LUBATKIN - Towars Reconciliations of Market Performance

8/13/2019 LUBATKIN - Towars Reconciliations of Market Performance

1/17

Towards Reconciliation of Market Performance Measures to Strategic Management ResearchAuthor(s): Michael Lubatkin and Ronald E. ShrievesReviewed work(s):

Source: The Academy of Management Review, Vol. 11, No. 3 (Jul., 1986), pp. 497-512Published by: Academy of ManagementStable URL: http://www.jstor.org/stable/258307.

Accessed: 05/12/2011 07:12

Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at.http://www.jstor.org/page/info/about/policies/terms.jsp

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms

of scholarship. For more information about JSTOR, please contact [email protected].
http://www.jstor.org/action/showPublisher?publisherCode=aomhttp://www.jstor.org/stable/258307?origin=JSTOR-pdfhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/stable/258307?origin=JSTOR-pdfhttp://www.jstor.org/action/showPublisher?publisherCode=aom


2/17

? Academy of Management Review, 1986, Vol. 11, No. 3, 497-512.

Towards Reconciliation ofMarket Performance Measuresto Strategic Management ResearchMICHAELLUBATKINUniversity of Connecticut

RONALDE. SHRIEVESUniversity of TennesseeFour research issues are identified that highlight the contrasting per-spectives of strategic management and finance on event-studymethodology. These issues then are used to evaluate five financeprocedures used to calculate market-based performance measures.In each case, alternative procedures are recommended to make thesemeasures more relevant both conceptually and statistically, for strate-gic management research.

Parallel bodies of research about mergers havedeveloped independently in the fields of strate-gic management and finance. In these two fields,similar conclusions are not reached. The consen-sus of studies appearing in the strategic manage-ment literature suggests that mergers, or certaintypes of mergers, may improve the performanceof the acquiring firm (e.g., Kitching, 1967;Lubatkin, 1983; Porter, 1980; Rumelt, 1974). Incontrast, the consensus of market-based perfor-mance studies appearing in the finance litera-ture indicates that mergers do not lead to posi-tive performance outcomes, or at best, lead tosmall gains (for summaries, see Halpern, 1983;Jensen & Ruback, 1983;Weston, 1981). The find-ings of the finance studies generally are consis-tent with the notion that the market for acquisi-tions is perfectly competitive in the sense thatstockholders of the acquired firm capture anymerger benefits through the premiums paid fortheir securities. In such a market, the diversifica-

tion strategy that the acquiring firm follows isirrelevant to its shareholders' welfare.The apparent contrast in these conclusions isnot surprising. Commenting specifically on thefields of strategic management and finance,Bettis (1983) stated that The gap (in paradigmand methodology) generally is so broad thatresearchers on both sides forget that they oftenanalyze the same phenomena albeit from differ-ent perspectives (p. 413).The present authors agree. Focusing on corpo-rate event studies, it is argued that hypothesesconcerning the two disciplines often are funda-mentally different. For example, strategy re-searchers focus on more than one test of perfor-mance applied by more than one category ororganizational assessor (Rumelt, 1974). The liter-ature on mergers was selected to highlight thispoint because mergers have been widely stud-ied in both fields. (In this paper, the terms mer-gers and acquisition are used interchange-ably to mean any transaction that forms one eco-nomic unit from two or more previous ones.)However, the issues raised here are relevant toany major corporate event studied in the man-agement field.

Correspondence should be addressed to Michael Lubatkin,Business Environment and Policy, Box U41-B, University ofConnecticut, Storrs, CT 06268. 497


3/17

The goal of this paper is to demonstrate howthe principal empirical methodology used infinance studies can be adapted to researchers inmanagement. (The capital asset pricing model,or CAPM, and the market model are the princi-pal versions of this methodology.) This goal is aparticularly timely goal since researchers in man-agement recently have given more attention tothese models (e.g., Bettis, 1983; Lubatkin, 1983;plus two sessions held at the annual meeting ofthe Academy of Management, 1984).

First, market-based performance measures aredescribed and the contrasting perspectives takenby researchers of the two fields are discussed.From these contrasting perspectives, four re-search issues are identified. These issues include:differences in time frame, sampling frame, sta-tistical methods, and performance analysis.Within this framework, it is argued that fiveprocedures, common to this frame methodology,may be inappropriate for testing hypotheses fromthe strategic management literature. For eachprocedure, alternative procedures which recon-cile the basic market-based performance mea-sures to the research objectives of strategic man-agement are suggested. A concluding sectiondraws together the recommendations of thispaper to show how each can be used in conjunc-tion with the other.

The Case for Market-BasedPerformance MeasurementResearch in strategic management is foundedon the notion that strategy influences corporateperformance. To date, this notion has more con-ceptual appeal than empirical support. Consider,for example, the literature on corporate diversi-fication. A widely held belief is that firms thatdiversify in a related manner will perform betterthan firms that diversify in an unrelated manner.Rumelt (1974) first found empirical support forthis relatedness prescription. Subsequent studies,however, have uncovered results that seriouslyquestion its adequacy (e.g., Bettis & Hall, 1982;Christensen & Montgomery, 1981).

Other policy prescriptions have met a similarfate. For example, studies that test for a linkbetween corporate performance outcomes andmerger strategies (e.g., Kitching, 1967; Singh &Montgomery, 1984), organizational structures(e.g., Chandler, 1962; Lorsch & Allen, 1973;Rumelt, 1974), strategic planning systems (e.g.,Kudla, 1980; Wood & LaForge, 1979), composi-tion of corporate governance (e.g., Schmidt, 1977;Vance, 1978), executive succession (e.g., Lie-berson & O'Connor, 1972; Weiner & Mahoney,1981), and so on, all arrive at weak or inconsis-tent findings.Is the theory at fault, or are the measures usedto evaluate corporate performance inappropri-ately selected? A review of the literature on cor-porate performance measures suggests the lat-ter may be true.Strategy research traditionally has defined per-formance by some accounting-based index suchas return on assets and/or sales growth. Each ofthese measures, however, captures only onedimension of performance (Dalton, Todor, Sten-dolin, Fielding, & Porter, 1980;Ford & Schellen-berg, 1982). In addition, the importance of eachmeasure may differ across strategic contexts(Cameron & Whetten, 1981; Gupta & Govinda-rajan, 1984). Steers (1975), for example, recom-mended that performance be measured alongmultiple dimensions and that weights be as-signed to each dimension to reflect the specificstrategy of the focal organization. This elabo-rate approach, however, would not necessarilygenerate accurate indices of corporate perfor-mance. Measurement problems associated withaccounting-based measures are as well docu-mented as those of hybrid measures (e.g., price/earnings) which incorporate both accounting andmarket-based measures (e.g., Hong, 1977;Lev &Sundar, 1979; Rappaport, 1983). Finally, tradi-tional measures of performance are not neces-sarily correlated with the value of the firm (Bea-ver, Kettler, &Scholes, 1970;Gonedes, 1973).Theimportance of this latter point cannot be under-stated. High valuation justifies attractive com-pensation packages, inhibits proxy battles for

498


4/17

control, and enhances the firm's future effective-ness by allowing less costly access to additionalequity and debt capital (Branch & Gale, 1983).High valuation, therefore, is a performance goalthat extends beyond the domain of stockholders.Given the pitfalls of employing traditional mea-sures of corporate performance, are there other,more reliable measures? A strong case has beenmade for a measure developed from capital mar-ket theory. Stated simply, this model assessesthe impact of an event (e.g., a change in leader-ship) on a firm's security by estimating normalor expected return to its stock in the absence ofan event (Fama, Fisher, Jensen, & Roll, 1969).This is done by adjusting the firm'sobserved com-mon stock returns (appreciation plus dividends)for general stock market movements over theperiod surrounding the corporate event of inter-est. The abnormal or unexpected return to thestock represents the difference between its ob-served return and its normal or expected return,given the general market effects.Of course, abnormal returns do not measurerealized operating performance; rather, they cap-ture investors' anticipation of firm specific eventson future performance. To the extent that thecapital markets are efficient, that is, securityprices reflect all available information (Fama,1976), any change in price represents the pres-ent value of the change in the expected cashflow to the firm.There are a number of advantages to measur-ing performance in this manner. First, stockprices represent the only direct measure of stock-holder value. Second, stock prices are believedto be fully specified; that is, they are not limitedto a specific aspect of performance such as salesgrowth or profits, but rather reflect all relevantinformation aspects of performance. Third, stockprices are readily available for all publicly tradedfirms and their competitors. Fourth, stock pricesare reported objectively. Fifth, stock prices havebeen shown to see through managers' attemptsto manipulate reported accounting measures.Sixth, the abnormal returns measures that arecomputed from the stock price compare favor-

ably to the less specified measures of marketperformance such as annual changes in stockprice. Unlike the latter measures (e.g., Weiner &Mahoney, 1981), abnormal returns are adjustedto account for general market movements, infla-tion, and the firm's market risk, or beta. Finally,these measures provide a basis for evaluatinginvestors' assessment of the impact of a manage-rial decision (e.g., to merge, to divest, to reorga-nize, etc.), or the impact of events outside thedirect control of management (e.g., a rivalrousact by a competitor, a precipitous rise in energyprice, etc.).There are also limitations to measuring corpo-rate performance in this manner. For example,an assumption underlying the construction of thismeasure is that the only stakeholder that mattersis the fully diversified investor. This assumption,fundamental to modern financial theory, runscounter to the notions of strategic management.Strategic management recognizes the need forbusiness organizations to be accountable tomany stakeholder groups, each evaluating per-formance along different criteria. Strategic man-agement also recognizes the importance of man-aging unsystematic risks such as entry barriers.(The issue of whether the management of unsys-tematic risk is compatible with modern financialtheory is debated between Bettis, 1983, andPeavy, 1984.) The point of this paper, therefore,is not to suggest that abnormal returns representthe only true measure of performance; rather,that this measure reflects the viewpoint of thecommon shareholder better than do accounting-based measures, and with modifications, mayprove to be a powerful test of corporate per-formance.

Contrasting Conceptual FocusMarket-based performance measures havebeen used in financial economics to study theeffects of events such as dividend announce-ments (e.g., Fama et al., 1969), merger an-nouncements (e.g., Mandelker, 1974), tenderoffers (e.g., Dodd &Ruback, 1977), and changes

499


5/17

in capital structure (Masulis, 1983) on commonstock prices. Recently, researchers in strategicmanagement have given it more attention, usingit to evaluate the impact of mergers (Burgman,1983; Lubatkin, in press; Singh & Montgomery,1984), divestitures (Montgomery, Thomas, &Kamath, 1984), diversification (Montgomery &Singh, 1984),product-market interventions (Bettis,Chen, & Mahajan, 1984), executive succession(Reiganum, 1985), strategic planning systems(Kudla, 1980), and strikes (Newmann, 1980). Op-portunities to employ this methodolody, however,are not limited to research in strategic manage-ment. Other applications include research inorganizational behavior (e.g., to assess the im-pact of changes in top management reward sys-tems and changes in top management leader-ship styles on stockholders) and organizationaltheory (e.g., to assess the impact of structuralchanges and changes in corporate governance).

A danger exists, however, when a techniquedeveloped in one discipline is borrowed withoutfirstquestioning the assumptions underlying thattechnique. A paradigm of strategic manage-ment, for example, is that a corporate action suchas a merger is the outcome of a series of relatedevents or tactics where each increases or de-creases the probability of the final outcome (seeMintzberg's, 1973, discussion of strategic ges-talts ).The full performance impact of the mergercannot be assessed solely by observing thereturns associated with the final event (e.g., themerger's announcement date, or its legal trans-action date). In the case of mergers, these eventsor tactics related to the merger process include:the decision to begin an acquisition program,the decision about which specific diversificationstrategy to employ, the act of purchasing stockin various possible targeted firms prior to anyformal takeover attempt, the disclosure of infor-mation that clarifies which firm or firms aretargeted, any opposition to the takeover attempt,the formal announcement by the acquiring firm,and any other act that provides information aboutfinal merger act.

Researchers in finance, however, generallyview corporate events quite differently than doresearchers in strategic management. Events aredefined more in tactical terms, perhaps becausethe power of the market model is enhanced bythe fact that the timing of the impact of a tactic ismore easily identified. (Research on merger tac-tics under the heading market for corporatecontrol is surveyed by Jensen & Ruback, 1983.)While questions directed at tactics are interesting,equally interesting questions concerning thenature of strategic events generally have beenoverlooked. This is not to say that finance doesnot recognize limitations of event-study research.Recent literature reviews of merger studies attestto this (Copeland &Weston, 1983;Halpern, 1983;Jensen &Ruback, 1983;Weston, 1981).This litera-ture, however, offers few recommendations toassist the study of broader questions that relateto strategic events.

The research issue sections that follow show(a) how the prevalent finance perspective hasinfluenced the manner in which the market-based performance measures are constructed,and (b) how these measures can be adapted tobecome more consistent with the strategic man-agement paradigm.Research Issue 1: Time Frame-Selecting theRelevant Horizon LengthResearchers who use the market model meth-odology select either daily or monthly returnsdata. In both cases, a security's returns aredefined as the change (daily or monthly) instockholder's wealth and are based upon theclosing price of a security after adjusting for anystock splits, additional stock issues, and divi-dends. Following a technique related to the capi-tal asset pricing model, a time series of a se-curity's returns ordered relative to an event ofinterest (such as a merger) are regressed againsta comparably ordered time series of returns cal-culated on a broad-based stock market index.The residuals from such a regression are inter-preted as abnormal returns, or the security's

500


6/17

returns in excess of the market's expected (ornormal) returns. When monthly returns data areused, the stock returns for the firm and the mar-ket usually are regressed over 60 months beforeand then 60 months after the event of interest.When daily returns are used, the stock returns ofthe firm and the market typically are regressedover 150 trading days before and then 150 trad-ing days after the day of the event of interest.Before 1978, most published event studies usedmonthly returns data. However, as the availabil-ity of daily returns data has increased, so has itsuse.Proponents of daily returns data claim that itsuse increases the power of statistical tests byallowing the researcher to isolate more effec-tively the market's reaction to one particularevent or tactic with a known date (Brown &Warner, 1980). (Examples of merger tactics werepresented in the previous section. For a goodsummary of nonmerger-related tactics such asprovocative price changes, establishment offighting brands, etc., see Porter, 1980.)The pres-ent authors assert, however, that using dailyreturns data may be inappropriate when theresearch objective is to assess the full impact ofa strategic event.First, strategic events cannot be dated preciselybecause they represent the outcome of a seriesof related events. To understand the impact thatthese related events can have on the perfor-mance measures for a particular merger, it isimportant to recognize that the capital marketsact in a relatively efficient manner (i.e., the futurebenefits of an action are incorporated rapidlyinto a security's price). Since strong evidencesupports the notion that the market is efficient,(Copeland & Weston, 1983; Jensen & Ruback,1983), each merger-related event results in areassessment of value which will be capitalizedinto the firm's stock price as the event becomesknown to the marketplace. For example, abnor-mal returns associated with the merger an-nouncement will reflect only the valuation impactof the marginal information contained in thatevent. A recent study by Schipper and Thomp-

son (1983) lends support to this conjecture. Theyexamined the market's response to a firm's firstpublic announcement of its intention to engagein an acquisition program and found that someof the value of later mergers is capitalized intothe firm's stock price at the time that the acquisi-tion program is announced.Second, the short time horizon employed whenusing daily returns data may not capture the fullseries of strategic event-related returns. Indeed,merger studies that employed monthly returnsdata reported abnormal market returns 18months or more prior to the merger (e.g., Elgers&Clark, 1980;Langetieg, 1978;Mandelker, 1974).To the extent that the prior related informationhas positive value (as was found by Schipper &Thompson) and that this information becomespublic more than 150days before the event, stud-ies that employ daily returns data will understatestrategic event-related abnormal returns.

This problem can be minimized by extendingthe time horizon (i. e., to at least 6 years, or about1560 trading days). To do so, however, woulddeny the principal advantage of using dailyreturns data which is to compute abnormalreturns over short horizons, thereby reducingbias caused by the influence of extraneousevents. In addition, computing abnormal returnsdaily over 1560days rather than monthly over 60months is like measuring the length of a footballfield with a micrometer rather than with a yard-stick. Assuming the results would be the same,the added computational cost would be wasted.More importantly, the results may not be thesame. Researchers have observed bias intro-duced with the use of daily returns due to cer-tain characteristics of the data (e.g., Cohen,Hawawini, Maier, Schwartz, &Whitcomb, 1983).This bias is tolerated, however, when the objec-tive of the research is to focus on one isolatedevent or tactic.Monthly returns data also may introduce bias.While their required long horizon reduces biasdue to the elimination of related events, it alsoincreases the likelihood that extraneous eventswill be captured. Fortunately, in large samples,501


7/17

the influence of these extraneous events will bepartially averaged away, while the influence ofrelated events will be captured systematically tothe extent that these related events occur consis-tently across the sample.This bias associated with extraneous eventsalso can be reduced by shortening the horizon.However, the market model's regression coeffi-cients have been shown to be sensitive to thelength of the time period over which they areestimated (Gonedes, 1973). The use of five yearsof data when using monthly returns, therefore,has been rationalized as representing an accept-able tradeoff between estimating reasonably sta-ble coefficients and capturing the impact of extra-neous events. In addition, there is no theoreticaljustification forshortening the horizon. The lengthof the horizon depends on the nature of relatedevents, of which little is known: When do theybegin? Does their sequence, in terms of theirorder and the time interval that separates them,vary across firms? Do investors' interpretations ofeach event vary across firms? Are strong earn-ings and high liquidity an event extraneous tomerger, or a cause of merger? If it is a cause,will investors begin to discount the value of alater merger at the time when they becomeaware of this performance trend? These andother questions highlight opportunities for futureresearch.In summary, it is argued that the properties ofmonthly returns data make the data more appro-priate to use when studying the market's re-sponses to a strategic event, while daily returnsdata are more appropriate when studying tacticsof strategies. In each case, however, tradeoffsmust be recognized.Research Issue 2: Sampling Frame-Selectingthe Relevant Sampling Units

Market model event studies which use monthlydata returns commonly exclude firms that haveparticipated in the same type of event duringsome specific period around the date of the eventof interest (e.g., Choi &Philippatos, 1983;Lange-tieg, Haugen, &Wichern, 1980).According to its

proponents, this period of clean data (typicallythree years before and three years after the eventbeing studied) helps to ensure that regressioncoefficients estimated over the full 60 monthsbefore and 60 months after the event will reflectonly the influence of a single event. (The use ofdaily returns data is partially justified as a meansof avoiding the necessity of applying a cleandata screening criterion, since it is unlikely thatan event firm will experience the same eventtwice within the shorter horizon involved.)

Clean data may reduce estimation error, buttwo problems arise. First, it may result in a non-representative sample. For example, the sam-ple of mergers that pass this screening criterionincludes only acquiring firms that are relativelyinactive in the acquisition market. This system-atic exclusion of more frequently merging firmshas been casually observed, but not documented(e.g., Langetieg et al., 1980, p. 372). It is, how-ever, well documented in strategic managementstudies that active acquirers differ strategically(e.g., Rumelt, 1974),structurally (e.g., Pitts, 1976),and experientially (Jemisen & Sitkin, 1986) frominactive acquirers. To the extent that a firm'sactivity in the acquisition market is related to itsperformance outcome from merger, the cleandata screening procedure may result in biasedestimates of the impact of mergers.A second problem of the clean data criterionis that the screen reduces the sample size. Thereduced sample may result in an overall erosionof the power of the significance test to resolvehypotheses of interest, even if the selection cri-teria results in smaller estimation errors.The following exercise will approximate theextent to which the clean data screening proce-dure affects sample characteristics and resultsin sample size reduction in merger studies. Tothis end, merger activity for the population ofNew YorkStock Exchange (NYSE)acquiring firmsand for two samples drawn from the populationare documented. The information for the popula-tion is taken from the Federal Trade Com-mission's listing of large, nonpartial mergers dur-ing the period 1948-1979. This NYSE population,

502


8/17

like the typical market-based merger sample,includes only mergers in which the acquired firmwas a manufacturing or mining firm with assetsexceeding $10 million in book value and wherethe ownership position in the acquired firmexceeded 50 percent.Two samples of acquiring firms then are devel-oped from this population. The first sample re-quires only that there is a lengthy period ofmonthly stock return data available, both pre-and post-merger, for the surviving firm(see notesto Table 1 for details). The second sample ad-ditionally requires that the surviving firm doesnot participate in another large merger for 36months before or 36 months after the merger inquestion. This latter sample represents the typi-cal clean merger sample found in finance stud-ies which employ monthly return data. By con-struction (though not by research intent), theacquiring firms in the second sample are merger

inactive.Using the merger history information for allNYSE acquiring firms and carefully accountingfor name changes, a merger activity, or fre-quency measure, is developed for each of thesamples. This measure is calculated by count-ing the number of large mergers in which eachacquiring firm participated during 1948-1979. Forexample, if an acquiring firm participated in fourmergers during that time period, each of its merg-ers received a frequency measure of four. Forcomparison purposes, the number of mergersfor the population and each sample which fallinto various merger activity (frequency) catego-ries are presented in Table 1.The second sample (clean data) differs in con-tent and in size from the population and firstsample. Only 21 percent of the NYSE populationof large mergers were by acquiring firms thathad only one large merger; 40 percent of thesecond sample of mergers were by single-mergerfirms. Similarly, 47 percent of the NYSE popula-tion of mergers were by acquiring firms that hadmore than three large mergers; only 24 percentof mergers in the second sample are representedas active acquirers. The first sample, however,

is similar in content to the NYSE population. Tothe extent that active acquirers (e.g., firstsample)differ from the inactive acquirers (e.g., secondsample) in terms of strategy, structure, andperformance, the commonly used clean datasampling procedure results in nonrepresentativemerger samples. In addition, the size of the sec-ond sample is almost 60 percent smaller thanthe first (315 vs. 768 mergers).In summary, it is argued that the results ofstudies employing the clean data screening crite-rion cannot be interpreted as representative ofthe effects of events in general without explicitlyconsidering the impact of sample content andsample size differences on the statistical results.Research Issue 3A: Statistical MethodsCorrecting for Sampling Dependencies

Cross-sectional dependencies may exist be-tween the time series of abnormal returns esti-mated for each event-firm in a sample. Whenthis occurs, the variance of the performance mea-sures will be inflated, lowering the power of thestandard statistical tests.Researchers in finance believe that the pri-mary occurrence of such dependencies is whensample events cluster by calendar time. Thisassumption may be soundly based: business andeconomic regression applications involving timeseries data document the problem of autocorre-lation. For example, Jarrell and Bradley (1980)found that government regulation influenced themarket valuation of a number of different securi-ties that shared a common event month.To overcome these temporal dependencies, theabnormal returns associated with mergers thatshare the same calendar month are averagedwith a procedure referred to as the Jaffee (1974)/Mandelker (1974) adjustment procedure. Theequally weighted series of abnormal returns thatresults from this procedure are each treated as asingle case for statistical tests (see Brown &Warner, 1980, for a more detailed description ofthis procedure).Researchers preoccupied with temporal de-pendencies, however, may overlook a potentially

503


9/17

o\O J)CD M 00CM *n r M o)0) CU . N-~IC LO nJ - CDn CD ON (S 6cat & do6 i 0)

o o(C CY) OD C) N $o -Z- CY) o 0t > > - *n v N o o c o o

0 Z; O~C~)QD 00 4 - l C *n a)^ . , Na: s sC-, Y) C -----'LI) G) C4) _C CY) 0 0.

C') o _ 0uD

so ~~~~~O

W W CDuM ,O OLr) - ,, W - (N Cs -. d4 ar) CD00 d4 00 CD d4 CD CD C)

Vz Z - (0d4 CD )- 0 00 CY)C) C) o o

0-Q . . . . . . . .

d4-CY) CY) o0 1-4 C14 C1 CY) d4 0 0 E 4 m

0) l-z Z

HO S0 =C')

2 ~ ~ ~~~ a)Z; Q e sc se occ sa o ot~~~~~~~~ 4 * M} C CM d4 Mn c V ctr- oooc CD d2 > 2~~~~ o 1 Cs CDr- WDM - (1 (1 (1 (1 C1? u i DCD ')D 'O CCoo $QC

? ~~~~~~~.-; r-~ - o-o C-4 c6 c-S c. o c-s -,i u-,0 cq CY) LO_(0 _' r ( 0 0l) CY 'CCCO C 0)

r~~~ ~l0L ( N((000) ) ) ) ~ _

2 g a~~~~C)M d4 M W 0 aV MD 00 C) CqCD d40W:U~~~~~~~~ Wi V - W~r)- d; (14 (D 0 -C(Nooot_Co - SCY) 0)Zf f r- - C_ CY) _ (D (N U) 0 L CYCY @0 o-(40 - 0,,rq Co CY)C) I'll (D

m~~~~~~e4 af) r-M M E--(4(4( N

-i)N -Cl l )- - - - - - - - -

-a .; S0 )m CD C4 C4 - d C4 -tCZ;csCS 0 C)( 0Q

ci .O~ 6, 0

0 - 0)

U) (0'l)'C'L) W-C')Cl0)- I'-N-C)D-' 0) 0o 0). v

4 - -

z -0

504


10/17

more important source of dependency, namely,that which is firm-specific. Firms differ in theirability to obtain positive market performanceresults. For example, firms differ in the charac-teristics of the industry of which they are a mem-ber (Christensen &Montgomery, 1981), the strat-egy that they follow within their industry (Porter,1980), the strategy that they follow when diversi-fying into other industries (Yip, 1982), their inter-vention capabilities (Hitt, Ireland, &Palia, 1982),the fit between their strategy, organizationalstructure, culture, and leadership style (Porter,1985), and the degree to which their manage-ment team sees themselves as an agent ofstockholders (Fama, 1980). Each of these firm-specific factors influences the stock returns of asecurity whether or not an event occurs. Unlesscontrolled, these factors may bias the returnsobserved for and ascribed to an event.Other firm-specific factors deal more directlywith the administrative processes involved withplanning, implementing, and controlling anevent. In the case of mergers, these factorsinclude the ability to adequately investigate acandidate's competitive position (e.g., Ebeling &Doorley, 1983), to identify undervalued securi-ties (e.g., Allen, Oliver, & Shwallie, 1981), toassess strategic fit(e.g., Salter &Weinhold, 1978),to assess organizational fit (e.g., Kitching, 1967),to assess cultural fit (e.g., Marks, 1982), and tomanage the acquisition process itself while mini-mizing administrative impediments (Jemison &Sitkin, 1986).Each of these factors may influencethe timing and magnitude of merger-related mar-ket returns. For example, investors may respondearlier and with more certainty to the first fewacquisition tactics of a firm that has a proventrack record of successful mergers than theywould to a firm that has been unsuccessful or toone without prior acquisitions (Asquith, Bruner,& Mullins, 1983;Hofer & Chrisman, 1984; Lubat-kin, 1982).Collectively, these firm-specific influences maycreate a dependency problem in samples thatcontain more than one merger consummated bythe same acquiring firm. Table 2 depicts the like-

lihood of this occurring. The two samples referredto in the previous section are compared afterthey have been adjusted either by common cal-endar time ( time-adjusted ) or by commonacquiring firm. In the latter case ( firm-adjust-ment ), all mergers completed by the sameacquiring firm are treated as a single obser-vation.As expected, the incidence of common acquir-ing firms in the first sample (containing mergeractivev as well as merger inactive acquiringfirms) is high; the firm-adjustment procedurereduces the observations by 56 percent. Even inthe second sample, which involves only merg-ers completed by relatively inactive firms, thenumber of observations is reduced by 18percentafter the firm-adjustment procedure.To summarize, if cross-sectional dependenciesdue to firm-specific effects are important, as man-agement literature suggests, then many eventstudies may have understated the significanceof their results due to the reduced power of signifi-cance tests.Research Issue 3B: Statistical Methods-Assuming a Homogeneous Population

Researchers in finance often assume thatevents such as mergers represent a homoge-neous occurrence. For example, they commonlytest the null hypothesis that mergers in generaldo not provide any benefits to the stockholders ofthe acquiring firms. (A recent exception is thestudy by Wansley, Lane, & Yang, 1983.)Table 2Content Analysis of Sample by Adjustment Pro-cedures

First sample Second sample ( clean data)OriginalSample Size: 768 315

After TimeAdjustment: 247 177After FirmAdjustment: 339 257

505


11/17

In contrast, the central objective of strategicmanagement research is identification of discretediversification characteristics in order to comparethe performance outcomes associated with eachcharacteristic. This literature strongly suggeststhat events such as mergers do not representhomogeneous phenomena, but rather can becategorized along a number of dimensions,including the diversification strategy employedby the acquiring firm (Kitching, 1967; Lubatkin,1983), the market structure characteristics of theparticipating firms (Porter, 1980; Yip, 1982), theability of the acquiring firm's management teamto consolidate the acquired firm's managementteam and operations (Jemisen & Sitkin, 1986),the accumulated experience of the acquiringfirm's management team in the acquisition mar-ket (Burgman, 1983; Hofer & Chrisman, 1984;Lubatkin, 1983),and the relative size of the merg-ing firms (Dundas & Richardson, 1982;Kitching,1967).The fact that the event studies from the field offinance often assume that their uncategorizedsample of events such as mergers are drawnfrom a homogeneous population may have ledthem to report results which fail to reflect impor-tant regularities in returns to merger activity.Research Issue 4: Abnormal PerformanceAnalysis-Selecting the Relevant Benchmarkof Normal Return

A security's price performance can only beconsidered 'abnormal' relative to a particularbenchmark. Thus, it is necessary to specify amodel generating 'normal' returns before abnor-mal returns can be measured (Brown &Warner,1980, p. 207).The assumption underlying the mar-ket model is that the correct benchmark is thereturn after adjusting for market returns. Themeasure of abnormal return, therefore, is theresidual or error term in the regression of themerging firm's period-by-period returns on thoseof a general stock market index. The basic re-search question tested in event studies that adoptthis benchmark is: Does the performance of anevent firm differ from what is expected after con-

trolling for the general or market-wide influenceson the stock's returns?Researchers have suggested various modifica-tions of the basic market model to control addi-tional systematic influences on returns. For ex-ample, an industry factor has been suggestedas a second independent variable to betterexplain the variation in a security's performance(Langetieg, 1978).The obvious difficulty with theindustry factor, however, is that there is no cor-rect industry context for many diversified firms.

As a result, few studies have adopted this mea-sure.A second correction factor that recently hasreceived more support involves a well-matchedcontrol group (e.g., Choi & Philippatos, 1983;Langetieg, 1978). For each event firm, research-ers select a control firm, one that shares similarindustry membership, asset and sales size, andfinancial characteristics. The market model isthen run on both firms. The control firm's ab-normal performance is subtracted from the eventfirm's abnormal performance. The differencescore is theoretically free of a number of poten-tial methodological biases. As Choi and Philip-patos (1983)observed with merger studies, how-ever, there is a problem with this control pro-cedure: If he impact of the merger is perceivedby other firms in the industry (in the form of mar-ket share, output pricing, and other actions), thecontrol firm'sperformance may not be totally freeof the event (p. 243). A second problem is thatclosely comparable firms may not be availablefor all firms selected in a sample. Finally, re-searcher bias and error may be introduced whenchoosing these control firms.Management literature on topics as diverse asturnaround (Hofer, 1980; O'Neill, in press), diver-sification strategies (Salter & Weinhold, 1978),executive succession (Weiner &Mahoney, 1981),and mergers (Levitt, 1975) suggest a differentbenchmark, one that addresses more directly theconcerns of managers who are trying to improvetheir firms' performance. Translating this notionto capital market studies, the correct benchmarkof normal performance becomes the firm's own

506


12/17

abnormal performance over some period of timebefore the market notices the influence of somestrategic event. The basic research question forevent studies that employ capital market mea-sures and adopt this benchmark or control is:Does the anticipated event induce a significantchange in the performance of the event firm?Therefore, two new measures of abnormal per-formance (average paired-difference and cu-mulative paired-difference) are suggested asmore consistent with the paradigms of strategicmanagement. These measures are constructedby first partitioning the time series of abnormalreturns for each event firm into a benchmarkperiod and an event-impact period. The bench-mark period is that period before the market dem-onstrates an awareness of an impending event;the event-impact period is that period beginningwith the first sign of awareness and continuingup to the event-date.

A paired-difference score then can be calcu-lated for each event firm by subtracting the aver-age monthly (daily) returns estimated during itsbenchmark period from that estimated during itsevent-impact period. As constructed, the paired-difference score represents the average monthly(daily) change in abnormal returns. This differ-ence score is averaged with difference scorescomputed for other firms in the sample that sharesimilar events to form an average paired-differ-ence score, or APD. (This measure is compara-ble to the traditionally computed average abnor-mal return score, or AR.) Multiplying the APDscore by the number of months (days) in theevent-impact period gives the cumulative changein abnormal performance associated with anevent, or CPD. (This measure is comparable tothe traditional cumulative average abnormalreturn score, or CAR.)A limitation of the difference score is that itmay not be possible to identify the precise dateof market awareness for each event firm becausea firm's returns are influenced by many events-some related to the event of interest and othersnot. Assuming that these unrelated events donot occur systematically across a sample of firms

sharing similar events, however, a portfoliogrouping procedure can be used to aid in esti-mating this date. Here, the plot of the time seriesof cumulative abnormal returns for the sampleof event firms is used (Fama et al., 1969). Whilethis plot may reveal a real trend, it also may givethe appearance of one when none is present(Brown &Warner, 1980). Traditionally computedmeasures of abnormal returns, however, alsosuffer from a similar limitation.The principal advantages of the paired-difference procedure come from its selectedbenchmark. By using the market's evaluation ofa firm's own performance during some noneventperiod as the appropriate benchmark, this proce-dure should provide an additional control fornonevent-related, firm-specific influences thatmay influence stock returns over much or all ofthe estimation period. (These influences wereidentified in the previous research issue section.)In addition, this procedure also should help mini-mize errors in the estimation of the regressioncoefficients that may result from problems withexperimental design, for example, when theactual impact of the event occurs within the coeffi-cient estimation period, and when the coefficientsare influenced by multiple events. Finally, byallowing the impact period to be determined bythe trend of a sample's time series of abnormalreturns, the difference procedure recognizes thatthe time period when the impact an event hason returns is likely to vary systematically by sam-ple characteristics.A recent study by Lubatkin (1986) provides ini-tial empirical support for the difference proce-dure. He found the APD and CPD measures tobe consistent in direction and magnitude withtheir counterparts (AR and CAR), but statisticallymore precise.In summary, it is argued that the traditionalmeasures of abnormal return may not be appro-priate for researchers in management. Asidefrom providing results that are less meaningfulto managers as those provided by the differencemeasures, the traditional measures also may suf-fer from a number of potential biases.

507


13/17

Recommendations to ResearchersIn the sections on research issues, how theprevalent finance perspective has influenced theconstruction of the market-based performancemeasures was shown. It was stressed that re-searchers in finance have tended to view corpo-rate events quite differently than do researchersin strategic management: finance views corpo-rate events such as mergers in discrete, tacticalterms rather than as an outcome of a series of

related events. Finance measures, therefore,may be incomplete measures of strategic acts.Four research issues were identified to high-light this point. Within this framework, alterna-tive procedures were introduced that are moreconsistent with the paradigms of strategic man-agement. The following recommendations showhow each can be used in conjunction with theothers. In the process, the present authors hopeto demonstrate how the principal empirical meth-odology used in finance studies can be adaptedto the research objectives of management.The first issue concerns the selection of the rel-evant time frame to assess abnormal returns.Recent finance researchers favor short time hori-zons and, therefore, daily returns data becausethe researcher may effectively isolate one par-ticular event with a known date. It was argued,however, that short horizons-while appropri-ate for assessing tactics-are inappropriate forassessing strategic acts because the flow of infor-mation regarding strategic events cannot bedated precisely. Rather, in these cases the rele-vant information is likely to be transmittedthrough a series of related events that occur overrelatively long horizons. Therefore, usingmonthly returns data when investigating strate-gic acts is recommended. While the use of thisdata may reduce the precision of statistical tests,this loss can be minimized when used in con-junction with the benchmark employed by thepaired-difference procedure. Discussed in thefourth research issue section, the properties ofthis benchmark are intended to add precision byproviding a control for extraneous events.

The second issue concerns the selection of therelevant sample frame. Finance researchersfavor a frame made up of events that are iso-lated-not preceded by or followed by othersimilar events. This sampling criteria helps toensure that the regression coefficients estimatedfor each event reflect only the influence of thatsingle event. It was argued, however, that inaddition to reducing sample size, this frame maynot permit inferences to be made about the gen-eral population when the element of interest rep-resents a strategic event. With strategic events,the performance impact of each of a successionof similar events rnay be expected to differ.Therefore, a sample frame that is not restrictedto isolated events is more appropriate.The selection of the unrestricted frame, how-ever, may involve a tradeoff of reliability forgeneralizability. Therefore, where possible, theresults of investigations of strategic acts shouldbe presented for both sample frames. In this way,readers consider the separate effects of con-tamination and differences in sampling content.Of course, not all studies can report their resultsboth ways: it would make little sense to employthe screen when the research objective is to studyfirms that merge frequently. However, to theextent that the contamination is captured overmuch or all of the estimation period, the bench-mark used by the paired-difference proceduremay be able to partially control for this bias. Anadditional control for this bias is the firm-adjustment procedure. As described in the thirdresearch issue section, this procedure averagestogether the abnormal returns estimated formergers that share a common acquiring firm andthen treats each average as a single observation.Used together, the two procedures allow a re-searcher to employ a sample frame that betterrepresents the population while minimizing (butnot eliminating) the impact of contamination biason sample statistics.The third issue is concerned with the appropri-ate statistical methods. Here, finance research-ers make two assumptions that are inconsistentwith the paradigms of strategic management.

508


14/17

First, they assume that the primary source ofsampling dependencies is caused by temporalconsiderations, and, therefore, employ an adjust-ment procedure designed to correct for thisproblem. Inferred from the literature of strategicmanagement, however, are firm-specific de-pendencies-potentially a more important sourceof sampling dependencies, particularly in sam-ple frames that contain nonisolated events. Afirm-specific adjustment procedure was de-scribed to correct for this problem.This procedure is not appropriate, however,for all research objectives. For example, it makeslittle sense to average abnormal returns acrossevents completed by the same firm when theobjective is to test for a relationship between afirm's experience with an event and the perfor-mance outcome it received from the event. Inthis case, the paired-difference procedure is rec-ommended as an alternative to control for firm-

specific influences.Researchers who have other objectives areconfronted with a choice. While the two adjust-ments can be computed simultaneously, theresult-an average taken across time and firms-has questionable meaning for hypothesis testingin strategic management. It is suggested, there-fore, that the best procedure is to test depen-dency on a sample-by-sample basis, since thelevel of sampling dependencies of both typeswill vary with the sampling procedures. In caseswhere both are present, the results should bepresented in both forms.A second statistical assumption made by fi-nance researchers is that events such as merg-ers represent a homogeneous occurrence. Incontrast, the literature of strategic managementsupports the position that performance differ-ences are likely to be observed when a sampleof events are stratified along a number of pre-scribed dimensions. Here, the recommendationis straightforward: to the extent that events differalong various dimensions, it is of primary impor-

tance for researchers to group them by similarcharacteristics before applying standard statisti-cal techniques to the respective firm's perfor-mance measures.The final research issue has to do with theselection of the proper benchmark of normalreturns when calculating abnormal returns.Finance researchers assume that the correctbenchmark is some general index of marketreturns. It was argued that this benchmark maynot account for other nonevent-related, firm-specific influences that may influence stock re-turns over much or all of the estimation period.A paired-difference procedure was described tocontrol for these extraneous influences. It is rec-ommended that this procedure be used as a finalverification of the measurement of a corporateevent's impact on stockholder wealth. As sug-gested, the properties of the benchmark makethe procedure used with the difference proce-dure ideally suited for those studies where re-search objectives require long time horizons(Research Issue 1);those that prevent the explicitrecognition of the potential combined effects ofcontamination and sample bias (Research Issue2); and those that prevent the use of the firm-adjustment procedure (Research Issue 3).To summarize, the preceding recommenda-tions were presented as guidelines to assistresearchers in strategic management. It is in-tended that they be used in conjunction with eachother. The objectives of the research, however,will determine which procedures can be em-ployed.The position taken in this paper has been thatmarket-based performance measures can pro-vide researchers of strategic management witha powerful test of corporate performance, thoughnot the only test. This discussion, however,underscores the necessity forresearchers to ques-tion the assumptions underlying the techniquesof another discipline before borrowing them.

509


15/17

ReferencesAllen, M. G., Oliver, A. R., & Shwallie, E. H. (1981)The keyto successful acquisitions. Journal of Business Strategy,2(2), 14-24.Asquith, P., Bruner, R. F., &Mullins, D. W. (1983)The gainsto bidding firms from merger. Journal of Financial Econom-ics, 11i, 121-139.Beaver, W., Kettler, P., & Scholes, M. (1970) The associationbetween market determined and accounting determinedrisk measures. The Accounting Review, 45, 645-682.Bettis, R. A. (1983) Modern financial theory, corporate strat-

egy, and public policy: Three conundrums. Academy ofManagement Review, 8, 406-415.Bettis, R. A., Chen, A., Mahajan, V. (1984) Assessing theimpact of market interventions on firm's performance.Working paper 84-700, Southern Methodist University.Bettis, R. A., & Hall, W. K. (1982) Diversification strategy,accounting determined risk, and accounting determinedreturn. Academy of Management Journal, 25, 254-264.Branch, B., & Gale, B. (1983) Linking corporate stock perfor-mance to strategy formulation. Journal of Business Strat-egy, 4(1), 51-58.Brown, S. J., & Warner, J. B. (1980) Measuring security priceperformance. Journal of Financial Economics, 8, 205-258.Burgman, R. J. (1983) A strategic explanation of corporateacquisition success. Unpublished doctoral dissertation,Purdue University.Cameron, K. S., & Whetten, D. A. (1981) Perceptions oforganizational effectiveness over organizational life cycles.Administrative Science Quarterly, 26, 525-544.Chandler, A. (1962) Strategy and structure. Cambridge, MA:M. I. T. Press.Choi, D., & Philippatos, G. C. (1983) An examination ofmerger synergism. Journal of Financial Research, 6(3),239-256.Christensen, H. K., & Montgomery, C. A. (1981) Corporateeconomic performance: Diversification strategy versus mar-ket structure. Strategic Management Journal, 2(4), 327-344.Cohen, K. J., Hawawini, G. A., Maier, S. F., Schwartz, R. A.,

& Whitcomb, D. K. (1983) Friction in the trading processand the estimation of systematic risk. Journal of FinancialEconomics, 12, 263-278.Copeland, T. E., &Weston, J. F. (1983) Financial theory andcorporate policy. Reading, MA: Addison-Wesley.Dalton, D. R., Todor, W. D., Stendolin, M. J., Fielding, G. J.,& Porter, L. W. (1980) Organization structure and perfor-mance: A critical review. Academy of ManagementReview, 5, 49-64.

Dodd, P., & Ruback, R. (1977) Tender offers and stockholderreturns. Journal of Financial Economics, 5, 351-373.Dundas, K., & Richardson, P. R. (1982) Implementing theunrelated product strategy. Strategic Management Jour-nal, 3, 287-301.Ebeling, H. W., & Doorley, T. L. (1983) A strategic approachto acquisitions. Journal of Business Strategy, 3(3), 44-54.Elgers, P. T., & Clark, J. J. (1980) Merger types and share-holder returns: Additional evidence. Financial Manage-ment, 9(2), 66-72.Fama, E. F. (1976) Foundations of finance. New York: BasicBooks.Fama, E. F. (1980) Agency problems and the theory of thefirm. Journal of Political Economy, 88, 288-307.Fama, E. F., Fisher, L., Jensen, M., & Roll, R. (1969) Theadjustment of stock prices to new information. InternationalEconomic Review, 10, 1-21.Ford, J. D., &Schellenberg, D. H. (1982)Conceptual issues of

linkage in the assessment of organizational performance.Academy of Management Review, 7, 49-58.Gonedes, N. J. (1973) Evidence on the informational contextof accounting numbers: Accounting-based and market-based estimates of systematic risk. Journal of Financialand Quantitative Analysis, 8(3), 407-443.Gupta, A. K., & Govindarajan, V. (1984) Business unit strat-egy, manufacturing characteristics and business unit effec-tiveness at strategy implementation. Academy of Manage-ment Journal, 27, 25-41.Halpern, P. (1983) Corporate acquisitions: A theory of specialcases? A review of event studies applied to acquisitions.Journal of Finance, 2, 297-318.Hitt, M. A., Ireland, R. D., & Palia, K. A. (1982) Industrialfirms'grand strategy and functional importance: Moderat-ing effects on technology and uncertainty. Academy ofManagement Journal, 25, 265-298.Hofer, C. W. (1980) Turnaround strategies. Journal of Busi-ness Strategy, 1(1), 19-31.Hofer, C. W., &Chrisman, J. J.(1984) First diversification andthe strategic management process: A new perspective.Paper presented at the annual meeting of the Academy ofManagement, Boston.Hong, H. (1977) Inflation and the market value of the firm:Theory and tests. Journal of Finance, 32, 1031-1048.Jaffe,J. (1974) Special information and insider trading. Journalof Business, 47, 410-428.

510


16/17

Jarrell, G., & Bradley, M. (1980) The economic effects of fed-eral and state regulations of cash tender offers. Journal ofLaw and Economics, 23, 371-407.Jemison, D. B., & Sitkin, S. B. (1986) Corporate acquisitions:A process perspective. Academy of Management Review,11, 145-163.Jensen, M., & Ruback, R. (1983) The mark for corporate con-trol: The scientific evidence. Journal of Financial Eco-nomics, 11, 5-50.Kitching, J. (1967) Why do mergers miscarry? Harvard Busi-ness Review, 45(6), 84-101.Kudla, R. J. (1980) The effects of strategic planning on com-

mon stock returns. Academy of Management Journal, 23,5-20.Langetieg, T. C. (1978)An application of a three-factor perfor-mance index to measure stockholder gain from merger.Journal of Financial Economics, 6, 365-383.Langetieg, T. C., Haugen R. A., & Wichern, D. W. (1980)Merger and stockholder risk. Journal of Financial andQuantitative Analysis, 15(3), 689-710.Lev, V., & Sundar, S. (1979) Methodological issues in the useof financial ratios. Journal of Accounting and Economics,1(3), 187-210.Levitt, T. (1975) Dinosaurs among bears and bulls. HarvardBusiness Review, 53(1), 41-53.Lieberson, S., &O'Connor, J. 1972)Leadership and organiza-tional performance: A study of large corporations.American Sociological Review, 37, 117-130.Lorsch, J. W., & Allen, S. A. (1973) Managing diversity andinterdependence: An organizational study of multi-divisional firms. Cambridge, MA: Harvard University

Press.Lubatkin, M. H. (1982) A market analysis of diversificationstrategies and administrative experience on the perfor-mance of merging firms. Unpublished doctoral dissertation,University of Tennessee.Lubatkin, M. H. (1983) Merger and the performance of theacquiring firm. Academy of Management Review, 8,218-225.Lubatkin, M. H. (in press) Merger strategies and stockholder

value. Strategic Management Journal.Mandelker, G. (1974) Risk and return: The case of mergingfirms. Journal of Financial Economics, 1, 303-335.Marks, M. L. (1982) Merging human resources. Mergers andAcquisitions, 17(2), 38-44.Masulis, R. W. (1983)The impact of capital structure changeon firm value: Some estimates. Journal of Finance, 38(1),139-178.

Mintzberg, H. (1973) The nature of managerial work. NewYork:Harperand Row.Montgomery, C., Thomas, A., & Kamath, R. (1984)Dives-titure,marketvaluation, and strategy.Academy of Man-agement Journal, 24(, 830-840.Newmann, G. (1980)The predictabilityof strikes:Evidencefrom the stock market. Industrial Labor Relations Review,33(4),525-535.O'Neill,H. M. (in press) An analysis of turnaroundstrategyin commercial banking. Journal of Management Studies.Peavy, J. W. (1984)Modem financialtheory,corporatestrat-egy and public policy:Anotherperspective. Academy of

ManagementReview, 9, 152-157.Pitts,R. A. (1976)Diversificationtrategiesand organizationalpolicies of large diversified firms.Journalof Economicsand Business, 28(3), 181-188.Porter,M.(1980)Competitive trategy.New York:Free Press.Porter,M. (1985)Competitiveadvantage. New York:FreePress.Rappaport,A. (1983)Corporateperformancestandardsandstockholder value. Journal of Business Strategy, 3(4), 28-38.Reiganum,M. R. (1985)Theeffectof executivesuccession onstockholder wealth. Administrative Science Quarterly, 30,46-60.Bumelt, R. (1974) Strategy, structure, and economic perfor-mance. Cambridge, MA:HarvardUniversityPress.Salter, M., &Weinhold,W. (1978)Diversificationvia acqui-sition:Creating value. Harvard Business Review, 56(4),166-177.Schipper, K., &Thompson, R. (1983)Evidence on the capital-izedvalue ofmergeractivity oracquiringfirms.JournalofFinancial Economics, 11, 85-119.Schmidt, R. (1977) The board of directors and financialinterests. Academy of Management Journal, 20, 677-682.Singh, J. H., &Montgomery,C. A. (1984)Corporateacquisi-tions and economic performance. Paper presented at theannual meeting of the Academy of Management, Boston.Steers, R. M. (1975)Problems n the measurement of organi-zational effectiveness. Administrative Science Quarterly,20, 546-558.Vance, S. C. (1978)Corporategovernance:Assessing corpo-rateperformanceby boardroomattributes. ournalof Busi-ness Research, 6, 203-220.Wansley, J. W., Lane, W. B., &Yang, H. C. (1983)Abnormalreturns oacquiredfirmsby type ofacquisitionand methodof payment. Financial Management, 12(3), 16-22.

511


17/17

Weiner, N., & Mahoney, T. A. (1981) A model of corporateperformance as a function of environmental, organizational,and leadership influences. Academy of Management Jour-nal, 24, 453-470.

Weston, J. F. (1981)Developments in finance theory. FinancialManagement, 10(2), Tenth Anniversary Issue, 5-22.

Wood, D. R., & LaForge, R. L. (1979) The impact of compre-hensive planning on financial performance. Academy ofManagement Journal, 22, 516-526.Yip, G. S. (1982) Diversification entry: Internal developmentversus acquisition. Strategic Management Journal, 3,33 1-346.

Michael Lubatkin is Assistant Professor of BusinessEnvironment and Policy in the School of BusinessAdministration, University of Connecticut, Storrs.Ronald Shrieves is Professor of Finance in the Schoolof Business Administration, University of Tennessee,Knoxville.

512

Documents

LUBATKIN - Towars Reconciliations of Market Performance