Are Material Weaknesses in Internal Controls Associated with Poor M&A Decisions?

Evidence from Goodwill Impairment

Dennis Caplan

Associate Professor

University at Albany (SUNY)

Saurav K. Dutta

Associate Professor

University at Albany (SUNY)

and

Alfred Zhu Liu*

Assistant Professor

University at Albany (SUNY)

*Contact for correspondence: Alfred Zhu Liu, Department of Accounting and Law, School of

Business, University at Albany, SUNY, Albany, NY 12222.

Email: zliu3@albany.edu.

Office: (518) 956-8355.

We thank workshop participants at University at Binghamton for helpful comments.

Are Material Weaknesses in Internal Controls Associated with Poor M&A Decisions?

Evidence from Goodwill Impairment

ABSTRACT

This paper examines the effect of the quality of a firm’s internal control over financial reporting

(ICFR) on the quality of corporate M&A decisions. We use material weaknesses in internal

control (ICMWs) from SOX 404 audits as a proxy for the quality of a firm’s ICFR, and use

future goodwill impairment to proxy for the quality of managers’ M&A decisions. We find that

goodwill recognized from acquisitions in years concurrent with ICMW has a greater rate of

impairment in subsequent years than goodwill recognized from acquisitions in years without

ICMW, thereby establishing a link between ICMW and goodwill impairment. We further show

that disclosure and remediation of ICMWs appear to improve valuations of subsequent

acquisitions. Our study contributes to the literature on internal controls by documenting

unanticipated benefits of SOX 404 audits on managerial performance, and to the goodwill

literature by identifying ICMW as a determinant of goodwill impairment.

Keywords: internal control; material weaknesses; SOX 404; goodwill impairment; mergers and

acquisitions.

1

INTRODUCTION

This paper examines and documents an unintended benefit of Section 404 of the

Sarbanes-Oxley Act (SOX) related to managerial decisions in mergers and acquisitions

(M&As). Although Section 404 was intended to improve the quality of external reporting

by public companies (Coates and Srinivasan 2014), an emerging body of literature suggests

that an unanticipated benefit might be an improvement in companies’ managerial decision-

making. Feng, Li, and McVay (2009) finds that firms with ICMWs have lower quality

management forecasts, and concludes: “weak internal controls also have the potential to

affect managers’ decisions related to production, capital investment, mergers and

acquisitions, research and development, advertising, and hiring or expansion decisions”

(207). Several recent studies provide empirical support for this conjecture by showing that

ICMW affects overall investment efficiency (Cheng, Dhaliwal, and Zhang 2013) and

operational efficiency (Cheng, Goh, and Kim 2014; Feng, Li, McVay, and Skaife 2015).

Our paper extends this line of research by studying whether acquiring firms with

ICMWs make suboptimal M&A decisions as reflected in the overvaluation of target firms.

We emphasize the association between weak internal controls and overvaluation of M&As

for the following reasons. ICMW could signal poor managerial ability (Li, Sun, and

Ettredge, 2010; Wang 2010). Some commonly-cited internal control weaknesses, such as

senior management incompetency, lack of competent accounting personnel, and poor

corporate governance, directly reflect on managerial ability. Other internal control

weaknesses could provide indirect evidence of managerial ability because the design and

implementation of internal controls is a primary management responsibility. Poor

managerial ability is associated with low-quality M&A decisions (Goodman, Neamtiu,

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Shroff, and White 2014). Therefore, the existence of ICMW could manifest in poor

managerial decisions in M&A activities. Furthermore, ICMW firms operate in a poor-

quality information environment, and hence managerial decisions are based on low quality

information (Feng et al. 2009). This can be particularly costly in an M&A setting as M&A

decisions require a broad set of inputs, are often economically significant transactions, and

involve uncertainty in valuation due to subjective estimates of synergies (McNichols and

Stubben 2015).

We identify lower quality M&A decisions through future goodwill impairment,

consistent with a stream of prior literature (Hayn and Hughes 2006; Gu and Lev 2011; Li,

Shroff, Venkataraman, and Zhang 2011; Olante 2013, Goodman et al. 2014). Using a

sample of U.S. firms that engage in M&A transactions between 2004 and 2010, we find

that new goodwill recognized in ICMW years has higher impairment over the subsequent

three years than new goodwill recognized in non-ICMW years. Additionally, we identify

specific ICMW categories that are more relevant for M&A decisions and show that they

accentuate the negative effect. We also find that ICMWs identified in management's

interim assessments per SOX 302, without a confirming year-end SOX 404(b) auditor

opinion, have little impact on the quality of M&A decisions. We find that goodwill

recognized after the remediation of ICMW is impaired at a lower rate, likely reflecting

improved valuation of new acquisitions after the ICMWs are remediated. Finally, we find

that the disclosure of ICMW does not affect the tendency of managers to engage in future

M&A transactions. However, in the two years following the revelation of ICMW, these

firms recognize less new goodwill than matched non-ICMW firms, likely resulting from

more conservative valuation in acquisitions.

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Our paper contributes to the literature in several ways. We provide new evidence

that material weaknesses in internal control have a detrimental effect on managers’ M&A

decisions, which adds to the stream of research on the economic consequences of

ineffective internal controls. Our findings corroborate and extend Cheng et al. (2013) and

directly link ICMWs to the quality of M&A decisions as evidenced by goodwill

impairment.1 These findings provide additional support for the notion that ICMWs

negatively affect managerial performance, which extends the emerging line of research on

the unanticipated benefits of Section 404 of the Sarbanes-Oxley Act (e.g.: Feng et al. 2009;

Ashbaugh-Skaife, Collins, Kinney, and LaFond 2008).

Our paper also contributes to the research on the determinants of the quality of

M&A decisions. Much of this research examines the relationship between acquisition

performance and the characteristics of the target firm (e.g., McNichols and Stubben 2015;

Martin and Shalev 2016; Raman, Shivakumar, and Tamayo 2013; Skaife and Wangerin

2013). Goodman et al. (2014), on the other hand, focuses on attributes of the acquiring firm,

specifically managerial forecasting ability as evidenced by management forecast accuracy.

We extend this line of research by demonstrating that ICMW at the acquiring firm

adversely affects the quality of managers’ acquisition decisions. Moreover, when the

ICMW is remediated, the quality of managers’ acquisition decision improves. Since ICMW

can be an indicator of poor managerial ability, our finding corroborates Goodman et al.

(2014).

1 Biddle, Hilary, and Verdi (2009) and Cheng et al. (2013) examine the association between ICMWs and

investment efficiency. These papers include acquisitions as one component of an aggregate measure of

corporate investment which also includes capital expenditures and R&D expenses.

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The rest of the paper proceeds as follows. The next section discusses the related

literature and develops our hypothesis. We then identify the measures used in our analysis

and discuss our sample selection procedures. In the following sections, we report the results

of the hypothesis testing, including the robustness tests, and the results of additional tests

examining the effects of various characteristics of ICMW. The last section of the paper

offers concluding remarks.

BACKGROUND, MOTIVATION AND HYPOTHESIS DEVELOPMENT

Survey of the ICMW Literature

The Sarbanes-Oxley Act (SOX) of 2002 requires periodic assessment and

disclosure of the quality of a company’s internal controls over financial reporting. Section

404(a) prescribes rules and responsibilities for managers to maintain adequate internal

controls and provide an assessment of the effectiveness of internal controls in the annual

report. Section 404(b) requires the company’s auditor to separately assess and report on the

effectiveness of internal controls in the annual report.

Following the enactment of SOX, a large and growing body of accounting research

has investigated attributes of firms that report ICMWs and the information content of these

disclosures (Doyle, Ge, and McVay 2007a, 2007b; Beneish, Billings, and Hodder 2008;

Hammersley, Myers, and Shakespeare 2008; Kim and Park 2009; Myllymäki 2014), as

well as the economic ramifications (Ogneva, Subramanyam, and Raghunandan 2007;

Dhaliwal, Hogan, Trezevant, and Wilkins 2011). Some of this research also examines the

effects of ICMW remediation (Ashbaugh-Skaife et al. 2008; Bedard, Hoitash, Hoitash, and

Westermann 2012; Hammersley, Myers, and Zhou 2012; Gordon and Wilford 2012).

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While most of this literature documents that ICMWs reduce the quality of

information firms provide to external users (Doyle et al. 2007a; Chan, Farrell, and Lee

2008), a few studies explore the effect of ICMWs on the quality of information available to

management. For example, Feng et al. (2009), and Li, Peters, Richardson, and Watson

(2012) find that management forecast accuracy is lower among firms with ineffective

internal controls. Feng et al. (2009) concludes that ICMWs affect the quality of internal

management reports and thus management guidance. Cheng et al. (2013) shows that

ICMWs negatively affect overall investment efficiency, but disclosure of ICMWs partially

mitigates that problem. Feng et al. (2015) reports that inventory-related ICMW is

associated with lower inventory turnover and higher inventory impairment. Cheng et al.

(2014) finds that operational efficiency is lower in firms disclosing material weaknesses in

internal controls and concludes that effective internal controls enhance the quality of

internal reports, leading to better operational decisions. Deficiencies in internal control over

financial reporting result in less reliable financial information (Ashbaugh-Skaife, Collins,

Kinney, and LaFond 2009), which in turn affects managerial decisions on production and

investment (Lambert, Leuz, and Verrecchia 2007).

Survey of the M&A Literature

Merger and acquisition transactions are often large in magnitude and impose

significant consequences on the companies involved. In general, determining the

acquisition price is a complex, subjective process that involves an assessment of the target

firm’s value and also the value of the synergy with the acquiring firm (Penman 2013, 93;

Holthausen and Zmijewski 2014, 675). This process relies on managerial judgment and

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ability, hence the quality of investment decisions is associated with managerial ability

(Goodman, et al. 2014). In addition, valuing the target requires due diligence on the part of

the acquirer to assess source credibility (e.g., the quality of the target’s information system)

and the specific information provided by the target (Skaife and Wangerin 2013). Due

diligence involves accounting and IT personnel and the application of accounting, tax and

finance skills and expertise (Protiviti 2014). Incompetent management and ineffective due

diligence can lead to M&A decisions that are costly to the acquiring firm.

A Real-World Example

We use the following real-world example to illustrate how ICMW relating to

management incompetence affects the quality of M&A decisions. Integrated Alarm

Services Group, Inc. (IASG), based in Albany, NY, provided services to commercial and

residential security alarm providers. In 2003 and 2004, IASG paid $126 million, mostly in

cash, to acquire multiple companies (IASG 2005). Prior to the acquisitions, IASG had

revenue of $24 million and total assets of $46 million. Two-thirds of the $126 million

acquisition price was reported as goodwill, amounting to $84 million. In its 2004 annual

report, the first year a SOX 404 audit was required, IASG reported numerous material

weaknesses including the following. The company:

“failed to design appropriate company wide policies and policies over

the …treasury and risk management functions.”

“did not maintain a sufficient complement of personnel with an appropriate level

of accounting knowledge, …”

“had a shortage of finance and accounting staff … and individuals in the finance

function who did not have the appropriate skills, training and experience to meet

the objective that should be expected of these roles.”

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“did not have effective controls over the process of identifying and accumulation

of all required supporting information to ensure … support for the accounting

positions taken on non-routine transactions.” (IASG 2005, 37-38)

Shortly after these acquisitions, in 2006, IASG reported its “first impairment ever”

for $65 million, about 70% of its 2006 revenue and 70% of the beginning-of-year goodwill

(IASG 2007). As illustrated by the events at IASG, our research design exploits the unique

attributes of goodwill and its impairment to evaluate the quality of M&A decisions made

concurrently with an ICMW.

The above example highlights two factors that influence our hypothesis

development. First, ICMW can indicate deficiencies in managerial ability that result in

poor M&A decisions. At IASG, the lack of skill, training and experience among finance

personnel manifested in IASG’s overpaying for its acquisitions. Second, overpayment leads

to the recognition of significant goodwill at the time of the acquisition and goodwill

impairment in subsequent periods as explained below.

The acquisition price is partitioned into two components: the fair value of net

identifiable assets acquired, and goodwill (Halsey and Hopkins 2017, 59). U.S. GAAP

states that goodwill consists of “expected synergies from combining operations of the

acquiree and the acquirer, intangible assets that do not qualify for separate recognition, or

other factors” (ASC 805-30-50-1-a). Overpayment, the excess of the acquisition price over

the value of the target to the acquirer,2 can be one of the “other factors.” In the case of

IASG, two-thirds of the acquisition prices in 2003 and 2004 was recorded as goodwill.

Moreover, goodwill is not subject to either immediate expensing (as with R&D) or

regular depreciation (as with PP&E), instead it is retained on the balance sheet and a

2 The value of a target to the acquirer includes the fair value of the target’s net identifiable assets, the

expected synergies, and intangible assets that are not separately recognized.

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periodic assessment of the value of goodwill is required under ASC 350. Impairment of

goodwill is not a regular component of the financial statements, and when it does occur, it

represents an acknowledgement by management that the company's expected economic

benefits from the acquisition will not be realized.3

Hypothesis Development

Material weaknesses in internal control can indicate deficiencies in management

competence and M&A due diligence. Since management is responsible for designing and

implementing effective internal controls, the existence of ICMW signals poor managerial

ability. For example, some internal control weaknesses, such as incompetence of senior

management or poor corporate governance, directly reflect on managerial ability.

Furthermore, certain deficiencies in internal control, such as an ineffective internal audit

department or audit committee, increases the likelihood that company personnel will fail to

exercise due diligence in the acquisition process. In addition to managerial ability,

information quality affects M&A decisions. ICMW is often associated with poor

information quality. Managers relying on poor information are likely to make suboptimal

decisions.

More formally, consider a single acquirer bidding for a target company. Figure 1

depicts four mutually exclusive and collectively exhaustive outcomes. The acquirer’s

estimate E of the value of the target is a function of the quality of its information system

and managerial ability. An acquisition will occur only when the acquirer and target agree

on a price P that exceeds the target’s reservation price but is less than E.

3 At IASG there was no goodwill impairment until 2006.

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The cells on the main diagonal present the preferred outcomes. In cell (1), an

acquisition occurs at a price P only if the acquirer’s estimate E is greater than P. This is the

preferred outcome for the acquirer if and only if the economic but unobserved value of the

target, V, is also greater than P. Cell (1) signifies a value-increasing acquisition resulting in

economic rents because the firm purchases the target at a price less than the economic

value of the target to the firm. Conversely, an acquisition will not occur at price P when E

< P. This is the preferred outcome for the buyer if and only if V < P as in cell (4), which

signifies the correct decision of not buying the target at a price higher than the target’s

value to the firm. The cells on the minor diagonal present negative outcomes for the

acquirer. Cell (2) denotes a case in which the acquirer fails to buy the target at a price less

than the target’s value to the firm, which leads to an opportunity cost. Cell (3) signifies the

case in which the buyer acquires the target at a price in excess of its economic value to the

firm, which leads to subsequent goodwill impairment once the true value of the target

becomes known.

From an empirical standpoint, cells (1) and (3) in the left-hand column are

observable. ICMW firms are more likely than non-ICMW firms to experience outcomes in

cell 3, which leads to the following hypothesis.

Hypothesis: Goodwill impairment is higher for acquirers with ICMWs than for acquirers

without an ICMW.

RESEARCH DESIGN AND SAMPLE

In this section we define key variables, describe the sample selection process, and

present descriptive statistics of our sample.

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Key measures

We use newly recognized goodwill to identify a firm’s M&A activities in a given

fiscal year. Under U.S. GAAP, goodwill arises solely from the excess of acquisition cost

over the fair value of net identifiable assets acquired.4 Conceptually, goodwill captures “the

portion of the [acquisition] premium related to expected synergies” (FASB 2001, 6) or

“intangible factors that allow a business to earn above-average profits” (Baker, Christensen,

and Cottrell 2011, 17). We compute the newly recognized goodwill (GW_NEW) in a given

year as follows:

GW_NEW = GW_EOY + GDWLIP – GW_BOY (1)

where GW_EOY is goodwill at the end of the current year; GDWLIP is goodwill

impairment in the current year, and GW_BOY is goodwill at the beginning of the current

year.5 All variables are scaled by total assets at the beginning of the current year.

We use total goodwill impairment over the subsequent three-year period

(GWIP3YR) to proxy for overpayment for M&As, similar to Goodman et al. (2014, 20).6

As noted in the previous section, overestimation of the value of synergy can result in

overpayment. Overpayment results in “recognition of subsequent impairment losses on

goodwill” (Hamlen, Huefner, and Largay 2016, 54). We collect information on these

goodwill-related variables from Compustat.

4 This definition is appropriate for most of our sample. For M&As dated in reporting periods beginning on or

after December 15, 2008, goodwill is defined as the excess of the fair value of the acquired company over the

fair value of its net assets. We included in all our analyses year dummies. We also perform a sensitivity

analysis excluding 2009 and 2010 for the three-year goodwill impairment test and find qualitatively similar

results (not tabulated). 5 Our sample firms report no goodwill amortization in the sample period as SFAS 142 effectively terminated

goodwill amortization for reporting periods beginning on or after December 15, 2001. The newly recognized

goodwill, GW_NEW, is usually positive. In some cases, GW_NEW is negative, which is primarily driven by

the write-off of goodwill associated with discontinued operations. We delete these observations from our

sample because they do not represent new acquisitions. 6 As shown in Table 6, our findings are not sensitive to the length of the impairment window.

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We identify ICMWs using the auditor’s opinion on the effectiveness of a firm’s

ICFR as required by SOX Section 404(b). We collect auditors’ opinions primarily from

Audit Analytics based on Form 10-K or 10-K/A and secondarily from Compustat

(AUOPIC = 1 for effective internal control or 2 for material weakness in internal control).7

We define an indicator variable, ICMW, which equals 1 if a firm’s auditor identifies

material weaknesses in the firm’s internal controls in the current year and 0 otherwise. All

variables are defined in the Appendix.

Main model

We use the following cross-sectional regression to test whether ICMWs in the

acquisition year lead to greater future goodwill impairment:

GWIP3YR = α + β1×GW_NEW + β2×GW_NEW×ICMW + β3×ICMW + β4×GW_BOY

+ β5×GW_BOY×ICMW + β6×PE + β7×DAC + β8×EQI + β9×TOBINQ + β10×VOL_OCF

+ β11×VOL_SALE + β12×VOL_INVEST + β13×SIZE + β14×TANGIBILITY + β15×IO

+ β16×LEVERAGE + β17×ADJRET + β18×ROA + β19×BIG4 + β20×FE_DISP + Industry

Fixed Effects + Year Fixed Effects + ε (2)

The dependent variable GWIP3YR measures the total goodwill impairment over the

subsequent three fiscal years, scaled by total assets at the beginning of the current year (i.e.,

the year of acquisition). For example, for an acquisition made in 2005 (firm-year 2005),

GWIP3YR is computed by taking the cumulative impairment in 2006, 2007 and 2008 and

dividing by the firm’s total assets at the beginning of 2005.

7 We compare the SOX 404 coverage of the two sources and find that Audit Analytics covers fewer

companies but is more accurate in including ICMWs expressed in updated filings (Form 10-K/As). We

therefore supplement the ICMW sample using Compustat. We verified the Compustat data by reading the

firms’ 10-Ks or 10-K/As.

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Companies that have a larger amount of goodwill are likely to report greater

impairment in the subsequent years. The total goodwill can either be newly recognized in

the current year or carried over from prior years. GW_ NEW measures new goodwill

recognized in the year of acquisition (i.e., current year) and GW_BOY measures the

carrying value of goodwill from acquisitions made in prior years.

We hypothesize that companies with ICMWs are more likely to overpay for

acquisitions resulting in greater impairment in subsequent periods. That is, all else equal,

goodwill recognized in an ICMW year will lead to a larger amount of goodwill impairment

in subsequent years than goodwill recognized in a non-ICMW year. We use the interaction

term, GW_NEW×ICMW, to measure this moderating effect of ICMWs on the relation

between current year new goodwill and total impairment over the subsequent three-year

period. This interaction term is the variable of interest. We expect a negative coefficient as

goodwill impairment is recorded as a loss and has a negative value.

Impairment of goodwill can arise from either overpayment at the time of acquisition

or adverse events subsequent to acquisition (Hayn and Hughes 2006, Li et al. 2011). We

include the interaction between ICMW and beginning-of-year goodwill, GW_BOY×ICMW,

to disentangle the effect of managerial decisions during an acquisition from the effect of

changes in subsequent measurement of goodwill impairment. For example, control

weaknesses could be associated with less timely impairment decisions, and their

remediation could lead to recognition of previously delayed goodwill impairment.

Similarly, the auditor’s scrutiny subsequent to ICMW disclosure could increase. PCAOB

auditing standards require that “the auditor should obtain a sufficient understanding of …

internal control over financial reporting … to assess the factors that affect the risks of

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material misstatement and … design further audit procedures” (PCAOB 2010, A5-9).

Consequently, auditors may exert greater scrutiny in response to a firm’s ICMW, leading to

more timely recognition of goodwill impairment.

If future goodwill impairment results solely from more timely recognition of

impairment due to the reasons mentioned above, both previously and newly recognized

goodwill should be affected. Hence, we should observe negative coefficients on both

GW_NEW×ICMW and GW_BOY×ICMW, indicating a deficiency in the goodwill

impairment process. On the other hand, if future goodwill impairment is caused by poor

acquisition decisions in the ICMW year, we should observe a negative coefficient on

GW_NEW×ICMW but not on GW_BOY×ICMW.8

We include additional variables in the regression to control for other factors that

may also affect goodwill impairment. Following Gu and Lev (2011), we include the price-

to-earnings ratio (PE), discretionary accruals (DAC), and net proceeds from equity

issuances (EQI) to control for the impact of stock overvaluation on goodwill impairment.

Gu and Lev (2011) reports that managers of overvalued firms are more likely to engage in

M&A transactions that later lead to greater goodwill write-offs and finds that the above

three factors are associated with goodwill impairment. We use the industry-adjusted PE

following Gu and Lev (2011). We compute DAC as in Gu and Lev (2011). We use current

year proceeds from equity issuances (both common and preferred) scaled by beginning-of-

year total assets to measure equity issuances (DeAngelo, DeAngelo, and Stulz 2010; Alti

8 We conduct a robustness test by including auditor switches up to three years after ICMW disclosure as an

additional control and our result is not qualitatively affected (not tabulated). In our sample, more ICMW firms

switch from Big 4 auditors to non-Big 4 auditors than from non-Big 4 auditors to Big 4 auditors. In another

robustness test, we include post-ICMW restatements as an additional control and find essentially the same

result (not tabulated).

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and Sulaeman 2012). Using equity issuances in the current year instead of prior years also

helps control for the effect of stock-based acquisitions as prior research shows that stock

acquisitions on average perform worse than cash acquisitions.

We include Tobin’s Q (TOBINQ) as a control variable. Tobin’s Q is commonly

used as a proxy for market timing in the mergers and acquisitions literature (e.g., Dong,

Loncarski, Horst, and Veld 2012). However, Tobin’s Q could also proxy for the market’s

expectations for a firm’s growth potential and managerial capability (e.g., Lang, Stulz, and

Walkling 1989). We therefore include it as a control variable without specifying the

direction of its impact on goodwill impairment. In addition, we include several variables to

control for firm risk, including firm size (SIZE), financial leverage (LEVERAGE), asset

tangibility (TANGIBILITY), and volatilities in operating cash flows (VOL_OCF), sales

(VOL_SALE), and investments (VOL_INVEST). We include ROA and firm’s adjusted

stock returns (ADJRET) to measure firms’ performance and use institutional ownership (IO)

to control for corporate governance (Shleifer and Vishny 1986).9 Finally, we include the

dispersion of forecast errors in analysts’ annual EPS forecasts (FE_DISP) as a control

variable since uncertainty may lead to overpayment for acquisitions.

Emerging evidence shows that higher audit quality increases the likelihood of

goodwill impairment (Shipman, Carcello, and Neal 2016; Ghosh, Xing, and Wang 2016).

When goodwill is material, auditors are required to evaluate both management’s process

for identifying impairment and the reasonableness of management’s estimates. The skills

required to perform these audit procedures may include an understanding of

macroeconomic conditions and an evaluation of industry and market trends. Since Big 4

9 Our result is not affected if the number of analysts following a firm is added to the regression as an

additional control variable. We keep only IO because the two variables are highly correlated.

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auditors may be better equipped to make these assessments, they may be associated with

higher financial reporting quality. We therefore include an indicator variable BIG4 to

control for this effect.

Sample selection and descriptive statistics

We start our sample period in 2004, as that is the first year in which extensive

coverage of auditors’ opinions on internal controls became available. We end the sample

period at 2010 because we require a subsequent three-year goodwill impairment window to

proxy for the negative consequence of suboptimal M&A decisions.10

Hence our sample

spans 2004-2010. Since our focus is on managers’ M&A decisions, we exclude firms that

do not recognize new goodwill in the entire sample period. This generates an initial sample

of 11,350 firm-years from 2,576 firms that have completed M&A deals in at least one year

from 2004 to 2010.11

Of these, auditors identify ICMWs for 659 firm-years at 466 firms.

After we delete firm-years with missing financial or governance information, our final

sample consists of 4,948 firm-years from 1,498 firms, of which 191 firm-years from 148

firms have ICMWs. Table 1 summarizes the sample selection procedures.

Table 2 reports descriptive statistics of ICMWs for our final sample. Panel A

reports the distribution of our final sample as well as the frequency of sample material

weaknesses by fiscal year. Consistent with the trend reported in other studies, there is a

higher incidence of ICMWs in the first four years following the adoption of the Sarbanes-

Oxley Act of 2002 (e.g., Skaife, Veenman, and Wangerin 2013). Panel B reports the

10

For a merger occurring in 2010, we collect goodwill impairment data from 2011, 2012 and 2013. 11

We keep the non-M&A years of the 2,576 firms to avoid a sample selection bias that might arise from

using M&A years only. Controlling for goodwill impairment after a non-M&A year also helps identify the

incremental effect of M&A decisions. We find qualitatively similar results using M&A years only.

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distribution of ICMWs in our sample across industries. Business Services is the most

frequently represented industry in our sample with 44 firm-years, which accounts for

approximately 23% of our sample ICMW observations. Panel C shows the number of years

for which the auditor reported ICMW for a sample firm. 114 of the 148 ICMW firms (77%)

report ICMW only once. The average number of ICMW years for the sample of ICMW

firms is 1.3 and the maximum is 4.

Table 3 reports the characteristics of the sample firms. Panel A reports the statistics

for the entire sample and Panel B compares the mean/median/standard deviation of ICMW

and non-ICMW firm-years. The total goodwill impairment in the subsequent three years

(GWIP3YR), on average, is about 2% of total assets for the full sample and 3.3% of total

assets for the ICMW firm-years. The average newly-recognized goodwill (GW_NEW)

each year is about 3.5% of total assets for the full sample and 4.6% for the ICMW firm-

years. The beginning-of-year goodwill (GW_BOY), on average, is about 14.6% of total

assets for the full sample and 12.3% for the ICMW firm-years. ICMW firm-years also have

notably higher PE ratios and abnormal accruals. In summary, compared with non-ICMW

firm-years, ICMW firm-years appear to have higher newly recognized goodwill and larger

future goodwill impairment.12

Table 4 presents the pair-wise Pearson correlations for all variables in Equation (2).

The correlation between GWIP3YR and ICMW is -0.047 and significantly different from

zero at the 0.01 level. Since GWIP3YR captures the goodwill impairment and normally has

a negative value, the negative correlation suggests that three-year goodwill impairment is

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With respect to other firm characteristics, our sample firms are on average larger firms than the average of

the Compustat population in the same period. In addition, the ICMW subsample appears to be more

overpriced, has lower institutional ownership and higher performance volatility, and is less likely audited by

Big 4 auditors.

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greater after an ICMW year than after a normal year. The negative correlations between

GWIP3YR and a group of control variables suggest that goodwill impairment is higher

when firms have greater newly recorded and preexisting goodwill, are more likely

overpriced, consistent with the finding in Gu and Lev (2011), and face greater uncertainty

and higher firm risk. The positive correlation between GWIP3YR and other control

variables suggest that goodwill impairment is lower when firms are larger, have better

accounting and stock performance, are audited by Big 4 auditors, have more tangible assets,

and are considered by the market to have higher growth potential.

MAIN RESULTS AND ROBUSTNESS TESTS

In this section we first show that goodwill recognized by firms with ICMWs in the

year of acquisition has a greater impairment rate in the subsequent three years than

goodwill recognized by firms with no ICMW in the year of acquisition. Next we control for

other determinants of goodwill impairment in the multivariate analysis and present

evidence in support of our hypothesis that firms with ICMWs have higher impairment rates

of goodwill in post-acquisition years. We then show that our findings are robust across

different specifications. Lastly, we use propensity score matching to account for possible

endogeneity in ICMW and show that our results are not sensitive to this issue.

Univariate analysis

We compare the three-year impairment of new goodwill recognized for acquisitions

made in ICMW and non-ICMW years. We report the results in Table 5. Panel A reports the

18

comparison based on ICMW partition for all firm-years, and Panel B reports the

comparison between ICMW and non-ICMW firms only for the M&A years.

Table 5 Panel A shows that in the three years following the acquisition year, the

average total goodwill impairment is 2.00% of total assets reported at the beginning of the

acquisition year. The average goodwill impairment is 3.31% for the 191 ICMW firm-years

compared to 1.94% for the 4,757 non-ICMW firm-years. The difference of 1.37% of total

assets is statistically significant, and economically significant based on the common

materiality thresholds of the major US public accounting firms (Eilifsen and Messier 2015).

Additionally, 37.17% of ICMW firm-years report goodwill impairment compared to 29.05%

of non-ICMW firm-years.

Table 5 Panel B reports results for only those firm-years with M&A activity. The

results are qualitatively similar to Panel A. In the three years following the acquisition year,

the average total goodwill impairment is 2.33% of total assets reported at the beginning of

the acquisition year. The average goodwill impairment is 4.17% for the 131 ICMW firm-

years compared to 2.26% for the 3,518 non-ICMW firm-years. The difference of 1.91% of

total assets is both economically and statistically significant. Additionally, 43.51% of

ICMW firm-years report goodwill impairment compared to 32.58% of non-ICMW firm-

years. The findings in Table 5 provide preliminary support for our hypothesis.

Multivariate analysis

Table 6 Column 1 reports the regression result for Equation 2. The coefficient on

GW_NEW is negative (-0.049 with a p-value of 0.00 for a two-tailed test). The coefficient

on the interaction term, GW_NEW×ICMW, is negative (-0.113 with a p-value of 0.00 for a

19

two-tailed test). A negative coefficient means a greater impairment rate for goodwill

recognized in the presence of ICMW. The impairment rate of goodwill recognized in

ICMW years (the sum of -0.049 and -0.113) is about three times the impairment rate of

goodwill recognized in non-ICMW years (-0.049). This result supports our hypothesis that

ICMWs in the acquisition year are associated with higher goodwill impairment rates in

subsequent years, and shows that the impact is economically significant. The coefficients

on GW_NEW and GW_BOY are both significantly negative, suggesting that a larger base

of goodwill, either pre-existing or newly acquired, is associated with greater future

impairment. The coefficient on GW_BOY×ICMW is not significantly different from zero,

confirming that the effect of ICMW on subsequent goodwill impairment is only through

new goodwill.13

With respect to other control variables that are statistically significant in

the regression, the signs are consistent with our predictions. In all regressions, we include

industry and year dummies following Gordon and Wilford (2012).

In untabulated tests, we also include the interaction terms between GW_NEW and

year dummies to control for the possibility that the quality of goodwill recorded in M&A

waves and in crisis/boom periods is different from that in other periods. The results are

qualitatively similar. We also find that the negative impact of ICMW on the impairment

rate of newly recognized goodwill is significantly larger after the 2007 financial crisis. This

finding suggests that ICMW has a greater impact on acquisition decisions when valuation

uncertainty is higher.

13

Since the magnitude of goodwill from current year (3.5% of total assets) is far smaller than the magnitude

of goodwill from prior years (14.6% of total assets), prior years' goodwill (GW_BOY) would contribute a

much larger portion of subsequent impairment than new goodwill (GW_NEW) recognized in the ICMW year

if the subsequent impairment is driven by remediation of deficiencies in the recognition of goodwill

impairment. The insignificant coefficient on GW_BOY×ICMW suggests that this is not the case.

20

Overall, the main result in Table 6 Column 1 provides evidence in support of our

hypothesis that new goodwill recognized in an ICMW year impairs at a higher rate in

subsequent years than new goodwill recognized in a non-ICMW year. This effect holds

after we control for stock overvaluation, market timing, firm risk, uncertainty, and

monitoring measures.

Robustness Tests

We test the robustness of the main result in several ways and report the results in

columns 2 through 5 of Table 6.14

First, instead of using a dummy variable to identify

whether a firm has an ICMW in a particular year, we use the number of ICMWs (ICMW_n)

in that year as an alternative measure of the strength of a firm’s ICFR. We replace ICMW

(and ICMW in interaction terms with other variables) with ICMW_n. We report the result

in Column 2 of Table 6. The coefficient on GW_NEW×ICMW_n is -0.071 with a p-value

of 0.00 for a two-tailed test. This suggests that a firm reporting a greater number of

material weaknesses in internal controls reports larger goodwill impairment in the

subsequent three years. Again, the interaction term GW_BOY×ICMW_n is not significant,

corroborating that ICMW affects future impairments through newly acquired goodwill in

the year of ICMW, and not through pre-existing goodwill.

We then test if the main result is sensitive to the length of the testing window. The

length of the impairment window is important because it should be long enough for

accounting to capture the ramifications of poor M&A decisions, but short enough to

minimize the impact of confounding events. Following Goodman et al. (2014), we use a

14

In all regressions, we test for multicollinearity and find no concerning VIFs.

21

three-year window to assess goodwill impairment in our main test.15

We extend the testing

window to four and five years and report the results in columns 3 and 4 of Table 6.

Consistent with our prediction, the coefficient on GW_NEW×ICMW remains significantly

negative even in the 4- and 5-year impairment windows.16

Finally, we test if the result would be different if we consider goodwill impairment

as a censored measure of acquisition performance. We replicate the main test using the

Tobit model because arguably, goodwill impairment captures only the downside of

acquisition performance, as it has an upper-bound of zero. We report the result of the Tobit

regression in Column 5 of Table 6. The coefficient on GW_NEW×ICMW is essentially

unaffected by this difference in regression specification.

Overall, the results presented in Table 6 support our contention that material

weaknesses in internal controls adversely impact managers' judgment in the context of

M&A decisions. This adverse impact manifests itself in the acquiring firm overpaying for

an acquisition, leading to impairment of goodwill in the following years. Empirically, this

is evident from the significant negative coefficient on the interaction term

GW_NEW×ICMW. It shows that new goodwill recognized in ICMW years is subject to

greater impairment in subsequent years than new goodwill recognized in non-ICMW years,

controlling for a multitude of other factors. The result is not sensitive to alternative

measures of ICMW, alternative impairment windows, or use of a Tobit model.

In addition, we perform the following robustness tests (results not tabulated):

15

A three-year window would be considered a long window in an event study. However, since goodwill

impairment tests are normally performed annually, a three-year window most likely includes only three

assessments of goodwill. 16

In untabulated tests, we also used a dummy variable that indicates whether a firm reports goodwill

impairment in the testing window as the dependent variable. The coefficients are positive but not significantly

different from zero at conventional levels.

22

Managerial ability: Goodman et al. (2014) report that managers’ ability to generate

earnings forecasts is closely related to the quality of their investment decisions, including

acquisitions. To disentangle this effect from the effect of ICMW on the quality of

acquisition decisions, we use the accuracy of management earnings forecast as a proxy for

managers’ forecasting ability following Goodman et al. (2014) and include it as an

additional control variable in a robustness test. Although the additional data requirement

results in a significantly smaller sample, we find qualitatively similar results. That is,

goodwill recognized in ICMW years is impaired at a significantly higher rate in subsequent

periods after controlling for managers’ forecasting ability.

Managerial optimism: Higher post-acquisition goodwill impairment can also result from

managers being overly optimistic. Prior studies show that overconfident managers overpay

for target companies in acquisitions (Malmendier and Tate 2008) and issue earnings

forecasts that are more optimistic (Hribar and Yang 2015). We find that managers in

ICMW firm-years issue more optimistic earnings forecasts than in non-ICMW firm-years.

However, our result about ICMW’s impact on future goodwill impairment is not

qualitatively affected after controlling for managers’ forecast optimism.

Overvalued stock: Managers may use overvalued stock for acquisition, and the reversal of

stock performance (decline in stock price) is an indicator of impaired goodwill. We

perform two additional tests to rule out this alternative explanation of future goodwill

impairment. In one test we include stock repurchases as an additional control variable. In

the other test we identify a subsample of cash-only acquisition firm-years. We find

qualitatively similar results in both tests. In particular the result based on the subsample of

23

cash-only acquisitions provides strong evidence that our finding is not driven by

overvalued stock.

Alternative measures of acquisition performance: Using long-term post-acquisition stock

returns as an alternative measure of acquisition performance, we find that ICMW firms

have significantly worse long-term abnormal stock returns for up to three years after

acquisitions than non-ICMW firms. We also examine post-acquisition increases in income

before extraordinary items and operating cash flows.17

We find that the ROA measure is in

the predicted direction and statistically significant. The cash flow measure is in the

predicted direction but the statistical significance is sensitive to whether the change is

computed from year to year (Hayn and Hughes 2006) or by comparing the three-year

averages pre- and post-acquisitions (Goodman et al. 2014).18

Accounting rule change: Our sample firms’ goodwill impairment span the period from

2005 to 2013. GAAP related to periodic goodwill impairment tests changed in 2011. To

test if our result is sensitive to this accounting rule change, we delete firm-years for which

the future goodwill impairment includes data after 2012. We find qualitatively similar

results using this subsample.

Measurement error in goodwill impairment: We refine our measure of goodwill

impairment by excluding the potential impact of the impairment of other intangible assets

that might be classified as goodwill impairment by Compustat. We find qualitatively

17

Lee (2011) provides evidence that goodwill impairment after SFAS 142 predicts future cash flows. 18

We also use the frequency of reporting future goodwill impairment as an alternative dependent variable in

the regression analysis. The coefficient on the interaction between ICMW and an indicator variable for new

goodwill is positive but not statistically significant. However, in both the univariate and the propensity

matched comparisons, we find that ICMW firms are significantly more likely to report future goodwill

impairment than non-ICMW firms. These results provide some support that ICMW firms are more likely to

report future goodwill impairment.

24

similar results following the adjustment in Ramanna and Watts (2012) and also using an

alternative measure that excludes the impairment of other intangibles.

Propensity-score matched comparison of goodwill impairment

Since ICMW could be associated with firm characteristics that are also related to

M&A decisions but are not captured by the control variables in Equation (1), we follow a

growing literature that uses a propensity-score matching approach to examine the effect of

a potentially endogenous treatment (e.g., Cheng et al. 2013; Amin, Krishnan, and Yang

2014; Amel-Zadeh and Zhang 2015). We use the following logistic regression to construct

a control sample of firms that have no ICMWs but possess characteristics similar to ICMW

firms with respect to M&A and goodwill.

prob(ICMW = 1) = f(α + β1×GW_BOY + β2×GW_NEW + β3×IO + β4×PE + β5×DAC

+ β6×EQI + β7×LogMVE + β8×LogSALE + β9×LogMTB + β10×TOBINQ + β11×REPO

+ β12×ROA + β13×EXTRAGROW + β14×NSEG + β15×FOREIGN + β16×REST

+ β17×VOL_OCF + β18×VOL_SALE + β19×VOL_INVEST + β20×LEVERAGE

+ β21×TANGIBILITY + β22×ADJRET + β23×BIG4 + β24×FE_DISP + β25×MKTRET

+ ε). (3)

We include several additional independent variables for propensity matching,

primarily following Cheng et al. (2013). LogMVE is the natural log of market value of

common equity at the end of the current year. LogSALE is the natural log of total sales in

the current year. LogMTB is the natural log of the market-to-book ratio at the end of the

current year. NSEG is the number of operating segments. EXTRAGROW is an indicator

variable that equals one if a firm’s year-over-year industry-adjusted sales growth falls into

the top quintile and zero otherwise. FOREIGN is an indicator variable that equals one if a

25

firm reports nonzero foreign currency translations in the current year and zero otherwise.

REST is the restructuring charges in the current year, scaled by the firm’s end-of-year

market value.19

We match an ICMW firm-year with a non-ICMW firm-year based on the

predicted likelihood of having ICMW in audit opinions using the nearest neighbor

approach. We report the results of the propensity-matched comparison of goodwill

impairment in Table 7.

We first report our validity check on the propensity-matched pairs. Table 7 Panel A

compares the characteristics of ICMW firm-years and the matched non-ICMW firm-years.

If the matching is effective, there should be no significant differences along the dimensions

that are used in creating the matched pairs. Panel A shows that indeed the ICMW firm-

years and the matched non-ICMW firm-years are not significantly different across any of

the matched dimensions.

Table 7 Panel B shows the difference in three-year goodwill impairment between

ICMW firm-years and propensity-score matched non-ICMW firm-years. The average

impairment after ICMW firm-years is -0.034 while that after non-ICMW firm-years is only

-0.016. The difference is -0.018, significant at the 0.01 level for a two-tailed comparison. In

addition, 37.43% of ICMW firm-years report goodwill impairment in subsequent years

19

The coefficients in the logistic regression are reported here (p-values are reported in parentheses under the

coefficients):

prob (ICMW = 1) = f(-0.211 + 0.280×GW_NEW – 0.655×GW_BOY - 0.096×IO + 0.001×PE + 1.379×DAC

(0.48) (0.46) (0.03) (0.61) (0.51) (0.01)

+ 0.975×EQI – 0.096×LogMVE - 0.102×LogSALE + 0.415×LogMTB – 0.198×TOBINQ + 2.375×REPO

(0.20) (0.14) (0.09) (0.00) (0.01) (0.07)

- 3.965×ROA - 0.042×EXTRGROW + 0.031×NSEG + 0.085×FOREIGN + 1.178×REST + 0.596×VOL_OCF

(0.00) (0.71) (0.58) (0.33) (0.72) (0.54)

+ 0.608×VOL_SALE +0.389×VOL_INVEST – 0.063×LEVERAGE -0.200×TANGIBILITY – 0.071×BIG4

(0.04) (0.25) (0.84) (0.34) (0.54)

- 0.127×ADJRET – 2.777×FE_DISP + 0.248×MKTRET)

(0.14) (0.86) (0.27)

The Wald χ2 for the regression is 149.3, significant at the 0.01 level for a two-tailed test.

26

compared to 27.27% of non-ICMW firm-years. The difference is 10.16% and significant at

the 0.05 level for a two-tailed comparison. We then restrict the comparison between ICMW

and non-ICMW firms only for M&A years (GW_NEW>0) and report the results in the

lower half of Panel B. The differences are essentially unchanged. Panel B shows that

ICMW firm-years are more likely to report goodwill impairment in subsequent years and

report greater losses. These results are qualitatively similar to those presented in Table 5.

We also use the propensity-score matched subsample to replicate our main

regression and report the results in Table 7 Panel C. The coefficient on the interaction term

GW_NEW×ICMW remains significantly negative for three-, four-, and five-year goodwill

impairment at conventional levels for two-tailed tests. The results are consistent with those

reported in Table 6, Columns (1), (3) and (4).

ADDITIONAL TESTS

We perform additional tests to further our understanding of how ICMWs affect

acquisition decisions and hence subsequent goodwill impairment. We first examine

whether certain types of ICMWs are more relevant to acquisition decisions and goodwill

impairment than others. We next evaluate whether ICMWs identified by management in

SOX 302 disclosures affect future goodwill impairment when there is no accompanying

SOX 404 auditor report. We then examine whether remediation of ICMW reduces the

subsequent impairment rate of newly-recognized goodwill. Finally, we examine how the

revelation of ICMW in the current year affects managers’ future acquisition decisions as

reflected in new goodwill recognition.

Effect of acquisition-related ICMWs

27

Not all ICMWs are homogenous in their effects on financial reporting and

managerial decision-making. Doyle et al. (2007a) finds that internal control weaknesses at

the company level, but not those related to specific accounts, contributes to lower accruals

quality. Cheng et al. (2013) reports that company-level ICMWs but not account-level

ICMWs are related to the investment inefficiency in the year prior to the ICMW disclosure.

Following this line of research, we predict that categories of material weaknesses

that have a direct bearing on M&A decisions will incrementally increase future goodwill

impairment. In our setting, certain ICMWs are more closely related to acquisition decisions.

For example, an ICMW that is described as relating to “acquisition” or “goodwill” is more

likely to affect management's judgment related to M&A than ICMWs that identify

“allowance for doubtful accounts” or “sales returns.” We identify a subset of ICMWs that

are arguably more relevant to M&A decisions through a keyword search of the ICMW

reason codes provided in Audit Analytics. We conduct the keyword search across both the

"Reason Phrase" and "Reason Description" fields. We select the following words that can

be related to M&A activity: “acquisition,” “consolidation,” “goodwill,” and “subsidiary.”20

Additionally, we select the following words that indicate the presence of ICMWs that can

affect a broad range of high-level decisions, including M&A: “competence,” “competency,”

“estimate,” “internal audit” and “information technology.”21

20

We find the following Audit Analytic “Reason Keys” that correspond to these keywords: 1 (Acquisition(s)

during the past year (404 exemption)), 16 (PPE, intangible or fixed asset (value/diminution) issues), 22

(Information technology, software, security & access issue), 24 (Consolidation, (Fin46r/Off BS) & foreign

currency translation issue), 35 (Acquisition, merger, disposal or reorganization issues), 38 (Foreign, related

party, affiliated and/or subsidiary issues), 41 (Tax expense/benefit/deferral/other (FAS 109) issues), 46 (Other

auditors work being relied upon (404 exemption)), 56 (Acquisition, etc. - integration and/or challenges noted)

and 77 (Non-routine transaction control issues). 21

Audit Analytic “Reason Keys” corresponding to these keywords are: 13 (Senior management competency,

tone, reliability issues), 18 (Insufficient or non-existent internal audit function), 22 (Information technology,

28

We use an indicator variable ICMWMA to identify firms that are affected by these

weaknesses. ICMWMA equals one if a firm’s ICMW includes weaknesses identified by the

above reason keys and zero otherwise. We add ICMWMA and its interaction with

GW_NEW to Equation 2 and report the result in Table 8 Column 1. The coefficient on

GW_NEW×ICMWMA is -0.242 and significantly different from zero at the 0.01 level. This

suggests that the selected internal control weaknesses have a greater than average negative

impact on subsequent impairment of new goodwill.

Do short-lived ICMWs affect M&A decisions?

ICMWs included in our study are reported by companies’ auditors under SOX

Section 404. Assessment of ICFR can come from three sources per the requirements of

SOX 302, SOX 404(a), and SOX 404(b). SOX 302 requires top corporate officers (CEO

and CFO) to present their assessments of the effectiveness of ICFR in periodic filings.

Section 404 requires managers (404a) and auditors (404b) to issue separate assessments of

the effectiveness of ICFR in the 10-K filings. ICMW assessments by management and by

auditors are almost always consistent in these filings. In our sample, all but 16 SOX 404(b)

ICMWs are also identified by management in accompanying SOX 404(a) disclosures.

However, among the non-ICMW firm-years in our sample, there are 166 firm-years with

ICMWs identified by management in 10-Q filings per SOX 302 but not by the companies’

auditors in the year-end 10-K filings per SOX 404(b).

software, security & access issue), 32 (Inventory, vendor and cost of sales issues), 44 (Accounting personnel

resources, competency/training), 52 (Information technology, software, access/security issues) and 67 (Senior

management tone and/or self- dealing issues).

29

There is no direct evidence about how these unaudited ICMWs in the interim

reports would affect managers' M&A decisions, while the indirect evidence is mixed. On

the one hand, Moody’s (2005) points out that control problems that are “correctable within

a short period” do not necessarily affect bond issuers’ credit worthiness. On the other hand,

Beneish et al. (2008) find that SOX 302 but not SOX 404 disclosures are associated with

negative announcement returns and increases in equity cost of capital among the initial

SOX filers. We investigate whether ICMWs disclosed in management's interim assessment

per SOX 302 (i.e., the unaudited 10-Qs) have the same effect on M&A decisions as those

in the auditors' annual assessment per SOX 404(b) in 10-Ks. It is an empirical question as

to whether these short-lived, remediated ICMWs impair managers' M&A decisions.

We define an indicator variable, ICMW10Q, which equals one if in any part of the

year management identifies ICMW in its SOX 302 assessment that is not accompanied by a

SOX 404 ICMW, and zero otherwise. This variable thus identifies ICMWs recognized by

management in the interim quarters but not by auditors at year-end. The interaction

between ICMW10Q and GW_NEW captures the effect of ICMW10Q on the impairment rate

of new goodwill in the years in which ICMW is solely based on managers’ assessments.

We report the result in Column 2 of Table 8.

The coefficient on GW_NEW× ICMW10Q is 0.028 and not significantly different

from zero (the p-value for a two-tailed test is 0.43). This suggests that unlike the ICMWs

reported by auditors, ICMWs based solely on managerial assessment do not have a

negative impact on future impairments of new goodwill. Further examination of the sources

of these ICMWs shows that most of them affect only one or two quarters of a year. These

30

short-lived ICMWs that are promptly remedied are less likely to affect the quality of M&A

decisions because an acquisition usually involves a lengthy due process.

Remediated ICMWs

Prior research suggests that remediation of ICMWs should enhance the quality of

information available to managers (e.g., Ashbaugh-Skaife et al. 2008; Cheng et al. 2013;

Cheng et al. 2014). To the extent that remediation of ICMWs improves the quality of

information available for–or due diligence exercised in–acquisition decisions, managers’

valuation of a target improves and overpayment for acquisitions decreases, resulting in

lower subsequent goodwill impairment.

We examine how the remediation of a prior-year ICMW affects managers’ M&A

decisions in the current year. Prior literature has shown that remediation of ICMWs leads to

operational and financial benefits. Cheng et al. (2014) documents improvements to firms’

operational efficiency upon remediation. Gordon and Wilford (2012) shows that firms that

remediate ICMWs lower their equity cost of capital. Cheng et al. (2013) shows that after

remediation, ICMW firms’ investment efficiency improves. In our setting, we predict that

ICMW remediation leads to better M&A decisions, resulting in lower subsequent

impairment rates of goodwill from post-remediation acquisitions.

We use an indicator variable, REMEDY, to identify firms that have ICMWs in the

prior year but no ICMWs in the current year. We add REMEDY and its interaction with

GW_NEW as additional variables to Equation 2. We report the result in Column 3 of Table

8. The coefficient on GW_NEW×REMEDY is 0.087 and significantly different from zero

at the 0.01 level. This suggests that goodwill recorded in the year following the remediation

31

of ICMW has a lower rate of future impairment.22

Remediation of prior year ICMWs thus

seems to improve the quality of managers’ M&A decisions. The sum of the coefficients of

GW_NEW×ICMW and GW_NEW×REMEDY is not significantly different from zero,

suggesting that goodwill impairment by firms with remediated ICMWs is similar to firms

with effective internal controls.

Awareness of ICMW and its Effect on Future M&A Decisions

A few recent studies examine changes in managers’ performance and decisions

around the disclosure of ICMWs and document a positive economic impact associated with

the disclosure of ICMW (e.g., Cheng et al. 2013, Cheng et al. 2014). Disclosure of ICMW

can induce other stakeholders to increase their monitoring and scrutiny of management's

operating decisions. Cheng et al. (2013) argues that this increased monitoring leads

managers to make better investment decisions. In our setting, identification of ICMWs

informs managers that they are observing low-quality signals from a weak ICFR

environment. In addition to increased monitoring, this knowledge, by itself, should make

managers less confident in the valuation inputs and consequently more conservative in their

acquisition decisions, resulting in less goodwill recognized for these acquisitions.

We test if the revelation of ICMW alters managers' behavior in future M&A

activities and induces managers to make more conservative bids, without regard to whether

the ICMW has been remediated. Awareness of ICMW reduces the likelihood of

overpayment and results in recognizing less goodwill on completed acquisitions. Managers

22

In an alternative design, we include indicator variables of ICMWs in three consecutive years and find

similar results.

32

might even engage in fewer M&A transactions. In either case, we predict that ICMW firms

will recognize less new goodwill in years subsequent to the identification of ICMW.

We compare newly-recognized goodwill in the two years following the year of

ICMW disclosure with that for propensity-matched firm-years without ICMW. We use the

same propensity-score matching procedures as in Table 7 and report the results of the

matched comparison in Table 9 Panel A. The smaller number of matched pairs is due to the

additional requirement for non-missing values of future new goodwill.23

Panel B shows that in the two years following the ICMW disclosure, ICMW firms

recognize 55% less new goodwill than matched non-ICMW firms (0.029 vs. 0.064, which

is significantly different from zero at the 0.01 level for a two-tailed test). We also compare

the percentage of firm-years that recognized new goodwill in the subsequent two years to

examine if ICMW firms are less likely to engage in M&A transactions. About 46.8% of

ICMW firm-years and about 52.2% of matched non-ICMW firm-years report new goodwill

in at least one of the two subsequent years, suggesting that they engage in M&A activities.

The difference is not statistically significant. Thus, after ICMW disclosure, ICMW firms

are not less likely to engage in M&A transactions, but are more conservative in valuing the

target firms in such transactions than non-ICMW firms, as evidenced by less new goodwill.

Combining the information in both panels further helps interpret the results. We

include goodwill recognized in the current year as one of the matching criteria, and Table 9

Panel A shows that the significantly lower goodwill recognition by ICMW firms does not

exist in the year in which ICMW is disclosed. This suggests that the observed difference is

not a continuation of an existing pattern of M&A decisions. These results are consistent

23

Our results are qualitatively similar if we replace missing new goodwill with zero.

33

with the notion that when managers become aware that their accounting systems are likely

to generate poor accounting information, they become more conservative when engaging in

M&A activities. They would be less likely to acquire targets at high premiums, resulting in

less goodwill impairment in the future. Our results are consistent with prior research that

suggests managers behave more conservatively following ICMW disclosure (Cheng et al.

2013, Sun 2016).

CONCLUSION

This paper studies the economic consequences of the quality of internal control over

financial reporting in the context of M&A decisions. We posit that ICMW firms have low

quality internal accounting information, which adversely affects managers’ assessment of

potential synergy with a target, a critical yet highly subjective assessment in M&A

decisions. Overestimating the value of synergy results in overvaluation of the target firm

and, likely, overpayment for the target. The payment in excess of the target’s economic

value to the acquiring firm results in goodwill that is recognized and subsequently impaired.

We find strong evidence that firms recognize greater goodwill impairment on

acquisitions made in the year of ICMW disclosure than on acquisitions in firm-years with

no ICMW disclosure. This is consistent with our prediction that firms with ICMWs

overpay in M&A transactions. Our results are robust to different specifications of the

goodwill impairment period, measuring ICMW by the number of material weaknesses

instead of an indicator variable, and controlling for endogeneity in ICMW through

propensity score matching.

Additionally, we identify categories of ICMWs that have a greater impact on

acquisition quality. We also document lower impairment rates of goodwill recognized for

34

acquisitions made in the year of ICMW remediation. We further show that although the

propensity of engaging in new acquisitions is not markedly lower for ICMW firms, they do

recognize less new goodwill in the future, which is consistent with managers bidding more

conservatively for targets following ICMW disclosure.

To summarize, our study provides evidence that material weaknesses in internal

control over financial reporting impair managers' M&A decisions. The negative economic

consequence is reflected in higher subsequent goodwill impairment. We also find that both

the disclosure and the remediation of material weaknesses in internal control affect

managers' M&A decisions.

Our study contributes to an emerging literature that documents unintended benefits

of SOX 404 implementation on management decision-making and performance. Other

studies in this literature document ICMW’s effects on investment and operating efficiency;

we demonstrate ICMW’s effect on the quality of acquisition decisions. In addition, our

approach complements prior studies that report how the target firm’s information quality

affects acquisition performance by examining the acquiring firm’s information quality. Our

study also contributes to the literature on goodwill impairment by identifying an additional

determinant of goodwill impairment.

35

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40

Figure 1

M&A Decision Outcomes and Economic Consequences

(A)

E> P

(B)

E < P

V > P

Outcome

Consequence

(1)

Target acquired

Economic Rents

(2)

Target not acquired

Opportunity Cost

V < P

Outcome

Consequence

(3)

Target acquired

Impairment

(4)

Target not acquired

Figure 1 depicts four mutually exclusive and collectively exhaustive outcomes of managers’ M&A decisions.

An acquisition will occur at price P only if the buyer’s estimate E is greater than P (E>P), in which case the

transaction will either generate economic rents (when V>P as in (1)) or cause future goodwill impairment

(when V<P as in (3)). An acquisition will not occur at price P if the buyer’s estimate E is less than P (E<P),

leading the buyer to either forgo a value-increasing acquisition (when V>P as in (2)) or avoid a value-

destroying acquisition (when V<P as in (4)). Cell (1) and Cell (4) on the main diagonal present the preferred

outcomes. Cell (1) and Cell (3) in Column A are available for our empirical test. Cell (3) is the primary

reason for goodwill impairment in our setting.

41

Appendix. Variable Definitions

Variable

Definition

ADJRET

A firm’s 12-month cumulative abnormal returns in fiscal year t based on

CAPM. Monthly returns are extracted from CRSP.

BIG4

An indicator variable equals one if a firm’s auditor is one of the Big 4 auditors

and zero otherwise (Compustat item AU).

DAC

Discretionary accruals in fiscal year t, estimated following Gu and Lev (2011)

using Compustat data.

EQI

Issuance of common and preferred shares (Compustat item SSTK) net of stock

repurchase (Compustat item PRSTKC) in year t, scaled by beginning-of-year

total assets (Compustat item AT).

EXTRGROW

An indicator variable equal to 1 if year-over-year industry-adjusted sales

(Compustat item SALE) growth falls into the top quintile and 0 otherwise.

FE_DISP

The dispersion of financial analysts’ annual EPS forecasts from I/B/E/S.

FOREIGN

An indicator variable equal to 1 if a firm has a non-zero foreign currency

translation (Compustat item CICURR) in fiscal year t and 0 otherwise.

GW_BOY

Beginning-of-year goodwill (Compustat item GDWL) in fiscal year t, scaled

by beginning-of-year total assets (Compustat item AT).

GW_NEW

Newly recognized goodwill in fiscal year t, scaled by beginning-of-year total

assets. See Equation (1).

GWIP3YR

Cumulative annual goodwill impairments for fiscal years t+1, t+2 and t+3

scaled by beginning-of-year total assets in fiscal year t. non-zero GWIP3YR

has a negative value.

ICMW

A dummy variable for material weakness in internal control in fiscal year t

based on audit opinion per SOX 404(b). 1 = yes and 0 = no. We extract the

information from Audit Analytics (IC_IS_EFFECTIVE) and Compustat

(AUOPIC) and manually verified it.

ICMW_n

Number of material weaknesses in internal control in fiscal year t.

ICMWMA

An indicator variable for material weakness in internal control in fiscal year t

that we consider to be directly related to mergers and acquisitions or goodwill

impairments. See footnotes 20 and 21 for details.

ICMW10Q

An indicator variable for ICMW reported by management per SOX 302

requirements. It equals 1 if a firm reports ICMW by management at any

quarter during a year while its auditor does not report ICMW at year-end, and

0 otherwise. It captures cases in which only interim ICMWs are reported.

IO

Average institutional ownership (Form 13F) in fiscal year t.

42

LEVERAGE

A firm’s financial leverage computed as long-term debt (Compustat item

DLTT) scaled by the sum of long-term debt and market value of equity (CSHO

× PRCC_F) in fiscal year t.

LogMTB

The natural logarithm of the end-of-year ratio of the market value (Compustat

item CSHO × PRCC_F) over book value of common equity (CEQ) in fiscal

year t.

LogMVE

The natural logarithm of the end-of-year market value of common equity

(Compustat item CSHO × PRCC_F) in fiscal year t.

LogSALE

The natural logarithm of the annual sales (Compustat item SALE) in fiscal year

t.

MKTRET

The annual buy-and-hold value-weighted market returns.

NSEG

Number of operational segments (Compustat item SID) in fiscal year t.

PE

Price-to-earnings ratio computed following Gu and Lev (2011).

ROA

Return on assets computed as net income (Compustat item NI) divided by

begging-of-year total assets (AT) in fiscal year t.

REMEDY

An indicator variable, equal to 1 if a firm reports ICMW in fiscal year t-1 and

reports no ICMW in fiscal year t, and zero otherwise.

REST

Total restructuring charges (Compustat item RCP) in year t scaled by the firm's

market capitalization in fiscal year t.

REPO

The percentage of outstanding common shares that are repurchased during

fiscal year t (Compustat item CSHOPQ/CSHO).

SIZE

The natural logarithm of a firm’s total assets at the end of fiscal year t

(Compustat item TA).

TANGIBILITY

Property, plant, and equipment (net) (Compustat item PPENT) as a percentage

of total assets in fiscal year t.

TOBINQ

Tobin’s Q computed as the market value of the firm (market value minus book

value of shareholders’ equity plus total assets, Compustat item

PRCC_F×CSHO+AT-CEQ) divided by the book value of total assets.

VOL_INVEST

The volatility in total investments (the sum of capital expenditure, acquisitions,

and research and development minus sale of property, plant and equipment) in

the five fiscal years before fiscal year t.

VOL_OCF

The volatility in operating cash flows (Compustat item OANCF) in the five

fiscal years before fiscal year t.

VOL_SALE

The volatility in total sales (Compustat item SALE) in the five fiscal years

before fiscal year t.

43

Table 1. Sample Selection Procedures

All

ICMW

Subsample

Criteria

Firms

Firm-

Years Firms

Firm-

Years

Initial Sample:

Total observations with auditor opinion on internal

control in Audit Analytics and Compustat and data

about goodwill*

3,955

19,051

762

1,071

Less: firms that have no acquisition over the entire

sample period 1,379 7,701

296

412

Observations from firms that engage in M&A at least

once during the sample period 2,576 11,350

466

659

Less: firm-years that do not have all data to control for

stock overvaluation

637 4,816 247 375

Less: firm-years that do not have all data to control for

performance and uncertainty

375

1,337

65 87

Less: firm-years that do not have all data to control for

other firm characteristics

66 249 6 6

Final Sample:

1,498

4,948

148

191

*We require data for goodwill impairment and amortization, the beginning-of-year, end-of-year, and change

in the current year goodwill, and the 3-year goodwill impairment. Our sample goodwill impairment includes

data up to 2013. Since we require goodwill impairment data for three consecutive years after a fiscal year, our

sample for ICMW analysis effectively ends in 2010.

44

Table 2. Descriptive Statistics of Sample ICMWs

Panel A. Distribution of final sample by fiscal year

Fiscal Year All

Firm-Years

ICMW

Firm-Years

Non-ICMW

Firm-Years

2004 552 43 509

2005 652 44 608

2006 786 47 739

2007 799 34 765

2008 570 6 564

2009 733 7 726

2010 856 10 846

Total 4,948 191 4,757

Panel B. Frequency of sample ICMW by industry (top 10 industries)

SIC2 Name Frequency Percentage

73 Business Services 44 23.04%

36 Electr, Oth Elec Eq, Ex Cmp 25 13.09%

35 Indl,Comml Machy,Computer Eq 16 8.38%

38 Meas Instr; Photo Gds; Watches 15 7.85%

48 Communications 7 3.66%

87 Engr,Acc,Resh,Mgmt,Rel Svcs 7 3.66%

28 Chemicals & Allied Products 6 3.14%

33 Primary Metal Industries 6 3.14%

59 Miscellaneous Retail 6 3.14%

56 Apparel and Accessory Stores 5 2.62%

Other industries 54 28.27%

Total 191 100%

Panel C. Number of ICMW years for sample ICMW firms

# of Years # of Firms Percentage # of Firm-Years

Firms with 1 114 77.03 114

2 28 18.92 56

3 3 2.03 9

4 3 2.03 12

Total # of ICMW Firms 148 100% 191

Average # of ICMW per ICMW firm 1.29

45

Table 3. Descriptive Statistics of Firm Characteristics

Panel A. Statistics for the whole sample

Variable Mean Std Dev Min Q1 Median Q3 Max

GWIP3YR -0.020 0.056 -0.340 -0.002 0.000 0.000 0.000

GW_NEW 0.035 0.085 0.000 0.000 0.005 0.027 0.574

GW_BOY 0.146 0.149 0.000 0.021 0.098 0.231 0.602

SIZE 7.424 1.621 4.181 6.268 7.315 8.408 11.960

PE 8.461 30.823 -16.944 -2.249 0.325 6.662 222.279

DAC 0.014 0.063 -0.150 -0.015 0.007 0.036 0.290

EQI -0.021 0.072 -0.335 -0.036 0.000 0.007 0.207

IO 0.773 0.198 0.151 0.670 0.820 0.926 1.000

TOBINQ 2.028 1.117 0.838 1.276 1.683 2.396 6.880

LEVERAGE 0.146 0.158 0.000 0.004 0.101 0.225 0.689

TANGIBILITY 0.218 0.204 0.003 0.068 0.155 0.300 0.847

VOL_OCF 0.047 0.041 0.003 0.021 0.036 0.060 0.237

VOL_SALE 0.138 0.130 0.004 0.052 0.098 0.180 0.726

VOL_INVEST 0.084 0.108 0.000 0.021 0.047 0.097 0.589

FE_DISP 0.002 0.003 0.000 0.000 0.001 0.002 0.018

ADJRET 0.087 0.067 0.002 0.040 0.072 0.116 0.355

ROA 0.904 0.295 0.000 1.000 1.000 1.000 1.000

BIG4 -0.020 0.056 -0.340 -0.002 0.000 0.000 0.000

Panel B. ICMW vs. non-ICMW firm-years

ICMW Firm-Years Non-ICMW Firm-Years

Variable Mean Std. Dev. Median Mean Std. Dev Median

GWIP3YR -0.033 0.075 0.000 -0.019 0.055 0.000

GW_NEW 0.046 0.108 0.003 0.034 0.084 0.005

GW_BOY 0.123 0.126 0.083 0.147 0.149 0.098

SIZE 6.543 1.408 6.326 7.459 1.619 7.355

PE 20.592 50.955 2.346 7.974 29.637 0.272

DAC 0.031 0.087 0.017 0.013 0.061 0.006

EQI -0.004 0.065 0.002 -0.022 0.072 0.000

IO 0.738 0.219 0.798 0.774 0.197 0.821

TOBINQ 1.904 0.868 1.679 2.033 1.126 1.684

LEVERAGE 0.136 0.172 0.055 0.146 0.157 0.102

TANGIBILITY 0.198 0.198 0.117 0.219 0.204 0.156

VOL_OCF 0.063 0.049 0.049 0.047 0.040 0.035

VOL_SALE 0.188 0.168 0.124 0.136 0.128 0.097

VOL_INVEST 0.107 0.125 0.064 0.083 0.108 0.046

FE_DISP 0.002 0.003 0.001 0.002 0.002 0.001

46

ADJRET -0.003 0.479 -0.053 0.039 0.445 0.003

ROA 0.071 0.063 0.055 0.087 0.067 0.072

BIG4 0.843 0.365 1.000 0.906 0.292 1.000

Obs. 191 4,757

This table reports the descriptive statistics of sample firms’ goodwill, goodwill impairment, and firm

characteristics used in later analyses. Variables in bold fonts indicate that the means are significantly different

between ICMW and non-ICMW firm-years at the 0.05 level for two-tailed comparisons. All continuous

variables are winsorized at the top and the bottom 1 percentile, respectively. See the Appendix for variable

definitions.

47

Table 4. Pairwise Pearson Correlations

ICMW GWIP3YR (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

(1) GWIP3YR -0.047

(0.00)

(2) GW_NEW 0.027 -0.147

(0.05) (0.00)

(3) GW_BOY -0.031 -0.185 0.210

(0.03) (0.00) (0.00)

(4) SIZE -0.109 0.068 -0.023 0.075

(0.00) (0.00) (0.10) (0.00)

(5) PE 0.079 -0.021 0.026 -0.074 -0.140

(0.00) (0.14) (0.07) (0.00) (0.00)

(6) DAC 0.055 -0.032 0.016 -0.032 -0.096 -0.046

(0.00) (0.02) (0.25) (0.02) (0.00) (0.00)

(7) EQI 0.047 -0.036 0.132 0.008 -0.075 0.098 0.050 (0.00) (0.01) (0.00) (0.59) (0.00) (0.00) (0.00)

(8) IO -0.036 -0.090 0.073 0.177 0.085 -0.014 -0.056 -0.098

(0.01) (0.00) (0.00) (0.00) (0.00) (0.34) (0.00) (0.00)

(9) TOBINQ -0.022 0.144 0.002 -0.072 -0.215 0.065 0.108 -0.254 -0.014

(0.12) (0.00) (0.90) (0.00) (0.00) (0.00) (0.00) (0.00) (0.31)

(10) LEVERAGE -0.012 -0.057 0.034 0.041 0.321 -0.035 -0.058 0.151 -0.033 -0.461 (0.39) (0.00) (0.02) (0.00) (0.00) (0.01) (0.00) (0.00) (0.02) (0.00)

(11) TANGIBILITY -0.020 0.079 -0.123 -0.257 0.064 -0.004 -0.049 0.025 -0.066 -0.082 0.218

(0.16) (0.00) (0.00) (0.00) (0.00) (0.80) (0.00) (0.08) (0.00) (0.00) (0.00)

(12) VOL_OCF 0.077 -0.035 0.065 -0.179 -0.398 0.117 0.153 0.053 -0.036 0.254 -0.233 -0.115

(0.00) (0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00)

(13) VOL_SALE 0.077 -0.070 0.057 -0.045 -0.273 0.041 0.121 0.058 -0.031 0.078 -0.117 -0.042 0.481

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.03) (0.00) (0.00) (0.00) (0.00)

(14) VOL_INVEST 0.043 -0.103 0.105 0.282 -0.134 0.030 0.093 0.148 0.030 0.028 0.072 0.029 0.150 0.235

(0.00) (0.00) (0.00) (0.00) (0.00) (0.04) (0.00) (0.00) (0.03) (0.05) (0.00) (0.04) (0.00) (0.00) (15) FE_DISP 0.036 -0.056 -0.045 -0.165 -0.079 0.113 0.025 0.090 -0.144 -0.243 0.274 0.049 0.108 0.101 -0.008

(0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.08) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.56)

(16) ADJRET -0.018 0.028 -0.018 -0.060 -0.003 -0.008 0.090 0.090 -0.041 -0.016 0.041 0.009 0.012 0.020 0.018 0.037 (0.19) (0.05) (0.21) (0.00) (0.86) (0.59) (0.00) (0.00) (0.00) (0.26) (0.00) (0.50) (0.39) (0.17) (0.21) (0.01)

(17) ROA -0.045 0.103 0.082 -0.068 -0.169 -0.270 0.213 -0.250 0.006 0.663 -0.409 -0.030 0.266 0.151 0.020 -0.170 0.037

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.65) (0.00) (0.00) (0.04) (0.00) (0.00) (0.16) (0.00) (0.01) (18) BIG4 -0.041 0.030 -0.013 0.073 0.295 -0.031 -0.009 -0.078 0.206 -0.010 0.041 0.102 -0.125 -0.086 -0.016 -0.110 -0.010 -0.038

(0.00) (0.04) (0.35) (0.00) (0.00) (0.03) (0.54) (0.00) (0.00) (0.48) (0.00) (0.00) (0.00) (0.00) (0.27) (0.00) (0.50) (0.01)

This table reports the pair-wise correlations between variables used in our multivariate analysis. The numeric column heading refers to the corresponding

variable by the same number as listed in the far-left column. p-values are reported in parentheses based on two-tailed tests.

48

Table 5. Future Goodwill Impairment for Firms with and without ICMW

Panel A. All firm-years that satisfy data requirements for the main regressions

All

ICMW=1

ICMW=0

Diff.

Mean impairment

-0.0200

-0.0331

-0.0194

-0.0137***

(p-value)

(0.01)

% reporting impairment 29.37% 37.17% 29.05% 8.12%**

(p-value) (0.02)

Obs. 4,948 191 4,757

Panel B. Only firm-years that satisfy data requirements for the main regressions and have non-

zero new goodwill

All

ICMW=1

ICMW=0

Diff.

Mean impairment

-0.0233

-0.0417

-0.0226

-0.0191***

(p-value)

(0.01)

% reporting impairment 32.97% 43.51% 32.58% 10.94%***

(p-value) (0.01)

Obs. 3,649 131 3,518

This table compares three-year goodwill impairment between ICMW firm-years and non-ICMW firm-years.

Goodwill impairment is scaled by the beginning-of-year total assets and we winsorized the extreme values

at the top and the bottom 1 percentile, respectively. p-values for mean comparisons are based on two-tailed

t-tests. The results are qualitatively similar when 4-year or 5-year goodwill impairments are used.

49

Table 6. ICMW and the Impairment Rate of Newly Recognized Goodwill

Main Result Robustness Tests

(1)

(2) (3) (4) (5)

3-year

# of MW

4-year

5-year

Tobit

Independent

Predicted

Coefficient

Coefficient

Coefficient

Coefficient

Coefficient

Variables

Sign

(t-statistics)

(t-statistics)

(t-statistics)

(t-statistics)

(t-statistics)

GW_NEWa

-

-0.049 ***

-0.060 ***

-0.064 ***

-0.066 ***

-0.085 ***

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

GW_NEW×ICMW

a, b

-

-0.113 ***

-0.071 ***

-0.152 ***

-0.133 ***

-0.113 ***

(0.00)

(0.00)

(0.00)

(0.00)

(0.01)

ICMW

a

?

-0.000

0.000

0.005

0.013

-0.009

(0.93)

(0.60)

(0.46)

(0.15)

(0.20)

GW_BOY -

-0.055 ***

-0.044 ***

-0.066 ***

-0.069 ***

-0.115 ***

(0.00)

(0.00)

(0.01)

(0.01)

(0.00)

GW_BOY×ICMW

+

?

0.041

0.008

0.041

-0.033

0.037

(0.46)

(0.31)

(0.51)

(0.77)

(0.60)

PE

-

-0.000

-0.000

-0.000 **

-0.000 *

-0.000 ***

(0.27)

(0.14)

(0.03)

(0.09)

(0.00)

DAC

-

-0.016

-0.021

-0.007

-0.020

-0.052

(0.38)

(0.21)

(0.73)

(0.34)

(0.40)

EQI

-

0.011

-0.009

0.005

-0.012

0.019

(0.36)

(0.11)

(0.80)

(0.66)

(0.27)

TOBINQ

+/-

0.007 ***

0.006 ***

0.010 ***

0.011 ***

0.034 ***

(0.00)

(0.00)

(0.00)

(0.00)

(0.00)

ADJRET

-

-0.001

-0.000

-0.000

0.000

-0.004

(0.73)

(0.94)

(0.90)

(0.85)

(0.48)

VOL_OCF

-

-0.024

-0.037

-0.042

-0.048

-0.186 **

(0.41)

(0.14)

(0.40)

(0.33)

(0.04)

VOL_SALE

-

-0.020 **

-0.013

-0.027 *

-0.048 **

-0.034 **

(0.04)

(0.13)

(0.06)

(0.02)

(0.04)

VOL_INVEST

-

-0.015

-0.015

-0.022

-0.018

-0.021

50

(0.33)

(0.14)

(0.13)

(0.27)

(0.21)

FE_DISP

-

-0.949 ***

-0.548 ***

-1.287 ***

-1.060 **

-2.846 ***

(0.00)

(0.00)

(0.00)

(0.03)

(0.00)

SIZE

+

0.002 ***

0.002 **

0.003 ***

0.003 ***

0.002

(0.01)

(0.05)

(0.00)

(0.00)

(0.33)

TANGIBILITY

+

0.024 **

0.015 *

0.042 **

0.047 ***

0.049 ***

(0.03)

(0.06)

(0.02)

(0.00)

(0.00)

LEVERAGE

+/-

-0.010

-0.014

-0.005

-0.012

-0.054 ***

(0.40)

(0.25)

(0.74)

(0.46)

(0.00)

ROA

+

0.037

0.028

0.040

0.038

0.029

(0.14)

(0.16)

(0.31)

(0.44)

(0.45)

IO

+/-

-0.014 **

-0.011 **

-0.021 ***

-0.023 **

-0.049 ***

(0.02)

(0.03)

(0.01)

(0.02)

(0.00)

BIG4

+

0.005 *

0.007 **

0.004

0.008

0.003

(0.08)

(0.02)

(0.54)

(0.28)

(0.68)

Intercept

0.023 ***

-0.043 *

0.014

0.019

0.071 ***

(0.01)

(0.09)

(0.37)

(0.44)

(0.01)

Fixed Effects

Industry

Industry

Industry

Industry

Industry

& Year & Year & Year & Year & Year

Obs.

4,948

4,537

4,776

3,853

4,948

Adj. R2 0.158 0.195 0.165 0.199 0.472

F-statistics (χ2 in 5) 9.78 *** 11.44 *** 9.98 *** 10.15 *** 681.62 ***

This table reports the regression result of Equation (2). The dependent variable in the main result (column (1)) is three-year goodwill impairment. The

independent variable of interest is the interaction term GW_NEW×ICMW. ICMW is a dummy variable that equals 1 if a firm’s auditor identifies material

weakness in the firm’s internal control. See the Appendix for variable definitions. Column (1) reports the main result. Columns (2)-(5) report the results of

robustness tests. We replace the ICMW dummy with the number of ICMW weaknesses and report the result in Column (2). We compute alternative measures of

future goodwill impairment using 4-year and 5-year windows and report the result in Column (3) and Column (4), respectively. We replicate the regression in

Column (1) using a Tobit model and report the result in Column (5). p-values are reported in parentheses based on two-tailed tests and ***, **, * represent

significance at the 0.01, 0.05, and 0.10 level, respectively. Industry and year fixed effects are included in all regressions. Significance levels are computed using

firm- and year-adjusted error terms in all regressions. All continuous variables are winsorized at the top and bottom one percentile. a.The predicted sign is negative because goodwill impairment is reported as a loss and has a negative value.

b.ICMW in Column 2 is replaced with the ICMW_n, the number of material weaknesses in ICFR in a given year.

51

Table 7. Propensity-Score Matched Comparison of Future Goodwill Impairment

Panel A. Difference between ICMW firms and matched non-ICMW firms

Mean

Variable Treat

Matched

Difference (p-value)

GW_NEW 0.047

0.041

0.006 (0.55)

GW_BOY 0.125

0.124

0.001 (0.93)

IO 0.742

0.745

-0.003 (0.89)

PE 19.775

17.286

2.489 (0.62)

DAC 0.031

0.024

0.007 (0.38)

EQI -0.005

-0.008

0.003 (0.66)

LOGMVE 6.702

6.646

0.057 (0.67)

LOGSALE 6.442

6.428

0.014 (0.92)

LOGMTB 0.925

0.882

0.044 (0.48)

TOBINQ 1.910

1.814

0.095 (0.25)

ROA 0.059

0.057

0.001 (0.75)

EXTRAGROW 0.144

0.096

0.048 (0.15)

NSEG 2.602

2.554

0.048 (0.55)

FOREIGN 0.684

0.684

0.000 (1.00)

REST -0.005

-0.003

-0.002 (0.14)

VOL_OCF 0.064

0.060

0.003 (0.49)

VOL_SALE 0.191

0.185

0.006 (0.55)

VOL_INVEST 0.109

0.101

0.008 (0.70)

LEVERAGE 0.134

0.142

-0.008 (0.63)

TANGIBILITY 0.202

0.214

-0.012 (0.56)

BIG4 0.845 0.861 -0.016 (0.66)

FE_DISP 0.002 0.002 0.000 (0.87)

REPO 0.021 0.020 0.001 (0.74)

ADJRET -0.001

-0.069

0.068 (0.13)

MKTRET 0.103

0.121

-0.018 (0.74)

Panel B. Comparison of future goodwill impairment

3-year impairment ICMW=1

Matched

ICMW=0

Diff.

Propensity

Diff.

All firm-years

Mean -0.034

-0.016

-0.018***

0.000

P-value

(0.01)

(0.99)

% reporting loss 37.43%

27.27%

10.16%**

0.000

P-value

(0.04)

(0.99)

Obs. 187

187

Only firm-years with positive new goodwill (M&A years)

Mean -0.043

-0.024

-0.019**

0.000

P-value

(0.01)

(0.99)

% reporting loss 43.75%

31.25%

12.50%**

0.000

P-value

(0.04)

(0.99)

Obs. 128

128

52

Panel C. Replication of main regression using PSM subsample (condensed results)

VARIABLES 3-yr impairment 4-yr impairment 5-yr impairment

GW_NEW×ICMW -0.075* -0.100*** -0.099**

(0.08) (0.01) (0.02)

GW_NEW -0.116* -0.133** -0.115**

(0.06) (0.01) (0.03)

ICMW -0.020*** -0.022* -0.014

(0.00) (0.07) (0.51)

… … … …

R-squared 0.335 0.345 0.384

Observations 374 356 306

F-statistics 2.04*** 2.00*** 1.95***

This table compares future goodwill impairment between propensity matched ICMW firm-years and non-ICMW

firm-years. The propensity scores are computed using the following logit regression:

prob(ICMW = 1) = f(α + β1GW_BOY + β2GW_NEW + β3IO + β4PE + β5DAC + β6EQI + β7LogMVE +

β8LogSALE + β9LogMTB + β10TOBINQ + β11ROA + β12EXTRAGROW + β13NSEG + β14FOREIGN + β15REST

+ β16VOL_OCF + β17VOL_SALE + β18VOL_INVEST + β19LEVERAGE + β20TANGIBILITY + β21ADJRET +

β22×BIG4 + β23×FE_DISP + β24×MKTRET + ε).

See the Appendix for variable definitions. p-values are reported in parentheses based on two-tailed comparisons

and ***, **, * represent significance at the 0.01, 0.05, and 0.10 level, respectively. Goodwill impairment is

winsorized at the top and bottom one percentile.

53

Table 8. Results for Additional Analysis

(1)

MA Related

(2)

Interim ICMW

(3)

Remediated

Coefficient

Coefficient Coefficient

(t-statistics)

(t-statistics) (t-statistics)

GW_NEW -0.054 ***

-0.050 *** -0.049 ***

(0.00) (0.00) (0.00)

GW_NEW×ICMW -0.226 ***

-0.112 *** -0.113 ***

(0.00) (0.00) (0.00)

GW_NEW×ICMWMA -0.242 ***

(0.01)

GW_NEW×ICMW10Q 0.028

(0.43)

GW_NEW×REMEDY

0.087 ***

(0.00)

ICMW -0.002 0.000

0.000

(0.81) (1.00) (0.99)

ICMWMA 0.004

(0.74)

ICMW10Q 0.006

(0.33)

REMEDY

0.006

(0.32)

GW_BOY -0.044 ***

-0.055 *** -0.055 ***

(0.00) (0.00) (0.00)

GW_BOY×ICMW 0.035 0.041

0.041

(0.43) (0.46) (0.46)

PE -0.000 -0.000

-0.000

(0.40)

(0.27) (0.27)

DAC -0.020 -0.017

-0.017

(0.22)

(0.38) (0.36)

EQI -0.011 **

0.011 0.011

(0.02) (0.37) (0.39)

TOBINQ 0.006 ***

0.007 *** 0.007 ***

(0.00)

(0.00)

(0.00)

ADJRET -0.001 -0.001

-0.001

(0.57)

(0.73)

(0.77)

VOL_OCF -0.037 *

-0.024 -0.024

(0.10)

(0.40)

(0.41)

VOL_SALE -0.013 -0.021 ** -0.021 **

(0.14)

(0.04)

(0.04)

VOL_INVEST -0.015

-0.015

-0.015

(0.13)

(0.31)

(0.32)

FE_DISP

-0.596 ***

-0.974 *** -0.991 ***

54

(0.00)

(0.00)

(0.00)

SIZE 0.002 **

0.002 *** 0.002 ***

(0.04)

(0.01)

(0.01)

LEVERAGE -0.015 -0.010

-0.010

(0.23)

(0.41)

(0.41)

TANGIBILITY 0.016 **

0.024 ** 0.024 **

(0.05)

(0.03)

(0.02)

ROA 0.025 0.037

0.038

(0.16)

(0.14)

(0.14)

IO -0.011 **

-0.014 ** -0.014 **

(0.02)

(0.02) (0.02)

BIG4

0.007 **

0.005 * 0.005 *

(0.02)

(0.08)

(0.08)

Intercept 0.025 -0.047 * -0.047 *

(0.16) (0.06) (0.06)

Fixed effects

Industry &

Year

Industry &

Year

Industry &

Year

Obs.

4,849

4,948

4,948

Adj. R2 0.202 0.142 0.142

F-statistics 12.70 *** 9.60 *** 9.62 ***

This table reports the regression results of all additional analysis. The dependent variable is three-year goodwill

impairment. The independent variables of interest are the interaction of GW_NEW with the following variables: 1)

ICMWMA, which presents the incremental effect of certain internal control material weaknesses (see footnotes 20

and 21 for the reason keys) that are considered critical to acquisition decisions compared with the average effect

over all ICMW reason keys; 2) ICMW10Q, which presents the separate effect of internal control material

weaknesses that are reported by management based on SOX 302 but not recognized by auditors based on SOX 404;

3) REMEDY, which presents the effect of the remedied ICMW in the previous year on new goodwill recognized in

the current year. See the definitions of other variables in the Appendix. p-values are reported in parentheses based

on two-tailed tests and ***, **, * represent significance at the 0.01, 0.05, and 0.10 level, respectively. Significance

levels are computed using firm- and year-adjusted error terms in all regressions. All continuous variables are

winsorized at the top and bottom one percentile.

55

Table 9. Propensity Score Matched Comparison of Goodwill Recognition in the First Two Years

after ICMW

Panel A. Difference between ICMW firms and matched non-ICMW firms

Mean

Variable Treat Matched Difference (p-value)

GW_NEW 0.048 0.049 -0.001 (0.91)

GW_BOY 0.125 0.139 -0.014 (0.32)

IO 0.741 0.763 -0.022 (0.29)

PE 19.932 16.158 3.774 (0.42)

DAC 0.031 0.040 -0.009 (0.31)

EQI -0.005 -0.005 0.000 (1.00)

LOGMVE 6.700 6.631 0.069 (0.60)

LOGSALE 6.442 6.455 -0.013 (0.93)

LOGMTB 0.929 0.857 0.072 (0.25)

TOBINQ 1.914 1.828 0.085 (0.33)

ROA 0.059 0.058 0.000 (0.91)

EXTRAGROW 0.145 0.113 0.032 (0.35)

NSEG 2.606 2.618 -0.012 (0.87)

FOREIGN 0.688 0.699 -0.011 (0.82)

REST -0.005 -0.005 0.000 (0.73)

VOL_OCF 0.064 0.063 0.001 (0.91)

VOL_SALE 0.192 0.194 -0.003 (0.72)

VOL_INVEST 0.107 0.112 -0.005 (0.87)

LEVERAGE 0.133 0.139 -0.007 (0.70)

TANGIBILITY 0.198 0.172 0.027 (0.14)

MKTRET 0.103 0.105 -0.002 (0.88)

ADJRET -0.001 0.055 -0.056 (0.28)

BIG4 0.844 0.855 -0.011 (0.77)

FE_DISP 0.002 0.002 0.000 (0.54)

Panel B. Comparison of two-year new goodwill

This table compares newly recognized goodwill in subsequent two years between propensity matched ICMW firm-

years and non-ICMW firm-years. The propensity scores are computed using the following logit regression:

prob(ICMW = 1) = f(α + β1GW_BOY + β2GW_NEW + β3IO + β4PE + β5DAC + β6EQI + β7LogMVE + β8LogSALE

+ β9LogMTB + β10TOBINQ + β11REPO + β12ROA + β13EXTRAGROW + β14NSEG + β15FOREIGN + β16REST +

β17VOL_OCF + β18VOL_SALE + β19VOL_INVEST + β20LEVERAGE + β21TANGIBILITY + β22ADJRET +

β23×BIG4 + β24×FE_DISP + β25×MKTRET + ε).

See the Appendix for variable definitions. p-values are reported in parentheses based on two-tailed comparisons.

Goodwill impairment is winsorized at the top and bottom one percentile.

ICMW=1

Matched

ICMW=0

Diff.

Propensity

Diff.

New goodwill (mean) 0.029

0.064

-0.035***

0.000

P-value

(0.01)

(0.99)

% reporting new goodwill 46.8%

52.2%

5.4%

0.000

P-value

(0.30)

(0.99)

Obs. 186

186