The Impact of Post-Merger Accounting Integration on Long-Term M&A Success

Tom Adams thomas.adams_iii@uconn.edu

Youree Kim

youree.kim@uconn.edu

Todd Kravet todd.kravet@uconn.edu

University of Connecticut

School of Business 2100 Hillside Road, Unit 1041A

Storrs, CT 06269-1041

Preliminary and Incomplete

Current Draft: July 2018 We thank Jenny Luchs, Sarah Parsons, and workshop participants at the University of Connecticut for helpful comments. We also thank the University of Connecticut for financial support.

The Impact of Post-Merger Accounting Integration on Long-Term M&A Success

ABSTRACT: M&As involve a substantial amount of accounting work from due diligence to integration accounting. In this study, we investigate whether accounting-related integration issues during the immediate post-merger period are associated with internal information quality and the combined entity’s long-term M&A success. We expect that firms with greater accounting-related integration issues, which includes purchase price allocation and the integration of accounting systems, will experience poorer internal information and long-term post-acquisition operating performance. We first document that our inverse measures of accounting-related integration quality, abnormally high audit fees and audit report lags in acquisition years, are positively associated with management EPS guidance error. We then document that our measures are negatively (positively) associated with long-term changes in acquirer cash flows (post-M&A goodwill impairments). Our results are consistent with integration quality, in the period immediately following deal completion, affecting both the quality of information produced from the acquirer and target’s newly integrated accounting system as well as long-term post-M&A financial outcomes. JEL codes: G34, M42 Keywords: Mergers & Acquisitions, Accounting Integration, Audit Fees, Audit Report Lags, Management Guidance, Goodwill Impairments

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I. INTRODUCTION

The integration process immediately after mergers and acquisitions are completed is

expected to be important in realizing the synergies from combing the acquirer and target firm.

Once the acquisition is completed, the acquirer then starts integrating accounting systems of the

two entities, focusing on setting up common controls for various operational segments such as

compliance, reporting, or procurement, in order to operate as one company. Internal information

produced from the merged accounting system of the acquirer and target provides feedback to

management about the progress of the integration and is used to make decisions about the

integration. We expect difficulties with the integration that are related to accounting systems to

be particularly important because these issues likely result in worse internal information

available to managers and worse post-acquisition outcomes.

In this study, we examine whether accounting-related integration issues are associated

with firms’ post-merger internal information quality and their long-term acquisition outcomes.

Accounting-related integration issues are complications in the integration of a target into their

acquirer that affect the accounting system of the combined entity and can include operational

integration issues that affect financial reporting or issues directly stemming from the combining

of accounting systems and applying acquisition accounting under SFAS 141, Business

Combination (FASB, 2001a).1 We posit that quality of information produced from the

accounting system is important during the integration period as this is a critical time for setting

the stage for the combined entity’s operation (Haspeslagh et al. 1991). In line with this notion,

Feldman and Spratt (1998) argue that the first 100 days (i.e., approximately one quarter of a

1 The accounting standards effective during our sample period are SFAS 141, Business Combinations, and SFAS 142, Goodwill and Other Intangible Assets. (FASB 2001a, 2001b). SFAS 141 is revised and replaced by SFAS 141R after 2009 (FASB 2007).

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year) is when all the critical actions should be launched, as this is likely to be the outer boundary

of the patience of the related parties of the combined firm such as customers and investors. We

expect accounting-related integration issues to adversely affect firms’ internal information and,

thus, disclosure quality. We also expect accounting-related integrations issues to be associated

with less successful acquisitions for two reasons. First, underlying operational integration issues

that affect accounting systems can cause acquirers not to realize expected synergies. Second,

poorer internal information stemming from accounting-related integration issues results in worse

managerial decision-making with respect to the integration. Quality of accounting during this

early post-merger period can be important because management is critically dependent on the

accounting information as this information provides data-driven input to their integration

decisions.

We use two measures of accounting-related integration issues based on abnormal audit

fees and abnormal audit report lags in the fiscal year the acquisition is completed. These

measures are based on prior audit research (Bamber et al. 1993; Knechel and Payne 2001;

Ashbaugh et al. 2003; Krishnan and Yang 2009) and capture instances where, during the

accounting integration period, either (1) the acquirer paid higher than expected audit fees or (2)

the acquirer’s auditor took longer than expected to sign its audit opinion. We argue that higher

than expected fees and/or longer than expected audit report lags capture difficulties experienced

by the acquirer in integrating the target company’s financial reporting and internal control

systems into its own. These two measures have a positive but modest correlation indicating they

capture different aspects of accounting-related integration issues. These also capture ex-post

actual integration issues that allow us to test whether acquirers and investors can predict which

acquisitions are likely to have integration issues.

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We first test our argument that accounting-related integration issues decrease the quality

of accounting information produced from the newly integrated financial reporting and internal

control systems. We proxy for the quality of accounting information using management guidance

error (Rogers and Stocken 2005; Feng et al. 2009). Consistent with our predictions, we find that

both our proxies of accounting-related integration issues are positively associated with

management guidance error. This association is consistent with accounting-related integration

issues leading to worse internal accounting information and, thus higher management EPS

guidance error. Because accounting-related integration issues lead to lower internal information

quality we expect it also to result in worse decision-making during the integration and, thus

poorer post-acquisition outcomes.

We next test whether accounting-related integration issues are associated with long-term

post-acquisition outcomes. Specifically, we examine changes in acquirer cash flows and

goodwill impairments for the three-year period following the acquisition. We expect that when

post-acquisition accounting integration goes well (poorly), the newly integrated financial

reporting and internal control systems will produce higher (lower) quality information, which

will then be used by management to make better post-acquisition operational decisions. If good

(bad) information is used in these decisions, we expect to observe good (bad) long-term

outcomes. Underlying operational integration issues can also result in accounting integration

issues and cause poorer long-term outcomes. Consistent with our argument, we find that our

measures of accounting-related integration issues, abnormal audit fees and abnormal audit report

lags in the fiscal year of the acquisition, are negatively associated with post-acquisition changes

in cash flows. We also find that accounting-related integrations issues are positively associated

with goodwill impairments indicating that acquiring managers are more likely to overpay in

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acquisitions with integration issues. This result suggests that acquiring managers are not able to

foresee these integration issues because otherwise they would have lowered the purchase price

and avoided an impairment. Overall, our empirical results are consistent with accounting-related

integration issues affecting both the quality of information produced from the newly integrated

firm’s accounting system and its long-term outcomes.

Prior research investigates whether various pre-acquisition acquirer accounting-related

characteristics are associated with acquisition performance based on acquirer announcement

returns (e.g., Biddle and Hilary 2006; McNichols and Stubben 2008; Biddle et al. 2009; Francis

and Martin 2010).2 A majority of these studies are based on the argument that acquisitions are

settings where managers’ incentives can diverge from shareholders resulting in agency costs but

that higher-quality or more informative accounting can increase monitoring and, thus decrease

agency costs. Our tests examine ex-post integration, rather than ex-ante acquirer accounting

characteristics expected to affect monitoring, and our arguments relate to the importance of

successful integration of accounting systems in the combining of business. Importantly, our

findings that ex-post accounting-related integration issues are positively (negatively) associated

with managerial forecasts errors (post-acquisition profitability) are incremental to the effect of

ex-ante acquirer characteristics, such as acquirers’ internal control weaknesses and earnings

quality. In further tests, we find that acquirer announcement returns cannot predict accounting-

related integration issues suggesting that while investors are aware that acquirer characteristics

2 Another branch of research looking at the pre-merger accounting quality at the target firm gauges its overall influence on M&A deal characteristics and on the profits to shareholders of the merging parties (Raman et al. 2013; Skaife and Wangerin 2013; Marquardt and Zur 2015; McNichols and Stubben 2015; Chen et al. 2017). For example, Marquardt and Zur (2015) find that target firm accounting quality influences the structure of the deal as well as the speed and the likelihood of deal completion. Skaife and Wangerin (2013) find that acquirers offer higher premiums for targets with low quality financial reporting, and that low quality financial reporting increases the likelihood of deal renegotiation. Chen et al. (2017) find that target firm accounting comparability is positively associated acquisition profitability.

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are associated with acquisition outcomes there is no evidence investors are able to foresee ex-

post accounting-related integration issues. Overall, the results suggest that accounting-related

integration issues have an important association with firms’ post-acquisition information quality

and acquisition profitability but these integration issues are difficult for investors and acquiring

managers to predict ex-ante.

This study makes several contributions. First, we contribute to the literature examining

the importance of accounting quality in acquisitions. While prior research finds that ex-ante

acquirer and target characteristics are associated with acquirer announcement returns we focus

on ex-post realized integration issues and find that accounting-related integration issues are

important for the success of acquisitions but are difficult to predict by acquirers and investors.

Further, our ex-post measures of accounting integration are incrementally associated with

acquisition outcomes relative to ex-ante acquirer accounting characteristics. Second, we extend

literature examining the importance of the integration process in acquisitions. Prior literature

suffers from noisy measures of integration issues that are often measured ex-ante, such as

diversifying and foreign acquisitions. We develop an alternative measure of integration issues

based on the integration of accounting systems measured ex-post in the fiscal year the acquisition

is completed. Consistent with the long-standing argument that the acquisition integration process

is important, we find that the integration process is associated with ex-post acquisition

performance. However, we also find that investors are not able to predict at announcement

whether there will be accounting-based integration issues suggesting that it is difficult to foresee

which business combinations will encounter integration difficulties.

Lastly, we extend to the literature investigating the role of audit fees and audit report

lags. Investors are not likely to find the information about the amount of audit and other fees

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disclosed in the proxy statement or 10-K useful, because while audit fee negotiation might have

information useful to investors, they do not learn the actual audit fees paid to the auditor until

disclosed in the following year’s definitive proxy statement (Hackenbrack et al. 2014). Also,

investors think that the auditor’s opinions and disclosures are often boilerplate and produce a

“largely uninformative pass-fail report” (Harris 2017). Therefore, in the context of acquisitions,

our study can show a new role of audit fees and audit report lags as predictors for future long-

term operating performance of the combined entity, which may be particularly useful to

investors.

The rest of the paper is organized as follows. In section II we discuss related literature

and develop our hypotheses. In section III we discuss research design. In section IV we discuss

sample selection and results. In section V we discuss the results of path analyses. In section VI

we provide the results from additional analyses. Finally, in section VII we provide concluding

remarks.

II. RELATED LITERATURE AND HYPOTHESES DEVELOPMENT

Accounting in Mergers and Acquisitions

Prior studies suggest that accounting plays an important role and involves a large volume

of work over the course of an acquisition (e.g., Lajoux and Elson 2000; Wangerin 2012; Shalev

et al. 2013; Marquardt and Zur 2015). When the acquisition process begins, the acquirer works

on the firm valuation to determine the price of the target and conducts preliminary due diligence.

In the due diligence phase of a deal, the review of financial statements is considered the “single

most important aspect of due diligence” (Lajoux and Elson 2000). After the acquisition

agreement is signed, a public disclosure of the acquisition and transactional due diligence follow.

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Subsequent to the deal closure, the acquirers must allocate the purchase price to all separately

identifiable classes of assets acquired and liabilities assumed based on estimated fair values as of

the acquisition date according to SFAS 141, Business Combinations. Also, under SFAS 142,

Goodwill and Other Intangible Assets, any unallocated purchase price is recorded as goodwill to

reflect expected synergies and other future economic benefits resulting from the business

combination. Finally, once the purchase price allocation is done, the acquirer conducts more

detailed work on accounting integration, setting up centralized information flow for various

business segments such as compliance, planning and analysis, and reporting.

Quality of Accounting and its Impact on the M&A Process

Prior research investigating the role of accounting information in acquisitions often

focuses on the pre-merger accounting quality and its impact on deal outcomes. Previous studies

linking accounting quality to investment efficiency often look at the acquirer’s pre-acquisition

accounting quality (Biddle and Hilary 2006; McNichols and Stubben 2008; Biddle et al. 2009;

Francis and Martin 2010; Goodman et al. 2014; Kravet 2014; Harp and Barnes 2017). These

studies are generally based on the argument that acquirers with higher accounting quality will

make better investment decisions because their investment decisions will be reported more

accurately and transparently, which facilitates better decision-making and monitoring. Therefore,

pre-acquisition accounting quality represents an acquirer characteristic that is expected to be

associated with investment decisions. Biddle and Hilary (2006) relate accounting quality to

investment cash-flow sensitivity, implying that firms with poor accounting quality are more

likely to rely on internally generated cash flows rather than external financing to fund

investments. Also, McNichols and Stubben (2008) find that firms that manipulated earnings tend

to overinvest during the misreporting period, and Francis and Martin (2010) report that firms

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with more conservative accounting make more profitable acquisitions. Harp and Barnes (2017)

find that problems in an acquirer’s internal control environment have negative operational

implications for acquisition performance. In general, these findings suggest that acquirers’ ex-

ante accounting quality improves investment efficiency by reducing information asymmetry and

increasing monitoring. Our study is interested in ex-post integration issues with respect to

accounting systems, which is not necessarily due to or related to agency problems between

managers and other stakeholders. Furthermore, our study differs from this literature to the extent

that integration issues do not arise because of acquirer characteristics but from idiosyncratic

factors related to the specific deal.

Another stream of literature focuses on the pre-merger quality of accounting at the target

firm by investigating (1) the relation between the quality of financial information provided by the

target and the profits to shareholders of the merging parties and (2) the target financial reporting

quality and its impact on the overall acquisition process (Raman et al. 2013; Skaife and

Wangerin 2013; Marquardt and Zur 2015; McNichols and Stubben 2015; Chen et al. 2018).

These studies are limited to acquisitions where the target is a public firm and publicly available

data is available. For example, McNichols and Stubben (2015) examine the relation between

target firm accounting quality and acquirer (target) profit from an acquisition and document a

positive (negative) relation between these two components. Marquardt and Zur (2015) contend

and find that high quality accounting information at the target firm reduces the costs of the

acquisition process, increases the likelihood of deal completion, and predict that target

accounting quality is negatively (positively) associated with the likelihood of an auction

(negotiation). These studies investigate the implications of target firms’ accounting information

quality from the acquirers’ perspective. They focus on how a target firm’s accounting quality can

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influence the acquiring firm’s investment decisions. Overall, this line of research suggests that

the target firm accounting quality affects deal structures in mitigating adverse selection risk and

influences shareholder values for both the target and the acquirer.

We extend this literature examining the relation between pre-acquisition accounting

quality and acquisition decisions by considering how and whether ex-post accounting-related

integration issues for the immediate post-merger period, which includes both purchase price

allocation and accounting system integration, is associated with internal information quality and

long-term success of acquisitions. By looking at the ex-post integration period our study captures

a period that is likely critical for the combined entity’s future performance and that cannot be

completely predicted from acquirer and target pre-acquisition characteristics, thereby giving a

more complete picture of the importance of accounting integration in acquisitions. We especially

focus on operating performance measures rather than short-term measures such as cumulative

abnormal returns surrounding the deal announcement date, because it is not clear if integration

issues can be predicted at acquisition announcements and synergies expected from the deal can

only be fully realized by better operating performance on a long-term basis.

Few studies look at the accounting for the immediate post-merger period and its

association with the combined entity (Bens et al. 2012; Shalev et al. 2013; Paugam et al. 2015).

Shalev et al. (2015) find that CEOs whose compensation packages rely more on earnings-based

bonuses are more likely to over allocate the purchase price to goodwill, the largest asset recorded

post-merger. Paugam et al. (2015) finds that abnormal goodwill allocation is associated with a

negative market reaction when the purchase price allocation is disclosed and a higher likelihood

of future goodwill impairment. As noted here, despite the expectation that post-acquisition

accounting integration is important, this line of literature often focuses on purchase price

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allocation only and ignores the overall integration of accounting systems. Studies involving the

quality of both purchase price allocation and integration accounting have been relatively

unexplored by the literature, especially in terms of long-term operating performance.

Accounting for Immediate Post-Merger Period and Management Guidance

The period immediately following deal completion is a critical time for the newly merged

firm. During this period, many things can go wrong. For example, the acquirer must perform a

purchase price allocation, whereby the transaction value is allocated to acquired assets and

liabilities (ASC 805). This process is complex and requires significant managerial judgment

(Laux and Leuz 2009). Managers often make mistakes; acquisition-related restatements comprise

8-17 percent of all financial restatements (Scholz 2008, 2014). In addition to performing a

purchase price allocation, an acquirer must integrate the target’s accounting and internal control

systems into its own. Recognizing these complexities, GAAP provides acquirers with a one-year

grace period, the measurement period, during which the acquirer can make adjustments to

acquisition-related accounting (ASC 805-25-19). Similarly, SEC FAQ No. 3 provides acquirers

with a one-year exemption from testing acquiree internal controls (SEC 2004). Thus, both the

FASB and SEC formally recognize the accounting and internal control complexity of the post-

merger period.

For these (and other) reasons, acquisitions are not always successful. High quality

integration, particularly in the period immediately following acquisition completion (e.g., the

first 100 days), is paramount for long-term acquisition success (Feldman and Spratt 1998;

Homburg and Bucerius 2006). An often overlooked, but a vitally important part of post-merger

integration, is the melding of the acquirer’s and acquiree’s accounting and internal control

systems (Deloitte 2018). Information produced from these systems is used, by managers, to make

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operational decisions, prepare financial statements, and to make both mandatory and voluntary

disclosures (Feng et al. 2009). If the integration of the acquirer’s and acquiree’s accounting and

internal control systems goes well (poorly), we expect that the newly integrated systems will

produce high (low) quality information.

We proxy for the quality of information produced from a firm’s post-acquisition

accounting and internal control systems with management earnings forecast error. Firm

managers often voluntarily disclose earnings forecasts to manage expectations, reduce

information asymmetry between the firm and its stakeholders, and to reduce litigation risk

(Rogers and Stocken 2005; Hirst et al. 2008; Feng et al. 2009). Many attributes of management

guidance have been studied, including its determinants, its characteristics, and its consequences

(Hirst et al. 2008). However, the attribute most relevant to our prediction is guidance error. The

accuracy of management forecasts depend largely upon the inputs to the forecasts (Feng et al.

2009). Thus, we posit that if integration quality is high (low), the firm’s systems will produce

high (low) quality information, which will lead to more (less) accurate guidance. Stated formally

in the alternative form, our first hypothesis is:

H1: Accounting-related integration issues are negatively associated with the accuracy of post-acquisition management guidance.

Accounting for the Immediate Post-Merger Period and the Acquisition Success

If poorer accounting-related integration results in lower quality internal accounting

information then we expect poorer managerial decision-making during the integration that is

based on accounting information. We therefore posit that great accounting-related integration

issues during the immediate post-merger period contributes to poorer acquisition outcomes,

because it produces less accurate and less useful accounting information and increases

information asymmetry between the acquired and the acquirer during probably the most critical

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period for the combined firm (Haspeslagh et al. 1991; Angwin 2004). It is also possible that

accounting-related integration issues can stem from broader integration issues related to

combining operations. If operational integration issues lead to lower than expected synergies,

then we expect also expect these type of integration issues to lead to worse acquisition outcomes.

Overall, we expect greater accounting-related integration issues to be associated with worse

acquisition outcomes.

However, if managers can anticipate integration issues then they can either not pursue the

deal or adjust the purchase price they offer to reflect the expected integration costs. If managers

forgo deals with high integration costs or pay a lower premium then we would not expect an

association between accounting-related integration issues and acquisition outcomes.

Given these arguments, we expect worse (better) quality of accounting for the immediate

post-merger period to be associated with worse (better) long-term success of acquisitions. As

such our second hypothesis, is as follows (alternative form):

H2: Accounting-related integration issues are negatively associated with the long-term success of acquisitions.

III. RESEARCH DESIGN

Abnormal Audit Fees and Audit Report Lags as Measures of Integration Issues

We develop two ex-post measures of realized accounting-related integration issues that

we expect to capture difficulty in accounting for the acquisition transaction and in combining of

accounting systems. Thus, the accounting quality measures which can reflect managers’ use of

discretion more would not be an effective proxy for the M&A setting. Therefore, we use two

alternative measures of accounting quality, abnormal audit fees and abnormal audit report lags,

which should be less influenced by firm risks or management’s earnings manipulation (Bamber

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et al. 1993; Knechel and Payne 2001; Ashbaugh et al. 2003; Krishnan and Yang 2009; Hribar et

al. 2014). Both abnormal audit fees and abnormal audit report lags are inverse measures of

accounting-related integration quality.

We expect accounting-related integration difficulties to directly affect auditors in their

audit of the combined firm’s financial reporting. Difficulty in accounting for the acquisition or

combining accounting systems results in increased effort and audit risk for auditors as it is more

difficult to provide assurance that the combined financial statements are consistent with GAAP.

Prior literature finds that increased auditor effort and risk results in higher audit fees and longer

audits (Bamber et al. 1993; Knechel and Payne 2001; Ashbaugh et al. 2003; Krishnan and Yang

2009). We use abnormal audits fees in the fiscal year the acquisition is completed to capture

instances where, during the accounting integration period, the acquirer paid higher than expected

audit fees. We use abnormal audit report lag in the fiscal year the acquisition is completed to

capture instances where, during the integration period, the acquirer’s auditor took longer than

expected to sign its audit opinion. We argue that higher than expected fees and/or longer than

expected audit report lags capture difficulties experienced by the acquirer in integrating the

target company’s financial reporting and internal control systems into its own. We do not use

measures based on accounting numbers (e.g., accrual quality) because they are correlated with

firms’ operating risk or can be easily manipulated by managers (Marquardt and Zur 2015; Bens

et al. 2012).

Abnormal Audit Fees and Audit Report Lags and Management Guidance Error

We estimate the following model using OLS to test the association between management

EPS guidance error and abnormal audit fees and abnormal audit report lags.

GUIDANCE_ERROR = α0 + α1Integration Issue Measure + αiControli + Industry & Year Fixed Effects + ε

(1)

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The dependent variable, GUIDANCE_ERROR, is the absolute difference between management

EPS guidance and actual EPS (measured in the first full fiscal year following the deal), scaled by

share price one month prior to fiscal year-end (Rogers and Stocken 2005; Feng, Li, and McVay

2009). We also specify the dependent variable, GUIDANCE_ERROR, as an indicator variable

coded 1 for high guidance error; coded 0 otherwise.3 Integration Issue Measure represents our

three test variables, which are used alternately in model 1. First, ABN_AFEE is the acquirer’s

abnormal audit fees (i.e., regression residual audit fee), measured in the fiscal year the deal is

completed using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003).4 Results

from estimating the audit fee model are included in Appendix B.5 Second, ABN_AUDRPT_LAG

(abnormal audit report lag) is the audit report lag measured for the fiscal year the deal is

completed less the average audit report lag in the three years preceding the deal, where audit

report lag is defined as the number days between fiscal year-end and the audit opinion signature

date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang

2009).6 Third, INTEGRATION_ISSUES, aggregates ABN_AFEE and ABN_AUDRPT_LAG by

transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values (each

ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration

3 High and low amounts of guidance error are determined using sample median guidance error. When we use the indicator variable, HIGH_GUIDANCE_ERROR, we estimate model 1 using a logistic regression model. 4 In addition to including all variables from the Ashbaugh et al. model, we include lagged audit fees as a control for firm-specific factors that could affect current year audit fees but that are unrelated to the acquisition in the current year. 5 We chose the Ashbaugh et al. (2003) audit fee model because, relative to other audit fee models, it requires only a limited set of Audit Analytics and Compustat based variables. Yet, the model has high explanatory power; results from our model estimation, presented in Appendix B, yielded an adjusted-R2 of 0.883. Further, the Asbaugh et al. model includes a control variable for M&A activity, which is necessary in our setting. 6 Results, presented later in the paper, are robust to measuring audit report lags using both raw audit report lag days (i.e., not abnormal audit report lag days) and the natural log of audit report lag days.

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issues (ranging in value from 0 to 2). We expect positive coefficients for all three test variables

indicating that greater accounting-related integration issues is positively associated with

management guidance accuracy.

Control variables are based on prior acquisition research and include pre-acquisition

measures of acquirer size (Moeller et al. 2004; ACQ_SIZE), profitability (Dechow et al. 2011;

ACQ_ROA), leverage (Maloney et al. 1993; ACQ_LEV), book-to-market ratio (Dong et al., 2006;

Tuch and O’Sullivan, 2007; ACQ_BTM), and return volatility (Dechow et al. 2011;

ACQ_STD_RET). We also include controls capturing acquirer pre-acquisition internal control

(Darrough et al. 2018; ACQ_ICW) and financial reporting quality (Kothari et al. 2005; Krishnan

et al. 2011; ABS_PADACC), as well as the acquirer’s use of a Big 4 audit firm (BIG4). Finally,

we include deal specific controls for the target company’s public/private status (PUBLIC_MA),

deal announcement period returns (MM_RET3), relative deal size (DL_REL_SIZE), form of

payment (Ghosh 2001; Gu and Lev, 2011; DL_STOCK), type of deal (i.e., diversifying vs. non-

diversifying, Morck et al. 1990; DL_DIVER), and length of time to deal completion (Wangerin

2012; DL_DUE_DIL).

Abnormal Audit Fees and Audit Report Lags and Long-Term Post-M&A Performance

To test the association between accounting-related integration issues we use two

measures of long-term, post-acquisition performance established in prior literature: changes in

acquirer cash flows between the pre- and post-acquisition periods and post-acquisition goodwill

impairments. We first estimate the following model using OLS to test the association between

long-term changes in acquirer cash flows and abnormal audit fees and abnormal audit report

lags:

CHG_CFO = γ0 + γ1Integration Issue Measure + γiControli + Industry & Year Fixed Effects + θ

(2)

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The dependent variable in model 2, CHG_CFO, is the difference between the average cash flow

from operations (CFO) for the post-acquisition periods, t+1 through t+3, and the pre-acquisition

periods, t-3 through t-1, where period t is the year in which the acquisition was completed. If

CFO data are unavailable for periods t+3 or t-3, average CFO is calculated using two (instead of

three) years of data in the pre- and post-deal periods. Integration Issue Measure represents our

three test variables, ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES, which are

used alternately in model 2. All of these variables were defined above. Our second hypothesis

predicts that high (low) post-acquisition accounting integration quality will translate into strong

(poor) long-term, post-acquisition financial performance. As such we expect to find a negative

association between our measures of accounting-related integration issues, ABN_AUDFEE and

ABN_AUDRPT_LAG, and CHG_CFO. The control variables used are consistent with those used

in model 1 and are defined, in detail, in the appendix.

We next estimate the following model using logistic regression to test the association

between long-term changes in acquirer cash flows and abnormal audit fees and abnormal audit

report lags:

Pr(GW_IMPAIRMENT = 1) = F{δ0 + δ1Integration Issue Measure + δiControli + Industry & Year Fixed Effects}

(3)

The dependent variable in model 3, GW_IMPAIRMENT, is an indicator variable coded 1 if the

acquirer recorded a goodwill impairment in the year of the deal (period t) or in the post-deal

period (periods t+1 through t+3); coded zero otherwise (Chen et al. 2018). Goodwill captures

expected synergies and future economic benefits generated in a business combination that do not

meet the criteria for asset recognition (Johnson and Petrone 1998). Therefore, goodwill

impairments in the years following acquisitions provide evidence that synergies were not

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achieved or future economic benefits were not realized. Integration Issue Measure represents our

three test variables, ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES, which are

used alternately in model 3. All of these variables were defined above. Our second hypothesis

predicts that strong (poor) post-M&A accounting integration will be associated with good (bad)

financial outcomes. Therefore, we predict a positive association between our inverse measures of

post-M&A accounting quality, ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES,

and post-acquisition goodwill impairments. All control variables are defined in detail in the

Appendix and are consistent with those discussed in connection with model 1 above.

IV. SAMPLE SELECTION AND RESULTS

Sample and Data

Table 1 details our sample selection procedures. We begin with a sample of acquisitions

obtained from the Thomson One SDC database. We require acquisitions to have been completed

and to have involved a US public acquirer and a US target (either public or private).7 While our

analyses cover firm years between 2002 and 2016, we restrict our sample to M&A deals

completed before 2013 so that we have sufficient data to measure our dependent variables,

GUIDANCE_ERROR, CHG_CFO, and GW_IMPAIRMENT in the post-deal period. We also

focus on the post-SOX period (i.e., years 2002 and later), require relative deal sizes (the ratio of

transaction value to acquirer market value) of ≥ 5 percent, and non-missing acquirer identifiers.8

Our initial sample obtained from the SDC database included 5,725 acquisition observations.

Finally, after requiring data necessary for the construction of our dependent, control, and test

7 In our multivariate analyses, we control for the target company’s public/private status. 8 One of our control variables, ACQ_ICW, requires internal control data which is available only in periods 2002 and later (Darrough, Huang, and Zur 2018)

18

variables, our final sample for the management guidance error, change in cash flows, and

goodwill impairment analyses includes 1,314, 2,681, and 3,976 observations, respectively.9

Management Guidance Error

Next, we begin our analyses of post-acquisition accounting integration’s effect on

management guidance error. As discussed above, post-acquisition management guidance error

proxies for the quality of accounting information produced from the newly integrated financial

reporting and internal control systems of the acquirer and target companies. Our expectation is

that, if post-merger integration goes well (poorly), the quality of accounting information

produced from the financial reporting and internal controls systems will be high (low).

Management guidance error is an inverse measure of accounting information quality. Similarly,

abnormal fees and abnormal audit report lags are inverse measures of accounting integration

quality. Therefore, we expect to find a positive association between our measures of accounting

integration quality, ABN_AFEE and ABN_AUDRPT_LAG, and GUIDANCE_ERROR.

Table 2, Panels A and B, present descriptive statistics for our dependent variable

(GUIDANCE_ERROR) and our three test variables (ABN_AFEE, ABN_AUDRPT_LAG, and

INTEGRATION_ISSUES), as well as t-tests for our two test variables and control variables.

When we partition our sample into high/low guidance error groups (Panel B) we find that mean

abnormal audit fees and abnormal audit report lags for the high guidance error group

(HIGH_GUIDANCE_ERROR = 1) are significantly larger than those for the low guidance error

group (HIGH_GUIDANCE_ERROR = 0). The same is true for our aggregate integration issues

9 These final observation counts relate to analyses that use ABN_AFEE as a test variable. When ABN_AUDRPT_LAG is used as our test variable our sample sizes for the management guidance error, change in cash flows, and goodwill impairment analyses are further reduced to 1,277, 2,659, and 3,942 observations respectively. When INTEGRATION_ISSUES is used as our test variable our sample sizes for the management guidance error, change in cash flows, and goodwill impairment analyses are further reduced to 1,227, 2,538, and 3,760 observations respectively.

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measure.10 Next, among our control variables (the bottom portion of Panel B), we find firms with

high guidance error have significantly higher book-to-market ratios (ACQ_BTM), return

volatility (ACQ_STD_RET), and relative deal sizes (DL_REL_SIZE). We also find that firms

with high guidance error purchased public targets (PUBLIC_MA) and used Big 4 auditors (BIG4)

less often. These same firms are smaller and less profitable (ACQ_SIZE and ACQ_ROA).

In Table 2, Panel C, we provide a Pearson correlation matrix. Consistent with our

univariate results presented in Panel B, we find positive correlations between

GUIDANCE_ERROR and ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES,

although the correlation for ABN_AFEE is not significant (p=0.21). We also note that our two

proxies for post-acquisition integration quality, ABN_AFEE and ABN_AUDRPT_LAG, have a

positive correlation of 0.140 (p≤0.01), which provides some comfort that these proxies are not

entirely overlapping measures of our theoretical construct. Finally, as expected, both ABN_AFEE

and ABN_AUDRPT_LAG are highly correlated with the aggregate INTEGRATION_ISSUES

measure (Pearson correlations of 0.692 and 0.567, respectively; p≤0.01 for both).

Next, Table 3 provides results from our multivariate tests of H1. Again, our expectation

is that when post-acquisition accounting integration goes well (poorly) the quality of accounting

information produced from the financial reporting and internal control systems will be positively

(negatively) affected. We, therefore, expect to find positive associations between

GUIDANCE_ERROR and ABN_AUDFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES.

Consistent with this prediction, in columns 1-3 we find a positive associations for all three test

variables. In addition, when we specify our dependent variable as a binary variable measuring

high and low amounts of guidance error in columns 4-6, we find positive associations for all

10 The high/low guidance error sample partitions in Table 2, Panel B are defined using sample medians.

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three test variables. Overall, the results in columns 1-6 are consistent with post-acquisition

accounting integration quality affecting the quality of information produced from the acquirer’s

post-merger accounting and internal control systems.

Finally, among our control variables we find evidence that GUIDANCE_ERROR is

negatively associated with the use of a Big 4 auditor (BIG4, columns 1-3), M&A announcement

returns (MM_RET3, columns 1-3), and acquirer size (ACQ_SIZE, columns 1-6) and profitability

(ACQ_ROA, column 5). Conversely, GUIDANCE_ERROR is positively associated with

discretionary accruals (ABS_PADACC, column 5), book-to-market ratio (ACQ_BTM, columns 4-

6), return volatility (ACQ_STD_RET, columns 1-6), relative deal size (DL_REL_SIZE, columns

1 and 2), and diversifying M&A deals (DL_DIVER, columns 1-3).

Long-Term Post-M&A Outcomes: Changes in Cash Flows and Goodwill Impairments

Next, we begin our analyses of post-acquisition outcomes. As discussed above, we

predict that when post-acquisition accounting integration goes well (poorly), higher (lower)

quality accounting information will be produced by the financial reporting and internal control

systems, and as a result better (worse) operational decisions will be made. Better (worse)

operational decisions should, then, lead to better (worse) long-term acquisition outcomes. As

before, we proxy for accounting integration quality using both ABN_AFEE and

ABN_AUDRPT_LAG. Our proxies for long-term post-acquisition outcomes are changes in

acquirer cash flows (between the pre- and post-M&A periods) and post-acquisition goodwill

impairments, CHG_CFO and GW_IMPAIRMENT, respectively.

In Table 4, Panels A and B we present descriptive statistics and univariate comparisons

of our key variables. Specifically, in the top portion of Panel B, we present mean and median

values for CHG_CFO across quintiles of ABN_AFEE, ABN_AUDRPT_LAG, and

21

INTEGRATION_ISSUES. We note that for all three test variables, CHG_CFO is most negative in

quintile five. In the bottom portion of Panel B, we present frequencies of goodwill impairments

across quintiles of ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES. We note

that, across the ABN_AFEE (INTEGRATION_ISSUES) quintiles, the proportion of observations

experiencing a post-acquisition goodwill impairment increases from 29.1 (30.6) percent in

quintile one to 37.5 (38.4) percent in quintile five. We also note that the proportion of

observations experiencing a goodwill impairment is highest in the fourth and fifth quintiles of

ABN_AUDRPT_LAG. Finally, Table 4, Panel C presents descriptive statistics for our control

variables. The descriptive statistics are relatively consistent with those presented and discussed

in Table 2.

Next, Table 5 provides results from our multivariate tests of H2. Our expectation is that

strong (poor) post-acquisition accounting integration quality will result in good (bad) accounting

information being produced from the newly integrated financial reporting and internal control

systems. Since this information is used in post-acquisition operation decisions, we predict that

strong (poor) accounting integration quality will lead to good (bad) long-term outcomes. As

before, ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES, are inverse measures

of accounting integration quality. Therefore, we expect to find negative (positive) associations

between CHG_CFO (GW_IMPAIRMENT) and these measures. Consistent with these

predictions, we find a negative associations for ABN_AUDRPT_LAG and

INTEGRATION_ISSUES in columns 2 and 3 and positive associations, for ABN_AFEE,

ABN_AUDRPT_LAG, and INTEGRATION_ISSUES, in columns 4-6. The finding that greater

integration issues are more likely to result in goodwill impairments suggest that acquiring

managers cannot foresee these issues because otherwise they would forgo the acquisition or

22

lower the purchase price. Overall, this evidence is consistent with accounting integration quality

affecting long-term post-acquisition outcomes.

Among our control variables we find evidence consistent with acquirer pre-acquisiton

size and leverage positively affecting changes in cash flows (ACQ_SIZE and ACQ_LEV,

columns 1-3). In addition, acquirers that are more profitable pre-merger (ACQ_ROA, columns 1-

3), pay for the acquisition with at least 50 percent stock (DL_STOCK, columns 1-3), and who

take longer to complete the acquisition after deal announcement (DL_DUE_DIL, column 1)

experience smaller changes in cash flows between the pre- and post-acquisition periods.

Acquirers who have experienced internal control issues pre-acquisition (ACQ_ICW, columns 4-

6), have higher discretionary accruals (ABS_PADACC, column 5), are larger (ACQ_SIZE,

columns 4-6), have higher book-to-market ratios (ACQ_BTM, columns 4-6), have more stock

price volatility (ACQ_STD_RET, columns 4-5), acquire larger targets (DL_REL_SIZE, columns

4-6), and acquire targets in different industries (DL_DIVER, columns 4-6) are more likely to

experience a goodwill impairment. Finally, acquirers who use Big 4 auditors (BIG4, columns 4-

6), have larger announcement period returns (MM_RET3, columns 4 and 6), and are more

profitable pre-acquisition (ACQ_ROA, column 4) are less likely to experience a goodwill

impairment post-merger.

Summary of Multivariate Results

Overall, our multivariate results presented in Tables 3 and 5 provide evidence consistent

with accounting integration quality, as proxied by ABN_AFEE, ABN_AUDRPT_LAG, and

INTEGRATION_ISSUES, affecting (1) the quality of accounting information produced by the

newly integrated financial reporting and internal control systems (as proxied by management

guidance error, GUIDANCE_ERROR) and (2) long-term post-acquisition outcomes as proxied

23

by changes in cash flows between the pre- and post-M&A periods (CHG_CFO) and post-M&A

goodwill impairments (GW_IMPAIRMENT).

V. PATH ANALYSES

In prior sections we have argued that post-merger accounting integration quality should

affect the quality of accounting information produced from the financial reporting and internal

control systems. This, in turn, should affect long-term post acquisition outcomes. We then

presented results supporting individual pieces of this argument. However, we have not yet

presented results that support the argument in its entirety. Said differently, we argue that

accounting integration issues affect long-term post acquisition outcomes through the quality of

information produced within the newly merged firm. However, we have not yet documented

results demonstrating that information quality is the path or mechanism through which

integration issues affect long-term outcomes.

In Figure 1, we provide such evidence. We present the results of path analyses. As the

diagrams in Panel A and Panel B suggest, we model the quality of accounting information

produced by the newly integrated financial reporting and internal control systems (as proxied by

HIGH_GUIDANCE_ERROR) as a mediator in the relationship between post-merger accounting

integration issues (INTEGRATION_ISSUES) and long-term post-merger outcomes (CHG_CFO

in Panel A and GW_IMPAIRMENT in Panel B). A mediator variable is an intervening variable

through which an antecedent variable is proposed to influence an outcome variable (Hayes 2018,

78). In our case, HIGH_GUIDANCE_ERROR is the mediator variable, INTEGRATION_ISSUES

is the antecedent variable, and either CHG_CFO or GW_IMPAIRMENT are the outcome

variables (depending on the Panel in Figure 1). The antecedent variable can affect the outcome

variable through two paths. One path leads directly from the antecedent to the outcome and is

24

called the direct effect. A second path leads from antecedent to the outcome through the mediator

and is called the indirect effect (Hayes 2018, 79). Therefore, based on the arguments presented

earlier, we expect to find a significant negative (positive) indirect effect in the relationship

between INTEGRATION_ISSUES and CHG_CFO (GW_IMPAIRMENT). That is, we expect to

find evidence that the quality of information produced within the firm (HIGH_GUIDANCE_

ERROR) is the mechanism through which post-merger accounting integration issues

(INTEGRATION_ISSUES) affects long-term post-merger outcomes (CHG_CFO and

GW_IMPAIRMENT).

Our results, presented in Figure 1 are consistent with these expectations. In Panel A, we

find a significant positive association between HIGH_GUIDANCE_ERROR and

INTEGRATION_ISSUES (path coefficient of 0.530; p-value≤0.01). We also find a significant

negative association between HIGH_GUIDANCE_ ERROR and CHG_CFO (path coefficient of -

0.008; p-value=0.07). Ultimately, however, we are interested in whether INTEGRATION_

ISSUES has an indirect effect on CHG_CFO (i.e., is there evidence that integration issues affect

changes in cash flows through high guidance error?). Indeed, we find evidence of such an

indirect effect as documented in the bottom portion of the path diagram in Figure 1, Panel A.

Specifically, we find a statistically significant negative indirect effect (path coefficient of -0.008;

confidence interval of -0.020 to -0.001) but a statistically insignificant direct effect. This result

suggests that INTEGRATION_ISSUES have a negative effect on CHG_CFO, but only through

HIGH_GUIDANCE_ERROR. We find similar results in Panel B, which uses GW_IMPAIRMENT

as the outcome variable. That is, INTEGRATION_ ISSUES have a positive and significant

indirect effect on GW_IMPAIRMENT (path coefficient of +0.023; confidence interval of +0.011

25

to +0.039) but an insignificant direct effect.11 Overall, the results presented in Figure 1 suggest,

consistent with our prediction, that post-merger accounting integration issues

(INTEGRATION_ISSUES) affect long-term post-merger outcomes (CHG_CFO and

GW_IMPAIRMENT) through the quality of information produced by the newly merged firm’s

accounting and internal control systems (HIGH_GUIDANCE_ERROR). The results also suggest

– because we find evidence of indirect effects, but no evidence of a direct effects – we have

identified the mechanism through which post-merger integration affects long-term post-merger

outcomes.

VI. ADDITIONAL ANALYSES

Acquirers Prior Integration Experience

We next consider the possibility that some acquirers have more experience in performing

post-merger integration than others. Our interest is in determining whether our results are driven

by the quality of integration for the acquirer’s current acquisition or by the acquirer’s past

experience(s) with integration. As such, we add two new variables, measuring prior acquisition

experience, to our models (1a) and (1b) (i.e., our GUIDANCE_ERROR models) and also to our

models (2) and (3) (i.e., our CHG_CFO and GW_IMPAIRMENT models). First, the variables

Prior Deals ABN_AFEE and Prior Deals ABN_AUDRPT_LAG measure average abnormal audit

fees and average abnormal audit report lags for all deals executed by the acquirer in the three

years preceding the current deal. These variables are meant to capture an acquirer’s prior

integration successes (or failures). It is possible that these past integration experiences could

affect the quality of the current post-merger integration. Second, the variable

ACQUISITION_INTENSITY is the natural log of the number of acquisitions executed by the

11 Technical details regarding the estimation of our path models are provided in the footer of Figure 1.

26

acquirer in the three years preceding the current deal. In contrast to the Prior Deals ABN_AFEE

and Prior Deals ABN_AUDRPT_LAG variables, which measure prior integration successes (or

failures), ACQUISITION_INTENSITY is meant to measure any recent acquisition integration

experience, successful or not.

Results are presented in Table 6. Panel A presents results for our GUIDANCE_ERROR

tests, while Panel B presents the results for our CHG_CFO and GW_IMPAIRMENT tests. In

Panel A, we find positive associations for both ABN_AFEE (columns 1 and 3) and

ABN_AUDRPT_LAG (columns 2 and 4). Aside from a weak negative association for Prior Deals

ABN_AFEE (column 3), results for our new measures are insignificant suggesting that guidance

errors are driven by current integration issues. In Panel B, we similarly find that our CHG_CFO

results are driven by current integration issues, rather than prior integration experiences. For our

GW_IMPAIRMENT tests (columns 3 and 4), we continue to find positive associations for both

ABN_AFEE and ABN_AUDRPT_LAG. In addition, we find positive associations for

ACQUISITION_INTENSITY suggesting that (a) consistent with Hayn and Hughes (2006), firms

that go on an acquisition “spree” are more likely to experience a goodwill impairment, and (b)

current integration issues have an incremental effect on the likelihood of goodwill impairment,

over and above, recent acquisition experience.

Can Investors Predict Integration Issues?

Prior research finds that investors expect that acquirer accounting quality is negatively

associated with acquisition profitability by finding a positive association between acquirer pre-

acquisition accounting quality and acquirer announcement return (e.g., McNichols and Stubben

2008; Biddle et al. 2009; Francis and Martin 2010). We test whether investors can predict ex-

post accounting-related integration issues at the announcement of the deal. We regress our

27

accounting integration quality variables, ABN_AFEE and ABN_AUDRPT_LAG, on the acquirer

announcement return. In Table 7 we present our results. We do not find that acquirer abnormal

announcement are associated with our accounting integration quality variables at conventional

levels. The t-statistics for the coefficients on ABN_AFEE and ABN_AUDRPT_LAG are -0.490

and -0.260, respectively. Overall, this result suggest that ex-post integration issues are difficult

for investors to foresee and are not just a function of acquirer or target pre-acquisition

characteristics that are known to investors at announcement.

Falsification Tests

We conclude our analyses by performing a set of falsification tests. If our measures of

post-merger accounting integration quality, ABN_AFEE and ABN_AUDRPT_LAG, are picking

up current, rather than prior time invariant, firm-specific issues, we would expect to find no

associations between our dependent variables and ABN_AFEE and ABN_AUDRPT_LAG

measured one-year prior to the current acquisition. To test this prediction, we construct two new

lagged measures of abnormal audit fees and abnormal audit report lags, Pre-M&A ABN_AFEE

and Pre-M&A ABN_AUDRPT_LAG, and replace our main test variables with these measures.

Results are presented in Table 8; Panel A presents results for our GUIDANCE_ERROR analyses

and Panel B presents results for our CHG_CFO and GW_IMPAIRMENT analyses. Consistent

with our expectations, we find no significant associations for our lagged test variables, which

suggests our main test variables are picking up current integration issues.

VII. CONCLUSION

This study examines accounting quality for the immediate post-merger period which

covers both purchase price alocation and integration accounting on the combined company’s

28

long-term operating performance. We first hypothesize that strong (poor) quality accounting

system integration during this period will result in an accounting system that produces high (low)

quality information post-merger. Since managers use this information to generate internal

forecasts, we predict that strong (poor) accounting system integration will result in less (more)

management guidance error. Our results are consistent with this prediction.

We next hypothesize that strong (poor) quality accounting system integration will result

poor long-term, post-M&A performance for the firm. Our results indicate that our measures of

accounting system integration quality, abnormal audit fees and audit report lags, are negatively

(positively) associated with changes in acquirer cash flows between the pre- and post-merger

periods (goodwill impairments in the post-merger periods).

Our results contribute to M&A literature by examining how accounting quality in the

period immediately following an M&A affects long-term M&A outcomes; this is a previously

unexplored area of research. Second, we contribute to the M&A literature by introducing

abnormal audit fees and abnormal audit report lags as two possible measures of post-M&A

accounting integration quality.

29

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Shalev, R., I.X. Zhang, Y. Zhang. 2013. CEO Compensation and Fair Value Accounting: Evidence from Purchase Price Allocation. Journal of Accounting Research 51 (4): 819-854.

Skaife, H.A., D.D. Wangerin. 2013. Target Financial Reporting Quality and M&A Deals that Go Bust. Contemporary Accounting Research 30 (2): 719-749.

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Wangerin, D.D. 2012. M&A Due Diligence and Its Consequences for Post-Acquisition Financial Statements. Working paper, Michigan State University.

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Appendix A Variable Definitions

Variable Definition GUIDANCE_ERROR The absolute difference between management EPS guidance and actual EPS

(measured in the first full fiscal year following the deal), scaled by share price one month prior to fiscal year-end (Rogers and Stocken 2005; Feng, Li, and McVay 2009).

HIGH_GUIDANCE_ERROR An indicator variable coded 1 if GUIDANCE_ERROR is ≥ sample median GUIDANCE_ERROR; coded 0 otherwise.

INTEGRATION_ISSUES This variable aggregates ABN_AFEE and ABN_AUDRPT_LAG by transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values for ABN_AFEE and ABN_AUDRPT_LAG (each ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration issues (ranging in value from 0 to 2).

CHG_CFO Change in industry-adjusted acquirer cash flows (CFO) between pre- and post-deal periods, where CFO is calculated as cash flow from operations divided by average total assets. CHG_CFO is the difference between the average CFO for the post-deal periods, t+1 through t+3, and the pre-deal periods, t-3 through t-1, where period t is a year in which the acquisition was completed. If CFO data are unavailable for periods t+3 or t-3, average CFO is calculated using two (instead of three) years of data in the pre- and post-deal periods.

GW_IMPAIRMENT GW_IMPAIRMENT is an indicator variable coded 1 if the acquirer recorded a goodwill impairment in the year of the deal (period t) or in the post-deal period (periods t+1 through t+3); coded zero otherwise.

ABN_AFEE ABN_AFEE is the acquirer’s abnormal audit feess, measured in the year of the deal using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003). Results from this estimation are presented in Appendix B.

ABN_AUDRPT_LAG Audit report lag measured for the year of the deal less average audit report lag in the three years preceding the deal, where audit report lag is defined as the number days between fiscal year-end and the audit opinion signature date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang 2009).

ACQ_ICW Indicator variable coded 1 if the acquirer reported ICWs in the fiscal year prior to the deal announcement; coded 0 otherwise (Darrough, Huang, and Zur 2018).

ABS_PADACC Absolute performance-adjusted discretionary accruals (Kothari, Leone, and Wasley 2005; Krishnan, Wen, and Zhao 2011).

PUBLIC_MA An indicator variable coded 1 if the target company was a publicly traded company prior to the M&A; coded 0 otherwise.

BIG4 An indicator variable coded 1 if the acquirer used a Big 4 auditor (PwC, KPMG, Deloitte, or E&Y); coded 0 otherwise.

MM_RET3 Announcement returns measured as three-day market model adjusted returns. ACQ_SIZE Acquirer size. Measured as the natural logarithm of acquirer's market value

fifty trading days prior to an acquisition announcement. ACQ_ROA Acquirer's pre-acquisition ROA. Measured as operating income after

depreciation scaled by average total assets (oiadp / ((at +lag_at)/2)) at the fiscal year end prior to acquisition announcement. Industry-adjusted.

ACQ_LEV Acquirer's pre-acquisition leverage. Measured as the sum of short-term debt and long-term debt scaled by total assets ((dlc+dltt)/at) at the fiscal year end prior to acquisition announcement.

Appendix A continues on next page.

34

Appendix A (continued) Variable Definitions

Variable Definition ACQ_BTM Acquirer's pre-acquisition book-to-market ratio. Measured as book value of

equity divided by market value of equity (ceq/(prcc_f *csho)) at the fiscal year end prior to acquisition announcement.

ACQ_STD_RET Acquirer's standard deviation of daily returns computed over the one-year period ending one month before an acquisition announcement (One-year period is deemed to have 250 trading days).

DL_REL_SIZE Relative deal size. Measured as the ratio of the transaction value to the market value of the acquirer.

DL_STOCK Indicator variable equal to one if at least 50 percent of the consideration paid for the target consists of stock, and zero otherwise.

DL_DIVER Indicator variable equal to one for a diversifying acquisition (i.e., an acquisition where the acquirer and target operate in different two-digit SIC codes), and zero otherwise.

DL_DUE_DIL Proxy for managerial effort in M&A. The number of weekdays between the signing of the acquisition agreement and the effective date of the deal.

Audit Fee Model (Appendix B)

lnAUDIT_FEE The natural log of the audit fee in millions of dollars.

BIG4 An indicator variable coded 1 if the firm used a Big 4 auditor (PwC, KPMG, Deloitte, or E&Y); coded 0 otherwise.

lnMVE The natural log of the firm’s market value of equity defined as the firm’s price per share at fiscal year-end multiplied by the number of shares outstanding measured in millions of dollars.

MERGER An indicator variable coded 1 if the firm engaged in an M&A (identified by Compustat variable AQC); coded 0 otherwise.

FINANCING An indicator variable coded 1 if the number of shares outstanding increased by at least 10 percent or long-term debt increased by at least 20 percent; coded 0 otherwise.

MB The firm’s market to book ratio defined as its market value of equity divided by book value.

LEVERAGE The firm’s total liabilities divided by total assets.

ROA The firm’s return-on-asset ratio calculated as income before extraordinary items divided by average total assets.

AR_IN The sum of the firm’s receivables and inventory divided by its total assets.

NEGATIVE_ROA An indicator variable coded 1 if the firm’s ROA was negative; coded 0 otherwise.

SPECIAL_ITEM An indicator variable coded 1 if the firm reports special items (Compustat variable SPI).

Lagged lnAUDIT_FEE The natural log of prior year’s audit fee in millions of dollars.

35

Appendix B Audit Fee Model

Dependent Variable =

lnAUDIT_FEE

Coefficient t-statistic Intercept -1.208*** -47.05 BIG4 0.082*** 12.83 lnMVE 0.139*** 56.33 MERGER 0.092*** 19.38 FINANCING 0.057*** 12.75 MB -0.007*** -11.60 LEVERAGE 0.175*** 19.02 ROA 0.017 0.98 AR_IN -0.029*** -2.76 NEGATIVE_ROA 0.092*** 14.03 SPECIAL_ITEM 0.078*** 16.61 Lagged lnAUDIT_FEE 0.723*** 154.71 Industry Fixed Effects Included Year Fixed Effects Included Adjusted-R2 0.883 N 61,088

This appendix presents the results from estimating the audit fee model from Ashbaugh, LaFond, and Mayhew (2003) for the period 2002-2013 for the full population of firms with data available in the Audit Analytics and Compustat databases. In addition to including all variables from the Ashbaugh et al. model, we include lagged audit fees as a control for firm-specific factors that could affect current year audit fees. The residual values obtained from estimating the model represent our abnormal audit fee measure, ABN_AFEE. The dependent variable, lnAUDIT_FEE, is the natural logarithm of audit fees. All variables are defined in detail in Appendix A. All continuous variables have been winsorized at the 1st and 99th percentiles.

36

TABLE 1 Sample Selection

N for Dependent Variable:

GUIDANCE_ERROR CHG_CFO GW_IMPAIRMENT (1) (2) (3) M&A data from SDC: completed deals involving US public acquirers and

US targets (both public and private), between 2002 and 2013a, with non-missing acquirer identifiers (necessary for merger with Compustat and Audit Analytics databases), and where relative deal size exceeds 5%.

5,725 5,725 5,725

Less:

M&A transactions with missing dependent variable data (3,989) (2,426) 0 M&A transactions with missing control variable data (346) (423) (1,462)

Subtotal 1,390 2,876 4,263

Observations with non-missing ABN_AFEE test variable 1,314 2,681 3,976 Observations with non-missing ABN_AUDRPT_LAG test variable 1,277 2,659 3,942 Observations with non-missing INTEGRATION_ISSUES test variable 1,227 2,538 3,760

This table summarizes our sample selection process. One column is presented for each dependent variable – GUIDANCE_ERROR, CHG_CFO, and GW_IMPAIRMENT – used in our analyses. GUIDANCE_ERROR is the absolute difference between management EPS guidance and actual EPS (measured in the first full fiscal year following the deal), scaled by share price one month prior to fiscal year-end (Rogers and Stocken 2005; Feng, Li, and McVay 2009). CHG_CFO measures change in industry-adjusted acquirer cash flows (CFO) between pre- and post-deal periods, where CFO is calculated as cash flow from operations divided by average total assets. CHG_CFO is the difference between the average CFO for the post-deal periods, t+1 through t+3, and the pre-deal periods, t-3 through t-1, where period t is a year in which the acquisition was completed. If CFO data are unavailable for periods t+3 or t-3, average CFO is calculated using two (instead of three) years of data in the pre- and post-deal periods. GW_IMPAIRMENT is an indicator variable coded 1 if the acquirer recorded a goodwill impairment in the year of the deal (period t) or in the post-deal period (periods t+1 through t+3); coded zero otherwise. Finally, we use three test variables in our analyses: ABN_AFEE and ABN_AUDRPT_LAG. ABN_AFEE is the acquirer’s abnormal audit fees, measured in the year of the deal using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003). Second, ABN_AUDRPT_LAG (abnormal audit report lag) is audit report lag measured for the year of the deal less average audit report lag in the three years preceding the deal, where audit report lag is defined as the number days between fiscal year-end and the audit opinion signature date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang 2009). Third, INTEGRATION_ISSUES, aggregates ABN_AFEE and ABN_AUDRPT_LAG by transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values for ABN_AFEE and ABN_AUDRPT_LAG (each ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration issues (ranging in value from 0 to 2). All variables, including control variables, are defined in detail in Appendix A. a Our analyses cover the years 2002 to 2016. However, we restrict our sample to M&A deals completed between 2002 and 2013 so that we have sufficient data to measure our dependent variables in the post-deal period (i.e., after 2013).

37

TABLE 2 Management Guidance Error: Descriptive Statistics, t-tests, and Correlation Matrix

Panel A: Descriptive Statistics for Dependent and Test Variables N Mean STD P25 Median P75 GUIDANCE_ERROR 1,314 0.027 0.099 0.0007 0.002 0.008

ABN_AFEE 1,314 0.052 0.377 -0.162 0.019 0.250

ABN_AUDRPT_LAG 1,277 3.908 15.231 -4.000 1.667 10.667

INTEGRATION_ISSUES 1,227 1.003 0.476 0.667 1.000 1.333

Panel B: t-tests for Test and Control Variables HIGH_GUIDANCE_ERROR

= 1 = 0 t-statistic

Mean Median Mean Median (1) vs. (3)

(1) (2) (3) (4) (5) ABN_AFEE 0.082 0.044 0.023 0.012 2.85*** N 657 657 ABN_AUDRPT_LAG 5.095 2.000 2.719 1.000 2.79*** N 638 639 INTEGRATION_ISSUES 1.040 1.000 0.966 0.889 2.73*** N 613 614

HIGH_GUIDANCE_ERROR

= 1 = 0 t-statistic

Mean Median Mean Median (1) vs. (3)

(1) (2) (3) (4) (5) ACQ_ICW 0.059 0.000 0.043 0.000 1.38 ABS_PADACC 0.625 0.083 0.494 0.063 1.41 PUBLIC_MA 0.193 0.000 0.271 0.000 -3.34*** BIG4 0.863 1.000 0.922 1.000 -3.49*** MM_RET3 0.020 0.009 0.014 0.009 1.40 ACQ_SIZE 6.472 6.441 7.345 7.293 -10.83*** ACQ_ROA 0.121 0.124 0.145 0.140 -5.53*** ACQ_LEV 0.201 0.171 0.216 0.199 -1.52 ACQ_BTM 0.580 0.507 0.449 0.383 7.49*** ACQ_STD_RET 0.030 0.027 0.024 0.022 8.84*** DL_REL_SIZE 0.275 0.145 0.239 0.123 2.02** DL_STOCK 0.140 0.000 0.135 0.000 0.24 DL_DIVER 0.393 0.000 0.365 0.000 1.02 DL_DUE_DIL 66.055 39.000 72.518 47.000 -1.21 N 657 657

Table continues on next page.

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TABLE 2 (continued) Management Guidance Error: Descriptive Statistics, t-tests, and Correlation Matrix

Panel C: Correlation Matrix (N=1,314 except for ABN_AUDRPT_LAG and INTEGRATION_ISSUES, where N=1,277 and 1,227, respectively) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (1) GUIDANCE_ EPS_ERROR

1.000

(2) ABN_AFEE 0.035 1.000 (3) ABN_AUDRPT_ LAG

0.132 0.140a 1.000

(4) INTEGRATION_ ISSUES

0.071 0.692 0.567 1.000

(5) ACQ_ICW 0.028 -0.017 -0.105 -0.116 1.000 (6) ABS_PADACC 0.035 0.032 0.028 0.011 0.093 1.000 (7) PUBLIC_MA -0.017 0.099 -0.010 0.093 -0.045 -0.001 1.000 (8) BIG4 -0.183 0.058 -0.030 0.055 -0.154 -0.082 0.033 1.000 (9) MM_RET3 -0.043 -0.002 0.031 0.014 0.054 0.015 -0.151 0.016 1.000 (10) ACQ_SIZE -0.184 -0.002 -0.074 -0.033 -0.079 -0.028 0.319 0.271 -0.160 1.000 (11) ACQ_ROA -0.065 0.021 -0.012 0.003 -0.062 0.000 0.001 0.033 0.011 0.227 1.000 (12) ACQ_LEV -0.026 -0.055 -0.046 -0.071 0.033 -0.028 -0.037 -0.009 0.050 0.141 0.088 1.000 (13) ACQ_BTM 0.057 0.030 0.002 0.023 -0.036 -0.118 -0.065 -0.039 0.019 -0.332 -0.351 -0.005 1.000 (14) ACQ_STD_RET 0.106 0.098 -0.043 -0.002 0.072 0.095 -0.127 -0.083 0.093 -0.499 -0.193 -0.142 0.205 1.000 (15) DL_REL_SIZE 0.074 0.162 0.024 0.157 0.023 0.027 0.232 0.023 0.129 -0.124 -0.033 0.106 0.108 0.049 1.000 (16) DL_STOCK 0.011 0.030 0.021 0.061 0.028 0.020 0.345 -0.011 -0.121 0.022 -0.196 -0.053 -0.050 0.079 0.206 1.000 (17) DL_DIVER 0.085 0.003 -0.022 -0.009 -0.046 -0.034 -0.062 -0.079 -0.047 -0.020 0.069 0.053 0.000 -0.017 -0.072 -0.062 1.000 (18) DL_DUE_DIL -0.005 0.035 0.015 0.061 -0.021 -0.012 0.356 0.023 -0.046 0.258 -0.037 0.120 -0.064 -0.126 0.250 0.306 -0.113

***, **, * indicate significance at the 0.01, 0.05, 0.10 levels, respectively (two-tail). In Panel A, we present descriptive statistics for our dependent and test variables. GUIDANCE_ERROR is the absolute difference between management EPS guidance and actual EPS (measured in the first full fiscal year following the deal), scaled by share price one month prior to fiscal year-end (Rogers and Stocken 2005; Feng, Li, and McVay 2009). Alternatively, we use the variable HIGH_GUIDANCE_ERROR, which is an indicator variable coded 1 if GUIDANCE_ERROR is ≥ sample median GUIDANCE_ERROR and coded 0 otherwise. We use three test variables in our analyses. First, ABN_AFEE is the acquirer’s abnormal audit fees, measured in the year of the deal using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003). Second, ABN_AUDRPT_LAG (abnormal audit report lag) is audit report lag measured for the year of the deal less average audit report lag in the three years preceding the deal, where audit report lag is defined as the number days between fiscal year-end and the audit opinion signature date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang 2009). Third, INTEGRATION_ISSUES, aggregates ABN_AFEE and ABN_AUDRPT_LAG by transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values for ABN_AFEE and ABN_AUDRPT_LAG (each ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration issues (ranging in value from 0 to 2). All variables, including control variables, are defined in detail in Appendix A. All continuous variables have been winsorized at the 1st and 99th percentiles. In Panel B, we present t-tests for differences in mean values between HIGH_GUIDANCE_ERROR = 0 and HIGH_GUIDANCE_ERROR = 0 subsamples. Finally, in Panel C we present a Pearson correlation matrix. Correlations where p-value ≤ 0.10 are listed in bold font. a N=1,227; p-value≤0.01.

39

TABLE 3 The Impact of Accounting Integration Issues on Management Guidance Error

Dependent Variable = GUIDANCE_ERROR HIGH_GUIDANCE_ERROR (1) (2) (3) (4) (5) (6) Coefficient (t-statistic) Intercept 0.057 0.070** 0.038 0.11 -0.766 -0.985

(2.17) (1.98) (1.42) (0.17) (-1.05) (-1.32) ABN_AFEE 0.011* 0.490***

(1.72)

(2.83) ABN_AUDRPT_LAG 0.001*** 0.015***

(3.40) (3.33) INTEGRATION_ 0.026*** 0.541*** ISSUES (3.97) (3.49) ACQ_ICW -0.009 -0.002 -0.003 -0.094 -0.014 0.064

(-0.57) (-0.08) (-0.18) (-0.32) (-0.05) (0.21) ABS_PADACC 0.001 0.001 0.001 0.057 0.068* 0.058

(0.52) (0.40) (0.45) (1.49) (1.93) (1.51) PUBLIC_MA -0.003 0.002 -0.005 -0.211 -0.197 -0.263

(-0.40) (0.14) (-0.61) (-1.20) (-1.09) (-1.42) BIG4 -0.035** -0.041** -0.036*** -0.008 0.025 -0.034

(-2.56) (-2.23) (-2.62) (-0.04) (0.11) (-0.15) MM_RET3 -0.068* -0.111* -0.076* 0.312 0.321 0.422

(-1.69) (-1.93) (-1.77) (0.37) (0.36) (0.47) ACQ_SIZE -0.008*** -0.013*** -0.009*** -0.280*** -0.237*** - 0.275***

(-3.73) (-3.54) (-3.78) (-4.69) (-3.81) (-4.33) ACQ_ROA -0.020 -0.015 -0.020 -1.465 -1.947** -1.523

(-0.56) (-0.30) (-0.51) (-1.61) (-2.01) (-1.54) ACQ_LEV 0.007 0.038 0.016 0.248 0.186 0.203

(0.45) (1.42) (1.00) (0.66) (0.48) (0.51) ACQ_BTM 0.004 0.000 0.006 0.972*** 1.161*** 1.142***

(0.38) (-0.02) (0.60) (3.90) (4.42) (4.28) ACQ_STD_RET 0.604* 0.936* 0.584* 30.745*** 44.567*** 40.065***

(1.89) (1.73) (1.84) (3.96) (5.39) (4.84) DL_REL_SIZE 0.018* 0.029* 0.018 -0.056 0.025 -0.032

(1.65) (1.78) (1.58) (-0.26) (0.11) (-0.14) DL_STOCK -0.009 -0.017 -0.007 -0.104 -0.240 -0.148

(-0.81) (-1.01) (-0.65) (-0.51) (-1.11) (-0.68) DL_DIVER 0.014** 0.019** 0.014** 0.146 0.144 0.167

(2.42) (2.12) (2.29) (1.11) (1.06) (1.21) DL_DUE_DIL 0.000 0.000 0.000 0.001 0.001 0.001

(1.17) (1.28) (1.32) (1.19) (0.90) (1.23) Industry Fixed Effects Included Included Included Included Included Included Year Fixed Effects Included Included Included Included Included Included Adjusted/Pseudo-R2 0.113 0.130 0.131 0.229 0.263 0.265 N 1,314 1,277 1,227 1,314 1,277 1,227

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TABLE 3 (continued) The Impact of Integration Issues on Management Guidance Error

This table presents the results from estimating the following models:

GUIDANCE_ERROR = α0 + α1* Integration Issue Measure + αi*Controli + Industry & Year Fixed Effects + ε

(1a)

Pr(HIGH_GUIDANCE_ERROR = 1) = F{β0 + β1* Integration Issue Measure + βi*Controli + Industry & Year Fixed Effects}

(1b)

Results presented in columns 1 and 2 were obtained, using model 1a, from OLS regressions (heteroscedasticity robust standard errors). Results presented in columns 3 and 4 were obtained, using model 1b, from logistic regressions. The samples used are summarized in Table 1. The dependent variable GUIDANCE_ERROR is the absolute difference between management EPS guidance and actual EPS (measured in the first full fiscal year following the deal), scaled by share price one month prior to fiscal year-end (Rogers and Stocken 2005; Feng, Li, and McVay 2009). Alternatively, in columns 3 and 4 we use the variable HIGH_GUIDANCE_ERROR, which is an indicator variable coded 1 if GUIDANCE_ERROR is ≥ sample median GUIDANCE_ERROR and coded 0 otherwise. We include three, alternative Integration Issue Measures. First, ABN_AFEE is the acquirer’s abnormal audit fees, measured in the year of the deal using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003). Second, ABN_AUDRPT_LAG (abnormal audit report lag) is audit report lag measured for the year of the deal less average audit report lag in the three years preceding the deal, where audit report lag is defined as the number days between fiscal year-end and the audit opinion signature date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang 2009). Third, INTEGRATION_ISSUES, aggregates ABN_AFEE and ABN_AUDRPT_LAG by transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values for ABN_AFEE and ABN_AUDRPT_LAG (each ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration issues (ranging in value from 0 to 2). All variables, including control variables, are defined in detail in Appendix A. All continuous variables have been winsorized at the 1st and 99th percentiles. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively (two-tail).

41

TABLE 4 Post-M&A Outcomes: Descriptive Statistics and Univariate Comparisons

Panel A: Descriptive Statistics for Dependent and Control Variables N Mean STD P25 Median P75 CHG_CFO 2,681 -0.015 0.083 -0.051 -0.008 0.021 ALM_ABN_AFEE 2,681 0.041 0.390 -0.168 0.012 0.237 ABN_AUDRPT_LAG 2,659 4.498 25.724 -4.333 1.333 10.000 INTEGRATION_ISSUES 2,538 1.000 0.470 0.667 1.000 1.333

N Mean STD P25 Median P75 GW_IMPAIR 3,976 0.347 0.476 0.000 0.000 1.000 ALM_ABN_AFEE 3,976 0.052 0.389 -0.171 0.014 0.245 ABN_AUDRPT_LAG 3,942 4.111 17.921 -4.333 1.333 11.000 INTEGRATION_ISSUES 3,760 1.000 0.476 0.667 1.000 1.333

Panel B: Univariate Comparisons

CHG_CFO Means and Medians

Quintile ABN_AFEE

N (1)

Mean (2)

Median (3)

Quintile ABN_AUDRPT_

LAG N (4)

Mean (5)

Median (6)

Quintile INTEGRATION_

ISSUES

N (7)

Mean (8)

Median (9)

1 536 -0.010 -0.003 1 523 -0.013 -0.007 1 540 -0.010 -0.004 2 536 -0.011 -0.005 2 536 -0.009 -0.006 2 390 -0.012 -0.004 3 537 -0.015 -0.008 3 531 -0.009 -0.005 3 536 -0.010 -0.005 4 536 -0.013 -0.011 4 538 -0.015 -0.007 4 536 -0.012 -0.007 5 536 -0.025 -0.016 5 531 -0.019 -0.008 5 536 -0.022 -0.017

Total 2,681 -0.015 -0.008 Total 2,659 -0.013 -0.006 Total 2,538 -0.013 -0.006

Table continues on next page.

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TABLE 4 (continued) Post-M&A Outcomes: Descriptive Statistics and Univariate Comparisons

Panel B: Univariate Comparisons (continued)

GW_IMPAIRMENT Frequencies

Quintile ABN_AFEE

N (1)

Freq. (2)

Prop. (2) ÷ (1)

Quintile ABN_AUDRPT_

LAG N (1)

Freq. (2)

Prop. (2) ÷ (1)

Quintile INTEGRATION_

ISSUES N (1)

Freq. (2)

Prop. (2) ÷ (1)

1 795 231 29.1% 1 794 276 34.8% 1 817 250 30.6% 2 795 287 36.1% 2 791 250 31.6% 2 584 187 32.0% 3 796 265 33.3% 3 780 249 31.9% 3 772 284 36.8% 4 796 298 37.4% 4 793 300 37.8% 4 776 286 36.9% 5 794 298 37.5% 5 784 299 38.1% 5 811 311 38.4%

Total 3,976 1,379 34.7% Total 3,942 1,374 34.9% Total 3,760 1,318 35.1%

Panel C: Descriptive Statistics for Control Variables CHG_CFO GW_IMPAIRMENT

Mean Median Std. Dev. Mean Median Std. Dev. ACQ_ICW 0.067 0.000 0.250 0.073 0.000 0.260 ABS_PADACC 0.600 0.081 1.767 0.597 0.084 1.635 PUBLIC_MA 0.226 0.000 0.418 0.211 0.000 0.408 BIG4 0.788 1.000 0.409 0.783 1.000 0.412 MM_RET3 0.016 0.007 0.080 0.015 0.006 0.079 ACQ_SIZE 6.320 6.295 1.712 6.283 6.261 1.716 ACQ_ROA 0.098 0.109 0.119 0.096 0.108 0.123 ACQ_LEV 0.206 0.164 0.198 0.212 0.169 0.207 ACQ_BTM 0.587 0.504 0.425 0.572 0.496 0.412 ACQ_STD_RET 0.030 0.026 0.015 0.030 0.026 0.016 DL_REL_SIZE 0.318 0.158 0.443 0.305 0.152 0.427 DL_STOCK 0.194 0.000 0.396 0.188 0.000 0.391 DL_DIVER 0.353 0.000 0.478 0.363 0.000 0.481 DL_DUE_DIL 77.679 49.000 98.326 70.796 45.000 89.576

2,681 3,976

Table continues on next page.

43

TABLE 4 (continued) Post-M&A Outcomes: Descriptive Statistics and Univariate Comparisons

Panel A provides descriptive statistics for the dependent and independent test variables used in our post-M&A outcomes analyses. Panel B provides univariate comparisons of dependent variables, CHG_CFO and GW_IMPAIRMENT, across quintiles of our independent test variables, ABN_AFEE, ABN_AUDRPT_LAG, and INTEGRATION_ISSUES. Finally, Panel C provides descriptive statistics for our control variables. The dependent variable CHG_CFO measures change in industry-adjusted acquirer cash flows (CFO) between pre- and post-deal periods, where CFO is calculated as cash flow from operations divided by average total assets. CHG_CFO is the difference between the average CFO for the post-deal periods, t+1 through t+3, and the pre-deal periods, t-3 through t-1, where period t is a year in which the acquisition was completed. If CFO data are unavailable for periods t+3 or t-3, average CFO is calculated using two (instead of three) years of data in the pre- and post-deal periods. GW_IMPAIRMENT is an indicator variable coded 1 if the acquirer recorded a goodwill impairment in the year of the deal (period t) or in the post-deal period (periods t+1 through t+3); coded zero otherwise. We use three test variables in our analyses. ABN_AFEE is the acquirer’s abnormal audit fees, measured in the year of the deal using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003). Second, ABN_AUDRPT_LAG (abnormal audit report lag) is audit report lag measured for the year of the deal less average audit report lag in the three years preceding the deal, where audit report lag is defined as the number days between fiscal year-end and the audit opinion signature date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang 2009). Third, INTEGRATION_ ISSUES, aggregates ABN_AFEE and ABN_AUDRPT_LAG by transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values for ABN_AFEE and ABN_AUDRPT_LAG (each ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration issues (ranging in value from 0 to 2). All variables, including control variables, are defined in detail in Appendix A. All continuous variables have been winsorized at the 1st and 99th percentiles.

44

TABLE 5 The Impact of Integration Issues on Post-M&A Outcomes

Dependent Variable = CHG_CFO GW_IMPAIRMENT (1) (2) (3) (4) (5) (6) Coefficient (t-statistic) Intercept -0.033** -0.027 -0.023 -1.601*** -1.446*** -1.751***

(-2.13) (-1.63) (-1.40) (-4.78) (-4.05) (-4.65) ABN_AFEE -0.007 0.300***

(-1.45) (3.23) ABN_AUDRPT_LAG -0.0002*** 0.004***

(-2.76) (2.92) INTEGRATION_ -0.009** 0.365*** ISSUES (-2.45) (4.42) ACQ_ICW -0.006 -0.006 -0.007 0.241* 0.230* 0.291**

(-0.87) (-0.82) (-0.97) (1.83) (1.75) (2.18) ABS_PADACC 0.001 0.001 0.001 0.022 0.035* 0.023

(1.33) (1.31) (1.23) (1.03) (1.70) (1.09) PUBLIC_MA 0.004 0.004 0.006 -0.037 -0.042 -0.062

(0.97) (0.91) (1.24) (-0.36) (-0.41) (-0.59) BIG4 0.000 0.002 -0.001 -0.306*** -0.272*** -0.307***

(0.07) (0.34) (-0.17) (-3.16) (-2.84) (-3.11) MM_RET3 -0.022 -0.031 -0.025 -0.766* -0.750 -0.954**

(-0.87) (-1.23) (-0.99) (-1.68) (-1.62) (-2.02) ACQ_SIZE 0.004*** 0.003** 0.003*** 0.115*** 0.104*** 0.114***

(2.96) (2.39) (2.80) (3.85) (3.44) (3.70) ACQ_ROA -0.292*** -0.267*** -0.284*** -0.675** -0.430 -0.555

(-12.30) (-10.81) (-11.74) (-2.01) (-1.30) (-1.59) ACQ_LEV 0.043*** 0.040*** 0.040*** 0.042 0.117 0.140

(4.32) (4.24) (4.04) (0.22) (0.61) (0.71) ACQ_BTM 0.001 -0.002 0.000 0.642*** 0.683*** 0.688***

(0.25) (-0.36) (-0.09) (6.79) (7.19) (7.08) ACQ_STD_RET -0.260 -0.178 -0.158 7.714** 5.773* 5.799

(-1.49) (-1.00) (-0.90) (2.51) (1.88) (1.81) DL_REL_SIZE -0.004 -0.007 -0.005 0.176* 0.322*** 0.253***

(-0.86) (-1.63) (-1.09) (1.98) (3.45) (2.64) DL_STOCK -0.012** -0.011** -0.013** 0.076 0.079 0.061

(-2.39) (-2.03) (-2.51) (0.74) (0.75) (0.56) DL_DIVER -0.005 -0.005 -0.004 0.198*** 0.216*** 0.213***

(-1.60) (-1.59) (-1.26) (2.70) (2.92) (2.82) DL_DUE_DIL -0.00002* -0.00002 -0.00002 0.000 -0.0004 -0.0005

(-1.89) (-1.24) (-1.64) (-0.74) (-0.90) (-0.94) Industry Fixed Effects Included Included Included Included Included Included Year Fixed Effects Included Included Included Included Included Included Adjusted/Pseudo-R2 0.152 0.131 0.142 0.093 0.095 0.100 N 2,681 2,659 2,538 3,976 3,942 3,760 N (GW Impairment) 1,379 1,374 1,318

Table continues on next page.

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TABLE 5 (continued)

The Impact of Integration Issues on Post-M&A Outcomes This table presents the results from estimating the following models:

CHG_CFO = γ0 + γ1* Integration Issue Measure + γi*Controli + Industry & Year Fixed Effects + θ (2)

Pr(GW_IMPAIRMENT = 1) = F{δ0 + δ1* Integration Issue Measure + δi*Controli + Industry & Year Fixed Effects}

(3)

Results presented in columns 1 and 2 were obtained, using model 1a, from OLS regressions (heteroscedasticity robust standard errors). Results presented in columns 3 and 4 were obtained, using model 1b, from logistic regressions. The samples used are summarized in Table 1. The dependent variable CHG_CFO measures change in industry-adjusted acquirer cash flows (CFO) between pre- and post-deal periods, where CFO is calculated as cash flow from operations divided by average total assets. CHG_CFO is the difference between the average CFO for the post-deal periods, t+1 through t+3, and the pre-deal periods, t-3 through t-1, where period t is a year in which the acquisition was completed. If CFO data are unavailable for periods t+3 or t-3, average CFO is calculated using two (instead of three) years of data in the pre- and post-deal periods. GW_IMPAIRMENT is an indicator variable coded 1 if the acquirer recorded a goodwill impairment in the year of the deal (period t) or in the post-deal period (periods t+1 through t+3); coded zero otherwise. We include three, alternative Integration Issue Measures. First, ABN_AFEE is the acquirer’s abnormal audit fees, measured in the year of the deal using the audit fee model from Ashbaugh, LaFond, and Mayhew (2003). Second, ABN_AUDRPT_LAG (abnormal audit report lag) is audit report lag measured for the year of the deal less average audit report lag in the three years preceding the deal, where audit report lag is defined as the number days between fiscal year-end and the audit opinion signature date (Bamber Bamber, and Schoderbek 1993; Knechel and Payne 2001; Krishnan and Yang 2009). Third, INTEGRATION_ISSUES, aggregates ABN_AFEE and ABN_AUDRPT_LAG by transforming each into decile ranks (0 to 9) and dividing by 9. The two transformed values for ABN_AFEE and ABN_AUDRPT_LAG (each ranging in value from 0 to 1) are then added, resulting in an aggregate measure of integration issues (ranging in value from 0 to 2). All variables, including control variables, are defined in detail in Appendix A. All continuous variables have been winsorized at the 1st and 99th percentiles. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively (two-tail).

46

TABLE 6 Additional Analyses: Acquirers Prior Integration Experience

Panel A: the Impact of Integration Issues on Management Guidance Error Dependent Variable = GUIDANCE_ERROR HIGH_GUIDANCE_ERROR (1) (2) (3) (4) Coefficient (t-statistic) Intercept 0.055** 0.072** 0.063 -0.736

(2.16) (2.08) (0.09) (-1.01) ABN_AFEE 0.011* 0.501***

(1.74) (2.88) Prior Deals ABN_AFEE -0.029 -0.560*

(-1.64) (-1.68) ABN_AUDRPT_LAG 0.001*** 0.015***

(3.45) (3.35) Prior Deals ABN_AUDRPT_LAG 0.000003 -0.0002

(0.01) (-0.04) ACQUISITION_INTENSITY 0.012 0.013 0.117 0.105

(1.54) (1.31) (0.83) (0.74) Controls Included Included Included Included Industry Fixed Effects Included Included Included Included Year Fixed Effects Included Included Included Included Adjusted/Pseudo-R2 0.117 0.131 0.231 0.264 N 1,314 1,277 1,314 1,277

Panel B: the Impact of Integration Issues on Post-M&A Outcomes Dependent Variable = CHG_CFO GW_IMPAIRMENT (1) (2) (3) (4) Coefficient (t-statistic) Intercept -0.034** -0.028* -1.459*** -1.327***

(-2.23) (-1.66) (-4.32) (-3.70) ABN_AFEE -0.006

0.298***

(-1.45)

(3.20)

Prior Deals ABN_AFEE -0.011

0.081

(-1.52)

(0.49)

ABN_AUDRPT_LAG -0.0002*** 0.004*** (-2.77) (2.96)

Prior Deals ABN_AUDRPT_LAG 0.0001 0.0002 (0.96) (0.10)

ACQUISITION_INTENSITY -0.0008 -0.001 0.368*** 0.365*** (-0.28) (-0.30) (4.90) (4.95)

Controls Included Included Included Included Industry Fixed Effects Included Included Included Included Year Fixed Effects Included Included Included Included Adjusted/Pseudo-R2 0.1520 0.130 0.101 0.103 N 2,681 2,659 3,976 3,942 N (Goodwill Impairment) 1,379 1,374

Table continues on the next page.

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TABLE 6 (continued) Additional Analyses: Acquirers Prior Integration Experience

This table tests whether an acquirer’s (a) prior integration experience, or (b) recent acquisition intensity are associated with management guidance error (Panel A) and post-M&A outcomes (Panel B). In Panel A, models (1a) and (1b) were modified to incorporate measures of prior integration experience and recent acquisition intensity:

GUIDANCE_ERROR = α0 + α1* Integration Issue Measure + α2*Prior Integration Issue Measure + α3*ACQUISITION_INTENSITY + αi*Controli + Industry & Year Fixed Effects + ε

(1a)

Pr(HIGH_GUIDANCE_ERROR = 1) = F{β0 + β1* Integration Issue Measure + β2*Prior Integration Issue Measure + β3*ACQUISITION_INTENSITY + βi*Controli + Industry & Year Fixed Effects}

(1b)

Results presented in columns 1 and 2 were obtained, using model 1a, from OLS regressions (heteroscedasticity robust standard errors). Results presented in columns 3 and 4 were obtained, using model 1b, from logistic regressions. The samples used are summarized in Table 1. In Panel B, models (2) and (3) were modified to incorporate measures of prior integration experience and recent acquisition intensity:

CHG_CFO = γ0 + γ1* Integration Issue Measure + γ2*Prior Integration Issue Measure + γ3*ACQUISITION_INTENSITY + γi*Controli + Industry & Year Fixed Effects + θ

(2)

Pr(GW_IMPAIRMENT = 1) = F{δ0 + δ1* Integration Issue Measure + δ2*Prior Integration Issue Measure + δ3*ACQUISITION_INTENSITY + δi*Controli + Industry & Year Fixed Effects}

(3)

In both Panels A and B, we include two Prior Integration Issue Measures. First, Prior Deals ABN_AFEE is the average of the acquirer’s abnormal audit fees for all deals executed in the three-year period preceding the current acquisition. Second, Prior Deals ABN_AUDRPT_LAG is the average of the acquirers abnormal audit report lags for all deals executed in the three-year period preceding the current acquisition. All control variables are included in the estimation of models (1a) and (1b) but are not presented here in the interest of brevity. All variables are defined in Appendix A and in previous tables. All continuous variables have been winsorized at the 1st and 99th percentiles. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively (two-tail).

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TABLE 7 Additional Analyses: Do Announcement Period Returns Predict Integration Issues?

Dependent Variable =

ABN_AFEE ABN_AUDRPT_LAG (1) (2) (3) (4)

Coefficient t-statistic Coefficient t-statistic Intercept 0.010 1.110 2.847*** 9.900 MM_RET3 -0.060 -0.490 -1.072 -0.260 Adjusted-R2 0.000 0.000 N 5,086 4,928

This table tests whether announcement period returns (MM_RET3) can predict integration issues as measured by ABN_AFEE and ABN_AUDRPT_LAG. Results in all columns were obtained by estimating OLS models with heteroscedasticity robust standard errors. Consistent with our main sample, samples here were restricted to observations (a) falling in the years 2002 and later, (b) with relative deal sizes ≥ 5%, and (c) non-missing dependent and independent variables. The results above suggest that announcement period returns have little explanatory power in predicting post-merger integration issues. All variables are defined, in detail, in previous tables and in the Appendix. All continuous variables have been winsorized at the 1st and 99th percentiles. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively (two-tail).

49

TABLE 8 Falsification Tests: Test Variables Measured in the Pre-M&A Period

Panel A: Management Guidance Error Dependent Variable =

GUIDANCE_ERROR HIGH_GUIDANCE_ERROR

(1) (2) (3) (4)

Coefficient (t-statistic) Intercept 0.076** 0.088** -0.632 -0.525

(2.16) (2.36) (-0.90) (-0.72) Pre-M&A ABN_AFEE -0.010 -0.274

(-0.70) (-1.43) Pre-M&A ABN_AUDRPT_LAG 0.0001 0.002

(0.51) (0.74) Controls Included Included Included Included Industry Fixed Effects Included Included Included Included Year Fixed Effects Included Included Included Included Adjusted/Pseudo-R2 0.118 0.118 0.254 0.258 N 1,216 1,128 1,216 1,128

Panel B: Post-M&A Outcomes Dependent Variable =

CHG_CFO GW_IMPAIRMENT

(1) (2) (3) (4)

Coefficient (t-statistic) Intercept -0.036 -0.043 -1.318*** -0.954

(-1.61) (-0.84) (-2.62) (-1.29) Pre-M&A ABN_AFEE -0.006 0.143

(-1.23) (1.49) Pre-M&A ABN_AUDRPT_LAG -0.00003 -0.001

(-0.52) (-0.51) Controls Included Included Included Included Industry Fixed Effects Included Included Included Included Year Fixed Effects Included Included Included Included Adjusted/Pseudo-R2 0.125 0.117 0.093 0.093 N 2,506 2,370 3,723 3,518 N (GW_IMPAIRMENT) 1,311 1,246

This table presents the results of falsification tests in which the independent test variables, ABN_AFEE and ABN_AUDRPT_LAG, were measured one year prior to the M&A deal (i.e., period t-1, where period t is the fiscal year during which the M&A deal was completed). Panel A presents the results from estimating models 1a and 1b (our management guidance error models) using pre-M&A test variables. Panel B presents the results from estimating models 2 and 3 (our change in cash flow and goodwill impairment models). All variables are defined, in detail, in previous tables and in the Appendix. All continuous variables have been winsorized at the 1st and 99th percentiles. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively (two-tail).

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Figure 1 Path Analysis: HIGH_GUIDANCE_ERROR as Mediator Variable

Panel A: Changes in Cash Flows as Outcome Variable (N=872)

Figure continues on next page.

INTEGRATION_ ISSUES

HIGH_ GUIDANCE_ERROR

CHG_CFO

+0.530 (p-value≤0.01)

-0.008 (p-value=0.07)

Direct Effect: -0.065

(Confidence Interval: -0.120 to +0.007)

Indirect Effect through HIGH_GUIDANCE_ERROR: -0.008

(Confidence Interval: -0.020 to -0.001)

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Figure 1 (continued) Path Analysis: HIGH_GUIDANCE_ERROR as Mediator Variable

Panel B: Goodwill Impairments as Outcome Variable (N=1,227) Figure 1 presents the results of two path analyses. Panel A uses CHG_CFO as the outcome variable, while Panel B uses GW_IMPAIRMENT. In these analyses we are interested in testing whether post-merger accounting integration issues affect post-merger outcomes through the quality of information produced from the newly merged firm’s accounting system. In other words, we are testing whether HIGH_GUIDANCE_ERROR is a mediator variable in the relationship between INTEGRATION_ISSUES and our two post-merger outcome variables, CHG_CFO and GW_IMPAIRMENT. Using Stata (package “binary_mediator”), we ran path analyses in which the following models were estimated:

HIGH_GUIDANCE_ERROR = f(INTEGRATION_ISSUES, ACQ_ICW, ABS_PADACC, PUBLIC_MA, BIG4, MM_RET3, ACQ_SIZE, ACQ_ROA, ACQ_LEV, ACQ_BTM, ACQ_STD_RET, DL_REL_SIZE, DL_STOCK, DL_DIVER, DL_DUE_DIL, Industry & Year Fixed Effects)

Figure continues on next page.

INTEGRATION_ ISSUES

HIGH_ GUIDANCE_ERROR

GW_IMPAIRMENT

+0.549 (p-value≤0.01)

+0.587 (p-value≤0.01)

Direct Effect: +0.072

(Confidence Interval: -0.007 to +0.134)

Indirect Effect through HIGH_GUIDANCE_ERROR: +0.023

(Confidence Interval: +0.011 to +0.039)

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Figure 1 (continued) Path Analysis: HIGH_GUIDANCE_ERROR as Mediator Variable

CHG_CFO or GW_IMPAIRMENT = f(INTEGRATION_ISSUES, ACQ_ICW, ABS_PADACC, PUBLIC_MA, BIG4, MM_RET3, ACQ_SIZE,

ACQ_ROA, ACQ_LEV, ACQ_BTM, ACQ_STD_RET, DL_REL_SIZE, DL_STOCK, DL_DIVER, DL_DUE_DIL, Industry & Year Fixed Effects)

CHG_CFO or GW_IMPAIRMENT = f(HIGH_GUIDANCE_ERROR, INTEGRATION_ISSUES, ACQ_ICW, ABS_PADACC, PUBLIC_MA, BIG4,

MM_RET3, ACQ_SIZE, ACQ_ROA, ACQ_LEV, ACQ_BTM, ACQ_STD_RET, DL_REL_SIZE, DL_STOCK, DL_DIVER, DL_DUE_DIL, Industry & Year Fixed Effects)

Within the larger path model, when either HIGH_GUIDANCE_ERROR or GW_IMPAIRMENT are used as the dependent variable, a logit model is estimated. When CHG_CFO is used as the dependent variable, an OLS model is estimated. The path model estimates both the direct and indirect effects of post-merger accounting integration issues on post-merger outcomes and tests each for significance (bootstrapped standard errors based on 1,000 repetitions are used to calculate bias-corrected confidence intervals). In the bottom portion of each path diagram above, the estimated direct and indirect effects and their associated bootstrapped bias-corrected confidence intervals (90 percent) are reported. Similar results are obtained when we use Stata’s “sem” command. However, we do not report results based on this command because we use a binary mediator variable (which requires the estimation of a logit model as part of the path model) and the “sem” command treats all mediator variables as linear (output is based upon OLS estimates). Finally, our results remain significant when we allow for interaction effects between our test variable (INTEGRATION_ISSUES) and our mediator variable (HIGH_GUIDANCE_ERROR) by implementing the Stata package “paramed” (based on Valeri and VanderWeele 2013). All significant path coefficients are reported in bold font. All variables are defined in Appendix A and in prior tables.