jurnal merger akuisisi

Do mergers and acquisitionscreate shareholder wealth in the

pharmaceutical industry?Mahmud Hassan

Rutgers Business School, Newark, New Jersey, USA

Dilip K. PatroDepartment of Treasury, Washington, District of Columbia, USA

Howard TuckmanFordham University, New York, USA, and

Xiaoli WangBear Sterns, USA

AbstractPurpose – The purpose of this paper is to analyze mergers and acquisitions (M&A) focusing on theUS pharmaceutical industry in the period 1981-2004. This industry is chosen because it is global, itengages intensively in M&A which it uses to both complement and substitute for early stage research,and because the potential abnormal returns to blockbuster drugs are substantial. It is assumed that ifabnormal returns to M&A exist in the short and long run, this is the industry to find them.

Design/methodology/approach – The study examines short-term abnormal returns separatingmergers from acquisitions and US-based from foreign-based M&A targets. It examined 405 mergersand acquisitions during 1981-2004 to address the issues of our research.

Findings – Evidence of short and long-term abnormal returns, as well as accounting and efficiencyeffects are found for acquisitions but not for mergers. However, the tests do suggest that mergers withUS-based targets are not value destroying. It is also found that there are differences as to the effects ofacquisitions of foreign-based, as opposed to US-based targets.

Originality/value – Taken in total, the results provide support for the view that in thepharmaceutical industry, acquisitions of US-based companies have a positive impact on wealthcreation for company shareholders.

Keywords Pharmaceuticals industry, Acquisitions and mergers, Shareholders, Stock returns,United States of America

Paper type Research paper

NomenclatureORET ¼ operating cash flow return defined as the pretax income before depreciation over

market value of the companyEORET ¼ excess ORET above equally weighted industry averageVORET ¼ excess ORET above value weighted industry averageROA ¼ return on AssetEROA ¼ excess ROA above equally weighted industry averageVROA ¼ excess ROA above value weighted industry averageROE ¼ return on equityEROE ¼ excess ROE above equally weighted industry average

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1750-6123.htm

All views expressed in this paper are those of the authors, not of their respective employers.

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International Journal ofPharmaceutical and HealthcareMarketingVol. 1 No. 1, 2007pp. 58-78q Emerald Group Publishing Limited1750-6123DOI 10.1108/17506120710740289

VROE ¼ excess ROE above value weighted industry averageTAT ¼ total asset turnover defined as sales over total assetsFAT ¼ fixed assets turnover defined as sales over total fixed assetsFACE ¼ sales over fixed assets capital expenditureRDE ¼ R&D expenses over total assetsRDS ¼ R&D expenses over salesSGR ¼ sales, general and administrative expenses over total assetsSGS ¼ sales, general and administrative expenses over sales revenueLRAT ¼ labor related expenses over total assetsLSAL ¼ labor related expenses over sales revenueEGR ¼ employee growth rate defined as the change in number of employees over

preceding year

IntroductionWhether acquiring company shareholders experience a wealth effect from mergers andacquisitions is a matter of ongoing debate among academic researchers[1]. Some arguethat mergers and acquisitions (M&A) create synergies that benefit both the acquiringcompany and the consumers (Weston et al., 2004). Others argue that M&A activitiescreate agency problems, resulting in less than optimal returns (Jensen, 1986). Becausethe net effects of M&A activity remain unclear despite a number of studies, a need existsfor continued research on this subject. This paper focuses on M&A activity in thepharmaceutical industry because it is global, engages intensively in M&A which it usesas both complement and substitute to early stage research, and because the potentialabnormal returns to blockbuster drugs are substantial. If abnormal returns exist, this isa likely industry to experience them. Our study examines short-term abnormal returnsseparating mergers from acquisitions and US-based from foreign-based M&A targets.

In this section, we present the central issue addressed in this paper. The secondsection amplifies our reasons for choice of the pharmaceutical industry, thethird section discusses the relevant literature, and the fourth section discusses the dataand methodology. Our findings are presented and discussed in the fifth section andconclusions are discussed in the final section.

Writing in Hogarty (1970) reviews 50 years of research and finds no major empiricalstudies that conclude mergers are more profitable than alternative investments. After 35years, although we have a better understanding of the causes and consequences ofmergers and acquisitions (M&A) activities, it is not clear that mergers create positivewealth effects for the acquiring companies. During this period, the literature grew toinclude studies that range from straightforward event studies looking at abnormalreturns before and after mergers to more complex theoretical models involving signalingmechanisms by acquirers through bidding (Fishman, 1988). The evidence indicates thattarget companies earn significant positive abnormal returns but that the experience ofacquiring firms is mixed (Jensen and Ruback, 1983; Huang and Walkling, 1987).

The motivations for M&A activities, as well as the factors that determine acquirerperformance, are also of interest. Traditionally, the literature views M&A activities asvalue-creating, indicating that the synergies of M&A come from a broad range ofsources such as revenue enhancement, cost reduction, access to new products, tax gains,etc. (Weston et al., 2004; Singal, 1996). Based on such theories, the combined returns forthe target and acquirer in a merger should be positive. In contrast, theories based on theagency costs of free cash flow and managerial entrenchments argue that mergers

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destroy wealth and predict that the combined returns from a merger will be negative.According to Jensen (1986), availability of free cash flow can lead to value-reducingmergers, while Shleifer and Vishny (1989) state that managers may make investmentsthat increase managerial value to shareholders but that do not improve shareholders’returns. The evidence also suggests that payment method can influence whether M&Areturns are positive, and if so, by what amount (Mitchell and Mulherin, 1996).

Choice of the pharmaceutical industryThis paper is focused specifically on the pharmaceutical industry for several reasons.First, the industry is global in nature and engages in M&A activity extensively. Hence,findings for the industry have broad applicability. Second, the industry is differentfrom most others because of the high cost of bringing a drug to market and thedocumented low rate of success for drugs coming through the pipeline. There is aninherent incentive for a company to use M&A activity either to supplement or tosubstitute for early stage research. A finding of abnormal short-term returns might beexpected given the higher returns needed to offset higher risks. Similarly, findings ofenhanced post-M&A efficiency and accounting effects would seem to reflect thesynergies claimed in company explanations of their reasons for merging. Third,the industry has a well-known propensity to seek M&A with companies that haveso-called “blockbuster drugs” with the potential to produce billions in revenue: e.g.Pfizer’s cholesterol lowering drug Lipitor was acquired by M&A activity and is a megablockbuster with the 2005 global sales of over $12 billion (Bloomberg News, 2006).Given the potential for high returns from these types of M&A, it seems likely that ifM&A is wealth enhancing, we should find this effect for the pharmaceutical industry.Finally, the monopoly or oligopoly structures that exist in several pharmaceuticalproduct-markets support the expectation of abnormal returns from M&A, at leastwhile patent protection is in effect (Bottazzi et al., 2001). Since, over 80 percent ofrevenue is lost at the time of patent expiration and the patent period is relatively short,the window for abnormal returns in the long run may be limited (Berndt, 2001).

Literature reviewIn the recent finance literature, most empirical analyses of the returns to M&A are basedon event studies and the findings from these differ depending on whether the research isfocused on the target or the acquiring companies. Varying time frameworks, abnormalreturn metrics, benchmarks and weighting procedures also make comparisons difficultand measurement of long-term abnormal performance complex. Loderer and Martin(1992) investigate 304 mergers and 155 acquisitions that took place from 1965 to 1986 anddocument a negative but statistically insignificant abnormal return over the fivesubsequent years (significant measured over three years) for mergers and positive but aninsignificant abnormal return for acquisitions. Using a market model with a movingaverage method for beta estimation, Firth (1980) finds an insignificant abnormal return of0.01 percent over the 36 months following the bid announcement by examining 434successful bids and 129 unsuccessful bids in the UK over the period 1965-1975. In contrast,Agrawal et al. (1992), Loughran and Vijh (1997), Asquith et al. (1983) and Andre et al. (2004)document significant and negative announcement period abnormal returns post M&A.

The evidence does suggest that targeted (viz., acquired) companies attainsignificant positive returns from M&A. For example, Jensen and Ruback (1983)

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report a 30 percent target return in tender offers and a 20 percent target return inmergers. Likewise, investigating 169 transactions from the period 1977 to 1982,Huang and Walkling (1987) show a return for their event window of 14.4 percent forstock offers and 29.3 percent for cash offers. In contrast, the returns to acquiringcompanies in the short-term vary by type of deal and no clear conclusion of positivereturns emerges in the literature. Travos (1987) examines 167 M&A transactions from1972 to 1981 and finds an average bidder return of 21.6 percent in stock transactionsand 20.13 percent in cash deals. Asquith et al. (1983) find a positive return of 0.20percent for acquiring companies paying cash and a negative return of 22.40 percentfor those offering stock. Andrade et al. (2001) find that for the acquiring companies, 100percent cash deals are associated with better returns than transactions with stock.

Existing evidence on long-term acquirer performance is also mixed but suggestsnegative post merger performance. Agrawal et al. (1992), using data for 973 mergers, findsignificant negative abnormal returns over five years after merger. Loughran and Vijh(1997) report a statistically significant return of215.9 percent for buying and holding thestocks of the acquiring companies for five years. Andre et al. (2004) examine 267 Canadianmergers and acquisitions for 1980-2000 using different calendar-time approachesincluding and excluding overlapping cases. They report significant negative returns forCanadian acquirers over the three-year post-event period. In contrast, Healy et al. (1992)examine post acquisition performance for the 50 largest US mergers between 1979 andmid-1984 and note that merged firms show significant improvements in asset productivityrelative to the respective industry average, leading to higher operating cash flow return.

Some researchers have investigated cross-border mergers and acquisitions and,again, the results are mixed but predominantly negative. Black et al. (2001) documentsignificant negative returns to US bidders during the three and five years followingcross-border mergers. Gugler et al. (2003) also demonstrate that cross-borderacquisitions create a significant decrease in the market value of the acquiring firm overa five-year post acquisition period. In contrast, Conn et al. (2001) do not find evidence ofpost acquisition negative returns for cross-border acquisitions.

Moeller et al. (2004) studied the effect of firm size on abnormal returns fromacquisitions. The study used over 12,000 acquisitions from 1980 to 2001 in theUSA, and found that acquisitions by smaller firms lead to statistically significanthigher abnormal returns than acquisitions by larger firms. It speculated that the largerfirms offer premium prices on their acquisitions and end up having net wealth loss.

A limited number of studies investigate various effects of M&A in thepharmaceutical industry, albeit using a different methodological approach from theabove studies. Nicholson and McCullough (2002) examine mergers between biotechcompanies and pharmaceutical companies to determine whether or not these arecharacterized by asymmetric information. Danzon et al. (2004) investigate M&A in thebiotech-pharma industry controlling for propensity to merge as defined by probabilityto merge due to patent expiration, depleted product pipelines, and observable firmcharacteristics. Using a model that endogenizes the propensity to merge (ptm), they findthat firms with high ptm scores have low growth rates in R&D expenditure and salesregardless of whether they merge or not, implying a negative post-merger effect oninternal R&D and on sales. Large firms merge to fill gaps in the production pipeline andanticipated patent expirations, while small firms merge as an exit strategy. Smaller


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companies do not have the large field sales force needed to market a drug effectively somany of these smaller companies develop compounds and align with larger companies.

Our paper builds on the abnormal returns methodology using the Fama-FrenchCalendar Time Portfolio approach. To deal with the cross-sectional dependenceproblem inherent in M&A studies, we also implement a weighted least square (WLS)methodology (weighted with the number of observations) to mitigate the low-power ofthe Calendar Time Portfolio approach in detecting long-run abnormal performance.Furthermore, we provide a separate analysis of the effects of domestic and foreignM&A and add to the post M&A analysis a study of select profitability and operationalefficiency measures. The approach is described in more detail below.

Data and methodologyThe mergers and acquisitions database for this study is constructed from the SecuritiesData Company (SDC) Platinum using data for the 1981-2004 period. It focuses on UScompanies making M&A activities in the US market as well as non-US markets.Announcement dates of the intended transactions are based on information fromFactiva. After exclusion of companies with data unavailable in Center for Research inSecurity Prices (CRSP) database, or with questionable M&A dates, the final databaseconsists of 405 mergers and acquisitions, of which 315 are US-based targets(78 percent) and 90 (22 percent are foreign-based targets (non-US transactions)[2].Of the total events, 64 percent are mergers and 36 percent acquisitions. Table I reportsthe number of M&A events in each year and in different categories[3].

The event study methodology is used to examine short-term stock price reaction toM&A announcements. We use both a market model with value weighted market indexand the Fama-French three-factor model (also with value weighted market index) toadjust for risk and estimate abnormal return.

The traditional market model to estimate abnormal returns is:

Ri;t ¼ ai þ biRm;t þ 1i;t ð1Þ

where Ri,t is its return for firm i on day t and Rm,t is the corresponding return on theCRSP value-weighted market index. The abnormal return for each day for each firm isthen obtained as:

ARi;t ¼ Ri;t 2 ðai þ biRm;tÞ ð2Þ

where ai and bi are estimated from equation (1) using data from the appropriateestimation window. We also estimate abnormal returns using the Fama-Frenchthree-factor model[4]. Abnormal returns are averaged for each event day across firms(where t ¼ 0 is the announcement day) and cumulative abnormal returns (CARs) arecomputed for the window of interest by summing average abnormal returns for thewindow.

The estimation period for the parameter estimation is constructed in the followingmanner. We start with an announcement date such as June 1. An estimation periodwindow is then constructed for a defined period such as the pre-merger period tradingday 2281 to 230; e.g. 280 trading days prior to June 1 ending 30 trading days beforeJune 1. If another event occurs for the acquiring company within 281 trading days ofthe first event it is identified as an over-lapping event and we control for the multiple

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events by retaining the estimation window period but moving the test window.We also perform an analysis based on a separate database, which excludes theoverlapping events.

We use the Fama-French Calendar-Time Portfolio approach to explore long-termstock performance of the acquiring companies[5]. This method controls forcross-section dependence across firms and, for each period, an event portfolio isformed to include all companies that have completed the event within the prior nperiods. Excess returns for the event portfolio are regressed on the Fama-French threefactors defined as follows:

Rp;t 2 rf ;t ¼ aþ bðRm;t 2 rf ;tÞ þ Sð pÞSMBt þ hð pÞHML þ 1p;t ð3Þ

The intercept a is the estimated abnormal return during the event window. FollowingAndre et al. (2004), we also introduce a non-overlap sample to address the cross-sectionaldependence problem induced by overlapping observations[6]. For evaluatingaccounting and operational performance on a longer term basis, we extend ouranalysis over a ten year period – five years before and five years after the M&A event.

To complement the Fama-French Calendar Time Portfolio approach, we perform apost M&A analysis of the profitability and operating efficiency measures of the

YearAll

M&A

M&AUS

targets

M&Aforeigntargets

MergersUS

targetsAcquisitionsUS targets

Mergersforeigntargets

Acquisitionsforeigntargets

Allmergers

Allacquisitions

1981 1 1 1 11982 6 6 3 3 3 31983 3 3 1 2 1 21985 5 5 3 2 3 21986 7 7 2 5 2 51987 5 5 2 3 2 31988 8 3 5 2 1 4 1 6 21989 12 8 4 5 3 3 1 8 41990 13 10 3 7 3 3 10 31991 37 30 7 13 17 5 2 18 191992 28 18 10 11 7 3 7 14 141993 16 10 6 6 4 3 3 9 71994 23 16 7 13 3 4 3 17 61995 22 19 3 13 6 1 2 14 81996 18 13 5 10 3 1 4 11 71997 28 21 7 14 7 3 4 17 111998 24 20 4 11 9 1 3 12 121999 28 24 4 21 3 3 1 24 42000 21 18 3 13 5 3 16 52001 27 22 5 19 3 4 1 23 42002 21 17 4 13 4 2 2 15 62003 36 29 7 16 13 4 3 20 162004 17 11 6 7 4 5 1 12 5Total 405 315 90 205 110 52 38 257 148

Note: The number of M&A events in the pharmaceutical industry for each year and category

Table I.Number of mergers and

acquisitions in the USpharmaceutical industry


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company. The study is performed on two databases, the first focuses on acquiringcompanies only and the second includes acquirer and acquired summed together. Thefirst analysis is used to determine if the acquired company benefited from thetransaction while the second looks at the effects on the whole[7]. We follow the methodproposed by Healy et al. (1992) of using pretax operating cash flow return on assets(ROA) to measure financial improvement in operating performance. The advantage ofthis method is that, unlike earnings-based performance measures, operating cash flowperformance is unaffected by depreciation and good will and it is comparable on both across-section and a time-series basis when firms use different methods of accountingfor a merger. We also select several traditional accounting measures: ROA and returnon equity (ROE). Pretax operating cash flow return is defined as operating incomebefore depreciation over market value of assets.

Empirical resultsIn this section we present and discuss our empirical findings.

Short-term event window resultsTable II reports results for the short-horizon event study based on Fama-French 3factor model using the value weighted market portfolio[8]. Panel A reports the resultsof M&A for the US-based target companies while Panel B is for foreign-based targetM&A events. For each panel, we separately report the result for the merger andacquisition groups[9].

It is clear from Panel A that there are significantly different announcement effectson the stock prices of the mergers (“M”) and “acquisitions” (“A”) groups. Consider thewindow of 21 to þ1 days: the value of CAR for “M” group is very small (mean of 0.57percent) and not statistically significant different from zero. On the contrary, the CARfor the “A” group is larger (mean of 4.17 percent) and statistically significant for boththe t-test and the generalized sign z-test. A similar conclusion holds when we explorethe results for other event window such as (21, 0) and (0, 1). When we define thewindow as (þ1, þ 30), mean CAR for “M” group rises to 3.45 percent and becomessignificant at 5 percent level, while the CAR for “A” group is still higher (mean is4.14 percent) but is only marginal significant (not significant at 10 percent with thet-test but significant at 5 percent level with generalized sign z-test). When we grow thewindow further to (þ31, þ 250), CAR for “M” group shows a non-significant decline to25.14 percent, while CAR for “A” group has an increase to 4.57 percent also notsignificant. Clearly, the results do not suggest sustained abnormal profits for “M”events, but they do for “A” events, in the short run. When the “M” and “A” groups arecombined (not shown in the table), window (21, þ 1) has a mean significant CAR of1.81 percent. The results for window (1, 30) are also positive and significant, while theresults for window (þ31, þ 250) become negative (mean CAR is 21.85 percent) but arenot statistically significant. We conclude that pharmaceutical industry acquisitionactivities involving US transactions create short-term abnormal returns while“mergers” activities do not and that acquisitions create value to pharmaceuticalindustry, while mergers do not destroy value.

Do US company M&A activities aimed at foreign-based targets have a different effect?Panel B of Table II presents the data on this question. Measured sequentially forevent windows (21, þ 1), (þ1, þ 30), (þ31, þ 250), the mean CAR values for “M”

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are 20.55, 24.30, and 20.79 percent, while for “A” the mean values are 2.14, 22.14, and215.01 percent. However, most of the results are not statistically significant. For M&Awith foreign-based targets, the market may view merger and acquisition as negative andrespond accordingly. However, the CAR for window (230, 21) is positive for both “M”and “A” groups, perhaps suggesting a possible information leakage that causes people toprofit in the pre acquisition period. Note that the CAR of the “M” group for window (21, 0)is significantly negative while the CAR of “A” group for window (230, 21) is

Event window NMean of CAR

(percent)Medianof CAR

Positive : negative(percent) T

Generalizedsign Z

Panel A: short-term event study for M&A with US-based targetsMergers (US targets)(230, 2 1) 125 1.57 0.96 65:60 0.749 1.025(21,0) 125 0.18 20.48 58:67 0.338 20.228(21, þ 1) 125 0.57 20.24 61:64 0.855 0.309(0, þ 1) 125 0.40 0.36 67:58 0.737 1.384$(þ1, þ 30) 125 3.45 0.83 67:58 1.649 * 1.384$(þ31, þ 250) 125 25.14 22.64 54:71 20.907 20.945(þ1, þ 250) 125 21.69 23.23 61:64 20.279 0.309Acquisitions (US targets)(230, 2 1) 66 21.27 1.05 34:32 20.384 0.752(21,0) 66 2.24 2 0.28 32:34 2.624 * * 0.258(21, þ 1) 66 4.17 1.31 43:23 3.994 * * * 2.972 * *

(0, þ 1) 66 4.54 2.62 44:22 5.332 * * * 3.218 * * *

(þ1, þ 30) 66 4.14 3.10 40:26 1.254 2.232 *

(þ31, þ 250) 64 4.57 3.56 34:30 0.511 0.998(þ1, þ 250) 66 8.57 6.58 37:29 0.9 1.492$Panel B: short-term event study for M&A with US-based targetsMergers (foreign targets)(230, 2 1) 22 3.54 4.03 14:08 0.831 1.458$(21,0) 22 22.83 0.49 13:09 2 2.571 * * 1.031(21, þ 1) 22 20.55 0.73 14:08 20.405 1.458$(0, þ 1) 22 20.15 0.97 14:08 20.137 1.458$(þ1, þ 30) 22 24.30 0.09 11:11 21.01 0.178(þ31, þ 250) 22 20.79 21.89 11:11 20.068 0.178(þ1, þ 250) 22 25.09 24.44 10:12 20.414 20.249Acquisitions (foreign targets)(230, 2 1) 21 13.71 0.86 12:09 2.458 * * 0.816(21,0) 21 0.51 0.43 11:10 0.353 0.379(21, þ 1) 21 2.14 1.12 13:08 1.21 1.253(0, þ 1) 21 1.64 0.32 12:09 1.139 0.816(þ1, þ 30) 21 22.14 1.50 11:10 20.383 0.379(þ31, þ 250) 21 215.01 231.07 7:14 20.994 21.368$(þ1, þ 250) 21 217.15 227.23 6:15 21.065 2 1.804 *

Notes: The symbols $, *, * *, * * * denote statistical significance at the 10, 5, 1 and 0.1 percent levels,respectively, and the numbers in parentheses are t-values. The table reports results from event studiesaround announcement of mergers or acquisitions using the Fama-French three factor model. Two testresults are reported – the t-test by Brown and Warner, and generalized sign z-testSources: Brown and Warner (1980, 1985)

Table II.Abnormal returns in thepharmaceutical industry

results from theFama-French three-factor

model


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significantly positive. This is consistent with the information leakage argument and withour previous finding that markets view acquisitions as more favorable than mergers.Figure 1 shows the trend of CAR over time for “M” and “A” groups separately, andprovides support for our findings.

Long-term stock performanceWhile short-term effects are of interest for the immediate trading opportunities theycreate, more relevant is whether M&A activities have long-term sustainable positiveeffects. To examine long-term stock performance, we first estimate as from theFama-French Calendar Time Portfolio model and then look at long-term accountingperformance using several measures of pre and post profitability and operationalefficiency, testing if the differences are statistically significant. The strategy of using atwo-pronged approach to test for these effects is helpful, because it creates a body ofstatistical evidence to capture specific dimensions of M&A activities and theredundancy reinforces confidence in our findings.

The results shown in Table III are consistent with the findings of the short-termevent study. Specifically, acquisitions of US-based targets are more likely to havepositive abnormal returns than mergers with US targets. There are no abnormalreturns for the US target merger group for the seven periods shown in Table IIIranging from year one to year five and for the period as a whole. In sharp contrast, theacquisition of US target group shows a positive abnormal return for the fivesubsequent years after the announcement: the a for the entire period of 60 months is1.33 percent and is significant at the 1 percent level. It is interesting to note that the afor the combined M&A database is substantially smaller than for acquisition alone(0.72 percent) and statistically significant at the 10 percent level (not shown in thetable), consistent with the finding that the US-based acquisition group is more likely tooutperform US-based merger group. This also implies that studies combiningthe mergers and acquisitions together are less likely to detect positive abnormalreturns.

Analysis of the foreign-based target data suggests a slightly different story. Mergeractivity is found to have a positive effect (3.1 percent) in the first 12 months post-mergerand is significant at the 10 percent level. However, for the remainingperiods, merger activity does not have a statistically significant impact on abnormalreturns. This finding is consistent with what we have found in the short-term eventstudy. Acquisition activity in the foreign-based targets group is not statisticallysignificant and the abnormal returns for all of the individual periods are much smallerthan the results seen for US targets. This seems to suggest that acquisitions offoreign-based targets by US companies are less likely to lead to abnormal profits thanacquisitions of domestic companies in the long run. There are many possible reasons forthis, such as: the effects of differences in culture on acquisition success, less transparentpre-acquisition data for the foreign acquired company, problems in integratingforeign-based accounting and IT systems, etc. It is interesting to note that when theUS-based target data and the foreign-based target data are combined, the abnormalreturn is positive for the five-year period (1.33 percent) and statistically significant at the1 percent level.

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Figure 1.Figure of CARs based onFama French three factor

model using the valueweighted market index.

(a) US-based targets“mergers” group;

(b) US-based targets“acquisitions” group;

(c) foreign-based targets“mergers” group;

(d) foreign-based targets“acquisitions” group

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Notes: Figure 1 shows the trend in CAR overtime. The results are based on the non-overlapping database. An event is identified as an overlapping event if ithappens within 281 trading days of the previous included event. The results for database including overlapping events are similar and thus are not reported here

Ret

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Post M&A performanceTable IV reports the financial performance of US-based targets for ten-yearperiod – five years before and five years after the event. Pre M&A performance(period 5-1) is calculated as the weighted average of the acquirers and targets, whilepost M&A performance (period 1-5) is based only on acquiring company data. We alsolook at the financial performance pre and post M&A for acquirers only and results aresimilar.

Panel A shows the mean level of each profitability measure five years before andfive years after M&A. ORET represents operating cash flow return, which is defined aspretax income before depreciation divided by the market value of the company.EORET and VORET are the excess ORET above equally weighted industry averagesand value weighted industry averages, respectively. Similarly, EROA and VROA arethe excess ROA based on the equally weighted industry average and value weightedindustrial average, respectively. EROE and VROE are the ROE computed in the sameway as defined above. Panel B of Table IV reports two sample t-test results for eachprofitability measure. The null hypothesis for each test is that the mean level for thepre M&A period is not significantly different from the mean for the post M&A period.A negative t-value indicates a smaller mean level for the pre M&A period, and viceversa. The acquisition group shows a significant increase in the ORET after M&A andthe t-value is negative and significant. In contrast, ORET for the US-based “merger”group does not show significant changes after M&A. (The t-value is not significantlydifferent from zero). The same is true for VORET.

For the ROE measures (ROE, EROE, VROE), neither the mergers nor the acquisitionsgroups showed improvement after M&A (t-values are not significantly different fromzero). Interestingly, the two-sample t-test shows that the merger group experienced asignificant improvement after the M&A, for ROA, and EROA and VROA, while theacquisition group only had marginal improvements in ROA and VROA. The differencebetween the ROA and ROE measures may reflect one or more of the following possibilities.There may be an accounting problem in trying to capture intangible assets and/or equity,which affects ROE. Alternatively, when a company with a high market to book ratiomerges with, or acquires, a lower market to book company, ROE will increase. A thirdpossibility is that a company may de-leverage post merger, causing equity to increasewhile debt decreases. If assets are sold off to pay down debt then equity may not change.

Table V provides the select operating efficiency measures for the US-based targetspre and post M&A. Pre-M&A performance for period 5-1 is based on the market valueweighted average of both the acquirers and targets, while post-M&A performance inperiod þ1 to þ5 is based on acquiring company value. TAT is total asset turnovercalculated as sales over total assets, FAT is fixed asset turnover (sales over fixedassets), FACE is calculated as sales over Fixed Asset Capital Expenditure, RDEand RDS are R&D expenses over total assets and R&D expenses over sales,respectively, and SGR and SGS are selling, general and administrative expenses overtotal assets and sales, respectively. LRAT and LSAL are labor-related expensesover total assets and salesm, respectively. Finally, EGR is the employment growth ratecalculated as change in number of employee over the last year.

Panel A shows the mean values for the selected measures and Panel B providestwo-sample t-tests. A significant positive t indicates a decrease after M&A while anegative t implies an increase. The results are mixed. Total asset turnover ratio (TAT)

IJPHM1,1

68

Ev

ent

per

iod

(mon

ths)

US

targ

ets

mer

ger

sU

Sta

rget

sac

qu

isit

ion

sU

Sta

rget

sM

&A

For

eig

nta

rget

sm

erg

ers

For

eig

nta

rget

sac

qu

isit

ion

sF

orei

gn

targ

ets

M&

AA

llm

erg

ers

All

acq

uis

itio

ns

All

M&

A

0-12

0.01

060.0301

0.017

0.03

080.

0081

0.02

110.

0124

0.0267

0.0174

(1.4

9)(2.68** )

(2.70** )

(1.7

7$)

(0.3

8)(1

.58)

(1.8

4$)

(2.63** )

(2.98** )

0-24

0.00

430.0176

0.00

860.

0002

0.00

040.

0005

0.00

390.0149

0.0076

(0.7

5)(2.40* )

(1.8

1$)

(0.0

1)(0

.02)

(0.0

5)(0

.72)

(2.27* )

(1.70$)

0-36

0.00

650.0139

0.0089

0.00

770.

0105

0.00

840.

0067

0.0131

0.0087

(1.3

2)(2.45* )

(2.14* )

(0.6

)(0

.76)

(0.9

9)(1

.4)

(2.44* )

(2.21* )

0-48

0.00

50.0126

0.00

740.

0069

0.01

490.

0099

0.00

520.0127

0.0076

(1.1

2)(2.57* )

(1.9

7$)

(0.6

5)(1

.1)

(1.2

1)(1

.21)

(2.65** )

(2.08* )

0-60

0.00

430.0132

0.00

720.

0021

0.01

570.

0072

0.00

40.0133

0.0071

(1.0

1)(2.77** )

(1.9

7$)

(0.2

2)(1

.24)

(0.9

6)(1

.00)

(2.86** )

(2.00* )

13-3

60.

0027

0.00

770.

0041

20.

0004

0.01

120.

0033

0.00

240.

0082

0.00

41(0

.49)

(1.1

8)(0

.93)

(20.

02)

(0.6

2)(0

.32)

(0.4

7)(1

.38)

(0.9

9)37

-60

20.

0008

0.00

90.

0021

20.

0105

0.02

620.

0052

20.

0015

0.01

0.00

23(2

0.16

)(1

.27)

(0.4

8)(2

0.72

)(1

.05)

(0.4

)(2

0.31

)(1

.43)

(0.5

2)

Notes:

Th

esy

mb

ols

$,* ,

** ,

**

*d

enot

est

atis

tica

lsi

gn

ifica

nce

atth

e10

,5,1

and

0.1

per

cen

tle

vel

s,re

spec

tiv

ely

,an

dth

en

um

ber

sin

par

enth

eses

are

t-v

alu

es.A

bn

orm

alre

turn

s( a

)are

bas

edon

the

Fam

a-F

ren

chca

len

dar

tim

ep

ortf

olio

app

roac

h.W

LS

isim

ple

men

ted

wh

ere

the

wei

gh

tsar

eth

en

um

ber

ofob

serv

atio

ns.

Nu

mb

ers

inth

ep

aren

thes

esar

eth

et-

val

ues

Table III.Long horizon event study

based on Fama-Frenchcalendar time portfolio

approach


wealth?

69

PanelA:meanvalueof

profitabilitymeasurespreandpostM&A

Period

ORET

EORET

VORET

ROA

EROA

VROA

ROE

EROE

VROE

(1)US-basedmergers

25

20.

0154

0.09

682

0.01

572

0.15

540.

5246

20.

1558

20.

1260

0.12

852

0.12

692

42

0.00

190.

0984

20.

0021

20.

1502

0.55

342

0.15

052

0.55

202

0.25

082

0.55

282

30.

0085

0.09

770.

0083

20.

1253

0.44

042

0.12

572

0.17

250.

0087

20.

1734

22

20.

0033

0.10

252

0.00

352

0.08

730.

5305

20.

0877

20.

0611

0.16

812

0.06

192

12

0.02

050.

1154

20.

0207

20.

1439

0.54

832

0.14

432

0.25

372

0.04

912

0.25

450

0.01

470.

1377

0.01

452

0.00

430.

6728

20.

0047

0.06

990.

2122

0.06

901

20.

0030

0.08

882

0.00

312

0.00

550.

5626

20.

0058

20.

3745

20.

1741

0.06

902

20.

0429

0.06

012

0.04

312

0.02

790.

5949

20.

0283

20.

5618

20.

3662

20.

5626

30.

0729

0.17

980.

0727

0.11

270.

7985

0.11

230.

2084

0.35

460.

2076

40.

0799

0.21

000.

0797

0.13

430.

8612

0.13

390.

2405

0.56

680.

2398

50.

0582

0.16

660.

0579

0.14

940.

9310

0.14

900.

2632

0.55

220.

2622

(2)US-basedacquisitions

25

0.03

070.

1514

0.03

052

0.10

420.

5479

20.

1047

20.

0716

0.11

172

0.07

262

40.

0342

0.13

220.

0340

20.

0894

0.49

102

0.08

980.

0517

0.38

920.

0508

23

0.03

810.

1382

0.03

792

0.01

000.

5981

20.

0103

0.05

270.

2790

0.05

202

20.

0463

0.15

200.

0461

20.

0028

0.64

872

0.00

310.

1018

0.23

160.

1009

21

0.03

500.

1723

0.03

472

0.01

830.

7759

20.

0186

20.

0699

0.15

822

0.07

070

0.02

810.

1394

0.02

792

0.05

430.

6359

20.

0546

20.

0607

0.27

192

0.06

151

0.03

030.

1244

0.03

012

0.00

370.

6708

20.

0041

20.

0273

0.34

212

0.06

152

0.04

610.

1502

0.04

592

0.01

940.

5869

20.

0197

4.44

334.

7043

4.44

243

0.07

690.

1719

0.07

670.

0708

0.78

830.

0705

0.17

380.

4351

0.17

294

0.08

680.

1873

0.08

660.

0595

0.67

050.

0591

0.15

820.

3943

0.15

735

0.08

130.

1950

0.08

110.

0667

0.80

120.

0664

0.16

540.

6736

0.16

47PanelB:twosamplet-testof

profitabilitymeasures(tvalueisbasedon

meanlevelpreM&A

andmeanlevelpostM&A)

Variable

Method

Variances

DF

tvalue

Pr.

jtj

(1)USmerger

OR

ET

Poo

led

Eq

ual

153

21.

260.

2106

Sat

tert

hw

aite

Un

equ

al78

.72

1.15

0.25

65E

OR

ET

Poo

led

Eq

ual

153

21.

040.

2982

Sat

tert

hw

aite

Un

equ

al77

.12

0.94

0.35

26V

OR

ET

Poo

led

Eq

ual

153

21.

260.

2105

Sat

tert

hw

aite

Un

equ

al78

.72

1.15

0.25

64R

OA

Poo

led

Eq

ual

153

23.

070.0025**

Sat

tert

hw

aite

Un

equ

al14

32

3.88

0.0002**

(continued

)

Table IV.Pre and post measures ofM&A profitability

IJPHM1,1

70

ER

OA

Poo

led

Eq

ual

153

22.

80.0058**

Sat

tert

hw

aite

Un

equ

al11

82

3.21

0.0019**

VR

OA

Poo

led

Eq

ual

153

23.

070.0025**

Sat

tert

hw

aite

Un

equ

al14

32

3.88

0.0002**

RO

EP

oole

dE

qu

al15

32

0.39

0.69

49S

atte

rth

wai

teU

neq

ual

71.6

20.

330.

7453

ER

OE

Poo

led

Eq

ual

153

20.

320.

7523

Sat

tert

hw

aite

Un

equ

al73

.42

0.27

0.79

02V

RO

EP

oole

dE

qu

al15

32

0.39

0.69

49S

atte

rth

wai

teU

neq

ual

71.6

20.

330.

7452

(2)USacquisition

OR

ET

Poo

led

Eq

ual

164

22.

120.0354*

Sat

tert

hw

aite

Un

equ

al10

82

2.06

0.0415*

EO

RE

TP

oole

dE

qu

al16

42

0.87

0.38

49S

atte

rth

wai

teU

neq

ual

102

20.

830.

4079

VO

RE

TP

oole

dE

qu

al16

42

2.12

0.0355*

Sat

tert

hw

aite

Un

equ

al10

82

2.06

0.0416*

RO

AP

oole

dE

qu

al16

42

1.66

0.09

88$

Sat

tert

hw

aite

Un

equ

al16

32

1.93

0.0549

*

ER

OA

Poo

led

Eq

ual

164

21.

350.

1776

Sat

tert

hw

aite

Un

equ

al13

42

1.42

0.15

73V

RO

AP

oole

dE

qu

al16

42

1.66

0.09

88$

Sat

tert

hw

aite

Un

equ

al16

32

1.93

0.05

49$

RO

EP

oole

dE

qu

al16

42

1.51

0.13

42S

atte

rth

wai

teU

neq

ual

57.9

21.

110.

2715

ER

OE

Poo

led

Eq

ual

164

21.

650.

1002

Sat

tert

hw

aite

Un

equ

al58

.12

1.22

0.22

62V

RO

EP

oole

dE

qu

al16

42

1.51

0.13

42S

atte

rth

wai

teU

neq

ual

57.9

21.

110.

2715

Notes:

Th

esy

mb

ols

$,* ,

** ,

**

*d

enot

est

atis

tica

lsi

gn

ifica

nce

atth

e10

,5,1

and

0.1

per

cen

tle

vel

s,re

spec

tiv

ely

.Pro

fita

bil

ity

mea

sure

sof

M&

Aw

ith

US

-bas

edta

rget

sb

efor

ean

daf

ter

M&

Aco

mp

leti

ond

ate.

Per

iod

rep

rese

nts

the

tim

ere

late

dto

the

M&

Aev

ent

ann

oun

cem

ent.

Bef

ore

M&

Ap

erfo

rman

ce(p

erio

d2

5to

per

iod

0-1)

isca

lcu

late

das

the

wei

gh

ted

aver

age

bet

wee

nac

qu

irer

san

dta

rget

sw

hil

eth

eaf

ter

M&

Ap

erfo

rman

ce(p

erio

d1-

5)is

bas

edon

lyon

the

acq

uir

ing

com

pan

ies.

OR

ET

isth

eop

erat

ing

cash

flow

retu

rnd

efin

edas

the

pre

tax

inco

me

bef

ore

dep

reci

atio

nov

erm

ark

etv

alu

eof

the

com

pan

y(m

ark

etv

alu

eof

the

stoc

kþ

boo

kv

alu

eof

the

deb

t).

EO

RE

Tan

dV

OR

ET

are

the

exce

ssO

RE

Tab

ove

equ

ally

wei

gh

ted

ind

ust

ryav

erag

ean

dv

alu

ew

eig

hte

din

du

stry

aver

age,

resp

ecti

vel

y.

ER

OA

and

VR

OA

are

the

exce

ssR

OA

bas

edon

the

equ

ally

wei

gh

ted

ind

ust

ryav

erag

ean

dv

alu

ew

eig

hte

din

du

stri

alav

erag

e.E

RO

Ean

dV

RO

Ear

efo

rth

eR

OE

,re

spec

tiv

ely

.A

llth

ere

sult

sar

eb

ased

onth

esa

mp

les

excl

ud

ing

over

lap

pin

gev

ents

Table IV.


wealth?

71

PanelA:Meanvalueof

variousoperatingefficiency

measurespreandpostM&A

Period

TAT

FAT

FACE

RDE

RDS

SGA

SGS

LR

AT

LSAL

EGR

(1)US-basedtarget

mergers

25

0.78

134.

8767

58.9

104

0.16

811.

6002

0.48

870.

5616

0.23

160.

2316

20.

4410

24

0.77

749.

0877

20.2

421

0.19

914.

1103

0.52

210.

5284

0.22

090.

2209

0.16

692

30.

7106

5.80

7620

.569

80.

1481

1.07

080.

4442

0.91

210.

2202

0.22

020.

2301

22

0.72

035.

1360

20.0

809

0.15

102.

5922

0.45

790.

6101

0.21

690.

2169

0.10

812

10.

7467

7.40

2827

.520

10.

1322

2.02

810.

4316

0.60

380.

2580

0.25

800.

1519

00.

7111

5.77

8021

.387

30.

1710

15.9

245

0.38

070.

8745

0.24

980.

2498

0.23

141

0.73

585.

3884

21.0

350

0.13

992.

2529

0.37

890.

6536

0.25

370.

2537

0.10

162

0.72

806.

4852

33.5

637

0.12

910.

5257

0.39

610.

6603

0.24

630.

2463

0.06

843

0.76

116.

2510

25.9

459

0.11

061.

0793

0.39

690.

5731

0.24

690.

2469

0.08

864

0.72

376.

5499

27.2

216

0.13

993.

7074

0.38

100.

5832

0.24

220.

2422

0.08

565

0.79

064.

9043

27.7

690

0.10

600.

2336

0.39

420.

4782

0.26

760.

2676

0.04

56(2)US-basedtarget

acquisitions

25

0.80

773.

0070

31.1

195

0.15

500.

7956

0.34

150.

3599

0.20

080.

2008

16.0

000

24

0.85

763.

3303

35.8

816

0.15

360.

4550

0.34

700.

3710

0.26

700.

2670

0.07

892

30.

8674

3.77

6356

.069

30.

1179

0.36

150.

3426

0.36

180.

2266

0.22

660.

2142

22

0.83

043.

5053

29.1

663

0.12

640.

7925

0.33

360.

3881

0.23

990.

2399

0.04

752

10.

7961

3.33

0231

.493

50.

1137

0.67

090.

3367

0.42

560.

2277

0.22

770.

1005

00.

5467

8.20

7723

.113

60.

1066

0.63

740.

2998

0.79

380.

2519

0.25

190.

4671

10.

5498

8.18

6628

.522

90.

0786

1.01

150.

3028

0.69

340.

2541

0.25

410.

2051

20.

5409

7.25

3656

.156

20.

0721

0.72

120.

2715

0.45

910.

2587

0.25

870.

0941

30.

5550

6.97

7596

.616

60.

0730

0.86

230.

2767

0.45

960.

2562

0.25

620.

0789

40.

6212

7.43

2210

0.99

560.

0640

0.35

830.

2765

0.44

310.

2333

0.23

330.

0783

50.

6189

7.51

1250

.537

90.

0833

0.35

620.

2793

0.45

720.

2303

0.23

030.

0263

PanelB:Twosamplet-testof

operatingefficiency

measuresbefore

andafter

M&A

(tvalueisbasedon

(meanlevelof

before

M&A

–meanlevelof

after

M&A))

Variable

Method

Variances

DF

t-value

Pr.

jtj

(1)US-basedtarget

mergers

TA

TP

oole

dE

qu

al57

12

0.01

0.99

03S

atte

rth

wai

teU

neq

ual

569

20.

010.

9903

FA

TP

oole

dE

qu

al56

30.

430.

6679

Sat

tert

hw

aite

Un

equ

al46

50.

430.

67F

AC

EP

oole

dE

qu

al66

10.

220.

8256

Sat

tert

hw

aite

Un

equ

al48

20.

260.

7975

RD

EP

oole

dE

qu

al54

22.

30.0219*

Sat

tert

hw

aite

Un

equ

al53

82.

30.022*

RD

SP

oole

dE

qu

al51

40.

640.

5198

Sat

tert

hw

aite

Un

equ

al51

40.

650.

5164

(continued

)

Table V.Measures of operatingefficiency pre and postM&A

IJPHM1,1

72

SG

AP

oole

dE

qu

al42

22.

770.0059**

Sat

tert

hw

aite

Un

equ

al33

12.

780.0058**

SG

SP

oole

dE

qu

al40

10.

490.

6231

Sat

tert

hw

aite

Un

equ

al20

70.

440.

6588

LR

AT

Poo

led

Eq

ual

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Table V.


wealth?

73

does not change post M&A for the merger group but for the acquisitions group itsignificantly decreases. FAT and FACE are statistically significant for the acquisitiongroup indicating an improvement post M&A, but for the merger group, the t-values arenot significant. RDE and RDS are important to the pharmaceutical industry becausethey indicate what happens to research post merger. For both the merger andacquisition groups, RDE are significantly positive, suggesting an increase of R&Dexpenses over assets and, for RDS, the results are mixed and the t-tests not consistentlysignificant. SGA and SGS show the ratios of administrative, general and salesexpenses to assets and sales and for both the merger and acquisition groups, SGA arepositive and significant, suggesting an increase in efficiency post M&A.

Finally, the three measures for labor use – LRAT, LSAL and EGR – also reflectmixed performance. For the merger group, LRAT is negative and significant, LSAL ispositive and significant, and EGR is not significant. For the acquisition group, LRAT isnot significant. Both LSAL and EGR are significant and positive indicating animprovement in efficiency for labor utilization. Taken in total, these results suggestthat the acquisition group fairs better than the merger group but that at least some ofthe expected synergies do not materialize.

ConclusionsWhat can be said of these results taken as a whole? First, despite the attractiveness ofmergers in the pharmaceutical industry, we find no abnormal returns from mergers foracquiring companies. This holds true both for US pharmaceutical acquirers that mergewith other US-based companies and for those that merge with foreign-based targets.In both cases, the overwhelming evidence is that mergers do not give rise to eithershort- or long-term abnormal profits for the pharmaceutical industry. Indeed, theanalysis in the last section indicates that several of the statistically significant effectson operational efficiency are the reverse of what is predicted by those who argue forsynergies. While there is evidence of an improvement in ROA, the fact that ROE doesnot improve raises questions about the value of these mergers. Interestingly, for theacquiring group, there is some improvement in cash flow and in ROA but many of themeasures are not statistically significant. This result raises some doubt of the efficacyof the mergers of very large companies that have taken place in the industry in the lastfew years, viz., Pfizer and Warner Lambert[10].

An important finding of our research is that when pharmaceutical acquisitions areanalyzed separately from mergers, the results indicate a statistically significantpositive abnormal return for acquiring companies for both short and longer terms.This makes intuitive sense because bigger pharmaceutical companies acquire a patent,division, or a smaller biotech company for strategic reasons and the market reactspositively if the acquisition is considered value-adding to the existing product portfolioof the acquiring company. In contrast, mergers, particularly of large companies, maycontain return reducing, as well as profit enhancing, elements or they may not besufficient to augment a weak pipeline. As a result, the merged company (measuredfrom the perspective of the acquirer) may end up with modest or even negative returns.This would also be the case if the “winner’s curse” prevails and the bidding getssufficiently high so that the target draws off the profit, leaving modest or no returns tothe acquirer. Earlier studies that combine mergers and acquisitions as one group

IJPHM1,1

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cannot detect the difference in the record of success of the acquisition group and, hence,may give rise to misleading conclusions.

Consider next our findings for selective measures of accounting and operatingperformance, which suggest that the desired effects of M&A (i.e. greater profitabilityand improved efficiency) are more likely to be achieved through acquisitions thanthrough mergers. When a test is found to be both statistically significant and in theexpected direction, it is far more likely to be found for the acquisition than the mergergroup. Our study also suggests that US acquisitions of foreign-based companies byeither merger or acquisition are less likely to be successful than M&A with US-basedcompanies. This may be due to differences in accounting policies, language, culture, orlegal systems. There is also some evidence of information leakages that occurpre-merger that may cloud the findings.

We suspect that acquisitions are simpler for a company to absorb. They usuallyinvolve a single unit or product rather than a whole company and hence are more likelyto target areas of synergy and need. The cultural issues are easier to understand andmanage and this reduces absorption time and the concomitant time to completion,which is important since the pharmaceutical industry has limited years of protectionfor its patents. Acquisitions also make it much clearer where the control lies and whatis expected of the acquired company[11].

These observations notwithstanding, the fact that acquisitions are more likely thanmergers to accomplish the goals of the acquirer suggests that they might be the largestpart of M&A activity, but in actuality the opposite is the case. In the database forwhich we have financial data (405 companies), mergers represent 64 percent of theactivity and acquisitions only 36 percent. Why does the industry favor merger whenacquisitions seem to be more profitable? In part, this may reflect the desire of the largepharmaceutical companies to takeover whole companies to gain access to a freshpipeline of new compounds and/or to buy competitors to reduce competition.An acquisition event can occur only when the target company offers tender to sell as anexit strategy. It may also be true that acquisitions are harder to find and/or moredifficult to bring to fruition.

Either way, it is puzzling that companies in the pharmaceutical industry continue topredominantly engage in mergers given the results reported above. If mergers do notincrease the value of the acquirer’s wealth, one might expect to see them decrease overtime in favor of other acquisition modes but the numbers in Table I indicate no cleartrend in mergers and acquisitions over time. Perhaps, the answer lies in what Hameland Prahalad (1994) refer to as the strategic architecture of a company: its acceptedstandards of behavior, structure of values, and financial structure, etc. Alternatively,mergers may be like venture capital acquisitions where the expectation is that mostdeals will fail but a few will bring in large enough profits to justify the wholeacquisition program. Clearly, additional work is needed to explain why mergerscontinue to retain their popularity in the pharmaceutical industry while acquisitionsappear to be more economically and operationally sound.

Notes

1. Specifically, a merger is defined as the union of two previously separate companies, while anacquisition involves purchase of a target company’s unit, division, patent or other assets.


wealth?

75

A transaction is identified as acquisition from the description of the M&A or from thehistory file in the SDC database.

2. A separate database is constructed for overlapping events and parallel results are obtainedfor all of the tables reported below. The non-overlapping sample has a total of 278 events,229 domestic transactions and 49 cross-border transactions. Because the findings are similar,we report only the results from the non-overlapping database in this paper. Results for theother data can be obtained from the authors.

3. For the analysis of post M&A accounting performance, we further restrict the study to thosedata for which both acquirers and targets are available; this results in 155 M&A cases.

4. Data for the three factors are obtained from Professor French’s web site.

5. As shown in Lyon et al. (1999), the Fama-French Calendar-Time Portfolio approach is one ofthe best methods to estimate long-term abnormal performance.

6. Overlap is present if an event occurs within one year of a previously included event by thesame acquiring firm. Note that only the non-overlapping results are reported in this paperbut the overlap findings are available from the authors.

7. Post M&A performance is calculated as the market value weighted average of acquirer andtargets while the after M&A performance is based on acquirer only.

8. Results based on market model using value-weighted portfolio are similar and thus are notreported here.

9. A separate set of equations are run using size-based variables to test for a size effect. Theseincluded both linear, dummy variable, and log specifications to test for abnormal CARreturns based on size. The results did not find size significant and they did not change theresults reported in this section in a material way.

10. Recall that our tests do not involve exploration of whether the strategic goals of thesemergers have been achieved in the non-financial domain.

11. Interestingly, the results reported in this paper are also consistent with what peopleassociated with new business development in the industry have suggested fits their ownexperience.

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About the authorsMahmud Hassan, PhD is a Professor of Finance and Economics, Director of PharmaceuticalManagement Program, and the Director of the Lerner Center for Pharmaceutical ManagementStudies at the Rutgers Business School – Newark and New Brunswick, Rutgers University,USA. Professor Hassan has publications in the Journal of Finance, Journal of Business, Journal ofHealth Economics, Inquiry, JAMA, Health Affairs, and many other journals. Mahmud Hassan isthe corresponding author and can be contacted at: [email protected]

Dilip K. Patro, PhD is a Senior Financial Economist at the Office of the Comptroller ofCurrency, Washington, DC. His current research is focused on analyzing flows into internationalmutual funds, systemic risk for bank holding companies and examining behavior of cross listedfirms during currency crises. He has taught at the Rutgers Business School and at Smith Schoolof Business.

Howard Tuckman, PhD is the Dean of the Graduate School of Business Administration andthe Dean of the Business Faculty at Fordham Business School, Fordham University, USA. He isalso a Professor in the Finance area. Dean Tuckman has written over 100 articles and 7 booksand works in the area of pharmaceutical and biotech research.

Xiaoli Wang, PhD is a Quantitative Investment Strategist at Bear Sterns. At the time of thisresearch, she was at the Rutgers Business School doing her PhD in Finance. She also has anMBA from the Rutgers Business School.

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