merger gains and the dimensions of advisor...

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Merger Gains and the Dimensions of Advisor Quality

This version: September 2014

Abstract: This paper provides new evidence on the role of financial advisors in the US M&A market. I evaluate the industry and size-class expertise of buy-side (acquirer) advisors relative to sell-side (target) advisors, and examine how the differences in expertise affect merger outcomes of acquirers from 1994 to 2012. I find that acquirers hiring advisors with relatively greater industry or size-class expertise than targets gain significantly higher short-term abnormal returns in public acquisitions. The effect is more pronounced in stock deals, vertical deals, deals with large relative size, or when acquirers have previous acquisition experience. I also show that the impact of the relative buy-side expertise is greater than the relative sell-side expertise. Besides, relative industry expertise of acquirer advisors is positively related to post-merger ROAs and negatively related to post-merger R&D when both parties are publicly traded. In addition, I show that the probability of being hired as acquirer advisor increases as the industry and size-class expertise of an advisor increases. Keywords: Merger and Acquisition, Relative Advisor Quality, Industry Expertise, Size-class Expertise, Buy-side Expertise, Sell-side expertise, Investment Banks JEL Classification Codes: G34, G24, G14

I acknowledge the helpful comments of George Bittlingmayer, Felix Meschke, Christopher Anderson, Donna Ginther, Paul Koch, Bob DeYoung, Jide Wintoki, Lei Li, Ferhat Akbas, Bradley Goldie, and seminar participants at the University of Kansas, Ohio University, and University of Minnesota-Duluth. Please do not cite without permission.

Han Yu School of Business

University of Kansas Lawrence, Kansas, 66045

[email protected]

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1. Introduction

Do investment banks add value in mergers and acquisitions? Plausibly, financial advisors may

mitigate information asymmetry and reduce transaction costs between merging parties. Previous

research offers some evidence on whether financial advisors lead to better outcomes. On the one hand,

financial advisors do not lead to greater wealth gains for targets and acquirers (e.g. McLaughlin (1990,

1992), Servaes and Zenner (1996), Rau (2000), Hunter and Jagtiani (2003), Ismail (2010), Bao and

Edmans (2011)). On the other hand, some recent studies show that higher-quality advisors help clients

gain higher returns (Kale et al. (2003), Golubov et al. (2012) and Stock (2012)). Overall, it seems that

having an advisor does not help, but having a good advisor does help. However, this literature leaves

several questions unaddressed. For example, do merger gains (both short-term and long-term) depend on

own-side advisor quality or also on how the advisor of the counterparty behaves? Does the effect of

quality vary depending on the dimension, such as industry focus or size-class experience? Are buy-side

expertise and sell-side expertise equally important? And also, does the advisor’s quality over various

dimensions affect the chance of being chosen by acquirers?

In this paper, I examine the relation between relative advisor quality and both the short-term and

long-term merger gains of acquirers. The role of financial advisor in the M&A markets includes

estimating deal value, identifying potential matches, advising negotiation strategies, and providing

financing solutions. Different from the absolute quality of advisors, the relative quality measures how an

acquirer’s advisor performs comparing to that of the targets. I construct measures of relative advisor

quality using a large sample of US mergers that involve at least one advisor on both sides from 1994 to

2012. Specifically, the relative advisor quality is constructed based on measures of industry and size-

class expertise, which capture the quality of advisors in segmented markets.

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In the first part of the analysis, I find evidence that relative advisor quality measured by industry

and size-class experience increases short-term announcement returns of acquirers, but the impact is only

significant when buying public targets. The effect is more pronounced when the relative size of the deal

increases, in stock deals, or vertical deals, and in deals conducted by sequential acquirers. Specifically,

when buying public targets, if the acquirer advisor’s industry expertise is one standard deviation (4.66%)

greater than that of the target advisor, the acquirer gains 0.30% or $44.4 million over the (-1, +1)

window, which equals 16% of the value increase of an average acquirer’s merger gains. Similarly, a one

standard deviation difference (6.10%) of buy-side size-class expertise increases acquirer gains by 0.41%

or $57.7 million, which equivalent to 22.7% of the value change to an average acquirer over the three-

day window.

I also consider the endogeneity problem of client-advisor choice. Bao and Edmans (2011) show

that investment banks have fixed effects on acquirers’ performance, indicating certain deal

characteristics such as deal complexity, information asymmetry that pertaining to merger gains may also

be related to advisor choices. To address the concern that this endogeneity could potentially bias the

OLS regression estimates, I use the two-stage Heckman (1979) procedure and add the inverse Mills

ratios obtained in the first-stage regressions into the second-stage regressions as the control of selection

bias. I find the positive effect of relative industry expertise is robust after controlling for selection bias.

However, the positive effect of relative size-class expertise becomes insignificant in the second-stage

regression. Overall, the evidence is consistent with the view that the positive impact of a relatively better

advisor is more important when acquirers’ negotiation power decreases or when the takeover

environment for acquirers is less favorable.

I also present evidence of how buy-side and sell-side expertise impact merger gains differently.

Most advisors have both buy-side and sell-side advisory experiences, but the buy-side relative expertise

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benefits acquirer merger gains more than that of the sell-side relative expertise. This indicates that the

role of an advisor differs when serving acquirer versus serving targets. For example, although greater

sell-side expertise greatly mitigates the target-side information asymmetry, the greater buy-side

expertise of an advisor brings more value to acquirer by identifying better matches, providing favorable

negotiation terms, and arranging financing solutions.

In alternative tests, I rule out the explanation that the impact of relative advisor is dominated by

the absolute (single-side) quality of acquirer advisors. Actually, I show that the relative but not the

absolute industry quality increases acquirer merger gains, consistent to the findings of Kale et al. (2003).

In addition, I show that deal complexity such as large deal size and vertical acquisitions are positively

but insignificantly related to significance of relative advisor quality in public deals. I also rule out the

possibility that relative advisor quality affects acquirer gains due to the use of stock payment. Overall,

these factors alone cannot proxy for differences between the public and private takeover environments.

In the public corporate control market, the increased bargaining power of targets, the lower chance of

exploit information by acquirers, and the increased litigation risk all contribute to the decreased

negotiation power of acquirers. The findings show that the impact of relative advisor increase as an

acquirer becomes less dominant in the takeover process. Therefore, a relatively better financial advisor

would be greatly appreciated by the market of their role in helping acquirers to gain more or lose less.

In the second part of the analysis, I examine how relative advisors affect the post-merger

performance of acquirers. I find relatively better industry expertise significantly increases post-merger

ROAs and reduces post-merger R&D costs. The finding is in line with several explanations. For

example, advisors help with the “make or buy” decision and help acquirers identify knowledge-based or

technology-based mergers. Buying targets with “external knowledge” promotes innovation and

profitability and save the R&D costs of acquirers.

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Given the evidence of positive impact of a relatively better advisor, I extend the study to show

whether the industry or size-class expertise is related the possibility of an advisor being selected. I

construct an expanded sample of 658,154 potential advisor-acquirer pairs. Consistent with Chang et al.

(2013), I show that high industry and size-class expertise significantly increase the chance of an advisor

being hired by the acquirer. More importantly, the buy-side expertise of an advisor plays a more

important role than the sell-side expertise in the selection process, indicating the importance of

incorporating the specialization feature of the advisory industry in M&As studies.

This paper contributes to the discussion of M&A advisory service and is related to empirical

studies on advisor quality and merger gains (e.g. McLaughlin (1990, 1992), Chemmanur and Fulghieri

(1994), Servaes and Zenner (1996), Rau (2000), Hunter and Jagtiani (2003), Kale et al. (2003), Golubov

et al. (2012), Stock (2012), and Chang et al. (2013)). Except for Kale et al. (2003), most of the above

studies focus on the absolute quality of an advisor. This paper, however, is the first to document the

effect of relative advisor by measuring their business focus and strengths in several dimensions. Thus,

this paper tells more than the findings of relative market share in Kale et al. (2003). I show that the

advising industry is not simply divided into superstars and also-rans. Both the advisor choice and market

valuation take into account of an advisor’s focus in a particular industry or size group. This paper also

shows that the effect of relative advisor is not universal, similar to the effect of absolute advisor quality

suggested in Golubov et al. (2012). The use of a relatively better advisor is positively related to the

difficulty of acquisition negotiation faced by acquirers, such as when buying public targets versus

buying private targets.

Second, this is the first study that differentiates the buy-side from the sell-side expertise of M&A

advisors. Because of the different goals and duties of serving acquirers versus serving targets, many

financial advisors specialize in either the buy-side or the sell-size, particularly in term of their industry

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and size-class expertise. Thus, to capture the preference and quality of advisor rather than simple

involvement in the advisory market, I quantify the buy-side and sell-side expertise for each advisor and

construct relative quality measured conditional on the service type. I find that the buy-side quality

matters more for acquirers. Also, the improved post-merger profitability and the reduction of R&D costs

are mostly pronounced when measuring the relative advisor quality conditional on the buy-side expertise.

Furthermore, when both the buy-side and the sell-side expertise are controlled for, the chance of being

selected by an acquirer depends more on the buy-side quality. Thus, this study reveals that the quality of

advisor should be measured by considering the service type.

Thirdly, this is the first study to show that relative advisor quality increases post-merger

performance. Previous studies (Kale et al. (2003), Golubov et al. (2012), etc) mainly focus on the impact

of advisor quality on short-term merger outcomes. In this paper, however, I show that relative advisor

quality is significantly related to the increased post-merger profitability and reduced R&D costs in

public acquisitions. This finding presents additional evidence of the source of gains in M&As and is

consistent with the view that advisors not only help their clients in the negotiation process, but that they

also create synergy by identify better mergers, especially in the knowledge-based deals.

The rest of the paper is organized as follows. Section 2 discusses the relevant literature and

hypotheses development. Section 3 describes the sample construction and main variables used in the

analysis. Section 4 provides analysis results of relative advisor quality and short-term merger gains of

acquirers. Section 5 presents the results of how relative advisor quality affects post-merger performance.

Section 6 examines how industry and size-class expertise determine the choice of an advisor in an

expanded analysis. And section 7 concludes.

2. Related Literature and Hypotheses Development

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2.1 Literature Review

The role of financial advisors in the M&A market has received a fair amount of attention.

Theoretically, advisors provide expertise that efficiently facilitates financial transactions. Financial

advisors are information collectors and producers. Their service includes analyzing potential merger

plans for their clients, providing fairness opinions, giving suggestions on the valuation of targets,

offering advice on negotiation strategies, and arranging financing solutions for clients. Theoretical

models predict that if a product is repeatedly purchased and the quality can only be assessed after the

transaction is complete, the seller has incentives to offer high quality goods to keep a good reputation for

future business (Klein and Leffler (1981), Shapiro (1983), and Allen (1984)). Therefore, a longer track

record should be associated with higher quality of goods or services.

Empirical studies on financial advisors have tested this line of argument but have reached mixed

conclusions. On the one hand, early research such as Bowers and Miller (1990) shows that top-tier

advisors are able to identify deals with higher total synergies. On the other hand, Servaes and

Zenner(1996) study the choice of retaining a financial advisor in the M&A market and find acquirers do

not seem to benefit from the advisory service provided by investment banks after controlling for deal

characteristics. Rau (2000) shows that the market share of an investment bank, taken as a measure of

quality, is positively related to the contingent fee payments charged by the bank and the percentage of

deals completed in the past by the bank, but is unrelated to the performance of the acquirers advised by

the bank in the past. Moreover, top-tier investment banks seem to deliver lower announcement returns

than non-top-tier banks. Ismail (2010) also fails to find a positive relation between advisor reputation

and stock performance.

Several studies mention that one potential reason of the inconsistent findings of advisor role in

early studies is the deal completion incentive. McLaughlin (1990, 1992) examines the advisory fee

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structure in tender offers and finds a large portion of the fee is contingent on deal completion and the fee

amount is positively related to the transaction value. Thus, the acquirer adviser has a great incentive to

push the deal to completion, but has less of an incentive to negotiate a lower offer price for the acquirer.

Similarly, Hunter and Jagtiani (2003) report the magnitude of the acquirer advisor fee is $2.4 million (or

0.84% of the transaction value) on average. Thus, acquirer advisors may not have interests that are

aligned with their clients’. Some study also mentions that advisors may have deal-picking ability. For

example, Bao and Edmans (2011) study the fixed effects on bidder returns of financial advisors and find

persistent performance at the individual bank level. They show that certain banks have the ability to

identify promising acquisitions or negotiate terms, or can be trusted to turn down bad deals. Golubov et

al. (2012) revisit the relation between financial advisor reputation and announcement returns controlling

for the choice of top-tier versus non-top-tier advisors. They find acquirers gain significantly higher

returns when they hire top-tier advisors in public acquisitions.

Some recent studies also shed lights on the importance of boutique banks or advisors’ industry

expertise. Song and Wei (2013) compare boutique banks versus full-service investment banks, but they

do not find that boutique advisors deliver superior abnormal returns after controlling for reputation.

Stock (2012) studies the industry expertise of acquirer advisor and finds that higher industry expertise is

positively related to the acquirer announcement returns, and the target industry expertise seems to be

more important. Chang et al. (2013) show that the industry expertise significantly increases the chance

of an advisor being selected by the acquirer.

Because of the target and the acquirer work interactively during the takeover process, when both

sides hire advisors, the relative quality of the advisors would influence how acquirer and target split the

merger gains. As mentioned in Brealey and Myers (2000), "Their gain is your cost." A relatively better

advisor has more experience in negotiating, advising, or providing strategic solutions. Thus, the merger

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gain to the target or the acquirer not only depends on her own advisor but also depends on the quality of

the counterparty’s advisor. Kale et al. (2003) study the relative market share of acquirer advisors and

find that advisors with relatively high market shares help extract more value. The evidence indicates that

advisors with higher market shares are able to deliver higher quality of service.

In sum, prior work on financial advisors mainly focuses on the determinants of the advisor

choice and the value impact based on the absolute (one-side) quality of M&A advisors. Only one paper

(Kale et al. (2003)) addresses how the relative quality between acquirer and target advisors affects the

shares of merger gains, but the study is based on the overall market share rather than taking into account

the advisor expertise in other dimensions.

2.2 Hypotheses Development

This study emphasizes the importance of measuring the advisor quality in terms of their industry

and size-class expertise rather than the overall market share. The overall market shares of advisors have

been widely used in practical and in most of the prior studies. However, in fact, the advisory industry is

segmented, with advisors specialized in sub-markets such as particular industries or particular sizes of

transactions. Although a top-five advisor may be active in many industries, boutique investment banks

tend to focus on specific sectors or size classes.1 From the supply side, large, full-service banks who

dominant the market tend to have the ability to pick large customers to gain economy of scale; and

boutique investment banks may find serving a specific size-class or industry more cost efficient. On the

demand side, large or national companies are free to choose any investment banks, but smaller and 1For example, Energy Spectrum Advisor Inc., a Dallas based boutique advisor company focuses exclusively in the energy industry for over 150 transactions since 1997. Signal Hill, a boutique investment bank of M&A advisory and private capital firm, has been working largely in business service, IT industry, education, and healthcare service. The annual M&A advisor awards are represented by different categories of investment banks. For example, awards for deals over $1 billion in 2012 were given to large banks such as Band of America, Goldman Sachs, Barclays Capital, etc. Awards for deals in the $500 million to $1 billion range were given to large banks as well as small banks such as FTI consulting, WellPoint Inc., Cerberus Capital Management LP, and etc. Additional size categories include $250 to $500 million, $100 to $250 million, $50 to $100 million, etc.

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regional companies tend to choose advisors who are more accessible and familiar with their industries

and regions. This two-sided matching problem means that “better” and “worse” only have meanings

with respect to the attributes of a particular deal.2

There are two potential roles that an advisor can help with their clients during the acquisition–

deal creation and value allocation. Through the deal creation channel, a financial advisor uses its

professional knowledge to identify a potential match and proposes plans to bring the two firms together.

Advisors also contribute through the value allocation channel. Specifically, advisors provide strategic

advice during the takeover process, share their own expertise and experience in the M&A market to help

clients negotiate with the counterparty to obtain better terms.

The above-mentioned two roles of advisor are not mutually exclusive. Kale et al. (2003) and

Golubov et al. (2012) find evidence in support of both channels in public acquisitions.3 Similar to prior

studies, I also use advisory experience to proxy for quality since prior studies show that in a repeated

service market, the quality of the service is positively related to the number of business provided.

Different from Kale et al. (2003) who use ratio format, I define relative advisor quality as the difference

between the acquirer and the target advisor quality.4 A high relative advisor quality indicates the

acquirer advisor has greater expertise than the target advisor in a specially area of service. For example,

presumably, greater experience will mean more thorough due diligence, which reduces information

asymmetry. Furthermore, a relatively more experienced acquirer advisor is likely better able to help

2Consider a manufacturing company acquiring an oil company. The manufacturing company chooses to hire a boutique investment bank that focuses on takeovers in the oil industry, and the target hires a nationwide investment bank that advises all types of deals. Although the national investment bank has a higher overall market share, if its experience in the oil industry is less than the boutique investment bank, the solutions and strategies provided by the boutique investment bank may be more effective in the takeover process. Similarly, the size-class expertise measures the strength of advisors in dealing with mergers in a specific size range. 3 Kyle et al. (2003) use a sample of tender offers. Since tender offers are public acquisitions, their findings are in line with Golubov et al. (2012), who present advisor quality matters only in public deals. 4All but one relative measures in this paper compare the expertise of acquirer advisor versus the target advisor. There is only one measure that aim to compare the buy-side and sell-side expertise of the same advisor.

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acquirers identify value-enhancing merger pairs as well as help with better post-merger integration and

financing solutions.

Therefore, the main hypothesis of this study is that the relative advisor quality of industry and

size-class expertise increases the merger gains of acquirers (H1). The predicted sign of the impact of

relative advisor quality on acquirer merger gains is positive.

3.Sample Construction and Variables

3.1 Samples

[Insert Table 1 Here]

To construct advisor quality measures, I start from extracting a total of 59,431 M&A deals from

SDC between 1990 and 2012. Acquirer advisor quality measures are constructed based on a total of

16,097 transactions that involve at least one acquirer advisor and target advisor quality measures are

constructed based on a sample of 21,618 transactions with at least one target advisor. I use the Fama

French 12 industry classification to partition the industry group based on targets’ or acquirers’ primary

SIC codes. The size-class categories are obtained by sorting all transactions into five deal-size quintiles

based on the adjusted deal value. Both industry and size-class expertise measures are calculated as

rolling three-year averages.

The sample of analysis is collected from the SDC M&A database and consists of all US

acquisitions announced between January 1994 and December 2012. Transactions must be completed and

have disclosed transaction values. I delete incomplete deals because it is hard to compare the advisor

quality in withdrawn deals with completed deals as advisor quality may also impact the completion rate

(Golubov et al. (2012), Chang et al. (2013)), and the analysis of post-merger performance also require

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deal status to be completed. Panel B in Table 1 lists additional filtrations for constructing the sample of

analysis.

• Toehold and percentage of shares held after transaction: I require an acquirer does not own the target

before the transaction but fully control the target after the deal is completed. I keep an acquisition if the

bidder owns less than 50% of the target prior to the bid and owns greater than 50% of the target.

• Compustat information: In order to measure firm performance changes such as profitability,

leverage, liquidity, and R&Ds before and after the transaction, I require an acquirer to have

CUSIP number from Compustat.

• Filtration of other deal types: Certain deal types that are included in the SDC data are

fundamentally different from mergers and acquisitions, thus I delete deals of restructures, spin-

offs, repurchases, self-tenders, reverse takeovers, privatizations, and divestitures.

• Information on merger outcomes: I require an acquirer to have sufficient data from CRSP and

Compustat to measure merger outcomes such as short-term and long-term stock returns and post-

merger performance changes.

• Acquirer advisor: The analysis measures the effect of advisor’s quality on merger outcomes as

well as the determinants of advisor choice, thus I require an acquirer hires at least one financial

advisor.

The final sample consists of 2,735transactions with acquirer advisers and 2,120 of them have

sufficient information to calculate relative advisor quality for regression analysis.5

3.2 Measures of Advisor Quality

5 I am aware that the sample size in this paper is slightly smaller comparing to recent studies examining financial advisors and merger gains. For example, Golubov et al. (2012) examine the acquirer advisor quality using a total of 4,451 acquisitions. The main reason of the sample size difference is that they do not require targets to hire a financial advisor, while my study focuses on the relative advisor quality, which I require both acquirers and targets hire financial advisors.

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3.2.1 Measures of Absolute (Single-Side) Advisor Quality

For each of the industry and size-class subgroup, I construct three types of absolute quality

measures for each advisor: the overall quality, the buy-side expertise, and the sell-side expertise. Panel

B in Table 2 reports the summary statistics of the absolute advisor quality of the 2,735 deals included in

the analysis sample.


The overall quality: The overall quality measures capture the general experience of an advisor in

a specific industry or size-class.

If an acquirer hires bank i, then the acquirer-industry expertise is defined as follows:

Eq. (1)

The measure equals the number of deals advised by bank i in a specific industry m divided by the total

number of deals occurred in industry m in the same year, averaged from the previous three years. For

instance, there are a total of 10, 20, and 10 transactions in industry m during year t-3, t-2, and t-1.

Among which, JP Morgan Chase advises 2, 4, and 3 deals. The industry expertise in year t of JP Morgan

Chase equals to 23.33% (= (2/10 + 4/20 + 3/10)/3). If an advisor doesn’t provide service in industry m in

a specific year, then the industry expertise equals zero. I sort transactions in to 12 industries following

follow Fama and French 12 industry classification.

The size expertise equals the number of deals advised by bank i in size group k divided by the

total number of deals occurred in size group k in the same year, averaged from the previous three years

as follows:

Eq. (2)

index.year t indexindustry acquirer m index,bank investment i

,3/]Deals ofNumber Deals ofNumber

Deals ofNumber Deals ofNumber


[expertiseIndustry 1- tm,

1- tm, i,

2- tm,

2- tm, i,

3- tm,

3- tm, i,tm,i,

，

++=

index.year t index, class-sizek index,bank investment i

,3/]Deals ofNumber Deals ofNumber



[expertise class-Size1- tk,

1- tk, i,

2- tk,

2- tk, i,

3- tk,

3- tk, i,tk,i, ++=

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For instance, there are a total of 50, 60, 45 acquisitions in size group k in year t-3, t-2, and t-1and

Citigroup advises 10, 30, and 15 deals during these three years. The size-class expertise of Citigroup

equals to 34.44% (= (10/50 + 30/60 + 15/45)/3) in year t. If an advisor doesn’t provide service in size

group k in a specific year, then the size-class expertise equals zero. I sort transactions into five quintile

groups based on the inflation-adjusted transaction value.

The methodology is similar to the model of advisor choice in Chang et al. (2013), and the model

of underwriter choice of Ljungqvist et al. (2006). The overall quality measures take into account of all

deals that an advisor has been involved in without differentiating the role of a buy-side agent or a sell-

side agent. The advantage of using the overall quality measure is that they are easy to observe by

companies and investors and they closely mimic the public perception of an investment bank in the

M&A advising market.

The mean (median) value of overall industry advisor quality is 3.46% (2.78%), with the value

ranging from zero (when a bank has no involvement in an industry) to 17.57%. The mean (median)

value of the overall size-class quality is 3.88% (2.34%), with a minimum of zero (when a bank has no

involvement in a size-class) and a maximum of 19.51%.

The buy-side expertise: The buy-side expertise measures an advisor’s prior experience serving as

an acquirer advisor. The formula is similar to equation (1) or (2) with the denominator changes to

number of deals with acquirer advisors in an industry or in a size-class subgroup, and the numerator

changes to number of deals a bank serves as a buy-side (acquirer) advisor.

The sell-side expertise: The sell-side expertise is calculated by dividing number of deals an

advisor served as a sell-side (target) advisor over the total number of deals with target advisors.

Each deal has four ratios of the size-class expertise: the buy-side and sell-side expertise of the

acquirer advisor and the buy-side and sell-side expertise of the target advisor. Similarly, for industry

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expertise, each deal has four ratios: two measure buy-side and sell-side expertise of the acquirer advisor

and the other two measure those of the target advisor.

Differentiating the buy-side versus the sell-side provides an opportunity for us to take a closer

look at the expertise of advising type rather than the simple involvement in a transaction. And it is

closely related to investment banking and the literature of agency theory. Groysberg, Healy, and

Chapman (2008) show that buy-side analysts made more optimistic and less accurate forecasts. Frey and

Herbst (2014) show that the impact of buy-side analysts is more pronounced than that of sell-side

analysts. Principle-agent studies in other field have also addressed similar issues. For example, Haire,

Linquist, and Hartley (1999) show that both plaintiff and defendant attorneys are less likely to find

judicial support if they are lack of the experience of such specialized attorney expertise.

As far as I know, this is the first study that emphasis the difference between the buy-side and the

sell-side expertise of an M&A advisor. As discussed in the previous section, the intentions of hiring an

advisor differ between targets and acquirers, thus the nature of buy-side and sell-side service is different

for M&A advisors. When an advisor gets involved in a total of 200 deals and serves as buy-side advisor

in 130 deals, he has more expertise of buy-side rather than sell-side. As shown in Panel A of table 2,

although the top five banks remain the same in both the buy-side and sell-side column, some banks in

lower ranks show advising type preference. For example, Banks of America ranks number eight as a

buy-side advisor while ranks 12 as a sell-side advisor; and advisors like Broadview, Bankers Trust, and

William Blair ranks top 20 as sell-side advisors but they are off the top-20 chart as buy-side advisors.

Thus, the buy-side and sell-side measures provide more accurate information of the specialization of

advisors and are more capable of measuring quality rather than involvement.


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Panel A of Table 3 shows that the average buy-side quality of the acquirer industry is 3.92% and

the average sell-side quality in the acquirer industry is 5.21%. The maximum value of sell-side quality is

over 40% while the maximum ratio of buy-side quality is lower at 20.15%. This pattern shows that some

advisors prefer serving targets than serving acquirers. As for the target industry, the buy-side and the

sell-side quality are similar at 3.49% and 3.46%, respectively. Advisors also have different buy-side and

sell-side ratios of the size-class expertise. The mean buy-side expertise is 4.41% and the mean sell-side

expertise is 3.51%. Therefore, table 3 shows the first piece of evidence that advisors have different

preference of service type and the difference is only revealed when differentiating the buy-side quality

versus the sell-side quality.

3.2.2 Measures of Relative Advisor Quality

Based on the absolute advisor quality constructed above, I measure the relative advisor quality

by taking the difference between the acquirer and target quality, in the way of both unconditional and

conditional.

Unconditional relative quality: The unconditional measure is the difference between the acquirer

and target overall advisor quality of a specific industry or size-class group, regardless of the serving side.

For a given transaction, it measures the relative involvement of the acquirer’s bank over the general the

target’s bank.

Panel A in Table 3 reports the summary of relative advisor quality measures. Out of the 2,735

transactions in the sample that involve acquirer advisor, 2,443 of them have non-missing relative advisor

quality measures. As for the unconditional relative expertise, the average ratios are close to zero, with

acquirer advisor seems to have only slightly higher (0.11%) ratio than the target advisor on average.

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Showing that in the M&A market, the overall quality of acquirer advisors is similar to that of the targets

advisors.

Conditional on the serving-side: The second relative quality is conditional on whether a bank is

serving the target or acquirer. Specifically, This measure equals the acquirer advisor’s buy-side expertise

minus the target advisor’s sell-side expertise. For example, if both JP Morgan and Banks of America are

more specialized in serving acquirers than serving targets in a given industry, then if Banks of America

advises an acquirer but JP Morgan advises the target, Banks of America’s relative quality would be

higher than JP Morgan’s even if JPMorgan may have greater overall experience. It is worth mentioning

that the relative quality captures expertise in two industries if the deal is a vertical merger. For example,

a retail company buys an energy company, and then the relative quality conditional on the serving side

equals to the acquirer advisor’s buy-side quality of retail industry minus the target advisor’s sell-side

quality of energy industry. For size-class expertise, the relative measure equals acquirer advisor’s buy-

side expertise minus target advisor’s sell-side expertise of a specific size group.

Table 3 reports the mean (median) level of relative industry quality conditional on the serving

side is 0.68% (0.4%), and the mean (median) relative size-class quality conditional on the serving side is

0.88% (0.39%), indicating the acquirer advisor’s buy-side expertise is slightly higher than the target

advisor’s sell-side expertise. Also, the magnitudes of the two conditional measures of relative quality are

greater than unconditional measures. Therefore, although the overall involvement of acquirer and target

advisors are similar, the buy-side and sell-side expertise are quite different.


3.3. Dependent Variables

18

I analyze the effect of relative advisor on both the short-term and post-merger performance of

acquirer companies.

• Short-term returns: The short-term market reactions of acquisitions are measures by the three-

day (-1, 1) cumulative abnormal returns (CARs) of acquirers. I use the market model estimated

from (-360, -30) days to determine the expected returns of acquirers. In unreported robustness

checks, I use a wider window of (-10, 1) to account for the early information leakage of the

transaction. I winterized the sample at 1% and 99% to eliminate the impact of extreme values.

Analyses of CARs (-1, 1) and CARs (-10, 1) yield similar results.

• Post-merger performance: I estimate the post-merger performance using the change of major

financial ratios three years after the merger announcements. I measure the acquirer profitability

using ROA, the financial risk using the leverage, and the intangible/costs using R&D ratios. The

details of the construction of financial ratios are reported in appendix A.


Panel A of Table 4 and 5 report the merger outcomes of the full sample as well as of the sample

of public transactions. The average three-day announcement returns of acquirers are negative at -1.9%

showing that acquirer investors lose on average. The public deals show a similar but significant CARs (-

1, 1) of -1.8%. The results in table 4 and 5 also show that the post-merger profitability and the Tobin’s

Q of acquirers decrease significantly during the sample period, and the leverage ratio increase about 5%.

To sum, the univariate results of merger outcomes show that acquirers do not gain, in both the full

sample and the sample of public deals.

3.4 Independent Variables – Deal and Firm Characteristics

19

I extract deal characteristics from the SDC database and firm financials from Compustat to

construct measures of information asymmetry, deal complexity, as well as the strategies applied during

the takeover process. Panel B in Table 4 and Table 5present the summary of independent variables of

2,423deals in the full sample and 1,483 deal in the sample of public acquisition.

The inflation-adjusted transaction value represents the deal size and is a proxy for deal

complexity. Large targets have more complex firm structures and have more lines of business, therefore

are relatively difficult to value. Since complex deals require more time and resources to complete, the

transaction and contracting costs are higher. The average deal size in the sample varies from $3 million

to $219.6 billion and is averaged at $1.85 billion. The deal size for public deals is higher at $2.39 billion.

The relative size measures the size of the deal compared to the pre-merger acquirer market size,

which is calculated based on acquirer market value 30 days prior to the merger announcement. A higher

relative size indicates greater impact of buying a target to the acquirer and such deals are more

meaningful for acquirers. Also, as the relative size increases, the negotiation power of acquirer decreases.

With the average (medina) acquirer market value measured at $17.1 (2.5) billion, the mean (median)

level of relative size is 0.67 (0.19) in the full sample, showing the average deal size is smaller than the

acquirer size. The mean relative size in the sample of public deals is slightly higher than the full sample,

measured at 0.86.

Deal payment method is another important characteristic that often time affects merger outcomes.

Stock deals are difficult to value and arrange than cash deals and are also related to speculative merger

arbitrage trading. Besides, prior studies present that managers at acquiring companies are more likely to

time the market when using stock payments. Of the 2,423 deals in the full sample, about 39%

acquisitions use cash-only payment and the remaining deals uses a combination of equity and other with

20

an average of 52.55% stocks. For public deals, the use of equity payment is similar to the full sample,

with 34% acquisitions use cash payment.

The full sample includes only 12% tender offers. Kale et al. (2003) reach their main conclusion

based on a sample of tender offers. Thus, this study provides a more comprehensive view of how

relative advisor quality affects merger outcomes, in both tender offers and other public acquisitions.

I also control the following deal characteristics that exhibit similar patterns for both the full

sample and the sample of public deals.

The average number of SIC codes of a target in the full sample is 2.61. When the target has more

than one SIC codes, the information asymmetry is higher since the target is involved in multiple lines of

business and is difficult to value. As the number of bidders increases, competition becomes intense and

transaction costs increase. The number of bidders varies from one to four with an average of 1.005 in the

full sample. 36% of targets and the acquirers are in the same industry, in which cases the information

asymmetry is lower; while the rest of the acquisitions are vertical, which entails higher information

asymmetry and potentially calls for better advisors. Also, 28% of the deals are cross state, meaning the

acquisition occurs between companies from different states. On the one hand, acquirers increase market

power as they expand across geographic regions; on the other hand, as the distance between the two

companies increases, it is hard for the acquirer to evaluate the business environment of the target, thus

the deal may become risky and hard to value.

Around 1% of the deals are hostile, which increases the acquisition costs than friendly deals.

Acquisitions with pending target litigation issues (1%) or in need of regulatory approvals (71%) also

command more resources to complete. At last, around 11% targets have anti-takeover tactics, which

increase the deal complexity and transaction costs.

21

The acquirer’s M&A experience is measured by two variables: if the acquirer is a frequent buyer

and whether the deal is the first merger conducted by the acquirer. The table shows that most of the

acquirers are repeated players in the M&A market. On average, 22% acquirers are first-time buyers and

31% of them are frequent buyers, defined as firms completed at least five mergers before the current

transaction.

As mentioned in the previous section, all transactions in the sample are completed acquisitions.

On average, it takes an average of 129 days to complete a deal (resolution speed). And consistent with

previous studies showing evidence of market timing, acquirer’s stock returns increase 19% during the

period right before merger announcements (run-up).

I control two other advisor properties in the regression. On average, there are 12% of the deals

hiring more than one advisor (multiple advisors). Often times, these deals are large in size and are

complex for one advisor to handle. Also, about 30% of acquirers in the full sample and 36% in the

public sample hire top-tier advisors, which defined as the top five advisors. Panel A in table 2 presents

the names of the all-time top 20 investment banks and the total number of deals they advised. The

ranking is quite stable in the sense that these investment banks seldom drop from the top 20 list although

they might switch places from year to year. In the robustness analysis, I show that the top-tier ranking

does not affect the main effect of relative advisor quality on merger outcomes.

I also control several firm level characteristics of acquirers. Institution holding is on average

about 43%. In the full sample, acquirers are generally profitable with an average ROA ratio of 3% and

maintain a relatively low leverage (20%) before merger. The average Tobin’s Q is 2.48, representing

future growth opportunity of acquirers. The average R&D level is 8%, representing the intangibles and

average costs of acquirers.

22

4. Relative Advisor Quality and Short-term Announcement Returns

4.1. OLS Analysis of Acquirer Merger Gains

In this section, I establish the main wealth effect of the relative advisor quality. The regression

model is as follows:

𝐶𝐴𝑅𝑠 −1, 1 ! = 𝑎! + 𝑏×𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒𝑄𝑢𝑎𝑙𝑖𝑡𝑦! + 𝑐×𝐷𝑒𝑎𝑙𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠! + 𝑒! Equation (3)

The merger gains are measured as cumulative abnormal returns (CARs) during the three-day window of

takeover announcements. The main variables of interest are relative quality of industry and size-class

expertise, as described in the previous section. The estimates are measured using the heteroskedasticity-

robust standard errors. And I control for advisor clustering, year fixed effects, and industry fixed effects.

[Insert Table 6 and 7 Here]

Table 6 reports the full sample estimation of relative industry and size-class expertise,

respectively. As mentioned in previous section, advisor’s relative quality may be more important when

the acquirer’s negotiation power decreases, such as when facing a public target rather than a private

target. Thus, I repeat the analysis in the subsample of public targets in Table 7. The coefficients of

relative advisor quality, both the industry and the size-class expertise, are not significant in the full

sample. But Table 7 shows that the coefficients of relative advisor quality are significantly positive in

the subsample of public transactions, measured in both unconditional and conditional format. The

coefficients vary from 6.386 to 7.016 in the regressions of industry expertise and from 5.219 to 6.783 in

the analysis of size-class expertise; and the effect is more significant when measuring the relative

expertise in the conditional format. The economic magnitude of the effect is also meaningful. For

example, one standard deviation increase of unconditional industry relative advisor quality increase the

acquirer CARs by 0.29% during the three-day window, which equals to 16 percent increase of an

average acquirer’s announcement returns. And one standard deviation of conditional relative industry

23

expertise increases the acquirer CARs by 0.30%. Similar results are shown in the analysis of relative

size-class expertise. The economic impacts vary from 0.31% to 0.41% given one standard deviation

increase of relative size-class expertise.

In addition, I find acquirer CARs are negatively related to the size of the deal and stock payment.

These are consistent with prior literature that large mergers with positive NPVs tend to have lower

return rate for acquirers; and merger arbitrage is likely to affect acquirer returns used in stock deals.

Also, consistent with market timing hypothesis, the pre-merger run-up is negatively related to acquirer

announcement returns.

Overall, I find relative adviser quality of industry and size-class expertise does not impact

acquirer merger gains universally. The significantly positive impacts are only present in the sample of

public transactions, showing that acquirer shareholders’ gains are more sensitive to the choice of a better

advisor in the public corporate control market.

4.2. Selection Bias and Two-Stage Analysis

So far, the significant results in the subsample of public transactions are established based on the

analysis results of robust OLS regressions, assuming the choice of advisor is exogenously determined.

Previous studies (e.g. Servaes and Zenner (1996), Golubov et al. (2012)) have shown that certain

transactions characteristics that are related to acquirer or target merger gains may also be important

determinants of the advisor choice. A causal relationship cannot be established between financial

advisors and merger gains unless this endogeneity is taken into account. It is also important to study if

the industry and size-class expertise matters in the selecting procedure of acquirer advisor or if there is

persistency in the advisor choice decision. Therefore, I employ the two-stage Heckman (1979) model. In

the first-stage regressions, I use probit regressions to model the decision of hiring an advisor of superior

24

size-class or industry expertise with controls of deal characteristics. The dependent variable in the first-

stage regression equals one if a superior advisor is hired, while equals to zero otherwise. A superior

advisor is defined as a top-five buy-side or sell-side advisor in the acquirer industry, or a top-five buy-

side or sell-side in the corresponding size class, based on the number of deals completed in the previous

three-year. In the second-stage regressions, I re-estimate the impact of relative advisor quality with the

correction of the selection bias by implementing the inverse Mills ratios obtained from the first-stage

analysis.

One restriction of implementing the two-stage procedure is that at least one variable that is

present in the first stage should not be included in the second stage (Wooldridge (2012)). In other words,

it is advised to have at least one variable that impacts the choice of choosing a superior advisor does not

impact the merger gains. I construct variables “superior rate” by calculating the three-year rolling

average of buy-side or sell-side top-five hiring rate, in the acquirer’s industry level or the corresponding

size deal group. The “superior rate” variables are not only instruments that satisfies the criterion of the

Heckman two-stage models, they are also useful in telling whether there is industry or size-class advisor

hiring persistency. Besides the “superior rate” variables, I also include deal characteristics to proxy for

the deal complexity and information asymmetry.


Table 8 reports the results of the first-stage probit regressions. Using the main sample shown in

Panel A, the variables “superior rate” are positive but insignificant related to the advisor choice in all

four regressions, indicating the choice of a superior advisor, either at the industry or the size-class level

is not persistent in the full sample. However, in the subsample of public targets reported in Panel B,

“superior rate” are significantly positive in all models. The marginal effects are reported in the

parenthesis showing the persistency of hiring a superior advisor is also economically meaningful. For

25

example, 1% increase of superior rate increases the probability of hiring a superior industry buy-side

advisor by 0.460% and a superior industry sell-side superior advisor by 0.319%. Similarly, 1% increase

of superior rate increases the probability of hiring a superior size-class buy-side advisor by 0.456% and

a superior size-class sell-side advisor by 0.503%.

The results of first-stage regressions are also consistent with prior studies of advisor

determinants. Deal characteristics such as deal size, anti-takeover measures, pre-merger institutional

ownership are positively related to the choice of a superior advisor. As the deal size increases, the

complexity of the deal increases; and the anti-takeover measures used by targets also increase the

transaction costs. To summarize, I find strong industry-level and size-class level persistency in the

choice of choosing superior advisors in public transactions.

From the first-stage equation, I construct “inverse Mills ratio” that I add as an additional variable

in the second-stage regression. A significant “inverse Mills ratio” reflects selection bias, indicating

certain characteristics that influencing the likelihood of choosing a superior advisor further impact the

merger gains (CARs).


In Table 9, I report the second-stage regressions of the relative advisor effect in the sample of

public transactions. The coefficient of the “inverse Mills ratio” is insignificant in the regression

estimating the effect of relative industry advisor quality, indicating the OLS results of Table 7 is reliable.

The coefficients of relative industry expertise remain significant in the second-stage regression.

On the other hand, the right two columns in Table 9 show the results of relative size-class

expertise. The “inverse Mills ratios” are significantly positive. In addition, the magnitude of the relative

size-class quality coefficients decrease; and they all become statistically insignificant. These results

reflect the presence of selection bias, showing certain observed or unobserved characteristics that

26

increase the likelihood of hiring a superior advisor of size-class expertise also increase the acquirer

CARs. The results are consistent with the argument that better deals are match with superior advisors in

a specific size group, and in turn theses superior advisors are able to negotiate better terms for their

clients.

Overall, I find that the effects of industry expertise are robust to selection bias while the size-

class expertise becomes insignificant once I control the choice of superior advisors. The absence of

significance of size-class expertise could be due to the advisory industry characteristics. First, both

practitioners and academic use models that often compare M&A deals of similar sizes. Acquirers easily

get information of their competitors’ takeover transactions, such as deal type, advisor identity, and

advisor reputation. Second, the advisory market is naturally divided into size-based categories. For

example, the annual M&A awards are given based on small size, medium size, or large size group. In

other words, the size-class market for advisory service is quite competitive, and both acquirers and

advisors make informed decision such as whom to hire and what type of deals to serve. Thus, the results

show that size-related credentials of an advisor are easier to observed, and attracts more attention

relative to industry-related credentials.

4.3. Robustness tests


4.3.1. Sensitivity Analysis of Deal Characteristics

In previous sections, I establish the main finding that relative advisor quality significantly

impacts acquirer merger gains in public transactions. In this section, I conduct further analyses to

differentiate how the impact of relative advisor quality may vary based on deal characteristics. The

results are reported in table 10.

27

First, I present how the impact of relative advisor quality varies based on relative deal size.

Relative size is defined as the ratio of the transaction value over the acquirer market size. When

acquirers buy small sized targets comparing to their own size, they are usually the dominators of the

deals and are able to set favorable terms in the takeover process because there is limited alternatives for

the targets. As the relative size increases, the voices of targets get louder and their strategies become

more influencing. In other words, the dominant role of acquirers becomes less significant as they buy

larger companies relative to their own sizes. I divide the public target sample into small and big relative

size groups. Consistent to the above argument, the impacts of relative advisor quality of both the

industry and size-class expertise are significantly positive in relatively large deals. However, when

acquirers buy relatively small targets, the impact of relative advisor quality become insignificant.

Secondly, I partition the public transaction sample into stock payment versus cash payment deals.

Stock deals are usually more complex. Both the estimate of exchange ratio and financing arrangements

require professional advise from investment banks. Thus, the market is more likely to be convinced if

the valuation process in stock deals is provided by relatively better advisors and financing arrangements

are backed by reputable investment banks. Thus, the role of acquirer advisors becomes more important

in stock deals. Consistently, Table 10 shows that the coefficients of relative advisor quality are

significantly related to acquirer merger gains in stock deals but insignificant in cash deals.

Moreover, I partition the sample of public targets into vertical and horizontal deals. I find that the

role of relative advisor quality is significantly meaningful in vertical acquisitions. That is, when

acquirers purchase companies from different industries that acquirers are not familiar with, hiring a

relatively better advisor shows significant benefits. However, the impact of relative advisor quality is

less important when acquirers conduct horizontal mergers.

28

Last but not the least, I partition the sample of public transactions based on the acquirer’s prior

M&A experience. 1137 of the public deals are conducted by acquirers who have completed more than

one acquisitions; while only 341 deals are conducted by first-time acquirers. Previous studies have

shown that short-term merger gains of repeated acquirers are lower and more negative comparing to

first-time buyers, indicating over-spending or over-confidence of hubris acquirer managers. Table 10

shows that the market view it as a positive sign if a repeated acquire hires a relatively better acquirer

advisor, indicating a professional evaluation plays a more important role of assuring the quality of the

deals to the market.

Overall, the sensitivity tests present that relative advisor quality is beneficial to acquirers in

public transactions, especially when the deal is of relatively large size, using stock payment, vertical

merger, or conducted by repeated acquirers.


4.3.2. Buy-Side versus Sell-Side Expertise

Another feature that I consider is how the buy-side and sell-side expertise differs. Previous

studies on financial advisors all measure quality or reputation based on the general involvement of

advisors in the M&A market. However, the buy-side and sell-side advisory services have different

focuses due to different demands of acquirers and targets. For acquirers, they focus on identifying better

mergers and gaining better terms in negotiation, thus acquirer advisors are relatively more aggressive in

identifying potential synergy, helping negotiating, arranging financial solutions. On the other hand,

targets care more about better deal prices, low risk of post-merger lawsuits, which makes the role of the

target advisor is to evaluation if the acquirer’s offer is fair, to obtain the advisor’s assurance to avoid

29

post-merger lawsuits against target managers. Although investment banks often serve both sides, they do

have different buy-side and sell-side experience, especially at industry level or size level.

Given the difference between the buy-side and sell-side service, I measure relative advisor

expertise conditional on both the buy-side and the sell-side. Specifically, I measure the difference of

buy-side expertise of both sides’ advisors and the sell-side difference of both sides’ advisors. Using the

example giving in section three, the buy-side relative expertise captures Banks of America and JP

Morgan’s quality difference when they were both providing buy-side service; the sell-side expertise

captures the quality difference when they were both provide selling-side service. This set of measures

aim to differentiate whether the acquirer will benefit more from an advisor with better sell-side expertise

or an advisor with better buy-side expertise. 6

For industry expertise, the mean ratios of relative quality conditional on buy-side and sell-side

are negative at -0.62% and -0.38%, but median values are close to zeros. Similarly, the relative size-

class expertise of buy- and sell-side ratios average at 0.36% and 0.06%, both are positive but with close

to zero medians.7 Table 11 reports the regression results of acquirer merger gains on relative buy- and

sell-side expertise in the sample public transaction. I find both measures significantly increase acquirer

merger gains but the impact of buy-side expertise is more important than the sell-side expertise. For

example, 1% difference in the buy-side industry expertise of acquirer and target advisor increases the

merger gains by 6.682%; but 1% difference in the sell-side industry expertise only increases merger

gains by 3.346%. Similar results are shown in the regressions of size-class expertise. Thus, this analysis

presents insights that market view buy-side expertise more valuable for acquirers. Hiring a relatively

better buy-side advisor almost double the benefits of hiring a relatively better sell-side advisor.

6 The buy-side relative quality measures only the buy-side difference of acquirer industry and the sell-side relative quality measures only the sell-side difference of target industry. This is because when cross matching serving side with acquirer/target industries, many advisors would have zero readings of buy-side (sell-side) expertise in target (acquirer) industry 7 The summary of buy-side and sell-side relative expertise is reported in Appendix B.

30


4.3.3. Same-Bank Relative Expertise

I also control for the same-bank expertise difference conditional on the acquirer advisor. Since

most banks in the sample serve both targets and acquirers, it could be more plausible for acquirers to

hire advisors with more extensive buy-side expertise. Because such advisors potentially understand

better the needs and tasks of buy-side service. Thus, if the relative advisor quality matters, the greater

buy-side quality could benefits acquirers more significantly. Strictly speaking, this measure is not

comparing acquirer advisors with target advisors. Rather, they capture a given advisor’s relative

advantage on the buy-side service versus the sell-side service. Each transaction yields two relative

quality measures conditional on the acquirer advisor, one for the industry expertise and the other

measuring the size-class expertise.

The relative quality conditional on the advisor’s own expertise are both positive, with industry

relative expertise averaged at 1.06% and size-class relative expertise averaged at 0.8%. Thus, acquirer

advisors in general have more buy-side experience than sell-side experience.8 Table 12 shows that the

same bank’s relative buy-side quality matters, when measured at size-class level. If a bank had both buy-

side and sell-side serving experience, the more experience they had advising acquirers in the past, the

greater merger gains an acquirer obtains.

4.4. Alternative explanations

4.4.1. Advisor Reputation

8 The summary statistics of same-bank relative expertise and the Pearson correlations between different relative quality measures are also reported in Appendix B.

31

In the previous section, I show that greater quality of acquirer advisor relative to target advisor

significantly increases the short-term announcement returns of public deals. In this section, I examine

whether the impacts of relative industry and size-class expertise are suppressed by simply hiring a

reputable top-tier advisor. Similar to prior studies, I use the top-tier advisor to proxy for the most

reputable advisors. Golubov et al. (2012) show that top-tier advisor increase acquirer CARs in public

deals but Kale et al. (2003) report that controlling for the relative advisor market share, the top-tier

advisor does not impact the wealth gains in tender offers. I use the similar method as in Fang (2005) and

Golubov et al. (2012). An investment bank is defined as top-tier if it is ranked top five based on all deals

completed in the past three years. The measure is simple and powerful as it captures the two-tiered

structure of advisory industry acknowledged by both practitioners at Wall Street and the academic

literature. Consistent with Golubov et al. (2012), Goldman Sachs, JP Morgan, Morgan Stanley, Credit

Suisse First Boston, and Merrill Lynch have the most appearances in the top-five list, showing the top-

tier is quite stable over time.


Table 13 reports the results of relative advisor quality with the control of two-tier advisor

ranking. Coefficients of the top-tier advisor are insignificant in the regression of industry relative

expertise. The main findings of relative industry expertise remain significant, showing that the benefit of

hiring an acquirer advisor with relatively greater industry expertise is not suppressed by the two-tier

advisor reputation. However, the relative advisor quality of size-class expertise becomes insignificant

and coefficients of top-tier advisor significantly positively related to acquirer gains, indicating that the

effect of relative advisor quality overlapped with the impact of top-tier advisors. In other words, Table

13 reveals that the top-tier ranking is quite stable based on either deal size groups or the overall advisory

32

market, but advisors with greater industry-expertise is not captured by a simple two-tier partition of the

advisory market.

4.4.2 Deal Characteristics


I show that relative advisor quality significantly increase acquirer merger gains in public deals,

but not in the full sample. This is consistent with the view that the impact of relative advisor quality

increase as the negotiation power of acquirer decreases and takeover environment becomes less

favorable to acquirers. But there is also evidence in Table 10 that the impact of relative advisor quality

varies with merger characteristics. Thus, one might argue that public transactions may not only proxy

for the negotiation power, rather it is related to other explanations, such as deal complexity, deals size,

etc. For example, private target are on average smaller; and acquirers are more likely to use stocks in

complex deals, such as public transactions. To test these alternative explanations, I re-estimate model (3)

by dividing the full sample into different subsamples based on deal characteristics. Table 14 reports the

coefficients of relative advisor quality measures, in different subsample analysis.

First, I divide all acquisitions, including both public and private deals, into five groups based on

the inflation-adjusted deal value. I then estimate the effect of relative advisor quality of each size group.

Table 14 reports the coefficients of the smallest and largest group, which each contains 524 to 525 deals.

The coefficients in the small size group are negative while in the largest size group are positive, showing

the relative advisor quality benefits large deals but is negatively related to merger gains in small deals.

However, none of the coefficient in the subsample of deal size is statistically significant. Therefore,

large deal size and public targets might be related based on some common factors such as deal

complexity, but the size difference alone cannot explain the significant finding of relative advisor

quality in public deals.

33

Next, I divided the full sample based on relative size. Previous studies have shown that deals size

and relative size capture different aspects of the deal. Conditional on the same target size, a deal of

larger relative size means the takeover is more meaningful to the acquirer, while a small relative size

indicates a more dominant position of the acquirer. The result in Table 14 shows that as relative size gets

larger, the benefit of hiring a relatively better acquirer advisor increases as well. However, none of the

coefficients is significant, showing relative size is related but is an insufficient proxy for the public

corporate control environment.

Furthermore, I partition the full sample into stock deals versus cash deals, where stock deals

include all transactions involving stock payment while cash deals pay 100% in cash. The coefficients of

relative advisor quality in either subsample are insignificant, and the magnitude of coefficients does not

differ much. Thus, I show that the payment method does not proxy for difference between public and

private deals, thus cannot explain the significance of relative advisor quality in public deals.

Besides, I examine subsamples of tender offers versus mergers. The purpose of this analysis is to

compare the findings with Kale et al. (2003). They find relative advisor market share significantly

increase acquirer wealth gains using a group of 324 tender offers. Thus, I compare my results with their

findings and analyze whether the source of positive effect is due to the deal type of tender offers or the

difference between public and private deals. A tender offer is a open proposal raised directly to the

shareholders of the target firm, could be friendly or hostile and could be with or without the support of

board or directors; while a merger is an agreement of offer price reach by acquirer’s and target’s board

of director. Recent research (Offenberg and Pirinsky (2013)) has shown that the main difference

nowadays between tender offers and mergers are: tender offers might be faster to complete, but paid at a

high premium. In the subsample analysis, I re-estimate the effect of relative advisor quality in the

subsamples of 278 tender offers and 1,813 mergers. The coefficients in the subsample of tender offers

34

are positive and large in magnitude, similar to those in Table 7. However, the coefficients are not

statistical significant, showing that tender offers have similar deal structures to other type of public

acquisitions, but are unable to fully explain the significant findings in public deals.

Last, I partition the sample into 1,331 vertical and 766 horizontal acquisitions. Vertical deals are

between firms from different industry, based on their three-digit industry code; while horizontal

acquisitions between firms from the same industry. Usually, the information asymmetry is more severe

when an acquirer buys a target from different industries, because the acquirer in such situation may not

understand the business environment of the target. Thus, as the information asymmetry gets severe, the

demand of the high quality advisor increase and the effect of the advisor quality could be more

pronounced. Both Panel A and Panel B in table 11 show that coefficients of horizontal and vertical are

insignificant when including both public and private deals. But the coefficients in the subsample of

horizontal deals are negative or much smaller in magnitude than vertical deals, indicating the

information asymmetry difference between vertical and horizontal deals may partially explain the effect

of relative advisor quality in public deals.

4.5. Why Relative Quality Matters in Public Deals?

So far, findings show that the relative advisor quality significantly benefits acquirer

announcement returns in public transactions. The results is consistent to the finding of Golubov et al.

(2012), which present top-tier advisors increase acquirer gains in public deals only. One explanation is

that when an acquirer becomes less dominant in a takeover process, a relatively better advisor helps the

acquirer to negotiation better deal outcomes, thus increasing acquirer gains or decreasing acquirer losses.

While the effect of advisor becomes less noticeable in deals that acquirers have already dominated. In

this study, the variable “public transaction” is a proxy for the negotiation power of acquirers or the

35

overall takeover environment. Prior studies show that acquirers gain less when acquiring public targets

comparing to acquiring private targets (Fuller, Netter, and Stegemoller (2002), Capron and Shen (2007)).

Consistently, Table 4 and 5 show similar results that on average acquirers lose significantly by 1.8%

during the announcement period when buying public target but not in the full sample.

The decrease of acquirer negotiation power in public deals is due to several reasons. First, it is

difficult for acquirers to exploit information of public targets. As Capronand Shen (2007) mentioned, the

market of corporate control for public targets is competitive, which serves as an information processing

and asset valuation mechanism for all potential bidders, thus making it difficult for acquirers to exploit

private information as they do when buying private targets. Second, public targets have more alternative

choices in terms of financing, looking for better buyer, or negotiating. For example, Fuller, Netter, and

Stegemoller (2002) mention that shareholders of public targets have greater bargaining power than

private targets. They can use shareholder approvals, anti-takeover defenses, and other tactics to defer

unflavored buyer or to negotiate better terms. And third, the post-merger integration costs (such as

litigation costs) are relatively high in public deals. Acquirers buying public targets facing undisclosed or

hidden problems that may lead to future lawsuits or investigations (Golubov et al. (2012)).

Therefore, in the situation when acquirers find difficulty to control the takeover by their own, the

expertise and knowledge of a specialized third-party, M&A advisor, becomes more important and

beneficial. The role of acquirer advisor is important as they help negotiate with the counterparty, review

deal terms, or identify any undisclosed or hidden problems of targets. In other words, the impact of a

relatively better advisor is more significant when the takeover environment is less favorable to acquirers.

Although the greater demand of the advisor quality plays the most important role in

understanding the different impacts on public and private acquisitions, one cannot rule out the

possibility that the advisors may perform better in public deals when they face greater reputation

36

pressure from the market. On the one hand, the market closely follows public deals and financial

analysts and media provides extensive coverage of deal details (Rhee and Valdez (2009)), on the other

hand, private deals are often announced only when completed, at which point the job of advisor has

already been done (Officer, Poulsen, and Stegemoller (2009)). Thus, as advisors get greater visibility in

public deals, they may have the incentive to perform better.

5. Relative Advisor Quality and Post Merger Outcomes


In this section, I examine whether the relative advisor quality impact the long-run performance

of the acquiring firm. As mentioned in previous section, advisors can create value by identifying better

mergers or helping negotiate better terms for their clients. The two effects are not mutually exclusive. If

a relatively better advisor help the acquirer identify a good match, then the post-merger firm

performance could be better. Also, advisors may provide suggestions as to integrate the two merging

firms in order to decrease the costs or more efficiently utilize financial resources. In table 13, I examine

how relative advisor quality affects the changes of three major financial ratios of acquirers. I use ROA to

measure the firm profitability, leverage to measure the use of financial resources and financial risks, and

R&Ds to proxy for operational costs. For the change of each ratio, I estimate the effect of relative

advisor quality in the full sample as well as the subsamples of private and public deals. Panel A reports

the analysis of industry expertise and Panel B reports the regression results of size-class expertise.

In the full sample analysis of industry expertise, all coefficients of relative advisor quality are

positive in the models of ROA change. First, there is some evidence that relative advisor quality

significantly increases the post-merger profitability of acquirers. The effect is more pronounced in the

public sample, consistent with the findings of short-term stock returns that relative advisor quality is

37

more desirable and influencing in public deals. Specifically, 1% difference between the acquirer

advisor’s buy-side expertise and the target advisor’s sell-side expertise significantly increases the

acquirer ROA by 0.21%. Second, the general measure of relative quality is not significant while

measures based on buy-side and sell-side expertise capture the value impact, indicating rather than using

general measures of overall involvement in the advisory market, it is necessary to differentiate buy-side

and sell-side expertise to capture the impact of advisor quality. And third, the last column of ROA

regression shows that the relative quality conditional on the acquirer advisor’s own buy-versus-sell

expertise significantly increases the profitability of acquirers in private deals. This own-bank relative

quality measure does not impact the short-term CARs in previous tests and but becomes very

importance in boosting acquirer profitability in private deals. Given one percent difference in the buy-

versus-sell expertise of acquirer advisor, the economic magnitude of ROA increase is 1.48%.

The regressions of post-merger R&D change also show that relative quality is importance for

buying public targets. Similar to the analysis of ROA, the unconditional measure of relative advisor

quality does not reveal any value effect. However, all three conditional measures that compare the

expertise of acquirer and target advisors yield significantly negative coefficients in public deals,

showing that greater acquirer advisor quality effectively decrease the post-merger costs. The impact is

most pronounced in column 4 where 1% difference in the sell-side expertise between acquirer and target

advisors decreases the R&D costs by 0.18%. That is, with extensive sell-side expertise, the acquirer

advisor helps the acquirer integrate with the target and effectively drop abundant research costs.

The results of the increased ROA and the decreased R&Ds show that one source of the gain by

hiring a better acquirer advisor is that the advisor could help identify better match of technology

integration. Rather than developing technology on their own, acquirers can obtain new technology

38

through purchasing; this reduces the risk of R&D failure and implements the technology development

into production quickly.

The result of leverage change mainly show that a relatively better acquirer advisor helps reduce

the leverage of private deals. This is consistent with the view that one motivation of acquisition is to

utilize the spare borrowing resources of targets. And this is especially pronounced when buying private

targets, which usually have low financial leverages.

Results in Panel B show that, unlike the industry expertise, advisors with relatively superior size-

class expertise do not improve the post-merger performance of acquirers. The only significant

coefficient is the buy-versus-sell expertise of acquirer advisor, which is negatively related to the change

of leverage in public deals. Thus, advisors with better buy-side expertise might their clients reduce the

financial costs in public acquisitions.

6. Advisor Choice and Dimensions of Advisor Quality


In this section, I examine how the absolute (not relative) industry and size expertise affect the

advisor choice and analyze how the buy-side and sell-side expertise affects the choice differently. I take

an approach similar to Ljungqvist, Marston, and Wilhelm (2006) and Chang et al. (2013), in which I

estimate the choice of advisor based on an expanded sample of all competing advisors. Specifically, I

match each deal with all active advisors available in the market in a given year. The dependent variable

equals one if the bank is chosen by the acquirer, and zero otherwise. For example, if there are m deals

occurred in a given year with n possible advisors in the market, then the matching results in m×n

acquirer-advisor pairs. The original sample of 2,735 deals with acquirer advisor thus expands to a

sample of 658,154 acquirer-advisor pairs.

39

The results of the probit analysis are reported in Table 14. The main question I aim to answer is

how industry and size-class expertise affect the selection of an advisor among all potential pairs. In each

model, I include both the buy-side and sell-side expertise as to show which type is more likely to impact

the decision. Panel A reports the probit results of industry expertise. First, both the acquirer-industry

expertise and target-industry expertise are significantly positively related to the chance of being selected

as an acquirer advisor. Also, the first two models show that the chance of being selected as an acquirer

advisor increase more if the buy-side expertise is higher. For example, conditional on the acquirer-

industry quality, 1% increase of buy-side expertise increases the chance of being selected by 0.4%,

while 1% increase of sell-side expertise only increases the chance by 0.18%. Model three includes all

four expertise measures: acquirer industry buy- and sell-side expertise and target industry buy- and sell-

side expertise. The result shows that the buy-side expertise always dominates the sell-side expertise,

although the understanding of target industry is more preferred. This is consistent with the view that the

acquirer advisor is needed to mitigate the information asymmetry between the target and acquirer. When

an acquirer is lack of sufficient information of the target, an advisor with extensive knowledge of the

target industry is more appreciated.

In column four and five, I include interactive terms of advisor expertise with public deals.

Previous sections have shown that the effect of relative advisor quality is significantly positive in public

deals, and a superior advisor is more likely to be chosen in public deals as well. The results show that

two types of expertise that are more positively significant in public deals are the buy-side expertise of

acquirer industry (marginal effect = 0.169) and sell-side expertise of target industry (marginal effect =

0.117), indicating that acquirers who buy public targets prefer advisors with greater buy-side expertise

of the acquirer industry and sell-side expertise of the target industry.

40

Panel B of Table 14 reports the probit regressions of size-expertise. The main results are similar

to the analysis of industry expertise. Both buy- and sell-side expertise are significantly positively related

to the chance of an advisor being chosen. The possibility increases by 0.224% if the buy-side expertise

is 1% higher while the possibility increases only 0.099% if the sell-side expertise is 1% higher, showing

that the buy-side expertise is more important than the sell-side expertise.

Overall, the analysis of advisor choice shows that greater industry and size-class expertise

increase the possibility of an advisor being chosen among all available pairs, and the buy-side expertise

is particularly important for acquirer advisors.

7. Conclusion

In this study, I examine the role of the relative advisor quality in the US merger market from

1994 to 2012. My study measures the relative advisor quality in terms of their industry expertise, size-

class expertise, as well as buy- versus sell-side expertise. I examine how these factors affect both the

short-term and long-term merger outcomes of acquirers.

I find that the relative industry expertise of acquirer advisor to target advisor significantly

increases the short-term announcement returns of acquirers in public transactions. The effect is robust

with the control of selection bias. On the other hand, the relative size-class expertise is positively related

to acquirer CARs in public deals but the effect disappears after controlling for the selection bias. In the

robustness tests, I find the impact of relative advisor quality varies conditional on deal characteristics. I

also show that the buy-side relative expertise increases acquirer merger gains more than the sell-side

relative expertise.

I further show that the significance of relative advisor expertise in public transactions is different

from the advisor ranking effect. The size effect and vertical acquisitions are related to the impact of

relative advisors, but cannot fully explain the reason that the effect is significantly present in public

41

deals. I provide the explanation that, in public corporate control market, when the takeover environment

becomes less favorable for acquirers to exploit value or dominate negotiation, the importance and

impact of hiring a relative better advisor increase. The finding is consistent with Golubov et al. (2012),

who find top-tier advisors only improve the acquirer merger gains in public acquisitions.

I further show that relative advisor quality is related to improved post-merger performance of

public acquisitions. Specifically, acquirers hiring relatively better advisor of industry expertise improve

the post-merger profitability and reduce the R&D costs in public deals, showing one possible source of

gains by hiring a superior acquirer advisor is through better matches with targets of advanced

technology. However, the size-expertise doesn’t impact post-merger performance of acquirers in either

the full sample or public deals.

Last but not the least; I examine the advisor choice by using an expanded sample, which includes

all possible acquirer-advisor pairs. I find that the possibility of an advisor being chosen is positively

related to greater industry and size expertise. The finding is in general consistent to Chang et al. (2013).

Furthermore, I show that the magnitude of impact of buy-side expertise on the advisor choice dominates

that of the sell-side expertise.

To sum, this paper conduct a comprehensive study of how relative advisor quality affects

acquirer merger outcomes and is closely related to several studies in explaining the effect of M&A

advisors. The recent studies of M&A advisors pay greater attention to the segmented feature of advisory

market. Chang et al. (2013) and Stock (2012) study the advisor choice and industry expertise, and Song

and Jie(2013) study the impact of boutique bank rather than prestigious banks. This paper is the first to

show that relative advisor quality at industry and size-class level improves both the short-term and long-

term merger gains of acquirers. This is also the first paper that differentiates the buy-side expertise

42

versus the sell-side expertise and show that the impact of buy-side expertise dominates that of the sell-

side expertise.

The implication of the study is that (1) both investors and the practitioners of M&A market need

to understand not only the absolute quality of financial advisor, but also how the relative quality matters.

And (2) we need to understand better the difference between public acquisitions and private acquisitions

in the M&A market and the mechanism of how financial advisors improve public deals.

43

References Agrawal, A., Cooper, T., Lian, Q., and Wang, Q., (2012). “Common advisers in mergers and acquisitions: determinates and consequences.” Working paper. Allen, Franklin, 1984, Reputation and Product Quality, The RAND Journal of Economics, Vol. 15(3), pp. 311-327 Allen, L., J. Jagtiani, et al. (2004). "The role of bank advisors in mergers and acquisitions." Journal of Money, Credit and Banking 36(2): 197-224. Amihud, Y., B. Lev, et al. (1990). "Corporate control and the choice of investment financing: The Case of Corporate Acquisitions." The Journal of Finance 45(2): 603-616. Asquith, P., Robert F. Bruner, and David W. Mullins Jr. (1983). "The gains to bidding firms from merger." Journal of Financial Economics 11: 121-39. Bao, J. and A. Edmans (2011). “Do investment banks matter for M&A returns?” Review of Financial Studies, 24 (7): 2286-2315 Baron, D. P. (1982). "A model of the demand for investment banking advising and distribution services for New Issues." The Journal of Finance 37(4): 955-976. Berger, P. G., and Eli Ofek, (1995). "Diversification’s effect on firm value." Journal of Financial Economics 37: 39-66. Bodnaruk, A., M. Massa, et al. (2009). "Investment banks as insiders and the market for corporate control." Review of Financial Studies 22(12): 4989-5026. Bowers, H. M. and R. E. Miller (1990). "Choice of investment banker and shareholders' wealth of firms involved in acquisitions." FM: The Journal of the Financial Management Association 19(4): 34. Bradley, M., A. Desai, et al. (1988). "Synergistic gains from corporate acquisitions and their division between the stockholders of target and acquiring firms." Journal of Financial Economics 21(1): 3-40. Cain, M. D. and D. J. Denis (2010). "Do fairness opinion valuations contain useful information?" SSRN eLibrary. Capron , Laurence andJung-Chin Shen, 2007, Acquisitions of private vs. public firms: Private information, target selection, and acquirer returns, Strategic Management Journal, Vol 28 (9), pp. 891 - 911 Chang, X., Shekhar, C., Tam, L., and Yao, J., 2013, Industry Expertise, Information Leakage, and the Choice of M&A Advisors, working paper Chemmanur, T. J. and P. Fulghieri (1994). "Investment bank reputation, information production, and financial intermediation." The Journal of Finance 49(1): 57-79. Chemmanur, T., Ertuhrul, M, Krishan, K., (2013), Is it the investment bank or the investment banker? A study of the role of investment banker human capital in acquisitions, working paper Davidoff, S. M. (2006). "Fairness opinions." American University Law Review, Vol. 55, p. 1557. Davidson Iii, W. N. and T. Shenghui (2007). "Target firms and in-house mergers and acquisitions." Journal of Applied Finance 17(1): 21-28.

44

Delong, G. and R. Deyoung (2007). "Learning by observing: information spillovers in the execution and valuation of commercial bank M&As." The Journal of Finance 62(1): 181-216. DeLong, G. L. (2001). "Stockholder gains from focusing versus diversifying bank mergers." Journal of Financial Economics 59(2): 221-252. Elijah Brewer, III, William E. Jackson, III, et al. (2000). "The price of bank mergers in the 1990s." Economic Perspectives(Q I): 2-23. Fang, Lily Hua, (2005). “Investment bank reputation and the price and quality of underwriting Services.” Journal of Finance60: 2729–2761. Forte, G., Iannotta, G., and Navone, M., (2010). “The banking relationship's role in the choice of the target's advisor in mergers and acquisitions.” European Financial Management 16 (4), 686-701. Frey, S., and Herbst, P., 2013, The Influence of Buy-Side Analysts on Mutual Fund Trading, Journal of Banking and Finance, Forthcoming Fuller, K., Netter, J., and Stegemoller, M., (2002),What Do Returns to Acquiring Firms Tell Us? Evidence from Firms that Make Many Acquisitions, the Journal of Finance 57 (4) Golubov, A., Petmezas, D. and N.G. Travlos (2012), “When it pays to pay your investment banker: new evidence on the role of financial advisors in M&As.” the Journal of Finance 67 (1) , pp. 271 – 311. Groysberg, B., Healy, H., and Chapman, Craig, 2008. Buy-Side vs. Sell-Side Analysts’ Earnings Forecasts. Financial Analysts Journal. 64(4): pp. 25-39 Haire, S.B., Hartley, R., and Lindquist, S.A., 1999. “Attorney Expertise, Litigant Success, and Judicial Decisionmaking in the US Courts of Appeals.” Law & Society Review 33, pp. 667–685. Hankira, Y., C. Rauchb, et al. (2009). "Do investors know better than regulators? Evidence from international bank M&A." Working paper. Heckman, James, (1979), Sample selection bias as a specification error, Journal of the econometric society, Vol 47 (1) pp. 153 - 161 Holliday, K. K. (2006). "What should you expect from a merger advisor?" ABA Banking Journal 98(12): 42-47. Hunter, W. C. and J. Jagtiani (2003). "An analysis of advisor choice, fees, and efforts in mergers and acquisitions." Review of Financial Economics 12(1): 65(81).27 Hunter, W. C. and M. B. Walker (1990). "An Empirical Examination of Investment Banking Merger Fee Contracts." Southern Economic Journal 56(4): 1117-1130. Ismail, A. (2010), “Are good financial advisors really good? The performance of investment banks in the M&A market.” Review of Quantitative Finance and Accounting 35, 411-429. Jarrell, G. A. and A. B. Poulsen (1989). "The returns to acquiring firms in tender offers: Evidence from three decades." FM: The Journal of the Financial Management Association 18(3): 12-19. Jensen, M. C. and W. H. Meckling (1976). "Theory of the firm: managerial behavior, agency costs and ownership structure." Journal of Financial Economics 3(4): 305-360.

45

Kale, J. R., O. Kini, et al. (2003). "Financial advisors and shareholder wealth gains in corporate takeovers." The Journal of Financial and Quantitative Analysis 38(3): 475-501. Klein, B., and Leffler, K., 1981, The role of market forces inassuaring contractual performance, Jornal of Political Economy, Vol 89 (4), pp. 615-641 Kisgen, D. J., J. Qj" Qian, et al. (2009). "Are fairness opinions fair? The case of mergers and acquisitions." Journal of Financial Economics 91(2): 179-207. Kosnik, R. D. and D. L. Shapiro (1997). "Agency conflicts between investment banks and corporate clients in merger and acquisition transactions: causes and remedies." The Academy of Management Executive (1993-2005) 11(1): 7-20. Levitt, S. D. and C. Syverson (2008). "Market distortions when agents are better informed: the value of information in real estate transactions." Review of Economics and Statistics 90(4): 599-611. Ljungqvist, A., F. Marston, and W.J. Wilhelm. 2006. “Competing for Securities Underwriting Mandates: Banking Relationships and Analyst Recommendations.” Journal of Finance, 61, pp. 301-340. Ma, (2005). “Mergers and investment banks: how do banks help targets?” Working paper McConnell, J., and Sibilkov, V., (2011). “Client performance, choice of investment bank advisors in corporate takeovers, and investment bank market share.” Working paper McLaughlin, R. M. (1990). "Investment-banking contracts in tender offers: An empirical analysis." Journal of Financial Economics 28(1-2): 209-232. McLaughlin, R. M. (1992). "Does the form of compensation matter? Investment banker fee contracts in tender offers." Journal of Financial Economics 32(2): 223-260. Michel, A., I. Shaked, et al. (1991). "An evaluation of investment banker acquisition advice: The shareholders' perspective." The Journal of the Financial Management Association 20(2): 40-49. Moeller, S. B., F. P. Schlingemann, et al. (2004). "Firm size and the gains from acquisitions." Journal of Financial Economics 73(2): 201-228. Moeller, S. B., F. P. Schlingemann, et al. (2005). "Wealth destruction on a massive scale? A study of acquiring-firm returns in the recent merger wave." The Journal of Finance 60(2): 757-782. Morck, R., Shleifer, A., Vishny, R.W. (1990). "Do managerial objectives drive bad acquisitions? " The Journal of Finance 45: 31- 48. Muscarella, C. J. and M. R. Vetsuypens (1989). "A simple test of Baron's model of IPO underpricing." Journal of Financial Economics 24(1): 125-135. Offenberg, David and Christo Pirinsky, 2013, How do Acquirers Choose between Mergers and Tender Offers? Working paper. Officer, Poulsen, and Stegemoller (2009), Target-firm information asymmetry and acquirer returns, Review of Finance, Vol. 13 (3): 467-493.

46

Rau, P. R. (2000). "Investment bank market share, contingent fee payments, and the performance of acquiring firms." Journal of Financial Economics 56(2): 293-324. Rhee, M., and Valdez, M., 2009,Contextual Factors Surrounding Reputation Damage With Potential Implications for Reputation Repair, Academy of Management Review, Vol 34 (1), pp. 146 - 168 Rhodes-Kropf, M., D. T. Robinson (2004). "Market valuation and merger waves. " The Journal of Finance 59: 2685- 2718 Rhodes-Kropf, M., D. T. Robinson, et al. (2005). "Valuation waves and merger activity: The empirical evidence." Journal of Financial Economics 77(3): 561-603. Robert, D., E. Douglas, et al. (2009). "Mergers and acquisitions of financial institutions: a review of the post-2000 literature." Journal of Financial Services Research 36(2): 87-110. Roll, R. (1986). "The hubris hypothesis of corporate takeovers." Journal of Business 59, 197-21628 Schwert, G. W. (1996). "Markup pricing in mergers and acquisitions." Journal of Financial Economics 41(2): 153-192. Schiereck, D., Sigl-Grub, C., and Unverhau, J., (2009). “Investment bank reputation and shareholder wealth effects in mergers and acquisitions.” Research in International Business and Finance 23 (3), 257-273. Schwert, G. W. (2000). "Hostility in takeovers: in the eyes of the beholder?" The Journal of Finance 55(6): 2599-2640. Servaes, H. and M. Zenner (1996). "The role of investment banks in acquisitions." Review of Financial Studies 9(3): 787-815. Shapiro, Carl, 1983, Premiums for High Quality Products as Returns to Reputations, The Quarterly Journal of Economics, vol. 98 (4), pp. 659-79 Shleifer, A. and Robert W. Vishny (1986). "Large shareholders and corporate Control. " The Journal of Political Economy, 94, (3): 461-488. Shleifer, A. and Robert W. Vishny (2003). "Stock market driven acquisitions. " Journal of Financial Economics 70: 295-311 Song, Weihong and Wei, Jie (2013), The value of “boutique” financial advisors in mergers and acquisitions, Journal of Corporate Finance (20): 94 – 114 Stock, Pascal (2012), The Advising Investment Bank's Industry Expertise and Access to the Bidder's Private Information: Their Positive Influence on the Performance in Acquisition Sequences, working paper. Stulz, R. M., R. A. Walkling, et al. (1990). "The distribution of target ownership and the division of gains in successful takeovers." The Journal of Finance 45(3): 817-833. Travlos, N. G. (1987). "corporate takeover bids, methods of payment, and bidding firms' stock returns." The Journal of Finance 42(4): 943-963. Wasserstein, B. (2000). "Big deal: mergers and acquisitions in the digital age." Warner Books, Inc.

47

Wang, W., and Whyte, A. M., (2010).“Managerial rights, use of investment banks, and the wealth effects for acquiring firms' shareholders.”The Journal of Banking and Finance 34 (1), 44-54. Wooldridge, J., 2012, Introductory econometrics: A modern approach, 5th Ed., Cengage LearningThe Journal of Banking and Finance 34 (1), 44-54.

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Table 1: Sample Construction

A: Sample construction of advisor quality measures

Steps Filtration details Number of Deals 1 All SDC acquisition deals with information of deal value 1990 to 2012 59,431 2 Delete observations without an acquirer advisor or a target advisor

Sample of advisor quality measures of acquirer Sample of advisor quality measures of target

16,097 21,618

B: Sample construction of the analysis of merger outcomes

Steps Filtration details Number of Deals 1 All SDC acquisition deals with information of deal value 1994 to 2012. 52,448 2 Delete 8,639 observations with toehold <= 50 and % of shares held >= 50%. 43,809 3 Delete 23,590 deals without acquirer Cusip/ Permno number to extract Compustat

information. 20,219

4 Delete 6,444 restructures, spin-offs, repurchases, self-tenders, reverse takeovers, privatizations, and divestitures.

13,775

5 Delete deals without either market reaction or post-merger performance information of acquirers.

9,199

6 Deals with acquirer advisors. (Stage 1) (Deals with target advisors.) (Delete deals without target advisor or acquirer advisor.)

2,735 (3,986) (4,605)

7 Final sample retaining deals with both acquirer and target advisors to calculate relative quality. (Stage 2)

2,120

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Table 2: Top 20 banks from 1990 to 2012

Target advisor # of deals $ of deals (in Billions)

Acquirer advisor # of deals $ of deals (in Billions)

1 Goldman Sachs 1,411 3536.8 Goldman Sachs 944 3001.0 2 Morgan Stanley 1019 2532.1 Morgan Stanley 880 2452.8 3 Credit Suisse First

Boston 772 1241.7 Merrill Lynch 845 2461.7

4 Merrill Lynch 738 1424.1 Credit Suisse First Boston

815 1652.0

5 JP Morgan Chase 634 974.8 JP Morgan Chase 638 2077.9 6 Lehman Brothers 602 798.3 Lehman Brothers 587 1419.5 7 HoulihanLokey 519 111.7 Citigroup 584 2143.6 8 Citigroup 471 652.7 Bank of America 467 1154.8 9 Donaldson Lufkin &

Jenrette 449 307.3 UBS 407 729.3

10 Lazard 404 682.8 Donaldson Lufkin & Jenrette

375 561.4

11 UBS 392 704.0 Lazard 343 883.1 12 Bank of America 376 200.8 Deutsche Bank 342 771.8 13 Bear Stearns 315 608.8 Bear Stearns 335 860.8 14 Keefe Bruyette&

Woods Inc 295 82.9 HoulihanLokey 266 143.7

15 Deutsche Bank 269 249.8 Keefe Bruyette& Woods Inc

219 96.7

16 Sandler O'Neill Partners

249 105.7 Sandler O'Neill Partners

192 44.3

17 Broadview 237 48.5 Salomon Brothers 172 291.9 18 Jefferies & Co Inc 235 97.4 Smith Barney 161 158.9 19 Bankers Trust 209 180.7 Chase Manhattan

Bank 148 500.6

20 William Blair & Co 183 54.7 Barclays 148 403.0

50

Table 3: Summary statistics of advisor quality

A: Single-side advisor quality

Mean Median Std Min Max N Industry overall 0.0346 0.0278 0.0313 0 0.1757 2,735 A-industry buy-side 0.0392 0.0300 0.0365 0 0.2015 2,735 A-industry sell-side 0.0521 0.0436 0.0440 0 0.4007 2,735 T- industry buy-side 0.0349 0.0295 0.0271 0 0.1756 2,735 T-industry sell-side 0.0346 0.0242 0.0342 0 0.2129 2,735 Size-class overall 0.0388 0.0234 0.0419 0 0.1951 2,735 Size-class buy-side 0.0441 0.0267 0.0462 0 0.2024 2,735 Size-class sell-side 0.0351 0.0205 0.0427 0 0.2195 2,735

B: Relative advisor quality

Mean Median Std Min Max N Relative industry expertise Unconditional 0.0011 0.0000 0.0409 -0.2191 0.1364 2,443 Conditional on serving side 0.0068 0.0040 0.0466 -0.2128 0.1711 2,443 Relative size-class expertise Unconditional 0.0018 0.0001 0.0594 -0.1806 0.1851 2,014 Conditional on serving side 0.0088 0.0039 0.0610 -0.2074 0.1955 2,014

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Table 4: Summary statistics of deal characteristics and acquirer merger outcomes (Full sample)

A: Summary statistics of acquirer merger outcomes

Deal outcomes Mean Median Std Min Max N Acquirer CARs (-1, 1) -0.019 -0.004 0.091 -0.454 0.491 2,732 Acquirer CARs (-10, 1) -0.001 -0.009 0.123 -0.645 0.680 2,732 Acquirer BHAR (1 year) -0.002 -0.072 0.879015 -1.873 23.549 2,735 Acquirer BHAR (3 years) -0.042* -0.217 1.257276 -3.174 18.969 2,735 Acquirer Δ ROA (3 years) -0.058*** -0.006 0.376 -11.533 2.173 2,723 Acquirer Δ Q (3 years) -0.745*** -0.102 3.566 -56.91 74.39 2,636 Acquirer Δ Leverage (3 years) 0.047*** 0.018 0.180 -1.196 2.417 2,703 Acquirer Δ R&D (3 years) 0.001 0.000 0.107 -1.213 1.547 2,723

B: Summary statistics of deal characteristics

Deal characteristics Mean Median Std Min Max N Transaction Value ($Mil) 1,852.90 328.42 7,589.74 3.00 219,656.73 2,423 Transaction Value (log) 5.90 5.79 1.68 1.10 12.29 2,423 Acquirer Market Size ($Bil) 17.18 2.50 45.36 0.003 692.95 2,423 Relative Size Ratio 0.67 0.19 7.74 0 369.01 2,423 Public Target (Dummy) 0.69 1 0.46 0 1 2,423 Cash deal (Dummy) 0.39 1 0.52 0 1 2,423 % of Stock Payment 52.55 57.69 44.39 0 100 2,336 Tender Offer (Dummy) 0.12 0 0.33 0 1 2,423 Target SIC codes 2.61 2 1.96 1 25 2,423 Bidder Number 1.05 1 0.29 1 4 2,423 Horizontal Deals (Dummy) 0.36 0 0.48 0 1 2,423 Cross-State Deals (Dummy) 0.28 0 0.45 0 1 2,423 Hostile (Dummy) 0.01 0 0.11 0 1 2,423 Tender Merger (Dummy) 0.86 1 0.34 0 1 2,423 Toehold (%) 0.89 0 5.06 -0.05 49.77 2,215 Litigation (Dummy) 0.01 0 0.13 0 1 2,423 Regulatory (Dummy) 0.71 1 0.45 0 1 2,423 Anti-takeover (Dummy) 0.11 0 0.31 0 1 2,423 First Acquisition (Dummy) 0.22 0 0.42 0 1 2,423 5+ Acquisitions 0.31 0 0.46 0 1 2,423 Pre-announcement run-up 0.19 0.02 2.04 -1.04 48.19 2,422 Resolution Speed (days) 129 108 105 0 906 2,423 Multiple Acquirer Advisor 0.12 0 0.33 0 1 2,423 Top-tier advisor 0.30 0 0.45 0 1 2,735 Pre-merger Institutional ownership 0.43 0.48 0.33 0 1.76 2,423 Pre-merger ROA 0.03 0.03 0.16 -3.82 0.93 2,419 Pre-merger Leverage 0.20 0.17 0.18 0 1.19 2,413 Pre-merger Tobin’s Q 2.48 1.51 3.38 0.42 58.04 2,368 Pre-merger R&Ds 0.08 0.05 0.11 0.00 1.86 2.419

52

Table 5: Summary statistics of deal characteristics and acquirer merger outcomes (Public Targets)

A: Summary statistics of acquirer deal outcomes

Deal outcomes Mean Median Std Min Max N Acquirer CARs (-1, 1) -0.018*** -0.010 0.078 -0.453 0.491 1,532 Acquirer CARs (-10, 1) -0.018*** -0.014 0.106 -0.645 0.590 1,532 Acquirer BHAR (1 year) -0.021 -0.051 0.480 -1.873 5.161 1,532 Acquirer BHAR (3 year) -0.046* -0.170 1.052 -3.174 15.343 1,532 Acquirer Δ ROA (3 years) -0.048*** -0.005 0.363 -11.533 2.072 1,527 Acquirer Δ Q (3 years) -0.601*** -0.082 3.056 -56.911 6.106 1,493 Acquirer Δ Leverage (3 years) 0.047*** 0.028 0.164 -0.756 2.417 1,516 Acquirer Δ R&Ds (3 years) -0.006** 0.000 0.073 -0.928 0.874 1,532

B: Summary statistics of deal characteristics

Deal characteristics Mean Median Std Min Max N Transaction Value ($Mil) 2,395.01 479.75 8,804.48 7.61 219,656.7 1,483 Transaction Value (log) 6.22 6.17 1.70 2.03 12.29 1,483 Acquirer Market Size (Bil) 14.08 22.37 34.64 0.02 459.23 1,483 Relative Size Ratio 0.86 0.25 9.86 0.001 369.01 1,483 Cash deal (Dummy) 0.34 1 0.53 0 1 1,483 % of Stock Payment 57.60 70.08 43.64 0 100 1,422 Tender Offer (Dummy) 0.18 0 0.39 0 1 1,483 Target SIC codes 2.90 2 2.21 1 25 1,483 Bidder Number 1.05 1 0.28 1 4 1,483 Horizontal Deals (Dummy) 0.36 0 0.48 0 1 1,483 Cross-State Deals (Dummy) 0.30 0 0.46 0 1 1,483 Hostile (Dummy) 0.01 0 0.12 0 1 1,483 Tender Merger (Dummy) 0.98 1 0.10 0 1 1,483 Litigation (Dummy) 0.02 0 0.15 0 1 1,483 Toehold (%) 0.55 0 4.02 0 49.77 1,343 Regulatory (Dummy) 0.78 1 0.41 0 1 1,483 Anti-takeover (Dummy) 0.17 0 0.37 0 1 1,483 First Acquisition (Dummy) 0.23 0 0.42 0 1 1,483 5+ Acquisitions 0.32 0 0.46 0 1 1,483 Pre-announcement run-up 0.13 0.02 1.38 -1.03 48.19 1,482 Resolution Speed (days) 135.84 120 89.22 0 906 1,483 Multiple Acquirer Advisor 0.14 0 0.35 0 1 1,483 Top-tier advisor 0.36 0 0.48 0 1 1,483 Pre-merger Institutional ownership 0.44 0.49 0.32 0 1.26 1,483 Pre-merger ROA 0.03 0.03 0.15 -3.82 0.60 1,480 Pre-merger Leverage 0.13 0.16 0.49 -11.13 0.85 1,478 Pre-merger Tobin’s Q 0.20 0.17 0.16 0 1.13 1,476 Pre-merger Operation Cash Flow 2.28 1.43 3.23 0.42 58.04 1,459 Pre-merger R&D 0.07 0.04 0.09 0.00 1.17 1,480

53

Table 6: The effect of relative expertise on short-term announcement returns CAR (-1, 1)

Independent Var. Industry Expertise Size expertise Coff. StdE Coff. StdE Coff. StdE Coff. StdE Intercept 4.765*** 1.81 4.752*** 1.81 4.388*** 1.488 4.358*** 1.487 Unconditional relative quality 1.212 3.95 1.656 2.894 Conditional relative quality 1.864 3.43 2.708 2.574 Deal value (log) -0.394*** 0.12 -0.396*** 0.12 -0.379*** 0.12 -0.381*** 0.13 Relative size -0.004 0.00 -0.005 0.00 -0.004 0.00 -0.004 0.00 Run-up -0.463** 0.20 -0.463** 0.20 -0.449** 0.18 -0.451** 0.18 Public target -2.773*** 0.47 -2.771*** 0.47 -2.847*** 0.47 -2.849*** 0.47 Stock deal -1.757*** 0.40 -1.755*** 0.40 -1.746*** 0.36 -1.748*** 0.36 Horizontal 0.667* 0.36 0.667* 0.36 0.672** 0.25 0.676** 0.29 Cross-state 0.568 0.43 0.569* 0.43 0.597 0.50 0.598 0.50 Tender offer 1.059* 0.58 1.057 0.58 1.145** 0.57 1.138** 0.57 First merger -0.742* 0.44 -0.742* 0.44 -0.745* 0.39 -0.745* 0.39 Anti-takeover -0.712 0.54 -0.712 0.54 -0.866* 0.48 -0.872* 0.49 Litigation -3.286* 1.88 -3.285* 1.88 -3.314*** 1.17 -3.307*** 1.17 Regulatory 0.003 0.44 0.006 0.44 0.089 0.34 0.094 0.34 Institutional Ownership -0.560 0.62 -0.562 0.62 -0.657 0.59 -0.660 0.59 R2 (%) 11.23 11.23 11.24 11.26 F-ratio 4.70*** 4.71*** 13.71*** 13.63*** N 2097 2097 2012 2012

54

Table 7: The effect of relative expertise on short-term announcement returns CAR (-1, 1), public deals

Independent Var. Industry Expertise Size expertise Coff. StdE Coff. StdE Coff. StdE Coff. StdE Intercept 3.535* 2.17 3.541* 2.17 3.552 2.34 3.475 2.32 Unconditional relative quality 7.016* 4.21 5.219 3.25 Conditional relative quality 6.386** 3.19 6.783** 2.88 Deal value (log) -0.641*** 0.13 -0.653*** 0.13 -0.626*** 0.12 -0.631*** 0.13 Relative size -0.006 0.01 -0.006 0.01 -0.004 0.00 -0.005 0.00 Run-up -0.392* 0.20 -0.393* 0.20 -0.402** 0.18 -0.405** 0.17 Stock deal -2.846*** 0.45 -2.836*** 0.45 -2.933*** 0.44 -2.931*** 0.45 Horizontal 0.452 0.42 0.445 0.42 0.417 0.34 0.420 0.34 Cross-state 0.366 0.48 0.370 0.48 0.418 0.60 0.428 0.59 Tender offer 0.415 0.59 0.416 0.59 0.452 0.57 0.438 0.57 First merger -0.672 0.53 -0.674 0.53 -0.674 0.52 -0.678 0.52 Anti-takeover -0.502 0.58 -0.500 0.58 -0.634 0.56 -0.654 0.56 Litigation -2.592 1.89 -2.595 1.90 -2.592** 1.02 -2.581** 1.02 Regulatory -0.182 0.52 -0.173 0.52 -0.111 0.43 -0.089 0.43 Institutional Ownership 0.608 0.74 0.608 0.74 0.471 0.66 0.452 0.64 R2 (%) 12.94 12.95 12.93 13.06 F-ratio 3.68*** 3.71*** 16.83*** 16.85*** N 1478 1478 1420 1420

55

Table 8: The choice of hiring superior acquirer advisors

Panel A: Full sample

Independent Var. Estimate STD Estimate STD Estimate STD Estimate STD Superior rate Acquirer industry buy-side

0.497 (0.174) 0.41

Acquirer industry sell-side

0.342 (0.120) 0.39

Size-class buy-side 0.802 (0.268) 0.53

Size-class sell-side 0.827 (0.277) 0.63

Deal value (log) 0.192*** 0.02 0.192*** 0.02 0.165*** 0.03 0.165*** 0.03 Public target 0.015 0.06 0.014 0.06 -0.061 0.07 -0.064 0.07 First merger -0.062 0.06 -0.061 0.06 0.016 0.06 0.012 0.06 Stock deal -0.067 0.06 -0.068 0.06 0.043 0.06 0.039 0.06 Number of bidders -0.070 0.12 -0.071 0.12 -0.120 0.12 -0.121 0.12 Horizontal 0.009 0.05 0.010 0.05 -0.020 0.05 -0.020 0.05 Target SICs -0.012 0.01 -0.012 0.01 0.010 0.01 0.010 0.01 Anti-takeover 0.010 0.09 0.012 0.09 0.218*** 0.09 0.206*** 0.09 Regulatory 0.021 0.06 0.020 0.06 -0.005 0.06 -0.002 0.06 Institutional Ownership

0.283*** 0.08 0.286*** 0.08 0.332*** 0.08 0.339*** 0.08

Tender offer -0.014 0.09 -0.014 0.09 0.228** 0.09 0.226** 0.09 R2 (%) 10.66 10.63 12.11 12.08 F-ratio 210.85*** 210.35*** 230.73*** 231.08*** N 2,735 2,735 2,735 2,735

56

Panel B: Public sample

Independent Var. Estimate STD Estimate STD Estimate STD Estimate STD Superior rate Acquirer industry buy-side

1.255** (0.460) 0.56

Acquirer industry sell-side

0.871* (0.319) 0.52

Size-class buy-side 1.298* (0.456) 0.78

Size-class sell-side 1.432* (0.503) 0.89

Deal value (log) 0.173*** 0.02 0.173*** 0.02 0.154*** 0.03 0.148*** 0.03 First merger -0.026 0.08 -0.022 0.08 0.103 0.08 0.097 0.08 Stock deal -0.066 0.09 -0.066 0.09 0.012 0.09 0.000 0.09 Number of bidders -0.036 0.12 -0.035 0.12 -0.133 0.12 -0.130 0.12 Horizontal 0.025 0.07 0.026 0.07 -0.061 0.07 -0.059 0.07 Target SICs -0.015 0.02 -0.014 0.02 0.01 0.02 0.011 0.02 Anti-takeover 0.039 0.09 0.045 0.09 0.269*** 0.09 0.251*** 0.09 Regulatory 0.052 0.08 0.050 0.08 -0.026 0.08 -0.023 0.08 Institutional Ownership 0.392*** 0.11 0.398*** 0.11 0.481*** 0.11 0.491*** 0.11 Tender offer -0.033 0.10 -0.033 0.10 0.189** 0.10 0.181* 0.10 R2 (%) 12.58 12.56 9.35 9.17 F-ratio 137.26*** 137.93*** 106.03*** 104.16*** N 1,533 1,533 1,533 1,533

57

Table 9: Two-stage regressions of public deals

Independent Var. Industry Expertise Size expertise Coff. StdE Coff. StdE Coff. StdE Coff. StdE Intercept 3.564 2.15 3.511 2.15 5.185 3.36 5.055 3.37 Unconditional relative quality 6.828* 4.12 1.673 3.98 Conditional relative quality 6.598** 3.25 3.60 3.65 Deal value (log) -0.643*** 0.13 -0.649*** 0.13 -0.778** 0.34 -0.769** 0.34 Relative size -0.005 0.00 -0.005 0.01 -0.005 0.01 -0.006 0.01 Run-up -0.392* 0.20 -0.393* 0.20 -0.411** 0.20 -0.415** 0.20 Stock deal -2.848*** 0.45 -2.837*** 0.45 -2.992*** 0.46 -2.996*** 0.46 Horizontal 0.455 0.42 0.447 0.42 0.430 0.42 0.430 0.42 Cross-state 0.369 0.48 0.375 0.48 0.451 0.49 0.453 0.49 Tender offer 0.415 0.59 0.416 0.59 0.320 0.61 0.322 0.61 First merger -0.676 0.53 -0.682 0.53 -0.859 0.55 -0.858 0.55 Anti-takeover -0.497 0.58 -0.492 0.58 -0.657 0.59 -0.662 0.59 Litigation -2.594 1.89 -2.597 1.90 -2.688 1.90 -2.683 1.89 Regulatory -0.185 0.53 -0.176 0.52 -0.074 0.53 -0.059 0.53 Institutional Ownership 0.603 0.75 0.615 0.75 0.328 0.75 0.352 0.75 IMR 0.012 0.25 -0.023 0.25 0.504** 0.23 0.431* 0.23 R2 (%) 12.94 12.95 13.51 13.51 F-ratio 3.61*** 3.64*** 3.32*** 3.32*** N 1479 1479 1445 1445

58

Table 10: Relative advisor expertise and public deal characteristics

Relative industry expertise Relative size-class expertise Unconditional Conditional on

serving side Unconditional Conditional on serving

side Deal Type Coff. StdE Coff. StdE Coff. StdErr Coff. StdE Relative size >= 0.5 (540)

17.872** 8.387 14.495** 7.62 12.159** 6.27 13.179*** 5.39

Relative size < 0.5 (957)

1.786 4.36 -0.005 3.81 0.980 3.26 0.735 2.90

Stock (977) 9.130** 4.53 8.394** 4.19 9.569** 4.28 11.622*** 4.47 Cash (501)

0.689 6.59 2.989 5.18 -4.246 5.26 -2.401 4.82

First merger (341) -1.766 9.37 3.972 7.79 -2.682 9.21 1.293 7.62 Repeated merger (1137)

10.792** 4.84 8.272** 4.25 8.119*** 3.13 9.033*** 2.94

Vertical (936) 13.265*** 5.32 12.972*** 4.54 7.035* 4.38 9.335*** 3.74 Horizontal (541) -1.329 7.68 -3.043 6.79 3.052 5.32 3.301 4.41

59

Table 11: Buy-side versus sell-side relative expertise, public deals

Independent Var. Relative Industry Expertise Relative Size expertise Coff. StdE Coff. StdE Coff. StdE Coff. StdE Intercept 3.495 2.22 3.352 2.17 3.595 2.33 3.559 2.34 Buy-side relative quality 6.682** 3.38 8.017** 3.17 Sell-side relative quality 3.346* 1.85 2.706 3.13 Deal value (log) -0.616*** 0.13 -0.642** 0.13 -0.614*** 0.13 -0.635*** 0.13 Relative size -0.004 0.00 -0.004 0.01 -0.005 0.00 -0.003 0.00 Run-up -0.351* 0.21 -0.394* 0.20 -0.408** 0.17 -0.395** 0.18 Stock deal -2.837*** 0.42 -2.846 0.45 -2.936*** 0.45 -2.927*** 0.44 Horizontal 0.450 0.43 0.443 0.42 0.435 0.34 0.407 0.34 Cross-state 0.346 0.49 0.361 0.48 0.430 0.59 0.405 0.60 Tender offer 0.412 0.61 0.412 0.59 0.448 0.56 0.453 0.57 First merger -0.670 0.52 -0.670 0.53 -0.685 0.53 -0.672 0.52 Anti-takeover -0.483 0.53 -0.487 0.58 -0.645 0.57 -0.618 0.55 Litigation -2.631 1.93 -2.592 1.90 -2.573** 1.00 -2.604** 1.02 Regulatory -0.176 0.54 -0.180 0.52 -0.122 0.43 -0.118 0.43 Institutional Ownership 0.622 0.73 0.619 0.74 0.417 0.66 0.486 0.66 R2 (%) 12.86 16.17 13.12 12.83 F-ratio 4.18*** 3.11*** 17.21*** 4.14*** N 1478 1478 1420 1420

60

Table 12: Same-bank relative expertise, public deals

Independent Var. Relative Industry Expertise

Relative size-class Expertise

Coff. StdE Coff. StdE Intercept 3.562 2.15 3.480 2.22 Same-bank relative quality 8.115 7.95 13.316** 5.67 Deal value (log) -0.672*** 0.13 -0.680*** 0.13 Relative size -0.004 0.00 -0.003 0.01 Run-up -0.390** 0.20 -0.388* 0.18 Stock deal -2.819*** 0.45 -2.810*** 0.43 Horizontal 0.473 0.42 0.477 0.33 Cross-state 0.362 0.48 0.350 0.58 Tender offer 0.397 0.59 0.363 0.57 First merger -0.645 0.53 -0.658 0.49 Anti-takeover -0.469 0.58 -0.462 0.52 Litigation -2.613 1.90 -2.599 1.03 Regulatory -0.178 0.53 -0.187 0.41 Institutional Ownership 0.633 0.74 0.572 0.62 R2 (%) 12.77 13.02 F-ratio 3.78*** 20.24*** N 1482 1482

61

Table 13: Relative expertise and two-tier advisor ranking, public deals

Independent Var. Industry Expertise Size expertise Coff. StdE Coff. StdE Coff. StdE Coff. StdE Coff. StdE Coff. StdE Intercept 3.576* 2.16 3.568 2.16 3.637* 2.14 4.565 3.34 4.523 3.34 4.613 3.28 Unconditional relative quality

6.126** 3.08 0.520 4.15

Conditional relative quality

5.887* 3.51 2.591 3.81

Top-tier advisor 0.141 0.47 0.077 0.49 0.451 0.39 1.185** 0.49 1.031** 0.50 1.239*** 0.41 Deal value (log) -0.651*** 0.13 -0.658*** 0.13 -0.682*** 0.13 -0.776 0.34 -0.766 0.34 -0.802 0.33 Relative size -0.005 0.00 -0.005 0.00 -0.003 0.00 -0.005 0.00 -0.006 0.00 -0.005 0.00 Run-up -0.392** 0.20 -0.393** 0.20 -0.396** 0.20 -0.412 0.20 -0.416 0.20 -0.415 0.20 Stock deal -2.847*** 0.45 -2.837*** 0.45 -2.833*** 0.45 -2.990 0.46 -2.995 0.46 -2.907 0.45 Horizontal 0.454 0.42 0.448 0.42 0.470 0.42 0.442 0.43 0.441 0.43 0.509 0.41 Cross-state 0.369 0.48 0.373 0.48 0.357 0.48 0.462 0.49 0.463 0.49 0.436 0.47 Tender offer 0.418 0.59 0.418 0.59 0.400 0.59 0.329 0.61 0.331 0.61 0.261 0.58 First merger -0.674 0.53 -0.678 0.53 -0.636 0.53 -0.867 0.55 -0.865 0.55 -0.830 0.53 Anti-takeover -0.500 0.58 -0.496 0.58 -0.472 0.58 -0.660 0.59 -0.665 0.59 -0.528 0.56 Litigation -2.593 1.90 -2.596 1.90 -2.598 1.90 -2.678 1.90 -2.675 1.90 -2.669 1.88 Regulatory -0.186 0.52 -0.178 0.52 -0.191 0.52 -0.085 0.53 -0.071 0.53 -0.143 0.51 Institutional Ownership 0.592 0.74 0.599 0.75 0.591 0.74 0.342 0.75 0.363 0.75 0.487 0.74 R2 (%) 12.94 12.95 12.80 13.55 13.58 13.49 F-ratio 3.61*** 3.65*** 3.68*** 3.34*** 3.34*** 3.50*** N 1479 1479 1483 1445 1445 1509

62

Table 14: The effect of relative expertise on short-term announcement returns CAR (-1, 1), deal characteristics

Panel A: Industry expertise

Unconditional Conditional on serving side

Conditional on buy-side

Conditional on sell-side

Conditional on A bank

Deal Type Coff. StdE Coff. StdE Coff. StdErr Coff. StdE Coff. StdE Large deals (525)

7.332 9.31 6.714 8.19 2.185 6.70 4.604 10.14 23.692 20.63

Small deals (524) -1.199 5.61 -2.482 5.05 -1.991 3.98 -1.362 5.57 -7.850 10.76 Relative size < 0.5 (1537) -1.811 4.19 -0.872 3.66 -2.389 3.12 -2.018 4.07 2.703 8.50 Relative size >= 0.5 (560) 10.898 9.09 10.298 8.17 5.149 6.76 9.714 10.09 22.796 20.51 Stock (1290) 1.317 5.30 1.187 4.63 0.205 4.05 1.999 5.50 -2.857 13.02 Cash (807)

0.180 5.67 2.566 4.86 0.756 4.04 1.328 6.03 7.277 9.57

Tender offers (278)

14.044 11.65 10.110 8.51 7.107 7.62 16.247 12.37 -3.711 17.41

Mergers (1813) 2.396 4.27 3.528 3.64 1.102 3.19 2.617 4.27 9.333 8.25 Vertical (1331) 6.435 5.18 7.497* 4.43 5.369 3.99 7.385 5.23 9.415 9.30 Horizontal (766) -6.145 6.55 -5.984 5.86 7.557 4.95 -8.715 6.77 4.268 16.16

63

Panel B: Size-class expertise





Deal Type Coff. StdE Coff. StdE Coff. StdErr Coff. StdE Coff. StdE Large deals (507) 7.579 7.13 8.173 6.61 6.692 6.97 6.455 6.48 1.909 12.87 Small deals (505) 0.666 4.33 -0.852 4.32 1.402 4.30 0.107 3.96 -3.931 7.45 Relative size < 0.5 (1446) -3.065 3.66 -1.884 3.40 1.256 3.65 -5.144 3.32 12.500 8.57 Relative size >= 0.5 (560) 9.458* 5.53 10.228** 4.99 9.019* 5.22 7.740 5.31 4.454 13.03 Stock deal (1235) 3.139 5.25 4.622 4.79 5.218 4.96 1.480 4.78 10.291 8.72 Cash deal (777) -0.271 4.13 -0.669 3.95 1.990 4.20 -1.758 3.70 5.314 7.32 Tender offers (268) 5.426 7.42 6.958 7.27 8.640 7.89 2.699 6.30 13.263 9.75 Mergers (1738) 2.396 4.27 3.698 3.36 4.846 3.50 0.125 3.52 12.426** 6.58 Vertical deal (1276) 3.163 4.62 4.473 4.07 5.799 4.25 11.102 4.37 10.803* 7.73 Horizontal deal (736) 0.387 5.68 0.937 5.11 2.538 5.63 -1.081 4.87 8.071 10.00

64

Table 15: The effect of relative expertise on changes of acquirer performance

A: Industry Expertise

1. Unconditional 2. Conditional on serving side

3. Conditional on buy-side

4. Conditional on sell-side

5. Conditional on A bank

Dependent variables Coff. StdE Coff. StdE Coff. StdErr Coff. StdE Coff. StdE ROA change 0.219 0.17 0.211* 0.10 0.126 0.10 0.185 0.15 0.423** 0.22 Public 0.313 0.21 0.305* 0.16 0.208** 0.11 0.354** 0.18 0.223 0.20 Private -0.053 0.38 -0.018 0.34 -0.140 0.25 -0.345 0.39 1.479** 0.61 R&D change -0.051 0.08 -0.072 0.05 -0.058 0.04 -0.074 0.06 -0.076 0.08 Public -0.143 0.12 -0.151** 0.08 -0.107** 0.05 -0.180** 0.08 -0.039 0.07 Private 0.177 0.12 0.178 0.13 0.122 0.11 0.229 0.14 0.011 0.28 Leverage Change -0.126 0.10 -0.116 0.08 -0.114 0.07 -0.182* 0.10 0.126 0.19 Public -0.063 0.11 -0.051 0.10 -0.061 0.09 -0.147 0.11 0.350* 0.21 Private -0.363** 0.18 -0.380 0.15 -0.318*** 0.12 -0.372** 0.18 -0.592* 0.36

65

Panel B: Size-class expertise





Dependent variables Coff. StdE Coff. StdE Coff. StdErr Coff. StdE Coff. StdE ROA change 0.047 0.15 0.042 0.12 -0.05 0.11 0.16 0.16 -0.21 0.29 Public 0.112 0.15 0.096 0.11 0.003 0.08 0.151 0.17 -0.264 0.33 Private -0.381 0.63 -0.27 0.52 -0.321 0.47 -0.345 0.64 -0.02 0.39 R&D change -0.002 0.03 -0.009 0.03 -0.013 0.03 0.003 0.03 -0.046 0.07 Public -0.024 0.03 -0.031 0.04 -0.041 0.04 -0.013 0.03 -0.064 0.09 Private 0.202 0.14 0.199 0.12 0.152 0.11 0.204 0.13 0.08 0.17 Leverage Change -0.031 0.07 -0.065 0.06 -0.084 0.07 0.007 0.06 -0.255** 0.12 Public -0.04 0.07 -0.064 0.07 -0.092 0.07 -0.003 0.07 -0.193** 0.09 Private 0.042 0.2 0.005 0.18 -0.05 0.17 0.115 0.19 -0.33 0.27

66

Table 16: The choice of acquirer advisor – Expanded Analysis

Panel A: The choice of acquirer advisor versus industry expertise

Independent Var. Model 1 Model 2 Model 3 Model 4 Model 5 Estimate STD Estimate STD Estimate STD Estimate STD Estimate STD Acquirer Industry buy-side Expertise

6.611*** (0.404) 0.37

3.017*** (0.207) 0.61

5.298*** (0.323) 0.59

Acquirer Industry sell-side Expertise

2.586*** (0.185) 0.31

1.052** (0.085) 0.48

2.143*** (0.153) 0.51

Target Industry buy-side Expertise

7.641*** (0.476) 0.53

4.21*** (0.341) 0.82

6.798*** (0.423) 0.86

Target Industry sell-side Expertise

4.234*** (0.299) 0.47

2.53*** (0.205) 0.73

3.237*** (0.228) 0.78

A-industry Buy-side*public 2.362*** (0.169) 0.76

A-industry Sell-side*public 0.715 (0.051) 0.64

T-industry Buy-side*public 1.496 (0.106) 1.10

T-industry Sell-side*public 1.655* (0.117) 0.98

Deal value (log) 0.032*** 0.01 0.027*** 0.01 0.037*** 0.01 0.032*** 0.01 0.027*** 0.01 Public target -0.020 0.02 -0.020 0.02 -0.017 0.02 -0.140*** 0.03 -0.114*** 0.03 First merger 0.002 0.02 -0.019 0.02 -0.004 0.02 0.001 0.02 -0.02 0.02 Stock deal -0.009 0.02 -0.027 0.02 -0.024 0.02 -0.010 0.02 -0.029 0.02 Number of bidders -0.022 0.05 -0.018 0.05 -0.032 0.05 -0.024 0.05 -0.02 0.05 Horizontal -0.006 0.02 -0.004 0.02 -0.025 0.02 -0.006 0.02 -0.005 0.02 Target SICs 0.004 0.01 0.007 0.01 0.006 0.01 0.004 0.01 0.007 0.01 Anti-takeover -0.045 0.03 -0.021 0.03 -0.039 0.03 -0.034 0.03 -0.011 0.03 Regulatory -0.016 0.02 0.005 0.02 -0.007 0.02 -0.016 0.02 0.005 0.02 Institutional Ownership 0.107*** 0.03 0.074*** 0.03 0.100*** 0.03 0.106*** 0.03 0.073*** 0.03 R2 (%) 6.66 6.54 6.62 6.83 6.66 Wald test 1189.95*** 1200.85*** 1044.55*** 1225.16*** 1,226.48*** N 658,154 658,154 658,154 658,154 658,154

67

Panel B: The choice of acquirer advisor versus size-class expertise

Independent Var. 1 2 Estimate STD Estimate STD Size-class buy-side Expertise

9.055*** (0.224) 0.35

11.162*** (0.276) 0.64

Size-class sell-side Expertise

3.504*** (0.099) 0.4

3.344*** (0.094) 0.79

Size-class Buy-side*public

-2.996*** (-0.084) 0.75

Size-class Sell-side*public

0.402 (0.011) 0.91

Deal value (log) -0.024*** 0.01 -0.026*** 0.01 Public target -0.003 0.02 0.062*** 0.02 First merger 0.003 0.02 0.006 0.02 Stock deal 0.009 0.02 0.008 0.02 Number of bidders -0.003 0.04 0.004 0.04 Horizontal 0.003 0.02 0.003 0.02 Target SICs -0.002 0.00 0.000 0.00 Anti-takeover 0.016 0.03 0.015 0.03 Regulatory -0.005 0.02 -0.008 0.02 Institutional Ownership -0.010 0.02 -0.009 0.02 R2 (%) 11.14 11.29 Wald test 3983.32*** 4010.55*** N 658,154 658,154

68

Figure 1: Dynamic variation of top-tier banks

69

Figure 2: Variation of top-tier banks by deal size-class

70

Appendix A: Variable Definitions

This table defines variables used in this study. Acquisition deal characteristics and advisor quality are calculated based on information from SDC database. Corporate financial variables are from Compustat annual database. Stock trading and share information are from CRSP database.

Dependent variable Definition Top-Tier deals (1/0) = 1 if acquirer (or target) hires an top-five investment bank. Acquirer (or target) CARs [-10,1] = The cumulative abnormal returns from -10 to +1 days around the merger

announcement date, computed based on two-factor market model using CRSP value-weighted index as market returns.

Advisor quality variable Top-Tier (dummy) = 1 if the investment bank is ranked top-five based on number of deals advised

during the previous three years. Acquirer top-five rate = The average percentage rate of hiring a top-five acquirer advisor of similar

deals (same year, and same industry) during the previous three years. Target top-five rate = The average percentage rate of hiring a top-five target advisor of similar deals

(same year, and same industry) during the previous three years. Market share = The average of previous three years’ market share of bank i. each year t’s

market share is calculated as ( ). 0 if no deal advised

during previous three years. Size-class expertise = The average of previous three years’ market share of bank i in size group k.

Each year t’s market share in size group k is calculated as

( ).Deals are sorted into five size groups based on the

transaction value (less than 10 $mil; 10 $mil to 100 $mil, 100 $mil to 500 $mil, 500 $mil to 1 $bil, and above $1bil). Transaction values are inflation adjusted. = 0 if no deal advised in a certain size group k during previous three years.

Acquirer-industry expertise = The average of previous three years’ market share of bank i in acquirer industry m. Each year t’s market share industry m is calculated as

( ).Industry code is based on Fama-French 12 industry

classification. 0 if no deal advised in industry m during previous three years. Target-industry expertise = The average of previous three years’ market share of bank i in acquirer

industry n. Each year t’s market share industry m is calculated as

( ).Industry code is based on Fama-French 12 industry

classification. 0 if no deal advised in industry n during previous three years. Relative Top-Tier = Acquirer top-tier - Target top-tier. Relative market share = Acquirer market share - Target market share. Relative size-class expertise = Acquirer size-class expertise - Target size-class expertise. Relative acquirer-industry m expertise

= Acquirer industry expertise in industry m - Target industry expertise in industry m.

Relative target-industry n = Acquirer industry expertise in industry n - Target industry expertise in

t

ti,

advised deals of #advised deals of #

kt,

kt,i,


mt,

mt,i,


nt,

nt,i,


71

expertise industry n. Deal Characteristics Transaction value = Transaction value from SDC database, inflation adjusted. Relative Size = Transaction value divided by the market value of acquirer. Toehold = Percentage of the target owned by the acquirer prior to the acquisition

announcement. Majority = 1 if the acquirer seeks a majority ownership of more than 50% and owns less

than 50% before the deal. Merger of equal = 1 if the deal is a merger of equal. Tender offer = 1 if the deal is a tender offer. Hostile = 1 if the deal is hostile. Stock deals = 1 if the deal involves stock payment. Regulatory = 1 if acquisition requires regulatory approval. Litigation = 1 if the target has a pending litigation issue. Anti-takeover measure = 1 if the target has anti-takeover measures. Hi-tech acquirer = 1 if the acquirer is a high-tech company defined by SDC. Hi-tech target = 1 if the target is a high-tech company defined by SDC. Number of bidders = The number of competing bidders. Diversified merger = If the acquirer and target are from different industry, classified as Fama-

French 12 industry classification. Number of target SIC codes = Number of different SIC codes of the target . First merger = 1 if the deal is the first acquisition of the acquirer. Previous merger record = The number of deals the acquirer has completed previously. Completed (1/0) = 1 if the acquisition is completed or unconditional. Speed = Time interval between the announcement day and the withdrawal or

completion of the deal. Financial variables = Acquirer total assets = Acquirer total assets at the end of fiscal year t-1, inflation adjusted. Acquirer ROA = Acquirer net income divided by total assets at the end of fiscal year t-1. Acquirer leverage = (Long term debt + debt in current liability)/total assets of acquirer evaluated at

the end of fiscal year t-1. Acquirer free cash flow = The acquirer operating cash flow divided by total assets at the end of fiscal

year t-1. Acquirer Tobin’s Q = (Total assets + market value of equity – book value of equity)/total assets of

acquirer, evaluated at the end of fiscal year t-1. Target total assets = Target total assets at the end of fiscal year t-1, inflation adjusted. Target ROA = Target net income divided by total assets at the end of fiscal year t-1. Target leverage = (Long term debt + debt in current liability)/total assets of Target evaluated at

the end of fiscal year t-1. Target free cash flow = The Target operating cash flow divided by total assets at the end of fiscal year

t-1. Target Tobin’s Q = (Total assets + market value of equity – book value of equity)/total assets of

Target, evaluated at the end of fiscal year t-1. Other variables Acquirer (or target) Industry = Fama-French 12 industry classification. Year = Year of the deal announcement date.

72

Appendix B: Summary statistics of relative advisor measures in robustness analysis

A: Single-side advisor quality

Mean Median Std Min Max N A-industry buy-side 0.0392 0.0300 0.0365 0 0.2015 2,735 A-industry sell-side 0.0521 0.0436 0.0440 0 0.4007 2,735 T-industry buy-side 0.0349 0.0295 0.0271 0 0.1756 2,735 T-industry sell-side 0.0346 0.0242 0.0342 0 0.2129 2,735 Size-class buy-side 0.0441 0.0267 0.0462 0 0.2024 2,735 Size-class sell-side 0.0351 0.0205 0.0427 0 0.2195 2,735

B: Conditional relative advisor quality (buy-side, sell-side, and same-bank)

Mean Median Std Min Max N Relative industry expertise Conditional on buy-side -0.0062 -0.0002 0.0583 -0.3269 0.1600 2,443 Conditional on sell-side -0.0038 -0.0004 0.0409 -0.2046 0.1722 2,443 Conditional on acquirer advisor 0.0106 0.0059 0.0196 -0.1182 0.1337 2,443 Relative size-class expertise Conditional on buy-side 0.0036 0.0002 0.0579 -0.1794 0.1991 2,014 Conditional on sell-side 0.0006 0.0000 0.0678 -0.2107 0.2113 2,014 Conditional on acquirer advisor 0.0080 0.0041 0.0355 -0.1523 0.1275 2,014

C: Pearson Correlation of relative industry expertise




Conditional on acquirer advisor

Unconditional 1.000 0.912*** 0.798*** 0.886*** 0.319***

Conditional on serving side

1.000 0.850*** 0.906*** 0.484***


1.000 0.820*** 0.309***


1.000 0.069***


1.000

73

D: Pearson Correlation of relative size-class expertise





Unconditional 1.000 0.919*** 0.877*** 0.960*** -0.249***

Conditional on serving side

1.000 0.871*** 0.846*** 0.098***


1.000 0.710*** 0.137***


1.000 -0.446***


1.000

74

Appendix C: Summary Statistics of Top-tier Banks

A: Top-tier (top 5 banks) by deal characteristics

Top 5 bank % (acquirer side) Top 5 bank % (target side) All deals 30.78 28.99 Public acquirer 31.17 29.27 Public acquirer and public target 36.94 29.80 Cash deals 31.16 30.71 Stock deals 29.82 26.25 Tender offers 39.47 37.01 Mergers 31.78 16.10 Horizontal 30.51 29.20 Vertical 31.11 28.49 First time acquirer 26.27 26.46 6+ deal acquirer 35.18 34.14

B: Top-tier (top 5 banks) by industry classification

Industry code Acquirer industry rate (%) Target industry rate (%) Consumer Durables 60.76 61.75 Chemicals and Allied Products 55.54 58.70 Utilities 51.78 51.66 Energy 43.97 45.67 Consumer Non-Durables 40.13 40.98 Retails 38.13 36.41 Telecommunication 36.78 36.30 Manufacture 36.71 36.97 Healthcare and Medical products 33.27 32.60 Misc. 31.70 30.54 Business Equipment 28.44 30.17 Finance 26.45 26.72

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