stockholder gains from focusing versus diversifying bank ... · diversifying bank mergersq gayle l....

qThis study is based on research for my dissertation. I am especially grateful to members of mycommittee, Yakov Amihud (co-chair), Anthony Saunders (co-chair), David Yermack, David Backus,and Gregory Udell, for useful comments and insights. I owe a special debt of gratitude to ProfessorAmihud for his guidance and support. I also appreciate the helpful comments of Mark Flannery,Loretta Mester, Mike Pagano, Jonathan Rose, G. William Schwert (the editor), Dan Weaver, and ananonymous referee as well as comments from seminars at Baruch College, the Federal Reserve Bankof Philadelphia, and New York University. Any errors are solely my responsibility.

*Corresponding author. Tel.: 212-802-6413.

E-mail address: gayle}[email protected] (G.L. DeLong).

Journal of Financial Economics 59 (2001) 221}252

Stockholder gains from focusing versusdiversifying bank mergersq

Gayle L. DeLong*

Department of Economics and Finance, Zicklin School of Business, Baruch College,City University of New York, New York, NY 10010, USA

Received 7 January 1999; received in revised form 29 October 1999

Abstract

This paper shows bank mergers that enhance value upon announcement can bedistinguished from those that do not create value. I classify mergers of banking "rmsaccording to activity and geographic similarity (focus) or dissimilarity (diversi"cation),and examine the abnormal returns to each group as a result of the merger announcement.Mergers that focus both activity and geography enhance stockholder value by 3.0%while the other types do not create value. Analysis reveals that abnormal returns uponmerger announcement increase in relative size of target to bidder, but decrease in thepre-merger performance of targets. ( 2001 Elsevier Science S.A. All rights reserved.

JEL classixcation: G14; G21; G34; L89

Keywords: Banks; Mergers; Market reaction

0304-405X/01/$ - see front matter ( 2001 Elsevier Science S.A. All rights reserved.PII: S 0 3 0 4 - 4 0 5 X ( 0 0 ) 0 0 0 8 6 - 6

1Berger et al. (1999) provide a comprehensive analytic narrative of current research in theconsolidation of the "nancial services industry.

2See Palia (1995), Pillo! and Santomero (1998), and Rhoades (1994) for reviews, as well as Boydand Graham (1998) and Houston and Ryngaert (1994) for individual studies.

1. Introduction

Bank consolidation has been a trend in the United States since the mid-1980s.Begun as a way for poorly performing banks to exit the industry, mergerscontinued even after the industry returned to pro"table in 1992. Mishkin (1998)observes that regulatory and technological changes allow banks to expandgeographically and become larger. He predicts that the trend will continue andthat in twenty years the number of banks will be less than half the currentnumber. Although the number and size of mergers within the banking industryhave steadily increased,1 there is no clear evidence that banking mergers areeconomically valuable to shareholders upon announcement. Several studies "ndthat, on average, the sum of the weighted gains to the partners arising frommergers is negligible.2 This setting raises the question of whether all bankmergers have an insigni"cant value e!ect, or whether it is possible to distinguishthe types of mergers that lead to signi"cant gains from those that do not addvalue.

This study examines the wealth e!ect of bank mergers by distinguishingbetween types of mergers. Speci"cally, mergers are classi"ed according to theirfocus or diversi"cation along the dimensions of activity and geography. Thestudy determines the value e!ect, for bidders and for targets of mergers, and thecombined value e!ect for these players, for each group according to the focusingversus diversifying classi"cation. The results show that bank mergers that focusboth geography and activity are value-increasing, whereas diversifying mergersdo not create value.

Evidence on the detrimental value e!ect of diversifying mergers is provided byM+rck et al. (1990) for industrial "rms. However, M+rck, Shleifer, and Vishnyexamine only returns to bidders. While the results for bidders could indeedre#ect the economic inferiority of diversifying mergers, another possible inter-pretation of their results is that managers pursue their own interests (seeAmihud and Lev, 1981) and thus overpay for mergers that provide them withthe private bene"t of diversi"cation. The second interpretation implies a valuetransfer from bidder to target in diversifying mergers, but not necessarilyeconomic value destruction in such mergers. It is therefore impossible toestablish whether diversifying mergers are economically undesirable. A naturalquestion that follows from M+rck, Shleifer, and Vishny's study is: What is thee!ect of diversifying mergers on the total value of bidder and target? Thisquestion has not been comprehensively answered in the literature. Houston and

222 G.L. DeLong / Journal of Financial Economics 59 (2001) 221}252

3The debate concerns the value of universal banking where commercial banks provide all"nancial services, including securities and insurance, to their clients. For an overview of universalbanking, see Saunders and Walter (1996).

Ryngaert (1994) provide a partial answer regarding geographic diversi"cation.A major debate in the industrial organization of the banking industry, however,concerns activity diversi"cation.3 This paper examines the e!ect of diversifyingboth geography and activity. Studying the value e!ects of activity-diversifyingmergers allows us to make inferences on the desirability of various organiza-tional structures in the banking industry, such as universal banking.

In addition to examining the total value that diversifying mergers create ordestroy, this study di!ers from M+rck et al. (1990) in other respects. First, itexamines diversi"cation not only in terms of activity, as do M+rck, Shleifer, andVishny, but also in terms of geography. Looking at geographic diversi"cation inthe United States is important, because regulation at the state level in#uencesnot only the market for corporate control but also activities in which banks mayengage. Such regulation means that the "ndings of M+rck, Shleifer, and Vishnyare not immediately applicable to banking. Regarding corporate control, Cor-nett et al. (1998) show interstate bank mergers, which were relatively highlyregulated during the time of their study, tended to be non-value-maximizing forbidders upon announcement. On the other hand, intrastate bank mergers, whichwere subjected to few or no restrictions, did not destroy bidder value. Palia(1993) "nds regulatory restrictions in#uence the merger premium paid by thebidder to the target. States with restricted branching make the targets moreappealing, and therefore increase the premium, while states that allow multi-bank holding companies increase the number of bidders and also increase thepremium. Location of a bank in#uences not only the market for corporatecontrol, but also the characteristics of a bank's assets. A bank's loan portfolio isgreatly in#uenced by the regulation in its area, given that some states allow theirbanks to engage in underwriting securities and insurance while other states bansuch activities. Di!erent regulatory environments therefore in#uence businessdecisions. With unregulated "rms, business decisions are based on pro"t maxi-mization. If these "rms focus and then deviate from their original activities, thediversi"cation could be harmful. With banks, we do not know if focusing isa matter of choice or of regulation. Perhaps states with fewer controls on bankactivities produce diversi"ed banks that perform better than restricted banksthat are forced to focus their activities. Thus, the question in banking remainseven after considering the work of M+rck, Shleifer, and Vishny.

Second, focusing on the banking industry provides a control for industry-speci"c factors that could a!ect returns. The usual control in event studies is themarket return. There could be other factors particular to each industry thatshould be included in order to obtain uncontaminated results. Sweeney and

G.L. DeLong / Journal of Financial Economics 59 (2001) 221}252 223

Warga (1986), for example, show that investors demand a premium for interestrate risk. By focusing on the banking industry, my study corrects for interest rateand other bank-related risks.

Third, if any inter-industry e!ects exist, studying intra-industry mergersminimizes this impact. The "ndings of M+rck et al. (1990) could be the result ofa tendency for some industries to engage in a value-maximizing type of merger,while other industries engage in a non-value-maximizing type of merger. Indeed,Himmelberg et al. (1999) "nd that inter-industry e!ects in#uence "rm perfor-mance to the point of creating spurious results in analyzing the link betweenownership and performance. Since I focus on the banking industry, the resultscan be considered robust and immune to inter-industry di!erences.

Finally, activity diversi"cation is subtler than a mere classi"cation in di!erentindustries. Here, I examine the past movement of stock returns to determinewhether partners engage in di!erent types of risks and are therefore involved indi!erent types of activities. Unlike previous studies, I examine stock returnsusing cluster analysis to determine activity focus. Thus, this study presents a newand subtler dimension of diversifying versus focusing mergers and the e!ects ofthese types of mergers on the total value created by the transaction.

Evidence presented here suggests that bank mergers that focus both geogra-phy and activity create value upon announcement, while those that diversifyeither geography or activities, or both, do not create value. Overall, mergers inthe banking industry neither create nor destroy shareholder wealth, but mergersthat focus both geography and activities earn a positive 3.0% return. Bidders inthis group do not destroy value, while bidders in the other groups do destroyvalue. Targets that enter into focusing mergers do not earn signi"cantly more orless than targets in the other groups. Thus, my "ndings not only substantiatethose of M+rck et al. (1990) for the banking industry, but also enhance them byshowing that the loss of diversifying bidders is not the result of a wealth-transferfrom bidder to target.

The next section explores value-maximizing reasons for banks to engage ineither diversifying or focusing mergers. I then describe the methodology ofclassifying mergers, followed by a discussion on market reactions to the varioustypes of mergers. The "nal section summarizes the results.

2. Value-maximizing reasons for banks to merge

Miller and Modigliani (1961) show that the value to an acquirer of takingover an ongoing concern can be expressed as the present value of the target'searnings and the discounted growth opportunities the target o!ers. As long asthe expected rate of return on the growth opportunities is greater than the costof capital, the merged entity creates value and the merger should be undertaken.However, when the expected rate of return on these growth opportunities is less


than the cost of capital, the merged entity destroys value and the merger shouldnot take place.

Sources of the potential above-market rate of return can be seen more clearlyby examining the value additivity principle (VAP). According to this principle,the market value of a "rm is equal to the sum of the market values of theindividual discounted cash #ows of that "rm (see Haley and Schall, 1979).Assuming a merger does not create new cash #ows, VAP implies that the marketvalue of a merged entity is merely the sum of the market values of the individual"rms. The merger itself does not enhance the value of a "rm. Rather, investors areindi!erent to receiving income from a combined entity or from individual "rms.

Mergers, however, could create new cash #ows and thereby enhance the valueof the partners. The cash #ows come from saving direct and indirect costs as wellas increasing revenues. The following sections examine diversi"cation as well asfocusing reasons for value-maximizing banking "rms to engage in mergers.

A diversifying merger occurs when a bank merges with a "rm engaged indi!erent activities or located in markets di!erent from its own. Theoretically,diversifying "rms, which include "rms created through diversifying mergers,could create value by forming an e!ective internal capital market, therebylowering the cost of capital. Under the very strong assumption of management'sability to determine outcome of projects perfectly, Stein (1997) shows thatdiversi"cation can lead to lower cost of capital for a "rm. Management alwayspicks the winners and funnels resources to the projects that pay o! more thanother projects. Diversi"ed "rms would have uncorrelated projects from which tochoose and thereby create value in more states of the world than focused "rmswith highly correlated projects. Houston et al. (1997) show that bank holdingcompanies create internal capital markets in order to lower the cost of capital.Hubbard and Palia (1999) conduct empirical tests of the value of e$cientinternal capital markets. They "nd when external capital markets are relativelyundeveloped, as they were in the United States during the 1960s compared withthe information-laden decades that followed, internal capital markets serve toovercome ine$cient external markets. The greater the information asymmetriesbetween managers and the external market, the more valuable the internalmarket. All bidders, even those engaged in diversifying mergers, generallyearned positive abnormal returns during the 1960s. As informational asymmet-ries dissipate, so too the value of diversifying mergers and therefore the rewardsto the bidders of such mergers.

Like diversi"cation, focusing can have either a geographic or an activitydimension. Focusing mergers occur when the two partners engage in similaractivities or are located in the same market. Focusing mergers could create valuein several ways, including the replacement of less e$cient with more e!ectivemanagers (see Jensen and Ruback, 1983), the increase of market power (seeBerger and Humphrey, 1993), the reduction of overinvestment (see Amihudand Lev, 1981), or economies of scale (see Clark, 1988). Focusing mergers


consistently created positive abnormal returns for bidders, not only during the1960s as Hubbard and Palia (1999) "nd, but also during the 1970s and 1980s.Similarly, M+rck et al. (1990) examine mergers between "rms in related indus-tries "rms, which they de"ne as partnering "rms that share a 4-digit StandardIndustrial Classi"cation (SIC) code or whose correlation coe$cients are abovethe median for the sample. In their study, they discover that focusing mergerscreate positive abnormal returns for bidders, while diversifying mergers destroyvalue. The di!erence between diversifying and focusing mergers was morepronounced in the 1980s than in the 1970s. This "nding adds further validity tothe internal capital market argument described in the previous paragraph. Asinformation becomes more readily available, the value of e$cient internalcapital markets falls.

In summary, if mergers can create e$cient internal markets and such marketsare important, we should see no di!erence in returns to diversifying and focusingmergers.

3. Classifying mergers

To determine whether mergers that focus enhance value more than mergersthat diversify, I classify mergers according to whether they focus or diversifyalong geographic and activity areas. My sample consists of domestic U.S.mergers announced between 1988 and 1995 between publicly traded "rms whereat least one is a banking "rm, as reported by the Securities Data Company. Therequirement that both bidder and target be traded "rms limits the sample size.An alternative to the event study method is used by Palia (1993), who examinesthe ratio of price paid by the bidder divided by the target's book value of equity.The bid premium method allows the study of targets that are not traded. Allmergers in the sample were completed as of June 1996. I de"ne a merger as one"rm obtaining more than 50%, or adding to a lower percentage in order toreach more than 50%, of the voting shares of another "rm. This purchase resultsin the de-listing of the stock of the target "rm. Stock market data on both "rmshad to be available from the Center for Research in Security Prices (CRSP), andthe "rms had to be in existence for at least one year before the mergerannouncement in order to have enough information to classify their activities.Of the 729 mergers listed by the Securities Data Company, I am able to use 280.Of the mergers listed, 203 were excluded because they involved at least onenon-U.S. partner. These mergers are not covered in this study becausenon-controlled e!ects, such as di!erences in corporate control markets anddisclosure requirements, could confound the results and lead to incorrect con-clusions. Further reasons for exclusion include mergers that have at least onepartner not included in the CRSP database (111) and less than one year ofpre-announcement data available (135).


4Berger et al. (1995) present an informative discussion of the intricacies and evolution of U.S.banking law and regulation.

Table 1 provides descriptive statistics about the mergers used in this study.Panel A shows the median size of mergers by type. The median target-to-bidderratio of mergers that focus activities and geography is much larger, at 18%, thanratios of other types of mergers, which measure less than 8%. I investigatepotential implications of this di!erence in relative size in Section 4.5. PanelB shows the number of mergers by year and by type. Note that the number ofmergers that diversify geography, whether they focus or diversify activity,greatly increased over the sample period from 7 in 1988 to 34 in 1995. Until theMcFadden Act was superceded by the Riegle-Neal Interstate Banking andBranching E$ciency Act, U.S. regulations restricted banks from using relativelyinexpensive branches to expand into new states. Although the Riegle-Neal Actdid not go into e!ect until 1996, several states prepared for the change inregulation by relaxing restrictions in the early 1990s.4 Fig. 1, which shows thepercent of merger by type, substantiates this trend. More mergers in this studydiversify geography (57%) than focus geography (43%). Table 1 also shows thatin 1990 the number of mergers falls compared with the previous year for allgroups except mergers that diversify geography and focus activity. In 1990, theUnited States experienced a recession. Section 4.1, below, discusses the in#uenceof the business cycle and the type of merger that creates the most stockholdervalue upon announcement.

3.1. Geographic focus versus diversixcation

I classify mergers where acquirers and targets are headquartered in the samestate as in-market while those headquartered in di!erent states are market-extending. I use this method of classi"cation even when the acquirer already hasoperations, other than its headquarters, in the state where the target is head-quartered. The fact that the acquirer is devoting more resources to an areaoutside its original state suggests that the acquirer is extending its market. Ina separate test, I expand the de"nition of in-market to include mergers where theacquirer and the target are present in the same state but not necessarilyheadquartered.

3.2. Activity focus versus diversixcation

Determining whether a merger brings together two "rms with similar activ-ities is not as straightforward as determining geographic focus. When two "rmswith di!erent 2-digit SIC codes merge, the merger is diversifying. In this study,however, most of the partners falls into the large 2-digit category used for banks,


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Fig. 1. Categories of bank mergers. The sample consists of 280 domestic U.S. mergers announcedfrom 1988 to 1995 between publicly traded "rms for which at least one is a banking "rm. The chartshows the percentage of mergers in the sample that diversify or focus activity as well as thepercentage that diversify or focus geography. It also shows percentages for the four subgroups:GFAF mergers are those that focus both geography and activity, GDAF diversify geography butfocus activity, GDAD mergers diversify both geography and activity, and GFAD mergers focusgeography but diversify activity.

which is SIC 60. The 4-digit SIC codes represented in the sample include SIC6021, for `National Commercial Banksa, SIC 6022, for `State Banks, Member ofFederal Reservea, and SIC 6023, for `State Banks, Insureda.

I use two measures of similarity to classify partners according to activity. The"rst is Ward's method of cluster analysis. For a discussion of the variousmethods of cluster analysis, see Lehmann et al. (1998). This approach forms datainto clusters around a centroid, or mean. In my study, each observationrepresents a vector of variables, and begins as an individual cluster. Thecentroid, then, is the vector of variables. The two observations that are closest interms of squared Euclidean distance are joined together to form a cluster. Thevariables of the vectors are then averaged to form the centroid of the new cluster.The distance between the actual observations and this new centroid is the


within-group variation of the cluster. Ward's method seeks to minimize the ratioof within-group variation to between-group variation, that is, the squaredEuclidean distances between the centroids of the various clusters. The processcould continue until one all-encompassing cluster is formed, but, to be useful,the process is stopped before that point is reached. One gauge for determiningwhen to stop the process is to examine the amount of total variation, which isthe sum of squared Euclidean distances from each observation to the centralmean, that a formation of clusters explains. As the number of clusters increases,more of the total variation is explained. The all-encompassing cluster explainsnone of the variation, while the individual observations explain 100% of thetotal variation. The process can be stopped, for example, when the clusterformation explains 50% of the total variation.

Brown and Goetzmann (1997) use similarity of stock market returns todetermine mutual fund styles. They examine linear models that assign factorloadings to variables that may describe funds, but "nd that these models do notallow for the dynamic strategies that mutual fund managers often pursue. Sincestock returns re#ect the exposure a fund has to industry sectors, they re#ect thestrategy of the fund manager. By classifying mutual funds according to changesin their past monthly returns directly, Brown and Goetzmann allow portfolioweights to change every month, thereby alleviating a major problem of linearmodels. The classi"cation methodology explains the cross-sectional dispersionof fund returns by forming groups that minimize the within-group distancesamong the monthly returns. This approach allows the mutual funds to fallnaturally into groups based upon various strategies. Brown and Goetzmann"nd that their classi"cation methodology is a better predictor of future perfor-mance than classi"cations that use latent variable factor loadings, pre-speci"edfactors, or the styles reported by fund managers.

Applying cluster analysis to stock market returns enables the classi"cation ofmerging partners into groups. Similar to Brown and Goetzmann (1997), I formclusters of banking "rms by observing the similarity of their stock marketreturns. Merger partners that fall into the same cluster are focusing theiractivities, while those that fall into di!erent clusters are activity-diversifying.I use stock returns since they re#ect the pricing of various types of risk to whicha "rm exposes itself, and therefore provide a measure of the similarity betweentwo "rms.

One could argue that two partners involved in the same activities may notappear in the same cluster if the external shocks are di!erent. I examine thispossibility with regards to one of the major risks that permeates banking, whichis interest rate risk. Banks can incur losses when interest rates change unex-pectedly. Further, banks can expose themselves to varying degrees of this kind ofrisk through the di!erent types of activities they pursue. A bank that engages inmortgage lending exposes itself to more interest rate risk than a bank that hasa large percentage of its assets in government securities. Sweeney and Warga


(1986) "nd that investors demand a premium in advance from "rms that take onmore interest rate risk. Further, they "nd that this premium is proportional tothe amount of risk to which the "rm is exposed. Flannery and James (1984)reach a similar conclusion when they examine only banking "rms. Since interestrates are uniform within the United States, the location of the bank within theUnited States is not important. The stock market reacts to banks dependingupon the various exposures taken by banks to interest rate risk. The logicalextension of Sweeney and Warga's work suggests that the market will demanda premium for any type of risk, such as credit risk or foreign exchange risk, towhich a bank exposes itself. Stock prices, therefore, re#ect the types of risksa bank takes on through its various activities.

Another potential challenge to properly classifying a sample using historicalstock returns is hedging. For example, suppose that two banks are involved inmortgage lending, but one hedges its interest rate risk. The market will demanda higher advance premium from the bank that does not hedge, since that bankhas a higher exposure to interest rate risk. The stock market returns will not beas similar as two banks engaged in the same activities that do not hedge.The very act of hedging, which can include the use of swaps, options, futures,and forwards, is an activity. While the two banks overlap in one area, theproportion of the hedging bank's portfolio involved in mortgage lending issmaller than that of the non-hedging bank. The activities of the banks aretherefore not similar.

Taking these potential problems into consideration, I examine the stockmarket returns in order to classify the banks in my study. For each "rm, I forma vector of monthly stock returns, running from December to December, for thecalendar year preceding the announcement of the merger. Many of the "rmswere not in existence for longer than a year before the announcement, makinglonger periods of observation impossible. Examining years separately has theadvantage of automatically removing any drift to the mean from the data.I control for geographic focus or diversity by "rst dividing the sample accordingto geographic similarity using the methodology explained in the previoussection. This step creates 16 sets of data, or eight separate years divided intogroups of either geographically focusing or geographically diversifying mergingpartners. I perform cluster analysis on each of the 16 groups individually, andstop when the clusters explain approximately 40}50% of the total variation. Thenumber of clusters varies for each year, and ranges from three for geographicallyfocusing mergers announced in 1988 to six for geographically diversifyingmergers announced in 1995. In ten sets of data, I remove outliers and cluster theremaining observations.

Observation of the categories into which the merger partners fall suggests themethod is able to distinguish between merger types. Table 2 shows the clusterformation for one of the 16 sets, the geographically diversifying mergerpartners in 1990. Note that savings and loan institutions fall into the same


Table 2Cluster formation for geographically diversifying mergers, 1990

The following is an example of the groupings that are formed when cluster analysis is applied to themonthly stock market returns of merger partners in this study. I divide the sample of 560 mergingpartners according to the year the merger is announced. I then control for geographic focus ordiversi"cation, and perform cluster analysis on each group. Since I look at 8 years of mergerannouncements (1988}1995), I obtain 16 subsets of merging partners.

Clusters Merging partners

Cluster 1 Brook"eld Bancshares, ILSellersville S&L, PAFirst Home Federal S&L, FL

Cluster 2 Comerica Inc., Detroit, MINorwest Corp, MNHuntington Bancshares, Inc., OHUS Bancorp, ORWyoming National Bancorp, WYFirst Financial Bancorp, OHNational City Corp, OHFayette Federal Savings Bank, INBanks of Iowa, Inc., IONational Penn Bancshares, Inc., PABucheye Financial Corp, OHFirstar Corp., WIValley National Bancorp, NJMay#ower Financial Corp., CA

Cluster 3 InBancshares, CAHeartFed Financial Corp., CA

Cluster 4 Citicorp, NYBankAmerica Corp., CAUnited Banks of Colorado, CO

Outlier Benjamin Franklin Federal S&L, OR

cluster (Cluster 1), regional banks fall into another (Cluster 2), and the twomoney-center banks (BankAmerica and Citibank) fall into Cluster 4. Otherchecks of how well this method identi"es classi"cations show that four of the "vemergers initiated by the Minneapolis-based First Bank System, a bank holdingcompany known for its focusing strategy, are activity-focusing. Norwest, a bankholding company also based in Minneapolis and known for its diversifyingstrategy, initiated seven activity-diversifying mergers out of a total of eightmergers in this study. Finally, NationsBank, a North Carolina bank holdingcompany, was involved in three activity-focusing and three activity-diversifying


mergers. NationsBank, which is now called Bank of America Corp. as a result ofa merger, is not known for any particular merger strategy.

To check the robustness of the "rst measure, I use a second method tomeasure activity relatedness of partner activities, which is the correlation coe$c-ient of the stock returns of the partners. Following M+rck et al. (1990), I examinethe correlation coe$cients of daily stock returns of the partners in the year priorto the merger announcement. Speci"cally, I look at the period 300 to 51 daysbefore the merger announcement when such data are available. In three cases,I have slightly fewer observations, while I have at least 200 observations in allcases. As with the "rst measure, I control for geography by dividing the mergersinto two groups, geographically focusing and geographically diversifying, andanalyze the groups separately. I consider mergers with correlation coe$cientsabove the median of the group to be focusing and those below to be diversifying.Since this measure separates the data into only two activity groups, whichclassify above and below the median, it does not provide the richness of clusteranalysis that divides the data along several lines of activities. Also, the methodo-logy creates two groups of equal size when there may actually be more mergersthat are focusing or more that are diversifying. Using correlation coe$cients,however, reduces the in#uence of any outliers on the classi"cation scheme andalso serves to verify the general results of cluster analysis.

4. Market reactions to various types of mergers

To determine whether the market distinguishes between announcements ofdiversifying or focusing mergers, I establish whether merger partners experienceabnormal returns upon the announcement of the merger. I use the standardevent study methodology (see Brown and Warner, 1985) with the market model

ARit"R

it!(aι#bιRMt

), (1)

in which ARit

is the abnormal return for stock i at time t, Rit

is the return onstock i at time t, and R

Mtrepresents the return on the market at time t.

I estimate the intercept and slope coe$cients, alpha and beta, using dailyreturns collected 300 to 51 days before the merger announcement, when suchdata were available. In cases where the "rm had not been in existence for a full300 trading days before the announcement so as to yield 250 observations in theestimation period, I include it as long as there was at least one year of data,yielding approximately 200 observations in the estimation period. The marketreturn is the value-weighted index of returns including dividends for the com-bined New York Stock Exchange, American Stock Exchange, and NASDAQtaken from CRSP data. Cumulative abnormal returns (CAR) are compiledfrom ten days before the merger announcement to one day following the


announcement. Extending the window forward in time to ten days after theannouncement does not signi"cantly alter the results. Furthermore, the eventwindow is not important if all groups are a!ected equally. That is, if there is nocorrelation between pre-event leakage and type of merger, widening or narrow-ing the window will change the level of CAR, but not the relationship among thegroups. When I extend the event window to (t"!50, 10), the group of mergersthat focus both geography and activity is the only one to earn positive returns.The major di!erence between the shorter window and the longer one is that the"gures are statistically signi"cant in the former, but not in the latter. This resultoften occurs when event windows are widened as a result of the increasedvolatility of returns over time.

To determine the extent to which a particular type of merger creates ordestroys value, I examine the average cumulative abnormal returns of thespeci"c group. In order to test whether a merger is value-enhancing or destroy-ing, I examine the abnormal returns to both bidder and target. The gains invalue can be shared between the partners, but usually not evenly. Althoughtargets usually gain 20% to 30% upon announcement (see Jensen and Ruback,1983), targets typically are about 10% of the size of bidders. Even if bidders loseonly 1% of value, the loss more than o!sets the gain to targets. Previous studiesthat look at the combined return to bidder and target examine the weightedaverage of the returns to the bidder and the target (see Bradley et al., 1988;Cybo-Ottone and Murgia, 1996). The weight used is "xed, and based on therelative asset or market values of the merger partners, and is determinedsometime prior to the event window. The problem with this approach is that thebase changes such that percentage increases and percentage decreases are notsymmetric. The following example illustrates this point. Assume that the marketvalue of the bidder is the same as the target on the date the weight is "xed. Thisassumption results in a weight of 50% for both partners. Assume further thatthe market value of each partner is set at 100 so that the portfolio is worth 200. Ifthe market value of the bidder increases by 20 to 120, or a 20% increase, and themarket value of the bidder falls by 20 to 80, a similar 20% decrease, the weightedaverage return to the bidder and target is the same as the return to thehypothetical portfolio, or zero. The portfolio is still worth 200. If, then, thevalue of the bidder decreases by 20 back to 100, or a fall of 16%, and the value ofthe target increases 20 to 100, indicating a rise of 25%, the weighted average ofthe return is 0.5]!16%#0.5]25%, or 4.5%. In fact, the portfolio is stillworth 200, and has created no value.

The results for a few individual mergers di!er substantially for the twomethods of calculation. In one case, a bidder that represented 87% of the mergerlost 1.4%, and its target gained 17.4% yielding a weighted average loss of 1.4%of value. Had the partners been joined during the period, they would havecreated 7.3% value. The results for most mergers, and therefore the overallresults of this study, are basically the same regardless of method. For example,


mergers that focus both geography and activity create 2.9% value for weightedpartners and 3.0% value for the hypothetical partners. Overall, the weightedpartners lost 0.11%, but the hypothetical portfolios created 0.04%. Neithervalue is statistically di!erent from zero.

In order to obtain the combined value, I construct a hypothetical portfolio ofthe two merging partners, and determine the daily changes in the sum of theirmarket values, using

RPt"lnM [ (M<B

t#M<T

t)/(M<B

t~1#M<T

t~1)!1]#1N. (2)

In Eq. (2), RPt

is the return on the portfolio at time t, M< represents the marketvalue of the partner, with B designating bidder and T designating target, at timet or t!1. This methodology takes into consideration the relative size of thepartners. A relatively large bidder that loses value o!sets the gains of a smalltarget so that the return on the hypothetical portfolio is zero or negative. Thevalue creation of a relatively large target, however, could o!set the losses ofa relatively small bidder so that the combined partners create value. I also lookat returns to bidders and targets separately in order to determine how thereturns are divided. Since correlation is not a problem for individual "rms,I compare stock prices, including dividends, at time t and t!1 to obtain thesereturns.

To test for signi"cance, I use the Z-statistic described by Dodd and Warner(1983). Z-statistics control for the number of observations in the estimationperiod as well as market #uctuations during the event window. This test statistichas a normal shape, with values ranging from zero to one. The null hypothesisbeing tested is the possibility of no abnormal returns resulting from the mergerannouncements. As noted by Dodd and Warner (1983), the average CAR andZ-statistic may have opposite signs. This result can occur if most CARs arepositive and the sample includes a few extreme negative outliers with very largestandard deviations. In such cases, both average CAR and the Z-statistic aretypically close to zero.

I also examine whether the di!erences between various types of mergers arestatistically signi"cant. The t-statistic measures the statistical signi"cance of thedi!erence between the means of the two groups. It divides the di!erence inmeans by a control for the variance of the CARs and the size of the groups beingexamined as follows:

t"x61!x6

2Jv

1/n

1#v

2/n

2

. (3)

In Eq. (3), x6iis the mean of sample i, v

iis the variance of sample i, and n

iis the

size of sample i. The t-statistic is distributed as a Student-t under the nullhypothesis of no di!erence in abnormal returns to the two groups.


Non-parametric tests indicate whether outliers drive the results. The Wil-coxon signed rank test indicates whether the median is statistically di!erentfrom zero. The p-value associated with this test is always positive even thoughthe median CAR can be negative. Also, I perform the binomial test to indicatewhether the percentage of positive abnormal returns is statistically signi"cant.The Z-statistic associated with this test will be negative when the percentage ofpositive results is less than 50%.

4.1. Diversifying versus focusing mergers

I "rst look at whether the market distinguishes between mergers that focusgeography and those that diversify geography. I also test whether abnormalreturns are di!erent for merging partners that focus their activities as opposed tothose that diversify activities. The market does not distinguish geographicallyfocusing mergers from geographically diversifying ones, but it does favor activ-ity-focusing mergers over those that diversify into other products. Column 2 ofTable 3 shows that geographically focusing and geographically diversifyingmergers neither create nor destroy value for the combined partners. The non-parametric tests suggest that the abnormal returns are not driven by outliers.Column 2 of Table 4 shows that activity-focusing mergers enhance value by1.5%, and activity-diversifying mergers destroy value by 0.9%. The individualamounts, as well as the di!erence, are statistically signi"cant at least at the 5%level. The non-parametric tests in this case, however, are not statisticallysigni"cant, and suggest the results may be driven by outliers. An additional testnot reported in the tables indicates whether two groups come from the samedistribution. The non-parametric Wilcoxon two-sample test produces a p-valueof 0.0, which suggests the di!erence between the two activity groups, focus anddiversi"cation, is statistically signi"cant at the 1% level. Focusing activity,therefore, tends to enhance value, while focusing geography does not destroyvalue.

Note that, in a recession, geographic diversi"cation becomes more importantbecause it spreads risks. For example, a bank located in both Texas andPennsylvania would be better o! than a bank located only in Texas when oilprices fall. A recession occurred during the course of the period studied here.Real gross domestic product in the United States declined in the second half of1990 and the "rst quarter of 1991. The National Bureau of Economic Researchdistinguished this recession as an 8-month contraction, from a peak in July 1990to a trough in March 1991. Fig. 2 shows the average CAR by year and by mergertype for U.S. mergers. Note that investors bid down the price of mergers thatfocus both geography and activity in 1990. After the recession, the market againappreciated mergers that focused geographically. To test this reaction morespeci"cally, I regress combined CAR against three dummy variables. One forgeographic focus, one for activity focus, and one for geographically focusing


Tab

le3

Cum

ulat

ive

abno

rmal

retu

rns

(CA

Rs)

tom

erge

rsac

cord

ing

toth

efo

cus

ordi

vers

i"ca

tion

with

resp

ect

toge

ogr

aphy

The

sam

ple

cons

ists

of28

0do

mes

tic

U.S

.mer

gers

anno

unc

edbe

twee

n19

88an

d19

95be

twee

npub

licly

trad

ed"rm

sfo

rw

hic

hat

leas

ton

eis

aba

nkin

g"rm

.The

sam

ple

isdiv

ided

into

grou

ps

acco

rdin

gto

geogr

aphi

cdi

vers

i"ca

tion

.A

geogr

aphic

ally

focu

sing

mer

ger

ison

ein

whic

hbo

thpa

rtner

sar

ehe

adqua

rter

edin

the

sam

eU

.S.st

ate,

and

div

ersify

ing

mer

gers

are

those

for

whi

chth

epar

tner

sar

ehea

dquar

tere

din

di!er

ent

stat

es.R

eturn

sfo

rth

eco

mbin

edpar

tner

sar

edet

erm

ined

by

com

par

ing

tota

lm

arke

tva

lue

of

the

two

attim

et

with

tota

lm

arket

valu

eat

t!1:

RP t"

ln[(M<

B t#

M<

T t)/(M<

B t~1#

M<

T t~1)]

.R

etur

nsfo

rbi

dder

san

dta

rget

sco

mpa

repr

ices

inper

iod

tw

ith

thos

ein

per

iod

t!1.

Abno

rmal

retu

rns

are

calc

ulat

edus

ing

stan

dar

dev

entst

udy

met

hodol

ogy

(see

Bro

wn

and

War

ner,

1985

)w

ith

the

mar

ket

mode

l.C

AR

s(t"

!10

,1)

are

aver

aged

for

each

group

.Tes

tsof

stat

istica

lsign

i"ca

nce,

eith

erZ

-sta

tist

ics

orp-

valu

es,ar

ein

par

enth

eses

.P

-val

ues

are

used

only

toin

dic

ate

whet

her

the

med

ian

isst

atistica

llydi!er

entfrom

zero

,an

dar

eba

sed

onth

eW

ilcoxo

nsign

edra

nk

test

.

Com

bin

edpar

tner

sBid

der

sT

arge

ts

Num

ber

ofA

vera

geM

edia

n%

Ave

rage

Med

ian

%A

vera

geM

edia

n%

mer

gers

CA

RC

AR

Positive

CA

RC

AR

Positive

CA

RC

AR

Positive

Mer

ger

type

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pan

elA:

Cum

ulat

ive

abno

rmal

retu

rns

Geo

grap

hic

focu

s12

10.

620.

3553

.8(!

1.46

!1.

7234

.718

.30

14.8

189

.3(0

.96)

(0.4

7)(0

.82)

(!3.

11)!

(0.0

1)!

(!3.

36)!

(28.

10)!

(0.0

0)!

(8.6

4)!

Geo

grap

hic

dive

rsi"

cation

159

!0.

40!

0.34

45.3

!1.

85!

1.68

32.7

15.3

218

.46

88.1

(!0.

77)

(0.2

7)(!

1.19

)(!

4.71

)!(0

.00)

!(!

4.36

)!(2

2.95

)!(0

.00)

!(9

.60)

!

Tota

l28

00.

04%

!0.

13%

48.9

%!

1.68

%!

1.70

%33

.6%

16.6

1%16

.74%

88.6

%(0

.05)

(0.7

7)(!

0.36

)(!

5.60

)!(0

.00)

!(!

5.50

)!(3

5.77

)!(0

.00)

!(1

2.91

)!

Pan

elB:

Diw

eren

ces

betw

een

grou

psG

eogr

aphic

focu

sve

rsus

geogr

aphic

div

ersi"ca

tion

1.02

0.69

8.5

0.39

!0.

042.

02.

98!

3.65

1.21

t-st

atistics

1.46

0.60

1.26

!Si

gni"

cantat

1%le

vel


Tab

le4

Cum

ulat

ive

abno

rmal

retu

rns

(CA

Rs)

tom

erge

rsac

cord

ing

toth

efo

cus

ordi

vers

i"ca

tion

with

resp

ect

toac

tivi

ties

The

sam

ple

cons

ists

of28

0do

mes

tic

U.S

.mer

gers

anno

unc

edbe

twee

n19

88an

d19

95be

twee

npub

licly

trad

ed"rm

sfo

rw

hic

hat

leas

ton

eis

aba

nkin

g"rm

.A

nac

tivi

ty-focu

sing

mer

ger

isone

inw

hich

both

par

tner

sfa

llin

toth

esa

me

clust

er,an

ddiv

ersify

ing

mer

gers

are

those

whe

reth

epar

tner

sar

ein

di!

eren

tcl

uste

rs.

Ret

urns

for

the

com

bine

dpar

tner

sar

ede

term

ined

by

com

par

ing

tota

lm

arket

valu

eat

t!1:

RP t"

ln[(

M<

B t#M<

T t)/(M<

B t~1#

M<

T t~1)]

.R

etur

nsfo

rbid

der

san

dta

rget

sco

mpar

eprice

sin

period

tw

ith

thos

ein

period

t!1.

Abn

orm

alre

turn

sar

eca

lcul

ated

usin

gst

anda

rdev

ent

study

met

hodo

logy

(see

Bro

wn

and

War

ner

,19

85)w

ith

the

mar

ket

mod

el.C

AR

s(t"

!10

,1)

are

aver

aged

for

each

grou

p.Tes

tsof

stat

istica

lsig

ni"

cance

,eithe

rZ

-sta

tist

icsorp-

valu

es,a

rein

par

enth

eses

.P-v

alue

sar

euse

don

lyto

indic

atew

het

her

them

edia

nis

stat

istica

llydi!

eren

tfrom

zero

,an

dar

ebas

edon

the

Wilc

oxo

nsign

edra

nk

test

.

Com

bined

par

tner

sBid

der

sT

arge

ts

Num

ber

ofA

vera

geM

edia

n%

Ave

rage

Med

ian

%A

vera

geM

edia

n%

mer

gers

CA

RC

AR

Positive

CA

RC

AR

Positive

CA

RC

AR

Positive

Mer

ger

type

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pan

elA:

Cum

ulat

ive

abno

rmal

retu

rns

Act

ivity

focu

s11

21.

470.

4556

.3!

0.95

!1.

1237

.517

.61

16.7

791

.1(2

.67)

!(0

.22)

(1.3

2)(!

2.50

)"(0

.00)

!(!

2.65

)!(2

7.99

)!(0

.00)

!(8

.69)

!

Act

ivity

div

ersi"ca

tion

168

!0.

91!

0.89

44.1

!2.

17!

2.16

31.0

15.9

416

.48

86.9

(!2.

12)"

(0.1

4)(!

1.54

)(!

5.18

)!(0

.00)

!(!

4.94

)!(2

3.32

)!(0

.00)

!(9

.57)

!

Tota

l28

00.

04%

!0.

13%

48.9

%!

1.68

%!

1.70

%33

.6%

16.6

1%16

.74%

88.6

%(0

.05)

(0.7

7)(!

0.36

)(!

5.60

)!(0

.01)

!(!

5.50

)!(3

5.77

)!(0

.00)

!(1

2.91

)!

Pan

elB:

Diw

eren

ces

betw

een

grou

psA

ctiv

ity

focu

sve

rsus

activi

tydiv

ersi"ca

tion

2.38

1.33

12.2

1.22

1.04

6.6

1.67

0.29

4.17

t-st

atistics

3.44

!1.

77#

0.74

!Si

gni"

cantat

1%le

vel

"Si

gni"

cant

at5%

leve

l#Si

gni"

cantat

10%

leve

l


Fig. 2. Average combined CARs for US mergers by year and by type. The sample consists of 280domestic U.S. mergers announced from 1988 to 1995 between publicly traded "rms for which at leastone is a banking "rm. The chart shows the average by year for all the mergers in the sample as well asfour subgroups: GFAF mergers are those that focus both geography and activity, GDAD mergersdiversify both geography and activity, GFAD mergers focus geography but diversify activity, andGDAF diversify geography but focus activity. Returns to the combined merger partners aredetermined by comparing total market value of the two at time t with total market value att!1: RP

t"ln[ (M<B

t#M<T

t)/(M<B

t~1#M<T

t~1) ]. Abnormal returns are calculated using stan-

dard event study methodology (see Brown and Warner, 1985) with the market model for the timeperiod 10 days before to one day after the merger announcement.

mergers in 1990, as follows (t-statistics in parentheses):

Dummy for Dummy for Dummy forCumulative geographically activity geographicallyabnormal "a #focusing #focusing #focusing mergersreturns mergers mergers in 1990

CAR" !1.39 #1.33 #2.26 #!1.59(!3.03)HHH (1.95)H (3.38)HHH (!0.66) (4)

The results shown in Eq. (4) suggest that the market signi"cantly values bothgeographic and activity focus. Returns to mergers that focus geography in 1990,however, are indistinguishable from zero.


4.2. Combination mergers

Although the market seems to favor activity-focusing mergers, it may reactdi!erently when a merger focuses both activity and geography. In order to testthis hypothesis, I divide mergers into four mutually exclusive and exhaustivecategories. The "rst includes mergers that focus both geography and activity.The second includes mergers that diversify geography and activity. The thirdgroup includes mergers that focus geography and diversify activity, and thefourth group includes mergers that diversify geography and focus activity.Table 5 shows the CARs for these four groups. In Table 5, column 2 showsaverage CARs for the combined bidder and target, while columns 5 and 8 showaverage CARs for bidders and targets separately. Looking at the sub-groups ofmergers reveals di!erences. While three of the four groups do not create value,mergers that focus both geography and activities earn a positive 3.0% for thecombined partners. Bidders in this group do not destroy value, and targets thatenter into focusing mergers earn 18.7%. Results of non-parametric tests suggestthat the abnormal returns are not driven by outliers. Panel B of Table 5 revealsthat combined partners of mergers that focus both geography and activity earnat least 2.9% more than partners in any other type of merger, an amount that isstatistically di!erent from the other groups. The di!erences in earnings for theremaining groups are statistically indistinguishable from each other. In addition,I test for robustness by dividing the sample into two groups, mergers announcedbetween 1988 and 1992, and those announced from 1993 to 1995. I "nd nostatistically signi"cant di!erences between the two groups.

When I expand the de"nition of in-market merger to include mergers wherethe partners each have operations, but not necessarily headquarters, in overlap-ping states, mergers that focus both geography and activity still earn signi"-cantly more than the other groups. Although the point estimate of 2.1% isstatistically signi"cant at the 1% level (Z-statistic"3.2), it is smaller than theresult derived using the more restrictive de"nition. The market appears toconsider the home market as the place where the bank "rst establishes itself.Table 6 shows the results using categories determined by correlation coe$cients.Column 2 shows that mergers that focus both geography and activity earn 1.1%,an amount that is statistically signi"cant (Z-statistic"2.0), while the othertypes of mergers neither create nor destroy value. For geographicallydiversifying mergers, correlations range from !13.0% to 53.7%, with 5.5%being the median. For geographically focusing mergers, correlations rangefrom !19.5% to 66.2%, with a median of 3.8%. I use states as thegeographic delineations to make my results comparable to other studies, suchas Cornett and Tehranian (1992), that look at geographic focus and diversi-"cation in those terms. The remainder of the paper focuses on classi"cationsderived from cluster analysis using the more restrictive de"nition of in-marketmerger.


4.3. Value to bidders of mergers

Only bidders involved in mergers that focus both geography and activitydo not destroy value as a result of a merger announcement. Columns 5}7 inTable 5 show that while the other types of mergers destroy value for bidders,mergers that focus both geography and activity neither create nor destroy value.Panel B of Table 5, column 5 shows that the market appears to distinguishamong di!erent types of bidders such that bidders of mergers that focus bothgeography and activity earn at least 2.0% more than bidders in the other typesof mergers.

4.4. Value to targets of mergers

Targets everywhere and always tend to experience an increase in value. Thisobservation makes sense, since the shareholders of a company would not agreeto a takeover unless they experience a bene"t. This result also agrees with the"ndings of Jensen and Ruback (1983) regarding mergers in all industries. Datafor targets in Table 5, column 8 show that the average return is 16.6%. Targetsof geographically and activity-focusing mergers tend to earn the highest abnor-mal returns (18.7%), but, as shown in panel B of Table 5, the returns are notstatistically di!erent from the other groups. Targets of mergers in other indus-tries tend to earn even higher abnormal returns, as high as 25}30%. The numberof bidders in the United States is restricted as a result of activity constraintsarising from the Glass-Steagall Act and geographic constraints posed by theMcFadden Act, as well as the paucity of hostile takeovers in the bankingindustry. These constraints could help to explain the relatively low returns totargets in bank mergers.

4.5. Inyuence of other factors

Besides the type of merger, several factors may in#uence the market's reactionto the announcement of a bank merger. To establish that the di!erences betweenthe groups of mergers are not the result of other in#uences, I control forvariables that previous literature shows can be important to the announcementoutcome. These factors are: pre-merger performance of the target, relative size oftarget vis-a-vis the bidder, too-big-to-fail guarantees from the government, aswell as government assistance to failing banks, payment and accountingmethods, attitude of takeover, and number of bidders.

Palepu (1986) "nds that "rms are more likely to be acquired if they have lowstock performance. To control for under-performing targets that may createmore value for the merger partners, I regress the cumulative abnormal returns(CAR) of the combined merger partners on the target's pre-merger performance.I measure such performance as the return of bank i for the period 300 to 51 days


Tab

le5

Cum

ulat

ive

abnor

mal

retu

rns

(CA

Rs)

tom

erge

rsac

cord

ing

toth

efo

cus

ordiv

ersi"ca

tion

of

thei

rge

ogr

aphy

and

activi

tyusing

clust

eran

alys

isto

det

erm

ine

activi

tyfo

cus

The

sam

ple

consist

sof2

80do

mes

tic

U.S

.mer

gers

anno

unc

edbe

twee

n19

88an

d19

95be

twee

npu

blic

lytr

aded"rm

sw

her

eat

leas

tone

isa

ban

king"rm

.A

geog

raph

ical

lyfo

cusing

mer

geris

one

inw

hic

hbo

thpa

rtne

rsar

ehe

adqu

arte

red

inth

esa

me

U.S

.sta

te,a

nddi

vers

ifyin

gpar

tner

sar

ein

di!er

entst

ates

.A

nac

tivi

ty-foc

using

mer

ger

ison

ein

whi

chbo

thpar

tner

sfa

llin

toth

esa

me

clus

ter

and

div

ersify

ing

part

ner

sar

ein

di!

eren

tcl

ust

ers.

Ret

ur

ns

for

the

com

bin

edpar

tner

sar

edet

erm

ined

byco

mpar

ing

tota

lm

arket

valu

eof

the

two

attim

et

with

tota

lm

arke

tva

lue

att!

1:R

P t"ln

[(M<

B t#

M<

T t)/(M<

B t~1#

M<

T t~1)]

.Ret

urnsfo

rbid

der

san

dta

rget

sco

mpar

eprice

sin

period

tw

ith

those

inper

iod

t!1.

Abn

orm

alre

turn

sar

eca

lcula

ted

usin

gst

anda

rdev

ent

study

met

hodo

logy

(see

Bro

wn

and

War

ner

,19

85)w

ith

the

mar

ket

mod

el.C

AR

s(t"

!10

,1)

are

aver

aged

for

each

grou

p.Tes

tsof

stat

istica

lsig

ni"

cance

,eithe

rZ

-sta

tist

icsorp-

valu

es,a

rein

par

enth

eses

.P-v

alue

sar

euse

don

lyto

indic

atew

het

her

them

edia

nis

stat

istica

llydi!

eren

tfrom

zero

,an

dar

ebas

edon

the

Wilc

oxo

nsign

edra

nk

test

.

Com

bined

par

tner

sBid

ders

Tar

gets

Num

ber

ofA

vera

geM

edia

n%

Ave

rage

Med

ian

%A

vera

geM

edia

n%

mer

gers

CA

RC

AR

Positive

CA

RC

AR

Positive

CA

RC

AR

Positive

Mer

ger

type

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pan

elA:

Cum

ulat

ive

abno

rmal

retu

rns

Geo

grap

hic

and

activi

tyfo

cus

533.

001.

1964

.20.

14!

0.69

43.4

18.6

615

.16

94.3

(3.9

0)!

(0.1

9)(2

.06)

"(!

0.30

)(0

.41)

(!0.

96)

(23.

96)!

(0.0

0)!

(6.4

6)!

Geo

grap

hic

and

activi

tydiv

ersi"ca

tion

100

!0.

69!

0.74

43.0

!1.

80!

1.81

33.0

14.5

318

.49

88.0

(!0.

96)

(0.0

5)#

(!1.

40)

(!3.

52)!

(0.0

0)!

(!3.

40)!

(16.

76)!

(0.0

0)!

(7.6

0)!

Geo

grap

hic

focu

san

dac

tivi

tydiv

ersi"ca

tion

68!

1.23

!1.

4845

.6!

2.70

!2.

2127

.918

.02

14.7

985

.3(!

2.17

)"(0

.55)

(!0.

73)

(!3.

88)!

(0.0

1)!

(!3.

64)!

(16.

33)!

(0.0

0)!

(5.8

2)!

Geo

grap

hic

dive

rsi"

cation

and

activi

tyfo

cus

590.

04!

0.13

48.9

!1.

68!

1.70

33.6

16.6

116

.74

88.6

(0.0

5)(1

.00)

(!0.

36)

(!5.

60)!

(0.0

0)!

(!5.

50)!

(35.

77)!

(0.0

0)!

(12.

91)!

Tota

l28

00.

04%

!0.

13%

48.9

%!

1.68

%!

1.70

%33

.6%

16.6

1%16

.74%

88.6

%(0

.05)

(0.7

7)(!

0.36

)(!

5.60

)(0

.00)

!(!

5.50

)!(3

5.77

)!(0

.00)

!(1

2.91

)!


Pan

elB:

Diw

eren

ces

betw

een

grou

ps

Geo

grap

hic

/act

ivity

focu

svs

.ge

ogra

phi

c/ac

tivi

tydi

vers

i"ca

tion

3.69

1.93

21.1

51.

951.

1310

.44.

13!

3.32

6.3

t-st

atistics

3.27

!1.

74#

1.26

Geo

grap

hic

/act

ivity

focu

svs

.ge

ogra

phi

cfo

cus/

activi

tydi

vers

i"ca

tion

4.23

2.67

18.6

2.85

1.53

15.5

0.63

0.37

9.1

t-st

atistics

3.47

!2.

46"

0.22

Geo

grap

hic

/act

ivity

focu

svs

.ge

ogra

phic

dive

rsi"

cation/

activi

tyfo

cus

2.90

1.40

15.0

2.08

0.52

11.2

1.99

!1.

886.

2

t-st

atistics

2.48

"1.

72#

0.77

Geo

grap

hic

/act

ivity

div

ersi"ca

tion

vs.

geog

raphi

cfo

cus/

activi

tydi

vers

i"ca

tion

0.54

0.74

!2.

60.

900.

395.

1!

3.49

3.70

2.7

t-st

atistics

0.68

1.29

!0.

99

Geo

grap

hic

/act

ivity

div

ersi"ca

tion

vs.

geog

raph

icdi

vers

i"ca

tion/

activi

tyfo

cus

!0.

79!

0.52

!6.

20.

13!

0.61

0.8

!2.

141.

44!

0.10

t-st

atistics

!1.

120.

17!

0.65

Geo

grap

hic

focu

s/ac

tivi

tydiv

ersi"ca

tion

vs.

geog

raph

icdi

vers

i"ca

tion/

activi

tyfo

cus

!1.

33!

1.26

!3.

6!

0.77

!1.

00!

4.3

1.35

!2.

25!

2.8

t-st

atistics

!1.

58!

0.93

0.47

!Sig

ni"

cantat

1%le

vel

"Si

gni"

cant

at5%

leve

l#Sig

ni"

cantat

10%

leve

l


Tab

le6

Cum

ulat

ive

abno

rmal

retu

rns(C

AR

s)to

mer

gers

acco

rdin

gto

the

focu

sordiv

ersi"ca

tion

ofth

eirge

ogra

phy

and

activi

tyus

ing

corr

elat

ion

tode

term

ine

activi

tyfo

cus

The

sam

ple

cons

ists

of28

0do

mes

tic

U.S

.mer

gers

anno

unc

edbe

twee

n19

88an

d19

95be

twee

npub

licly

trad

ed"rm

sfo

rw

hic

hat

leas

ton

eis

aba

nkin

g"rm

.Age

ogr

aphic

ally

focu

sing

mer

geris

one

inw

hic

hbo

thpar

tner

sar

ehea

dquar

tere

din

the

sam

eU

.S.s

tate

,and

div

ersify

ing

mer

gers

are

those

whe

reth

epar

tner

sar

ein

di!

eren

tst

ates

.An

activi

ty-focu

sing

mer

geris

one

whe

reth

eco

rrel

atio

nco

e$ci

entoft

he

part

ners'st

ock

retu

rnsis

abov

eth

em

edia

n.

Ret

urns

for

the

com

bin

edpar

tner

sar

edet

erm

ined

byco

mpar

ing

tota

lm

arket

valu

eof

the

two

attim

et

with

tota

lm

arke

tva

lue

att!

1:R

P t"ln

[(M<

B t#M<

T t)/(M<

B t~1#

M<

T t~1)]

.R

etur

nsfo

rbid

ders

and

targ

ets

com

pare

price

sin

per

iod

tw

ith

thos

ein

period

t!1.

Abn

orm

alre

turn

sar

eca

lcula

ted

using

stan

dar

dev

ents

tudy

met

hodo

logy

(see

Bro

wn

and

War

ner

,198

5)w

ith

them

arket

model

.CA

Rs(t"

!10

,1)a

reav

erag

edfo

rea

chgr

oup.T

ests

ofst

atistica

lsign

i"ca

nce

,either

Z-s

tatist

ics

orp-

valu

es,a

rein

par

enth

eses

.P-v

alue

sar

euse

donly

toin

dic

ate

whe

ther

the

med

ian

isst

atistica

llydi!er

entfrom

zero

,an

dar

eba

sed

onth

eW

ilcoxo

nsign

edra

nk

test

.

Com

bin

edpa

rtner

sBid

ders

Tar

gets

Num

ber

ofA

vera

geM

edia

n%

Ave

rage

Med

ian

%A

vera

geM

edia

n%

mer

gers

CA

RC

AR

Positive

CA

RC

AR

Positive

CA

RC

AR

Positive

Mer

ger

type

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Pan

elA:

Cum

ulat

ive

abno

rmal

retu

rns

Geo

grap

hic

and

activi

tyfo

cus

611.

100.

1552

.5!

1.12

!1.

4534

.417

.60

14.6

090

.2(1

.95)

"(0

.80)

(0.3

8)(!

1.35

)(0

.02)

"(!

2.43

)"(1

8.86

)!(0

.00)

!(6

.27)

!

Geo

grap

hic

and

activi

tydiv

ersi"ca

tion

80!

0.54

!0.

2845

.0!

1.39

!1.

1633

.814

.70

18.4

991

.3(!

0.62

)(0

.43)

(!0.

89)

(!2.

38)"

(0.0

0)!

(!2.

91)!

(14.

37)!

(0.0

0)!

(7.3

8)!

Geo

grap

hic

focu

san

dac

tivi

tydiv

ersi"ca

tion

600.

140.

3855

.0!

1.80

!1.

9935

.019

.01

15.0

288

.3(!

0.61

)(0

.52)

(0.7

7)(!

3.06

)!(0

.03)

"(!

2.32

)"(2

0.88

)!(0

.00)

!(5

.94)

!

Geo

grap

hc

div

ersi"ca

tion

and

activi

tyfo

cus

79!

0.25

!0.

2545

.6!

2.32

!2.

3231

.615

.96

15.9

684

.8(!

0.47

)(0

.50)

(!0.

79)

(!4.

30)!

(0.0

0)!

(!3.

26)!

(18.

09)!

(0.0

0)!

(6.1

9)!

Tota

l28

00.

04%

!0.

13%

48.9

%!

1.68

%!

1.70

%33

.6%

16.6

1%16

.74%

88.6

%(0

.05)

(0.7

7)(!

0.36

)(!

5.60

)!(0

.00)

!(!

5.50

)!(3

5.77

)!(0

.00)

!(1

2.91

)!


Pan

elB:

Diw

eren

ces

betw

een

grou

ps

Geo

grap

hic

/act

ivity

focu

svs

.ge

ogra

phi

c/ac

tivi

tydi

vers

i"ca

tion

s1.

640.

437.

50.

27!

1.16

0.6

2.90

!3.

89!

1.1

t-st

atistics

1.60

0.27

0.81

Geo

grap

hic

/act

ivity

focu

svs

.ge

ogra

phi

cfo

cus/

activi

tydi

vers

i"ca

tion

0.95

!0.

23!

2.5

0.69

!0.

91!

0.6

!1.

41!

0.42

1.9

t-st

atistics

0.78

0.62

!0.

47

Geo

grap

hic

/act

ivity

focu

svs

.ge

ogra

phic

dive

rsi"

cation/

activi

tyfo

cus

1.34

0.40

6.9

1.20

0.87

2.8

1.64

!1.

365.

4

t-st

atistics

1.26

1.15

0.62

Geo

grap

hic

/act

ivity

div

ersi"ca

tion

vs.

geog

raphi

cfo

cus/

activi

tydi

vers

i"ca

tion

!0.

69!

0.66

!10

.00.

420.

83!

1.2

!4.

323.

473.

0

t-st

atistics

!0.

750.

52!

1.09

Geo

grap

hic

/act

ivity

div

ersi"ca

tion

vs.

geog

raph

icdi

vers

i"ca

tion/

activi

tyfo

cus

!0.

30!

0.03

!0.

600.

941.

162.

2!

1.26

2.53

6.5

t-st

atistics

!0.

421.

28!

0.34

Geo

grap

hic

focu

s/ac

tivi

tydiv

ersi"ca

tion

vs.

geog

raph

icdi

vers

i"ca

tion/

activi

tyfo

cus

0.39

0.63

9.4

0.52

0.33

3.4

3.05

!0.

943.

5

t-st

atistics

0.41

0.59

0.98

!Sig

ni"

cantat

1%le

vel

"Si

gni"

cant

at5%

leve

l#Sig

ni"

cantat

10%

leve

l


before the merger announcement minus the return on the bank index for thesame period.

In addition, I include the relative size of the merger partners. Recall thatTable 1 shows target-to-bidder ratio of mergers that focus both activity andgeography tend to be larger than ratios of the other types of mergers. These arethe mergers that create the most value. Moreover, James and Weir (1987) "nda positive relationship between an acquirer's returns and the relative size of thetarget to bidder. My results could be driven by relative size rather thandiversi"cation or focus. I therefore include the log of the relative size oftarget-to-bidder measured by market equity values ten days before the mergerannouncement.

Another issue concerning size is that some banks are `too-big-to-faila (TBTF).Governments decide that certain banks are so vital to the smooth operation ofan economy that the banks are not permitted to fail. In the United States, thispolicy became explicit in 1984 when the U.S. Comptroller of the Currency, theauthority responsible for regulating national banks, testi"ed to Congress that 11banks were so important, partly based on their sizes, that they would not bepermitted to fail. O'Hara and Shaw (1990) detail the bene"ts of being too bigto fail. Without assurances, uninsured depositors and other liability holdersdemand a risk premium. When a bank is not permitted to fail, the risk premiumis no longer necessary. Furthermore, banks covered under the policy have anincentive to increase their riskiness so as to enjoy higher expected returns.Mergers, therefore, may push banks into this desirable category. The larger theresulting institution, the more attractive the investment and the higher should bethe abnormal return upon the merger announcement. This e!ect, however,could be reduced if the institutions involved in mergers already enjoy the TBTFstatus. I control for TBTF by including the log of the assets, adjusted to 1995U.S. dollars, of the resulting merged entity.

Subsidies could also be in the form of government assistance to failed orfailing banks. Abnormal returns could be the transfer of wealth from taxpayersto merging entities. I therefore include a dummy variable to indicate when theU.S. Federal Deposit Insurance Corporation assists a merger.

Another factor that could be important to market reaction is the method ofpayment. Amihud et al. (1990) discuss two hypotheses concerning the choice of"nancing. First, since cash acquisitions create an immediate liability for thetarget stockholders, while payments made in stock are taxable only when theyare redeemed, the tax e!ects hypothesis suggests target stockholders wouldprefer stock. Second, the information asymmetries hypothesis posits that bid-ders want to use stock to purchase a target when the bidders know their stock isovervalued. Investors recognize this situation, and drive down the price of thebidder's stock upon announcement. Another aspect of this hypothesis reachesa di!erent conclusion. Stockholders of targets who know their stock is under-valued prefer payment in stock rather than cash so they may enjoy the bene"ts


Table 7Regression with control variables

This table analyzes the in#uence of the pre-merger performance of the target, relative target-to-bidder size,government assistance, accounting and payment methods, hostile takeovers, and number of bidders. The sampleconsists of 280 domestic U.S. mergers announced between 1988 and 1995 between publicly traded "rms for whichat least one is a banking "rm. The sample is divided into four groups according to geographic and activity focus ordiversi"cation. Returns to the combined merger partners are determined by comparing total market value of thetwo at time t with total market value at t!1: RP

t"ln[(M<B

t#M<T

t)/(M<B

t~1#M<T

t~1)]. Abnormal returns are

calculated using standard event study methodology (see Brown and Warner, 1985) with the market model. Thedependent variable is the vector of cumulative abnormal returns for the combined bidder and target 10 days beforeto one day after the merger announcement. CAR is regressed against a vector composed of the pre-mergerperformance of the target, the logs of the target-to-bidder market ratios, and the combined partners' assets, as wellas dummy variables to indicate government assistance, payment in cash or stock, the pooling method ofaccounting, a hostile merger, and more than one bidder. Additionally, vectors of dummy variables control for thetypes of mergers in this study: a vector of dummy variables that indicate whether a merger (1) diversi"es bothgeography and activity; (2) focuses geography and diversify activity; or (3) diversi"es geography and focusesactivity. The constant represents the portion of returns that is not explained by the other variables, and can includethe return to mergers that focus both geography and activity. I use White's method (1980) to correct forheteroskedasticity.¹-statistics are the coe$cients of this regression divided by their respective standard errors, andappear in parentheses.

Independent variable Estimate Estimate(t-value) (t-value)

Constant (including mergers that focus geography and activity) !6.41% 5.75%(2.29)" (3.37)!

Mergers that diversify geography and activity !3.16 !3.90(!2.31)" (!2.77)!

Mergers that focus geography and diversify activity !4.56 !5.09(!2.85)! (!3.11)!

Mergers that diversify geography and focus activity !2.40 !2.75(!1.64) (!1.91)#

Pre-merger performance of target !4.57 !2.09(!2.39)" (!2.39)"

Ln (market equity of target/market equity of bidder) 0.98 0.83(2.34)" (2.41)"

Combined assets of partners 0.01(0.46)

Government assistance (dummy) 1.88(1.20)

Payment in cash (dummy) 1.11(0.65)

Payment in stock (dummy) !0.05(!0.03)

Pooling accounting method (dummy) !1.03(!1.02)

Hostile takeover (dummy) 8.79(1.06)

More than one bidder (dummy) !0.63(!0.49)

Adjusted R2 18.4% 14.9%F-statistic 3.98! 6.83!

Number of observations 159 167

! Signi"cant at 1% level"Signi"cant at 5% level# Signi"cant at 10% level


of corrected valuation. While theory o!ers several con#icting interpretations ofthe choice of "nancing, empirical evidence by Amihud et al. (1990) and Travlos(1987), among others, shows that bidders paying for their acquisitions in cashearn signi"cantly more than bidders who use stock. I include a dummy variableto indicate when the bidder pays for the merger in cash and a dummy variable toindicate when stock is used. This strategy does not result in overspeci"cationsince 22 bidders use a hybrid type of "nancing, namely both cash and stock topay for their targets.

Implications of the accounting method used to combine the bidder and targetcould also be important to market reaction. The purchase method is similarto the purchase of any capital good. The assets and liabilities of the targetare restated at current market value and recorded as of the e!ective date of themerger. Any di!erence between the purchase price and the current market valueis recorded as goodwill to be expensed after the merger is completed. Thepooling method simply superimposes the target's balance sheet upon the ac-quirer's balance sheet at book value, starting from the beginning of the mergeryear. No marking-to-market valuation occurs, and no goodwill appears asa result of the merger. While pooling means fewer expenses arise to depressfuture earnings, and it is usually the preferred accounting method, severalrestrictions apply. Acquirers must obtain or increase current holdings to at least90% of the target. Payment must be made in stock, and acquirers may not buyback shares for at least two years after the merger is e!ective. Adhering to theserequirements could hamper corporate strategy. If the acquirer expects the targetto create excess cash #ow, the acquirer may want the #exibility to buy backshares. To control for any in#uence of the accounting method, I includea dummy variable for mergers that use the pooling method.

Hostile takeovers could create more value than non-hostile mergers. Mikkel-son and Ruback (1985) investigate whether the reason for a merger in#uencesreturns to targets. They examine Schedule 13D forms, which investors obtainingmore than 5% of a "rm must "le with the Securities and Exchange Commission.When the "ler intends to change the control of the "rm, returns are higher thanwhen the intent is, for example, solely for investment. If the additional value thata target receives is more than a transfer of wealth from the bidder to the target,a hostile takeover should create more value than a friendly merger. I control forthis possibility by including a dummy variable to indicate when a merger ishostile.

Bradley et al. (1988) show that multi-bidder contests tend to increase returnsto targets and decrease returns to bidders. These di!erences in returns comparedto single-bidder contests occur only when a subsequent bid is made and notupon the initial announcement of the merger. They also "nd that total synergis-tic gains are higher in multiple-bidder acquisitions, such that the target gainsmore than the bidder loses. To control for any in#uence of multiple bidders,I include a dummy variable to indicate more than one bidder for a target.


The regression that examines the various hypotheses discussed above is:

Cumulative abnormal return"a

#b1]dummy to indicate mergers that diversify geography and activity

#b2]dummy to indicate mergers that focus geography but

diversify activities

#b3]dummy to indicate mergers that diversify geography but

focus activity

#b4]pre-merger performance of the target

#b5]ln[ (market value of target)/(market value of bidder) ]

#b6]ln(assets of bidder plus assets of target)

#b7]dummy to indicate mergers that receive governmental assistance

#b8]dummy to indicate merger paid for with cash

#b9]dummy to indicate merger paid for with stock

#b10

]dummy to indicate merger that uses pooling accounting method

#b11

]dummy to indicate a hostile takeover

#b12

]dummy to indicate more than one bidder. (5)

The intercept represents the portion of returns that is not explained by the othervariables. This amount includes, but is not limited to, the return to mergers thatfocus both geography and activity as well as payment in cash and stock, andmergers that use the purchase method of accounting. b

1, b

2, and b

3represent

the additional market reaction due to the merger being of a di!erent type thanthose that focus both geography and activity, among other things.

I run a weighted ordinary least squares (OLS) regression using White'smethod (1980) to control for heteroskedasticity. The "rst column in Table 7shows that pre-merger performance of the target and the target-to-bidder ratiosare statistically signi"cant. I remove the insigni"cant variables from this analysisand run the regression again. The results of the second regression are shown inthe second column of Table 7. The coe$cients on the types of mergers that donot focus geography and activity are negative and statistically signi"cant. Thisresult is consistent with the results shown in Table 5, which shows that the valuecreated by mergers that focus both geography and activity is signi"cantlygreater than the value created by the other types.

In addition, part of the value enhancement is associated with relative size andpre-merger performance of the target. The positive coe$cient on the relativemarket value of target-to-bidder suggests that the larger the target is relative to


the bidder, the greater the CAR. This result substantiates the "ndings of Jamesand Weir (1987). The signi"cantly negative coe$cient on the pre-merger perfor-mance of the target variable reveals that the poorer the target performs, vis-a-visthe bank index, the greater the value increase upon announcement of a merger.This "nding suggests either the market expects the merger to enhance theperformance of the target, or that the FDIC may be providing subsidies, asdiscussed earlier in this section. The "rst hypothesis is related to the "ndings ofM+rck et al. (1990), who "nd a negative relationship between the preannounce-ment performance of targets and returns to bidding "rms upon the announce-ment of an acquisition when they examine non-bank "rms. Since the coe$cienton the variable that measures FDIC assistance directly is insigni"cant, thesecond hypothesis is not substantiated.

5. Conclusion

From this study, the focus hypothesis appears to apply to bank mergers. Byexamining mergers that involve a banking "rm in the context of the focus versusdiversi"cation debate, I "nd that the market does distinguish among varioustypes of mergers. I divide mergers into four groups depending upon activity andgeographic focus and diversi"cation. Mergers between partners that focus theireography and activity enhance value more than any other type. These resultshold for various time periods.

Focus and diversi"cation, however, are not the sole in#uences on returns tocombined merger partners. Analysis reveals that CARs increase in relative targetto bidder size and decrease in the pre-merger performance of targets. Furtherdimensions, such as the type of corporate governance (see Brickley and James,1987; Hubbard and Palia, 1997) or agency costs (see Cornett et al., 1998) couldalso in#uence the return on bank mergers. Further analysis into these areascould be a useful extension of this study.

The results must be understood with caution. Although stock prices reveal themarket's expectation of future cash #ows, actual performance may di!er frommarket expectations. This observation is especially true for bank mergers.Although the prior conditions to predict successful mergers may exist, theirpresence may be di$cult to discern. Berger and Humphrey (1992) "nd noapparent correlation between mergers that reduce costs and those that could bepredicted to do so in advance. Berger et al. (1998) o!er a potential explanationwhen they discuss the e!ects of bank mergers. Only the static e!ect of combiningbalance sheets is easily observable. The other, dynamic e!ects, such as restruc-turing and changes in lending practices, greatly in#uence the performance ofa merged bank, yet are extremely di$cult to predict at the time of the mergerannouncement. Flannery (1999) posits that the market is entering new territorywith today's mergers. The mega-mergers between diverse "nancial "rms into


di!erent geographic areas are unlike those in the past, and stockholders needupdated insights to access the bene"ts of these new types of mergers.

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