Investment banks or boutiques: Why are they chosen and what firm type makes the better M&A deals?
An empirical investigation regarding domestic M&A deals in the USA
Master Thesis
Author: S.R. Faber ANR (SNR): 215677 (1275258) Program: Master of Finance Supervisor: dr. C.A.R. Schneider University: University of Tilburg, Netherlands
Abstract
Between 2000 and 2015 there has been an increase in the M&A advisory business for boutiques advisors. The main difference between boutique advisors and full-service investment banks is that boutique advisors generally have knowledge in a specific sector or have a specialization on M&A, whereas full-service investment banks often cover a wide variety of services. This thesis investigates whether the findings found by Song et al. (2013) are still valid using another dataset. Song et al. (2013) investigated the firms’ choice for a boutique- or full-service advisor and what consequences it has for the deal outcome. It was found that boutique advisors (mixed advisory teams) have a higher probability of getting hired, when the deal size is small (high). In addition to this, boutique advisors generate higher deal premiums on both the target and acquirer side of the deal. No statistical evidence is found to draw conclusions regarding the influence of advisors on deal duration and deal completion. Overall, no significant statement can be made regarding the performance of boutique advisors when they are compared to full-service advisors.
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1. Introduction
Over the last years there has been an increase in mergers and acquisitions (hereafter: M&A) deals, both domestic and international. Thomson Reuters published an overview of 2015, and found that it has been the biggest year for mergers and acquisitions ever, with worldwide announced deals worth of a total of $4.7 trillion1, compared to $3.3 trillion in 2014 (an increase of 42%). America’s M&A volume increased 43.3% during 2015 compared to 2014, accruing nearly $2.9 trillion in activity from 15,922 announced deals. The majority of these deals were advised by at least one financial advisor, known to be an investment bank, an investment boutique or a mix of both. The report that was published by Thomson Reuters in 2015 also reports about the advising parties and how they did over 2015. One interesting factor to consider is that the advising companies that were ranked as number one to seven are all investment banks in the performance rankings, and thereafter it becomes more a mix of boutiques and investment banks. Both investment banks and boutiques provide financial advising services to their clients. However, boutiques are usually independent (free of conflicts), specialized in a specific branch and have an expertise in M&A, yet they are often not well-known and small. Meanwhile, investment banks are often large and well-known, and have, besides M&As, other fields of expertise. Despite this, Reuters found that, since the financial crisis boutiques have been aggressively grabbing market share from M&A deals from their investment bank counterparts. In 2016, the boutiques accounted for 34% of the fees paid out to advising fees, an increase of 20% compared to 20072. This is a strong indicator that the boutiques have gained increasing popularity over the last years, and this reflects on the results they achieved. Song et al. (2013) wrote an analysis about factors that make firms chose for a boutique advisor or a mixed advisory team and the consequences for the deal outcome. The paper was focused on the differences between investment banks, boutiques and a mix of both on the takeover premium, deal duration and deal completion using data between 1995 and 2006. As stated above, much has happened in M&A over time. The aim of this paper is to find out whether the findings of Song et al. (2013) are still valid, using data between 2000 and 2015. Research will be conducted under the two hypotheses that were used by Song et al. (2013) which are the scale and the skill hypotheses. The scale hypothesis will investigate whether the
1 Thomson Reuters Website; ArticleNr: MA-4Q15-(E). Accessed at: http://share.thomsonreuters.com/general/PR/MA-4Q15-(E).pdf (page 4) Note: These numbers are the deals in which a US company is the target company. This may be domestic and international deals. 2 Retrieved from http://www.reuters.com/article/us-banks-boutiques-strategy-idUSKBN1432WH
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 3 deal size determines the choice between an investment bank and a boutique. On the other hand, the skill hypothesis will investigate whether the complexity of the deal determines the choice between an investment bank and a boutique, and states that in a complex deal it is more likely that a boutique will be chosen as an advising company due to their specific knowledge of a branch. The data finds contradictory evidence regarding the scale hypothesis: it is found that boutique advisors are less likely to be hired over full-service advisors in larger deals; however, it is also found that mixed advisory teams are more likely to be hired over a full- service advisor when deal size increases. These findings are valid for both acquirer advisors and target advisors. Furthermore, it was found that the evidence regarding the skill hypothesis is inconclusive and insignificant. From the perspective of the acquirer it is found that boutiques are more likely to be hired in hostile deals and cross-industry deals, yet less likely to be hired in deals with a competing offer. Mixed advisory teams, on the other hand, are more likely to be hired by an acquirer in cross-industry deals, yet less likely to get hired in hostile and deals with a competing offer. From a target advisor perspective, it is found that both boutique advisors and mixed advisory teams are less likely to be hired in stock deals and hostile deals, which is opposing the skill hypothesis. The results regarding the impact of advisors on deal outcomes find that boutiques on both the acquirer and target side of the deal significantly increase the deal premium. Adding to this, the results regarding deal duration imply that the advisor choice does play a significant role, contradictory to the deal completion where the advisor choice does not play a significant role. It is found that the deal characteristics have a greater significant impact on both deal duration and deal completion. This paper contributes to the existing literature financial advisor and M&A literature as it will investigate whether previous research is still valid while other more recent data is used. In previous research it was firstly concluded that firms that face difficult deals are more likely to employ a boutique as an advising company. Secondly, it was concluded that boutique advisors provide better deal outcomes, and that they are therefore gaining in popularity. Finally, Song et al. (2013) found that the deal premium decreases when a boutique advisor is consulted on the acquirer side of the deal, boutique advisors take significantly longer to complete a deal and that the choice of an advisor does not matter for the probability of completion. Using another dataset, it is concluded that acquirer boutiques significantly
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 4 increase the deal premium in a deal. The results regarding deal duration and deal completion are insignificant. The remainder of this paper is structured as follows. Section 2 will review the existing literature regarding the effect of financial advisors on M&As. In section 3 the hypotheses will be stated and explained, equitable to their empirical testability. Section 4 describes the data, and section 5 will report the empirical findings. The paper will be concluded in section 6.
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2. Literature review
Previous literature regarding M&As is mainly focused on abnormal stock returns around the M&A announcement date, with control variables regarding the target company, the acquiring company, and the deal itself. The literature regarding the importance of advisors in M&A deals is more limited and can be divided into two sub-categories: (i) the early literature mainly focuses on contract terms and fees transactions, and (ii) the more recent literature focuses more on reputation effects on the completion and advisor choice. Angwin and Karamat (2015), consider reputation to be a mechanism for increasing the likelihood of repeat participation in M&A activity by developing attributes that positively influence M&A deal characteristics, and for clients to engage investment banks with the relevant traits matching 230 the deal criteria to improve deal performance. First of all, Hunter and Jagtiani (2003) and Ma (2005) find that top-tier advisors are generally more capable of bringing a deal to completion than lower tier advisors. In addition to this, Graham, Walter, Yawson and Zhang (2017) find that compared to non-industry specialists, advisors specializing in the target industry help acquirers garner higher announcement returns. Forte, Iannotta, and Navone (2010) find that the abnormal returns of target company shareholders increase with the intensity of the previous banking relationship. On the other hand, Schiereck, Sigl-Grüb and Unverhau (2009) find that there is no significant difference in wealth for transactions advised by different advisor tiers. In addition to this, Allen, Jagtiani, Peristiani, & Saunders (2002) find that acquirers tend to choose their own banks (banks they had a previous relationship with) as advisor in mergers. Kale, Kini, & Ryan (2003) find that the absolute wealth gain, as well as the share of the total takeover wealth gain accruing to the acquirer (target), increases (decreases) as the reputation of the acquirers’ advisor increases relative to that of the target. Next to that Kale et al. (2003) also find that the total wealth created in the takeover is positively related to the reputation of acquirer and target advisors. While bidder advisor reputation is positively related to the probability of bid success in their sample, they also present some evidence to suggest that bidders with better advisors are more likely to withdraw from potentially value-destroying takeovers (Kale et al., 2003). Ismail (2010), however, finds that the market share based reputation league-tables, could be misleading and therefore, the selection of investment banks should be based on their track record in generating gains to their clients. Rau (2000) finds that market share by an investment bank is unrelated to previous performance. Song et al. (2013) believe that
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 6 reputation is vaguely defined and does not necessarily reflect a result of previous performance. Golubov, Petmezas and Travlos (2012) find that top-tier advisors, based on market share, have a better ability to deliver higher bidder returns, because they have a better ability to identify more synergistic combinations. The choice for an advisor, however, cannot be limited to market share of reputation effects. Servaes and Zimmer (1996) found that the choice of an investment bank depends on the complexity of the transaction, the type of transaction, the previous acquirer take-over experience and the degree of diversification of the target firm. Kien, Thuy & Phuong (2017) found evidence from the Malaysian M&A market that targets and acquirers are more likely to hire an investment bank when the deal takes place in a crisis period and the deal value is high. Finally, Allen, Jagtiani, Peristiani & Saunders (2004) found that acquirers would preferably hire a bank as advisor they have had a previous relationship with in their mergers. Chang, Shekhar, Tam & Yao (2016) find that an investment bank's expertise in merger parties’ industries increases its likelihood of being chosen as an advisor, especially when the acquisition is more complex, and when a firm in M&A has less information about the merger counterparty. Various studies also find a link between the advisor choice and the implications it has for the deal outcome. Song et al. (2013) find lower deal premiums when acquirers hire boutique advisors. In addition, Song et al. (2013) also found that boutique advisors spend more time, probably on due diligence and negotiation, to complete deals. Chahine & Ismail (2009) found that the reputation of investment banks does not affect the level of the premium. Rau (2000) found that in both mergers and tender offers, bank market share is positively related to the contingent fee payments charged by the bank. If we extend this and assume that larger fees mean a bigger market share of the advisor, then some more conclusions can be drawn. Walter, Yawson & Yeung (2008) point out that advisors with higher market share receive higher fees for advising deals and complete deals faster (which does not necessarily reflect a higher probability of deal succeeding). Finally, Agrawal, Cooper, Lian & Wang (2013) did research regarding the effect of separate or common advisors in M&As. They found that deals with common advisers take longer to complete and provide lower premiums to targets. Further down in this paper, the advisors will be divided into tiers. Equitable to Song et al. (2013), Meggingson and Weiss (1991) and Rau (2000) a three-tier ranking system will be applied. Since Rau (2000) and Ismail (2010) found that market share does not necessarily reflect previous performance, this will be done based on the total deal size the advisors
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 7 participate in during the full sample (2000 until 2015). In case an advisory team worked on a deal together, the advisory team will get assigned into a tier. More explanation will be given when the tiers will be discussed. In the regression tables, firm specific fundamentals are used such as the Market to Book Value of both the acquirer and the target (MV/BV), Return on Equity (ROE), Target Debt/Equity ratio (D/E) and the growth in sales over the fiscal year prior to the announcement of the target. These fundamentals provide a full picture of the financial situation of the target company: profitability (ROE), capital structure (D/E Ratio) and growth potential (sales growth). The Market to Book value of both the target and the acquirer are taken into account as a proxy for size.
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3. Hypotheses and empirical testability
3.1 Hypotheses Before the hypotheses will be described and explained, the three main differences between investment banks and boutique banks will be investigated. First of all, Stowel (2012) finds that boutique banks principally focus on small specialized transactions in financial services (M&A, debt restructuring and small money services) whereas investment banks focus on higher transactions and focus on the full-service (mortgage services and deposit taking). In addition to this, Song et al. (2013) and Allen et al. (2003) also states that boutiques are usually smaller and independent which makes them less vulnerable to conflicts of interest. The second difference is that boutique banks are generally smaller and less well-known and therefore have less market share than investment banks. In the M&A advisory market share is important because the industry often equates the market share with quality (Song et al., 2013). Lastly, Hunter and Jagtiani (2003) found there is a difference between the fees that investment banks and boutiques charge for their services as an advisor. This is in-line with the market share statement that was made earlier, since the boutiques have to ask for lower fees in order to compensate for the lack of market share they have. Since the line between an investment bank and a boutique is thin, from now on the investment banks will be referred to as ‘full-service’ companies, since they have activities in the full spectrum of investment banks. The probabilities for the decision of consulting an investment bank or a boutique will be investigated using two hypotheses, equitable to the hypotheses used by Song et al. (2013). The scale hypothesis argues that larger firms with higher deal sizes go hand in hand with a higher probability that an investment bank will be considered as financial advisor. This could be due to the fact that the larger firms, with larger deals prefer to have a well-known advisor. Another interpretation could be that the investment bank and the larger clients have a previous relationship with each other. As Allen et al. (2004) and Puri (1996) find, this could be due to the ‘certification effect’ (Song et al., 2013). This leaves the smaller firms that do smaller deals with the boutique advisors, since they are either unappealing for the investment banks (they are not worth the time of the investment banks) or it provides a cheap alternative to the investment banks since their deals provide less income from fees.
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Scale hypothesis: Full-service advisors are more likely to get hired when deal size increases.
Alternatively, the skill hypothesis implies that when a deal is more complicated, a boutique is more likely to be hired over an investment bank, due to their specific knowledge of an industry.
Skill hypothesis: Boutique advisors or mixed advisory teams are more likely to get hired when deals are more complicated.
Note that the two hypotheses (skill and scale hypotheses) are not necessarily mutually exclusive. It is very possible that a smaller firm prefers a boutique for their knowledge, while another smaller firm prefers a boutique because they are cheaper. Regarding deal completion, the hypotheses are referring to deal premium, deal duration and deal completion (whether the deal is completed or not). The hypothesis on the deal premium states that boutique advisors or mixed advisory teams on the target side of the deal increase the deal premium. On the acquiring side of the deal, the hypothesis states that deal premium will decrease. This difference is caused by the different roles that the advisors play on the side they are advising: the acquirer firm wants to buy the target for a price preferably as cheap as possible, whilst the target firm wants to have a price as high as possible. Boutique advisors generally have specific sector knowledge or a speciality in M&A, therefore similar results should reflect this as well.
Deal premium hypothesis: The deal premium of acquirers (targets) will decrease (increase) when a boutique advisor or a mixed advisory team is hired.
The skill hypothesis states that boutique advisors and mixed advisory teams are more likely to be hired when deals are more complex. A consequence of this is that the deals that are advised by boutique advisors or mixed advisory teams take longer to complete or get withdrawn. There is, nonetheless, no direct link between the scale hypothesis and deal outcomes. Deal duration hypothesis: Boutique advisors or mixed advisory teams take longer to complete deals.
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3.2 Empirical implications of advisor choice There are a number of testable empirical implications that can be tested regarding the scale and the skill hypotheses. First of all, the scale hypothesis will be tested based on the deal size for the deals in the sample. If the scale hypothesis holds, both the target and acquirer are more likely to hire a full-service advisor when the deal size is high. Secondly, the skill hypothesis will be tested based on variables that came forward out of research done by Servaes and Zimmer (1996), who found that the complexity can be tested using the deal attitude (hostile deals are more complex than friendly deals), the industries that the target and acquiring firm operate in (same industry generally means an easier deal) and the presence of another bidder for the target company (more bidders complicate a deal). In addition to this, Song et al. (2013) also added offers in which at least 50% of the consideration is acquirers’ equity to this list (stock offering complicates a deal as it makes it harder to price the offer). If the skill hypothesis holds, it implies that both target and acquiring companies are more likely to hire a boutique advisor or a mixed advisory team, when the deal is hostile. In a hostile deal, the target company does not want to participate in a deal, so it gets more complicated from an acquirer perspective. In order to persuade the target (or its shareholders) to make a deal, the acquirer needs the expertise to make a plan that the target company will want to participate in. Secondly, acquirers are more likely to hire a boutique advisor or a mixed advisory team when the target company operates in a different industry than the acquiring company does. It is assumed that an acquirer will need more industry expertise to make the deal succeed, which can be offered by a boutique advisor or a mixed team. Thirdly, acquirers are more likely to hire a mixed team or a boutique advisor when there is a competing bid, as they want to make sure that they will get the opportunity to buy the target firm. Competing offers have no impact for target companies as they benefit from multiple biddings, since this will increase their negotiating power. Finally, when there is an offer in which the acquirer offers at least 50% shares of its own firm as consideration, the target company is more likely to hire a boutique advisor or a mixed team. Offers in which at least 50% of the consideration is acquirers’ equity are harder to value than cash, and thus the deal becomes harder to valuate.
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3.3 Empirical implications for deal outcomes The two most important factors the advisor of a M&A deal possibly can influence, are the deal premium (the price paid for the target firm on top of the value of the target firm) and the deal duration (number of days between deal announcement and deal completion). The second part of this paper will investigate whether the choice for an advisor has a marginal effect on the deal premium and deal duration. In addition to this, it will also be investigated whether the choice of an advisor has an impact on deal completion. Firstly, the effect of advisors will be tested by an OLS regression in which deal premium is the depended variable. Dummy variables will be created for each advisor type, on both the target and acquiring side. Full-service advisors are considered to be the base-level. In addition to this, all the variables that complicated a deal, and thus might increase the deal premium, cannot be ignored and therefore will be included in the regression as well. In the second model, various fundamentals will be included as well. Three regressions will be run: once for the full sample, once for the merger subsample and once for the tender subsample. Each regression will have a model that does include various fundamentals and one that does not. Song et al. (2013) found possible endogeneity between boutique advisors and various fundamentals of the firms included in the deal. Therefore, they applied a two-step treatment procedure to take this into account. In the first step of the two-step treatment procedure, a probit model on an acquirers’ dummy is run with deal characteristics (deal size, stock, hostile and cross-industry) along with fundamentals and time dummy variables. Out of this probit model a hazard rate is estimated which is thereafter included in the regression. Secondly, a similar regression will be run for the deal duration in days, in which deal duration is the dependent variable. The deal duration is calculated in Microsoft Excel, using the ‘datedif’ function. Weekends and possible holidays are included in the calculations for the deal duration at all cases. Equitable to the regression regarding the deal premium, three regressions will be run (full-sample, merger subsample and tender subsample) with each two models (including deal characteristics, excluding fundamentals and including both deal characteristics and fundamentals). The dummy variables that represent the advisors on the target and the acquiring side will be included as well. This model will not contain a two-step treatment procedure. Finally, a probit regression will be run on deal completion. The dependent variable will be a dummy variable that equals one if the deal is completed and zero if the deal is withdrawn.
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The marginal effects represent the probability on completion with certain advisors, certain deal characteristics and fundamentals (only model 2 of each (sub)sample). If the hypothesis regarding the deal premium holds, the premium paid in deals is higher when a boutique advisor or a mixed advisory team is higher on the target side of the deal, and lower on the acquiring side of the deal. Secondly, when the deal duration hypothesis holds, target and acquirer boutique advisors or mixed advisory teams will take longer to complete or withdraw deals.
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4. Data collection and descriptive statistics
4.1 Data collection The data of M&A transactions is retrieved from the Securities Data Corporation (SDC) U.S. Mergers and Acquisitions database. Completed and withdrawn deals are selected from 2000 until 2015, in which the target and the acquirer are publicly traded companies. Further criteria are: the deal value has a minimum value of $10 million, less than 50% of the target company’s shares were owned by the acquiring company before the transaction, the deal must have at least one advisor on both the target or the acquiring side of the deal and have a take- over premium between -50% and +200%. All these criteria, except for the period, are equitable to the criteria utilized by Song et al. (2013). The selection of another period is relevant since it will shine a possible new light of the findings of Song et al. (2013). The period from 2000 until 2015 is chosen since it gives insight in years around and during the financial crisis from 2007 until 2009. Using the website of the advising company or the company page on the website of Bloomberg, all advisors are classified as a boutique advisor or a full-service advisor3. The criteria to classify an advisor as a boutique is that the firm must have a specialisation in M&A and furthermore only specialises on ‘small money’ activities such as financial advisory or valuation. Any company that has activities such as trading, sales and lending is considered to be a full-service advisor. Figure 1 presents an overview of the number of deals between 2000 and 2015 and the proportion of those deals that is advised by a boutique advisor. The graph shows a number of interesting aspects. First of all, market share of boutique advisors grew between 2000 and 2015. Boutique advisors that acted on behalf on the acquirer (target) had 4.4% (6.6%) market share in 2000, which has grown to 12.7% (14.9%) in 2015. Secondly, there is a peak in target boutique advisory services line around 2009, whilst there is a decrease in the amount of deals compared to 2008. This could be due to the financial crisis: companies prefer knowledge of a branch over reputation or market value. Thirdly, there is an exceptional increase in both acquirer and target boutique advisors in 2003, where the advising services for acquirers (targets) increased from 5.5% (3.3%) to 11.1% (12.7%) and the total deals announced increased as well. This could be due to the early recession of the 2000’s, in which firms prefer to have sector knowledge when conducting M&A deals. All the findings could be in line with
3 See appendix 1 for the list of individual advisory firms and to what category they have been allocated to.
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 14 the skill hypothesis: in difficult times, advisors are more likely to consult sector experts in order to guide the deal into the right direction. As stated earlier, the graph displays interesting results between the 2007-2009 years. Therefore, this period will be monitored in addition to the two hypotheses.
Figure 1: The graph reports the market share of boutique advisors in U.S. M&A deals between 2000 and 2015. The bars present the number of announced deals (left axis) and the lines present the proportion of those deals that is advised by a boutique advisor (right axis). The proportion in which the boutique advisor acted on behalf of the acquirer (target) is presented in the dashed (solid) line.
4.2 Terminology It is important to first go over some terms that are often used in M&A deals. Deal duration refers to the number of days between the announcement date of the deal and the completion date (when the deal succeeds) or the withdrawn date (when the deal is withdrawn). The takeover premium is the difference between the price offered and the market value of the target company. Equitable to Song et al. (2013) the method of Officer (2003) will be used to calculate the takeover premium. Primarily, the share price and the shares outstanding 43 days prior to the deal announcement will be used to calculate the targets market value. If this value cannot be retrieved or is unreasonable (outside the range of -50% to 200%), the change in percentage between the initial offer and the final offer price will adjust the share price, after which the market value 43 days before the deal announcement
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 15 will be calculated once more. If this market value does not return a reasonable value or cannot be retrieved, the market value of the target company four weeks prior to the deal announcement, as reported by SDC, will be used to calculate the takeover premium. Any remaining unreasonable takeover premiums or missing values will be deleted. The combination of these three methods reduces the probability of inaccurate information and missing observations (Song et al., 2013).
4.3 Summary statistics Table 1 reports the summary statistics for advisors on both the target and acquiring side of the deals. The findings indicate that the choice for an advisor on the acquiring side of the deal is mainly dependent on the deal value. The average deal value for full-service advisors is over $2.4 billion, whereas the average deal value for boutique advisors is almost $1.0 billion. However, the average deal value with a mixed advisory team appears to be even higher than a full-service advisory (team) with an average of almost $7.9 billion. The acquiring advisor summary statistics reports that mixed advisory teams have a significant higher average acquirer size than full-service advisors. A mixed advisory team has both the reputation and scale advantages of a full-service advisor and also the sector knowledge of boutique advisors, which makes it a strong mix in M&A deals. Therefore, they will most likely be hired in complex and in deals with a high deal size. The same conclusion can be drawn when comparing the average acquirer size of full-service advisors and boutique advisors: boutique advisors have a significant higher average acquirer size. There is not a direct explanation for this finding. In fact, I would expect that full-service advisors have a higher average acquirer size than boutique advisors as larger firms often want a big and well- known advisor. Furthermore, the relative size on the acquiring side shows that boutique advisors generally have a higher relative acquirer to target size than the full-service advisors (32.07 versus 81.98) when they are advising acquiring firms. This could be due to the fact that acquiring firms are willing to give the boutique advisors a change when there is a smaller takeover target, whilst they are more likely to consult a full-service advisor when the target firm is larger. The average relative size difference between full-service and mixed team advisors is insignificant, so no conclusions can be drawn from this result. When looking at the target advisor summary statistics, similar results as the acquirer summary statistics with respect to the average deal size are found: full-service advisors
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 16 averagely have a significant higher deal size than boutique advisors. The average deal size of full-service advisors is $2.6 billion, whereas the average deal size for boutique advisors is $1.2 billion. Mixed advisory teams appear to do the highest deals with an average of $5.7 billion. Furthermore, the summary statistics display that boutique advisors do significantly less deals in which at least 50% of the consideration is acquirers’ equity. The proportion of these deals for boutique advisors is 42.72%, whereas the full-service advisors have a proportion of 55.05%. In addition to this, mixed advisory teams have a proportion of 41.38% of these deals. These findings are opposing the skill hypothesis. There is no evidence to make a statement regarding hostile deals. Finally, it is found that mixed advisory teams averagely have larger acquiring firms when they are consulted as advisors by target firms. Mixed advisory teams provide the resources (scale advantages) of full-service advisors and the knowledge of boutique advisors, and therefore have a strong negotiating position to get the best deal, which could make them a popular advisor among target firms.
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s i s e h T r e t s a M Table 1 Summary statistics of the Acquirer advisors in M&A deals between 2000 and 2015 sorted by financial advisor. ‘Acquirer Size’ is the market value of the acquiring firm 43 days before the deal announcement. ‘Relative size’ is the ratio of acquirers’ size by the target size. ‘Acquirer (Target) MV/BV’ is the Market to book value of the acquirer (target) for the prior fiscal year (COMPUSTAT ((24*25)/60)). ‘% Hostile’ is the percentage of hostile. ‘% Tender’ is the percentage of deals in which a tender offer was used. ‘Stock offer' is the percentage of deals in which at least 50% of the consideration is acquirers’ equity. ‘Premium’ is the average take-over premium. Number of non-missing observations is shown in the parenthesis. ***, ** and * indicates significance at 1, 5 and 10% level respectively.
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r e b a F s a B Panel A: Summary statistics acquirer advisors Average Average Average Acquirer Target % % % Premium Deal Size Acq. Size relative MV/BV MV/BV Hostile Tender Stock (mil) (mil) size (%) offer
– (1) Full-Service 2473.4 13356.0 32.07 5.616 2.856 1.799 13.25 52.70 52.52
6 1 0 2 ( e c n a n i F . c S M (1668) (1583) (1440) (1560) (1448) (1668) (1668) (1668) (1668)
(2) Boutique 994.4 20592.7 81.98 6.159 2.245 1.342 16.11 50.34 57.52 (149) (140) (116) (135) (118) (149) (149) (149) (149)
(3) Mix 7875.4 22426.7 9.455 4.256 2.857 1.379 12.41 50.34 49.01 (145) (135) (125) (134) (131) (145) (145) (145) (145)
Total 2760.3 14560.4 33.83 5.557 2.814 1.733 13.40 52.34 52.64 (1962) (1858) (1681) (1829) (1697) (1962) (1962) (1962) (1962) Mean ** * ***
- difference: + - - - + + - + - 2017) (1) – (2) Mean
difference: - *** - ** + + - + + + +
(1) – (3)
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s i s e h T r e t s a M Panel B: Summary statistics target advisors Average Average Average Acquirer Target % % % Premium Deal Value Acq. Size relative MV/BV MV/BV Hostile Tender Stock (mil) (mil) size (%) offer
(1) Full-Service 2585.7 14161.1 32.87 4.915 2.742 1.932 13.14 55.05 51.92 (1553) (1480) (1347) (1457) (1357) (1553) (1553) (1553) (1553)
–
r e b a F s a B (2) Boutique 1224.3 11241.8 58.65 4.178 2.201 1.456 16.50 42.72 56.80 (206) (190) (164) (187) (166) (206) (206) (206) (206)
(3) Mix 5654.4 21057.2 17.51 12.00 3.962 0.493 12.32 41.38 53.93 (203) (188) (170) (185) (174) (203) (203) (203) (203)
– Total 2760.3 14560.4 33.83 5.557 2.814 1.733 13.40 52.34 52.64
6 1 0 2 ( e c n a n i F . c S M (1962) (1858) (1681) (1829) (1697) (1962) (1962) (1962) (1962)
Mean Difference: + ** + - + + + - + *** - (1) – (2) Mean Difference: - *** - ** + - - + + + *** -
(1) – (3)
-
2017)
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5. Empirical results
In this section, the empirical investigation of the two hypotheses will be performed and explained. First, I will focus on the factors that influence the decision for a full-service advisor, a boutique advisor or a mixed advisor team. After which I will investigate how the decision of a certain advisor influences the deal outcomes, such as the deal premium and the deal duration. Finally, the impact on the advisor will be investigated on deal completion.
5.1 Empirical results on advisor choice Table 2 reports a multivariate regression of the probability that a boutique advisor or a mixed advisory team will be preferred compared to a full-service advisor. A total of six multivariate regressions had been run, where advisor choice is the depended variable for all of them and the full-service advisor as base level. In the first regression of every model, the deals advised by a boutique advisor are compared to full-service advisors, and the second regression compares mixed advisory teams to full-service advisors. These regressions have been run for the full sample, the merger subsample and the tender subsample. The decision for a multi logit regression has been made due to comparison possibilities the model offers. In addition to this, the fact that the model offers possibilities to look at different aspects is a strong motivation to choose for this model. Full-service advisors have been selected as base, as the aspects of the skill hypothesis state that acquirers or targets are more likely to choose for a boutique advisor or a mixed advisory team over a full-service advisor. Therefore, both boutique advisors and mixed advisory teams will be both compared to full-service advisors. Panel A of Table 2 reports the results of the acquirer-side of the deal. The results of the full sample show that the deal size is an important factor in the decision for an advisor concerning acquiring firms. Model 1 shows that acquirers are more likely to hire a full-service advisor when the deal size increases. In fact, when deal value increases by $500 million, the likelihood of the acquirer hiring boutique advisor over a full-service advisor decreases by 34.8%. Model 2 of the full sample shows that an increase in deal size, leads to a higher probability for the acquirer to hire a mixed advisory team over a full-service advisor: when the deal size increases by $500 million, the probability for an acquirer to hire a mixed advisory team over a full-service advisor increases by 48.7%. Even though these findings provide mixed results regarding the scale hypothesis, they make economic sense. Full-service
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 20 advisors that have a scale advantage over boutique advisors, are usually better known and have a bigger market share. The higher probability for acquirers to hire a mixed advisory team over a full-service advisor in larger deals makes sense as well, since it is a perfect mix between specific sector knowledge (boutique) and scale advantage (full-service); therefore, they can be consulted in larger and more complex deals. The results regarding the skill hypothesis reflect mostly statistical insignificant results. However, the economic significance of the findings has a magnitude that cannot be ignored. It is found that the probability that acquirers will hire a boutique advisor (mixed advisory team) over a full-service advisor increases with 31.9% (29.6%), when the acquirer and the target operate in different industries, ceteris paribus. Contrary to this, the probability for an acquirer to hire a boutique advisor (mixed advisory team) over a full-service advisor will decrease (increase) by 21.1% (1.2%), when there is a competing offer for the target company, ceteris paribus. Finally, the results show that the probability for the acquirer to hire a boutique advisor (mixed advisory team) over a full-service advisor increases (decreases) by 66.9% (72.2%), when the deal is hostile, ceteris paribus. The merger subsample in Panel A of Table 2 (model 3 and 4) finds similar results regarding the deal size and the probability of hiring a boutique advisor or a mixed advisory team: the probability for an acquirer to hire a boutique advisor (mixed advisory team) decreases (increases) by 31.1% (49.3%) compared to a full-service advisor when deal size increases by $500 million. The findings regarding hostile deals, competing bids and cross- industry deals are all comparable to the findings in the full sample. The results are not statistically significant, however the economic significance can not be ignored, equitable to the full sample. The probability for an acquirer to hire a boutique advisor (mixed advisory team) over a full-service advisor increases (decreases) by 87.2% (99.9%) when the deal is hostile. When the target firm and the acquiring firm operate in different industries, the probability for the acquirer to hire a boutique advisory (mixed advisory team) over a full- service advisor increases by 38.3% (36.6%). Competing bids lower the probability for an acquirer to hire a boutique advisor (mixed advisory team) by 9.2% (13.4%). The tender subsample in Panel A of Table 2 (model 5 and 6) provides comparable results as the full sample and the merger subsample: an increase in deal size of $500 million decreases (increases) the probability for an acquirer to hire a boutique advisor (mixed advisory team) by 55.2% (41.6%). Equitable to the full sample and the merger subsample, the results regarding the skill hypothesis are statistically insignificant. The economic significance
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 21 will be explained hereafter. A hostile deal increases (decreases) the probability for an acquirer to hire a boutique advisor (mixed advisory team) by 13.9% (32.8%) compared to a full- service advisor. The probability for an acquirer to hire a boutique advisor (mixed advisory team) increases by 6.7% (19.0%) if the deal is a cross-industry deal. Finally, if there are competing offers, the probability for an acquirer to hire a boutique advisor (mixed advisory team) decreases (increases) by 15.5% (106.5%). Summarizing, it can be concluded that the results regarding the scale hypothesis are mixed: higher deal size decreases the probability to hire a boutique advisor, but increases the probability for an acquirer to hire a mixed advisory team. The results regarding the skill are statistically insignificant and conflicting. Acquirers are, as the skill hypothesis states, more likely to hire a boutique or a mixed advisory team in cross-industry deals. However, they are more likely to hire a boutique and less likely to hire a mixed advisory team in hostile deals. If there are competing offers, acquirers are less likely to hire a boutique advisor and a mixed advisory team. From a target perspective, conflicting evidence regarding the scale hypothesis is found, equitable to the acquirer side: when deal size increases by $500 million, the probability for a target company to hire a boutique advisor (mixed advisory team) decreases (increases) by 26.7% (35.3%). For a target, the skill hypothesis mainly focuses on the hostile deals and deals with an offer in which at least 50% of the consideration is acquirers’ equity. Even though not all results are statistically significant, they are economically significant. The probability that a target company will hire a boutique advisor (mixed advisory team) decrease by 28.0% (100%) when the deal is hostile ceteris paribus. Offers in which at least 50% of the consideration is acquirers’ equity decrease the probability for the target to hire a boutique advisor (mixed advisory team) by 43.0% (18.1%) over a full-service advisor. These findings are opposing the skill hypothesis. The merger subsample finds similar findings regarding the scale hypothesis as the full sample: an increase in deal size of $500 million decreases (increases) the probability for a boutique advisor (mixed advisory team) to be hired over a full-service advisor by 27.2% (35.1%). Besides, it is found that offers in which at least 50% of the consideration is acquirers’ equity lead to a decrease in probability for a boutique advisor (mixed advisory team) 41.3% (15.5%) to get hired over a full-service advisor. Even though the results regarding hostile deals are statistically insignificant, hostile deals lead to an increase
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(decrease) in probability for a boutique advisor (mixed advisory team) to get hired over a full- service advisor of 126.6% (100%). The tender subsample, equitable to the full sample and the merger subsample, finds conflicting evidence regarding the scale hypothesis: an increase in deal size of $500 million decreases (increases) the probability for a boutique advisor (mixed advisory team) to be hired over a full-service advisor by 28.6% (48.1%). Offers in which at least 50% of consideration is acquirers’ equity and hostile deals do not have a statistical significance on the advisor choice. The economic interpretation of the coefficients is that in offers in which at least 50% of consideration is acquirers’ equity the probability for a boutique advisor (mixed advisory team) to get hired over a full-service advisor decreases by 51.8% (56.7%). Hostile deals lower this probability by 100% for both boutique advisors and mixed advisory teams. Two other interesting findings, which are included in neither hypothesis, are the Pre-2007 and Post-2009 dummies. Panel A reports that the probability for an acquirer to hire a boutique advisor (mixed advisory team) decreases with of 51.2% (62.2%) when the deal was before 2007. The merger subsample shows similar results and finds that boutique advisors (mixed advisory teams), have a significant lower probability to get hired over a full-service advisor in deals before 2007 of 51.7% (68.5%). The tender subsample results are insignificant; however, they imply that a boutique advisor (mixed advisory team) are 52.5% (43.2%) less (more) likely to be hired over a full-service advisor if the deal was before 2007. This is an interesting finding, since one would expect that boutique advisors and mixed advisory teams would have a higher probability to get hired as they have a specialisation in certain sectors or their core business is driven by advising M&A transactions. Even though the post-2009 variable is insignificant, it reports that acquirers are more likely to hire a mixed advisory team if the deal took place after 2009, whereas the results regarding the boutique advisors are insignificant. On the target side of the deal it is found that targets are less likely to hire a boutique advisor in deals before 2007. There is conflicting evidence regarding the probability for mixed advisory teams to be hired by targets, in deals before 2007. In deals after 2009, the results state that target companies are more likely to hire a boutique advisor or a mixed advisory team, although not all results are statistically significant.
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Table 2 Determinants of the choice of advisors. The table is based on M&A deals between 2000 and 2015. Panel A shows the determinants for the acquirer side and Panel B shows the determinants for the target side. ‘ln(Deal Size)’ is the natural log of the Deal Size in millions. ‘Stock’ is a dummy variable that equals one if at least 50% of the consideration is stock of the acquirer. ‘Toehold’ is the percentage of target shares, held by the acquirer before the deal announcement. ‘Hostile’ is a dummy variable that equals one if the deal is hostile. ‘Cross-industry’ is a dummy variable that equals one if acquirer and the target operate in different industries. ‘Competition’ is a dummy variable that equals one if there is more than one bidder. ‘The acquirer (target) MV/BV’ is the market value to book value for the acquirer (target) for the prior fiscal year (COMPUSTAT 24*25/60). ‘Target ROE’ equals the return on common equity over the prior fiscal year (COMPUSTAT 20/[60+60(t-1)/2]. ‘Sales growth’ is the proportional change in sales over the prior fiscal year. ‘Target D/E’ is the debt to equity ratio of the target company prior to the fiscal year (COMPUSTAT 6/60). ‘Pre-2007’ is a dummy variable that equals one if the year of the deal announcement was before 2007. ‘Post-2009’ is a dummy variable that equals one if year of the deal announcement was after 2009. p-values are displayed in parentheses bellow the coefficients. ***,**, and * represent a significance level of 1, 5 and 10% respectively.
Panel A: Multivariate regression on the advisor choice on the acquirer side Full sample Merger subsample Tender subsample Boutique Mix Boutique Mix Boutique Mix ln(DealSize) -0.427*** 0.397*** -0.372*** 0.401*** -0.803*** 0.348 (0.000) (0.000) (0.000) (0.000) (0.001) (0.126)
Stock -0.158 0.248 -0.040 0.273 -0.208 -0.394 (0.471) (0.242) (0.868) (0.230) (0.863) (0.732)
Toehold -0.021 0.023 -0.007 0.023 -0.878 -0.014 (0.629) (0.226) (0.858) (0.239) (0.783) (0.930)
Hostile 0.512 -1.280 0.627 -13.736 0.130 -0.397 (0.511) (0.219) (0.571) (0.984) (0.917) (0.734)
Cross-industry 0.277 0.259 0.324 0.312 0.065 0.174 (0.191) (0.227) (0.169) (0.176) (0.903) (0.780)
Competition -0.237 0.012 -0.097 -0.144 -0.169 0.725 (0.628) (0.972) (0.860) (0.742) (0.884) (0.309)
Acquirer MV/BV 0.001 -0.002 0.001 -0.003 0.066 -0.017 (0.662) (0.559) (0.620) (0.538) (0.154) (0.828)
Target MV/BV 0.030* 0.010 0.018 0.012 0.062 0.069 (0.091) (0.567) (0.474) (0.488) (0.464) (0.340)
Target ROE -0.040 0.023 -0.065 0.061 0.252 -0.049 (0.489) (0.757) (0.331) (0.438) (0.610) (0.686)
Target Sales Growth -0.083 -0.723** -0.052 -0.635 -1.524 -1.457 (0.613) (0.047) (0.657) (0.103) (0.187) (0.232)
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Panel A Continuation
Target D/E -0.054*** -0.023 -0.051*** -0.021 -0.083 -0.120 (0.001) (0.143) (0.003) (0.198) (0.440) (0.240)
Pre-2007 -0.717*** -0.971*** -0.727** -1.154*** -0.746 0.359 (0.008) (0.001) (0.019) (0.000) (0.226) (0.690)
Post-2009 -0.055 0.232 0.054 0.104 -0.512 1.192 (0.846) (0.379) (0.865) (0.712) (0.480) (0.162)
Constant 0.478 -4.953*** 0.031 -4.908*** 2.890** -5.576*** (0.327) (0.000) (0.954) (0.000) (0.037) (0.001) Observations 1525 1303 222 Pseudo R2 0.102 0.106 0.151
Panel B: Multivariate regression on the advisor choice on the target side Full sample Merger subsample Tender subsample Boutique Mix Boutique Mix Boutique Mix ln(DealSize) -0.311*** 0.302*** -0.318*** 0.301*** -0.337* 0.393* (0.000) (0.000) (0.000) (0.000) (0.062) (0.084)
Stock -0.562*** -0.200 -0.533** -0.168 -0.729 -0.834 (0.003) (0.273) (0.010) (0.389) (0.524) (0.473)
Toehold -0.005 0.031* -0.007 0.010 -0.127 0.206** (0.874) (0.071) (0.813) (0.652) (0.556) (0.036)
Hostile -0.328 -14.181 0.818 -13.035 -14.701 -15.882 (0.668) (0.974) (0.335) (0.972) (0.990) (0.990)
Cross-industry -0.130 -0.005 -0.210 0.038 0.309 -0.470 (0.497) (0.980) (0.330) (0.855) (0.501) (0.428)
Competition 0.243 0.980*** 0.024 0.916*** 0.419 1.104* (0.502) (0.000) (0.959) (0.003) (0.560) (0.092)
Acquirer MV/BV 0.000 0.001 0.000 0.001 -0.009 0.056 (0.966) (0.278) (0.975) (0.277) (0.825) (0.218)
Target MV/BV 0.012 0.005 -0.010 0.005 0.052 0.026 (0.473) (0.657) (0.693) (0.624) (0.431) (0.779)
Target ROE -0.007 0.048 -0.035 0.051 0.023 0.229 (0.903) (0.520) (0.587) (0.510) (0.903) (0.693)
Target Sales Growth 0.082 -0.513* 0.055 -0.547* 1.053** -0.982 (0.286) (0.069) (0.233) (0.085) (0.025) (0.369)
Target D/E -0.025* 0.005 -0.023 0.005 -0.025 0.035 (0.080) (0.182) (0.121) (0.204) (0.783) (0.778)
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Panel B Continuation
Pre-2007 -0.656*** 0.002 -0.705*** -0.264 -0.468 1.391 (0.006) (0.994) (0.009) (0.354) (0.425) (0.100)
Post-2009 0.290 0.514* 0.155 0.380 0.891 0.869 (0.231) (0.058) (0.570) (0.183) (0.113) (0.346)
Constant 0.311 -4.319*** 0.461 -4.136*** -0.158 -6.114*** (0.463) (0.000) (0.328) (0.000) (0.890) (0.000) Observations 1525 1303 222 Pseudo R2 0.083 0.083 0.185
In Table 4 the same regressions were run once more; however, only deals greater or equal than $500 million were taken into account as a robustness check. $500 million dollars has been set as cut-off point because it represents about the 50th percentile of the full sample, the 75th percentile of the boutique advisor subsample, the 50th percentile of the full-service subsample and the 20th percentile of the mixed team subsample. Table 3 reports the exact results.
Table 3 Distribution of deal sizes on the acquirer side.
Percentile Full sample Boutique subsample Full-service subsample Mixed team subsample 10th 43.03 21.36 46.35 202.26 20th 94.24 36.25 97.41 507.23 25th 129.32 43.62 132.22 864.28 30th 168.39 54.15 170.96 1197.82 33th 190.07 66.56 193.82 1268.64 40th 279.20 88.95 279.91 1475.37 50th 466.85 146.99 451.61 2060.38 60th 809.54 188.39 767.99 2722.80 66th 1180.03 267.65 1039.73 3572.17 70th 1470.74 396.67 1409.09 4241.79 75th 1910.29 572.92 1819.06 6115.93 80th 2612.14 841.32 2491.28 7805.69 90th 6164.67 1898.66 5783.33 22966.58 Total # of 1962 149 1668 145 observations
Table 4 shows the multivariate logarithmic regressions from an acquirer (target) perspective in Panel A (Panel B) equitable to Table 2. First, the results of the acquiring firms will be discussed, and afterwards the results of the target firms. The two hypotheses (scale and skill) will be investigated once more.
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Equitable to Table 2, in the full sample mixed results are found regarding the scale hypothesis. The probability for an acquirer to hire a boutique advisor (mixed advisory team) decrease (increase) by 36.6% (34.7%) if the deal size increases by $500 million ceteris paribus. The results regarding the skill hypothesis are mostly insignificant; however, they have an economic impact. The probability for an acquirer to hire a boutique advisor (mixed advisory team) increases (decreases) by 70.7% (69.9%) if the deal is hostile ceteris paribus. Furthermore, it is found that acquirers are 101.3% (26.7%) more likely to hire a boutique advisor (mixed advisory team) when the acquirer and target operate in different industries. Finally, the results show that an acquirer is 36.9% (17.4%) less (more) likely to hire a boutique advisor (mixed advisory team) when there are competing offers. The merger subsample provides comparable results as the full sample regarding the deal size, hostile deals, cross-industry deals and deals with a competing offer. The main difference with respect to the full sample is that mixed advisory teams have a lower probability to get hired over a full-service advisor by an acquirer. All results regarding the skill hypothesis are statistically insignificant, except for the cross-industry probability effect on boutique advisors, which implied that boutique advisors have a 135.6% higher probability to get hired over a full-service advisor when it is a cross-industry deal ceteris paribus. The tender subsample provides results that are all insignificant. In general, the results indicate the same as was previously concluded in the full sample and the merger subsample, by exception of the hostile deals that have a negative impact on the probability for a boutique to get hired over a full- service provider. Summarizing, the results of the robustness show the same evidence regarding the scale hypothesis. Equitable to the full sample the $500 million or higher deals show that boutiques advisors (mixed advisory teams) are less (more) likely to be hired by an acquirer when the deal size is higher. The results of the skill hypothesis vary more. Hostile deals in both regressions generally display a higher (lower) probability for an acquirer to hire a boutique advisor (mixed advisory team). In the full sample, the results regarding competing offers deals were inconclusive, whereas in the $500 million or higher deals it generally shows a higher probability for a boutique advisor and a mixed advisory team to get hired over a full- service advisor. On the other hand, the results for competing offers showed positive results for the probability to get hired for a boutique advisor and a mixed advisory team, where the robust sample shows a negative effect on the probability for a boutique to get hired and a
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 27 positive effect on the probability for a mixed advisory team to get hired. I would like to stress again that most of the results are statistically insignificant. Looking at the full sample results in Panel B of Table 4 the results do not find significant evidence that a boutique advisor is less likely to be hired when the deal size increases. There is sufficient evidence to conclude that mixed advisory teams are more likely to be hired when deal size increases: an increase in deal size of $500 million leads to a 35.3% increase in probability that a mixed advisory team will be hired instead of a full-service advisor. When looking at the skill hypothesis variables on the target size, it is found that hostile deals do not have a significant statistical impact in the choice of an advisor. The economic impact of hostile deals on the probability for a target company to hire a boutique advisor (mixed advisory team) over a full-service advisor is a decrease of 46.3% (100%) in probability ceteris paribus. Offers in which at least 50% of the consideration is acquirers’ equity do not have a significant statistical impact on mixed team advisory teams, but they do have a statistical impact on the probability to hire a boutique advisor: it lowers the probability for a target to hire a boutique advisor (mixed advisory team) over a full-service advisor by 62.4% (10.6%). The merger subsample finds similar results for the probability regarding the scale hypothesis: mixed advisory teams have a significant higher probability to get hired by a target company of 37.2% when the deal size increases $500 million. Equitable to the full sample, the results of the deal size on the probability for a boutique advisor to get hired over a full-service advisor is statistically insignificant. Hostile deals have an insignificant impact on the probabilities for a boutique advisor or a mixed advisory team to get hired. Equitable to the full sample, offers in which at least 50% of the consideration is acquirers’ equity have a negative significant (insignificant) impact on the probability for a boutique advisor (mixed advisory team) to get hired over a full-service advisor by a target firm. Hostile deals do not statistically influence the probability for both boutique advisors and mixed advisory teams to get hired over a full-service advisor. The economic impact of offers in which at least 50% of the consideration is acquirers’ equity is a decrease of 61.6% (8.1%) in the probability for a boutique advisor (mixed advisory team) to get hired by a target firm over a full-service advisor. The economic impact of hostile deals on the probability for a boutique advisor (mixed advisory team) to get hired over a full-service advisor increases (decreases) by 3.4% (100%) when the deal is hostile ceteris paribus. Finally, the tender subsample does not find statistical evidence to support both the scale and skill hypothesis. The coefficients imply that boutique advisors (mixed advisory teams) are more (less) likely to be hired in larger deals and
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 28 offers in which at least 50% of the consideration is acquirers’ equity, and hostile deals lower the probability for a boutique advisor and a mixed advisory team to get hired over a full- service advisor by a target. Summarizing, the robustness multivariate logarithmic regression finds similar results regarding the scale hypothesis as the full sample regression, except for the tender subsample for which the findings were statistically insignificant. The findings regarding the skill hypothesis are, equitable to the full multivariate logarithmic regression, that is, inconclusive. Hostile deals were found to have a negative economical in the full sample, and a comparable conclusion can be drawn in the $500 million or more multivariate logarithmic regression. Offers in which at least 50% of the consideration is acquirers’ equity were found to have a significant negative economic impact on the probability for a boutique advisor to get hired by a target firm. A similar result is also found in the $500 million or higher deals.
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Table 4 Determinants of advisor choice for acquirers (targets) are displayed in Panel A (Panel B). Only deals equal or greater than $500 million are taken into account. Meaning and calculation of the independent variables are equal to table 2. p-value is displayed in the parentheses. ***, **, and * represent a significance level of 1, 5 and 10% respectively.
Panel A: Multivariate regression on the advisor choice on the acquirer side Full sample Merger subsample Tender subsample Boutique Mix Boutique Mix Boutique Mix ln(DealSize) -0.456** 0.298*** -0.451** 0.292*** -0.678 0.348 (0.026) (0.002) (0.040) (0.003) (0.299) (0.380)
Stock 0.063 0.313 0.229 0.337 -17.264 -0.045 (0.875) (0.195) (0.597) (0.190) (0.997) (0.972)
Toehold 0.011 0.027 0.010 0.027 0.748 -0.017 (0.786) (0.189) (0.797) (0.201) (0.913) (0.931)
Hostile 0.535 -1.199 1.283 -13.172 -16.519 -0.683 (0.623) (0.252) (0.311) (0.981) (0.997) (0.601)
Cross-industry 0.700* 0.237 0.857** 0.273 0.841 0.333 (0.059) (0.334) (0.042) (0.302) (0.414) (0.651)
Competition -0.461 0.160 -0.224 0.015 -20.697 0.736 (0.547) (0.660) (0.789) (0.973) (0.988) (0.332)
Acquirer MV/BV 0.002 -0.003 0.001 -0.004 0.123 -0.012 (0.390) (0.452) (0.681) (0.448) (0.254) (0.904)
Target MV/BV 0.024 -0.001 -0.044 0.001 0.013 -0.035 (0.278) (0.943) (0.422) (0.957) (0.913) (0.707)
Target ROE -0.117 0.058 -0.151** 0.091 -0.928 -0.632 (0.125) (0.476) (0.048) (0.270) (0.243) (0.221)
Target Sales Growth -0.258 -0.410 0.087 -0.275 -2.889 -1.852 (0.563) (0.247) (0.825) (0.455) (0.249) (0.272)
Target D/E -0.057** -0.007 -0.046* -0.007 0.000 0.060 (0.014) (0.695) (0.087) (0.698) (0.999) (0.665)
Pre-2007 -0.535 -0.928*** -0.694 -1.042*** 0.339 -0.234 (0.284) (0.004) (0.235) (0.003) (0.754) (0.818)
Post-2009 0.180 0.307 0.388 0.222 -1.589 0.793 (0.713) (0.301) (0.484) (0.486) (0.296) (0.376)
Constant 0.403 -4.227*** 0.259 -4.148*** 2.087 -5.019 (0.795) (0.000) (0.878) (0.000) (0.652) (0.103)
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Panel A Continuation
Observations 808 690 118 Pseudo R2 0.077 0.089 0.178
Panel B: Multivariate regression on the advisor choice on the target side Full sample Merger subsample Tender subsample Boutique Mix Boutique Mix Boutique Mix ln(DealSize) -0.020 0.302*** -0.007 0.316*** 0.014 -0.067 (0.888) (0.001) (0.963) (0.001) (0.974) (0.867)
Stock -0.978*** -0.112 -0.954** -0.085 0.341 -0.234 (0.005) (0.622) (0.011) (0.723) (0.812) (0.856)
Toehold 0.014 0.018 0.019 0.016 -0.305 0.061 (0.640) (0.403) (0.555) (0.483) (0.716) (0.697)
Hostile -0.622 -14.471 0.034 -13.418 -15.390 -16.624 (0.560) (0.978) (0.977) (0.975) (0.994) (0.993)
Cross-industry -0.484 -0.224 -0.960** -0.147 0.467 -0.946 (0.164) (0.350) (0.037) (0.565) (0.499) (0.198)
Competition 0.106 1.054*** 0.301 1.085*** -0.767 0.989 (0.835) (0.000) (0.611) (0.001) (0.516) (0.185)
Acquirer MV/BV -0.003 -0.004 -0.003 -0.004 -0.016 0.077 (0.496) (0.372) (0.597) (0.291) (0.814) (0.252)
Target MV/BV 0.006 0.004 -0.022 0.002 0.018 0.004 (0.771) (0.797) (0.559) (0.906) (0.839) (0.972)
Target ROE 0.067 0.113 0.031 0.113 0.428 0.312 (0.464) (0.147) (0.759) (0.148) (0.578) (0.717)
Target Sales Growth 0.432** -0.479 0.319 -0.612 2.042 -0.119 (0.033) (0.168) (0.223) (0.123) (0.117) (0.928)
Target D/E -0.015 0.002 -0.009 0.002 -0.005 0.093 (0.485) (0.901) (0.689) (0.902) (0.969) (0.551)
Pre-2007 -0.100 -0.194 0.012 -0.386 -0.282 0.897 (0.832) (0.527) (0.983) (0.238) (0.801) (0.323)
Post-2009 1.014** 0.420 0.876* 0.289 1.653* 0.830 (0.022) (0.168) (0.094) (0.374) (0.073) (0.376)
Constant -2.310** -4.124*** -2.320* -4.122*** -3.074 -2.206 (0.043) (0.000) (0.069) (0.000) (0.348) (0.465) Observations 808 690 118 Pseudo R2 0.079 0.077 0.198
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However, both the full sample and the robustness check were performed under the ambiguous assumption that the takeover premium has to be between -50% and +200%, as was done by Song et al. (2013). Also the three different methods regarding the calculation of the takeover premium seemed arbitrary to me. Therefore, I include one more robustness check under which the takeover premium is calculated based on the market value of the target firm four weeks prior to the deal announcement and made no restrictions regarding the range of the takeover premium. The findings are displayed in Table 5. Panel A of Table 5 finds similar effects regarding the deal size as were found in Panel A of Table 3 and 4: The probability for an acquirer to hire a boutique advisor (mixed advisory team) decreases (increases) as deal premium increases. In hostile deals acquirers are more (less) likely to hire a boutique advisor (mixed advisory team) and competing offers lower the probability for an acquirer to hire a boutique advisor (mixed advisory team). Lastly, it is found that cross-industry deals increase the probability for an acquirer to hire a boutique advisor (mixed advisory team). All these results are similar to the earlier findings in Table 3 and Table 4. When looking in at Panel B, the results regarding the scale hypothesis appear to be comparable to Panel B in Table 3 and Table 4: as deal size increases the probability for a target company to hire a boutique advisor (mixed advisory team) decreases (increases). Even though the results regarding the skill hypothesis are insignificant for both boutique advisors and mixed advisory teams; hostile deals mostly seem to have a negative impact on the probability to get hired for mixed advisory teams, whereas the results regarding boutique advisors are inconclusive. Offers in which at least 50% of the consideration is acquirers’ equity lower the probability for boutique advisors and mixed advisory teams to get hired over a full-service advisor. The results regarding boutique advisors are statistically significant, whereas the results regarding mixed advisory teams are not.
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Table 5 Determinants of advisor choice for acquirers (targets) are displayed in Panel A (Panel B). Robustness regressions using one calculation method for the takeover premium and no restrictions on the takeover premium range were applied. Meaning and calculation of the independent variables are equal to Table 2. p-value is displayed in the parentheses. ***, **, and * represent a significance level of 1, 5 and 10% respectively.
Panel A: Multivariate regression on the advisor choice on the acquirer side Full sample Merger subsample Tender subsample Boutique Mix Boutique Mix Boutique Mix ln(DealSize) -0.420*** 0.386*** -0.357*** 0.388*** -0.904*** 0.312 (0.000) (0.000) (0.000) (0.000) (0.000) (0.193)
Stock -0.105 0.319 0.023 0.324 0.495 -0.177 (0.641) (0.159) (0.929) (0.185) (0.680) (0.887)
Toehold -0.015 0.027 -0.001 0.027 -0.710 -0.001 (0.715) (0.161) (0.973) (0.170) (0.819) (0.994)
Hostile 2.515*** 0.631 2.667* -11.426 2.746* 1.417 (0.006) (0.572) (0.053) (0.988) (0.075) (0.299)
Cross-industry 0.271 0.463** 0.349 0.539** -0.045 0.334 (0.212) (0.037) (0.152) (0.025) (0.933) (0.614)
Competition -0.605 -0.006 -0.408 -0.166 -0.899 0.527 (0.367) (0.989) (0.624) (0.795) (0.518) (0.498)
Acquirer MV/BV 0.001 -0.001 0.001 -0.002 0.079 -0.027 (0.659) (0.794) (0.606) (0.745) (0.107) (0.710)
Target MV/BV 0.032* 0.016 0.020 0.017 0.104 0.083 (0.082) (0.371) (0.415) (0.332) (0.375) (0.310)
Target ROE -0.041 0.010 -0.065 0.056 0.364 -0.058 (0.473) (0.899) (0.325) (0.505) (0.474) (0.649)
Target Sales Growth -0.061 -0.690* -0.045 -0.626 -1.296 -0.887 (0.640) (0.061) (0.642) (0.120) (0.246) (0.388)
Target D/E -0.054*** -0.027 -0.051*** -0.022 -0.113 -0.132 (0.003) (0.131) (0.008) (0.226) (0.338) (0.245)
Pre-2007 -0.899*** -1.019*** -0.932*** -1.158*** -0.870 -0.101 (0.001) (0.001) (0.003) (0.000) (0.175) (0.918)
Post-2009 -0.253 0.128 -0.180 -0.048 -0.514 1.202 (0.380) (0.647) (0.578) (0.872) (0.487) (0.163)
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Panel A Continuation
Constant 0.584 -4.940*** 0.088 -4.878*** 3.420** -5.308*** (0.241) (0.000) (0.875) (0.000) (0.017) (0.004) Observations 1376 1173 203 Pseudo R2 0.100 0.100 0.164
Panel B: Multivariate regression on the advisor choice on the target side Full sample Merger subsample Tender subsample Boutique Mix Boutique Mix Boutique Mix ln(DealSize) -0.331*** 0.325*** -0.350*** 0.326*** -0.313* 0.402* (0.000) (0.000) (0.000) (0.000) (0.079) (0.093)
Stock -0.552*** -0.301 -0.520** -0.280 -0.473 -0.757 (0.006) (0.124) (0.017) (0.178) (0.676) (0.526)
Toehold 0.000 0.026 0.000 0.000 -0.086 0.216** (0.993) (0.151) (0.996) (0.994) (0.626) (0.032)
Hostile 0.617 -13.314 2.186 -11.003 -13.146 -14.839 (0.591) (0.983) (0.119) (0.981) (0.990) (0.990)
Cross-industry -0.046 -0.018 -0.130 0.026 0.448 -0.567 (0.815) (0.931) (0.558) (0.906) (0.322) (0.376)
Competition 0.368 0.853** -0.068 0.699 0.519 1.026 (0.403) (0.014) (0.920) (0.115) (0.461) (0.149)
Acquirer MV/BV 0.000 0.001 0.000 0.002 -0.007 0.048 (0.897) (0.137) (0.927) (0.120) (0.864) (0.309)
Target MV/BV 0.014 0.014 -0.011 0.016 0.106 0.072 (0.402) (0.284) (0.674) (0.226) (0.190) (0.506)
Target ROE -0.003 0.040 -0.032 0.039 0.066 0.520 (0.963) (0.597) (0.626) (0.629) (0.775) (0.483)
Target Sales Growth 0.069 -0.427 0.049 -0.508 0.844* -0.666 (0.276) (0.124) (0.256) (0.120) (0.053) (0.474)
Target D/E -0.027* 0.006 -0.023 0.005 -0.081 0.001 (0.077) (0.285) (0.148) (0.281) (0.385) (0.996)
Pre-2007 -0.664*** 0.101 -0.795*** -0.163 -0.065 1.372 (0.009) (0.729) (0.005) (0.600) (0.913) (0.110)
Post-2009 0.350 0.595** 0.143 0.470 1.163** 0.774 (0.167) (0.044) (0.615) (0.135) (0.044) (0.407)
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Panel B Continuation
Constant 0.387 -4.521*** 0.657 -4.341*** -0.614 -6.178*** (0.380) (0.000) (0.185) (0.000) (0.593) (0.001) Observations 1376 1173 203 Pseudo R2 0.082 0.085 0.170
5.2 Empirical results on deal outcomes Two main deal outcomes will need to be considered when doing research regarding the deal outcomes: deal premium and deal duration. As stated before, the deal premium is the percentage paid on top of the value of the target firm as a percentage of the deal size. Deal duration is the number of days it took to complete/withdraw the deal (difference in number of days between announcement date and completion/withdrawn date). In this section, the effect of advisors on these two aspects will be investigated. Song et al. (2013), found correlation in their research between boutique advisors and various fundamentals. Therefore, they applied a two-step treatment procedure in order to account for possible endogeneity. In the sample that was used for this thesis, this seems not to be the case. Table 2 shows little significant fundamentals on both the acquirer and the target side of the deals. However, the slight possibility on endogeneity might influence the results, therefore a two-step treatment procedure that Song et al. (2013) applied will be utilized. Song et al. (2013) used a treatment equation and a regression equation on the deal premium, as Maddala (1983, pp. 120-123). Since Song et al. (2013) found correlation between boutique advisors and various fundamentals; they initially ran a probit regression where the dependent variable is a dummy variable that equals one if the target or acquiring advisor was a boutique advisor and zero otherwise. The first-stage treatment rule was given by; ∗ 퐵표푢푡𝑖푞푢푒푖 = 휑푍푖 + 푢푖
Afterwards, they obtained probit estimations for the treatment equation (lambda). This estimation was included in the regression as a hazard rate. The second-stage regression is given by;
퐷푒푎푙 푃푟푒푚𝑖푢푚푖 = 훼 + 훽푋푖 + 훾퐵표푢푡𝑖푞푢푒푖 + 휆ℎ푖 + 휀푖
The difference between Eq. (2) and OLS is that the dummy variable on the use of boutique advisors in Eq. (2) is augmented by the hazard rate obtained from Eq. (1). The
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 35 variables that include a vector Xi in Eq. (2) are shown to have a significant impact on deal premium in previous research (Song et al., 2013).
5.2.1 Deal premium The deal premium is the most important factor that an advisor is able to influence as a deal outcome, and it is also considered to be a greatly important factor for shareholders of both companies. Table 6 reports results how advisors influence the deal premium in which all coefficients are percentages. The regression includes advisor tiers to account for reputation effects. Equitable to prior studies, a three-tier ranking system is utilized based on the total deal size during the full sample period. Ranks 1-5 are considered to be Tier 1 advisors, Ranks 6-20 are considered Tier 2 advisors, and everything thereafter is considered a Tier 3 advisor. Dummy variables are generated to indicate whether an advisor is Tier 1 or Tier 3; Tier 2 is the default group. In addition to this, Song et al. (2013) also included an interaction term between an acquirer boutique advisor and a Tier 3 advisor. Since there are no Tier 1 acquirer boutique advisors in the sample, no interaction term between Tier 1 and a boutique advisor will be included. The full sample results, column 1 (2), show that an acquirer boutique advisor increases the deal premium by 19.4% (16.4%) and a target boutique advisor increases the deal premium by 7.2% (column 2) ceteris paribus. It was expected that boutique advisors would decrease the deal premium on the acquirer side, and increase it on the target side, which makes the evidence inconclusive. Furthermore, it is found that numerous deal characteristics also have a significant impact on the deal premium: it is found that an increase in deal size of $500 million is followed by a slightly higher deal premium of 7.4% (7.2%). Furthermore, offers in which at least 50% of consideration is acquirers’ equity have an immense negative impact on the deal premium: in offers in which at least 50% of the consideration is acquirers’ equity the deal premium decreases by 12.2 % (11.0%). Offers in which at least 50% of consideration is acquirers’ equity are generally harder to price, so target firms might agree to a lower premium earlier than a low full-cash offer. Besides, hostile deals lower the deal premium by 15.6% (10.6%). This does not make economic sense as I would expect a higher offer if the target firm does not want to cooperate in a deal, which leads to a higher premium. Finally, it is found that tender deals increase the deal premium by 13.4% (14.1%). This could be due to the fact that in a tender, the synergies are usually higher, and therefore a higher price can be offered compared to a merger. In column 2 a couple of the fundamentals are found to be
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 36 statistically significant; however, the economic impact of the fundamentals does not exceed an effect of 0.7% on the deal premium. Finally, if the acquirer advisor is a boutique and a Tier 3 advisor, the deal premium decreases by 16.2% ceteris paribus. In the next two models, the Tier 2 advisors will be investigated. The data that is taken into account when constructing these models has to have either an acquirer Tier 2 advisor or a target Tier 2 advisor. The results of the Tier 2 advisors, column 3 (4), display an interesting result: no significant evidence is found regarding the effect of acquirer and target advisors in the Tier 2 subsample. Similar results are found regarding the deal size (an increase in deal size of $500 million leads to an increase of 10.7% (12.2%)) and offers in which at least 50% of consideration is acquirers’ equity (decrease of 12.2% (9.5%)) compared to the full sample. Also, the effect of tender deals for Tier 2 advisors is significant: a tender deal leads to an increase in deal premium of 13.7% (14.5%) when it is a tender deal ceteris paribus. Furthermore it is found that hostile deals lower the deal premium by 26.7% (29.6%). Finally, it is also found that various fundamentals have a statistical impact on the deal premium, however; the economic effect is limited. The merger subsample, columns 5 (6), finds that acquirer boutique advisors increase deal premium by 24.7% (25.8%) and target boutique advisors increase deal premium by 6.3% (9.1%) ceteris paribus. This implies that the deal premium in which two boutique advisors are consulted (on both the acquirer and target side), has a higher deal premium of 30.0% (34.7%) compared to a deal in which two full-service advisors are consulted. This is inconclusive regarding the hypothesis on the deal premium. Next to that, it is found that hostile deals do not have a significant statistical and economic impact on the deal premium. This result was not expected, if it is compared to the results in the full sample and Tier 2 subsample. Furthermore, offers in which at least 50% of consideration is acquirers’ equity lower the deal premium by 11.5% (10.5%). The fundamentals (equitable to the full sample) do not have a significant economic impact. Equitable to the full sample an acquirer Tier 3 boutique advisor lowers the deal premium by 21.5%. The tender subsample, column 7 (8), finds no evidence regarding the effect of the advisors on the deal premium. However it is found that deal characteristics have a significant impact on the deal premiums in tender deals. First of all, the results display that an additional $500 million in the deal size increases the deal premium by 20.1% (31.7%). Offers in which at least 50% of consideration is acquirers’ equity in tender deals lower the deal premium by 19.7% (13.0%), which is equal to the previous results. Contradictory to the results in the merger
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 37 subsample, hostile deals have a negative impact on the deal premium in tender deals and lead to a decrease in deal premium of 29.9% (30.6%). Furthermore, it is also found that cross- industry deals lower the deal premium by 7.4% (10.7%) and that competing offers, even though not statistically significant, increase the deal premium by 9.4% (10.9%). Finally, contradictory to previous results, the tender subsample shows a relative strong economic impact of various fundamentals on the deal premium. Finally, the time dummy variables will be analysed. In the full sample and merger subsample, it is found that deal premiums were significantly higher in the years before 2007 (excluding 2007 itself) than the period of 2007-2009, while the Tier 2 and the tender subsamples reported insignificant results. On the other hand, it was found that tender deals had a lower deal premium in the period after 2009, than in the period of 2007-2009. The full sample, Tier 2 subsample and the merger subsample reported insignificant results. During the crisis period, managers might have become more careful and make sure they do not want to overpay in tender deals, as all the risk is theirs. In a merger the risk is shared between the two firms that are involved in the merger. Summarizing, it is found that boutique advisors have a strong positive economic impact on the deal premium on both sides of the deal, whereas mixed advisory teams have a weak impact on the deal premium on both sides of the deal. Even though not all results are statistically significant it appears to have a general pattern, except for the tender subsample. These results provide mixed evidence with respect to the deal premium hypothesis: target boutique advisors indeed generate a higher deal premium, whilst it was found that acquirer boutique advisors also generate higher deal premiums, which was not expected to be the case. Panel B and C of Table 6 perform the two-step procedure as stated earlier. Panel B performs it with respect to boutique advisors and Panel C with respect to mixed advisory teams. Full-service is considered to be the base-level once more. The results of Panel B of Table 6 report the following results. In the full sample, column 1 (2), the results show that boutique advisors have a statistical significant positive impact on the deal premium. In addition to this, it is also found that higher deal sizes generally have a higher premium, offers in which at least 50% of the consideration is acquirers’ equity lower the premium and tender offers results in a higher premium. Most of the findings were also found in Panel A of Table 6, except the deal size which was found to be insignificant and has a significant impact in the two-step treatment procedure. Surprisingly hostile deals no longer have a significant effect on the deal premium.
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The merger subsample in Panel B, detects the same results as the merger subsample: boutiques advisors generally generate higher deal premiums than full-service advisors on both the target and acquirer side. In offers in which at least 50% of consideration is acquirers’ equity, the deal premiums appear to be significantly lower than full-service advisors. Finally, the tender subsample finds that boutique advisors, on the acquirer side, have a positive, yet insignificant impact on the deal premium, and boutique advisors on the target side have an insignificant negative impact. Even though it is insignificant, this is conflicting with the results provided by the OLS regression on deal premium. Finally, it is found that offers in which at least 50% of consideration is acquirers’ equity (not statistically significant) and hostile deals have a negative impact on the deal premium, whereas larger deals generally have higher premiums. These results are equal to the findings from the OLS regression. It appears the lambda coefficient is statistically significant in the full sample only; therefore, the endogeneity might have less of an impact than expected. The same two-step treatment procedure is run once more on mixed advisory teams, to account for possible endogeneity in that subsample. Panel C of Table 6 displays the following results: The full sample results that mixed advisory teams on the acquirer (target) side lower (higher) the deal premium, although the effect is not statistically significant and limited economical significant. This finding is equitable to the findings in the OLS regression in Panel A. Furthermore, it is found that larger deal sizes lead to higher deal premiums, offers in which at least 50% of the consideration is acquirers’ equity and hostile deals lead to lower deal premiums, and tender deals generally generate higher deal premiums. Equitable to Panel B, there is not sufficient evidence to conclude that cross-industry deals have a negative impact on the deal premium. Finally, the lambda coefficient is significant in the full sample, which could be an indicator that there is endogeneity in the sample. The merger subsample finds insignificant results on deal premium when the acquirer and the target advisor are mixed advisory teams. This finding is equitable to the findings in Panel A. Furthermore, it is found that an increase in deal size leads to an increase in deal premium, and offers in which at least 50% of the consideration is acquirers’ equity lower the deal premium, both findings are in line with the OLS regression (Panel A) and the full sample results (Panel C). The hostile variable is omitted due to collinearity, so no conclusions can be drawn regarding these results.
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The tender subsample finds an insignificant lower (higher) premium when acquirers (targets) consult a mixed advisory team, equitable to the OLS regression (Panel A). Deal size and offers in which at least 50% of consideration is acquirers’ equity appear to be insignificant factors in tender deals when determining the deal premium, however their coefficients are still as expected. Hostile deals still have a strong negative impact on the deal premium, equitable to Panel A. All in all, the two-step treatment procedures do not reflect other results than the OLS regression. Also, the lambda is statistically insignificant in three of the six models, which could indicate there is insignificant endogeneity.
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 40
s i s e h T r e t s a M Table 6 The impact of advisors on the deal premium. Panel A shows an OLS regression where ‘premium as percentage of deal size’ is the dependent variable. The coefficients represent percentages. ‘Acquirer/Target Boutique/Mixed’ are dummy variables that equal one if the advisor of the acquirer (target) is a boutique or a mixed advisory team respectively. ‘Acq. (Target) tier #’ are dummy variables that equal one if the advisor of the acquirer (target) is a tier 1 or tier 3 advisor. ‘Acq. Advisor tier 3 * Acq. Boutique’ is an interaction term that equals one if the acquiring advisor is a boutique and a tier 3 advisor. The model displays the full sample in model 1 and 2, the Tier 2 only in column 3 and 4, the merger subsample in 5 and 6 and the tender subsample in 7 and 8. Panel B shows a two- step treatment procedure on boutique advisors. Panel C shows the same two-step treatment procedure on mixed advisory teams. p-value is displayed in
parentheses. ***,** and * represent a significance level of 1, 5 and 10% respectively. –
r e b a F s a B
Panel A: OLS Regression on deal premium Full sample Tier 2 only Merger subsample Tender subsample (1) (2) (3) (4) (5) (6) (7) (8)
– Acquirer Boutique 19.415** 16.355* 8.442 8.883 24.704** 25.849** 2.815 -26.155
6 1 0 2 ( e c n a n i F . c S M (0.024) (0.099) (0.264) (0.288) (0.015) (0.027) (0.869) (0.201) Acquirer Mixed -2.594 -0.067 -4.151 -6.099 -1.773 1.008 -4.823 -1.499 (0.527) (0.988) (0.636) (0.516) (0.685) (0.837) (0.692) (0.904) Target Boutique 5.513 7.170* 7.506 9.779 6.296* 9.137** 3.120 4.593 (0.100) (0.069) (0.265) (0.184) (0.083) (0.036) (0.733) (0.635) Target Mixed 0.540 1.091 -0.021 1.375 0.214 0.259 2.497 3.605 (0.879) (0.789) (0.998) (0.888) (0.955) (0.953) (0.828) (0.760) ln(DealValue) 1.193* 1.165 1.722 1.968 1.050 0.709 3.236 5.101** (0.061) (0.117) (0.192) (0.159) (0.116) (0.367) (0.170) (0.044) Stock -12.168*** -10.959*** -12.241*** -9.548** -11.534*** -10.505*** -19.713* -13.023 (0.000) (0.000) (0.005) (0.044) (0.000) (0.000) (0.055) (0.244)
Toehold -0.066 -0.168 0.020 0.084 -0.034 -0.187 -0.241 -0.371 -
2017) (0.806) (0.579) (0.970) (0.874) (0.907) (0.563) (0.774) (0.694) Hostile -15.577** -10.631 -26.748* -29.614* -0.012 4.510 -29.880** -30.584** (0.047) (0.203) (0.099) (0.065) (0.999) (0.716) (0.011) (0.014)
Cross Industry -1.916 -3.672 -0.577 -0.713 -1.020 -2.727 -7.351 -10.717*
(0.366) (0.126) (0.891) (0.875) (0.654) (0.296) (0.227) (0.096)
Competition 0.579 0.299 4.621 9.487 -3.920 -4.054 9.405 10.938
|
(0.889) (0.945) (0.571) (0.256) (0.413) (0.425) (0.287) (0.231)
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s i s e h T r e t s a M Panel A Continuation Full sample Tier 2 only Merger subsample Tender subsample (1) (2) (3) (4) (5) (6) (7) (8)
Tender 13.396*** 14.098*** 13.692** 14.461** (0.000) (0.000) (0.025) (0.025) Acq. advisor tier 1 0.160 -0.972 0.708 -0.822 -4.799 -5.374 (0.962) (0.789) (0.846) (0.837) (0.584) (0.553)
– Acq. advisor tier 3 0.623 -0.179 0.674 -1.111 -3.153 -1.647
r e b a F s a B (0.840) (0.958) (0.839) (0.764) (0.725) (0.857) Target advisor tier 1 3.345 3.843 2.489 3.567 4.457 0.492 (0.354) (0.330) (0.529) (0.411) (0.641) (0.961) Target advisor tier 3 2.465 4.659 2.497 4.927 1.127 0.692 (0.412) (0.155) (0.441) (0.165) (0.894) (0.939) Acq. advisor tier 3 * -16.157* -9.240 -21.547* -18.092 1.501 34.022
– Aboutique
6 1 0 2 ( e c n a n i F . c S M (0.093) (0.407) (0.054) (0.163) (0.945) (0.166) Acq/Tar Size 0.026*** 0.022*** 0.018** 0.045*** (0.000) (0.003) (0.016) (0.000) Acquirer MV/BV 0.053*** -0.099 0.051*** 1.183** (0.003) (0.138) (0.004) (0.043) Target MV/BV 0.165 0.204 0.365** -1.745** (0.311) (0.372) (0.046) (0.028) Target ROE -0.142*** -0.152** -0.166*** 1.729 (0.006) (0.013) (0.002) (0.106) Target Sales Growth 0.639 2.357 1.356 -1.025 (0.419) (0.153) (0.127) (0.577)
Target D/E Ratio -0.701 -1.070 -0.711 -4.600 -
2017) (0.254) (0.113) (0.249) (0.504) Pre-2007 8.400*** 9.449*** 8.522 8.528 9.608*** 10.526*** 5.433 7.219 (0.003) (0.003) (0.123) (0.146) (0.002) (0.003) (0.468) (0.360)
Post-2009 -1.692 -2.072 -6.923 -5.774 0.326 0.894 -14.834* -20.229** (0.581) (0.549) (0.260) (0.377) (0.922) (0.814) (0.083) (0.022)
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Panel A Continuation s i s e h T r e t s a M Full sample Tier 2 only Merger subsample Tender subsample (1) (2) (3) (4) (5) (6) (7) (8)
Constant 43.120*** 40.163*** 41.471*** 36.279*** 41.959*** 40.980*** 55.962*** 41.966** (0.000) (0.000) (0.000) (0.002) (0.000) (0.000) (0.003) (0.030)
Observations 1,962 1,519 536 450 1,699 1,298 263 221
R-squared 0.053 0.086 0.055 0.107 0.032 0.060 0.085 0.211
–
r e b a F s a B Panel B: Impact of boutique advisors on deal premium – Two step procedure
Full Sample Merger subsample Tender subsample Step (1) Step (2) Step (1) Step (2) Step (1) Step (2)
– Acquirer Boutique 9.443** 10.646** 5.659
(0.040) (0.036) (0.623) 6 1 0 2 ( e c n a n i F . c S M Target Boutique 6.254 8.379* -3.026 (0.114) (0.056) (0.756) ln(DealValue) -0.207*** 3.578*** -0.196*** 2.420* -0.304*** 7.127** (0.000) (0.002) (0.000) (0.053) (0.000) (0.029) Stock -0.240*** -8.569*** -0.208** -9.534*** -0.383 -13.603 (0.005) (0.002) (0.025) (0.001) (0.397) (0.239) Toehold -0.134 -0.127 -0.333 (0.660) (0.695) (0.717) Hostile -0.108 -10.671 0.200 1.562 -0.366 -26.844** (0.756) (0.204) (0.675) (0.901) (0.522) (0.030)
Cross Industry 0.031 -3.715 0.022 -2.641 0.103 -10.561 -
2017) (0.716) (0.127) (0.815) (0.316) (0.636) (0.114) Competition 0.269 -4.343 8.247 (0.951) (0.395) (0.367)
Tender 14.570***
(0.000)
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s i s e h T r e t s a M Panel B Continuation Full Sample Merger subsample Tender subsample Step (1) Step (2) Step (1) Step (2) Step (1) Step (2) Acq. advisor tier 1 -2.268 -2.403 -2.553 (0.525) (0.541) (0.776) Acq. advisor tier 3 -1.657 -2.526 -1.544 (0.606) (0.474) (0.856)
– Target advisor tier 1 4.729 4.326 0.963
r e b a F s a B (0.233) (0.321) (0.927) Target advisor tier 3 5.153 5.207 2.468 (0.110) (0.135) (0.785) Acq/Tar Size 0.000* 0.000 0.000 (0.093) (0.454) (0.207) Acquirer MV/BV 0.000 0.000 0.006
– (0.778) (0.767) (0.744)
Target MV/BV 0.012 0.004 0.041 6 1 0 2 ( e c n a n i F . c S M (0.114) (0.655) (0.185) Target ROE -0.020*** -0.019*** -0.036 (0.002) (0.005) (0.380) Target Sales Growth -0.009 -0.025 0.037 (0.738) (0.439) (0.704) Target D/E Ratio 0.038 0.029 0.367* (0.286) (0.285) (0.082) Pre-2007 -0.355*** 14.221*** -0.344*** 14.192*** -0.372 10.104 (0.001) (0.000) (0.006) (0.000) (0.156) (0.245) Post-2009 0.170 -4.456 0.164 -0.133 0.263 -24.319***
(0.148) (0.212) (0.211) (0.973) (0.347) (0.008) -
2017) Lambda 59.199** 36.662 48.307 (0.011) (0.193) (0.154) Constant 0.508** 13.935 0.425* 24.354* 0.995* 21.742
(0.010) (0.279) (0.052) (0.085) (0.057) (0.477)
Observations 1,519 1,519 1,298 1,298 221 221
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s i s e h T r e t s a M Panel C: Impact of mixed advisory teams on deal premium – Two step procedure Note: In the merger subsample, hostile is omitted due to collinearity. 13 observations have been dropped and were not taken into account when constructing the table. Full Sample Merger subsample Tender subsample Step (1) Step (2) Step (1) Step (2) Step (1) Step (2)
Acquirer Mixed -0.206 0.005 -0.330
– (0.964) (0.999) (0.980)
r e b a F s a B Target Mixed 1.144 -0.190 5.878 (0.780) (0.966) (0.629) ln(DealValue) 0.219*** 4.868*** 0.216*** 3.239** 0.283*** 5.550 (0.000) (0.002) (0.000) (0.047) (0.001) (0.257) Stock -0.057 -12.907*** -0.010 -11.537*** -0.554 -18.147 (0.501) (0.000) (0.911) (0.000) (0.235) (0.165)
– Toehold -0.204 -0.155 -0.420
(0.505) (0.635) (0.663) 6 1 0 2 ( e c n a n i F . c S M Hostile -1.113** -25.612*** -0.965* -34.686** (0.013) (0.009) (0.078) (0.037) Cross Industry 0.088 -1.619 0.121 -0.888 -0.064 -10.209 (0.319) (0.509) (0.207) (0.743) (0.784) (0.134) Competition 0.456 -3.806 7.054 (0.918) (0.469) (0.446) Tender 15.165*** (0.000) Acq. advisor tier 1 -4.047 -3.944 -4.199 (0.258) (0.320) (0.638)
Acq. advisor tier 3 -1.641 -2.162 -2.798 -
2017) (0.616) (0.548) (0.746) Target advisor tier 1 3.681 2.440 0.753 (0.351) (0.577) (0.942)
Target advisor tier 3 4.664 4.416 0.842
(0.157) (0.220) (0.927)
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s i s e h T r e t s a M Panel C Continuation Full Sample Merger subsample Tender subsample Step (1) Step (2) Step (1) Step (2) Step (1) Step (2)
Acq/Tar Size -0.001 -0.001 -0.001 (0.317) (0.386) (0.485) Acquirer MV/BV 0.001 0.000 0.011 (0.304) (0.333) (0.594)
– Target MV/BV -0.001 0.001 0.007
r e b a F s a B (0.907) (0.924) (0.806) Target ROE 0.002 0.002 -0.011 (0.196) (0.289) (0.785) Target Sales Growth 0.019 0.037 -0.024 (0.577) (0.321) (0.715) Target D/E Ratio -0.269** -0.268** -0.281
– (0.026) (0.046) (0.372)
6 1 0 2 ( e c n a n i F . c S M Pre-2007 -0.176 6.038* -0.314** 7.397* 0.650** 8.486 (0.127) (0.071) (0.012) (0.068) (0.043) (0.458) Post-2009 0.174 1.429 0.100 2.612 0.496 -19.065* (0.146) (0.702) (0.439) (0.507) (0.140) (0.087) Lambda -78.649*** -48.191* -32.493 (0.004) (0.096) (0.601) Constant -2.408*** 33.822*** -2.346*** 38.058*** -3.224*** 49.861* (0.000) (0.000) (0.000) (0.000) (0.000) (0.091)
Observations 1,519 1,519 1,285 1,285 221 221
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However, as stated before Song et al. (2013) applied a rather arbitrary calculation and range for the deal premium, which might have an influence on the results. Therefore, as a robustness check the OLS regression will be ran once more, where the deal premium is calculated in an uniform way, without a range in which the deal premium must be in. Table 7 displays the results. Panel A of Table 7 reports the full sample results. The results are in line with the findings that were found in Panel A of Table 6 as well: Target boutique advisors whereas offers in which at least 50% of the consideration is acquirers’ equity lower deal premiums. When looking at the economic significance regarding the deal size it is found that in the models where no fundamentals are included the deal size has a strong negative impact on deal premium, and when various fundamentals are included it has a weak positive impact on the deal premium, where it was weakly positive using the deal premium of Song et al. (2013). However, in Table 6 it was found that hostile deals generally have a lower deal premium than friendly deals. The results in Table 7 are opposing this view and find that hostile deals have a statistically insignificant higher deal premium. In addition to this, the effect of tender deals in the full sample results is no longer statistically significant. When looking at the coefficient, it had a strong positive effect in Table 6, and in Table 7 it has a strong negative effect (weak positive effect) on the deal premium in model 1 (2). In model 3 (4) it is found that target boutique advisors have a significant strong positive impact on the deal premium (insignificant strong negative impact). Model 3 also reports a negative influence of the deal size on the deal premium: an increase in deal size of $500 million leads to a decrease in deal premium of 368%. Equitable to Table 6, it was found that offers in which at least 50% of the consideration is acquirers’ equity lower the premium and competing offers increase the deal premium. The economic effect of various fundamentals is limited: it does not influence the deal premium more than 3.4%. The merger subsample in model 5 (6) found similar results as the full sample regarding target boutique advisors and offers in which at least 50% of the consideration is acquirers’ equity. In the merger subsample it is also found that hostile deals have a higher premium than friendly deals, which was also found in Table 6, although not statistically significant. Finally, the tender subsample, model 7 (8) finds new results regarding the role of advisors: no statistical significant is found regarding target boutique advisors, however, it is found that target mixed advisory teams have a significant higher deal premium than target full-service advisors. Furthermore it is also found that offers in which at least 50% of the consideration is
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 47 acquirers’ equity lower the premium, hostile deals increase the premium and competing offers increase the premium. Finally it is found that various fundamentals have a strong impact on the deal premium, in which the target MV/BV have a positive influence on the deal premium, and Acquirer MV/BV, Target ROE and Target D/E-Ratio have a negative influence on the deal premium. In addition to this, it can be doubted that the instruments that Song et al. (2013) applied in their two-step treatment procedure actually influence the advisor choice, and do not influence the deal premium. Panel A of Table 6 found a significant relationship between the deal characteristics used by Song et al. (2013). I included a scope dummy variable that equals one if the target or acquirer had a previous relationship with the advisor five years prior to the announcement date. However, this instrument appeared to have an insignificant effect on the advisor choice. Secondly, I included the advisor tiers as a proxy for size of the advisor in the treatment equation, although this showed to be insignificant as well. Other possible independent variables that influence the advisor choice, as Table 2 shows, also have an influence on the deal premium and are therefore unsuitable to include in the treatment equation.
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s i s e h T r e t s a M Table 7 The impact of advisors on the deal premium. Panel A shows an OLS regression where ‘premium as percentage of deal size’ is the dependent variable. The coefficients represent percentages. All independent variables The model displays the full sample in model 1 and 2, the Tier 2 only in column 3 and 4, the merger subsample in 5 and 6 and the tender subsample in 7 and 8. Panel B shows a two-step treatment procedure on boutique advisors. Panel C shows the same two-step treatment procedure on mixed advisory teams. p-value is displayed in parentheses. ***,** and * represent a significance level of 1, 5 and 10% respectively.
– OLS Regression on deal premium
r e b a F s a B Full sample Tier 2 only Merger subsample Tender subsample (1) (2) (3) (4) (5) (6) (7) (8)
Acquirer Boutique 84.032 -5.265 -101.290 -8.584 117.539 -18.753 4.479 -17.212 (0.961) (0.921) (0.609) (0.683) (0.957) (0.780) (0.973) (0.764) Acquirer Mixed -421.099 -18.905 42.169 -9.607 -442.765 -20.350 27.643 -7.844
– (0.615) (0.452) (0.861) (0.693) (0.645) (0.481) (0.780) (0.828)
6 1 0 2 ( e c n a n i F . c S M Target Boutique 1,744.197*** 47.977** 459.930** -15.894 2,059.869*** 58.076** 20.566 12.814 (0.009) (0.024) (0.010) (0.396) (0.008) (0.022) (0.770) (0.626) Target Mixed -8.716 -6.044 -57.627 9.501 -100.656 -10.197 224.758** 3.358 (0.990) (0.788) (0.805) (0.726) (0.904) (0.693) (0.013) (0.920) ln(DealValue) -54.369 0.899 -59.305* 3.188 -45.122 0.350 -20.795 7.171 (0.672) (0.826) (0.093) (0.367) (0.756) (0.939) (0.278) (0.338) Stock -462.649 -28.679** -103.212 -26.245** -481.503 -27.515* -27.033 -55.333 (0.291) (0.033) (0.376) (0.029) (0.317) (0.060) (0.765) (0.122) Toehold -12.189 -0.639 -1.982 -0.680 -16.958 -0.557 -9.684 -0.821 (0.819) (0.691) (0.889) (0.614) (0.783) (0.761) (0.133) (0.754) Hostile 300.686 40.493 -53.882 21.093 -305.276 64.303 -3.914 19.826
- (0.918) (0.602) (0.952) (0.804) (0.954) (0.648) (0.980) (0.715) 2017) Cross Industry -462.296 12.406 118.291 -15.278 -523.093 18.056 -47.608 -11.096 (0.281) (0.345) (0.288) (0.178) (0.293) (0.235) (0.325) (0.550) Competition -186.911 14.422 80.376 65.009** -250.286 21.187 162.291** 3.503
(0.859) (0.631) (0.800) (0.027) (0.858) (0.598) (0.031) (0.899)
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s i s e h T r e t s a M Continuation Full sample Tier 2 only Merger subsample Tender subsample (1) (2) (3) (4) (5) (6) (7) (8)
Tender -499.928 0.277 -182.008 18.346 (0.439) (0.988) (0.251) (0.250) Acq. advisor tier 1 -19.947 -15.414 -92.319 -14.458 36.837 -32.057
– (0.976) (0.435) (0.908) (0.533) (0.592) (0.215)
r e b a F s a B Acq. advisor tier 3 528.059 -3.036 523.179 -3.771 -47.423 -12.829 (0.403) (0.871) (0.477) (0.863) (0.499) (0.621) Target advisor tier 1 205.956 5.440 210.031 2.802 5.890 -3.985 (0.781) (0.804) (0.810) (0.913) (0.940) (0.895) Target advisor tier 3 181.976 16.877 213.765 13.316 21.609 31.181 (0.767) (0.349) (0.766) (0.523) (0.745) (0.227)
– Acq. advisor tier 3 * -888.475 -8.090 -1,025.992 5.879 -24.150 -8.577
e c n a n i F . c S M Aboutique (0.640) (0.892) (0.669) (0.937) (0.885) (0.900) Acq/Tar Size 0.023 0.031* -0.005 0.061** (0.457) (0.080) (0.912) (0.036) Acquirer MV/BV 0.076 0.176 0.087 -1.972 (0.417) (0.408) (0.390) (0.261) Target MV/BV 0.291 0.125 0.718 8.393*** (0.763) (0.858) (0.547) (0.003)
6 1 0 2 ( Target ROE -0.154 -0.128 -0.152 -13.421*** (0.571) (0.404) (0.605) (0.000) Target Sales Growth -2.320 -3.450 -2.230 1.843
(0.574) (0.462) (0.651) (0.718) -
2017) Target D/E Ratio -1.267 -0.442 -1.313 -11.595 (0.694) (0.785) (0.702) (0.545) Pre-2007 -1,005.491* 12.097 28.822 12.365 -1,216.718* 9.911 32.977 47.375**
(0.081) (0.488) (0.844) (0.399) (0.073) (0.629) (0.585) (0.040) Post-2009 -1,218.645* 22.232 212.831 -2.080 -1,410.261* 29.492 -28.720 -21.264
(0.051) (0.244) (0.197) (0.901) (0.053) (0.186) (0.676) (0.400)
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s i s e h T r e t s a M Continuation Full sample Tier 2 only Merger subsample Tender subsample (1) (2) (3) (4) (5) (6) (7) (8)
Constant 1,485.008 49.311 435.665 49.955* 1,617.277 51.304 205.854 24.456 (0.246) (0.213) (0.120) (0.083) (0.275) (0.260) (0.163) (0.659)
– Observations 1,814 1,369 486 401 1,574 1,167 240 202
r e b a F s a B R-squared 0.009 0.015 0.033 0.067 0.010 0.016 0.092 0.162
–
6 1 0 2 ( e c n a n i F . c S M
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2017)
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Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 51
5.2.2 Deal duration Another aspect of a M&A deal that the advisor can influence is the deal duration. The deal duration is the difference in weekdays between the announcement date and the completion date or withdrawn date, depending on whether the deal is completed or withdrawn. Table 6 reports the results of an OLS regression in which the dependent variable is the difference of days between announcement and completion or withdrawal. The deal duration is calculated in Microsoft Excel, using the ‘datedif’ function and includes possible weekends and holidays. The most important results that are shown in Table 8, is that the advisor does not have a statistically significant impact on deal duration (row 1 – 4 of all columns). When looking at the advisor tiers, it is found that tier 3 acquirer (targets) advisors take (in)significantly longer to complete the deal, whereas tier 1 advisors complete deals insignificantly faster. Apparently, for the deal duration it does not statistically matter if the advisor is a full-service advisor, a boutique advisor or a mixed advisory team. However, when interpreting the economic significance, numerous interesting findings are found. In the full sample, it is found that both acquirer boutique advisors take longer to complete deals, whereas target boutique advisors and target mixed advisory teams take shorter to complete deals. This is inconclusive with respect to the deal duration hypothesis. The results regarding acquirer mixed advisory teams are inconclusive. The merger subsample finds inconclusive results regarding this same hypothesis as well. The economic interpretation finds results that find that acquirer boutique advisors take longer to complete deals. Target boutique advisors and mixed advisory teams complete deals faster, whereas acquirer mixed advisory teams find both economical evidence that they complete deals faster or slower, depending on the model. Finally, the tender subsample finds that acquirer and target boutiques complete deals faster, whereas acquirer mixed advisory teams take longer to complete the deal. The findings regarding target mixed advisory teams are inconclusive: Model 5 finds shorter deal duration, whereas model 6 finds longer deal duration. The findings of the merger and tender subsample are also inconclusive regarding the deal duration hypothesis. In addition to this, it is found that deal characteristics have a statistical significant impact on the deal duration. The full sample, column 1 (2), reports that larger deals increase the deal duration: an increase of deal size by $500 million would take 56.6 (69.8) days longer to complete ceteris paribus. In addition to this, it is found that offers in which at least 50% of the consideration is acquirers’ equity take longer to complete. The difference between offers in which at least 50% of the consideration is acquirers’ equity and a deal in which less acquirers’
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 52 equity is offered takes 23.5 (25.3) days longer to be completed or be withdrawn. Furthermore, hostile deals appear to take longer to complete than friendly deals. If the deal is hostile, the deal duration increases by 65.8 (72.4) days. Surprisingly, it is also found that cross-industry deals take 12.2 (11.5) days less to complete, compared to same industry deals. In line with the skill hypothesis, it was expected that firms that operate in the same industry would be able to complete a deal faster due to the similarities both companies have, which apparently seems not the case. It could be due to the in-depth preparation that is necessary for a cross-industry takeover, which is generally rather underestimated in same industry takeover. Finally, it is found that tender deals take 51.7 (51.2) days shorter to complete. A possible explanation for this is that, in a tender, the target company becomes part of the acquiring company, whereas in a merger, the two companies fuse together. The financial fundamentals are not statistically significant. When looking at the merger subsample, column 3 (4), similar results as the full sample are found. However, contradictory to the full sample, hostile deals do not take significantly longer than friendly deals. The tender subsample, column 5 (6), shines a new light on a number of findings. First of all, it is found that hostile deals take statistical and economical significantly longer to complete than friendly deals: if the tender deal is hostile, it takes 123.9 (114.4) days longer to be completed or withdrawn. Secondly, no statistical evidence is found regarding offers in which at least 50% of the consideration is acquirers’ equity; however, the economic significance is still fair. In addition to this, it is also found, contradictory to the full sample and merger subsample, that competing offers influence the deal duration: competing offers prolong deal duration by 24.2 (24.3) days when a competing offer exists. Finally, besides the Target MV/BV, it is found that the D/E Ratio has a prolonging effect on deal duration; however, the economic significance is limited: a 100% increase of Target MV/BV leads to a 2.2 day shorter deal duration.
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Table 8 The effect of advisor choice on deal duration. Deal duration is measured as the number of days between the announcement of the deal and the completion or withdrawal. Deal duration is the dependent variable. This table contains the full sample in model 1 and 2, the merger subsample in model 3 and 4 and the tender subsample in model 5 and 6. All independent variables have been explained in previous tables. P-values are displayed in the parentheses. ***,** and * represent a significance level of 1, 5 and 10% respectively.
OLS regression on deal duration Full sample Merger subsample Tender subsample (1) (2) (3) (4) (5) (6)
Acquirer Boutique 4.539 1.451 8.001 3.987 -15.378 -11.323 (0.560) (0.881) (0.358) (0.718) (0.335) (0.539) Acquirer Mixed 4.913 -2.808 3.321 -4.742 8.008 7.644 (0.552) (0.770) (0.715) (0.659) (0.662) (0.707) Target Boutique -2.489 -1.955 -0.643 -0.407 -7.584 -7.578 (0.713) (0.815) (0.932) (0.966) (0.583) (0.632) Target Mixed -8.502 -8.075 -10.491 -10.471 -1.124 3.396 (0.237) (0.351) (0.183) (0.276) (0.948) (0.860) ln(DealValue) 9.101*** 11.231*** 9.682*** 11.969*** 8.886** 9.671** (0.000) (0.000) (0.000) (0.000) (0.012) (0.019) Stock 23.545*** 25.263*** 22.752*** 23.922*** 12.778 26.147 (0.000) (0.000) (0.000) (0.000) (0.409) (0.152) Toehold 0.810 1.149* 0.715 1.061 1.405 0.922 (0.137) (0.074) (0.232) (0.134) (0.267) (0.549) Hostile 65.841*** 72.433*** -4.169 8.191 123.928*** 114.399*** (0.000) (0.000) (0.862) (0.763) (0.000) (0.000) Cross Industry -12.183*** -11.541** -14.920*** -14.511** 5.453 4.468 (0.004) (0.024) (0.002) (0.011) (0.552) (0.670) Competition 5.475 5.261 -0.503 -3.417 24.197* 24.335 (0.512) (0.571) (0.960) (0.760) (0.069) (0.101) Tender -51.667*** -51.194*** (0.000) (0.000) Acq. advisor tier 1 -6.713 -9.128 -9.053 -11.826 -0.932 -0.888 (0.308) (0.227) (0.223) (0.169) (0.941) (0.950) Acq. advisor tier 3 19.578*** 17.251** 18.376*** 16.539** 19.728 15.823 (0.001) (0.013) (0.006) (0.035) (0.110) (0.254) Target advisor tier 1 -2.471 -2.711 -7.736 -8.885 7.781 13.154 (0.735) (0.746) (0.348) (0.351) (0.589) (0.427) Target advisor tier 3 9.054 4.519 7.880 3.111 10.627 10.439 (0.135) (0.516) (0.242) (0.689) (0.402) (0.477) Acq/Tar Size -0.012 -0.020 0.005 (0.342) (0.216) (0.753) Acquirer MV/BV -0.018 -0.015 -0.679 (0.632) (0.698) (0.470) Target MV/BV -0.549 -0.703* -2.188* (0.113) (0.080) (0.091) Target ROE 0.754 0.322 1.030 (0.654) (0.869) (0.732) Target Sales Growth -1.517 -1.573 10.804 (0.245) (0.245) (0.335)
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 54
Continuation
Target D/E Ratio 0.124 0.119 3.330* (0.262) (0.304) (0.057) Pre-2007 -0.504 -3.422 -0.374 -3.312 1.286 -4.251 (0.929) (0.610) (0.953) (0.665) (0.909) (0.741) Post-2009 13.312** 11.777 15.816** 14.581* 0.316 4.113 (0.032) (0.109) (0.022) (0.080) (0.980) (0.775) Constant 61.224*** 55.581*** 61.905*** 56.766*** -3.769 -3.855 (0.000) (0.000) (0.000) (0.001) (0.893) (0.902)
Observations 1,962 1,519 1,699 1,298 263 221 R-squared 0.130 0.142 0.073 0.087 0.295 0.325
5.2.3 Deal completion The last thing that will be investigated is whether the advisor choice has an influence on the deal completion. This has no direct link regarding the skill or scale hypothesis; however, it is another vital aspect that an advisor has an influence on. Table 9 reports a probit regression on deal completion including a variety of deal characteristics and fundamentals. First of all, it appears that the marginal effects of advisors are not statistical significant, except for model acquirer boutique in model 1, and neither economically significant. This includes the tiers that the advisors are allocated to. The main factors that influence deal completion appear to be, equitable to deal duration, the deal characteristics. In the full sample, column 1 (2), it is found that multiple main deal characteristics have a statistical and economic impact on deal completion. Firstly, it is found that hostile deals have a lower probability of being completed than friendly deals. If the deal is hostile, the probability of completion decreases by 60.4% (56.1%) ceteris paribus. Secondly, it is found that competing offers on the target firm lower the probability of the deal being completed by 38.9% (37.1%) ceteris paribus. These findings make economic sense. A target firm that does not want to be taken over, and therefore forces the acquiring firm to make a hostile offer, increases the probability for the deal to be blown off. In addition to this, if there are multiple bids on the table, the target firm will probably go with the deal that is best for them (highest price, best future prospects etc.) and thus will increase the probably of a failed deal for the (alleged) acquiring firm. Contradictory to all the negative consequences, a number of positive aspects were found as well. First of all, tender deals are more likely to succeed than mergers. If the deal is a tender deal, the probability of a successful deal increases by 3.1% (4.1%). Also, a higher premium goes hand in hand with a slightly higher chance on deal success. If
Master Thesis – B a s F a b e r – MSc. Finance (2016 - 2017) | 55 the premium is high (higher than the median of the full sample in this case), the probability of succeeding the deal increases by 5.8% (5.5%) ceteris paribus. Equitable to the variables that decrease the probability of a successful deal, the results make economic sense. When the deal premium is higher it is an incentive for both the target and the acquirer to go on with the deal. The target will receive a higher sum of money on top of the value of the firm where the acquirer generally overpays, and therefore does not want to back down in a later stadium of the deal. The higher probability for success in tender deals makes sense as well. Since in a tender deal the acquiring firm takes over the target firm, the latter would mainly be integrated into the acquiring firm whereas, in a merger, the two companies would have to work parallel to each other and determine what would benefit them both. In addition to this, Target Tier 1 advisors lower the probability for deal completion by 4.2%. In model 2, several fundamentals are included in the regression; however, the economic significance is ignorable: the impact does not exceed an increase of 0.3% of the probability of a successful deal completion. Acq/Tar size is statistically significant, even though the economic impact is zero. When looking at the merger subsample, column 3 (4), similar results to the full sample results. A clear exception is the deal size that has a negative connection with deal completion: an increase of $500 million in deal size leads to a decrease in deal completion of 3.7% (0.0%). Hostile lowers the probability of a successful merger deal by 62.1% (58.2%) ceteris paribus. It appears that the completion of merger deals is more dependent on competing offers than the full sample: a competing offer reduces the probability of the deal being completed by 45.0 (44.9%) ceteris paribus. It is found that high premium increases the probability of deal completion by 6.1% (5.8%). Equitable to model 2, the fundamentals reported in model 4 do not make an economic significant impact on the probability of deal completion. Next to that, the tender subsample, model 5 (6), shows comparable results as the full sample and the merger subsample regarding the effect on hostile deals on the probability of deal completion: a hostile deal decreases the probability of deal completion by 63.5% (53.6%). Also, the comparable effect is found regarding competing offers, but the effect is less economically significant than it is in the full sample and in the merger subsample. It is found that competing offers decrease the probability of the deal being completed by 21.1% (15.5%). Contradictory to the full sample and the merger subsample, the tender subsample also finds a statistical link between deal completion and deal value: a higher deal value appears to go hand in hand with a higher probability of completion. If the deal value increases by $500 million, the probability of the deal being completed increases by 14.9% (13.1%). A
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side note on this analysis is that the deals that were advised by boutiques on the acquirer side in the tender subsample have not been taken into account as all deals were completed, which cannot be the case in a probit regression. The 24 (21) observations have not been taken into account in the deal characteristics and fundamentals either. Finally, the results of the time indicator dummy variables will be investigated. Model 1 – 4 provide a statistically significant increase in deal completion if the deal took place before 2007. The economic effect varies between an increase in probability of 4.3% to 5.6%. There is not sufficient statistical evidence to draw the same conclusion in model 5 and 6. The economic effect of these models reports an increase of 3.9% and 2.9% if the deal took place before 2007. Furthermore, models 1 and 3 report that the probability of deal completion is significantly larger (2.9% and 3.6%) when the deal took place after 2009. The other models result in statistical insignificance and economic effects that vary between a decrease of 0.6% and an increase of 2.4% in probability when the deal took place after 2009.
Table 9 Probit regression on deal completion. Marginal effects are displayed. Deal completion is a dummy variable that equals one if the deal is completed and zero if it is withdrawn. This table contains the full sample in model 1 and 2, the merger subsample in model 3 and 4 and the tender subsample in model 5 and 6. ‘High Premium’ is a dummy variable that equals one, if the deal premium is above the median of the full sample and zero otherwise. All other independent variables have been explained in previous tables. p-values are displayed in the parentheses. ***,** and * represent a significance level of 1, 5 and 10% respectively.
Note: In model 5 (6), acquirer boutiques completed all the deals and therefore this could not have been included in the model. The 24 (21) observations that were advised by boutique advisors in the tender subsample have not been taken into account when constructing the model.
Probit regression on deal completion Full sample Merger subsample Tender subsample (1) (2) (3) (4) (5) (6)
Acquirer Boutique 0.036* 0.034 0.025 0.025 (0.090) (0.144) (0.277) (0.355) Acquirer Mixed 0.010 0.014 0.005 0.008 0.028 0.032 (0.612) (0.529) (0.818) (0.739) (0.542) (0.397) Target Boutique 0.013 0.003 0.009 -0.004 0.035 0.027 (0.510) (0.889) (0.664) (0.870) (0.438) (0.496) Target Mixed 0.009 0.012 0.014 0.014 -0.021 0.014 (0.635) (0.571) (0.486) (0.541) (0.751) (0.788) ln(DealValue) -0.005 0.001 -0.006* -0.000 0.024* 0.021* (0.171) (0.854) (0.084) (0.916) (0.052) (0.057) Stock -0.004 -0.004 -0.001 0.000 -0.063 -0.027 (0.714) (0.774) (0.938) (0.980) (0.260) (0.584)
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Continuation Full sample Merger subsample Tender subsample (1) (2) (3) (4) (5) (6)
Toehold 0.000 0.000 -0.000 -0.000 0.029 0.020 (0.874) (0.899) (0.928) (0.931) (0.465) (0.543) Hostile -0.604*** -0.561*** -0.621*** -0.582*** -0.635*** -0.536*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Cross Industry -0.018 -0.019 -0.012 -0.014 -0.036 -0.032 (0.128) (0.157) (0.335) (0.336) (0.269) (0.292) Competition -0.389*** -0.371*** -0.450*** -0.449*** -0.211*** -0.155** (0.000) (0.000) (0.000) (0.000) (0.002) (0.015) Tender 0.031* 0.041** (0.054) (0.017) High Premium 0.058*** 0.055*** 0.061*** 0.058*** 0.012 0.023 (0.000) (0.000) (0.000) (0.000) (0.706) (0.458) Acq. advisor tier 1 0.017 0.017 0.024 0.025 -0.012 -0.005 (0.319) (0.367) (0.183) (0.215) (0.807) (0.907) Acq. advisor tier 3 0.002 0.004 0.008 0.009 -0.030 -0.017 (0.889) (0.803) (0.634) (0.649) (0.540) (0.691) Target advisor tier 1 -0.027 -0.042* -0.027 -0.044* -0.073 -0.066 (0.197) (0.077) (0.242) (0.099) (0.227) (0.229) Target advisor tier 3 0.012 0.010 0.010 0.007 -0.010 0.002 (0.484) (0.572) (0.565) (0.714) (0.832) (0.963) Acq/Tar Size 0.000* 0.001* 0.000 (0.076) (0.063) (0.487) Acquirer MV/BV -0.000 -0.000 -0.003 (0.684) (0.775) (0.277) Target MV/BV -0.002* -0.002* 0.000 (0.080) (0.091) (0.965) Target ROE 0.003 0.003 0.004 (0.585) (0.636) (0.751) Target Sales Growth 0.000 0.000 0.002 (0.906) (0.931) (0.960) Target D/E Ratio 0.001 0.001 -0.001 (0.298) (0.369) (0.877) Pre-2007 0.047*** 0.043** 0.056*** 0.051*** 0.039 0.029 (0.001) (0.010) (0.001) (0.007) (0.309) (0.397) Post-2009 0.029* 0.018 0.036** 0.024 -0.006 -0.001 (0.051) (0.282) (0.020) (0.184) (0.885) (0.972)
Observations 1,962 1,519 1,699 1,298 239 200 Pseudo R2 0.220 0.214 0.212 0.207 0.373 0.381
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6. Conclusions
In the beginning of this thesis, a graph showed the increasing popularity of boutique advisors in USA domestic M&A deals. The graph showed the amount of deals advised by a boutique advisor on the target side increased from 4.4% to 12.7% and from on the acquirer side 6.6% to 14.9% between 2000 and 2015. Song et al. (2013), wrote an interesting paper regarding the factors that make a firm to consult a boutique advisor or a mixed advisory team over a full-service advisor and what consequences this decision has for the deal outcome. The aim of this thesis is to see whether the conclusions that were made by Song et al. (2013) are robust enough to hold with another dataset. Whereas Song et al. (2013) used data from 1995 to 2006, the data used for this thesis is from 2000 to 2015. It was found that the probability for a boutique to get hired is higher when deals are smaller. Furthermore, it was investigated whether the deal attitude, offers in which at least 50% of the consideration is acquirers’ equity, cross-industry deals and competing offers have an impact on the decision for a firm to consult a boutique advisor, however no statistical evidence was found to support these findings. The results regarding mixed advisory teams found they are more likely to get hired when the deal size increases. Insignificant and inconclusive results were found with respect to the skill hypothesis. In addition to this, it was investigated what effect boutique advisors have on deal outcomes. First of all, it was found that acquirer boutiques have a positive effect on the deal premium, equitable to target boutique advisors. Furthermore, it was found that both acquirers and target advisors do not have a significant impact on the deal duration, whereas it is mainly dependent on deal characteristics. Similar results were found regarding the deal completion. Summarizing, the findings do not provide a conclusive and significant insight in the decisions for firms to hire a boutique advisor and what consequences it has for the deal outcomes.
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Advisor Category Appendix 1: Advisor categories Brown Brothers Harriman & Co Full-Service Bryan, Worley & Co Full-Service Advisor Category Bryant Park Capital Boutique 2ND GENERATION CAPITAL Boutique Burke Capital Group Boutique ABN AMRO Bank Full-Service Burnham Hill Partners Full-Service Acclaim Finl Grp Venture III Full-Service C.K. Cooper & Company Boutique Adams Harkness & Hill Inc Full-Service Cain Brothers Co. Boutique Advest Inc Full-Service Canaccord Genuity Full-Service AG Edwards Inc Full-Service Canadian Imperial Bk Commerce Full-Service AGC Partners Boutique Candlewood Partners LLC Boutique AGM Partners LLC Boutique Cantor Fitzgerald Inc Full-Service AgriCapital Corp. Boutique Capital Market Securities Inc Boutique Alex Sheshunoff & Co Boutique Capital One Southcoast Inc Full-Service AlixPartners LLC Boutique Capital Resources Group Inc Full-Service Allen & Co Inc Full-Service Capitalink LC Boutique Allen C. Ewing Boutique Carnegie AS Full-Service Alliant Partners Boutique Carpenter & Co Full-Service ALPS Securities Co Ltd Full-Service Carson Medlin Co Boutique Alterity Partners Boutique Cassel Salpeter & Co LLC Boutique Ambassador Financial Group Inc Full-Service Castle Creek Financial LLC Boutique American Appraisal Assoc., Inc Full-Service CE Unterberg Towbin Full-Service Anderson & Strudwick Full-Service Cedar Hill Advisors LLC Boutique Aquilo Partners Inc Boutique Centerview Partners LLC Boutique Arthur Andersen Corp. Fin. Full-Service Chaffe & Associates Boutique Asante Partners LLC Full-Service Chanin Capital Partners Full-Service Atlas Advisors LLC Boutique Chartwell Capital Ltd Full-Service Austin Associates Inc Full-Service Chase Full-Service Avondale Partners Full-Service Chatsworth Securities LLC Full-Service B Riley & Company Full-Service Chesapeake Group Boutique Bain & Co Full-Service Chestnut Securities Inc Full-Service Bank of America Full-Service Chilmark Partners Boutique BancBoston Robertson Stephens Full-Service CIBC World Markets Inc Full-Service Bank Analysis Center Boutique CIT Group Inc Full-Service Bank Street Group LLC Boutique Citi Full-Service BankersBanc Capital Corp Full-Service Cochran Caronia Waller Full-Service Banks Street Partners LLC Boutique Cochran, Caronia & Co. Boutique Barclays Full-Service Cohen & Steers Capital Advisor Boutique Batchelder & Co Full-Service Cohen Bros & Co Boutique Baxter Fentriss & Co Boutique Collins Stewart Ltd Full-Service BB&T Capital Markets Full-Service Colonnade Advisors LLC Boutique Bear Stearns & Co Inc Full-Service Commonwealth Associates Full-Service Berenson & Co LLP Full-Service Communications Equity Boutique Berkshire Capital Boutique Consensus Advisory Services Boutique Berkshire Partners Full-Service Covert & Co Boutique Berwind Financial Group Boutique Covington Associates Boutique BlackRock Inc Full-Service Cowen & Co Full-Service Blackstone Advisory Partners Full-Service Craig-Hallum, Inc. Full-Service BMO Capital Markets Full-Service Credit Suisse Full-Service BMO Nesbitt Burns Inc Full-Service Cummings & Co LLC Boutique BNP Paribas Full-Service Curtis Full-Service BNY Capital Markets Inc Full-Service Cypress Associates LLC Boutique Boenning & Scattergood Full-Service DA Davidson & Co Inc Full-Service Brean Murray Carret & Co LLC Full-Service Dahlman Rose Weiss LLC Full-Service Breckenridge Full-Service Dain Rauscher Corp Full-Service Broadhaven Capital Partners Boutique Daiwa Securities Co Ltd Full-Service Broadview Full-Service Danielson Capital LLC Full-Service Brocair Partners LLC Boutique Davenport Full-Service Brookwood Associates Boutique David A. Noyes Full-Service
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Advisor Category Advisor Category De Guardiola Advisors Full-Service Grant Samuel & Associates Pty Boutique Dean Bradley Osborne Partners Boutique Grant Thornton Full-Service Delaware Bay Group Inc Full-Service Greenbridge Parnters LLC Boutique Deloitte & Touche Corp Finance Full-Service Greene Holcomb & Fisher LLC Boutique DeSilva & Phillips Boutique Greene Holcomb & Lannin LLC Boutique Deutsche Bank Full-Service Greenhill & Co, LLC Boutique Donaldson Lufkin & Jenrette Full-Service GreenOak Real Estate Advisors Full-Service Donnelly Penman French Haggart Boutique Greentech Capital Advisors Boutique Dresdner Kleinwort Wasserstein Full-Service Griffin Financial Group LLC Boutique Dresner Partners Boutique Griffin Securities Inc Full-Service Duff & Phelps Boutique Groton Partners LLC Boutique Ernst & Young LLP Full-Service Guggenheim Partners LLC Full-Service E. R. Keller Full-Service Hadley Green Securities Full-Service Earlybirdcapital Inc Boutique Hambrecht & Quist Full-Service Edelman & Co Ltd Full-Service Hankin & Co Boutique Edge Healthcare Partners LLC Boutique Harris Nesbitt Full-Service Emerging Growth Equities Ltd Full-Service Harris Williams & Co Boutique Endicott Financial Advisors LL Full-Service HAS Associates Full-Service Energy Capital Solutions LLC Boutique HC Wainwright & Co Inc Full-Service Epoch Partners Full-Service Healthcare Growth Partners Inc Boutique Eureka Capital Markets LLC Boutique Hicks Muse Tate & Furst Inc Boutique Evercore Group Boutique Hoak Breedlove Wesneski & Co Boutique Evolution Media Capital LLC Full-Service Hoefer & Arnett Inc Full-Service Ewing Monroe Bemiss & Co. Boutique Houlihan Lokey Boutique FAC/Equities Full-Service Houlihan Smith & Co Boutique Fairmount Partners LLC Boutique Hovde Group LLC Full-Service Falls River Group LLC Boutique Howard Frazier Barker Elliot Full-Service FBR Capital Markets Corp Full-Service Howard Lawson Boutique Feldman Financial Advisors Inc Boutique HSBC Holdings PLC Full-Service Ferris Baker Watts Boutique Hyde Park Capital Advisors LLC Boutique Fig Partners LLC Full-Service Imperial Capital LLC Full-Service Financial Technology Partners Boutique ING Barings Full-Service Financo Boutique Integrated Finance Ltd Full-Service Findley Group Full-Service Intrepid Investment Bankers Boutique FinPro Inc Full-Service Investment Bank Services Inc Full-Service First Albany Boutique ISI Group Inc. Full-Service First Analysis Securities Full-Service J.H. Chapman Group Boutique First Security Van Kasper & Co Full-Service James H Avery Co Full-Service First Union Corp Full-Service Janney Montgomery Scott LLC Full-Service Fleet Boston Corp Full-Service JC Flowers & Co LLC Full-Service FMV Opinions Boutique Jefferies LLC Full-Service Foros Boutique Jefferson Capital Group Full-Service Fox-Pitt Kelton Boutique JF Capital Advisors Boutique Freeman & Co Boutique JJB Hilliard WL Lyons Inc Full-Service Friedman Billings Ramsey Group Full-Service JMP Securities LLC Full-Service Friend & Co Full-Service Johnson Rice & Co Full-Service FTI Capital Advisors LLC Full-Service Joseph Perella Full-Service FTN Midwest Research Securitie Full-Service JP Morgan Full-Service Fuji Corporate Advisory Co Ltd Full-Service Kafafian Group Inc Full-Service GCA Savvian Group Corp Boutique Kaufman Brothers LP Full-Service Gemini Partners L.P. Full-Service KBW Effectenbank NV Full-Service George K Baum & Co Full-Service Keane Advisors LLC Boutique Georgeson Shareholder Full-Service Keefe Bruyette & Woods Inc Full-Service Gerard Klauer Mattison & Co Full-Service Keefe Ventures LLC Full-Service Gleacher & Co Full-Service Key Banc Capital Markets Full-Service Gleacher Partners Boutique KeyCorp/McDonald Investments Full-Service Global Hunter Securities LLC Boutique KKR Capital Markets LLC Full-Service Goldman Sachs & Co Full-Service KPMG Full-Service
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Advisor Category Advisor Category Ladenburg Thalmann & Co Full-Service Newbury Piret & Co Inc Boutique Lane Berry & Co Int'l LLC Boutique Nomura Securities Full-Service Lazard Boutique North Point Advisor LLC Boutique Leerink Partners LLC Boutique Northeast Capital Full-Service Leerink Swann & Co Boutique Northland Securities Group LLC Full-Service Legacy Partners Group LLC Boutique Oppenheimer & Co Inc Full-Service Legg Mason & Co Inc Full-Service Orr Group Boutique Lehman Brothers Full-Service Ostrowski & Company Inc Boutique Lexicon Partners Boutique P&M Corporate Finance LLC Boutique Lincoln Partners LLC Boutique Pacific Crest Securities Inc Full-Service LionTree Advisors LLC Boutique Pagemill Partners LLC Boutique LMC Capital LLC Full-Service PaineWebber Full-Service Loop Capital Markets Full-Service ParaCap Group LLC Full-Service LRG Capital Group Full-Service Paragon Capital Group LLC Boutique Lutz Advisors Inc Full-Service Parker-Hunter Inc Full-Service Macquarie Group Full-Service Paul J Taubman Boutique Mann Armistead & Epperson Ltd. Boutique Perella Weinberg Partners LP Full-Service Maren Group LLC Boutique Periculum Capital Co llc Boutique Marshall and Stevens Inc Full-Service Perseus Group LLC Full-Service Maxim Group LLC Full-Service Peter J. Solomon Co Ltd Boutique McAdams Wright Ragen Inc Full-Service Petkevich & Partners LLC Full-Service McColl Partners LLC Boutique Petrie Partners LLC Boutique McConnell Budd & Romano Full-Service Philo Smith Full-Service McConnell, Budd & Downes Full-Service Philpott Ball & Co Boutique McDonald Investments Full-Service Pinnacle Capital Corp Full-Service McKinnon & Company, Inc. Full-Service Piper Jaffray Inc Full-Service MCS Capital Markets LLC Full-Service PJT Partners LP Full-Service Mercanti Group Boutique PricewaterhouseCoopers Full-Service Mercer Capital Management Full-Service Professional Bank Services Full-Service Mercury Partners LLC. Full-Service Prudential Securities Inc Full-Service Merrill Lynch Full-Service Prudential Volpe Technology Gr Full-Service Merriman Capital Inc Full-Service Punk Ziegel & Co LP Boutique Merriman Curhan Ford & Co Full-Service Putnam Lovell Group Inc Full-Service Methuselah Advisors LLC Boutique Qatalyst Partners Boutique Milestone Advisors LLC Boutique Quadrangle Group LLC Full-Service Miller Buckfire Boutique Quarterdeck Investment Partner Full-Service Millstein & Co LP Full-Service RA Capital Advisors Boutique Mitchell Energy Advisors LLC Boutique Ragen MacKenzie Group Inc Full-Service Mizuho Securities Co Ltd Full-Service Randall & Dewey Inc Full-Service MJ Capital Partners LLC Boutique Raymond James Financial Inc Full-Service Moelis & Co Boutique RBC Full-Service Molecular Securities Inc Full-Service RBS Full-Service Monocacy Finl Advisors LLC Full-Service RCS Capital Corp Full-Service Monroe Securities Inc Full-Service Regal Capital Advisors LLC Full-Service Montgomery & Co Boutique Relational Advisors LLC Full-Service Mooreland Partners LLC Boutique Renninger & Associates LLC Boutique Morgan Joseph & Co Inc Boutique Revolution Partners LLC Full-Service Morgan Keegan Inc Full-Service River Branch Capital LLC Boutique Morgan Stanley Full-Service Robert A. Stanger & Co Full-Service MTS Health Partners LP Boutique Robert W Baird & Co Inc Full-Service MTS Securities LLC Full-Service Robertson Stephens & Co Full-Service Mufson Howe Hunter & Co LLC Boutique Robinson-Humphrey Co Full-Service Nassau Group Boutique Rohatyn Associates LLC Full-Service NatCity Investments Inc Full-Service Roman Friedrich & Co Ltd Boutique National Bank Financial Inc Full-Service Roth Capital Partners Inc Boutique Navigant Capital Advisors LLC Full-Service Rothschild & Co Full-Service Needham & Co LLC Full-Service RP Financial LC Boutique New York Capital Resources Full-Service Ryan Beck & Co Full-Service
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Advisor Category Advisor Category Sagent Advisors Inc Boutique ValueScope Inc Full-Service Salem Partners Full-Service Venturi & Co Boutique Salomon Smith Barney Full-Service Vinings-Sparks IBG, L.P. Full-Service Samco Capital Markets Full-Service Wells Fargo & Co Full-Service Sanders Morris Harris Inc Full-Service Wallach Full-Service Sandler O'Neill Partners Full-Service Waller Capital Boutique Sands Brothers & Co Ltd Full-Service Warburg Dillon Read Inc Full-Service Savvian LLC Boutique Wasserstein Perella Group Inc Full-Service Sawaya Segalas & Co LLC Boutique WBB Securities LLC Full-Service Scotiabank Full-Service Wedbush Securities Full-Service Scotia Waterous Inc Full-Service Wellington West Capital Inc Full-Service Scott & Stringfellow Full-Service Western Asset Mgmt Co Full-Service Scura Rise & Partners LLC Boutique Western Financial Corp Full-Service Seidler Corp Full-Service Western Reserve Partners LLC Boutique Seven Hills Partners Full-Service William Blair & Co Full-Service SG Barr Devlin Boutique Willis Capital Markets Boutique SG Cowen Securities Corp Full-Service Wit Soundview Group Inc Full-Service Shasta Partners LLC Boutique WR Hambrecht & Co LLC Full-Service Sheffield Merchant Banking Full-Service Wunderlich Securities Inc Full-Service Sheshunoff & Co Inc Boutique WWC Capital Group LLC Full-Service Signal Hill Capital Group LLC Boutique XMS Capital Partners LLC Boutique Simmons & Co International Boutique SMH Capital Inc Full-Service Smith Capital Inc Full-Service Snyder & Co Full-Service Societe Generale Full-Service Sonenshine Pastor & Co LLC Boutique SoundView Technology Group Inc Boutique Southwest Securities Group Inc Full-Service St Charles Capital Boutique Standard & Poors Securities Full-Service Stanford Group Company Full-Service Starmann, Starshak & Welnhofer Boutique Stephens Inc Full-Service Sterne Agee & Leach Inc Full-Service Stifel Nicolaus & Co Inc Full-Service Stone Key Partners Boutique Stone Ridge Partners LLC Boutique Stout Risius Ross Inc Boutique SunTrust Banks Full-Service Susquehanna Financial Group Full-Service T. Stephen Johnson & Assoc. Boutique TD Securities Inc Full-Service Teneo Holdings Boutique Tengram Capital Partners LLC Full-Service ThinkEquity Partners Full-Service Thomas Weisel Partners Full-Service Threadstone Advisors LLC Boutique TM Capital Boutique Triangle Capital Partners LLC Boutique Trident Securities Boutique Tudor Pickering & Co LLC Full-Service UBS Full-Service Union Bank of Switzerland Full-Service Updata Capital Inc Boutique US Bancorp Full-Service USBX Advisory Services LLC Full-Service Valence Group Boutique
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