A comparative view on the effects of anti-takeover provisions on acquirer performance

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A comparative view on the effects of anti-takeover provisions on

acquirer performance

Master thesis

Finance and International Financial Management Faculty of Economics and Business, University of Groningen

Abstract

This thesis studies the effect of anti-takeover provisions (ATPs) on acquirer performance for different institutional contexts. Anti-takeover provisions are widely criticized for inhibiting the market for corporate control to fulfil its disciplinary action. They prevent this by entrenching management, thereby allowing for the possibility of managers to engage in value-destroying acquisitions. Previous research mainly focuses on the United States and generalizes the results for other countries. Following the framework set up by Masulis et al. (2007), I obtain data on mergers and acquisitions and on the amount of ATPs present in each acquiring firm. Then I use the observed cumulative abnormal return (CAR), generated around an acquisition announcement, to determine whether an acquisition is value destroying or not. This allows me to examine what the effect of ATPs, controlling for other return determinants, are on the CAR and to compare the effects between each institutional context. I find that the previous results, ATPs leading to value destroying acquisitions, may not be generalizable and that the institutional context seems to influence the relationship between ATPs and acquirer returns.

Author: Sikander Kraaijeveld Student number: s2154676

Program: MSc Finance & MSc International Financial Management Supervisor: dr. Y.R. Kruse

Date: January 12th ₂₀₁₇

Field Key Words: Corporate governance, anti-takeover provisions, market for corporate control, mergers and acquisitions

Word count: 12908

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1. Introduction

After the financial crisis there was a large drop in both the number, and especially the value, of mergers and acquisitions in North America, Western Europe and the world as a whole (M&A Statistics - Worldwide, Regions, Industries & Countries, 2016). The financial crisis is associated with many companies filing for bankruptcy, becoming nationalized, being in financial distress, or being placed under supervision (Acharya et al., 2009). Hence, there was not much appetite for mergers and acquisitions.

In recent years however, the M&A market has rebounded, potentially fuelled by historically low interest rates. In 2015, the total value of deals worldwide almost equalled that of its previous high in 2008 (M&A Statistics - Worldwide, Regions, Industries & Countries, 2016).

This surge, a possible new merger wave, after the pre financial crisis wave (Netter et al., 2011, p. 2333), once again raises the question whether these M&A’s lead to value to the acquirer. The value of M&A’s to acquirer performance, remains a heated debate amongst scholars, since on the one hand value is created, as the announcement-period share price increase is positive (Andrade et al., 2001), but on the other hand acquirers underperform in the long run (André et al., 2004). One system that should help to ensure the efficiency of these deals is the market for corporate control (Manne, 1965; Morck et al., 1989). Past research shows that acquirers that make value-decreasing acquisitions later tend to be the target of an acquisition (Mitchell and Lehn, 1990). Larger companies are more likely to be disciplined in this way, when making multiple ill-advised acquisitions, and their CEO’s are replaced quicker than those of smaller companies (Offenberg, 2009).

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managerial entrenchment (Bebchuk et al., 2009). Managerial entrenchment can lead to higher takeover premiums (DeAngelo and Rice, 1983), but it can also lead to lower firm valuation (Bebchuk et al., 2009). Furthermore, entrenchment increases the likelihood of firms engaging in value destroying acquisitions (Masulis et al., 2007). The primary focus of the discussion is on the United States (US) and the period before the financial crisis, 1990-2003 (Masulis et al., 2007; Humphery and Powell, 2008). The literature tends to generalize its findings to all countries with ATPs and does not consider the possible influence of the institutional context. Even though institutional contexts and corporate governance systems are converging internationally (Rubach and Sebora, 1998), they cannot be assumed to be the same across countries. Furthermore, the M&A market and institutional contexts changed since the crisis, for example with an increase in government intervention (Rao-Nicholson and Salaber, 2016).

The aim of this paper is to find answers to several questions

1. Do the findings of earlier papers, focused on the US, hold in different institutional contexts?

2. Do the findings of earlier papers, focusing on the US, hold after the crisis? 3. If they do not, what is the reason behind it?

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chosen because of their similarities in market and economic development, but dissimilar institutional contexts (Shleifer and Vishny, 1997).

Furthermore, the time horizon is changed compared to the papers by Masulis et al. (2007) and Humphery and Powell (2008), Masulis et al. (2007) examine the US take over market from 1990 to 2003. In order to find out whether financial crises and merger waves have an effect on the acquisitions made by acquirers with ATPs, this research focuses on the time horizon 2008-2013. This approach is relevant as it shows how firms behave after the financial crisis and the pre financial crisis merger wave, where it is argued that the negative effect found by Masulis et al. (2007) is not present during a merger wave (Obaydin, 2014). Another reason for choosing this time horizon is that through learning about corporate governance during the 2000’s, investors are better able to evaluate poorly governed firms, which affects the previously found results (Bebchuk et al., 2013).

This paper is beneficial to shareholders, as they find out in what countries their value is better protected from value-destroying acquisitions and in which countries this is poorly done. Additionally, this paper also gives shareholders insight into whether US companies with many ATPs still go unpunished when making value-destroying acquisitions.

The successful acquisitions studied in this paper are classified by whether they are value destroying or not. This classification is based on the cumulative abnormal return (CAR) generated by the announcement of each acquisition, where a negative CAR is value destroying. Subsequently, a comparison between the different countries is made.

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The remainder of this paper is organized as follows: the second section gives a review of the existing literature concerning ATPs and institutional contexts. The third section explains the development of my hypotheses. In the fourth section I describe the dataset that I hand collected and explain the methodology used in order to answer my hypotheses. The fifth section consists of the empirical results, robustness checks and a discussion of both. In the sixth section I conclude the paper and address its limitations.

2. Literature review

The separation of ownership and control led to the principal agent relationship, where the agent is expected to maximize the value of the principal. The idea that the agent maximizes the value of the principal has long been overturned, due to, among others, agency costs, which “are as real as any other costs” (Jensen and Meckling, 1976, p. 357). These costs arise due to the self-interest of people, first termed by Adam Smith in his book the wealth of nations from 1776. Corporate governance is a method used by the owners of the company to reduce agency cost by aligning the incentives of management with that of themselves, in order to assure that they earn a maximum return on their investment (Shleifer and Vishny, 1997; Becht et al., 2003). ATPs hinder the efficiency of corporate governance (Bebchuk et al., 2009), as they weaken the disciplining ability of the market for corporate control by reducing the probability of a hostile takeover being successful (Rowoldt and Starke, 2016). Thus, if ATPs hinder corporate governance and affect firm performance (Gompers et al., 2003; Bebchuk et al., 2009), why are they there?

2.1 Anti-takeover provisions

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why this wave led to more ATPs is that it created highly specialized companies and a competitive market for control (Shleifer and Vishny, 1991). This also sparked an increase in anti-takeover laws, lobbied for by managers as another way to prevent a takeover and to ensure their job stability (Shleifer and Vishny, 1991). ATPs are nowadays in place in companies all over the world, but remain a point of controversy (Straska and Waller, 2014)

2.2 Theory

ATPs are a point of controversy as they are argued to both benefit and harm the wealth of shareholders (Straska and Waller, 2014). DeAngelo and Rice (1983) discuss both outcomes using the managerial entrenchment and bargaining hypotheses. The managerial entrenchment hypothesis posits that ATPs serve to secure managerial jobs at the expense of shareholders and hence decrease shareholder wealth. The hypothesis is based on four assumptions: firstly, managers value control over assets and this desire pits their interests against those of the shareholders, creating agency costs. The second assumption is that one of the governance mechanisms that is mitigating the agency costs is the market for corporate control (Manne, 1965). The third assumption is related to the second as it assumes that the market for corporate control is a governance mechanism superior to other mechanisms, such as large shareholder monitoring, in disciplining some managerial inefficiencies. The fourth assumption is that the protection of management’s jobs by ATPs comes at a cost to shareholders.

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rights, such as a supermajority provision, this issue is solved (Stulz, 1988). The third argument states that ATPs, which grant the manager negotiating rights, increase the manager’s bargaining power, and thus a higher takeover premium is negotiated as the manager needs to be compensated for the lost utility when losing their job (Harris, 1990).

Furthermore, there is the long-term benefit hypothesis, where ATP’s benefit shareholders through helping to avoid managers making myopic decisions (Stein, 1988) and inducing them to agree to implicit long-term employment contracts containing deferred compensation (Knoeber, 1986).

2.3 Empirics

Numerous studies investigate the relationship between ATPs and shareholder wealth. Evidence supporting the bargaining hypothesis is found as ATPs don’t reduce takeover probability but increase the takeover premium (Comment and Schwert, 1995). Furthermore, poison pills are positively related to increases in takeover premiums and unrelated to the probability of takeover success (Heron and Lie, 2006) and an increase in the size of a golden parachute increases the takeover premium (Machlin et al., 1993). The long-term benefit hypothesis is partially supported for firms, with delay provisions, in concentrated industries with scarce targets (Kadyrzhanova and Rhodes-Kropf, 2011) and for firms with low shareholder concentration and low managerial control of voting rights (Straska and Waller, 2010).

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outperformed those with a high score. According to Bebchuk and Wang (2013) this relationship gradually diminished due to the “learning” effect; attention to governance by investors, the media and academics significantly increased in the 1990’s and remained constant in the 2000’s. They show, taking the learning effect into consideration, that the relationship becomes insignificant in the 2002-2008 period. What did persist is the negative association with firm value.

The governance index by Gompers et al. (2003) contains 24 provisions, where Bebchuk et al. (2009) identified six of these that are both negatively correlated with firm valuation and share performance. These ATPs are: staggered boards, poison pills, golden parachutes, limits to shareholder bylaw amendments and supermajority requirements for charter and merger amendments. ATPs however, aren’t the sole method for management to become entrenched. There is also the possibility to become indispensable as a manager, for example by investing in a type of business activity that only the specific manager knows how to work with (Shleifer and Vishny, 1989). This makes it harder to replace these managers and gives them an opportunity to require higher wages (Shleifer and Vishny, 1989).

2.4 Anti-takeover provisions in takeovers

ATPs do not merely lead to lower firm performance and a decrease in shareholder wealth, they also open the window for empire building through acquisitions (Masulis et al., 2007). Morck et al., (1990) find that shareholder value maximization is not always the motive behind an acquisition, as non-value maximizing managers buy pieces of a targeted company in order to enlarge their own business empires. The market for corporate control is supposed to be an efficient system in order to prevent these acquisitions, as bad buyers tend to become targets for acquisitions (Mitchell and Lehn, 1990).

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market for corporate control (Mitchell and Lehn, 1990). In their paper, Masulis et al. (2007) did not consider Bebchuk et al.’s (2013) “learning” explanation or the effect of a merger wave on acquirer returns on this relationship. These explanations are important as, for example, during a merger wave a manager may use ATPs to pursue non-value maximizing acquisitions (Obaydin, 2014). Both explanations could weaken the effect found by Masulis et al. (2007), as they affect the acquirer’s performance (Bebchuk et al., 2013) and the manager’s investment decisions (Obaydin, 2014). The argument that a merger wave affects the announcement returns is supported by Goel and Thakor (2010). They find that acquisitions, after the beginning of the wave, tend to have lower returns and are more likely to be initiated by self-interested managers. Additionally, acquisitions during merger waves are accompanied by weaker CEO turnover-performance sensitivity, poorer quality of analysts forecast and the acquisitions are usually initiated by firms with relatively weaker corporate governance (Duchin and Schmidt, 2013). This implies reduced monitoring and lower penalties for announcing inefficient acquisitions, subsequently possibly exacerbating the agency problem (Duchin and Schmidt, 2013)

There is recent evidence suggesting that the market for corporate control works efficiently when there are no ATPs in place. In Australia for example, it is prohibited to use ATPs, which leads to acquirers earning value for their shareholders and lower takeover premiums than in the US (Humphery-Jenner and Powell, 2011).

2.5 Institutional context

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shareholders. It is not clear as to how the UK would perform based on this, because they have both aspects of common and civil law (Cools, 2005).

On the other hand, it is argued that the distribution of powers influences how well a shareholder is protected (Cools, 2005). From this perspective the US performs worse than France and the rest of Continental Europe. This is because in the US the company board can run the company without much shareholder intervention; whilst in Continental Europe the law prevents the board from being able to ignore the shareholder’s views (Cools, 2005). The enforceability of the laws themselves also affects the shareholder protection in a given country (la Porta et al., 1998). The lower enforceability of norms in Continental Europe are argued to be a good reason to expand the power of market control mechanisms (Cuervo, 2002)

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Another institutional factor to consider is the government, which can indirectly, through legislation, and directly, through intervention get involved in a takeover (Dinc and Erel, 2013). The difference in legislation can, for example, be witnessed by the recent passing of a law in France that doubles the voting rights of shareholders after 2 years (Johnson, 2015). Direct government intervention is more focused on foreign, hostile, takeovers, suggesting that it is done out of a protectionist motive (Starke and Rowoldt, 2016), but not as a form of improving the inefficiencies in the market for corporate control.

3. Hypothesis development

The previous section gives a short review of the tremendous amount of research done and literature written on the effect of ATPs. One of the widely accepted views is that ATPs exacerbate the agent-principal conflict through entrenching management and preventing the disciplinary actions of the market for corporate control. Entrenched management is allowed to decrease shareholder value by pursuing value-destroying acquisitions (Masulis et al., 2007).

The US is the country most research focuses on, whilst the Continental European model is rather different compared to the Anglo-Saxon one. This leads to different institutional contexts and to different corporate governance systems. For example in Continental Europe, the shareholders have more power to intervene with the actions of the board (Cools, 2005). Hence I hypothesize the following:

H1: The effects of anti-takeover provisions are different when the institutional context is changed.

As the United Kingdom uses both types of law and is part of the Anglo-Saxon model, which relies more on the market mechanism, it can be deduced that they differ from Continental Europe. Therefore my second hypothesis is:

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Even though there is a surge in M&A activity in the last 2 years, there is not yet a merger wave. As Obaydin (2014) argues that the negative acquirer returns solely exist during a merger wave and Duchin and Schmidt (2013) argue that the agency problem may be exacerbated by a merger wave, it is unlikely that the returns are negative in the years after and during the crisis, because this period is not signified by a merger wave. Moreover, the impact of learning about corporate governance also influences this relationship positively, as investors are able to better evaluate poorly governed companies (Bebchuk et al., 2013). Hence, my third hypothesis is:

H3: Anti-takeover provisions during the period 2008-2013 do not negatively affect acquirer performance in the US.

4. Methodology and data

The aim of this paper is to determine whether ATP’s still negatively affect acquirer returns and if this varies with the institutional context. The hypotheses, formulated in order to establish this effect, are tested empirically by following the framework set up by Masulis et al. (2007). Multiple ATP indices are used to evaluate the relationship with acquirer returns. Furthermore, I take measures to control for the possible endogeneity problem and examine the impact of an international acquisition. In the estimated model the acquirer returns are regressed on firm-level ATP indices. The regression is performed using ordinary least squares with White’s heteroskedasticity consistent standard errors. White’s standard errors are used in order to improve the fitting of the model. The equation is as follows (1):

𝐶𝐴𝑅𝑖 = ∝𝑖 + 𝛽1,𝑖∗ 𝐴𝑇𝑃𝑖+ 𝛽2,𝑖∗ 𝐷𝑒𝑎𝑙𝑖+ 𝛽3,𝑖∗ 𝐵𝑖𝑑𝑑𝑒𝑟𝑖 + 𝜀𝑖 (1)

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differences between institutional contexts, I run the regression for each country separately.

4.1 Sample description

In order to start the compilation of the hand-selected dataset, information on mergers and acquisitions is extracted from the Zephyr database. Zephyr contains detailed information on M&A, IPO, private equity and venture capital deals and rumours. Zephyr is part of Bureau van Dijk Electronic Publishing, which collects M&A data and information about corporate ownership structures. Furthermore, Zephyr also provides detailed (financial) company information. In the articles that are based on the article by Gompers et al. (2003) the RiskMetrics database (formerly the IRRC database) is used in order to retrieve data on ATPs. This database however, only contains data on firms from the S&P 500. I therefore use Thomson Reuters Datastream to obtain information on ATPs from firms across the 4 countries and construct a new database. Reviewing each individual company year-by-year and noting whether there are ATPs in place or not is done to build this database.

The dataset is comprised of acquisitions that were announced between January 1st_{, 2008 and December 31}st_{, 2013 and also meet the following requirements:}

1. The acquisition is completed

2. The acquirer controls less than 50% of the target’s shares prior to the announcement and owns 100% of the target’s shares after the transaction 3. The deal value that is disclosed in Zephyr is equal to, or more than,

EUR 1 million

4. The acquirer has annual financial statement information and stock return data (210 trading days prior to the announcement) available from Thomson Reuters Datastream

5. The acquirer has information available on all the anti-takeover provisions investigated from Thomson Reuters Datastream

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673 acquisitions, made by 316 firms, that meet the requirements. In my sample the average firms makes about 2 acquisitions in the period between 2008-2013. In the dataset of Masulis et al. (2007), which contains 3,333 acquisitions during a period of 13 years, the average firm makes 3 acquisitions.

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4.2 Variables

In the following 3 subsections I present an overview of the formulation and measurement of the 3 categories of variables examined. Acquirer return is the dependent variable, ATP indices are the key explanatory variables and bidder- and deal specific characteristics are the control variables.

4.2.1 Acquirer return

Acquirer announcement effects are measured by the market model adjusted stock returns, generated around the announcement date of an acquisition. The information on announcement dates is obtained from the Zephyr database. During the window consisting of event days -2 and +2, where event day 0 is the announcement date of the acquisition, I compute the 5-day cumulative abnormal returns (CARs). I use a 5-day window instead of a 1 or 3-day window following Fuller et al. (2002), in order to deal with possible inaccuracies in the

Table 1: Sample description by announcement year and country

The sample consists of 673 completed acquisitions, listed in Zephyr, by US, UK, French and German firms with information on ATPs available from Thomson Reuters Datastream between 2008-2013. Variable

definitions can be found in the appendix. Year Number of

acquisitions Percentage of sample Mean acquirer market value of equity (B Euro) Mean deal value (B Euro) Mean relative deal size (Median) (Median) (Median)

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announcement dates reported by the Zephyr database (Bollaert and Delanghe, 2015). This method captures most, if not all, of the announcement effect without introducing considerable noise into my analysis (Fuller et al., 2002).

The abnormal returns are estimated through the use of the market model. The market model parameters are estimated over the 200-trading day period, from event day -210 to even day -11, and the acquirer’s home country stock market index is used as the market index.

𝐴𝑅_𝑖,𝑡 = 𝑅_𝑖,𝑡− 𝛼_𝑖 − 𝛽_𝑖𝑅_𝑀,𝑡 (2)

As shown in equation (2) the abnormal returns are calculated by deducting the expected stock return (𝛼_𝑖+ 𝛽_𝑖𝑅_𝑀,𝑡) from the acquirer’s stock return (𝑅_𝑖,𝑡), where 𝑅𝑀,𝑡 represents the acquirer’s domestic country index. The CAR is

calculated as follows:

𝐶𝐴𝑅_{𝑖,𝑗.𝑘} = ∑𝑘 𝐴

𝑡=𝑗 𝑅𝑖,𝑡 (3)

Where the individual abnormal returns, 𝐴𝑅_𝑖,𝑡, are summed from period t=j to t=k days to obtain the CAR.

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4.2.2 Anti-takeover provisions indices

I use the two corporate governance indices created by Bebchuk et al. (2009) (BCF) and Bebchuk and Cohen (2005) (BC) to test the effect of ATPs on acquirer returns. The BCF index consists of governance provisions that are significantly correlated with firm value and shareholder returns (Bebchuk et al., 2009). These provisions are: staggered boards, poison pills, golden parachutes, limits to

shareholder bylaw amendments and supermajority requirements for charter and merger amendments. Two of these provisions are not represented in the

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Table 2: Announcement abnormal returns and governance indices

The sample consists of 673 completed acquisitions, listed in Zephyr, by US, UK, French and German firms with information on ATPs available from Thomson Reuters Datastream between 2008-2013. Variable definitions can be found in the appendix.

Announcement abnormal returns

US UK France Germany All cash All stock Public Private Subsidiary

Car Mean 0.739 0.910 0.192 0.837 1.100 1.945 0.233 1.158 0.973

Median 0.249 0.620 0.205 0.800 0.503 -1.508 -0.383 0.626 0.566

Number of obs 428 159 59 27 549 9 151 522 158

Anti-takeover provision indices

US UK France Germany

BCF 2.771 2.025 2.339 2.296

Median 3 2 2 2

Correlation with CAR 0.023 0.083 0.134 0.549

Staggered board 0.450 0.409 0.915 0.333

Median 0 0 1 0

Correlation with CAR 0.074 0.057 -0.204 -0.130

Table 3: Differences in CARs

The sample consists of 673 completed acquisitions, listed in Zephyr, by US, UK, French and German firms with information on ATPs available from Thomson Reuters Datastream between 2008-2013. Variable definitions can be found in the appendix. ***, **, * stand for statistical significance at the 1%, 5% and 10% level, respectively.

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4.2.2.1. Univariate analysis

In table 3 the results of my univariate analysis are presented. On average, in all 4 countries, the dictator acquirers experience positive CARs around their acquisition announcements, whilst democracy acquirers experience lower CARs and in France and Germany they even experience negative CARs. This is inconsistent with the findings by Masulis et al. (2007), but does give an indication that supports my hypotheses. Tests for differences in means or medians indicate that the difference is statistically significant between the US BC-index dictator and democracy median. Furthermore, table 3 reports that there is a significant difference between the German BCF-index dictator and democracy mean returns. The correlation of the BCF index with the CARs, which for the US is presented in table 4 and for the UK, France and Germany in tables 11-13 in the appendix, is positive for all the 4 countries. These results are supportive of my hypotheses, however it is not possible to draw reliable inferences from this, as the correlation matrices and univariate analyses do not take into account the correlations between ATPs and other variables that determine acquirer returns. The differences between dictator and democracy portfolios may be due to different methods of payment used or the target status. Table 2 indicates that these can lead to varying abnormal returns.

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Table 4: Correlation matrix US

The sample consists of 428 completed acquisitions, listed in Zephyr, by US firms with information on ATPs available from Thomson Reuters Datastream between 2008-2013. Variable definitions can be found in the appendix.

CAR BCF BC Size Tobin’s

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4.2.3. Bidder characteristics

Consistent with Masulis et al. (2007) I control for the following bidder characteristics: firm size, Tobin’s q, leverage and free cash flow (FCF). To this list I add whether a firm has a blockholder, depicted by a single shareholder that owns at least 5 percent of the common shares outstanding. All of these variables are measured at the fiscal year-end prior to the acquisition announcement. Pre-announcement stock price run-up is also controlled for and is measured over the 200-trading day window from event day -210 to event day -11.

Moeller et al. (2004) find that the size of the acquirer is negatively correlated with the acquirer’s CAR, and this evidence is robust. The authors interpret the evidence to be supportive of the managerial hubris hypothesis (Roll, 1986), which posits that acquiring managers belief that they can manage the target firm more efficiently than the current management. In accordance with this hypothesis they find that on average larger acquirers pay higher premiums and that these acquisitions are likely to generate negative synergies. Another implication of firm size is that it can be seen as an alternative takeover defence, as it requires more resources to take over a larger firm. Hence, managers of large firms are expected to be more entrenched and more inclined to make an acquisition that is value reducing. In my dataset I define firm size as the log transformation of the acquirer’s total assets. This is done in order to increase comparability between different firms.

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Leverage is seen as a governance mechanism on its own. According to the monitoring hypothesis banks, who are the main providers of loans, are more efficient in monitoring borrowing firms than equity or public debt holders, as they have access to inside information (Fama, 1985). Hence, the hypothesis implies that private debt may be an efficient mechanism to reduce agency costs, consequently leading to better performance (Berlin and Loeys, 1988). However, high debt to equity ratios reduces the size of future FCFs due to interest payments and restricts the manager’s opportunities. It further provides an incentive for the manager to improve firm performance as, due to debt covenants, managers may lose control and could even be replaced by the creditors (Baird and Rasmussen, 2006). The paper by Garvey and Hanka (1999) underlines the importance of leverage as a control variable, as they argue that it is related to a firm’s takeover defence. I expect leverage to positively affect CAR. Leverage is defined as the firm’s book value of long-term and short-term debt divided by the market value of its total assets.

The free cash flow hypothesis of Jensen (1986) expects a negative effect of current FCF on CAR. This is because firms with higher cash flows have more resources available to the manager to engage in empire building. On the other hand, high FCFs can be a proxy for good managerial performance, which would mean that they also make good acquisition decisions. The effect of FCF on CAR therefore, could turn out to be either negative or positive. I define FCF as operating income before depreciation minus interest expense, minus income taxes and minus capital expenditures, which is then be divided by the book value of total assets.

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reduction in agency costs (Chang, 1998). In the US this may not be prevalent due to the block holders usually being modest in size and facing many institutional and legal barriers (Edmans, 2009). However, it is unlikely that this is also the case in the UK and especially not in the Continental European countries as they have stronger shareholder protection (La Porta et al., 1998; Cools, 2005). Therefore, I expect that the influence of a blockholder is ambiguous. Consistent with the paper by Becht and Röell (1999) a block holder is defined as a single shareholder that owns 5 percent or more of the common shares outstanding. In the dataset it is represented by a dummy variable, where 1 equals the existence of a block holder during the year of the acquisition announcement and 0 otherwise.

The last acquirer variable I control for is the stock price run up before the acquisition announcement. In their 2003 paper Gompers et al. report that acquirers with more ATPs in place realize a lower stock return performance and hence argue that stock price run-up has a negative impact on acquirer’s returns. The pre-announcement stock price run-up of the acquirer is measured by the acquirer’s buy-and-hold abnormal return over the 200-trading day window, from event day -210 until event day -11, where the country’s market index is used as the benchmark.

4.2.4. Deal characteristics

The deal characteristics that I control for consist of target ownership status, method of payment, relative deal size, whether it is a diversifying, serial or cross border acquisition and whether both the target and acquirer are from high tech industries.

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significantly positive returns when their targets are private or subsidiaries and significantly negative returns with public targets (Fuller et al., 2002). Moeller et al. (2004) support this: they find similar results and also report that acquiring subsidiary targets generates the highest returns. For Western European acquirers similar results are found (Doukas, Gonenc and Plantinga, 2014). Hence, I expect a negative influence of acquiring a public target and a positive effect when the target is private. To take these results into account I create three dummy variables: public, private and subsidiary to define targets in these categories.

How a transaction is financed also affects the stock market’s response to an acquisition announcement. Paying for a transaction with stock has been known to significantly negatively affect the acquirer’s abnormal returns, which is not experienced when the transaction is paid with cash (Amihud et al., 1990; Servaes, 1991). These findings are associated with the adverse selection problem in equity issuance as analysed by Myers and Majluf (1984). When the target is private and the transaction is financed with stock, the returns are less negative and can even be positive. This can be explained by the creation of new blockholders in the acquirer when concentrated held private target firms are acquired with stock (Chang, 1998; Fuller et al., 2002). The acquirer’s shareholder can therefore benefit from the increased monitoring of these new blockholders. Consistent with Masulis et al. (2007) I interact the 3 target status indicators with the 2 methods of payment and create the following six deal categories: private all

cash, private stock, public all cash, public stock, subsidiary all cash and subsidiary stock. To avoid multicollinearity with the intercept the subsidiary stock deal

variable is removed from the dataset.

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I also create a high tech dummy variable that equals one when both the acquirer and the target are firm in high tech industries and zero otherwise. I base my definition of high tech industries on that of Loughran and Ritter (2004). Consistent with Masulis et al. (2007) I interact it with deal size and expect the interaction term to have a negative effect on acquirer returns. I expect this as acquisitions of high tech targets may have some value destroying effects due to informational asymmetries and high premiums of the targets (Aybar and Ficici, 2009). Furthermore, integration of relatively comparable sized firms does not tend to go smoothly, as human capital, which is essential to these companies, is often lost during acquisitions (Masulis et al., 2007). Lastly, acquirers in these types of transactions also tend to underestimate the costs and overestimate the possible synergies generated when combining both firms (Meier et al., 2012).

There is extensive research done on the effect of diversification on firm value. Some managers make diversifying acquisitions in order to potentially benefit themselves, through decreasing the cash flow risk (John et al., 2008), but destroying shareholder value (Morck et al., 1990). Another tactic used by managers to strengthen their position is to buy unrelated assets that fit their strengths, which makes it costlier for shareholders to replace them (Shleifer and Vishny, 1989). In contrast, research done on the diversification discount shows that diversification does not have to lead to lower firm value and could be associated with possible higher firm values (Campa and Kedia, 2002). The influence on acquirer returns is therefore ambiguous. I classify an acquisition as diversifying if the target and the acquirer are not in the same Fama and French industry. I create a dummy variable of 1 for diversifying acquisitions and 0 for non-diversifying.

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The last deal characteristic I control for is whether the acquisition is cross border. There is a diversification benefit from operating in different countries, due to having cash flows currencies that are not perfectly correlated (Shapiro, 1978). However, there are also costs related to operating in a foreign country such as political risk (Mahajan, 1990), foreign exchange risk (Solnik, 1974) and asymmetric information (Reeb et al., 1998). Empirical results show that the effect of an international acquisition on acquirer returns depends on whether the firm is already operating in the foreign country. The firm experiences significant positive abnormal returns if it is not yet operating in the foreign country and negative insignificant returns are experienced when the firm is already operating in the foreign country (Doukas and Travlos, 1988). The impact on acquirer returns is thus ambiguous. I form a dummy variable with 1 for a cross border transaction and 0 otherwise.

4.2.5. Variable summary statistics

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variables on acquirer returns. For example, leverage and private target status are positively correlated to CAR. Overall the correlation between all the variables seems low, except for private and public targets, as they are mutually exclusive. Furthermore, the BCF index and BC index have a correlation of 0.695, hence I estimate two separate regressions in order to determine the influence of each index on CAR.

Table 5: Variable summary statistics

The sample consists of 673 completed acquisitions, listed in Zephyr, by US, UK, French and German firms with information on ATPs available from Thomson Reuters Datastream between 2008-2013. Variable

definitions can be found in the appendix. CAR and anti-takeover indices

US UK France Germany

Mean Median Mean Median Mean Median Mean Median CAR 0.739% 0.249% 0.910% 0.620% 0.192% 0.205% 0.837% 0.800% BCF index 2.771 3.000 2.025 2.000 2.339 2.000 2.296 2.000

BC index 0.451 0.000 0.409 0.000 0.915 1.000 0.333 0.000 Bidder characteristics

Firm size (B EUR) 23.332 6.775 32.946 2.667 86.165 17.906 314.244 50.593 Market value of

equity (B EUR) 23.611 8.605 10.482 4.274 16.149 8.542 35.501 35.204 Tobin’s q 2.062 1.721 1.883 1.933 1.527 1.307 1.632 1.394 Leverage 0.131 0.101 0.146 0.144 0.170 0.167 0.170 0.144 Free cash flow 0.059 0.081 0.083 0.089 0.045 0.045 0.055 0.058

Stock price run-up 0.058 0.007 0.080 0.077 0.085 0.022 0.062 -0.017 Blockholder 0.965 1.000 0.899 1.000 0.797 1.000 0.815 1.000 Deal characteristics Public target dummy 0.292 0.000 0.119 0.000 0.119 0.000 0.000 0.000 Private target dummy 0.708 1.000 0.881 1.000 0.881 1.000 1.000 1.000 Subsidiary target dummy 0.201 0.000 0.176 0.000 0.475 0.000 0.556 1.000 Cash deal dummy 0.984 1.000 0.981 1.000 0.475 0.000 0.444 0.000

Stock deal dummy 0.152 0.000 0.038 0.000 0.085 0.000 0.037 0.000 Diversifying acquisition dummy 0.119 0.000 0.157 0.000 0.136 0.000 0.037 0.000 Relative deal size 0.104 0.033 0.049 0.013 0.050 0.017 0.038 0.013 High tech dummy 0.386 0.000 0.145 0.000 0.220 0.000 0.296 0.000

Cross border

dummy 0.229 0.000 0.541 1.000 0.729 1.000 0.741 1.000 Serial acquisition

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5. Empirical results

5.1. Baseline analysis

In table 6 the estimates from the baseline regression of acquirer returns are shown. The dependent variable is the 5-day CAR generated around the acquisition announcement. As mentioned before the main explanatory variables are the BCF and BC indices. The standard errors are adjusted for heteroskedasticity by the use of White’s standard errors. The results show that the French BCF index has significantly positive effects on the CAR, which is evidence in support of my hypothesis that the institutional context affects the effect of ATPs on CAR. The sign and size of the coefficient estimates also give weak evidence in support of my other hypotheses: the effect of the UK is smaller, or even slightly negative, than in Continental Europe. There is also an indication that there is no negative effect of ATPs on the CAR of US firms, although these findings are not significant. Furthermore, there is a significant negative relationship between free cash flow and CAR, and between diversifying acquisitions and CAR in the US. There is a significant positive relationship between France CAR and both private stock deal and high tech x relative deal size. There is a significant negative relationship between CAR and both size and private cash deals. The BC index appears to have a negative, but insignificant, effect on CAR in both the UK and Germany. Many of the signs of the coefficient of control variables are as I expected, based on the literature. The adjusted 𝑅2_are

rather low, but this is, in the case of the US, consistent with the findings or Masulis et al. (2007).

5.2. Industry analysis

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the regressions again, but without the possible endogeneity associated with these variables. Following the paper by Gillan et al. (2003) I substitute the firm level Tobin’s q, leverage and FCF for the industry median. The industry median is calculated separately year-by-year for each country and for each of the Fama and French industries.

The estimates from the industry regression can be found in table 7. Besides from the French BCF coefficient being less significant there are no significant results. The coefficients of the Anglo Saxon countries are however negative in this regression, but insignificant. As the adjusted 𝑅2_{for the US is lower, which is the}

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Table 6: Baseline regression

The sample consists of 673 completed acquisitions, listed in Zephyr, by US, UK, French and German firms with information on ATPs available from Thomson Reuters Datastream between 2008-2013. Variable definitions can be found in the appendix. ***, **, * stand for

statistical significance at the 1%, 5% and 10% level, respectively. In parentheses are t-statistics based on White’s standard errors.

US UK France Germany (1) (2) (1) (2) (1) (2) (1) (2) Anti-takeover provision BCF index 0.000 0.000 0.032*** 0.042 (0.154) (-0.002) (3.238) (1.655) BC index 0.008 -0.004 0.001 -0.027 (1.174) (-0.510) (0.046) (-1.035) Bidder characteristics: Size -0.015 -0.013 0.012 0.011 -0.027** -0.017 -0.016 -0.003 (-1.453) (-1.455) (1.294) (1.263) (-2.543) (-1.336) (-1.495) (-0.238) Tobin’s q -0.005 -0.004 -0.010 -0.011 -0.006 -0.003 -0.022 -0.047 (-1.256) (-1.158) (-0.914) (-0.993) (-0.414) (-0.203) (-0.738) (-1.268) Free cash flow -0.064*** -0.065*** 0.300 0.301 -0.126 -0.024 0.103 0.183

(-4.355) (-4.445) (1.256) (1.222) (-0.898) (-0.150) (0.383) (0.628)

Leverage 0.034 0.035 0.008 0.011 -0.048 -0.032 -0.044 -0.119

(0.761) (0.801) (0.303) (0.412) (-0.619) (-0.395) (-0.405) (-0.982) Stock price run up -0.013 -0.012 -0.013 -0.011 0.051** 0.041 -0.014 -0.054

(-1.512) (-1.467) (-0.726) (-0.655) (2.319) (1.470) (-0.340) (-1.419) Blockholder -0.014 -0.015 0.007 0.007 -0.023 -0.010 -0.048** -0.030

(-1.030) (-1.045) (0.481) (0.477) (-1.081) (-0.690) (-2.403) (-1.009) Deal characteristics:

Relative deal size -0.009 -0.010 -0.001 0.001 -0.032 0.029 0.237 0.529** (-0.684) (-0.732) (-0.025) (0.030) (-0.406) (0.286) (1.371) (2.372) High tech 0.012 0.012 -0.006 -0.005 -0.022 -0.019 0.028 0.029

(1.587) (1.465) (-0.388) (-0.308) (-0.992) (-0.696) (0.711) (0.712) High tech x relative deal

size -0.033 -0.035 0.082 0.071 0.319** 0.124 -0.362 0.842

(-0.999) (-1.075) (0.913) (0.834) (2.210) (0.592) (-0.170) (0.498) Diversifying acquisition -0.018* -0.018*** 0.014 0.015 0.015 0.002 0.082 0.132

(-1.867) (-1.851) (0.889) (0.934) (0.749) (0.108) (1.102) (1.476) Public target x stock deal -0.014 -0.014 -0.018 -0.017 -0.003 0.002

(-0.858) (-0.865) (-0.556) (-0.520) (-0.097) (0.058) Public target x cash deal 0.012 0.010 0.020 0.019 0.013 0.023

(0.479) (0.419) (0.429) (0.416) (0.544) (1.129)

Private target x stock deal -0.008 -0.010 0.001 0.001 0.046** 0.044** -0.015 0.026 (-0.401) (-0.475) (0.017) (0.033) (2.109) (2.148) (-0.366) (0.702) Private target x cash deal 0.009 0.007 -0.004 -0.005 -0.028** -0.022 -0.006 -0.011

(0.353) (0.266) (-0.119) (-0.143) (-2.293) (-1.583) (-0.288) (-0.395) Subsidiary target x cash

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Table 7: Industry regression

US UK France Germany (1) (2) (1) (2) (1) (2) (1) (2) Anti-takeover provision BCF index -0.001 -0.001 0.021** 0.033 (-0.348) (-0.176) (2.058) (1.417) BC index 0.004 -0.011 -0.011 -0.024 (0.522) (-1.250) (-0.391) (-1.067)

Firm & industry characteristics:

Size -0.022* -0.020** 0.003 0.002 -0.021** -0.013 -0.018 -0.002 (-1.982) (-1.973) (0.471) (0.322) (-2.253) (-1.366) (-1.519) (-0.137) Industry Tobin’s q -0.013 -0.012 -0.011 -0.012 -0.013 -0.013 -0.035 -0.054*

(-0.987) (-0.914) (-0.983) (-1.026) (-0.812) (-0.704) (-1.696) (-1.789) Industry free cash flow -0.044 -0.044 0.241* 0.257* 0.093 0.158 0.234 0.310

(-0.232) (-0.228) (1.852) (1.880) (0.383) (0.621) (1.159) (1.424) Industry leverage 0.050 0.051 0.118 0.120 -0.065 -0.056 -0.159 -0.239* (0.712) (0.720) (1.216) (1.215) (-0.707) (-0.660) (-1.669) (-2.092) Deal characteristics:

Relative deal size -0.009 -0.009 0.012 0.015 -0.057 -0.003 0.065 0.323** (-0.615) (-0.616) (0.249) (0.327) (-0.906) (-0.043) (0.417) (2.716) High tech 0.013* 0.013 0.019 0.021 -0.017 -0.019 0.018 0.026

(1.698) (1.662) (1.508) (1.459) (-0.881) (-1.009) (0.818) (1.027) High tech x relative deal size -0.057** -0.060** -0.247 -0.270 0.317** 0.178 -0.057 1.293

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5.3. Sensitivity analyses

To see whether the previous findings are robust to deviations I perform multiple sensitivity analyses.

5.3.1. Dummy variable approach

Up to this point the BCF and BC indices are treated as continuous variables. To check the robustness of the model I take another approach and treat them as being in the dictator portfolio or not. I define two dummy variables: dictator BCF and dictator BC. The dummy variable is equal to 1 if the firm has above median BCF or BC index values and 0 otherwise. This approach confirms the significant relationship between ATPs and CAR in France. The signs of the coefficients are also as I hypothesized.

5.3.2. Different announcement windows

Up to this point I maintain a constant value for CAR, based on the sum of the 5-day (+2,-2) abnormal return, to test the relations. As a robustness check, I also calculate the acquirer’s CAR over a 3-day (+1, -1) and 7-day (+3,-3) announcement period. I estimate the firm level regressions again and in tables 9 and 14, in the appendix, the results are reported of these regressions, respectively. In the 3-day window the significance of the ATPs relationship with CAR in France falls, but this may be related to the lower adjusted 𝑅2_reported.

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Table 8: Dummy regression

US UK France Germany Anti-takeover provision Dictator BCF index 0.003 0.001 0.032** 0.037 (0.422) (0.149) (2.036) (1.230) Bidder characteristics: Size -0.015 0.012 -0.021* -0.015 (-1.446) (1.284) (-1.901) (-1.237) Tobin’s q -0.005 -0.010 -0.003 -0.035 (-1.240) (-0.930) (-0.217) (-1.001)

Free cash flow -0.065 0.302 -0.101 0.247

(-4.357) (1.238) (-0.623) (0.772)

Leverage 0.035 0.009 -0.037 -0.088

(0.780) (0.344) (-0.486) (-0.707)

Stock price run up -0.013 -0.013 0.055** -0.018

(-1.527) (-0.727) (2.195) (-0.437)

Blockholder -0.015 0.006 -0.020 -0.046*

(-1.040) (0.479) (-0.991) (-2.097)

Deal characteristics:

Relative deal size -0.009 -0.001 -0.030 0.305**

(-0.706) (-0.022) (-0.341) (2.255)

High tech 0.012 -0.006 -0.030 0.031

(1.611) (-0.403) (-1.161) (0.738)

High tech x relative deal size -0.033 0.085 0.281* -0.219

(-1.018) (1.020) (1.689) (-0.101)

Diversifying acquisition -0.018* 0.014 0.012 0.096

(-1.820) (0.884) (0.592) (1.195)

Public target x stock deal -0.014 -0.018 -0.012

(-0.850) (-0.546) (-0.388)

Public target x cash deal 0.011 0.020 0.020

(0.441) (0.427) (0.859)

Private target x stock deal -0.009 0.001 0.038* -0.011

(-0.421) (0.014) (1.711) (-0.278)

Private target x cash deal 0.008 -0.004 -0.025* -0.003

(0.320) (-0.124) (-1.911) (-0.130)

Subsidiary target x cash deal 0.000 -0.009 0.005 -0.023

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Table 9: 3 day regression

US UK France Germany (1) (2) (1) (2) (1) (2) (1) (2) Anti-takeover provision BCF index 0.002 0.003 0.014* 0.047 (1.060) (0.417) (1.839) (1.697) BC index 0.004 -0.006 -0.008 -0.029 (0.863) (-0.830) (-0.318) (-0.933) Bidder characteristics: Size -0.008* -0.008 -0.010 -0.012 -0.008 -0.003 -0.014 0.000 (-1.678) (-1.618) (-1.213) (-1.471) (-1.141) (-0.417) (-1.147) (0.003) Tobin’s q -0.001 -0.001 -0.014* -0.016* 0.001 0.003 -0.023 -0.051 (-0.338) (-0.210) (-1.752) (-1.893) (0.046) (0.265) (-0.697) (-1.196) Free cash flow -0.091*** -0.091*** 0.342** 0.334** -0.052 -0.014 0.222 0.310

(-6.040) (-6.058) (2.330) (2.370) (-0.389) (-0.095) (0.754) (0.928)

Leverage 0.022 0.023 0.059 0.060 0.000 0.008 0.015 -0.070

(0.677) (0.711) (0.795) (0.812) (0.007) (0.141) (0.130) (-0.512) Stock price run up -0.007 -0.007 -0.007 -0.004 0.042** 0.035* 0.017 -0.027

(-1.011) (-0.979) (-0.261) (-0.131) (2.153) (1.815) (0.354) (-0.612) Blockholder -0.006 -0.006 -0.016 -0.016 -0.013 -0.007 -0.045* -0.026

(-0.436) (-0.446) (-1.522) (-1.534) (-0.726) (-0.489) (-2.035) (-0.785) Deal characteristics:

Relative deal size -0.004 -0.004 0.026 0.029* -0.117** -0.087* 0.066 0.395 (-0.341) (-0.372) (1.377) (1.658) (-2.515) (-1.713) (0.368) (1.503)

High tech 0.005 0.004 0.003 0.008 -0.020 -0.017 0.052 0.053

(1.060) (0.855) (0.167) (0.415) (-1.178) (-0.997) (1.131) (1.093) High tech x relative deal size -0.028 -0.027 -0.016 -0.052 0.357*** 0.245 -1.053 0.279

(-0.915) (-0.887) (-0.105) (-0.312) (3.026) (1.512) (-0.468) (0.144) Diversifying acquisition -0.009 -0.010 0.005 0.007 -0.001 -0.007 0.084 0.140

(-1.294) (-1.324) (0.513) (0.670) (-0.045) (-0.476) (1.072) (1.397) Public target x stock deal -0.010 -0.011 0.035 0.038 0.003 0.005

(-0.696) (-0.701) (0.622) (0.693) (0.157) (0.243) Public target x cash deal 0.025 0.025 -0.032* -0.032* 0.021 0.025

(0.973) (0.989) (-1.760) (-1.811) (1.215) (1.515)

Private target x stock deal 0.003 0.003 -0.004 -0.004 0.045** 0.044** 0.033 0.079* (0.188) (0.192) (-0.354) (-0.314) (2.260) (2.226) (0.725) (1.918) Private target x cash deal 0.027 0.028 -0.013 -0.014 -0.015 -0.011 0.000 -0.006

(1.102) (1.118) (-1.055) (-1.126) (-1.489) (-1.196) (0.005) (-0.204) Subsidiary target x cash deal -0.001 -0.001 0.004 0.007 0.007 0.009 -0.017 -0.018

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5.3.3. Cross border

Even though I control for whether an acquisition is cross border through a dummy variable in the baseline regression I re-run the regressions with acquisitions that are either cross or non-cross border. The estimates for the cross border regressions reported in table 10 show that for France there is a significant positive effect between the BCF index and CARs at the 1% level. The results also indicate that there is a positive effect in Germany. For the BC index the relationship is also positive but insignificant. In contrast the sign for the coefficient of both the BCF and BC index for the US and UK indicate that there may be a negative relationship with CAR. In the appendix I included table 15, which reports the results for the non-cross border acquisitions. This table only includes the US and UK, as the size of the sample of France and Germany was insufficient. These estimates are in contrast with the results from the cross border regression, as there is weak evidence that the CARs are positively affected by the BCF index for both countries. There is a weakly significant effect of the BC index on US CARs.

5.3.4. Financial crisis

As the financial crisis can also be expected to have an effect on CARs and the investment decisions of managers, I split up the sample of each country into two periods. These periods are 2008-2010 and 2011-2013, where the earlier period is assumed to be the period with the most severe impact of the financial crisis. Due to space constraints and the results not shedding new light on a different perspective, the results are not reported in this paper.

5.3.5. Outliers

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Table 10: Cross border regression

US UK France Germany (1) (2) (1) (2) (1) (2) (1) (2) Anti-takeover provision BCF index -0.004 -0.004 0.041*** 0.012 (-1.001) (-0.673) (3.774) (1.859) BC index -0.009 -0.009 0.018 0.013 (-0.850) (-0.920) (0.420) (1.184 Bidder characteristics: Size -0.001 -0.002 0.020 0.019 -0.041** -0.018 -0.014** -0.014** (-0.172) (-0.260) (1.314) (1.297) (-2.113) (-0.792) (-3.028) (-2.829) Tobin’s q 0.001 0.000 -0.009 -0.009 -0.016 -0.016 0.028* 0.036* (0.118) (-0.009) (-0.744) (-0.728) (-0.972) (-0.813) (2.171) (2.127) Free cash flow -0.052 -0.049 0.296 0.312 0.026 0.235 -0.283*** -0.335**

(-0.792) (-0.738) (1.170) (1.183) (0.107) (0.803) (-4.814) (-3.561) Leverage 0.006 0.004 0.000 0.015 -0.092 -0.016 0.112* 0.114*

(0.120) (0.072) (0.005) (0.403) (-0.838) (-0.113) (2.367) (2.175) Stock price run up -0.049* -0.049* -0.021 -0.021 0.050 0.060 -0.077*** -0.083***

(-1.813) (-1.817) (-1.357) (-1.400) (1.515) (1.324) (-8.641) (-8.009) Blockholder 0.036** 0.036* 0.016 0.014 0.006 -0.004 -0.024* -0.027*

(2.049) (1.948) (1.049) (0.981) (0.132) (-0.089) (-2.336) (-1.942) Deal characteristics:

Relative deal size -0.029 -0.024 0.012 0.015 -0.066 -0.007 0.384*** 0.455*** (-1.470) (-1.296) (0.423) (0.533) (-0.769) (-0.063) (8.830) (7.469) High tech 0.027** 0.028** -0.005 -0.008 -0.027 -0.042 -0.045* -0.056**

(2.141) (2.270) (-0.358) (-0.546) (-0.962) (-1.149) (-2.364) (-3.365) High tech x relative deal size -0.001 0.006 0.416* 0.409 0.271 0.159 1.081 1.220

(-0.007) (0.032) (1.785) (1.545) (1.572) (0.565) (0.867) (1.060) Diversifying acquisition -0.047*** -0.047*** 0.051 0.054 0.006 -0.018 -0.030 -0.043

(-2.888) (-2.971) (1.304) (1.416) (0.382) (-0.696) (-1.065) (-1.415) Public target x stock deal 0.070 0.069 -0.043 -0.046

(1.126) (1.160) (-1.154) (-1.174)

Public target x cash deal -0.004 -0.009 0.033 0.025 0.022 0.031 (-0.153) (-0.353) (0.518) (0.416) (0.685) (1.178)

Private target x stock deal -0.026 -0.027 -0.045* -0.039 0.081** 0.076 0.041*** 0.044*** (-1.028) (-1.055) (-1.847) (-1.536) (2.335) (1.460) (7.450) (4.584) Private target x cash deal 0.020 0.014 -0.008 -0.017 -0.021 -0.011 -0.013 -0.016

(0.876) (0.681) (-0.321) (-0.739) (-1.497) (-0.531) (-1.537) (-1.992) Subsidiary target x cash deal 0.005 0.004 -0.013 -0.013 0.018 0.016 -0.007 -0.007

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6. Conclusion and limitations

ATPs are seen as a governance mechanism that can be used by management to entrench themselves and allow for the possibility of value-destroying acquisitions without the market for corporate control being able to fulfil its disciplinary role. This paper presents evidence that this view of ATPs may not be justified.

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My findings lead me to formulate a strategy for future research. In trying to establish a stronger relationship between CARs and ATP indices the learning effect and the effect of a merger wave should be controlled for. The learning effect could be operationalized as the amount of attention paid to corporate governance in academic articles and the media. Additionally the importance of corporate governance reports and databases such as the Asset4 by Thomson Reuters should be taken into consideration. A suitable operationalization of a merger wave could be the amount of takeovers and takeover attempt in a year and compare the yearly differences to establish when a merger wave is occurring and when it is not. Controlling for these factors hopefully gives both practitioners and academics an improved understanding of the influence of ATPs on firm value and performance. It would also be interesting to analyse countries, where not much attention is paid to corporate governance and investigate whether the relationship established by Masulis et al. (2007) holds. Lastly, as my results indicate that the differences between the Anglo Saxon and Continental European cause the difference in the effects of ATPs on CAR, it would be an idea to research what aspect of these models causes this. An example of this could be the role of banks, which is larger in the Continental European model.

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A comparative view on the effects of anti-takeover provisions on acquirer performance