27 Master thesis
Corporate governance during merger waves: does it
influence long-term performance?
By Dave Knipmeijer
MSc. Finance
Faculty of Economics and Business University of Groningen
Field Key Words: Valuation, Stock Selection, M&A, Analyst performance Corporate Governance, Ethics & SRI
JEL Classifications: G00, G14, G34
Author: Dave Knipmeijer
Studentnr: s2019213
Study program: MSc Finance
Supervisor: Prof. Dr. Wolfgang G. Bessler Abstract
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1. Introduction
Mergers and acquisitions are the largest and most externally observable form of corporate investment (Masulis, Wang & Xie, 2007). The process during a merger or acquisition is complex and can take a very long time. It is an interaction between many different stakeholder groups and consists of many phases with different stakeholders groups (Parvinen & Tikkanen, 2007). Because a lot of assets are involved with a merger or an acquisition, this form of investment can lead to conflicts between shareholders and managers.
For example the empire-building problem. This theory suggests that managers maximize their own utility instead of their shareholders’ value. They take over other firms for power and profit motives instead of maximizing the shareholder wealth (Trautwein, 1990). Corporate governance mechanisms try to minimize these conflicts during mergers and acquisitions. The market for corporate control, for example, keeps managers facing more pressure, which leads to better acquisitions decisions (Masulis et al, 2007).
1.1 Merger waves
Economic theory comes up with an explanation why mergers might occur: efficiency related reasons. Reasons are economies of scale and scope, attempts to create market power, removing incompetent target management and taking advantage of opportunities for diversification (Andrade, Mitchell & Stafford, 2001). Some mergers can be explained by this theory, but in the merger literature there is some consistent empirical evidence: mergers tend to cluster by time and industry1. Table 1 shows an overview of the worldwide merger waves. Every wave has a different facet and only last for a maximum of 15 years. Figure 1 shows the fifth and the sixth merger waves in the U.S. In the figure the pattern of multiple waves is visible. It is clear that periods of high merger activity are followed by lower ones. These mergers and acquisitions, clustered by time, can lead to potential problems and conflicts.
Duchin & Schmidt (2013) show evidence that during periods of high merger activity (hereafter merger waves) poor merger and acquisition decisions are made. These so called bad acquisitions occur because of several reasons.
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Table 1
Overview merger waves worldwide The evolution of M&A. Source: KPMG (2011)2.
Waves Period Facet
First wave 1893 – 1904 Horizontal mergers Second wave 1919 – 1929 Vertical mergers
Third wave 1955 – 1970 Diversified conglomerate mergers
Fourth wave 1974 – 1989 Co-generic mergers, hostile takeovers, corporate raiders
Fifth wave 1993 – 2000 Cross border, mega mergers
Sixth wave 2003 - 2008 Globalisation, private equity, shareholder activism
Figure 1
Merger waves in the US
Dollar value and number of mergers, 1986 to 2010.This figure presents aggregate merger volume in 2010-adjusted U.S. dollars and the number of mergers by year. Merger data from SDC. Source: Ahern & Harford (2014).
Acquisitions during merger waves exhibit greater uncertainty, poorer quality of analytics’ forecasts and weaker CEO turnover-performance sensitivity. In theory, because of limited resources, the quality of analytics’ and investors’ forecasts declines
2 KPMG (2011). Merger wave table. Retrieved from:
http://www.kpmg.com/za/en/issuesandinsights/articlespublications/transactions-restructuring/pages/seventh-wave-of-ma.aspx.
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(Duchin & Schmidt, 2013). And in general CEO’s are evaluated based on their own decisions, but also compared to their peers. When their peers perform just as bad as the CEO himself, it’s less likely that he will be penalized. These conditions may reduce external monitoring and can lead to agency driven acquisitions. Agency problems can be defined as problems that arise from the conflicting interests of agents (management) and principals (shareholders) (Hillier, Grinblatt & Titman, 2012). So agency driven acquisitions are acquisitions that are not in the interests of the owners of the firm and thus will increase the agency problem.
1.2 Corporate governance during merger waves
There is evidence that corporate governance during merger waves is weaker than during acquisitions outside the merger wave (Duchin & Schmidt, 2013). It’s interesting to study if acquirers with better corporate governance systems have better acquisitions during merger waves, because of the evidence that some merger waves are unavoidable for some firms. Harford (2005) for example describes the neoclassical model, which says that aggregate mergers are caused by economic disturbance. Due the economic disturbance the industry has to reorganize. Harford (2005) for example supports the neoclassical model, which says that aggregate mergers are caused by the clustering of shock-driven industry merger waves, not by attempts to time the market. Some acquisitions during merger waves are therefore inevitable, so it is interesting to study a mechanism that is able to reform: the firm’s corporate governance system. It has to be mentioned that it is not always possible to reform a firm’s corporate governance system into every possible preferred state, because of law restrictions. For example in Germany it is forbidden to have a one-tier system. According to the German Corporate Governance Code publicly listed firms require a two-tier board system.
1.3 In-wave acquisitions
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getting lower returns because of the conditions during in-wave mergers or is there a significant difference between firms because of the differences in the levels of corporate governance mechanisms.
The objective of this study is to find out whether corporate governance mechanisms have a positive influence on returns around M&A announcements during merger waves. This leads to the following research question:
“Does the level of corporate governance positively influences the long-term returns of mergers and acquisitions during merger waves?”
In the next section I will provide an overview of related literature on the topic of corporate governance and merger waves. After this I will discuss the data and methods used. Then I will discuss the results. At the end of this paper the conclusion and some final remarks are discussed.
2. Literature review
2.1 Corporate governance
When looking at corporate governance and firm performance in general, the well-known paper “Separation of Ownership and Control” by Fama and Jensen (1983) comes in mind. In short, it is the board’s task to monitor the CEO in order to align his incentives with those of the shareholders. This means that, according to the neoclassical theory of the firm, managers have to maximize shareholder wealth (Mueller, 2006).
However this alignment is not always accomplished, still a lot of firms suffer from agency problems. These agency problems can lead to massive scandals. In the beginning of the twenty-first century the US was confronted with a lot of corporate frauds and scandals (Enron & Worldcom). In 2002, there was a major change in the US corporate governance system due the Sarbanes-Oxley act. Investors experienced more protecting by improved reliability and accuracy of corporate disclosures.
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2.1.1 Corporate governance and returns
If a corporate governance structure helps to align the interests of the CEO with the interests of the shareholders (maximum return) we should see differences in returns between firms with strong and weak corporate governance structures. For example Gompers, Ishii & Metrick (2003) find a strong correlation between corporate governance and stock returns. They developed a governance index and followed a strategy where they sold the stocks with the lowest index and kept the stocks with the highest index. This resulted in an abnormal return of 8,5% per year.
Further Cunat, Gine & Guadalupe (2012) present evidence that corporate governance provisions have a causal effect on firms’ market value and long-term performance. However, the relation between corporate governance and firms’ returns is not linear. There seems to be a point where too much corporate governance has a negative impact on the firms’ returns. The Sarbanes-Oxley act, for example, has caused significant net costs on firms like extra money on consultants, lawyers & auditors (Zhang, 2007; Solomon & Brian-Low, 2004). To avoid these high costs, a lot of firms decided to go private (Gleason, Payne & Wiggenhorn, 2007). The costs of the act exceeded the benefits for some firms what indicates that the corporate governance structures surpassed their optimal state.
2.1.2 Corporate governance and M&As
Corporate governance also tends to have a positive influence on the returns of M&A deals in the short-run and the long run. Rani, Yadav & Jain (2014) find that acquirers with a higher corporate governance score have better post-M&A financial performance.
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For my study I will not use all the corporate governance measures as Duchin & Schmidt (2013) did, because of limited time and resources. I will discuss CEO duality, board independence and CEO compensation.
2.2 CEO duality
CEO duality is a board leadership structure where an individual serves as the CEO and the chairman of the board of directors (Yang and Zhao, 2014). Prior research provides evidence that CEO duality negatively affects firm performance because of several reasons. Fama & Jensen (1983) for example propose that CEO duality might hinder monitoring abilities of the board. Further duality can firmly strengthen a CEO’s position at the top of an organization, which could lead to reduced monitoring and discipline (Mallette and Fowler, 1992; Jensen, 1993). Further Core, Holthausen and Larcker (1999) mention that CEO duality is associated with weaker corporate governance and that the CEO in this situation is able to extract extra compensation from the firm. Finally there is lower sensitivity of CEO turnover to firm performance with the presence of CEO duality (Goyal & Park, 2002).
So there is a lot of evidence that CEO duality negatively affects firm performance. However there are some exceptions. Using data on U.S. firms, Boyd’s (1995) findings suggest that duality leads to better firm performance under conditions of complexity. In highly complex industries CEO duality can be an advantage, because in these kinds of industries, strategic decisions would be made more frequently (Boyd, 1995). It is interesting to find out if there is a difference in abnormal returns between firms with CEO duality in highly complex industries and low complex industries. Complexity can be measured by using the Herfindahl index (Boyd, 1995). However my dataset did not contain that many firms with duality, so it was not possible to provide evidence for this theory myself. For now a board structure without CEO duality is a proxy for a corporate governance mechanism that is good.
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2.3 Board independence
In large corporate governance laws and codes the general idea is that firms with a majority of independent directors make major corporate decisions in the best interests of shareholders. A director is independent if he/she is not employed by the firm or does not supply services to the firm. Or in general it does not have a conflict of interest in the accomplishment of her oversight mission (Tirole, 2006). A board is independent when more than 50% of the directors are independent directors. A lot of evidence is found on these subjects included the reasons why an independent board contribute to a better corporate governance structure.
Villiers, Naiker & Staden (2011) for example, they conclude that a higher concentration of independent directors will lead to a higher level of effective monitoring. When directors are independent, a CEO holds less power over them. The independent directors experience less power of the CEO, because the independent directors’ careers do not depend on the CEO. Further board independence set more appropriate compensation for CEOs (Core et al, 1999). The CEO’s compensation structure can be set in such a way that it maximizes the value for outside shareholders. CEOs have higher compensation packages at firms that experience larger agency problems, which leads to less shareholder value.
Finally, Byrd and Hickman (1992) say that independent boards benefit shareholders. They found that bidding firms with a majority of independent outside directors earn higher announcement abnormal returns than firms without a majority of independent directors. However there’s evidence that when the concentration of independent directors exceeds a certain level, the relation between the fraction of independent directors and shareholder wealth becomes negative (Byrd et al, 1992). A board structure where more than 60% is an independent director can have negative influences on the shareholder wealth. I will come back later on this in the robustness check section.
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Hypothesis 2: The presence of board independence will have a positive influence on the long-term success of a mergers or acquisition during a merger wave.
2.4 CEO compensation
Like Duchin and Schmidt (2013) I will use the CEO compensation measure: equity based compensation relative to the total compensation. The general idea of equity-based compensation is that decisions of a CEO will affect its own compensation. This should lead to pursuing the interests of the shareholders, because the higher the stock returns the higher the CEO compensation. Jensen and Murphy (1990) quote: “The larger the share of company stock controlled by the CEO and senior management, the more substantial the linkage between shareholder wealth and executive wealth”. So with a certain amount of company stock, the CEO tends to pursue the interests of the shareholders. Mehran (1995) confirms this by concluding that high levels of equity holdings by the CEO and equity-based compensation are positively related to firm performance.
On the other hand, there is evidence that equity based CEO compensation leads to more risk-taking behaviour. CEOs with equity-based CEO compensation become more acquisitive and the abnormal returns of these acquisitions are not constantly positive for the shareholders of the acquiring firm (Boulton, Braga-Alves & Schilingemann, 2014). For my study I will use the level of equity-based CEO compensation as a proxy for good corporate governance, because there is a lot of evidence that this kind of compensation positively influences long-term returns. For example, there is a positive correlation between equity-based compensation received by acquiring managers and stock price respond around and after acquisition announcements (Datta, Iskandar-Datta & Raman, 2001).
The presence of equity-based compensation can help align the interests of managers with those of shareholder. So CEO equity-based compensation will lead to better long-term returns for a firm, according to the literature.
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2.5 Firm performance
To measure firm performance, I use the long-term performance. I prefer the long-term performance to the short term because it reflects if synergy gains or losses have been made. This is important for the shareholders of the bidding firms (Bessler & Schneck, 2015).
Further, long-term value is relevant to all stakeholders, because companies maximizing their total value for the shareholders also create more employment, give a better treatment to their employees and carry on more social responsibility (Koller, Goedhart & Wessels, 2010).
3. Data and methods
3.1 Data
Data requirements: (i) Acquirer is a publicly traded firm from the United Kingdom, because of data availability. In the United Kingdom, mergers and acquisitions are more common than in continental Europe (Hillier et al, 2012); (ii) It had a minority stake of less than 50% before the deal and a majority stake of 51% or more after the deal; (iii) The deal value was at least € 25 million; (iv) deal was completed; (v) no requirement for the target firms. In the end most of the target were private limited companies (LTDs) so for most of the target no data was available.
I started with a dataset I used for the course Corporate Governance with data from BoardEx. The BoardEx dataset only provided data from the period 2005-2008.
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Table 2
Sample Construction
This table summarizes the construction of the complete dataset.
Table 3 Summary statistics
This table provides summary statistics of all the variables used in this paper from 2005-2008. The description of the variables can be found in the table B.1 variable descriptions (appendix B). The table shows the sample size, mean, standard deviation of the sample, the minimum and the maximum values of the independent and control variables. The sample period is 2005-2008.
Construction of dataset No. of deals
Deal type: mergers and acquisitions; minimum deal value: 25 mln euro and geography: United Kingdom. Period: 2003-2011
2.678
Available corporate governance data 290 Dataset without missing values 201
Variable N Mean Std. dev. Min Max
Dependent variable BHAR (250) in % 201 -0,015 0,127 -0,568 0,288 Independent variables Board independence 201 0,776 0,418 0 1 CEO duality 201 0,104 0,307 0 1 CEO equity-based compensation/total 201 0,641 0,515 0,010 3,44 Control variables Total assets (in € x1.000) 201 73.361.607 ,164 179.718.663 ,771 109.700 ,000 1.272.700.000 ,000 Leverage 201 0,623 0,198 0,080 1,418 Return on assets 201 0,072 0,098 0,597- 0,622 Cash only 201 0,791 0,408 0 1
Relative deal size
(In %) 201 15,890 25,503 0,036 145,195
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3.2 Summary statistics
The summary statistics of the dependent, independent and control variables are presented in table 3. Variables with a minimum of zero and a maximum of one represent dummy variables.
3.3 Merger wave
Before starting to look at merger and acquisition deals, it was necessary to visualize the merger wave that is going to be investigated. I started with the sixth wave: a range of merger and acquisition deals in the period 2003-2008 with a minimum value of 25 million in the United Kingdom. In this period 2.678 M&A deals took place with a total value of € 1.280.863 (mln). Figure 2 shows the merger wave in two ways: the number of deals and the aggregate deal value. It is clear that the top of the wave was in the year 2007. In that year the most mergers and acquisitions took place with in total the highest aggregate value. Further I am interested in the in-wave acquisitions. So I will look at periods with a relative large amount of deals. Because of data availability problems, I was restricted to the period 2005-2008.
Figure 2 Merger wave
Merger wave (2003-2010) in the United Kingdom. Source: Zephyr.
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3.4 Dependent variable
The objective of this thesis is to look at influence of the governance mechanisms during merger waves on the long-term returns. To measure the long-term effects I will use the commonly used post-merger buy-and-hold abnormal Returns (BHAR). BHAR uses the geometric return instead of the commonly used arithmetic return, which is a better measure for the long run. The BHAR for the individual firm is defined as:
!"#$!,! = (1 + !!,! !"# !!! ) − (1 + !!"#$!!"#$,! !"# !!! )
For the post-merger BHAR I used a holding period of 250 days. Further I used several benchmarks for the acquirers. I used: FTSE 250, FTSE 100, FTSE Small Cap, FTSE AIM, FTSE FLEDGLING and FTSE Techmark.
Long horizon buy-and-hold abnormal returns are in many cases not normally distributed, but positively skewed. This will lead to negatively biased t-statistics (Lyon, Barber & Tsai, 1999). Lyon et al. (1999) developed a skewness adjusted t-test for positively skewed buy-and-hold abnormal returns. So for the parametric test, we have the normal t-test (1) and the skewness adjusted t-test (2):
1) !!"#$ = !"#$ !"# √!! !"#$
2) !!"#$%# = ! ! + !! !!!+ !!! ! where
I tested for normality and came with the following results. The null-hypothesis for residual normality is rejected very strongly (the p-value for the Jarque-Bera test is 0,000). So the values are non-normally distributed so I need to use the second t-test. This resulted in a t-statistic of -1,746 with a p-value of 0,082, which is significant at 10% significance level. In other words, the buy-and-hold abnormal returns are statistically different from zero (at a 10% significance level).
3.5 Independent variables
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2. Board Independence: This is a dummy variable with a value of one if the board is independent. This means that more than 50% of the directors of the board has to be independent. The dummy variable has a value of zero when the board in not independent.
3. CEO Equity Based Compensation: This variable reflects the relative amount of equity CEO compensation to the total CEO compensation.
3.6 Control variables
For the different regressions I will control for acquirer characteristics and deal characteristics.
3.6.1 Acquirer characteristics
According to Duchin & Schmidt (2013) size is negatively correlated with acquirer’s returns. Therefore we control for the size of the acquirer itself. The acquirer size is measured by the natural log of the total assets. Also leverage tends to have an effect on acquirer returns. Leverage can have a positive influence on acquirer returns, when it triggers managers to improve their performance. Managers of a highly leveraged firm can feel the pressure of its creditors, which can lead to the incentive to improve performance (Masulis et al., 2007). Leverage is measured by dividing the total liabilities by the total assets.
Further I control for return on assets because it shows the efficiency of a firm generating earnings with its assets. The return on assets reflects how well the assets of the business are used to generate profit (Weetman, 2011). If high return on assets reflects efficient management, I expect that return on assets will have a positive correlation with the acquirer returns. Return on assets is measured by dividing the net income by the total amount of assets.
3.6.2 Deal characteristics
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zero if this was not the case. I will come back later on this phenomenon in the robustness checks part. As mentioned before: size is negatively correlated with acquirer’s returns. Therefore I will control for the relative deal size. The relative deal value is calculated by dividing the deal value by the market capitalization at the moment of announcement.
Finally the industry relatedness of an acquisition is important. The control variable industry relatedness measures the industry relatedness of a merger or an acquisition. Morck, Schleifer & Visney (1990) describe the difference between a related and an unrelated (diversifying) acquisition. The returns to the bidding firm shareholders are lower when the firm undergoes an unrelated merger or acquisition. They conclude that firms with industry related acquisitions have higher returns than firms with diversifying acquisitions. Unrelated acquisitions can have risk-reducing and survival motives, which leads to overpayment by the managers. To measure the industry relatedness of a M&A I used a dummy variable with a value of one for a merger or acquisition in the same industry and zero if the M&A was across industries. To compare the different industries the five-digit UK SIC code was used. Table A.1 shows the pair wise correlation between the variables. The correlations between the variables are not very high, so multicollinearity does not seems to be a problem. For an overview of all the variables, see table B.1.
3.7 Methods
For testing the individual effects of the corporate governance mechanisms on the buy-and-hold abnormal returns, I used the multivariate ordinary least squares analysis. Each corporate governance variable has its own model. Therefore three models are used for the OLS regression:
!"#$ 250
= !!+ !!∗ !"# !"#$%&' + !!∗ !"#$%"& !"#$"%&'( + !!,! (1)
!"#$ 250
= !!+ !!∗ !"#$% !"#$%$"#$"&$ + !!∗ !"#$%"& !"#$"%&'( + !!,! (2) !"#$ 250 =
!!+ !!∗ !"# !"#$%& !"#$% !"#$%&'()*"& + !!∗ !"#$%"& !"#$"%&'( + !!,!
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It is of course more likely that firms will have installed multiple corporate governance mechanisms at the same time. So I will test each possible combinations of the mechanisms I used in my thesis.
4. Results
Table 4 shows the results obtained from the different multivariate OLS regressions that regress the buy-and-hold abnormal returns with a holding period of 250 days on the independent variables: CEO duality, board independence and CEO equity compensation. In general the OLS regressions did not result in many significant variables. Only the independent variable “Board independence” and the control variable “Return on assets” are statistically different from zero. Further the explanatory power of the models is not very high. The models have a very low R-squared ranging from 0,061 to 0,087.
4.1 CEO duality
The results in table 4 show that CEO duality has a slightly negative effect on the buy-and-hold abnormal returns with a holding period of 250 days. However these results are not significantly different from zero. Therefore the following hypothesis cannot be supported with statistical evidence:
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Table 4
Results Multivariate OLS Regression
Table 4 represents the results of the three different OLS regressions. Column (1) shows the results of the effects of CEO duality on the long-term returns, where column (2) shows the effect of board independence on the long-term results. Finally column (3) shows the effect of the level of CEO equity based compensation on the long term BHAR.
In figure 3 I visualized the difference between acquirers with no CEO duality and acquirers with CEO duality. I used the difference between the two, because the differences were minimal and hard to see if both types were visualised in the graph. In the first 125 days the acquirers with no CEO duality outperformed the acquirers with CEO duality. After 125 days the difference between acquirers without and with CEO duality becomes minimal.
BHAR [250] (1) (2) (3) CEO duality -0,001 (0,001) Board Independence 0,001* (0,001)
CEO Equity Based Compensation 0,000
(0,000) Total assets (ln) 0,000 (0,000) 0,000 (0,000) 0,000 (0,000) Leverage 0,001 (0,001) 0,001 (0,001) 0,001 (0,001) Return on assets 0,006*** (0,002) 0,006*** (0,002) 0,006*** (0,002) Cash only -0,000 (0,001) -0,000 (0,001) -0,000 (0,001)
Relative deal size -0,000
(0,001) -0,000 (0,001) -0,000 (0,001) Industry relatedness -0,000 (0,000) -0,000 (0,000) -0,000 (0,000) R-squared 0,087 0,070 0,061 N 201 201 201
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Figure 3
Difference in long-term performance between acquirers with no CEO duality and acquirers with CEO duality
4.2 Board independence
The results in table 4 show that board independence has a minimum positive effect on the 250 days buy-and-hold abnormal returns. This result is statistical significant at a 10% significant level.
In figure 4 the results are visualized. It is clear that the acquirers with an independent board outperform the acquirers with no independent board. The cumulative BHAR of the firms with an independent board is positive in the first hundred days, but becomes slightly negative after hundred days. Acquirers with no independent board experience negative return for almost the whole BHAR holding period. In the first days after the announcement they have positive returns, but it declines after five days.
So in general the presence of an independent board has a positive influence on the buy-and-hold abnormal returns with a holding period of 250 days. The following hypothesis is not rejected:
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Figure 4
Long-term performance acquirers with and without Board independence
4.3 CEO Equity Based Compensation
Table 4 column (3) shows the effects of the level of CEO Equity Compensation relative to the total CEO compensation on the buy-and-hold abnormal returns. The effects on the long-term returns are minimal and also statistical insignificant. Because of this statistical insignificance it is hard to say anything about the effect of CEO equity based compensation on the long-term returns. So the following hypothesis cannot be supported with statistical evidence:
Hypothesis 3: The level of equity-based CEO compensation will have a positive influence on the long-term success of a mergers or acquisition during a merger wave.
4.4 Corporate Governance mechanisms
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Table 5
Results Multivariate OLS Regression
This table shows the results of different combinations of corporate governance mechanisms. Column (1) shows the effects of CEO duality and board independence on the BHAR with a holding period of 250 days. Column (2) shows the effect of CEO duality and CEO equity based compensation on the BHAR, where column (3) shows the effects of board independence and CEO equity based compensation. Finally column (4) shows the effects of the corporate governance mechanisms together on the BHAR. BHAR(250) (1) (2) (3) (4) CEO duality -0,000 (0,001) -0,001 (0,001) -0,000 (0,001) Board Independence 0,001 (0,001) 0,001* (0,001) 0,001 (0,001) CEO Equity Based
Compensation 0,000 (0,000) 0,000 (0,000) 0,000 (0,000) Total assets (ln) 0,000 (0,001) 0,000 (0,000) -0,000 (0,000) -0,000 (0,000) Leverage 0,001 (0,001) 0,001 (0,001) 0,001 (0,001) 0,001 (0,001) Return on assets 0,006*** (0,002) 0,006*** (0,002) 0,006*** (0,002) 0,006*** (0,002) Cash only -0,000 (0,001) -0,000 (0,001) -0,000 (0,001) -0,000 (0,001) Relative deal size -0,000
(0,001) -0,000 (0,001) -0,000 (0,001) -0,000 (0,001) Industry relatedness -0,000 (0,000) -0,000 (0,000) -0,000 (0,000) -0,000 (0,000) R-squared 0,071 0,068 0,075 0,076 N 201 201 201 201
*,** and *** represent 10%, 5% and 1% significance level
5. Additional analyses and robustness checks
5.1 Cycle of the wave: momentum
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classify the first 20% in a merger wave as early movers. My dataset does not contain the whole merger wave, so I classified the first 20% of mergers and acquisition in my dataset as early movers. I created a dummy variable with a value of one if the acquirer was an early mover and a zero if the acquirer was a follower.
The results of the robustness check are shown in table 6 column (1). The early mover advantage is not present in the table. The effects on the long-term buy-and-hold abnormal returns are minimal and not statistical significant. Almost all the independent and control variables are not statistically different from zero, so the variables do not explain the success of a M&A at all.
5.2 Level of board independence
As I mentioned before a board which is too independent can have negative influences on the long-term returns. There’s evidence that when the concentration of independent directors exceeds a certain level, correlation with results becomes negative (Byrd et al, 1992). A board structure where more than 60% are independent directors can have negative influences on the shareholder wealth. The results of the OLS regression are displayed in table 6 column (2). They show something different than the theory explains. Boards that consist of more than 60% independent directors have a positive influence on the abnormal returns. However the independent and controlling variables are not significantly different from zero.
5.3 Method of payment: cash versus shares
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have a minimal negative impact on the long-term returns. However, with a very low R-squared (0,051) the model has almost no explanatory power. Further the variables are not statistically different from zero.
Table 6
Additional analyses and robustness checks
Table 6 shows the results of the additional analyses and robustness checks. Column (1) shows the effect of the early mover advantage on the BHAR with a holding period of 250 days. Column (2) shows the effect of too much board independence on the BHAR (250). Finally column (3) shows the results of the method of payment on the BHAR (250). BHAR [250] (1) (2) (3) Early mover 0,000 (0,000) Board independence (>60%) 0,000 (0,000) Cash only -0,000 (0,000) -0,000 (0,001) -0,000 (0,001) Total assets (ln) 0,000 (0,000) 0,000 (0,000) 0,000 (0,000) Leverage -0,000 (0,000) 0,001 (0,001) 0,001 (0,001) Return on assets 0,003*** (0,001) 0,006*** (0,002) 0,006*** (0,002)
Relative deal value -0,000
(0,000) -0,000 (0,001) -0,000 (0,001) Industry relatedness -0,000 (0,000) -0,000 (0,000) -0,000 (0,000) R-squared 0,075 0,058 0,056 N 201 201 201
*,** and *** represent 10%, 5% and 1% significance level 6. Conclusion, limitations and future research
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deals. This because of the fact that some databases with corporate governance data were not available. In the result section it is shown that most of the variables are insignificant and that the models have very low explanatory power. Therefore it is very hard to answer the research question if corporate governance mechanism positively affects long-term M&A returns during a merger wave. Only the corporate governance mechanism board independence has a positive statistical significant influence on the long-term returns. This means that there is evidence that board independence has a little effect on the buy-and-hold abnormal returns with a holding period of 250 days. But in general the research question cannot be answered with significant evidence.
The fact that almost all the variables are statistical insignificant can have several causes. Of course it is possible that corporate governance mechanisms do not explain a large part of M&A results. Other factors like method of payment and timing of the acquisition could have much more influence than corporate governance mechanisms. However in my study they did not had any impact on the results. Further the merger and acquisition process is very complex. With different negotiation rounds and so many stakeholders, the effect of corporate governance could be minimal.
Another explanation of the insignificant results could be the small sample: a sample of 201 deals is not that big. With a larger sample, the variables could be significant which will lead to a better explanatory model. I also did not take into account the minimum relative deal value. In large organizations an acquisition of less than 1% will not have an impact on the abnormal returns. If I did remove the relative small deals, my dataset would become even smaller than it is already.
Overall, I think my results could have some potential. With a larger sample my results could be significant. Only then I could provide significant evidence about the effect of a corporate governance mechanism on the long-term M&A returns. At the moment I cannot answer my hypothesis because of the statistical insignificance of almost all the variables and the very low explanatory power of the models (r-squared lower than 10%).
6.1 Future research
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Corporate governance mechanisms should align the interests of the managers and the stakeholders and thus should contribute to maximum shareholder value. However there are some signals that too much corporate governance does not benefit firms and their shareholders. For example after the Sarbanes-Oxley act, many firms went private because of the high costs. It is interesting to find out if the corporate governance level in some countries has exceeded its optimal state.
7. References
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27 Appendix A Table A.1 Correlation matrix 1 2 3 4 5 6 7 8 9 10 BHAR (250) 1 Board Independence 0,151 1 Cash only 0,047 0,223 1 CEO Duality -0,082 -0,402 -0,185 1
CEO Equity Based Compensation 0,080 0,111 0,061 0,073 1
Industry relatedness -0,066 0,047 0,045 -0,137 0,045 1
Leverage 0,108 0,220 0,257 -0,105 0,028 -0,040 1
Relative deal value -0,075 -0,148 -0,435 0,167 -0,142 -0,011 -0,196 1
Return on assets 0,215 0,121 0.206 -0,058 0,017 -0,004 0,202 -0,209 1
28 Appendix B Table B.1 Variables defined Variable Definition
BHAR(250) Post-merger buy-and-hold abnormal returns from the announcement date until 250 days after the announcement.
Board independence Dummy variable that equals one when more than 50% of the board consists of independent directors and zero when less than 50% of the board are independent directors.
Board independence (>60%)
Dummy variable that equals one when more than 60% of the board consists of independent directors and zero when less than 60% of the board are independent directors.
Cash Only Dummy variable that equals one when the merger or acquisition is entirely financed with cash.
CEO Duality Dummy variable that equals one when the CEO is also the chairman of the board of directors
CEO Equity Based Compensation
Equity Linked CEO Compensation as a proportion of Total CEO Compensation.
Early mover The first 20% of the acquirers in the merger wave. Dummy variable with a value of one if the acquirer was an early mover or a zero if this was not the case.
Industry relatedness Dummy variable with a value of 1 for a merger or acquisition in the same industry and 0 if the M&A was across industries.
Leverage Total value of debt divided by the total amount of assets. Relative deal size Value of the deal relative to the market capitalization at the
announcement date of the acquisition.
Return on Assets Net income divided by the total amount of assets.
