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Return to Invested Capital and the Performance ofMergers and Acquisitionshttp://orcid.org/0000-0001-6935-3646Jun “QJ” Qian, Julie Lei Zhu

To cite this article:http://orcid.org/0000-0001-6935-3646Jun “QJ” Qian, Julie Lei Zhu (2017) Return to Invested Capital and the Performance ofMergers and Acquisitions. Management Science

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MANAGEMENT SCIENCEArticles in Advance, pp. 1–17

http://pubsonline.informs.org/journal/mnsc/ ISSN 0025-1909 (print), ISSN 1526-5501 (online)

Return to Invested Capital and the Performance ofMergers and AcquisitionsJun “QJ” Qian,a Julie Lei Zhub

aFanhai International School of Finance, Fudan University, 200433 Shanghai, China; b Shanghai Advanced Institute of Finance, Shanghai JiaoTong University, 200030 Shanghai, ChinaContact: [email protected], http://orcid.org/0000-0001-6935-3646 (JQ); [email protected] (JLZ)

Received: June 25, 2014Revised: March 19, 2016; December 8, 2016Accepted: December 21, 2016Published Online in Articles in Advance:June 27, 2017


Copyright: © 2017 INFORMS

Abstract. We evaluate the efficiency of capital deployment for acquiring firms beforemerg-ers and acquisitions (M&As), defined as the return on invested capital net of the cost ofcapital, and link thismeasure to firms’ postacquisition performance. Acquirerswith higherpreacquisition net returns on investment have superior long-run operating and stock per-formance than do acquirers with lower returns. Acquirers with low net returns on invest-ment also underperformmatching nonacquirers. The relationship between preacquisitioninvestment return and postacquisition performance is weakened when chief executiveofficer turnover occurs after deal completion. These results imply that managerial abilityin deploying capital and creating value for shareholders persists through M&As.

History: Accepted by Wei Jiang, finance.Funding: Financial support from Boston University and Shanghai Advanced Institute of Finance is

gratefully acknowledged.Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2017.2766.

Keywords: merger and acquisition • invested capital • stock return • return on assets • CEO turnover

1. IntroductionLarge-scale mergers and acquisitions (M&As) requiresubstantial capital. While good M&As lead to growthand value creation, bad M&As generate massive lossesfor acquiring firms’ shareholders (e.g., Moeller et al.2005). An extensive strand of literature focuses on thevaluation of acquirers at the time of the M&A trans-action and establishes the links among valuation, thecharacteristics of M&A deals, and the performanceof merged firms.1 However, prior literature generallytakes valuation as given without examining acquirers’performance in creating value for their shareholdersleading up to the M&As.In this paper, we develop a new measure on acquir-

ing firms’ investment efficiency prior to the announce-ment of M&A deals, and we link this ex ante measureto postmerger performance. Our study can thereforereveal whether the recognition of capital deploymentefficiency is a source of market (mis)valuation. To eval-uate an acquiring firm’s efficiency in capital deploy-ment, we use the net return on invested capital (ROIC),which is defined as the return on capital raisedfrom equity holders and debtholders, in excess of theweighted average cost of capital (WACC).We hypothesize that managerial ability in deploy-

ing capital to productive projects and creating valuefor shareholders persists. Acquirers with higher netreturns on investment prior to the acquisitions areexpected to continue to deliver superior returns to

their shareholders in the upcoming M&A transaction,whereas acquirers with lower returns on investmentare likely to repeat subpar performance.

We conduct three sets of tests to examine ourmain hypothesis. First, we examine whether the netreturn on investment of an acquiring firm, constructedbefore the M&A deal announcement date, can predictthe firm’s postacquisition operating and stock perfor-mance. A positive link, especially the “predictability”of the ex ante investment efficiency on postacquisitionstock returns, would provide support for our hypoth-esis that managerial ability in deploying capital per-sists. It would also imply that investors and the marketdo not fully recognize how efficiently acquirers havebeen in utilizing capital before the M&A deal. Sec-ond, we examine whether the M&A deal itself affectspostacquisition performance by comparing acquirerswithmatching nonacquirers. This set of tests can revealwhether completing a large M&A deal can magnifyan acquirer’s investment deficiency. Third, since ourgeneral hypothesis is related to the characteristics ofmanagement, we study the effects of CEO turnover onthe link between preacquisition investment efficiencyand postacquisition performance. We hypothesize thatwhen the CEO—arguably the most important agent—leaves the acquiring firm following theM&Adeal com-pletion, the link between preacquisition net return oninvestment and postacquisition performance should beweakened.2

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Qian and Zhu: Return to Invested Capital and the Performance of Mergers and Acquisitions2 Management Science, Articles in Advance, pp. 1–17, ©2017 INFORMS

Our sample includes more than 3,500 completedM&A deals announced during the period of 1980–2013. The dependent variables in our empirical tests are(a) the merged firm’s announcement period abnormalreturns, (b) the merged firm’s long-run abnormal buy-and-hold stock returns (BHARs), and (c) the postac-quisition return on assets (ROA). The key explanatoryvariable in the regression models is the net returns oninvested capital, ROIC−WACC, constructed at the fis-cal year-end before the M&A deal announcement date.Our empirical tests yield three sets of results. First,

we find that the announcement period returns of ac-quirers with high net returns on investment are not sig-nificantly different from those with low returns. Thisresult suggests that, at the M&A deal announcement,the market does not distinguish acquirers with differ-ent levels of investment efficiency. Preacquisition netreturns on investment, however, strongly predict thestock returns and the operating performance of post-merger firms during the first three years after the dealcompletion date. For example, as the net return oninvestment increases by one standard deviation (11%),the acquirer’s buy-and-hold abnormal return duringthe first year postacquisition increases by 6.3% (thesample average return for the year is −1.1%); its ROAduring the same year rises by 4.4%—given the samplemean of 6.9% during the same year, the effect is eco-nomically significant. Using calendar-time portfolios,we find that a strategy of long high investment returnacquirers and short low investment return acquirersgenerates a return of 16% during the first year postac-quisition.Second, relative to matching nonacquirers, low effi-

ciency acquirers perform worse, in terms of both long-run abnormal stock returns and ROA, after the comple-tion of the M&A deal. The fact that undertaking M&Adeals affects acquirers’ postacquisition performancerejects the alternative hypothesis that the predictabil-ity of preacquisition investment efficiency is drivenby the persistence of certain firm characteristics (e.g.,profitability). Finally, in our tests studying the effectsof CEO turnover on postacquisition performance, weinclude a standard set of corporate governance mea-sures as controls. We find that when there is CEOturnover in either the first or second fiscal year afterdeal completion, the link between the ex ante invest-ment return and the ex post acquirer performance isweakened. These results therefore support the hypoth-esis that our investment measure depicts managerialeffectiveness in deploying capital, of which the CEO isan integral part.

Taken together, our results confirm the validity ofour investment return measure in assessing manage-ment efficiency prior to M&A deals, and they implythat investors and the market do not fully under-stand how acquirers have differed in their capacity to

generate returns to investment (net of the cost of capi-tal) before the announcement of M&A deals. For prac-tical purposes, our measure of investment efficiencycan be used by the board of directors and shareholdersof acquiring firms to make prudent decisions beforeapproving large-scale M&A deals. This measure canalso be used to investigatemanagement effectiveness inother corporate events, such as seasoned equity offer-ings (SEOs).

Prior work (e.g., Ang and Cheng 2003, Rhodes-Kropfet al. 2005, Dong et al. 2006) has examined misvalua-tions of merging firms at the time of theM&A deal andtheir impact on subsequent performance. Bouwmanet al. (2009) find that postmerger operating perfor-mance is negatively related to the valuation level ofthe market, in that merged firms perform worse dur-ing high valuation periods. Our paper builds on thisline of research and explores possible channels of mis-valuations of acquiring firms. Our results suggest thatinvestors’ failure in fully recognizing the differences inmanagerial efficiency in capital deployment can lead tomisvaluations before M&As.3 Leverty and Qian (2010)use different measures of efficiency and find that themarket reacts more favorably to deals made by moreefficient acquirers. By contrast, we link our simplemeasure of preacquisition investment efficiency to thepostacquisition long-term stock and operating perfor-mance of the acquiring firms.

Section 2 of the paper describes our hypotheses andthe empirical methodologies. Section 3 presents theempirical results on the association between the netreturn on investment and the postacquisition perfor-mance of merged firms. Finally, Section 4 presents con-cluding remarks. The appendix provides explanationsof the variables used in the empirical tests.

2. Hypothesis Development andEmpirical Methodologies

We first develop three specific hypotheses to exam-ine different aspects of our main premise—managerialefficiency in capital deployment persists throughM&As. We then define the measure of investment effi-ciency for acquiring firms and the empirical proce-dure in examining the relationship between the pre-merger net returns to investment and postacquisitionperformance.

2.1. HypothesesIf our main hypothesis holds, in that acquiring firmsthat have generated higher net returns from invest-ment continue to deliver superior returns in the M&Adeals, then the net return on investment, constructedbefore the M&A deal announcement date, should pre-dict the firm’s postacquisition operating and stock per-formance. This is our first hypothesis.We examine both

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Qian and Zhu: Return to Invested Capital and the Performance of Mergers and AcquisitionsManagement Science, Articles in Advance, pp. 1–17, ©2017 INFORMS 3

the announcement period and the long-run postacqui-sition abnormal returns of the merged firms; we alsoexamine merged firms’ operating performance. On theother hand, if investors and the market begin to appre-hend or fully recognize acquirers’ investment efficiencybefore the announcement of M&A deals, then thepredictability of the preacquisition investment mea-sure on postacquisition performance would weaken ordisappear.Second, we examine whether the M&A deal itself

affects postacquisition performance by comparing ac-quirers with matching nonacquirers as well as faileddeals. More specifically, we hypothesize that the groupof high efficiency (low efficiency) acquirers outper-forms (underperforms) matching nonacquirers andfirms that fail to complete the M&A deal. This set oftests can reveal whether a largeM&A deal canmagnifyinvestment efficiency or deficiency in capital deploy-ment. They can also help us rule out the alternativehypothesis that the link between preacquisition invest-ment efficiency and postacquisition performance isdriven by the persistence of certain firm characteristics.

Third, we postulate that investment efficiency relatesto the quality of the acquirer’s management. Sincethe CEO is the most important member of the man-agement team, we study the effects of CEO turnoveron the link between preacquisition investment effi-ciency and postacquisition performance. Under thenull hypothesis—the CEO does not affect the firm’sinvestment efficiency—the link would not change uponthe departure of the CEO. By contrast, if the CEOparticipates in the investment decision process and isresponsible for the implementation of new projectsincludingM&A transactions, the link ought to beweak-ened if the CEO leaves the acquiring firm followingM&A deal completion. These tests can also lend fur-ther support to our main hypothesis that it is the man-agerial effectiveness in capital deployment that persiststhrough M&As.

2.2. Measure of Investment EfficiencyOur goal is to construct a measure that captures man-agerial efficiency in utilizing all the capital raised bythe acquiring firm before launching large-scale acqui-sitions. Accordingly, we define our efficiency measureas the ROIC in excess of WACC. The return in ROIC isnet operating profits (NOPAT) , or net income addingback after-tax interest expenses. Invested capital (Com-pustat item ICAPT) is defined as the sum of long-termdebt, minority interests, preferred equity, and com-mon equity. ROIC is then calculated as NOPAT scaledby lagged invested capital. Instead of using forecastedfuture cash flows net of initial investment costs (e.g.,in the case of calculating project net present values(NPVs)), we use realized earnings net of WACC; thisapproach can avoid the (possible) biases in forecasts ofearnings and cash flows and is therefore more reliable.

WACC is derived using the following procedure:(1) Following Fama and French (1992, 1997), amongothers, we use a portfolio approach to find individ-ual stock betas—that is, the beta of each stock is theassociated Fama–French 48-industry portfolio beta;4we then calculate the cost of equity using the capitalasset pricing model (CAPM) (the market risk premiumused in the CAPM is the average premium over therisk-free rate for the Center for Research in SecurityPrices (CRSP) value-weighted index over the preced-ing 30 years). (2) We infer the after-tax cost of debtfrom interest expenses, total interest-bearing debt, andthe marginal tax rate.5 (3) We use the market value ofequity and book value of total debt as weights in theWACC formula.6 For robustness, we also use the unlev-ered (industry) cost of equity as individual firm’s costof equity estimates. Finally, net investment return iscalculated as ROIC minus WACC.7

2.3. Performance Measures of Merged FirmsWe follow the CAPM to estimate abnormal announce-ment period returns:

ARit � (Rit −R f t) − βit · (Rmt −R f t),

where Rit is firm i’s return on date t, Rmt is the mar-ket return on date t, R f t is the risk-free rate, and βit isestimated based on returns from past 60 months usingthe CAPM. We calculate cumulative abnormal returns(CARs) by summing the abnormal daily returns overa three-day event window around the M&A dealannouncement date.

To measure the long-run stock performance of mer-ged firms, we follow the literature on long-run eventstudies and use the “buy-and-hold” returns of a sam-ple firm less the buy-and-hold return of a properly cho-sen benchmark portfolio.8 The BHAR is calculated as

BHARiT �

s+T∏t�s(1+Rit) − 1−RpT ,

where Rit is the month t return for firm i, RpT isthe benchmark portfolio return, and T is the timehorizon over which returns are calculated. We usethe characteristic-based portfolio constructed in Danielet al. (1997) and Wermers (2003; hereafter DGTW)as our benchmark portfolio.9 The DGTW benchmarkportfolio for a given stock during a given monthis constructed to directly match that stock’s threemain characteristics: size, (industry-adjusted) market-to-book (MTB) ratio, and past momentum. Therefore,DGTW form benchmarks that directly match the char-acteristics of the stocks being evaluated.

In constructing the ex ante efficiencymeasure of cap-ital usage, we use NOPAT scaled by lagged investedcapital minus WACC as the net return on invested cap-ital. Since NOPAT is defined as earnings adding back

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after tax interest expenses, it is not affected by themethods of payment in acquisitions. Hence, NOPAT-scaled lagged assets (ROA) is our measure for operat-ing performance in the postacquisition period. In ourregression analysis, we exclude the period betweenthe M&A deal announcement and the completion datefrom the postacquisition period to account for the dif-ferences in the timing of consolidating targets underthe purchase or pooling method (Healy et al. 1992).We also include an indicator for the pooling methodand another indicator that equals 1 if more than 50%of the consideration for acquisition is paid for with theacquirer’s stock.

2.4. Framework for Analyzing PostmergerPerformance

To examine our first hypothesis, the link between theex ante efficiencymeasure and the ex post performanceof themerged firms, we runmultivariate regressions tocontrol for factors that may affect a firm’s performance.Specifically, we estimate the following model:

Announcement Return (or BHAR or Postmerger ROA)� β1Invest_Return+Firm and deal controls+Fixed effects+ ε. (1)

In Equation (1), the dependent variable is the an-nouncement return (AR)—three-dayCARs, or the one-,two-, and three-year postacquisition BHARs or ROA ofthe postmerger acquiring firm. Invest_Return, our mainvariable of interest, is each acquiring firm’s ex ante effi-ciency measure of capital deployment obtained at thefiscal year end prior to the deal announcement; follow-ing our discussion, we expect β1 to be positive.Firm controls are the variables that have been shown

to affect the performance of the postmerger firm. Theseinclude the acquiring firm’sMTB, calculated one quar-ter before theM&A announcement date; preannounce-ment cash levels (Acquirer Cash); Accruals; and netoperating assets (NOA), which is the balance sheetrepresentation of the cumulative accruals; and PreAn-nReturn, the mean preannouncement return of acquir-ers from 200 days to 31 days prior to the deal announce-ment date, to account for possible short-run pricemomentum. We also include a Small Acquirer indica-tor, defined as an acquiring firm with its market cap-italization below the 25th percentile of NYSE firms asof the fiscal year-end immediately before the M&Aannouncement date.For M&A deal characteristics, we first include Rela-

tive Size, defined as the transaction value divided by theacquirer’s market capitalization at the end of the fiscalyear immediately before the deal announcement date,as a factor that may affect the acquirer’s postacquisi-tion performance.10 We also include three indicators ascontrols: (1)Diversify equals 1 if the target and acquirer

have different two-digit SIC codes and 0 otherwise,(2) Pooling equals 1 if the acquirer uses the poolingmethod, and (3) Tender is equal to 1 if the acquisitionis a tender offer and 0 otherwise. As discussed earlier,we include an indicator (Stock) that equals 1 if morethan 50% of the deal is paid for with the acquirer’sstock. Finally, we denote privately owned target firmsby the indicator Private Target and acquiring firmsmak-ing multiple acquisitions (during the sample period)by the indicator Serial.For fixed effects in Equation (1), we include a set of

industry indicators. In addition, as recommended byPetersen (2009), we include year indicators and clus-ter standard errors by year to control for the cross-correlation among acquiring firms each year.

To examine our second hypothesis, we match eachacquirer in the sample with a nonacquirer based onindustry, size, and investment efficiency before theannouncement of the M&A deal. Specifically, for eachacquirer during the period of M&A deal announce-ment to completion, we identify all firmswith the sametwo-digit SIC code and total assets between 50% and200% of that of the sample firm (and obtain informa-tion during the same period). Those firms that wereinvolved in a merger bid during the three years beforethe M&A deal announcement or the three years afterdeal completion are excluded. We then select the firmwith its investment efficiency closest to that of theacquiring firm as thematching nonacquirer. Aswe sep-arately examine high and low efficiency acquirers andcompare them with their matching groups, we splitthe acquirers and matching nonacquirers into threegroups: high investment efficiency group (net invest-ment returns in the top 20th percentile of all firms), lowinvestment efficiency group (net investment returns inthe bottom 20th percentile), and medium group (therest of the firms in the middle of the distribution).

For the comparison between completed and faileddeals, we adopt a procedure following Savor and Lu(2009): (1) for each completed or failed deal, we iden-tify up to 10 matching nonacquirers using a proceduresimilar to the one described in the paragraph above;(2) the abnormal buy-and-hold return of each acquirerequals the difference between its own buy-and-holdreturn and that of the return of the matching port-folio of nonacquirers; and (3) we split the completedand failed deals into stock- and cash-financed groups.Finally, within each of the two groups, we further splitfirms into high, low, andmedium investment efficiencyfirms.

To examine the third hypothesis, we introduce CEOturnover into our baseline model:

Three-year BHARs or Three-year average postmerger ROA� β1Invest_Return+ β2CEO_TurnoverYr1+ β3CEO_TurnoverYr2

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+ β4Invest_Return×CEO_TurnoverYr1+ β5Invest_Return×CEO_TurnoverYr2+CEO and governance measures+Firm and deal controls+Fixed effects + ε. (2)

In Equation (2), we first ensure that the same CEOwas in place starting from one fiscal year beforethe announcement of the M&A deal and contin-ued as the CEO of the firm until the completion ofthe deal. We then study whether the link betweenpreacquisition investment efficiency and postacquisi-tion performance is affected by the departure of theCEO after deal completion—either during the firstfiscal year (CEO_TurnoverYr1) or second fiscal year(CEO_TurnoverYr2) after deal completion. If this rela-tionship weakens or disappears—as shown by nega-tive interaction terms (β4 , β5 < 0) in Equation (2)—thiswould corroborate the hypothesis that our investmentreturn measure captures managerial effectiveness, ofwhich the CEO is a significant part. Since the informa-tion on CEO turnover is missing for some acquiringfirms, we include a separate indicator for missing CEOinformation.For corporate governance measures, we include

(1) the E-index (Bebchuk et al. 2009), (2) the fraction ofthe board of directorswho are outsidemembers, (3) thefraction of insider ownership, and (4) whether the CEOis the chairman of the board. We also include CEOage and tenure as controls.11 The inclusion of thesevariables is based on the hypothesis that managerialeffectiveness depends on strong governance, includ-ing monitoring by the board. For each of these vari-ables we also include a separate indicator for missinginformation.

3. Data and Empirical ResultsFrom the Securities Data Company (SDC) U.S. M&Adatabase, we identify all completed acquisitions,including the acquisitions of privately owned targetsannounced during the period of 1980–2013, and applystandard sample selection criteria. As is common prac-tice, we exclude financial institutions and regulatedutility firms.12 In addition, if an acquirer announcesmultipleM&A deals in the same year, we only keep thefirst (qualified) deal made in the year.13 Finally, we onlyinclude deals in which sufficient Compustat and CRSPdata are available to calculate the variables as shown inTable 1. This procedure yields a sample of more than3,500M&Adeals, for which we analyze the ex ante effi-ciency measure in capital usage and ex post operatingperformance and abnormal stock returns.14Panel A of Table 1 reports the summary statistics

of abnormal returns of the acquiring firms during theannouncement and postacquisition periods, as wellas their ROA during the first three years after deal

completion. These are the dependent variables in ourempirical tests. Both the mean and median values ofthe acquirers’ postacquisition period abnormal returnsare negative. Panel B reports summary statistics ofthe acquirers, and panel C reports deal characteristics.The average preacquisition net return on investment(Invest_Return) is 2.5% (median is 3.8%), and the stan-dard deviation of this variable is 11.3%.More than one-quarter of the acquirers have net returns less than 0,suggesting that they invest in negative NPV projectsbefore launching the acquisition. The average MTBratio of acquirers is 3.45; consistent with prior litera-ture, the average level of Accruals is negative (−0.03),while the average acquirer holds about 21% cash overtotal assets.

Panel C shows that about 12% of the deals are ten-der offers, while 48% of the deals are paid for withmore than 50% of the acquirer’s stock. About 16% ofthe deals use the pooling method to account for theacquisitions (note that Statement of Financial Account-ing Standards 141 requires all firms to use the pur-chase method for acquisitions initiated after June 30,2001). Forty-two percent of the deals involve acquiringa privately owned target, and these targets are muchsmaller than public targets on average (the transac-tion value involving private targets is about one-tenthof that for public targets). Sixty-eight percent of theacquirers make more than one acquisition over thesample period (“serial” acquirers). Overall, the sum-mary statistics (see Table 1) are similar to those in morerecent M&A studies (e.g., Bouwman et al. 2009, Oler2008, Netter et al. 2011).

In the rest of this section, we present results frommultivariate regression analyses on the announcementperiod (event) returns, postacquisition long-run abnor-mal stock returns and operating performance, and anumber of additional results and robustness checks.

3.1. Announcement Period Returns andPostacquisition Performance

The dependent variable in column (1) of Table 2 isthe three-day CAR of the acquiring firms. After con-trolling for acquiring firm and M&A deal characteris-tics, we do not find a significant relationship betweenthe net returns on investment and the acquirer’sannouncement period return. This result suggests that,at the announcement of the M&A deal, the marketdoes not distinguish acquirers with different levelsof investment efficiency. We also obtain a number ofother results consistent with prior research. For exam-ple, the market responds negatively to stock acquisi-tions (e.g., Servaes 1991, Rau and Vermaelen 1998),while the announcement period return is higher if theacquirer experiences larger preannouncement stockreturns (e.g., Jegadeesh and Titman 1993). We also findthat the announcement returns decrease in the level

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Table 1. Summary Statistics

Panel A: Dependent variables

Abnormal Announcement BHAR BHAR years BHAR years ROA Avg. ROA Avg. ROAReturns (%) year +1 (%) 1 and 2 (%) 1–3 (%) year +1 (%) years 1 and 2 (%) years 1–3 (%)

Mean 0.1 −1.1 −3.4 −5.9 6.9 7.1 7.4p50 −0.1 −5.0 −10.8 −17.4 8.2 8.1 8.2sd 7.0 59.9 89.1 110.0 16.5 17.3 14.7p25 −2.5 −28.6 −45.0 −57.6 3.1 3.2 3.6p75 2.5 18.0 22.5 26.7 14.3 14.1 14.1

Panel B: Acquiring firm characteristics

Invest_Return (%) MTB Small Acquirer Accruals NOA Acquirer Cash

Mean 2.5 3.45 0.13 −0.03 1.08 0.21p50 3.8 1.95 0.00 −0.03 0.80 0.12sd 11.3 17.75 0.34 0.10 6.93 0.22p25 −2.8 1.38 0.00 −0.07 0.50 0.03p75 10.0 3.12 0.00 0.01 1.11 0.33

Panel C: M&A deal characteristics

Relative Size Diversify Pooling Stock Tender PreAnnReturn Private Target Serial

Mean 0.29 0.44 0.16 0.48 0.12 0.13 0.42 0.68p50 0.14 0.00 0.00 0.00 0.00 0.10 0.00 1.00sd 0.33 0.50 0.37 0.50 0.33 0.22 0.49 0.47p25 0.04 0.00 0.00 0.00 0.00 0.04 0.00 0.00p75 0.41 1.00 0.00 1.00 0.00 0.18 1.00 1.00

Notes. This table presents the summary statistics of acquirers’ announcement period returns, long-term abnormal stock returns, and ROAduring the postacquisition period (panel A); these are dependent variables used in subsequent tests. It also shows summary statistics ofacquiring firm characteristics (panel B) and M&A deal characteristics (panel C). The summary statistics are based on a sample of about 3,500acquisitions during the period of 1980–2013, with nonmissing deal characteristics, and have sufficient Compustat and CRSP data to calculatethe necessary accounting and return variables. All the accounting variables are measured at the fiscal year-end before the deal announcementdate. Acquisitions are included in this sample if (a) the acquirer is a U.S. public firm listed on one of the major exchanges; (b) the deal valueis at least $10 million; (c) the acquirer obtains 100% of the target assets and the deal is completed; (d) the method of payment is cash, stock,or a mixture of the two; and (e) the deal is announced during 1980–2013. If an acquirer announces multiple deals in the same year, the first(qualified) deal is retained in the sample (while other deals are dropped). See the appendix for the definitions of all the variables.

of the acquirer’s cash holdings, consistent with Har-ford (1999), who interprets this result as the markettaking cash-rich acquirers to have more severe agencyproblems, as indicated by Gao et al. (2013). Finally, themarket reacts positively to the acquisition of a privatelyowned target (e.g., Chang 1998, Netter et al. 2011).The next three columns of Table 2 report the regres-

sion results for the BHARs, adjusted by the DGTWbenchmark return, calculated over the one-year, two-year, and three-year horizons after acquisition. Wefind that high investment return acquirers have signif-icantly better postacquisition returns than low returnacquirers over all three windows after the deal com-pletion date; all the results are statistically significantat the 5% or 1% level. The magnitude of the resultsis also large: for instance, when the investment returnincreases by one standard deviation (11%), the three-year buy-and-hold abnormal return increases by 10%(� 0.907× 0.11, column (4); the mean three-year abnor-mal return for the sample is −5.9%). These results sug-gest that the preacquisition net return on investment

is a strong predictor of long-run postacquisition stockperformance.

The acquirer’sNOAand accruals prior to the acquisi-tion announcement date are both negatively associatedwith long-run abnormal returns during the postac-quisition period (coefficient on NOA significant at the5% level over the one- and two-year horizons), consis-tent with prior literature (e.g., Louis 2004, Richardsonet al. 2005). The long-run abnormal returns are lowerfor acquirers with greater preannouncement price run-ups (statistically significant in the one- and two-yearwindows) and for stock acquisitions (significant in thethree-year window), also consistent with prior studies.We also find that serial acquirers perform better, per-haps because of their experience in the earlier deals.

Table 3 reports results for operating performance(ROA) of acquiring firms in the first fiscal year afterdeal completion (column (1)), the average ROA duringthe first and second fiscal years after acquisition (col-umn (2)), and the average ROA during the first threefiscal years after the acquisitions (column (3)). As in the

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Table 2. Regression Analysis of Short-Run and Long-Run Abnormal Stock Returns

(1) (2) (3) (4)Abnormal Announcement BHAR BHAR BHAR

Variables Returns year +1 years 1 and 2 years 1–3

Invest_Return 0.003 0.577∗∗∗ 0.554∗∗∗ 0.907∗∗(0.15) (6.49) (3.92) (2.48)

MTB 0.001 0.007 0.001 −0.001(1.08) (0.82) (0.12) (−0.08)

Small Acquirer 0.012∗∗ 0.078∗∗ 0.058 0.080(2.24) (2.61) (1.33) (1.09)

Accruals −0.005 −0.199∗ −0.113 −0.270(−0.20) (−1.70) (−0.67) (−1.18)

NOA −0.000 −0.002∗∗∗ −0.001∗∗ 0.001(−0.15) (−5.45) (−2.35) (0.90)

Acquirer Cash −0.029∗∗ 0.250 0.123 0.338(−2.38) (1.49) (0.92) (0.82)

Relative Size 0.002 −0.010 0.039 0.132(0.23) (−0.23) (0.57) (1.20)

Diversify −0.000 −0.025 0.002 0.049(−0.07) (−1.37) (0.07) (1.05)

Pooling −0.004 0.025 0.028 0.155∗(−1.17) (0.45) (0.78) (1.82)

Stock −0.017∗∗∗ 0.025 −0.024 −0.119∗∗(−3.57) (0.77) (−0.87) (−2.74)

Tender 0.006 −0.020 −0.041 −0.086∗∗(1.12) (−0.98) (−0.99) (−2.17)

PreAnnReturn 0.078∗∗∗ −0.341∗∗∗ −0.461∗∗∗ −0.322(4.15) (−3.46) (−3.68) (−1.14)

Private Target 0.019∗∗∗ −0.046 −0.064 −0.118∗∗(4.96) (−1.54) (−1.62) (−2.74)

Serial −0.004 0.101∗∗∗ 0.206∗∗∗ 0.388∗∗∗(−1.22) (5.31) (5.79) (5.49)

Constant 0.002 0.074 0.656∗∗∗ −1.068∗∗∗(0.10) (0.77) (5.01) (−7.65)

Year and industry fixed effects Yes Yes Yes YesObservations 3,533 3,165 3,065 2,735Adjusted R-squared 0.06 0.03 0.04 0.03

Notes. This table reports results from ordinary least squares (OLS) regressions of the acquirers’ three-day abnor-mal announcement period returns and BHARs (buy-and-hold abnormal returns) for the three years after acqui-sition. Invest_Return is defined as ROIC (NOPAT over invested capital, or Compustat item ICAPT) minus WACC,measured at the fiscal year-end prior to the deal announcement. See the appendix for other variable defini-tions. Standard errors are clustered by year. Industry and year dummies are included but not reported. Robustt-statistics to heteroscedasticity are provided in parentheses.∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels, respectively.

long-run stock return regressions (see Table 2), postac-quisition operating performance is significantly betterfor acquirers with higher preacquisition net investmentreturns; all results are statistically significant at the 1%level (columns (1)–(3)).When the preacquisition invest-ment return increases by one standard deviation (11%),the acquirer’s ROA during the first year after deal com-pletion rises by 4.4% (� 0.403 × 0.11, column (1)), andits average ROA during the first three years after dealcompletion rises by 3% (� 0.273 × 0.11, column (3)).Given that the mean ROA of merged firms is 6.9%during the first year and 7.4% (per year) during thethree years after deal completion, the positive associa-

tion between premerger net returns on investment andpostacquisition ROA is also economically significant.

We also find that operating performance is worseif the acquirer has higher levels of accruals, or NOAsor (preannouncement) cash holdings; uses stock asthe main method of payment; or acquires a privatelyowned target. The relationship between cash leveland long-run postacquisition operating performance isconsistent with the findings of Oler (2008).15 Operatingperformance is better if the acquirer makes multipleacquisitions, has higher preacquisition MTB, or usesthe poolingmethod. As additional controls, we includematching (nonacquiring) firms’ operating performance

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Table 3. Regression Analysis of Postacquisition OperatingPerformance

(1) (2) (3)ROA Avg ROA Avg ROA

Variables year +1 years 1 and 2 years 1–3

Invest_Return 0.403∗∗∗ 0.329∗∗∗ 0.273∗∗∗(13.77) (10.48) (11.43)

MTB 0.006∗ 0.006∗∗ 0.008∗∗∗(1.94) (2.09) (3.88)

Small Acquirer −0.007 −0.012 −0.009(−0.80) (−1.39) (−1.02)

Accruals −0.085∗∗ −0.127∗∗∗ −0.109∗∗∗(−2.06) (−3.09) (−3.10)

NOA −0.001∗∗∗ −0.000∗∗∗ −0.000(−3.65) (−7.35) (−0.15)

Acquirer Cash −0.133∗∗∗ −0.135∗∗∗ −0.147∗∗∗(−7.75) (−6.98) (−6.97)

Relative Size −0.012 −0.009 −0.007(−1.36) (−1.03) (−0.83)

Diversify −0.004 −0.003 −0.001(−0.83) (−0.51) (−0.12)

Pooling 0.033∗∗∗ 0.022∗∗ 0.028∗∗∗(3.07) (2.40) (3.61)

Stock −0.017∗ −0.017∗∗ −0.023∗∗∗(−1.89) (−2.36) (−3.56)

Tender −0.008 −0.005 −0.005(−1.26) (−0.84) (−0.78)

PreAnnReturn −0.082∗ −0.045 −0.056(−1.89) (−1.02) (−1.22)

Private Target −0.057∗∗∗ −0.053∗∗∗ −0.053∗∗∗(−6.03) (−5.25) (−4.53)

Serial 0.026∗∗∗ 0.026∗∗∗ 0.020∗∗∗(4.69) (5.40) (4.59)

Match_ROAYr1 0.135∗∗∗(6.50)

AvgMatch_ROAYrs1–2 0.162∗∗∗(6.55)

AvgMatch_ROAYrs1–3 0.146∗∗∗(4.24)

Constant −0.026 −0.000 0.006(−1.23) (−0.02) (0.11)

Year and industry Yes Yes Yesfixed effects

Observations 2,943 2,504 2,146Adjusted R-squared 0.33 0.34 0.35

Notes. This table reports OLS regressions of acquirers’ ROA forthe three years after acquisitions. Invest_Return is defined as ROIC(NOPAT over invested capital, or Compustat item ICAPT) minusWACC, measured at the fiscal year-end prior to the deal announce-ment. Match_ROAYr1 is ROA for the one-year after acquisition formatching firms not involved in M&A deals (and the other two vari-ables denote the averages of matching ROAs in the first two andthree years). See the appendix for all the other variable definitions.Industry and year dummies are included but not reported. Robustt-statistics to heteroscedasticity are provided in parentheses.∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels,

respectively.

during the postacquisition period to ensure that theresults are not driven by the (possible) mean reversionproperties of operating performance (e.g., Nissim andPenman 2001).16 The ROA of matching firms is posi-tively and significantly related to the acquirers’ postac-quisition ROA, illustrating the success of the matchingprocedure.

In summary, results from Tables 2 and 3 show thatthe ex ante investment efficiency measure strongly pre-dicts the postacquisition stock and operating perfor-mance of acquiring firms. Combining the announce-ment period results with the long-run abnormalreturns results (see Table 2), we conclude that the mar-ket, triggered by an M&A deal announcement, doesnot recognize high investment return acquirers’ greaterefficiency in capital deployment, as these acquirers out-perform the low net return acquirers during the threeyears after the acquisition. Overall, these results pro-vide support for our first hypothesis.

3.2. The Effects of the M&A Deal and CEOTurnover on Postacquisition Performance

Results from the previous section establish a positivelink between preacquisition investment efficiency andpostacquisition performance for the acquiring firms.An alternative hypothesis stipulates that the positivelink found in Tables 2 and 3 can be explained bythe persistence of certain (observable or unobservable)firm characteristics, such as profitability. To rule outthe alternative hypothesis, we examine the effects ofthe M&A deals on the postacquisition performance foracquirers with different levels of investment efficiency.

Table 4 presents results from comparing acquir-ers and matching nonacquirers. By construction, thenonacquirers are similar to the acquirers in terms ofsize, industry, and investment efficiency (before theM&Adeal announcement) but have not engaged in anyM&A transaction three years before the announcementdate and three years after the completion date of theacquirer’s deal. It is clear that the group of low effi-ciency acquirers performs substantially worse than thegroup of matching nonacquirers in both BHAR andROA. The magnitude of the differences in both BHARsand ROA is large, and the differences across all postac-quisition periods are statistically significant. For exam-ple, during the first year after deal completion, low effi-ciency acquirers underperformmatching nonacquirersby 20.5% in terms of BHARs and 2.56% in ROA (pan-els A and B; both of these differences are significantat 1%). These results suggest that completing a largeM&A deal can exacerbate the deficiency in managerialeffectiveness in capital deployment, and they supportour second hypothesis. The group of high efficiencyacquirers performs better than that of the nonacquirersin terms of BHAR during the first-year after acquisi-tion, but not in subsequent years. There is no signifi-cant difference in ROA between the two groups.

In addition, we conducted tests comparing com-pleted and failed deals (results available in the onlineappendix) following Savor and Lu (2009). These resultsare similar to those reported in Table 4. For exam-ple, among stock-financed deals, the group of low effi-ciency acquirers underperforms matching firms that

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Table 4. Comparing Acquirers with Matching Nonacquirers

Low investment efficiency High investment efficiency

Panel A: Buy-and-hold abnormal returns

BHAR BHAR BHAR BHAR BHAR BHARN year 1 years 1 and 2 years 1–3 N year 1 years 1 and 2 years 1–3

Acquirers 733 −18.18% −18.51% −17.62% 733 3.68% −8.32% −14.03%Matched nonacquirers 733 2.35% −4.77% 5.13% 733 −7.15% −13.56% −17.59%Diff. of acquirers and matched nonacquirers 733 −20.52%∗∗∗ −13.74%∗∗ −22.75%∗∗ 733 10.83%∗∗∗ 5.24% 3.56%t-statistic (−4.62) (−2.23) (−2.23) (2.61) (0.86) (0.46)

Panel B: Postacquisition ROA

ROA Avg ROA Avg ROA ROA Avg ROA Avg ROAN year 1 years 1 and 2 years 1–3 N year 1 years 1 and 2 years 1–3

Acquirers 748 −3.88% −2.12% −1.31% 720 7.34% 6.80% 7.10%Matched nonacquirers 748 −1.32% −0.06% 0.55% 720 7.92% 7.35% 7.36%Diff. of acquirers and matched nonacquirers 748 −2.56%∗∗∗ −2.06%∗∗∗ −1.86%∗∗ 720 −0.59% −0.55% −0.27%t-statistic (−3.31) (−2.81) (−2.58) (−0.87) (−0.96) (−0.51)

Notes. This table reports the comparison of one-, two-, and three-year postacquisition BHARs (panel A) and ROA (panel B) for acquirers andmatching nonacquirers.Wematch each acquirerwith a nonacquirer based on industry, size, and investment efficiency before the announcementof the M&A deal. For each acquirer during the period of M&A deal announcement to completion, we identify all firms with the same two-digitSIC code and total assets between 50% and 200% of that of the sample firm (and obtain information during the same period). Those firmsthat were involved in a merger bid during the three years before the M&A deal announcement or the three years after deal completion areexcluded. We then select the firmwith its investment efficiency closest to that of the acquiring firm as the matching nonacquirer. We separatelyexamine high and low efficiency acquirers and compare them to their matching groups. High (low) investment efficiency groups are identifiedusing the top (bottom) quintile of all acquirers’ investment efficiency estimated at the fiscal year-end prior to acquisition announcement.∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels, respectively.

do not complete their M&A deals in all three years interms of BHARs; statistical significance is weaker per-haps because of the small sample on failed deals. Thegroup of high efficiency acquirers outperforms match-ing acquirers that do not complete their deals in thetwo- and three-year windows in terms of BHARs (withmarginal statistical significance).17 To summarize, onthe basis of the comparisons with matching nonac-quirers and failed deals, we conclude that the M&Adeal does affect postacquisition performance as statedin our second hypothesis. The results also reject thenotion that the predictability of investment efficiencyis driven by the persistence of certain characteristics ofthe acquiring firm.Next, in examining our third hypothesis (and Equa-

tion (2)), we add two sets of variables, CEO turnoversand corporate governancemeasures, to seewhether theCEO is an important factor of managerial effectivenessin capital deployment. Results on the buy-and-holdstock returns (operating performance) are reported inpanel A (panel B) of Table 5. In these models, we en-sure that the same CEO is in place for the acquiringfirm between one fiscal year before the deal announce-ment date and the deal completion year. Sample size issmaller as a result of the data requirements on CEOs.Column (1) in panel A repeats the analysis as shownin column (4), Table 2, on the three-year buy-and-holdreturns (ona smaller sample).Wecontinue tofindapos-itive and significant relationship between acquirers’ net

investment returns and postacquisition stock returns(with the magnitude of the coefficient greater than thatin column (4) of Table 2).

In column (2) of panel A, we add CEO turnover indi-cators (during the first and second fiscal year after dealcompletion) and their interactions with the preacquisi-tion investment return. We include a separate indicatorfor missing CEO turnover information. In column (3),we add the governance variables (again, we include aseparate indicator for missing information on each ofthe variables). In both columns, we find that acquir-ers’ stock performance is worse when there is CEOturnover after deal completion. Since CEO turnoverfollowing the completion of an M&A deal is probablynot exogenous, this result can be explained by the factthat CEO turnover occurs following poor performanceof the acquiring firm.

The focus of the test is on the interaction term be-tween (demeaned) investment return and the indica-tor on CEO turnover: if the CEO is not part of ac-quirer effectiveness in capital deployment, then thelink between preacquisition investment efficiency andpostacquisition performance should not change whenthere is CEO turnover. In other words, the coefficienton the interaction term ought to be 0 regardless of theoutcome of the possibly endogenous turnover (in termsof the quality of the newCEO relative to that of the out-going CEO).We find that the interaction terms are neg-ative in both columns (2) and (3) and are statistically

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Table 5. The Effects CEO Turnover on PostacquisitionLong-Run Performance

Panel A: Long-run buy-and-hold abnormal returns

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

Variables years 1–3 years 1–3 years 1–3

Invest_Return 0.937∗∗ 0.576∗∗ 0.545∗∗(2.48) (2.36) (2.23)

CEO_Turnover Yr1 −0.235∗∗ −0.217∗∗(−2.39) (−2.60)

CEO_Turnover Yr1×Invest_Return −0.083∗ −0.253∗(−1.90) (−1.93)

CEO_Turnover Yr2 −0.205∗∗∗ −0.175∗∗(−3.33) (−2.68)

CEO_Turnover Yr2×Invest_Return −0.264 −0.039∗(1.35) (1.85)

Missing CEO_Turnover −0.148∗∗∗ −0.082∗(−2.78) (−1.76)

CEO_Age 0.048(0.23)

CEO_Tenure 0.041(0.83)

E-Index −0.023(−1.33)

Indep_Board 0.127(0.91)

Same CEO_Chair 0.103∗∗(2.32)

Insider_Ownership −0.008∗∗(−2.23)

Missing governance indicators YesConstant −1.039∗∗∗ −0.766∗∗∗ −0.943

(−7.89) (−4.48) (−0.97)Firm and deal controls Yes Yes YesYear and industry fixed effects Yes Yes YesObservations 2,633 2,633 2,633Adjusted R-squared 0.03 0.08 0.08

significant at the 10% level. Moreover, F-tests showthat the CEO turnover indicators and their interactionterms with investment returns are jointly significant atthe 1% level in both columns. We obtain very simi-lar results in panel B: (1) CEO turnover has a nega-tive impact on postacquisition operating performanceof the merged firm, (2) the interaction terms betweenCEO turnover and preacquisition investment returnsare negative (statistically significant at the 10% level inboth columns), and (3) F-tests for the joint significanceof CEO turnover variables show that they are signifi-cant at the 5% level.The results from both panels therefore reject the

notion that the CEO is not part of the managerial teamthat exhibits a certain degree of investment efficiency.The results support our third hypothesis that CEO isindeed a critical factor of the acquiring firms’ invest-ment efficiency. After controlling for CEO turnovers,the link between preacquisition investment return andpostacquisition performance still exists (see the coef-ficients on Invest_Return in columns (2) and (3) in

Table 5. (Continued)

Panel B: Postacquisition operating performance

(1) (2) (3)Avg ROA Avg ROA Avg ROA

Variables years 1–3 years 1–3 years 1–3

Invest_Return 0.273∗∗∗ 0.181∗∗∗ 0.178∗∗∗(11.00) (4.87) (4.79)

CEO_Turnover Yr1 −0.011∗ −0.011(−1.87) (−1.13)


CEO_Turnover Yr2 −0.014∗ −0.013∗(−1.98) (−1.84)


Missing CEO_Turnover −0.006 0.012(−1.43) (1.46)

CEO_Age 0.034∗(1.74)

CEO_Tenure 0.007(1.21)

E-Index −0.000(−0.16)

Indep_Board −0.019(−1.10)

Same CEO_Chair 0.017∗∗∗(2.77)

Insider_Ownership −0.000(−0.83)

Missing governance indicators YesConstant 0.068∗∗∗ 0.094∗∗∗ −0.090

(2.83) (5.00) (−1.17)Firm and deal controls Yes Yes YesYear and industry fixed effects Yes Yes YesObservations 2,060 2,060 2,060Adjusted R-squared 0.35 0.32 0.32

Notes. This table reports OLS regressions of acquirers’ BHARs(panel A) and average ROA (panel B) for the three years after acqui-sitions on CEO turnover indicators and their interactions with preac-quisition investment return, aswell as governancevariables.Wemakesure that the same CEO was in place starting from one fiscal yearbefore the announcement of the M&A deal and continued as theCEO of the firm until the completion of the deal. CEO_Turnover Yr1(CEO_Turnover Yr2) is an indicator equal to 1 if the acquirer changesits CEO during the first (second) fiscal year after acquisition comple-tion and 0 otherwise. Missing CEO_Turnover is an indicator equal to1 if CEO turnover data is missing and 0 otherwise. See the appendixfor other variable definitions. Standard errors are clustered by year.Robust t-statistics to heteroscedasticity are provided in parentheses.∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels,

respectively.

both panels), suggesting that there are other firm-levelfactors beyond CEO and the government measuresincluded in the tests that affect investment efficiency.

3.3. Additional Results and Robustness TestsIn this subsection, we present and discuss results froma number of additional tests and robustness checkson the methodologies of calculating stock returns,

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sampling periods, and different specifications of con-structing the ex ante investment efficiency measure.3.3.1. Calendar-Time Approach to Calculate Stock Re-turns. The abnormal buy-and-hold returns in Table 2,Table 4, and panel A of Table 5 are based on an event-time approach. That is, abnormal returns are calcu-lated across M&A transactions for one- to three-yearwindows after the completion of these transactions,even though the acquisitions occur at different calen-dar times. One potential problem with this approachis that the significance of long-run returns can beoverstated because of cross-correlations among returns(e.g., Bernard 1987, Mitchell and Stafford 2000, KothariandWarner 2004).An alternative approach is to use calendar-time re-

turns: tracking the performance of an event portfolioin calendar time. This approach weighs each monthequally and tests a strategy of investing equal amountsin eachmonth, and it is therefore immune to the poten-tial cross-correlation problem. We track the perfor-mance of an event portfolio in calendar time relative toan asset pricing model. The event portfolio is formedeach period to include acquiring firms that have com-pleted the event (M&A deal) in the prior years (years 1,2, or 3). In addition, we use the calendar-time approachto calculate the abnormal returns for the strategies ofgoing short on low net investment return acquirers andgoing long on high net investment return acquirers.

For each month during our sample period, we cre-ate high and low net investment return event portfoliosas follows: the high return (low return) portfolio con-sists of all the acquirers that completed an acquisitionwithin the previous one, two, or three years. Portfoliosare rebalanced monthly to drop all the acquirers thatreach the end of their one-, two-, or three-year periodand add all the acquirers that have just completed anM&A transaction. The portfolio excess returns are thenregressed on the Fama–French (2015) five factors: theFama–French (1993) three factors, the profitability fac-tor (Hou et al. 2015), and the Carhart (1997)momentumfactor.Formally, a pooled portfolio regression is estimated

as follows:Rp , t −R f , t �ap + bp(Rm , t −R f , t)+ spSMB + hpHML

+ mp MOM + rpRMW + δ1Dlow

+ δ2Dlow × (Rm , t −R f , t)+ δ3Dlow × SMB+ δ4Dlow ×HML + δ5Dlow ×MOM+ δ6Dlow ×RMW + ep , t ,

where Rp , t is the event portfolio return, (Rm , t −R f , t) isthe excess market return over the risk-free rate, SMBis small minus big stock portfolio, HML is high MTBminus lowMTB stock portfolio, RMW is the profitabil-ity factor, and MOM is the momentum factor. Theintercept denotes the event portfolio excess returns.To estimate the difference between the returns of high

and low investment return event portfolios, we cre-ate a dummy variable in the above equation, Dlow,which equals 1 if the event portfolio return is a lownet investment return and 0 otherwise. The coefficienton the Dlow indicator, δ1, thus captures the differencebetween the low investment return portfolio and all theother stocks. Low investment return firms are identi-fied using the bottom quintile of all acquirers’ invest-ment returns in the fiscal year-end prior to the dealannouncement date.

Table 6, panel A presents the regression results forthe event portfolios. The coefficients on Dlow are neg-ative and statistically significant (at less than the 1%level in all three columns), reinforcing the notion thatacquirerswith low investment efficiencyexperience sig-nificantly lower long-runabnormal returns thanacquir-ers with higher investment efficiency. Note that all fourpricing factors are significant with expected signs, butadding them and interactions with the investment effi-ciency variable does not affect the main results. Theseresults also help rule out the alternative hypothesis thatthe return predictability of the investment measure isdriven by the persistence of certain firm characteristics(e.g., profitability).

In addition, we calculate mean abnormal monthlyreturns from long and short portfolios consisting ofacquirers that completed acquisitions within the pre-vious one-, two-, and three-year windows. Panel Bshows that this strategy generates significant abnormalreturns in all three years, with an abnormal return of1.33% per month for the first year, which correspondsto an annual return of 16% (� 1.33% × 12), 0.58% permonth for the first two years, and 0.33% per monthfor the first three years. Overall, the results usingthe calendar-time approach corroborate the results ofusing the event-portfolio approach in Table 2 and con-firm that acquirers’ ability to generate high net returnsfrom invested capital prior to the M&A deal is animportant predictor of postacquisition abnormal stockreturns.3.3.2. Robustness Checks. We have shown in differ-ent tests with different dependent variables that theex ante investment efficiency measure is an importantpredictor for acquirers’ postacquisition performance.All the results presented so far are based on the entiresample period of 1980–2013. It has been documentedthat acquisitions tend to cluster in time (see, for exam-ple, Holmstrom and Kaplan 2001): for example, thenumber of deals is much greater in the late 1990sthan in the 1980s. Moreover, Moeller et al. (2005) showthat shareholders of acquiring firms experience muchgreater losses in the late 1990s than in earlier peri-ods. To rule out the possibility that our findings aredriven by the deals made in a particular period (e.g.,the late 1990s), we split the sample period into threesubperiods: acquisitions announced from 1980 to 1990,from 1991 to 2000, and from 2001 to 2013.

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Table 6. Long-Run Stock Returns Using Calendar-TimePortfolios

Panel A: Pooled regressions

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

Variables 12 months 24 months 36 months

Rm , t −R f , t 1.184∗∗∗ 1.131∗∗∗ 1.124∗∗∗(36.86) (43.18) (47.61)

SMB 0.564∗∗∗ 0.558∗∗∗ 0.549∗∗∗(11.53) (14.01) (15.28)

HML −0.255∗∗∗ −0.232∗∗∗ −0.196∗∗∗(−5.23) (−5.81) (−5.45)

MOM −0.068∗∗ −0.164∗∗∗ −0.18∗∗∗(−2.34) (−6.92) (−8.45)

RMW 0.426∗∗∗ 0.342∗∗∗ 0.274∗∗∗(6.46) (6.36) (5.65)

Dlow −0.013∗∗∗ −0.006∗∗∗ −0.004∗∗(−5.47) (−3.42) (−2.13)

Dlow × (Rm , t −R f , t) −0.064 −0.021 0.016(−1.14) (−0.47) (0.39)

Dlow × SMB 0.062 0.054 0.114∗(0.74) (0.78) (1.84)

Dlow ×HML −0.22∗∗∗ −0.157∗∗ −0.138∗∗(−2.6) (−2.28) (−2.21)

Dlow ×MOM −0.282∗∗∗ −0.283∗∗∗ −0.233∗∗∗(−5.61) (−6.9) (−6.3)

Dlow ×RMW −1.442∗∗∗ −1.264∗∗∗ −1.033∗∗∗(−12.62) (−13.56) (−12.3)

Intercept 0.001 0.000 0.000(0.57) (0.05) (0.44)

Observations 996 996 996Adjusted R-squared 0.80 0.85 0.87

Panel B: Hedge returns

Prior Prior Prior12 months 24 months 36 monthsMonthly Monthly Monthlyreturn return return

Long (%) 1.36 1.03 1.07Short (%) 0.03 0.45 0.74Long− Short (%) 1.33 0.58 0.33p-value <0.001 0.052 0.167

Notes. This table presents stock return results on calendar-time port-folios. Panel A presents a pooled portfolio OLS regression, where theindicator variable Dlow equals 1 if the event portfolio is a low invest-ment efficiency acquirer and 0 otherwise. Low investment efficiencyfirms are identified using the bottom quintile of all acquirers’ invest-ment efficiency estimated at the fiscal year-end prior to acquisitionannouncement. For each month from 1980 through 2013, portfoliosare formed based on acquirers’ net investment returns and from allsample firms that completed an acquisition in the previous one year(first column), the previous two years (second column), and the pre-vious three years (third column); t-statistics are provided in paren-theses. Panel B presents abnormal returns on the investment strategyof shorting all low net investment return acquirers and going long onall high net investment return acquirers. Abnormal returns are cal-culated using the DGTW benchmark portfolio, where each acquirerwas matched for size, industry-normalized MTB, and momentum.∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels,

respectively.

Table 7, panel A reproduces the coefficients on themain variable of interest (Invest_Return) from Tables 2and 3 (the baseline models). We rerun stock return andoperating performance regressions for each of the threesubperiods and report the coefficients on the same vari-able in panels B, C, andD. It is clear that themain resulton the positive relationship between the ex ante invest-ment efficiencymeasure and ex post acquirer stock andoperating performance is not driven by any particu-lar sample period. In terms of the magnitude of thecoefficients on investment efficiency in postacquisitionBHAR regressions, it appears that the degree of “pre-dictability” is the strongest in the 1980s, and then itfalls over the next two decades. A weaker predictionsuggests that firms and the market began to under-stand the ROIC measure over time—in the limit, if themarket fully understands this measure in the pricing ofacquirer stocks, then we would not observe a positiveand significant coefficient on the investment returns inthe BHAR regressions. So these patterns are consis-tent with the argument that as corporate governanceimproves over time, the predictability of the ex antemeasure also weakens. Of course, a number of otherfactors could also affect the results—the nature of themerger waves in different decades, the characteristicsof focused industries and firms, and so on.

Our investment measure is derived based on the dif-ference between ROIC and the acquiring firm’s WACCcalculated prior to the M&A deal announcement. Inpanel E we use only ROIC as the measure and dropWACC. We can see that ROIC itself can predict postac-quisition abnormal stock returns and operating perfor-mance (ROA). These results imply that the predictabil-ity of investment efficiency is tightly linked to the qual-ity of the investment projects, and not just to the costof capital needed to raise funds in order to finance theprojects. Finally, we use an alternative method to cal-culate WACC (and that our measure is still ROIC −WACC). To reduce possible measurement errors result-ing from effects of different levels of leverage of firmsfrom the same industry, we use unlevered (industry)cost of equity as the input in the WACC formula.18Results using this approach, as shown in panel F, arequite similar to those reported in panel A.

4. Summary and Concluding RemarksAn extensive strand of literature documents misvalu-ations of acquiring firms’ stocks at the time of M&Astransactions, and it links the valuation of stocks to thecharacteristics of M&A deals and the performance ofthe acquirers. In this paper, we develop a new, simplemeasure on the acquiring firm’s investment efficiencyand link it to postmerger performance. Our measure isthe return on invested capital—all the equity and debtcapital—net of its cost of capital.

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Table 7. Regressions Relating Preacquisition Net Investment Returns to Postacquisition Performance: Robustness Tests

BHAR BHAR BHAR ROA Avg ROA Avg ROAExplanatory variables year +1 years 1 and 2 years 1–3 year +1 years 1 and 2 years 1–3

Panel A: Full sample (replication of Tables 2 and 3)Invest_Return 0.577∗∗∗ 0.554∗∗∗ 0.907∗∗ 0.403∗∗∗ 0.329∗∗∗ 0.273∗∗∗

(6.49) (3.92) (2.48) (13.77) (10.48) (11.43)Observations 3,165 3,065 2,735 2,943 2,504 2,146

Panel B: First subperiod 1980–1990Invest_Return 0.850∗ 1.080∗∗ 1.555∗∗ 0.369∗∗∗ 0.328∗∗ 0.349∗∗

(1.99) (2.81) (2.40) (3.97) (2.80) (2.57)Observations 530 518 486 484 427 392

Panel C: Second subperiod 1991–1999Invest_Return 0.638∗∗∗ 0.385∗∗ 0.939∗ 0.402∗∗∗ 0.347∗∗∗ 0.267∗∗∗

(3.47) (2.86) (1.98) (10.71) (9.43) (8.04)Observations 1,293 1,253 1,127 1,139 944 828

Panel D: Third subperiod 2000–2013Invest_Return 0.384∗∗∗ 0.391∗ 0.380∗ 0.381∗∗∗ 0.298∗∗∗ 0.238∗∗∗

(3.23) (1.95) (1.85) (7.47) (5.85) (6.01)Observations 1,342 1,294 1,122 1,320 1,133 926

Panel E: Using ROIC only as the investment efficiency measure (and drop WACC)ROIC 0.229∗∗ 0.332∗∗ 0.723∗∗ 0.438∗∗∗ 0.372∗∗∗ 0.305∗∗∗

(2.73) (2.18) (2.50) (13.74) (11.07) (11.62)Panel F: Using unlevered beta to calculate WACC

Invest_Return 0.338∗∗∗ 0.477∗∗∗ 0.846∗∗ 0.390∗∗∗ 0.322∗∗∗ 0.275∗∗∗(3.12) (3.90) (2.08) (13.02) (9.94) (10.94)

Notes. This table reports the coefficients on the net investment return variable for different regression specifications and with differentsubsamples. Panel A reproduces the results from baseline models from Tables 2 and 3. In the other panels, we vary either the sample periodor the specification. All regressions include the same set of explanatory variables as those reported in Tables 2 and 3. Robust t-statistics toheteroscedasticity are provided in parentheses.∗, ∗∗, and ∗∗∗ indicate significance at the 10%, 5%, and 1% levels, respectively.

With more than 3,500 M&A deals announced dur-ing the 1980–2013 period, we find that acquirers withhigher net returns on investment prior to the acqui-sition have significantly better operating performanceand long-run abnormal stock returns after acquisition.The positive link between the ex ante investment effi-ciency and ex post performance indicates that man-agerial ability in deploying capital persists and thatinvestors and the market do not fully recognize thedifferences in management’s ability to deploy capi-tal before the acquisition. After the completion of theM&Adeal, low efficiency acquirers performworse thanmatching nonacquirers in terms of both abnormal stockreturns and ROA. These results imply that completingthe large investment project exacerbates the problemsin managerial deficiency for acquiring firms. Finally,when there is CEO turnover after deal completion,the link between preacquisition net return on invest-ment and postacquisition performance is weakened.This result suggests that the acquirer’s investment effi-ciency relates to the characteristics of the firm’s man-agement team, of which the CEO is an integral part.

Our paper contributes to the literature on marketefficiency around corporate events by introducing anew measure of investment efficiency and by docu-menting a positive link between this ex ante measure

and the ex post acquisition performance. Prior liter-ature has shown that large-scale M&As can lead tosubstantial losses for acquiring firms’ shareholders,despite “due diligence” efforts before completing thedeals. We offer a new factor that the acquiring firm’sshareholders and board of directors should consider—the firm’s efficiency in utilizing capital leading up tothe M&A transaction. In particular, if management hasmisallocated capital to negative NPV projects, share-holders should be cautious in approving the newM&Adeal to avoid further losses. Our measure and similarmethodologies can also be used to investigate manage-ment effectiveness surrounding other corporate events.

AcknowledgmentsThe authors thank department editor Wei Jiang, two anony-mous referees, Franklin Allen, Sanjai Bhagat (WesternFinance Association (WFA) discussant), Brian Bolton (WFAco-discussant), Patricia Dechow, Kenneth French, SharonKatz, Duane Kennedy, Jun Liu, Derek Oler, Stephen Penman,Doug Skinner, Phil Strahan, Kent Womack, Yu Yuan, andseminar/session participants at Boston College, Boston Uni-versity, Dartmouth College, New York University, Ameri-can Accounting Association meetings, MIT Asia Account-ing Conference, and Western Finance Association meetingsfor useful comments. Research assistance by Haofei Wang isgratefully acknowledged. All errors are the authors’ own.

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Appendix. Definitions of Variables

NOPAT Net operating profit is defined as net income adding back after tax interest expense.ROA Return on assets is defined as NOPAT scaled by lagged assets.ROIC Return on invested is defined as NOPAT scaled by lagged invested capital (Compustat item

ICAPT). Invested capital is the sum of long-term debt, minority interests, preferred equity, andcommon equity.

WACC Weighted average cost of capital of a firm is calculated by the following procedure: (1) Using aportfolio approach to find individual stock betas, where the beta of each stock is the associatedFama–French (FF) 48-industry portfolio beta. We assign each stock to FF 48 portfolios at theend of June of year t based on its four-digit SIC code. Value-weighted industry return iscalculated based the lagged market capitalization of each stock in an industry portfolio. Weestimate industry β using at least 24 months and up to 60 months of lagged industry returns.(2) Estimating the firm’s cost of equity using the CAPM; the market risk premium assumed inCAPM is the average premium over the risk-free rate for the CRSP value-weighted index overthe preceding 30 years. (3) Inferring after-tax cost of debt from interest expense, totalinterest-bearing debt, and the marginal tax rate. (4) Using the market value of equity and bookvalue of total debt as relative weights in the WACC formula.

Invest_Return Net investment return is defined as ROICminusWACC, measured at the fiscal year end prior tothe deal announcement.

Acquirer Cash The acquirer’s cash and short-term investments at the end of fiscal year immediately prior to theacquisition announcement divided by lagged assets.

MTB The acquirer’s market-to-book ratio at one quarter prior to acquisition announcement.Small Acquirer An indicator variable equal to 1 if an acquirer’s market capitalization is below the 25th percentile

of NYSE firms and 0 otherwise.Accruals Total accruals are defined, following Fairfield et al. (2003), as follows: ACC�∆WC−DEP, where

∆WC� change in working capital� change in accounts receivable+ change ininventories+ change in other current assets− change in accounts payables− change in othercurrent liabilities; and DEP is depreciation and amortization.

NOA The net operating assets are calculated, following Fairfield et al. (2003), as follows:NOA�AR+ INV+OTHERCA+PPE+ INTANG+OTHERLTA−AP−OTHERCL−OTHERLTL,where AR is accounts receivables; INV is inventory; OTHERCA is other current assets; PPE isnet property, plant, and equipment; INTANG is intangibles; OTHERLTA is other long-termassets; AP is accounts payable; OTHERCL is other current liabilities; and OTHERLTL is otherlong-term liabilities.

Stock An indicator variable equal to 1 if more than 50% of the consideration is paid using theacquirer’s own stock and 0 otherwise.

Diversify An indicator variable equal to 1 if the acquirer and target are not in the same primary industry,defined as two-digit SIC code, and 0 otherwise.

Relative Size The transaction value divided by the acquirer’s market capitalization at the end of fiscal yearprior to the acquisition announcement.

Pooling An indicator variable equal to 1 if an acquisition is accounted for under pooling and 0 otherwise.PreAnnReturn Acquirer’s average stock return measured over 200 days to 31 days before the announcement

date.Private Target An indicator variable equal to 1 if the target is not a listed company and 0 otherwise.Serial An indicator variable equal to 1 if the acquirer has made more than one acquisition during the

sample period and 0 otherwise.CEO_Age The age of the acquirer’s CEO at the year of the acquisition announcement.CEO_Tenure The number of years from being the CEO to the year of the acquisition announcement.CEO_Turnover Yr1 An indicator variable equal to 1 if the acquirer changes its CEO during the first fiscal year after

acquisition completion and 0 otherwise.CEO_Turnover Yr2 An indicator variable equal to 1 if the acquirer changes its CEO during the second fiscal year

after acquisition completion and 0 otherwise.Missing CEO_Turnover An indicator variable equal to 1 if CEO turnover data are missing and 0 otherwise.Same CEO_Chair An indicator variable equal to 1 if the CEO and the chairman of board of the acquirer are the

same person at the date of acquisition announcement and 0 otherwise.

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Appendix (Continued)

E-Index The entrenchment index, constructed by Bebchuk et al. (2009), measures the effectivenessof corporate governance. The index identifies six provisions that matter the most:staggered boards, limits to shareholder bylaw amendments, poison pills, goldenparachutes, supermajority requirements for mergers, and supermajority requirementsfor charter amendments. The entrenchment index is constructed by counting the numberof such provisions that apply to the company at the end of the fiscal year immediatelyprior to the acquisition announcement.

Indep_Board The percentage of the independence directors on the acquirers’ board at the end of thefiscal year immediately prior to the acquisition announcement.

Insider_Ownership The percentage of shares owned by the insider of the company at the end of the fiscal yearimmediately prior to the acquisition announcement.

Missing Gov Indicator An indicator variable equal to 1 if any of the governance variables is missing and 0otherwise.

Abnormal Announcement Return The CARs is the sum of abnormal daily returns over a three-day event window around theM&A deal announcement date. Abnormal daily return is calculated by deductingexpected stock return estimated from CAPM from actual stock return. Beta used forCAPM is estimated from monthly stock return in the past 60 months before theannouncement date, and a minimum of 24 months of nonmissing returns is required.

BHAR The buy-and-hold returns of a sample firm less the buy-and-hold return of a properlychosen benchmark portfolio. We use the characteristic-based portfolio constructed inDaniel et al. (1997) and Wermers (2003) as our benchmark portfolio. The DGTWbenchmark portfolio for a given stock during a given month is constructed to directlymatch that stock’s three main characteristics: size, (industry-adjusted) MTB ratio, andpast momentum.

Endnotes1Shleifer and Vishny (2003) argue that overvalued (undervalued)acquirers are more likely to use stock (cash) to purchase targets’assets, and such stock-based (cash-based) acquisitions tend to under-perform (outperform) themarket in the long run. Empirical evidencehas provided support for these arguments.2Prior literature (e.g., Malmendier and Tate 2005, 2008; Billett andQian 2008; Benmelech and Frydman 2015) finds that the personaltraits and experience of chief executive officers (CEOs) influence theirinvestment decisions.3Edmans et al. (2012) and Baker et al. (2012) examine the effects oftarget valuation and prices on the likelihood of receiving an offer andthe offer premium. In addition, using a sample of SEOs in the 1980s,Loughran and Ritter (1997) find that the operating performance ofissuing firms peaks around the time of the offering but deterioratesafterward.4We assign each NYSE, AMEX, and NASDAQ stock to an indus-try portfolio at the end of June of each year based on its four-digitStandard Industrial Classification (SIC) code. We derive the indus-try portfolio beta by regressing value-weighted industry portfolioreturns on the market returns over the past 60 months. The industryportfolio return is based on the lagged market capitalization of eachstock in the portfolio (each stock must have at least 24 months oflagged returns available).5Following Nissim and Penman (2001), the marginal tax rate isdefined as the top statutory federal tax rate plus 2% average state taxrate.6 In addition, we use the traditional approach to find individual stockbeta, which is based on estimations using the past 60monthly returns(with at least 24 months’ returns available); with this approach,we winsorize the cost of equity estimates (based on betas and theCAPM) to lie in the range of 3%–30%. This approach yields similarresults as those reported in our paper. See the online appendix for asummary of different methods to derive WACC.

7Our approach is similar to the concept of residual earnings, whichis the NPV of an investment project and includes a charge for capitalemployed against earnings (e.g., Feltham and Ohlson 1995, Penman2003). Leverty and Qian (2010) use “frontier efficiency analysis” witha firm’s revenue as the output, and they use a number of inputsincluding physical and financial assets and the firm’s cost structureto evaluate the efficiency of value creation by both acquirers andtargets.8For acquiring firms that are delisted during the return period, theremaining return for the period is calculated by first applying CRSP’sdelisting return and then reinvesting any remaining proceeds in thesize-matched portfolio (where size is measured as market capitaliza-tion at the start of the return accumulation period). For firms thatare delisted for poor performance (CRSP delisting codes 500 and520–584) and missing delisting returns, we follow Penman and Zhu(2014) and apply a delisting return of −100%; results are robust tousing a delisting return of zero.9As shown in Daniel and Titman (1997), among others, the“characteristics” of stocks (the DGTW portfolios) provide betterex ante forecasts of the cross-sectional patterns of future stockreturns; characteristic matching also does a better job of match-ing future realized returns. Hence, this procedure should havemore statistical power than factor-based models to detect abnor-mal performance (Wermers 2003). The DGTW benchmarks areavailable at http://terpconnect.umd.edu/∼wermers/ftpsite/Dgtw/coverpage.htm (last accessed June 7, 2017).10For controls related to the size of merging firms, we also use a con-tinuous variable denoting acquirer size (instead of an indicator onsmall acquirers) plus relative size, as well as the size of the acquirerand target separately. Main results are robust to these alternativespecifications.11 Jenter and Lewellen (2015) find that M&A deals are more likely tosucceed when the target CEO is closer to retirement age; this effect isweaker in better-governed firms. Jenter and Kanaan (2015) find thatCEOs are more likely to be dismissed after poor industry andmarketperformance.

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http://terpconnect.umd.edu/~wermers/ftpsite/Dgtw/coverpage.htm


12Our sample selection criteria include the following: the deal valuemust be at least $10 million (for both public and private targets), theacquiring firmmust be publicly listed and traded on one of the majorU.S. exchanges, and all partial acquisitions (i.e., acquiring less than100% of the target assets) are to be excluded. With these screeningcriteria, we obtain about 11,000 completed deals from the SDC overthe specified sample period.13 In earlier versions of the paper, we chose the largest deal (made in agiven year) for serial acquirers; this approach generated a somewhatdifferent sample, but our main results are robust to this samplingprocedure.14Specifically, requiring that each acquirer have nonmissing data onCRSP to calculate announcement returns reduces the sample from11,000 to about 7,000 deals. If we also require nonmissing data onCompustat to calculate investment efficiency measures, we end upwith about 3,500 deals. The screening and selection procedure andthe resulting sample are consistent with those reported in Netteret al. (2011).15See Almeida et al. (2011) for an alternative explanation. They arguethat acquirers may pursue liquidity-driven mergers even if thesedeals do not have operational synergy.16Candidates for firmsmatching an acquirer are those with the sametwo-digit SIC codes and with asset size (at the end of fiscal yearbefore the deal announcement date) that is 50%–200% of that of theacquirer; firms that have not made an acquisition during the threeyears prior to and three years after the deal announcement year areranked based on their MTB, and the firm with the closest MTB ischosen as the matching firm.17Our results differ from those of Savor and Lu (2009). They findthat, as a group, completed deals perform worse than failed deals,whereas we differentiate acquirers by their investment efficiencyand find different results compared with matching failed deals. Mal-mendier et al. (2016) examine failed deals and conclude that targetsof cash deals are undervalued prior to receiving the takeover bid,whereas targets of stock deals are fairly valued.18To obtain unlevered (industry) cost of equity, we first obtain unlev-ered equity beta from a levered firm; see, for example, Chapter 19 ofBrealey et al. (2011) for how to unlever a levered firm’s beta. We thencalculate each firm’s cost of equity using the CAPM and then obtainthe industry-level cost of equity and assign it as the input for thefirm’s cost of equity in the WACC formula.

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http://ssrn.com/abstract=414420

Return to Invested Capital and the Performance of Mergers ...es.saif.sjtu.edu.cn/attachments/publication/2017/... · QianandZhu: Return to Invested Capital and the Performance of

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