M&A expert=0 with the closest propensity score based on a caliper of 0.01. The definition of M&A expert is MA_EXP_GR30 in Columns 1-3, and MA_EXP_PCT in Columns 4-6. Columns 1 and 4 report the regression results in the full sample, Columns 2 and 5 report the results in the sample of firms in industries with supplementary FASB and/or Audit Guidance (i.e. high industry accounting complexity), and Columns 3 and 6 report the results in the sample of firms in industries without supplementary FASB or Audit Guidance (i.e. low industry accounting complexity). Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.

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Table 4 - M&A Expertise and Auditor Change during Acquisition year Alternate Construction - If Auditor Change=1

Hypothesized DV = MA_EXP_GR30 DV = MA_EXP_PCT DV = IND_EXP Directional (1) (2) (3) (4) (5) (6) (7) (8) (9) Expectation Full Complex Non-



Complex ACQ + 0.035* 0.045* 0.015 0.014 0.031* -0.021 0.011 -0.002 0.035 (1.70) (1.84) (0.40) (0.78) (1.45) (-0.74) (0.48) (-0.08) (0.67) SIZE ? 0.023*** 0.023*** 0.025*** 0.023*** 0.021*** 0.027*** 0.029*** 0.025*** 0.037*** (4.06) (3.04) (2.68) (4.52) (3.17) (3.29) (4.78) (3.36) (3.25) INV_REC ? 0.006 0.038 -0.029 -0.028 -0.014 -0.044 -0.051 -0.029 -0.074 (0.18) (0.90) (-0.63) (-1.02) (-0.40) (-1.06) (-1.60) (-1.05) (-1.25) LEV + 0.003** 0.002 0.003 0.000 -0.000 0.001 0.001 -0.001 -0.001 (2.16) (1.59) (1.07) (0.08) (-0.35) (0.63) (0.29) (-0.27) (-0.43) |DACC| + -0.004 -0.020 0.036 -0.032 0.003 -0.083** 0.004 -0.041 0.069 (-0.15) (-0.57) (0.80) (-1.48) (0.10) (-2.25) (0.12) (-1.20) (0.98) CFFO + 0.008 0.003 0.006 -0.009 -0.010 -0.007 -0.001 -0.021* 0.014 (0.69) (0.22) (0.40) (-1.40) (-1.01) (-0.89) (-0.09) (-1.89) (1.04) ROA - -0.004 -0.006 0.001 -0.003 -0.004 -0.001 -0.001 -0.002 0.003 (-0.96) (-1.30) (0.07) (-1.19) (-1.17) (-0.42) (-0.31) (-0.53) (0.54) TENURE ? 0.003** 0.002* 0.003 0.003** 0.005*** 0.001 0.004** 0.004* 0.004 (2.23) (1.65) (1.49) (2.41) (2.78) (0.54) (2.22) (1.73) (1.40) LOSS + -0.032** -0.022 -0.047** -0.001 -0.005 0.005 0.011 -0.009 0.037 (-2.29) (-1.17) (-2.19) (-0.11) (-0.29) (0.23) (0.73) (-0.48) (1.40) GCO + 0.026* 0.041** 0.012 0.010 0.001 0.021 0.021 0.013 0.038 (1.96) (2.22) (0.57) (0.99) (0.07) (1.03) (1.35) (0.68) (1.32) #OFF_CLIENTS ? 0.011*** 0.011*** 0.011*** 0.002*** 0.002*** 0.002** 0.002*** 0.001 0.003*** (17.10) (13.72) (10.36) (3.93) (3.01) (2.48) (2.95) (1.45) (2.72) Constant -0.175*** -0.138*** 0.041 -0.177*** 0.068 -0.038 -0.177*** -0.209*** -0.280 (-4.05) (-2.83) (0.50) (-3.29) (0.31) (-0.72) (-3.69) (-3.02) (-0.85) Observations 1,166 638 528 1,527 850 677 1,158 637 521 Pseudo R2 0.462 0.485 0.425 0.125 0.155 0.092 0.100 0.073 0.121

Table 4 presents an alternative auditor change analysis. We first limit the sample to firms switching auditors in the current year where the prior year auditor was neither an M&A expert nor an industry expert. We then examine whether the new auditor was more likely to be an M&A expert (where M&A expert is MA_EXP_GR30 in Columns 1-3, and MA_EXP_PCT in Columns 4-6) or an industry expert (columns 7-9) during a year in which the firm engaged in an acquisition. The same control variables from model (1) are retained in this analaysis. Columns 1, 4 and 7 report the regression results in the full sample, Columns 2, 5 and 8 report the results in the sample of firms in industries with supplementary FASB and/or Audit Guidance (i.e. high industry accounting complexity), and Columns 3, 6 and 9 report the results in the sample of firms in industries without supplementary FASB or Audit Guidance (i.e. low industry accounting complexity). Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.

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Table 5 - M&A Expertise and M&A Related Misstatements during Acquisition year DV = MA_MISSTATE Hypothesized MA_EXP_GR30 MA_EXP_PCT Directional (1) (2) (3) (4) (5) (6) Expectation Full Complex Non-Complex Full Complex Non-Complex MA_EXP ? -0.055 0.227 -0.539 -0.099 -0.483 0.303 (-0.17) (0.53) (-1.17) (-0.30) (-1.11) (0.62) IND_EXP ? -0.125 0.025 -0.281 -0.114 0.077 -0.350 (-0.48) (0.07) (-0.78) (-0.43) (0.20) (-0.97) ACQ + 0.378 0.740** -0.445 0.115 0.205 -0.119 (1.39) (2.30) (-0.89) (0.47) (0.68) (-0.29) MA_EXP * ACQ - -0.851** -2.027*** 0.668 -0.467 -1.425* 0.015 (-2.35) (-3.76) (1.23) (-0.91) (-1.30) (0.02) IND_EXP * ACQ ? -0.077 -0.679 0.482 -0.150 -0.917 0.580 (-0.19) (-1.10) (0.85) (-0.35) (-1.44) (0.98) SIZE - 0.096 0.164* 0.024 0.096 0.163* 0.028 (1.35) (1.79) (0.21) (1.36) (1.78) (0.25) BM + 0.129* 0.270*** -0.027 0.130* 0.273*** -0.027 (1.70) (2.82) (-0.36) (1.73) (2.90) (-0.36) NEW_FIN + 0.404** 0.308 0.518** 0.415** 0.351 0.525** (2.45) (1.40) (2.13) (2.53) (1.58) (2.14) LOSS + 0.553*** 0.568** 0.512 0.585*** 0.625*** 0.508 (2.62) (2.35) (1.34) (2.75) (2.58) (1.32) BIG4 - -0.363 -0.730* 0.128 -0.340 -0.583 -0.095 (-1.10) (-1.68) (0.25) (-1.07) (-1.46) (-0.19) INIT ? -0.552** -0.357 -0.901* -0.541* -0.330 -0.902* (-1.96) (-1.01) (-1.81) (-1.92) (-0.94) (-1.82) ROA ? 0.006 0.070* -0.132 0.008 0.084** -0.129 (0.10) (1.75) (-1.26) (0.14) (2.02) (-1.23) LEV ? 0.027 0.104** -0.120 0.026 0.105** -0.114 (0.58) (2.10) (-1.43) (0.55) (2.10) (-1.39) FOR + 0.252 0.441 0.010 0.216 0.357 0.013 (1.05) (1.50) (0.03) (0.89) (1.16) (0.03) #OFF_CLIENTS ? 0.004 0.005 0.003 0.001 0.003 -0.001 (0.76) (0.83) (0.41) (0.22) (0.56) (-0.08) Constant -12.345*** -12.561*** -3.255*** -12.354*** -12.693*** -3.213*** (-14.55) (-11.89) (-3.49) (-14.20) (-11.91) (-3.54) Observations 15,121 8,232 6,889 15,071 8,194 6,877 Pseudo R2 0.081 0.094 0.107 0.080 0.087 0.105

Table 5 presents logistic regression results based on model (2) examining the influence of auditor M&A expertise on the likelihood of M&A related misstatements. The definition of M&A expert is MA_EXP_GR30 in Columns 1-3, and MA_EXP_PCT in Columns 4-6. Columns 1 and 4 report the regression results in the full sample, Columns 2 and 5 report the results in the sample of firms in industries with supplementary FASB and/or Audit Guidance (i.e. high industry accounting complexity), and Columns 3 and 6 report the results in the sample of firms in industries without supplementary FASB or Audit Guidance (i.e. low industry accounting complexity). Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.

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Table 6 - M&A Expertise and M&A Related Misstatements during Acquisition year PSM Analysis matched by MA_EXP

DV = MA_MISSTATE Hypothesized MA_EXP_GR30 MA_EXP_PCT Directional (1) (2) (3) (4) (5) (6) Expectation Full Complex Non-


Complex MA_EXP ? -0.236 0.170 -0.385 -0.096 -0.421 0.020 (-0.89) (0.45) (-0.89) (-0.26) (-0.84) (0.04) IND_EXP ? -0.448* -0.316 -0.309 -0.679* -0.082 -1.078** (-1.65) (-0.82) (-0.92) (-1.93) (-0.15) (-2.34) ACQ + 0.413 0.903** -0.523 0.110 0.920* 0.001 (1.23) (2.09) (-0.92) (0.25) (1.73) (0.00) MA_EXP * ACQ - -0.464 -1.895*** 0.981 -0.645 -2.025* -0.008 (-0.99) (-2.60) (1.35) (-1.07) (-1.71) (-0.01) IND_EXP * ACQ ? -0.262 -1.066 0.195 0.534 -0.800 0.905 (-0.53) (-1.33) (0.28) (0.90) (-0.88) (1.04) Constant -3.423*** -3.420*** -3.502*** -3.512*** -3.396*** -3.650*** (-15.32) (-10.99) (-10.71) (-12.83) (-8.69) (-9.78) Observations 9,542 5,120 4,106 5,952 2,692 2,740 Pseudo R2 0.037 0.080 0.033 0.040 0.073 0.030

Table 6 presents the misstatement analysis using matched samples based on a multivariate propensity score, including all control variables in model (1) as determinants of misstatements. Before matching observations, the sample is limited to firm-year observations where the prior year auditor was neither an M&A expert nor an industry expert. Firm-year observations where M&A expert=1 are matched to observations where M&A expert=0 with the closest propensity score based on a caliper of 0.01. The definition of M&A expert is MA_EXP_GR30 in Columns 1-3, and MA_EXP_PCT in Columns 4-6. Columns 1 and 4 report the regression results in the full sample, Columns 2 and 5 report the results in the sample of firms in industries with supplementary FASB and/or Audit Guidance (i.e. high industry accounting complexity), and Columns 3 and 6 report the results in the sample of firms in industries without supplementary FASB or Audit Guidance (i.e. low industry accounting complexity). Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.

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Table 7 - M&A Expertise and M&A Related Misstatements during Acquisition year Alternate Construction – MA_MISSTATE_ALT=1 if M&A Misstatement firm-year, 0 if other (non M&A) Misstatement firm-year

DV = MA_MISSTATE_ALT Hypothesized MA_EXP_GR30 MA_EXP_PCT Directional (1) (2) (3) (4) (5) (6) Expectation Full Complex Non-Complex Full Complex Non-Complex MA_EXP ? 0.154 0.584 -0.482 0.048 -0.350 0.394 (0.42) (1.17) (-0.90) (0.14) (-0.74) (0.76) IND_EXP ? 0.056 0.147 -0.146 0.044 0.227 -0.179 (0.20) (0.39) (-0.34) (0.15) (0.58) (-0.44) ACQ + 0.277 0.744** -0.444 0.044 0.198 -0.125 (0.97) (2.10) (-0.90) (0.16) (0.63) (-0.27) MA_EXP * ACQ - -0.828** -1.940*** 0.558 -0.627 -1.481* -0.213 (-2.14) (-3.40) (0.91) (-1.15) (-1.30) (-0.29) IND_EXP * ACQ ? 0.009 -0.644 0.656 -0.040 -0.828 0.725 (0.02) (-0.93) (0.97) (-0.09) (-1.23) (1.12) SIZE - -0.014 0.062 -0.155 -0.013 0.061 -0.142 (-0.18) (0.62) (-1.17) (-0.17) (0.60) (-1.07) BM + 0.127 0.242** 0.026 0.132 0.257** 0.015 (1.30) (2.13) (0.24) (1.35) (2.16) (0.14) NEW_FIN + 0.349** 0.258 0.456* 0.378** 0.345 0.450* (2.01) (1.11) (1.70) (2.17) (1.45) (1.69) LOSS + 0.401* 0.457* 0.369 0.406* 0.432* 0.412 (1.93) (1.87) (0.95) (1.94) (1.75) (1.07) BIG4 - -0.547 -0.969** 0.050 -0.478 -0.762 -0.153 (-1.50) (-2.00) (0.10) (-1.37) (-1.63) (-0.28) INIT ? -0.489 -0.453 -0.699 -0.476 -0.421 -0.702 (-1.60) (-1.18) (-1.28) (-1.55) (-1.09) (-1.31) ROA ? 0.038 0.031 0.074 0.042 0.047 0.070 (0.48) (0.46) (0.46) (0.53) (0.60) (0.43) LEV ? 0.046 0.066 0.022 0.049 0.072 0.021 (0.61) (0.73) (0.17) (0.67) (0.83) (0.16) FOR + 0.432 0.534 0.395 0.404 0.506 0.343 (1.59) (1.50) (0.90) (1.51) (1.44) (0.79) #OFF_CLIENTS ? 0.001 -0.000 0.002 0.001 0.002 -0.002 (0.20) (-0.08) (0.21) (0.14) (0.36) (-0.22) Constant -12.899*** -14.073*** -1.541 -12.960*** -14.951*** -1.548 (-15.01) (-13.74) (-1.50) (-15.29) (-17.33) (-1.54) Observations 1,795 1,028 767 1,786 1,019 767 Pseudo R2 0.077 0.112 0.089 0.075 0.101 0.088

Table 7 presents logistic regression results based on model (2) with an alternate sample and construction of the dependent variable, MA_MISSTATE_ALT. The sample is limited to misstatement firm-year observations only. Further, the treatment sample consists of M&A misstatement firm-years, and the control sample consists of non-M&A misstatement firm-years. The definition of M&A expert is MA_EXP_GR30 in Columns 1-3, and MA_EXP_PCT in Columns 4-6. Columns 1 and 4 report the regression results in the full sample, Columns 2 and 5 report the results in the sample of firms in industries with supplementary FASB and/or Audit Guidance (i.e. high industry accounting complexity), and Columns 3 and 6 report the results in the sample of firms in industries without supplementary FASB or Audit Guidance (i.e. low industry accounting complexity). Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.

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Table 8 - M&A Expertise and Audit Fees - If no Auditor changes in year t, and t+1 DV = LOG_AUDFEE Hypothesized (1) (2) Directional Expectation MA_EXP_GR30 MA_EXP_PCT MA_EXP ? 0.146*** 0.011 (7.33) (0.62) IND_EXP + 0.118*** 0.116*** (7.25) (6.99) ACQ + 0.090*** 0.071*** (5.21) (4.93) MA_EXP * ACQ ? -0.056*** -0.054** (-2.71) (-2.38) IND_EXP * ACQ ? -0.022 -0.016 (-1.04) (-0.71) SIZE + 0.476*** 0.481*** (74.27) (75.04) LOSS + 0.121*** 0.120*** (9.26) (9.14) CRATIO - -0.018*** -0.018*** (-7.00) (-6.95) ZFC + -0.000 0.000 (-0.10) (0.05) CFFO ? -0.238*** -0.240*** (-9.59) (-9.53) INV_REC + 0.250*** 0.254*** (5.06) (5.10) SEGMENTS + 0.141*** 0.144*** (10.45) (10.58) FOR + 0.279*** 0.280*** (15.30) (15.26) EMPLOYEES + 0.036*** 0.034*** (6.45) (6.10) REPORT_LAG + 0.003*** 0.003*** (7.83) (7.55) ROA - -0.021 -0.021 (-1.17) (-1.12) LEV + 0.008 0.007 (0.54) (0.43) GCO + 0.004 -0.001 (0.13) (-0.02) ICW + 0.251*** 0.250*** (13.24) (13.14) RESTATE + 0.096*** 0.096*** (8.97) (8.95) CLIENT_IMPORT + 0.171*** 0.142** (2.76) (2.31) #OFF_CLIENTS + 0.004*** 0.005*** (9.99) (17.49) Constant 9.730*** 9.759*** (52.35) (49.90) Observations 19,936 19,877 Pseudo R2 0.873 0.872

Table 8 presents OLS regression results based on model (3) examining the influence of auditor M&A expertise on audit fees. The definition of M&A expert is MA_EXP_GR30 in Column 1, and MA_EXP_PCT in Column 2. Each regression includes two-digit SIC code dummies and year fixed effects. Numbers in parentheses are test statistics based on robust standard errors clustered at the firm-level. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively, with probability levels one-tailed for hypothesized directional expectations.