Roll Et Al. - Market Response to European Regulation of Business Combinations

8/8/2019 Roll Et Al. - Market Response to European Regulation of Business Combinations

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Market Response to European Regulation of Business CombinationsAuthor(s): Nihat Aktas, Eric de Bodt, Richard RollSource: The Journal of Financial and Quantitative Analysis, Vol. 39, No. 4 (Dec., 2004), pp. 731-757Published by: University of Washington School of Business Administration

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JOURNAL F FINANCIAL NDQUANTITATIVENALYSIS VOL. 9, NO. 4, DECEMBER 004COPYRIGHT 004, SCHOOLOF BUSINESS DMINISTRATION,NIVERSITYF WASHINGTON, EATTLE, A98195

MarketResponse to European Regulation fBusiness CombinationsNihat Aktas, Eric de Bodt, and Richard Roll*

AbstractAcquisitions, mergers, and other business agreements ace increasing regulatory crutiny,even when they involve firms domiciled outside the territory f regulatory authorities. Re-cent examples include mergers between American firms that were approved by Americanregulators but blocked by European egulators. Regulatory eciprocity eems a likely futuretrend. There are obvious consequences for the successful completion of future business

combinations. This paper explains the regulatory procedures of the European Commissionwith respect to business combinations, documents the price reactions of subject firms ondates from the initial announcement o the final regulatory decision, and studies whetherEuropean regulators end to shield European firms from foreign competition. Our mainresults are: i) the market clearly reacts to European egulatory ntervention ven when thesubject firms are non-European, i) the probability of intervention s not related to the na-tionality of the bidder, however, ii) when intervention does occur, the market anticipates twill be more costly when the bidder s non-European, o protectionism annot be rejectedoutright, and iv) regulatory nterventions are anticipated by investors, so they affect theinitial announcement eturns.

I. IntroductionThe summer of 2001 witnessed an unprecedented event in the history of busi-

ness combinations. Two American companies, General Electric and Honeywell,obtained approval to merge from all American regulatory agencies, but regulatorsin Europe blocked the merger. There have been few, if any, events that point so

vividly to global market integration. Two decades ago, Europeans would have

scarcely noticed mergers beyond their borders, but now are paying close attention

and have erected a system of severe sanctions against non-European firms thatmight be tempted to defy their regulatory edicts. Sanctions include fines and/orexclusion of offending companies from European markets.

*Aktas, [email protected], Universit6 Catholique de Louvain, 1 place des Doyens, 1348Louvain-la-Neuve, Belgium; de Bodt, [email protected], Universit6 de Lille 2-Esa, 1 placeD6liot - BP381, 59020 Lille C~dex, France; Roll, [email protected], he Anderson School,UCLA, Los Angeles, CA 90095. We thank Eugene Fama, Michel Levasseur, Stephen Ross, partic-ipants at the UCLA Business Forecast, the 2002 EFMA Conference, the 2002 EFA Conference, the2002 London Business School finance seminar, and Jonathan Karpoff (the editor), for constructivecomments and suggestions.

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732 Journal of Financial and Quantitative Analysis

European hallenges to the GE/Honeywell merger were followed closely inthe American inancial press and, because of its size and especially the outcome,it is probably one of the best-known events of its kind to date. It is, however, farfrom the first. Two other widely publicized cases were the Boeing/McDonnellDouglas merger of 1997, which was finally approved after a number of con-cessions by the companies involved (see Aktas et al. (2001)), and the proposedEMI/Time Warner deal of 2000, which was scuttled (see The Wall Street Journal(2000)).

In total, from the beginning of their activities in September 1990 throughJuly 2002, the regulatory authorities of the European Commission (EC) exam-ined 2,055 proposed business combinations of all types (mergers, acquisitions,

jointventures, and

agreementso share assets). A

completelist is available at

http://europa.eu.int/comm/competition/mergers/cases/. ublic regulation of M&Ahas long attracted he attention of the academic community. Most of the litera-ture finds little evidence that antitrust activities foster competition. Why does itexist? Bittlingmayer and Hazlett (2000), focusing on the Microsoft case, sug-gest three possibilities: private advantages rom antitrust egulation, bureaucraticself-interest, and political extraction. EC activities provide a useful context totest for one type of private advantage: protecting ocal firms from internationalcompetition.

This is a key question. If fundamentally rotectionist, he adoption of mergerregulations by several jurisdictions could become a serious barrier o efficiencyenhancing global business combinations. Imagine two merging irms doing busi-ness in, for instance, five regions. If regulatory heories and actions were roughlyindependent across regions, even a modest probability of blockage by any singleregional regulator could translate nto a very large probability of blockage by atleast one.

Bris and Cabolis (2002) identify 42 countries hat have enacted merger aws.They refer to the Wilkinson Sword/Gillette ase, where 14 different agencies were

involved. A recent example involves Microsoft, which last year settled an an-titrust case brought by U.S. authorities without making any egregious conces-sions. On February 12, 2003, the financial press reported hat Microsoft com-petitors, ncluding Nokia, Sun Microsystems, and AOL Time Warner, ad filed a260-page anti-competition omplaint with the EC, which, according o Microsoft," ... contains the same arguments hat were made by our competitors n the U.S.proceedings." Some commented that Microsoft faces a higher hurdle n Europethan t leaped over in the U.S., because " ... the EC is looking for a way to distin-guish itself from the U.S." in regulatory matters. If true, this appears o be exactlythe type of jeopardy engendered by multiple regulatory urisdictions. If protec-tionism is a widespread motivation or merger regulation, blockage could be evenmore likely due to retaliation of one regulatory body against another.

A study of intervention y European egulators, who have been highly activeover the past decade, should be informative with respect to the long-term conse-quences of this looming possibility. Our goals are to provide a systematic accountof the stock market's response to European regulatory activities and to test forthe existence of protectionism. We also study a more general ssue: do investorsanticipate regulatory ntervention, .e., are returns observed around a merger an-


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Aktas, de Bodt, and Roll 733

nouncement nfluenced by anticipation of regulatory osts? If so, announcementreturns will not capture he complete economic value of the merger.

We have collected a virtually complete record of EC regulatory actionsthrough 2000 involving publicly traded companies, along with stock price andvolume responses in the respective local markets around action announcementdates. One thing is clear immediately: although he extent of European egulationwas not widely appreciated by the U.S. public prior to the GE/Honeywell event,stock markets eemed to understand t very well. There are strong price reactionsto European egulatory nnouncements. The specifics, to be described n detail inthe paper, are fascinating. Our results contrast with those of Brady and Feinberg(2000), who are working with a sample of 27 firms for which they do not findsignificant stock price reactions around European egulatory nterventions whatthey call "case specific effects").

The data reveal that mergers with greater promise of value creation attractcloser scrutiny from EC regulators, which is consistent with their stated anti-monopoly objective. Non-European irms are not scrutinized more often thanEuropean irms, which is also evidence against protectionism. But when a firmis subjected to an in-depth investigation, the market anticipates a much highercost when the bidder is non-European. This is consistent with a protectionisteffect of European regulatory activities and could arise from more stringent at-

titudes against foreign bidders or from less effective lobbying by foreigners, orboth. Overall, he empirical evidence about possible protectionism s mixed. Pro-tectionism cannot be dismissed outright but further esearch will be necessary toclarify the true motives of European egulators.

We find also that investors anticipate regulatory activities. Initial announce-ment returns hould be interpreted s the wealth effect of the combination ess theanticipated cost of regulatory ntervention. The interaction s complex becausemore valuable mergers attract closer scrutiny by the EC regulators, hus raisingthe intervention probability and lowering the observed return, which reduces the

probability, nd so on.Our work offers some enhancements o existing empirical methods. Ourevent study takes simultaneous account of non-normality nd autocorrelation fabnormal eturns, of event-induced ariance Boehmer et al. (1991)) and of eventclustering. To study determinants f regulatory ntervention, we adopt an orderedprobit model. This controls for endogeneity between a merger's wealth creation(investors' reactions) and the probability of intervention regulators' reactions).We use both linear and truncated egressions when studying determinants f re-turns. Although Eckbo et al. (1990) advocated he truncated method for studying

voluntary orporate vents to reduce potential self-selectivity bias, it has not beenused frequently n subsequent mpirical work.

The paper s organized as follows. Section II provides a brief literature e-view. Section III describes European egulatory procedures with respect to busi-ness combinations. Section IV describes he data n detail, and Section V presentsan event study for a comprehensive ample and for sub-samples categorized bysize, country, and other pertinent attributes f individual cases. Section VI studiesthe determinants f the probability of regulatory ntervention and the impact of


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A. The Scope of Intervention

An important novelty introduced by Regulation EC n' 4064/89 (passed in

1989 and first implemented n 1990) is the one shop principle. In general, pan-European regulations about business agreements and dominant position abusesallow for concurrent nforcement of national regulations. But EC regulation akesexclusive precedence or mergers and acquisitions of European "dimension." Ac-cording to Article 12 of n 4064/89, a combination s considered o be of Euro-pean dimension when the two following conditions are met: The total world-widegross sales of all concerned irms exceed 5 billion euros; and the European ndi-vidual gross sales of at least two of the concerned irms exceed 250 million euros,unless every concerned irm makes at least two-thirds of its gross sales in a singlemember state. Alterations made in 1997 to the basic regulation urther oweredthe thresholds, so the current criteria sweep under EC purview most significantbusiness combinations. They imply that national regulations by the individualmember states have been relegated o a role of secondary mportance.

B. Juridical ompetenceThe EC's rather xclusive authority might explain the favorable reception of

the regulations by major European irms, simply because they shorten he lengthof anti-trust rocedures. The EC's decisions are final and need the approval of nohigher judicial authority. Indeed, there is no appeal other than to a "tribunal epremiere nstance" (a country court) or to the EU court. This allows the EC tonegotiate remedial actions from a strong position; the firms nvolved usually wishto avoid a prolonged court appeal of uncertain outcome (Winckler and Brunet(1998), p. 14).

There is one important difference between European and American com-bination control systems. The American system stipulates that the authorities(Department of Justice and Federal Trade Commission) must obtain the consentof a judge for every ban, whereas the EC on its own authority an block what itconsiders an objectionable ombination.

C. Procedures

A proposed business combination must advise the Commission no later thanone week after a deal agreement the public announcement f a takeover, an ex-change offer, or acquisition of control). There are some noteworthy differencesfrom American procedures, ncluding: notification o the EC can be given onlyafter the official signing of a deal agreement and European regulators are sup-posed to maintain ull confidentiality about all information eceived following anotification. Confidentiality s obligatory until the authorities decide to block thecombination or allow it to proceed, and a combination cannot be completed be-fore the initial notification and, to take effect, it must be declared acceptable afterthe investigation.

As Article 10 of Regulation 4064/89 specifies, the EC has one month tocomplete its preliminary analysis (time runs from the moment it receives com-plete information). This period s called Phase I. It culminates n a decision based


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mainly on the information ontained n the notification. Four decisions are possi-ble: i) the combination does not constitute a combination of European dimensionand hence is not subject to review (Article 6.1.a of Regulation 4064/89); ii) thecombination s compatible with the rules of the Common Market (Article 6.1.bof Regulation 4064/89) and is therefore approved; ii) although he combinationis basically compatible with the rules of the Common Market, he combinationwill be permitted only if certain conditions are met (Article 6.1.b is of Regulation4064/89, and Article 1.5.a of Regulation 1310/97); and iv) doubts are cast on theproposed combination. A more detailed analysis will be undertaken. This ex-tended nvestigation s called Phase II (Article 6.1.c of Regulation 4064/89). Thecriteria hat bring this denouement have never been clarified.

Once the detailed (Phase II) investigation s underway, he EC has four addi-tional months to complete its investigation and to rule on the compatibility of thecombination with European aw. At the end of the (up to) four-month period, theEC may issue three possible rulings about the proposed combination: approval;approval ubject to certain conditions; and unacceptable. If the combination hasalready been consummated, he EC can order the separation of the firms or ofthe grouped assets, the end of common control, or any action that could restorecompetition.

The last two outcomes supposedly reflect doubts the EC has concerning he

compatibility of the combination with competition.In such an

event,the EC

must communicate ts objections to the parties involved and provide them theopportunity o present their points of view. As stressed by (Winckler and Brunet(1998), p. 65), "such a communication of grievances plays an important part inthe procedure, ince the Commission can base its final decision only on objectionsfor which the interested parties were given the opportunity o put forward heirobservations." The interested parties have the right to examine the case file andcan demand a hearing.

Leparmentier 2001) discusses differences n the objectives of American and

European egulations. European egulators are supposed o examine only the po-tential creation of a dominant position. American regulators FTC and DOJ) lookat efficiency as well as the broader nterests of consumers. Such fundamental if-ferences arouse repidation hat global business combinations ould become moreand more difficult f regulatory authorities ail to harmonize heir approaches.

IV. Data

A. Actionsbythe DGC Directorate eneral orCompetition fthe EC)

Table 1 provides summary nformation about proposed combinations thatnotified he EC since the inception of regulations n 1990 through he latest monthin our data sample (December 2000). The entries after the last column show thenumber of outcomes by type of decision. As of December 2000, the DGC hadtaken 78 proposed mergers and acquisitions hrough Phase II. Among them, 15were approved outright, 47 were approved ubject to various conditions, and 13were declared incompatible with EU conditions and were therefore forbidden.


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Another three cases were resolved differently (by referral o an individual ECmember state or by restoration f effective competition).

TABLE

European Commission EC) Regulatory Outcomes or Proposed Business Combinations

Years

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 Total

No. ofcases notifying he EC 12 63 60 58 95 110 131 172 235 292 345 1573

Cases withdrawn-Phase 3 1 6 4 5 9 5 7 8 48

Termination fter Phase I 7 55 57 54 86 102 118 131 229 260 328 1427Outside EC jurisdiction 2 5 9 4 5 9 6 4 6 1 1 52Approved without onditions 5 47 43 49 78 90 109 118 207 236 293 1275Approved ubject o conditions 3 4 2 3 2 12 19 28 73Other decisions after Phase Ia 1 1 1 3 7 4 4 6 27

Phase II proceedings nitiated 6 4 4 6 7 6 11 12 20 19 95

Cases withdrawn-Phase I 1 1 4 5 6 17

Decision after Phase II 5 4 3 5 7 7 11 9 10 17 78Approved 1 1 1 2 2 1 1 3 0 3 15Approved ubject o conditions 3 3 2 2 3 3 7 4 8 12 47Prohibited 1 1 2 3 1 2 1 2 13Other decisions of Phase Ilb 2 1 0 3

Other ecisionsc 1 2 2 4 1 3 4 6 14 13 5 55

Number f proposed business combinations hat have notified EC regulatory uthorities ach year since the inception fthe legal requirement n 1990 through he latest month n our data sample (December 2000). Entries n the last column

give the total number f outcomes by type of decision. PhaseI

terminationases

endafter a one-month

nvestigationperiod. Phase IIcases are subjected o an in-depth nvestigation, hichcan take up to four additional months.

apartial r full referral o an individual C member tate.bPartial eferral o an individual Cmember tate or restoration f effectivecompetition.cPrevious decision revoked, mposition ffines, or relief rom prior uspension.Source: DGC, "Merger askForce"

B. Market rice,VolumeData, and Deal Features

Stock price and volume data were obtained from Datastream accessed at

the Universit6 de Lille 2. For announcement dates, four separate sources werechecked: Reuters, Bloomberg (through Dexia bank), the SDC Database editedby Thomson Financial and, depending on the country, the financial press (LesEchos, Financial Times, and The Wall Street Journal). The SDC Database andthe financial press have also been used to collect supplementary nformation uchas the size of the deal, the means of payment, the type of combination, and thepresence of rumors n the months preceding he combination.

Much information s available at http://www.europa.eu.int/comm/competi-tion, the official DGC Web site, including statistics on interventions by the DGC,current egislative amendments, and final decision reports (some are download-able in *.pdf). Among the interesting nformation n these reports are diagnosticsprovided by the DGC, which allows one to classify combinations nto four cat-egories: i) firms that do not operate in the same industry sector; ii) firms thatoperate n the same sector but not in the same geographical area; iii) firms thatoperate n the same sector and in the same geographical area, but have only lim-ited sales volume in that area; and iv) firms that operate n the same sector, n thesame geographical area, and have significant sales volume. Because we do nothave sector concentration measures such as the Herfindahl ndex, this information


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is particularly aluable. Only firms that operate in category iv represent a riskthat the combination will increase sector concentration s evaluated by the DGCexperts.

Because the firms nvolved were traded on various national exchanges, t wasnecessary to collect local market nformation about each exchange and to selecta market ndex (which will be employed in the usual way to construct abnormalreturns). The countries nvolved, the stock market ndexes selected, and the localcurrencies are listed in Table 2. We also collected currency xchange rates, short-term nterest rates (we use the U.K. Cash Deposit U.S.$ one-month rate for somerobustness checks), and the MSCI World Price Index data from Datastream.

TABLETiming, inalDecisions, ndDomiciles fProposedCombinations

Panel A. Year f Notification

90 91 92 93 94 95 96 97 98 99 00 Total

12 44 37 40 53 66 59 86 105 150 222 874

Panel B. Final DecisionProhibition Approval ubject o Conditions Outright pproval Referral Total

9 102 759 4 874

Panel C. Home Country Local Market ndex,and Currency

Country N Index Currencya

Australia 5 S&PASX200 DollarAustria 8 Weiner Boerse Index Schilling*Belgium 24 Brussels all Shares Franc*Bermuda 2 MSCIWorld rice Index DollarCanada 21 Toronto 00 DollarDenmark 11 Copenhagen SE Kr6neFinland 24 HEX Markka*France 221 CAC40 Franc*Germany 267 DAXKurs Price Index Mark*Greece 2 DJ EuroStoxxPrice Index EuroHong Kong 1 Hang Seng DollarIreland 2 Ireland E Punt*Italy 74 MilanComit Lira*

Japan 35 NIKKEI 25 YenLuxembourg 1 Luxembourg E 13 Franc*Netherlands 88 CBSAllShare Guilder*Norway 11 Oslo SE General Kr6nePortugal 3 DJ EuroStoxxPrice Index EuroSingapore 1 Singapore DBS50 Price Index DollarSouth Africa 7 JSE Industrial RandSpain 26 Madrid E General Peseta*Sweden 61 Affarsvarlden eighted all shares Kr6neSwitzerland 57 Swiss Market ndex FrancU.K. 250 FTSE 00 PoundU.S. 334 S&P500 Dollar

Total 1535

InTable 2, the panels break down he sample by (A)year of notification f the proposed combination o the EC, B)final

regulatory ecision type, and (C) country of domicile. Panels A and B pertain o combinations while panel C reportsindividual irms n the combinations. Panel C also gives the local market ndex used in the study and local currency hatwas converted ntoU.S.dollars at the spot exchange rate.

aSince January 1, 1999, euroland ountries ndicated by an * have maintained ixed exchange rates with he euro (andhence witheach other)

C. Firms and Cases with Available Data

It usually takes quite a while after an intervention or the EC to file an official

report on its Web site. Consequently, we were obliged to restrict our analysis to


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notifications rom 1990 through 2000 inclusive; later cases were mostly incom-plete. The total number of notified combinations during this period was 1,573(see Table 1).

Of these 1,573 notifications, 1,560 final decisions, comprised of 1,505 majordecisions and 55 other decisions (see notes in Table 1), were reached by the endof 2000. We study only the major decisions. Many proposed business combina-tions involve small or closely held firms with no readily available market priceinformation, so they could not be included in this study. In 874 of the 1,505major decisions, at least one of the subject irms was listed on a national stock ex-change. Table 2 provides a breakdown by year of notification, inal decision, andhome country for these 1,535 individual isted firms. But our tests require alsothat both the bidder and the target be listed, leaving 443 combinations and 886firms. Of the 443 combinations, 68 are public offerings, 64 are mergers, and 311are acquisitions; 169 involve a bidder domiciled outside the European ommunity.

Our final sample varies slightly from analysis to analysis depending on twofactors: the calendar date and the set of explanatory variables. The date mattersbecause sometimes firms are delisted prior o the final regulatory decision. Hence,they must be dropped rom the calculations. The explanatory ariables are some-times not available; e.g., the deal value and the means of payment. We reportthe actual size of the analyzed sample in each table. We note when the inclusion

of some variable has a significant mpact on the composition of the sample (e.g.,changing he composition among public offerings, mergers, and acquisitions).

V. MarketResponse to EC Regulatory ctionsThis section reports observed abnormal eturns around he initial announce-

ment date of the combination and around several regulatory decision dates, pro-vides tests for the relevance of the home country of the bidder, and assesses theinitial announcement eturn as a predictor of the final regulatory outcome.

A. Methods

The accepted method for isolating the impact of a particular vent on marketvaluations s the event study. Since its origination by Fama et al. (1969), therehave been many variations on the basic theme, all consisting of statistical pro-cedures designed to measure the event more precisely. In the sequel below, weemploy several variants n an effort to assure that the results are robust.

The first step in isolating the effect of an event is to construct a model for

normal returns; .e., individual firm returns hat would have occurred n the ab-sence of the event. We decided to try three different procedures, each of whichhas appeared many times in other papers and each possessing various merits andpossible problems. They are the simple market model, the market model with pa-rameters stimated by the Scholes and Williams (1977) method, and the constantmean return model.

Parameters or each model are estimated using 200 daily observations roma period prior o the initial announcement. Thirty days immediately preceding heannouncement vent window are excluded since they might be contaminated by


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information eakage. Eleven observations constitute our event window, five daysbefore and five days after the event date, which is day zero. The same parameterestimates are employed for all event windows, even events other than the initialannouncement such as the final resolution disclosure), because data subsequentto the initial announcement re possibly abnormally nfluenced by the proposedcombination.

Unreported esults show that abnormal returns do not adhere very well tothe spherical Gaussian specification. They are significantly non-normal n a largemajority of instances (which is typical for financial returns), and there is slightbut significant autocorrelation. These are adequate reasons for trying alternativestatistical approaches.

The cumulativeaverage

abnormal return scomputed

from theregressionresiduals (or from the mean deviations in the case of the constant mean model

return), irst averaging across firms relative to the announcement dates and thenaccumulating he averages rom the day prior o the event window.

Inferences about the observed cumulative abnormal eturn CAR) face notonly the difficulties of non-normal isturbances nd autocorrelation, ut also cross-sectional correlation because mergers and acquisitions are known to cluster intime) and event-induced volatility. Solutions to these problems have been exten-sively studied in the literature see Ruback (1982), Corrado 1989), Boehmer et

al. (1991), Salinger (1992), and Cowan and Sergeant 1996)) but there has beenno procedure or resolving all problems simultaneously. Our procedure shouldhelp ameliorate his situation.

Building on the Boehmer et al. (1991) method, our adjusted estimate of thevariance of abnormal eturns for the market model 1) is

(1) Var[CARr]=

T

+ 2(T-where T is the number of daily abnormal eturns accumulated n CAR, a 2 is theestimated residual variance, U is the estimation period length in days, Tm is themean of the market return over the estimation period, Var(rm) s its variance, rT0is the cumulated market return rom the beginning of the event window up totime T, and Cov[Rt, RlI] is the estimated first-order utocovariance during theestimation window.

As in the Boehmer et al. (1991) method, Var[CART] s used to standard-

ize the observed CART. Standardized CARTS or the N stocks are then aver-aged cross-sectionally o obtain he cumulative average abnormal eturn, CAART,whose standard rror will be 1 v by construction provided hat the individualelements of the average are cross-sectionally uncorrelated nd that the residualvariance does not change during the event window). The resulting t-statisticsare robust o event-induced variance, which is taken nto account by the adjustedstandard rrors of CART.

1The approach is easily extended to the constant mean return model and the Scholes/Williamsmethod.


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To tackle the normality problem and to improve he power of the test, we donot rely on an asymptotic p-value but use a percentile bootstrap approach Efronand Tibshirani 1993)). The procedure s very intuitive. From the original datamatrix, we draw with replacement 500 bootstrap amples of the same size as theoriginal. For each bootstrap ample, we apply the corrected Boehmer et al. (1991)method. The estimated bootstrap -statistics provide an empirical distribution owhich the t-statistic obtained from the original data can be compared. This pro-duces a bootstrap -value estimate. As Horowitz (2001) shows, this substantiallyimproves the speed of convergence of the estimated p-value and does not rely onnormality. Hence, our approach s an alternative o Corrado's 1989) and s robustto both departures rom normality and to event-induced volatility. Unreported e-sults show that, while the event study method is highly robust o the choice of aspecific return-generating rocess, using bootstrap -values can significantly alterthe conclusions, particularly with small samples.

Event clustering in time remains an issue. In some cases, there is perfectoverlap because several firms are involved in the same proposed combination.In that situation, we adopt the Mandelker 1974) and Jaffe (1974) procedure offorming one portfolio or each combination. Each firm s weighted n the portfolioby its market value as of the last day of the estimation window. Most of our resultsare at combination evel and hence are resolved by the perfect overlap portfoliomethod. For

partial overlap,he

jointestimation

procedure advocated by Salinger(1992) is computationally omplex and would imply estimating a matrix of size88,600 x 886! Instead, we propose n Section VII a robustness check, based alsoon a bootstrap procedure, which shows that our results are not sensitive to thisproblem.

Finally, we stress that our abnormal returns capture only the unanticipatedpart of the information elease around event dates (see Malatesta and Thompson(1985) or Schipper and Thompson (1983) for analyses of partially anticipatedevents) and that all statistical ests of differences between sub-samples are based

on a presumption f independence.

B. Preliminary Results

Our first results, shown in graph A of Figure 1, depict cumulative averageabnormal eturns CAAR) for all firms in the sample at the initial announcementdate. Note that both the bidder and target firms along with joint ventures areincluded. All returns are converted nto U.S. dollars at spot exchange rates. Localmarket ndexes are used as proxies for the market portfolio.

As could be expected, a sizeable price movement occurs on the first an-nouncement of a proposed business combination. It exceeds 2% on the day of theannouncement nd the two preceding days. Evidently, there is either leakage orinsider trading n some cases or imprecision n announcement ate determination.Figure 1 clearly highlights the robustness of CAAR estimation o the choice of aparticular ormal return model. Since the three methods of computing cumulativeabnormal eturns give similar results, we will henceforth present only those ob-tained with the market model, but will provide some additional obustness hecksin Section VII.


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742 Journal of Financial nd Quantitative Analysis

FIGUREMarket eaction t the Initial nnouncement f a Business Combination

Graph A. Initial nnouncement f Combination, llFirms,U.S.$,and Local Indexes

Graph B. Initial nnouncement f Combination, llFirms, nd Market Model

Graph A plots cumulative verage abnormal eturns CAARs) orall firms n the sample around he initial eal announce-ment date for three different methods of estimating bnormal eturns MM:market model, CMRM: onstant mean returnmodel, and SW: Scholes and Williams model). Allprices are converted nto U.S. dollars. Local market ndexes are usedas proxies or he market portfolio.Graph B shows the impact of the currency local vs. U.S. dollar) nd the index (MSClWorld Price Index vs. local ndexes), using the market model.

Graph B of Figure 1 shows the impact of currency local vs. U.S. dollar) andof the index (MSCI World Price Index vs. local indexes). There s little differencebetween using local currencies and dollars because exchange rate movements arevirtually ndependent cross event periods and are swamped by stock price move-


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ments. Similarly, he results are not very influenced by the index. Consequently,hereafter we present results only in U.S. dollars using local indexes.

C. Return Effects around Announcements and DGC Decision Dates

1. Initial nnouncement ate

For each business combination, we assigned the role of bidder to one firmand the role of target o a second firm. Usually, the notification o the EC explicitlystates which firm is the bidder. If this were not true in a particular ombination,we consulted the financial press and made a best effort to ascertain each firm'srole.

Table 3 reports nitial announcement CAARs for bidders, targets, and theircombination. Not surprising given past empirical studies of mergers, there isa large abnormal price increase for target firms and it is statistically significant.Target irms have significant abnormal positive performance up to five days priorto the announcement. The targets' CAAR over the event window is 10.15%,which is somewhat ower than in previous studies. For example, Mulherin andBoone (2000) find a target CAAR of 20.2% in a sample of 281 combinationsduring 1990-1999 and Andrade et al. (2001) report 15.9% during 1990-1998;both were for U.S. combinations only.

TABLEPrice Reaction o the Initial Announcement f a Business Combination

RelativeDate

-5 -4 -3 -2 1 0 1 2 3 4 5

Bidders N = 583)CAAR(%) -0.22 -0.32 -0.36 -0.38 -0.04 0.07 0.14 0.65 0.00 -0.04 -0.15p-value 0.00 0.00 0.01 0.01 0.97 0.20 0.18 0.02 0.62 0.66 0.74

Targets N 487)CAAR %) 0.58 0.88 1.24 2.04 5.58 8.20 8.70 8.96 9.12 9.10 10.15p-value 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Combinations N= 441)CAAR %) 0.03 0.01 -0.01 -0.05 0.53 1.02 0.96 1.66 1.01 0.99 1.51p-value 0.37 0.95 0.29 0.45 0.00 0.00 0.00 0.11 0.00 0.00 0.13Mean Difference,Target- Bidderp-value 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

CAARs round he initial nnouncement ate (day 0) of proposed combinations or bidders, argets, and combinations(bidders plus targets weighted by their respective market alues on the last day of the estimation window prior o theannouncement). Estimation s by the market model with ocal indexes converted nto U.S. dollars; p-values are from apercentile bootstrap based on the modifiedBoehmer t al. (1991) method described n Section V.

Our bidding firms have significant negative returns rom days -5 through-2. Over the 11-day window, the bidder CAAR is -0.15% (not far from the-0.37% found by Mulherin and Boone (2000) and somewhat ess negative thanthe - 1% reported by Andrade et al. (2001)). For the combined firms, bidder plustarget, here are significant positive returns on the announcement ate tself and onthe previous -1) and following (+1) days. Over the event window, the combined

2See, for example, the review paper by Jensen and Ruback (1983) and other studies in the samespecial issue of the Journal of Financial Economics, Andrade, Mitchell, and Stafford 2001) or Mul-herin and Boone (2000).


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CAAR is 1.51% (as compared with 3.51% in Mulherin and Boone (2000) and1.4% n Andrade et al. (2001)).

Differences between targets and bidders are highly significant. Using thelast day of the estimation window market value and the sub-sample for whichwe have both a quoted target and a quoted bidder, the aggregated dollar gain is$147,159 million for targets but the loss is $229,033 million for bidders, a materialnet reduction n value. This loss is comparable o that reported by Moeller etal. (2004) using a sample of 12,023 U.S. acquisitions during 1980-2001. Theyestimate a total value destruction f $218 billion for acquirers' hareholders, mostof which is attributable o large firms. Although we have fewer acquiring irms,they are mostly large.

Later, we focusmainly

on the combination evel. This choice is dictatedboth by econometric considerations combination portfolios overcome the eventclustering problem) and, more importantly, y the motivation or our study. Weare not trying to uncover he determinants f becoming a target or a bidder, but tounderstand he impact of DGC intervention n combinations.

2. End of Phase I

Table 4 shows CAARs for combined firms at the end of Phase I by decisiontype. Outright uthorization s apparently o surprise ince the CAAR is insignif-

icant. The most striking result is the clear difference (statistically significant ata 5% level) between the market's reaction to authorization with conditions andan in-depth nvestigation. The former s good news even though the conditionscould imply costs to the involved firms, because the combination s tentativelyacceptable and the DGC investigation s closed. The latter s bad news (a PhaseII investigation akes time and its outcome is uncertain).

TABLEAnnouncement f Phase I Termination

RelativeDate

-5 -4 -3 -2 -1 0 1 2 3 4 5

Outright uthorization N - 348)CAAR %) 0.04 0.04 -0.20 -0.32 -0.20 -0.13 -0.20 -0.26 -0.28 -0.36 -0.48p-value 0.23 0 31 0.35 0 16 0.48 0.57 0.71 0.79 0.61 0.39 0.13

Authorization ithConditions N= 38)CAAR(%) 0.18 0.91 1.19 1.07 1.22 1.69 1.96 1.73 1.44 1.33 1.48p-value 0.79 0 02 0 04 0.06 0.03 0.03 0.03 0.09 0.12 0.21 0.19

In-Depth Phase II) nvestigation N = 32)CAAR %) 0.08 -0.80 -0.61 -0.65 -0.72 -1.33 -1.61 -1.78 -1.78 -2.33 -2.65p-value 0.81 0.09 0.13 0.18 0,21 0.15 0.07 0.05 0.02 0.01 0.01

Tests of Differences n CAARs

Outright uthorization =Authorization ithConditionsp-value 0.72 0.04 0.00 0,02 0.06 0.03 0.06 0.10 0.13 0.21 0.13

Outright uthorization In-Depth nvestigationp-value 0.53 0.09 0.23 0 28 0 27 0.16 0.07 0.04 0.03 0.02 0.03

Authorization ithConditions - In-Depth nvestigationp-value 0.73 0.01 0 03 0 05 0.05 0.01 0.01 0.01 0.01 0.01 0.00

CAARs around the announcement date (day 0) at the end of an EC Phase I investigation, categorized by decision type(outright authorization, authorization subject to conditions, and in-depth investigation).

Estimation is by the market modelwith local indexes converted into U.S. dollars; p-values are from a percentile t bootstrap based on the modified Boehmeret al. (1991) method described in Section V.


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3. End of Phase II

Table 5 presents results for the end of Phase II. There is a positive reactionaround he decision

date,which could

signifythat the end of

uncertaintys

goodnews, but nothing is significant. This might be due to the CAAR mixing dif-ferent decisions (prohibition, authorization, uthorization ubject to conditions)and to possible market anticipation of the final decision. Splitting the analysis bydecision type would not improve power because sub-sample sizes are so small.

TABLEAnnouncement f Phase IITermination

Relative Date-5 -4 -3 -2 1 0 1 2 3 4 5

AllOutcomes N -=30)CAAR %) -0.40 0.00 0.01 -0.35 0.15 0.13 0.30 0.20 -0.26 -0.72 -0.43p-value 0.12 0.97 0.57 0.60 0.61 0.96 0.83 0.72 0.73 0.35 0.59

CAAR or all proposed combinations t the end of an EC Phase II nvestigation, nnounced on day 0. The results heremixdifferent ecisions (prohibition, utright uthorizations, nd authorizations ubject o conditions). Estimation s by themarket model with ocal indexes converted nto U.S. dollars; p-values are from a percentile bootstrap based on themodifiedBoehmer t al. (1991) method described nSection V

D. Announcement ffectsby HomeCountry f the BidderSuspicion about EC motives has been frequently articulated n the non-Euro-

pean press. Do EC anti-merger activities differentially mpact non-Europeanfirms, perhaps eflecting de facto protectionism f European ivals? To shed somelight on this issue, Table 6 presents results for bidders and for combinations afterdividing the sample between EC and non-EC bidders.3 The table includes fivepanels. Panel A presents the CAAR around he initial announcement ate; panelB, outright authorization fter Phase I; panel C, authorization ubject to condi-

tions after Phase I; panel D, announcement f a Phase II investigation at the endof Phase I; and panel E, decisions at the end of Phase II.Table 6, panel A shows no significant difference upon original deal an-

nouncement. Panel B reveals that, n case of outright authorization, ombinationsinvolving non-EC bidders seem to undergo slight wealth destruction near -1%on the 11-day event window) while those involving EC bidders show no signif-icant reaction. The difference between EC and non-EC bidders s, however, notsignificant and, at the combination evel, unreported esults show that it is onlymarginally ignificant n the few days following the announcement ate.

Panel C provides a more interesting result. There is a clear domicile dif-ference in case of authorization ubject to conditions at the end of Phase I. ForEC bidders and combinations nvolving them, there is almost no reaction. Fornon-EC bidders and combinations nvolving them, there is a strong positive (upto 9% for bidders) mpact. The differences between EC and non-EC sub-samplesare significant, both at the bidder and the combination evels. Clearly, he marketinterprets n authorization ubject o conditions as good news for non-EC bidders.

3The bidder s a U.S. firm n 121 (71%) of the 169 combinations n which the bidder s a non-ECfirm.


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TABLEPriceReactions orEuropean ndNon-European idding irms

CAARN (%) p-Value

Panel A. Initial nnouncement f Business CombinationsEC bidders 367 -0.21 0.44Non-EC idders 216 -0.06 0.60Combinations ithEC bidders 272 1.75 0.13Combinations ithnon-EC idders 169 1.13 0 00Difference,ECvs. non-EC idders 0.41Difference, ombinations ith and without EC bidders 0 25Panel B. Announcement f Outright uthorization fterPhase I

EC bidders 305 0.24 0.24Non-EC idders 183 -0.29 0.39

Combinations ithEC bidders 212 -0.32 0.28Combinations ithnon-EC idders 136 -0.74 0.09Difference,ECvs. non-EC idders 0.20Difference, ombinations ith and without EC bidders 0.41Panel C. Announcement f Authorization ubject o Conditions fter Phase /

EC bidders 35 0.34 0.74Non-EC idders 12 9.60 0.03Combinations ithEC bidders 29 0.70 1.00Combinations ithnon-EC idders 9 4.01 0.11Difference,ECvs. non-EC idders 0.02Difference, ombinations ith and without EC bidders 0.11Panel D. Announcement fPhase //IInvestigationEC bidders 23 -0.74 0.53

Non-EC idders 21 -2.71 0.01Combinations ithEC bidders 17 -1.58 0.37Combinations ithnon-EC idders 15 -3.87 0.05Difference,ECvs. non-EC idders 0.03Difference, ombinations withand withoutEC bidders 0.01Panel E Announcement f Decision after Phase IECbidders 25 0.84 0.52Non-EC idders 21 2.34 0.06Combinations ithEC bidders 17 -1.38 0.21Combinations ithNon-EC idders 13 0.80 0.47Difference,ECvs. non-EC idders 0.14Difference, ombinations ithand without EC bidders 0.24

CAARs nd their associated p-values are reported or he 11-day event window or bidders and for business combinationsafter dividing he sample between EC and non-EC bidders. Panel A presents the CAAR t the initial nnouncement;panel B, outright uthorization fter Phase I; panel C, authorization ubject o conditions fter Phase I;panel D, Phase IIinvestigation t the end of Phase I; and panel E the end of Phase II. Tests of differences re also given for both biddersand combinations. Estimation s by the market model with ocal indexes converted nto U.S. dollars; p-values are from apercentile bootstrap based on the modified Boehmer t al. (1991) method described n Section V

This result might at first sight seem surprising but it can probably be interpretedas follows: authorization ubject to conditions s good news for non-EC biddersbecause investors had feared an in-depth nvestigation.

Panel D shows what is probably he most important esult. While the valuedestruction around he announcement of a Phase II investigation (at the end of

Phase I) is insignificant or EC bidders and combinations nvolving them, it is sig-nificantly negative for non-EC bidders and combinations nvolving them, (morethan -2.5% with a p-value less than 1% for non-EC combinations).4 Moreover,the differences between the two sub-samples are significant. Evidently the mar-ket anticipates a much higher cost for non-EC bidders as a result of a Phase IIinvestigation.

4Ellert (1976) found a negative reaction to the announcement hat the merger of two U.S. firmswas to be challenged by American regulators. We thank J. Fred Weston for reminding us of Ellert'swork.


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One might have thought this result could be ascribed to systematic differ-ences between the deals initiated by EC and non-EC bidders. But this can beruled out by the results in panel A. If there were systematic differences, theywould have influenced value creation on the initial announcement date, which isclearly not the case. This leaves two possible explanations: the market nitiallyanticipates more costly DGC decisions for non-EC bidders, or the market antici-pates a better obbying effort by EC bidders presumably upported by their homecountry authorities). Either explanation mplies protectionism. This finding alsoopens another question. Is the probability of DGC intervention higher for com-binations involving non-EC bidders? Section VI investigates this question in amultivariate etting. Finally, panel E depicts the end of Phase II. Phase II termi-nation seems to convey slightly better news for combinations nvolving non-ECbidders, but most of the results are insignificant. Interpretations hould thereforebe made with care, keeping in mind the mix in the sample of several decisiontypes (see Section V.C).

E. Announcement ffects s Predictions f FinalOutcome

On the initial announcement of a proposed business combination, marketparticipants must consider the likely outcome of regulatory action, but it seems

possiblethat the

regulatorshemselves are influenced

bythe initial

price responseto a proposed deal. For example, suppose on occasion there really are somemonopoly rents to be gained from a merger; if the market assesses this possi-bility correctly, here should be a larger han average price rise of both bidder andtarget around he initial announcement. But if regulators are doing a good job,this should be associated with a higher probability of a subsequent prohibition.Table 7 is consistent with this idea. Combinations hat eventually proceed to anin-depth Phase II investigation by the regulators have larger price increases onthe initial announcement date (at least up to day +4). Perhaps regulatory suspi-cion is

aroused by the announcement ate return or, alternatively, he potential ormonopoly rents is independently determined by the regulators o be worthy of aPhase II investigation.

TABLEInitialDeal Announcement Effect and Eventual Regulatory Outcome

RelativeDate

-5 -4 -3 -2 1 0 1 2 3 4 5

Combinations nding n Phase Iand Phase II ProceedingsPhase I Combinations N 406)CAAR(%) 0.01 -0.02 -0.05 -0.14 0.35 0.86 0.86 1.62 0.94 0.91 1.49p-value 0.71 0.55 0.19 0 99 0.00 0.00 0.00 0.15 0.00 0.00 0.07Phase //II ombinations N = 35)CAAR(%) 0.21 0.34 0.45 0.95 2.59 2.83 2.11 2.20 1.74 1.88 1.78p-value 0.08 0.11 0.25

0.070.00 0.00 0.00 0.00 0.00 0.00 0.00

Difference, Phase II vs. Phase I Combinations

p-value 0.11 0.11 0.15 0.10 0.00 0.00 0.02 0.43 0.07 0.03 0.30Table 7 presents the CAAR at the initial announcement date for business combinations ending in Phase I and thoseproceeding through Phase II. Estimation is by the market model with local indexes converted into U.S. dollars; p-valuesare from a percentile t bootstrap based on the modified Boehmer et al. (1991) method described in Section V.


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VI. Determinants f the Probability f Intervention nd ofValue Creation

To this point, we have relied mainly on univariate tatistics of price reactionsaround various announcement dates. Even though some interesting results haveemerged, here are important uestions whose answers seem likely to be providedonly by a multivariate pproach. For example, to properly answer the questionraised in Section V.D (does the bidder's home country nfluence the probabilityof DGC intervention?), we should control for other possible determinants f theprobability of intervention. t would also be interesting o uncover variables hatinfluence the magnitude of the price movement around he initial announcementof the proposed combination. These questions raise serious endogeneity prob-lems. As pointed out in Section V.C, we cannot rule out an endogenous relationbetween the observed CAR and the probability of DGC intervention. We firstintroduce a method to resolve this particular onundrum.

A. Self-Selectivity iasand Investor/Regulator ndogeneityEckbo et al. (1990) are probably he first to directly address the endogene-

ity problem. They develop a model that takes into account self-selectivity bias involuntary orporate vents and endogeneity between the announcement eturn ndthe probability of intervention; .e., investors' simultaneous assessments of valuecreation and the probability of regulatory ntervention and regulators' simulta-neous observations of announcement eturns and decisions to intervene. Eckboet al. apply their model to U.S. mergers and acquisitions. With respect to self-selectivity bias, they argue hat when corporate vents result from voluntary deci-sions (such as corporate acquisitions, IPOs, or SEOs), rational management willundertake nly those combinations hat are anticipated o be value creating. (Thisdoes not mean, of course, that all combinations will be value creating ex post.)Self-selection truncates he distribution f the observed CAR. To account for thisphenomenon, he authors advocate runcated egressions such as

(2)0

(see Green (2003), p. 760) where yiis the dependent variable or observation , xiis the vector of independent ariables, is the normal density function, 4 is its cu-mulative, a is the truncation oint, cr s the standard eviation of the disturbances,and 3 is the set of coefficients. Estimation s by maximum ikelihood. The re-

sults presented by the authors ndicate the importance of self-selectivity. Usingtruncated egression, hey show, inter alia, that the larger he bidder relative o thetarget, the smaller the gain to the bidder, while ordinary non-truncated) stima-tion does not find a significant effect. Despite its apparent power, the truncatedregression approach has not been widely employed. In Section VI.C, we presentresults using both techniques.

Consider next the simultaneous estimation of CAR determinants and theprobability of regulatory ntervention. Eckbo et al. (1990) tackle the same prob-lem but base their analysis on an assumption hat nvestors and regulators possess


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independent ets of information. This seems at odds with the reality. Represen-tatives of the DGC M&A task force clearly reveal their keen interest n financialmarket data in conversations with the authors. Moreover, results below suggestthat EC regulators ase their decisions partly on stock price movements around heannouncement date. Section VI.B studies determinants f the probability of in-tervention while Section VI.C looks for determinants f the initial announcementCAR. To resolve the investors vs. regulators ndogeneity problem, we employ atwo-step instrumental ariable approach. Also, as in the univariate analysis, weundertake a bootstrap corresponding o each multivariate model. Moreover, weenlarge the bootstrap o 2,500 replications o accommodate he double source ofvariability mplicit in the two-stage instrumental ariables estimation.

B. Determinants fthe DGC's Probability f Intervention

Considering he limited sample size and the nature of the dependent variable(whether here is an intervention), we code intervention as a qualitative variablewith three possible levels. This variable akes the value 1 in case of outright autho-rization after Phase I, the value 2 in case of authorization ubject to conditions atthe end of Phase I, and the value 3 in case of an in-depth Phase II) investigation.It does not reflect the final outcome after Phase II.

To estimate the determinants of probability of intervention, we then fit anordered probit model of the form,

(3) Pr(Outright Authorization) = 0,

Pr(Authorization Subject to Conditions) = (0),

Pr(In-Depth Investigation) = (0),

where X is a vector of explanatory ariables and 3 is a corresponding ector of co-efficients, the pi coefficients are thresholds, and denotes the normal cumulative

density function. Estimation s by maximum ikelihood.The explanatory ariables are: Estimated CAR is an instrument or CAR (see

explanation below); DGC Experts' Diagnostic is a dummy variable hat takes thevalue 1 if the DGC determines hat the involved firms are not in the same sector,in the same geographical area, or have insufficient sales (see Section IV.B for de-tails); Outside EC is a dummy variable hat takes the value 1 if the home countryof the bidder s outside the EC; Large EC Country s a dummy variable hat takesthe value 1 if the home country of the bidder s one of the large EC countries Ger-many, France, Spain, Italy, or U.K.); Target Size is the market value of the targetevaluated at the end of the estimation period; Bidder/Target Returns Correlationis the correlation oefficient of the target and bidder returns evaluated during heestimation period (a proxy for the sector and geographical proximity of the targetand the bidder), and Deal Value is the deal value in millions of dollars. To alle-viate concerns about nvestors vs. regulators ndogeneity, we form an instrumentfor the CAR by regressing t on the following variables: Outside EC, Large ECCountry, Deal Value, Target Size, Bidder/Target Returns Correlation, which areall described above and, n addition, on the following variables which are not ex-planatory variables n the ordered probit); Bidder Market Value, the market value


21/28


of the bidder evaluated at the end of the estimation period; Target/Bidder ize Ra-tio, the target o bidder size ratio; Tender Offer, a dummy variable aking he value1 if the combination s a public offering; Cash Offer, a dummy variable aking hevalue 1 if the combination s 100% cash, Stock Offer, a dummy variable akingthe value 1 if the combination s 100% stock; Rumor, a dummy variable akingthe value 1 if there have been rumors n the financial press during he six-monthspreceding he combination, and Bidder Past Performance, he accumulated idderperformance uring he estimation period. The instrument ncluded n the orderedprobit s Estimated CAR, the fitted value of CAR from the above regression.

The results are in Table 8. Panel A reports the full model. Panel B ex-plores the impact of removing DGC Experts' Diagnostic, a variable subject topossible bias because it could be manipulated by the DGC. Being nonlinear,an ordered probit model does not provide coefficients that directly measure themarginal effects of explanatory ariables, but the procedure advocated n Greene(2003), p. 738) can be used to estimate these marginal effects. They are reportedin the Marginal Effects section of the table.5

The main conclusions include the following. The Outside EC dummy vari-able has no impact on the probability of intervention in either version of themodel). While Section V found evidence that higher costs are imposed on non-EC bidders by DGC intervention, here is no greater probability of intervention.

The combination of these two results suggests that EC bidders engage in moreeffective political lobbying once the DGC has intervened. The Large EC Countrydummy variable is also not significant at the usual statistical levels. Its boot-strap p-value is around 17% to 18%. In the Marginal Effects section of Table 8,Large EC Country s negative for Outright Authorization nd positive for Autho-rization Subject to Conditions; .e., bidders from the larger European countriesare (insignificantly) ess likely to receive outright authorization nd more likelyto be subjected to an intensive investigation. Estimated CAR, the instrumentalvariable, s not significant. DGC Experts' Diagnostic, Bidder/Target eturns Cor-

relation, and Deal Value are all significant n panel A. DGC Experts' Diagnostichas a negative effect on the probability of intervention and a bootstrap p-valueof 0.033) while Bidder/Target eturns Correlation nd Deal Value a have positiveeffect. As panel B shows, removing DGC Experts' Diagnostic does not changethe conclusions; hence there s little evidence to conclude that the DGC willfullymanipulates ts characterization f the combination. Bidder/Target Returns Cor-relation measures he return orrelation of the two subject companies prior o theannouncement, "smoking gun" that the combination might be anti-competitive.Hence, it not surprisingly makes DGC intervention more likely. Similarly, the

size (Deal Value) of the proposed combination ncreases DGC scrutiny.

C. Determinants f Announcement ate Returns

The previous section found that the probability of regulatory ntervention spredictable o some extent from determinants nown on the announcement ate ofthe deal. Investors surely realize this and exploit it when coming to a consensus

5The marginal impact of variable 1X on probability Pr(Decision Type) is an estimate ofOPr(Decision Type)/OXi.


22/28


23/28


TABLEDeterminants f CAR t the Initial nnouncement ate

Panel A. Linear Panel B. Truncated

Coefficient t-Value p-Value Coefficient t-Value p-Value

Intercept -0.106 -2.247 0.009 -0.582 -2.941 0.006

rj=2(Probability f Authorization ubject o Conditions) 0.894 1.886 0.035 2.815 2.307 0.016

Prj=3 Probability f In-Depth nvestigation) 0.762 2.150 0 013 2.243 1.991 0.023Deal Value -0.004 -1.427 0.068 -0.011 -1.296 0.094Tender Offer 0.018 1.881 0.010 0.039 0.687 0.240Cash Offer 0.004 0.318 0.590 -0.014 -0.257 0.478Stock Offer -0,023 -1.228 0.040 -0.023 -0,305 0.411Rumor -0,001 -1.301 0.144 0.001 0.182 0.681Bidder Past Performance -0.015 -0.973 0.108 0.013 0.254 0 506r 0.004 0.967 0.216R2 0.375F 25.417

A linear model (panel A) and the truncated egression model advocated n Eckbo et al. (1990) (panel B) are estimated.The dependent variable sthe CAR round he initial nnouncement fthe business combination. o measure he marginalimpact on CARof anticipated egulatory ntervention, redicted values for the probabilities f intervention ere obtainedfrom n ordered Probitmodel estimated upon he announcement ate of the proposed combination. FPrj=2s the estimatedprobability f ending Phase Iwithapproval ubject o conditions. Prj._3 s the estimated probability f an in-depth PhaseII nvestigation. Other determinants f CARare Deal Value in billions f dollars), Tender Offer a dummy ariable akingthe value one if he combination s a public offering),Cash Offer a dummy ariable aking he value 1 ifthe combination s100% ash), Stock Offer a dummy ariable aking he value 1 if he combination s 100% tock), Rumor a dummy ariabletaking he value 1 if there have been rumors n the financial ress during he six months preceding he combination), ndBidder Past Performance the accumulated bidder performance uring he estimation period). The truncated model isestimated by maximum ikelihood nd a is the estimated standard deviation f the disturbances The t-values and p-values are obtained rom a bootstrap procedure. Sample size is 348 inboth panels.

In panel A, the OLS estimation, the two intervention variables, the prob-ability of being authorized subject to conditions (Prj=2)

and the probability ofbeing subjected o an in-depth nvestigation Prj=3), have significant positive coef-ficients. This suggests that nvestors ake into account he likelihood of regulatoryintervention when evaluating proposed business combinations. The impact of anin-depth nvestigation s the more significant of the two effects but its coefficientis slightly smaller. The positive impact of these probabilities might at first seem

surprising ince they signify more probable egulatory ntervention; ut rememberthat most interventions nd with approval, ven after Phase II, and an interventionprobably ignals a belief by the regulatory uthorities hat considerable value is tobe created by the proposed combination. n Table 4, we showed that authorizationsubject o conditions after Phase I is actually good news, even better han outrightauthorization. The same figure showed that going into a Phase II investigationis bad news, but this is still consistent with the positive effect of the probabil-ity of In-Depth Investigation Prj=3) in Table 9 since a higher ex ante probabilityimplies more value creation (not counting the costs of intervention). If we had

been able to uncover determinants f wealth creation alone, the marginal mpactof a Phase II investigation hould have been negative. But the positive coefficienton(Prj=3) shows that the investigation has incremental nformation ontent aboutwealth creation, which dominates he negative aspect of regulatory ntervention.

The existence of previous rumors has a negative and (almost) significant m-pact. When the market anticipates the combination, part of the value creationis already ncorporated n prices before the official announcement. Stock Offercombinations create ess value. This is a well-known result n the M&A literature(see, e.g., Travlos (1987)). Payment n stock is supposedly a signal of overvalu-


24/28


ation. Tender Offer creates more value, a well-known result. Lastly, the size ofthe combination Deal Value) has a negative and fairly significant mpact on thevalue creation. In percentage return erms, arge deals create ess value.

Are these results robust o self-selectivity bias? Panel B of Table 9 attemptsto answer this question. The coefficients of Prj=2 and Prj=3 remain positive andretain heir significance. Deal Value keeps its negative coefficient but drops n sig-nificance. Tender Offer keeps its positive sign and Stock Offer keeps its negativesign but neither remains significant. Rumor drops from marginally ignificant toinsignificant. Cash Offer has an insignificant coefficient in both panels. We con-clude that standard CAR results should be interpreted with care, at least in thecontext of voluntary corporate events decided by economically motivated man-agers (see Eckbo et al. (1990)). Some apparently mportant determinants f CARcan vanish when truncated egression s employed.

VII. Robustness Checks

We have already checked the sensitivity of CAAR estimation o the methodused for establishing a normal return using the constant mean return, he simplemarket model, and the Scholes and Williams (1977) specification). We have alsoexamined

sensitivity currencydenomination local vs. U.S. dollar) and

pricendex

(local market vs. a global index (see Section V.B)). In this section, we presentsome additional hecks of robustness against other potential problems.

TABLE 0Checks of Robustness

RelativeDate

-5 -4 -3 -2 -1

Panel A. Are he Results Robust o the Bidder Price Run-Up?MarketModel,Estimated ntercept N = 1535)CAAR %) -0.09 -0.14 -0.27 -0.19 0.46p-value 0.30 0.14 0.03 0.33 0.00CAPMwithRiskless Rate (N = 1535)CAAR %) -0.09 -0.14 -0.26 -0.19 0.47p-value 0.26 0.15 0.03 0.24 0.00Panel B. Are the Results Robust o Quotation uspensions?WithQuotation uspensions N = 267 combinations)CAAR %) -0.02 -0.16 -0.30 -0.20 0.59p-value 0.63 0 22 0.08 0.40 0.00Without uotation uspensions N= 244 Combinations)CAAR %) -0.01 -0.11 -0.28 -0.15 0.64p-value 0.44 0.39 0.10 0.71 0.00Panel C. Are the Results Robust o Event Clustering?NotAccounting orEvent ClustenringN = 1535)CAAR %) -0.09 -0.14 -0.27 -0.19 0.46p-value 0 27 0.14 0.05 0.30 0.00

Taking ccount of Clustering N = 1535)CAAR %) -0.09 -0.14 -0.27 -0.19 0.46p-value 0 23 0.02 0.00 0.18 0.00

0 1 2 3 4 5

0.91 0.85 1.46 0.73 0.48 1.050.00 0.00 0.04 0 00 0.00 0.01

0.92 0.86 1.47 0.74 0.49 1.060.00 0.00 0.05 0.00 0.01 0.01

0.890.00

0.920.00

0.800.00

0.790.00

0.640.00

0.620 00

0.720.00

0.740 00

0.390.01

0.370 02

0.390.02

0.340 02

0.91 0.85 1.46 0.73 0.48 1.050.00 0.00 0.07 0.00 0.00 0.01

0.91 0.85 1.46 0.73 0.48 1.050.00 0.00 0.00 0.00 0.00 0.00

Panel A examines whether bidder price ncreases before he business combination ffect he results by biasing he inter-cept of the market model. Panel B presents results obtained with and without ases where there has been a quotationsuspension. Panel C explores he potential ias that partial vent clustering ould generate by comparing ootstrappedp-values withand without vent clustering.


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A. Bidders' rice Run-Up

Previous iterature has found bidders generally experience abnormally oodreturns before the announcement f a proposed combination. This could bias theintercept of the simple market model. To check the robustness of our previous re-sults to this potential problem, we replace the estimated ntercept by the risk-freerate multiplied by 1 - /3. Our proxy for the risk-free rate s the U.K. Cash DepositU.S.$ one-month ate. The MSCI World Price Index s our market portfolio proxyhere. Table 10, panel A compares he results for the 1,535 firms sample. There svirtually no difference.

B. Quotationuspension

In a significant number of cases (23 business combinations), reported vol-umes are zero around he event date. We inquired about this puzzling circum-stance with Datastream and learned that the zero volume usually corresponds oquotation suspension. To assure this did not influence our results, we reran ourtests without these cases. Table 10, panel B compares he results; again, no sig-nificant differences appear.

C. ClusteringAs discussed in Section V.A, event clustering can be treated n two different

ways. If there s perfect overlap, one can adopt the portfolio formation procedureintroduced by Mandelker 1974) and Jaffe (1974). When the overlap s only par-tial, Salinger (1992) advocates a joint estimation procedure. But his procedureis not well suited to large sample sizes, even with cheap computing power (seeSection V.A for more about this).

So to evaluate the potential mpact of partial overlap, we have bootstrappedthe initial data matrix

by includingeach observation n the

bootstrap ample onlyif it does not overlap with another observation. This procedure provides bootstrapsamples without any clustering. Table 10, panel C compares the bootstrap p-values obtained using the original procedure and the new procedure excludingany event clustering. The slight variations observed between the two sets of p-values are not sufficient to raise doubt about the results already presented n thispaper.

VIII. Summarynd Conclusions

Government regulation of business combinations s becoming an interna-tional phenomenon. Over the past decade, for example, regulators rom the Euro-pean Commission have increasingly ntervened n proposed mergers and acquisi-tions, even for entirely non-European ombinations ully approved by their homecountries. EC regulators have the power to block combinations rom virtually anycountry f the subject companies do significant business within Europe. It seemslikely that other jurisdictions will reciprocate and some, such as the U.S., havealready done so.


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This regulatory rend poses obvious potential difficulties or the efficient or-ganization of global industry. magine that on some future date there are five sep-arate regulatory blocks, each with its own approach o approving or prohibitingbusiness arrangements. To the extent that regulators act independently, he prob-ability of simultaneous approval could be small, even if authorization s likelywithin each single jurisdiction. Some degree of regulatory harmonization willundoubtedly rise, but it would be altogether utopian o anticipate perfect cooper-ation.

To our knowledge, this paper is the first to present global evidence aboutmarket reactions to EC regulatory events during the period 1990-2000. We firstdescribe the regulatory process in Europe, which conforms to a strict timetableand thus allows accurate measurement f market price reaction o regulatory de-cisions. According to current EC regulatory aw, only the larger proposed busi-ness combinations are subject to intervention. Intervention hen proceeds n twostages. Phase I provides for a preliminary nvestigation with four possible out-comes: not subject to EC intervention, approval, approval ubject to conditions,and further nvestigation. Most investigations erminate without urther nvestiga-tion. However, a significant number of proposals are subjected o a more thoroughexamination, called Phase II, which terminates definitively n approval, approvalsubject to conditions, or prohibition.

We examine the abnormal market price movements of 1,535 firms (874 busi-ness combinations) rom 19 countries as they moved through he EC regulatoryprocess. To estimate observed cumulative abnormal eturns and their associatedp-values, we adopt the Boehmer et al. (1991) approach, which takes account ofevent-induced ariance n returns. We modify this approach o accommodate irst-order auto-correlation f returns and employ a percentile bootstrap procedure oaccount for non-spherical disturbances. Our multivariate stimation controls forendogeneity between regulatory ntervention and wealth creation. We also checkwhether self-selectivity bias and event date clustering have influenced he results.

We find clear confirmation hat investors take anticipated regulatory nter-vention into account when considering a business combination. Observed cumu-lative abnormal eturns around business combination announcements must there-fore be interpreted onditionally with respect to the probability and costs of regu-lation.

We find that mergers with greater promise of value creation attract closerscrutiny rom EEC regulators, which is consistent with their stated anti-monopolyobjective. Also, EEC regulators have not subjected non-European irms to exten-sive scrutiny more often than European irms, which, taken by itself, would also

be consistent with an anti-monopoly motivation.However, heir own country regulators ikely scrutinized non-European irms

prior o consideration by the EEC, so the surviving combinations constitute a lessmonopoly-prone ample. This implies that an equal probability of interventionby the EEC is actually biased and perhaps protectionist. Moreover, when non-European irms are subjected to an in-depth nvestigation by EC regulators, hemarket anticipates a much higher cost than for European irms, also suggestinga protectionist dimension n European egulatory activities arising from a stricter


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attitude oward oreign firms or from more effective lobbying by European irms,or both.

The results about motivation are mixed. It is possible, of course, that Euro-pean regulators ometimes have pristine anti-monopoly motives while their mo-tives are less laudable n other cases. Further esearch s required o sort out thefrequency and extent of these conflicting motives.

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Documents

Roll Et Al. - Market Response to European Regulation of Business Combinations