A Firm’s Earnings and the Likelihood of its Acquisition:

Earnings Management by Acquirers

Ashiq Ali

The University of Texas at Dallas

800 W Campbell Rd, Richardson, TX 75080

Todd Kravet

University of Connecticut

2100 Hillside Road, Unit 1041A, Storrs, CT 06268

Bin Li

The University of Texas at Dallas

800 W Campbell Rd, Richardson, TX 75080

This Draft: December 2014

[Preliminary. Please do not quote without permission. Comments welcome.]

Abstract:

We examine the association between a firm’s earnings and its likelihood of being

acquired. Prior research argues that firms with poorer performance are more likely to be

taken over because acquirers can unlock value by managing the firms more efficiently

(Palepu 1986). However, extant evidence on the association between earnings and

takeover probability is mixed. We argue that firms with high earnings can also be

attractive takeover targets because of acquirers’ incentives to manage reported earnings.

We show that a firm’s takeover likelihood is positively (negatively) associated with

industry-adjusted ROA when the firm’s earnings is above (below) industry average ROA.

We also show that the positive association is more pronounced when acquirers’ have

greater incentives, lower costs, and better opportunity to manage earnings through

acquisitions. The positive association is also more pronounced when the deal

characteristics are likely to facilitate earnings management. Finally, we find that investors

react less favorably to the announcement of the acquisitions of targets with higher

earnings.

______________________________________________

We appreciate the helpful comments of Sunny Yang and workshop participants at the

University of Connecticut and University of Texas at Dallas.

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A Firm’s Earnings and the Likelihood of its Acquisition:

Earnings Management by Acquirers

1. Introduction

We examine the association between a firm’s earnings and the likelihood of it

being acquired. Prior research argues that underperforming firms are more likely to be

taken over, because acquirers can unlock value by taking such actions as increasing

monitoring, replacing management, and restructuring operations (Palepu 1986). However,

prior literature finds mixed results on the association between earnings and takeover

probability (e.g., Palepu 1986; Cremers et al. 2009; Berger and Ofek 1996; Billett and

Xue 2007).1 We argue firms with relatively high earnings are also attractive takeover

targets because acquirers seek to report earnings growth for opportunistic reasons. We

predict a positive (negative) association between earnings and takeover when firms

outperform (underperform) relative to their peer firms. We examine this prediction as

well as test whether the predicted association is due to real earnings management activity.

We use a sample of 3,463 takeovers of public firms from 1990 to 2013 to estimate

our takeover probability model. Consistent with our expectations, we find a non-linear

association between earnings and takeover probability. When return on assets (ROA) is

above the annual industry average, there is a significantly positive association between

industry-adjusted ROA and takeover probability. This association is significantly

negative when industry-adjusted ROA is below the annual industry average. This non-

linear association is incremental to the previously identified determinants of takeover

probability. Furthermore, we do not find a similar non-linear association when we use

1 The mixed evidence in prior studies is all the more puzzling because, when prior studies use stock price-

based measures, their results are always consistent with the inefficient management hypothesis (e.g., Palepu

1986; Mitchell and Lehn 1990; Martin and McConnell 1991; Berger and Ofek 1996).

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unadjusted ROA rather than industry-adjusted ROA suggesting that firms’ earnings

performance relative to their industry is important in classifying them as having

inefficient management or attractive targets for acquirers to report earnings growth.

Next, we provide several results that suggest that acquirers targeting firms with

relatively high earnings is a real earnings management activity. First, we find that the

positive association between firms’ earnings and their takeover probability is greater for

acquirers likely to benefit more from earnings management. Acquirers we expect to

benefit more from earnings management are public as against private acquirers, acquirers

that report a string of earnings growth before the acquisitions, acquirers with CEO’s

having higher pay-for-performance sensitivity, and acquirers suspected of earnings

management in the acquisition completion year, as characterized by prior studies. These

findings support the earnings management explanation given that prior literature suggests

public firms rather than private firms and those with longer patterns of earnings growth or

higher pay-for-performance CEO compensation have stronger incentives to continue

reporting earnings increases (Klassen 1997; Barth et al. 1999; Beatty et al. 2002; Cheng

and Warfield 2005; Burns and Kedia 2006; Bergstressor and Philippon 2006; Efendi et al.

2007; Myers et al. 2007). Second, we find that acquirers with lower costs of real earnings

management (higher Z-score) or higher costs of accrual earnings management (bloated

balance sheet) are more likely to acquire targets with higher earnings. Third, we find that

acquirers with greater opportunity to manage earnings are more likely to acquire targets

with higher earnings. The association between positive industry-adjusted earnings and

takeover probability is significantly greater for acquirers with less monitoring (no

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blockholders and lower board independence) and greater agency costs of free cash flow

(high free cash flow with lower growth opportunities).

Fourth, we find that the positive association between firms’ industry-adjusted

ROA and their takeover probability is more likely when deal characteristics facilitate

earnings management. Specifically, the positive association between industry-adjusted

ROA and takeover probability obtains only when the relative size of the target to the

acquirer is small and when the time from the acquisition announcement to completion is

short. These results are consistent with managers’ incentive to use the acquisition to

manage earnings. Additionally, we find that the persistence of target firms’ pre-

acquisition earnings for the post-acquisition acquirers’ (merged entity’s) earnings is

greater for targets with positive industry-adjusted ROA than with negative industry-

adjusted ROA. This result suggests that targets with positive industry-adjusted ROA

continue to have a favorable effect on acquirers’ earnings even after acquisitions,

consistent with the earnings management hypothesis. On the other hand, the negative

industry-adjusted ROA does not persist, presumably because acquirers turnaround the

unprofitable target firms.

Finally, we show that acquirers’ announcement returns are significantly lower for

acquisitions of targets with ROA above the industry average than of targets with ROA

below the industry average. This result is consistent with acquiring managers being

opportunistic and sacrificing firm value to show increasing reported earnings through

acquisitions of profitable targets. Overall, we present a comprehensive set of results that

are consistent with the positive association between takeover probability and positive

industry-adjusted ROA being due to acquirers’ real earnings management activity. While

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there are potential alternative explanations for each of our separate tests, the totality of all

results are consistent with the earnings management explanation.

Our study makes the following contributions to the literature. We explain why

prior work finds mixed results for the association between earnings and takeover

probability (e.g., Dietrich and Sorensen 1984; Palepu 1986; Ambrose and Megginson

1992; Cremers et al. 2009). We show that there is a negative association between

negative industry-adjusted earnings and takeover probability, consistent with the

inefficient management hypothesis. Moreover, we show that takeover probability

increases with positive industry-adjusted earnings.

We also contribute to the literature on real earnings management (e.g., Penman

and Zhang 2002; Roychowdhury 2006; Cohen et al. 2008; Cohen and Zarowin 2010;

Zang 2012). This literature examines real decisions that are used to manage earnings,

such as cutting R&D expenses and advertising expenses, overproduction of inventory,

and offering sales incentives. We show that acquisitions represent another form of

earnings management. Our study complements the findings in the literature that managers

do use acquisitions for opportunistic reasons, such as increasing their compensation,

which is tied to successful completion of an acquisition (Grinstein and Hribar 2004;

Harford and Li 2007; Fich et al. 2014).

The remainder of the paper is organized as follows. Section 2 discusses prior

literature and develops the hypotheses. Section 3 presents our research design and

Section 4 presents the results of our tests and Section 5 concludes.

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2. Literature review and hypothesis development

2.1.Inefficient management hypothesis

Prior literature argues takeover is an important mechanism in capital markets to

replace (and deter) firms that are operating inefficiently (e.g., Marris 1963; Manne 1965;

Jensen 1986). Marris (1963) and Manne (1965) argue that when managers fail to

optimally maximize profit their firm’s stock price will be lower than it would otherwise

be. This situation creates a takeover opportunity where an acquirer can replace inefficient

managers with efficient ones. This argument suggests that firms with lower profitability

are more likely to be acquired. Prior studies, however, find mixed results when testing the

association between earnings and takeover probability. Appendix A provides a survey of

the papers that include firm-level takeover models in major accounting and finance

journals. 2 Overall, we find that earnings measures are generally insignificant when

included as independent variables in takeover probability models. Even though some

studies show significant coefficients on earnings variables in the takeover models, the

sign of the coefficients appears inconsistent.

Using measures of firm performance other than accounting earnings, prior

research finds results consistent with takeovers being motivated by inefficiency in target

firms (e.g., Palepu 1986; Mitchell and Lehn 1990; Martin and McConnell 1991; Berger

and Ofek 1996). Palepu (1986) finds that abnormal stock returns are negatively

associated with takeover probability, indicating that firms that experience a decrease in

value (presumably due to poor managerial decisions) are more likely to be acquired.

2 Specifically, we search in all available issues of Journal of Accounting and Economics, Journal of

Accounting Research, Journal of Finance, Journal of Financial Economics, Review of Financial Studies,

and The Accounting Review up to 2012 for papers that tabulate a firm-level multivariable test where a

takeover indicator variable is the dependent variable and the sample includes U.S. firms. We also include

studies published in other journals that we identified during a search of the takeover literature.

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Mitchell and Lehn (1990) find that acquirers that make poor acquisitions are more likely

to be acquired themselves and subsequently, to have their poor acquisitions divested.

Martin and McConnell (1991) find that takeovers are positively associated with top

management turnover. In addition, when target firms under-perform peer firms (based on

abnormal stock returns) their managers are more likely to be replaced after acquisitions.

Berger and Ofek (1996) find that firms with greater value loss due to diversification are

more likely to be acquired and that value-destroying diversified firms are more likely to

be acquired by leveraged buyout (LBO) transactions and broken up into stand-alone firms.

Our study attempts to explain why prior research does not find an association between

earnings and takeover probability.

2.2.Profitable firms as takeover targets

Managers with incentives to report earnings growth can quickly manipulate

earnings through acquisitions. Prior research finds that managers have incentives to

manipulate earnings to show earnings growth (e.g., Burgstahler and Dichev 1997;

Matsumoto 2002; Skinner and Sloan 2002; Donelson et al. 2013). Prior research also

shows that managers take real actions for the purpose of earnings manipulation, such as

cutting R&D and advertising expenses, overproducing inventory, and providing sales

incentives (e.g., Penman and Zhang 2002; Roychowdhury 2006; Cohen et al. 2008;

Cohen and Zarowin 2010). We argue that firms use acquisitions as well to manipulate

earnings. Using acquisitions for opportunistic reasons is not inconceivable, given that

prior studies have argued that managers make acquisition for empire building and for

increasing their compensation (Jensen 1986, Grinstein and Hribar 2004; Harford and Li

2007; Fich et al. 2014).

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Bruner (2004) discusses a momentum strategy where firms’ growth strategy is

focused on making acquisitions to grow earnings or sales. Anecdotal evidence also

suggests firms use acquisitions to grow earnings. Bruner (2004) provides the example of

Tyco International, Ltd, which had a long-standing strategy of growth through acquisition

rather than growing organically.3 General Electric’s Chief Financial Officer (CFO) stated,

“Of course we’re buying earnings when we do an acquisition” and the North American

Chief of Retailer Financial Services stated that they “may hunt for acquisitions if his

division might miss its annual earnings target” (Smith et al. 1994). Morgenson (1999)

raises the issue of firms using acquisitions to report favorable net income and discusses

the case of HFS reporting earnings growth rates of more than 30 percent after making a

series of acquisitions while that rate was estimated to be 11 percent when excluding the

effect of the acquisitions.

Acquisitions increase net income in the year of the deal such that managers can

acquire target firms with a relatively high degree of certainty about the net income effect

of the acquisition. Although the acquisition accounting changed over time, we expect the

overall incentive to acquire target firms with higher earnings to persist throughout our

sample period.4 If an acquirer purchases a target with net income, then the net income of

the combined company will be greater than that of the acquirer alone, as long as any

additional expense from marking the target’s assets up from their book value do not

decrease net income into a net loss.

3 Tyco International, Ltd. made 226 reported acquisitions from 1985 to 2006 per SDC (Bruner 2004),

however, this number likely understates the amount of acquisitions because in 2002 it was reported that

Tyco made 700 acquisitions from 1998 to 2001 that were never disclosed (Maremont 2002). 4 The most significant accounting rule change was in 2001, when SFAS 141 and 142 (FASB 2001a, b)

eliminated the pooling-of-interests method (hereafter, pooling) and goodwill amortization. The effect of

the acquisition on net income is usually more favorable under the pooling method (Lys and Vincent 1995;

Ayers et al. 2000; Ali and Kravet 2014), but at the same time, the elimination of the goodwill amortization

made the effect of acquisitions on net income using the purchase method more favorable.

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The above arguments suggest that acquisitions of firms performing poorly are

made to enhance efficiency; the poorer the firm’s performance, the more likely it is going

to be acquired. Whereas, acquisitions of firms performing well are made to report

earnings growth by acquirers; the more profitable the firm, the more attractive target it is.

We argue that firms’ earnings relative to their peer firms is more important in predicting

their probability of being acquired rather than firms’ earnings per se. We therefore

propose:

H1. Positive (negative) industry-adjusted ROA is positively (negatively) related to the

likelihood of being acquired.

2.3. Benefits to earnings management

We expect that when the benefits to acquirers from earnings management are

greater then acquirers are more likely to acquirer targets with higher industry-adjusted

earnings to manage earnings. Accordingly, we propose the following hypothesis:

H2. The positive association between industry-adjusted ROA and the likelihood of being

acquired is increasing in the benefits associated with earnings management.

We operationalize this hypothesis using the acquirers’ public versus private status,

pattern of earnings growth before the acquisition, pay-for-performance sensitivity, and

reporting of earnings after the acquisition that is suspect of earnings management. Prior

research argues that managers with strong capital market incentives are likely to report

earnings growth (Levitt 1998; Subramanyam 1996; Barth et al. 1999; Skinner and Sloan

2002). Prior research also finds that capital market pressure to manage earnings is greater

for public firms than private firms (Klassen 1997; Ke et al. 1999; Beatty et al. 2002;

Givoly et al. 2010). Thus, compared to private acquirers, public acquirers are more likely

to acquire targets for the purpose of reporting favorable earnings.

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Barth et al. (1999) find that firms with patterns of increasing earnings have higher

price-earnings multiples than other firms and the price-earnings multiples decrease

substantially when earnings decrease after a pattern of earnings increases. Therefore,

managers have incentives to maintain patterns of increasing earnings and prior research

finds that firms manage earnings to maintain patterns of earnings increases (e.g., Myers et

al. 2007; Beatty et al. 2002). We expect that acquirers with patterns of increasing

earnings are more likely to acquire targets with higher income to report earnings growth

than acquirers without patterns of increasing earnings. We examine patterns of four, five,

and six years of annual earnings increases based on Barth et al.’s (1999) finding that

firms’ have increased price-earnings multiples after five annual earnings increases.

Prior research finds that equity compensation creates an incentive for managers to

manage earnings (Cheng and Warfield 2005; Burns and Kedia 2006; Bergstressor and

Philippon 2006; Efendi et al. 2007; Cornett et al. 2008). We examine whether acquirers

with higher pay-for-performance sensitivity are more likely to acquire targets with higher

earnings. We measure acquiring CEOs’ pay-for-performance sensitivity, PPS, following

Bergstresser and Philippon (2006) and examine the association between industry-adjusted

earnings and the takeover probability by acquirers with high PPS and those with low

PPS.5

Firms that meet or just beat earnings benchmarks are more likely to be managing

earnings because of the incentives to meet or beat earnings benchmarks (e.g., Burgstahler

and Dichev 1997; Roychowdhury 2006; Donelson et al. 2013). We examine whether

5 We measure CEO pay-for-performance sensitivity (PPS) following the method described by Bergstresser

and Philippon (2006). We first calculate ONEPCT as the total change in the value of the CEO’s stock and

stock option portfolio in response to a one-percent change in the stock price using the method described by

Core and Guay (2002). Next, we calculate PPS as ONEPCT/(ONEPCT + Salary + Bonus).

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acquirers that meet or just beat earnings benchmarks in the year the acquisition is

completed are more likely to have acquired targets with higher earnings than other

acquirers. We follow Cohen et al. (2008) to identify “suspect” acquirers that are likely to

have manipulated earnings based on three benchmarks that firms have incentives to meet.

First, we label an acquirer as a “suspect” if its net income before extraordinary items

scaled by total assets lies in the interval [0, 0.005) in the 365-day period after the

effective date of the takeover. Next, we identify an acquirer as a “suspect” if the change

in net income before extraordinary items scaled by total assets lies in the interval [0,

0.005) around the effective date of the takeover. Finally, we obtain an acquirer’s analyst

forecasts for annual earnings reported in the 365-day period after the effective date of the

takeover, and then take the median of the forecasts announced in the 365-day period prior

to the effective date as the consensus forecast. We compute the forecast error (FE) as the

difference between actual earnings per share (EPS) and the consensus forecast, and

define acquirers where the forecast error is one cent per share or less ($0.00 ≤ FE ≤ $0.01)

as “suspects”.

2.4. Costs of accrual and real earnings management

We expect acquirers with lower costs to doing real earnings management or

higher costs to doing accruals earnings management are more likely to acquire targets

with higher industry-adjusted profitability to manage earnings. Accordingly, we propose

the following hypothesis:

H3. The positive association between industry-adjusted ROA and the likelihood of being

acquired is increasing in the cost of accruals earnings management and decreasing in the

cost of real earnings management.

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Prior research identifies several factors that act as constraints on real and accrual

earnings management, such as financial distress and balance sheet bloat (Barton and

Simko 2002; Cohen and Zarowin 2010; Zang 2012). Real earnings management is

costlier for firms in financial distress because they have fewer resources that can be used

to manage earnings (Zang 2012). We classify firms with Altman Z-scores below (above)

the annual industry average as financially distressed (healthy). Zang (2012) finds that

firms with bloated balance sheets, as reflected in higher net operating assets (NOA), are

more likely to use real earnings management because their use of accrual earnings

management is constrained (Barton and Simko 2002). We classify firms with NOA above

(below) the annual industry average as those more likely to use real earnings

management.6

2.5. Opportunity to manage earnings

We expect that managers with greater opportunity to use acquisitions to manage

earnings are more likely to do so. Accordingly, we propose the following hypothesis:

H4. The positive association between industry-adjusted ROA and the likelihood of being

acquired is increasing in the opportunity managers have to manage earnings.

Prior research finds that firms with greater monitoring by institutional owners are

less likely to manage earnings and, more specifically, less likely to use real activities to

manage earnings (Dechow et al. 1996; Bushee 1998; Roychowdhury 2006; Zang 2012).

Monitoring by more independent boards is also associated with less earnings

management (Dechow et al. 1996; Klein 2002) and more profitable acquisitions (Byrd

and Hickman 1992). These results suggest that blockholders and independent boards are

6 NOA is calculated as shareholders’ equity less cash and marketable securities plus total debt divided by

sales.

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effective in monitoring opportunistic acquisition deals. We define acquirers with

blockholders as those with at least one institutional shareholder with at least a five

percent ownership position. We classify firms’ with the percentage of independent board

members below (above) the annual industry average as having non-independent

(independent) boards.

Jensen (1986) argues that firms generating free cash flow but that have poor

investment opportunities will be subject to the agency costs of free cash flow because

managers have incentives to invest free cash flow rather than pay dividends or repurchase

shares. Lang et al. (1991) find that firms with low Tobin’s Q and high free cash flow

make less profitable acquisitions, consistent with Jensen’s (1986) arguments. Harford

(1999) also finds that acquirers with high cash holdings make less profitable acquisitions.

Acquirers are classified as having greater opportunity to manage earnings due to high-

agency problem when their Tobin’s Q is below the annual industry average and free cash

flow is above the annual industry average; all other public acquirers are classified as

having low-agency problem.7

2.6. Deal characteristics associated with earnings management

In order for acquirers to credibly manage earnings using acquisitions we expect

the characteristics of the deal to facilitate the earnings management. Accordingly, we

propose the following hypothesis:

H5. The positive association between industry-adjusted ROA and the likelihood of being

acquired is increasing in deal characteristics that facilitate earnings management.

7 Free cash flow is calculated as cash flow from operations minus capital expenditures, scaled by beginning

total assets.

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We consider the following deal characteristics that are likely to facilitate earnings

management through acquisitions. First, acquirers are not likely to have the motive to

manage earnings when acquiring targets that are large relative to the acquirer’s size. We

define relatively small (large) transactions as those where the relative size is less than

(greater) than the third quartile of the distribution in our sample.9 Second, if acquirers are

using acquisitions to manage earnings then the time interval between the beginning of

transaction negotiations and the deal completion is likely to be relatively short. We

consider the time interval as short when the amount of time between the acquisition

announcement and completion date is less than the third quartile of the distribution in our

sample.10

Finally, if acquirers use acquisitions to manage earnings then targets’ earnings are

likely to be persistent. If an acquirer acquires a target with high earnings in the year

before the acquisition but the earnings are less persistent then the effectiveness of the

acquisition in increasing the acquirer’s net income in the subsequent years is lower.

Therefore, we examine whether targets’ cumulative earnings over multiple years is

positively associated with takeover probability, as well as whether the association

between acquirers’ post-acquisition earnings and targets’ pre-acquisition earnings is high.

9 In our sample the third quartile is 42.0 percent, which is calculated as the transaction value divided by the

acquirers’ pre-acquisition market value. Our findings do not change if we use the median value of 13.9

percent. 10 In our sample the third quartile is a 147 day difference between the acquisition announcement and

completion date. Our findings do not change if we use the median value of 98 days.

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2.7. Investors’ assessment of acquisitions

If targets with abnormally high earnings are acquired for opportunistic reasons

then we expect unfavorable reaction to the stock price of the acquisitions at the time the

acquisition announcement. Accordingly, we propose the following hypothesis:

H6. Acquirers’ announcement returns are negatively associated with targets’ industry-

adjusted ROA.

3. Research design and takeover model results

3.1.Takeover model

To examine whether target firms’ earnings are associated with their likelihood of

being acquired, we estimate a modified version of Cremers et al. (2009) model, which

controls for factors prior literature finds to be associated with takeover probability (e.g.,

Dietrich and Sorensen 1984; Palepu 1986; Ambrose and Megginson 1992; Edmans et al.

2012):

TAKEOVERi,t+1 = α0 + α1 IA_Qi,t + α2 IA_PP&Ei,t + α3 IA_Ln(CASH)i,t

+ α4 BLOCKHOLDERi,t + α5 SIZEi,t + α6 INDUSTRYi,t

+ α7 IA_LEVERAGEi,t + α8 ARETi,t + α9 LOSSi,t + α10 IA_ROAi,t + εt ,

(1)

where TAKEOVERi,t is an indicator variable that equals one if firm i receives a completed

takeover bid in fiscal year t+1, and zero otherwise; IA_Qi,t is Tobin’s Q adjusted for the

industry mean for firm i and year t; IA_PP&Ei,t is the ratio of net plant, property, and

equipment (PP&E) to total assets adjusted for industry mean; IA_Ln(CASH)i,t is the

natural logarithm of cash holdings adjusted for the industry mean; BLOCKHOLDERi,t is

an indicator variable that equals one if firm i has at least one institutional shareholder

with at least a five percent ownership position, and zero otherwise; SIZEi,t is the natural

logarithm of the market value of equity at the end of fiscal year t; INDUSTRYi,t is an

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indicator variable equal to one if the industry has at least one takeover in fiscal year t, and

zero otherwise; IA_LEVERAGEi,t is the leverage ratio, defined as total liabilities divided

by total assets, adjusted for the industry mean; ARETi,t is the abnormal stock return

calculated as firms’ 12-month buy and hold return over fiscal year t minus the value-

weighted 12-month buy and hold return over the same time period. LOSSi,t is an indicator

variable equal to one if income before extraordinary items in fiscal year t is less than or

equal to zero, and zero otherwise. IA_ROAi,t is income before extraordinary items divided

by average total assets, ROAi,t, and adjusted for the industry mean. Our analysis primarily

focuses on industry-adjusted earnings because a target firm’s peers are potential

alternative targets for the acquirer, and the selection of target firms involves an in-depth

comparison of peer firms (Bruner 2004; Pearl and Rosenbaum 2009; Chen et al. 2014).

Consistent with this notion, it is common in the mergers and acquisitions literature to do

industry adjustments when analyzing takeover decisions. 11 All industry-adjusted

variables are calculated by subtracting the industry mean where industries are defined by

Fama-French 49 industry classifications (Fama and French 1997). All continuous

variables are winsorized at the 1st and 99th percentiles, and details of variable definitions

are provided in Appendix B.

Since we have opposite predictions for the relations between earnings and the

likelihood of takeover for firms whose earnings are above the industry average and

whose earnings are below the industry average, we modify of Eq. (1) in two ways. We

11 The effect of acquisitions on earnings per share (EPS) depends on the number of new shares issued to

target shareholders. If EPS increases (decreases) from the acquisition then the acquisition is considered

accretive (dilutive). Because we are interested in studying firms’ takeover probability it is not possible to

know for firms that are not acquired what the EPS effect of their takeover would be as there is no acquirer.

Instead, we focus on firms’ earnings and their takeover probability with the assumption that acquirers

fixated on EPS can structure the acquisition of target firms with higher earnings to be more accretive than

acquisitions of those with lower earnings.

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expect a positive correlation between TAKEOVERi,t+1 and IA_ROAi,t (α10 > 0) for firm-

years with IA_ROAi,t ≥ 0 and a negative correlation between TAKEOVERi,t+1 and

IA_ROAi,t (α10 < 0) for firm-years with IA_ROAi,t < 0. First, we include an interaction

term, IA_ROAi,t × BELOW_IA_ROAi,t, to differentiate when firms’ earnings are above or

below the industry average. BELOW_IA_ROAi,t is equal to one if IA_ROAi,t is less than or

equal to zero, and zero otherwise. The coefficient on IA_ROAi,t captures the association

with takeover probability when earnings is above the industry average and we expect the

coefficient to be positive. The coefficient on IA_ROAi,t × BELOW_IA_ROAi,t captures the

association with takeover probability when earnings is below the industry average and we

expect the coefficient to be negative. Second, we estimate Eq. (1) separately for firm-

years with IA_ROAi,t ≥ 0 and with IA_ROAi,t < 0 with the expectation that the coefficient

on IA_ROAi,t is positive and negative, respectively.

3.2. Sample selection

Our initial sample consists of all firm-years for the period 1990 to 2012 on the

Compustat annual file with the necessary data available to calculate our variables. We

identify firms that are acquired from 1991 to 2013 using the Securities Data Corporation

(SDC) M&A database. We include acquisitions where a majority of the target firm is

acquired, which SDC classifies as acquisitions of assets (AA), acquisitions of majority

interest (AM), and mergers (M). Stock return data are obtained from the CRSP stock files.

Our sample begins at 1990 because SDC coverage in the 1980’s is not as complete as in

later periods. We exclude financial firms (SIC codes 6000 through 6999) because they

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have a different regulatory structure than other firms. Our final sample consists of 75,479

firm-year observations of which there are 3,463 takeovers.

Table 1, Panel A presents the descriptive statistics of our main variables. The

mean value of TAKEOVER indicates that on average firms face a 4.6 percent

unconditional takeover probability. The variables adjusted by industry have mean values

close to zero by construction. The remaining variables mean and median values appear to

be consistent with prior research. Panel B presents the descriptive statistics for takeover

and non-takeover firms separately and tests whether the mean and median values are

significantly different between the two groups. Takeover firms have significantly lower

ROA and industry-adjusted ROA than non-takeover firms. Therefore, the univariate

statistics are consistent with the inefficient management hypothesis. The differences in

control variables are consistent with our expectations. Panel C presents the Spearman and

Pearson correlation coefficients for the variables in our model. Both IA_ROA and ROA

are significantly negatively correlated (Spearman and Pearson) with TAKEOVER, which

is also consistent with the inefficient management hypothesis that firms with poorer

performance are more likely to be acquired. We examine this association further in our

takeover probability model.

3.3.Test of the piecewise linear relations between earnings and takeover probability (H1)

Table 2 presents the results from estimating the takeover probability model. In

Panel A, we use industry-adjusted ROA, IA_ROA. The results on control variables are

consistent with prior research (Dietrich and Sorensen 1984; Palepu 1986; Ambrose and

Megginson 1992; Cremers et al. 2009; Cai and Tian 2009; Edmans et al. 2012). The

negative coefficient on IA_Q is consistent with more highly valued firms being less likely

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to be acquired (Cremers et al. 2009; Edmans et al. 2012). The negative coefficient on

IA_PP&E is consistent with it being costlier to merge firms with fixed assets than

intangible assets (Cai and Tan 2009; Ali and Kravet 2014). We find that blockholders are

positively associated with takeover (Cremers et al. 2009; Edmans et al. 2012), consistent

with it being less costly for blockholders to monitor managers (Shleifer and Vishny 1986).

The significantly negative coefficient on firm size is consistent with there being greater

transaction costs to acquire larger firms (Palepu 1986; Cremers et al. 2009). Firms are

more likely to be acquired if there was at least one acquisition in their industry in the

prior year (Cremers et al 2009). Consistent with prior research (Cremers et al. 2009; Ali

and Kravet 2014), we also find that IA_LEVERAGE is positively associated with takeover

probability consistent with distressed firms becoming takeover targets because their

excessive leverage limits their access to financing.

The coefficient on IA_ROA in column 1 is 0.073 and not significant at

conventional levels (10%). This result is consistent with prior research that fails to find

results consistent with the efficient management hypothesis when examining the

association between earnings and takeover probability.

In column 2, we modify equation 1 to test for non-linearity in the association

between TAKEOVER and IA_ROA by including IA_ROA × BELOW_IA_ROA and

BELOW_IA_ROA in the model. Consistent with our hypothesis, we find when firms’

profitability is greater than their peers’ earnings, there is a positive association between

earnings and takeover probability; and when firms’ profitability is less than their peers’

earnings, the association between takeover probability and earnings is significantly lower.

In column 2, the coefficient on IA_ROA is significantly positive and the coefficient on

19

IA_ROA × BELOW_IA_ROA is significantly negative indicating a non-linear association

between takeover probability and earnings. The combined coefficient IA_ROA + IA_ROA

× BELOW_IA_ROA is also significantly negative indicating IA_ROA is negatively

associated with takeover probability when earnings are below the industry average.

In columns 3 and 4, we estimate Eq. (1) for observations where BELOW_IA_ROA

is equal to zero and one, respectively. When firms’ earnings are greater than their peer

firms’ earnings (column 3) there is a significantly positive association between IA_ROA

and takeover probability. To illustrate the economic significance of these results, we

report next to each coefficient the change in probability of a takeover due to a one

standard deviation increase in each independent variable (from 0 to 1 for indicator

variables), holding all other variables are their mean value. Focusing on column 3, a

standard deviation increase in IA_ROA is associated with an increase in takeover

probability of 0.8 percent, which is a 17.4 percent increase relative to the unconditional

probability of takeover (4.6 percent) in our sample. The two largest effects out of

variables in our model are 2.3 and -1.1 percent for BLOCKHOLDER and SIZE,

respectively. Relative to these two factors IA_ROA appears to have an economically

significant association with takeover probability. When firms’ earnings are less than their

peer firms’ earnings (column 4) there is a significantly negative association between

IA_ROA and takeover probability. This result is consistent with the inefficient

management hypothesis. When IA_ROA is negative, a standard deviation decrease in

IA_ROA is associated with an increase in takeover probability of 0.4 percent, which is a

8.7 percent increase relative to the unconditional probability. In sum, industry-adjusted

20

earnings have a non-linear and economically significant association with takeover

probability.

In Panel B, we replace IA_ROA with ROA, which is not industry-adjusted. In

contrast to Panel A, when including ROA in the model without allowing for non-linearity,

in column 1, the coefficient on ROA is significantly negative, consistent with the

inefficient management hypothesis. In column 2, when allowing for a non-linear

association between ROA and takeover probability by including the interaction of ROA ×

LOSS, the coefficient on ROA is not significant while the coefficient on ROA × LOSS is

significantly negative. When we estimate the model separately for firm-years without

losses (column 3), we find no evidence of a positive association between ROA and

takeover probability. In column 4, using only firm-years with losses, we find the

coefficient on ROA is significantly negative, consistent with the inefficient management

hypothesis. Overall, the results suggest that adjusting earnings for the annual industry

average is important for documenting the positive association between earnings and

takeover probability.

4. Tests of real earnings management hypotheses

4.1. Benefits of earnings management to acquirers (H2)

4.1.1. Public versus private acquirers

We expect that, compared to private acquirers, public acquirers are more likely to

acquire targets for the purpose of reporting favorable earnings. To examine the

probability of takeover by public and private firms we estimate our takeover probability

model using a multinomial logistic model. We create a categorical dependent variable,

TO_ACQ_PUBLIC, that is equal to one if a firm receives a completed takeover bid in

21

year t+1 by a public acquirer (Public_Acquirer), equal to two if a firm receives a

completed takeover bid in year t+1 by a private acquirer (Private_Acquirer), and zero if a

firm is not acquired. The results are presented in Table 3. Column 1 shows a significant

non-linear association between earnings and takeover probability by public acquirers,

similar to the results in Panel A of Table 2. Interestingly, we find the results on earnings

are opposite in column 2 for private acquirers. Firms with earnings above the industry

average have a negative association with takeover probability by private acquirers, which

can potentially be explained by targets with higher abnormal earnings being more costly

to acquire because public acquirers are willing to pay a premium for higher earnings. We

also find the coefficient on IA_ROA is significantly higher in column 1 and column 2

consistent with publicly acquirers being more likely than private acquirers to select

targets with higher earnings. The coefficient on IA_ROA + IA_ROA × BELOW_IA_ROA

is significantly more negative in column 1 than column 2. This result is consistent with

findings in prior literature that private acquirers prefer mature targets with a stable cash

flow stream (Jensen 1986; Bargeron et al. 2008; Eckbo and Thorburn 2008). Bargeron et

al. (2008) find that private acquirers are more likely to select targets with lower growth

opportunities and higher cash flow from operations than public acquirers. Target firms

with lower earnings relative to their peers are less likely to have abundant free cash flow

and thus, would not be attractive targets for private acquirers.12 Overall, the results are

consistent with public acquirers selecting targets with higher earnings because these firms

have capital market incentives to pursue a strategy of increasing earnings through

acquisitions.

12 Results for control variables are also consistent with this notion. Firms with more cash and without losses

are also more likely to be acquired by private acquirers and not public acquirers.

22

4.1.2. Acquirers with patterns of increasing earnings

We expect that acquirers with patterns of increasing earnings are more likely to

acquire targets with higher income to report earnings growth than acquirers without

patterns of increasing earnings. We create a categorical dependent variable,

TO_ACQ_STRING, that is equal to one if a firm receives a completed takeover bid in

year t+1 by a publicly-held acquirer with a pattern of earnings increases and where the

relative size and time to completion are below the third quartile for our sample 13

(Acq_4yr_String, Acq_5yr_String, and Acq_6yr_String), equal to two if a firm receives a

completed takeover bid in year t+1 by a publicly-held acquirer without a pattern of

earnings increases (Acq_No_4yr_String, Acq_No_5yr_String, and Acq_No_6yr_String),

equal to three if a firm receives a completed takeover bid in year t+1 by a public acquirer

where data is not available to calculate their earnings pattern, a public acquirer where the

relative size or time to completion is above the fourth quartile, or by a private acquirer

(Private/Public Unknown), and zero otherwise.

Table 4, Panel A presents a comparison of the percentage of acquirers with a

pattern of earnings increases for acquisitions where the target’s earnings are above and

below the industry average. There are 1,084, 1,048, and 1,001 acquisitions where there is

available data to calculate acquirers’ earnings pattern over four, five, and six years,

respectively. We observe that the percentage of acquirers with patterns of four, five, and

six annual earnings increases is significantly higher when the targets’ earnings is above

the industry average than when below the industry average. This result is consistent with

firms making acquisitions of targets with higher earnings to maintain patterns of earnings

13 We use the relative size and time to completion restriction to capture deals that are more likely to be used

for earnings management. We find below that these deal characteristics are likely to be associated with

using acquisitions to manage earnings.

23

increases or firms avoiding acquisitions of targets with lower earnings to maintain

patterns of earnings increases.

Panel B presents the results from the multinomial logistic regression. Columns 1

and 2 present the results based on four years of earnings increases. For brevity, hereafter

we do not present the coefficients for the probability of being acquired by a private

acquirer or public acquirer without the required data (Private/Public Unknown).14 In this

table we also do not present coefficients for the control variables. In columns 1 and 2, the

coefficient on IA_ROA is significantly positive. This result indicates that firms with

higher earnings are more likely to be acquired by acquirers with and without four years of

annual earnings increases. In column 3, the coefficient on IA_ROA is significantly

positive while the coefficient on IA_ROA in column 4 is not significant. This result

indicates that firms with higher earnings are more likely to be acquired by acquirers with

four years of annual earnings increases but not by acquirers without four years of

earnings increases. We find the strongest results in columns 5 and 6 when examining

acquirers with six years of earnings increases. The coefficient on IA_ROA is significantly

positive in column 5 but is not significant in column 6, and the difference in these

coefficients is significant. This result is consistent with acquirers with six years of

earnings increases making acquisitions of targets with higher earnings to maintain their

pattern of earnings growth.

We also find that acquirers without a pattern of earnings increases (columns 2, 4,

and 6) are more likely to acquire targets reporting losses. The coefficient on LOSS is

significantly positive in these columns. However, acquirers with patterns of earnings

14 When examining the probability of takeover by private acquirers as a separate outcome in this test and

those below we find similar results to those reported in Table 3.

24

increases (columns 1, 3, and 5) are not more likely to acquire targets reporting losses.

These results are inconsistent with the argument that more efficient firms, presumably

those with patterns of earnings increases, acquire less efficient firms (e.g., Marris 1963;

Manne 1965; Jensen 1986), which suggests that financial reporting incentives are causing

these results. Overall, the results suggest that acquirers choose targets with higher

earnings and avoid targets with lower earnings to maintain reporting patterns of earnings

increases.

4.1.3. Acquirers with higher pay-for-performance sensitivity

We expect that if higher PPS increases managers’ incentive to use acquisitions to

manage earnings then the association between positive industry-adjusted earnings and

takeover probability should be greater for takeover by firms with high PPS than with low

PPS. We create a categorical dependent variable, TO_ACQ_PPS, that is equal to one if a

firm receives a completed takeover bid in year t+1 by a publicly-held acquirer with their

CEO’s pay-for-performance sensitivity greater than the annual industry average

(Acquirer_High_PPS), equal to two if a firm receives a completed takeover bid in year

t+1 by a publicly-held acquirer with their CEO’s pay-for-performance sensitivity lower

than the annual industry average (Acquirer_Low_PPS), equal to three if a firm receives a

completed takeover bid in year t+1 by a public acquirer without the data available to

calculate PPS or by a private acquirer (Private/Public Unknown), and zero if a firm is not

acquired.

The results are presented in Panel A of Table 5. We find that firms with higher

earnings are more likely to be acquired by acquirers with high PPS but not by acquirers

with low PPS, which is consistent with equity compensation creating incentives for

25

managers to use acquisitions to manage earnings. The coefficient on IA_ROA is

significantly positive in column 1 while the coefficient in column 2 is significantly

negative and the difference in coefficients between column 1 and 2 is also significant.

The negative coefficient on IA_ROA in column 2 can potentially be explained by it being

more costly to acquire targets with higher earnings because high-PPS acquirers are

willing to pay a premium for higher earnings. The coefficient on IA_ROA + IA_ROA ×

BELOW_IA_ROA is significantly negative in column 1 but is not significant in column 2,

untabulated. Overall, the results are consistent with PPS creating an incentive for

managers to use acquisitions to manage earning.

4.1.4. Acquirers suspected of managing earnings to meet or beat earnings benchmarks

We expect that acquirers reporting earnings in the acquisition completion year

that are suspect of being managed are more likely to have acquired targets with higher

earnings than other acquirers. We create a categorical dependent variable,

TO_ACQ_SUSPECT, that is equal to one if a firm receives a completed takeover bid in

year t+1 by a publicly-held acquirer that reports suspect earnings in the year the

acquisition is completed (Acquirer_Suspect), equal to two if a firm receives a completed

takeover bid in year t+1 by a publicly-held acquirer that does not report suspect earnings

in the year the acquisition is completed (Acquirer_Nonsuspect), equal to three if a firm

receives a completed takeover bid in year t+1 by a public acquirer without the data

available to calculate suspect earnings or by a private acquirer (Private/Public Unknown),

and zero if a firm is not acquired.

The results are presented in Panel B of Table 5. We find that firms with higher

earnings are more likely to be acquired by acquirers with suspect earnings than those

26

without suspect earnings. The coefficient on IA_ROA is significantly positive in column 1

and 2. Most importantly, the coefficient on IA_ROA is significantly higher in column 1

than column 2 indicating that targets with higher earnings are more likely to have been

acquired by acquirers suspected of managing earnings than other acquirers. The

coefficient on IA_ROA + IA_ROA × BELOW_IA_ROA is not significantly different in

columns 1 and 2 suggesting that the difference between acquirers suspected of managing

earnings and those not suspecting is in the selection of targets with higher earnings but

not lower earnings. Overall, the results are consistent with acquirers successfully

managing earnings to meet or beat earnings benchmarks select targets with higher

earnings, presumably these targets enable the acquirers to meet or beat earnings

benchmarks.

4.2. Acquirers with real and accrual earnings management constraints (H3)

Table 6 presents the results from testing the association between firms’ industry-

adjusted ROA and the probability of takeover by acquirers with varying degree of

constraints to use real earnings management. For the proxies of lower cost of real

earnings management (high Z-score) and higher cost of accrual earnings management

(high NOA) we create a categorical dependent variable, TO_ACQ_CONSTRAINT, that is

equal to one if a firm receives a completed takeover bid in year t+1 by an acquirer more

likely to use real earnings management (Acquirer_High_Zscore and

Acquirer_High_NOA), equal to two if a firm receives a completed takeover bid in year

t+1 by an acquirer less likely to use real earnings management (Acq_Low_Zscore and

Acq_Low_NOA), equal to three if a firm receives a completed takeover bid in year t+1 by

an acquirer where data is not available to determine the acquirers’ real earnings

27

management constraint proxies (Private/Public Unknown), and zero if a firm is not

acquired. Panel A and B presents the results from estimating the multinomial logistic

regression using acquirers’ Z-score and NOA, respectively.

In Panel A we find when firms’ industry adjusted earnings is positive it has a

significantly higher association with the probability of being acquired by an acquirer less

constrained to use real earnings management than an acquirer more constrained to use

real earnings management. Specifically, the coefficient on IA_ROA in column 1 is

significantly positive and significantly higher than the coefficient on IA_ROA in column

2. In Panel B, we find that the coefficient on IA_ROA is higher in column 1 than column

2, however the difference is only marginally significant (p-value = 0.107). Overall, the

results suggest that acquirers are more likely to purchase targets with higher earnings

when it is less costly for the acquirers to use real earnings management.

4.3. Acquirers with greater opportunity to manage earnings (H4)

Table 7 presents the results from testing the association between firms’ industry-

adjusted ROA and the probability of takeover by acquirers with greater opportunity to

manage earnings. Firms with less monitoring (no blockholders and less independent

boards) and greater free cash flow with less growth opportunities provide greater

opportunity to manage earnings. We create a categorical dependent variable,

TO_ACQ_OPPORTUNITY, that is equal to one if a firm receives a completed takeover

bid in year t+1 by an acquirer with greater opportunity to manage earnings

(Acquirer_NoBlock, Acquirer_Brd_NoInd, and Acq_HighFCF_LowQ), equal to two if a

firm receives a completed takeover bid in year t+1 by an acquirer less opportunity to

manage earnings (Acquirer_Block, Acquirer_Brd_Ind, and Acq_Other), equal to three if a

28

firm receives a completed takeover bid in year t+1 by a public acquirer where data is not

available to determine the acquirers’ earnings management opportunity proxies or by a

private acquirer (Private/Public Unknown), and zero if a firm is not acquired.

We find that firms with higher earnings are more likely to be acquired by

acquirers with greater opportunities to manage earnings than by other acquirers.

Specifically, in Panel A, B, and C the coefficient on IA_ROA is significantly higher in

column 1 than 2. The difference in the coefficients on IA_ROA + IA_ROA ×

BELOW_IA_ROA in column 1 and 2 is not significant. These results are consistent with a

greater opportunity to manage earnings influencing the probability of acquiring targets

with higher earnings but not lower earnings. Overall, these results are consistent with

targets with higher abnormal earnings being more likely to be acquired by firms with

greater opportunity to manage earnings.

4.4. Characteristics of the deal (H5)

To examine whether the probability of takeover varies with deal characteristics

that are likely to facilitate earnings management, we consider whether the target’s size is

small relative to that of the acquirer’s and whether the acquisition completion time is

short. We create a categorical dependent variable, TO_ACQ_CHARACTERISTIC, which

equals one if a firm receives a completed takeover bid in year t+1 by a publicly-held

acquirer where the deal characteristics are likely to facilitate earnings management

(Acquirer_Low_Rel_Size and Acquirer_Short_Interval), equal to two if a firm receives a

completed takeover bid in year t+1 by a public acquirer where the deal characteristics are

not likely to facilitate earnings management (Acquirer_High_Rel_Size and

29

Acquirer_Long_Interval), equal to three if a firm receives a completed takeover bid in

year t+1 by a private acquirer (Private), and zero if a firm is not acquired.

The results are presented in Table 8. In Panel A, we find that firms with higher

earnings are more likely to be acquired when the relative size of target is low but not

when the relative size of target is high. In Panel B, we find that firms with higher

earnings are more likely to be acquired by acquirers that complete the transaction more

quickly. In both Panels A and B, the coefficient on IA_ROA is significantly positive in

column 1 and is not significant in column 2 and the difference in coefficients between

column 1 and 2 is also significant. The coefficient on IA_ROA + IA_ROA ×

BELOW_IA_ROA is not significantly different between columns 1 and 2. These results

are consistent with relative small target size and quick completion time facilitating

earnings management motivated acquisitions, but not being related to acquisitions due to

inefficient management. Overall, the results indicate the positive association between

IA_ROA and TAKEOVER is greater in acquisitions that facilitate earnings management.

We also examine the association between takeover probability and firms’ three-

year average earnings, because if acquirers choose targets to report earnings growth it

would imply that persistently higher earnings are important for acquirers to successfully

manage earnings. We calculate all of the variables in our model using the three-year

average over fiscal years t, t-1, and t-2 (IA_Q3, IA_PP&E3, IA_Ln(CASH), SIZE3,

IA_LEVERAGE3, ARET3, LOSS3, IA_ROA3, BELOW_IA_ROA3), except for

BLOCKHOLDER3 and INDUSTRY3, which capture if there was a blockholder over the

prior three years or at least one acquisition in the industry over the prior three years,

respectively. Table 9 presents results from a logistic regression estimating equation (1)

30

using the three-year average variables. Column 1 does not allow for a non-linearity in the

association between earnings and takeover probability and the coefficient on IA_ROA3 is

significantly positive. In column 2, the coefficient on IA_ROA3 is significantly positive

indicating that when three-year average ROA is above the industry average there is a

positive association between three-year average industry-adjusted earnings and takeover

probability. The coefficients on IA_ROA × BELOW_IA_ROA and IA_ROA + IA_ROA ×

BELOW_IA_ROA are not significant indicating that when three-year industry-adjusted

earnings is negative there is no association with takeover probability. However, the

coefficient on LOSS3 is significantly positive indicating that firms with a negative three-

year average ROA are more likely to be acquired. This result indicates that the presence

of a loss is more important than the size of the loss when examining earnings over longer

periods. In column 3 and 4, we estimate the model separately for firms with three-year

average ROA above and below the industry average, respectively. Consistent with the

results in column 2, we find that ROA above the industry average is positively associated

with takeover probability (column 3) and ROA below the industry average is not

associated with takeover probability (column 4). LOSS3 is significantly positive in both

columns 3 and 4 suggesting that whether a firms’ three-year average earnings is above or

below the industry average they are more likely to be acquired when reporting an average

loss over three consecutive years. Overall, the results indicate that firms are more likely

to be acquired when their three-year average industry-adjusted earnings are higher.

4.4.1. Persistence of Target’s Pre-Acquisition Earnings

If acquirers choose targets with higher earnings to report earnings growth then we

expect targets’ pre-acquisition earnings to be positively associated with acquirers’ post-

31

acquisition earnings when targets have earnings above the industry average. We test this

association by using OLS to estimate the following equation:

ACQ_ROAi,t+1 = β0 + β1 ACQ_ROAi,t + β2 ROAi,t + β3 ACQ_SIZEi,t

+ β4 ACQ_Qi,t + β5 ACQ_LEVi,t + β6 SIZEi,t

+ β7 Qi,t + β8 LEVERAGEi,t + εi,t ,

(2)

where ACQROAi,t+1 is the acquirers’ ROA in the first complete fiscal year after the

acquisition is completed. We also examine future earnings two (ACQROAi,t+2) and three

(ACQROAi,t+3) years ahead. ACQROAi,t and ROAi,t are the acquirers’ and targets’ ROA,

respectively, in the fiscal year before the acquisition announcement. We also control for

acquirers’ and targets’ size (ACQ_SIZE and SIZE), Tobin’s Q (ACQ_Q and Q), and

leverage (ACQ_LEVERAGE and LEVERAGE).

Table 10 presents the results and we estimate the model separately for

observations with annual ROA above and below the industry average (odd and even

columns, respectively). Columns 1 and 2 examine one-year ahead earnings and the

coefficient on ACQ_ROA is significantly positive in both columns. The results indicate

that acquirers’ pre-acquisition earnings are persistent. Importantly, the coefficient on

ROA is significantly positive in column 1 but is not significant in column 2. This result is

consistent with the argument that acquirers select targets with higher abnormal earnings

that are likely to be persistent; and select targets with lower abnormal earnings to

improve the efficiency of the targets so the lower abnormal earnings do not persist. We

find generally similar results when examining persistence in two-year ahead (columns 3

and 4) and three-year ahead earnings (columns 5 and 6). The persistence of acquirers’

and targets’ earnings appear to decrease as the earnings horizon is extended such that the

coefficient on ROA is marginally significant in column 5. Overall, targets’ earnings above

32

the industry average persist in acquirers’ post-acquisition earnings, which is a necessary

condition if acquirers use acquisitions to report higher earnings; while targets’ below the

industry average earnings do not persist in acquirers’ post-acquisition earnings, consistent

with acquirers restructuring targets so that low earnings do not persist.

4.5.Acquirers’ Announcement Returns (H6)

We next examine the acquirers’ announcement returns to test whether investors

view acquisitions of targets with higher abnormal earnings unfavorably. This analysis is

limited to public acquirers where we can calculate announcement returns. ACQ_SCAR is

the acquirer’s five-day (-2, +2) cumulative abnormal return calculated using the market

model following prior studies (e.g., Masulis et al. 2007; Harford et al. 2012). Table 11,

Panel A presents a univariate analysis of acquirers’ announcement returns by whether the

target has earnings above or below the industry mean. We find that the mean acquirer

announcement return for acquisitions of targets with profitability below the industry

average is -0.5% while it is -1.8% for acquisitions of targets with earnings above the

industry average. In Panel B, we perform a multivariable test of the association between

targets’ earnings and acquirers’ announcement returns. The dependent variable is

ACQ_SCAR. In column 1, our variable of interest is whether target’s earnings are below

the industry average (i.e., BELOW_IA_ROA = 1). In column 2, we group observations

based on targets’ earnings into four groups using a cutoff of 0.10 (-0.10) for targets’

earnings above (below) the industry average. If firms’ IA_ROA is positive and is above

(below) 0.10 we set BIG_ABOVE_IA_ROA (SMALL_ABOVE_IA_ROA) equal to one, and

zero otherwise. If firms’ IA_ROA is negative and is above (below) -0.10 we set

SMALL_BELOW_IA_ROA (BIG_BELOW_IA_ROA) equal to one, and zero otherwise.

33

We also control for acquirers’ ROA (ACQ_ROA), acquirers’ Tobin’s Q (ACQ_Q),

acquirers’ size (ACQ_ln(MV)), relative transaction size (RELATIVE_SIZE), hostile bids

(HOSTILE), all cash bids (CASH), all stock bids (STOCK), and the number of bidders

(N_BIDS) (Travlos 1987; Fuller et al. 2002; Moeller et al. 2004; Dong et al. 2006; Chen

et al. 2007).

In column 1, the coefficient on BELOW_IA_ROA is 0.011 and significant at the 5%

level, which indicates that acquirers’ announcement returns are significantly lower by 1.1%

on average when targets earnings are above the industry average relative to when

earnings are below the industry average. In column 2, we include

D_BIG_BELOW_IA_ROA, D_SMALL_BELOW_IA_ROA, and

D_SMALL_ABOVE_IA_ROA so that the coefficient on each of these variables reflects the

comparison with acquisitions of targets with the highest relative earnings (i.e., where

D_BIG_ABOVE_IA_ROA is equal to one). The estimated coefficients on

D_BIG_BELOW_IA_ROA, D_SMALL_BELOW_IA_ROA, and

D_SMALL_ABOVE_IA_ROA are all significantly positive. These results indicate that

acquirers’ announcement returns are significantly lower when they acquire targets with

relatively high earnings, which suggests that investors view acquiring targets with

relatively higher earnings as value-decreasing.

5. Conclusion

We examine the association between firms’ earnings and their likelihood of being

acquired. We find a non-linear association between earnings and takeover probability.

When ROA is above the industry average, there is a significantly positive association

between industry-adjusted ROA and takeover probability and this association is

34

significantly negative when ROA is below the industry average. This result is consistent

with the inefficient management hypothesis as well as with the notion that firms with

higher abnormal earnings are attractive takeover targets because it can favorably affect

the acquirers’ reported earnings.

We carry out several tests to examine whether the acquisitions of targets with

higher abnormal earnings is a real earnings management activity. First, we find that the

positive association between firms’ industry-adjusted earnings and their takeover

probability is greater for acquirers likely to benefit more from earnings management.

Second, we find that acquirers with lower costs of real earnings management or higher

costs of accrual earnings management are more likely to acquire targets with higher

positive industry-adjusted earnings. Third, we find that acquirers with greater opportunity

to manage earnings are more likely to acquire targets with higher positive industry-

adjusted earnings. Fourth, we find that the positive association between firms’ industry-

adjusted earnings and their takeover probability is greater when deal characteristics are

more likely to facilitate earnings management. Finally, we find that investors view the

acquisition of targets with higher abnormal earnings unfavorably as reflected in acquirers’

announcement returns. While any one of our results individually could potentially be due

to an alternative explanation as well, we think that overall our results suggest that

acquisitions of targets with higher earnings are a real earnings management activity.

Finally, our results also contribute to the takeover literature in finance and accounting by

documenting how and why earnings are associated with takeover probability.

35

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40

Appendix A. Examples of research on the relation between earnings and the likelihood of takeover

Authors Year Journal Title Table

Earnings

Variable

Relation between

Takeover Likelihood

and Earnings

Variable

Carleton, Guilkey, Harris, and

Stewart 1983 JF

An empirical analysis of the role of the medium of

exchange in mergers 3, 4 ROA

Insignificant or

significantly positive

Dietrich and Sorensen 1984 JBR An application of logit analysis to prediction of

merger targets 1 ROS / Times

Interest Earned Insignificant

Palepu 1986 JAE Predicting takeover targets 3 ROE Insignificant

Pastena and Ruland 1986 TAR The merger / bankruptcy alternative 3 None

Mikkelson and Partch 1989 JFE Managers' voting rights and corporate control 4, 5 None

Ambrose and Megginson 1992 JFQA

The role of asset structure, ownership structure, and

takeover defenses in determining acquisition

likelihood 4 None

Shivdasani 1993 JAE Board composition, ownership structure, and hostile

takeovers 4,6,7 ΔEBIT Insignificant

Chaplinsky and Niehaus 1994 JF The role of ESOPs in takeover contests

3 None

Comment and Schwert 1995 JFE Poison or placebo? Evidence on the deterrence and

wealth effects of modern antitakeover measures 3

None

Berger and Ofek 1996 JF Bustup takeovers of value-destroying diversified

firms 3,4 ROA Insignificant or

significantly positive

Billett 1996 JB The relationship between risky debt and the firm's

likelihood of being acquired 2, 4 Industry-

adjusted ROA Insignificant

Powell 1997 JBFA Modelling takeover likelihood

3, 4

ROCE and

Industry-

adjusted

ROCE

Insignificant or

significantly negative

41

Field and Karpoff 2002 JF Takeover defenses of IPO firms

7 None

Rauh 2006 JFE Own company stock in defined contribution pension

plans: A takeover defense? 9 None

Billett and Xue 2007 JF The takeover deterrent effect of open market share

repurchases 2 Industry-

adjusted ROA Significantly positive

Powell and Yawson 2007

JBFA

Are corporate restructuring events driven by

common factors? Implications for takeover

prediction

3,

Appendix

Free cash

flows Insignificant

Bates, Becher, and Lemmon 2008

JFE Board classification and managerial entrenchment:

Evidence from the market for corporate control 6 None

Cumming 2008 RFS Contracts and exits in venture capital finance 5, 6, 8 None

Bodnaruk, Massa, and Simonov 2009 RFS Investment banks as insiders and the market for

corporate control 3 ROE Insignificant

Cremers, Nair, and John 2009 RFS Takeovers and the cross-section of returns

2 ROA Insignificant or

significantly negative

Ivashina, Nair, Saunders,

Massoud, and Stover 2009 RFS Bank debt and corporate governance

2,4,5,6,7,

8,10

Industry-

adjusted ROA

Insignificant or

Significantly positive

Giroud and Mueller 2010 JFE Does corporate governance matter in competitive

industries 6 None

Cornett, Tanyeri, and Tehranian 2011 JCF The effect of merger anticipation on bidder and

target firm announcement period returns 2 ROA Significantly positive

Kadyrzhanova and Rhodes-

Kropf 2011 JF

Concentrating on governance 4 None

Sokolyk 2011 JCF The effects of antitakeover provisions on acquisition

targets 3, 5 None

Edmans, Goldstein, and Jiang 2012 JF The real effects of financial markets: The impact of

prices on takeovers 2, 3, 5 None

Puri and Zarutskie 2012 JF On the life cycle dynamics of venture-capital- and

non-venture-capital-financed firms 7 None

Appendix A presents papers that include tabulated multivariable firm-level tests, where the dependent variable is the probability of takeover. We identified these

42

papers by conducting a search of Journal of Accounting and Economics (JAE), Journal of Accounting Research (JAR), Journal of Finance (JF), Journal of

Financial Economics (JFE), Review of Financial Studies (RFS), and The Accounting Review (TAR) for years 1980-2012 and selecting papers with merger,

acquisition, or tender (singular and plural forms) in the title, abstract, or keywords. We then select papers that tabulate tests, in which the dependent variable

indicates whether or not a firm is acquired and the sample includes U.S. firms. We also include additional published papers identified in our review of the

literature and these include papers from Journal of Business (JB), Journal of Business Research (JBR), Journal of Corporate Finance (JCF), and Journal of

Quantitative and Financial Analysis (JFQA). The earnings related variables we include return on assets (ROA), return on equity (ROE), Return on sales (ROS),

return on capital employed (ROCE), free cash flows, and change in earnings before interest and taxes (ΔEBIT),

43

Appendix B. Variable definitions

Variable Symbol Definitions

Takeover indicator TAKEOVERi,t+1 An indicator variable that equals one if firm i receives a completed takeover bid in fiscal

year t+1, and zero otherwise.

Raw return-on-asset ROAi,t Income before extraordinary items (Compustat: IB) in fiscal year t scaled by average total

assets (Compustat: AT).

Industry-adjusted return-on-asset IA_ROAi,t Firm i’s return on assets (ROA) minus average return-on assets in the same industry-year

using the Fama-French 49 industry classifications.

3-year average industry-adjusted

return-on-asset

IA_ROA3i,t Firm i’s three-year average return on assets (ROA) minus the three-year average return-on

assets in the same industry-year using the Fama-French 49 industry classifications.

Below average ROA indicator BELOW_IA_ROAi,t An indicator variable that equals one if firm i’s industry-adjusted return on assets for fiscal

year t is less than or equal to zero (IA_ROAi,t < 0), and zero otherwise.

Below 3-year average ROA

indicator

BELOW_IA_ROA3i,t An indicator variable that equals one if firm i’s three-year average industry-adjusted return

on assets for fiscal year t is less than or equal to zero (IA_ROA3i,t < 0), and zero otherwise.

3-year average industry-adjusted

Tobin's Q

IA_Q3i,t Firm i’s three-year average Tobin's Q (Compustat: (PRCC_F × CSHO + LT)/AT) minus the

three-year average Tobin's Q in the same industry-year using Fama-French 49 industry

classifications.

Industry-adjusted PP&E IA_PP&Ei,t Firm i’s ratio of property, plant, and equipment to total assets (Compustat: PPENT/AT)

minus the average ratio of property, plant, and equipment to total assets in the same

industry-year using Fama-French 49 industry classifications.

3-year average industry-adjusted

PP&E

IA_PP&E3i,t Firm i’s three-year average ratio of property, plant, and equipment to total assets

(Compustat: PPENT/AT) minus the three-year average ratio of property, plant, and

equipment to total assets in the same industry-year using Fama-French 49 industry

classifications.

Industry-adjusted logarithm of

cash

IA_Ln(CASH)i,t Firm i’s logarithm of cash and short-term investments (Compustat: CHE) minus average

logarithm of cash in the same industry-year using Fama-French 49 industry classifications.

Industry-adjusted leverage IA_LEVERAGEi,t Firm i’s leverage (Compustat: LT/AT) minus average leverage in the same industry-year

using Fama-French 49 industry classifications.

3-year average industry-adjusted

leverage

IA_LEVERAGE3i,t Firm i’s three-year average leverage (Compustat: LT/AT) minus the three-year average

leverage in the same industry-year using Fama-French 49 industry classifications.

Industry acquisition activity INDUSTRYi,t An indicator variable equal to one if firm i’s industry has at least one takeover in fiscal year

t, excluding firm i, using Fama-French 49 industry classifications, and zero otherwise.

The indicator of a blockholder BLOCKHOLDERi,t An indicator variable that equals one if firm i has at least one institutional shareholder with

at least a five percent ownership position (Thomson-Reuters Institutional Holdings 13F

Database), and zero otherwise.

The indicator of a blockholder BLOCKHOLDER3i,t An indicator variable that equals one if firm i has at least one institutional shareholder with

44

over last three years at least a five percent ownership position (Thomson-Reuters Institutional Holdings 13F

Database) over the prior three years, and zero otherwise.

Firm size SIZEi,t Firm i’s natural logarithm of market value of equity (Compustat: PRCC_F × CSHO) at the

end of fiscal year t.

Annual returns ARETi,t Firm i’s abnormal stock return calculated as firm i’s 12-month buy and hold return over

fiscal year t minus the CRSP value-weighted 12-month buy and hold return over the same

time period.

3-year average annual returns ARET3i,t Firm i’s average abnormal stock return calculated as the average of firm i’s 12-month buy

and hold return over fiscal years t, t-1, and t-2 minus the CRSP value-weighted 12-month

buy and hold return over the same time period.

Accounting loss indicator LOSSi,t An indicator variable equal to one if firm i’s income before extraordinary items in fiscal

year t is less than or equal to zero, and zero otherwise.

Accounting 3-year average loss

indicator

LOSS3i,t An indicator variable equal to one if firm i’s three-year average income before extraordinary

items over fiscal years t, t-1, and t-2 is less than or equal to zero, and zero otherwise.

Acquirer’s 5-day announcement

returns

ACQ_SCARi,t Acquirers’ five day cumulative abnormal returns calculated using the market model. The

market model parameters are estimated over the period (-210, -11) (e.g., Masulis et al.,

2007; Harford et al., 2012).

Big below average ROA

indicator

D_BIG_BELOW_IA_ROAi,t An indicator variable equal to one if BELOW_IA_ROAi,t is equal to one and IA_ROA is less

than -0.10, and zero otherwise.

Small below average ROA

indicator

D_SMALL_BELOW_IA_ROAi,t An indicator variable equal to one if BELOW_IA_ROAi,t is equal to one and IA_ROA is

greater than or equal to -0.10, and zero otherwise.

Small above average ROA

indicator

D_SMALL_ABOVE_IA_ROAi,t An indicator variable equal to one if BELOW_IA_ROAi,t is equal to zero and IA_ROA is less

than 0.10, and zero otherwise.

Big above average ROA

indicator

D_BIG_ABOVE_IA_ROAi,t An indicator variable equal to one if BELOW_IA_ROAi,t is equal to zero and IA_ROA is

greater than or equal to 0.10, and zero otherwise.

Acquirer return-on-asset ACQ_ROAi,t Acquirer’s income before extraordinary items (Compustat: IB) in fiscal year t scaled by

average total assets (Compustat: AT).

Acquirer one-year ahead future

return-on-asset

ACQ_ROAi,t+1 Acquirer’s income before extraordinary items (Compustat: IB) in the first fiscal year after

the acquisition is completed scaled by average total assets (Compustat: AT).

Acquirer two-year ahead future

return-on-asset

ACQ_ROAi,t+2 Acquirer’s income before extraordinary items (Compustat: IB) in the second fiscal year after

the acquisition is completed scaled by average total assets (Compustat: AT).

Acquirer three-year ahead future

return-on-asset

ACQ_ROAi,t+3 Acquirer’s income before extraordinary items (Compustat: IB) in the third fiscal year after

the acquisition is completed scaled by average total assets (Compustat: AT).

Acquirer Tobin’s Q ACQ_Qi,t Acquirer’s market-to-book value of assets ratio (Compustat: (PRCC_F × CSHO + LT)/AT).

Acquirer market value ACQ_SIZEi,t Acquirer’s natural logarithm of market value of equity (Compustat: PRCC_F × CSHO) at

the end of fiscal year t.

Relative transaction size DEAL_PCTi,t Transaction value divided by the acquirer’s market value of equity at the quarter ending

45

prior to the acquisition announcement.

Hostile deal HOSTILEi,t An indicator variable equal to one if the transaction is hostile, per SDC, and zero otherwise.

All cash CASHOi,t An indicator variable equal to one if all cash is used as payment, and zero otherwise.

All Stock STOCKOi,t An indicator variable equal to one if all common stock is used as payment, and zero

otherwise.

Number of bids N_BIDSi,t The number of bidders for the target firm.

46

Table 1

Summary statistics

Panel A. Descriptive statistics

Variable N Mean Std. Dev. P5 Q1 Median Q3 P95

TAKEOVER 75,479 0.046 0.209 0.000 0.000 0.000 0.000 0.000

ROA 75,479 -0.021 0.204 -0.458 -0.035 0.034 0.078 0.169

IA_ROA 75,479 0.001 0.185 -0.373 -0.030 0.028 0.089 0.232

IA_Q 75,479 -0.010 1.332 -1.477 -0.686 -0.290 0.239 2.621

IA_PP&E 75,479 0.000 0.191 -0.266 -0.115 -0.031 0.092 0.362

IA_Ln(CASH) 75,479 0.014 2.135 -3.725 -1.364 0.104 1.442 3.522

IA_LEVERAGE 75,479 -0.002 0.229 -0.336 -0.165 -0.016 0.132 0.392

INDUSTRY 75,479 0.286 0.452 0.000 0.000 0.000 1.000 1.000

BLOCKHOLDER 75,479 0.685 0.464 0.000 0.000 1.000 1.000 1.000

SIZE 75,479 5.574 2.173 2.158 3.964 5.476 7.052 9.433

ARET 75,479 0.039 0.651 -0.737 -0.347 -0.068 0.243 1.225

LOSS 75,479 0.324 0.468 0.000 0.000 0.000 1.000 1.000

BELOW_IA_ROA 75,479 0.357 0.479 0.000 0.000 0.000 1.000 1.000

Panel B: Descriptive statistics by takeover

TAKEOVER = 0

(N = 72,016)

TAKEOVER =1

(N=3,463)

Variable Mean Median Std. Dev.

Mean Median Std. Dev.

ROA -0.020 0.034 0.203

-0.048*** 0.024*** 0.219

IA_ROA 0.002 0.029 0.184

-0.016*** 0.021*** 0.198

IA_Q 0.004 -0.281 1.340

-0.297*** -0.445*** 1.125

IA_PP&E 0.001 -0.030 0.191

-0.020*** -0.049*** 0.185

IA_Ln(CASH) 0.028 0.116 2.146

-0.271*** -0.143*** 1.861

IA_LEVERAGE -0.003 -0.017 0.228

0.023*** -0.001*** 0.241

INDUSTRY 0.284 0.000 0.451

0.317*** 0.000*** 0.465

BLOCKHOLDER 0.681 1.000 0.466

0.776*** 1.000*** 0.417

SIZE 5.600 5.506 2.185

5.035*** 4.940*** 1.819

ARET 0.043 -0.063 0.651

-0.058*** -0.153*** 0.629

LOSS 0.321 0.000 0.467

0.394*** 0.000*** 0.489

BELOW_IA_ROA 0.355 0.000 0.478 0.408*** 0.000*** 0.492

47

Panel C. Correlation coefficients (below diagonal: Pearson; above diagonal: Spearman)

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

(1) TAKEOVER

-0.020 0.024 -0.035 0.033 -0.055 -0.025 -0.031 0.020 0.015 0.043 -0.053 -0.042

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(2) IA_ROA -0.021

-0.830 0.833 -0.667 0.171 0.126 0.249 -0.231 0.019 0.067 0.327 0.283

(0.000)

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(3) BELOW_IA_ROA 0.024 -0.657

-0.724 0.681 -0.148 -0.115 -0.219 0.181 -0.017 -0.066 -0.309 -0.259

(0.000) (0.000)

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(4) ROA -0.029 0.926 -0.599

-0.811 0.290 0.118 0.245 -0.220 -0.041 0.078 0.381 0.326

(0.000) (0.000) (0.000)

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(5) LOSS 0.033 -0.611 0.681 -0.677

-0.153 -0.125 -0.212 0.116 0.036 -0.078 -0.356 -0.297

(0.000) (0.000) (0.000) (0.000)

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(6) IA_Q -0.047 -0.063 -0.053 -0.060 -0.036

0.002 0.140 -0.072 -0.019 -0.004 0.353 0.332

(0.000) (0.000) (0.000) (0.000) (0.000)

(0.549) (0.000) (0.000) (0.000) (0.322) (0.000) (0.000)

(7) IA_PP&E -0.022 0.118 -0.094 0.111 -0.104 -0.026

0.004 0.068 -0.012 -0.008 0.146 0.074

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(0.299) (0.000) (0.001) (0.024) (0.000) (0.000)

(8) IA_Ln(CASH) -0.029 0.254 -0.215 0.234 -0.210 0.075 -0.017

-0.013 -0.001 0.115 0.713 0.120

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(0.001) (0.874) (0.000) (0.000) (0.000)

(9) IA_LEVERAGE 0.024 -0.228 0.202 -0.208 0.159 -0.043 0.054 -0.034

-0.004 -0.017 0.050 -0.048

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(0.313) (0.000) (0.000) (0.000)

(10) INDUSTRY 0.015 -0.001 -0.017 -0.035 0.036 0.000 -0.001 -0.001 0.001

-0.026 -0.033 -0.039

(0.000) (0.786) (0.000) (0.000) (0.000) (0.932) (0.820) (0.857) (0.857)

(0.000) (0.000) (0.000)

(11) BLOCKHOLDER 0.043 0.113 -0.066 0.123 -0.078 -0.040 -0.014 0.105 -0.023 -0.026

0.167 0.051

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(0.000) (0.000)

(12) SIZE -0.054 0.327 -0.308 0.332 -0.354 0.239 0.116 0.715 0.018 -0.033 0.143

0.280

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

(0.000)

(13) ARET -0.033 0.194 -0.175 0.185 -0.182 0.324 0.053 0.054 -0.051 -0.028 0.014 0.168

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Notes to Table 1:

This table presents descriptive statistics (Panel A), descriptive statistics by takeover (Panel B), and the correlation coefficients (Panel C) for the variables used in

our takeover probability model. In Panel B, we test the difference in values for takeover and non-takeover firms. In Panel C, p-values are presented in

parentheses below the correlation coefficients. Variable definitions are provided in Appendix B. *, **, and *** denote two-tailed statistical significance at 10%,

5%, and 1%, respectively.

48

Table 2

Probability of takeover: Logistic regression

TAKEOVERt+1 = α0 + α1IA_Qt + α2IA_PP&Et + α3IA_Ln(CASH) t + α4BLOCKHOLDERt + α5SIZEt + α6INDUSTRYt + α7IA_LEVERAGEt + α8ARETt

+ α9LOSSt + α10IA_ROAt + α11IA_ROAt × BELOW_IA_ROA t + α12 BELOW_IA_ROA t + εt

Panel A. Industry-adjusted ROA

Variable

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

All All BELOW_IA_ROA = 0 BELOW_IA_ROA = 1

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.134*** -0.7%

-0.152*** -0.8%

-0.112*** -0.5%

-0.202*** -1.2%

(-7.24)

(-8.24)

(-4.79)

(-6.73)

IA_PP&E -0.420*** -0.3%

-0.422*** -0.3%

-0.401*** -0.3%

-0.489*** -0.4%

(-4.19)

(-4.18)

(-3.03)

(-3.14)

IA_Ln(CASH) 0.022* 0.2%

0.019 0.2%

0.000 0.0%

0.033 0.3%

(1.69)

(1.43)

(0.01)

(1.64)

BLOCKHOLDER 0.591*** 2.3%

0.608*** 2.3%

0.605*** 2.1%

0.591*** 2.6%

(13.58)

(13.87)

(10.44)

(8.96)

SIZE -0.134*** -1.1%

-0.128*** -1.1%

-0.136*** -1.0%

-0.103*** -1.0%

(-8.77)

(-8.34)

(-7.01)

(-4.21)

INDUSTRY 0.131*** 0.5%

0.125*** 0.5%

0.058 0.2%

0.218*** 1.0%

(3.41)

(3.24)

(1.16)

(3.62)

IA_LEVERAGE 0.496*** 0.4%

0.511*** 0.4%

0.557*** 0.4%

0.521*** 0.5%

(6.29)

(6.52)

(4.83)

(4.90)

ARET -0.001 0.0%

0.003 0.0%

-0.022 0.0%

0.031 0.1%

(-0.02)

(0.10)

(-0.53)

(0.62)

LOSS 0.130*** 0.5%

0.154*** 0.6%

0.203*** 0.7%

0.154* 0.7%

(2.72)

(2.81)

(2.61)

(1.96)

IA_ROA 0.073 0.1%

1.178*** 0.8%

1.279*** 0.8%

-0.443*** -0.4%

(0.54)

(4.17)

(4.42)

(-2.69)

IA_ROA × BELOW_IA_ROA

-1.496*** -1.1%

(-4.68)

BELOW_IA_ROA

-0.011 0.0%

(-0.20)

Year fixed effects Included

Included

Included

Included

N 75,479

75,479

48,534

26,945

Pseudo R2 3.6% 3.7% 4.3% 3.1%

Test: IA_ROA + IA_ROA × BELOW_IA_ROA χ2 = 4.37**

49

Panel B. Unadjusted ROA

Variable

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

All All LOSS = 0 LOSS = 1

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.144*** -0.7%

-0.156*** -0.8%

-0.104*** -0.5%

-0.195*** -1.2%

(-7.88)

(-8.02)

(-3.65)

(-7.15)

IA_PP&E -0.410*** -0.3%

-0.407*** -0.3%

-0.373*** -0.2%

-0.517*** -0.5%

(-4.10)

(-4.06)

(-3.01)

(-3.05)

IA_Ln(CASH) 0.023* 0.2%

0.020 0.2%

-0.008 -0.1%

0.061*** 0.6%

(1.79)

(1.56)

(-0.51)

(2.78)

BLOCKHOLDER 0.600*** 2.3%

0.603*** 2.3%

0.603*** 2.0%

0.564*** 2.7%

(13.75)

(13.80)

(10.51)

(8.46)

SIZE -0.132*** -1.1%

-0.127*** -1.1%

-0.131*** -1.0%

-0.116*** -1.2%

(-8.81)

(-8.31)

(-6.99)

(-4.44)

INDUSTRY 0.131*** 0.5%

0.131*** 0.5%

0.106** 0.4%

0.141** 0.7%

(3.41)

(3.43)

(2.11)

(2.33)

IA_LEVERAGE 0.468*** 0.4%

0.484*** 0.4%

0.555*** 0.4%

0.478*** 0.5%

(6.01)

(6.16)

(4.55)

(4.63)

ARET 0.009 0.0%

0.011 0.0%

-0.004 0.0%

0.037 0.1%

(0.28)

(0.36)

(-0.09)

(0.75)

ROA -0.312*** -0.2%

0.653 0.5%

0.337 0.2%

-0.411*** -0.4%

(-3.24)

(1.46)

(0.68)

(-3.22)

ROA × LOSS

-0.962** -0.8%

(-2.03)

LOSS

0.101* 0.4%

(1.87)

Year fixed effects Included

Included

Included

Included

N 75,479

75,479

51,003

24,476

Pseudo R2 3.6% 3.6% 4.1% 3.1%

Test: IA_ROA + IA_ROA × BELOW_IA_ROA χ2 = 1.87

50

Notes to Table 2:

This table presents the test of the association between earnings and probability of takeover. Panel A presents the results using industry-adjusted earnings

(IA_ROA) and Panel B presents the results using unadjusted earnings (ROA). The sample includes all observations in Compustat with the required data available

and excludes financial firms. The dependent variable is TAKEOVERi,t, an indicator variable equal to one if firm i receives a completed takeover bid in fiscal year

t+1. Variable definitions are provided in Appendix B. We include year fixed effects. Standard errors used to calculate z-statistics, presented in parentheses, are

White adjusted and clustered by firm and year. The marginal effects column presents change in the probability of takeover for a one standard deviation increase

in the variable, or a change from 0 to 1 for an indicator variable, with all other independent variables taking the mean value. At the bottom of column 2 we

present a test of whether the combined coefficient IA_ROA + IA_ROA × BELOW_IA_ROA is significant. *, **, and *** denote two-tailed statistical significance

at 10%, 5%, and 1%, respectively.

51

Table 3

Probability of takeover by publicly-held versus privately-held acquirer: Multinomial logistic

regression

Variable

(1)

(2)

Public_Acquirer (N = 2,579)

Private_Acquirer (N = 884)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.160*** -0.6%

-0.173*** -0.2%

(-7.79)

(-4.61)

IA_PP&E -0.454*** -0.2%

-0.372* -0.1%

(-4.01)

(-1.79)

IA_Ln(CASH) -0.008 -0.1%

0.090*** 0.1%

(-0.58)

(3.38)

BLOCKHOLDER 0.598*** 1.7%

0.697*** 0.5%

(11.96)

(8.10)

SIZE -0.030* -0.2%

-0.416*** -0.7%

(-1.81)

(-13.67)

INDUSTRY 0.123*** 0.4%

0.124 0.1%

(2.81)

(1.62)

IA_LEVERAGE 0.203** 0.1%

1.249*** 0.2%

(2.22)

(8.86)

ARET 0.024 0.0%

-0.058 0.0%

(0.70)

(-0.88)

LOSS 0.284*** 0.8%

-0.241** -0.2%

(4.53)

(-2.34)

IA_ROA 1.720*** 0.9%

-1.199* -0.2%

(5.52)

(-1.94)

IA_ROA × BELOW_IA_ROA -2.290*** -1.2%

1.496** 0.2%

(-6.39)

(2.22)

BELOW_IA_ROA -0.054 -0.2%

0.043 0.0%

(-0.81)

(0.40)

Year fixed effects Included

N 75,479

Pseudo R2 4.5%

Test: IA_ROA: (1) = (2) χ2 = 18.18***

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1) = (2) χ2 = 7.18***

Notes to Table 3:

This table presents the test of the association between earnings and probability of takeover by publicly-held and

privately-held acquirers. The sample includes all observations in Compustat with the required data available and

excludes financial firms. The dependent variable is a trichotomous variable equal to one if a firm receives a

completed takeover bid in year t+1 by a publicly-held acquirer (Public_Acquirer), equal to two if a firm receives a

completed takeover bid in year t+1 by a privately-held acquirer (Private_Acquirer), and zero otherwise. We use a

multinomial logistic regression to estimate the equation. Variable definitions are provided in Appendix B. We

include year fixed effects. Standard errors used to calculate z-statistics, presented in parentheses, are White adjusted

and clustered by firm. The marginal effects column presents change in the probability of takeover for a one standard

deviation increase in the variable, or a change from 0 to 1 for an indicator variable, with all other independent

52

variables taking the mean value. At the bottom of the table we present a test of whether the coefficients on IA_ROA

and IA_ROA + IA_ROA × BELOW_IA_ROA are significantly different in columns 1 and 2. *, **, and *** denote

two-tailed statistical significance at 10%, 5%, and 1%, respectively.

53

Table 4

Probability of takeover by acquirers with patterns of increasing earnings

Panel A: Percent of acquirers with pattern of earnings increases by whether targets’ earnings are above

or below industry average

N Percent of Acquirers with Pattern of Earnings

Increases

All

BELOW_IA_ROA = 0 BELOW_IA_ROA = 1

Difference

t-statistic

Acq_4Yr_String 1,084 21.7% 16.2% -2.24**

Acq_5Yr_String 1,048 16.7% 10.4% -2.81***

Acq_6Yr_String 1,001 13.9% 6.6% -3.50***

Panel B: Probability of takeover by acquirers with patterns of increasing earnings: Multinomial logistic

regression

Variable

(1) (2)

(3) (4)

(5) (6)

Acq_4Yr_S

tring

(N = 213)

Acq_No_4Yr

_String

(N = 871)

Acq_5Yr_

String

(N = 151)

Acq_No_5Yr

_String

(N = 897)

Acq_6Yr_S

tring

(N = 113)

Acq_No_6Yr

_String

(N = 888)

Coefficient

(z-statistic)

[Marginal

Effect]

Coefficient

(z-statistic)

[Marginal

Effect]

Coefficient

(z-statistic)

[Marginal

Effect]

Coefficient

(z-statistic)

[Marginal

Effect]

Coefficient

(z-statistic)

[Marginal

Effect]

Coefficient

(z-statistic)

[Marginal

Effect]

Loss 0.193 0.483***

-0.011 0.420***

-0.176 0.413***

(0.88) (4.69)

(-0.04) (4.11)

(-0.49) (4.03)

[0.0%] [0.4%]

[0.0%] [0.3%]

[0.0%] [0.3%]

IA_ROA 1.992** 0.936*

2.314** 0.513

2.688** 0.293

(2.10) (1.70)

(2.32) (0.93)

(2.52) (0.53)

[0.1%] [0.1%]

[0.1%] [0.1%]

[0.1%] [0.0%]

IA_ROA × BELOW_IA_ROA -1.884* -1.547**

-1.994 -0.980

-2.963** -0.502

(-1.65) (-2.51)

(-1.55) (-1.57)

(-2.01) (-0.79)

[0.0%] [-0.2%]

[0.0%] [-0.2%]

[0.0%] [-0.1%]

BELOW_IA_ROA -0.205 -0.163

-0.260 -0.162

-0.466 -0.147

(-0.89) (-1.43)

(-0.94) (-1.45)

(-1.30) (-1.32)

[0.0%] [-0.2%]

[0.0%] [-0.2%]

[0.0%] [-0.2%]

Control Variables Included Included

Included Included

Included Included

Year fixed effects Included Included

Included Included

Included Included

N 75,479

75,479

75,479

Pseudo R2 3.8% 3.8% 3.7%

Test: IA_ROA: (1) = (2) χ2 = 0.95

(3) = (4) χ2 = 2.57

(5) = (6) χ2 = 4.04**

Test: IA_ROA +

IA_ROA × BELOW_IA_ROA: (1) = (2) χ2 = 1.18

(3) = (4) χ2 = 0.80

(5) = (6) χ2 = 0.00

54

Notes to Table 4:

This table presents the test of the association between earnings and probability of takeover by acquirers with patterns

of earnings increases, acquirers without patterns of earnings increases, and privately held acquirers or acquirers

without available data to calculate patterns of earnings. Panel A presents a comparison of the percentage of acquirers

with a pattern of annual earnings increases for four, five, and six years by whether the target firms’ earnings are

above or below the industry-average. Panel B presents results from the multinomial logistic regression. The sample

includes all observations in Compustat with the required data available and excludes financial firms. We use a

multinomial logistic regression to estimate the equation. In Panel B, the dependent variable is a categorical variable

equal to one if a firm receives a completed takeover bid in year t+1 by a publicly held acquirer with a pattern of

annual earnings increases (ACQ_4YR_STRING, ACQ_5YR _STRING, and ACQ_6YR_STRING), equal to two if a

firm receives a completed takeover bid in year t+1 by a publicly held acquirer without a pattern of earnings increases

(ACQ_NO_4YR_STRING, ACQ_NO_5YR_STRING, and ACQ_NO_6YR_STRING), equal to three if a firm receives

a completed takeover bid in year t+1 by an acquirer where data is not available to determine if there is a pattern of

earnings increases (PRIVATE/UNKNOWN), and zero if a firm is not acquired. Variable definitions are provided in

Appendix B. For brevity, we do not present results for estimating the probability of PRIVATE/UNKOWN and

estimated coefficients for control variables. We include year fixed effects. Standard errors used to calculate z-

statistics, presented in parentheses, are White adjusted and clustered by firm. The marginal effects, presented in

brackets, are the change in the probability of takeover for a one standard deviation increase in the variable, or a

change from 0 to 1 for an indicator variable, with all other independent variables taking the mean value. At the

bottom of the table we present a test of whether the coefficients on IA_ROA and IA_ROA + IA_ROA ×

BELOW_IA_ROA are significantly different between columns. *, **, and *** denote two-tailed statistical

significance at 10%, 5%, and 1%, respectively.

55

Table 5

Probability of takeover by acquirers with greater benefits to managing earnings

Panel A: Probability of takeover by acquirers with high PPS: Multinomial logistic regression

Variable

(1) (2)

Acquirer_High_PPS

(N =337)

Acquirer_Low_PPS

(N = 420)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.164*** 0.0% -0.282*** -0.1%

(-3.68) (-4.94)

IA_PP&E -0.227 0.0% -0.845*** 0.0%

(-0.77) (-3.43)

IA_Ln(CASH) -0.066 0.0% -0.006 0.0%

(-1.55) (-0.16)

BLOCKHOLDER 1.374*** 0.3% 0.986*** 0.2%

(7.75) (6.98)

SIZE 0.435*** 0.2% 0.228*** 0.1%

(10.17) (5.79)

INDUSTRY 0.113 0.0% -0.031 0.0%

(0.93) (-0.28)

IA_LEVERAGE 0.101 0.0% 1.146*** 0.1%

(0.35) (5.44)

ARET 0.094 0.0% -0.433*** -0.1%

(0.98) (-3.69)

LOSS -0.081 0.0% -0.120 0.0%

(-0.39) (-0.76)

IA_ROA 2.585*** 0.1% -2.826*** -0.1%

(4.11) (-2.76)

IA_ROA × BELOW_IA_ROA -3.662*** -0.1% 3.819*** 0.1%

(-3.36) (3.12)

BELOW_IA_ROA -0.491** -0.1% -0.100 0.0%

(-2.28) (-0.66)

Year fixed effects Included

Included

N 69,816

Pseudo R2 6.2%

Test: IA_ROA: (1) = (2) χ2 = 20.67***

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 3.36*

56

Panel B: Probability of takeover by acquirers suspected of earnings management: Multinomial logistic

regression

Variable

(1) (2)

Acquirer_Suspect

(N =154 )

Acquirer_Nonsuspect

(N = 1,696)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.235*** 0.0% -0.159*** -0.4%

(-2.77) (-6.54)

IA_PP&E 0.133 0.0% -0.442*** -0.2%

(0.33) (-3.25)

IA_Ln(CASH) 0.035 0.0% -0.015 -0.1%

(0.58) (-0.87)

BLOCKHOLDER 0.847*** 0.1% 0.554*** 1.0%

(4.12) (9.18)

SIZE -0.139** 0.0% 0.012 0.1%

(-2.09) (0.61)

INDUSTRY 0.255 0.0% 0.126** 0.2%

(1.48) (2.35)

IA_LEVERAGE 0.280 0.0% 0.095 0.0%

(0.84) (0.85)

ARET 0.204 0.0% 0.037 0.0%

(1.63) (0.89)

LOSS 0.263 0.0% 0.328*** 0.6%

(1.03) (4.39)

IA_ROA 3.844*** 0.1% 1.508*** 0.5%

(3.76) (4.00)

IA_ROA × BELOW_IA_ROA -5.085*** -0.1% -2.267*** -0.8%

(-4.43) (-5.27)

BELOW_IA_ROA 0.108 0.0% -0.064 -0.1%

(0.39) (-0.79)

Year fixed effects Included

Included

N 75,479

Pseudo R2 4.0%

Test: IA_ROA: (1) = (2) χ2 = 4.70**

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 0.54

Notes to Table 5:

This table presents the test of the association between earnings and probability of takeover by publicly held

acquirers with high pay-for-performance sensitvity, publicly held acquirers with low pay-for-performance sensitivity,

and acquirers without data available to calculate pay-for-performance sensitivity or that are privately held. The

sample includes all observations in Compustat with the required data available and excludes financial firms. We use

a multinomial logistic regression to estimate the equation. In Panel A, the dependent variable is a categorical

variable equal to one if a firm receives a completed takeover bid in year t+1 by a publicly held acquirer with high

pay-for-performance sensitivity (Acq_High_PPS), equal to two if a firm receives a completed takeover bid in year

t+1 by a publicly held acquirer with low pay-for-performance sensitivity (Acq_Low_PPS), equal to three if a firm

57

receives a completed takeover bid in year t+1 by a publicly held acquirer without the data available to calculate pay-

for-performance sensitivity or that is privately held (Private / Public Unknown), and zero if a firm is not acquired. In

Panel B, the dependent variable is a categorical variable equal to one if a firm receives a completed takeover bid in

year t+1 by a publicly held acquirer suspected of managing earnings in the year the acquisition is completed

(Acq_Suspect), equal to two if a firm receives a completed takeover bid in year t+1 by a publicly held acquirer not

suspected of managing earnings in the year the acquisition is completed (Acq_Nonsuspect), equal to three if a firm

receives a completed takeover bid in year t+1 by a publicly held acquirer without the data available to calculate

earnings management likelihood or that is privately held (Private / Public Unknown), and zero if a firm is not

acquired. Variable definitions are provided in Appendix B. We include year fixed effects. Standard errors used to

calculate z-statistics, presented in parentheses, are White adjusted and clustered by firm. The marginal effects

column presents change in the probability of takeover for a one standard deviation increase in the variable, or a

change from 0 to 1 for an indicator variable, with all other independent variables taking the mean value. At the

bottom of the table we present a test of whether the coefficients on IA_ROA and IA_ROA + IA_ROA ×

BELOW_IA_ROA are significantly different in columns 1 and 2. *, **, and *** denote two-tailed statistical

significance at 10%, 5%, and 1%, respectively.

58

Table 6

Probability of takeover by acquirers with real earnings management constraints

Panel A: Probability of takeover by acquirers in financial distress: Multinomial logistic regression

Variable

(1) (2)

Acquirer_High_Zscore (N = 578) Acquirer_Low_Zscore (N = 1,046)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic) Marginal Effect

IA_Q 0.000 0.0% -0.282*** -0.4%

(0.01) (-8.25)

IA_PP&E -1.105*** -0.1% -0.130 0.0%

(-4.97) (-0.79)

IA_Ln(CASH) 0.008 0.0% -0.021 -0.1%

(0.26) (-0.99)

BLOCKHOLDER 0.744*** 0.5% 0.502*** 0.5%

(6.91) (6.70)

SIZE -0.041 -0.1% 0.021 0.1%

(-1.23) (0.85)

INDUSTRY 0.252*** 0.2% 0.083 0.1%

(2.82) (1.20)

IA_LEVERAGE -0.252 0.0% 0.343** 0.1%

(-1.33) (2.49)

ARET -0.031 0.0% 0.067 0.0%

(-0.43) (1.28)

LOSS 0.519*** 0.3% 0.228** 0.3%

(4.31) (2.42)

IA_ROA 2.418*** 0.3% 1.124** 0.2%

(4.11) (2.31)

IA_ROA × BELOW_IA_ROA -2.949*** -0.3% -1.971*** -0.4%

(-4.25) (-3.59)

BELOW_IA_ROA -0.156 -0.1% -0.050 -0.1%

(-1.14) (-0.50)

Year fixed effects Included Included N 75,479 Pseudo R2 3.9% Test: IA_ROA: (1) = (2) χ2 = 2.95*

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 0.54

59

Panel B: Probability of takeover by acquirers with balance sheet bloat: Multinomial logistic regression

Variable

(1) (2)

Acquirer_High_NOA (N = 489) Acquirer_Low_NOA (N = 1,218)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.162*** -0.1% -0.165*** -0.3%

(-3.83) (-5.54)

IA_PP&E 0.294 0.0% -0.723*** -0.2%

(1.27) (-4.55)

IA_Ln(CASH) -0.019 0.0% -0.012 0.0%

(-0.61) (-0.59)

BLOCKHOLDER 0.701*** 0.4% 0.560*** 0.7%

(6.15) (7.87)

SIZE -0.002 0.0% 0.007 0.0%

(-0.06) (0.29)

INDUSTRY 0.193* 0.1% 0.104 0.1%

(1.95) (1.64)

IA_LEVERAGE 0.072 0.0% 0.162 0.0%

(0.35) (1.26)

ARET 0.098 0.0% 0.004 0.0% (1.43) (0.07) LOSS 0.221* 0.1% 0.347*** 0.5% (1.75) (3.92) IA_ROA 2.511*** 0.3% 1.239*** 0.3% (3.77) (2.80) IA_ROA × BELOW_IA_ROA -2.914*** -0.3% -2.033*** -0.5% (-3.71) (-4.06) BELOW_IA_ROA 0.239* 0.1% -0.186* -0.3% (1.73) (-1.94) Year fixed effects Included Included

N 75,479

Pseudo R2 3.8%

Test: IA_ROA: (1) = (2) χ2 = 2.59

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 0.70

60

Notes to Table 6:

This table presents the test of the association between earnings and probability of takeover by acquirers more and less likely to be using real earnings

management. We classify firms as having a higher likelihood to use real earnings management when they have high Altman’s Z-scores (Altman 1968, 2000)

(Panel A) and high net operating assets (Panel B). The sample includes all observations in Compustat with the required data available and excludes financial

firms. We use a multinomial logistic regression to estimate the equation. Variable definitions are provided in Appendix B. We include year fixed effects.

Standard errors used to calculate z-statistics, presented in parentheses, are White adjusted and clustered by firm. The marginal effects column presents change in

the probability of takeover for a one standard deviation increase in the variable, or a change from 0 to 1 for an indicator variable, with all other independent

variables taking the mean value. At the bottom of the table we present a test of whether the coefficients on IA_ROA and IA_ROA + IA_ROA × BELOW_IA_ROA

are significantly different in columns 1 and 2. *, **, and *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively.

In Panel A, the dependent variable is a categorical variable equal to one if a firm receives a completed takeover bid in year t+1 by an acquirer with a high Altman

Z-Score (Acquirer_High_Zscore), equal to two if a firm receives a completed takeover bid in year t+1 by an acquirer with a low Altman Z-score

(Acquirer_Low_Zscore), equal to three if a firm receives a completed takeover bid in year t+1 by an acquirer where data is not available to determine the

acquirers’ Z-score (Private/Public Unknown), and zero if a firm is not acquired.

In Panel B, the dependent variable is a categorical variable equal to one if a firm receives a completed takeover bid in year t+1 by an acquirer with high NOA

(Acquirer_High_NOA), equal to two if a firm receives a completed takeover bid in year t+1 by an acquirer with low NOA (Acquirer_Low_NOA), equal to three if

a firm receives a completed takeover bid in year t+1 by an acquirer where data is not available to determine the acquirers’ NOA (Private/Public Unknown), and

zero if a firm is not acquired.

61

Table 7

Probability of takeover by acquirers with greater opportunity to manage earnings

Panel A: Probability of takeover by acquirers with and without blockholders: Multinomial logistic regression

Variable

(1) (2)

Acquirer_NoBlock (N = 367) Acquirer_Block (N = 903)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.175*** -0.1% -0.125*** -0.2%

(-3.37) (-3.79)

IA_PP&E -0.380 0.0% -0.412** -0.1%

(-1.33) (-2.41)

IA_Ln(CASH) -0.011 0.0% -0.010 0.0%

(-0.29) (-0.44)

BLOCKHOLDER 0.557*** 0.2% 0.676*** 0.7%

(4.32) (7.95)

SIZE 0.066* 0.1% -0.074*** -0.2%

(1.65) (-2.81)

INDUSTRY -0.020 0.0% 0.036 0.0%

(-0.17) (0.49)

IA_LEVERAGE -0.202 0.0% 0.171 0.0%

(-0.87) (1.19)

ARET -0.098 0.0% 0.020 0.0%

(-1.06) (0.36)

LOSS 0.455*** 0.2% 0.291*** 0.3%

(3.08) (2.92)

IA_ROA 3.649*** 0.3% 1.380*** 0.3%

(5.07) (2.76)

IA_ROA × BELOW_IA_ROA -4.561*** -0.3% -1.644*** -0.3%

(-5.59) (-2.83)

BELOW_IA_ROA 0.139 0.1% -0.125 -0.1%

(0.84) (-1.15)

Year fixed effects Included Included

N 75,479

Pseudo R2 3.5%

Test: IA_ROA: (1) = (2) χ2 = 6.89***

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 1.72

62

Panel B: Probability of takeover by acquirers’ board independence: Multinomial logistic regression

Variable

(1) (2)

Acquirer_Brd_NoInd

(N = 388)

Acquirer_Brd_Ind

(N = 517)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.141*** -0.1% -0.107*** -0.1%

(-3.13) (-2.63)

IA_PP&E -0.575** -0.1% -0.338 0.0%

(-2.35) (-1.52)

IA_Ln(CASH) -0.091*** -0.1% -0.011 0.0%

(-2.60) (-0.38)

BLOCKHOLDER 0.616*** 0.3% 0.769*** 0.6%

(4.83) (6.39)

SIZE 0.118*** 0.1% 0.062* 0.1%

(3.10) (1.81)

INDUSTRY 0.119 0.1% 0.106 0.1%

(1.05) (1.10)

IA_LEVERAGE 0.218 0.0% 0.616*** 0.1%

(1.02) (3.11)

ARET -0.029 0.0% 0.032 0.0%

(-0.34) (0.43)

LOSS 0.256 0.1% 0.236* 0.2%

(1.64) (1.82)

IA_ROA 2.356*** 0.2% 0.250 0.0%

(3.38) (0.38)

IA_ROA × BELOW_IA_ROA -3.059*** -0.3% -0.568 -0.1%

(-3.73) (-0.72)

BELOW_IA_ROA 0.060 0.0% -0.248* -0.2%

(0.36) (-1.78)

Year fixed effects Included Included

N 60,550

Pseudo R2 3.9%

Test: IA_ROA: (1) = (2) χ2 = 4.91**

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 0.40

63

Panel C: Probability of takeover by acquirers with high free cash flow and low growth opportunities: Multinomial logistic regression

Variable

(1) (2)

Acquirer_HighFCF_LowQ

(N = 727)

Acquirer_Other

(N = 977)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.337*** -0.4% -0.068** -0.1%

(-7.83) (-2.36)

IA_PP&E -0.308 0.0% -0.510*** -0.1%

(-1.54) (-2.91)

IA_Ln(CASH) -0.009 0.0% -0.019 0.0%

(-0.34) (-0.86)

BLOCKHOLDER 0.616*** 0.5% 0.590*** 0.6%

(6.64) (7.45)

SIZE 0.009 0.0% 0.001 0.0%

(0.30) (0.03)

INDUSTRY 0.206*** 0.2% 0.082 0.1%

(2.58) (1.15)

IA_LEVERAGE 0.504*** 0.1% -0.148 0.0%

(3.01) (-1.03)

ARET 0.070 0.0% 0.004 0.0%

(1.06) (0.07)

LOSS 0.422*** 0.3% 0.199** 0.2%

(3.73) (2.12)

IA_ROA 2.382*** 0.3% 0.877* 0.2%

(4.42) (1.78)

IA_ROA × BELOW_IA_ROA -3.199*** -0.5% -1.442** -0.3%

(-5.11) (-2.56)

BELOW_IA_ROA -0.220* -0.2% 0.060 0.1%

(-1.75) (0.60)

Year fixed effects Included Included

N 75,479

Pseudo R2 3.8%

Test: IA_ROA: (1) = (2) χ2 = 3.84**

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 0.09

64

Notes to Table 7:

This table presents the test of the association between earnings and probability of takeover by acquirers more and less likely to be using real earnings

management. We classify firms as having a higher likelihood to use real earnings management when they no blockholders (Panel A), low board independence

(Panel B), and greater free cash flow with lower growth opportunities (Panel C). The sample includes all observations in Compustat with the required data

available and excludes financial firms. We use a multinomial logistic regression to estimate the equation. Variable definitions are provided in Appendix B. We

include year fixed effects. Standard errors used to calculate z-statistics, presented in parentheses, are White adjusted and clustered by firm. The marginal effects

column presents change in the probability of takeover for a one standard deviation increase in the variable, or a change from 0 to 1 for an indicator variable, with

all other independent variables taking the mean value. At the bottom of the table we present a test of whether the coefficients on IA_ROA and IA_ROA + IA_ROA

× BELOW_IA_ROA are significantly different in columns 1 and 2. *, **, and *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively.

In Panel A, the dependent variable is a categorical variable equal to one if a firm receives a completed takeover bid in year t+1 by an acquirer without a

blockholder (Acquirer_NoBlock), equal to two if a firm receives a completed takeover bid in year t+1 by an acquirer with a blockholder (Acquirer_Block), equal

to three if a firm receives a completed takeover bid in year t+1 by an acquirer where data is not available to determine if the acquirer has a blockholder (Private/

Public Unknown), and zero if a firm is not acquired.

In Panel B, the dependent variable is a categorical variable equal to one if a firm receives a completed takeover bid in year t+1 by an acquirer with low board

independence (Acquirer_Brd_NoInd), equal to two if a firm receives a completed takeover bid in year t+1 by an acquirer with high board independence

(Acquirer_Brd_Ind), equal to three if a firm receives a completed takeover bid in year t+1 by an acquirer where data is not available to determine the acquirers’

board independence (Private/Public Unknown), and zero if a firm is not acquired.

In Panel C, the dependent variable is a categorical variable equal to one if a firm receives a completed takeover bid in year t+1 by an acquirer with high free cash

flow and low Tobin’s Q (Acq_HighFCF_LowQ), equal to two if a firm receives a completed takeover bid in year t+1 by an acquirer without high free cash flow

and low Tobin’s Q (Acq_Other), equal to three if a firm receives a completed takeover bid in year t+1 by an acquirer where data is not available to determine the

acquirers’ free cash flow and Tobin’s Q (Private/Public Unknown), and zero if a firm is not acquired.

65

Table 8

Probability of takeover by characteristics of the deal

Panel A: Probability of takeover by acquirers’ relative transaction size: Multinomial logistic regression

Variable

(1) (2)

Acquirer_Low_Rel_Size

(N = 2,093)

Acquirer_ High_Rel_Size

(N = 486)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.146*** -0.4% -0.205*** -0.1%

(-6.50) (-4.49)

IA_PP&E -0.448*** -0.2% -0.444* 0.0%

(-3.61) (-1.73)

IA_Ln(CASH) -0.003 0.0% -0.029 0.0%

(-0.21) (-0.87)

BLOCKHOLDER 0.594*** 1.4% 0.690*** 0.3%

(10.81) (5.88)

SIZE -0.079*** -0.4% 0.160*** 0.2%

(-4.33) (4.20)

INDUSTRY 0.107** 0.2% 0.205** 0.1%

(2.21) (2.06)

IA_LEVERAGE 0.154 0.1% 0.397* 0.0%

(1.55) (1.80)

ARET 0.028 0.0% 0.020 0.0%

(0.75) (0.24)

LOSS 0.379*** 0.9% -0.110 -0.1%

(5.45) (-0.78)

IA_ROA 2.090*** 0.9% 0.181 0.0%

(6.21) (0.25)

IA_ROA × BELOW_IA_ROA -2.678*** -1.2% -0.515 0.0%

(-6.95) (-0.59)

BELOW_IA_ROA -0.095 -0.2% 0.101 0.1%

(0.13) (-1.54)

Year fixed effects Included

Included

N 75,479

Pseudo R2 4.6%

Test: IA_ROA: (1) = (2) χ2 = 5.94**

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 0.23

66

Panel B: Probability of takeover by time to complete transaction: Multinomial logistic regression

Variable

(1) Table (2)

Acquirer_Short_Interval

(N = 1,932)

Acquirer_ Long-Interval

(N = 647)

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q -0.132*** -0.2% -0.229*** -0.2%

(-5.71) (-5.48)

IA_PP&E -0.317** -0.1% -0.801*** -0.1%

(-2.52) (-3.39)

IA_Ln(CASH) 0.034** -0.1% -0.123*** 0.1%

(1.98) (-4.79)

BLOCKHOLDER 0.718*** 0.9% 0.316*** 0.3%

(12.19) (3.47)

SIZE -0.131*** -0.1% 0.233*** 0.1%

(-6.86) (7.64)

INDUSTRY 0.110** 0.1% 0.165* 0.2%

(2.19) (1.93)

IA_LEVERAGE 0.144 0.0% 0.385** 0.0%

(1.38) (2.18)

ARET 0.054 0.1% -0.073 0.0%

(1.40) (-0.97)

LOSS 0.310*** 0.7% 0.250** 0.0%

(4.23) (2.17)

IA_ROA 2.442*** 0.7% -1.123 -0.1%

(7.29) (-1.46)

IA_ROA × BELOW_IA_ROA -3.173*** -0.9% 1.035 0.0%

(-8.20) (1.19)

BELOW_IA_ROA -0.154** -0.2% 0.143 0.0%

(-1.96) (1.16)

Year fixed effects Included

Included

N 75,479

Pseudo R2 4.7%

Test: IA_ROA: (1) = (2) χ2 = 18.41***

Test: IA_ROA + IA_ROA × BELOW_IA_ROA: (1)=(2) χ2 = 2.37

Notes to Table 8:

This table presents the test of the association between earnings and probability of takeover by publicly held

acquirers’ transaction decisions, which include relative target size and time to complete the transaction. The sample

includes all observations in Compustat with the required data available and excludes financial firms. We use a

multinomial logistic regression to estimate the equation. In Panel A, the dependent variables is a categorical variable

equal to one if a firm receives a completed takeover bid in year t+1 by a publicly held acquirer where the relative

transaction size is small (Acq_Low_Rel_Size), equal to two if a firm receives a completed takeover bid in year t+1

by a publicly held acquirer where the relative transaction size is large (Acq_High_Rel_Size), equal to three if a firm

receives a completed takeover bid in year t+1 by a private acquirer (Private), and zero if a firm is not acquired. In

67

Panel B, the dependent variables is a categorical variable equal to one if a firm receives a completed takeover bid in

year t+1 by a publicly held acquirer where the time between the announcement and completion of the acquisition is

short (Acq_Short_Interval), equal to two if a firm receives a completed takeover bid in year t+1 by a publicly held

acquirer where the time between the announcement and completion of the acquisition is long (Acq_Long_Interval),

equal to three if a firm receives a completed takeover bid in year t+1 by a privately acquirer (Private), and zero if a

firm is not acquired. Variable definitions are provided in Appendix B. Variable definitions are provided in Appendix

B. We include year fixed effects. Standard errors used to calculate z-statistics, presented in parentheses, are White

adjusted and clustered by firm. The marginal effects column presents change in the probability of takeover for a one

standard deviation increase in the variable, or a change from 0 to 1 for an indicator variable, with all other

independent variables taking the mean value. At the bottom of the table we present a test of whether the coefficients

on IA_ROA and IA_ROA + IA_ROA × BELOW_IA_ROA are significantly different in columns 1 and 2. *, **, and

*** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively.

68

Table 9

Probability of takeover using firms’ three-year average earnings: Logistic regression

Variable

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

All All BELOW_IA_ROA3 = 0 BELOW_IA_ROA3 = 1

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

Coefficient

(z-statistic)

Marginal

Effect

IA_Q3 -0.125*** -0.5%

-0.132*** -0.5%

-0.118*** -0.4%

-0.160*** -0.8%

(-5.23)

(-5.44)

(-3.70)

(-3.85)

IA_PP&E3 -0.309** -0.2%

-0.309** -0.2%

-0.284* -0.2%

-0.364* -0.3%

(-2.44)

(-2.43)

(-1.76)

(-1.81)

IA_Ln(CASH)3 0.013 0.1%

0.013 0.1%

0.002 0.0%

0.017 0.2%

(0.77)

(0.74)

(0.08)

(0.64)

BLOCKHOLDER3 0.748*** 2.7%

0.755*** 2.7%

0.735*** 2.3%

0.768*** 3.4%

(12.67)

(12.71)

(9.61)

(8.13)

SIZE3 -0.141*** -1.1%

-0.140*** -1.1%

-0.160*** -1.1%

-0.083** -0.8%

(-7.12)

(-7.06)

(-6.49)

(-2.55)

INDUSTRY3 0.221*** 0.8%

0.215*** 0.8%

0.288*** 0.9%

0.096 0.4%

(3.06)

(2.97)

(3.15)

(0.82)

IA_LEVERAGE3 0.562*** 0.4%

0.578*** 0.4%

0.600*** 0.4%

0.568*** 0.5%

(5.48)

(5.60)

(4.10)

(3.92)

ARET3 -0.178*** -0.2%

-0.169*** -0.2%

-0.201** -0.2%

-0.177* -0.3%

(-2.72)

(-2.59)

(-2.36)

(-1.72)

LOSS3 0.235*** 0.8%

0.246*** 0.9%

0.248*** 0.8%

0.285*** 1.2%

(4.28)

(3.99)

(2.90)

(3.11)

IA_ROA3 0.395** 0.2%

0.580** 0.3%

0.894** 0.4%

-0.106 -0.1%

(1.99)

(2.10)

(2.32)

(-0.36)

IA_ROA3 × BELOW_IA_ROA3

-0.367 -0.2%

(-1.48)

BELOW_IA_ROA3

-0.038 -0.1%

(-0.59)

Year fixed effects Included

Included

Included

Included

N 59,237

59,237

39,538

19,699

Pseudo R2 3.7% 3.8% 4.4% 2.9%

Test: IA_ROA3 + IA_ROA3 × BELOW_IA_ROA3 χ2 = 0.91

69

Notes to Table 9:

This table presents the test of the association between three-year average earnings and probability of takeover. The sample includes all observations in Compustat

with the required data available and excludes financial firms. The dependent variable is TAKEOVERi,t, an indicator variable equal to one if firm i receives a

completed takeover bid in fiscal year t+1. Variable definitions are provided in Appendix B. We include year fixed effects. Standard errors used to calculate z-

statistics, presented in parentheses, are White adjusted and clustered by firm. The marginal effects column presents change in the probability of takeover for a

one standard deviation increase in the variable, or a change from 0 to 1 for an indicator variable, with all other independent variables taking the mean value. At

the bottom of column 2 we present a test of whether the combined coefficient IA_ROA + IA_ROA × BELOW_IA_ROA is significant. *, **, and *** denote two-

tailed statistical significance at 10%, 5%, and 1%, respectively.

70

Table 10

Post-Acquisition Persistence of Target’s Earnings

Variable

(1) (2)

(3) (4)

(5) (6)

ACQ_ROAt+1 ACQ_ROAt+2 ACQ_ROAt+3

BELOW_

IA_ROA = 0

BELOW_

IA_ROA = 1

BELOW_

IA_ROA = 0

BELOW_

IA_ROA = 1

BELOW_

IA_ROA = 0

BELOW_

IA_ROA = 1

ACQ_ROA 0.512*** 0.829***

0.342*** 0.588***

0.243*** 0.320***

(5.18) (3.20)

(3.36) (3.76)

(3.90) (4.06)

ROA 0.256*** 0.112

0.235*** 0.027

0.131* 0.059*

(3.71) (1.11)

(2.88) (0.71)

(1.87) (1.78)

ACQ_SIZE 0.011*** 0.031***

0.010*** 0.015***

0.010*** 0.018***

(4.28) (3.21)

(4.10) (3.88)

(4.09) (3.99)

ACQ_Q 0.004 -0.001

0.001 -0.026

-0.002 -0.011*

(0.78) (-0.14)

(0.28) (-1.64)

(-0.67) (-1.94)

ACQ_LEV 0.036 -0.008

0.038 0.053

0.008 0.010

(1.24) (-0.05)

(1.44) (1.52)

(0.40) (0.32)

SIZE -0.005** -0.009

-0.001 -0.005

0.000 -0.008*

(-1.98) (-1.21)

(-0.28) (-0.74)

(0.15) (-1.93)

Q -0.017*** -0.003

-0.018*** 0.008

-0.004 0.009

(-2.80) (-0.37)

(-3.72) (1.02)

(-1.57) (1.63)

LEVERAGE -0.043** 0.129

-0.032 0.024

-0.018 0.011

(-1.98) (1.50)

(-1.60) (1.01)

(-1.21) (0.45)

Year fixed effects Included Included

Included Included

Included Included

N 970 594

899 531

831 483

Adj. R2 25.5% 14.3% 18.3% 29.4% 14.6% 27.8%

Notes to Table 10:

This table presents the test of the association between acquirers’ post-acquisition earnings and both the acquirers’ and targets’ pre-acquisition earnings. The

sample includes all completed acquisitions of publicly held target firms with the required data available and excludes financial firms. Variable definitions are

provided in Appendix B. We include year fixed effects. Standard errors used to calculate t-statistics, presented in parentheses, are White adjusted and clustered

by firm. *, **, and *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively.

71

Table 11

Acquisition Announcement Returns

Panel A. Univariate analysis

Variable N Mean Std. Dev.

N Mean Std. Dev.

Mean

Diff. t-stat.

BELOW_IA_ROA = 0 BELOW_IA_ROA= 1

ACQ_SCAR 1,328 -0.018 0.081

811 -0.005 0.091

-0.013 (-3.34)

Panel B. Association between acquirers’ announcement returns and targets’ earnings

Variable

(1) (2)

ACQ_SCAR ACQ_SCAR

BELOW_IA_ROA 0.011**

(2.52)

D_BIG_BELOW_IA_ROA

0.023***

(3.18)

D_SMALL_BELOW_IA_ROA

0.016**

(2.32)

D_SMALL_ABOVE_IA_ROA

0.012**

(2.19)

ACQ_ROA -0.002 -0.000

(-0.08) (-0.00)

ACQ_Q -0.003* -0.003

(-1.76) (-1.60)

ACQ_SIZE -0.001 -0.001

(-0.89) (-0.77)

DEAL_PCT -0.017*** -0.017***

(-3.23) (-3.23)

HOSTILE -0.011 -0.010

(-1.08) (-1.04)

CASHO 0.017*** 0.017***

(3.34) (3.35)

STOCKO -0.019*** -0.019***

(-3.05) (-2.97)

N_BIDS 0.011**

(2.52)

Year fixed effects Included Included

N 1,742 1,742

Adj. R2 6.1% 6.3%

Notes to Table 11:

This table presents the test of the association between acquirers’ announcement returns and targets’ earnings. Panel

A present the univariate test while Panel B presents the multivariable test. Variable definitions are provided in

Appendix B. We include year fixed effects. Standard errors used to calculate t-statistics, presented in parentheses,

are White adjusted and clustered by firm. *, **, and *** denote two-tailed statistical significance at 10%, 5%, and

1%, respectively.