Effect of M&A announcement on acquirer stock prices in the ...1193658/FULLTEXT01.pdfMaster Thesis...

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Master Thesis Repair- DDM IFM (2016-2018)

Effect of M&A announcement on acquirer stock prices in

the Pharmaceutical sector and the role of bid premium

Pulkesh Mishra

S2831805

Supervisor: Prof. Adri De Ridder

Co-Assessor: Dr. Wim Westerman

Abstract:

A majority of previous studies reveal evidences of negative or no abnormal returns for the

bidder/acquirer firm upon the announcement of a merger or acquisition (M&A). Additionally,

these studies stress on the importance of ‘bid premium’ announced as a key factor

influencing acquirer returns post M&A announcement. This paper aims to find validity for

the above-mentioned statements in case of a ‘Pharmaceutical sector setting’ because not

many previous studies have analyzed the role of bid premium influencing abnormal stock

returns for the acquirer/bidder firm in M&A’s taking place in the pharmaceutical sector. We

applied ‘event study methodology’ to study the abnormal returns’ and our results suggest

positive returns to M&A announcements around the world for the period from 1997-2015.

Furthermore, we carried out an OLS regression to observe the influence of ‘bid premium’

(announced at the time of M&A announcement), on the abnormal stock returns. We control

for acquirer firm characteristics by adding them as control variables in the analysis. Our

findings suggest that bid premium negatively affects the acquirer abnormal returns around the

time of the M&A announcement.

Keywords: Pharmaceutical sector, M&A, bid premium, Cumulative abnormal returns (CAR)

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1. Introduction

Mergers and Acquisitions (M&A) has been a popular tool for expanding activities business

effectively since the past few decades. The reasons behind such M&A’s are different across

different industries, similarly the effects and end results of such M&A’s vary accordingly.

Mittra (2007) defines M&A activities as a help for companies to overcome difficulties arising

out of technological shocks and financial deficits due to constant need for remaining innovative

and maintaining commercial sovereignty. A previous study by Bower (2001) mentions five

broad reasons for carrying out an M&A:

-Deal with overcapacity through consolidation in mature industries.

-Collaborate with competitors in geographically fragmented industries, thereby increasing

asset base and reducing competitive pressure.

-Extend into new products or markets.

-Substitute for internal and expensive Research and Development (R&D) or acquire new R&D

capabilities and expand the R&D base of the company.

-Exploit eroding industry boundaries by inventing an industry.

On the basis of geographical alignment, M&A’s can be categorized into two broad forms, the

first one being a merger or acquisition between two companies of the same country, i.e., a

domestic M&A, while the other one being an M&A between two companies coming from two

different countries, i.e., a ‘cross-border M&A’. Based on their nature, M&A’s are classified as

horizontal, vertical and mixed as described by Evans (2000). Clougherty and Duso, (2009)

define horizontal mergers as those which are done to decrease competition, where a company

acquires or merges with another company that manufactures or sells the same product in the

market thereby giving serious competitive threats. Whereas, according to Arnold (2010) when

instead of producing the same product, two different companies in the industry produce

products that can be used as inputs in either’s production process, i.e., the input of one is the

output of another, such mergers come under the classification of vertical mergers. Thus, as

concluded by Harrigan (2003), an acquisition could be favourable for a company to gain cost

advantages from the synergies resulting from such a merger. In case of mixed mergers, Church

(2008) explains that neither is there a competition between the firms involved in the M&A, nor

does one use the output of another as an input. According to Morresi and Pezzi (2014), such

mergers are generally a result of companies wanting to enter new business districts and/or for

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increasing their financial capabilities.

For industries, such as the ‘Pharmaceutical industry’, mergers and acquisitions are important,

not just in their profit enhancing abilities, but in some cases, also for their survival in the highly

competitive pharmaceutical industry. Few unique characteristics of the pharmaceutical

industry define the meaning of mergers and acquisitions in this field in a slightly different

manner when compared to the other industries. The generic cause across all industries for

M&A’s is growth with respect to size and scale of operations. For a pharmaceutical company,

aspects such as competition, survival and research and development (R&D) are of crucial

importance, which makes M&A’s a highly suitable option for them. The landscape of the

pharmaceutical industry is rapidly changing, and companies in this industry require a strong

capital base to cope with the changes, moreover, this industry is also highly regulated by the

government. Due to these reasons, it becomes difficult for smaller companies to keep up with

the rapid changes because of an inadequate capital base. Such companies who cannot compete

in the market because of their size and capital restraints, find it viable to be absorbed by larger

and better-capitalized companies through an M&A activity.

The concentration in the pharmaceutical industry has increased rapidly over the past. The total

value of M&A activities in this industry has been over $500 billion, because of which the top

10 pharmaceutical firm’s share went up from 20% in 1985 to almost 50% by the end of 2002,

as shown in their research by Danzon et al. (2005). Recent statistics provided by the ‘Institute

of Mergers, Acquisitions and Alliances’ (IMAA) show that as of 2017, the pharmaceutical

industry is ranked fourth among 91 industries, when compared across industries on the basis

of value of the M&A deals. The total value of the M&A deals in the pharmaceutical industry

comes to 2719 Billion US$. Demirbag et al. (2007), finds that the competition between

pharmaceutical companies as well as the pressure for globalization is increasing rapidly and

thus generating sufficient profit is becoming difficult, which is a major reason for companies

entering into M&A methods as a growth strategy to maintain their position in the markets.

Moreover, outsourcing R&D activities is another major reason for pharmaceutical companies

merging and acquiring other firms. When a company faces difficulty to expense for the required

research and development of a new drug it can acquire another company to overcome such a

problem. According to Berndt (2001), about eighty percent of revenue is lost at the time of

patent expiry. Since the patent period is not very long in duration, inorganic expansion means

can facilitate overcoming negative effects of patent exhaustion by way of acquiring new

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external technologies that maintain the technological superiority of the pharmaceutical

companies in such a case as mentioned in the study by Higgins and Rodriguez (2006). The

pharmaceutical industry thus engages in M&A activities extensively. Additionally, according

to Hassan et. al. (2012), factors such as high cost of bringing drugs to the market and lower

rate of success also motivate the pharmaceutical companies to take the support of M&A

activities for survival.

Measurement of M&A success involves the future profits of the combined firm post the

completion of the M&A, but we specifically focus on the acquirer company profits in our study.

With regard to the acquirer company, Diaz et. al. (2009) find that M&A success is not only

dependant on future profits, but also on the company’s ability to complete the deal at a price

that does not exceed the (expected) profits. As supported by previous research from Moeller

et. al. (2005), this motivates us to consider the importance of other factors such as “Bid

Premium” when explaining the returns to stockholders of both the acquirer (bidder) and target

firm post the M&A activity, which eventually determines the success or failure of such an

activity.

Bid premium refers to the price that the acquirer firm pays at the time of the M&A, to the target

firm’s shareholder over and above the market price of the target firm’s shares. Such a payment

is made so that the target firm’s shareholders are satisfied and thus agree to give up their

shareholding in return for a considerate amount of compensation in exchange for their

shareholding, so the M&A is carried out smoothly and the acquirer gains full control of the

target entity. However, the case with bid premiums becomes quite cumbersome due to various

factors. Greenfield (1992) in his research concludes that the target company shareholders must

agree to a bid premium and give up their stake in the company. Unless the price offered is high

enough to satisfy them, they would be reluctant to accept the offer, as they would like to wait

for a better takeover bid offer from another company, which could pay more. This means that

a greater bid premium would enhance the possibility of obtaining the consent for the takeover

from the target firms’ shareholders who allow a smooth takeover process after receiving the

high premiums for their share in the target firm. From the point of view of the acquirer

company, where such a bid premium is perceived differently, emphasis is laid on synergies

that arise from the M&A. Moreover, the quantum of bid premium is also of key importance for

the acquirer as it influences shareholder’s reaction to M&A announcement.

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Section 2 of our study expands on the literature relating to the behavior of abnormal returns

around M&A announcements that have been previously observed by various authors who

conducted such studies on different time scales and geographical alignments, thereby helping

us to formulate our hypotheses that we test further in our study. Section 3 introduces the

methodology that we will use to test our hypotheses along with descriptive statistics of the data

on which we will conduct our study. In the next section, we report our findings and analyze

them to test if we can obtain evidence to support our hypothesis or not. After analyzing the

reported findings, we discuss them in the following section along with factors that influence

our findings, thereby concluding the study.

2 Literature Review

The concentration in the pharmaceutical industry has increased rapidly over the past. The total

value of M&A activities in this industry has been over $500 billion, because of which the top

10 pharmaceutical firm’s share went up from 20% in 1985 to almost 50% by the end of 2002,

as shown in their research by Danzon et al. (2005). However, the question here arises if the

large volume of M&A transactions in this sector is justified when compared to the returns of

the companies because of the M&A. There have been many researches on this topic in the past

that give inconclusive results on this matter.

Pharmaceutical sector is one where firms possess many firm-specific assets such as patents and

other technological superiority increasing tools, which give the firms an edge over competitors.

Although such resources are internally unique, their utility cannot be very long-lived due to the

ever-changing technological environment and needs of the product market prevalent in this

industry. Hence, pharmaceutical firms need to opt for an M&A on various occasions. Research

and development plays a pivotal role as the most important knowledge based resource in such

firms. The pharmaceutical sector is heavily reliant on the knowledge-based resources for a

healthy survival.

R&D investment is a very crucial factor here because, firms with high R&D expenses may not

be interested in M&A’s as they tend to exhaust large sums of money in developing in-house

technology. On the other hand, firms may instead of spending a large sum on R&D, invest in

M&A to increase technological superiority by inorganic growth means (Vyas et. al., 2012).

This phenomenon is traditionally known as the ‘make or buy’ strategy (Miyazaki, 2009).

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R&D is an integral part of the pharmaceutical sector, and over the years, it has been observed

that R&D expenditure has increased heavily across all industries, and more so for the

pharmaceutical industry.

Hassan et al. (2007), suggest that target firms in the pharmaceutical sector experience positive

abnormal returns upon M&A activity whereas, acquiring firms experience negative or no

abnormal returns upon the same. Hagedoorn and Duysters (2002) also take into account another

important aspect related to M&A’s which is the strategic and organizational fit of M&A’s and

conclude that M&A’s are profitable for companies in a high-tech environment.

An alternative study by Rawani et al. (2010), suggests that there is a positive market reaction

after M&A announcement for both the target and the bidder firms in the pharmaceutical

industry, whereas another research done by Wong and Yin (2009) on a sample obtained from

the Asian companies found evidence that M&A announcements in this sector have a negative

impact on target firms and positive impact on the bidder firms. A study by Mann and Kohli

(2012) also found evidence suggesting that both domestic as well as cross-border acquisitions

in the pharmaceutical sector create value for the target company shareholders. However, a

similar research done by examining German acquisitions in the U.S.A between 1990-2004 by

Bassen et al. (2010) showed positive effect of cross-border mergers and acquisitions for the

acquiring company.

A further look into the reasons behind such stock price movements gives us interesting insights

to this subject. Various previous studies have suggested different reasons that influence

acquirer stock returns in a major way. It has been found Gregory (1997) that acquisition of a

hostile nature or that by a tender offer generate higher returns compared to friendly mergers or

acquisitions. Moreover, Healy et. al. (1997) also provide evidence for higher acquirer returns

when acquirer company management owns large stakes in the target firm. On the contrary,

when acquirer company management does not own enough target equity beforehand, it

signifies agency problems in the management that could lead the acquirer firm shareholders to

believe that the management prefer growth strategies including value-destroying mergers over

shareholder value maximization and this would in such a case lead to lower acquirer returns

post M&A announcement.

Yook (2003) in his study finds evidence that all cash bids generate higher returns for acquirer

company than an announcement of a merger or acquisition via stock. The given explanatory

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logic behind such a behaviour is that stock acquisition may make the acquirer company

shareholders to believe that the shares held by them are overpriced, which is in line with the

fact that managers plan to issue shares at the high point in the stock market cycle. Doukas et.

al. (2001) also suggest that M&A’s involving corporate diversification are followed by lower

stock returns for acquirer firms as it could lead to a reduction in the investors’ confidence in

the company. Another interesting finding from a study by Rau and Vermaelen (1998) reveals

that acquisition of low market to book value firms leads to higher acquirer returns post an

M&A. The statistics provided by their research shows such returns are 12% higher on an

average, whereas M&A’s involving targets that have a high market to book value see negative

returns post announcement. Such behaviour can be explained by the fact that shareholders see

a lot of profit potential in a low market to book value firm, as the prices will rise and normalize

around the market value, whereas in case of a high market to book value target firm the

acquiring shareholders do not see such potential and hence are not so much in favour of the

M&A.

It would be very interesting to see how the above discussed factors influence the acquirer

returns in the pharmaceutical industry setting, and see if the returns at announcement are

negatively or positively affected by them, or if they are not affected at all. This brings us to our

first hypothesis, which will test the impact of announcement of pharmaceutical M&A’s on the

stock prices of the acquirer firms:

Hypothesis 1: The stock returns of the acquiring or bidder firm does show positive abnormal

change upon announcement of an M&A in the pharmaceutical industry.

Our first hypothesis is a major step towards the second and main hypothesis whereby we further

investigate into the factors that affect the behaviour of the acquirer stock returns post an M&A

announcement with the main focus on the effect of ‘bid premium’ on such returns.

There have been previous studies involving bid premium whereby the authors have been able

to provide informational insights, which are very useful for the purpose of our study. Loderer

and Martin (1990) have concluded that acquiring firms experience more losses when buying

large target firms because they generally end up overpaying in such cases. Such a move is

likely to be motivated by the misjudgement of the decision-making managers who overestimate

the potentially accruing benefits post the M&A to be higher than they actually are, which

eventually results in loss to the acquirer firm shareholders. Roll (1986) describes this

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phenomenon as ‘hubris hypothesis’ in his study, moreover the managers may also have

personal benefits associated with higher premiums which results in the managers preferring

personal interests over shareholder interests in the acquiring firm.

Antoniou et. al. (2007) find evidence for the fact that the more benefits acquirers expect to earn

from an M&A, the more willing they will be to pay a higher price for the acquisition. In such

a case, the higher the potential synergies expected, the higher the premium, and as a result, the

shareholders of the acquirer firm perceive it as a good sign thereby increasing the abnormal

stock returns. This phenomenon is traditionally known as the ‘Synergy hypothesis’.

Sirower (1997) on the other hand emphasizes more on the flipside of the synergy hypothesis

thereby explaining the reason behind negative relationship between premium and abnormal

returns for the acquirer post an M&A deal. According to the overpayment hypothesis, when

the acquiring company pays a premium to the target firm, which is higher than the market

expected profits, it leads to a decline in the stock returns of the acquiring company. Sirower

(1997) terms this as the ‘Overpayment hypothesis’. Ruback (1982) further addresses this as the

“winner’s curse” whereby the company that gains the final control ends up paying an

excessively high price for the acquisition, thus explaining the negative relationship between

bid premium and acquirer abnormal returns.

Another reason for such negative returns as discussed by in their research by Shleifer and

Vishny (1997) is the existence of ‘agency problems’. Agency problems arise when the acquirer

company managers who are entrusted with the decision-making in an M&A, end up paying a

high premium in order to cater to personal gains rather than thinking about the overall profits

to be obtained from the deal. Hence, such an attitude from officials at top-level management

also leads to a negative impact on the stock price of the acquirer company. Although there are

evidences of higher premiums associated with taking over large firms, many other studies give

us evidences of lower bid premiums associated with M&A’s involving large firms.

Alexandridis et. al. (2013) find that since there are high values at stake and efficient mangers

are capable of properly measuring accurate values of large targets, they are not willing to

overpay, moreover, integration post an M&A involving large targets are also a very

cumbersome and costly affair, due to which the cost of integration may exceed the integration

expenses. Hayward (2002) suggests that post-merger integration is a function of organizational

size, and according to a report by Deloitte (2012) it has been found that 70% of value erosion

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in M&A deals involve the post-merger integration factor. Alexandridis et. al. (2013) even

reported that losses to acquiring firms increase with the target size. Such reasons generally

compel the acquirers to make offers involving lesser premium and the targets in many cases

accept such offers. Gorton et. al. (2009) find that as there are lesser bidders for large targets, it

results in reduced competition thereby mitigating the “winner’s curse” due to which lower

premiums are accepted by the target firm. Additionally, Demsetz and Lehn (1985) conclude

that larger firms exhibit a less concentrated ownership and taking this finding further Bauguess

et. al. (2009) give evidence for the fact that due to a less concentrated ownership of the target

firm, acquisition becomes easier if the insiders of the target firm are convinced to accept the

smaller premium.

Recent studies on this subject matter also discuss about other synergy hypotheses related to the

M&A sector, which are useful in the context of our research. According to a study by

Loukianova et. al. (2017) synergies are classified into two groups, i.e., ‘financial’ and

‘operational’. Baldi and Tregeorgis (2009) describe financial synergies as a combination of

capital structures of the merged companies, which leads to higher expected cash flows and

decreased discount rates. Rahatullah (2013) further emphasizes that financial synergies can

also result in tax benefits and enhanced debt capacity that decrease the overall cost of capital

for the combined companies. Operational synergies, on the other hand are reflected in greater

revenue, savings in cost, investment cutbacks and better market position according to the study

by Hamza et. al. (2016). Kruse et. al. (2007) even provides evidence for operational synergies

in their research on 69 Japanese M&A’s whereby they find evidence of better operating

performance of the companies in the said sample. In an analysis by Devos et. al. (2009) where

he analysed a sample of 264 companies involving in M&A activities. The merger gains in his

analysis were 10.03% of the combined equity value of both the firms. Upon breaking down the

gains, it was observed that 8.38% of the said gains accrued to operational synergies. Hamza et.

al. (2016) also concluded in his analysis of 59 French mergers that operational synergies

mattered more when it came to measurement of M&A success.

Pharmaceutical industry is one of the industries that depends a great deal on operational

synergies with regard to M&A’s. One of the main reasons for this are the characteristics of this

industry, which rely heavily on operational effectiveness for success. It would thus be

interesting to study how acquirer abnormal returns would differ in case of pharmaceutical

companies and what is the role of bid premium in determining such abnormal stock returns.

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Considering the fact that operational synergies play an important role in this industry, and

M&A activities have been more successful in this industry as compared to other industries, we

would expect bid premium to have a positive influence on the abnormal returns, because it

would be an indicator of operational synergies between the firms entering the M&A. This

brings us to the second and main hypothesis that we test in our study, which is as follows:

Hypothesis 2: The abnormal stock returns of the acquiring or bidder firm are positively related

to the bid premium offered to the target in an M&A activity in the pharmaceutical industry.

The abnormal returns for an acquirer firm are affected not only by the quantum of bid premium

announced but many other factors as well. One of the most important factors influencing

behavior of returns for a company post an M&A are the bidder firm characteristics themselves.

Moreover, previous studies have found evidence that other factors such as payment method of

acquisition amount by the acquirer also affect their abnormal returns. Large cash payments for

takeover lead to reducing the liquidity for the acquirer can have a negative influence on the

shareholders of that firm. Georgen and Renneboog (2004) have concluded in their study that

paying the acquisition amount entirely in cash results in negative returns for the acquirer.

Another major factor influencing the acquirer firm’s abnormal returns post an M&A

announcement is the geographical orientation of the companies involved in it. If the acquirer

and target belong to the same country, they share similar cultural attributes and possess

knowledge of the local business environment which can result in smoothening the M&A

process between them. Risk of foreignness prevails in case of cross-border M&A’s, which can

make the it difficult to assess if the activity will be a success, on the contrary, in case of cross-

border M&A’s, there is an exchange of new knowledge between companies which can be

beneficial for both the acquirer and the target involved in such an activity. Deng and Yang

(2015) find that cross-border M&A’s experience higher returns post announcement compared

to domestic. According to them, the cause for such high returns in case of cross-border deals

is the market seeking and resource seeking objectives of acquirer firms who gain access to new

markets and new technological or managerial capabilities post such an activity. Thus, we can

observe that the above factors have been found to affect the acquirer’s post announcement

returns significantly and need to be controlled for in order to analyze the relationship between

abnormal returns and bid premium efficiently.

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3. Data and Methodology

Methodology

The methodology that we use to study the abnormal stock returns will be a classical event study

following MacKinley (1997), which aims to measure the abnormal returns in the stock prices

of publicly traded stocks that occur in conjunction with a specific event as in the case of Warner

and Brown (1980). Furthermore, the ‘market model’ proposed in previous research by Strong

(1992), is used as the point of reference in the event study. The reason behind this is to observe

the performance of the stock returns around the announcement period with the market index.

The event window is the number of days for which we measure the effect of an event and this

window should be appropriate enough to capture the effect of the event. In this case, we will

restrict the event window to 3 days, i.e., 1 trading day before and one day after the event

including the day of the event. This decision is in line with previous research done on a similar

research objective by Zhang and Wiersema (2009). Similarly, the estimation period to assess

the values of alphas and betas of the stocks would be kept at 150 trading days, it is not a part

of the event window as normal returns need to be observed separately from the event related

returns. However, we extend the scope of our analysis and validate our results for the 3-day

event window, by adding two other event windows which have lengths of 11 days and 51 days

respectively.

FIGURE 1: Estimation Window and Event Window

Estimation window of alpha and beta Event window

T -151 (T-2) (T-1) (T) (T+1)

( T-6) (T-5) (T) (T+5)

(T-26) (T-25) (T) (T+25)

Pre-event estimation window: 150 trading days before event

Event window: (-1, +1), (-5,+5) and (-25,+25)

The formula for the market model is as follows:

𝑅𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝑚𝑡 + 𝜀𝑖 (1)

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Where 𝑅𝑖𝑡 equals return on the security at date t, 𝑅𝑚𝑡 equals return on the market portfolio on

day t, 𝜀𝑖 equals the zero-mean disturbance term and 𝛼𝑖 and 𝛽𝑖 are the parameters of the market

model estimated by running an ordinary OLS regression over the estimation window.

To calculate the abnormal returns (𝐴𝑅𝑖𝑡), we first calculate the daily stock returns of the firms

in our sample and then subtract the returns on the corresponding market index from the stock

returns. If the firm performs better than the market index, it will experience positive abnormal

returns and whereas if the market performs better than the firm, negative abnormal returns will

be experienced. We will use the Cumulative Abnormal Returns (CAR’s) technique in our

analysis to study the overall conclusion of abnormal returns in the event window.

The formula for CAR is:

𝐶𝐴𝑅𝑖 = ∑ 𝐴𝑅𝑖𝑡𝑇𝑡=1 (2)

For the second hypothesis, we would perform another regression analysis whereby the

dependent variable is the CAR’s obtained from the previously mentioned analysis and we

determine the influence of ‘bid premium’ on the CAR’s, also test if there exists a linear

methodology used by Diaz et. al. (2009) in their study which was also done on a similar

approach. Thus, the CAR estimates that will be used will be from the window {-1,+1}. We do

estimate other CAR windows also for the purpose of validating our choice, and we do this by

comparing the results returned by the various event windows chosen. We additionally estimate

the CAR for an 11 day event window {-5,+5} and 51 day event window {-25,+25}. We find

that the behavior of CAR’s does not significantly change between the shorter windows, i.e, for

the 3 day and 11 day event windows as CAR’s in both cases remains positive. The 51 day event

window returned negative CAR values. Such a behavior of CAR in the broader event window

could be because the longer period includes significant effects of activities and events other

than the effects of just the M&A announcement. Moreover, due to the continuous functions

and activities performed in a company, the effect of M&A announcement weakens with time

as stock prices are then influenced by the new events and activities other that occur within the

broader event window. Hence it becomes difficult to evaluate the effects that other activities

have on a broader event window rather than just the M&A announcement. Thus, the choice of

a 3-day event window interval has been made because it registers the most immediate effect of

the M&A announcement. Beitel et. al. (2004) in their research find that the most significant

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value changes in case of M&A’s occurs on the announcement day or the day before the

announcement. Such a window is also one of the most used in previous studies. The descriptive

statistics of the 11 day {-5,+5} and 51 day {-25,+25} event windows have been included in the

appendix section.

To test the linear relationship between the CAR’s and the bid premium we would use the

following regression model:

𝐶𝐴𝑅𝑖 = 𝛼 + 𝛽1𝑃𝑅𝐸𝑀𝐼𝑈𝑀𝑖 + 𝛽2𝐷𝐸𝐴𝐿𝑆𝐼𝑍𝐸𝑖 + 𝛽3𝑅&𝐷𝐼𝑁𝑇𝐸𝑁𝑆𝐼𝑇𝑌𝑖 + 𝛽4𝑙𝑛𝑆𝐼𝑍𝐸𝑖 +

𝛽5𝑀𝑇𝐵𝑉𝑖 + 𝛽6𝑙𝑛𝑇𝑂𝑇𝐴𝐿𝐴𝑆𝑆𝐸𝑇𝑆𝑖 + 𝛽7𝑃𝐸𝑅𝐴𝑇𝐼𝑂𝑖 + 𝛽8𝑙𝑛𝑀𝐾𝑇𝐶𝐴𝑃𝑖 + 𝛽9𝐶𝐴𝑆𝐻𝐷𝑈𝑀𝑀𝑌𝑖 +

𝛽10𝐶𝑅𝑂𝑆𝑆𝐵𝑂𝑅𝐷𝐸𝑅𝐷𝑈𝑀𝑀𝑌𝑖 + 𝜀𝑖 (3)

The bid premium is our main independent variable whereas the others are acquirer firm

characteristics which are used here as control variables. Premium, which in this case is our

independent variable, refers to the ratio of the bid price to the market price of the target to be

acquired, and is given as a percentage in the database we will use to accumulate our data

sample. According to the related literature it is expected that premium initially has a positive

influence on the CAR’s but the influence gets negative as the premium increases after a certain

point. The previous theories defining such a phenomenon state that after increasing beyond a

certain point, bid premium signifies overpayment on part of the acquirer due to which the

shareholder’s confidence in the acquirer firm begins to decline.

The other variables included in the regression equation are acquirer firm characteristics that

are included as control variables which are factors that influence the outcome, but often arise

from the experimental design, they are not the variables of interest. In other cases, they

are confounding factors that cannot be catered for by adjusting the experimental design. Their

impact can be minimal or great, but once their presence is detected, attempts should be made

to neutralise them. This is usually done by including them as independent variables, that are

then ignored or just acknowledged. Deal Size refers to the complete value which the deal

entails. The effect of deal size on the CAR can influence the relation between the CAR and

premium as we already discussed in our literature that generally a larger deal value would

attract a large premium, but there are instances where very high value deals are completed with

a lesser premium amount. Research and Development Intensity (R&D intensity) refers to the

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ratio of research and development (R&D) of the previous year divided by the sales of the

previous year of the acquirer firm. This characteristic is important for us as we are analyzing

the pharmaceutical industry, where R&D is of crucial importance, as already discussed before,

it determines the operational effectiveness of the company. We thus need to eliminate any bias

it could create in our main analysis of determining the relationship between the bid premium

and CAR’s.

All other control variables included in the research are acquirer firm characteristics namely size

of the firm which is calculated as the log returns of a firm’s market value, ratio of market to

book values of the firm, the log returns of total assets of the acquirer firm, the price-earnings

ratio, and the log returns of the market capitalization of the acquiring entity. All the above-

mentioned values are obtained for the year previous to the one in which the M&A activity takes

place. We use logarithm values instead of the raw values provided by the database, as using

logs normalizes and de-trends the data and we thus measure the data relating to all the variables

on a comparable metric. This is in accordance with the methodology applied by Diaz et. al.

(2013) in their research where their approach was similar to our study. Finally, two dummy

variables are added to the regression. The cash dummy takes a value of one if the deal is done

completely in cash and zero otherwise. This dummy variable has been added to our regression

model to check how are M&A deals being affected when the payment for the acquisition is

done in cash compared to when the payment is done by other means such as shares of the

acquirer company. A cross-border dummy that takes a value of one if the M&A is done outside

the home country of the acquirer and zero otherwise has also been added to the regression

analysis as a control variable. The cross-border dummy helps to understand how implications

of M&A activities vary across the world. Some of the variables are more effective in affecting

the CAR in certain geographic conditions, hence this variable helps to understand and address

the geographical disparities that arise due to the globally spread sample size of our study.

Data

The main sample for our research comprises of 140 M&A activities in the pharmaceutical

sector, around the globe in the period ranging from 1997-2015. Our analysis includes 129

M&A’s out of the 140 due to unavailability of complete data for 11 such activities whereby all

the data such as R&D, Market Capitalization and P/E ratio was not available, so an unbiased

analysis was not possible if we included those variables. Moreover, 3 out of the above 11

excluded M&A activities had zero CAR due to a constant listed stock price, which would have

15

created a major bias in the regression analysis, as a constant stock price would mean a zero-

abnormal return and such returns cannot affect the CAR. All data about the deals was obtained

from the ‘Orbis’ database. Few criteria, which were followed in obtaining the sample, were

that only merger and acquisition deals between the said period are chosen. The acquirer in our

case is a listed acquirer who has no stake in the target company prior to the M&A, but takes

over complete stake in the target company post the M&A. The status of all the deals stands at

‘confirmed completed’, which means no deals where there may be chance of cancellation or

there is no confirmation for a completion, are omitted. All the acquirer companies in our sample

belong to the category ‘Manufacture of basic pharmaceutical products and pharmaceutical

preparations’. Lastly, only those deals that had a bid premium amount mentioned as on the

announcement date were included in the sample. The details about the stock prices of the

acquirer company were obtained from ‘Datastream’ which we later used to calculate the

CAR’s. Other details about the firm characteristics were also obtained from Datastream. Lastly,

we compared stock prices of acquirer companies with the stock indices pertaining to the home

country of the acquirer firm. Price details about stock indices were obtained from Yahoo

‘Finance’.

All currency values in our analysis are in US Dollar (US$). For countries that did not use US$,

we converted their currency to US$ before including them in our study. This was required to

conduct our study on a comparable metric in terms of the currency, across our global sample.

16

Descriptive Statistics

The above table shows us that the U.S.A is the region where nearly half the M&A activities

from our sample have taken place. Moreover, it can be further inferred that there is a gradual

increase in the number of M&A activities over the years. The acquisition of ‘Warner Lambert’

by ‘Pfizer’ in 2000 with a deal size of $90 Billion is the largest acquisition ever made in the

pharmaceutical industry and is also a part of our sample. ‘Sanofi’s’ acquisition of ‘Aventis’ is

the largest non-U.S.A based M&A till date with a deal size of $67 Billion, whereas the

acquisition of ‘Allergan’ by ‘Actavis PLC’ in 2004, is the largest cross-border M&A recorded

with a deal size of $71 billion.

Region No. of M&A's

U.S.A 61

Europe 37

Rest of the World 31

Year No. of M&A's

1997 0

1998 1

1999 0

2000 1

2001 5

2002 5

2003 6

2004 5

2005 8

2006 9

2007 5

2008 13

2009 9

2010 12

2011 9

2012 10

2013 11

2014 10

2015 10

TABLE 1: Sample Composition

No. of Observations: 129

Panel B: Classification by Year

Panel A: Classification by Region

17

Table 2 above shows the descriptive statistics of the abnormal returns used to test the first

hypothesis. It can be seen that none of the returns obtained are negative. The mean for the day

after the event day is the highest, whereas the standard deviation is the highest on the day of

the event. To test the normality of the cumulative abnormal returns a ‘Jarque-Bera’ (JB) test

for normality was also performed which gave a JB statistic of 35.17 with a p-value of 0.00,

thus giving proof of non-normality of the data. Hence, we had to choose a non-parametric test

for the measurement of our first hypothesis. The Wilcoxon Signed Rank Test for One Sample

was used.

The mean and median value for bid premium are 37% and 24% respectively, which is higher

than normal when compared to other industries, as according to Kengelbach and Roos (2011),

the average bid premium in case of an M&A across all industries are around 20-30%. The

Mean Median Std. Dev Max. Min. Skewness Kurtosis

AR(t-1) 0.003 0.001 0.029 0.19 -0.1 1.8 13.89

AR(t) 0.002 0.003 0.06 0.365 -0.16 2.27 12.61

AR(t+1) 0.004 0.002 0.04 0.15 -0.08 1.1 3.16

CAR (t-1,t+1) 0.009 0.005 0.073 0.31 -0.14 1.26 3.43

TABLE 2: Descriptive Statistics of Abnormal Returns (AR's) & Cumulative Abnormal Return (CAR)

Number of Obs.: 129

TABLE 3: Descriptive Statistics of Independent and Control Variables

No. of M&A's Variable

Bid Premium

Log (Mkt Cap.)

No. of M&A's

Log (MV)

Log (Total Assets)

R&D Intensity

MTBV

P/E Ratio

N umber of Observations: 129 N umber of Observations: 129

KurtosisSkewnessStd. DevMedianMean

26.214.030.580.240.37

0.77-1.221.097.677.29

0.34-1.011.064.654.29

1.31.1211.127.326.89

54.3471.640.140.45

128.89-11.35265.773.49-18.26

119.2110.647812.5820.881.77

18

contrast observed in the mean and median values of variables ‘market to book value’ and price-

earnings ratio is because our sample contains a few very large sized companies from the

Pharmaceutical industry. Companies such as ‘Pfizer’, ‘GlaxoSmithKline’ and others, have

exceptionally high P/E ratio and market to book values, when compared to other companies in

the sample that are much smaller in size. This influences the sample average in our case.

Similarly, in case of R&D intensity, where it can be logically inferred that the larger companies

in the sample do invest heavily in R&D to maintain market position and strive towards

innovation at a fast pace. Another reason that affects the statistics in case of R&D intensity, is

the fact that many acquirer companies in our sample do not invest in R&D at all until the M&A

takes place, instead, they acquire R&D capabilities inorganically. Moreover, before conducting

the regression analysis we also checked the sample for homogeneity biases and autocorrelation.

The ‘White test’ for heteroscedasticity returned an F-statistic significant at all levels, inferring

that the error terms were heteroskedastic. The correlation matrix was also obtained from

’Eviews’, which showed that a high correlation coefficient for three variables. We controlled

for correlation by performing three different regression analysis and including all the three

correlated variables separately in different regression analysis. Additionally, all the regression

analysis was performed after adjusting for heteroscedasticity and autocorrelation by the means

of using ‘HAC standard errors’ in the Eviews analysis. The outputs of the White test and

correlation matrix have been added in the ‘Appendix’ section.

4. Results and Analysis

TABLE 4: Wilcoxon Signed Rank Test (One Sample)

Variable T-Critical Value T-Statistic

CAR (t-1,t+1) 387695 4339***

(0.000)

CAR (t-5,t+5) 387695 4029***

(0.000)

CAR (t-25,t+25) 387695 3336***

(0.000)

Number of Observations: 129

Significance Levels of 10%, 5% and 1% are presented by *, ** and *** respectively

The above table presents the results of the Wilcoxon Signed Rank test done on the CAR to test

if the M&A activity influences the CAR significantly. The null hypothesis being tested in case

of the above test is “The median of CAR (t-1,t+1) is equal to zero”. The output obtained shows

19

a t-statistic of 4339, for the 3-day event window, and this t-statistic value is significantly lower

than the corresponding critical value, with less than 1% level of significance. Hence we can

reject the null hypothesis that the abnormal returns of companies involving in M&A activity

do not show any abnormal change upon announcement of the said activity. The CAR obtained

is positive for the 3-day event window, and we can thus infer that, the stock prices show a

positive change upon announcement of the M&A in the event windows, thereby confirming

the first hypothesis. Our results are in line with the results obtained by Rawani et. al. (2010)

and Wong and Yin (2009) who also find positive abnormal returns for acquirer post

announcement of M&A. We additionally test the CAR’s for two other event windows as well,

to see how the trend of the CAR’s change with a change in the length of the event window.

The additionally added event windows have a length of 11-day window and 51-day event

window. The t-statistics obtained for the 11-day and 51-day event windows are 4029 and 3336

respectively, which are significantly lower than the corresponding critical values. Both the

values are highly significant at 1% level. The descriptive statistics for the CAR’s from both the

additional event windows have been added to the appendix section.

We observe a similar result for the shorter event windows, i.e., the 3-day and 11-day event

window, as both return positive value for the CAR’s, however, the 51-day event window

returns a negative CAR. Such discrepancy between the short and broad event windows could

be because the 51-day event window is very broad to capture the effects of M&A

announcement without being affected by other events that occur in the company during the

length of the event window. Since a majority of event windows returned a positive CAR and

as the effect of M&A announcement does not last very long due to other major events that

happen within the broad event window, we choose the 3-day event window for testing the

second hypothesis of this study.

For testing the second hypothesis we regressed the dependent variable CAR, on the

independent and control variables. It was observed that the Logs of Market Capitalization,

Market Value and Total Asset returned high correlation coefficients and hence we did not

regress them on the CAR in the same regression. We therefore performed three different

regressions including only one of the three correlated variables at a time. Furthermore, we

segregated our sample on a geographical basis to evaluate how the relationship between the

dependent and independent changes with respect to change in the region to which the sample

belongs. We segregated the global sample of 129 observations into three regions whereby we

20

ended up obtaining 61 U.S.A specific M&A’s, 37 Europe specific M&A’s and 31 from rest of

the world for which we performed individual regression analysis following the methodology

mentioned in this study. We report our results in three tables that are given below whereby

Table 5 gives the regression output for all variables except Logs of Market Value and Total

Assets, Table 6 lists the output excluding Logs of Market Capitalization and Total Assets in

the analysis and finally, Table 7 excludes the Logs of Market Capitalization and Market value.

These exclusions have been made to prevent any bias that correlation with regard to the

mentioned variables can create. The regression outputs are given below.

Variable

Coefficient (Only

U.S.A)

Coefficient (Only

Europe)

Coefficient (Rest of the

world) Coefficient (Global)

Constant -0.12 -0.075 0.109 -0.06

(0.141) (0.491) (0.0486) (0.414)

Bid Premium -0.02 -0.089 -0.036** -0.02**

(0.122) (0.109) (0.047) (0.041)

R&D Intensity 0.03** 0.024 0.0007** -0.002

(0.043) (0.218) (0.022) (0.44)

Price-Earnings Ratio -0.00002 -0.000004 -0.0006*** -0.00001**

(0.94) (0.356) (0.005) (0.02)

Market to Book Value -0.00009 -0.003 0.005 -0.00007***

(0.107) (0.445) (0.211) (0.001)

Log of Deal Size 0.008 -0.027 0.003 0.0007

(0.397) (0.226) (0.515) (0.89)

Log of Market Capitalization 0.009 0.041* -0.012 0.01

(0.433) (0.068) (0.215) (0.23)

Cross-Border Dummy 0.008 0.053 0.009 0.02

-0.392 (0.143) (0.727) (0.12)

Cash Dummy 0.026 -0.06 -0.012 -0.007

(0.139) (0.105) (0.723) (0.53)

F-Statistic 2.15 1.95 1.044 1.36

Probability of F-Statistic 0.047 0.091 0.434 0.22

R-Squared 0.25 0.358 0.275 0.08

Number of Observations 61 37 31 129

Note: The dependent variable, CAR, is calculated from the 3-day event window of abnormal stock returns

of the acquirer. The main independent variable in our analysis is bid premium, which is the excess amount

paid to the target firm shareholders at the time of acquisition by the acquirer firm. The other variables are

added as control variables . R&D intensity refers to the ratio of previous R&D investment to previous year

sales. Previous year alues of Price-Earnings ratio and Market to book value are used. Log of Deal Size and

and Market Capitalization are obtained by compressing their actual previous year values into Log values

to conduct the analysis on a comparable metric across all variables. Cash Dummy takes a value of 1 if the

acquisition amount is paid entirely in cash and 0 otherwise. Cross-Border Dummy takes a value of 1 if the

M&A is done across countries and 0 if the M&A is done domestically.

TABLE 5: OLS Regression (Excluding Variables: Log of Market value and Log of Total Assets)

Significance Levels of 10%, 5% and 1% are presented by *, ** and *** respectively.

Dependent Variable: CAR

21

Variable

Coefficient (Only

U.S.A)

Coefficient (Only

Europe)

Coefficient (Rest of the

world) Coefficient (Global)

Constant -0.089 0.021 0.08*** -0.015

(0.106) (0.858) (0.009) (0.774)

Bid Premium -0.02 -0.104 -0.036** -0.02*

((0.803)) (0.142) (0.173) (0.022) (0.057)

R&D Intensity 0.03** 0.027 0.001** -0.002

(0.04) (0.194) (0.034) (0.29)

Price-Earnings Ratio 0.00001 -0.000004 -0.0007 -0.00001**

(0.9522) (0.418) (0.003) (0.017)


(0.11) (0.5) (0.202) (0.001)

Log of Deal Size 0.01 -0.014 0.005 0.0024

(0.375) (0.532) (0.452) (0.644)

Log of Market Value 0.002 0.028 -0.015 0.002

(0.86) (0.328) (0.181) (0.803)

Cross-Border Dummy 0.008 0.052 0.01 0.018

(0.48) (0.168) (0.682) (0.141)

Cash Dummy 0.031* -0.04 -0.01 -0.002

(0.07) (0.315) (0.772) (0.876)

F-Statistic 2.06 1.58 1.12 1.13


R-Squared 0.24 0.31 0.29 0.07


Note: The dependent variable, CAR, is calculated from the 3-day event window of abnormal stock returns of

the acquirer. The main independent variable in our analysis is bid premium, which is the excess amount paid

to the target firm shareholders at the time of acquisition by the acquirer firm. The other variables are added

as control variables . R&D intensity refers to the ratio of previous R&D investment to previous year sales.

Previous year alues of Price-Earnings ratio and Market to book value are used. Log of Deal Size and and

Market Value are obtained by compressing their actual previous year values into Log values to conduct the

analysis on a comparable metric across all variables. Cash Dummy takes a value of 1 if the acquisition amount

is paid entirely in cash and 0 otherwise. Cross-Border Dummy takes a value of 1 if the M&A is done across

countries and 0 if the M&A is done domestically.

TABLE 6: OLS Regression (Excluding Variables: Log of Market Capitalization and Log of Total Assets)



22

We observe that the variable of constant returns no significant value except in table 6 when

regressed with respect to the ‘Rest of the world’ sample, excluding the Logs of Market

Capitalization and Total Assets whereby the constant returns a highly significant and positive

value of 0.08 significant at a 1% level. The bid premium variable returns a negative but

significant value for the global sample in all the above reported outputs. The value in all the

regression outputs is about -0.02 which is significant at 5% levels in tables 5 and 7 whereas it

is significant at 10% level in table 6. Apart from global sample, the bid premium returns a

Variable

Coefficient (Only

U.S.A)

Coefficient (Only

Europe)

Coefficient (Rest of

the world) Coefficient (Global)

Constant -0.117 -0.067 0.021 -0.05

(0.139) (0.562) (0.714) (0.48)

Bid Premium -0.022 -0.084 -0.023 -0.021**

(0.117) (0.115) (0.301) (0.04)

((0.314)) R&D Intensity 0.03** 0.026 0.008** -0.001

(0.032) (0.206) (0.018) (0.547)

Price-Earnings Ratio 0.00004 -0.000004 -0.0006*** -0.00001**

(0.883) (0.315) (0.009) (0.023)


(0.1) (0.8) (0.252) (0.002)

Log of Deal Size 0.007 -0.018 -0.001 -0.0003

(0.491) (0.37) (0.841) (0.949)

Log of Total Assets 0.009 0.031 0.004 0.01

(0.464) (0.181) (0.742) (0.314)

Cross-Border Dummy 0.007 0.052 -0.004 0.017

(0.429) (0.184) (0.886) (0.156)

Cash Dummy 0.027 -0.053 -0.01 -0.006

(0.133) (0.195) (0.773) (0.608)

F-Statistic 2.14 1.717 0.96 1.33


R-Squared 0.25 0.329 0.25 0.082


Note: The dependent variable, CAR, is calculated from the 3-day event window of abnormal stock

returns of the acquirer. The main independent variable in our analysis is bid premium, which is the

excess amount paid to the target firm shareholders at the time of acquisition by the acquirer firm. The

other variables are added as control variables . R&D intensity refers to the ratio of previous R&D

investment to previous year sales. Previous year alues of Price-Earnings ratio and Market to book value

are used. Log of Deal Size and and Total Assets are obtained by compressing their actual previous year

values into Log values to conduct the analysis on a comparable metric across all variables. Cash Dummy

takes a value of 1 if the acquisition amount is paid entirely in cash and 0 otherwise. Cross-Border Dummy

takes a value of 1 if the M&A is done across countries and 0 if the M&A is done domestically.

TABLE 7: OLS Regression (Excluding Variables: Log of Market Capitalization and Log of Market Value)



23

negative and significant value of -0.036 for the rest of the world sample in tables 5 and 6. All

other coefficients of the bid premium variable return negative but insignificant values. We

observe a slightly negative relationship between the bid premium and CAR in our analysis. We

thus find no evidence to support our second hypothesis whereby we predict a positive

relationship between the CAR and the bid premium.

Among the other variables included in the regression, we find a significant and positive

relationship of R&D intensity with our dependent variables for the U.S.A and rest of the world

sample. R&D intensity returns a coefficient of 0.03 for the U.S.A specific sample in all the

three tables whereas in case of the rest of the world sample the values range between 0.007 to

0.01, whereby all the above values of R&D intensity are significant at the 5% level. R&D

intensity does not return any significant values in case of the Europe specific and the global

sample. Thus, we observe that higher R&D intensity does lead to a slightly higher abnormal

return for acquirer post an M&A announcement but only for specific regions such as in the

U.S.A sample and rest of the world sample. We cannot infer the same for the global sample.

Price-Earnings ratio variable is also found to be slightly negative and highly significant in case

of the rest of the world sample where it returns a value of about -0.0006 in all the three tables

that are significant at the 1% level. Price-Earnings ratio returns a significant and negative value

in case of the global sample as well. Its coefficient in case of the global sample is -0.00001

significant at the 5% level, and the same value has been observed in all the regression outputs

obtained in tables 5,6 and 7. It is thus observed that a high Price-Earnings ratio negatively

affects the acquirer abnormal returns post an M&A announcement, but this can only be held

true in case of the global sample and we find no evidence for this in case of the segregated

region specific samples.

Market to Book value variable returns a significant and negative value of -0.0007 for the global

sample, which is highly significant at the 1% level. It returns insignificant values in case of all

the region-specific samples that we tested. Apart from the above only the Cash Dummy

variable returned a significant value. In table 6 where the Logs of Market Capitalization and

Total Assets were excluded from the regression, the Cash Dummy for the U.S.A specific

sample returned a positive value of 0.031 significant at the 10% level. In Table 5, Log of

Market Capitalization returned a positive and significant coefficient, significant at the 10%

level, for the European region-specific sample. All other variables included in our study

returned insignificant values with which we were unable to make any inferences.

24

The outputs obtained give evidence that the relationship between the dependent and

independent variables do get affected by inclusion and exclusion of highly correlated variables

in the analysis. We controlled for correlation by not including highly correlated variables in

the same regression analysis, and it is due to this that we observe certain variables to be

significant in one analysis whereas they may not be significant in the other. We observed that

the cash dummy only returned a significant coefficient in case of the U.S.A specific region, in

Table 6 where the variables Log of Market Capitalization and Log of Total Assets were

excluded in the regression, but no significant values of the cash dummy coefficient was

returned when those variables were included. Moreover, we also observed that in Table 5, the

coefficient Log of Market Capitalization was significant in case of Europe, but in Table 7, the

coefficient was insignificant. This signifies that correlation among variables could have

affected our analysis in a major way if we did not control for such correlation, and in case of

correlation, any inferences that we make from our results would be incorrect.

5. Discussion and Conclusion

The first hypothesis in our study aimed at finding if acquirer firms experience positive

abnormal returns around the announcement of the M&A activity. A majority of previous

studies such as Hasan et. al. (2007) found either negative or no abnormal returns around the

event announcement as in the case of Mishra and Chandra (2010). One of the principal causes

for us to find positive abnormal returns in case of M&A announcement is the fact that our

research was conducted in the context of a specific industry setting, i.e., the pharmaceutical

industry. As previously discussed, due to a few unique characteristics of the pharmaceutical

industry such as its technology-intensive and the capital-intensive nature, creation of synergies

plays an important role in determining company profitability. Rawani et. al. (2010) also

performed their study on the pharmaceutical industry sample and found a similar result and

found positive abnormal returns.

Positive behavior of the abnormal returns gives evidence that when an M&A is announced,

acquirer firm’s shareholders expect the generation of synergy effects from combining the

management and technological resources of the two merging companies, which would enhance

company profitability post the completion of such an M&A. This is in line with the findings of

Antoniou et. al. (2007) who finds that the stockholders of the bidder firms react according to

25

the expected synergies post the M&A rather than thinking of the M&A activity as a value

decreasing activity.

Another probable cause for us to experience positive returns upon regression in this study is

the fact that our sample contained target firms which are from to the pharmaceutical industry

also, this implies that almost all the mergers were horizontally or vertically oriented. Such an

orientation is more likely to promote operational synergies post an M&A activity, as companies

are familiar with the industry characteristics, which enables them to integrate each other

effectively post an M&A activity. The behavior of the acquirer shareholders which we analyzed

in this study gives evidence that they are confident of the operational synergies to occur post

completion of the M&A activity. The shareholders’ reaction signifies that they are aware of

the dynamism and competition involved in the pharmaceutical industry, which requires high-

end R&D capabilities and fast-paced innovation to survive and compete. Inorganic means such

as M&A activities facilitate the companies with a competitive advantage over the competitors.

Apart from testing for abnormal returns around an M&A event, we further used the CAR’s

calculated to test the first hypothesis as our dependent variable, and regressed them on the

independent variable bid premium, to test if there exists a relationship between the CAR and

bid premium. Considering the approach by Antoniou et. al. (2007) where he suggested that

operational and financial synergies are generated post an M&A, and such potential synergies

increases shareholder confidence in the company, which was supported by Diaz et. al. (2009),

we formed our second hypothesis that bid premium would be positively related to CAR. The

results obtained upon the regression analysis gave evidence for a negative relationship between

the two said variables. As previously mentioned, we also incorporated acquirer firm

characteristics in the regression analysis as control variables to test their effect on the CAR and

eliminate the potential bias that could arise if such variables are not controlled for.

For testing our second hypothesis, we collected a global sample, and our final dataset 129

observations after filtering the data, according to the specific requirements of the analysis. We

checked for correlation effects in our analysis by excluding variables that were highly

correlated, which lead to us estimating three different regressions whereby highly correlated

variables were included in a separate regression model from each other. We further segregated

our sample region-specifically so that we can observe how relationship between the CAR’s

and the independent variables varies across regions on the globe.

26

The first regression done on the global sample showed evidence of a significant relationship

between the dependent variable CAR and the main independent variable bid premium but not

as per what we had hypothesized in our literature. The coefficient of bid premium showed a

value of -0.02 which was significant at the 10% level, thus showing no support for our second

hypothesis.

Although our findings do not fall in line with the synergy hypothesis suggested by Antoniou

et. al. (2007), they are in line with overpayment hypothesis as suggested by Sirower (1997).

The shareholders consider high bid premiums that are announced on the announcement date,

as value destroying activity for the acquirer firm because high premiums could affect the

liquidity of the firm, and this is thus reflected in the stock prices around the M&A

announcement. Diaz et. al. (2009) argue that the relationship between the returns and bid

premium depends on the magnitude of the premium, according to which a lower bid premium

would have a positive influence over the CAR, because the shareholders are confident that the

expected synergies post the completion of the M&A will outweigh the cost of the M&A, and

will increase profitability of the company in the long run. However, when the amount of

premium increases, the shareholders consider it as wasteful expenditure. They are then

concerned about the adverse effect that high costs of M&A will have over the liquidity of the

company. Shareholders thus start to lose confidence in the company, hence, bid premium starts

having a negative influence on the CAR once the magnitude of the premium increases and the

M&A deal seems overpriced to the shareholders.

Among the control variables used in the regression R&D intensity is positively related to the

returns, but only for the region-specific samples of the U.S.A and the ‘rest of the world’. This

gives evidence for the fact that firms in the U.S.A and other Non-European countries in the

world, which invest heavily in R&D receive slightly positive returns around M&A

announcement. This could be due to the fact that the shareholders in that region expect greater

operational synergies to arise post the M&A, giving us enough evidence to infer that R&D

intensity influences CAR to a higher degree in certain regions compared to others where R&D

intensity is not significantly influential in determining the behavior of CAR post an M&A

announcement. Log of market capitalization is found to be positively associated with the

returns for the European sample, meaning that shareholders expect firms with higher market

capitalization to succeed in M&A’s as compared to firms that have a comparatively lower

27

market capitalization, but we can make such an inference only for the European region and not

others.

Price-Earnings ratio and market to book value of the firm are negatively associated with the

returns, that too at a high significance level of 5%. This can be explained in accordance with

the argument given by Yook (2003) in his study whereby his proposition is that shareholders

do not react to M&A’s positively if they believe that the shares of the acquirer firm held by

them are overpriced. Furthermore, Gu and Lev (2011) have explained that overpriced shares

do revert to their intrinsic value which leads to negative post acquisition returns for the bidders.

The dummy variables returned insignificant coefficients in our analysis except for one instance,

where the cash dummy returned a positive coefficient significant at 10% level for the U.S

specific sample in Table 6. This implies that when the entire acquisition amount is paid by

cash, shareholders react positively around the announcement. The cash dummy did not return

any significant value in case of other regions’ sample, which could mean the payment method

in case of an M&A does not influence shareholders reaction to an M&A announcement. The

cross-border dummy also returned no significant coefficients. Thus, we can infer from our

results that shareholders behavior around M&A announcement, does not get influenced by the

fact if the M&A activity is being conducted domestically or across the border. Our study does

not find any support the proposition made by Bassen et. al. (2010) who found positive returns

for cross-border mergers.

In summary, our study which was aimed at analyzing the M&A related scenario in the

pharmaceutical sector did provide interesting findings with regard to the reaction of

shareholders to M&A announcement and further studied factors influencing such a reaction.

With the results obtained from conducting this study we can infer that, in the pharmaceutical

sector positive abnormal returns are experienced upon announcement of an M&A activity

which is in accordance with a few previous studies discussed in the literature. Bid premium is

not positively associated with such abnormal returns, in fact it has a negative influence on the

CAR, which means that the overpayment hypothesis prevails over the synergy hypothesis in

this regard. The R&D intensity of the acquirer is observed to be having a significant influence

on these returns and this is probably due to the pharmaceutical sector setting of our study.

Additionally, our findings suggest that companies with a lower price-earnings ratio and lower

market to book value would be less likely to experience negative abnormal stock returns upon

28

an M&A announcement. Moreover, it is important for companies to refrain from

overestimating company value, as this makes shareholders lose confidence in the company,

which ultimately affects the returns. Increasing transparency and maintaining a good

governance structure helps to increase shareholder’s trust in the firms making them more likely

to experience positive abnormal returns as shareholders are confident of the synergies that a

firm can gain if they merge with or acquire a suitable target.

.

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Institute of Mergers, Acquisitions and Alliances., 2017. Accessed 2 February 2018,

<https://imaa-institute.org/mergers-and-acquisitions-statistics/

Appendices:

Appendix 1: White Test for Heteroskedasticity

White Heteroskedasticity Test

Variable Coefficient

F-Statistic 1.11

(0.334)

Obs*R-Squared: 66.9


Appendix 2: Wilcoxon Signed Rank Test of 11-day and 51-day Event Window

Wilcoxon Signed Rank Test (One Sample)

Variable T-Critical Value T-Statistic

CAR (t-5,t+5) 387695 4029***

(0.000)

CAR (t-25,t+25) 387695 3336***

(0.000)

Number of Observations: 129


35

Appendix 3: Correlation matrix

Appendix 4: Descriptive Statistics of AR’s and CAR’s for 11-Day Event Window

JB Statistic: 10.19 P-Value: 0.006, *Non-Normal Data

Appendix 5: Descriptive Statistics of AR’s and CAR’s for 51-Day Event Window

Mean STD TestStat Skewness Kurtosis Max Min Median

AR(t-5) -0.001 0.025 -0.349 0.366 7.720 0.130 -0.097 0.000

AR(t-4) 0.001 0.023 0.720 2.315 9.556 0.122 -0.044 -0.001

AR(t-3) 0.000 0.020 0.041 -0.122 4.137 0.070 -0.090 0.001

AR(t-2) 0.001 0.024 0.663 1.097 10.054 0.142 -0.079 0.001

AR(t-1) 0.003 0.031 1.174 2.078 12.579 0.189 -0.105 0.000

AR(T) 0.000 0.052 0.110 1.180 6.882 0.283 -0.153 0.003

AR(t+1) 0.006 0.036 1.739 1.229 3.443 0.146 -0.079 0.002

AR(t+2) -0.006 0.032 -2.286 -0.875 8.114 0.112 -0.185 -0.004

AR(t+3) -0.001 0.028 -0.589 3.036 16.743 0.167 -0.058 -0.004

AR(t+4) -0.002 0.025 -0.740 -0.699 15.230 0.123 -0.155 -0.001

AR(t+5) 0.000 0.021 -0.066 0.034 9.238 0.104 -0.098 0.001

CAR 0.002 0.114 0.185 0.679 3.211 0.394 -0.359 -0.009

DescriptiveStatisticsofAbnormalReturns(AR's)andCumulativeAbnormalReturns(CAR's)for11-DayEventWindow

NumberofObservations:129

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JB Statistic: 25.61 P-Value: 0.000, *Non-Normal Data

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