• No results found

The announcement effect of pharmaceutical M&As on the amount of R&D and the stock price of the target

N/A
N/A
Protected

Academic year: 2021

Share "The announcement effect of pharmaceutical M&As on the amount of R&D and the stock price of the target"

Copied!
27
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

The announcement effect of pharmaceutical M&As on the

amount of R&D and the stock price of the target

Abstract

This research examines the effects of 113 pharmaceutical mergers between 2003 and 2013 on both target shareholder return and the amount of R&D. Using an event study, the abnormal returns are estimated for the ten-year period between 2003 and 2013. This research finds a cumulative abnormal return for the target shareholder of 8.50%, significant at a 1% level. Furthermore this study finds that R&D expense does not significantly decline after M&As.

Bachelor Thesis Economics and Business

Specialisation Finance and Organization ECTS: 12

Name: Thijs Algra

Student number: 6293697 Thesis Supervisor: S. Changoer

(2)

I. Introduction

Pharmaceutical firms have played a prominent role in the wave of international mergers and acquisitions. The pharmaceutical industry has been characterized by successive waves of merger activities and industry consolidation since the 1980s (Danzon et al., 2007). In reviewing the wave theory of mergers, Danzon et al. (2007) suggests that large mergers have historically been clustered by industry. The attempted takeover of AstraZeneca by Pfizer in May 2014 is part of a merger wave within the pharmaceutical industry. The continuing trend of large-scale mergers and acquisitions within the pharmaceutical industry raises questions about the strategic value and sustainability of further consolidation (Mittra, 2007). Politicians express their concerns regarding the future of research. According to them the main incentive for large pharmaceutical companies to acquire a company is based on shareholder return. By cutting on R&D costs, they are afraid that the development of new medicines will become at risk. Finance Minister of Sweden Anders Borg responded to Pfizer’s attempted takeover of AstraZeneca that there are risks in terms of Swedish research concerning new drug development, the entire life science cluster, and also potentially jobs in Sweden. Anders Borg interprets Pfizer’s potential takeover as a sign of short-term capitalism, which he believes is not in the interest of important life-science research in Sweden, in the UK or elsewhere. He states that Pfizer had acquired companies that together employed about 124,000 people. While the net increase in its workforce since then was only around 25,000, indicating that nearly 100,000 employees had lost their jobs. He does not provide data whether this decrease in workforce affects the amount of R&D after the merger.

In order to check whether this expectation is correct, this paper examines the effect of mergers on research within the pharmaceutical industry. By examining the

(3)

effects of mergers within the pharmaceutical industry on both the amount of R&D and shareholder returns for the target company, this paper provides data to test the expectation of the Swedish Minister of Finance. This study answers two research questions. First, have the pharmaceutical mergers from 2003 to 2013 significant decreased the R&D expense after the merger? Second, have the pharmaceutical mergers in the same period realized significant abnormal returns for the target shareholders around the merger announcement date?

I find a cumulative abnormal announcement return of 8.50% for the target shareholders. This finding is statistically significantly higher at a 1% level. Furthermore the influence of M&As on several variables is examined. Contrary to the expectations of the Swedish Minister of Finance this research shows that the amount of R&D expense after merger does not significantly decrease after M&As.

This study contributes to the existing literature in three ways. First, I find evidence contrary to the concerns concerning the attempted takeover of AstraZeneca by Pfizer. Compared to a control group of non-mergers within the pharmaceutical sector, R&D expense of merged pharmaceutical companies does not decline significantly.Whereas Tjandrawinata and Simanjuntak (2012) find a similar effect on R&D expense by focusing on only the eight largest pharmaceutical companies, this paper examines the effect on R&D expense of 113 pharmaceutical M&As. Second, this study finds abnormal returns for the target shareholders, whereas Higgins (2006) only finds abnormal returns for the acquirer. Finally, this research examines a new time period. Previous researches find abnormal returns for the target till about 2007. The research focuses on the time period in which there is a continuous trend of large-scale mergers within the pharmaceutical sector (Ornaghi, 2009).

(4)

The remainder of the paper is organized as follows. In section 2, I provide a brief discussion of the relevant literature; Section 3 discusses the empirical methodology and data used in our analysis; Section 4 presents and discusses our empirical findings and Section 5 summarizes the analysis and discusses the implications of our results.

II. Literature review

II.I Background literature

In the literature several motives are found for mergers and acquisitions. Many theories have emerged to understand for whom and how value is created through acquisitions. According to Andrade et al. (2001) there are five motivations to acquire a company: create market power, diversification, econonomies of scale or other synergies, market

discipline, and self-serving strategic motivations.

Another incentive to merge is the opportunity to trade otherwise non-marketable resources and to buy or sell resources in bundles (James, 2002). By looking at three cases from the pharmaceutical industry, James (2002) seeks to begin the development of a resource-based perspective on the management of M&As.

Another theory concerning the motivation for mergers and acquisitions created by Ravenscraft and Scherer (1987) is the monopoly theory of mergers. This theory refers to the price-raising opportunities arising from consolidated market control, especially following horizontal mergers. Also diversification is a frequently used motivation factor to acquire a company.

Each strategic motivation has its own set of problems and concerns. By classifying acquisitions into appropriate categories of transactions, we could be able

(5)

to determine which strategic motivation for mergers is flawed, and thereby predisposed to destroy shareholder value. Researchers can then begin to focus on these different underlying motivations and determine why a particular strategy has been successful or unsuccessful (Higgins, 2006). Whether a particular strategy has been successful is discussed at section II.III.

II.II Reasons for M&As

All acquirers share the motive of increase the value of their company and in order to do so they intend to obtain positive abnormal returns. Ravenscraft and Scherer (1989) find that acquired companies do not benefit, because mergers do not improve profitability on average. An extensive review of the value-creating performance of acquisitions reported in numerous studies form the US, the UK and some continental countries provides clear evidence that shareholders of acquiring companies experience wealth losses on average, or at best, break even. Only among pooling-of-interest mergers partners, mergers or acquisitions between strategically related firms generate abnormal returns for both shareholders of acquiring and target firms. Pooling Of Interests refers to a method of accounting that allows for assets to be evaluated by book value rather than market value. This allows them to option to work without adding in goodwill what apparently positively influences the effect on the shareholder return of the acquirer. On the other hand, this study examines that target companies experience abnormal returns of 20% to over 43%.

Danzon et al. (2007) provide another motive for pharmaceutical M&As specifically. By examining the determinants and effects of M&A activity in the pharmaceutical biotechnology industry from 1988 to 2001. The paper shows that for

(6)

large firms mergers are a response to expected excess capacity due to patent expirations and gaps in a firm’s product pipeline.

There is a strong belief within some parts of the industry that survival requires companies to grow in size in terms of marketing and R&D activities (James, 2002). By acquiring smaller companies, the large companies intend to save costs from the combination of R&D efforts and sales. According to James (2002) M&A-related cost savings may achieve a short-term boost to profitability, but it is definitely not sure that M&As create synergies that can provide a platform for future business growth.

The study of Berger and Ofek (1995) focuses on another frequently used reason to acquire a company: diversification. By examining all firms during the financial services industry, they show the effects of diversification on a firm value. They show that diversification on average implies a 13% tot 15% loss to the firm’s actual value, the loss for the merged company is smaller when the segments of the diversified firms are in the same two-digit SIC code. The loss is also reduced by tax benefits of diversification. Based on this result it is expected that the diversification loss will be smaller within pharmaceutical mergers.

However, Barney (1988) examines that relatedness is not a sufficient condition for bidding firms to earn abnormal returns. They define the relatedness hypothesis and argue that relatedness is not a sufficient condition for a bidding firm to earn abnormal returns. The relatedness hypothesis defines two firms as related when the net present value of the cash flow of the combination of these firms is greater than the sum of the net present value of the cash flows of these firms independently. Shareholders of bidding firms will earn only normal economic returns from M&As if the value of the combined cash flow and bidding firms is publicly known. A strategically related merger or acquisition may create economic value, but this value

(7)

will be distributed in the form of abnormal returns to the shareholders of target firms. In order for relatedness to generate abnormal returns for acquirers, a variety of specific conditions must be met. Only when bidding firms enjoy private and uniquely synergistic cash flows with targets or unexpected cash flows resulting form synergies, will acquiring a related firm result in abnormal returns for the shareholders of acquiring firms.

II.III Measures influence factors

One of the challenges in analysing mergers and acquisitions is to find appropriate measures in order to explain the reaction of the market. The influence of a merger on the profitability depends on several factors.

One of these factors is the difference between a hostile and a friendly takeover. Schnitzer (1996) defines a bid as hostile if the target company publicly rejects it, or if the acquirer describes it as unsolicited and unfriendly. The raider’s decision on whether to attempt a hostile takeover or a friendly merger is in effect a choice between two different bargaining situations. With a hostile tender offer he only has to deal with shareholders. Once he opts for a merger he has to deal with both shareholders and manager, where the manager has better information about the scope of the potential value increase. Morck et al. (1988) find that targets of hostile takeovers tend to be older and more slowly growing than targets of friendly mergers. According to them friendly acquisitions are motivated by synergy gains whereas hostile tender offers are used if the intention is to discipline the incumbent management.

Huang and Walkling (1987) examine the effect of hostility versus friendly takeovers as well. They hypothesize that abnormal returns for target shareholders will

(8)

be higher in tender offers than in mergers and also in resisted offers than in unresisted offers. Their study includes all M&A-announcements from 1977 until September 1982. Based on these M&As, they find an average CAR for the acquirers for tender offers of 27.5%, whereas for mergers this percentage is 22.6%. Both of their hypotheses are confirmed, although the second one insignificant. Tender offers yield significantly higher returns than mergers and resisted offers earn statistically insignificant higher returns than unresisted offers. Furthermore they find an average abnormal return of 23.4% for shareholders for the two-day announcement period, which is significantly positive.

Another factor that affects the performance of a merger is the mode of financing. An acquirer chooses to takeover a company by cash, stock financing or a combination of these two. Datta et al. (1992) show that the use of way of financing has several effects on the wealth of both the target and bidding firms’ shareholders. First, previous literature shows that cash offers are associated with significantly and substantially higher returns compared to stock offers. Financial theory suggests that issuance of stock is viewed negatively by the capital markets. Second, since a cash transactions results in a tax liability on the shareholders they seek a higher premium. Third, the mode of payment influences the speed and thereby the cost of the transaction. In stock offers, the bidding firm is required to obtain approval of the Securities and Exchange Commission (SEC) before target shareholders can exchange their shares. Thereby it increases the competitiveness of the acquisition market and benefits targets at the expense of bidders.

(9)

II.IV M&As within the pharmaceutical industry

The pharmaceutical industry displays several key characteristics that are critical to understanding its challenges. The time it takes from discovery of a drug to Federal Drug Administration (FDA) approval, is on average fourteen to fifteen years. Pharmaceutical companies have seen M&As as a means of addressing the problems created by patent expiry, the growing costs of developing new drugs and the need for global marketing presence (James, 2002). Other motives to acquire a company are synergies and economies of scale.

Consistent with the idea of cutting on R&D costs, the research of Higgins and Rodriguez (2006) show that biopharmaceutical firms can successfully outsource R&D through acquisitions. One of the methods to decrease R&D expense is through the acquisition of external technologies. By focusing on research and development-directed acquisitions they are able to measure the acquirer firm performance directly related to R&D productivity. Next to the efficiency increase in R&D, they find by examining 160 pharmaceutical mergers between 1994 and 2001 that on average acquirers obtain significant positive returns.

Ravenscraft and Long (2000) present similar findings. They find positive abnormal stock market reactions to the announcement of 65 pharmaceutical deals. According to them the primary motive to merge stems from the elimination of excess capacity and inefficiencies induced by the changing industry structure and firm product portfolio. In order to maintain profitability, pharmaceutical firms have to improve R&D productivity, cut costs or limit buyer power. The increase in efficiency results in a decrease of R&D expenses after companies merge. Furthermore they find evidence for gains from mergers motivated by economies of scale are positively

(10)

correlated to product market demand, what causes mergers to happen in rising product markets.

Contrary to these positive results, Ornaghi (2009) examines that companies that merged between 1988 and 2004 have on average worse performances than the group of non-merging firms. He examines that research inputs and outputs declined in the same year and all the year after the deals. Furthermore he finds that mergers have a negative effect on the R&D intensity too.

In spite of these negative results, there is still a continuing trend of large-scale mergers. After the study of Ornaghi (2009) there have been a lot of mergers in the pharmaceutical industry. The trend of large-scale pharmaceutical mergers still continues. Firms that experience the greatest deterioration in their R&D productivity are most likely to undertake the acquisition of a research-intensive firm. In 2006 for example Bayer acquired Schering AG for $19.6 billion and more recently, in 2009 Pfizer acquired Wyeth for $7.0 billion (Comanor, 2011). Pfizer and Wyeth invested approximately $11.3 billion in R&D a year prior to their merger. After they merged, they spent 9.4 billion a year in 2010. Three years later they reduced the spending on R&D to $6.6 billion (YCHARTS, 2013).

III. Hypothesis development

On the basis of the literature review the hypotheses for this paper are:

H1: The pharmaceutical mergers between 2003 and 2013 did result in positive

(11)

H1 is divided in the following sub-hypotheses:

H1a: Sales experience has a positive effect on the abnormal return

H1b: Cash financed M&As generate relatively greater positive abnormal returns than

M&As financed with equity

Since the pharmaceutical industry and the reasons to acquire are similar the last decade (Comanor, 2011) and based on the previous literature, it is expected that the pharmaceutical mergers have resulted in abnormal positive returns for the target shareholders around the merger date.

H2: R&D expenditures decrease in a pharmaceutical merged company relatively

compared to a group of non-mergers

It is is expected that the amount of research decline relatively in merged pharmaceutical companies, based on the research of Higgins and Rodriguez(2006).

IV. Research Design

IV.I Data

The dataset will consist of the mergers within the pharmaceutical industry during the period 2003-2013. The timeframe includes the most recent largest mergers and will indicate whether the expectations of the Swedish Minister of Finance are legitimate. Data with respect to the acquisitions will be obtained by selecting the pharmaceutical delistings (SIC code 2834 and 2835) from the Security Data Corporation database.

(12)

The abnormal announcement returns are calculated, by using the formulas that will be discussed in section IV.II. After the missing data is filtered out, my sample consists out of 113 pharmaceutical mergers and acquisitions.

In order to calculate the abnormal returns, three databases are used. First, Zephyr will provide the information concerning announcement dates of M&As and characteristics such as method of payment and deal value. Second, the stock market returns are obtained from Datastream. The M&As for which there is no publicly information with respect to the stock market returns, will be filtered out of the sample, resulting in a bias. Third, the Fama-French Portfolios and Factors Database is used for the calculation of the benchmark return. The Fama-French three-factor model contains four variables: risk-free rate, market premium, SMB and HML. These variables are available in the database. Table 1 summarizes an overview of the data.

To determine what products are in development for acquirer firms, we use the NDA pipeline files from 2003 to 2013. In order to examine if the amount of research of a pharmaceutical merged company does change significantly, the NDA pipeline files of the pharmaceutical mergers will be compared to the NDA pipeline files of a control group of non-mergers. The pipeline data will be obtained from the NDA pipeline files.

IV.II Research method

To test the first hypothesis, I run the following regression.

(13)

Where:

- denotes the cumulative abnormal return - is the constant in the model

- is a dummy variable for cash financing (Zephyr) - is a dummy variable for equity financing (Zephyr)

- is a dummy variable for research experience (NDA pipeline files) - is a dummy variable for the target patent product sales in same

therapeutic category (FDA files)

- is a dummy variable for a friendly takeover (Zephyr) - is the error term

Abnormal returns are calculated to find out how share prices react to an M&A-announcement. An M&A-announcement gives the market new information about a share of a certain company. In order to compute the cumulative abnormal returns around the time of the acquisition announcement we use event study methodology. Given the constraints of announcements concerning among which for example new drug discoveries, regulatory changes the best measure is the stock market reaction using fairly narrow windows (Smith, Kriege, 2010). The (Martynova, 2006)window is set at 3 days: the announcement day and the day before and after. denotes the 3-day cumulated abnormal return. The abnormal returns are calculated by comparing realized returns with benchmark returns. The benchmark return is obtained from the Fama-French Potfolios and Factors database (F-F database) and is used in order to show what the realized return should have been if there was no M&A-announcement.

(14)

The abnormal returns are calculated as follows:

(2)

Where:

- is the abnormal return of stock i at time t

- is the realized return of stock i at time t (Datastream)

- is the benchmark return for stock i at time t (F-F database)

The expected return is commonly computed in three ways: capital asset pricing model, the market model and the Fama and French three-factor model (Martynova, Renneboog, 2008). This paper uses the Fama and French three-factor model in order to calculate the benchmark returns. The parameters are estimates for a period of 200 days before the event till 30 days prior the event.

( ) ( ) ( ) (3)

Where:

- is the benchmark return for stock i - is the risk-free rate (F-F database)

- is the market return at time t (F-F database)

- SMB is the size portfolio return (F-F database)

- HML is the book-to-market portfolio return (F-F database) - is the coefficient for systematic risk

(15)

- is the coefficient for the average excess return for SMB - is the coefficient for the average excess return for HML - is the error term of stock i

In order to get an average estimation of all individual abnormal returns, the formula from the research of Martynova and Renneboog (2006) is used. Formula 3 provides the effect of M&A announcements.

(4)

Where:

- is the cumulative abnormal return for sector i

- t denotes the event window( , ) for -1 ≤ < ≤ +1 - N is the number of M&As

- denotes the starting date of the event window - denotes the end date of the event window

- is the abnormal return of stock i at time t

Once we have obtained the abnormal return for the pharmaceutical sector, the paper focuses on what caused this reaction in the share price. Based on the literature review, this study looks at method of payment, sales experience and research & development. After computing the cumulative abnormal returns (CARs), the CAR will be present against different variables including the way of financing, the relatedness of the transaction, the research and the sales experience. Cumulative abnormal returns

(16)

(CAR’s) are one way to analyse the transactions success of mergers and acquisitions. Post-acquisition accounting data is not appropriate for the current analysis because it overlooks the health of the acquiring company’s product pipeline, which represents potential future significant cash flows that are not recorded in available accounting data. This is to examine if these variables influence the stock return.

When a merger or acquisition is completely financed by cash, the dummy variable is equal to 1. M&As financed by a mix of equity and cash are excluded in this specific analysis, given the inability to attribute any potential CAR to a specific method of payment. When the merger of acquisition is financed by equity only, has a value of 1. Both dummy variables have a value of 0 when the method of payment for an M&A is mixed. If the acquiring firm has products in its own pipeline within the same therapeutic category as the target firm, the research experience variable equals one (Higgins, 2006). To determine if it influences significantly if the acquiring firm has patented product sales within the same therapeutic category as the target firm, the sales variable is included. The sales variable is an indicator that equals one if the acquiring firm has patented product sales within the therapeutic category as the target firm prior to the acquisition (Higgins, Rodriguez, 2006). Dummy variable denotes the bidding attitude, which is equal to one if the attitude is friendly and 0 if the attitude is hostile. Furthermore three other variables are added to control for various financial characteristics of the target firm: a measure of free-cash flow, Tobin’s Q and the log value of market capitalization of the target firm.

There are other factors, for example technical shocks, which influence the performance of both the merged and the non-merger companies in the pharmaceutical industries. Therefore a control group of non-merging pharmaceutical firms with

(17)

similar stock market value will be composed. These data are obtained from Compustat and the results are checked on robustness. Assuming the exogenous technological shocks will affect both merging- and non-merging firms in a similar way, the differences are caused by the merger.

For the second hypothesis the following model is estimated:

Where:

- is the amount of R&D expense - is the constant in the model

- is a dummy variable for cash financing (Zephyr) - is a dummy variable for equity financing (Zephyr)

- is a dummy variable for research experience (NDA pipeline files) - is a dummy variable for the target patent product sales in same

therapeutic category (FDA files)

- is a dummy variable for a friendly takeover (Zephyr) - is a dummy variable for the expected R&D expense (Zephyr) - is an interaction term for a merger between two pharmaceutical

companies (Zephyr)

- is a dummy variable for pipeline products of the target (NDA Pipeline files)

(18)

To determine the effect of M&As on R&D expense, the RHS variable indicates the normal level of R&D expense that is estimated by the model of Berger (1993). Dummy has a value equal to one if there is a merger between two pharmaceutical companies. Dummy equals one if the target has products in pipeline in registration phase at the moment of the merger (Higgins, Rodriguez, 2006). , and have the same meaning as in formula 4. These are included to control for other factors that might affect R&D expense.

V. Empirical Findings

V.I Descriptives and univariate analysis

This section contains the findings of the research. By assessing the stock market returns, this study shows the effect of pharmaceutical M&As on four specific independent variables: the relatedness of the transaction, the method of payment, sales experience and research experience. In table 1 I present the variable correlations; in table 2 the descriptive statistics and table 3 the return to the shareholders of the target. After discussing the descriptive statistics, in the first section the result of the abnormal return is explained. The effects on these variables will be discussed in the subsequent section.

Table 2 shows the descriptive statistics of the various variables. Relatively large acquisitions among which Wyeth by Pfizer and AG by Bayer result in an average value of 113 deals of over 5 billion dollars. The greatest part of these M&As are financed by cash, whereas Higgins and Rodriguez (2006) shows equity is the most frequently used method of payment for the greatest part of pharmaceutical M&As

(19)

within his sample. The effects of the method of payment on shareholder return are discussed later in this section. The range and distribution of the control variables Free-cash flow, Tobin’s Q and Log market cap is consistent with previous research. Table 3 shows that all variables slightly correlate with each other. There are no problems with multicollinearity.

V.II Abnormal announcement return

Table 3 shows the results for the abnormal return for the target shareholders of the pharmaceutical companies. For the target shareholders a cumulative abnormal return of 8.50% during a 3-day event window is found. This result is statistically significant higher at a 1% level and is consistent with both hypothesis H1 and previous literature (Higgins, Rodriguez, 2006). Higgins and Rodriguez (2006) examine an abnormal return for both acquiring and target firms. Ravenscraft and Long (2000) find a positive abnormal stock return for target shareholders as well. By examining sixty-five pharmaceutical deals occurring between 1985 and 1996, they find average abnormal stock market reactions of 13.31% for the target and -2.12% for the bidder.

In order to try to explain what caused the abnormal return, regression 1 is regressed on various variables among which the method of payment. The literature finds that cash-financed transactions generate superior returns than those financed with stock (Andrade et al., 2001). Higgins and Rodriguez (2006) find contradicting results and so does this study. While cash transactions, on average, produce cumulative abnormal returns of 7.89%, those transactions financed with equity produced returns of 9.32 %. Both results are statistically significant at a level of 5%. This result does not confirm hypothesis H1B.

(20)

Table 1

Correlation matrix for pharmaceutical industry

1 2 3 4 5 6 7 8 9 10

1. Free-cash flow 0.1542 0.5820 0.1576 -0.0534 -0.0380 0.0489 0.2746 0.1876 0.1368 2. Tobin 0.1542 0.1355 -0.0586 -0.0178 -0.1095 -0.0525 0.0105 -0.0656 0.0234 3. Log market cap 0.5820 0.1355 0.2130 -0.1232 -0.0483 0.0928 0.0205 0.0798 0.0854 4. Sales experience 0.1576 -0.0586 0.2130 -0.0823 0.0321 0.1548 0.2745 0.1046 0.1940 5. Research experience -0.0534 -0.0178 -0.1232 -0.0823 -0.0734 -0.0643 -0.0877 -0.0526 -0.0257 6. Stock deal -0.0380 -0.1095 -0.0483 0.0321 -0.0734 -0.3023 -0.0450 0.0781 -0.0955 7. Cash deal 0.0489 -0.0525 0.0928 0.1548 -0.0643 -0.3023 0.0565 -0.0326 0.0610 8. Pharmer 0.2746 0.0105 0.0205 0.2745 -0.0877 -0.0450 0.0565 0.1238 0.0893 9. NDA 0.1876 -0.0656 0.0798 0.1046 -0.0526 0.0781 -0.0326 0.1238 0.0348 10. Friendly 0.1368 0.0234 0.0854 0.1940 -0.0257 -0.0955 0.0610 0.0893 0.0348

The matrix shows correlation between the various variables for the pharmaceutical M&As. Pearson correlations are given above the diagonal and the Spearman correlations are given below the diagonal. The bold variables indicate significance at 10% or lower. The Pearson and Spearman correlations are the same in this study. Description of the variables can be found in appendix 1.

Table 2

Descriptive statistics

Variable Mean Q1 Median Q3

Standard Deviation

Deal value ($Mil) 5,785.54 1700.98 2.900,83 18,390.29 10,924.06

Free-cash flow 501.23 102.85 402.55 2978 1103.45

Tobin 3.32 1.01 2.8 5,78 3.43

Log market cap 6.78 3.95 5.87 8,78 2.45

Score (in year of

acquisition) 4.32 2.01 3.38 6.02 7.96 Stock deal 0.28 0 0 1 0.39 Cash deal 0.36 0 0 1 0.48 Research experience 0.39 0 0 1 0.46 Sales experience 0.25 0 0 1 0.39 Pharmer 0.78 1 1 1 0.32 NDA 0.12 0 0 0 0.31 Friendly 0.94 1 1 1 0.03

The table shows the descriptive statistics for 113 pharmaceutical mergers and acquisitions in the 2003 to 2013 period.

(21)

The explanation for this contradictory result according to Higgins and Rodriguez (2006) is that equity payments help alleviate the moral hazard problem and align the interests of the target-firm employees with those of the acquiring firm. Although both this study and Higgins and Rodriguez (2006) show that equity financed transactions outperformed cash transactions, table 2 shows that the most frequently used method of payment switched from equity to cash.

Table 3

Panel A: Abnormal Announcement Returns

Pharmaceutical

N 113

[-1,+1] 8.50%

(4.68)***

Panel B: Deal Characteristics

Predictions Equity -0.0125 - (2.47)** Cash -0.0235 + (1.71)* Research experience 0.0010 + (0.09) Sales experience 0.0612 + (3.78)*** Pharmer -0.0121 ? (0.65) NDA 0.0299 + (2.01)** Friendly 0.0011 + (0.11)

Panel A shows the abnormal returns of the pharmaceutical sector.

Panel B shows regression estimates and independent variables from regressing the cumulative abnormal return (CAR) on selected independent variables for 113 announcements of acquisitions. CARs are from the three-day event window. The period runs from 2003 to 2013.

t-statistics are reported in parentheses in the right column. In the right column I show my predicted effects of the deal characteristics on CAR., where + is positive effect on CAR, - is negative and ? could be either way.

(22)

Consistent with the findings of both Ravenscraft and Long (2000) and Higgins and Rodriguez (2006), this study finds that prior sales experience has a positive effect on the CAR of the target shareholders. Higgins and Rodriguez (2006) examine that acquiring firms with prior sales experience within the same therapeutic category as the acquisition had substantially greater average abnormal returns than those firms that did not. They find abnormal returns of 6.99% for acquirers with prior sales experience, whereas the acquirers without prior sales experience obtained abnormal returns of 3.08%. This study finds similar significant abnormal returns for target shareholders. Targets taken over by acquirers with prior sales experience obtained 10.20% on average, whereas targets taken over by acquirers without prior sales experience experienced an abnormal return of 7.02% on average. This finding is statistically significant at 1% and confirms hypothesis H1A.

Also existing research experience affects the abnormal return. Acquiring firms that have existing research experience within the same therapeutic category generate on average an abnormal return of 5.08%, in contrast to the CAR of 3.24% of acquirers without research experience.

Another variable that significantly influences the return of the target shareholders is NDA. The NDA variable equals 1 for targets with a product in the last phase of the NDA approval. The coefficient of 0.0299 is significant at 5%, referring to a greater positive return for the target shareholders with a product in the last phase. This finding is consistent with the study of Higgins and Rodriguez (2006), who examine presence of a new product in this phase generates greater abnormal returns for both the acquirer and target shareholders at a 1% significant level.

None of the remaining independent friendly versus hostile takeover and control variables free-cash flow and Tobin’s Q is significant. The result for the

(23)

variable friendly is in contrast to the findings of Huang and Walkling (1987) and does not provide statistical evidence for higher abnormal returns from friendly takeovers compared to hostile.

V.II Effects on R&D expenditure

In order to test the second hypothesis, regression 5 is regressed on the various variables. This regression focuses on five independent variables: products in pipeline, expected R&D expense, relatedness of the transaction, sales experience, and research experience. The relatedness of the transaction measures if both the acquirer and target operate within the same therapeutic category.

The expected R&D expense is based on the model of Berger (1993). The coefficient for the R&D expense variable indicates that R&D expense after pharmaceutical M&As does not significantly differ from non-mergers.

The coefficient for new products in the pipeline of the target of 0.0299 and is significant at 5%. Table 2 shows that 16 out of the 113 targets had at least 1 product in the final phase of the NDA.

Existing research experience affects the amount of R&D expenditures. Although this result is insignificant, the coefficient shows for those M&As indicates that R&D expenditures decrease after merger. One of the reasons for this decrease might be economies of scale, since the acquirer is already doing research within the same research field.

None of the other variables is statistically significant and therefore this study examines that these variables do not significantly affect the R&D expenditure after pharmaceutical M&As.

(24)

VI. Conclusion and Discussion

The study started with the expectations of the Swedish minister of Finance concerning the effects of pharmaceutical M&As on research and the shareholder return. In this paper, we have focused on the effects of M&As on these and two other variables. The research question was split into several hypotheses. First, the abnormal return for the target shareholders was determined. Subsequently this study focused on several factors in order to explain the abnormal return. The second hypothesis related to the change in R&D expense caused by M&As. I examine a significantly positive abnormal return on average for the target shareholders and thereby the first hypothesis is confirmed. This study does not find statistical evidence for a decrease in R&D expense after pharmaceutical M&As.

Further research could examine what other factors affect R&D expense. In this study we only took into account the new drugs in the pipeline of a target once they were in the final phase. I believe considerable opportunities exist for further empirical research into the previous phases of the NDA files. Finally, it would be of interest to take the revenue growth of a target company into account.

(25)

References

YCHARTS. (2013, 12 31). Pfizer Research and Develpment Expense (Quarterly). Retrieved 06 09, 2014, from YCHARTS:

http://ycharts.com/companies/PFE/r_and_d_expense

Andrade, G., Mitchell, M., Stafford, E. (2001). New evidence and perspectives on mergers. Journal of Economic Perspectives , 15, (2) 103-120.

Berger, P., Ofek, E. (1995). Symposium on Corporate Focus. Journal of Financial

Economics , 39-65.

Comanor, W., Scherer, F. (2011). Mergers and innovation in the Pharmaceutical Market. HKS Faculty Research Working Paper Series RWP11-043 .

Danzon, P., Epstein, A., Nicholson, S. (2007). Mergers and acquisitions in the pharmaceutical and biotech industries. Managerial and decision economics , 28, 307-328.

Datta, D., Pinches, G., Narayanan, V. (1992). Factors influencing wealth creation in mergers and acquisitions: A meta-analysis. Strategic Management , 67-84. Higgins, M., Rodriguez, D. (2006). The outsourcing of R&D through acquisition in the pharmaceutical industry. Journal of Financial Economics , 80, 351-383.

James, A. (2002). The Strategic Management of Mergers and Acquisitions in the Pharmaceutical Industry: Developing a Resource-based perspective. Technology

Analysis & Strategic Management , 14, 300-311.

Martynova, M., Renneboog, L. (2006). Mergers and acquisitions in Europe. ECGI

(26)

Mittra, J. (2007). Life science innovation and the restructuring of the

pharmaceutical: industry merger, acquisitions and strategic alliance behavior of large firms. Technology Analysis & Strategic Management , 19 (3), 279-301. Ornaghi, C. (2009). Mergers and innovation in big pharma. International Journal

of Industrial Organization , 27 (1), 279-301.

Schnitzer, M. (1996). Hostile versus Friendly Takeovers. Economica , 63 (249), 36-40.

Ravenscraft, D., Scherer, F. (2000). Paths to creating value in pharmaceutical mergers. Mergers and productivity , 287-326.

Ravenscraft, D., Long, W. (1989). The profitability of mergers. International

Journal of Industrial Organization , 101-116.

Tjandrawinata, R., Simanjuntak, D. (2012). The impact of mergers and

acquisitions in research-based pharmaceutical companies on productivity. Social

(27)

Appendix 1: Variable definitions

Variable Description

The abnormal return of stock i at time t The benchmark return for stock i at time t

The cumulative abnormal return for sector i Dummy equals 1 if acquisition was cash financed Dummy equals 1 if acquisition was stock financed Expected R&D expense

HML The book-to-market portfolio return Dummy equals 1 for a merger

Dummy equals 1 for term for a merger between two pharmaceutical companies

Dummy equals 1 if target has new product(s) in pipeline

Dummy equals 1 if acquiring company has pipeline research in the same therapeutic category as the target firm

The risk-free rate

The realized return of stock i at time t The market return at time t

Dummy equals 1 if acquiring company has patented pharmaceutical product sales in the same therapeutic category as the target firm

Referenties

GERELATEERDE DOCUMENTEN

It is interesting to note that whilst the number of narrative fiction films screened at this year’s festival is less than half the number of non-fiction documentary

perspective promoted by these teachers is positive or negative, the very fact that students are being told that the government does not care about their identity, history and

In order to perform the measurements for perpendicular polarization, the λ/2 plate is rotated by 45°, to rotate the laser polarization by 90°.The measurements were performed

The general mechanical design of the Twente humanoid head is presented in [5] and it had to be a trade-off between having few DOFs enabling fast motions and several DOFs

Subject to section 86(9) and (10), a credit provider who receives notice of court pro- ceedings contemplated in section 83 or 85, or notice in terms of section 86(4)(b)(i), may

Is the DOW-effect present in returns that are adjusted to the market beta, market capitalization and book-to-market ratio of firms listed on the Dutch

Moreover, the market betas of the portfolios with high customer satisfaction results (both based on relative and absolute ACSI scores) are considerably lower compared

Cumulative abnormal returns show a very small significant reversal (significant at the 10 per cent level) for the AMS Total Share sample of 0.6 per cent for the post event