University of Cincinnati

UNIVERSITY OF CINCINNATI

Date:______

I, ______, hereby submit this work as part of the requirements for the degree of: in:

It is entitled:

This work and its defense approved by:

Chair: ______

Essays in Mergers and Acquisitions: Roles of the Third Party

A dissertation submitted to the

Graduate School

of the University of Cincinnati

In partial fulfillment of the

requirements for the degree of

Doctoral of Philosophy

In the Department of Finance-Real Estate

College of Business

Jie Wei B.S., Wuhan University, 1997 M.S., University of California, Los Angeles, 2002

Committee Chair: Michael Ferguson, Ph.D.

May 2008

Abstract

Merger and Acquisition market is a very active section of the financial market,

involving multi-trillion dollar businesses every year. Extensive research has been done on

this field, mostly focusing on the two parties of the trade, namely the acquirer and the

target. However, the third party like deal advisors and risk arbitrageurs play very

important roles in these transactions too. They not only get directly involved by

negotiating the price and the terms, but also indirectly influence deal outcomes and

facilitate price discovery by trading both party’s equities.

In the first part of this study, we focus on risk arbitrageurs. They participate the

M&A games by providing the target shareholders a safe exit and make money from the

P − P speculative spread ( offer +1 ). But if and why risk arbitrageurs earn risk adjusted P+1

excess returns is a big unanswered question in the M&A literature. Our empirical study

shows that deal characteristics, as well as market conditions affect risk arbitrageur’s

return. More importantly, after considering the liquidity risk (for which we use VIX as the proxy) M&A arbitrageurs are not making excess return.

In the second part of this study, we turn our attention to financial advisors. We examine the effect of using boutique vs. full service investment banks as financial advisors on deal outcomes and shareholder’s wealth in M&A transactions. Boutique investment banks are defined as independent financial advisors whose focus is M&A

advising. This is the first paper to examine the role of financial advisors from this

perspective. We find that deal size and target management’s attitude towards the deal are

important factors that affect acquirer’s choice of boutique versus full service advisors.

We also find that on average, boutique advisors achieve a higher deal success rate while it takes them a longer time to complete deals. Boutique bank’s expertise in valuation is more appreciated than their independence by both the client and the market. They are better advisors in particular deals because of their expertise.

iii

Acknowledgements

This dissertation would not have been possible without the help, support, guidance and efforts of a lot of people. I would like to thank my committee members Dr.

Michael Ferguson, Dr. Weihong Song, Dr. Martin Levy for their guidance over the years.

I am also indebted to Dr. Steve Slezak, Dr. Brian Hatch, Dr. Steve Wyatt and Dr. Hui

Guo for their inspiring teaching over the years.

I am grateful for the financial support of the University of Cincinnati Graduate

Scholarship. I would also like to take this opportunity to thank several of my fellow PhD

students including Doina Chichernea, Anthony Holder, David Manzler and Joshua Knapp for interesting and helpful discussions and debates which helped me to remain calm and keep the sense of wonder during this process.

I am also especially grateful for the patience shown by my family throughout this

process. Without their support and encouragement I would not be able to finish this and maintain my sanity (if I am still considered as having some). Thanks a lot for everything.

Table of Contents

List of Tables and Figures…. ………………………………………………………… vii

Introduction …….……………………………………………………..…………………1

Essay 1: The Profitability of M&A Arbitrageurs

1. Introduction.……………………………………...…………..…………………4

2. Literature Review……………...…………………………………..……………9

3. Data ………………………………………………..…………………………13

4. Empirical Results

4.1 Methodology ……………………………………………….…...... 14

4.2 Testing Hypothesis…………………………………………………. 18

1) Controlling Idiosyncratic Risk………………………...……19

2) Controlling Liquidity Risk………………………………….21

3) Controlling both Idiosyncratic Risk and Liquidity Risk...….23

4) The Interaction between Idiosyncratic Risk and Liquidity Risk.27

4.3 Robustness Check …………………………………..………………29

5. Conclusion ……………………………………………………..…………….29

6. References………………………………………..……………..……. ……...31

7. Tables ………...……………………………………………………………….33

Essay 2: The Value of “Boutique” Financial Advisors in M&A

1. Introduction ……………...……………………..……………………………..42

2. Literature Review ……...……………………………..……………………….47

3. Data and Methodology ...……………..……………………………………….49

4. Empirical Results

4.1 Univariate Analysis………………………..……………………...... 50

4.2 Multivariate Analysis ……………………………………………….58

5. Conclusion …………………………………..………………………………..65

6. Appendix 1…………………………………..………………………………..67

7. Appendix 2…………………………………..………………………………..68

8. References……………………………………..……………………. ……….79

9. Tables and Figures ……………………………………………………………70

Concluding Remarks and Perspectives…………..……………………………...…….85

vii

List of Tables and Figures

Essay 1

Table 1.1 Sample Summary …………….……………………………………………….33

Table 1.2 Regression of Risk Arbitrage Return on Common Risk Factors………….…..34

Table 1.3 The Determinants of Probability of Deal Success…………………….………35

Table 1.4 The Idiosyncratic Risk Factor ………………. ……………………….………36

Table 1.5 The Market Liquidity Risk Factor…………………………………………...37

Table 1.6 Excess Returns in Different Portfolios

Panel A- Deal Size and the Medium of Payment………………..………………38

Panel B- Deal Size and Market Liquidity…………………..………………..38

Panel C- The Medium of Payment and Market Liquidity...…………………39

Panel D- Deal Size, the Medium of Payment and Market Liquidity………..39

Table 1.7 Interaction Effects of Deal Characteristics and Market Liquidity

Panel A - The Difference of Excess Return ……………………………………..40

Panel B -The Coefficients of VIX ……………………………………………41

Essay 2

Table 2.1 Sample Distribution …………………………….…………………….………70

Table 2.2 Deal Characteristics …………………………….…………………….………71

Table 2.3 The Comparison between Boutique and Full Service Banks ………….……..72

Table 2.4 Univariate Analysis

Panel A-Classified by Acquirer’s Advisor …………………………..………….74

Panel B- Classified by Target’s Advisor ……………………….…….…………75

viii

Table 2.5 Determinants of the Use of Boutique Advisor

Panel A- Acquirer’s Choice on Advisors………..……….…………..…………..76

Panel B- Target’s Choice on Advisors………..……….………………..………..87

Table 2.6 The Impact of Boutique Advisors on Deal Completion ….……….………….78

Table 2.7 The Impact of Boutique Advisors on Deal Duration… ….………..………….79

Table 2.8 The Impact of a Boutique Advisor on the Deal Premium

Panel A-OLS Regression ….………………….……………………..………….80

Panel B-Two Stage Least Square Regressions – Mergers ……………...……….81

Panel C-Two Stage Least Square Regressions – Tenders …………………...….82

Table 2.9 Impact of Boutique Advisor on Announcement Period Returns…………..….83

Figure 1 Time Trend of the Merge Wave and the Use of Boutique Advisors……….…..84

Introduction

M&A is a very active section of the financial market involving multi-trillion dollar businesses every year. Extensive research has been done on this battle field for corporate control, covering a wide range of topics. Most studies focus on two parties of the trade, namely the acquirer and the target. However, the third party of this game, like financial advisors and risk arbitrageurs, play very important roles too. Risk arbitrageurs buy target’s stocks when a deal is announced. By providing target shareholders a sure return and taking on the risk of deal failing, they make the return of the speculative

P − P spread ( offer +1 ). They not only facilitate the price discovery process by trading P+1 target’s (some times acquirer’s too when the medium of payment involves acquirer’s stock) equity, but also indirectly assist the deal completion. Financial advisors, as the third party, play an even more direct role than risk arbitrageurs. They help both sides negotiate the price and terms of the trade; they even help with the financing in some cases.

Their own identities and objectives directly affect the outcome of the takeover. However, research on these third party players is limited. There are puzzles and interesting open questions that have not been fully studied yet. In this study, we focus our attention on these third party players.

The first puzzle we try to resolve is the risk arbitrageur’s excess profit. If and why

M&A arbitrageurs earn risk adjusted excess returns is a big unanswered question in the

M&A literature. The goal of the first chapter of this study is to examine the risk and return relation of M&A arbitrageurs in different idiosyncratic and systematic

environments. On one hand, deal characteristics create idiosyncratic risk for arbitrageurs

by affecting the ex-ante probability of deal success. On the other hand, market liquidity

conditions also affect the probability of deal success and more importantly the

P − P speculative spreads ( offer +1 ), which is the source of these arbitrageur’s profit. For P+1

example, when the market experienced the credit crunch in mid 2007, speculative spreads

widened up dramatically. But both of idiosyncratic risk and liquidity risk have not been

taken into account formally in previous literature when examining M&A arbitrageur’s

return. In this article, we empirically examine the risk and return relationship by

considering both idiosyncratic and systematic risk including market liquidity risk. Our

study shows that deal characteristics, for example, the medium of payment, affects risk

characteristics of these arbitrageur’s portfolios. Also, both idiosyncratic risk partially

captured by the probability of deal success and market liquidity which we use VIX as the

proxy affect M&A arbitrageur’s return. More importantly, after considering the liquidity

risk they are bearing, M&A arbitrageurs are not making excess return.

This essay contributes to the existing research on M&A arbitrage in two aspects.

First, we provide evidence that M&A arbitrageurs bear both idiosyncratic risk and systematic liquidity risk. Second, we examine how excess return (α) is related to different

idiosyncratic and systematic market environments and shows that M&A arbitrageurs are not earning excess return.

In the second part of this study, we turn our attention to financial advisors. The

second essay of the dissertation examines the effect of using boutique vs. full service

investment banks as financial advisors on deal outcomes and shareholder’s wealth in

M&A transactions. Boutique investment banks are defined as independent financial advisors whose focus is M&A advising. This is the first paper to examine the role of financial advisors from this perspective. We identify 202 boutique banks from 533 M&A advisory firms for a large sample of M & A deals from 1995 to 2006 and analyze how firms choose boutique advisors vs. full service advisors. After controlling the endogenous choice of financial advisors by merging firms, we examine how investment bank’s expertise and independence affect deal premium, completion speed, and success rate and announcement period returns. We find that deal size and target management’s attitude towards the deal are important factors that affect acquirer’s choice of boutique versus full service advisors. We also find that on average, boutique advisors achieve a higher deal success rate while it takes them a longer time to complete deals. Boutique bank’s expertise in valuation is more appreciated than their independence by both the client and the market. They are better advisors in particular deals because of their expertise.

However, due to the nature of the boutique, there is a limitation on what kind of deals that these niche players can exploit the most of their advantage.

I will cover more of the background and literatures on these two topics as well as the methodology and details of empirical findings etc. in the following essays respectively.

Essay 1: The Profitability of M&A Arbitrageurs

1. Introduction

Risk arbitrageurs (or M&A arbitrageurs, these two terms are used interchangeably

through out this essay) are generally associated with the negative image of being greedy

and extraordinarily profitable, partially due to Ivan Boesky’s high profile insider trading

scandal. But most risk arbitrageurs are not violating laws. They make high profit by writing insurance against deal failures for target shareholders (Och and Pulvino 2005).

The significant roles they play in mergers and acquisitions are recognized. The puzzle is that they seem to make more return compared with the market risk they bear (in other words, as an insurance company, they are charging too high a premium).

Previous literatures argue that because risk arbitrageurs are holding a portfolio of

unrelated deals, the idiosyncratic part of the risk should be diversified away. Therefore,

they only bear the market risk. We argue that the idiosyncratic part of risk might not be

diversified away in the portfolio because there might not be enough number of deals

available to compose a diversified portfolio. If this is the case, the risk that arbitrageurs

take has both a systematic component and an unsystematic part.

The risk M&A arbitrageurs are bearing is the deal risk, which is affected by both

deal characteristics and market conditions. Speculative spread is determined according to

market’s estimation on the deal risk. On one hand, the speculative spread1, which is the

source of these arbitrageurs’ profit, is apparently affected by deal characteristics. The

unsystematic part might not be diversified away in their portfolios because of the limited

1 “Speculation spreads in acquisitions, defined as the percentage difference between the bid price and market price one- day after the initial announcement, are the starting point for arbitrage returns, a subject receiving increased attention in practice and in the literature.” Jindra and Walking (2004)

number of open deals. They will then require reward for bearing the idiosyncratic risk.

On the other hand, the speculative spread is tightly tied with market liquidity conditions.

The most recent evidence is the suddenly widened up speculative spreads when the

liquidity dried up in the market due to credit crunch in the mid 20072.

To investigate whether M&A arbitrageurs require risk premium for both of these

two types of risks, to explore the interaction of these two types of risk and to examine

whether they make excess return after adjusting for these risks, we study in this essay the

relation between risk and return of M&A arbitrageurs in different idiosyncratic and

systematic risk environments including market liquidity risk. The ex-ante probability of

the deal success, which is affected by certain deal characteristics, is used as the proxy for

idiosyncratic risk. As for the market liquidity risk, different from Mitchell and Pulvino

(2001), who use market up and down as the proxy for liquidity, we use VIX. Although

Mitchell and Pulvino (2001) show that arbitrageur’s returns are market risk neutral during

up markets and positively related to market risk during down markets, we think up and

down market is a fairly rough measurement of market liquidity risk. The novelty of our

study is that we use VIX as the proxy for market liquidity risk. This is a more appropriate

proxy because it is a continuous variable and it is more relevant for risk arbitrageurs

under the given situation. What is calculated in the speculative spread is the ex-ante

market liquidity condition, which is better captured by VIX.

2 See WSJ August 10th, 2007 --Arb Spreads Gone Wild

…Below is a look at the difference between where the stocks of some of the biggest leveraged-buyout targets are trading and the offers that are on the table for them. So-called arbitrage spreads are exploding today. (Normally, the average spread is somewhere in the neighborhood of 10%.)…..Sallie Mae: 100%, First Data: 75%, Clear Channel: 37%, BCE: 28%, TXU: 26%, Harrah’s: 25%, Alltel: 22%, Hilton: 21%

We are also interested in how idiosyncratic risk interacts with systematic liquidity risk and consequently affects arbitrageur’s total risk and return. Why do we care about the interaction of idiosyncratic risk and systematic liquidity risk? We believe that on top of regular risk factors, the payoff of the M&A arbitrageurs is also jointly determined by both deal characteristics and market liquidity conditions. First of all, low market liquidity condition might affect the deal success rate systematically. Secondly, when deal fails, the loss to risk arbitrageurs might be even bigger when market liquidity is low than when market liquidity is high, because arbitrageurs are stuck with a large number of target shares and they might demand liquidity instead of providing liquidity when the market is in the most need for it (Och and Pulvino2005). The price of target stocks might fall to a lower level in the low liquidity market than in the high liquidity market when the withdraw announcement is made.

It seems that both the idiosyncratic risk and the systematic liquidity risk as well as other systematic risk factors that arbitrageurs bear affect the required rate of return for arbitrageurs. In other words, expected payoff of arbitrageurs is jointly determined by deal characteristics (idiosyncratic risk) and market conditions (systematic risk). But how much systematic versus unsystematic risk do arbitrageurs bear and how does this influence the risk premium that M&A arbitrageurs require? Do they really earn risk adjusted excess return? These are the questions we attempt to answer in this paper.

Previous theoretical and empirical literature established several results regarding

M&A arbitrageurs: 1) M&A arbitrageurs do earn significant returns which cannot be justified by the risks considered so far in the existing literature, 2) Deal characteristics

(such as mediums of exchange, target’s attitude etc.) affect the ex-ante probability of deal success, which is the idiosyncratic risk M&A arbitrageurs bear, 3) Systematic risk characteristics change during market up vs. down time for M&A arbitrageurs.

First, the significant returns earned by M&A arbitrageurs motivate the business world to describe them as earning extraordinary profits. This claim has been confirmed by many academic studies (see Dukes, Frohlich and Ma (1992), Karolyi and Shannon

(1998), Baker and Savasoglu (2002), Mitchell and Pulvino (2001), Jindra and Walking

(2004) etc.). The extraordinary profits are however not justified by the systematic risk taken. The positive abnormal return that arbitrageurs earn has been one of the interesting and important puzzles in mergers and acquisitions literature. Since risks that M&A arbitrageurs take might have an undiversified idiosyncratic part, their risk and return relation can be very different when we consider how much risk premia arbitrageurs require for both types of risks. If arbitrageurs require risk premium for both types of risks, as well as the interaction of these two types of risks (for example, when changes of prob. of deal success interact with changes in the liquidity environment), do they make excess return after we adjust for all the risks they are taking?

The second finding about M&A arbitrageurs is that deal characteristics determine the ex-ante probability of deal success. For example, it is easier for larger firms to acquire smaller firms; horizontal mergers may face regulatory hurdles and have a lower rate of success; a higher premium increases the odds of target shareholders’ approval; the mediums of exchange signal different degrees of asymmetric information and therefore affect the odds of target shareholders’ approval. The probability of deal success is

affected by both idiosyncratic risk and systematic risk. If the idiosyncratic part cannot be

diversified away in the portfolio, arbitrageurs would require a premium for bearing it.

Although deals are unrelated in most cases, there might not be enough open deals to

compose a completely diversified portfolio. If this is the case, deal characteristics could

affect the rate of return required by the risk arbitrageurs. For example, studies found that

arbitrageur’s return is on average higher when the merger is cash financed rather than

stock financed. However, when we account for the required return for bearing market

risks, the pure stock portfolio actually has higher excess return, which is consistent with

the fact that stock deals have lower propensity to be completed (higher idiosyncratic risk).

The third finding about M&A arbitrageurs is that risk characteristics are different during market up and down times. Mitchell and Pulvino (2001) are the first to suggest that the risk that arbitrageurs bear is related to their returns in a different way in market down times and up times. They use a piece-wise linear model to fit the return of arbitrageurs and find that return is unrelated with market risk during market up time and significantly positively related to market risk during market down time. The authors therefore suggest that arbitrageurs earn excess returns because they provide liquidity, especially during market down time. More interestingly, the difference in loadings on betas in up market and down market is amplified by different mediums of exchange, indicating the interaction of deal risk and market risk. Up and down markets provide different systematic liquidity environments. Not only do the risk characteristics change, but the required risk premium also changes. Existing research has shown that asset pricing models such as CAPM and Fama-French three factor have much better

performance when conditioned on some macroeconomic variables.

Based on these three streams of research, we study the risk and return relation of

M&A arbitrageurs given the possibility that they might bear both types of risks and

whether there is excess return left on the table for M&A arbitrageurs after everything is

taken into account. As stated above, this article contributes to current research about

M&A arbitrageurs in following aspects. First, we provide evidence that M&A

arbitrageurs bear both idiosyncratic risk and systematic risk (including market liquidity

risk). Second, the idiosyncratic premium changes with market liquidity. Third, after we

examine how excess return (α) is related to different idiosyncratic and market

environments, we can finally answer the question whether risk arbitrageurs are earning

excess returns with a firm “NO”.

The essay is organized as follows. Section 2 discusses related literature. Section 3

describes the data. Section 4 presents empirical results and we conclude in section 5.

2. Literature Review

Previous studies on the profitability of risk arbitrage conclude that it generates

substantial returns. Dukes et al. (1992) find that during the period 1971-1985 investors could have earned a daily return3 of 0.47 percent (corresponding to an annualized return of more than 100 percent) by buying target’s stock on the day of tender offer announcements (cash only) and selling it when the deal is resolute. Jindra and Walking

(1999) report similar results using a sample of 361 cash tender offers between 1981 and

1995.

3This is total return – it is not adjusted for risk and no transactions cost are considered.

Studies that examine tender offers with both cash and stock as the method of

payment find less but still substantial abnormal returns. Lacker and Lys (1987) find

excess return of 5.3 percent 4 over the transaction period, which correspond to an

annualized excess return of 51.9 percent. Karolyi and Shannon (1998) report 26 percent

for a small sample of Canadian mergers. Baker and Savsoglu (2002)5 use a much larger sample of American mergers between 1978 and 1996 and conclude that the abnormal return is about 1 percent per month and 12.5 annualized after taking into account of transaction costs.

Finally, Mitchell and Pulvino (2001) analyze 4750 mergers from 1963 to 1998 and conclude that risk arbitrage of this sort generates risk adjusted excess returns of four percent per year. Their findings are robust to different measures of transaction costs.

Additionally, by fitting the return of arbitrageurs using a piece-wise linear model, they find that return is unrelated to market risk during market up time and significantly positively related to market risk during down time. Therefore, the authors suggest that arbitrageurs earn excess returns because they provide liquidity, especially during market down time. More interestingly, the difference of loadings on the beta in up market and down market is amplified by the different mediums of exchange, which implies that risk characteristics change when idiosyncratic risk interacts with the systematic environment.

However, the general conclusion in the literature is that M&A arbitrageurs do earn significant returns that do not seem to be justified by the amount of risk they bear – hence, this issue remains an open question.

4 This is risk adjusted using a market model, where the excess return is the residual from this model. 5 Both Baker and Savsoglu (2002) and Mitchell and Pulvino (2001) measure the risk adjusted excess return and take transaction costs into account.

One of the key issues in determining the risk adjusted excess return is defining the

risk factors taken into consideration. The risk that arbitrageurs bear has both an

idiosyncratic and a systematic part. In terms of systematic risk, previous literature uses

asset pricing models like CAPM or Fama-French model to measure the systematic risk

premium. However, liquidity, which has been widely accepted as an important risk factor in the asset pricing literature 6 , has not yet been adjusted for when measuring risk arbitrageur’s excess return. Furthermore, the liquidity risk relevant for risk arbitrageurs is the “look-ahead” liquidity factor. The innovation of this study is to use VIX to capture this “anticipation” in the market liquidity environment.

Mitchell and Pulvino (2001) is one of the few studies that suggest that market conditions create very different liquidity environments for arbitrageurs, which in turn affect their returns. Empirically, they are the first to examine the risk and return relation of M&A arbitrageurs during market up and down times and conclude that risk arbitrageurs make excess return by providing liquidity, especially in market down time.

Other than Mitchell and Pulvino (2001), not much has been done to study how the market

conditions affect risk arbitrageur’s return. However, previous research does find that more acquisitions occur when stock markets are booming than when markets are depressed. (See Jovanovic and Rousseau (2001); Rhodes-Kropf and Viswanathan (2002)).

In addition, Bouwman, Fuller and Nain (2006) focus on how announcement return (not

risk arbitrageur’s return but they are related) changes during high or low market valuation and found significant differences. Previous literature thus suggests that the risk return relation for M&A arbitrageurs is changing with changing market conditions. To address this issue, we propose conditioning the asset pricing models on macroeconomic variables

6 See Pastor and Stambaugh (2003), Sadka(2005)

(VIX) in order to control changing market conditions. This methodology is widely used in the asset pricing literature (see Ferson and Harvey (1999)) and has not been applied yet in the M&A research.

Another question that is not properly answered in previous literature is the risk premium that arbitrageurs demand for bearing idiosyncratic risk. The probability of deal success is a partial proxy for deal specific risk. Whether this idiosyncratic risk can be diversified away or not by holding a portfolio with open deals on the market is not quite clear. If the idiosyncratic risk component can’t be diversified (not because of lack of information, but because of the limited number of available deals), M&A arbitrageurs will require premium for bearing this kind of risk. Deal specific factors that affect the ex- ante probability of success are such as method of payment, managerial friendly or hostile attitude, number of bidders, etc. (see Baker and Savasoglu (2002), Hsieh and Walking

(2005)). For example, the methods of payment used are indicative of different degrees of asymmetric information which affect the probability of deal success. Consequently, the method of payment will affect M&A arbitrageur’s risk characteristics, return and of course excess returns. Other deal characteristics have also been shown to matter: Karolyi and Shannon (1998), for example, suggest that arbitrageurs might command a higher risk premium for smaller and less liquid deals.

In conclusion, we suggest that one of the reasons why M&A arbitrageur’s excess return remains an unexplained puzzle is the fact that previous literature did not consider idiosyncratic risk and systematic liquidity risk, nor the interaction between the these two types of risks. In this article, we use probability of deal success as the proxy for

idiosyncratic risk and use VIX to control market liquidity conditions. By taking into

account the risk that M&A arbitrageurs are bearing, which they ask for reward, we then

further examine whether they are earning risk adjusted excess return. Our empirical

evidence shows that the market is efficient and M&A arbitrageur’s return is proportional

to the risk they take.

3. Data

We started with all merger and acquisition offers recorded by Securities Data

Company (SDC) between 1986 and 2006. However, we restrict our attention to

announced mergers and tender offers where both target firms and acquirer firms are

American public traded companies. Different from many previous studies that focus on

specific type of transactions such as cash tender, we include both pure cash and pure stock as the medium of payment in our sample and we include both mergers and tenders.

The rationale for limiting to only 100 percent cash or 100 percent stock deals is to be able to calculate arbitrageur’s profits with relative accuracy. When payment method gets complicated, the trading strategies of arbitrageurs become even harder to conjecture and the measurement of arbitrageur’s profit become less accurate. 7 The initial sample

contains 6906 deals. However, after excluding collar offer deals and deals that have

duration less than 1 day, we are left with 5733 deals. Further elimination is to exclude

deals for which we cannot estimate probability of deal success based on some deal

characteristics and deals for which we cannot obtain stock price information of the

involved companies from CRSP.

7 A recent paper by Branch and Wang (2008) studies the collar stock swap deals. They found that this kind of deals have similar piece-wise linear relation as Mitchell and Pulvino (2001) found. But they are more sensitive to downside risk.

The final sample contains 4407 deals. The summary of the sample is presented in

Table 1.1. The average number of annual announced deals in our sample is 210 deals and on average 71% of these deals are pure cash deals. There are about 12% of deals that did not go through. The average size of the target is $2,606 million dollars, which is much smaller compared with the average size of the acquirer at $8, 8337 million dollars. Also, from the number of deals announced in each year, we can divide the sample period into three sub-periods, 1986~1993, 1994~2000, 2001~2006. Later, we will see how different time periods make a difference in our analysis.

[Insert Table 1.1 Here]

4. Empirical Results

4.1 Methodology

To estimate arbitrageur’s profits, we follow Baker and Savsoglu (2002) and

Mitchell and Pulvino (2001) using calendar portfolio approach. We calculate daily return for each deal using different methods for cash offers and stock offers respectively. We then take the average of the daily returns for all deals which remain open in that day. To obtain the monthly return for the portfolio, we accumulate daily return during the month.

1) Calculate Arbitrageur’s Portfolio return

T T T Pit + Dit − Pit−1 i) Daily Return on Cash offers: Rit = T Pit−1 T T T A A A A Pit + Dit − Pit−1 − Δ(Pit + Dit − Pit−1 − rf Pi1 ) ii) Daily Return on Stock offers: Rit = PositionValuet−1 Where T stands for the target and A stands for the acquirer and t indicates the time period.

N ∑(Ri ) iii) Daily Average Return: R = i=1 N is number of deals remain open on day dayh N h M iv) Monthly Return: Rmonthj = ∏(1+ Rit ) −1 M is number of trading days in month j t=m After we have a series of monthly portfolio return, the goal is to examine whether arbitrageurs are making risk adjusted excess profits.

2) Examine significance of α in

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML

We first examine the excess α in our complete sample (Table 1.2). Our result is very similar to what Baker and Savsoglu (2002) has obtained. The excess α is .6% monthly, which translates into 7.4% annual return. If we look at stock deals and cash deals separately, we find that stock deals have higher excess α. The monthly excesses α for stock deal portfolio is .85% and is .54% for cash deal portfolio, which translate into annual return of 10.7% and 6.7% respectively. Moreover, how these two portfolios

responded to market risk factor is very different. The β mkt is negatively insignificant for stock deal portfolio, while it is significant at .8 for cash deal portfolio. Furthermore, the

R2 for stock deal portfolio is much smaller than cash deal portfolio. In other words,

Fama-French three factor model can describe M&A arbitrageur’s risk return relationship much better for cash deal portfolio than for the stock deal portfolio. The medium of payment is deal specific but seems to matter not only in arbitrageur’s risk adjusted profit but also the required return for the systematic risk. This is a piece of evidence that idiosyncratic risk matters for risk arbitrageurs.

Another potential deal specific variable that might affect risk arbitrageur’s profit is deal size. Larger deals not only might provide more profits in dollar amount, but also provide a better liquidity environment for arbitrageurs to disguise their actions (See

Cornelli and Li 2000). So we examine separately the excess return for large deal portfolio and small deal portfolio. We define large deals as deal value that is more than $50 million here. From Table 1.2, we can see that small deal portfolio actually has a higher excesses return. Large deal portfolio has a .5% monthly excess return and small deal portfolio has a .8% monthly excess return, which is almost 60% higher than what large deal portfolio

has. Moreover, β SMB is insignificant in large deal portfolio and highly significant in small deal portfolio. This is not particularly surprising since this is the size factor. Again, this comparison shows us that deal specific variable can actually affect risk arbitrageur’s profit. Both medium of payment and size are deal specific. Stock deals have lower likelihood of deal completion therefore higher risk. Small size deals have worse liquidity environment (individual stock liquidity, not market liquidity). We see both of these two factors are associated with bigger unexplained return. Therefore, when we consider whether risk arbitrageurs are making risk adjusted excesses return, we should consider the idiosyncratic risk they are bearing.

[Insert Table 1.2 Here]

To find a proxy for the idiosyncratic risk of risk arbitrageur’s portfolio, we first estimate the probability of success of each deal using deal characteristics like medium of payment, attitude of the target management, market values of target and acquirer, etc. We then use the weighted average of probability of deal success as the probability of success

of M&A arbitrageur’s portfolios as the proxy for the idiosyncratic risk that these arbitrageurs are bearing. The advantage of this proxy is it combines several deal characteristics into one variable. The caveats of this approach are follows: first, by taking two averages (daily average then monthly average), the measurement of idiosyncratic risk becomes less accurate; second, market conditions can also affect the probability of deal success. Both of these two drawbacks actually work against us. If we can still find the significance of idiosyncratic risk, the actual influence should be bigger.

3) Estimate ex-ante prob. of deal success using deal characteristics

We first fit a logistic regression model using actual deal outcome as dependent variable and deal characteristics as independent variables.

Deal outcome (1 or 0) =

β 0 + β1Rmkt + β 2 Rmkt−1 + β 3 Rmkt−2 + β 4 LogT argetMV + β 5 LogAcquirerMV

+ β 6 IndustryDummy(Horizontal) + β 7 AcquirerAttitude + β8 Stock + β 9 NumberofBidders

+ β10 Dyr94~00 + β11Dyr01~06

After we estimate the probability of success for each deal, we then take the average for all the deals that remain open in that day as the daily average. The monthly measurement of idiosyncratic risk of the portfolio is obtained by taking average of all the trading days during that month. The down side of this method is that the measurement of idiosyncratic risk is diversified after taking average twice. However, since we cannot directly use prob. of success in time series regression, taking the average is the only viable method if we want to include some kind of measurement of idiosyncratic risk factor in the risk return analysis regression.

In Table 1.3, we present the model we use to estimate the probability of deal

success. The takeover premium is not included in the model because it is not significant.

Also, including the takeover premium will not improve the accuracy for estimating the probability of success, while it will reduce the sample size, because quite a few deals do not have this information. Similar to Baker and Savsoglu (2002), we find that target management attitude and market values of both target and acquirer are significant in estimating the likelihood of deal success. However, different from them, we find that medium of payment, number of bidders, and the industry dummy are significant factors in estimating deal success rate. Moreover, we find that lag market returns up to two periods positively affect deal success, which is consistent with Mitchell and Pulvino

(2001). One more point I would like to emphasize is the significance of the year dummy for the time period between year 1994 and year 2000. As we mentioned before, this period has more takeover activities. However, deals seem to fall apart a little easier in this period than in other time periods in our sample. The significance of all these factors demonstrates that deal risk is determined by both deal specific characteristics and market conditions. Using this model, we estimate the coefficient for each factor and then use the estimated coefficients to calculate the ex-ante probability of success for each deal.

[Insert Table 1.3 Here]

4.2 Testing Hypothesis

In order to gain a better understanding on M&A arbitrageur’s risk and return relation, we consider two risk factors that they might also bear on top of the regular risk factors considered in the previous literature: the idiosyncratic risk and the liquidity risk.

Therefore, the first step is to take into account the idiosyncratic risk.

1) Controlling Idiosyncratic Risk

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML + β iv IV

The proxy of the idiosyncratic risk for each deal is the estimated probability of deal success. The proxy of the idiosyncratic risk for risk arbitrageur’s monthly portfolio is the average probability of success of the portfolio. To control both the systematic risk and the idiosyncratic risk, we run the time series regression using all of the Fama-French three factors and the idiosyncratic risk factor (the estimated ex-ante probability of deal success).

In the first column of Table 1.4, we can see that all of the FF three factors are significant and the IV factor is negatively significant at 10% level. The higher the IV factor means the higher the average probability of deal success of the portfolio, which translates into the lower the idiosyncratic risk. The negative sign means that the lower the idiosyncratic risk, the lower the expected return, which makes perfect sense. Column2 through

Column4 in table 1.4 form three series of portfolios based on the idiosyncratic risk of each deal. By comparing these three columns we can see that these three series of portfolios react to FF factors in very similar way and the excess return α is highly significant and about the same across all three groups. One of the possible explanations for this is because the probability of deal success actually captures both systematic risk and idiosyncratic risk. The risk and return relation becomes murkier when we use probability of deal success to form portfolios. We learn from Table 1.2 that stock deal portfolio and cash deal portfolio have very different risk return relation and medium of payment is clearly deal specific. So in column 6 and 7, we repeat the analysis on

stock/cash portfolios separately. Again, stock portfolio has much lower R square and

insignificant β mkt . What is interesting is that stock portfolios have a higher excess return, consistent with their higher idiosyncratic risk. Moreover, the IV factor, which is significant in the complete sample now become insignificant in both the stock and cash portfolios. However, the significance of excess α that our puzzle dwells on disappears too.

From above analysis, it is obvious that idiosyncratic risk is a very important factor in measuring risk arbitrageur’s profit. However, how exactly this idiosyncratic risk factor works is not quite clear yet. One possible reason is that, the proxy of idiosyncratic risk for each deal is relatively accurate; however, the proxy of idiosyncratic risk for the portfolio is cursory. Therefore, when using the IV of the portfolio in the time series regression, we might lose some crucial information. Also, the probability of deal success is determined by both market and deal specific factors. To mitigate the problem, we examine portfolios formed on some deal specific factors like medium of payment and deal size in later section and we will return to the discussion of idiosyncratic risk there.

[Insert Table 1.4 Here]

The second step in this study is to examine arbitrageur’s risk and return relation in different liquidity conditions. This is different from Mitchell and Pulvino (2001). What is done in their paper is to examine arbitrageur’s risk and return relation in different market conditions by dividing months into up market and down market according to monthly market return. They found that the coefficient on market risk factor is very different in up and down markets. They conjecture that it is because of the different liquidity

environment. So we look at the liquidity environment using the VIX8. The advantage of this VIX variable is that it captures the anticipation of the market. Also, instead of fitting piece-wise linear CAPM as Mitchell and Pulvino (2001) did, we use VIX as the forth risk factor. The advantage of using a continuous variable as the proxy is to retain more precise information about this risk factor. So the second step is:

2) Controlling Liquidity Risk

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML + β liquidity Liquidity

The result is shown in the Table 1.5. In the first column, we run the regression without separating deals into different portfolios. Since VIX is only available after year1990, we have a shorter time series regression when using VIX as one of the risk factors. Still, we have 3770 deals in 204 months, which is a pretty decent sample size. From the significance of VIX and the insignificance of alpha, we can see that, VIX is able to capture the liquidity risk and explain away the excess return quite well. The reason is that the more relevant liquidity risk for M&A arbitrageurs is the “look-ahead” market condition. VIX captures what market perceives for the near future, which is the one that in theory is more likely to affect arbitrageur’s profit. The coefficient for VIX is positively significant and the significance of α disappears after controlling VIX. This means that after we control the liquidity risk that arbitrageurs are bearing, they are not making excess return. Furthermore, VIX is a continuous variable. The higher the VIX, the more volatile the market is likely to be. When market is expected to be volatile, liquidity is squeezed. The positive coefficient of VIX means that when liquidity is low, the expected

8 VIX is a weighted blend of prices for a range of options on the S&P 500 index. The VIX is quoted in terms of percentage points and translates, roughly, to the expected movement in the S&P 500 index over the next 30-day period, on an annualized basis.

return for arbitrageurs is higher. This is exactly what we expect from the liquidity risk argument. The empirical evidence fits our predictions from theory perfectly.

[Insert Table1.5]

From previous analysis we know that idiosyncratic risk is likely to play a role in the required return and it might also affect how risk arbitrageur’s required risk premium on market risk factors. In order to see the effect of idiosyncratic risk without using the noisy proxy of the estimated probability of success, we separate deals according to some deal characteristics and re-form portfolios. First, we form stock portfolios and cash portfolios. In the previous section, we already found that stock and cash portfolio react to market risk very differently. In other words, M&A arbitrageurs seem to ask for different risk premium for different deal characteristics. In this section, we see that they also react to liquidity risk differently. In stock portfolio, the coefficient of VIX is insignificant, while in cash portfolio it is highly significant. This indicates that there is interaction effect between deal characteristics and market liquidity condition. We will return to this interaction effect in a later section.

We then reform portfolios using large deals or small deals according to the deal value. Deals that are valued more than $50 million are considered as large deals and the rest of them are small deals. When VIX is used as the proxy, the significance of α disappears in either the large deal portfolio or the small deal portfolio. Interestingly, the risk factor is only significant in large deal portfolio. Consistent with previous findings that VIX are only significant in relatively lower idiosyncratic risk portfolios (we will return to the discussion of this in the interaction effect section). But the excess alpha is

again higher in small deal portfolios. Most importantly, when liquidity risk is taken into account, there is no excess profit for the arbitrageurs to make.

3) Controlling both Idiosyncratic Risk and Liquidity Risk

We have learned from the previous section that both idiosyncratic risk and systematic risk affect risk arbitrageur’s return. If we take into account the arbitrageur’s required return for just idiosyncratic risk, although the risk factor is significant, the excess return is also significant. But if we take into account market liquidity risk, not only the risk factor is significant, the excess return becomes insignificant. We have explained the excess return puzzle. However, since we use the probability of success (the total risk that arbitrageurs are taking) as the proxy for idiosyncratic risk, although we can explain away the excess return, it is not clear that whether deal specific risk matters.

Moreover, the result on interaction between idiosyncratic and systematic is worth of further investigation. In this section we use deal specific characteristics to form portfolios and examine the excess return in different market liquidity environments. This approach in fact examines whether risk arbitrageurs ask for reward for deal specific risk or/and market liquidity risk from another angle. If risk arbitrageurs do ask return for bearing these two types of risk but we only use Fama-French three factors to control the risk they are taking, we should see the excess return is higher in the riskier portfolios than in the safer portfolios.

a) Deal Characteristics

We have learned from previous section that if the medium of payment is stock, the probability of deal success goes down, therefore increasing the deal risk. We also know that when deal size increases, the probability of success decreases. Moreover, small stocks tend to have lower liquidity and it is harder for arbitrageurs to hide their trade.

Therefore, arbitrageurs might ask for a higher return. Hence, we can conjecture that if a portfolio is composed of small stock deals, the excess return should be higher than that of a portfolio composed of large cash deals. This is exactly what we found from the data ( It is shown in Table 1.6 Panel A ). Portfolios of small stock deals have a monthly excess return of 1.5%, while large cash deal portfolios have .4% of excess return. The difference is highly significant. 9 Portfolios with different combinations of these two deal characteristics all have the excess return that is consistent with the hypothesis that the higher the idiosyncratic risk, the higher the excess return (if we only consider the traditional market risk factors).

b) Deal Size and Market Liquidity

In Panel B of Table 1.6, we see that large deal portfolios have a higher excess return than small deal portfolios in both high market liquidity and low market liquidity.

The p-values that we calculate from bootstrapping samples show the differences are significant. Moreover, both large and small deal portfolios have higher excess return in a high VIX market than in a low VIX market. Although the difference is significant in large deal portfolios but not significant in small deal portfolios, small deal portfolios in

9 All the p-values associated with the comparisons between different portfolios’ alphas are calculated through bootstrapping samples.

low market liquidity still have the highest excess return while large deal portfolios in high market liquidity have the lowest excess return. This empirical evidence again indicates that both idiosyncratic risk and market liquidity help explain the excess return that Fama and French three factors cannot explain.

c) The Medium of Payment and Market Liquidity

If stock deal portfolios have a higher excess return and risk arbitrageurs ask for an even higher return when market liquidity is low, we should see that the excess return is higher for stock deal portfolios in a high VIX environment than that of cash deal portfolio in a low VIX environment. Again, the empirical evidence fits above hypothesis (Table

1.6 Panel C). The excess return for small stock deal portfolios in a high VIX (low liquidity) environment is .8% monthly while the excess return for the large cash portfolio in low VIX ( high liquidity) is .5% monthly and the p-value is smaller than .0001. The difference is highly significant. Moreover, given that it is a cash deal portfolio, the excess return is higher in low liquidity (High VIX) than in high liquidity (Low VIX) (.7% compared to.5%). The only thing that is a little odd is that the excess return of stock portfolio in a low VIX market is higher than in a high VIX market. From table 1.6- Panel

D, we can see the abnormality in Panel C is actually driven by the small stocks. Large stocks’ behavior is as predicted in a high/low VIX market. Therefore, the problem lies in the small stock portfolio. A further look at the coefficient of other market risk factors helps us understand what is happening here. In a low VIX environment, the small stock portfolio is risk neutral to the market (from the insignificance of the FF factor

coefficients). But in high VIX environment, the small stock portfolio is market sensitive

(It is positively affected by the SMB). Hence, market liquidity not only interacts with the idiosyncratic risk factors, but also with other market risk factors. Therefore, in terms of the excess return, it is not necessary that one has to be smaller / larger than the other one under this scenario.

[Insert Table 1.6 Panel A, B and C here] d) Deal Size, the Medium of Payment and Market Liquidity

In the last panel of table 1.6, we include both of the two deal characteristics, deal size and the medium of payment with market liquidity. As we have expected, small stock portfolio in low liquidity market has a lot higher excess return than large cash portfolio in high liquidity market. The behavior of the excess return of portfolios with different deal characteristics (size, medium of payment) in different liquidity environment (high VIX and low VIX) demonstrates that the higher the deal specific risk, the higher excess return is and worse liquidity environment adds on the required return of risk arbitrageurs.

[Insert Table 1.6 Panel D here]

The above results clearly show that risk arbitrageurs do ask return for both idiosyncratic risk and market liquidity risk. But how do these types of risk interact is still not clear. Are the increased required return due to deal specific risk (e.g. from cash deal portfolios to stock deal portfolio, or from large deal portfolio to small deal portfolio) larger in the low liquidity environment than in the high liquidity environment? In other words, does low market liquidity amplify the deal specific risk? This is the question we are trying to answer in the next section.

4) The Interaction between Idiosyncratic Risk and Liquidity Risk

In order to see the interaction effect of deal specific risk with market liquidity risk, we use two approaches: (1) examine the difference in returns of deal specific portfolios

(e.g. stock vs. cash, large vs. small) in high and low market liquidity. If the difference is significant in one liquidity environment but not significant in another, then market liquidity does affect idiosyncratic risk premium; (2) examine the coefficient of liquidity factor VIX in regressions of the returns of deal specific portfolios on market risk factors.

If the coefficients are significantly different, we can also conclude that market liquidity affects idiosyncratic risk premium.

In Table 1.7 Panel A, we show results of the first approach. Counter to our intuition that low market liquidity would amplify the idiosyncratic risk premium, deal specific risk actually matters more in the low liquidity (high VIX) environment. All the significance of the difference in excess return between deal characteristics are found within high market liquidity (low VIX) environment. For example, in table 1.7 Panel A, in the high VIX months, the excess return of the difference (small deal portfolio minus large deal portfolio, stock deal portfolio minus cash deal portfolio, small stock deal portfolio minus large cash deal portfolio) is not significant or marginally significant, while the excess return is highly significant in the low VIX months.

We show the results from second approach in table 1.7 Panel B. The coefficients of VIX are not significant in stock deal portfolios, which are supposed to have a higher idiosyncratic risk, but they are larger and significant in the cash deal portfolios. Results are consistent with findings in panel A, both of which show that in the low VIX

environment, deal characteristics actually matter more than in the high VIX environment.

All these results are not inconsistent with what we found before, because arbitrageurs do ask return for both deal risk and liquidity risk. They would ask for a higher return if a deal is riskier in terms of deal characteristics and if it is announced in low liquidity months. But counter to our intuition, changes of deal characteristics from safer to riskier matter more in a low liquidity environment than in a high liquidity environment. For example, for small stock portfolio, arbitrageurs do ask for a higher return than for large cash portfolio in both high VIX and in low VIX. But in low VIX, the difference is actually bigger than in high VIX. Why is this happening? One of the possible explanations is as follows. We know that risk arbitrageurs make return by providing insurance and the insurance is against two things: deal specific risk and market condition risk. When in a high liquidity environment, all deals are likely to succeed, market risk is low, and so the insurance is more against deal specific reasons. Hence deal specific risk matters a lot. But in a low liquidity environment, the insurance is against both market condition and deal specific risk. Moreover, M&A arbitrageurs might ask return for providing liquidity. Therefore, under this situation, market condition matters more than deal specific risk because it is now not only part of the insurance premium, but also the reason that the liquidity that arbitrageurs can provide is precious.

Our empirical analysis show that risk arbitrageurs not only ask for reward for bearing both idiosyncratic risk and market liquidity risk, but also the idiosyncratic risk premium they demand is different according to market liquidity condition.

4.4 Robustness Check

In order to see whether the result is driven by some influential data points and whether assumption of the normal distribution is violated, we check the Cook’s Distance, autocorrelation and heteroskedasticity and normality assumptions. There are no influential data points, the autocorrelation is insignificant and there is no violation to homoskedasticity. We also use robustness regression procedure (which is a form of weighted least squares regression and is done iteratively. At each step a new set of weights are determined based on the residuals) to re-run all the tests and the results remain similar.

5. Conclusion

Risk arbitrageurs have always been considered as making extraordinary profits by both business and academia. Recent studies found that their profits are not that out of line compared with the risk they are taking. However, they are still deemed as making excess return. If and why M&A arbitrageurs earn risk adjusted excess returns remains a big unanswered question in the M&A literature.

In this study, we consider two types of risks that these arbitrageurs are bearing and therefore might demand a premium, namely idiosyncratic risk and systematic liquidity risk. On one hand, deal characteristics create idiosyncratic risk for arbitrageurs by affecting the ex-ante probability of deal success. On the other hand, market liquidity conditions affect speculative spreads, which is the source of these arbitrageur’s profit.

Omitting these two risks might distort the risk and return relation in M&A arbitrageur’s

portfolios and raise the question concerning market efficiency. In this article, we empirically examine this risk and return relationship by considering both idiosyncratic and systematic risks including market liquidity risk. Our empirical results show that deal characteristics ( for example, the medium of payment) affect risk characteristics of these arbitrageur’s portfolios and therefore the return. Also, both idiosyncratic risk captured by the probability of deal success and market liquidity risk captured by the VIX affect M&A arbitrageur’s return. Moreover, the interaction of idiosyncratic risk and systematic risk in

M&A arbitrageur’s portfolio induces unique and interesting characteristics of the risk and return relation, which has also not been demonstrated in previous studies on this topic.

Most importantly, after considering the liquidity risk they are bearing, M&A arbitrageurs are not making excess returns.

Reference:

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Table1.1: Sample Summary We only include announced mergers and tender offers where both target firms and acquirer firms are American public traded companies. We include both pure cash and pure stock mergers and tenders in this sample. Duration is the number of days between a deal is announced and a deal is resolute.

Year Number of Percent Average Average Average Mergers of Cash Duration Target Acquirer Announced Deals (Days) Market Market Equity Equity (Million (Million Dollars Dollars) 1986 102 84.31 200.09 1059.24 2079.87 1987 158 82.28 286.49 3432.57 2461.42 1988 184 83.7 230.92 2438.69 2718.04 1989 193 77.2 279.82 1598.62 2275.7 1990 135 84.44 339.97 1164.61 2117.93 1991 103 56.31 250.68 1384.7 2424.56 1992 131 69.47 266.98 1531.85 2355.48 1993 166 78.31 236.05 1266.32 2287.55 1994 242 79.75 250.97 1855.94 2412.08 1995 292 65.75 220.87 1796.68 3734.88 1996 343 71.72 203.48 3076.56 5160.73 1997 329 60.18 170.88 2797.52 7458.82 1998 383 59.79 162.68 3420.3 8788.09 1999 369 62.06 167.21 1857.71 16796.3 2000 429 69.7 287.50 3763.37 14839.56 2001 206 63.59 161.89 2947.02 14003.98 2002 132 71.21 131.15 5345.5 14919.62 2003 128 67.19 148.0313 2060.1 13409.25 2004 127 64.57 160.78 4826.55 10899.45 2005 125 71.2 135.86 3050.11 22811.39 2006 130 80.77 107.18 4053.6 21143.1 Total/Average 4407 71.60 209.50 2606.07 8337.99

Table 1.2: Regression of Risk Arbitrage Return on Common Risk Factors *To Calculate Arbitrageur’s Portfolio return PT + DT − PT i) Daily Return on Cash offers: it it it−1 Rit = T Pit−1 T T T A A A A Pit + Dit − Pit−1 − Δ(Pit + Dit − Pit−1 − rf Pi1 ) ii) Daily Return on Stock offers: Rit = PositionValuet−1 N ∑(Ri ) iii) Daily Average Return: R = i=1 N is number of deals remain open on day h dayh N M iv) Monthly Return: M is number of trading days in month j Rmonthj = ∏(1+ Rit ) −1 t=m

v) Then run this regression: (RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML ** Stock deals are 100% paid by acquirer’s stocks. Cash deals are 100% paid by cash. ***Large deals are deals with more than 50 million dollar deal size Numbers in parenthesis are p-values

Complete Stock Deals Cash Deals Large Deals Small Deals Sample Number of Deals 4407 1322 3085 2647 1760 Number of Months 252 252 252 252 252 Intercept 0.0065 0.0085 0.0054 0.0050 0.0079 (0.00) (0.00) (0.00) (.00) (.00) Mkt 0.65 -0.06 0.83 0.59 0.75 (0.00) (0.34) (.00) (.00) (.00) SMB 0.23 0.22 0.25 0.05 0.54 (0.00) (0.00) (.00) (0.15) (.00) HML 0.29 0.15 0.35 0.20 0.44 (0.00) (0.08) (.00) (.00) (.00)

Adjusted-R2 0.78 0.036 0.82 0.69 0.63

Table 1.3: The Determinants of Probability of Deal Success

Deal outcome (1 or 0) = β 0 + β1Rmkt + β 2 Rmkt−1 + β3 Rmkt−2 + β 4 LogT argetMV + β 5 LogAcquirerMV

+ β 6 IndustryDummy(Horizontal) + β 7 AcquirerAttitude + β8 Stock + β 9 NumberofBidders

+ β10 Dyr94~00 + β11Dyr01~06

Independent Variable Coefficient Estimate P-Value Intercept 1.79 0.00 RMkt -0.03 0.95 RMkt-1 1.00 0.06 RMkt-2 1.18 0.03 Log of Target Market Equity -0.15 0.00 Log of Acquirer Market Equity 0.15 0.00 Industry Dummy 0.26 0.00 Hostile Dummy -1.13 0.00 Payment Dummy(Stock) -0.25 0.00 Number of Bidders -0.71 0.00 Year Dummy 2(1994~2000) -0.15 0.02 Year Dummy 4(2001~2006) -0.05 0.50 Number of Observations 4565 Pseudo R2 0.103

Table 1.4: The Idiosyncratic Risk Factor For the complete sample, we run this regression

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML + β iv IV High deal risk group are deals with low probability of success. From column 3~7, we run this regression for sub-samples: (RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML IV is the average ex-ante probability of deal success of the portfolio. The higher the IV is, the lower the idiosyncratic risk. Therefore, high deal risk groups actually has lowest third probability of success deals Complete High Medium Low Stock Cash Sample Deal Risk Deal Deal Risk Risk Number of 4407.00 1432.00 1483.00 1492.00 1322.00 3085.00 Deals Number of 252.00 251.00 250.00 252.00 251.00 252.00 Months

Intercept 0.062 0.006 0.006 0.007 0.049 0.029 (0.06) (0.02) (0.00) (0.00) (0.43) (0.38) Mkt 0.65 0.51 0.80 0.47 -0.06 0.83 (0.00) (0.00)) (0.00) (0.00) (0.32) (0.00) SMB 0.24 0.31 0.21 0.14 0.22 0.25 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) HML 0.29 0.29 0.38 0.18 0.15 0.36 (0.00) (0.00) (0.00)) (0.00) (0.08) (0.00) IV -0.06 -0.05 -0.03 (0.09) (0.52) (0.48) Adjusted R2 0.78 0.29 0.69 0.41 0.03 0.82

Table 1.5- The Market Liquidity Risk Factor

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML + β liquidity Liquidity The definition of recession from NBER website: The NBER does not define a recession in terms of two consecutive quarters of decline in real GDP. Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales. For more information, see the latest announcement on how the NBER's Business Cycle Dating Committee chooses turning points in the Economy and its latest memo, dated 07/17/03.

VIX is a weighted blend of prices for a range of options on the S&P 500 index. The VIX is quoted in terms of percentage points and translates, roughly, to the expected movement in the S&P 500 index over the next 30-day period, on an annualized basis.

Complete Stock Cash Large Small Sample(199 0~2000) Number of 3770.00 1204 2566.00 2273.00 1497.00 Deals Number of 204.00 204 204.00 204.00 204.00 Months

Intercept 0.0005 0.0108 -0.0012 -0.003 0.0003 (0.89) (0.17) (0.76) (0.38) (0.97) Mkt 0.65 -0.11 0.85 0.59 0.75 (0.00) (0.10) (0.00) (0.00) (0.00) SMB 0.21 0.17 0.23 0.01 0.54 (0.00) (0.02) (0.00) (0.69) (0.00) HML 0.29 0.07 0.37 0.19 0.46 (0.00) (0.39) (0.00) (0.00) (0.00) VIX 0.000318 -0.00011 0.000343 0.0004 0.0004 (0.06) (0.78) (0.07) (0.02) (0.20) Adjusted R2 0.75 0.03 0.79 0.65 0.62

Table 1.6- Risk Arbitrageur’s Excess Return on Different Portfolios

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML * Stock deals are 100% paid by acquirer’s stocks. Cash deals are 100% paid by cash. **Large deals are deals with more than 150 million dollar deal size Numbers in parenthesis are p-values ***VIX is a weighted blend of prices for a range of options on the S&P 500 index. The VIX is quoted in terms of percentage points and translates, roughly, to the expected movement in the S&P 500 index over the next 30-day period, on an annualized basis. High VIX are months with top 50% value of VIX through sample period.

Panel A: Deal Size and the Medium of Payment Small Large DifferenceBootstrap P-Value Stock Intercept 0.015 (0.00) 0.006 (0.00) 0.009 (0.00) Mkt -0.055 (0.60) -0.120 (0.00) SMB 0.380 (0.00) 0.039 (0.41) HML 0.264 (0.10) -0.025 (0.66) Cash Intercept 0.007 (0.00) 0.004 (0.00) 0.003 (0.00) Mkt 0.818 (0.00) 0.863 (0.00) SMB 0.487 (0.00) -0.146 (0.00) HML 0.433 (0.00) 0.193 (0.00) Difference 0.008 0.002 Difference 0.011 P-Value (0.00) (0.00) Bootstrap P-Value (0.00) Panel B: Deal Size and Market Liquidity Small Large Difference Bootstrap P-Value High Intercept 0.010 (0.00) 0.006 (0.00) 0.004 (0.00) VIX Mkt 0.679 (0.00) 0.390 (0.00) SMB 0.492 (0.00) -0.144 (0.02) HML 0.381 (0.00) -0.141 (0.03) Low Intercept 0.009 (0.00) 0.004 (0.01) 0.005 (0.00) VIX Mkt 0.727 (0.00) 0.729 (0.00) SMB 0.463 (0.00) -0.007 (0.88) HML 0.448 (0.00) 0.297 (0.00) Difference 0.000 0.001 Difference 0.005 P-Value Bootstrap (0.12) (0.00) P-Value (0.00)

Table 1.6-Contiuned Panel C: The Medium of Payment and Market Liquidity

Small Large DifferenceBootstrap P-Value Stock Intercept 0.008 (0.04) 0.011 (0.00) -0.004 (0.00) Mkt -0.085 (0.31) -0.181 (0.13) SMB 0.197 (0.03) 0.110 (0.40) HML 0.109 (0.35) 0.017 (0.90) Cash Intercept 0.007 (0.00) 0.005 (0.00) 0.001 (0.00) Mkt 0.876 (0.00) 0.774 (0.00) SMB 0.226 (0.00) 0.297 (0.00) HML 0.461 (0.00) 0.054 (0.32) Difference 0.001 0.006 Difference 0.002 P-Value Bootstrap (0.00) (0.00) P-Value (0.00)

Panel D: Deal Size, the Medium of Payment and Market Liquidity Small Large High Low Difference High Low Difference Stock Intercept 0.013 0.016 -0.003 0.007 0.006 0.002 0.007 (0.08) (0.01) (0.00) (0.00) (0.02) (0.00) (0.00) Mkt -0.119 -0.225 -0.112 -0.173 (0.47) (0.34) (0.02) (0.05) SMB 0.381 0.426 0.020 -0.159 (0.03) (0.10) (0.69) (0.10) HML 0.216 0.332 -0.048 -0.178 (0.34) (0.23) (0.46) (0.08) Cash Intercept 0.009 0.006 0.003 0.004 0.004 0.000 0.005 (0.00) (0.00) (0.00) (0.04) (0.02) (0.00) (0.00) Mkt 0.817 0.816 0.922 0.853 (0.00) (0.00) (0.00) (0.00) SMB 0.454 0.538 -0.178 -0.067 (0.00) (0.00) (0.00) (0.34) HML 0.493 0.159 0.342 -0.181 (0.00) (0.06) (0.00) (0.02) Difference 0.004 0.010 0.007 0.003 0.002 0.006 Difference 0.009 Bootstrap Bootstrap P-Value (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) P-Value (0.00)

Table1.7: Interaction Effect of Deal Characteristics and Market Liquidity

(R portfolio1 − RPortfolio2 ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML Different portfolios have different deal characteristics. Stock deal portfolio includes deals that use 100% stock as the medium of payment. Large deal portfolio includes deals that are more than 150 million dollars. High VIX are the months that have VIX value of the top 50% of the sample period.

Panel A: The Difference of Excess Return Low VIX High VIX Size Variable Estimate P-value Estimate P-value (small-big) Intercept 0.005 (0.06) 0.004 (0.10) Mktrf -0.002 (0.97) 0.288 (0.00) SMB 0.470 (0.00) 0.636 (0.00) HML 0.151 (0.07) 0.522 (0.00)

Medium Variable Estimate P-value Estimate P-value (Stock-Cash) Intercept 0.006 (0.08) 0.001 (0.82) Mktrf -0.955 (0.00) -0.961 (0.00) SMB -0.187 (0.19) -0.029 (0.78) HML -0.037 (0.81) -0.352 (0.01)

Size and Medium Variable Estimate P-value Estimate P-value (small stock-large cash) Intercept 0.012 (0.06) 0.008 (0.26) Mktrf -1.094 (0.00) -1.041 (0.00) SMB 0.483 (0.07) 0.559 (0.00) HML 0.517 (0.07) -0.125 (0.60)

Table1.7- Continued Panel B: The Coefficients of VIX

(RRiskArb − R f ) = α + β Mkt (RMkt − R f ) + β SMB SMB + β HML HML + β vixVIX Small Large

Stock Variable Estimate P-Value Estimate P-Value Intercept 0.011 (0.46) 0.003 (0.57) Mktrf -0.120 (0.37) -0.130 (0.00) SMB 0.393 (0.00) -0.017 (0.69) HML 0.248 (0.15) -0.087 (0.12) VIX 0.000122 (0.87) 0.000181 (0.45) Variable Estimate P-Value Estimate P-Value Cash Intercept -0.001 (0.92) -0.004 (0.43) Mktrf 0.818 (0.00) 0.905 (0.00) SMB 0.467 (0.00) -0.169 (0.00) HML 0.430 (0.00) 0.230 (0.00) VIX 0.000392 (0.10) 0.000368 (0.10)

Essay 2: The Value of “Boutique” Financial Advisors in M&A

1. Introduction

Financial advisors play a very important role in mergers and acquisitions. Their characteristics and roles influence the outcomes of the deals that these advisors are involved in. Previous studies (see Rau(2000), Hunter and Jagtiani (2003), Allen, Jagtiani,

Peristiani, Saunders (2004)) have shown that the market share of financial advisors – commercial banks and investment banks – affects not only the deal premium, the speed of completion, the success rate, but also the merging firm’s announcement period returns.

However, to date there is no evidence on the impact that advisor’s expertise and independence have on deal outcomes. Financial advisors who are independent and focus solely on providing advisory services are considered “boutique” investment banks by the industry. Their growing popularity is attested to by the hot IPO market for these boutique investment banks, especially in the last four years. Investor zest comes from boutique bank’s fast climbing market share. These small but specialized firms are stealing M&A business market share from their larger brethren at increasing rates. The combined market share of Goldman Sachs, Morgan Stanley and Merrill Lynch fell to 77% through August

2006 from 94% in 2005. Meanwhile mergers and acquisitions deals advised by Evercore,

Lazard, and Greenhill are running at 28% through August 2006, up from 18% in 2005.10

While boutique investment banks have recently gained attention and popularity in the market, there is little (or no) evidence that merging firms benefit from deals advised by these specialized firms. On one hand, boutique banks are independent and free of conflict, which should have positive effect on deal outcomes. On the other hand, they are

10 See Wall Street Journal, Oct.2nd, 2006. In the League tables, market shares of financial advisors may be double counted when a deal has more than one financial advisors.

usually small, less diversified and not well-known and therefore do not have much reputational capital (in terms of dollar amount) at stake, which in turn may be viewed as negative factors.

This paper is the first to examine the effect of using specialized, independent advisors in M&A transactions. We attempt to address several unanswered questions in this paper. First, how much market share have boutique investment banks gained in the recent sizzling corporate takeover market? Second, how do merging firms initially choose boutique banks versus full service banks? In other words, what deal and/or firm characteristics drive the choice? Third, is their growing popularity associated with superior performance at valuation and/or their independence? More generally, does the advisor’s expertise and independence affect deal outcomes? Using manually compiled data on a large sample of deals announced between 1995 and 2006, we examine whether the use of a boutique advisor, either by the target firm or by the acquiring firm, affects deal premiums, acquisition announcement returns, the probability of deal completion, and the speed of deal completion.

As boutique investment bank Lazard states on its company website, one of the main competitive advantages of this kind of financial advisors is their independence. “We are an independent firm, free of many of the conflicts that can arise at larger financial institutions as a result of their varied sales, trading, underwriting, research and lending activities. We believe that recent instances of perceived or actual conflicts of interest, and a desire to avoid any potential future conflicts, have increased the demand by managements and boards of directors for trusted, unbiased advice from professionals

whose main product is advice.” Moreover, while full service investment banks increasingly rely on their trading desks for profits and leave limited resources devoted to the traditional long-term investment-banking relationships, boutique banks thrive with their expertise on financial advice as this is their focus and main product.

However, full service banks still dominate the M&A advisory market

Diversification seems to be a double edged sword for the full service banks: on one hand, it creates potential conflicts of interest; on the other hand, prior relationships arising from other lines of business enhance the certification effect discussed in papers such as Allen et al (2004)11. For example, if a full service bank has prior lending relationships with the acquirer or the target, they have access to private information about the firm’s cash flows, financial resources and other crucial accounting information which can assure a more accurate valuation. Although boutique banks as niche players have less conflicts of interest issues, they are by nature less diversified and surely don’t benefit from the certification effect described above.

Another advantage of full service investment banks is that they tend to be larger banks therefore have more reputational capital (in terms of dollar amount) at stake. When we examine how the choice of a boutique vs. a full service bank affects announcement period returns and other deal outcomes, we recognize that the choice itself is endogenously determined by merging firms. Although boutique banks have the advantage of being independent and focused, they tend to be smaller and less known. The reputation of full service firms may affect how merging firms pick their advisors. For example, if the acquirer’s manager has a bad deal to push through, s/he might want to

11 In the study of Allen et al. (2004), this certification effect takes the form of increased abnormal return to targets whenever their merger advisor is their own bank (with whom the target has had a prior lending relationship).

choose firms that are willing to bend the rules for her/his firm. Large full service investment banks have their reputation at stake and are less likely to go along with management who look for private benefits through expansion. On the other hand, when the acquirer’s management faces a good deal, they might want to choose a reputable advisor to signal their valuation. It is an empirical question whether boutique bank’s expertise and independence can positively affect deal outcomes, given that firms may self-select certain financial advisors.

This article empirically examines how merging firms choose boutique vs. full service investment banks as financial advisors and the effect of using such a financial advisor on short term shareholder’s wealth effect and deal outcomes in corporate takeovers. Using the criterion of being independent and specialized in M&A advisory services, we manually classify financial advisors as boutique or full service investment banks 12 . We further analyze how acquirers/targets choose boutique vs. full service advisors. After controlling the endogenous choice of financial advisors, we focus on how investment bank’s expertise and independence affect takeover premium, announcement period return, deal completion speed, and completion rate.

We find that deal size is one important factor that affects the choice of financial advisors, and so is target’s capital structure and the medium of payment. A boutique investment bank is more likely to be chosen as the advisor for both acquirer and target when deal size is small and the equity ratio is high. Moreover, a target is more likely to choose a boutique investment bank as the advisor when the medium of payment is stock.

Furthermore, the recent increased popularity of boutique investment banks is actually a

12 To our knowledge, this is the first paper to make this distinction between financial advisors.

comeback. There is a significant drop of the use of boutique advisors during year 1998 to year 2000, but their popularity rises back since year 2001.

Deals advised by boutique investment banks have a higher percentage of success although it takes them longer time to complete deals. But after controlling deal characteristics, there is no significant difference in success rates between deals advised by full service banks or boutique banks. In terms of deal pricing, the acquirer’s boutique advisor is negatively related to deal premium, which is beneficial to acquirer’s shareholders. Moreover, the use of boutique banks by acquirers is significantly and positively related to the acquirer’s announcement abnormal returns.

This paper contributes to the literature in several ways. First, we identify 202 boutique banks from 533 M&A advising firms and we find that the recent increased popularity of the use of boutique investment bank is a comeback instead of a new trend.

Second, this is the first paper to analyze what drives the choice of boutique banks vs. full service banks. Third, our findings suggest that boutique advisor’s expertise in valuation is more appreciated than their independence by both the client and the market. They are more likely to be chosen as financial advisors in complicated deals and they are better advisors in these deals because of their expertise. On the other hand, if the advantage of using boutique banks as financial advisors is their independence, we should expect a positive effect of using boutique investment banks on deal outcomes regardless of the complexity of deals. Although boutique investment banks have an advantage in small but more complicated deals, due to the nature of their type of business, there is a limitation on what kind of deals these niche players can exploit to the most of their advantage.

2. Literature Review

Our paper is related to existing research on financial advisors in M&A. Previous literature has primarily focused on two aspects: the early literature analyzes the fee structure and contract terms of deals (McLaughlin (1990), Hunter and Walker (1990)); more recent studies investigate advisor’s reputation effects. Bowers and Miller (1990) and Michael et al. (1991) both find that high reputation investment bankers have the ability to detect better mergers, but do not provide any bargaining advantage. Servaes and

Zenner (1996) study the acquiring firm’s choice of using investment banks or not. They find that acquiring firms are more likely to use an investment bank when the acquisition is more complex and when they have less prior acquisition experience. However, returns are lower when investment banks are used. The distinction disappears after controlling the characteristics of the transaction. In other words, the returns earned by the acquirers do not depend on whether an investment bank is used, after controlling the determinants of investment banking choice. This result shows that controlling deal characteristics and the endogenous choice of financial advisors is critical for studying financial advisor’s effect on wealth gains.

Rau (2000) investigates the determinants of the market share of investment banks acting as advisors and how the investment bank’s market share affects their clients. He finds that acquirers advised by first-tier banks complete a significantly greater proportion of the tender offers, but similar proportions of mergers are completed across the different categories of investment banks. In mergers, clients of first-tier investment banks earn lower announcement-period excess returns. In tender offers, clients of first-tier

investment banks earn higher announcement-period excess returns. Rau’s (2000) study of the financial advisor’s effect on deal outcomes and client’s wealth gains characterizes advisors only from the perspective of market share. However, market share may be the result of a wide range of business characteristics; therefore we examine the question by focusing on the more fundamental characteristics that differentiate boutique vs. full services banks: financial advisor’s advisory focus and independence.

Hunter and Jagtianni (2003) also examine how market share affects deal outcomes. They find that top-tier advisors are more capable of completing deals. In terms of the speed of completing a deal, top tier advisors were found to be more efficient. But the announcement returns of acquiring firms hiring top tier investment banks are actually lower. In contrast, Kale, Kini and Ryan (2003) argue that one reason why previous studies do not find a significant role of advisor reputation may be that they failed to control the reputation of the opponent’s advisor. After controlling this and some other confounding factors, they document a significant advantage of employing prestigious financial advisors. However, it is not clear what the financial advisor’s reputation means other than their market share in the context of takeovers. Bao and Edmans (2006) contend that past performance is a more appropriate measure of investment bank reputation instead of market share. They document significant long-horizon persistence in the average announcement returns to acquisitions advised by an investment bank. Speed and raw completion ratio are also persistent; moreover, banks that outperform with respect to these measures also exhibit superior shareholder value creation.

In general, previous literature mainly focuses on the reputation of financial

advisors defined as their market share. A notable exception is Allen et al (2004). They study the tradeoff between “conflict of interests” and “bank certification effect” of using commercial banks with prior lending relations as financial advisors in mergers and acquisitions. They find positive evidence of a net bank certification effect for target firms only. In contrast, acquirer’s abnormal returns are either negative or insignificantly different from zero in all cases. Deviating from the perspective of reputation, Allen et al

(2004) focuses on how financial advisor’s prior relation with clients affects wealth gains and deal outcomes. While the motivation of our paper is similar to theirs, we examine whether the expertise and independence of financial advisors affect deal outcomes and shareholder’s wealth.

3. Data and Methodology

The sample of M&A is collected from the Securities Data Corporation’s (SDC)

U.S. Mergers and Acquisitions Database. We select both completed and withdrawn domestic M&A with announcement dates between 1995 and 2006, and require both the acquirer and the target to be public firms. For earlier deals, information about their financial advisors is much scarcer and therefore difficult for us to define whether they were diversified financial advisors or boutique investment banks. We focus on deals in which the acquirer has at least one advisor. We include only acquisitions in which the acquiring firms control less than 50% of the shares of the target firms before acquisition announcements and that deal values are at least $10 million. These selection criteria yield

2,604 deals. We further exclude deals that have a premium of more than 200% or less

than -50%.

Our final sample consists of 2,351 acquisitions from 1995 to 2006, involving 533 separate investment banks. Among these investment banks, 202 (37.9%) advisors are classified as boutique investment banks 13 . In terms of number of deals advised by boutique investment banks, 18.5% of the 2,351 deals have acquirers advised by boutique and 20.9% of them have targets advised by boutique. There are 348 tender offers and

2,003 mergers (See Table 2.1). Number of deals analyzed in various tests varies due to different sample selection criteria and data availability.

[Insert Table 2.1 Here]

In terms of the trend of the use of the boutique investment bank, the surge of the use of boutique bank since 2001 is not an increased popularity. Instead it is a comeback.

There is a drop on the popularity of the use of boutique in year 1998~ year 2000(Figure

1).

[Insert Figure 1 Here]

4. Empirical Results

4.1. Univariate Analysis

Table 2.1 shows the distribution of the sample. The whole sample has 2,351 unique deals. There are 87 deals that only have acquirer advisor(s) and no target advisor, while the rest have both acquirer advisor(s) and target advisor(s). From the acquirer advisor’s point, there are 1,712 (72.8%) deals advised by full service investment banks and 434 (18.5%) deals advised by boutique investment banks. From the target advisor’s

13 For each investment bank, we find the introduction of the company on its website. If the company’s website is not found, we search the internet for articles mentioning the company. Based on the introduction about the company and our definition on boutique banks, we classify investment banks into diversified full Service banks or boutique banks.

point, there are 1,458 (62%) deals advised by full service investment banks, and 491

(20.9%) deals advised by boutique investment banks. Additionally, we have 141 deals in which both the acquirer advisor and the target advisor are boutique investment banks.

[Insert Table 2.2 Here]

Table 2.2 provides a comparison of deal characteristics for different categories.

Deals are divided into four categories according to the acquirer-target advisor combination: full service vs. full service, full service vs. boutique, boutique vs. full service, and boutique vs. boutique. From the simple comparison between these four groups, we can see that the average deal size, acquirer size, relative size, acquirer M/B ratio, target M/B ratio are all much bigger when both sides are advised by full service investment bank than when both sides are advised by boutique investment bank. For example, the average deal size for deals advised by full service advisors on both sides is

2691.68 million dollars, while the average deal size for boutique vs. boutique is only

426.83 million dollars. Interestingly, the percentage of successful deals is higher in the boutique vs. boutique category than in any other combinations.

Table 2.2 suggests that the choice of boutique advisor is significantly related to the size of the deal and the size of the merging firms. Larger deals tend to be advised by full service investment banks at least on one side. Small deals are more likely to be advised by boutique banks. One possible reason for this difference is that boutique banks tend to be small and less known, which might make them humble on the fees they charge.

It is conceivable that smaller firms that cannot afford the high fees charged by full service investment banks might have to choose boutique banks.

While the comparison on deal characteristics can shed some light on the reasons why firms choose boutique vs. full service advisors, we are mainly interested in the effects of this choice on deal outcomes. To examine how the roles of boutique advisors differs from their larger competitors, we compare the duration, the premium, announcement period returns, the combined shareholder wealth and the success rate of deals advised by boutique and full service banks. We make these comparisons for the whole sample and for mergers and tender offers separately, and the results are presented in Table 2.3.

[Insert Table 2.3 Here]

Deal duration refers to the number of days between deal announcement and the effective date (if the deal succeeds) or the withdrawn date (if the deal fails). In order to obtain as much and as accurate information as possible about the deal premium, we follow the method described in Officer (2003) to calculate the premium. We first use the aggregate amount of each form of payment offered to target shareholders and compare it with the target’s market value of equity 43 days prior to the bid announcement to compute the premium. For deals that have this number missing or premium calculated out of the range (-50% to 200%), we use the initial offer price per share of the target stock and the percentage change of the final offer and compare the final offer price with target’s price 43 days prior to the bid announcement. For deals with missing values or outliers even using this method, we replace the either missing or inaccurate premium information by the recorded premium in the SDC database. By combining these three methods to calculate the deal premium, we try as best as we can to reduce the possibility

of missing premiums and inaccurate premium information.

The announcement period return is the short run performance of both target’s and acquirer’s stocks upon the announcement of the deal. We examine 3-day (-1, +1) announcement period abnormal returns. The combined wealth gain is calculated in two forms following Kim et al (1988) and Kale, Kini and Ryan (2003): (1) the weighted average of acquirer’s and target’s short run performance and (2) the dollar value of the aggregate short run gain or loss of both acquirers and targets.

Investigating the results in Table 2.3 provides a first insight into how the involvement of boutique banks affects the results of the deals. First, we look at how the acquirer’s choice of advisors affects deal outcomes (Panel A). We can see that on average, full service banks advised merger deals last 137 days with an average premium of

50.11%. Boutique advised deals last 152 days with 46.92% premium. The difference between boutique and full service advisors on duration is statistically significant, while the difference on premium is not. Another significant difference is the dollar amount combined wealth. Boutique advised deals are associated with less negative combined wealth in dollar amount. However, in terms of percentage gain or loss, there is no significant difference.

From the point of view of the target’s choice, there is no significant difference in any of the six deal outcome measurements (Panel B). However, it is interesting to note that when the target hires a boutique advisor, the choice of using boutique advisors or not by acquirers has explanatory power for both duration and deal premium. Panel C shows that when the target uses a boutique advisor, it takes a longer time to complete the deal if

the acquirer’s advisor is also a boutique bank. On average, the duration for boutique advisors on the acquirer side is 157 days compared to 138 days for full service acquirer advisors. However, the acquirer pays a significantly lower premium when using boutique advisors vs. full service advisor: 43.7% versus 52.3%.

The comparison indicates that the benefit to an acquirer using a boutique investment bank is most remarkable when the opponent’s advisor is a boutique investment bank. This is interesting because it suggests that an acquirer can benefit more than a target from using a boutique advisor. This could be due to the boutique’s independence. An advisor’s conflicts of interest are more likely to affect the acquirer than the target – when the acquirer’s advisor has a self interest in pushing through the deal, they might suggest that the acquirer pay a higher premium. It could also be due to the boutique’s greater expertise in valuation. The most complex part of a deal is target valuation. Therefore when the target firm hires a boutique advisor, it may signal that the asymmetric information about target’s value is larger. This would be exacerbated when the acquirer hires a full service advisor rather than a boutique advisor. Thus, if the full service advisor has greater conflicts of interest or if the boutique has greater skills in target valuation, the acquirer ends up paying a higher premium when hiring a full service advisor.

[Insert Table 2.4 Here]

Because mergers and tender offers are different along many dimensions, we examine them separately. Table 2.4 shows that merger deals advised by boutique banks on the acquirer side last on average 161 days, compared to deals advised by full service

banks, which last 146 days. The difference in the duration is statistically significant. In general, tender offers take a much shorter time to complete than merger deals. In the case of tender offers, boutique advised deals also last longer. On average, it takes 7 more days to complete boutique-advised deals than their full service advised counterparts, although the difference is not significant. This analysis shows that the significant difference in duration presented in Table 2.3 is probably driven by mergers rather than tender offers.

The use of boutique advisors for acquirers seems to matter more in mergers than in tender offers, which is reasonable since mergers involve more negotiation, and hence provide more opportunity for boutiques to fully exploit their advantages in being more specialized and focused than full service advisors.

In terms of premiums, there is no significant difference regardless of whether the acquirer’s advisor is a boutique or not in either mergers or tender offers. There is also no significant difference whether the target’s advisor is boutique or not in mergers. However, in tender offers, when the target’s advisor is a boutique investment bank, the premium is

76.7% on average, which is higher than full service banks advised deal’s average of

58.1%. Boutique investment banks seem to have an advantage in advising targets in tender offers.

Consistent with previous studies, we also find that the target’s announcement period return is much higher on average than acquirer’s announcement return. The average target 3-day CAR is 18.3% while the average acquirer CAR is -2.5%. To examine the difference between boutique and full service advisors, we look at mergers and tender offers separately. The average acquirer announcement period return is about

-3% in merger deals. Although deals advised by boutique advisors on the acquirer side have slightly higher acquirer CAR, the difference is not significant. For tender offers, the average acquirer CAR is 0.19% and 0.28% for boutique and full service acquirer advisor respectively and the difference is also not significant. The result is similar when classification is based on the target’s advisor. The difference of target CAR between deals advised by boutique or full service banks is not significant in either mergers or tender offers.

Furthermore, to understand whether the use of boutique advisors is associated with any wealth gains to society as a whole, we need to look at the effects on combined wealth. Following Kale, Kini and Ryan (2003), we create two variables CWLTH and

CPWLTH to capture the combined wealth gain or loss. CWLTH is the dollar amount of combined wealth generated during announcement period and is defined as follows:

CWLTH= TWLTH+AWLTH

TWLTH= TMV*TCAR*(1-Bidder Toehold)

AWLTH=AMV*ACAR

CPWLTH= TMV/ (AMV+TMV)*TCAR+AMV/ (AMV+TMV)*ACAR where TMV (AMV) is the market value of outstanding shares of the target (acquirer) four weeks before the announcement; TCAR is the target announcement period abnormal return. ACAR is the acquirer announcement period abnormal return. CPWLTH is the weighted average of TCAR and ACAR.

When examining the CWLTH, we find some significant results in terms of acquirer’s advisor choice. On average, boutique advised merger deals are associated with

value-creating deals. The average combined wealth gain for these deals is 61.91 million dollars. On the other hand, full service banks advised merger deals are associated with negative combined wealth loss of 239.09 millions on average. In tender offers, the opposite is observed. It seems that in mergers, boutique investment banks provide valuable service to acquirers while in tender offers, boutique investment banks produce more value to targets. When they advise targets, the target receives a higher premium on average. In contrast, when they advise the acquirer, the losses to the acquirer are so great that the combined wealth declines. However, if we look at the combined wealth gain in percentage forms, there is no significant difference across any scenario.

Another variable of interest in Table 2.4 is the percentage of successful deals. In mergers, deals advised by boutiques have success rates of 92.41% or 94.49% (depending on whether the classification is on acquirer or target side). Deals advised by full service banks only have 89.32% or 87.91% success rates, respectively. It seems that boutique advisors are more likely to get deals completed successfully.

The general conclusion stemming from Table 2.4 is that boutique banks appear to be superior advisors in certain cases. Although on average it takes a longer time for deals they advise to be completed, they are associated with higher percentage of successful deals than full service banks are. This higher success rate does not appear to be achieved by sacrificing quality as there is no significant difference in the premium, TCAR, ACAR, and CPWLTH in most cases. In some cases, deals advised by boutique banks reflect even better quality measurements. For example, when the target is advised by a boutique bank in a tender offer, they receive significantly higher premium. However, more analysis

needs to be done before we draw a conclusion. The significant difference in univariate analysis might change after we control the deal characteristics in multivariate analysis.

4.2 Multivariate Analysis

In this section, we study how merging firms choose their advisors and how the choice of boutique advisors affects deal outcomes when controlling deal characteristics.

Previous literature finds that the rank of an investment bank based on market share affects certain aspects of deal outcomes. In order to avoid the result being contaminated by this confounding factor, we run the robustness check by including the rank of the investment banks as an additional control. Following Rau (2000), we calculate the average yearly rank of each investment bank in this sample, according to the annual rank of each investment bank on the basis of the total value of transactions advised during the year from SDC’s advisor league tables. If a bank is not listed as having advised any acquisitions during the year, a rank of one plus the number of investment banks that participated in the market that year is assigned. Then we take the average rank across the years for each investment bank and use it as the advisor ranking variable. For deals that have multiple advisors, we use the lead advisor’s rank. We calculate average rank for each deal for both the acquirer and target advisors and include both of these two variables in the multivariate analysis. a) The Determinants of Use of Boutique Advisors

From the univariate analysis we can see that the use of boutique advisors is associated with particular deal characteristics. To further investigate the relation between

the choice of a boutique bank and deal characteristics, we run several probit regressions in which the dependent variable takes a value of 1 if a boutique advisor is used and 0 otherwise. The results are presented in Table 2.5.

[Insert Table 2.5 Here]

First, we examine the acquirer’s choice of advisors in mergers and tender offers respectively. All four models we run show that deal size is a very important determinant of the acquirer’s choice of advisor. The larger the dollar size of the deal, the less likely it is an acquirer will choose a boutique investment bank as their advisor. This is consistent with the univariate analysis: larger deals tend to be advised by full service investment banks at least one side. Although this might be due to the fee structure difference in boutique and full service banks, it does not affect our conclusion that deals with higher complexity are more likely to have boutique advisors. Another two significant determinants of an acquirer’s choice of advisor are the target’s sales growth rate during the prior fiscal year and the target’s equity to asset ratio for the prior fiscal year. The larger the changes in sales, the more likely it is for an acquirer to choose a boutique advisor. At the same time, the higher the equity/asset ratio, the more likely for an acquirer to choose a boutique advisor. When the change in sales is large, it indicates that the target is in the growth stage of the life cycle, which signals the complexity of valuating the target. In this situation, the expertise of a boutique bank is more valuable in arriving at an accurate valuation. Therefore, an acquirer is more likely to choose a boutique bank when target is growing rapidly. On the other hand, a high E/A ratio indicate that a large percent of target’s value is in equity form, which is harder to be valuated than debt is. This

signals that the target has a higher degree of asymmetric information, which makes the choice of boutique’s expertise more valuable.

In tender offers, deal size is also the significant determinant of advisor. Panel B of table 2.5 repeats the results of target’s choice of advisors. As was the case for acquirers, deal size is a highly significant determinant in both mergers and tender offers. But more interesting is that on the target’s side, the medium of exchange affects the decision to hire a boutique advisor. Targets are more likely to have boutique investment banks as advisors when acquirer’s stock is used as the medium of exchange. This is consistent with the hypothesis that the boutique bank has more expertise in valuation. When the target shareholders are to be paid with acquirer’s stock instead of cash, the asymmetric information problem is more severe and therefore it is harder for the target to accurately evaluate the offer. The target’s choice of a boutique bank in this circumstance indicates that the boutique bank’s expertise in valuation is recognized and valued.

b) The Impact of Boutique versus Full Service Advisors on Deal Outcomes

i) Deal Completion

We have seen in the univariate analysis that there is higher percentage of successful deals in boutique advised deals than the ones advised by full service investment banks.

Does boutique push deals to go through harder than their counterparts or are they more likely to be chosen as the advisors for those deals who are more likely to be successful?

After controlling the deal characteristics we find that using boutique advisors on either side does not improve the chance that deals become more successful. The rank of both

acquirer and target does not affect the successful rate either. Therefore, the higher successful rate seems to be caused by the fact that deals for which are more likely succeed are also more likely to have boutique advisors. Also, target’s management’s attitude towards the deal and having competition significantly reduce the successful rate.

[Insert Table 2.6 Here]

ii) Deal Duration

In order to see if the effect of the type of advisor on the duration of the deal is subsumed by other potential variables that were not considered in the univariate analysis, we proceed to analyze the combined effects of several factors in the case of mergers and tender offers.

[Insert Table 2.7 Here]

Table 2.7 shows that in mergers, deal size, the medium of payment, having toehold and whether there is a competing bidder are factors that affect the duration of the deal. All these factors indicate the complexity of the deal and therefore elongate the duration. Most importantly, the use of a boutique bank by either the acquirer or the target significantly elongates the deal’s duration in mergers. One possible explanation is that the boutique banks tend to be smaller and have fewer resources; therefore it takes them longer to complete the deals However, it might also be because boutique banks spend more time doing due diligence. Since we have controlled the rank of investment banks, which is a proxy for the size of the investment, boutique investment spending more time for due diligence is a plausible explanation. .

The significance of the use of boutique by either the acquirer or the target disappears when the analysis is done on tender offers. The significant factors that affect deal duration in tender offers are deal size, the medium of payment and the target management’s attitude towards the deal and year dummies. Interestingly, during the peak of the merger wave (year 1998~ year 2000), it takes significantly longer time to finish a tender offer.

iii) Deal Premium

When examining the effects of using boutique banks on the quality of transactions, we recognize that the use of a boutique bank is endogenously determined by deal or firm characteristics. This is confirmed by the results in the previous section on the choice of boutique advisor. There are several deal characteristics strongly related to the use of boutique advisors. This endogenous selection process therefore has the potential to bias estimates of the impact of using boutique advisors on the merger outcomes that we evaluate. To control the potential self-selection bias, we employ both OLS regressions and a two stage treatment procedure.

[Insert Table 2.8 Here]

The independent variables in the OLS regressions are dummy variables for boutique advisors to the acquirer, to the target, and other deal characteristics. We find that relative size, toehold, completion, target’s market to book ratio, and the rank of both acquirer and target significantly affect deal premium in mergers. The acquirer’s use of boutique advisor reduces the premium, but it is not significant. In the same time, for

tender offers, target’s use of boutique advisor is associated with higher premium.

However, this OLS regression does not consider the endogeneity problem of the choice of boutique advisors by the merging firms. We therefore use a two-stage treatment procedure to address this endogeneity problem. In the first step, we estimate the choice of boutique advisor when deal characteristics differ. The effect of boutiques on premium is then examined in the second step, where we estimate the deal premium using deal characteristics and boutique dummies.

The significance of the lambda coefficient confirms our concerns about endogeneity of the boutique dummy. After adjusting for self-selection bias for acquirer’s use of boutique advisor, we see in the second stage regression that a boutique advisor significantly lowers deal premiums for acquirer in both mergers and tender deals. Our multivariate analysis suggests that boutique advisors have bargaining advantage and valuation expertise, and that the advantage seems to be more on the acquirer side than on the target side.

iv) Announcement Period Returns

We also use both the OLS regressions and two-stage treatment procedures in analyzing announcement period returns. Again the significance of lambda in acquirer’s return regression shows the importance of controlling the endogeneity problem. When we first examine how a boutique advisor affects either the acquirer’s or the target’s short-run performance CARs14 (-1, +1) in mergers and tender offers using OLS regressions, we

14 When calculating CARs, the market model is estimated using continuously compounded returns over the 200-day period ending 50 days before the initial acquisition announcement. The CRSP value weighted index is used as a market proxy.

find no significance15. However, when we take into account the endogeneity problem and use a two-stage treatment procedure, some interesting results emerge. In mergers, the use of a boutique bank by the acquirer is significantly and positively related to the acquirer’s announcement abnormal returns and has no significant effect on target’s short run abnormal returns. Overall, it seems that the benefit of using a boutique advisor is more valuable for the acquirer’s shareholders than for the target’s shareholders and it is more prominent in mergers than in tenders. One possible explanation is that the conflicts of interest of full service banks are more likely to affect the acquirer’s shareholders than the target’s shareholders since conflicts of interest might induce advisors on the acquirer side to suggest higher premium or push through bad deals.

[Insert Table 2.9 Here]

v) Combined Wealth Gain

There are two ways to look at the combined wealth gain, in dollar amount and in percentage. After controlling the deal characteristics, we find that the use of boutique investment bank by either the acquirer or the target does not affect combined wealth.16 In this sense, deals advised by the two types of advisors are comparable. The benefit of using a boutique advisor in previous analysis does not come from some unknown deal characteristics associated with higher synergy.

5. Conclusion

15 Due to the limited space, OLS table is not present here. But it is available upon request. 16 The use of a boutique bank is associated with reduction on the combined wealth of in dollar amount by associated with positive increase of combined wealth in percentage. It is arguable with measurement is more appropriate in this context. Therefore, whether the use of boutique is associated with value-creating deals or value-destroying deals is still arguable.

In this article, we first investigate how merging firms choose boutique investment banks vs. full service banks as financial advisors. We then further analyze whether boutique investment banks or full service investment banks are better advisors in mergers and acquisitions after controlling the endogenous choice problem and other deal characteristics. Boutique investment banks have the expertise and independence, but they tend to be smaller and are definitely less diversified. Full service investment banks have resources and diversity, but suffer from potential conflicts of interest. Therefore, which one is the better advisor is subject to empirical examination.

Our empirical analysis suggests that boutique investment banks are more likely to be chosen as the advisors when the values of deals are smaller but with higher complexity, for example, when the large portion of payment involves equity valuation. These are the factors affecting the choice of using a boutique vs. full service advisors. Once the endogenous choice of the use of financial advisors is taken into account, the difference that a boutique advisor makes in deal outcomes is more noteworthy in mergers than in tender offers. It is also more remarkable on the acquirer side than on the target side.

On average, it takes a longer time to complete deals advised by boutique advisors, but this could be because they are doing the due diligence. There is some evidence that the use of boutique advisors by the acquirer can reduce the premium, and improve the acquirer’s short term performance in merger deals. There are also situations where full service and boutique advisors are comparable in their service, especially in tender offers.

Generally speaking, boutique investment bank’s expertise is recognized and valued, while issues such as conflicts of interest embedded in full service investment banks do

not seem to be a big concern to the market since we don’t see the consistent advantage of using a boutique banks in all deals. Instead, boutique investment bank’s positive effect is only noticeable in more complicated deals.

Our findings therefore suggest that boutique advisor’s expertise in valuation is more appreciated than their independence by both the client and the market. They are better advisors in more complex deals due to their specialization and expertise in M&A advisory. However, due to the nature of the boutique, there is a limitation on what kind of deals these niche players can exploit to the most of their advantage. The diversity and the broad spectrum of complexity of deals in the market determine that both boutique and full service advisors will have their own supporters and beneficiaries. The balance of their market share might shift from time to time, but it is likely to remain fairly stable.

Appendix 1: List of Variables Used in Tests 1. Boutique Dummy: whether the firm is advised by boutique investment banks. 2. Deal size: log of the transaction value reported by SDC. 3. Relative size: the ratio of acquirer size relative to target size. 4. Acquirer Size: the market value of equity of acquirer measured at two months prior to acquisition announcement. 5. Acq. M/B (Target M/B): the acquirer’s (target’s) market-to-book ratio, measured as the ratio of year-end market value of common stock to the book value of equity for the prior fiscal year (COMPUSTST items 24*25/60). 6. Same_Industry: a dummy variable equals to 1 if the acquirer and target have the same first 2-digits SIC. 7. Target ROE: the ratio of earnings to average equity for the prior fiscal year (COMPUSTAT items 20/ (60+60(t-1)). 8. Target sales growth: the proportional change in sales over the prior fiscal year. 9. Target D/E: the ratio of debt to equity of the target for the prior fiscal year (COMPUSTAT items 6/60) ; 10. Stock: is a dummy variable that takes value of 1 if at least 50% of transaction is paid by stock, 0 otherwise; 11. Hostile: is a dummy variable indicating target management’s attitude towards the deal, 0 if friendly, and 1 if hostile; 12. Tender: is a dummy variable indicating that deals are identified as a tender offer by SDC; 13. Toehold: is the fraction of the target’s stock held by the acquirer prior to merger announcement; deals in which the toehold is larger than 50% are dropped; 14. Competition: is a dummy variable equal to 1 if there is at least one competing bidder for the same target after the deal announcement and before the deal completion/termination; 15. Premium: is the percentage difference between the offer price and target share price four weeks prior to the announcement date. 16. Complete: is a dummy indicating whether the deal is completed or withdrawn. 17. ACAR: acquirer’s announcement period (-1, +1) abnormal return. 18. TCAR: target’s announcement period (-1,+1) abnormal return 19. CWLTH: Combined wealth in dollar amount. It is the market value of target times TCAR plus market value of acquirer times ACAR. 20. CPWLTH: Weighted average of ACAR and TCAR. The weight is the market value of the merging firms.

Appendix 2: Top Ranking Boutique Advisors

Acquirer Advisors Target Advisors Houlihan Lokey Howard & Zukin Houlihan Lokey Howard & Zukin Keefe Bruyette & Woods Inc Keefe Bruyette & Woods Inc Blackstone Group LP Sandler O'Neill Partners Greenhill & Co, LLC Greenhill & Co, LLC Sandler O'Neill Partners Blackstone Group LP Lazard Freres & Co LLC Evercore Partners Needham & Co Inc Petrie Parkman & Co Inc Wasserstein Perella Group Inc Peter J. Solomon Co Ltd Lazard Needham & Co Inc Simmons & Co International Lazard Freres & Co LLC Peter J. Solomon Co Ltd Duff and Phelps Duff and Phelps Lazard Robertson Stephens & Co Simmons & Co International Lazard Houses Hovde Financial, Inc. Broadview Associates Wasserstein Perella Group Inc Petrie Parkman & Co Inc Lazard Houses Hambrecht & Quist Robertson Stephens & Co Hoefer & Arnett Inc Broadview Associates Grant Thornton Relational Advisors LLC Salomon Brothers Hambrecht & Quist Oppenheimer & Co Inc Alliant Partners Adams Harkness & Hill Inc Updata Capital Inc Alliant Partners Gleacher NatWest Broadview Hoefer & Arnett Inc Hovde Financial, Inc. Broadview Fox-Pitt Kelton Salomon Brothers Cochran, Caronia & Co. SG Barr Devlin Berenson Minella Trident Securities Montgomery Securities Brown, Gibbons, Lang & Co LP

References Allen, Linda, Julapa Jagtiani, Stavros Peristiani, and Anthony Saunders, 2004. “The Role of Bank Advisors in Mergers and Acquisitions,” Journal of Money, Credit, and Banking 36, 197-224.

Bao, Jack, and Alex Edmans, 2006. “How should Acquirers Select Investment Banks Advisors?” Working paper, MIT.

Bowers and Robert Miller, 1990. “Choice of Investment Banker and Shareholders' Wealth of Firms Involved in Acquisitions,” Financial Management, 34-44.

Hunter, William C., Jagtiani, Julapa, 2003. “An Analysis of Advisor Choice, Fees, and Effort in Mergers and Acquisitions”, Review of Financial Economics, 12, 65-81

Hunter, William C., Walker, Mary B., 1990. “An Empirical Examination of Investment Banking Merger Fee Contracts”, Southern Economic Journal, 56, 1117-1130

Kale, Jayant, Omesh Kini, and Harley Ryan, Jr., 2003. “Financial Advisors and Shareholder Wealth Gains in Corporate Takeovers,” Journal of Financial and Quantitative Analysis 38, 475-501.

McLaughlin, R., 1990. “Investment-banking Contracts in Tender Offers: An Empirical Analysis,” Journal of Financial Economics 28, 209-232.

McLaughlin, R., 1992. “Does the Form of Compensation Matter? Investment Banker Fee Contracts in Tender Offers,” Journal of Financial Economics 32, 223-260.

Michael, A., Shaked, I., and Lee, Y., 1991. “An Evaluation of Investment Banker Acquisition Advice: The Shareholders’ Perspective”, Financial Management 20, 40-49

Officer, M.S., 2003. “Termination Fees in Mergers and Acquisitions”, Journal of Financial Economics, 69, 431-467

Rau, P., 2000. “Investment Bank Market Share, Contingent Fee Payments, and the Performance of Acquiring Firms”, Journal of Financial Economics 56, 293-324

Servaes, Henri, and Marc Zenner, 1996. “The Role of Investment Banks in Acquisitions,” Review of Financial Studies 9, 787-815.

Table 2.1—Sample Distribution

* Others are the advisors that cannot be classified as either boutique or full service banks ** M-B mix are deals they have both boutique and full service advisors *** Mix-Other are deals they have both classifiable advisors and unclassifiable advisors

Panel A—Classified by Acquirer Advisor FullService Boutique Others* M-B Mix- Total Mix** Other** Merger Obs. 1451 380 34 94 44 2003 % 72.44 18.97 1.7 4.69 2.2 Tender Obs. 261 54 6 19 8 348 % 75 15.52 1.72 5.46 2.3 2351 Panel B—Classified by Target Advisor FullService Boutique Others M-B Mix- No Total Mix Other Advisor Merger Obs. 1223 438 60 142 59 81 2003 % 61.06 21.87 3.12 7.39 3.07 Tender Obs. 235 53 8 30 16 6 348 % 67.53 15.23 2351

Table 2.2- Deal Characteristics: Deals Done by Different Advisor Combinations

The four categories are the combinations of different advisors for acquirer and target respectively. The first is the acquirer’s advisor and the second one is target’s advisor. The numbers in the parenthesis are the numbers of observations used to calculate the average. DealSize is the dollar amount of the value of the deal. RelativeSize is the ratio of acquirer size and target size in market value. AcquirerSize is the market value of acquirer in million dollars. AcquirerM/B is acquirer’s market to book ratio. TargetM/B is target’s market to book ratio. Premium is the percentage difference between acquirer’s offer price and market price. %Hostile is the percentage of deals that target incumbents hold hostile attitude towards the deal. %Tender is the percentage of deals that are tender offers. %StockOffer is the percentage of deals that are paid by Stock. %Completed is the percentage of deals that are completed.

FullService- Boutique- FullService- Boutique- FullService FullService Boutique Boutique Total DealSize($Mil) 2691.68 882.66 715.03 426.83 (1149) (302) (195) (141) (1787) RelativeSize(Mkt) 35.80 30.82 29.47 10.34 (1044) (272) (174) (110) (1600) AcquirerSize 13030.31 6076.45 9241.67 1447.43 (1086) (284) (180) (111) (1661) AcquirerM/B 5.39 17.29 5.67 2.77 (1074) (280) (181) (111) (1646) Target M/B 4.23 2.54 2.51 1.91 (1063) (279) (177) (112) (1631) Premium 49.52 52.35 49.21 43.77 (1149) (302) (195) (141) (1787) %Hostile 5.92% 2.65% 4.10% 1.42% (1149) (302) (195) (141) (1787) %Tender 16.10% 9.93% 16.41% 9.93% (1149) (302) (195) (141) (1787) %StockOffer 69.19% 76.82% 71.28% 73.76% (1149) (302) (195) (141) (1787) %Completed 87.99% 93.38% 90.26% 95.04% (1149) (302) (195) (141) (1787)

Table 2.3- The Comparison between Boutique and Full Service Banks

Duration is the number of days between deal announced and deal resulted. Premium is the percentage difference between acquirer’s offer price and market price. TCAR is target’s cumulative abnormal return between window (- 1,+1). ACAR is acquirer’s cumulative abnormal return between window (-1,+1). CWLTH is the combined dollar amount of wealth gain. CPWLTH is the weighted average of ACAR and TCAR. Completion is percentage of completed deals. The numbers in the parenthesis are the numbers of observations used to calculate the statistics.

Panel A –Classified by Acquirer’s Advisor FullService Boutique Difference T-Stat P-Value Duration 136.85 151.83 -14.98 -2.13 0.034 (1451) (336) Premium 50.11 46.92 3.18 1.29 0.196 (1451) (336) TCAR 18.31% 19.51% -1.20% -0.83 0.406 (1285) (270) ACAR -2.78% -2.61% -0.17% -0.30 0.767 (1236) (299) CWLTH -216.73 -22.85 -193.89 -1.88 0.060 (1075) (226) CPWLTH 0.69% 0.97% -0.28% -0.46 0.644 (1075) (226) %Completed 89.11% 92.26% (1451) (336) Panel B –Classified by Target’s Advisor Duration 138.19 144.14 -5.95 -1.18 0.239 (1344) (443) Premium 49.47 49.62 -0.15 -0.06 0.950 (1344) (443) TCAR 18.53% 18.48% 0.05% 0.04 0.970 (1174) (381) ACAR -2.73% -2.79% 0.06% 0.13 0.896 (1148) (387) CWLTH -185.02 -177.04 -7.99 -0.04 0.965 (980) (321) CPWLTH 0.81% 0.54% 0.27% 0.55 0.582 (980) (321) %Completed 88.32% 93.91% (1344) (443)

Panel C- Target is Boutique and Classified by Acquirer’s Advisor FullService Boutique Difference T-stat P-Value Duration 138.03 157.23 -19.20 -2.18 0.030 (302) (141) Premium 52.35 43.77 8.58 2.08 0.039 (302) (141) TCAR 18.84% 17.53% 1.31% 0.52 0.600 (275) (106) ACAR -2.86% -2.65% -0.21% -0.25 0.805 (260) (127) CWLTH -242.22 -1.71 -240.51 -1.02 0.309 (234) (87) CPWLTH 0.25% 1.31% -1.06% -1.15 0.252 (234) (87)

Table 2.4—Univariate Analysis

Duration is the number of days between deal announced and deal resulted. Premium is the percentage difference between acquirer’s offer price and market price. TCAR is target’s cumulative abnormal return during time window of (-1,+1). ACAR is acquirer’s cumulative abnormal return during time window of (-1,+1). CWLTH is the combined dollar amount of wealth gain. CPWLTH is the weighted average of ACAR and TCAR. Completion is percentage of completed deals. The numbers in the parenthesis are the numbers of observations used to calculate the statistics.

Panel A--Classified by Acquirer’s Advisor Merger FullService Boutique Difference T-Stat P-Value Duration 146.09 161.44 -15.35 -2.05 0.041 (1236) (290) Premium 48.07 45.12 2.95 1.14 0.253 (1236) (290) TCAR 16.05% 17.52% -1.47% -1.00 0.315 (1092) (232) ACAR -3.32% -3.06% -0.26% -0.43 0.670 (1045) (259) CWLTH -239.09 61.91 -301.00 -2.72 0.007 (911) (194) CPWLTH 0.14% 0.71% -0.56% -0.87 0.383 (911) (194) %Completed 89.32% 92.41% (1236) (290) Tender FullService Boutique Difference T-Stat P-Value Duration 83.67 91.24 -7.56 -0.43 0.669 (215) (46) Premium 61.84 58.30 3.54 0.51 0.611 (215) (46) TCAR 31.05% 31.63% -0.58% -0.13 0.895 (193) (38) ACAR 0.19% 0.28% -0.09% -0.07 0.942 (191) (40) CWLTH -92.54 -536.68 444.14 1.81 0.071 (164) (32) CPWLTH 3.74% 2.56% 1.18% 0.78 0.436 (164) (32) %Completed 87.91% 91.3% (215) (46)

Panel B--Classified by Target's Advisor Merger FullService Boutique Difference T-Stat P-Value Duration 148.01 151.84 0.48 -0.71 0.477 (1127) (399) Premium 47.81 46.64 1.18 0.48 0.630 (1127) (399) TCAR 16.26% 16.45% -0.19% -0.15 0.880 (983) (341) ACAR -3.29% -3.22% -0.07% -0.12 0.902 (955) (349) CWLTH -183.41 -194.32 10.91 0.05 0.957 (818) (287) CPWLTH 0.26% 0.20% 0.05% 0.09 0.926 (818) (287) %Completed 88.29% 94.49% (1127) (399) Tender FullService Boutique Difference T-Stat P-Value Duration 87.18 74.32 12.86 1.40 0.163 (217.00) (44.00) Premium 58.09 76.67 -18.58 -2.66 0.008 (217.00) (44.00) TCAR 30.19% 35.74% -5.55% -1.00 0.323 (191.00) (40.00) ACAR 0.03% 1.12% -1.09% -0.85 0.395 (193.00) (38.00) CWLTH -193.16 -31.14 -162.02 -1.21 0.230 (162.00) (34.00) CPWLTH 3.59% 3.34% 0.25% 0.17 0.867 (162.00) (34.00) %Completed 88.48% 88.64% (217) (44)

Table 2.5--Determinants of the Use of Boutique Advisor

RelativeSize is the ratio of acquirer size and target size in market value. Dealsize is the dollar amount of the value of the deal. Stock is a dummy variable indicating the medium of payment. Toehold is the fraction of the target’s stock held by the acquirer prior to merger announcement. Hostile is the dummy indicating target incumbent’s attitude. Industry is the dummy indicating whether the merger is horizontal. TargetM/B is target’s market to book ratio. TROE is target’s return of equity. TSale is defined as the sales growth rate from year t-1 to year t. TDE is target’s equity to total asset ratio. D1998 is a dummy indicating the announcement date of the deal is later than year 1997. D2001 is a dummy indicating the announcement date of the deal is later than year 2000. The numbers in the parenthesis are corresponding p-values.

Panel A- Acquirer’s Choice on Advisors Merger Tender Intercept 0.72 0.66 0.56 0.76 (0.000) (0.000) (0.003) (0.120) RelativeSize(Mkt) 0.00 0.00 0.00 0.00 (0.049) (0.061) (0.068) (0.479) DealSize -0.27 -0.27 -0.26 -0.30 (0.000) (0.000) (0.000) (0.000) Stock 0.13 0.13 0.13 -0.57 (0.183) (0.181) (0.193) (0.223) Toehold -0.01 -0.01 -0.01 -0.01 (0.382) (0.422) (0.416) (0.774) Hostile 0.21 0.19 0.24 -0.03 (0.365) (0.409) (0.307) (0.917) Industry 0.12 0.08 0.29 (0.173) (0.350) (0.162) Target M/B 0.00 -0.03 0.00 (0.483) (0.000) (0.993) TROE 0.00 0.08 (0.854) (0.704) TSale 0.07 -0.50 (0.069) (0.105) TDE 0.02 0.03 (0.000) (0.372) D1998 -0.29 -0.29 -0.29 -0.12 (0.003) (0.003) (0.004) (0.607) D2001 0.26 0.25 0.24 -0.02 (0.006) (0.009) (0.013) (0.925) Obs 1621 1618 1602 270 Pseudo R2 9.9% 10.2% 11.6% 14.2%

Panel B- Target’s Choice on Advisors Merger Tender Intercept 0.69 0.64 0.57 0.95 (0.000) (0.001) (0.003) (0.048) RelativeSize(Mkt) 0.00 0.00 0.00 0.00 (0.282) (0.318) (0.261) (0.520) DealSize -0.26 -0.25 -0.25 -0.30 (0.000) (0.000) (0.000) (0.000) Stock 0.19 0.19 0.20 0.07 (0.051) (0.053) (0.051) (0.838) Toehold -0.01 -0.01 -0.01 -0.10 (0.595) (0.617) (0.550) (0.217) Hostile -0.51 -0.53 -0.43 0.17 (0.100) (0.087) (0.152) (0.576) Industry 0.09 0.05 0.15 (0.287) (0.543) (0.482) Target M/B 0.00 -0.02 -0.03 (0.564) (0.000) (0.446) TROE 0.03 0.04 (0.664) (0.797) TSale 0.03 -0.05 (0.389) (0.789) TDE 0.03 0.01 (0.000) (0.879) D1998 -0.06 -0.06 -0.04 -0.40 (0.515) (0.518) (0.702) (0.095) D2001 0.19 0.19 0.13 0.40 (0.036) (0.039) (0.150) (0.118) Obs 1482 1479 1463 250 Pseudo R2 8.7% 8.9% 10.5% 11.9%

Table 2.6-The Impact of Boutique Advisors on Deal Completion

Merger Tender

Intercept 1.50 (0.000) 1.97 (0.026) Aboutique 0.06 (0.722) 0.40 (0.369) Tboutique 0.20 (0.165) 0.22 (0.615) RelativeSize(Mkt) 0.01 (0.009) 0.00 (0.523) DealSize -0.01 (0.742) 0.07 (0.563) Stock 0.19 (0.203) -0.08 (0.848) Toehold -0.01 (0.354) 0.01 (0.878) Hostile -2.08 (0.000) -1.74 (0.000) Competition -1.33 (0.000) -1.05 (0.002) ARank 0.00 (0.253) -0.01 (0.029) TRank 0.00 (0.168) 0.00 (0.926) D1998 -0.20 (0.131) -0.24 (0.492) D2001 0.39 (0.003) 0.04 (0.920) Obs 1325 219 Pseudo R2 26.8% 41.2%

Table 2.7-The Impact of Boutique Advisors on Deal Duration

Merger Tender Intercept 72.14 (0.000) -23.39 (0.486) Aboutique 13.56 (0.052) 15.90 (0.334) Tboutique 14.31 (0.018) -14.67 (0.351) RelativeSize(Mkt) -0.01 (0.720) -0.01 (0.549) DealSize 9.17 (0.000) 13.40 (0.003) Stock 14.40 (0.031) 49.80 (0.012) Toehold 1.95 (0.019) -0.62 (0.626) Hostile 25.70 (0.434) 82.56 (0.000) Competition 62.65 (0.000) 19.50 (0.354) ARank 0.02 (0.734) 0.06 (0.628) TRank 0.01 (0.783) 0.06 (0.610) D1998 -4.00 (0.532) 21.65 (0.098) D2001 -4.70 (0.436) -30.82 (0.030) Obs 1187 191 Adjusted- R2 4.69% 19.78%

Table 2.8-The Impact of Boutique Advisors on Deal Premium

ABoutique is the dummy indicating acquirer is using a boutique advisor. TBoutique is the dummy indicating target is using a boutique advisor. RelativeSize is the ratio of acquirer size and target size in market value. DealSize is log of dollar amount of the value of the deal. Stock is a dummy variable indicating the medium of payment. Toehold is the fraction of the target’s stock held by the acquirer prior to merger announcement. Complete is a dummy indicating whether the deal is completed. Complete is a dummy variable indicating whether the deal is completed or withdrawn. Competition is the dummy indicating whether the acquirer has competition. Hostile is the dummy indicating target incumbent’s attitude. AcquirerM/B is acquirer’s market to book ratio. TargetM/B is target’s market to book ratio. TROE is target’s return of equity. TSale is defined as the sales growth rate from year t-1 to year t. TDE is target’s equity to total asset ratio. ARank is the average rank of acquirer’s advisors. TRank is the average rank for target’s advisors. D1998 is a dummy indicating the announcement date of the deal is later than year 1997. D2001 is a dummy indicating the announcement date of the deal is later than year 2000. The numbers in the parenthesis are corresponding p-values. The dependent variable in the OLS regression and in the second stage of 2 stage treatment procedure is the deal premium in percentage. The dependent variable in the first stage of 2 stage treatment procedure is the dummy variable of boutique.

Panel A: OLS Regression Merger Tender Estimate P-Value Estimate P-Value Intercept 49.67 (0.000) 61.54 (0.010) Aboutique -3.79 (0.277) -11.78 (0.223) Tboutique -3.21 (0.291) 18.30 (0.046) Relativesize(Mkt) 0.04 (0.001) 0.02 (0.058) DealSize -1.29 (0.139) -2.40 (0.376) Stock -1.83 (0.563) -7.44 (0.481) Toehold -0.77 (0.032) -1.40 (0.061) Complete 9.74 (0.036) 10.90 (0.361) Competition 5.34 (0.322) -6.00 (0.551) Hostile 6.86 (0.364) 9.31 (0.378) Acquirer M/B 0.07 (0.187) 0.24 (0.685) Target M/B 0.52 (0.058) -0.75 (0.172) TROE -1.67 (0.368) -4.52 (0.389) TSale 0.31 (0.790) 0.39 (0.466) TDE -0.50 (0.137) -1.26 (0.633) ARank -0.04 (0.096) -0.02 (0.810) TRank 0.04 (0.100) 0.01 (0.859) D1998 -0.42 (0.892) 14.64 (0.053) D2001 -4.49 (0.119) -1.08 (0.893) Obs 1183 199 Adjusted- R2 2.80% 5.46%

Panel B-Two Stage Least Square Regressions On Only Mergers First Stage: Endogenous Variable ABoutique TBoutique Estimate P-Value Estimate P-Value Intercept 0.50 (0.026) 0.45 (0.026) DealSize -0.31 (0.000) -0.28 (0.000) Stock 0.07 (0.592) 0.16 (0.158) Toehold -0.04 (0.125) -0.01 (0.658) Hostile 0.27 (0.346) -0.55 (0.121) TSale 0.08 (0.048) 0.06 (0.113) TDE 0.08 (0.000) 0.08 (0.000) D1998 -0.16 (0.156) 0.01 (0.937) D2001 0.03 (0.787) 0.11 (0.277) Second Stage: OLS on Deal Premium Intercept 71.20 (0.000) 56.04 (0.000) ABoutique -37.86 (0.002) TBoutique -26.96 (0.011) RelativeSize(Mkt) 0.04 (0.001) 0.04 (0.000) DealSize -3.56 (0.001) -2.27 (0.051) Stock -0.12 (0.969) -0.36 (0.912) Hostile 6.62 (0.378) 4.08 (0.591) Toehold -0.96 (0.008) -0.77 (0.032) Complete 9.33 (0.042) 10.14 (0.029) Competition 4.04 (0.447) 4.75 (0.378) AMB -0.01 (0.396) 0.04 (0.331) ARank -0.04 (0.119) TRank 0.04 (0.142) D1998 -0.76 (0.802) 1.18 (0.698) D2001 -4.58 (0.105) -4.46 (0.126) Lambda 20.31 (0.004) 13.88 (0.031) Obs 1223 1183 Adjusted-R2 2.39% 2.38%

Panel C-Two Stage Least Square Regressions On Only Tenders First Stage: Endogenous Variable ABoutique TBoutique Estimate P-Value Estimate P-Value Intercept 1.57 (0.008) 0.99 (0.080)

DealSize -0.41 (0.000) -0.24 (0.007) Stock -0.24 (0.630) 0.04 (0.929) Toehold 0.01 (0.630) -0.09 (0.278) Hostile 0.22 (0.525) 0.30 (0.358) TSale -0.58 (0.125) -0.12 (0.553) TDE -0.07 (0.499) -0.17 (0.111) D1998 0.02 (0.938) -0.39 (0.132) D2001 -0.09 (0.759) 0.25 (0.385) Second Stage: OLS Regression on Deal Premium Intercept 101.72 (0.002) 64.20 (0.046) ABoutique -59.32 (0.103) TBoutique -7.10 (0.888) RelativeSize(Mkt) 0.02 (0.064) 0.02 (0.040) DealSize -8.45 (0.043) -3.64 (0.319) Stock -12.21 (0.262) -7.57 (0.473) Hostile 12.99 (0.225) 10.60 (0.328) Toehold -1.46 (0.052) -1.60 (0.050) Complete 14.19 (0.222) 14.23 (0.222) Competition -4.19 (0.675) -4.26 (0.671) AMB 0.05 (0.933) 0.30 (0.613) ARank -0.01 (0.866) TRank -0.01 (0.892) D1998 13.86 (0.057) 12.78 (0.130) D2001 3.23 (0.679) 0.81 (0.922) Lambda 31.68 (0.125) 14.79 (0.605) Obs 206 200 Adjusted-R2 4.87% 5.46%

Table 2.9- The Impact of Boutique Advisors on Announcement Period Return

ABoutique is the dummy indicating acquirer is using a boutique advisor. TBoutique is the dummy indicating target is using a boutique advisor. RelativeSize is the ratio of acquirer size and target size in market value. DealSize is log of dollar amount of the value of the deal. Stock is a dummy variable indicating the medium of payment. Toehold is the fraction of the target’s stock held by the acquirer prior to merger announcement. Complete is a dummy indicating whether the deal is completed. Complete is a dummy variable indicating whether the deal is completed or withdrawn. Competition is the dummy indicating whether the acquirer has competition. Hostile is the dummy indicating target incumbent’s attitude. AcquirerM/B is acquirer’s market to book ratio. TargetM/B is target’s market to book ratio. TROE is target’s return of equity. TSale is defined as the sales growth rate from year t-1 to year t. TDE is target’s equity to total asset ratio. ARank is the average rank of acquirer’s advisors. TRank is the average rank for target’s advisors. D1998 is a dummy indicating the announcement date of the deal is later than year 1997. D2001 is a dummy indicating the announcement date of the deal is later than year 2000. The numbers in the parenthesis are corresponding p-values. Only estimates of second stage are shown here. The dependent variable is announcement period return.

Second Stage Merger ACAR TCAR Intercept 0.95 (0.686) 14.97 (0.002) ABoutique 6.23 (0.024) TBoutique -5.04 (0.306) Premium -0.02 (0.004) 0.24 (0.000) RelativeSize(Mkt) 0.00 (0.252) 0.01 (0.073) DealSize -0.35 (0.165) -1.75 (0.001) Toehold 0.09 (0.322) 0.05 (0.772) Hostile 3.26 (0.077) 6.34 (0.067) Stock -3.64 (0.000) -3.81 (0.012) TMB -0.03 (0.560) -0.12 (0.319) TROE 0.39 (0.211) 0.18 (0.770) TSale -0.36 (0.150) -0.50 (0.333) Complete 2.95 (0.004) 4.99 (0.013) ARank -0.01 (0.041) TRank -0.02 (0.062) D1998 -1.58 (0.022) 2.05 (0.145) D2001 0.17 (0.791) 3.76 (0.005) Lambda -3.91 (0.014) 3.22 (0.278) Obs 1074 1060 Adjusted-R2 6.81% 26.89%

Figure 1: Time Trend of the Merge Wave and the Use of Boutique Advisors

350

300

250

200 Boutique Total 150 Percentage

100

5 6 7 8 9 0 1 2 3 4 5 6 9 9 0 0 0 0 19 199 199 199 19 20 20 200 200 200 20 20

Concluding Remarks and Perspectives

Although M&A activities have waves through out time, the battle field for corporate control has always been a popular topic for both business and academia. Every year, there are multi-trillion dollars involved in M&A transactions. Research on M&A has always been an important section of the corporate finance. However, most research on M&A studies the acquirer and the target. The third party of the game (financial advisors and arbitrageurs) has not been the center of the stage. Yet, they do play very important roles. M&A arbitrageurs do not directly participate in the negotiation. But by buying target’s stocks (if the medium of payment involve acquirer’s stock, they simultaneously short acquirer’s stock) they practically provide insurance for target shareholders against the possibility that the deal fails. Financial advisors get directly involved in the negotiation on the price and terms of the deal. Every so often, they also provide the fairness opinion and/or financing of the deal.

There are some academic papers about both of these two types of third party players. The puzzle about M&A arbitrageurs is that both business and academia think they are making extraordinary profits. If their profits are not in the line with the kind of risk they are taking, this would indicate market inefficiency. Although in previous literature researchers took into account market risk factors like Fama and French three factors, they did not explicitly consider the market liquidity and idiosyncratic risk.

Mitchell and Pulvino (2001) point out that the payoff of these arbitrageurs is like the payoff of a put option on the market index. However, the empirical evidence shows that the insurance premium is still too high. The excess return still exists.

In the first essay of this dissertation, we study whether the risk arbitrageur’s profit is really evidence of market inefficiency or they are making higher returns because they are taking more risks than we previously thought. There are at least two types of risks that have not been explicitly considered before, namely the idiosyncratic risk and the market liquidity risk. Our results show clearly that risk arbitrageurs do ask for higher return when deals are more likely to fail due to deal specific factors. Also, low market liquidity will not only affect the probability of success, but also the magnitude of loss if a deal fails. Therefore, arbitrageurs ask for higher returns when facing this kind of environment. The contributions of this study are as follows: first, we explicitly consider both the idiosyncratic risk and market liquidity risk and we find that risk arbitrageurs are not making excess return. The seemly free lunch is because of the omitted risk factors.

Second, we use VIX as the proxy for market liquidity, which has a great explanatory power because it is a “look ahead” measurement of the liquidity. The results of this study are very clean. M&A arbitrageurs are not making excess return.

The open question about financial advisors is broader. Previous literature mainly focuses on financial advisor’s reputation/market share. But reputation/market share is the result of more fundamental business characteristics. A recent paper by Allen et al. (2004) studies the trade off between conflicts of interests and the certification effect. Along this line, but from a totally different angle, we study the value of financial advisor’s independence and expertise in the second essay of the dissertation. The boutique investment banks are considered as the financial advisors with independence and expertise. The questions we address in this study are (1) how do firms choose boutique

investment banks vs. full service investment bank and (2) how the independence and expertise of boutique financial advisors affect the deal outcome and deal announcement returns. Although boutique investment banks have stolen some thunder from those large full service investment banks amid the frenzy of financial scandals, no academic paper ever investigated what drives the popularity of the boutique investment banks. This is the first paper to study financial advisors from this angle. We identify 202 boutique banks from 533 M&A advising firms. We provide empirical evidence that firms are more likely to choose boutique advisors when they face small but more complicated deals.

Additionally, the expertise of boutique advisors is more appreciated than their independence by both the clients and the market.

In this dissertation, we have studied two different but related questions about roles of the third party in M&A activities. There are a lot more interesting and important questions about the third party that can be addressed by future research. For example, the identity of these risk arbitrageurs and how their participation influences the probability of deal success; the fee structure of boutique financial advisors and how do they affect firm’s long term performance. A longer and tougher road is waiting to be explored, but the future research on this filed is exciting and promising.