ACCOUNTING WORKSHOP

Earnings management, audit adjustments, and the financing of corporate acquisitions: Evidence from China

By

Clive Lennox* Leventhal School of Accounting, University of Southern California

Zi-Tian Wang

School of Accountancy, Shanghai University of Finance and Economics, China

Xi Wu School of Accountancy, Central University of Finance and Economics, China

Thursday, June 2nd, 2016 1:20 – 2:50 p.m.

Room C06 *Speaker Paper Available in Room 447

  

Earnings management, audit adjustments,

and the financing of corporate acquisitions: Evidence from China

Clive Lennox Leventhal School of Accounting, University of Southern California, USA

Zi-Tian Wang School of Accountancy, Shanghai University of Finance and Economics, China

Xi Wu School of Accountancy, Central University of Finance and Economics, China

May 2016 Acknowledgements: This study has benefited from the comments of Udi Hoitash, Arnie Wright, and workshop participants at the University of Bath, Michigan State University, and Northeastern University. Xi Wu thanks the Inspection Bureau of the Chinese Ministry of Finance for providing data support.

  

Earnings management, audit adjustments,

and the financing of corporate acquisitions: Evidence from China

ABSTRACT

Acquirers are motivated to overstate earnings prior to stock-financed acquisitions

because this can increase the acquirer’s stock price, making it less costly to purchase

a target using the acquiring company’s stock. We hypothesize that audits help to

correct this tendency to overstate earnings prior to stock-financed acquisitions. We

test this using a difference-in-differences research design, which compares audit

adjustments for stock-financed and cash-financed acquirers in the periods before

versus after the acquisitions. Consistent with auditors correcting earnings

overstatements prior to stock-financed acquisitions, we find that auditors require

larger downward adjustments to earnings in the year before stock-financed

acquisitions.

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1. Introduction

When a company pays for an acquisition using its own stock, the cost of the acquisition is

lower when the acquirer’s stock price is higher. This means that acquirers have incentives to

overstate earnings when they plan to finance acquisitions using stock rather than cash.1

Auditors are responsible for testing whether the acquirers’ financial statements are fairly

presented and auditors should require downward adjustments to earnings when they find

that earnings are overstated. We argue that stock-financed acquirers are motivated to

overstate earnings and audits help to detect and correct these earnings overstatements.

Accordingly, we hypothesize that auditors help to prevent low quality financial reporting

prior to stock-financed acquisitions by detecting managers’ earnings overstatements and

requiring corrections through downward adjustments to earnings.

However, the results may not support this hypothesis for several reasons. First, the

managers of stock-financed acquirers may choose to report conservatively rather than

overstate earnings because target companies and potential litigants are likely to carefully

scrutinize the acquirer’s financial statements when the acquirer uses its own equity to pay

for the acquisition (Ball and Shivakumar, 2008).2 Second, when managers are motivated to

overstate earnings they are likely to try and hide the misstatements from their auditors.

                                                            1 Although stock financing increases an acquirer’s incentives to overstate earnings, stock financing can also help to deter earnings overstatements by the target company. For example, Hansen (1987) argues that any ex-post losses arising from overpayment cause the combined company’s stock price to decline, and these losses are shared between the target’s and acquirer’s shareholders. Therefore, stock-financed acquirers are able to protect themselves from overpaying for a target, by paying for the acquisition using equity rather than cash. Prior research is consistent with target companies being motivated to overstate earnings prior to the acquisition announcement date (Anilowski et al., 2009; Marquardt and Zur, 2010). However, research also suggests that target companies understate earnings during the period after the announcement date and before the completion date in order to provide a boost to the acquirer’s earnings and cash flows during the post-acquisition period (Chen et al., 2016b). In our sample, very few of the target companies are publicly traded so we do not examine the audit adjustments of target companies. Instead, we focus on the audit adjustments of acquiring companies. 2 Consistent with this argument, Ball and Shivakumar (2008) find that U.K. companies report conservatively prior to Initial Public Offerings (IPOs). We examine stock-financed acquisitions rather than IPOs because the audit adjustment data in China are only available after a company becomes publicly traded. We do not examine Seasoned Equity Offerings (SEOs) because in China equity is often sold to corporate insiders and existing major shareholders. This means that Chinese companies do not have strong incentives to overstate earnings prior to SEOs.

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Therefore, the benefits of an audit may be greater for correcting accidental misstatements

rather than deliberate misstatements (Botosan et al., 2016). Furthermore, an acquiring

company is unlikely to inform its auditor about an impending stock-financed acquisition

until the company is ready to make a public announcement about the acquisition. One

reason is the need for secrecy prior to the M&A announcement date. Another reason is that,

if an acquirer informed its auditor about an impending stock-financed acquisition, the

auditor would likely scrutinize the accounts more carefully and charge a higher audit fee.

Finally, our analysis of audit adjustments is based on data from China, where the legal

enforcement and investor protection are relatively weak as compared with more developed

markets (Allen et al., 2005; Chen et al., 2013; Wong, 2014; Chen et al., 2016a) and the threat of

legal liability toward auditors is relatively low as well (Chan et al., 2006; Chen et al., 2011).

This means that Chinese auditors may not have strong incentives to detect and correct

earnings overstatements.3, 4

We test our hypothesis using a difference-in-differences research design. The

treatment sample comprises stock-financed acquirers (STOCK = 1), while the control sample

comprises cash-financed acquirers (STOCK = 0). We code the fiscal year-end immediately

before the acquisition announcement as the pre-period (BEFORE = 1) and the fiscal year-end

immediately after the acquisition completion (or termination) date as the post-period

(BEFORE = 0). The treatment variable is the interaction term, STOCK × BEFORE. Consistent

                                                            3 We undertake our study in China because the data on audit adjustments are generally unavailable in other countries. In China, it is mandatory for every audit firm to report to the Ministry of Finance (MOF) the pre-audit and audited values of earnings and total assets for their publicly traded clients. The pre-audit data are not publicly available but the MOF has provided these data to us and other researchers for the purpose of academic study. Audit firms report the pre-audit values of earnings and total assets, but they are not required to report to the MOF other line items from the pre-audit financial statements across our whole sample period, such as the pre-audit values of operating cash flows. 4 Although auditors in China face a relatively low threat of civil litigation, they can suffer severe punishments from the regulatory agencies (Chen et al., 2011) and Chinese auditors increasingly have market-based incentives to develop and maintain reputations for high quality auditing (Chen et al., 2010; He et al., 2016).

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with the hypothesis, we find that auditors require larger downward adjustments to earnings

in the year before stock-financed acquisitions. This suggests that stock-financed acquirers

attempt to overstate earnings and auditors help to detect and correct these overstatements by

requiring earnings to be adjusted downwards.

In a supplementary analysis, we examine whether auditors are able to resolve all of

the accounting issues before the auditor issues an opinion on the acquirer’s financial

statements. If an acquirer refuses to make a necessary adjustment, the auditor can respond

by disclosing the problem in the audit report. We therefore examine whether auditors’

reports disclose unresolved problems at the end of the audit. We expect that such problems

are more likely to be disclosed prior to stock-financed acquisitions because managers of

stock-financed acquirers have incentives to overstate earnings. We test this using the same

difference-in-differences research design that we use in our tests of audit adjustments. We

find that auditors’ reports are more likely to disclose accounting problems in the period prior

to stock-financed acquisitions. This suggests that not all of the attempts to overstate earnings

are corrected through audit adjustments. Thus, the quality of the audited financial

statements remains relatively low prior to stock-financed acquisitions even though audit

adjustments are helpful for correcting earnings overstatements.

Our study contributes to two distinct literatures. Prior studies in the auditing

literature typically examine the cross-sectional associations between various proxies for

earnings management and audit characteristics. For example, Becker et al. (1998) examine the

association between accruals and Big N auditors.5 A short-coming of prior studies is that the

audit characteristics examined in prior studies are typically endogenous (e.g., Big N) which

makes it difficult to draw inferences. Another short-coming is that the cross-sectional

association tests do not reveal what auditors actually do to curb earnings management. Our                                                             5 Lennox et al. (2016) focus on the consequences of audit adjustments whereas our study focuses on the determinants of audit adjustments.

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study uses information on audit adjustments to provide more direct evidence about how

exactly auditors curb earnings management. Moreover, we employ a difference-in-

differences research design to strengthen our causal inferences. In addition, our study

examines a setting – namely stock-financed acquisitions – in which managers are strongly

motivated to overstate earnings.6

Our study also contributes to the earnings management literature. Prior studies have

used accruals variables to test whether managers overstate earnings before stock-financed

acquisitions, but the extant findings are rather mixed (Erickson and Wang, 1999; Louis, 2004;

Heron and Lie, 2002; Pungaliya and Vijh, 2009).7 The mixed results could be attributable to

well-documented biases and noise in accruals. The biases are particularly problematic

around significant corporate events such as acquisitions (Hribar and Collins, 2002). The

biases are potentially even more problematic when companies pay for acquisitions using

stock rather than cash because stock-financed acquirers tend to have stronger growth

prospects than cash-financed acquirers, which means that stock-financed acquirers tend to

have larger signed accruals even if they are not engaged in earnings management. Pungaliya

and Vijh (2009) argue that this could explain why some studies have found abnormally large

accruals prior to stock-financed acquisition. 8 Moreover, accruals are notoriously noisy

measures of earnings management (Dechow et al., 1995; Subramanyam, 1996; Dechow and

Dichev, 2002; Tucker and Zarowin, 2006; Dechow et al., 2010). This could explain why some

                                                            6 There is some experimental evidence on auditors’ decisions to waive proposed audit adjustments. For example, Cohen et al. (2011) find that auditors are more likely to waive adjustments when the CEO has more influence over the independence of the audit committee. Our study is different because we focus on non-waived rather than waived audit adjustments and we examine the setting of stock-financed acquisitions. 7 Erickson and Wang (1999) and Louis (2004) report significant evidence of earnings management. In contrast, Heron and Lie (2002) and Pungaliya and Vijh (2009) find insignificant results using larger samples and alternative research designs. 8 The problem of bias is greater when researchers calculate working capital accruals from the balance sheet (Hribar and Collins, 2002). This type of bias is also affected by the method of financing because companies have less need to convert working capital into cash when acquisitions are financed using stock rather than cash (Collins et al., 2012).

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accruals studies fail to find significant evidence of earnings management prior to stock-

financed acquisitions (Heron and Lie, 2002; Pungaliya and Vijh, 2009). Another explanation

for the insignificant results is that managers’ attempts to overstate earnings are detected and

corrected by auditors before the audited financial statements are released to investors.

The confounding factors that create bias and noise in accruals affect both the pre-

audit and audited earnings of a given company in the same fiscal year. These confounds are

eliminated in our analysis of audit adjustments because an audit adjustment captures the

difference between pre-audit and audited earnings (see Section 2.3 for further details). Our

audit adjustments results are consistent with managers attempting to overstate earnings

prior to stock-financed acquisitions and with auditors correcting these overstatements before

the audited financial statements are issued to investors.

Section 2 discusses the prior literature on earnings management prior to stock-

financed acquisitions and the role of auditors in curbing earnings management. It then

develops our hypothesis for audit adjustments. Section 3 describes the difference-in-

differences research design and presents the sample and descriptive statistics. Section 4

reports the main results for audit adjustments. Section 5 reports the results of supplementary

analyses examining audit reporting decisions, propensity score matching, and audit fees.

Section 6 concludes.

2. Prior research and hypothesis development

2.1 Earnings management prior to stock-financed acquisitions

Prior studies hypothesize that companies are motivated to manage earnings upward prior to

stock-financed acquisitions. This earnings management hypothesis has been tested using

accruals variables. In a sample of 55 stock-financed acquisitions, Erickson and Wang (1999)

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find that signed discretionary accruals are larger in the period before stock-financed

acquisitions compared with the period afterwards. Louis (2004) examines working capital

accruals for a sample of 236 stock-financed acquirers. Similar to Erickson and Wang (1999),

Louis (2004) finds that working capital accruals are larger prior to stock-financed

acquisitions compared with afterwards. 9, 10

Unfortunately, accruals are susceptible to producing false positives in tests of

earnings management. The risk of false positives is particularly high when researchers

examine changes in accruals around significant corporate transactions such as acquisitions

and stock offerings (Hribar and Collins, 2002; Dechow et al., 2010; Ball, 2013).11 Moreover,

companies tend to pay for acquisitions using stock rather than cash when they expect that

they will need to retain cash to finance future growth (Collins et al., 2012). Thus, stock-

financed acquirers typically have better growth prospects than cash-financed acquirers.

Moreover, high-growth companies tend to have larger signed accruals (Fairfield et al., 2003).

Therefore, stock-financed acquirers are likely to exhibit larger signed accruals due to their

strong growth prospects even if they are not managing earnings upwards (Pungaliya and

Vijh, 2009).12 In addition, when an acquirer chooses to finance the acquisition using its own

stock rather than cash, the acquirer has less need to generate cash from its non-cash working

                                                            9 Erickson and Wang (1999) and Louis (2004) also examine samples of cash-financed acquisitions but they do not use the cash-financed acquisitions to conduct difference-in-differences tests. 10 Gong et al. (2008) report that abnormal accruals are significantly greater than zero prior to 395 stock-financed acquisitions (see their Table 3). However, Gong et al. (2008) do not examine abnormal accruals during the post-acquisition period and they do not compare the abnormal accruals of stock-financed acquirers to the abnormal accruals of cash-financed acquirers. 11 Ball (2013, p. 851) comments that: “In study after study, an explanation of the reported results that does not involve manipulation is at least as plausible as one that does.” 12 Louis (2004) finds that stock-financed acquirers have significantly lower book-to-market ratios compared with cash-financed acquirers, which suggests that stock-financed acquirers have better growth opportunities. However, Louis (2004) does not control for the book-to-market ratio in his examination of abnormal accruals (see his Table 4). Pungaliya and Vijh (2009) argue that controlling for growth opportunities overturns the conclusion that companies manage earnings upwards prior to stock-financed acquisitions. Consistent with these studies, we find that stock-financed acquirers have better growth opportunities than cash-financed acquirers and we control for this in our research design.

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capital (e.g., by selling inventory or collecting accounts receivable). Therefore, a stock-

financed acquirer is less likely to reduce its non-cash working capital and is more likely to

increase its non-cash working capital prior to the acquisition date. This could also explain

why some studies find abnormally large working capital accruals prior to stock-financed

acquisitions.

There are two accruals studies that find insignificant results using larger samples

than those used by Erickson and Wang (1999) and Louis (2004). Heron and Lie (2002)

examine 427 stock-financed acquisitions, 342 cash-financed acquisitions, and 90 acquisitions

that are financed using both stock and cash. Across the three groups, Heron and Lie (2002)

find no significant association between abnormal accruals and the method of acquisition

financing.13 Pungaliya and Vijh (2009) examine 895 stock-financed acquisitions and 1,719

cash-financed acquisitions. After controlling for the effects of growth on discretionary

accruals, they find no evidence of upward earnings management prior to stock-financed

acquisitions. They suggest that the significant results of Erickson and Wang (1999) and Louis

(2004) might be attributable to the strong growth prospects of stock-financed acquirers rather

than upward earnings management.

On the other hand, the insignificant results in Heron and Lie (2002) and Pungaliya

and Vijh (2009) do not necessarily mean that companies do not attempt to manage earnings

upwards prior to stock-financed acquisitions. First, auditors can help to ensure that any

overstatements of the pre-audit earnings are detected and corrected during the audit. These

audit corrections mean that the audited earnings may not be overstated even if the pre-audit

earnings were overstated. We test this by directly examining audit adjustments to reported

earnings. Second, abnormal accruals are rather noisy measures of earnings management

                                                            13 Heron and Lie (2002) acknowledge the limitation that their earnings management tests rely on annual data. To the extent that companies overstate their (unaudited) quarterly earnings rather than their annual earnings, the tests of Heron and Lie (2002) may lack sufficient power to detect earnings management 

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(e.g., Dechow et al., 1995). Therefore, the insignificant results of Heron and Lie (2002) and

Pungaliya and Vijh (2009) could be attributable to low power tests (i.e., false negatives)

rather than an absence of earnings management. We examine this explanation by testing

whether pre-audit accruals have sufficient power to detect the earnings overstatements that

are corrected by auditors.

2.2 The effects of auditing on earnings management

Ours is not the first study to examine the effects of auditing on earnings management.

Indeed, there is a large auditing literature that correlates proxies for earnings management

with various audit characteristics, such as audit firm size (Becker et al., 1998; Francis et al.,

1999; Khurana and Raman, 2004; Lennox and Pittman, 2010; Chen et al., 2011), audit office

size (Francis and Yu, 2009; Francis et al., 2013), non-audit fees (Frankel et al., 2002; Ashbaugh

et al., 2003; Chung and Kallapur, 2003; Ferguson et al., 2004), auditor tenure (Johnson et al.,

2002; Myers et al., 2003; Chen et al., 2008; Chi et al., 2009; Davis et al., 2009), auditor industry

expertise (Balsam et al., 2003; Gul et al., 2009; Reichelt and Wang 2010), and audit market

concentration (Boone et al., 2012; Francis et al., 2012; Newton et al., 2013).

Although many studies have tried to assess the impact of auditing on earnings

management, they have been unable to provide direct evidence on how exactly auditors curb

earnings management. We address this limitation by examining the audit adjustments that

are booked to earnings. Another limitation is that many auditing studies rely on accruals

variables to identify earnings management. This is potentially problematic because accruals

contain significant biases and noise, particularly around significant transactions such as

acquisitions and equity offerings (Hribar and Collins, 2002; Dechow et al., 2010; Ball, 2013).

The biases increase the odds that a researcher will conclude that earnings management exists

when in fact it is absent (i.e., a false positive). On the other hand, noise increases the odds

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that a researcher will fail to find significant evidence of earnings management when in fact it

exists (i.e., a false negative).14

Moreover, simple cross-sectional analyses of the correlations between earnings

management and audit characteristics are prone to generating false positives because audit

characteristics are typically endogenous. For example, Lawrence et al. (2011) argue that the

negative correlation between earnings management and audit firm size is attributable to

client characteristics rather than superior audits by the Big N audit firms. They present

evidence that the Big N quality differential disappears when Big N clients are matched to

non-Big N clients based on their observable characteristics.15 Likewise, Minutti-Meza (2013)

reports that the negative correlations between earnings management and auditor industry

expertise become insignificant when clients are matched using propensity scores or simply

with client size.

We address these limitations in several ways. First, instead of conducting cross-

sectional comparisons, we employ a difference-in-differences research design that controls

for time-invariant differences between the treatment group (stock-financed acquirers) and

the control group (cash-financed acquirers). Second, we strengthen our inferences by

focusing on a specific setting, namely stock-financed acquisitions, where managers are

motivated to overstate earnings. Third, we use information on audit adjustments to identify

the mechanism though which auditors help to curb earnings management. Fourth,

                                                            14 Ball (2013, p. 850) argues that false positives are common in studies that use accruals variables to measure earnings management: “A powerful cocktail of authors’ strong priors, strong ethical and moral views, limited knowledge of the determinants of accruals in the absence of manipulation, and willingness to ignore correlated omitted variables in order to report a result, seems to have fostered a research culture that tolerates grossly inadequate research designs and publishes blatantly false positives. In my opinion it has, anyway. Of course earnings management goes on. Agency costs are positive. People have been tried and convicted. I personally have testified in several high profile cases and some of the malfeasance that took place in them was disgusting. But whether the typical research paper in this dismally unscientific literature has come even remotely close to reliably documenting ‘earnings management’ is another matter entirely.”

15 Contrary to Lawrence et al. (2011), DeFond et al. (2016) find that client characteristics do not fully explain the quality differential between Big N and non-Big N audits.

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examining audit adjustments allows us to avoid the non-accounting factors that create bias

and noise in accruals. This fourth point is further explained in Section 2.3.

2.3 Audit adjustments and the confounding factors that afflict accruals

An audit adjustment captures the difference between the pre-audit earnings reported by

management to the auditor (EPRE) and the audited earnings reported to investors at the end

of the audit (EAUD). The pre-audit earnings are influenced by the accounting choices of

management (AMGMT), while the audited earnings are influenced by the accounting choices of

both management and the audit firm (AMGMT+AUD).

The pre-audit earnings and the audited earnings correspond to the same fiscal year-

end. Therefore, they are equally affected by confounding factors - such as the company’s

growth prospects - that are not discretionary accounting choices (NA). This is important in

our setting because stock-financed acquirers tend to have relatively strong growth prospects

and companies with strong growth prospects tend to have larger accruals even if they are

not engaged in earnings management (Pungaliya and Vijh, 2009).

Our analysis of audit adjustments allows us to avoid these confounding factors. To

illustrate this, we express the equations for pre-audit earnings and audited earnings as

follows:

EPRE = AMGMT + NA (1)

EAUD = AMGMT+AUD + NA (2)

where EPRE = pre-audit earnings, EAUD = audited earnings, AMGMT = management’s

discretionary accounting choices over pre-audit earnings; AMGMT+AUD = management’s and

auditor’s discretionary accounting choices over audited earnings; NA = any factors that affect

pre-audit and audited earnings but are not discretionary accounting choices (e.g., a

company’s growth prospects).

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An audit adjustment to earnings is simply the difference between audited earnings

and pre-audit earnings. Subtracting (1) from (2) gives the audit adjustment to earnings:

EAUD - EPRE = AMGMT+AUD - AMGMT (3)

Eq. (3) shows that the audit adjustment (EAUD - EPRE) depends on the accounting choices of

management and the audit firm (i.e., AMGMT+AUD and AMGMT) but the adjustment variable is

not affected by the NA factors because NA is differenced out when we subtract (1) from (2).

This is important because NA are the non-accounting factors, such as a company’s growth

prospects, that can potentially confound the analyses of accruals. Therefore, examining the

determinants of audit adjustments allows us to avoid such confounds.

2.4 Hypothesis development

When an acquisition is financed using the acquirer’s stock, the shareholders of the target

company receive a specified number of the acquirer’s shares in return for their shares in the

target. The exchange ratio is usually agreed by the acquirer and target before any public

announcement of the acquisition (Erickson and Wang, 1999). The ratio is determined by the

relative stock prices of the acquirer and target (or the target’s appraised price if the target is a

private company). Consequently, a stock-financed acquirer has an incentive to overstate its

earnings in order to boost its stock price in the period before the M&A announcement date.16

Auditors are responsible for testing whether the financial statements are fairly

presented. If an auditor finds that the acquirer’s pre-audit earnings are overstated, the

auditor should propose a downward adjustment to earnings. The acquirer can accept the

proposed adjustment in which case earnings are adjusted downwards, or the acquirer can

refuse to make the adjustment. If the acquirer refuses to make the adjustment, the auditor

                                                            16 Studies in China find significantly positive earnings response coefficients, signifying that stock prices increase when Chinese companies report higher earnings (e.g., Gul et al., 2003).

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can disclose the accounting problem in the audit report. In practice, an auditor’s threat to

disclose the problem in the audit report is often (but not always) sufficient to persuade the

client to book the proposed adjustment.

We expect companies to overstate earnings prior to stock-financed acquisitions. We

also expect auditors to detect and correct these earnings overstatements. We therefore

hypothesize that earnings are adjusted downwards when audits take place in the period

prior to the announcement of stock-financed acquisitions.

H1 Auditors require larger downward adjustments to earnings prior to stock-financed acquisitions.

There are several reasons why the results may not support our hypothesis. First, Ball

and Shivakumar (2008) argue that managers are motivated to report conservatively prior to

equity offerings, which is opposite to the traditional view that managers overstate earnings

in order to boost the stock price (Teoh et al., 1998a, 1998b). Ball and Shivakumar (2008) argue

that equity issuers report conservatively because the IPO prospectuses are closely scrutinized

by auditors and other interested parties (e.g., potential litigants, regulators, the press, and

investors). In our setting, however, auditors may not know about the future stock-financed

acquisition until the point at which the M&A is publicly announced. Therefore, the financial

statements of stock-financed acquirers are unlikely to receive extra scrutiny from the auditor

in the period prior to the M&A announcement.17

Second, managers who deliberately overstate earnings may try to hide the

misstatements from their auditors (Botosan et al., 2016). This would make it harder for

                                                            17 The stock-financed acquirers have different characteristics than the cash-financed acquirers. Therefore, auditors may be able to anticipate the method of financing even if the acquirers do not inform their auditors. In Section 5.3, we use propensity score matching to match each stock-financed acquirer to a cash-financed acquirer and the resulting matched samples are very similar in terms of their observable characteristics. As reported in Section 5.3., our inferences for H1 are unchanged using the matched sample. This suggests that our results are not affected by the ability of auditors to anticipate the acquirer’s method of financing.

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auditors to detect and correct earnings overstatements prior to stock-financed acquisitions.

Relatedly, an acquirer may choose not to inform its auditor about a future acquisition until

the acquirer is ready to make a public announcement. One reason is that the acquirer would

want as few people as possible to know about its private negotiations with the target

company. Another reason is that the acquirer may not want the auditor to closely scrutinize

its financial statements because close scrutiny could result in more downward adjustments

to earnings and higher audit fees.18

Finally, auditors in China face a relatively low threat of being sued (Chan et al., 2006;

Chen et al., 2011). This means that auditors in China have less incentive to detect and correct

earnings overstatements.

3. Research design, sample, and descriptive statistics

3.1 Difference-in-differences research design

There is a downward adjustment to earnings when the audited earnings are less than the

pre-audit earnings; i.e., EAUD, it < EPRE, it. Under H1, auditors require larger downward

adjustments prior to stock-financed acquisitions. We test H1 by estimating the following

model that explains the absolute magnitudes of downward audit adjustments to earnings:

|ADJ_DNit |= α0 + α1 STOCKi + α2 BEFOREt + α3 STOCKi × BEFOREt + CONTROLS + u (4)

The dependent variable in eq. (4) is constructed using a company’s pre-audit earnings (EPRE,it)

and its audited earnings (EAUD,it). In particular, |ADJ_DNit| equals (|EAUD,it – EPRE,it|)/

|EPRE,it| when earnings are adjusted downwards (EAUD,it < EPRE,it), and zero when earnings

are not adjusted downwards (EAUD,it ≥ EPRE,it). Therefore, |ADJ_DNit| takes positive values

                                                            18 Based on our own audit experiences, an acquirer would usually not tell its auditor about a future acquisition until the acquirer is ready to make a public announcement. Consistent with auditors being unaware of impending acquisitions, we find in Section 5.4 that auditors do not negotiate higher audit fees before the public announcement of a stock-financed acquisition. 

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when earnings are adjusted downwards, and zero values otherwise. We estimate eq. (4)

using tobit regression because |ADJ_DNit| is truncated at zero.

As an alternative to using the absolute value of pre-audit earnings as the

denominator, we also scale by the absolute value of pre-audit total assets. The alternative

dependent variable (|ADJ_DN1it|) equals (|EAUD,it – EPRE,it|) / |TA PRE,it| when earnings are

adjusted downwards (EAUD,it < EPRE,it), and zero when earnings are not adjusted downwards

(EAUD,it ≥ EPRE,it), where TA PRE,it is pre-audit total assets. Once again, |ADJ_DN1it| takes

positive values when earnings are adjusted downwards, and zero values otherwise.

The STOCKi variable equals one if the acquisition of company i is financed using

stock, and zero if it is financed using cash.19 The BEFOREt variable equals one in the most

recent fiscal year immediately before the M&A announcement date, and zero in the first

fiscal year immediately after the M&A completion (or termination) date. The timing for

BEFOREt is illustrated in Figure 1. For example, when the M&A is first announced on July 1,

2010, the BEFOREt variable equals one for the previous fiscal year-end (i.e., Dec 31, 2009).20

When the M&A is completed or terminated on July 1, 2011, the BEFOREt variable equals zero

for the following fiscal year-end (i.e., Dec 31, 2011). Our treatment variable is the interaction,

STOCKi × BEFOREt. Under H1, auditors require larger downward adjustments to earnings

prior to stock-financed acquisitions. Therefore, we predict a positive coefficient on STOCKi ×

BEFOREt in eq. (4) (i.e., α3 > 0).

[INSERT FIGURE 1 HERE]

Similar to eq. (4), we also examine the upward adjustments to earnings:

|ADJ_UPit| = β0 + β1 STOCKi + β2 BEFOREt + β3 STOCKi × BEFOREt + CONTROLS + u (5)

                                                            19 All the acquisitions in our sample are financed using either cash or stock but not a combination of both stock and cash. 20 In China, every company is required to have a December 31 fiscal year-end.

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The |ADJ_UPit| variable equals (|EAUD,it – EPRE,it|)/|EPRE,it| when earnings are adjusted

upwards (EAUD,it > EPRE,it), and zero when earnings are not adjusted upwards (EAUD,it ≤

EPRE,it). We also estimate an alternative specification where upward adjustments are scaled by

the absolute value of pre-audit assets rather than the absolute value of pre-audit earnings.

The |ADJ_UP1it| variable equals (|EAUD,it – EPRE,it|)/|TAPRE,it| when earnings are adjusted

upwards, and zero when earnings are not adjusted upwards. We do not have a hypothesis

for upward adjustments so we do not make a prediction about the coefficient on STOCKi ×

BEFOREt in eq. (5).

3.2 Sample

The data on audit adjustments starts in 2006. We require one year of data prior to the M&A

announcement date when coding the pre-period (BEFOREt = 1), so our sample comprises

M&A deals announced on or after January 1, 2007. The sample ends with deals announced in

2013 because we require data for the year after the M&A completion or termination date

(BEFOREt = 0) and 2014 is the last year that audit adjustment data are available to us. Panel

A of Table 1 shows how the sample is constructed. We begin with 2,466 M&A

announcements where data are available from the CSMAR database in the year before the

M&A announcement and the year after the M&A completion or termination date. 21 This

yields an initial sample of 4,932 company-year observations, with two observations for each

M&A deal (= 2 × 2,466). We lose 262 observations (131 deals) where audit adjustment data

                                                            21 We manually checked each M&A record in the CSMAR database and traced the outcome of each verified M&A deal by the end of our sample period. There are 41 terminated stock-financed acquisitions and 118 terminated cash-financed acquisitions in our sample. Our results are qualitatively unchanged if we drop the terminated deals from the sample. In untabulated tests, we find that stock-financed acquisitions are more likely to be terminated than cash-financed acquisitions. This is consistent with stock-financed acquisitions having a higher likelihood of termination due to the risk of the acquirer’s equity being overvalued. Unfortunately, the number of terminated stock-financed acquisitions is too small for us to test whether stock-financed acquisitions are more likely to be terminated when there is a downward audit adjustment to earnings.

16  

are missing in the Ministry of Finance (MOF) database. We also drop 148 observations (74

deals) where there are inconsistencies between the CSMAR and MOF databases in the values

of audited earnings (EAUD,it).22

[INSERT TABLE 1 HERE]

Panel B of Table 1 reports the number of deals by announcement year. There are 273

deals in the first year (2007) and 285 in the final year (2013). The most deals are announced in

2008 (378), while 2007 has the fewest (273). In total, there are 2,035 cash-financed acquisitions

and 226 stock-financed acquisitions. Panel C shows there are 4,070 observations relating to

the cash-financed acquisitions (STOCKi = 0) and 452 observations relating to the stock-

financed acquisitions (STOCKi = 1).

3.3 Descriptive statistics

Table 2 presents descriptive statistics for all the variables used in our analyses. The

definitions of each variable are provided in Appendix A.

[INSERT TABLE 2 HERE]

As a percentage of the absolute value of pre-audit earnings, we find that the mean

signed audit adjustment (ADJit) is negative (-3.4%) while the median adjustment is zero

(0.0%). These statistics reflect that 47.81% of the audits in our sample are subject to

downward earnings adjustments, 31.11% have no adjustment to earnings, and 21.07% are

subject to upward earnings adjustments. Therefore, downward adjustments occur more than

twice as often as upward adjustments which is consistent with prior research (Kinney and

                                                            22 Lennox et al. (2016) find that the inconsistencies are partly explained by the MOF personnel entering data from the parent company accounts rather than the group accounts. After taking into account the rounding differences between the CSMAR and MOF databases, we define the two databases as being inconsistent with each other when the reported difference in audited earnings is at least ±1%.

17  

Martin, 1994; Lennox et al., 2016). Moreover, downward adjustments are typically larger

than upward adjustments. For example, Table 2 shows that the tenth and ninetieth

percentiles of signed adjustments (ADJit) are -16.2% and 2.5% respectively. Not surprisingly,

the adjustment magnitudes are smaller when we scale adjustments using the absolute value

of pre-audit total assets (ADJ1it). Table 2 presents similar descriptive statistics for the

absolute magnitudes of downward adjustments (|ADJ_DNit| and |ADJ_DN1it|) and

upward adjustments (|ADJ_UPit| and |ADJ_UP1it|).

Table 2 also presents descriptive statistics for the accruals variables. We construct two

alternative measures: 1) performance-matched total accruals (PMA), and 2) discretionary

accruals (DA) estimated using the modified Jones model.23 We expect that managers to

overstate their pre-audit earnings prior to stock-financed acquisitions, so we focus on the

absolute magnitudes of income-increasing pre-audit accruals (i.e., |PMA_UPPRE,it| and

|DA_UPPRE,it|). However, for the sake of completeness, we also examine the absolute

magnitudes of income-decreasing pre-audit accruals (|PMA_DNPRE,it| and |DA_DNPRE,it|),

the absolute magnitudes of income-increasing audited accruals (|PMA_UPAUD,it| and

|DA_UPAUD,it|), and the absolute magnitudes of income-decreasing audited accruals

(|PMA_DNAUD,it| and |DA_DNAUD,it|).

Finally, Table 2 presents descriptive statistics for the control variables. We control for

company size (SIZEit), the market-to-book ratio (MBit), and leverage (LEVit). We include an

indicator for state-owned enterprises (SOEit) because government ownership is common in

China (Wang et al., 2008). We control for corporate governance characteristics using the

proportion of independent directors on the board (IN_DIRit) and the number of board

members (BD_SIZEit). We control for a company’s performance using annual buy-and-hold

stock returns (BHRETit). We control for liquidity using the ratio of cash to total assets                                                             23 In untabulated tests, we also examine total accruals and performance-matched discretionary accruals. Our inferences are unchanged using these alternative measures of earnings management.

18  

(CASHit) and we also control for the company’s age (AGEit). Prior research finds that the Big

10 audit firms in China supply higher quality audits (DeFond et al., 2000; Chen et al., 2001),

so we also include an indicator for the Big 10 audit firms (BIG10it). Finally, we include an

indicator variable for audit firm changes (AUDCHit).

4. Main Results

4.1 Univariate results

Table 3 reports the univariate difference-in-differences tests for H1, which predicts that

auditors require larger downward adjustments to earnings prior to stock-financed

acquisitions. The downward adjustments are scaled by the absolute value of pre-audit

earnings or the absolute value of pre-audit assets (|ADJ_DNit| and |ADJ_DN1it|). We find

that the difference-in-differences tests are highly significant for both of the downward

adjustment variables (t-stats. = 4.28, 4.24). Therefore, consistent with H1, the downward

audit adjustments to earnings are significantly larger prior to the public announcement of

stock-financed acquisitions. Panel B reports the univariate results for the upward

adjustments to earnings (|ADJ_UPit| and |ADJ_UP1it|). The difference-in-differences tests

for upward adjustments are insignificant, signifying that auditors do not require larger

upward adjustments prior to stock-financed acquisitions.

[INSERT TABLE 3 HERE]

4.2 Multivariate results

Table 4 reports the results from tobit regressions, where the dependent variables capture the

downward adjustments (|ADJ_DNit| and |ADJ_DN1it|) and upward adjustments

(|ADJ_UPit| and |ADJ_UP1it|). Cols. (1) and (2) show that the coefficients on the treatment

variable, STOCKi × BEFOREt, are significantly positive in the models of downward

19  

adjustments (t-stats. = 3.039, 2.779). Therefore, consistent with H1, auditors require

significantly larger downward adjustments to earnings before the public announcement of

stock-financed acquisitions.

[INSERT TABLE 4 HERE]

Cols. (3) and (4) show that the coefficients on STOCKi × BEFOREt are negative in the

models of upward adjustments. This is consistent with auditors requiring smaller upward

adjustments prior to the public announcement of stock-financed acquisitions. However, in

contrast to the significant results for downward adjustments, the coefficients on STOCKi ×

BEFOREt are insignificant in the models of upward adjustments (t-stats. = -1.508, -1.286).

Results for the control variables show that the downward adjustments tend to be

significantly smaller when companies are larger (SIZEit) and have stronger stock return

performance (BHRETit). Downward adjustments are positively related to leverage (LEVit),

suggesting that companies with high leverage are more likely to overstate pre-audit

earnings. Consistent with Cohen et al. (2011), we also find that auditors require larger

downward adjustments when boards have a higher proportion of independent directors

(IN_DIRit). The other control variables are insignificant in explaining downward

adjustments. Cols. (3) and (4) find that upward adjustments are significantly smaller when

companies are younger (AGEit), have lower market-to-book ratios (MBit), larger boards

(BD_SIZEit), and the audit firm is newly appointed to the engagement (AUDCHit).

5. Supplementary analyses

5.1 Auditors’ reporting choices

Although an auditor can request an adjustment, it is the company’s own management that

ultimately decides whether to book the adjustment. This reflects the principle that a

company is responsible for preparing the financial statements, while the auditor is

20  

responsible for issuing an opinion on the company’s financial statements. When a company

refuses to make an adjustment, the auditor can respond by disclosing the problem in the

audit report. Therefore, to the extent that accounting problems are not fully resolved through

audit adjustments, we would expect to find more problems being disclosed in audit reports

prior to stock-financed acquisitions.

Panel A of Table 5 shows the different types of audit opinions in our sample. There

are 4,368 (96.59%) clean opinions and 154 unclean opinions (3.41%). Of the 154 unclean

opinions, there are 33 unqualified opinions that are modified due to accounting issues, 27

opinions that are qualified due to accounting issues, 2 opinion disclaimers that mention

accounting issues, 86 unclean opinions that are modified due to fundamental uncertainties

relating to going-concern, and 6 unclean opinions that are modified due to fundamental

uncertainties relating to impending lawsuits.

[INSERT TABLE 5 HERE]

The fundamental uncertainty opinions do not directly reference accounting issues.

Nevertheless, these types of opinion could indicate a lack of accounting conservatism

because auditors issue going-concern opinions to warn investors that the book values of

assets (reported under the going-concern assumption) are substantially higher than their

liquidation values. That is, auditors are more likely to issue going-concern modifications

when companies refuse to write down the book values of assets to their liquidation values.

Thus, going-concern modifications can signal a lack of accounting conservatism. On the

other hand, a going-concern modification can also reflect the auditor’s assessment that a

company is financially distressed. Therefore, it is not necessarily the case that the going-

concern modifications signal low financial reporting quality.

21  

With this in mind, we create two audit opinion variables: UNCLEANit and

OPINIONit. The UNCLEANit variable equals zero for the 4,368 clean opinions, and one for the

154 unclean opinions. The OPINIONit variable equals zero for the 4,368 clean opinions; one

for the 62 (= 33+27+2) unclean opinions that directly reference accounting problems; and two

for the 92 (= 86+6) unclean opinions that disclose fundamental uncertainties relating to

going-concern and impending lawsuits. 24

Panel B of Table 5 shows that 16.8% of audit opinions are unclean in the year prior to

stock-financed acquisitions, whereas only 7.1% are unclean in the year after stock-financed

acquisitions. In the sample of cash-financed acquisitions, 2.3% of audit opinions are unclean

in the pre-acquisition period and 2.7% are unclean in the post-acquisition period. The

difference-in-differences is 10.1% (= (16.8% - 7.1%) – (2.3% - 2.7%)) and is statistically

significant at the 1% level (z-stat. = 3.05). Therefore, auditors are significantly more likely to

issue unclean opinions prior to stock-financed acquisitions.

Panel C of Table 5 reports results for the difference-in-differences regressions, where

the dependent variables are UNCLEANit and OPINIONit.

UNCLEANit = 0 + 1 STOCKi + 2 BEFOREt + 3 STOCKi × BEFOREt + CONTROLS + u (6)

OPINIONit = γ0 + γ1 STOCKi + γ2 BEFOREt + γ3 STOCKi × BEFOREt + CONTROLS + u (7)

Eq. (6) is estimated using ordinary logit because the dependent variable is binary

(UNCLEANit = 0, 1); Eq. (7) is estimated using multinomial logit because the dependent

variable takes three values (OPINIONit = 0, 1, 2). Col. 1 of Table 5 presents equation (6) for

the unclean opinions; Col. 2 presents the equation for accounting-related problems (i.e.,

where OPINIONit = 1 in eq. (7)); Col. (3) presents the equation for the uncertainty-related

                                                            24 In our sample, some of the audit reports that disclose accounting-related problems also disclose fundamental uncertainties about going-concern. We assign these companies to the OPINIONit = 1 group because we are primarily interested in the audit reports that disclose accounting-related problems.

22  

opinions (i.e., where OPINIONit = 2 in eq. (7)). We employ the same set of control variables as

in Table 4 except that we add a control for the return on assets (ROA) because auditors are

more likely to issue going-concern opinions when companies are less profitable.25

Col. (1) finds a significant positive coefficient on STOCKi × BEFOREt in the regression

for unclean opinions (z-stat. = 2.110). Therefore, consistent with the univariate difference-in-

differences test reported in Panel B of Table 5, we find that auditors are more likely to issue

unclean opinions prior to stock-financed acquisitions. Col. (2) also finds a significant positive

coefficient on STOCKi × BEFOREt in the regression for the accounting-related unclean

opinions (z-stat. = 2.249). Therefore, auditors are more likely to disclose accounting problems

prior to stock-financed acquisitions. In contrast, Col. (3) finds an insignificant positive

coefficient on STOCKi × BEFOREt in the regression for the uncertainty-related unclean

opinions (z-stat. = 0.636). Together, these results imply that the significant results in Col. (1)

are mainly driven by the accounting-related unclean opinions rather than the uncertainty-

related unclean opinions.

Overall, these results suggest that auditors are more likely to disclose accounting

problems before companies engage in stock-financed acquisitions. This also suggests that not

all of the accounting problems are resolved through audit adjustments; i.e., there are

unresolved accounting problems in the audited financial statements even after the audit

adjustments have been booked. However, we are cautious in our conclusions as there are

only 154 unclean opinions and there are only 62 opinions that directly reference accounting

problems in the audited financial statements.

5.2 Accruals prior to stock-financed acquisitions

                                                            25 We do not include ROA in the models of audit adjustments because ROA is mechanically correlated with audit adjustments and both variables are a function of audited earnings.

23  

Prior studies use accruals variables to test for the existence of earnings management prior to

stock-financed acquisitions but they find mixed results (Erickson and Wang, 1999; Louis,

2004; Heron and Lie, 2002; Pungaliya and Vijh, 2009). Our results for audit adjustments are

consistent with auditors detecting and correcting managers’ attempts to overstate earnings

prior to stock-financed acquisitions (Table 4). Therefore, to the extent that the accruals

variables are able to detect earnings overstatements, we ought to find significantly larger

income-increasing accruals in the year prior to the announcement of stock-financed

acquisitions.

Table 6 reports the results for pre-audit accruals and audited accruals. We employ the

same set of control variables as in Table 4 but the results for the control variables are

suppressed for the sake of brevity. Panel A presents the performance-matched accruals,

while Panel B presents the discretionary accruals estimated using the modified Jones model.

We find that the STOCKi × BEFOREt coefficients are statistically insignificant for both the

pre-audit accruals and the audited accruals. Therefore, the accruals variables are unable to

detect managers’ attempts to overstate earnings prior to stock-financed acquisitions. This

suggests that the accruals variables generate false negatives because they are noisy proxies

for financial reporting quality. That is, the accruals variables generate insignificant results

despite that auditors are correcting earnings overstatements in the pre-audit financial

statements (Table 4) and auditors are disclosing accounting-related problems in the audited

financial statements (Table 5).

[INSERT TABLE 6 HERE]

5.3 Propensity-score matching

Our difference-in-differences research design helps to control for time-invariant differences

between the stock-financed and cash-financed acquirers. In particular, each acquirer is used

24  

as its own control because we compare audit outcomes in the years before versus after the

acquisition. Employing a difference-in-differences design is helpful, but there could still be a

concern that the results for STOCKi × BEFOREt are confounded by time-varying differences

between the stock-financed and cash-financed acquirers during the pre-acquisition year. To

address this concern, we match each stock-financed acquirer to a cash-financed acquirer

based on their observable characteristics in the year prior to the acquisition announcement

(i.e., when BEFOREt = 1).

We begin the matching analysis by estimating a logit model, where the dependent

variable (STOCKi) equals one if company i finances an acquisition in year t using stock, and

zero if the acquisition is financed using cash. The independent variables are measured in the

year immediately before the acquisition announcement: SIZEit-1, MBit-1, LEVit-1, SOEit-1,

BD_SIZEit-1, IN_DIRit-1, BHRETit-1, CASHit-1, AGEit-1, ROAit-1, BIG10it-1, and AUDCHit-1. The

logit results from this prediction model are shown in Appendix B.26 We find that companies

use stock rather than cash to finance acquisitions when they are smaller (SIZEit-1), have

higher market-to-book ratios (MBit-1), have higher leverage (LEVit-1), are older (AGE it-1), and

less profitable (ROAit-1). We use the coefficients from the model in Appendix B to calculate

the predicted probabilities of using stock rather than cash. We then match each stock-

financed acquirer to a cash-financed acquirer. The mean (median) absolute difference in the

propensity score between the treatment sample and the matched control sample is 0.002

(0.000), with the largest difference being only 0.063.27

Next, we check that matching eliminates the observable differences between the

                                                            26 The number of acquisitions decreases to 2,248 in Appendix B because there are 13 cash-financed acquisitions where the acquirer belongs to an industry which perfectly predicts the method of financing. We do not lose any stock-financed acquisitions when we estimate the model in Appendix B. The model is estimated using logit rather than probit because the logit results give better covariate balance and covariate balance is an important consideration when using propensity score matching (Shipman et al., 2016). 27 We match with replacement but our results are similar if we match without replacement after imposing the same caliper of 0.063. Under this alternative treatment, the STOCKi × BEFOREt coefficients are positive and significant in Cols. (1) and (2) of Table 4 (t-stats. =2.074, 2.062).

25  

treatment and control groups. Appendix C reports the differences between the treatment

sample of stock-financed acquirers and: 1) the full control sample of cash-financed acquirers,

and 2) the matched control sample of cash-financed acquirers. Appendix C reveals

significant differences between the treatment sample and the full control sample for the

following variables: SIZEit-1 (t-stat. = -7.632), MBit-1 (t-stat. = 3.724), LEVit-1 (t-stat. = 7.240),

BD_SIZEit-1 (t-stat. = -1.932), CASHit-1 (t-stat. = -4.341), AGE it-1 (t-stat. = 5.606), ROAit-1 (t-stat. =

-4.340), BIG10it-1 (t-stat. = -3.012), and AUDCHit-1 (t-stat. = 2.595). Appendix C also shows that

all of these differences become small and statistically insignificant when the treatment

sample is compared to the matched control sample. Therefore, propensity score matching

achieves covariate balance between the treatment group and the matched control group

(Shipman et al., 2016).

Finally, we re-run our tests of H1 using the matched control group. The sample size

drops from 4,522 to 904 observations (452 observations correspond to the pre- and post-

periods for the 226 stock-financed acquisitions; another 452 observations correspond to the

pre- and post-periods for the 226 matched cash-financed acquisitions). Table 7 presents the

results. Consistent with H1, we find significant positive coefficients on STOCKi × BEFOREt in

the models for downward audit adjustments (t-stats. = 2.386, 1.786).28

[INSERT TABLE 7 HERE]

5.4 Audit fees

Our audit adjustment results suggest that managers overstate earnings prior to stock-

financed acquisitions and auditors discover such overstatements during the course of the audit.

Auditors typically negotiate audit fees before the start of the audit and research shows that

auditors negotiate higher fees when they anticipate that the audit engagement will be

relatively risky (e.g., Hope et al., 2016). To the extent that auditors are able to anticipate                                                             28 The STOCKi × BEFOREt coefficients also remain positive and significant in Cols. (1) and (2) of Table 5 (z-stats. = 2.291, 3,107) and insignificant in Col. (3) of Table 5 (z-stat. = -0.161).  

26  

impending stock-financed acquisitions, we would expect auditors to negotiate higher audit

fees at the start of the audit. On the other hand, we would not expect higher audit fees if

auditors are unable to anticipate future stock-financed acquisitions.29 To investigate this, we

estimate an audit fee model using the same difference-in-differences research design used in

our previous analyses. In untabulated tests, we find an insignificant positive coefficient on

STOCKi × BEFOREt in the audit fee model (t-stat. = 1.451). The lack of significance is likely

attributable to auditors being uninformed about future stock-financed acquisitions when

they negotiate audit fees at the start of the audit.

6. Conclusions

Ours is the first study to directly examine how audits affect earnings when managers are

motivated to overstate earnings prior to stock-financed acquisitions. We find that auditors

require larger downward adjustments to earnings in the year before companies finance their

acquisitions using equity. This is consistent with auditors helping to maintain high quality

financial reporting by detecting and correcting managers’ overstatements of pre-audit

earnings. In addition, we find that auditors are more likely to disclose accounting problems

in their audit reports when audits take place before stock-financed acquisitions. This is

consistent with auditors disclosing low quality financial reporting in the audited financial

statements at the end of the audit.

Our study helps to reconcile the mixed evidence in prior studies concerning the

existence of upward earnings management prior to stock-financed acquisitions. Erickson and

Wang (1999) and Louis (2004) find abnormally large signed accruals prior to stock-financed

                                                            29 Companies are motivated to not inform their auditors about impending stock-financed acquisitions because otherwise auditors would respond by conducting additional audit procedures and charging higher audit fees. In addition, companies would likely prefer to keep the negotiations secret until they are ready to make a public announcement to the market.

27  

acquisitions, whereas Heron and Lie (2002) and Pungaliya and Vijh (2009) obtain

insignificant results. Consistent with managers attempting to overstate earnings prior to

stock-financed acquisitions, we find larger downward audit adjustments to earnings. Our

results also provide two possible explanations for the insignificant results reported in prior

studies (Heron and Lie, 2002; Pungaliya and Vijh, 2009). First, prior studies test for earnings

management in the audited financial statements. However, the audited financial statements

provide less evidence of earnings management than the pre-audit financial statements

because audits help to detect and correct earnings overstatements. These audit corrections

make it harder for researchers to detect upward earnings management when using data from

the audited financial statements. Second, prior studies rely on accruals variables to detect

earnings management but our results suggest that accruals provide low power tests for

earnings management. In particular, we find insignificant results for pre-audit accruals even

though our analysis of audit adjustments shows that auditors detect and correct

overstatements of pre-audit earnings prior to stock-financed acquisitions.

28  

REFERENCES

Allen, F., J. Qian, Qian, M., 2005. Law, finance, and economic growth in China. Journal of Financial Economics 77 (1): 57-116.

Anilowski, C., Marcias, A., Sanchez, J., 2009. Target firm earnings management and the method of sale: Evidence from auctions and negotiations. Working paper.

Ashbaugh, H., LaFond, R., Mayhew, B. W., 2003. Do nonaudit services compromise auditor independence? Further evidence. The Accounting Review 78(3), 611–639.

Ball, R., 2013. Accounting informs investors and earnings management is rife: Two questionable beliefs. Accounting Horizons 27(4): 847-853.

Ball, R., Shivakumar, L., 2008. Earnings quality at initial public offerings. Journal of Accounting and Economics 45: 324-349.

Balsam, S., Krishnan, J., Yang, J. S., 2003. Auditor industry specialization and earnings quality. Auditing: A Journal of Practice and Theory 22(2), 71–97.

Becker, C. L., DeFond, M. L., Jiambalvo, J., Subramanyam, K. R., 1998. The effect of audit quality on earnings management. Contemporary Accounting Research 15(1), 1–24.

Boone, J. P., Khurana, I. K., Raman, K. K., 2012. Audit market concentration and auditor tolerance for earnings management. Contemporary Accounting Research 29(4),1171–1203.

Botosan, C., Boyle, E., Lewis-Western, M., 2016. The impact of audits on financial statement error in the presence of incentive and opportunity. Working paper, University of Utah.

Chan, K. H., K. Lin, Mo, P., 2006. A political–economic analysis of auditor reporting and auditor switches. Review of Accounting Studies 11 (1): 21-48.

Chen, C. J. P., Chen, S., Su, X., 2001. Profitability regulation, earnings management and modified audit opinions: evidence from China. Auditing: A Journal of Practice and Theory 20: 9–30.

Chen, C. Y., Lin, C. J., Lin, Y. C., 2008. Audit partner tenure, audit firm tenure, and discretionary accruals: Does long auditor tenure impair earnings quality? Contemporary Accounting Research 25(2), 415–445.

Chen, H., Chen, J. Z., Lobo, G. J., Wang, Y., 2011. Effects of audit quality on earnings management and cost of equity capital: Evidence from China. Contemporary Accounting Research 28(3), 892–925.

Chen, K.-C., Q. Cheng, Y. C. Lin, Y.-C. Lin, Xiao, X., 2016. Financial reporting quality of Chinese reverse merger firms: The reverse merger effect or the weak country effect? The Accounting Review Forthcoming.

Chen, S., Thomas, J., Zhang, F., 2016. Spring-loading future performance when no one is looking? Earnings and cash flow management around acquisitions. Review of Accounting Studies Forthcoming.

29  

Chen, S., S. Y. J. Sun, and D. Wu. 2010. Client importance, institutional improvements, and audit quality in China: An office and individual auditor level analysis. The Accounting Review 85 (1): 127-158.

Chen, Z., B. Ke, Yang, Z., 2013. Minority shareholders’ control rights and the quality of corporate decisions in weak investor protection countries: A natural experiment from China. The Accounting Review 88 (4): 1211-1238.

Chi, W., Huang, H., Liao, Y., Xie, H., 2009. Mandatory audit partner rotation, audit quality, and market perception: evidence from Taiwan. Contemporary Accounting Research 26(2), 359–391.

Chung, H., Kallapur, S., 2003. Client importance, nonaudit services, and abnormal accruals. The Accounting Review 78(4), 931–955.

Cohen, J. R., Gaynor, L. M., Krishnamoorthy, G., Wright, A. M., 2011. The impact on auditor judgements of CEO influence on audit committee independence. Auditing: A Journal of Practice and Theory 30(4): 129-147.

Collins, D., Pungaliya, R., Vijh, A., 2012. The effects of firm growth and model specification choices on tests of earnings management. Working Paper. University of Iowa and Sungkyunkwan University.

Davis, L. R., Soo, B. S., Trompeter, G. M., 2009. Auditor tenure and the ability to meet or beat earnings forecasts. Contemporary Accounting Research 26 (2), 517–548.

Dechow, P., Dichev, I., 2002. The quality of accruals and earnings: the role of accrual estimation errors. The Accounting Review 77, 35–59.

Dechow, P., W. Ge, and C. Schrand, 2010. Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics 50: 344-401.

Dechow, P., Sloan, R., Sweeney, A., 1995. Detecting earnings management. The Accounting Review 70 (2): 193-226.

DeFond, M. L., Erkens, D. H., Zhang, J., 2016. Do client characteristics really drive Big N quality differentiation? Management Science Forthcoming.

DeFond, M. L., Wong, T. J., Li, S., 2000. The impact of improved auditor independence on audit market concentration in China. Journal of Accounting and Economics 28: 269–305.

DeFond, M. L., Zhang, J., 2014. A review of archival auditing research. Journal of Accounting and Economics 58: 275-326.

Erickson, M., Wang, S-W., 1999. Earnings management by acquiring firms in stock for stock mergers, Journal of Accounting and Economics 27, 149-176.

Fairfield, P., Whisenant, S., Yohn, T. L., 2003. Accrued earnings and growth: Implications for future profitability and market mispricing. The Accounting Review 78(1): 353-371.

Ferguson, M. J., Seow, G. S., Young, D., 2004. Nonaudit services and earnings management: UK evidence. Contemporary Accounting Research 21(4), 813–841.

30  

Francis, J. R., Maydew, E. L., Sparks, H. C., 1999. The role of Big6 auditors in the credible reporting of accruals. Auditing: A Journal of Practice & Theory 18(2), 17–34.

Francis, J., Michas, P. N., Seavey, S. E., 2012. Does audit market concentration harm the quality of audited earnings? Evidence from audit market in 42 countries. Contemporary Accounting Research 30(1), 325–355.

Francis, J. R., Michas, P. N., Yu, M. D., 2013. Office size of Big 4 auditors and client restatements. Contemporary Accounting Research, 30(4): 1626-1661.

Francis, J. R., Yu, M. D., 2009. Big 4 office size and audit quality. The Accounting Review 84 (5), 1521–1552.

Frankel, R. M., Johnson, M. F., Nelson, K. K., 2002. The relation between auditors’ fees for nonaudit services and earnings management. The Accounting Review 77, 71–105.

Gong, G., H. Louis, Sun, A., 2008. Earnings management, lawsuits, and stock-for-stock acquirer’s market performance, Journal of Accounting and Economics 46, 62-77.

Gul, F. A., Fung, S. Y. K., Jaggi, B., 2009. Earnings quality: Some evidence on the role of auditor tenure and auditors’ industry expertise. Journal of Accounting and Economics 47, 265–287.

Gul, F. A., S. Y. J. Sun, J. S. L. Tsui. 2003. Audit quality, earnings, and the Shanghai stock market reaction. Journal of Accounting, Auditing and Finance 18 (3): 411-427.

Hansen, R. G., 1987. A theory of the choice of exchange medium in the market for corporate control. Journal of Business 60, 75–95.

He, X., Pittman, J., Rui, O. M., 2016. Reputational implications for partners after a major audit failure: Evidence from China. Journal of Business Ethics forthcoming.

Heron, R., and E. Lie, 2002. Operating performance and the method of payment in takeovers. Journal of Financial and Quantitative Analysis 37, 137-155.

Hope, O.K., Hu, D., Zhao, W., 2016. Third-party consequences of short-selling threats: The case of auditor behavior. Working paper.

Johnson, V. E., Khurana, I. K., Reynolds, K. J., 2002. Audit-Firm tenure and the quality of financial reports. Contemporary Accounting Research 19(4), 637–660.

Khurana, I. K., Raman, K. K., 2004. Litigation risk and the financial reporting credibility of Big4 versus non-Big4 audits: evidence from Anglo-American countries. The Accounting Review 79(2), 473–495.

Kinney, W. R., Martin, R. D., 1994. Does auditing reduce bias in financial reporting? A review of audit-related adjustment studies. Auditing: A Journal of Practice & Theory 13,149–156.

Lawrence, A., Minutti-Meza, M., Zhang,P., 2011. Can Big4 versus non-Big4 differences in audit-quality proxies be attributed to client characteristics? The. Accounting Review 86(1), 259–286.

31  

Lennox, C., Pittman, J., 2010. Big Five audits and accounting fraud. Contemporary Accounting Research 27(1), 209–247.

Lennox, C., X. Wu, and T. Zhang, 2016. The effect of audit adjustments on earnings quality: Evidence from China. Journal of Accounting and Economics forthcoming.

Louis, H., 2004. Earnings management and the market performance of acquiring firms, Journal of Financial Economics 74, 121-148.

Marquardt, C., Zur, E., 2010. The role of accounting quality in the M&A market. Working paper.

Minutti-Meza, M., 2013. Does auditor industry specialization improve audit quality? Journal of Accounting Research 51(4), 779–817.

Myers, J.N., Myers, L. A., Omer, T.C., 2003. Exploring the term of the auditor–client relationship and the quality of earnings: A case for mandatory auditor rotation? The Accounting Review 78(3), 779–799.

Newton, N. J., Wang, D., Wilkins, M. S., 2013. Does a lack of choice lead to lower quality? Evidence from auditor competition and client restatements. Auditing: A Journal of Practice & Theory 32(3), 31–67.

Reichelt, K. J., Wang, D., 2010. National and office-specific measures of auditor industry expertise and effects on audit quality. Journal of Accounting Research 48(3), 647–686.

Pungaliya, R. and A. Vijh, 2009. Do acquiring firms manage earnings? Working paper, University of Iowa.

Shipman, J., Swanquist, Q., Whited, R., 2016. Propensity score matching in accounting research. The Accounting Review forthcoming.

Subramanyam, K., 1996. The pricing of discretionary accruals. Journal of Accounting and Economics 22, 249–281.

Teoh, S., Welch, I., Wong, T., 1998a. Earnings management and the long run underperformance of seasoned equity offerings. Journal of Financial Economics 50, 53–100.

Teoh, S., Welch, I., Wong, T., 1998b. Earnings management and the subsequent market performance of initial public offerings. Journal of Finance 53, 1935–1974.

Tucker, J., Zarowin, P., 2006. Does income smoothing improve earnings informativeness? The Accounting Review 81, 251–270.

Wang, Q., T. J. Wong, and L. Xia. 2008. State ownership, the institutional environment, and auditor choice: Evidence from China. Journal of Accounting and Economics 46 (1): 112-134.

Wong, T. J., 2014. Corporate Governance Research on Listed Firms in China: Institutions, Governance and Accountability. Foundations and Trends in Accounting 9 (4), 259–326.

32  

Figure 1 An illustration of the timing for the BEFOREt variable

2009 2010 2011

M&A announcement

date (e.g., July 1, 2010)

Year

M&A completion (or termination)

date (e.g., July 1, 2011)

Most recent fiscal year-end prior to the M&A

announcement date (i.e., BEFOREt = 1 for

fiscal year 2009)

Most recent fiscal year-end after the

M&A completion (or termination) date

(i.e., BEFOREt = 0 for fiscal year 2011)

33  

Table 1

The sample Panel A: Sample selection (2006 – 2014)

Sample of M&As from the CSMAR database, where data are available in the year before the M&A announcement date and the year after the M&A completion or termination date (see Fig. 1 for further details).

4,932 Less: observations with missing audit adjustment data from the Ministry of Finance database.

(262)

Less: observations where there are inconsistencies between the CSMAR and Ministry of Finance databases.

(148)

Final sample 4,522 Panel B: M&A deals by announcement year Cash-financed

deals Stock-financed

deals Total

2007 243 30 273 2008 325 53 378 2009 300 39 339 2010 311 26 337 2011 309 21 330 2012 294 25 319 2013 253 32 285 Total 2,035 226 2,261 Panel C: Number of observations in each year Cash-financed

observations (STOCKit = 0)

Stock-financed observations (STOCKit = 1)

Total 2006 243 30 273 2007 504 69 573 2008 609 71 680 2009 626 56 682 2010 608 60 668 2011 591 54 645 2012 577 64 641 2013 257 33 290 2014 55 15 70 Total 4,070 452 4,522

34  

Table 2 Descriptive statistics.

All the continuous variables are winsorized at the 1st and 99th percentiles to mitigate the impact of outliers.

N Mean S.D. P10 P25 P50 P75 P90 ADJit 4,522 -0.034 0.201 -0.162 -0.041 0.000 0.000 0.025 ADJ1it 4,522 -0.002 0.009 -0.007 -0.002 0.000 0.000 0.002 |ADJ_DNit| 4,522 0.059 0.152 0.000 0.000 0.000 0.041 0.162 |ADJ_DN1it| 4,522 0.003 0.008 0.000 0.000 0.000 0.002 0.007 |ADJ_UPit| 4,522 0.025 0.120 0.000 0.000 0.000 0.000 0.025 |ADJ_UP1it| 4,522 0.001 0.005 0.000 0.000 0.000 0.000 0.002 UNCLEANit 4,522 0.034 0.181 0.000 0.000 0.000 0.000 0.000 OPINIONit 4,522 0.054 0.304 0.000 0.000 0.000 0.000 0.000 |PMA_DNPRE,it| 4,522 0.035 0.088 0.000 0.000 0.000 0.036 0.086 |PMA_UPPRE,it| 4,522 0.038 0.077 0.000 0.000 0.004 0.048 0.101 |PMA_DNAUD,it| 4,522 0.033 0.079 0.000 0.000 0.000 0.036 0.088 |PMA_UPAUD,it| 4,522 0.039 0.079 0.000 0.000 0.006 0.049 0.106 |DA_DNPRE,it| 3,810 0.028 0.052 0.000 0.000 0.000 0.036 0.087 |DA_UPPRE,it| 3,810 0.033 0.055 0.000 0.000 0.004 0.048 0.098 |DA_DNAUD,it| 3,818 0.028 0.054 0.000 0.000 0.000 0.036 0.089 |DA_UPAUD,it| 3,818 0.034 0.056 0.000 0.000 0.006 0.050 0.102 STOCKi 4,522 0.100 0.300 0.000 0.000 0.000 0.000 0.000 BEFOREt 4,522 0.500 0.500 0.000 0.000 0.500 1.000 1.000 SIZEit 4,522 21.915 1.324 20.414 21.009 21.746 22.649 23.625 MBit 4,522 1.926 1.754 0.445 0.786 1.437 2.414 3.945 LEVit 4,522 0.488 0.219 0.179 0.326 0.499 0.645 0.752 SOEit 4,522 0.490 0.500 0.000 0.000 0.000 1.000 1.000 BD_SIZEit 4,522 2.196 0.206 1.946 2.197 2.197 2.197 2.398 IN_DIRit 4,522 0.365 0.049 0.333 0.333 0.333 0.385 0.429 BHRETit 4,522 0.125 0.683 -0.443 -0.181 0.007 0.284 0.822 CASHit 4,522 0.194 0.152 0.050 0.090 0.150 0.248 0.412 AGEit 4,522 2.006 0.787 0.693 1.386 2.303 2.639 2.773 ROAit 4,522 0.048 0.054 0.005 0.020 0.043 0.073 0.108 BIG10it 4,522 0.415 0.493 0.000 0.000 0.000 1.000 1.000 AUDCHit 4,522 0.080 0.272 0.000 0.000 0.000 0.000 0.000

35  

Table 3 Univariate difference-in-differences tests.

Panel A: Downward audit adjustments to earnings Stock-financed

(STOCKit = 1) Cash-financed (STOCKit = 0)

|ADJ_DNit| Pre-M&A period (BEFOREt = 1) 0.118 0.055 Post-M&A period (BEFOREt = 0) 0.053 0.058 Difference-in-differences test (0.118 - 0.053) - (0.055 - 0.058) t-stat. = 4.28***

|ADJ_DN1it| Pre-M&A period (BEFOREt = 1) 0.007 0.003 Post-M&A period (BEFOREt = 0) 0.003 0.003 Difference-in-differences test (0.007 - 0.003) - (0.003 - 0.003) t-stat. = 4.24*** Panel B: Upward audit adjustments to earnings Stock-financed

(STOCKit = 1) Cash-financed (STOCKit = 0)

|ADJ_UPit| Pre-M&A period (BEFOREt = 1) 0.033 0.027 Post-M&A period (BEFOREt = 0) 0.030 0.022 Difference-in-differences test (0.033 - 0.030) - (0.027 - 0.022) t-stat. = -1.02 |ADJ_UP1it| Pre-M&A period (BEFOREt = 1) 0.002 0.001 Post-M&A period (BEFOREt = 0) 0.001 0.001 Difference-in-differences test (0.002 - 0.001) - (0.001 - 0.001) t-stat. = 0.76

36  

Table 4

Audit adjustments prior to stock-financed acquisitions (H1). Cols. (1) to (4) are estimated using tobit because the dependent variables are truncated at zero. The standard errors are clustered by company and the t-statistics are reported in parentheses.

Downward adjustments to

pre-audit earnings

Upward adjustments to

pre-audit earnings |ADJ_DNit| |ADJ_DN1it| |ADJ_UPit| |ADJ_UP1it| (1) (2) (3) (4)

STOCKi -0.046** -0.001 0.020 0.000 (-2.112) (-1.072) (0.577) (0.297)

BEFOREt -0.009 -0.001 0.015 0.001 (-1.069) (-1.344) (1.268) (1.213)

STOCKi × BEFOREt 0.085*** 0.004*** -0.073 -0.003 (3.039) (2.779) (-1.508) (-1.286)

SIZEit -0.039*** -0.002*** -0.019** -0.001** (-6.597) (-5.391) (-2.363) (-2.097)

MBit -0.003 0.000 0.009* 0.001*** (-0.657) (1.330) (1.771) (2.887)

LEVit 0.107*** 0.005*** -0.093** -0.003 (3.011) (2.705) (-2.041) (-1.397)

SOEit -0.020 -0.002** -0.016 -0.001 (-1.441) (-2.186) (-0.834) (-1.318)

BD_SIZEit -0.018 0.000 -0.088** -0.003* (-0.614) (0.213) (-1.991) (-1.874)

IN_DIRit 0.229* 0.013** 0.067 0.005 (1.953) (2.049) (0.394) (0.679)

BHRETit -0.021*** -0.001* -0.010 -0.000 (-3.071) (-1.724) (-0.928) (-0.575)

CASHit -0.119*** -0.004 -0.026 0.002 (-2.778) (-1.637) (-0.424) (0.564)

AGEit 0.013 0.000 0.035*** 0.002*** (1.565) (1.005) (3.010) (3.125)

BIG10it 0.008 0.001 0.012 0.001 (0.692) (0.859) (0.723) (0.906)

AUDCHit -0.015 -0.000 -0.065** -0.003*** (-0.813) (-0.370) (-2.074) (-2.646)

CONSTANTit 0.775*** 0.030*** 0.203 0.004 (5.183) (3.616) (0.962) (0.492)

Industry dummies Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Observations 4,522 4,522 4,522 4,522

37  

Table 5 Audit opinions prior to stock-financed acquisitions.

Panel A: Descriptive statistics for audit opinions. N Percentage Non-clean audit opinions:

Unqualified opinions that are modified due to accounting issues 33 0.73% Except for qualifications due to accounting issues 27 0.60%

Opinion disclaimers due to accounting issues 2 0.04% Fundamental uncertainties about going-concern 86 1.90%

Fundamental uncertainties about impending lawsuits 6 0.13% Sub-total 154 3.41%

Clean audit opinions: 4,368 96.59% Total 4,522 100.00%

Panel B: Frequencies of non-clean audit opinions. Stock-financed

(STOCKit = 1) Cash-financed (STOCKit = 0)

Pre-M&A period (BEFOREt = 1) 0.168 0.023 Post-M&A period (BEFOREt = 0) 0.071 0.027 Difference-in-differences test (0.168 - 0.071) - (0.023 - 0.027) z-stat. = 3.05***

Panel C: Audit opinion models. In Col. (1) the dependent variable is UNCLEANit which equals zero if the audit opinion is clean, and one if the opinion is unclean. In Cols. (2) and (3) the dependent variable is OPINIONit which equals zero if the audit opinion is clean, one if it is unclean due to accounting-related issues, and two if it is unclean due to uncertainty-related issues (i.e., going-concern and impending lawsuits). Col. (1) is estimated using a binary logit model whereas Cols. (2) and (3) are estimated using a multinomial logit model because the dependent variable takes three values.

Unclean opinions

UNCLEANit = 1

Accounting-related unclean opinions OPINIONit = 1

Uncertainty-related unclean opinions OPINIONit = 2

(1) (2) (3) STOCKi -0.304 -1.948* 0.347 (-0.714) (-1.699) (0.682) BEFOREt -0.178 -0.350 -0.007 (-0.890) (-1.225) (-0.027) STOCKi × BEFOREt 0.868** 2.445** 0.317 (2.110) (2.249) (0.636) SIZEit -1.178*** -0.547*** -1.872*** (-6.822) (-2.905) (-7.134) MBit 0.114 0.124 0.078 (1.569) (1.425) (0.897)

38  

Table 5 (cont.) Audit opinions prior to stock-financed acquisitions.

(1) (2) (3) LEVit 4.299*** 3.071*** 5.119*** (6.310) (3.122) (6.374) SOEit -0.217 -0.722* 0.294 (-0.684) (-1.790) (0.613) BD_SIZEit 0.628 0.046 1.066 (0.755) (0.047) (0.831) IN_DIRit 5.146* 5.441 4.262 (1.754) (1.448) (1.131) BHRETit -0.264 -0.151 -0.278 (-1.286) (-0.486) (-1.487) CASHit 2.044 0.428 3.049 (1.486) (0.275) (1.481) AGEit 0.733*** 0.454 1.090** (2.634) (1.504) (2.327) ROAit -7.145*** -9.556*** -6.507** (-3.898) (-4.088) (-2.460) BIG10it -0.266 -0.722* 0.082 (-0.894) (-1.816) (0.199) AUDCHit 0.573 0.793* 0.470 (1.549) (1.886) (0.990) CONSTANTit -0.407 -11.313** 10.421* (-0.100) (-2.391) (1.650) Industry dummies Yes Yes Yes Year dummies Yes Yes Yes Observations 4,522 4,522 4,522

39  

Table 6 Accruals prior to stock-financed acquisitions.

The models are estimated using tobit regressions because the dependent variables are truncated at zero. The standard errors are clustered at the company level and t-statistics are

reported in parentheses. Panel A: Income-increasing and income-decreasing performance-matched accruals

Income-increasing accruals Income-decreasing accruals |PMA_UPPRE,it| |PMA_UPAUD,it| |PMA_DNPRE,it| |PMA_DNAUD,it|

STOCKi 0.011 0.011 0.007 -0.007 (1.071) (1.071) (0.530) (-0.623)

BEFOREt -0.003 -0.003 0.001 0.003 (-0.703) (-0.874) (0.296) (0.781)

STOCKi × BEFOREt 0.005 0.010 0.004 0.009 (0.319) (0.684) (0.226) (0.525)

Control variables Yes Yes Yes Yes Industry dummies Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Observations 4,522 4,522 4,522 4,522

Panel B: Income-increasing and income-decreasing discretionary accruals

Income-increasing accruals Income-decreasing accruals |DA_UPPRE,it| |DA_UPAUD,it| |DA_DNPRE,it| |DA_DNAUD,it|

STOCKi 0.004 0.006 -0.004 -0.005 (0.591) (0.820) (-0.533) (-0.657)

BEFOREt 0.002 0.003 -0.004 -0.003 (0.885) (1.114) (-1.354) (-1.074)

STOCKi × BEFOREt -0.001 -0.002 0.016 0.019 (-0.107) (-0.183) (1.570) (1.578)

Control variables Yes Yes Yes Yes Industry dummies Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Observations 3,810 3,818 3,810 3,818

40  

Table 7

Results using propensity score matching. We match each stock-financed acquisition to one cash-financed acquisition using the closest

propensity score. The propensity scores are estimated using the model shown in Appendix B. The standard errors are clustered at the company level with t-statistics and z-statistics

reported in parentheses.

Downward adjustments Upward adjustments |ADJ_DNit| |ADJ_DN1it| |ADJ_UPit| |ADJ_UP1it| (1) (2) (3) (4)

STOCKi -0.052 -0.001 0.034 -0.001 (-1.481) (-0.666) (0.628) (-0.213)

BEFOREt -0.033 -0.001 0.053 -0.000 (-0.844) (-0.587) (0.924) (-0.184)

STOCKi × BEFOREt 0.110** 0.005* -0.108 -0.002 (2.386) (1.786) (-1.418) (-0.642)

Control variables Yes Yes Yes Yes Industry dummies Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Observations 904 904 904 904

41  

Appendix A Variable definitions

Measures of audit adjustments

EPRE,it = Pre-audit earnings.

EAUD,it = Audited earnings.

TAPRE, it = Pre-audit total assets.

ADJit = Audit adjustments to earnings, scaled by the absolute value of pre-audit earnings: ADJit = (EAUD, it – EPRE, it)/|EPRE, it|.

ADJ1it = Audit adjustments to earnings, scaled by the absolute value of pre-audit total assets: ADJ1it = (EAUD, it – EPRE, it)/|TAPRE, it|.

|ADJ_DNit| = Absolute magnitude of downward audit adjustments to earnings, scaled by the absolute value of pre-audit earnings: |ADJ_DNit| = (|EAUD, it – EPRE, it|)/|EPRE, it| if EAUD, it < EPRE, it, and 0 otherwise.

|ADJ_DN1it| = Absolute magnitude of downward audit adjustments to earnings, scaled by the absolute value of pre-audit total assets: |ADJ_DN1it| = (|EAUD, it – EPRE, it|)/|TAPRE, it| if EAUD, it < EPRE, it, and 0 otherwise.

|ADJ_UPit| = Absolute magnitude of upward audit adjustments to earnings, scaled by the absolute value of pre-audit earnings: |ADJ_UPit| = (|EAUD, it – EPRE, it|)/|EPRE, it| if EAUD, it > EPRE, it, and 0 otherwise.

|ADJ_UP1it| = Absolute magnitude of upward audit adjustments to earnings, scaled by the absolute value of pre-audit total assets: |ADJ_UP1it| = (|EAUD, it – EPRE, it|)/|TAPRE, it| if EAUD, it > EPRE, it, and 0 otherwise.

Measures of performance-matched accruals

TAAUD,it = Audited total assets.

CFOPRE,it = Operating cash flows scaled by pre-audit total assets: CFOit / TAPRE, it.

CFOAUD,it = Operating cash flows scaled by audited total assets: CFOit / TAAUD,it.

ACCPRE,it = Pre-audit accruals scaled by pre-audit total assets: (EPRE, it - CFOit) / TAPRE,it.

ACCAUD,it = Audited accruals scaled by audited total assets: (EAUD, it - CFOit) / TAAUD,it.

42  

Appendix A (cont.) Variable definitions

PMAPRE,it

= Pre-audit performance-matched accruals. We match a sample company’s pre-audit accruals (ACCPRE,it) with another company’s pre-audit accruals based on the same year, industry, and closest value of CFOPRE,it.

PMAAUD,it = Audited performance-matched accruals. We match a sample company’s audited accruals (ACCAUD,it) with another company’s audited accruals based on the same year, industry, and closest value of CFOAUD,it.

|PMA_DNPRE,it| = Income-decreasing performance-matched pre-audit accruals: = | PMAPRE,it | if PMAPRE,it < 0 and 0 if PMAPRE,it > 0.

|PMAit_UPPRE,it| = Income-increasing performance-matched pre-audit accruals: = | PMAPRE,it | if PMAPRE,it > 0 and 0 if PMAPRE,it < 0.

|PMA_DNAUD,it| = Income-decreasing performance-matched audited accruals: = | PMAAUD,it | if PMAAUD,it < 0 and 0 if PMAAUD,it > 0.

|PMA_UPAUD,it| = Income-increasing performance-matched audited accruals: = | PMAAUD,it | if PMAAUD,it > 0 and 0 if PMAAUD,it < 0.

Measures of discretionary accruals

DAPRE,it = Pre-audit discretionary accruals, which are estimated using the modified Jones model: ACCPRE,it = a0 +a1(1/ TAPRE,it-1) + a2(ΔREVit - ΔRECit) + a3(PPEit) We scale each independent variable by the lagged value of pre-audit total assets (TAPRE,it-1).

DAAUD,it = Audited discretionary accruals, which are estimated using the modified Jones model: ACCAUD, it = a0 +a1(1/ TAAUD,it-1) + a2(ΔREVit - ΔRECit) + a3(PPEit) We scale each independent variable by the lagged value of audited total assets (TAAUD, it-1).

|DA_DNPRE,it| = Pre-audit income-decreasing discretionary accruals. = |DAPRE,it| if DAPRE,it < 0 and 0 if DAPRE,it > 0.

|DA_UPPRE,it| = Pre-audit income-increasing discretionary accruals. = |DAPRE,it| if DAPRE,it > 0 and 0 if DAPRE,it < 0.

|DA_DNAUD,it| = Audited income-decreasing discretionary accruals. = |DAAUD,it| if DAAUD,it < 0, and 0 if DAAUD,it > 0.

|DA_UPAUD,it| = Audited income-increasing discretionary accruals. = | DAAUD,it | if DAAUD,it > 0 and 0 if DAAUD,it < 0.

43  

Appendix A (cont.) Variable definitions

Measures of audit opinions

UNCLEANit = 0 for clean audit opinions, = 1 for unclean audit opinions.

OPINIONit = 0 for clean audit opinions, = 1 for unclean audit opinions that disclose accounting-related issues, and = 2 for unclean audit opinions that disclose uncertainty-related issues.

Treatment variables

STOCKi = 1 if company i engages in a stock-financed acquisition, and 0 if it engages in

a cash-financed acquisition.

BEFOREt = 1 for fiscal year t immediately before the M&A announcement date, and 0 for fiscal year t immediately after the M&A completion date (or termination date). See Fig. 1 for further details.

Control variables

SIZEit = Natural logarithm of total assets.

MBit = Market-to-book ratio.

LEVit = Total liabilities/total assets.

SOEit = 1 for stated-owned-enterprises, and 0 otherwise.

BD_SIZEit = Natural logarithm of the number of directors on the board.

IN_DIRit = Percentage of independent directors on the board.

BHRETit = Annual buy-and-hold returns.

CASHit = Cash and cash equivalents divided by total assets.

AGEit = Natural logarithm of the company’s age.

ROAit = Audited profits divided by total assets.

BIG10it = 1 for a Big 10 audit firm, and 0 otherwise.

AUDCHit = 1 if company i hires a new audit firm in year t, and 0 otherwise.

44  

Appendix B A logit model for predicting the method of acquisition financing.

The dependent variable equals one if the company finances an acquisition in year t using stock, and zero if cash. The independent variables are measured at year t-1. The z-

statistics are reported in parentheses.

SIZEit-1 -0.564*** (-6.781)

MBit-1 0.097** (2.003)

LEVit-1 1.945*** (5.222)

SOEit-1 -0.120 (-0.690)

BD_SIZEit-1 0.286 (0.654)

IN_DIRit-1 0.120 (0.067)

BHRETit-1 -0.140 (-1.276)

CASHit-1 -0.719 (-1.088)

AGE it-1 0.420*** (3.260)

ROAit-1 -2.328* (-1.813)

BIG10it-1 -0.180 (-1.046)

AUDCHit-1 0.209 (0.843)

CONSTANTit-1 8.038*** (3.814)

Year dummies Yes Industry dummies Yes

Observations 2,248 Pseudo R-square 14.75%

45  

Appendix C Univariate tests for the independent variables that are used to predict the method of financing in Appendix B.

Treatment sample

of stock-financed acquisitions (N = 226)

Full sample of cash-financed acquisitions

(N = 2,035)

Matched sample of cash-financed acquisitions

(N = 226)

(1) (2) (3) (1) vs (2) (1) vs (3) Mean Mean Mean t-stat. t-stat.

SIZEit-1 21.146 21.837 21.141 -7.632*** 0.042 MBit-1 2.426 1.957 2.631 3.724*** -0.866 LEVit-1 0.583 0.470 0.573 7.240*** 0.390 SOEit-1 0.465 0.492 0.465 -0.778 0.000 BD_SIZEit-1 2.175 2.203 2.196 -1.932** -1.203 IN_DIRit-1 0.362 0.363 0.360 -0.202 0.613 BHRETit-1 0.074 0.078 0.116 -0.078 -0.620 CASHit-1 0.161 0.212 0.172 -4.341*** -0.737 AGE it-1 2.197 1.857 2.203 5.606*** -0.092 ROAit-1 0.034 0.051 0.029 -4.340*** 0.629 BIG10it-1 0.283 0.385 0.301 -3.012*** -0.413 AUDCHit-1 0.124 0.075 0.142 2.595*** -0.554