for acquirers around deal announcements, implying that target size may proxy, among others,for the unobserved complexity inherent in large deals. We provide evidence in favor of thisinterpretation.

2012 Elsevier B.V. All rights reserved.

nce that large acquisitions destroymore value for acquiring companies. BusinessWeek (2002)h at l

Consulting Group (2007) shows that mega-deals priced atmore than $1 billion impair nearly twice as much value relative to smaller

Journal of Corporate Finance 20 (2013) 113

Contents lists available at SciVerse ScienceDirect

Journal of Corporate Finance

j ourna l homepage: www.e lsev ie r .com/ locate / jcorpf intransactions.2 Loderer and Martin (1990) argue that acquirers experience greater loses when buying large targets because they aremore likely to pay too much. This is possible if excessively confident managers, that overestimate their ability to extract acquisitionbenefits and thus overpay (Hayward and Hambrick, 1997; Malmendier and Tate, 2008; Roll, 1986), tend to bid for larger targets.Moreover, top executives may pay heftier premia for large firms because they often provide particularly high private benefits(Grinstein and Hribar, 2004; Harford and Li, 2007; Loderer and Martin, 1990; Morck et al., 1990).3

Nevertheless, there are several reasonswhy acquirerswould, instead, pay lower premiums for large targets. The high value-at-stakeassociated with buying large firms, for instance, can result in more accurate valuations or make managers and their boards morereports that 61% of merger deals wort The authors would like to thank Chris Brooks, Lubparticipants at the 2011 European Financial ManageManagement Association Annual Meeting, and theKyriacopoulos Chair in Finance. All errors are our own Corresponding author at: Department of Banking an

E-mail address: kfuller@bus.olemiss.edu (K.P. Fulle1 Mergers: Why Most Big Deals Don't Pay Off, Bus2 A Brave New World of M&A: How to Create Valu3 Besides, all else equal, large targets may have stro

0929-1199/$ see front matter 2012 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jcorpn.2012.10.006east $500 million end up being costly for shareholders.1 Similarly, research by BostonAcquirer returns

1. Introduction

There is compelling empirical evideAvailable online 16 November 2012

JEL classification:G14G30G34

Keywords:Public acquisitionsTarget sizePremiumthe overpayment potential is lower in acquisitions of large targets. Yet, they still destroymore valueDeal size, acquisition premia and shareholder gains

George Alexandridis a, Kathleen P. Fuller b,, Lars Terhaar a, Nickolaos G. Travlos c

a ICMA Centre, Henley Business School, Reading University, United Kingdomb School of Business, University of Mississippi, USAc ALBA Graduate Business School, Greece

a r t i c l e i n f o a b s t r a c t

Article history:Received 27 February 2012Received in revised form 26 October 2012Accepted 31 October 2012

This study examines the contradictory predictions regarding the association between the premiumpaid in acquisitions and deal size. We document a robust negative relation between offer premiaand target size, indicating that acquirers tend to pay less for large firms, notmore.We also find thatomir Litov, Christos Mavis, Nathalie Moyen, Evangelos Vagenas-Nanos, Hongxia Wang, Feng Zhang andment Association Conference, the 2011 Northern Finance Association Conference, the 2011 Financial2011 Southern Finance Association Meeting. Travlos acknowledges nancial support from the Kitty.d Finance, School of Business, University of Mississippi, University, MS, 38677, USA. Tel.: +1 662 915 5463.r).inessWeek, 14 October 2002.e from Mergers and Acquisitions, The Boston Consulting Group, July 2007.nger negotiating power and thus extract higher offers from acquirers.

ll rights reserved.

hesitant to offer hefty premiums. Along similar lines, the heightened complexity of integrating large businesses can make expectedsynergies from the combinationmore uncertain and, therefore, lead to acquirersmaking less generous offers in an effort tomitigate the

2 G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113additional potential complexity costs. The difficulty to assimilate large targets into a combined organization might also result in asmaller pool of potential acquirers. The fact that there are fewer acquirers for large targets (Gorton et al., 2009) can reduce competitionandmitigate the winner's curse problem, resulting in lower acquisition premia (Alexandridis et al., 2010). Besides, large firms tend tobe subject to less concentratedmanagerial ownership (Demsetz and Lehn, 1985) and, as a result, their insiders aremore likely to acceptsmaller premia (Bauguess et al., 2009).

Given the conflicting predictions regarding the association between the size of M&A deals and offer premiums, we empiricallyexamine its direction using a sample of 3691US public acquisitions announced between 1990 and2007. In addition,we further explorethe relationship between deal size, overpayment potential and acquisition losses. Our results show that large firms are acquired at asignificant discount relative to small ones. The average offer premiumpaid for targets in the top size tercile (36.5%) is 30% lower than fortargets in the bottom tercile (52.6%). The regression analysis shows that the offer premium decreases by 6.8% for every standarddeviation increase in the logarithm of our target size measure. This strong negative relationship persists through time and acrossindustry sectors, regardless of the premium measure employed and after controlling for differences in known quantifiable premiumdeterminants. Thus, we attribute it to the high value-at-stake and/or potential integration complexity associated with these deals.

Our initial evidence does not appear to be consistent with the perception that acquirers are more likely to overpay for largefirms. However, the offer premium itself is not a clean overpayment measure. Using a proxy proposed in recent behavioralcorporate finance literature, we examine whether the overpayment potential in large deals is stronger. Baker et al. (2012)document evidence of peak-price-driven bidding, reflected in a strong positive relation between target price 52-week highs andoffer premia. They find evidence that investors are more likely to perceive such anchoring behavior as overpaying and suggestthat the further the target's pre-announcement price from its 52-week high, the greater the overpayment potential. Based on thispremise, we find that the perceived overpayment is in fact lower for large targets. Further, Malmendier and Tate (2008) arguethat managerial over-optimism tends to result in excessive acquisition offers. We show that a CEO overconfidence measure basedon the timing of executive options' exercise reflects similar levels of managerial overconfidence for acquirers of small and largetargets. Consequently, the evidence altogether is not supportive of more systematic overpayment in large deals or a positiveassociation between acquisition size and paying too much.

Further, we explore the relationship between target size and acquisition returns. Using several value gain measures, we findthat losses to acquiring firms increase with the size of the target, despite the payment of lower control premia. In particular,acquisitions of large targets are associated with 2.37% lower announcement returns and a 35.38% lower return-per-dollar-spent,relative to deals involving small targets. This relation between target size and acquisition returns is economically significant; forevery standard deviation increase in our target size measure, the acquirer announcement return decreases by 1.1%. Moreimportantly, this inverse relation persists after controlling for the offer premium, an overpayment instrumental variable based onthe target's 52-week high, as well as for other known acquirer return determinants.

A plausible explanation for the documented relationships is associated with the relatively greater complexity inherent in largedeals and its connection with post-merger integration problems and costs that can impede the realization of potential synergies.4

Shrivastava (1986) argues that post-merger integration entails the physical, procedural, managerial and/or cultural combinationof firms, which further involve the integration of organizational systems and processes, corporate cultures, performance andreward systems as well as people. Shrivastava (1986) and Hayward (2002) also suggest that post-merger integration is a functionof organizational size and Ahern (2010) reports that integration costs are heftier in large deals. Particularly compelling is theobservation in a recent report by Deloitte (2012) that as much as 70% of the value erosion in M&A deals is believed to beassociated with inadequate post-merger integration. In fact, poor assimilation of corporate structures, operations and strategiesled to high profile failures in the AT&T-NCR, DaimlerChrysler, AOLTime Warner and SprintNextel mergers, to mention a few,even though the economic motives for the deals were clear. Accordingly, Harding and Rovit (2004) observe that top deal makersfavor a succession of smaller deals over complex megamergers. If market participants are, on average, more averse to dealcomplexity than corporate boards then they may sell-off firms acquiring large targets more, despite the lower premia offered.

According to our complexity hypothesis, an acquisition of a large target by a large acquirer can have much more of a negativeimpact on acquirer returns than a deal of comparable relative size that involves two relatively small firms. In support of this, wefind that the well documented negative effect of the target-to-bidder relative size on acquirer returns (Fuller et al., 2002) is onlysignificant when the target firm is particularly large. Moreover, the reported negative association between acquirer size and gainsto acquisitions (Moeller et al., 2004) appears to be driven by target size as deals made by large acquirers tend to result in moreextensive losses only when they involve large targets.

Our study offers important contributions to the existing literature. First, we document a robust size effect in the market forcorporate control; acquisitions of large targets are associated with significantly lower premiums. In fact, we find this negativerelation to be more pronounced than the effects of other known premium determinants. Second, our analysis shows that apositive relationship between deal size and overpayment is unlikely and highlights the roles of the high value-at-stake and dealcomplexity inherent in large deals as possible reasons for the lower premia offered. Third, we show that, despite the payment oflower premiums and the smaller overpayment potential, acquisitions of large targets destroy more value for acquirers. This suggeststhat large acquisitions aremore likely to fail in delivering the expected synergies and that additional complexity associatedwith these

4 Limited strategic rationale is also a frequently quoted reason for deal failure, although there is no reason to suspect that strategic t is poorer as the size of thedeal increases.

Table 1Sample distribution by announcement year and target size. The sample includes completed, domestic U.S. mergers and acquisitions of public targets by privateand public acquirers, announced between 1990 and 2007. Deals have transaction values of at least $1 million and the acquiring firm owns less than 10% of thetarget shares prior to the deal announcement and more than 50% upon completion of the transaction. Target firms are listed on NYSE, AMEX or NASDAQ and havedata available on CRSP and Compustat. The sample is split into three subgroups (Small, Medium and Large) based on MRTS (Market-Relative Target Size) which is

3G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113measured by the ratio of the market capitalization of the target firm (in $million) 1 month prior to the acquisition announcement and the median marketcapitalization of all Compustat firms at the announcement year.

MRTS

All Small Medium Large

Mean 3.94 0.14 0.64 11.06Median 0.58 0.13 0.58 3.60Min 0.004 0.004 0.288 1.204Max 387.7 0.287 1.198 387.7

Year All Small Medium Large

1990 71 28 22 211991 91 36 25 301992 95 34 37 241993 137 51 49 371994 188 61 74 531995 232 84 77 711996 262 86 95 811997 361 97 147 1171998 363 123 118 1221999 377 108 133 1362000 304 92 88 1242001 226 108 62 562002 135 63 45 272003 164 77 53 342004 155 56 49 502005 169 43 56 70transactions tend to outweigh any potential benefits from paying less. Finally, our work complements the literature documentingnegative effects of acquirer size and target-to-acquirer relative size on acquisition gains, as we demonstrate that these are, to a largeextent, driven by the large target size.

The remainder of this paper is organized as follows. Section 2 describes the data used in our investigation and the sample statistics.Section 3 examines the association between target size, offer premiums and potential overpayment. Section 4 explores the relationshipbetween target size and acquirer gains as well as its potential determinants. Finally, Section 5 provides concluding remarks.

2. Data and sample statistics

2.1. Sample criteria and distribution

The sample of acquisitions is from Thomson Financial SDC and includes U.S. completed deals announced between 1990 and2007, where the target is publicly-listed on NYSE, AMEX or NASDAQ and the acquirer is either public or private. Spin-offs,recapitalizations, self-tenders, repurchases, minority stake purchases, acquisitions of remaining interest, exchange offers andprivatizations are omitted. The transaction value is at least $1 million and the acquirer owns less than 10% of the target's sharesprior to the acquisition announcement andmore than 50% at the deal completion. Data on public acquirers and targets is availableon CRSP and Compustat. The final sample consists of 3691 acquisitions of listed targets.

The sample is partitioned in three equal terciles (small, medium and large) based on the size of the target firm relative to amarket median. Market-Relative Target Size (MRTS) is the market capitalization of the target 1 month prior to the acquisitionannouncement divided by the median market value of all Compustat firms at the announcement year.5

Table 1 presentsMRTS statistics and the sample distribution by announcement year for the threeMRTS groups. The number oftransactions in our sample ranges from 71 in 1990 to 377 in 1999. Also, the sample covers the aftermath of the decade of hostilebust-up takeovers in the 1980s (Mitchell and Mulherin, 1996), the decade of deregulation in the 1990s (Andrade et al., 2001) as

5 The direction of our results remains unchanged when i) partitioning the sample by an Industry-Relative Target Size (IRTS) measure, dened as the ratio of thetarget's market value to the median market value of all Compustat rms within the corresponding Fama/French industry at the announcement year, ii) usingCRSP market capitalization terciles when dividing target rms into size subsets, iii) partitioning the sample into ve, instead of three, size groups, iv) using targettotal assets instead of market value as a measure of size and v) using target-to-acquirer relative size instead of target size.

2006 176 41 55 802007 185 42 46 97Total 3691 1230 1231 1230

well as the recession following the burst of the technology bubble and the lead-up to the sixth merger wave propagated primarilyby high corporate cash balances and low-interest financing (Alexandridis et al., 2012). The market capitalization of the mediantarget in the small (large) MRTS tercile is 14% (360%) of the market value median in the corresponding year. The marketcapitalization of TimeWarner, the largest target in the sample, was approximately 388 times larger than the median market valueof Compustat firms in 2000.

The sample distribution shows that M&A activity built-up progressively throughout the 1990s and reached record levels in1999. During this period, the number of transactions was roughly equally distributed across the three different target size subsets.

a market value of approximately $3.1 billion (35.9 million) and is acquired at a deal value of $4.5 billion (81.9 million). The large subset

calculated as the offer premium (PREM) minus the mean premium paid for targets in the same industry (based on Fama and

4 G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113French 49 industries) at the announcement year and the year prior to the acquisition announcement, iii) PREMVW is the ratio ofthe offer price to the 30-day (45,15) volume-weighted average of the target's trading price, iv) PREMR is the cumulativeabnormal return to target shareholders calculated over a 190-day (63,+126) window around the deal announcement, as inSchwert (2000) and v) TCAR3 is the cumulative abnormal return to target shareholders calculated over a 3-day window aroundthe deal announcement.9 Remarkably, the mean premium (PREM) for large targets is only 36.5% compared to 52.6% for small

6 HIVAL is the percentage of deals taking place in a high valuation market, based on the de-trended, monthly P/E ratio of the S&P500 Index as in Bouwman et al.(2009).7 Ownership data is collected from denitive 14A lings (proxy statements) of the target rm, led with the U.S. Securities and Exchange Commission (SEC)

preceding the acquisition announcement. The ownership sample covers the period 19982007.8 As CEOs in our sample may undertake both acquisitions of large and small targets, we also compute this proxy for single acquirers and nd similar results.9 PREM and PREMVW are reported for observations between zero and two, as in Ofcer (2003). Our results are similar when this premium restriction is not

imposed. Market model parameters for PREMR and TCAR3 are estimated over a 200-day interval preceding the event window using benchmark returns of theCRSP value-weighted market index. Alternative parameter estimation windows do not signicantly affect our results.encompasses 92% of the total value spent on all deals in our sample. The acquirermarket capitalization (ASIZE), measured 1 month priorto the acquisition announcement, increaseswith target size, while large targets account for approximately 55% of the size of the acquirercompared to amean target-to-bidder relative size (RELSIZE) of 28% for small-target deals. The acquirer and targetmarket-to-book equityratios (AMTB and TMTB) suggest that both acquirers and targets involved in large acquisitions are relatively more highly valued (Jensen,2005; Moeller et al., 2005). There is also some evidence that more large targets are acquired during high market valuation periods(HIVAL).6

Acquisitions by private acquirers (PRIVATE) aremore concentrated in the small-target subset, as private companies are likely to bemore constrained in raising the necessary capital for acquisitions of sizeable targets than trade buyers. Further, an unsolicited offer(HOSTILE) is more likely when the target is large, although hostile offers are generally quite rare (1.3% of the sample). Large targetsalso seem to attract less competing bids (COMP) than smaller firms,which is consistentwith our competition conjecture. They are alsomore likely to be acquired with stock (ALLSTOCK) while small targets with cash (ALLCASH), in line with DeAngelo et al. (1984) andFaccio and Masulis (2005). Target inside ownership (INSIDE) is the percentage ownership pertaining to the target's directors andexecutives excluding the holdings of directors representing outside institutions, corporations and/or individual blockholders,following Bauguess et al. (2009).7 As originally hypothesized, insiders of large targets own significantly smaller stakes in their firmsrelative to directors and executives of small targets where managerial ownership is more concentrated. Moreover, large targets areless likely to be financially distressed than small targets, based on Altman (1968) Z-Scores (DISTRESS).

HUBRIS is the percentage of acquiring firms with top executives that do not exercise vested stock options although they are atleast 67% in-the-money, as in Malmendier and Tate (2005). Data on executive stock options is from proxy statements (DEF 14A)filed with the U.S. Securities and Exchange Commission (SEC) by the acquiring firm. Based on this measure, the percentage ofoverconfident CEOs is very similar for small and large-target deals.8 Hence, CEOs that make large acquisitions are not more oftenhubris infected. If overconfident executives have a tendency to overpay (Roll, 1986) our result may indicate that theoverpayment potential in large deals is not stronger, although the hubris measure may only capture one dimension of theoverpayment likelihood.

2.3. Premium statistics

Table 2 also reports acquisition premiums. We estimate five alternative premium measures: i) PREM is the ratio of the offerprice to the target's share price 1 month prior to the acquisition announcement, ii) IAPREM is the industry adjusted premium,The level-off in activity amid the 20012002 recessionwas associatedwith a significant decline in large-target deals as participants inthe market for corporate control refrained from committing funds towards acquisitions of larger companies. The takeover marketstarted recovering from its trough in 2003 and then approached activity levels approximately half of those achieved during theoverheatedmarket of the technology bubble. Inmore recent years of the sample, the number of large deals increased again, while theshare of transactions involving small targets gradually declined after 2001.

2.2. Deal and rm statistics

Table 2 reportsmean sample statistics for the threeMRTS subsets (small,mediumand large). Targetmarket- and total-asset-value aswell as the transaction value (all in 2007dollars) consistently increasewithMRTS. The average target in the large (small)MRTS-grouphas

Table 2Descriptive statistics. The sample includes domestic U.S. M&As of public targets by private and public acquirers between 1990 and 2007. Deal value is at least$1 m and the acquirer owns less than 10% of the target prior to the deal and more than 50% upon its completion. Targets are listed on NYSE, AMEX or NASDAQ and

5G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113have data available on CRSP and Compustat. The sample is split into three groups (Small, Medium and Large) based on MRTS (Market-Relative Target Size) whichis the ratio of the target's market value 1 month prior to deal announcement and the median market value of all Compustat firms at the announcement year. N isthe sample size. ASIZE (TSIZE) is the market capitalization of the acquirer (target) 1 month prior to deal announcement. Target total assets (TASSETS) are fromthe year end prior the deal announcement. Transaction value (TV), TASSETS, TSIZE and ASIZE are in million 2007 dollars. Relative size (RELSIZE) is the ratio ofASIZE and TV. TMTB (AMTB) is the market-to-book equity ratio of the target firm (acquirer). HIVAL is the percentage of deals taking place in a high valuationmarket, based on the de-trended, monthly P/E ratio of the S&P500 Index. PRIVATE is the percentage of deals where the acquirer is a private firm. HOSTILE is thepercentage of hostile deals. COMP is the percentage of deals with multiple bids. ALLCASH (ALLSTOCK) is the percentage of deals financed with 100% cash (stock).INSIDE is the percentage ownership of all directors and executives of the target excluding those representing outside institutions, corporations and individuals.DISTRESS is the percentage of target firms with Altman (1968) Z-scores below 1.80. HUBRIS is the percentage of acquiring managers with vested, unexercisedstock options that are at least 67% in-the-money. PREM is the ratio of the offer price to the target share price 4 weeks prior to the deal announcement. IAPREM isthe premium (PREM) minus the mean premium paid for targets in the same Fama/French industry at the announcement year and the year prior to the deal.PREMVW is the ratio of the offer price to the 30-day (45,15) volume-weighted average of the target's trading price. PREM and PREMVW are reported forobservations between 0 and 200%. PREMR is the cumulative abnormal return to target shareholders calculated over a 190-day (63,+126) window around thedeal announcement. TCAR3 is the target cumulative abnormal return calculated for a 3-day (1,+1) event window around the deal announcement. Differencesbetween Large and Small subsets are based on two-sample t-tests for means. a, b, and c denote statistical significance at the 1%, 5% and 10% levels, respectively.

N All Small Medium Large Difference

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

Panel A: deal and firm characteristicsatargets. Thus, acquirers pay about 30% lower premiums for large targets and the difference is statistically significant at the 1%level. Differences for IAPREM, PREMVW and PREMR are very similar.

Particularly compelling is the unreported observation that in 18 of the 20 largest transactions, the offer premium is below theaverage premium paid for targets in the same industry/year where industry classifications are based on the Fama/French 49industries. In unreported univariate tests, we also group target firms using the Fama/French 5 industry classification and augmentthis to examine the financial sector separately. On average, the lowest premia (PREM) are paid for targets within the financial(39.6%) andmanufacturing (40.8%) sectors, while the highest are paid for technology targets (52.3%). However, irrespective of theindustry classification, we find that large targets receive significantly lower premiums.

Fig. 1 illustrates premium differentials for small and large targets over time. The figure shows that although control premiumsvary throughout the sample period, they remain smaller in acquisitions of large firms.10 The smallest difference in premiumsbetween large and small deals is observed in the second half of the 1990s and after 2003. Interestingly, premiums for small targetsincrease significantly between 2000 and 2002, whereas those for large targets drop substantially. This may reflect the reluctanceof acquirers to commit funds towards acquisitions of larger companies during recessions and their preference for small targets forwhich they are seemingly willing to pay more. However, there is a considerable drop in premiums for small targets after 2002.

Overall, the reported statistics are consistent with the conjecture that acquirers pay, on average, significantly lower premia forlarge targets, which in turn implies that they are not more likely to overpay. In the next section, we further examine the relation

10 Patterns for medians are also very similar.

MRTS 3691 3.94 0.14 0.64 11.06 10.92TSIZE 3691 1101.0 35.9 171.3 3096.7 3060.8a

TVSum 3691 6,048,510 81,944 356,632 5,609,934 5,527,990a

Mean 1638.7 66.6 289.7 4560.9 4494.3a

TASSETS 3661 3070.2 173.4 583.7 7575.8 7402.4a

ASIZE 3037 13,758.7 4038.7 10,200.0 25,379.3 21,340.6a

RELSIZE 3037 39.90 28.28 34.87 54.53 26.25a

TMTB 2904 3.09 1.80 2.79 4.40 2.60a

AMTB 2931 4.67 3.58 4.31 5.91 2.33a

HIVAL 3691 38.93 36.42 41.19 39.19 2.77c

PRIVATE 3691 13.22 19.67 11.62 8.37 11.30aHOSTILE 3691 1.30 0.57 1.14 2.21 1.64a

COMP 3691 29.24 38.74 30.30 24.52 14.22aALLCASH 3691 31.40 41.46 32.58 20.16 21.03aALLSTOCK 3691 42.08 37.07 44.60 44.55 7.48a

INSIDE 2196 16.63 22.79 16.75 10.88 11.91aDISTRESS 2022 25.72 37.82 22.80 18.44 19.38aHUBRIS 1232 61.86 61.82 58.90 64.02 2.20

Panel B: premium statisticsPREM 3228 43.76 52.57 43.02 36.50 16.06aIAPREM 2987 0.26 7.24 0.80 6.42 13.66aPREMVW 3246 42.23 51.23 41.28 35.09 16.14aPREMR 3656 35.58a 44.67a 31.89a 29.50a 15.17aTCAR3 3687 20.32a 24.46a 19.76a 16.74a 7.72a

90%PREM-

MeanSmallLarge

6 G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113between target size and premiums in a multivariate framework and discuss its determinants. We also investigate whether there isgreater overpayment potential in large deals, despite the payment of lower premia.

3. Target size, acquisition premiums and potential overpayment

3.1. Premium regressions

In this section, we examine whether the inverse relation between target size and offer prices can be explained by other knownpremium determinants. Table 3 reports estimates fromOLS regressionswhere PREM is the dependent variable in specifications 1 to 4,PREMVW in regression 5, PREMR in regression 6 and the 3-day (1,+1) target abnormal return TCAR3 in regression 7. The mainexplanatory variable in all regressions is the natural logarithm ofMarket-Relative Target Size (LNMRTS). Its coefficient is negative andstatistically significant at the 1% level. In regression 1, a one standard deviation increase in LNMRTS reduces the offer premium byapproximately 6.8%.

Bargeron et al. (2008) show that unlisted acquirers pay lower premiums than their publicly-listed counterparts. Although privateacquirers are typically involved in smaller deals, making it unlikely that the listing status drives our results, we still control for thislisting effect. The indicator variable PRIVATE, taking the value of one when the acquirer is unlisted and zero otherwise, has a negativeand statistically significant coefficient in regression specification 2, corroborating that private acquirers pay lower premiums.Moreover, takeover premiums tend to increasewith the degree of competition in themarket for corporate control (Alexandridis et al.,2010; Walkling and Edminster, 1985). Thus, we include two competition variables in regressions 27, namely ACTIVITY and COMP.ACTIVITY is the ratio of the number of listed firms targeted in successful acquisitions within an industry at the announcement year tothe number of all firms listed in Compustat within the corresponding target industry and announcement year. Its coefficient is

10%20%30%40%50%60%70%80%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Year

Fig. 1. Acquisition premium over time: small vs. large targets. The sample includes completed, domestic U.S. mergers and acquisitions of public targets by private andpublic acquirers, announced between 1990 and 2007. Deals have transaction value of at least $1 million and the acquirer owns less than 10% of the target shares prior tothe announcement and more than 50% upon completion of the transaction. Target firms are listed on either NYSE, AMEX or NASDAQ and have data available on CRSPand Compustat. The sample is split into three groups (Small, Medium and Large) based onMRTS (Market-Relative Target Size) which is the ratio of themarket value ofthe target (in $million) 1 month prior to the acquisition announcement and the median market value of all Compustat firms at the announcement year. PREM is theratio of the offer price to the target share price 4 weeks prior to the acquisition announcement for observations between zero and two.positive and statistically significant in regressions 2 and 3 but turns insignificant when including industry and year fixed effects. Theindicator variable COMP, which controls for the presence of competing takeover bids, as reported in SDC, is statistically irrelevant.Wealso control for the level of takeover hostility using a binary variable capturing hostile/unsolicited offers (HOSTILE) and find thathostile acquisitions are associated with larger takeover premiums, as in Schwert (2000).

Statistics in Table 2 show that large (small) target firms are less likely to be acquired with cash (stock). Huang andWalkling (1987)and Savor and Lu (2009) document that premiums in cash-financed acquisitions are larger than those paid in share-for-sharetransactions, as target shareholders are to be compensated for the immediate tax implications of cash offers. The indicator variableALLCASH controls for this effect and is equal to one for pure-cash deals andzero otherwise. Results for the offer premium in regressions 1to 5 do not provide support for the tax compensation effect. Instead, it appears that cash payments are associated with a relativediscount.11 This may be driven by the lower likelihood of competing bids when the initial bidder opts for payment in cash (Berkovitchand Narayanan, 1990; Fishman, 1988, 1989) or by target shareholders requiring larger premia to accept the bidder's equity asacquisition currency. Returns for target firms (TCAR3) are higher in cash offers, which may merely reflect the higher likelihood of dealcompletion.12

We also add a corporate diversification indicator variable (DIVERS) to control for the fact that higher premiums are normallyoffered in intra-industry mergers (Officer, 2003). However, the coefficient of this variable that takes the value of one if theacquirer and target firms have different 2-digit SIC codes and zero otherwise, is statistically insignificant in most specifications.Table 2 indicates that a greater fraction of large deals are announced during periods of high market valuation, relative to small

11 Notably, offer premia for larger targets are lower, despite the fact that they are more frequently paid for with equity, as reported in Table 2.12 See for example Fishman (1989), Jennings and Mazzeo (1993) and Campa and Hernando (2009).

Table 3Premium regressions. The table reports OLS regression estimates of acquisition premium on the natural logarithm of Market-Relative Target Size (LNMRTS) and otherdeal, firm and market characteristics. The sample of acquisitions meets the criteria described in Table 1. PREM is the ratio of the offer price to the target share price4 weeks prior to the deal announcement, with observations between zero and two. PREMVW is the ratio of the offer price to the 30-day (45,15) volume-weightedaverage of the target's trading price, reported for observations between zero and two. PREMR is the cumulative abnormal return to target shareholders calculated overfor a 190-day window (63,+126) around the deal announcement. Target abnormal returns (TCAR3) are calculated for a 3-day (1,+1) announcement window.Market-Relative Target Size (MRTS) is measured by the ratio of the market value of the target (in $million) 1 month prior to the deal announcement and the median

7G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113market value of all Compustat firms at the announcement year. PRIVATE is an indicator variablewith value of one for acquisitions by unlisted acquiring firms. ACTIVITYis the ratio of the number of listed firms targeted in successful acquisitions within an industry and announcement year to the number of all firms listed on Compustatwithin the corresponding target industry and year. COMP, HOSTILE, CASH, DIVERS and HIVAL are indicator variables equal to one for acquisitions with multiple bids,with unsolicited bids, financed 100% in cash, for transactions where the acquirer and target operate in different industry sectors (2-digit SIC code), and for deals takingplacewithin a high valuationmarket, based on the de-trended, monthly P/E ratio of the S&P500 Index, respectively. lnTMTB and lnAMTB is the natural logarithmof thetarget and acquirermarket-to-book equity ratio, respectively. lnMRAS is the natural logarithm ofMarket-Relative Acquirer Size (MRAS), measured as acquirer marketvalue 1 month before the deal announcement over themedianmarket value of all Compustat firms in the announcement year. INSIDE is thepercentage ownership heldby all directors and executives of the target firm and their families minus the director ownership representing outside institutions, corporations and individuals. Someregression models also control for industry (INDUSTRY FE) and year fixed effects (YEAR FE), the coefficients of which are not reported. p-values are reported inbrackets; a, b, and c denote statistical significance at the 1%, 5% and 10% levels, respectively.

PREM PREMVW PREMR TCAR3

(1) (2) (3) (4) (5) (6) (7)deals. Similarly, Table 1 shows that the market for corporate control was most active around the peak of the technology bubble inthe 90s, a high market valuation period. Bouwman et al. (2009) report that acquirers pay lower premia during periods of highmarket valuation. We include an indicator variable (HIVAL), which is equal to one if the acquisition is announced within ahigh-valuation market, based on a de-trended monthly P/E ratio of the S&P500, and zero otherwise. In line with Bouwman et al.(2009), estimates of HIVAL suggest that acquirers pay lower premiums during high market valuation periods.13 Dong et al. (2006)document that acquirer and target firm valuations are also associated with the size of the premium. They show, for example, thathighly-valued targets receive lower bid premia and are, thus, subject to lower abnormal returns. As large targets tend to behighly-valued, as shown in Table 2, the negative relationship between target size and takeover premia may be driven by firmvaluation. Thus, we include the natural logarithms of the acquirer and target market-to-book ratios (lnTMTB and lnAMTB) in theregressions. As anticipated, target firm valuation has a negative impact on the offer premium while acquirer valuation has apositive effect.

13 Nathan and O'Keefe (1989) show a negative relation between acquisition premiums and the business cycle. If stock market performance moves ahead orparallel to the business cycle, then lower premiums will be paid in periods of positive market run-up. We measure market performance (RUNUP) using the buy-and-hold return of the S&P500 index for the 6 months (126,5) prior to the acquisition announcement. When we replace HIVALwith the buy-and-hold returnof the S&P500 index for the 6 months (126,5) prior to the acquisition announcement, we nd a negative relation between stock market performance and theoffer premium.

Intercept 0.424a 0.433a 0.343a 0.494a 0.496a 0.275a 0.247a

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)lnMRTS 0.041a 0.054a 0.063a 0.054a 0.051a 0.057a 0.039a

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)PRIVATE 0.114a

(0.000)ACTIVITY 1.030a 0.925a 0.048 0.143 1.159b 0.113

(0.000) (0.000) (0.894) (0.671) (0.019) (0.675)COMP 0.019 0.012 0.002 0.006 0.058 0.046

(0.534) (0.755) (0.963) (0.903) (0.409) (0.229)HOSTILE 0.168a 0.216a 0.147c 0.125 0.075 0.057

(0.001) (0.000) (0.083) (0.121) (0.541) (0.397)CASH 0.038b 0.075a 0.053b 0.046b 0.016 0.025

(0.031) (0.000) (0.032) (0.050) (0.642) (0.182)DIVERS 0.004 0.025 0.002 0.016 0.002 0.033b

(0.763) (0.136) (0.911) (0.454) (0.956) (0.043)HIVAL 0.081a 0.065a 0.039 0.078a 0.013 0.032

(0.000) (0.000) (0.188) (0.006) (0.753) (0.163)lnTMTB 0.038a 0.033a 0.041a 0.046a 0.048a

(0.001) (0.008) (0.001) (0.005) (0.000)lnAMTB 0.047a 0.023c 0.037a 0.064a 0.011

(0.000) (0.060) (0.001) (0.000) (0.224)lnMRAS 0.025a 0.019a 0.013b 0.033a 0.027a

(0.000) (0.005) (0.039) (0.000) (0.000)INSIDE 0.062 0.037 0.245a 0.099c

(0.363) (0.556) (0.007) (0.051)INDUSTRY FE NO NO NO YES YES YES YESYEAR FE NO NO NO YES YES YES YESN 3228 2369 1513 1038 1055 1172 1180R-Square 4.67% 10.22% 12.90% 23.93% 23.83% 20.18% 19.46%

Further, Moeller et al. (2004) argue that larger acquirers are more likely to overpay since managerial hubris is more of aproblem in large firms. Thus, we control for acquirer size by including the natural logarithm of the market-relative acquirer size(MRAS) in regressions 37. Its coefficient is positive and statistically significant in all specifications, in line with Moeller et al.(2004). Based on the reported estimates, the negative effect of the target size on premiums is nearly three times stronger than thepositive effect of acquirer size on premiums.14 Bauguess et al. (2009) document a positive link between target returns and insidemanagerial ownership. INSIDE is the percentage ownership of all directors and executives of the target excluding those thatrepresent outside institutions. While its coefficient is positive and insignificant in regressions 4 and 5, it is statistically importantin the remaining specifications as well as when regressed alone against each of the premium measures (in unreported

8 G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113regressions). In an unreported regression, we also include the stock option-based measure of CEO overconfidence (Holder67), asin Malmendier and Tate (2005), which significantly reduces the sample size, but find its effect to be insignificant. Finally, weinclude year and industry dummies (YEAR FE and INDUSTRY FE) to control for related fixed effects on the offer premium.

Overall, LNMRTS can independently explain more variation in acquisition premiums than any other single variable discussedabove. More importantly, the evidence here suggests that the inverse relation between target size and offer premiums cannot befully explained by known premium determinants. As a result, it may be also attributed to other residual and not easilyquantifiable factors. The high value-at-stake involved in acquiring a large target, for instance, may enhance managerial restraintand lead to less inflated valuations.15 Similarly, the potential complexity of integrating large targets and its relation with past highprofile merger failures may also make managers and their boards more cautious, resulting in lower offer premia.

3.2. Target size and overpayment potential

The strong negative association between target size and acquisition premiums makes a positive link between target size andoverpayment less likely. Yet, as the offer premium is not a clean overpayment measure, one cannot rule out entirely that acquirersmake more excessive offers in large deals. While actual overpayment is complex to estimate ex-ante, we use a measure ofpotential overpayment to provide some further insight into the relationship between acquisition size and overpayment.16 In arecent paper, Baker et al. (2012) argue that offer prices may also reflect psychological influences of largely irrelevant referencestock prices. They show that executives give special weight to the target's recent peak prices in valuations, reflected in a strongpositive relation between target price 52-week highs and offer premia. They also find evidence that investors tend to perceivesuch anchoring behavior or peak-price-driven bidding as overpaying and suggest that the further away the target'spre-announcement stock price from its 52-week high, the more likely the overpayment potential. Based on this premise, weassess whether the potential overpayment perception at the time of the deal announcement would be stronger for large firms byexamining the relation between target size and its pre-announcement price distance from a 52-week high reference point.Moreover, we explore whether the extent of reference price anchoring depends on target size. A finding pointing to similardistances of pre-announcement target prices from 52-week highs, coupled with comparable degrees of anchoring, for small andlarge targets, would indicate that investors are unlikely to perceive large deals as more expensive than small.

Table 4, Panel A reports mean offer prices and target price 52-week highs, both expressed as log percentage differences fromthe target 30-day pre-announcement stock price (OfferPREM and T52WkHiDIST, respectively) as in Baker et al. (2012).17 Onaverage, the pre-announcement target price distance from its 52-week high is significantly lower in large deals (26.96%) than insmall (40.84%). This suggests that the overpayment potential may be higher in small deals as the target 52 week-high is furtheraway from the pre-announcement price. However, for this to hold true, the degree of peak-price-driven bidding should becomparable within all size subsets. The difference between the 52-week premia and offer premia is quite small, irrespective oftarget size and averages to 1.95% across all subsets. In fact, this difference is even more trivial for small deals (0.62%) despite thelarger distance from 52-week highs. The fact that the offer price is very close to the 52-week high within all size groups provides afirst indication that the influence of the 52-week high is similar within all target size categories.

To establish whether the degree of anchoring is comparable irrespective of target size, we also regress offer premia(OfferPREM) on 52-week high distances (T52WkHiDIST) in Table 4, Panel B. Given the nonlinearity of the relationship that maybe caused by large outliers in the independent variable, we also report piecewise linear specifications that allow for a marginaleffect of different ranges T52WkHiDIST (up to 25%, between 25% and 75% and above 75%), as in Baker et al. (2012). The coefficientB in the simple linear specification (OLS) for the entire sample shows that a 1% rise in the 52-week high increases the offer priceby 0.18%.18 The magnitude of the B-coefficient is similar for all target size groups, reflecting that the degree of anchoring isanalogous. The corresponding beta coefficients in the piecewise linear specifications (B1, B2 and B3) corroborate that thedependence of the offer price on the 52-week-high is strong for typical reference price levels (i.e. T52WkHiDIST of up to 75%). Thefact that the pre-announcement prices of small targets are typically much further away from their 52-week high than for large

14 In unreported tests, we also examine univariately the mutually exclusive effects of acquirer and target size of the offer premium and nd consistent evidence.15 A related argument is that large deal size may motivate acquirers to hire more reputable nancial consultants that, all else equal, may be able to provide betteradvice and/or negotiate better deals, resulting in lower premia. However, in unreported tests, we nd that the nancial advisor quality of the target rm is alsopositively related with deal size. Controlling for the quality of acquiring rm nancial advisor following Golubov et al. (2012) does not alter our results.16 As better combinations tend to attract higher offer premiums, a pure overpayment measure would evaluate premiums in comparison to actual synergies.However, estimating actual synergies is an unattainable task, particularly for a large acquisition sample.17 The 52-week high stock price is measured over the 335 calendar days ending 30 days prior to the acquisition announcement.18 Including the inverse of the 30-day pre-acquisition price to control for the fact that prices in both sides of the equation are scaled by the 30-day pre-announcement price produces similar results.

Table 4Target size and potential overpayment. The sample of acquisitions meets the criteria described in Table 1. The sample is split into three groups (Small, Medium andLarge) based onMRTS (Market-Relative Target Size) which is the ratio of the target's market value 1 month prior to deal announcement and themedianmarket valueof all Compustat firms at the announcement year. N is the sample size. Panel A reports mean offer premium and target 52-week high premium by target size group.OfferPREM is the offer price expressed as a log percentage difference from the target stock price 30 calendar days prior to the acquisition. T52WkHiDIST is the 52-weekhigh stock price of the target firm ending 30 days prior to deal announcement, expressed as a log percentage difference from the target share price 30 calendar daysprior to the announcement. Differences between Large and Small subsets are based on two-sample t-tests formeans. Panel B reports estimates from regressions of theoffer premium on the target firm's 52-week high share price, as in Baker et al. (2012). Regression estimates are obtained from ordinary least squares regressions (OLS)and piecewise linear regressions (Piece) of the following form: OfferPREMit=a+B(T52WkHiDISTi,t30)+eit, as well as OfferPREMit=a+B1min(T52WkHiDISTi,t30,0.25)+B2max(0,min(T52WkHiDISTi,t-300.25,0.50))+B3max(T52WkHiDISTi,t300.75,0)+eit, respectively. a, b, and c denote statistical significance at the 1%, 5%and 10% levels, respectively.

Panel A All Small Medium Large Difference

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

N 3191 1002 1087 1102 OfferPREM (1) 35.04 41.46 34.19 30.05 11.53aT52WkHiDIST (2) 33.09 40.84 32.15 26.96 13.47aDifference (1)(2) 1.95a 0.62 2.03b 3.09a

Panel B All Small Medium Large

OLS Piece OLS Piece OLS Piece OLS Piece

B 0.179a 0.192a 0.137a 0.151a

R-square 9.05% 10.24% 9.23% 12.80% 6.34% 8.29% 6.885% 9.45%

9G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113targets implies that investors may perceive the extent of overpayment to be smaller in large deals. This provides further support(0.000) (0.000) (0.000) (0.000)B1 0.364a 0.252b 0.353a 0.428a

(0.000) (0.034) (0.000) (0.000)B2 0.214a 0.304a 0.159a 0.131a

(0.000) (0.001) (0.000) (0.006)B3 0.054b 0.065 0.014 0.009

(0.046) (0.162) (0.767) (0.858)N 3191 3191 1002 1002 1087 1087 1102 1102to our previous suggestion, that a positive association between target size and paying too much is less likely.

4. Target size and acquirer gains

4.1. Acquirer return analysis

In this section, we investigate how the investor's reaction to acquisition announcements varies by the size of the target. Table 5reports univariate results based on different measures of the expected acquisition benefit. ACAR3 is the acquirer cumulative

Table 5Target size and acquirer returns. The sample meets the criteria described in Table 1 and is split into three groups (Small, Medium and Large) based on MRTS(Market-Relative Target Size) which is the ratio of the target's market value 1 month prior to deal announcement and the median market value of all Compustatfirms at the announcement year. N is the sample size. Acquirer cumulative abnormal returns (ACAR3 and ACAR41) are calculated over 3-day (1,+1) and41-day (20,+20) event windows around the deal announcement, as in Brown and Warner (1985). %LOSERS is the percentage of acquiring firms subject tonegative abnormal returns, and %LARGELOSS are those acquirers experiencing abnormal returns below the median value of the negative return subset of the totalsample. $AR3 is the acquirer dollar return over the 3-day (1,+1) event window around the deal announcement, as in Malatesta (1983). RPDS3 refers to theacquirer abnormal return per dollar spent, estimated by the change in acquirer market capitalization over the 3-day (1,+1) announcement window relative tothe value of the transaction, as in Morck et al. (1990). WD3 is the bidder abnormal withdrawal returns, calculated for the 3-day (1,+1) event window aroundthe announcement of the deal cancelation. Market model parameters are estimated over a 200-day interval preceding the initial deal announcement, usingbenchmark returns of the CRSP value-weighted market index.

N All Small Medium Large Difference

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

ACAR3 3035 1.51a 0.45 1.08a 2.82a 2.37a%LOSERS 3035 60.92 56.02 61.37 64.64 8.62a

%LARGELOSS 3035 30.48 23.63 26.69 39.89 16.26a

ACAR41 3020 2.44a 0.33 2.73a 4.50a 4.83a$AR3 3035 173.94a 37.70 0.62 518.68a 556.38aRPDS3 2902 2.27 17.55 3.36 17.83a 35.38bWD3 633 0.03 1.56b 0.46 1.25b 2.81a

the natural logarithmof theMarket-Relative Acquirer Size (MRAS) to control for the fact that small acquirers tend to outperform large

10 G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113ones (Moeller et al., 2004). Its coefficient is positive and significant at the 1% level, although when regressed alone with acquirerreturns (in an unreported regression) its coefficient is negative and significant at the 1% level. This may be the result of the positivecorrelation between acquirer and target size (29%), prompting us to further investigate their mutually exclusive effect in the nextsection.

In regressions 3 and 4, we include the log difference of the offer price and the 30-day pre announcement target stock price(OfferPREM) as well as an overpayment instrumental variable (FittedPREM). FittedPREM measures the impact of the premiumcomponent that depends on the 52-week high and can be used as a pure overpayment indicator, according to Baker et al. (2012).Accordingly, in regression 3, a 1% increase in the offer premium is associated with a 0.36 basis points decrease in the bidderannouncement effect. Thus, the influence of the offer premium on acquisition gains, although statistically significant, is not large.However, investors are considerablymore concerned about the component of the premium that depends on the 52-week high as theannouncement effect decreases by 0.15% for a 1% increase in FittedPREM. In regressions 3 and 4, we also control for industry and yearfixed effects (INDUSTRY FE and YEAR FE) to account for possible biases from industry- and time-clustering ofmerger activity (Andradeand Stafford, 2004).

The coefficient of LNMRTS is negative and statistically significant at the 1% level in all specifications. In regression 4, a one standarddeviation increase in LNMRTS reduces the announcement period returns to acquirer shareholders by approximately 1.1%, reflecting itsstrong economic significance. More importantly, if the overpayment control variable captures at least a significant overpaymentcomponent, then the tests here suggest that investors are more disappointed about large deals for reasons beyond too high offerprices. In particular, the evidence altogether provides support to the view that other characteristics of large deals, such as theirheightened complexity, may also induce the inverse relation between deal size and acquirer returns.abnormal return reported for a 3-day (1,+1) announcement window. %LOSERS (%LARGELOSS) is the percentage of acquirersthat are subject to a negative ACAR3 (an ACAR3 lower than the median of all negative ACAR3s). ACAR41 is the acquirer abnormalreturn for a 41 day window starting 20 days prior to the acquisition announcement. The dollar return ($AR3) is the acquirer marketvalue gain in 2007 dollars and RPDS3 is the acquirer return-per-dollar-spent in the transaction, as inMalatesta (1983) andMorck et al.(1990), respectively. WD3 is the 3-day abnormal return on a separate sample of deal withdrawals from SDC that satisfies the samecriteria as our sample of completed deals. If the significantly lower announcement returns to acquirers of large targets are indicative ofvalue-destroying acquisitions, then the decision to abandon the deal should be associated with considerable market approval.Moreover, the withdrawal return should be free of value-adjustments related to the bidding firm's stand-alone valuation, since themarket would price such information at the time of the initial deal announcement.

By all measures, shareholders of acquirers that bid for larger firms are significantly worse off. ACAR3 is statistically insignificantfor acquisitions of small targets, but2.82% and significant at the 1% level for large-target deals. The return difference betweenacquisitions of small and large firms is even greater for 41-day returns. The percentage of acquirers that are subject to negativeabnormal returns (%LOSERS) as well as that of extreme losers (%LARGELOSS) are also higher in large deals. The average dealinvolving a large target wipes out approximately $518 million from the acquirer's market value. The return-per-dollar-spent isalso significantly lower in large deals; the acquirer value depreciates, on average, by 18% of the dollar amount paid for largetargets. Further, it appears that large deal withdrawals are associated with significant shareholder gains (1.25%), while acquirersthat withdraw from small deals suffer considerable losses (1.56%), which corroborates our previous results that investors viewacquisitions of large targets less favorably.

4.2. Acquirer return regressions

In this section, we test the robustness of the negative association between target size and acquirer returns in a multivariateframework by controlling for other effects that have been shown to explain acquirer gains. Among those variables, we also account forthe offer premium as well as the overpayment instrumental variable introduced in Section 3 in order to assess the target size effectover and above those influences. Table 6 reports regression results where the dependent variable is the 3-day acquirer abnormalreturn (ACAR3). The main explanatory variable is the natural logarithm ofMarket-Relative Target Size (LNMRTS).

Bradley et al. (1988) and Schwert (2000) report that acquirer abnormal returns are negatively associated with biddercompetition and takeover hostility, respectively. Therefore, we include variables to control for takeover competition (ACTIVITYand COMP) and hostile/unsolicited offers (HOSTILE), as defined in Section 3, but their coefficients are statistically insignificant.Travlos (1987) documents that offers involving stock-swaps result in more negative abnormal returns for acquirers than cashoffers. Controlling for the occurrence of stock-for-stock deals is particularly important because large deals are more likely paid forwith stock, as documented in Table 2. The coefficient of an all-equity indicator (ALLSTOCK) is negative and statistically significantat the 1% level in all specifications. Stock deals are associated with 1.7% lower abnormal returns in regression 2. Further, theinter-industry indicator (DIVERS) accounts for the fact that diversifying acquisitions are found to destroy shareholder value(Morck et al., 1990).

We also control for themarket valuation at the timeof themerger (HIVAL) butwe find its coefficient to be statistically insignificant.Previous research has shown that bidder and target firm valuations can affect returns to acquiring firms (Chemmanur et al., 2009;Moeller et al., 2005). Thus, we include themarket-to-book value of both the acquirer (AMTB) and the target (TMTB) in our regressions.In line with Moeller et al. (2004, 2005), we find evidence of significantly lower abnormal returns to high market-to-book acquirersaround the deal announcement. However, the target market-to-book is statistically insignificant in all specifications. We also include

11G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113Table 6Acquirer return regressions. This table reports OLS regression estimates of acquirer abnormal returns (ACAR3) on the natural logarithm ofMarket-Relative TargetSize (LNMRTS) and other deal, firm and market characteristics. Market-Relative Target Size (MRTS) is measured by the ratio of the target's market value (in$million) 1 month prior to the acquisition announcement and the median market value of all Compustat firms at the announcement year. OfferPREM is the logpercentage difference of the offer price from the target share price 30 calendar days prior to the announcement. FittedPREM is an instrumental variable, based ona piecewise linear regression of the control premium on the target firm's 52-week high share price, as in Baker et al. (2012). The piecewise linear regressionfollows: OfferPREMit=a+b1min(T52WkHiDISTi,t30,0.25)+b2max(0,min(T52WkHiDISTi,t300.25,0.50))+b3max(T52WkHiDISTi,t300.75,0)+eit, whereT52WeekHiDIST is the 52-week high stock price of the target firm ending 30 days prior to deal announcement, expressed as a log percentage difference fromthe target share price 30 calendar days prior to the announcement. ACTIVITY is the ratio of the number of listed firms targeted in successful acquisitions within anindustry in the announcement year to the number of all firms listed on Compustat within the corresponding target industry and announcement year. COMP,HOSTILE, STOCK and DIVERS are indicator variables that take the value of one for acquisitions with multiple bids, with unsolicited bids, financed 100% in stock andfor transactions where the acquirer and target operate in different industry sectors (2-digit SIC code), respectively. HIVAL is an indicator variable equal to one foracquisitions taking place within a high valuation market, based on the de-trended, monthly P/E ratio of the S&P500 Index. lnTMTB and lnAMTB are the naturallogarithm of the target and acquirer market-to-book equity ratio, respectively. lnMRAS is the natural logarithm of Market-Relative Acquirer Size (MRAS),measured as acquirer market value 1 month before the deal announcement over the median market value of all Compustat firms in the announcement year.Regressions (3) and (4) control for industry (INDUSTRY FE) and year fixed effects (YEAR FE), the coefficients of which are not reported. p-values are reported inbrackets; a, b, and c denote statistical significance at the 1%, 5% and 10% levels, respectively.

ACAR34.3. Additional univariate tests

An interesting result that emerges from the cross-sectional analysis is that acquirer size is positively associated with acquirerreturns, which is in stark contrast with the finding in Moeller et al. (2004). They document a strong negative association betweenacquirer size and gains from acquisitions, which they partly attribute to managerial hubris (and thus higher premia) beingmore of aproblem for large acquirers. Given the positive correlation between acquirer and target size, as it typically takes a large acquirer to buya large target, the positive relationship we documentmight be spurious. However, themotivation to examine the mutually exclusiveeffect of acquirer and target size goes beyond that. If, for example, the negative acquirer size effect is driven by deals where both theacquirer and the target are large, this may be consistent with our complexity conjecture, as one might argue that the most complexdeals are ones where two very large organizations need to assimilate into a combined entity.

Thus, in Table 7, we examine the relative effects of acquirer and target size on acquirer returns. Panel A reports ACARs byMRTS andMRAS terciles. On average, we find a negative relationship between acquirer size and abnormal returns reflected in the large minussmallMRAS difference (0.64%), which is statistically significant at the 10% level. However, this relationship is no longer negative fortargets of typical size (small andmedium subsets). Acquirer returns decreasewith acquirer size onlywithin the large target subgroup,which appears to be driving the overall inverse association. This is particularly intuitive as it shows that acquisitions by large acquirers

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

Intercept 0.017a 0.010b 0.030c 0.078a(0.000) (0.104) (0.602) (0.005)

lnMRTS 0.007a 0.008a 0.011a 0.010a(0.000) (0.000) (0.000) (0.000)

OfferPREM 0.036a(0.000)

FittedPREM 0.147a(0.000)

ACTIVITY 0.020 0.066 0.061(0.721) (0.381) (0.422)

COMP 0.011 0.009 0.011(0.325) (0.423) (0.279)

HOSTILE 0.014 0.017 0.014(0.379) (0.326) (0.413)

STOCK 0.017a 0.017a 0.014a(0.000) (0.001) (0.003)

DIVERS 0.004 0.003 0.003(0.321) (0.542) (0.508)

HIVAL 0.005 0.004 0.005(0.211) (0.549) (0.461)

lnTMTB 0.002 0.002 0.001(0.448) (0.434) (0.646)

lnAMTB 0.012a 0.011a 0.011a(0.000) (0.001) (0.000)

lnMRAS 0.004a 0.005a 0.005a

(0.001) (0.000) (0.000)INDUSTRY FE NO NO YES YESYEAR FE NO NO YES YESN 3035 1722 1527 1507R-square 1.74% 5.19% 12.61% 12.72%

Table 7Acquirer returns by target size, acquirer size and relative size. The sample meets the criteria described in Table 1. In Panel A, the sample is split into mutuallyexclusive groups (Small, Medium and Large) based on Market-Relative Target Size (MRTS) and Market-Relative Acquirer Size (MRAS), measured as target andacquirer market value, respectively, 1 month before the deal announcement over the median market value of all Compustat firms in the announcement year. InPanel B, the sample is split into mutually exclusive groups (Small, Medium and Large) based on Market-Relative Target Size (MRTS) and the relative size of the

(2) n 1013 216 394 403Large Mean 1.59a 0.27 0.52b 2.43a 2.70a

12 G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113(3) n 1012 110 290 612Difference (3)(1) 0.64c 0.83 0.98 1.01

Panel B MRTS

All Small Medium Large Difference

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

RELSIZE All Mean 1.51a 0.45 1.08a 2.82a 2.37an 3035 914 1038 1083

Small Mean 0.52a 0.17 0.59b 0.97a 0.80b(1) n 1067 403 421 243

a b a a atransactions (RELSIZE). RELSIZE is the ratio of the market capitalization of the acquirer 1 month prior to deal announcement and the transaction value. Thevariable of interest in Panels A and B is the cumulative acquirer abnormal return over the 3-day (1,+1) window around the deal announcement (ACAR3). Allvalues are shown in percentages. Differences between Large and Small subsets are based on two-sample t-tests for means. a, b, and c denote statisticalsignificance at 1%, 5% and 10% levels, respectively.

Panel A MRTS

All Small Medium Large Difference

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

MRAS All Mean 1.51a 0.45 1.08a 2.82a 2.37an 3035 914 1038 1083

Small Mean 0.95a 0.56 1.50b 1.42 0.86(1) n 1010 588 354 68Medium Mean 1.99a 0.51 1.12a 3.64a 3.13atend to result in more extensive value losses only when they acquire large targets. Therefore, it appears that the well-documentednegative effect of acquirer size on returns is largely driven by target size.19

We provide further insight by examining the association between target size and the target-to-bidder relative size. Fuller et al.(2002) report that the larger the target's market value relative to the bidder in acquisitions of listed targets, the lower the acquirerreturn. Panel B reports ACARs by MRTS and the target-to-acquirer relative size (RELSIZE). On average, acquirer returns decreasewith relative size, but this effect is driven by the large target subset, and overall is substantially less pronounced than thedocumented target size effect. Consequently, an acquisition of a large target by a large acquirer can have much more of a negativeimpact on acquirer returns than a deal of comparable relative size that involves two relatively small firms. This result is consistentwith the conjecture that the complexity inherent in large deals may to a large extent induce the positive relation between targetsize and acquirer losses.

5. Conclusion

We study the effect of target firm size on acquisition premia and returns to acquiring firm shareholders. We find that targetsize is negatively associated with offer premia and that the overpayment potential in large deals appears to be lower. Despite thefact that acquirers pay systematically lower premia and are less likely to overpay for larger targets, acquisitions of larger firmsdestroy more value for acquiring shareholders. This counterintuitive effect is particularly interesting. Our evidence is consistentwith the explanation that the additional complexity associated with large targets makes it more difficult for acquirers to attain theassumed economic benefits. Moreover, it is in line with the tenet that acquirer abnormal returns may greatly reflect influencesbeyond overpayment, such as concerns about the strategic potential and/or the complexity of the deal. Finally, we also establishthat the well documented negative effects of acquirer size and target-to-acquirer relative size on acquisition gains are largely theeffects of large target size.

19 Another interesting observation is that the combination of small acquirers with large targets results in negative (1.42%) but statistically insignicantabnormal returns. However, the size of this sub-sample is particularly small.

Medium Mean 1.96 0.96 1.72 2.96 2.00(2) n 1068 312 356 400Large Mean 2.15a 0.21 1.00 3.71a 3.50a

References

Ahern, K.R., 2010. Q-theory and acquisition returns. SSRN Working Paper.Alexandridis, G., Petmezas, D., Travlos, N.G., 2010. Gains from M&As around the world: new evidence. Financ. Manag. 39, 16711695.Alexandridis, G., Mavrovitis, C.F., Travlos, N.G., 2012. How Have M&As Changed: Evidence from the 6th Merger Wave. Eur. J. Financ. 18, 663688.Altman, E.I., 1968. Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. J. Finance 23, 589609.Andrade, G., Stafford, E., 2004. Investigating the economic role of mergers. J. Corp. Financ. 10, 136.Andrade, G., Mitchell, M., Stafford, E., 2001. New evidence and perspectives on mergers. J. Econ. Perspect. 15, 103120.Baker, M.P., Pan, X., Wurgler, J., 2012. The Effect of Reference Point Prices on Mergers and Acquisitions. J. Financ. Econ. 106, 4971.Bargeron, L., Schlingemann, F.P., Stulz, R.M., Zutter, C.J., 2008. Why do private acquirers pay so little compared to public acquirers? J. Financ. Econ. 89, 375390.Bauguess, S.W., Moeller, S.B., Schlingemann, F.P., Zutter, C.J., 2009. Ownership structure and target returns. J. Corp. Financ. 15, 4865.Berkovitch, E., Narayanan, M.R., 1990. Competition and medium of exchange in takeovers. Rev. Financ. Stud. 3, 153174.Bouwman, C.H., Fuller, K.P., Nain, A.S., 2009. Market valuation and acquisition quality: empirical evidence. Rev. Financ. Stud. 22, 633679.Bradley, M., Desai, A., Kim, E.H., 1988. Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms. J. Financ.

Econ. 21, 340.Brown, S.J., Warner, J.B., 1985. Using daily stock returnsthe case of event studies. J. Financ. Econ. 14, 331.Campa, J.M., Hernando, I., 2009. Cash, access to credit, and value creation in M&As. Working Paper. Banco de Espaa.

13G. Alexandridis et al. / Journal of Corporate Finance 20 (2013) 113Chemmanur, T., Paeglis, I., Simonyan, K., 2009. The medium of exchange in acquisitions: does the private information of both acquirer and target matter? J. Corp.Financ. 15, 523542.

Cools, K., Gell, J., Kengelbach, J., Roos, A., 2007. A Brave New World of M&A: How to Create Value from Mergers and Acquisitions. The Boston Consulting Group.DeAngelo, H., DeAngelo, L., Rice, E.M., 1984. Going private: minority freezeouts and stockholder wealth. J. Law Econ. 27, 367401.Deloitte, 2012. Post-Merger Integration Services (PMI). [online] Available at: http://www.deloitte.com/view/en_CH/ch/insights/privatebankingcentreofexecellence/

capturingopportunitythroughMA/postmergerintegrationservices/index.htm (accessed on 12 March 2012).Demsetz, H., Lehn, K., 1985. The structure of corporate ownership: causes and consequences. J. Polit. Econ. 93, 11551177.Dong, M., Hirshleifer, D., Richardson, S., Teoh, S.-H., 2006. Does investor misvaluation drive the takeover market? J. Finance 61, 725762.Faccio, M., Masulis, R.W., 2005. The choice of payment method in European mergers and acquisitions. J. Finance 60, 13451388.Fishman, M.J., 1988. A theory of preemptive takeover bidding. RAND J. Econ. 19, 88101.Fishman, M.J., 1989. Preemptive bidding and the role of the medium of exchange in acquisitions. J. Finance 44, 4157.Fuller, K.P., Netter, J., Stegemoller, M., 2002. What do returns to acquiring firms tell us? Evidence from firms that make many acquisitions. J. Finance 57, 17631793.Golubov, A., Petmezas, D., Travlos, N.G., 2012. When it pays to pay your investment banker: new evidence on the role of financial advisors in M&As. J. Finance 67,

271312.Gorton, G., Kahl, M., Rosen, R., 2009. Eat or be eaten: a theory of mergers and firm size. J. Finance 64, 12911344.Grinstein, Y., Hribar, P., 2004. CEO compensation and incentives: evidence from M&A bonuses. J. Financ. Econ. 73, 119143.Harding, D., Rovit, S., 2004. Mastering the Merger: Four Critical Decisions that Make or Break the Deal. Harvard Business School Press, Boston.Harford, J., Li, K., 2007. Decoupling CEO wealth and firm performance: the case of acquiring CEOs. J. Finance 62, 917949.Hayward, M.L.A., 2002. When do firms learn from their acquisition experience? Evidence from 1990 to 1995. Strateg. Manag. J. 23, 2139.Hayward, M.L.A., Hambrick, D.C., 1997. Explaining premiums paid for large acquisitions: Evidence of CEO hubris. Adm. Sci. Q. 42, 103127.Henry, D., Jespersen, F.F., 2002. Mergers: why most big deals don't pay off. BusinessWeek. (14 October, viewed 30 March 2010. b http://www.businessweek.com/

magazine/content/02_41/b3803001.htm >).Huang, Y., Walkling, R., 1987. Target abnormal returns associated with acquisition announcements: payment, acquisition form, and managerial resistance. J. Financ.

Econ. 19, 329349.Jennings, R.H., Mazzeo, M.A., 1993. Competing bids, target management resistance, and the structure of takeover bids. Rev. Financ. Stud. 6, 883909.Jensen, M.C., 2005. Agency costs of overvalued equity. Financ. Manag. 34, 516.Loderer, C., Martin, K., 1990. Corporate acquisitions by listed firms: the experience of a comprehensive sample. Financ. Manag. 19, 1733.Malatesta, P.H., 1983. The wealth effect of merger activity and the objective functions of merging firms. J. Financ. Econ. 11, 155181.Malmendier, U., Tate, G., 2005. CEO overconfidence and corporate investment. J. Finance 60, 26612700.Malmendier, U., Tate, G., 2008. Who makes acquisitions? CEO overconfidence and the market's reaction. J. Financ. Econ. 89, 2043.Mitchell, M.L., Mulherin, J.H., 1996. The impact of industry shocks on takeover and restructuring activity. J. Financ. Econ. 41, 193229.Moeller, S.B., Schlingemann, F.P., Stulz, R.M., 2004. Firm size and the gains from acquisitions. J. Financ. Econ. 73, 201228.Moeller, S.B., Schlingemann, F.P., Stulz, R.M., 2005. Wealth destruction on a massive scale? A study of acquiring-firm returns in the recent merger wave. J. Finance

60, 757782.Morck, R., Shleifer, A., Vishny, R.W., 1990. Do managerial objectives drive bad acquisitions? J. Finance 45, 3148.Nathan, K., O'Keefe, T., 1989. The rise in takeover premiums: an exploratory study. J. Financ. Econ. 23, 101119.Officer, M.S., 2003. Termination fees in mergers and acquisitions. J. Financ. Econ. 69, 431467.Roll, R., 1986. The hubris hypothesis of corporate takeovers. J. Bus. 59, 197216.Savor, P.G., Lu, Q., 2009. Do stock mergers create value for acquirers? J. Finance 64, 10611097.Schwert, G.W., 2000. Hostility in takeovers: in the eyes of the beholder? J. Finance 55, 25992640.Shrivastava, P., 1986. Postmerger integration. J. Bus. Strateg. 7, 6576.Travlos, N.G., 1987. Corporate takeover bids, method of payment, and bidding firms' stock returns. J. Finance 42, 943963.Walkling, R.A., Edminster, R.O., 1985. Determinants of tender offer premiums. Financ. Anal. J. 41, 2737.

Deal size, acquisition premia and shareholder gains1. Introduction2. Data and sample statistics2.1. Sample criteria and distribution2.2. Deal and firm statistics2.3. Premium statistics

3. Target size, acquisition premiums and potential overpayment3.1. Premium regressions3.2. Target size and overpayment potential

4. Target size and acquirer gains4.1. Acquirer return analysis4.2. Acquirer return regressions4.3. Additional univariate tests

5. ConclusionReferences