Does Financial Advisor Reputation matter for M&A Returns?

Master s Thesis Does Financial Advisor Reputation matter for M&A Returns? Empirical Evidence on the Role of Financial Advisors in European Mergers and Acquisitions August 2013 Jonas Ravn Nielsen MSc Finance Supervisor: Stig Vinther Møller Department of Business and Economics Aarhus University Business and Social Sciences

Acknowledgements In this preface, I would like to express my gratitude to some of the people who have helped me in various ways during the writing of this thesis. First of all, I would like to thank my supervisor, Associate Professor Stig Vinther Møller, for always being available for answering questions and providing supervision when needed. Further thanks goes out to all the members of Madklubben af 09.04.2011 for providing me with some much needed diversion from the, at times, exhausting thesis work. A special thanks goes out to Neymar, Hulk, Lebron James, Chris Froome, Alberto Contador, Jakob Fuglsang and a number of other outstanding athletes for making this summer very entertaining and helping inspire me with their brilliant performances. Last but not least, I would like to express my sincerest and heartfelt gratitude to my family. Your encouraging words and continuous support is very much appreciated. /Jonas Ravn Nielsen 12 August, 2013 2

Abstract The role of financial advisors in M&As has been investigated in several earlier papers with somewhat mixed results. This thesis presents new evidence on the role of financial advisors in European M&As by analysing a comprehensive and unique dataset of 1.096 European M&As from the time period 01/01/2000 to 31/12/2012. In conjunction with the overall topic and inspired by Golubov et al. (2012), a European advisor league table is constructed to classify financial advisors into top-tier and non-top-tier groups, respectively. The dataset is ultimately analysed in a cross-sectional regression setup and split into different subsamples to clarify, whether top-tier financial advisors create more value than non-top-tier advisors in public, private and subsidiary mergers, respectively. The value creation is measured by Cumulative Abnormal Returns (CAR). In this setup, the impact of financial advisor reputation on bidder returns is analysed while controlling for various relevant deal- and firm-specific characteristics. The findings in this paper indicate that for European M&As, advisor reputation does not have any significant effect on bidder returns in the full sample or any of the three subsamples. Thus, it is shown that top-tier financial advisors do not create any additional value for their customers in European mergers. This applies to public, private as well as subsidiary deals. In addition to this, it is shown that top-tier advisors are neither associated with faster deal completion. Keywords: Mergers and Acquisitions, Financial Advisors, M&A Advisory Services, Top-tier versus Non-top-tier, Value Creation, Time to Completion, Event Study, Cross-section Analysis, Europe 3

Content 1. Introduction... 6 1.1 Problem statement...7 1.2 Delimitations...7 2. Theory... 9 2.1 Merger waves...9 2.2 Relationship between reputation, quality and price...11 2.3 Earlier empirical findings...12 2.4 Control variables...14 2.4.1 Size...15 2.4.2 Relative size...15 2.4.3 Book-to-market...16 2.4.4 Run-up...16 2.4.5 Sigma...16 2.4.6 Leverage...16 2.4.7 Cash flows-to-equity...17 2.4.8 Hostile deals...17 2.4.9 Tender offer...17 2.4.10 Diversifying deals...18 2.4.11 Cross-border...18 2.4.12 Method of payment...19 2.4.13 Listed target...19 3. Methodology and data... 21 3.1 Event study...21 3.1.1 Market efficiency...21 3.1.2 Event- and estimation period...22 3.1.3 Thin trading...24 3.1.4 Clustering...24 3.1.5 Market Model...25 3.1.6 Regression analysis...26 3.2 Data...26 4

3.2.1 Sample selection and return data...27 3.2.2 Advisor League table...30 3.2.3 Regression variables...32 4. Analysis... 35 4.1 Sample descriptive statistics...35 4.2 Cross-section analysis...38 4.2.1 Analysis of bidder CAR...38 4.2.2 Controlling for target used top-tier...42 4.2.3 Sources of top-tier value creation...45 4.2.4 Time to completion...47 4.3 Robustness of results...49 4.3.1 Financial advisor classification...49 4.3.2 Event window and inputs for the market model...50 4.3.3 Other sensitivity tests...50 4.4 Generalizability...51 4.4.1 Year...51 4.4.2 Geography...52 4.4.3 Sector...53 5. Discussion... 55 5.1 The results in a larger perspective...55 5.2 Europe versus the U.S....55 5.3 Limitations of the study...56 5.4 Further Research...57 6. Conclusion... 58 7. References... 60 8. Appendices... 63 5

1. Introduction In some mergers there truly are major synergies though often times the acquirer pays too much to obtain them but at other times the cost and revenue benefits that are projected prove illusory. Of one thing, however, be certain: If a CEO is enthused about a particularly foolish acquisition, both his internal staff and his outside advisers will come up with whatever projections are needed to justify his stance. Only in fairy tales are emperors told that they are naked. - Warren Buffet (1997) Mergers and acquisitions (M&As) remain an important activity in the field of corporate finance, accounting for substantial reallocations of resources within the economy. From a corporation point of view, M&A activity provides firms with the opportunity to relatively quickly expand the business or enter new markets, industries etc.. Even though there are also great challenges associated with acquiring and integrating companies, it does not seem to keep company leaders from engaging in these often large transactions. Every year, firms spend trillions of dollars on M&A deals worldwide. From a research point of view, M&A is a very interesting and complex topic and as a consequence, it has already received plenty of attention in academic papers. It is obvious that a combination of many different factors decides, whether a specific merger turns out a success or a failure. The M&A advisory services provided by financial advisors are one of these factors. Engaging in a merger is a significant strategic decision that in most cases has great impact on all stakeholders of the companies involved. The nature and process of a merger can be very complex, creating the need for management to bring external expertise to the table. In 2007, during the peak of the latest merger wave, firms spent $4,2 trillion on M&As deals worldwide. In 85% of these deals, external financial advisors were involved, advising the 6

acquiring firm, the target or both. This generated an estimated $39,7 billion in advisory fees 1. Thus, financial advisors play an important part in many M&A deals and advising has become a large, profitable business. It seems plausible to assume that financial advisors differ in skills and quality. The industry of M&A advisory services is dominated by a group of so-called bulge bracket or top-tier firms. These top-tier firms have built up a reputation as experts in M&A transactions which, theoretically, should mean that they also deliver superior services. The question is if - and how - this is reflected in the abnormal returns that are observed around mergers. Do top-tier financial advisors create more value for their customers than non-top-tier advisors? In a recent study from the U.S. by Golubov et al. (2012), it is shown that top-tier financial advisors are associated with significantly higher bidder returns in M&As. Since most existing studies on this area rely on data from the U.S., this raises the interesting question if these findings also apply to European mergers and acquisitions. 1.1 Problem statement The aim of this paper is to investigate whether top-tier financial advisors create more value for their customers than non-top-tier advisors when advising on M&As. More specifically, the goal is to find out if there is any significant difference between hiring a top-tier or a non-toptier advisor from the perspective of the acquiring firm. This will be investigated and quantified via an event study approach, combined with a cross-sectional regression analysis. In order to classify the advisors, a league table is constructed. Since most existing studies focus on the U.S. market for M&As, this paper will also contribute to the existing literature by exploring a dataset exclusively containing mergers and acquisitions from Europe. Thus, the main research question of this paper is: Did the choice of employing a top-tier financial advisor affect bidder returns in European M&As during the period from 1/1/2000 to 31/12/2012? 1.2 Delimitations First of all, the terms mergers, acquisitions and M&As are used indiscriminately to describe all of the deals included in the sample. That is, transactions in which two 1 Statistics from Golubov et al. (2012). 7

independent companies come under common control. The same goes for the acquiring firm, which will sometimes simply be referred to as the acquirer or bidder. Thus, the technical details of differences between a merger and an acquisition and acquirer versus bidder will not be discussed further in this paper. The focus of the thesis is short-run abnormal returns earned by acquiring firms. This implies that only listed acquirers will be included in the study. Short-run event studies are sometimes supplemented by a study of long-term operational performance of the firms included. This however, would require substantial extra data work and is therefore deemed to be out the realistic scope of the thesis. It would also be interesting to study the effect of advisor reputation on abnormal returns of target firms, but this would require that targets were also listed on a stock exchange, reducing the sample size substantially as a consequence. Therefore, focus is solely on abnormal returns earned by acquiring firms. Another interesting issue is the advisory fees. It is commonly known that top-tier financial advisors charge an extra premium for their services. Thus, it would be interesting to investigate whether the advisors actually create enough extra value to compensate customers for the higher fees. However, as advisory fees were not disclosed in any of the databases used in this sample, this is not investigated in this paper. Furthermore, it should be noted that the analysis and findings of this paper are based on the assumption that the choice of advisor is exogenously determined. Finally, the paper is based on a sample of European M&As. The results will therefore primarily contribute to the understanding of the mechanisms of M&As within Europe and supplement the existing literature on this area. However, this also means that the results should not be generalized to other markets without further investigation. 8

2. Theory The purpose of this section is to describe the nature of mergers and acquisitions, and to give a brief presentation of the basic theory on which this paper relies. A short review of earlier findings on the topic is afterwards presented. Finally, rooted in findings from other empirical and theoretical papers, a discussion of the different control variables included in the analysis is provided. 2.1 Merger waves One of the most commonly discussed phenomena of M&A activity is that it tends to cluster in so-called merger waves. According to Depamphilis (2010), we have witnessed a total of six multi-year merger waves since the late 1890s (based on U.S. data). These waves all differed in terms of specific trends within M&As, like for instance horizontal consolidation (1900s and 1920s), growth of conglomerates (1960s), hostile takeovers (1980s) and cross-border transactions (2000s). When analysing historical data on M&A activity it is important to keep the historical development in mind for several reasons. For one, there is evidence that the stock market rewards firms that early on anticipates promising opportunities, and conversely punishes those that simply imitate the moves of other firms. First-movers seem to gain the highest returns compared to the later me too acquirers. This is a result of firms pursuing M&A opportunities early on in general pay lower prices for targets than those that are followers, as prices tend to escalate upwards when we approach the peak of a merger wave. This phenomenon was studied by McNamara et al. (2008), among others, who show that deals completed in the beginning of a wave, outperform both the overall stock market as well as deals completed later in the cycle. Secondly, the tendency of merger activity to cluster is relevant in event studies, as clustering of events might violate some of the design criteria of the study. This issue is also addressed later. There are basically two competing theories seeking to explain the phenomenon of merger waves; the neoclassical hypothesis and the behavioural hypothesis (Depamphilis (2010)). The neoclassical hypothesis argues that merger waves are a consequence of firms reacting to shocks in their industries and operating environment. Shocks could for instance be deregulatory changes, emergence of new technology, substituting products, distribution 9

channels or a sustainable rise in commodity prices. What determines the size and length of a merger wave is then the number industries affected by the shock and the magnitude of the shock. The second theory, the behavioural hypothesis, is based on the misvaluation hypothesis, implying that management use their own overvalued stock to buy the assets of lower-valued firms. Thus, this theory is based on the assumption that markets are not always efficient. That is, even though over time, asset values reflect their true economic value, temporary inefficiencies can occur. For M&As to cluster in waves, this theory requires that valuations of many firms must increase at the same time, compared to other firms. It is furthermore assumed that managers are rational and understand the market inefficiencies. Thus, managers whose stocks are believed to be overvalued, will seek to acquire companies whose stocks are lesser valued. The use of overvalued stock means that the acquirer can issue fewer shares and thereby reduce earnings dilution. With the influence of overvaluation in mind, the method of payment according to this theory would intuitively be stock. Various studies have confirmed that there is a positive correlation between long-term fluctuations in market valuations and takeover activity. However, it remains more unclear whether high valuations contribute to more takeover activity, or if increased M&A activity leads to higher market valuations. When comparing these two theories, there seems to be no clear answer to which one is most correct. Both theories are true to the observable reality, making them both plausible explanations for M&A activity. It could be that the two theories actually complement, and not necessarily substitute, each other. Figure I illustrates the cyclical nature of merger activity. As this paper focuses on the European M&A market, the figure shows the latest historical development of M&A activity within the European Union. 10

Figure I Historical Merger Activity in Europe The figure presents the historical merger activity in the European Union in the time period from 1998 to 2012. Aggregate deal value is based on completed deals where both target and acquirer are from the EU and is drawn from Zephyr. The MSCI Europe Total Return Index is drawn from Datastream. 1.000 900 800 700 600 500 400 300 200 100 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Aggregate deal value, USD Billion (LHS) MSCI Europe Total Return Index (RHS) 250 200 150 100 50 0 Source: Zephyr & Datastream As can be seen from figure I, M&A activity varies quite a lot over time, confirming the cyclical nature just described above. In the time period considered, the aggregate deal value peaks in years 2000 and 2007 with values around $850-$900 billion. The figure also shows that M&A activity is closely related to the general performance of the stock market, in this case exemplified by the MSCI Europe Total Return Index. The main point of this section is that merger waves exist, and that the time period considered seems to contain two peaks in merger activity. This is something which should be considered in the study as it might influence the results. 2.2 Relationship between reputation, quality and price From the existing theoretical literature on reputation, both in the banking industry specifically and in the products market in general, the conclusion can be drawn that reputation correlates positively with price as well as quality. The relationship between reputation, quality and price was first described in the classic theoretical articles of Klein & Leffer (1981), Shapiro (1983) and Allen (1984) who demonstrates that in a situation where a producer repeatedly sells its products in the market and when the quality of that product or service is ex-ante unobservable, a premium price arises as a means of quality assurance. This premium 11

compensates the seller for the resources spent on building up a reputation and furthermore, it ensures that the present value of future income is greater than the potential short-term profit that could be earned by slacking on quality and selling low quality goods or services at high quality prices. The theory described in these articles can very easily be related to the market for investment banking and financial advisory services. Also here, the quality of services is ex-ante unobservable and the banks have to sell their services to their clients repeatedly. According to Chemmanur & Fulghieri (1994), and as also mentioned briefly in the introduction, the investment banking industry is dominated by a group of so-called bulge bracket firms, also known as the top-tier investment banks. These firms have built up a reputation as experts in capital market transactions which, in theory, should ensure that they are able to charge premium fees in return for their superior services. Chemmanur & Fulghieri (1994) investigate the relationship between reputation, quality and price for the underwriting services of investment banks and find a positive relationship between the three. These findings are furthermore confirmed by Fang (2005). The authors also suggest that the same could apply to other services of investment banks. This could for instance be services such as advisory offered in connection with M&As. This thesis contributes by investigating if the M&A advisors with the best reputation are also the ones with the best services. That is, this paper will seek to test if the relationship between reputation and quality just described also applies to the European market for M&A advisory services. 2.3 Earlier empirical findings The role of financial advisors in the M&A market has already received a fair amount of attention in the academic literature. However, the results are somewhat mixed. This section contains a short summary of some recent empirical findings within the area. For instance, Bowers & Miller (1990) find that top-tier advisors are better at identifying deals with larger total synergies, but are not able to provide a bargaining advantage to capture a larger share of these synergies for the bidder. 12

In another study, Michel et al. (1991) show that deals advised by a relatively unknown financial advisor named Drexel Burnham Lambert outperformed deals advised by top-tier investment banks in terms of bidder CARs. Servaes & Zenner (1996) investigate the role of investment banks in a sample of U.S. acquisitions over the period from 1981 to 1992. They find that neither the use of an advisor in general, nor the use of a top-tier advisor has any effect on announcement period CAR. The authors furthermore find that bidders are more likely to hire advisors when the transaction is complex and when the bidders have less prior acquisition experience. This study is however only considering the largest transactions each year, meaning that the study might not be representative for all deals. Rau (2000) shows that, except from a subsample of tender offers, top-tier advisors do not make better deals measured in terms of bidder CARs. This is supported in two later studies by Hunter & Jagtiani (2003) and Ismail (2010), who both do not succeed in finding a positive relationship between advisor reputation and bidder returns. However, what Hunter & Jagtiani (2003) does find, is that top-tier advisors are more likely to complete their deals and complete them in less time than non-top-tier advisors. Kale et al. (2003) takes on a somewhat different approach, as this paper focus on the relative reputation between the advisors of the merging parties. By doing this, the authors attempt to capture the nature of the bargaining in a takeover. The investigation is conducted for a relatively small sample of 390 U.S. mergers in the period from 1981 to 1994. The authors find that bidder gains, total synergy gains and the acquirer s share of total synergies is positively related to the relative reputation of the advisor. It is also found that bidder advisor reputation is positively related to the probability of bid success. Furthermore the authors show that bidders with top-advisors are more likely to withdraw from potentially value-destroying acquisitions 2. In a recent study, Golubov et al. (2012) provide new interesting evidence on the role of financial advisors. As an important departure from previous studies, the authors separately examine different types of acquisitions based on the firm s listing status. In a comprehensive study of 4.803 American mergers and acquisitions over the period from 1996 to 2009 they 2 Contradicting the quote of Warren Buffet in the introduction. 13

find that top-tier advisors deliver higher bidder returns, but only in acquisitions where the target is also listed. The authors find that in public acquisitions, the use of a top-tier advisor is associated with a significant 1,53% improvement in bidder CAR, ceteris paribus. The authors attribute this to the fact that in public acquisitions, reputational exposure and required skills set are relatively larger for the advisors, as public deals are often more complex and are also subject to more attention from the surrounding environment, media, analysts, etc.. It is also shown that the improvement in value creation, as measured by bidder CAR, can be attributed to the so-called better merger and skilled negotiation hypotheses. The former referring to the theory that the top-tier advisors can identify and create larger synergy effects, while the latter refers to top-tier advisors, through better negotiation techniques, can accrue more of the total value to the bidder. Furthermore, the authors find a negative relationship between toptier advisors and the so-called time to resolution, implying that top-tier advisors in general close deals faster. The paper also looks into advisor fees and finds that, consistent with the premium price/premium quality theory discussed above, top-tier advisors charge premium fees for their premium services. The premium is, however, justified with the higher bidder return in mind. Overall, as this short literature review shows, the empirical evidence on the role of financial advisors and the relationship between advisor reputation and bidder abnormal returns in M&As is somewhat mixed. However, contrary to earlier studies, the most recent study by Golubov et al. (2012) manages to somehow confirm the quality of services provided by toptier advisors, and thereby also confirm that the theory of the relationship between reputation, quality and price also applies to the financial advisors in M&As. 2.4 Control variables Besides from the choice of advisor, there are a number of other deal- and firm-specific characteristics that varies across the different acquisitions. All of these can potentially affect bidder returns. This section presents the control variables that are included in the later crosssection analysis of this paper. These variables are included because they have previously been proved to affect bidder returns (CAR). By controlling for these characteristics in relation to CAR, it is possible to isolate the potential effect that stems from the choice of advisor. If the effect is still present after controlling for other characteristics, choice of advisor can be shown to have direct effect on the return. The purpose of this section is thus to provide an 14

understanding of why the different variables have been included in the cross-section. In the following, each control variable will be accounted for, and expectations with regards to effect (positive or negative) on bidder CAR will be presented. Exact definitions of all variables are presented later in table IV, in the data section, together with a description of the various data sources. 2.4.1 Size The size of the acquirer is one variable that varies significantly across deals. In a comprehensive investigation of approximately 12.000 U.S. mergers from 1980 to 2001, Moeller et al. (2004) show that bidder announcement returns and size of acquirer are inversely related. More specifically, the paper shows that relatively smaller firms tend to realize larger abnormal returns than larger acquirers. Small firms are here defined as the firms whose market capitalization is below the 25 th percentile on the NYSE. The authors attribute these findings to management overconfidence and the empire-building tendencies of large firms, also sometimes referred to as hubris. According to DePamphilis (2010) another explanation could be that smaller firms are generally more focused when making acquisitions, and furthermore more likely to make acquisitions related to products or markets which they already understand. Size is expected to be negatively related to bidder CAR. 2.4.2 Relative size Relative size measures the size of the target relative to the size of the bidder. Fuller et al. (2002) show that bidder returns are negatively related to relative size in public acquisitions, i.e. the larger target, relative to bidder, the lower the bidder return. The explanation could be that larger targets often have more bargaining power than relatively small targets. Also, acquiring firms may find it more difficult to integrate larger public firms into the company. However, the same relationship is shown to be positive for acquisitions of private and subsidiary targets. The authors point out that this is due to a fundamental difference in the division of gains and synergies in acquisitions of unlisted forms compared to public firms (more of the value accruing to bidder). These differences are believed to be magnified as the relative size increases. 15

Relative size is expected to be negatively related to CAR in public acquisitions and positively related to CAR in private and subsidiary acquisitions. 2.4.3 Book-to-market The book-to-market ratio of bidders is another variable that in earlier studies have been shown to impact bidder returns. In a study of 3.732 U.S. takeovers in the period from 1978 to 2000, Dong et al. (2006) find that bidders with higher book-to-market ratios also experience greater announcement period returns. Book-to-market is expected to be positively related to bidder CAR. 2.4.4 Run-up Run-up, i.e. the bidder s stock price development up to the merger announcement also impacts bidder CAR. This is shown by Rosen (2006) in an examination of more than 6.000 U.S. mergers announced in the period from 1982 to 2001. Rosen (2006) finds that the marketadjusted buy-and-hold stock return of the bidding firm is significant and positively related to bidder abnormal returns, indicating that some kind of bidder-specific momentum effect seems to exist. Run-up is expected to be positively related to bidder CAR. 2.4.5 Sigma Closely related to the run-up is the bidder s stock volatility. In an investigation of 4.300 U.S. acquisitions, Moeller et al. (2007) provide evidence that bidders with higher sigma experience relatively lower announcement period returns when it comes to stock acquisitions, but not cash offers. The authors argue that sigma can be interpreted as a proxy for diversity of opinion among analysts and information asymmetry. That is, the higher volatility in bidder stock returns, the higher diversity of opinion and degree of information asymmetry. Sigma is expected to be negatively related to bidder CAR. 2.4.6 Leverage Another bidder-characteristic that influences abnormal returns is the leverage ratio. This was investigated by Maloney et al. (1993) in a study of 428 mergers by companies listed on the New York Stock Exchange in the period from 1958 to 1982. The study finds a positive relationship between the leverage of the acquiring firm and the abnormal returns of the 16

acquirer at announcement. The authors argue that this might be due to the theory that a certain level of debt keeps managers disciplined and encourages them to make better decisions. Leverage is expected to be positively related to bidder CAR. 2.4.7 Cash flows-to-equity According to Jensen (1986), large free cash flows encourage managers to engage in empirebuilding acquisitions. This implies managers investing in negative NPV projects rather than paying out the free cash flow to investors. This is further supported by Lang et al. (1991) who find a negative relationship between the cash flows-to-equity ratio and the returns of bidder in a study of 209 mergers from the U.S.. Cash flows-to-equity is expected to be negatively related to bidder CAR. 2.4.8 Hostile deals Friendly takeovers are normally defined as deals in which a negotiated settlement is possible, without the acquirer resorting to aggressive tactics in the process of acquiring the target. A hostile takeover is a situation where the potential acquirer bypasses the target s board and management and goes directly to the target shareholders with an offer to purchase their shares, or a situation in where the management in target actively tries to avoid the takeover. For instance, if negotiations with the target management have proved unsuccessful, bidder can choose to take negotiations public by making a hostile tender offer to the target shareholders. Because such offers usually contain a premium to the current stock price, this puts pressure on the target management as they have to justify and argue how they can create more value for their shareholders. In relation to target, Schwert (2000) also finds that a hostile takeover is associated with higher target returns in an investigation of a U.S. sample. When it comes to bidder returns the same paper finds no significant effect. However, Servaes (1991) documents that hostile takeovers are associated with a lower return to bidder in a study of 704 takeovers from the period 1972-1987. Hostile takeovers are expected to be negatively related to bidder CAR. 2.4.9 Tender offer A tender offer is an open offer from a potential buyer to the existing target shareholders, to buy their shares in the target firm. Tender offers can be used in a number of circumstances, but they are most often a result of friendly negotiations between the acquirer and the target 17

firm s two boards (Depamphilis (2010)). Jensen & Ruback (1983) documents that tender offers are generally associated with positive bidder returns. Tender offers are expected to be positively related to bidder CAR. 2.4.10 Diversifying deals As mentioned in the section on merger waves, the 1960 s were dominated by a wave of diversifying mergers. Both advantages and disadvantages can be identified in relation to diversifying mergers. Lewellen (1971) argue in favour of the financial advantages that diversification can provide. Two companies with cash flows that are not perfectly correlated can merge and thereby reduce risk for their debt holders (by reducing the risk of default). This would ultimately result in a lower cost of debt. Lewellen (1971) also argues that there might be operational synergies if the resources of the two merging parties complement each other. On the other hand, Jensen (1986) argues that diversifying deals might be the result of empirebuilding as it is more prestigious for managers to be leading a large conglomerate rather than a small firm. This could encourage management to engage in diversifying mergers, even though the shareholders of the firm would actually be better off having the excess cash paid out as dividends and reinvest it themselves. Diversifying deals are, among others, investigated empirically by Morck et al. (1990), Fan & Goyal (2006) and Villalonga (2004). While the two first of these find that diversifying mergers create less value than other mergers, the latter finds the opposite. All three articles consider U.S. samples. The mixed empirical findings imply that diversifying deals can be both negatively or positively related to bidder CAR. 2.4.11 Cross-border Merging two firms from different countries demands some extra effort from the firms implied. There can be many motives for expanding internationally, but some of the reasons include geographic diversification, acceleration of growth in new markets, lower labour costs, unique intellectual property and minimization of tax liabilities. In a study of 4.430 acquisitions between 1985 and 1995, Moeller and Schlingemann (2005) find that U.S. firms that acquire foreign targets experience significantly lower announcement period returns compared to those who acquire domestic targets. This cross-border effect is further enhanced 18

over time as cross border deals are also negatively related to operational performance during the following 5 years. Chatterjee & Aw (2004) confirm the presence of the cross-border effect in Europe as well, as they find that U.K. firms acquiring continental European targets significantly underperform those acquiring domestic firms. Contributing factors to the crossborder effect include increased integration costs and risks, together with a higher degree of asymmetric information between bidder and target when the transaction is across borders. Cross-border deals are expected to be negatively related to bidder CAR. 2.4.12 Method of payment In mergers, payment can basically be stock, cash or a mix of these. The relation between method of payment and returns has been studied in several empirical papers. Martynova & Renneboog (2009) investigate this in a sample of 1.361 European mergers over the period from 1993 to 2001. The authors show that payment in stock is negatively related to bidder returns, whereas payment in cash is positively related. This is in line with findings in numerous other empirical papers (Moeller & Schlingemann (2005) among others). According to Martynova & Renneboog (2009), method of payment provides information about the acquirer. It seems investors consider equity issues a signal that the firm s stock is overvalued. The management of the acquiring firm should always choose the cheapest method of payment and if they felt that the stock was undervalued, they would therefore not choose pay with equity. The positive effect from payment in cash can be seen as a consequence of this as well. It is expected that payment in stock will be negatively related to bidder CAR, whereas payment in cash or a mix of stock and cash will be positively related to bidder CAR. 2.4.13 Listed target The owner structure in target also matters for bidder returns. The target can be public, private or subsidiary - the latter implying that the company is partly owned and controlled by another firm which owns more than half of the stock. The effect of this is investigated by Fuller et al. (2002), who finds that the 5-day bidder CAR (-2, +2) is -1,00% for listed targets, 2,08% for private targets and 2,75% for subsidiary targets, respectively. The authors attribute this difference to the illiquidity of unlisted targets, arguing that the higher returns should be interpreted as some kind of liquidity premium. The paper also points out that the seller of a 19

private company in many cases also is the founder, who due to for instance competitive conditions or a desire to cash out, wishes to sell. Ceteris paribus, this leaves the target in a weaker bargaining position. These findings are confirmed in Faccio et al. (2006) who finds an insignificant 5-day bidder CAR (-2, +2) of 0,38% for listed targets. For private targets, CAR is 1,48% and significant. The authors name this the listing effect. Public acquisitions are expected to be negatively related to CAR, while private and subsidiary acquisitions are expected to be positively related to bidder CAR. 20

3. Methodology and data This section addresses the various methodological aspects of the paper and the dataset on which the analysis is conducted. In particular, the relevant event study methodology is briefly presented and accounted for. The dataset is thoroughly presented with regards to sample selection, data sources and construction of variables as the data is comprehensive and a central part of this paper. 3.1 Event study Event studies are commonly used to measure the effect of a particular event on the value of a firm. Given rationality in the marketplace, the economic effect of an event should immediately and efficiently be incorporated in market prices. This means that by observing the development of asset prices around the event dates, it is possible to measure the economic impact. One of the key assumptions in an event study is that the market is efficient. This topic is therefore shortly addressed in the following. The outline of this particular event study is then afterwards presented and potential biases are furthermore discussed. 3.1.1 Market efficiency As already mentioned, the event study is based on the market reaction around the event date. Thus, it is crucial that the market agents are rational and immediately incorporate all new information into the price of each security. If this is the case, security prices can be used to measure the value creation related to the specific event. In the literature, three degrees of market efficiency are normally described. Fama (1970) describes the three degrees of market efficiency as follows: - Weak form: Prices reflect all historical information - Semi-strong form: Prices reflect all historical and publicly available information - Strong form: Prices reflect all historical, public and insider information In relation to Fama (1970) there is general consensus about the existence of the weak and semi-strong form of efficiency among market agents. The presence of the strong form only seems plausible in the case of key employees in companies or market makers with access to order books. 21

However, for the purpose of this paper, the assumption of semi-strong efficiency, meaning that the market incorporates all historical and public information in security prices, is sufficient. 3.1.2 Event- and estimation period In the time dimension, an event study consists of an estimation period and an event period. This is illustrated in figure II. Figure II Time line of Event Study Estimation window Event window T 0 T 1 0 τ T 2 2 L 1 L 2 2 Source: Campbell, Lo, MacKinlay (1997) The first thing defined is typically the event date (day τ=0). When studying the effect of for example regulatory changes or release of financial statements, there are normally no problems with identifying the exact event date. When dealing with M&As however, there are several possible event dates; the rumour date, the announcement date or the completion date. Rumours, information leakages etc. can mean that the stock price is affected even before an official announcement is made. This is sometimes almost unavoidable, especially in large transactions with many different stakeholders involved. Thus, in some cases it can be argued that the rumour date should serve as the event date, because the market already at this point in time incorporates the discounted value added by the anticipated merger, weighted by the probability that the merger is actually completed. However, rumours are attached with substantial uncertainty, and it can be hard to distinguish between reliable and unreliable rumours, leaving significant uncertainty in the estimates. To completely remove this uncertainty the completion date can be chosen. However, it at this point in time, most of the market reaction is expected to have already happened. In-between the rumour- and the 22

completion date is the announcement date, where the intention of the merger is officially announced. If the companies involved hired any advisors, this is typically also mentioned in the official merger announcement. As the announcement date balances the trade-off between the two other dates, it is most often picked as the event date when working with M&As. Therefore, the event date in this paper will also be defined as the announcement date. With the event date in place, the event window can now be defined. As illustrated in figure II, two dates are chosen, T 1 and T 2, so the total length of the event window becomes L 2. According to MacKinlay (1997), T 1 and T 2 will often be the same date, so the event window only includes the exact day of the event. However, there are good reasons to expand the event window further when working with M&As. For instance, if a merger is announced at the end of a trading day, the full market reaction may not be visible before the next day. Furthermore, a high degree of complexity in the deal structure can mean a delayed market reaction, as investors might need more time to fully analyse the effect of the merger and update their expectations accordingly. These are factors that favour an expansion of the event window forward in time. There are also arguments for a backwards expansion of the event window to include some days before the event date. These are primarily rooted in the previous mentioned possible information leakages, which could mean that the market has already priced in a lot of the value of the merger. In this paper, the primary event window has been set to (-2, +2), i.e. a total of 5 days 3. This is believed to be sufficient in order to avoid any loss of information, and is also in line with Golubov et al. (2012). As a last step, the estimation window is defined. A general rule is that the estimation window and event window should not overlap in order to prevent the event from influencing the normal performance model parameter estimates. Mackinlay (1997) proposes that the length of the estimation window, L 1, should be at least 120 days with daily observations, whereas Bartholdy et al. (2007) recommend at least 200 days. Thus, a 200 day estimation window is chosen, going from t= -210 to t= -11 relative to the event day. This is in line with Golubov et al. (2012), who use 200 observations as well. 3 As a robustness check, alternative event windows (-1, +1), (-5, +5) and (-10, +10) are also tested. Please see section 4.3.2. 23

3.1.3 Thin trading One of the potential biases in the event study is related to the illiquidity of some stocks. This paper uses daily data and this means that when considering stocks that are not traded every day, returns can possibly be zero. This is particularly important to keep in mind here, as this paper investigates European firms, some of which are traded on relatively small stock exchanges. According to Bartholdy et al. (2007), this issue will lead to underestimation of the variance of the returns. This leads to a possible bias in the test-statistic for the event date, where a possible abnormal return might mistakenly be attributed to the event even though in reality there is no effect. Bartholdy et al. (2007) define thinly traded stocks as stocks traded less than 40% of trading days. Stocks traded more than 80% of trading days are on the other hand defined as thickly traded. In the paper mentioned, it is found that tests perform well with regards to power and size as long as the number of events exceeds 50 which is the case for all subsamples in this paper. However, as an extra precaution to avoid any bias in the study, it is decided to remove firms from the sample that are not thickly traded in the estimation period (i.e. traded less than 80% of the trading days). 3.1.4 Clustering An event study relies on the assumption that abnormal returns across individual securities are uncorrelated. This makes it possible to calculate the variance of the aggregated sample CAR without concern about covariance between individual sample CARs. Clustering arise when event windows overlap in calendar time, creating covariance in the returns of the securities involved. As briefly discussed in section 2.1, clustering can be enhanced during merger waves where merger activity is peaking. It seems logical that on the top of a merger wave, problems with clustering can be significant, c.f. the earlier description of this phenomenon. As shown in figure I, the time period considered in this paper contains two years with significantly higher merger activity, which may indicate problems with clustering. The effect of clustering has been investigated empirically in numerous articles. Warner and Brown (1985) find that clustering does not cause a misspecification of the event study, as long as the market model is used. This is supported by Bernard (1987), who furthermore finds that correlation across securities is low for daily data, despite overlapping event windows. 24

In the light of these findings, and the fact that this paper uses the market model with different national stock indexes as market proxy, clustering is not expected to cause any bias in the results. 3.1.5 Market Model A number of different methods are available to calculate the normal return of the securities in the sample. The most commonly used is the market model and this is also the method that will be applied to estimate the normal returns in this paper, in line with the principles of Campbell, Lo, Mackinlay (1997). It is a statistical model that relates the return of a given security to the return of a market proxy. This means that the variation in returns caused by market movements is removed and thereby, the variance in abnormal returns is reduced, which increases the ability to detect event-related effects. The market model is specified as follows for any security, i: R E it i i mt it R it 0 var( ) it where R it and R mt are the returns of period t for security i and the market portfolio, respectively. α i is the average return on security i when the co-movement with the market, given by β i, is not taken into consideration. ε it is the abnormal return of period t for security i. The market model parameters are estimated via OLS for all securities in the sample for the estimation window, corresponding to t=-210 to t=-11 relative to the event date. Next, the abnormal returns are calculated for the event window. The abnormal returns for security i are given by: 2 i * * * it Rit i irmt The abnormal returns are then aggregated over time in order to capture the effect form the entire event period. This is denoted the Cumulative Abnormal Return (CAR) and is given by: t2 CARi ( t1, t2) t t1 * it 25

CAR can furthermore be aggregated across events to obtain the Cumulative Average Abnormal Return for the entire sample: N 1 CAR( t, t ) CAR ( t, t ) 1 2 i 1 2 N i 1 The Cumulative Average Abnormal Returns will not be tested further as the main purpose of this paper is to investigate the effect on the individual bidder CAR of hiring a top-tier advisor, but they are reported as part of the descriptive statistics in table V together with a test of difference in means. Thus, the main variable of interest is the bidder CAR, which is explained by various variables (including choice of advisor) in the later cross-sectional regression analysis. 3.1.6 Regression analysis As stated in the theory section, a lot of deal- and firm-specific characteristics influence the abnormal returns of a merger. In order to investigate the effect of top-tier advisors on bidder CAR, it is thus necessary to control for other characteristics that might have impact. This is done in a multiple regression which, given j events and m characteristics, is specified as follows c.f. MacKinlay (1997): CAR ( t, t ) x... x j 1 2 0 1 1 j m mj j E( ) 0 j where β 0, β m are regression coefficients and x 1j, x mj are the explanatory variables. From this regression, the effect of advisor choice on bidder CAR can be isolated and determined. The model is estimated via OLS, which requires homoskedastic and uncorrelated residuals. However, Campbell, Lo, MacKinlay (1997) propose the use of White s hetero-consistent standard errors, since the residuals can seldom be expected to fulfil these criteria. The assumptions of OLS are tested in appendix A. 3.2 Data The paper uses a unique dataset compiled from 4 different databases: Zephyr, Compustat, Bloomberg and Datastream. All these databases are considered as relatively reliable data sources. In the following, the sample selection procedure and return data is presented, accompanied by some of the underlying considerations. Moreover, an advisor league table is 26

constructed, in order to classify whether a deal was advised by a top-tier or non-top-tier advisor - something which is central to this paper. Finally, the data and variables used in the cross-section analysis are presented. 3.2.1 Sample selection and return data As the goal of this paper is to investigate whether the primary findings of Golubov et al. (2012) also applies to European mergers, the sample consists only of mergers from the European Union. Besides from this deviation, the search strategy is chosen so it to a very large extent matches the one from Golubov et al. (2012). This is done in order to ensure that results are comparable and that differences in results, if there are any, cannot simply be explained by different sample selection criteria. The sample is drawn from the M&A database Zephyr, using the search strategy displayed below in table I. Table I Search Strategy The table presents the search strategy used to draw the sample from the M&A database, Zephyr. This strategy yields an initial sample of 1.808 deals. Deal type Deal status World Regions Acquisition or merger Completed European Union (acquirer AND target) Time period 01/01/2000 until 31/12/2012 Percentage of stake Percentage of initial stake max 10% Deal value (million USD) >10 Listed Percentage of final stake min 50% Acquirer must be listed on a stock exchange Since this paper investigates the European M&A market, the search strategy is chosen so that both acquirer and target must be from the European Union. Furthermore, since we are only interested in transactions that represent a transfer of control from target to acquirer, the acquirer s initial ownership in target is set to a maximum of 10%, and a minimum of 50% after the deal is completed. Deal value is set to a minimum of $10 million. This is done in order to ensure that the merger is of significant size to the acquirer. Ideally, this restriction should be seen relatively to the acquirer (i.e. for instance a deal value of minimum 1% of acquirer market value), but the Zephyr database does not have this option in the search criteria. 27

This search strategy yields an initial sample of 1.808 deals. The sample is subsequently cleaned of leveraged buyouts, reverse takeovers and privatizations leaving 1.747 observations in the sample. Furthermore, a few deals are removed as the acquirer, despite of the restriction in the search strategy, is from a non-eu country. The number of observations in the sample is then 1.741. The next step includes removing observations where there is not sufficient return data available in Datastream to calculate announcement period returns, or where the stock is thinly traded (i.e. the stock is traded less than 80% of the trading days). When this is done, there are 1.221 observations left in the sample. As a last step, the deals where complete financial data from Compustat is not available to create all the variables for the cross-section analysis are excluded, leaving a final sample of 1.096 observations. This is the sample that is used in the rest of the paper and form the basis of the analysis. Returns for each individual acquirer are obtained from Datastream in line with the estimation and event window setup presented earlier. For the market model, market returns are also collected from Datastream. Each security is benchmarked against a national value-weighted stock index from its home country, which is standard procedure in multi-country event studies, according to Park (2004). The risk of using a national value-weighted index is that they in some cases can be dominated by a few, large companies (for instance the Novo Nordisk stock on the Danish OMXC20) and thus may not be optimal benchmarks for the market as a whole. A robustness check is therefore also conducted with the MSCI Europe total return index serving as market proxy 4. Benchmark indexes for all the countries included in the study are displayed in table II. 4 This robustness check is addressed in section 4.3.2. 28

Table II Overview: Benchmark indexes for the market model The table presents the different benchmark indexes used in the market model estimation. Market prices are drawn from Datastream and converted to log returns. Returns are based on total return indexes (codes RI/DSRI in Datastream) meaning that they include dividend payments, stock splits etc. All indexes are value-weighted. Home country Equity index Description Austria ATX20 The 20 most actively traded equities at the Vienna Stock Exchange. Belgium BEL20 The 20 equities traded at the Euronext Brussels. Germany DAX30 The 30 most valuable equities at the Frankfurt Stock Exchange. Denmark OMXC20 The 20 most actively traded equities at the Copenhagen Stock Exchange. Spain IBEX35 The 35 most actively traded equities at the Madrid Stock Exchange. Finland OMXH The 127 equities traded at the Helsinki Stock Exchange. France CAC40 The 40 most valuable equities at the Euronext Paris. United Kingdom FTSE 100 The 100 most valuable equities at the London Stock Exchange. Greece MSCI Greece The MSCI equity index for Greece. Hungary BUX The 13 most valuable equities at the Budapest Stock Exchange. Ireland ISEQ overall The 45 equities traded at the Irish Stock Exchange. Italy MIBTel The 40 equities traded at the Italian Stock Exchange. Luxembourg LuxX The 11 most valuable equities traded at the Luxembourg Stock Exchange. Netherlands AEX The 25 most actively traded equities at the Euronext Amsterdam. Poland WIG 20 The 20 most valuable equities on the Warsaw Stock Exchange. Portugal PSI The 47 equities traded at the Euronext Lisbon. Sweden OMXS30 The 30 most traded equities at the Stockholm Stock Exchange. Source: Datastream All prices are based on a total return index, meaning that they take dividend payments, stock splits etc. into account. This is preferable in order to give the most exact estimate of the firm s value creation to the shareholders. From these prices, the return series needed as inputs for the market model are constructed. When it comes to measuring returns, there are also several choices. In general, we distinguish between simple returns and log returns. The returns are computed as follows: r it R it Pit P it 1 1 P it log(1 rit ) log Pit 1 Where r it and R it are the simple return and the log return, respectively, from holding security i in the period t-1 to t. P it and P it-1 are the closing prices for security i at time t and time t-1, 29

respectively. For the purpose of this paper, log returns, also known as continuously compounded returns, are used as they have several advantages (Campbell, Lo, MacKinlay (1997)). One advantage is that the multi-period return is simply the sum of the one-period log returns, i.e. they can be aggregated over time. Furthermore, log returns increase the normality of the returns and eliminate negative values, which is important as the study relies on the assumption of normal distributed returns 5. 3.2.2 Advisor League table To measure advisor reputation, an advisor league table is constructed. This league table is central to this paper, as it will be used for the classification of top-tier and non-top-tier advisors, respectively. Table III presents the list of the top-25 investment banks in the European Union ranked according to the aggregated value of deals on which they they advised. The advisors are classified into top-tier and non-top-tier. Top-8 is classified as top-tier and all other advisors are classified as non-top-tier. This is in line with Fang (2005) and Golubov et al. (2012) who also use this method and also use top-8 as their cut-off point 6. They argue that this type of binary classification is handy for two reasons; first, it is economically intuitive as it captures the structure of the investment banking industry, where an investment bank is either considered a part of the top or it is not. Both earlier literature and the financial press provide examples of this. Second, the classification is also econometrically preferable, because a binary variable does not rely on the same assumptions as a continuous variable, being that the measure can capture reputation with precision and that it has a constant effect on the variables of interest. 5 See Appendix A for a test of the assumptions. 6 The robustness of the results is also checked by testing alternative cut-offs. Please see section 4.3.1. 30

Table III Advisor League Table This table presents financial advisor ranking of the top-25 investment banks according to the aggregated transaction value of deals on which they advised during the period from January 1, 2000 to December 31, 2012. The number of deals advised by each advisor and the average transaction value is also presented. The League table is drawn from the Zephyr M&A database. Both acquirer and target must be from the European Union for a deal to count. Total deal value is reported in USD million. Values are allocated fully to both bidder and target firm advisors, and to each advisor in the case of multiple advisors working on the same transaction. Rank Financial Advisor Total deal value Number of Deals Average deal value Top-Tier 1 Morgan Stanley 1.727.490 395 4.373 2 Goldman Sachs 1.582.345 269 5.882 3 Merrill Lynch 1.550.171 267 5.806 4 Rothschild 1.424.178 739 1.927 5 JP Morgan 896.043 273 3.282 6 UBS 894.036 267 3.348 7 Lazard 880.641 493 1.786 8 Deutsche Bank AG 848.251 284 2.987 Non-Top-Tier (only shown from Top-9 th to Top-25 th ) 9 Citigroup Inc. 823.417 211 3.902 10 BNP Paribas SA 650.378 268 2.427 11 ABN Amro 548.457 307 1.787 12 Lehman Brothers 546.458 194 2.817 13 HSBC Bank 460.519 190 2.424 14 Credit Suisse 415.025 116 3.578 15 UBS Warburg 388.804 125 3.110 16 Credit Suisse First Boston Corporation 325.052 171 1.901 17 Société Générale 291.919 125 2.335 18 KPMG Corporate Finance 227.927 582 392 19 Calyon SA 209.501 55 3.809 20 Greenhill & Company 184.110 53 3.474 21 Dresdner Kleinwort Wasserstein 171.421 111 1.544 22 Deloitte 169.909 269 632 23 Dresdner Kleinwort Benson Group Ltd 166.391 51 3.263 24 Mediobanca SpA 161.673 84 1.925 25 JP Morgan Cazenove 156.466 87 1.798 Source: Zephyr According to the league table, the top-8 investment banks within the European Union are Morgan Stanley, Goldman Sachs, Merrill Lynch, Rothschild, JP Morgan, UBS, Lazard and Deutsche Bank. Even though it is the European M&A market, the top is still dominated by the large American investment banks. However, compared to league tables from U.S. studies there are also deviations. Firms such as Rothschild, UBS and Deutsche Bank are present in the European top-8, and this is also intuitively appealing since they are Europe-based companies. Thus, despite some similarities, the league table shows that the advisors qualifying for a top-tier classification on the European M&A market, are not the exact same 31

as the top-tier advisors on the American M&A market investigated in other studies. It is also noticeable how much the average deal value varies, also within the top-8. Advisors such as Goldman Sachs and Merill Lynch tend to work on much larger deals, whereas for instance Rothschild is involved in smaller, but yet a higher number of deals. 3.2.3 Regression variables The cross-section analysis requires additional data for the control variables. As mentioned in the theory section, the control variables included are size, book-to-market, run-up, sigma, public deals, private deals, all-cash deals, payment includes stock, relative size, diversifying deals, cross-border, tender offers, hostile deals, leverage and cash flows-to-equity. As data for all of these variables is not accessible in Zephyr, from where the sample is initially drawn, it is necessary to merge the initial dataset with data from Bloomberg, Compustat and Datastream, to obtain all the data needed to conduct the analysis. Table IV summarizes definitions of all variables used in the regression analysis as well as the underlying data source. As far as possible, variables are constructed in line with Golubov et al. (2012) to secure comparability. However, this paper does not include takeover premium as an explanatory variable due to limited data. On the other hand, the cross-border variable is included as an extra variable to account for the perceived less integrated capital markets in the European Union relatively to the U.S.. The implications of cross-border deals were also discussed in section 2.4.11. In the sample, all target firms are classified as either public, private or subsidiary. This has been done manually, since the Zephyr database does not report the listing status for all firms. Therefore, it has been necessary to supplement with information from Bloomberg s M&A database to fill out some of the blanks and complete the dataset. Basically, firms reported as listed and with a valid ISIN code have been classified as public, firms reported as unlisted, n/a, or with no valid ISIN code have been classified as private and finally, firms where the deal comments explicitly states that the firm is a subsidiary are classified as subsidiary. 32

Table IV Regression variable definitions Panel A: Dependent variables and advisor reputation Variable CAR (-2, +2) Top-tier Synergy Gain (SG) Bidder s Share of Synergies (BSOS) Time to Completion Size Book-to-market Run-up Sigma Definition The cumulative abnormal return of the acquiring firm s stock in the 5-day event window (-2, +2) where 0 is the announcement day. CAR is calculated using log returns as inputs in the market model with the market model parameters estimated over the period starting 210 days and ending 11 days prior to the announcement. Value-weighted national market indexes are used as the market return. All return data is drawn from Datastream. Dummy-variable: one for transactions advised by one of the top-8 financial advisors according to the aggregated value of deals advised by each bank during the sample period, zero for all other financial advisors or if no advisor was used. The top-8 financial advisors in this paper are Morgan Stanley, Goldman Sachs, Merrill Lynch, Rothschild, JP Morgan, UBS, Lazard and Deutsche Bank (see advisor league table in table III). Financial advisor data for each deal in the sample is drawn from Zephyr, and is afterwards supplemented with additional data from Bloomberg s M&A database to fill out any missing values. Sum of bidder- and target dollar-denominated gains (the latter computed as the market value of equity 4 weeks prior to the announcement from Datastream in USD million times the CAR(-2, +2) for the two firms) Bidder dollar-denominated gain (computed as the market value of equity 4 weeks prior to the announcement from Datastream times CAR(-2, +2)) divided by Synergy Gain if Synergy Gain is positive and (1-Synergy Gain) if negative. Number of calendar days between announcement and completion dates, both as reported by Zephyr. Panel B: Bidder Characteristics Bidder market value of equity 4 weeks prior to the announcement from Datastream in USD million. Values are transformed from local currencies to USD by using the spot exchange rates on that same date, also from Datastream. Bidder book value of equity at the fiscal year-end prior to announcement divided by the market value of equity 4 weeks prior to announcement. Book value of equity is computed as total assets total liabilities from Compustat; Market value of equity is drawn directly from Datastream. Values are transformed from local currencies to USD by using the spot exchange rates on that same date, also from Datastream. Market-adjusted daily buy-and-hold return of the bidder s stock over the estimation period, beginning 210 days and ending 11 days prior to the announcement from Datastream. Standard deviation of the bidder s market-adjusted daily stock returns over the estimation period, beginning 210 days and ending 11 days prior to the announcement, from Datastream. (Continued) 33

Panel B: Continued Variable Leverage Cash flows-to-equity Deal value Public deals Private deals Subsidiary deals Relative size Hostile deals Tender offers Diversifying deals Cross-border All-cash deals All-stock deals Mixed deals Payment incl. stock Definition Total financial debt (long-term debt + debt in current liabilities) divided by the book value of total assets for the fiscal year prior to acquisition announcement from Compustat. Income before extraordinary items + depreciation dividends on common and preferred stock from Compustat, divided by market value of equity at fiscal year-end prior to announcement from Datastream. Values are transformed from local currencies to USD by using the spot exchange rates on that same date, also from Datastream. Panel C: Deal Characteristics Value of the transaction in USD million from Zephyr. Dummy variable: one for acquisitions of public firms, zero otherwise. Based on classification from Zephyr. Dummy variable: one for acquisitions of private firms, zero otherwise. Based on classification from Zephyr. Dummy variable: one for acquisitions of subsidiary firms, zero otherwise. Based on classification from Zephyr. Value of the transaction from Zephyr divided by acquirer s market value of equity 4 weeks prior to announcement from Datastream. Values are transformed from local currencies to USD by using the spot exchange rates on that same date, also from Datastream. Dummy variable: one for deals defined as hostile or unsolicited by Zephyr, zero otherwise. Dummy variable: one for deals defined as tender offers by Zephyr, zero otherwise. Dummy variable: one for cross-industry transactions, zero for same industry transactions. Industries are classified by the first two digits in the SIC code reported by Zephyr. Dummy variable: one for transactions where bidder and target are not from the same country according to Zephyr, zero otherwise. Dummy variable: one for deals in which payment is pure cash, zero otherwise. Based on information from Zephyr supplemented with Bloomberg. Dummy variable: one for deals in which payment is pure stock, zero otherwise. Based on information from Zephyr and supplemented with Bloomberg. Dummy variable: one for deals in which payment is neither all-cash or allstock, zero otherwise. Based on information from Zephyr and supplemented with Bloomberg to fill out any missing values. Dummy variable: one for deals in which payment includes some stock, zero otherwise. Based on information from Zephyr and supplemented with Bloomberg to fill out any missing values. Table IV completes the data presentation and the sample is now ready to be undertaken further analysis, in order to answer the questions raised in the problem statement. 34

4. Analysis In the following section, an analysis of the data is conducted to investigate whether top-tier advisors are able to create additional value for their clients. First, the dataset is characterized to get an impression of the various variables considered and clarify, if there is any variation across the subsamples that might influence the results. Second, regression analysis is carried out in order to isolate the effect from top-tier advisors and identify the sources of any additional value creation. 4.1 Sample descriptive statistics Table V presents descriptive statistics for the full sample as well as the top-tier and non-toptier subsamples, respectively. The means and medians for the subsamples are tested for differences in accordance with Keller (2005) 7. To test for differences in means, a t-test is applied. The t-test is a parametric test and is adjusted whenever the two subsamples have unequal variances. Variances are tested with an F-test. For the dummy variables, a z-test is applied to test for differences in proportions. Medians are tested with the nonparametric Wilcoxon-rank-sum test. Panel A illustrates statistics for bidder characteristics. The mean size of the sample is $9.499,614 million. Clients of top-tier advisors are considerably larger with a mean size of $23.405,637 million, compared to those of non-top-tier advisors, who has a mean size of only $7.529,594 million. Mean book-to-market for the bidders in the sample is 0,546. Bidders advised by top-tier advisors seem to have higher book-to-market ratios on average. The difference in means is statistically significant at the 10% confidence level, while for the medians it is significant at the 1% level. Mean bidder sigma (idiosyncratic volatility) in the sample is 0,020. For the top-tier sample the average sigma is 0,018 while for the non-top-tier it is 0,021. Thus, bidders advised by top-tier advisors appear to have a significantly lower sigma. Bidders on average experience a run-up of 9,4% when considering the full sample. The numbers are 7,1% and 9,8% for the top-tier and non-top-tier subsamples respectively. The difference is not statistically significant. Mean bidder leverage is 0,248 for the full sample. Bidders advised by top-tier advisors seem to be slightly more levered than non-top-tier clients, but the difference is not statistically significant. The mean cash-flows-to-equity 7 Further outline of the tests can be found appendix B. 35

Table V Sample descriptive statistics The table presents descriptive statistics for the sample of public, subsidiary and private mergers and acquisitions within the European Union, announced over the period of January 1, 2000 to December 31, 2012. The sample is drawn from the Zephyr M&A database. Panels A and B describe the mean, median and number of observations for bidder- and deal-specific characteristics, respectively, both for the full sample and as well as for top-tier and non-top-tier advisors. Top-tier advisors are defined as the top-8 financial advisors (see advisor league table in table III) by the aggregated value of deals on which they advised. Statistical tests for differences in means and equality of medians for each characteristic for top-tier versus non-top-tier are also presented. Means are tested with a t-test (z-test for the dummy variables) and medians are tested with the Wilcoxon-rank-sum test, all as described in Keller (2005). All variables and data sources are described in table IV. Panel A: Bidder characteristics Full sample (1) Top-Tier (2) Non-Top-Tier (3) Difference (2) - (3) Mean Median N Mean Median N Mean Median N p-value p-value Mean Median Size 9.499,614 2.280,797 1.096 23.405,637 5.971,892 136 7.529,594 1.964,747 960 0,000 0,000 Book-to-market 0,546 0,432 1.096 0,665 0,539 136 0,530 0,422 960 0,091 0,003 Sigma 0,020 0,017 1.096 0,018 0,015 136 0,021 0,017 960 0,002 0,000 Run-up 0,094 0,025 1.096 0,071 0,040 136 0,098 0,024 960 0,356 0,645 Leverage 0,248 0,244 1.096 0,261 0,251 136 0,246 0,243 960 0,356 0,506 Cash flows-to-equity 0,084 0,074 1.096 0,095 0,075 136 0,082 0,074 960 0,540 0,959 Panel B: Transaction characteristics Deal value 742,147 59,631 1.096 4.502,388 477,280 136 209,446 47,733 960 0,029 0,000 Relative size 0,135 0,036 1.096 0,211 0,082 136 0,125 0,033 960 0,003 0,000 Public deals 0,090-1.096 0,235-136 0,070-960 0,000 - Private deals 0,779-1.096 0,588-136 0,806-960 0,000 - Subsidiary deals 0,130-1.096 0,176-136 0,124-960 0,069 - Diversifying deals 0,508-1.096 0,397-136 0,524-960 0,003 - Hostile deals 0,005-1.096 0,022-136 0,003-960 0,003 - Tender offers 0,023-1.096 0,059-136 0,018-960 0,001 - All-cash deals 0,539-1.096 0,559-136 0,536-960 0,601 - All-stock deals 0,050-1.096 0,051-136 0,050-960 0,937 - Mixed deals 0,272-1.096 0,338-136 0,263-960 0,047 - Cross-border deals 0,437-1.096 0,544 136 0,422-960 0,004 - CAR (-2, +2) 1,111% 0,572% 1.096 0,679% 0,027% 136 1,171% 0,598% 960 0,298 0,403 36

ratio in the full sample is 0,084. The clients of top-tier advisors have a mean of 0,095, while the clients of non-top-tier advisors have a mean of 0,082. This difference is not statistically significant. Panel B presents the statistics for deal characteristics. The average deal value in the sample is $742,147 million. Not surprisingly, deals advised by top-tier advisors (mean of $4.502,388 million) are on average significantly larger than those advised by non-top-tier advisors (mean of $209,446 million). The difference in means is statistically significant on the 5% confidence level. It also seems intuitive that the largest advisors are more often advising on larger deals. The mean relative size of targets in the full sample is 13,5%. It is 21,1% and 12,5% for the two subsamples, respectively. The difference is statistically significant at the 1% confidence level. Thus, relative size also appears to be larger in deals where bidder employs a top-tier advisor, implying that firms involved in large deals, relative to their own size, are more likely to hire a top-tier advisor. Public deals constitute 9% of the sample, while 77,9% are private deals and 13% subsidiary. From the subsamples it is revealed that top-tier advisors are more likely to work on public (23,5%), and less likely to be involved in private acquisitions (58,8%). This difference is significant at the 1% confidence level. Top-tier advisors also advise on a larger proportion of subsidiary deals. The difference is in this case significant at the 10% level. Approximately 51% of the acquisitions in the sample are diversifying deals. Top-tier advisors appear to work on fewer diversifying deals (39,7%) than non-top-tier advisors (52,4%). The difference is statistically significant at the 1% level. Hostile deals only represent 0,5% of the entire sample. However, 2,2% of top-tier deals are hostile while only 0,3% of non-top-tier deals are resisted by the target management. This difference is also statistically significant at the 1% level. 2,3% of the deals in the sample are tender offers. 5,9% of top-tier deals are tender offers, while it is only 1,8% of the non-top-tier deals. This difference is also statistically significant at the 1% level. Looking at the method of payment, 53,9% of the deals in the sample are all-cash offers, 5% are all-stock deals and 27,2% involve some kind of mixed consideration. In the remaining 13,9% of the deals, the method of payment is not disclosed. There does not seem to be any significant difference between the top-tier and non-top-tier subsamples when it comes to method of payment, except that mixed payment seems to be slightly more common in top-tier deals, the difference being significant at the 5% level. Cross-border deals represent 43,7% of the full sample. For the toptier subsample the share is 54,4% and for the non-top-tier it is 42,2%, indicating that top-tier 37

advisors are more likely to participate in such cross-border transactions. The difference is statistically significant at the 1% level. The mean (median) bidder CAR for the full sample is 1,111% (0,572%). This number is statistically significant when considering the test-statistics used in standard event studies 8. Thus, the sample indicates that in general, merger announcements seem to generate significant and positive bidder returns within the European Union. More interestingly, deals advised by top-tier banks generate a mean (median) 5-day bidder CAR of 0,679% (0,027%) while deals advised by non-top-tier advisors produce a mean (median) bidder CAR of 1,171% (0,598%). These differences in bidder CARs are however not statistically significant. Based on this simple comparison of the subsamples, one could be led to the conclusion that top-tier advisors do not create more value than their non-top-tier competitors. However, this would be misleading, as this comparison does not take the effect from the other variables into consideration. For example, table V reveals that top-tier deals are generally hired by larger firms, for larger transactions, and are more often involved in cross-border deals. Therefore, firm- and deal-specific characteristics need to be controlled for in order to isolate the net effect from advisor reputation on bidder CAR. This can be done in a cross-sectional regression analysis, which is presented in the following section. 4.2 Cross-section analysis In this section, the data is further analyzed in a cross-sectional regression setting, to control for other firm- and deal-specific characteristics and thereby isolate the effect of advisor reputation on bidder CAR. Various models are presented and furthermore, the sources of any additional value creation are identified. The assumptions of the OLS-estimation have been tested and are reported in appendix A. 4.2.1 Analysis of bidder CAR In this section, the relationship between advisor reputation and bidder CAR is analyzed in a multivariate OLS-regression setting. The regressions control for various bidder- and dealspecific characteristics (as already outlined in previous sections) that are found to affect bidder returns. In the spirit of Golubov et al. (2012) and Masulis et al. (2007), six mutually exclusive variables are created in order to capture the interactions of target listing status and 8 These tests are however not explicitly presented in this paper as this is not the main focus of the study. 38

method of payment. These variables are: public deals x all-cash, public deals x payment incl. stock, private deals x all-cash, private deals x payment incl. stock, subsidiary deals x all-cash and subsidiary deals x payment incl. stock. To avoid perfect multicollinearity the last variable, subsidiary stock deals, is excluded from the regression equations. The regressions also control for fixed year effects, motivated by the earlier description of the cyclical nature of M&A activity and the economy in general. Fixed year effects are controlled for in accordance with Verbeek (2008). For simplicity, the fixed year coefficients are suppressed in the reported results, as they are not of primary interest. As recommended by MacKinlay (1997), White s standard errors are used as the models exhibits heteroskedasticity (see appendix A for test results). Advisor reputation might not be equally important for all types of deals, as shown in Golubov et al. (2012). As mentioned, this study finds advisor reputation to be more important in public deals compared to private and subsidiary deals. Consequently, the regressions are also run separately for subsamples of public, private and subsidiary deals, respectively (shown as specification (2), (3) and (4)). In these specifications, the interaction variables between target listing status and method of payment are naturally not included. Furthermore, the all-cash variable has been excluded as it exhibits strong negative correlation with the payment incl. stock variable 9. Table VI presents the results. In all specifications, the main variable of interest is the top-tier dummy-variable. This variable is taking the value of one if a top-8 advisor was advising on the deal, and zero otherwise. If this variable is statistically significant, it implies that top-tier advisors have significant impact on bidder CAR. Looking at the full sample in specification (1), it reveals that the top-tier variable has a positive, yet statistically insignificant coefficient for the full sample of 1.096 deals. This is also consistent with the study of Golubov et al. (2012). The control variables are in general insignificant, except for size and book-to-market. However, it is a general problem in other empirical studies as well to obtain significant control variables (Moeller et al. (2004) and Moeller & Schlingemann (2005)). 9 See correlation matrix in table XII, in appendix A. 39

Table VI Cross-sectional regression analysis of bidder CARs The table presents results of the cross-sectional regression analysis of bidder CARs. Via OLS bidder CARs are regressed on advisor reputation and other bidder- and deal-specific characteristics. Variables are defined in table IV. All regressions control for fixed year effects which is done by including dummy variables for each year in the sample. These coefficients are suppressed in the results. The letters a, b and c denote statistical significance at the 1%, 5% and 10% levels, respectively. The t-statistics reported in parenthesis are based on White s standard errors adjusted for heteroskedasticity. Intercept 0,0220 (0,71) 0,1467 b (2,47) Top-tier 0,0024 0,0070 (0,46) (0,54) Ln(size) -0,0040 b -0,0079 c (-2,54) (-1,91) Book-to-market -0,0060 c -0,0191 c (-1,96) (-1,97) Run-up -0,0010-0,0245 (-0,16) (-1,35) Sigma 0,8150-1,2251 (1,15) (-1,06) Public deals x all-cash -0,0110 (-1,16) Public deals x payment incl. stock -0,0163 (-1,32) Private deals x all-cash -0,0007 (-0,19) Private deals x payment incl. stock 0,0006 (0,06) Subsidiary deals x all-cash 0,0029 (0,65) Payment incl. stock 0,0069 (0,50) Relative size 0,0209-0,0088 (1,60) (-0,62) Diversifying deals 0,0024 0,0072 (0,70) (0,56) Cross-border -0,0008-0,0245 (-0,23) (-1,61) Tender offers -0,0104-0,0242 (-0,81) (-1,51) Hostile deals 0,0069 0,0151 (0,45) (0,69) Leverage -0,0200 c -0,0804 a (-1,86) Cash flows-to-equity 0,0103 (0,94) Full sample Public Private Subsidiary (-2,87) 0,1181 b (2,42) -0,0095 (-0,25) -0,0035 (-0,48) -0,0023 (-1,1) -0,0036 (-1,10) 0,0001 (0,02) 1,109 (1,29) -0,0007 (-0,07) 0,0330 (1,48) 0,0004 (0,09) 0,0003 (0,08) 0,0149 (0,71) -0,0123 (-0,96) 0,0050 (0,49) 0,1216 b (2,24) 0,0065 (0,56) -0,0101 a (-3,24) -0,0210 b (-1,98) 0,0170 (0,83) 0,6182 (0,86) -0,0133 (-0,33) -0,0143 (-0,36) 0,0140 (1,63) -0,0091 (-1,12) -0,0140 (-0,64) -0,0172 (-0,42) N 1096 99 854 143 Adjusted R 2 0,0462 0,0566 0,0532 0,0774 40

Size is negatively correlated with bidder CAR as expected, and this can be interpreted as evidence in favour of the hubris theory mentioned earlier. Note that size is included in the model as ln(size), and the coefficient should thus be interpreted as a 1% increase in bidder size will lead to a 0,004% decrease in bidder CAR for the full sample, ceteris paribus. Bookto-market is, more surprisingly, also negatively correlated with bidder CAR, implying that bidders with relatively higher book-to-market ratios, ceteris paribus, obtain lower returns. The signs on many of the control variables are not as expected. However, cross-border deals seem to be associated with a relatively lower return as expected. Furthermore, diversifying deals appear to be rewarded by investors with relatively higher returns as a result. The negative sign on the interaction variable public x payment incl. stock indicate that payment with stock is associated with a relatively lower bidder CAR in public deals. This confirms the expectation that investors interpret payment in stock as a sign that the bidder s stock is overvalued. Turning from the full sample to the subsamples in specification (2), (3) and (4), there are several differences in the models. The coefficients on book-to-market and size are negative in all subsamples as well. Judging by the signs of the coefficients, sigma is associated with a lower bidder CAR in public acquisitions but a higher return in private and subsidiary deals. The variable is insignificant though. When payment includes stock it has a positive effect in public deals but a negative effect in private and subsidiary deals. Relative size is negatively related to bidder CAR for public and subsidiary acquisitions but has a positive sign for private deals. This implies that in public and subsidiary deals, the larger the total deal value is relative to the bidder, the lower is the bidder s return. The opposite goes for private deals. Here it seems like the larger total deal value relative to the bidder, the larger the bidder return. Diversifying deals are generally associated with a higher bidder CAR for all subsamples. Cross-border deals are, just as expected, negatively related to bidder CAR in public and subsidiary acquisitions. In private deals however, the sign is positive, implying that acquiring a target from another country does not have negative impact on bidder CAR if the target is privately held. Quite surprisingly, it also appears that tender offers are associated with a negative impact on returns and that hostile deals are positively related to returns in public acquisitions. However, it should be noted that the number of hostile deals in the sample is very small, making this result a bit questionable. Leverage is negatively related to bidder CAR across all subsamples. In the public subsample it is even statistically significant at the 1% level, implying that highly levered acquirers can expect relatively lower returns when 41

taking over a listed firm. Cash flows-to-equity is positively related to bidder CAR for the public and private subsamples. In the public sample it is even statistically significant at the 5% level. This means that acquirers with a high cash flows-to-equity can expect relatively higher returns when acquiring public targets. For the subsidiary subsample the sign is however negative, implying the opposite effect when target is a subsidiary. This effect is however also insignificant. Looking at the adjusted R 2, the models are only able to explain between 4,6% and 7,7% of the variation in bidder CAR. This is not impressive, but on the other hand it is not much lower than other empirical papers. The main variable of interest, the top-tier dummy-variable, is not even remotely close to being statistically significant in any of the subsample specifications. However, the sign on the coefficient is positive for specifications (1), (2) and (4), implying that getting advice from a top-tier advisor is positively related to bidder CAR. For specification (3), the private subsample, the coefficient has a negative sign which is quite surprising. It was expected that the effect from top-tier advisors was smaller in private deals, but not negative. However, as the coefficients are all highly insignificant, the effect might as well be zero. Although the coefficients are insignificant they seem to indicate the same pattern as found in Golubov et al. (2012). The coefficient is largest for the public, smaller for the subsidiary and negative for the private subsample. This could be interpreted as weak evidence that advisor reputation is more important in public acquisitions, also in Europe, which could be due to the higher reputational exposure that advisors face when advising on public deals. However, the result does not reveal any significant effect on bidder CAR. Overall, the results are quite surprising and not consistent with the somewhat similar study by Golubov et al. (2012). To conclude, the results point towards the conclusion that financial advisor reputation does not have any significant effect on bidder CAR in European M&As - not even in public acquisitions - possibly indicating a difference between the European and the U.S. M&A market on this area. 4.2.2 Controlling for target used top-tier In an attempt to improve the regression, an additional variable is included and the regressions are run once again for the full sample as well as all 3 subsamples. A new variable, target 42

used top-tier, is implemented as an interaction variable together with the existing top-tier variable. This is supposed to capture the effect from when the target also hired a top-tier advisor. The reasoning behind this is that the presence of a top-tier advisor on the target side might undermine the effect of having a top-tier advisor on the bidder side, and thereby cancel out the effect of the top-tier variable alone. The new variable is expected to be negatively related to bidder CAR. The results are reported in table VII. Only difference from table VI is the inclusion of the additional variable just described. The implementation of the additional variable does seem to have some effect. The interaction variable Top-tier x target used top-tier is negative for the full sample as well as the public and private subsamples. In the private subsample it is statistically significant at the 1% confidence level. For the subsidiary subsample, the variable is positive. Looking at the toptier variable, this variable is still insignificant in the full sample and across all subsamples. However, it is somewhat closer to being significant, indicating that the inclusion of the additional variable actually was justified and that there seems to be some kind of cancellation effect when the target firm also hires a top-tier advisor. However, even after controlling for this effect by including the extra variable, the results still indicate that top-tier advisors do not have any significant impact on bidder CAR. 43

Table VII Cross-sectional regression analysis of bidder CARs - part II Controlling for target used top-tier advisor The table presents results of the cross-sectional regression analysis of bidder CARs. Via OLS, bidder CARs are regressed on advisor reputation and other bidder- and deal-specific characteristics, including if the target firm also used a top-tier advisor. Variables are defined in table IV. All regressions control for fixed year effects which is done by including dummy variables for each year in the sample. These coefficients are suppressed in the results. The letters a, b and c denote statistical significance at the 1%, 5% and 10% levels, respectively. The t- statistics reported in parenthesis are based on White s standard errors adjusted for heteroskedasticity. Intercept 0,0209 (0,67) 0,1414 b (2,34) Top-tier 0,0050 0,0184 (0,95) (1,09) Top-tier x Target used top-tier -0,0220 c -0,029 (-1,77) (-1,29) Ln(size) -0,0038 b -0,0071 (-2,40) (-1,65) Book-to-market -0,0059 c -0,0178 c (-1,93) (-1,76) Run-up -0,0010-0,0240 (-0,16) (-1,33) Sigma 0,8111-1,3661 (1,15) (-1,18) Public deals x all-cash -0,0100 (-1,05) Public deals x payment incl. stock -0,0143 (-1,15) Private deals x all-cash -0,0008 (-0,21) Private deals x payment incl. stock 0,0004 (0,04) Subsidiary deals x all-cash 0,0025 (0,57) Payment incl. stock 0,0091 (0,64) Relative size 0,0214-0,0072 (1,61) (-0,46) Diversifying deals 0,0023 0,0059 (0,67) (0,44) Cross-border -0,0006-0,0237 -(0,17) (-1,54) Tender offers -0,0072-0,0172 (-0,56) (-1,06) Hostile deals 0,0106 0,0150 (0,70) (0,73) Leverage -0,0206 c -0,0825 a (-1,91) Cash flows-to-equity 0,0097 (0,89) Full sample Public Private Subsidiary (-2,98) 0,1059 b (2,15) -0,0103 (-0,27) -0,0001 (-0,02) -0,0495 a (-3,93) -0,0022 (-1,04) -0,0035 (-1,09) 0,0003 (0,06) 1,1015 (1,29) -0,0011 (-0,11) 0,0335 (1,49) 0,0003 (0,06) 0,0005 (0,12) 0,0141 (0,65) -0,0132 (-1,03) 0,0047 (0,46) 0,1235 b (2,27) 0,0059 (0,51) 0,0329 (1,10) -0,0101 a (-3,25) -0,0196 c (-1,80) 0,0177 (0,86) 0,6098 (0,84) -0,0125 (-0,31) -0,0204 (-0,46) 0,0134 (1,55) -0,0095 (-1,16) -0,0177 (-0,80) -0,0165 (-0,39) N 1096 99 854 143 Adjusted R 2 0,0473 0,0655 0,0562 0,0726 44

4.2.3 Sources of top-tier value creation As the cross-section regression analysis does not reveal any significant positive effect from top-tier advisors on bidder CAR, any further investigation of the sources of top-tier value creation seems unnecessary. However, the investigation might serve as further confirmation of the results found in the previous section and therefore the analysis is done after all. The analysis is conducted in the spirit of Golubov et al. (2012) and basically investigates two hypotheses, namely the so-called better merger hypothesis and the skilled negotiation hypothesis, which were both explained earlier. The intuition is to test these two hypotheses by considering the total dollar-denominated Synergy Gain (SG) and the Bidder s Share of Synergies (BSOS), respectively. To conduct this analysis, it is necessary to obtain additional data to calculate target CARs as well. Thus, the analysis is restricted only to public deals where it was possible to obtain target stock prices. This limits the number of included deals in this part of the analysis to 80. This is deemed acceptable as this analysis primarily serves as an extra check of the previous results. The variables are defined in table IV. If the better merger hypothesis is true, the top-tier dummy variable should be positively related to SG, because top-tier advisors should be able to structure mergers with larger synergies (for example by finding better matches, i.e. target firms that better suits the portfolio of the acquiring firm). Additionally, if the skilled negotiation hypothesis is true, then the top-tier dummy variable should also be positively related to BSOS because top-tier advisors should be able to make a larger share of the total synergies accrue to the acquiring firm. Results are presented in table VIII. Specification (1) shows the analysis of Synergy Gains, while specifications (2) and (3) is for Bidder s Share of Synergies. The difference between specification (2) and (3) is that the latter has the target used top-tier interaction variable included which should improve the regression. The rationale is again that the variable is taking the presence of a top-tier advisor on the target side into account. This will reduce the ability of the acquiring firm s advisor to make a larger share of the synergies accrue to bidder. Just as expected, the results show that the top-tier dummy variable is also insignificant in explaining Synergy Gains (SG) and Bidder s Share of Synergies (BSOS). This means that, contrary to Golubov et al. (2012), there is no evidence in favour of the better merger or skilled negotiation hypothesis. 45

Table VIII Sources of top-tier value creation The table presents results of the cross-sectional OLS regression analysis of Synergy Gains (SG) and Bidder s Share of Synergies (BSOS) on advisor reputation and other firm- and deal-specific characteristics for a sample of European public acquisitions. Variables are defined in table IV. All regressions control for fixed year effects which is done by including dummy variables for each year in the sample. These coefficients are suppressed in the results. The letters a, b and c denote statistical significance at the 1%, 5% and 10% levels, respectively. The t-statistics reported in parenthesis are based on White s standard errors adjusted for heteroskedasticity. Intercept 1.444,6790 (0,67) Top-tier 367,8375 (1,21) Top-tier x Target used top-tier Ln(size) -40,1573 (-0,29) Book-to-market -179,6220 (-0,57) Run-up 1.881,1630 b (2,04) Sigma 7.761,6628 (0,20) Payment incl. stock 203,8170 (0,78) Relative size -314,1908 (-0,73) Diversifying deals -172,1809 (-0,58) Cross-border -105,7410 (-0,32) Tender offers -392,7161 (-0,95) Hostile deals 224,1858 (0,30) Leverage 320,1889 (0,44) Cash flows-to-equity 2.631,2190 (1,40) (1) SG (2) BSOS (3) BSOS 6,1210 b (2,08) -0,3745 (-0,42) -0,2787 (-1,38) 0,7219 (1,33) -0,9823 (-1,03) -1,2708 (-0,37) -0,6442 (-1,31) -0,2713 (-0,58) 0,1469 (0,17) -0,7608 c (-1,74) -1,3881 a (-2,69) -0,6576 (-0,47) -2,9322 b (-2,41) -0,6963 (-0,25) 5,4427 b (2,02) 0,0609 (0,07) -1,4750 c (-1,91) -0,1678 (-0,94) 0,8105 (1,42) -1,1773 (-1,34) -1,9586 (-0,60) -0,5088 (-1,07) -0,1479 (-0,34) 0,0454 (0,05) -0,7568 (-1,66) -1,1158 b (-2,21) -0,9075 (-0,78) -3,3558 a (-2,78) -2,1253 (-0,66) N 80 80 80 Adjusted R 2 0,0138 0,0211 0,0577 These results support the findings from the cross-sectional regression analysis of bidder CARs in the previous section, generally neglecting the effect of advisor reputation in European M&As. However, it is interesting that the interaction variable Top-tier x Target used top-tier is significant at the 10% level in specification (3). This indicates that even though the top-tier 46

variable itself is not significant, the presence of a top-tier advisor on the target side has a significant negative influence on BSOS. This can be interpreted as if a top-tier advisor on the target side is able to make more of the value from the merger accrue to the target. The results should however, as mentioned, be interpreted with some caution due to the fact that the analysis is based on only 80 deals. 4.2.4 Time to completion Although this study finds no evidence of top-tier advisors having any direct significant impact on bidder returns, there are also other aspects of the M&A process, where financial advisors can potentially influence the outcome. Since financial advisors are often in charge of the negotiation process, one of these is the time it takes from the announcement until completion of the deal. So even though hiring a top-tier advisor does not have any significant on acquirer returns (as found in the previous section), another incentive for hiring a top-tier advisor could be that they maybe have the ability to close deals faster. On one hand it seems plausible that top-tier advisors are able to complete deals more quickly due to their superior skills and expertise. Golubov et al. refer to this as the skilled advisor hypothesis. On the other hand, top-tier advisors might be more cautious because they have more reputational capital at stake, and thus, it seems plausible that they will take more time to carefully evaluate the terms of the transaction and negotiate better terms for the acquirer. Golubov et al. (2012) refer to this as the diligent advisor hypothesis. In their paper, the authors find a negative coefficient on the top-tier variable in this context, meaning that toptier deals in general are completed faster, which is supporting the skilled advisor hypothesis. In order to test these hypotheses for the European sample in this current paper, another regression analysis is conducted with the dependent variable being time to completion, denoting the number of calendar days between the announcement date and the completion date. The analysis is restricted to the subsample of public deals, simply because acquisitions of unlisted firms are often only announced when completed. This causes the duration of the bid to be only one day, even though this is not actually the case. Table IX presents the results. As can be seen from the results, the coefficient of the top-tier variable is positive, yet insignificant, indicating that deals advised by top-tier financial 47

Table IX Time to completion The table presents the results of the cross-sectional OLs regression analysis of the time to completion on advisor reputation together with other deal- and bidder-specific characteristics. The sample consists of public acquisitions announced over the period from 01/01/2000 to 31/12/2012. Both acquirer and target are from the European Union. Variables are defined in table IV. The symbols a, b and c denote statistical significance at the 1%, 5% and 10% levels, respectively. The t-statistics reported in parenthesis are based on White s standard errors adjusted for heteroskedasticity. Public deals Intercept 12,6616 0,17 Top-tier 15,3964 (0,68) Ln(size) 5,2784 (0,91) Book-to-market 4,1803 (0,24) Run-up -51,2940 a (-2,69) Sigma 924,0292 (0,72) Payment incl. stock 41,7131 b Relative size (2,13) 44,6036 b (2,48) Diversification 0,7795 (0,04) Cross-border 13,7261 (0,68) Tender offers 6,3451 (0,29) Hostile deals -26,9284 (-0,94) Leverage -58,6731 (-1,04) Cash flows-to-equity -5,5163 (-0,07) N 99 Adjusted R 2 0,1110 advisors are generally not completed faster in European M&As. Again, this is different from the empirical findings from both Hunter & Jagtiani (2003) and Golubov et al. (2012). It seems plausible that top-tier advisors generally tend to work with larger and more complex deals, and this could explain the positive relationship between the top-tier variable and time to completion. However, this has already been taken into account, as the regression also controls for various deal- and firm-specific characteristics, including size. With an adjusted R 2 of 0,111, the model is able to explain around 11% of the variation in time to completion. 48

The coefficient on the top-tier variable can be interpreted as top-tier deals on average taking 15,4 days longer to complete. The coefficient is however highly insignificant and should therefore be interpreted with caution. Furthermore it should be noted that the analysis is based on a relatively small subsample of only 99 public deals. The main conclusion is therefore that top-tier advisors are not associated with shorter time to completion as the top-tier variable is highly insignificant in the model. Overall, the results from this additional analysis are interpreted as further evidence of the nonexisting effect of advisor reputation in European M&As. 4.3 Robustness of results The main conclusion of the analysis is that top-tier advisors are not associated with higher bidder returns in European M&As, relative to their non-top-tier counterparts. Furthermore, it is found that top-tier advisors are not associated with shorter time to completion either. At best, they are associated with the same time to completion as non-top-tier advisors (the coefficient on the top-tier variable is positive, but not statistically different from zero). As already pointed out, these results deviate from recent empirical findings from the U.S. M&A market. This section addresses the robustness of the results. 4.3.1 Financial advisor classification The first sensitivity test concerns the advisor league table from section 3.2.2 and the top-tier definition. Given that the top-8 classification is arbitrary, a robustness analysis is performed using top-6 and top-10 cut-offs, respectively. Using the top-6 cut-off, i.e. leaving out Lazard and Deutsche Bank AG from the top-tier classification, actually causes the top-tier dummy variable to be significant at the 10% level for the full sample. However, it remains highly insignificant across all the subsamples, providing only very weak support of the hypothesis of top-tier advisors delivering superior services. Using the top-10 cut-off, thereby including Citigroup and BNP Paribas in the top-tier classification, does not change anything with regards to the results either. Overall, the sensitivity analysis of advisor classification confirms the robustness of the results. 49

4.3.2 Event window and inputs for the market model Another sensitivity test relates to the event window defined in section 3.1.2. Given that the primary event window (-2, +2) used to calculate bidder CAR is arbitrary, alternative event windows to calculate the short-run announcement period returns are tested. The alternative windows are (-1, +1), (-5, +5) and (-10, +10). As mentioned in section 3.1.2 the length of the event window balances a trade-off between being able to capture the entire market reaction caused by the merger announcement, nothing more and nothing less. The rationale behind narrowing the window to (-1, +1) would be that the market immediately values the merger when announced and that there are almost no information leakages before the official announcement. On the other hand, an expansion of the event window to (-5, +5) or (-10, +10) might capture more of the effect if there are indeed information leakages before the official announcement, or if the market needs some extra time to fully incorporate the value of the merger in the share price. The cross-sectional regressions are therefore also conducted with these alternative event windows. The conclusion however, remains the same for all three alternative windows as with the primary event window of (-2, +2). As inputs for the market model, national value-weighted equity indexes are used as shown in section 3.2.1. As already mentioned, this could possibly introduce a bias as some indexes are dominated by a few large-cap stocks. Therefore an alternative analysis is also conducted with the MSCI-Europe index as market proxy for all equities in the sample. Again, the results remain unchanged. Thus, the sensitivity analysis of the event window and the market model inputs confirms the robustness of the results. 4.3.3 Other sensitivity tests Other sensitivity tests relates to the control for fixed year effects. Even though it intuitively makes sense to include fixed year effects, the models have all been tested without fixed year effects included. This does seem to improve the fit of the models a little bit. However, the main conclusions remain the same. The results without fixed year effect are disclosed in appendix C. The return data has also been checked for outliers. A plot of the bidder CARs (scatter plots are disclosed in appendix B) reveals a couple of extremely positive CAR(-2, +2) values above 50

50%. Removing these observations from the analysis does however not change the results. As an additional check, the analysis is rerun with bidder returns winsorized at the 1 st and the 99 th percentile to control further for outliers. This does also not change the results. 4.4 Generalizability This section compares the sample with a benchmark of European M&As. As described in the data section, the sample was initially selected on the basis of a carefully determined search strategy. Furthermore, some deals were removed due to data insufficiencies. In order to conduct a short-run event study, daily data on stock prices for the acquiring firms is needed, in order to quantify how the market values the mergers. Thus, the sample selection was restricted to only listed bidders, which introduces a possible selection bias. The benchmark is not restricted by the same criteria, meaning that it has no lower limit on deal value and furthermore the bidder is not required to be listed. The purpose of this comparison is to control for any selection bias in the sample, and thereby find out if the results can be generalized to all M&As within the European Union. 4.4.1 Year The sample period in the study runs from 01/01/2000 to 31/12/2012. Figure III illustrates the yearly M&A activity across the years included in this period. Figure III M&A activity by year The figure presents the yearly, equal-weighted percentage distribution of the number of events in the sample period. The yearly M&A activity in the sample is compared with a benchmark of European M&As. The benchmark contains deals with values below $10 million and deals where the acquirer is not listed on a stock exchange. 16% 14% 12% 10% 8% 6% 4% 2% 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Sample Benchmark Source: Zephyr 51

As illustrated, the sample consists of relatively more events from 2000 to 2007 compared to the benchmark index. This could imply that listed acquirers made more large acquisitions in the beginning of the period (lower bound on deal value of $10 million). From 2008 and up to 2012, the sample contains fewer events compared to the benchmark. Thus, the sample is somewhat skewed, with a larger proportion of events from the first part of the time period considered. However, since all models in the analysis control for fixed year effects, this is not believed to have had any significant impact on the results. Therefore, the distribution of events across years does not create any concerns with regards to generalizability. 4.4.2 Geography This section considers the geographical location of the acquirers included in the sample. Figure IV illustrates and compares the sample with the benchmark. Figure IV Geographical location of acquirers The figure presents the equal-weighted, percentage distribution of events across countries in the sample period, based on the geographical location of the acquirer. The sample is compared with a benchmark of 57.154 European M&As from the same time period. As opposed to the sample, the benchmark contains deals with values below $10 million and deals where the acquirer is not listed on a stock exchange. 40% 35% 30% 25% 20% 15% 10% 5% 0% Sample Benchmark Source: Zephyr As can be seen from figure IV, the sample is generally quite similar to the benchmark when it comes to geographic location of acquirers. However, it should be noted how Finland and the Netherlands appears to be underrepresented in sample. On the other hand UK, Italy and 52

Ireland are more dominating in the sample than in the benchmark. One way to interpret this could be that bidders from Finland and the Netherlands are generally involved in smaller deals (<$10 million), and maybe bidders are not even listed. The opposite could then be the case for the UK, Italy and Ireland. Maybe these countries are heavily represented in the sample simply because they have larger, listed bidders who are generally involved in larger deals. This could be a plausible explanation for the difference. Overall, the geographical dispersion of acquirers in the sample versus the benchmark does not indicate any potential selection bias. 4.4.3 Sector This section investigates the dispersion of events across sectors (from the bidder s perspective). Table X summarizes the weight of each sector in the sample, and compares to it the benchmark. As the table shows, both the sample and the benchmark are dominated by the Personal, Leisure & Business Services sector. However, this sector is actually slightly underrepresented in the sample compared to the benchmark with 18% versus 14%. The Banking, Insurance & Financial Services also accounts for a substantial number of events in both the sample and the benchmark. So does the Industrial, Electric & Electronic Machinery sector. Wholesaling is on the other hand underrepresented in the sample. Only in 4% of the events is the acquirer from this industry while the proportion is 9% in the benchmark. Again, this can be interpreted as this sector containing mostly smaller M&A deals which could be the reason they did not make it into the final sample. 53

Table X Sector distribution of events The table presents the equal-weighted, percentage distribution of events across sectors in the sample period, based on the acquirer s primary sector. Sectors are based on the Zephus Classification Codes from Zephyr s M&A database. The sample is compared with a benchmark of 57.031 European M&As. As opposed to the sample, the benchmark contains deals with values below $10 million and deals where the acquirer is not listed on a stock exchange. Sector Sample Benchmark Deviation Agriculture, Horticulture & Livestock 1% 1% 0% Banking, Insurance & Financial Services 11% 10% 1% Biotechnology, Pharmaceuticals & Life Sciences 2% 1% 1% Chemicals, Petroleum, Rubber & Plastic 3% 3% 0% Communications 5% 2% 3% Computer, IT & Internet Services 7% 9% -2% Construction 4% 3% 1% Food & Tobacco Manufacturing 5% 3% 2% Hotels & Restaurants 3% 2% 1% Industrial, Electric & Electronic Machinery 9% 6% 3% Leather, Stone, Clay & Glass Products 1% 1% 0% Metals & Metal Products 2% 2% 0% Mining & Extraction 3% 1% 2% Miscellaneous Manufacturing 0% 0% 0% Personal, Leisure & Business Services 14% 18% -4% Printing & Publishing 3% 2% 1% Property Services 6% 4% 2% Public Administration, Education, Health Social Services 1% 4% -3% Retailing 4% 5% -1% Textiles & Clothing Manufacturing 1% 1% 0% Transport Manufacturing 1% 1% 0% Transport, Freight, Storage & Travel Services 3% 4% -1% Utilities 5% 2% 3% Wholesaling 4% 9% -5% Wood, Furniture & Paper Manufacturing 2% 2% 0% Unknown 0% 4% -4% Source: Zephyr Overall, the sample and the benchmark lines up fairly similar, except for the few, small deviations just described. These deviations could possibly create skewed results, as the bidder returns might differ from sector to sector. One way to investigate this could be to include sector-specific control variables in the cross-sectional regression analysis to see if they would help explain any of the variation in bidder CAR. However, the deviations reported are not deemed to be that extreme and therefore this is not implemented in the analysis. 54

5. Discussion This section provides a short discussion of the results from the analysis. First, the results will be considered in a broader perspective and in relation to existing research. Next, the differences found between the U.S. and the European M&A markets will be addressed. Furthermore, possible weaknesses and limitations of the study and the chosen method are discussed. Finally, it is briefly discussed, how further research could supplement the knowledge and findings from this paper. 5.1 The results in a larger perspective When comparing the findings from this paper with the earlier empirical findings as presented in section 2.3, this paper generally supports the results of Servaes & Zenner (1996), Rau (2000), Hunter & Jagtiani (2003) and Ismail (2010), who does also fail to find a significant positive relationship between advisor reputation and bidder returns. The findings from this thesis and these other papers implicate that the theoretical relationship between reputation, quality and price does not hold for M&A advisory services. Thus, top-tier advisors do not seem to offer superior services, when considering service quality by bidder returns or time to completion. The inspiration for this thesis primarily comes from the paper of Golubov et al. (2012). The motive was to investigate some of the same hypotheses, but on a European dataset of M&As. The results from this paper are however contrary to the recent findings of Golubov et al. (2012), which is surprising, as the sample selection and the method used is quite similar to the one used in this paper. It therefore seems logical to conclude that the different results are due to differences between the U.S. and the European M&A markets. 5.2 Europe versus the U.S. This thesis presents new evidence on the role of advisor reputation in European M&As. The conclusion is that hiring a top-tier advisor does not seem to be associated with any significant value gains for the acquiring firm. Furthermore, it appears that top-tier advisors are not associated with shorter time from announcement to completion. The results are quite surprising, as a similar study of a U.S. sample reveals completely different results for the U.S. market for M&A, as already mentioned. 55

Why does this difference exist? This thesis does not hold the answer to that question. Maybe advisor reputation is simply not as important in European M&As because the difference in advisor quality is not as big as in the U.S. As mentioned in section 3.2.2, the advisor league table does deviate from the one applied in U.S. studies (i.e. other firms are classified as toptier in Europe compared to the U.S.), so maybe the difference lies in the European financial advisors that are classified as top-tier in this study. Or maybe, European investors do simply not acknowledge the superior services of top-tier advisors to the same extent that American investors seem to do. Furthermore, while top-tier advisors in the U.S. are associated with faster deal completion (as found by Hunter & Jagtiani (2003) and Golubov et al. (2012)) this does not seem to hold in European M&As. It is difficult to say why this is the case. Maybe top-tier financial advisors on the American market simply focus more on closing deals fast (skilled negotiation hypothesis) whereas top-tier advisors in Europe tend to focus more on being as thorough as possible (diligent advisor hypothesis). Although this thesis is not able to clarify why there are differences between the U.S. and Europe when it comes to the role of financial advisors, it has provided new evidence that this is in fact the case. 5.3 Limitations of the study As also pointed out earlier in this paper, the event study method has certain limitations and weaknesses. There is no need account for these once again, however it is deemed relevant to briefly look at the possible implications for the results of the investigation. One issue is the announcement period returns. The longest event window used in this thesis is (-10, +10), assuming that this window is sufficient in order to capture the effect of the merger announcement. As mentioned earlier, this might not be the case if the market to a certain extent has already successfully decoded rumours or signals and consequently incorporated the effect of the merger in prices. In such cases, the event window CAR will be reduced and thereby the effect of the merger announcement will not be measured correctly. This study assumes exogeneity in the choice of advisor. This assumption is questionable at best. Golubov et al. (2012) applies a so-called Heckmann two-stage procedure that takes this into account. In the first stage, the binary choice of top-tier or non-top-tier advisor is modelled 56

and the second stage then corrects for the selection bias. However, as this procedure requires extra data, which was not accessible, it is not applied in this paper. It should also be noted that in Golubov et al. (2012), the use of this procedure does not change any of the main results. Furthermore, in order to be able to conduct a short-term event study, stock return data is needed in order to measure how the market reacts to a given merger announcement. As a consequence of this, the study is limited to listed acquirers. This was investigated in section 4.4 where the sample was compared to a benchmark of deals from Europe including unlisted acquirers. As this comparison showed no dramatic deviations, it seems reasonable to assume that the results apply to unlisted bidders as well. Last but not least, it should be mentioned that the results relies on the assumption that investors are able to efficiently value the effect of the M&As. The abnormal returns measured around the announcement dates then represent the value creation for acquirer shareholders. This method requires that no other firm-specific information that can affect stock returns is released within the event window. Also after the event window, no further price movements should be related to the merger announcement. Thus, for the method to be effective, investors should, relatively quickly, be able to correctly value the implications and possible synergies of a given merger. Based on section 3.1.1 about market efficiency this is deemed to be rather realistic, although critical. 5.4 Further Research As already mentioned, this paper does not take the possible endogeneity that arises from the advisor choice being correlated with some bidder- and deal-specific characteristics. Due to data limitations this was not possible in this study. Still, it would be interesting to see if the results from this paper continue to hold after controlling for endogeneity. As already mentioned under limitations, the results in this paper are relying on the assumption that markets are efficient in the valuation of every merger, implying that the stock price almost immediately reflects the future value that the merger is going to create. Thus, it is a short-term analysis. As a supplement, a study of long-term study could also be conducted with the purpose to investigate how the firms perform in the years after a merger, as it might take some time before synergies begin to show explicitly in the financials. Thus, it would be interesting to see if there is any difference in the long-term performance between firms that used a top-tier advisor and firms that did not. 57

6. Conclusion This thesis investigated whether financial advisor reputation matters for M&A returns in European mergers and acquisitions. The analysis is conducted on a comprehensive dataset compiled from four different databases. The dataset contains a total of 1.096 European mergers and acquisitions. Furthermore, a European advisor league table has been constructed in order to classify financial advisors into top-tier and non-top-tier, respectively. First, when simply comparing average bidder CARs for the top-tier and non-top-tier subsamples, no significant difference is found. However, as this method does not take any deal- or firm-specific characteristics into account, a cross-sectional regression analysis is conducted to control for these characteristics. The sample is also split into three subsamples based on the target s listing status (public, private or subsidiary), in order to find out if advisor reputation is equally important for these different types of deals. The cross-section analysis reveals some very interesting results, as it shows no significant effect from hiring a top-tier advisor compared to a non-top-tier advisor. Although the top-tier coefficient is generally positive in the cross-sectional analysis it is not statistically significant for any of the subsamples. The results do not change when controlling for when the target firm also employed a top-tier advisor. Thus, it can be concluded that hiring a top-tier advisor did not have any significant impact on bidder returns, in the time period considered in the analysis. Furthermore, the time from announcement to completion of the deal seems to be the same for deals involving top-tier advisors, which is surprising, because it was expected to be shorter as found in earlier research by Hunter & Jagtiani (2003) and Golubov et al. (2012). The results are robust to variations in choice of event window, market index, cut-off point in advisor league table and outliers. It is also shown that the sample is generally quite representative for the European M&A market as a whole with regards to time distribution of events, geography and sectors. The results stand in contrast to Golubov et al. (2012), which in a similar study based on a U.S. sample, find that advisor reputation has significant impact on bidder CAR in public 58

acquisitions and that top-tier advisors are generally associated with faster deal completion. As the sample selection criteria and the method used are quite similar, the results from that paper and this thesis should be somewhat comparable. Furthermore the results are not consistent with the theory of the reputation and quality relationship. Overall, this paper presents new empirical evidence on the role of financial advisors in European M&As. It is ultimately shown that hiring a top-tier advisor does not have significant impact on bidder returns in European M&As. This goes for public, private as well as subsidiary targets. 59

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