Legal Enforcement On Cross Border Mergers And Acquisitions Finance Essay - Free Essay Example

Sample details Pages: 29 Words: 8622 Downloads: 2 Date added: 2017/06/26 Category Finance Essay Type Argumentative essay Did you like this example? It is the conventional fact that there are wide discrepancies among corporate governance regimes around the globe (La Porta et al., 2000). According to the authors, such differences are mainly expressed in terms of legal protections of outside investors, namely shareholders and creditor. Many literatures have looked at agency cost contracting hypothesis that when a firm with lower shareholder protection are motivated to takeover a firm from the country with better corporate governance controls so that to be able to adapt to the better investors protection regimes (Kuipers et al., 2009). Don’t waste time! Our writers will create an original "Legal Enforcement On Cross Border Mergers And Acquisitions Finance Essay" essay for you Create order Kuipers et al. (2009) found support for agency cost contrasting hypothesis from evidence of higher returns for foreign targets (worse corporate governance regimes) and low and insignificant abnormal returns for U.S. targets (better corporate governance regimes) and also find acquirers abnormal returns are stronger wither higher shareholder protections. On the other hand, firms with better legal protection of investor rights are motivated to takeover firms with poorer protections. This is known as the positive spillover hypothesis (Martynova and Renneboog, 2008). For example, Wang and Xie (2009) found that total synergy of combined firms will increase when acquirers with higher shareholder protections are taking over targets with poorer shareholder protections. Similar results are confirmed by Bris et al. (2008) that market assigns more values to the acquires who are willing to adopt to better shareholder protection by cross-border MAs. Hence, the main contribution of this research paper is that it will mimic the research of Kuipers et al. (2009) but try to test the reverse sample setting of U.S. acquirers (better corporate governance) and foreign (poorer corporate governance). Not only will the short window study sample period be pushed forward to provide more recent evidence on positive spillover hypothesis, but also to study the long-horizon acquirers performance in order to provide evidence on persistent effect of corporate governance and legal environment on post-MA returns for acquirers. One of the main limitations of this research paper is that there is little representation of firms from emerging markets. As agency costs are more severe in emerging market countries (Demirgà ¼Ãƒ §-Kunt and Maksimovic, 1998), the misrepresentation will lead to biased and inconsistent estimates of regression models. Moreover, we have encounter difficulties when we are trying to gather data for block holder ownership of foreign target. Lins (2003) reveals, cash flow r ights and non-management block holder share ownership are key determinants of firm values. Omitting the key variable of determining the stock returns to both target and acquirers will cast some doubt on our later empirical findings. Indeed, the key assumption of this research procedure is derived from the last limitation that the main determinant of MAs returns to acquirers and targets are corporate governance regimes and legal environments. Moreover, we assume during the 15 years of study, sample firms are free from any impacts of economic cycle. It is an essential assumption because we are not controlling for seasonality during our sampling procedure and for the regression analysis. Although it might provide us with biased and inefficient estimates of the effect of corporate governance and legal environment on abnormal returns of targets and acquirers, the regression results are still arguably robust after including control variables and correcting for heteroscedasticity and Mu lticollinearity. The paper is organised as follows: Chapter 2 summarises the results and conclusions of previous studies on the subject; chapter 3 describes the development of 2 relevant hypotheses for regression analysis; chapter 4 provides detailed descriptions of data and sample selection; chapter 5 sets out both methodology for event studies and regression analysis; chapter 6 presents the results of event studies of U.S. acquirers and foreign targets; chapter 7 presents multivariate regression analysis results of MAs abnormal returns; chapter 8 discusses a range of limitation of this research; chapter 8 concludes the study and provides recommendations for future research. Chapter 2: Literature Review This thesis studies the impact of the legal environment and shareholder, creditor protection for target countries on the returns to foreign target shareholders, US acquirer shareholders. During the 11 years period from 1999 to 2011, there has been a surge of takeover activities with foreign acquirers and U.S. targets. For example, Thomson One Banker reports that total 394843 cases of cross-border MAs worth in total of $31.53 trillion for this 11 years period. After reviewing 22 existing literatures  [1]  from 1974 to 1998, Agrawal and Jaffe (2000) found that while target-firm shareholders in general benefit from positive short-term returns, investors in bidding firms normally experience share price underperformance in the months following acquisition, with negligible overall wealth gains for portfolio holders. In light of the increasing puzzling phenomenon, existing literatures in the field of finance have tried to explain the causes of variation in cross-border acquis itions abnormal returns. One of the traditional explanations is payment method hypothesis. For example, Goergen and Renneboog (2004) tests European domestic and cross-border MA deals, and concludes that means of payment in an offer has an impact on the share price. Moreover, the resource diversity hypothesis examined by Anand et al. (2005), which suggest that targets with access to heterogeneous resource environments provide the acquirer with greater potential for capability enhancement and financial performance.   Furthermore, neoclassical synergy hypothesis suggests that expectation and realisation of synergy gains are important determinant of merger activities and performance. Eun et al. (1996) find support for neoclassical synergy hypothesis using a sample of foreign acquisitions of U.S. firms during the period 1979-1990. In addition, Seth et al., (2000), however, also find that hubris hypothesis is coexisting with the neoclassical synergy hypothesis. They proposed tha t managerial hubris hypothesis is a plausible explanation for these cross-border MA. The hypothesis suggests that managers overestimated targets values and result in paying excess premium for the deals believing their valuations are correct (Roll, 1986). As a result, synergy will be insignificant and even be negative for target and portfolio of targets and acquires. Furthermore, MAs are motivated by firms intention to displace inefficient managements (Roll, 1987). However, Roll argued that firms can displace inefficient managements in a cheaper way, such as through proxy fights. Moreover, Jensen (1987) proposed the free cash flow hypothesis that firms with free cash flows need to either payout the cash as dividends or commit to future capital expenditures. Therefore, Jensen argued that MAs are the easiest way for firms to spend the free cash flows and grow expectedly. Last but not least, there is multinational network hypothesis proposed by Doukas and Travlos (1988), whom states tha t the ability of the firm to take better advantage of the benefits inherent in a multinational network by expanding through foreign direct investment activities. There are plenty of literatures to explain the difference between U.S. acquirer and Foreign targets in terms of investor protection and legal environment We are particularly interested in the valuation impact of legal environment of U.S. acquirers and foreign targets. Legal environments around the globe seem to have great diversity. La Porta et al. (1997) introduced the concept that differences in external corporate governance mechanisms which relate to investor protection against expropriation by insiders, along with the rigor of legal enforcement standards within countries, affects the nature and effectiveness of capital markets around the globe. Moreover, La Porta et al. (1998) identifies 4 tranches of legal origins in the world, namely: common-law, French-civil-law, German-civil-law and Scandinavian-civil-law. The authors show that common-law provides the strongest coverage of investor protections, while French-civil-law has the weakest; and German- and Scandinavian-civil-law locate in the middle. Besides, in a later study, La Porta et al. (2002) argue that the legal approach is a more fruitful way to understand corporate governance and its reform than the conventional distinction between bank-centered and market-centered financial systems. Despite there are handful of corporate scandals in the wake of calling for better corporate governance in the U.S. in early 2000s, its corporate governance structure is still one of the bests in the world. The central of corporate governance is agency problems, in which the most serious issue is expropriation by insiders (Shleifer and Vishny, 1997). Continental Europe and East Asian countries have relatively weaker investor protections due to the fact that block holders are prevalent and expropriation by insider is a severe threat (La Porta et al., 200 2). In other words, the authors conclude that better protection of minority shareholders and in firms with higher cash-flow ownership by the controlling shareholder lead to higher firm valuations. However, Rajan and Zingales (2003) criticised the method of LLSV (1997,1998) using time-invariant factors to explain financial development over time and proposed an interest group theory. That is the state of development of the financial sectors does not change monotonically and cross-countries difference in financial development also change considerably over time. Therefore, these patterns are not fully consistent with structural theories that attribute cross-countries difference in financial development to time-invariant factors, such as a countrys legal origin or culture. Using corporate governance differences to explain MA activities has been centre of many empirical studies. Previous studies focus on country-level corporate governance aspects. For example, Rossi and Volpin (2007 ) find that targets in cross-border MA deals are more frequently from countries with weaker investor protection than their acquirers country, suggesting a convergence in governance standards. Starks and Wei (2004) and Bris and Cabolis (2008) find a higher takeover premium when investor protection in the acquirers country is stronger than in the targets country. Following this path of research of corporate governance and legal environment from LLSV and various researchers, Kuipers et al., (2009) confirms agency costs contracting hypothesis that higher abnormal returns of foreign firms to acquirer U.S. corporations are explained by the adoption of better corporate governance regimes and legal environment. Therefore, it confirms and extends the findings in LLSV (2000, 2002) that shareholder protections in global capital markets are influential in cross-border tender offer MA deals. Similarly, Goergen and Renneboog (2008) introduced the idea that contractual devices, such as cross-bo rder mergers and acquisitions, allow companies to opt into better corporate governance structure hence achieve higher firm value. Gilson (2001) added that the convergence of corporate governance will take the contractual form due to inflexibility of response of international governance institutions to changed business circumstances. Alternatively, Martynova and Renneboog (2008) named the motivation of global corporate governance convergence as bootstrapping hypothesis. Furthermore, one of the questions is waiting to be answered is whether shareholder protections and legal enforcement would still be influential if the cross-border deal settings are reversed with U.S. acquirers and foreign targets, effectively to hold acquirers legal environment constant. Wang and Xie (2009) found that acquisitions of firms with poor corporate governance by firms with better corporate governance generate higher total gains. The authors argued that MA leads to change in control of combined companies and when bidding shareholders rights are greater than targets. The resulting change in controls will lead to improve of corporate governance at the targets and therefore higher synergy post-acquisition. Conversely, there is shareholder-expropriation  hypothesis (Goergen and Renneboog, 2008), which states that better investor protected firms takeover less investor protected firms are motivated by the incentives to opt to less shareholder and creditor protections. Such hypothesis suggests that managers of acquirers have different incentives to their shareholders or creditors, so that shareholder and creditor protections are weaker so that it is consistent with free cash flow hypothesis proposed by Jensen (1987). While plenty of MA performance studies have been conducted for the short event windows (including Kuipers et al., (2009), Bris and Cabolis (2008), Wang and Xie (2009)), Cartwright and Schoenberg (2006) identified mixed conclusions in the field of study of long-run p erformance of acquisitions. For example, Black et al. (2001) find significant negative returns to U.S. acquirers during the 3 and 5 years following cross-border mergers. Gugler et al. (2003) found cross-border acquisitions result in a significant decrease in the market value of the acquiring firm over the five post-acquisition years. Furthermore, Andrà © et al. (2004) found cross-border MAs result in a significant negative abnormal returns for Canadian bidders over 3 years period. However, Agrawal and Jaffe (2000) reviewed 22 literatures studied long horizon performance of post-merger, and found that these papers report uniformly that tender offers generates non-negative abnormal returns while mergers have more negative abnormal returns. Therefore, the research hypothesis of this paper is motivated by the lack of evaluation of long run performance of tender offers of foreign acquirers of U.S. targets and shareholder, creditor protection and legal enforcement mechanism. Such a st udy is valuable because it will provide insights into sustainable value created by security holder and stakeholder protection mechanisms in a reversed context of Kuipers et al. (2009)s studies. Chapter 3: Hypothesis development Hypothesis 1: Positive spillover hypothesis Kuipers et al., (2009) developed supports for their hypothesis of shareholder, creditor protection and legal enforcement standards differences are key determinants of value and volume of foreign acquirer with U.S. targets from period 1982 to 1991. It is inquisitive that whether the reverse data setting will also provide evidence to support this hypothesis. In other words, the settings of foreign targets of US acquirers should also display abnormal returns of target, acquirer and portfolio as a function of such shareholder and creditor protection mechanisms. If it is true then the hypothesis is in support of Kuipers et al., (2009) and verifies that the relationships are robust with reverse condition. Moreover, long-run performance of MAs should be evaluated to test for the persistence of the investor protection and legal enforcement effects. Martynova and Renneboog (2008) demonstrates positive spillover hypothesis of MAs, which state s that in a MA deal, when acquirers have better corporate governance will spillover their corporate governance regulations to targets and total synergies are partly contributed by the difference in corporate governance. Rossi and Volpin (2004) also found support in the case of studying determinants of cross-border MA volumes and conclude that   targets are from countries with poorer investor protection (shareholder and creditor) than their acquirers countries, suggesting that cross-border MAs play a governance role by improving the degree of investor protection within target firms. This is typically seen as a reverse phenomenon of Kuipers et al. (2009)s studies, which they conclude that companies with poorer shareholder protections will take over better protected firms so that lead to higher abnormal returns. Martynova and Renneboog (2008) named it bootstrapping hypothesis, that bidders with poorer corporate governance will choose to improve their shareholder and creditor protections by taking over targets with better corporate governance regulations. Accepting the positive spillover hypothesis in the setting of U.S. acquirers and foreign targets would be consistent as the contractual convergence hypothesis of Kuipers et al. (2009) and Goergen and Renneboog (2008). Therefore, the empirical analysis finds support for positive spillover hypothesis if there is a positive relationship between investor protections and target abnormal returns and negative relationship between investor protection and acquirer abnormal returns. Hypothesis 2: Free cash flow hypothesis Roll (1987) establish a free cash flow theory for mergers and acquisitions. Free cash flow is cash flow in excess of that required to fund all of a firms projects that have positive net present values when discounted at the relevant costs of capital. Such free cash flow must be paid out to shareholders in order to maximize value for shareholders (Roll, 1987). However, distributing the c ash to shareholders reduces the resources controlled by managers. Thus, this can reduce the managers power and potentially subject them to the monitoring by capital markets that occurs when firms must obtain new capital. Therefore, naturally, managers have incentives to expand their firms beyond the size that maximizes shareholder wealth. Growth entrenches managers by acquiring the resources under their control. Moreover, changes in management compensation are often positively related to growth. Empirical support for free cash flow hypothesis can be found in Lang et al. (1991). They find that bidder returns are significantly negatively related to cash flows for bidders with low Tobin q but not for high q bidders. Chapter 4: Data and sample selection Deal data we are intended to collect for the study of abnormal returns of acquisitions as a function of shareholder/creditor protection and legal environment is provided by Thomson One Banker. For U.S. acquirer and foreign targets, we require stock return data on DataStream International  [2]  . For both target and acquirer, we require no missing stock returns during the announcement period as well as long-horizon post-announcement period. Specifically, for the study of reverse effect of Kuipers et al. (2009) hypothesis will need Thompson One Banker deal sample collection from 01/01/1993 to 31/12/2008. The time period is chosen in order to capture the fifth merger wave starting from 1996 to 1998 and the sixth merger wave starting from 2002 to 2008 (Sudarsanam, 2010). The acquirer countries are set to global except U.S. and target country has been restricted to the U.S. Following conditions need to be confined for the data selection. The offers need to be completed with in the time periods specified from 1993 to 2008; Takeovers have to be successful, using Kuipers et al. (2009)s definition we define successful takeover as acquirer gaining 50% or more control of the voting shares of the target firm for the first time; Both bidding and target firms are publically trading; The value of the deal is more than $1 million; All deals have to be tender offers  [3]  ; Exclude industry of Finance (6 digit-Cusip code start with 6) and Utility (6 digit-Cusip code start with 49) as MAs of these industries subject to various regulations and government controls. All deal payment methods have to be equity  [4]  . We then match total of 214 transactions to corresponding stock price time-series data in DataStream International as follows: Individual stock price for acquirers and targets covering the window (-260 days, 10 days) which is used to carry out short-run studies; Individual stock price for acquirers covering the window (-26 0 days, 60 months) which is used to carry out long-run studies; The goal of gathering sufficient sample data would be compromised as there are many newly combined firms are not publically traded. Moreover, the 214 sample size further reduced to 145 bidder firms and 151 target firms after getting rid of wrong DataStream code and no estimation period stock return data. Table 1 below shows the sample collection by target nations. The sample covers 22 countries, totalling 151 target companies: Table 1. Sample collections for foreign targets. Target Nations Total Target numbers Target Nations United Kingdom 54 Canada Sweden 12 Australia Netherlands 5 Switzerland Denmark 4 Taiwan France 3 New Zealand Israel 2 Belgium Finland 1 Greece Chile 1 Hong Kong South Africa 1 Japan Germany 1 Thailand Croatia 1 India South Korea 1 Total Finally, the important part of data collection for cross-sectional regression is referred to La Porta et al. (1998), which argued that the allocation of countries to the legal families strongly influences the design of shareholder protection. A word of caution should be noticed that data collected in the studies for La Porta et al. (1998) is rather outdated and it has become one of the major drawbacks of this work. However, recent papers (e.g., Kuipers et al. (2009)) are still adopting such data set due to the thoroughness of the investigation La Porta et al. (1998) has created. Our sample countries can be grouped into two legal families, namely common law and civil law. La Porta et al. (1998) collected data from 49 countries around the globe and there are 18 countries belong to common law family, while the other 28 countries belong to civil law families  [5]  . La Porta et al. (1997) concludes that countries with poorer investor protection (depending on the legal family) ha ve smaller and narrower capital markets. Table 2 allocates our sample of 151 target firms into the four legal families: Table 2. Sample collections for different Legal Origins. English Common Law Total numbers French Civil Law Total numbers United Kingdom 54 France Canada 35 Netherlands Australia 14 Chile 1 Israel 2 Belgium 2 New Zealand 2 Greece 1 Thailand 1 Total 12 Hong Kong 1 South Africa 1 German Civil Law Total numbers India 1 Switzerland 4 Total 111 Germany 1 Scandinavian Civil Law Total numbers Taiwan 3 Sweden 12 South Korea 1 Denmark 4 Japan 1 Finland 1 Croatia 1 Total 17 Total 11 The detail of data composition is summarised as following: Shareholder protection: Anti-director rights (Investor protection index) which is the sum of the following 5 dummy variables; measures how strongly the law favours shareholders in the voting processes. More specifically, the index is formed by adding 1 when: (1) the country allows shareholders to mail their votes; (2) shareholders are not required to deposit their shares prior to their annual meeting; (3) cumulative voting is allowed; (4) opposed minority mechanism is in place; (5) there is equal or less than 10% of share capital that entitles a shareholders to call for an Emergency Shareholders Meeting. The index ranges from 0 to 5. Proxy by mail measures weather shareholders can vote with ease so that to enhance the protection of their rights. The dummy variable equals to 1 if the regulation exists, 0 otherwise. Shares blocked before meeting identifies which countries companies have required shareholders to lock their share before annual meeting, which is unfavourable to shareholders. We measure t he rule using dummy variable, equals to 1 if such regulation exists in the law, 0 otherwise. Cumulative voting, allow shareholders to exercise their voting rights in collective fashion, so that to give them more power over the board of directors. We measure the rule using dummy variable, equals to 1 if such regulation exists in the law, 0 otherwise. Opposed minority mechanism allows shareholders to reject the major changes to a company, therefore enhances their rights. We measure the rule using dummy variable, equals to 1 if such regulation exists in the law, 0 otherwise. % of share capital to call an Extraordinary Shareholders meeting, the higher the percentage the hard the shareholders to organise a meeting to challenge the management, so that the weaker the shareholder protection. We measure the rule using dummy variable, equals to 1 if such regulation exists in the law, 0 otherwise. Creditor protection: The proxy for creditor protection, which is the sum of the following 4 dummy variables; measures how strongly the law favours creditors. The index is form by adding 1 when: (1) there are restrictions for reorganisation; (2) there is no automatic stay on secured assets; (3) secured creditors get fist paid in events of reorganisation; (4) finally, management will be removed in reorganisations. Restrictions for reorganisation: equal 1 if the reorganization procedure needs creditors consent, 0 otherwise. Automatic stay on secured assets which protects managers as when the company defaults, secured creditor will not be able to possess the remaining assets. We measure the rule using dummy variable, equals to 1 if it allows for auto stay on in the law, 0 otherwise. Secured creditor first paid protects creditors in event of reorganisation, and it is rare that they are not assured the right to collateral during reorganisation. We measure the rule using dummy variable, equals to 1 i f secured creditors are first paid, 0 otherwise. Management stays on after reorganisation will weaken the creditor protection, therefore, the dummy variable equals to 1 if managers are required to stay on and 0 otherwise. Rule of Law measures the relatively narrow difference of legal rules among sample countries. More specifically, two types of law La Porta et al. (1998) have sampled are company laws and reorganisation laws. However, there have been obvious omissions of MAs regulations and rules mainly because these rules come from many sources and can be located within company laws, anti-trust laws and other financial and market regulations. Similarly, by adopting La Porta et al. (1998) s sample set, we are discarding rules imposed by security exchanges, regulations of banking and financial institutions. The proxy variable for rule of law is constructed according to La Porta et al. (1998) using the rule of law index consists of dummy variables for degree of law and order scal e from 0 to 10, with lower values for less tradition for law and order. Judicial Efficiency is an assessment of the efficiency and the integrity of the legal environment. The variable is scaled from 0 to 10, with lower scored means least efficient. Following La Porta et al. (1998), this index is typically interpreted as the investors assessment of general business conditions in a country. Interaction terms are shareholder protection*rule of law and creditor protection*rule of law, which are constructed according to Kuipers et al. (2009). Shareholder protection*rule of law measures the rights that minority shareholders effectively hold after controlling for their enforcement by the legal system. And creditor protection*rule of law: measure the rights that creditors effectively hold in a bankruptcy procedure. We interpret this variable as a proxy for the ease of access to credit markets which is same as Rossi and Volpin (2004). Control variables for control effect regressions are constructed using DataStream International. The list below shows the variables to be constructed, they are mainly set up according to Kuipers et al. (2009) while some others are composed with regard to other literatures on MAs stock returns: Deal-related: MultiBids: Bradley et al. (1988) showed that   competition among bidding firms increases the returns to targets and decreases the returns to acquirers. The dummy variable equals to 1 if more than one bid is made for the target, 0 otherwise. Opposed: Huang and Walkling (1987) provided evidence that abnormal returns earned by target firms at the time of deal announcements are related to degree of resistance. Ryngaert (1988) found that if target firm management is entrenched, opposed offers can lower target shareholder wealth. To categorise an offer as opposed by targets management if DataStream International classifies the deal as hostile takeover. Opposed is a dummy variable equal to one if target management actively opposes the offer. RelSize: Asquith et al. (1983) measures relative size of target to bidder using ratio of target to acquirer total assets. It is well established that larger the target relative to bidder, higher the target returns (Kuipers et al., 2009). At a basic level, the larger the target relative to the bidder, the greater the effect of the acquisition on the bidder, and the more likely a greater market reaction. The variable is the total asset ratio between target and bidder. Firm-specific variables to capture market imperfection and asymmetries. FXIntang: Swenson (1993) found that part of the high premium paid by bidder in foreign takeovers are attributed to higher level of foreign targets intangible assets relative to total assets. Thus, we calculate the variable as proportion of target firm total asset booked as intangible FXqHi: Tobins Q is a proxy variable for managerial talent and ability (Kuipers et al., 2009). On the other hand, researchers (e.g., Lang et al. (1989) and Lang et al. (1991)) have developed a measure of free cash flow using Tobins Q. To include this proxy variable is initiated by Lang et al. (1989) that lower q target shareholders will benefit more than higher q target shareholders. The dummy va riable equal to 1 if targets q is higher than one, 0 otherwise. USqHi: As per Lang et al. (1989) have predicted that shareholders of higher q bidders gain significantly more than the shareholders of lower q bidders. We are intended to use the proxy variable to capture the managerial effect for gains of U.S. bidders. The dummy variable equal to 1 if acquirers q is higher than one, 0 otherwise. Related: Wayne et al. (1993) found evidence that bidders pay slightly higher premium for targets whose operations are closely related to their own. Dummy variable equal to 1 if both target and acquirer are in the same 2-digit SIC classification, 0 otherwise. Chapter 5: Methodology To estimate short window abnormal returns, we employ a standard event study methodology using market model. We use single index market model method due to we can obtain more accurate estimation of normal return of stocks when they have previous trading before the announcement date of MAs. The alternative is the simpler and more straightforward method of market-adjust model. Such method is a crude calculation of abnormal returns as it assumes stocks has no estimation period normal returns (Binder, 1998). Moreover, market model approach is better as they are not limited to sample of acquirers that have only U.S. based American depositary receipt (Servaes and Zenner, 1994), and Eun et al. (1996) has to resort to the naive method of mean-adjusted returns methodology (Kuipers et al., 2009). Estimation period is set to a period of 200 days begins 260 days before and 61 days prior to the announcement day (AD) of the offer. It is worth to note that the date can be different from deal to deal therefore it is not calendar date. 200 days is long enough to provide a reasonable estimate of the normal return, and going back 61 days is sufficient to ensure that the announcement of takeover bid does correlated with estimation of normal return. Then we estimate each stocks abnormal return using market-models estimated standard error during the event period, and cumulative average abnormal returns (CAARs) are found for a range of event window lengths. We are following Kuipers et al. (2009)s methodology of constructing target, acquirer, market-model and abnormal returns. For U.S. acquirers and foreign targets, market model and abnormal returns are constructed using country-specific, equal-weighted market indices provided by DataStream International. At last, Bradley et al. (1988) sets out the way to construct the daily abnormal returns, cumulative abnormal returns and the standardised cumulative returns for portfolio comprises of target and acquirer. Naturally, time -series nature of stock price for individual firm would allow us to use continuous compounded returns as a more appropriate valuation method. One of the benefits of continuous compounded return is that it is more likely to fulfil the strict assumptions of standard parametric statistical tests than discrete return. Formula we are going to use to calculate continuous compounded return is: 5.1 Calculating normal return for estimation period Market-model, a statistical model, is used to estimate normal return, of estimation period. It is popular and also sufficient to be used for short window study: From which, . And is the abnormal returns or prediction error, which needs to be estimated. is a random variable that, by construction, must have an expected value of zero, and is assumed to be uncorrelated with , not autocorrelated and homoskedastic. Calculating cumulative average abnormal returns (CAARs) By controlling for the normal relationship between the stocks return and the market return, resulting in abnormal returns conditional on contemporaneous market returns with a lowest standard error  [6]  : Average Abnormal Returns, AARs is the sample mean: Where t is defined in trading days relative to the event date (e.g. t= -60 means 60 trading days before the event date). Over an interval of two or more trading days beginning with day T1 and ending with T2 within the event period, the cumulative average abnormal returns (CAART1,T2) is the result of the sum of the abnormal returns over the test period (TP): ; In order to infer whether the abnormal return is statistically significant or not, student-t-test would be conducted with hypothesis: Cross-sectional students t-test procedures are adopted from Brown and Warner (1985), and Standard deviation of average abnormal returns (AARt) is: Where there are n different stocks have time series data across different event periods. Cross-section test statistic (under the assumption of cross sectional independence) is: The test statistic is the ratio of the specific event date CAAR to its estimated standard deviation; the standard deviation is estimated from the time-series of AARs. Where T1 and T2 are time period when the event windows start and stop respectively. The statistics distribution follows a students t distribution with n-1 degree of freedom. The null hypothesis states that the difference between the expected return given the deal has been announced, yi, and the expected return in the absence of the event equals to zero. In other words, null hypothesis is there is no evidence of expected abnormal returns for the test period. By rejecting the Null Hypothesis, we can conclude that there is sufficient evidence that to support the existence of abnormal returns for acquire, target or the portfolio. We can set significant level at which one is willing to reject the null hypothesis (also known as the probability of making a Type I error). A typical study (e.g,, Kuipers et al. (2009)) would set significant level at a low value (1% or 5%). It will force us to find strong evidence to reject null hypothesis and therefore lead to less chance of making Type I error. This is desirable, as we know the probability of wrongly rejecting the hull hypothesis, but will have more uncertainty when we fail to reject the null hypothesis. As the availability of Eventus, which abstract data from Compustat/CRSP (U.S.) database, it makes analysis of U.S. Acquirers CARs a lot easier. I have obtained CUSIP code, which is an id entifier of securities assigned to all securities, and used it as ID to abstract CARs and parametric test statistics from Eventus. On the other hand, it is more difficult and cumbersome to carry out event studies for Foreign Targets due to Eventus does not contain non-U.S. security data. In order to carry out the short window event studies, Microsoft Excel is employed to carry out the regression of market model to estimate each individual foreign target. Then using the actual stock continuous returns and equivalent country specific market continuous returns to compute the abnormal returns (ARs) across a range of short run event dates. After calculating all ARs for all 151 foreign target firms, average abnormal returns (AARs) of each event date can be obtained by taking average of the 151 ARs on that specific event date. Finally, cumulative average abnormal returns (CAARs) for specified range of short event windows can be obtained by adding AARs that fall into the range. Long-run acquisition abnormal returns for U.S. acquirers We are constructing the long-horizon event study to test for the market efficiency and persistence of reaction effects. More importantly it is to provide abnormal returns for later regression analysis regarding the relations between long-run abnormal returns and investor protection as well as legal environment. Long-horizon event study is recommended to use a more sophisticated asset-pricing-model. Therefore, we are adopting Andrà © et al. (2004)s methodology of using Fama-French three factor model (FFTFM) (Fama and French, 1993) to estimate the calendar-time portfolio abnormal returns. Furthermore, with respect to the original preferences of Fama and French (1993) , we are adopting CARs as an estimation of abnormal returns rather than average-buy-and-hold abnormal returns. For each calendar month, we calculate the return on a portfolio composed of firms that undertake mergers and acquisitions within the preceding k years (k = 1 to 5) of the calendar month (t =1 to T). We th en estimate the regression using the calendar-time return on this portfolio: The dependent variable of the regression is the monthly excess return of the portfolios (Rp.t Rf.t), which corresponds for a given month, t, to the returns of the portfolio of MAs (Rp.t) less the risk-free rate (the monthly rate of 91-day U.S. Government Treasury bills, Rf,). The independent variables are the excess market return and two zero-abnormal return investment portfolios SMB (size) and HML (book-to-market ratio) which are constructed to mimic systematic risks. We have constructed SMB and HML consistent with Fama and French (1993). ÃÆ'Ã… ½Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢p, Sp, hp stand for the factor loadings of the portfolio on each risk factor: the market, SMB and HML. The parameter ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±p in the regression indicates the monthly average abnormal return of our MA sample. A positive ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±p indicates after controlling for market return, size and book-to-market effects, the firm earn s positive abnormal returns. Fama (1998) notes that if the model only partially gives explanation for the expected returns of the MA portfolios, then the value of ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±p will combine the abnormal return due to the event with the unexplained part of the return due to the misspecification of the model. 5.2 Multivariate Analysis Multivariate regressions are estimated to test the relationship between cumulative abnormal returns (independent variable Yi.t) and investor protection and legal environment (dependent variables Xi.t). The simple form of the cross-section multiplicative regression is: Where, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± is the intercept term and is the residual. The dummy variables of investor protection and degree of legal enforcement are adopted from La Porta et al. (1998), and it has been specified in data and sample selection of this study. Multiplicative cross-section regression model has been employed due to there are many dummy variables are used for shareholder and creditor protections as well as for legal enforcement. For example, Brambor et al. (2006) specified the simple form of multiplicative interaction cross-section regression can take the following form: Where X and Z are two qualitative variables can take bivariate values or continuous values. In the model, gives the one unit change i n X on the expected value of Y only when Z=0. In other words, by combining two models into one multiplicative interaction model, we have to interpret the relationship between dependent variable and one of independent variables conditioned by the other interacted independent variables. In this specific model above, measures the effect of unit change in interaction of X and Z on the expected value of Y. Conditional interpretation is paramount if the interaction model becomes more and more complex. Moreover, random errors are treated using Newy-Wests heteroskedasticity consistent efficient covariance, in each case to control for heteroskedasticity effect in panel data. Moreover, multicollinearity is checked by inspecting correlation matrix. 5.2.1 Control effects: Referring to Kuipers et al. (2009), for both the short-run reverse effect study and long-run study, firm-specific and deal-related dependent variables (included in Xi.t in the regression) are included. Therefore, we can examine the robustness of the findings regarding the determinants of target and acquirer abnormal returns in cross-border tender offers as a function of investor protection and legal environments, after controlling for possible endogenous variables. All the control variables have been specified in the data and sample selection part of this paper. And again, the control effects regressions random errors are treated using Newy-Wests heteroskedasticity consistent efficient covariance, in each case to control for heteroskedasticity effect in panel data. Moreover, multicollinearity is checked by inspecting correlation matrix. 5.2.2 Model Building Issues: The goal of multivariate regression study is use the simplest model to provide efficient and unbiased estimations of the complex relationships (Brooks, 2002). In order to test whether the regression model behaves as specified, we are imposing scrutiny checks to ensure the OLS assumptions are fully complied. The OLS assumptions are: The random errors have zero mean: So that the measure of mean of Y is only conditional on X variants. In fact, when there is a constant term included in the regression, this assumption will never be violated (Brooks, 2002). Variance of random errors is constant: This assumption is known as homoscedasticity, that is essential because it ensures each observation is equally reliable, so that the estimates of the regression coefficients are efficient and test for hypothesis concerning the coefficients are unbiased (P.Newbold et al., 2009). Otherwise, if the variance of error terms is not constant, we called it heteroskedasticity, and can be detected us ing White or Breusch-Pagan- Godfrey tests. Random errors are treated using Newy-Wests heteroskedasticity consistent efficient covariance, in each case to control for heteroskedasticity effect in panel data. The random errors are linearly independent of on another: this assumption ensures there are no autocorrelation between the error terms. It is essential because it ensures that mean value of Y depends only on X but not on the error terms. Moreover, it also ensures that the estimates of the regression coefficients are efficient and that tests concerning thesis coefficients are valid (P.Newbold et al., 2009). We can detect violation of this assumption using autocorrelation test of Durbin-Watson, if the test statistic is close to 2, then we will have zero correlation between error terms. No relationship between the random error and corresponding X variables: violation of this assumption will have non-stochastic X variables within the models. This is not desirable because infere nces carried out conditionally on the observed value of an X repressor will be invalid. However, if error terms have zero mean, the OLS estimator is still unbiased (Brooks, 2002). No Multicollinearity: It means there is no correlation between explanatory variables. So that they are orthogonal to one another and adding or removing variables will not cause the values of the coefficients on that other variables to change (Brooks, 2002) We will check for Multicollinearity between all of the control variables by constructing the correlation matrix by the help of Eviews 6. Removing any one of the two variables which have correlations higher than 0.5000 will leave us orthogonal variables in the regression model. Chapter 6: Abnormal returns post MA 6.1 Short-horizon Cumulative Abnormal Returns (CARs) for U.S. acquirers Table 3. U.S. acquirers cumulative abnormal returns (CARs). N=149 CAAR (-10,-1) -0.41% (-5,+5) 0.01% (-1,0) 0.08% (+1,+10) 0.29% The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. To estimate the abnormal returns, we followed the standard event study procedure using Eventus. First, Market Model is used, as it mentioned before in methodology, to estimate the expected (normal) returns for all securities across the estimation period of (-260, -60) and set minimum estimation period to be 3 days. We then estimate the abnormal returns (ARs) using OLS regression estimations, such that squared standard errors (SSEs) are minimised to present an accurate evaluation. Finally, calculate the Average Abnormal Returns (AARs) for each event window for all sample companies. Therefore, it is palpable to see that Cumulative Average Abnormal Return (CAAR) for each short-horizon event window is the cumulative sum of all AARs within that window. Table 3 re ports the cumulative abnormal returns (CARs) obtained from Eventus for U.S. acquirers for the complete sample over various event windows. To see whether the results indicate any statistical significance, we refer to the parametric test statistics. All cross-section t-test statistics are insignificant indicating CAARs of U.S. acquirers are equal to zero statistically. In terms of economic significance, CAARs for all announcement windows are also insignificant. However, a common trend for all companies is that stock CAARs become less negative as event windows distributed closer to announcement dates and become more negative as event windows cover boarder period relative to announcement dates. However, the overall CARs level is mild and close to zero. The result is consistent with Martynova and Renneboog (2008), that the authors found no significant relation between bidders corporate governance quality and acquirer CARs, therefore supports the positive spillover hypothesis. Table 4. Short-horizon U.S. acquirer cumulative average abnormal returns (CAARs) sort into four legal origins. English Common Law N=105 French Civil Law N=12 German Civil Law N=9 Scandinavian Civil Law N=19 2-tailed CAAR t CAAR t CAAR t CAAR (-10,-1) -0.16% -0.193 -0.73% -0.159 0.55% 0.382 -1.35% (-5,+5) 0.00% -0.001 -0.46% -0.217 2.92% 0.984 0.56% (-1,0) -0.20% -0.372 3.04% 1.694$ -0.45% -0.336 0.22% (+1,+10) 0.55% 0.800 2.07% 0.648 -0.19% -0.094 -0.58% The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. In table 4, the data from table 3 has been decomposed into four categories of legal origins around the globe. According to La Porta et al. (1998)s methodology, we have sort target countries into the four legal families, namely: English Common Law, French Civil Law, German Civil Law and Scandinavian Civil Law. It is apparent from this table that across the board insignificant t-test statistics apart from announcement window (-1,0) for French (1.694, significant at 10%) with CAAR equals 3.04%, which is economically significant. Despite positive CAARs for French during announcement window (+1,+10) has economically significant value of 2.07% , it is not statistically significant with t-test equals 0.648. Similar results can be found for Germans CAAR of 2.92% during event window (-5,+5) and Scandinavians CAAR of -1.35% for event window (-10,-1). However, small sample sizes for French (12), German (9) and Scandinavian (19) legal origins may explain the insignificant cross-sectional t-tests. We knew from La Porta et al. (1998) that English Common Law environment most excellently covers investor protection, while French Civil Law system performs worse with German and Scandinavian Civil Law locate in the middle. Further analysis from table 4 has confirmed this finding using CAARs of U.S. acquirers. This is demonstrated that that English common law origin MA deals pose the least variations of CAARs among the 4 legal families, while French poses the most variations, with German and Scandinavian locate in the middle. Fur ther to this point, U.S. acquirers with better shareholder and creditor protections takeover firms with worse legal protection of insider expropriation and experience negative or close to zero CAARs during the short-horizon announcement windows. Therefore, the results from table 4 are consistent with empirical results from Moeller and Schlingemann (2005), in which the authors also tested the short horizon CAARs of U.S. acquirers and foreign targets for sample period of 1985 to 1995. They found that bidder returns are positively associated to legal systems with better shareholder rights. This appears to provide a soft support for the positive spillover hypothesis proposed by Martynova and Renneboog (2008), such that bidders corporate governance quality does not lead to higher acquirers CAARs. 6.2 Short-horizon Cumulative Average Abnormal Returns (CAARs) for foreign targets Table 5. Foreign Targets cumulative average abnormal returns (CAARs). N=135 CAAR (-10,-1) 2.93% (-5,+5) 11.15% (-1,0) 5.04% (+1,+10) 4.37% The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. t (HC) is Heteroskedasticity consistent students t test. Table 5 shows the foreign target CAARs and cross-sectional t-tests obtained from Microsoft Excel using the Eventus methodology for U.S. acquirers. The results show drastic difference compared to U.S. acquirers CAARs, as foreign targets experience both statistically and economically significant CAARs. All 4 event windows display CAARs significant at 0.1% with the most economically significant CAARs of 11.15% occurring in event window (-5, +5). It is also worth to note that 2-day event window of (-1, 0), in which foreign targets experienced 5.04% CAAR with cross-sectional t-statistics equals 7.3612 (significant at 0.1%). The results show in table 5 are similar to the empirical results provided by Martyno va and Renneboog (2008) that the authors have 296 cross-border target samples and report CAAR of 15.61% with t-statistics equals 16.15 and also significant at 0.1%. This is no coincidence, as foreign targets have lower quality of corporate governance, hence, positive spillover of corporate governance will lead to higher synergy for targets shareholders post-MAs. Table 6. Short-horizon foreign targets cumulative average abnormal returns (CAARs) sort into four legal origins. English Common Law N=111 French Civil Law N=12 German Civil Law N=11 Scandinavian Civil Law N=17 2-tailed CAAR t CAAR t CAAR t CAAR (-10,-1) 3.09% 5.0726*** 2.28% 1.6217 1.94% 1.8095$ 2.94% (-5,+5) 10.81% 10.2499*** 9.97% 3.7207** 10.82% 2.5967* 14.49% (-1,0) 4.73% 7.9402*** 4.45% 2.5983* 3.87% 1.1682 8.19% (+1,+10) 4.36% 5.7351*** 4.80% 2.631* 5.83% 2.3226* 3.18% The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. t (HC) is Heteroskedasticity consistent students t test. Table 6 illustrates the foreign targets CAARs decomposed into the 4 legal origins specified in La Porta et al. (1998). Targets from all legal origins yield positive and statistically significant CAARs. Also, it is apparent from the table that targets from English and Scandinavian legal origins earns higher announcement returns than target companies from French and German legal origins. This finding is consistent with prior empirical study. For example, Martynov a and Renneboog (2008) found that companies of English or Scandinavian legal origin yield the highest CAARs, which are almost 2.5 times higher than the CAARs of target companies of French or German legal origin. However, the comparison between different legal origins may be biased due to limited sample sizes for French, German and Scandinavian legal origins during the 15 years period from 1993 to 2008. Moreover, the results are also complement to the findings of Bris and Cabolis (2008) that target shareholders earn significantly higher abnormal returns (5.52%) than opposite case (-13.41%) and conclude that higher legal protection of legal law of target firms will lead to higher target shareholders abnormal returns. 6.3 Long-horizon Cumulative Average Abnormal Returns (CAARs) for U.S. acquirers There are considerable amount of empirical studies of cross-border MAs are focusing on short-horizon performance of acquirers. One question needs to be answered, however, is whether U.S. acquirers, which have the best corporate governance regimes (Kuipers et al., 2009), will earn significant abnormal returns in the long-horizon. We know that there are mixed views of long-horizon performance of acquirers, due to different determinants are used to explain the abnormal returns of acquirers (Cartwright and Schoenberg, 2006). However, there are still some consistent views in the long run performance of acquirers. For example, Agrawal and Jaffe (2000) review 22 empirical studies and show that through tender offers, in a long run, acquirers earn non-negative abnormal returns. Table 7 Long-horizon. U.S. target, foreign acquirer abnormal returns (ARs) from FF3FM. Holding period N=156 Intercept t (HC) B(p) t (HC) S(p) t (HC) H(p) (+60 days, +3 Years) 0.0024 0.93 1.1245 21.56*** 0.2501 2.19* -0.0902 (+60 days, +4 Years) 0.0012 0.47 1.1614 22.44*** 0.3348 2.97** -0.0463 (+60 days, +5 Years) 0.0012 0.48 1.1834 23.10*** 0.3318 3.00** 0.0033 The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. t (HC) is Heteroskedasticity consistent students t test. Table 7 shows the regression results of Fama-French-3-factor-model (FF3FM) regression of the portfolio of U.S. acquirers. Intercept is the result for abnormal returns; B(p) is the factor loading of the portfolio for the market risk; S(p) is the factor loading of the portfolio for the risk of size effect, approximated by SMB and lastly, H(p) is the factor loading of the portfolio for the risk of financial distress, approximated by HML. For the sample size of 156, it is apparent from the table that abnormal re turns across three time periods are both economically and statistically insignificant after controlling for market risks, size effect and financial distress risks. Among these three factors, the market risk seems to explain most of the abnormal returns of the portfolio, with heteroskedasticity consistent t-statistics greater than 20 and significant at 0.1% level. On the other hand, size effects are statistically significant at 5% for 3 years period and 1% for both 4 and 5 year periods. In a contrary, there is no statistically significant relationship between portfolios abnormal returns and firm-specific financial distress risks. Table 8. Long-Horizon U.S. acquirer abnormal returns (ARs) sort into four legal origins from FF3FM. English N=109 Intercept (Abnormal Return) t (HC) B(p) t (HC) S(p) t (HC) H(p) (+60days, +3years) 0.0050 1.69* 1.0656 16.90*** 0.2869 2.50** -0.1561 -1.15 (+60days, +4years) 0.0039 1.34$ 1.1226 18.76*** 0.3282 2.76** -0.1168 -0.89 (+60days, +5years) 0.0034 1.19 1.1465 19.63*** 0.3165 2.74** -0.0984 -0.76 French N=12 (+60days, +3years) -0.0102 -1.70* 1.4198 8.93*** -0.2226 -0.94 0.1280 0.61 (+60days, +4years) -0.0134 -2.60** 1.4285 11.85*** -0.1283 -0.58 0.2842 1.44$ (+60days, +5years) -0.0104 -2.07* 1.5107 13.13*** -0.0800 -0.35 0.3954 2.02* German N=11 (+60days, +3years) -0.0031 -0.51 1.1340 8.90*** 0.5304 2.15* 0.6093 3.25*** (+60days, +4years) -0.0033 -0.59 1.0854 8.50*** 0.5962 2.36** 0.6610 3.43*** (+60days, +5years) -0.0024 -0.42 1.0558 7.69*** 0.6432 2.48** 0.6883 3.59*** Scandinavian N=20 (+60days, +3yea rs) -0.0017 -0.39 1.1966 11.83*** 0.2628 1.41$ -0.0469 -0.37 (+60days, +4years) -0.0024 -0.64 1.1519 12.33*** 0.3054 1.76* -0.0047 -0.04 (+60days, +5years) -0.0031 -0.81 1.2159 11.57*** 0.3538 2.10* 0.1308 1.06 The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a two-tail test. t (HC) is Heteroskedasticity consistent students t test. Table 8 shows the FF3FM regression results of portfolio of U.S. acquirers from table 7, which have been decomposed into the 4 legal origins. Interestingly, acquirers from English common law origin have experienced positive long run abnormal returns. Although the abnormal returns are not economically significant (around 0.5 and 0.3 percent), for period 3 years and 4 years, the abnormal returns are statistically significant at 5% and 10% respectively. In a contrary, French, German and Scandinavian civi l law origin all display negative abnormal returns for the portfolio of U.S. acquirers. Among the tree legal origins, portfolio of U.S. acquirers that took over targets within the French civil law origin displays both economically and statistically significant negative abnormal returns with German and Scandinavian origins display insignificant abnormal returns economically and statistically. More specifically, the long horizon abnormal returns for French origin are ranging from 1.02% to 1.34% and statistically significant at 5% and 1% respectively. It is worth to note that factor loading on market risks are highly positively significant (at 0.1% level) for all legal origins and time periods. While other two risks have moderate explanatory power, except for financial distress risks of German civil law origin. From which are positively significant at 0.1% level. In general, the results from FF3FM of Long horizon portfolio of U.S. acquirers are again consistent with the positive spillover hypothesis, such that, abnormal returns of portfolio for U.S. acquirers positively related to targets shareholder protection for English Common Law origin. The long-horizon empirical results are the extensions to Moeller and Schlingemann (2005), in which the authors tested the short horizon CAARs of U.S. acquirers and foreign targets. They found that bidder returns are positively associated to legal systems with better shareholder rights. The regression study has further confirmed such result in the long run. Chapter 7: Multivariate Regressions 7.1 Regression analysis of CAARs for foreign targets and U.S. Acquirers Table 9. ordinary least-squares regression of CAARs for U.S. acquirers and foreign targets Dependent Variable Foreign Target CAARs N=100 U.S. Acquirer CAARs N=94 ( 1 ) ( 2 ) ( 1 ) Independent Variables Constant 0.1977$ (0.1103) -0.5569*** (0.1711) -0.0752 (0.0548) Rule -0.0052 (0.0106) 0.0965*** (0.0228) 0.0089 (0.0094) Efficiency of Judicial System -0.0020 (0.0133) -0.0263* (0.0130) -0.0012 (0.0093) Shareholder R