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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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:
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 = ∑𝑇 𝐴𝑅𝑖𝑡
𝑡=1 (2)
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.
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
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’.
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
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
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
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.
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)
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)
Market to Book Value -0.00008 -0.003 0.005 -0.00007***
(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 Probability of F-Statistic 0.057 0.177 0.385 0.34 R-Squared 0.24 0.31 0.29 0.07 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 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)
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)
Market to Book Value -0.00008 -0.001 0.005 -0.00007***
(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 Probability of F-Statistic 0.048 0.138 0.49 0.233 R-Squared 0.25 0.329 0.25 0.082 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 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)
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.
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.
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.
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.
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.
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.
.
References:
Alexandridis, G., Fuller, K.P., Terhaar, L. and Travlos, N.G., 2013. Deal size, acquisition premia and shareholder gains. Journal of Corporate Finance, 20, 1-13.
Antoniou, A., Arbour, P. and Zhao, H., 2008. How much is too much: are merger premiums too high?. European Financial Management, 14(2), 268-287.
Arnold, RA., 2010. Microeconomics. 9, South Western Cengage Learning Inc. Mason, Ohio Baldi, F. and Trigeorgis, L., 2009. Assessing the value of growth option synergies from business combinations and testing for goodwill impairment: a real options
perspective. Journal of Applied Corporate Finance, 21(4), 115-124.
Bassen, A., Schiereck, D. and Wübben, B., 2010. M&A success of German acquisitions in the US–evidence from capital market and survey data. Applied Financial Economics, 20(7), 543-559.
Bauguess, S.W., Moeller, S.B., Schlingemann, F.P., Zutter, C.J., 2009. Ownership structure and target returns. Journal of Corporate Finance, 15, 48–65.
Beitel, P., Schiereck, D. and Wahrenburg, M., 2004. Explaining M&A success in European banks. European Financial Management, 10(1), 109-139.
Bower, J.L., 2001. Not All M&As Are Alike–and That Matters. Harvard Business Review, 79(3), 93-101
Brown, S.J. and Warner, J.B., 1980. Measuring security price performance. Journal of Financial Economics, 8(3), 205-258.
Church, J., 2008. Conglomerate Mergers. Issues in Competıtıon Law and Polıcy, BA Section of Antitrust Law 2, 1503-1552,
Clougherty, J.A. and Duso, T., 2009. The impact of horizontal mergers on rivals: gains to being left outside a merger. Journal of Management Studies, 46(8), 1365-1395.
Danzon, P.M., Nicholson, S. and Pereira, N.S., 2005. Productivity in pharmaceutical– biotechnology R&D: the role of experience and alliances. Journal of Health
Economics, 24(2), 317-339.
Demirbag, M., Ng, C.K. and Tatoglu, E., 2007. Performance of mergers and acquisitions in the pharmaceutical industry: a comparative perspective. Multinational Business
Review, 15(2), 41-62.
Demsetz, H. and Lehn, K., 1985. The structure of corporate ownership: Causes and consequences. Journal of Political Economy, 93(6), 1155-1177.
Deng, P. and Yang, M., 2015. Cross-border mergers and acquisitions by emerging market firms: A comparative investigation. International Business Review, 24(1), 157-172.
Devos, E., Kadapakkam, P.R. and Krishnamurthy, S., 2008. How do mergers create value? A comparison of taxes, market power, and efficiency improvements as explanations for
synergies. The Review of Financial Studies, 22(3), 1179-1211.
Dı́az, B.D., Olalla, M.G. and Azofra, S.S., 2004. Bank acquisitions and performance:
Diaz, B.D. and Azofra, S.S., 2009. Determinants of premiums paid in European banking mergers and acquisitions. International Journal of Banking, Accounting and Finance, 1(4), 358-380.
Doukas, J.A., Holmen, M. and Travlos, N.G., 2002. Diversification, ownership and control of Swedish corporations. European Financial Management, 8(3), .281-314.
Evans, M.H., 2000. Excellence in financial management.< http://www.exinfm. com/training/pdfiles/course07-1.>
Fang, Y., Francis, B. and Hasan, I., 2012. More than connectedness–Heterogeneity of CEO social network and firm value.
Goergen, M. and Renneboog, L., 2004. Shareholder wealth effects of European domestic and cross‐border takeover bids. European Financial Management, 10(1), 9-45.
Greenfield, L., 1992. Bidders still have deep pockets despite the recession. Acquisitions Monthly, 8.
Gregory, A., 1997. An examination of the long run performance of UK acquiring firms. Journal of Business Finance & Accounting, 24(7‐8), 971-1002.
Gorton, G., 2009. The subprime panic. European Financial Management, 15(1), 10-46.
Gu, F. and Lev, B., 2011. Overpriced shares, ill-advised acquisitions, and goodwill impairment. The Accounting Review, 86(6), 1995-2022.
Harrigan, K.R., 2003. Vertical integration, outsourcing, and corporate strategy. Beard Books, New York.
Hamza, T., Sghaier, A. and Thraya, M.F., 2016. How do Takeovers Create Synergies? Evidence from France. Studies in Business and Economics, 11(1), 54-72.
Hassan, M., Patro, D.K., Tuckman, H. and Wang, X., 2007. Do mergers and acquisitions create shareholder wealth in the pharmaceutical industry?. International Journal of Pharmaceutical and Healthcare Marketing, 1(1), 58-78.
Hayward, M.L. and Hambrick, D.C., 1997. Explaining the premiums paid for large acquisitions: Evidence of CEO hubris. Administrative Science Quarterly, 103-127.
Healy, P.M., Palepu, K.G. and Rubeck, R.S., 1997. Which takeovers are profitable? Strategic or financial?. Sloan Management Review, 38(4), 45.
Higgins, M.J. and Rodriguez, D., 2006. The outsourcing of R&D through acquisitions in the pharmaceutical industry. Journal of Financial Economics, 80(2), 351-383.
Keckley, P.H. and Coughlin, S., 2012. Deloitte 2012 survey of US health care consumers: the performance of the health care system and health care reform. Washington DC: Deloitte Center for Health Solutions
Kengelbach, J. and Roos, A.W., 2011. Riding the Next Wave in M&A: Where are the Opportunities to Create Value?. Boston Consulting Group, Incorporated.
Kohli, R. and Mann, B.J.S., 2012. Analyzing determinants of value creation in domestic and cross border acquisitions in India. International Business Review, 21(6), 998-1016.
Loderer, C. and Martin, K., 1997. Executive stock ownership and performance tracking faint traces. Journal of Financial Economics, 45(2),223-255.
Loukianova, A., Nikulin, E. and Vedernikov, A., 2017. Valuing synergies in strategic mergers and acquisitions using the real options approach. Investment Management and Financial Innovations 14(1), 236-247
MacKinlay, A.C., 1997. Event studies in economics and finance. Journal of Economic Literature, 35(1), 13-39.
Mishra, P. and Chandra, T., 2010. Mergers, acquisitions and firms’ performance: Experience of Indian pharmaceutical industry. Eurasian Journal of Business and Economics, 3(5), 111-126.
Mittra, J., 2007. Life science innovation and the restructuring of the pharmaceutical industry: Merger, acquisition and strategic alliance behaviour of large firms. Technology Analysis & Strategic Management, 19(3), 279-301.
Miyazaki, H., 2009. An analysis of the relation between R&D and M&A in high-tech industries. Applied Economics Letters, 16(2), 199-201.
Moeller, S.B., Schlingemann, F.P. and Stulz, R.M., 2005. Wealth destruction on a massive scale? A study of acquiring‐firm returns in the recent merger wave. The Journal of
Finance, 60(2), 757-782.
Morresi, O. and Pezzi, A., 2011. 21 years of international M&As and joint ventures by Italian medium-sized listed firms: Value creation or value destruction?. Research in International Business and Finance, 25(1), 75-87.
Rau, P.R. and Vermaelen, T., 1998. Glamour, value and the post-acquisition performance of acquiring firms. Journal of Financial Economics, 49(2), 223-253.
Rawani, A. and Kettani, H., 2010. Humanities, Historical and Social Sciences. Social Sciences, 26, 28.
Roll, R., 1986. The hubris hypothesis of corporate takeovers. Journal of Business, 197-216.
Ruback, R.S., 1982. The effect of discretionary price control decisions on equity values. Journal of Financial Economics, 10(1), 83-105.
Shleifer, A. and Vishny, R.W., 1997. A survey of corporate governance. The Journal of Finance, 52(2), 737-783.
Sirower, M.L., 1997. The synergy trap: How companies lose the acquisition game. Simon and Schuster.
Strong, N., 1992. Modelling abnormal returns: a review article. Journal of Business Finance & Accounting, 19(4), 533-553.
Vyas, V., Narayanan, K. and Ramanathan, A., 2012. Determinants of mergers and acquisitions in Indian pharmaceutical industry. Eurasian Journal of Business and Economics, 5(9), 79-102.
Wong, A. and Cheung, K.Y., 2009. The effects of merger and acquisition announcements on the security prices of bidding firms and target firms in Asia. International Journal of
Economics and Finance, 1(2), 274.
Yook, K.C., 2003. Larger return to cash acquisitions: Signaling effect or leverage effect?. The Journal of Business, 76(3), 477-498.
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
Significance Levels of 10%, 5% and 1% are presented by *, ** and *** respectively
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)
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 Test Stat 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
Descriptive Statistics of Abnormal Returns (AR's) and Cumulative Abnormal Returns (CAR's) for 11-Day Event Window
