Is it the Investment Bank or the Investment Banker? A Study of

the Role of Investment Banker Human Capital in Acquisitions

Thomas J. Chemmanur∗ Mine Ertugrul† Karthik Krishnan‡

Abstract

Using a hand-collected dataset of investment banker turnovers between 1996 and 2006, weaddress the following question: Does value creation by investment banks in acquisitions ariseprimarily from the reputation, culture, and other institutional strengths of a given investmentbank or does it also arise from the human capital accumulated by the individual investmentbankers employed by that bank? We postulate two channels through which investment bankerscan create value for the investment banks employing them and for the acquirers they advise: a“certification channel,”whereby investment bankers with greater human capital and reputationmay be able to convey their private information about synergy and other value-relevant variablesmore credibly to the financial market; and an “ability channel,” where investment bankerswith greater human capital are able to identify targets with greater synergistic value, betternegotiate deal terms with target firms, and have greater deal completion rates and shorter dealcompletion times. We test these hypotheses using the announcement effects of acquisitions, andthe post-acquisition operating and stock return performance of acquirers as outcome variables.Our findings can be summarized as follows. First, investment banker fixed effects explain asubstantial portion of the variation in acquisition outcomes, over and above the fixed effectsassociated with the investment bank itself. Further, proxies for investment banker human capitalsuch as education and connections are positively related to acquisition outcomes. Second, wheninvestment bankers with greater human capital switch from one investment bank to another,the losing investment bank experiences a decline in acquisition performance and the gaininginvestment bank experiences an increase in its acquisition performance. Third, acquirers followhigh human capital investment bankers who switch from one bank to another. Fourth, moves byinvestment bankers with high human capital are associated with a negative stock market reactionfor the losing bank and a positive market reaction for the gaining bank. Finally, investmentbanker human capital also has a positive impact on deal completion rates and times even afteraccounting for the fixed effects associated with the investment banks employing them.

∗Professor, Finance Department, Fulton Hall 440, Carroll School of Management, Boston College, Chestnut Hill,MA 02467, Tel: (617) 552-3980, Fax: (617) 552-0431, Email: chemmanu@bc.edu†Assistant Professor, Accounting and Finance Department, College of Management, University of Massachusetts,

100 William T Morrissey Blvd, Boston, MA 02125, Phone: (617) 287-7678. Email: mine.ertugrul@umb.edu‡Assistant Professor, Finance and Insurance Department, 414C Hayden Hall, College of Business, Northeastern

University, Boston, MA 02115, Tel: (617) 373-4707, Email: k.krishnan@neu.edu

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1 Introduction

The role of financial intermediaries such as investment banks in the financial markets has been

widely debated both in the academic and in the practitioner literature. In particular, the theoretical

literature has viewed investment banks as information producing intermediaries in the context of

financial market transactions such as new equity issues and acquisitions (see, e.g., Chemmanur

and Fulghieri (1994) or Pichler and Wilhelm (2001)). There has also been considerable empirical

evidence suggesting a positive relation between the reputation of the investment banks involved and

value gains by client firms in securities issues (see, e.g., Beatty and Welch (1996) and Chemmanur

and Krishnan (2012)) and acquisitions (see, e.g., Golubov, Petmezas, and Travlos (2012) and Kale,

Kini, and Ryan (2003)). A natural question that arises in the above context is regarding the

precise source of value creation by investment banks: Does this value creation arise primarily from

the reputation, culture, and other institutional strengths of a given investment bank or does it also

arise from the human capital accumulated by the individual investment bankers employed by that

bank?1 To the best of our knowledge, there has been no literature, theoretical or empirical, that

sheds light on the above issue. The objective of this paper is to fill this gap in the literature by

empirically disentangling the relative contributions of individual investment bankers from those of

the investment banks employing them using a hand-collected sample of investment bankers who

switch between investment banks.2

We address several interesting questions in this paper. First, do individual investment bankers

contribute to value gains in acquisitions independent of the investment banks employing them, and

if so, what are the relative proportions of value added by investment bankers versus the investment

banks employing them? Second, how do the characteristics of individual investment bankers such as

education, experience, and connections affect value addition by these investment bankers? Third,

how does the presence of a particular investment banker at a given bank affect the propensity

1The role played by culture of an investment bank in contributing to value creation for client firms has also beenwidely debated recently by practitioners. See, e.g., “Why I am Leaving Goldman Sachs,” (New York Times, March14, 2012) by a former Goldman Sachs investment banker.

2This topic is of interest to both academics and practitioners. Media reports suggest that Wall Street investmentbankers in general, and M&A advisors in particular, have been compensated extremely well over recent years. Further,the aftermath of the recent financial crisis has seen the rise of contentious debates about Wall Street compensation.If investment bankers add little or no value to their clients, are their high compensation levels sustainable in the longrun? Analyzing the association between investment bankers and acquisition outcomes may provide clues to answeringsuch questions. While we do not claim to explain Wall Street compensation levels in this paper, we provides evidenceon whether investment bankers add value to the investment banks employing them and to their client firms.

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of an acquirer to choose that investment bank to advise them?3 Fourth, how does an individual

investment banker switching from one investment bank to another affect the performance and

equity value of these investment banks? Finally, how do individual investment bankers affect the

probability of deal completion by their client firms and the time taken to complete the deal?

The theoretical framework we use to develop testable hypotheses relies on a variation of the

model of Chemmanur and Fulghieri (1994). Consider a setting where a potential acquirer hires

an investment bank to advise their firm on an acquisition. We assume that more able investment

banks are able to identify targets with greater synergistic value to the acquirer, to better negotiate

the terms of the deal (so that a larger share of the synergy accrues to the acquirer), and will be able

to complete deals in a shorter time. The above abilities of the investment bank are a function of not

only its reputation, culture, and its other institutional strengths, but also the human capital (e.g.,

education, connections) of the investment bankers employed by it. This, in turn, means that, when

an acquirer hires an investment bank to advise their firm on an acquisition, he takes into account

not only the institutional strengths of the investment bank, but also the human capital of the

investment bankers employed by it.4 We further assume that the true value of the synergy between

the acquirer and the target is private information to the investment banker. Finally, investment

bankers build reputation with the financial market through their performance over time, so that

more able and reputable investment bankers are able to convey their private information more

credibly to the financial market.

In such a setting, we expect that, first, investment bankers will be able to add value to the

acquiring firm, and this value will be reflected in both short- and long-run acquisition performance

measures. Thus, we expect that investment banker fixed effects will explain a substantial portion

of the variation in acquisition outcomes, over and above the fixed effects associated with the invest-

ment bank itself. This will also imply that proxies for investment banker human capital such as

3There are several anecdotes suggesting that the relationship between acquirers and individual investment bankersis of considerable importance in the acquirer’s choice of investment banks to advice them: see, e.g., the article, “CSFBwill share Reebok M&A fees after Taussig Defects to Lehman,”Bloomberg, November 15, 2005.

4Media articles provide anecdotal evidence that acquirers hire investment bankers that they perceive as havingskill and ability to add value. To quote Lee Shavel, the CFO of NASDAQ OMX Group Inc. and an ex-investmentbanker, “. . . a banker recently [approached us with something] we hadn’t spent a lot of time thinking about, and thebanker had a great ability to synthesize a complex situation, coherently articulate a rationale for the transaction andan execution strategy. . . .We were impressed with this banker’s ability to do that, and decided to hire him.” See,“How to Pick a Banker,”WSJ, April 3, 2012. The article goes on to indicate that successful deals may also benefitthe investment bank: “they have some incentive to see deals be successful, since it may lead to more deals.”

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education, experience, and connections will be positively related to acquisition outcomes. Second,

when bankers with higher ability switch from one investment bank to another, the losing invest-

ment bank will experience a decline in acquisition performance in the industry specialization of

the outgoing investment banker; and the gaining investment bank will experience an increase in its

acquisition performance in the industry specialization of the incoming investment banker. Third,

acquirers will follow high ability investment bankers who switch from one bank to another. Fourth,

moves by investment bankers with higher ability will be associated with negative market reaction

for the losing bank and a positive market reaction for the gaining bank. Finally, investment bankers

will also have an impact on deal completion rates and times even after accounting for the effects

associated with the investment banks employing them.5

Our analysis starts with associating investment banks and investment bankers with various

measures of acquisition outcomes of the deals advised by them. We use the three day cumulative

abnormal returns (CAR) of the acquiring firm around the deal announcement period as our first

measure of acquisition outcome. We also use the three year post-acquisition operating performance

measured by abnormal ROA (Chen, Harford, and Li (2007)) and the three year post-acquisition buy

and hold abnormal stock returns (BHAR) of the acquirer (Lyon, Barber, and Tsai (1999), Chen,

Harford, and Li (2007)) as measures of long-term performance. We measure the completion time

of the acquisition as the time (in days) from the announcement date to the completion date. We

then allocate acquisitions to our sample of investment bankers based on the industry specialization

of the banker and the investment bank advising the acquisition. Finally, we regress investment

bank and investment banker fixed effects on the acquisition outcome variables described above

controlling for deal, acquirer, and target characteristics, as well as for investment bank reputation.

The above procedure allows us to evaluate the contributions of investment bankers to the success

of the acquisition deals for which the investment bank employing them served as advisor, over and

above the contribution of the investment bank itself. We also evaluate the relative contribution of

the investment bank and investment bankers by regressing the acquisition outcome variables directly

on specific investment banker characteristics such as education, experience, and connections while

controlling for investment bank fixed effects and investment bank reputation.

5Note that deal completion times may be important to acquirers, since longer deal completion times may increasethe likelihood of a competing bid and may increase uncertainty regarding the price to be paid for the target due tochanging market conditions.

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Bertrand and Schoar (2003) use a fixed effects model to identify senior managements’impact

on firm strategy and performance. Their identification strategy requires a manager to work in

at least at two different firms within the sample period to separately identify manager and firm

fixed effects. More recently, Graham, Li and Qiu (2011) use the methodology of Abowd, Kramarz,

and Margolis (1999) to isolate manager fixed effects. Their idea is that the researcher can keep

the set of managers who have worked at only one firm in the sample as long as another manager

has either moved from or to that firm. This allows the researcher to conduct the analysis using a

larger number of individuals and isolate individual fixed effects from firm fixed effects. We use this

latter approach to include bankers that have deals associated with them while they are employed

at only one bank as well as bankers that have deals associated with them when they are employed

at multiple banks within our sample period. Thus, we are able to isolate investment banker fixed

effects from investment bank fixed effects in our analyses.6

We find that investment banker fixed effects are statistically significant determinants of ac-

quisition outcomes. We observe substantial increases in the explanatory power of our regressions

when we add investment banker fixed effects. Further, F-tests indicate that the investment banker

fixed effects are jointly significant for our measures of acquisition outcomes. Investment banker

fixed effects explain a greater fraction of the variation in acquisition outcomes than investment

bank fixed effects, indicating that the human capital of investment bankers is more important in

determining acquisition outcomes than reputation, culture, and other institutional strengths of in-

vestment banks. In particular, we find that investment banker fixed effects explain 20.2 percent of

the variation in acquisition CAR, compared to 2.2 percent of the variation in CAR explained by

investment bank fixed effects. Similarly, investment banker fixed effects explain 47.7 percent of the

variation in abnormal ROA, compared to the 9.8 percent of the variation explained by investment

bank fixed effects. Investment banker fixed effects explain 19.4 percent of the variation in BHAR,

compared to 11.8 percent for investment bank fixed effects.

We find significant dispersion in investment banker fixed effects for acquisition performance

in our sample. Using the standard error-weighted distribution of the estimated investment banker

fixed effects, we find that the banker at the 25th percentile is associated with a 3.9 percentage point

6We also conduct our fixed effects analysis with the more restricted sample of investment bankers that have dealsassociated with them at more than one bank (based on the methodology used in Bertrand and Schoar (2003)). Ourresults are similar to those reported in this paper.

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decrease in CAR whereas the banker at the 75th percentile is associated with a 2.7 percentage

point increase in CAR. In terms of long-term performance, we find that the difference between

the 75th and the 25th percentile banker fixed effects for long-term operating performance is 5.4

percentage points. Further, we find that investment banker performance is persistent across the

various investment banks that employ them. Therefore, our fixed effects results above do not

merely reflect the performance of an investment banker when he is employed at one bank. Rather,

it indicates that a banker’s association with performance follows him across the various banks that

employ him.

We also relate individual investment banker characteristics such as education to our acquisition

outcome measures. We find that Ivy League graduate education of the banker has a positive

association with acquisition performance for both short- and long-term performance measures.

Investment bankers with MBA degrees are associated with higher acquisition performance and this

relationship is stronger when the banker has an Ivy League MBA degree. Investment bankers

with Ivy League non-MBA degrees are also associated with positive long-term performance of the

acquirer. However, once we control for the number of education-based connections between the

investment banker and the top management or directors of the acquiring firm, the positive relation

between investment bankers’MBA degree and acquisition performance disappears. Moreover, MBA

degree connections are positively associated with acquisition performance. In contrast, the positive

relationship between Ivy League non-MBA degree and long-term acquisition performance survives

the introduction of the non-MBA degree connections variable in the regression. The above results

indicate that investment bankers’association with acquisition outcomes may be explained by both

connections and skill (as evidenced by the positive relation between Ivy League non-MBA degrees

and acquisition performance).7

The results of our empirical analysis on switching by investment bankers from one bank to

another also indicate that they add considerable value to the investment banks employing them

and to acquirer firms. We find that, after a high fixed effects investment banker leaves a bank,

the performance of acquisitions advised by that bank deteriorates (for acquirers in the outgoing

banker’s area of specialization). Similarly, after a high fixed effects investment banker joins a

7Note that we use skill as a blanket term to refer to both the quality of education received at Ivy League schoolsas well as the innate ability required to successfully pass through stringent admissions criteria used in Ivy Leagueschools.

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bank, the performance of acquisitions advised by that bank improves (for acquirers in the incoming

banker’s area of specialization). Thus, the results above indicate that investment bankers are

indeed associated with acquisition outcomes, and when they move to another investment bank,

their performance effect moves with them.

We also find that, when a high fixed effects banker moves from one investment bank to another,

acquirers are more likely to use the investment banker’s new employer as the advisor on the next

deal. Specifically, we find that a one interquartile increase in the switching investment banker’s

fixed effects (based on CAR) is associated with a 11 percentage point greater likelihood that the

investment banker’s previous client firm will use the new employer of that investment bank. We also

find that the investment bank’s stock price (for publicly traded investment banks) reacts positively

when a high fixed effects investment banker joins the investment bank and negatively when a high

fixed effects investment banker leaves the investment bank. These results suggest that the human

capital of investment bankers may be valuable assets for the investment banks employing them as

well as for client firms.

Finally, we find that investment banker fixed effects are significantly associated with deal com-

pletion times and deal completion probability over and above the fixed effects associated with their

investment bank. We find that the difference between the 75th and the 25th percentile banker fixed

effects for completion time is about 50 days. Further, various measures of investment banker human

capital are associated with deal completion times and deal completion hazard. MBA connections

and Ivy League non-MBA degrees of investment bankers are associated with lower deal completion

times and higher deal completion hazard. Investment bankers with MBA degrees are also associ-

ated with a higher hazard of acquisition completion. In many cases, investment banks only obtain

fees if acquisitions are completed. This suggests that the human capital of the investment banker

is quite valuable to the investment bank since greater completion hazard translates into higher

advisory fees.

We also perform a number of empirical tests to demonstrate the robustness of our results.

One potential concern is that our methodology for allocating investment bankers working for an

investment bank to a specific deal (for which the bank served as the advisor) may bias our results.

We are able to mitigate this concern using in-sample randomizations and out-of-sample simulations.

In particular, we show that any misspecification in our allocation of investment bankers to deals

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will, in fact, bias against finding any relationship between investment banker fixed effects and

acquisition performance.8

Ours is the first paper in the literature to document that the human capital of investment

bankers adds substantial value to the investment banks who employ them as well as to the client

firms advised by them. Our results show that investment bankers add value to the success of acqui-

sitions over and above the contribution of the reputation, culture, and other institutional strengths

of the investment bank employing them. Our results provide some justification for investment

banks using generous compensation packages to retain and hire valuable talent.9

Our empirical evidence also indicates that the association between investment bankers and

acquisition outcomes may reflect skill (proxied by Ivy League education) and connections. Such

skill (or ability) of the investment banker can thus be a valuable asset for investment banks as well

as for acquirers. The relation between banker MBA network connections and acquisition outcomes

is also suggestive of value added by investment bankers, and such value addition may come from

different channels. One possibility is that the positive association between network connections and

long-run deal performance reflects bankers using their connections to obtain high-quality deals for

their employers.10 Another explanation for the link between connections and acquisition outcomes

is that bankers use their connections to more easily convince management and/or the board of

the merit or lack of merit of a potential deal (to the extent that connections facilitate greater

trust or more effective communication of information). Bankers may also be able to gain more

information about the acquirer using their connections, which may help them to better understand

the feasibility of the deal and design the terms of the deal more effectively.

Finally, our empirical results allow us to rule out the possibility that investment bankers simply

“latch on” to valuable deals which have already been given to the investment bank (instead of

8Our results are broadly consistent with Card (1996), who shows that misspecification of a binary independentvariable will attenuate the coeffi cient on that variable, i.e., bias the coeffi cent toward zero.

9Media reports seem to support this argument indicating that “..across Wall Street, one thing never failed: Topdealmakers got steep increases. . . .” and “The search for new growth areas means that the war for talent is here tostay, and may even accelerate. . . . . . because developing a new business means hiring senior talent, with attractive paypackages. . . .”See “The Compensation Mirage.”(Investment Dealers’Digest, March 21, 2005)10An anecdote that supports the idea that banks hire bankers (at least partly) for their connections, is from “The

Accidental Investment Banker,”Jonathan A. Knee, 2006, who mentions that during an interview for an investmentbanking position at Morgan Stanley, he “..went through a list of a hundred or so company names he had come upwith . . . .. where he knew some senior executives or other ..”. Other quotes in the book also suggest a substantialrole for connections in obtaining advising mandates. For example, Knee mentions that “. . . .I had known S&S CEO,Jonathan Newcomb, who was interested in getting me involved in its sale to Pearson for $4.6 Billion. And I used myrelationship with my former client and colleagues to help us get hired by Reed Elsevier. . . .”

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adding value to deals or procuring valuable deals for the investment bank). If this were the case,

then the performance of deals would not deteriorate for an investment bank that loses a high

performance fixed effect investment banker; nor would the performance of deals improve for an

investment bank that gains a high performance fixed effect investment banker.

The rest of the paper is organized as follows. Section 2 discusses the relation of this paper to the

existing literature. Section 3 outlines the relevant theory and develops testable hypotheses. Section

4 outlines the empirical methodology for our fixed effects analysis. Section 5 describes our data

and sample selection procedures. Section 6 provides a discussion of our empirical results. Section

7 concludes.

2 Relation to the Existing Literature

This paper is related to several strands in the existing literature. The first is the theoretical and

empirical literature on the role of the investment bank as intermediaries in the financial market:

Chemmanur and Fulghieri (1994), Pichler and Wilhelm (2001), and Morrison and Wilhelm (2004)

are examples of the relevant theoretical literature; Beatty and Welch (1996) and Liu and Ritter

(2011) are examples are the relevant empirical literature.11 There is also a large on the role of

investment banks and the performance of acquisitions: see, e.g., Bowers and Miller (1990), Servaes

and Zenner (1996), Rau (2000), Kale, Kini, and Ryan (2003), Hunter and Jagtiani (2003), and Bao

and Edmans (2011). For instance, Kale, Kini, and Ryan (2003) find that investment bank reputation

affects the performance of acquisitions. Bao and Edmans (2011) use a fixed effects approach and

find that investment banks play an important role in the performance of acquisitions.

The second literature this paper is related to is that on CEO compensation and turnover and

the effect of CEOs on firm performance. Bertrand and Schoar (2003) study whether different

CEOs have different financial and investment policy styles and whether CEOs can affect firm

performance. Graham, Li, and Qiu (2011) find that management compensation fixed effects are

related to corporate policy fixed effects. Our paper is also related to Chemmanur and Paeglis (2005)

and Chemmanur, Paeglis, and Simonyan (2009). Chemmanur and Paeglis (2005) find that the

11Morrison and Wilhelm (2004) develop a model of partnerships in human capital intensive industries (such asinvestment banking) where it is diffi cult to contract upon the training effort of skilled agents, so that a sociallysuboptimal level of training may occur. They show how partnership organizations can overcome this problem bytying human and financial capital.

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quality of the top management can reduce IPO underpricing, bring in more institutional investors

and reputable underwriters, and improve post-IPO operating performance. Chemmanur, Paeglis,

and Simonyan (2009) find that higher management quality can also reduce information asymmetry

and allows the firm to use a larger fraction of equity (and less debt) to raise external financing.12

Finally, our paper is related to the literature on mutual fund managers. Chevalier and Ellison

(1999) investigate how mutual fund manager quality is related to fund performance. They find

that mutual fund managers who attend universities with high average test admission (SAT) scores

manage better performing funds.

3 Theory and Hypotheses

The theoretical framework we use to develop testable hypotheses relies on a variation of the model

of Chemmanur and Fulghieri (1994).13 Consider a setting where a potential acquirer hires an

investment bank to advise their firm on an acquisition. We assume that more able investment

banks are able to identify targets with greater synergistic value to the acquirer, to better negotiate

the terms of the deal (so that a larger share of the synergy accrues to the acquirer), and will be able

to complete deals in a shorter time. The above abilities of the investment bank are a function of not

only its reputation, culture, and its other institutional strengths, but also the human capital (e.g.,

education, connections) of the investment bankers employed by it. This, in turn, means that, when

12Our paper is also broadly related to the theoretical and empirical literature on the role of managerial humancapital in capital structure decisions: see Titman (1984) and Berk, Stanton, and Zechner (2010) for examples of thetheoretical literature and Chemmanur, Cheng, and Zhang (2012) for an example on the empirical literature. It isalso related to the literature on the role of human capital in asset pricing: see, e.g., Fama and Schwert (1977).13Strictly speaking, Chemmanur and Fulghieri (1994) model reputation acquisition by investment banks, not in-

dividual investment bankers. However, at the expense of some additional complexity, one can adapt their modelto incorporate reputation acquisition by individual investment bankers within an investment banking firm along thelines outlined by Tirole (1996). The three important building blocks of such a theory will be the following (as adaptedfrom Tirole (1996)). First, each investment banker is characterized by individual traits such as education, ability,and connections. Past individual behavior generates information about these traits and thereby generates individualreputations. The investment bank’s reputation will then be affected by that of the individual investment bankers itemploys. Second, while the identity of the investment bank employing a particular investment banker is perfectlyobservable, the past behavior of each investment banker is only imperfectly observed. Investment bank reputationwould play no role in the financial market if the past behavior of individual investment bankers were fully observed.Third, the past behavior of the investment bank (i.e., its reputation) affects the current behavior of the investmentbankers employed by it, and can therefore be used to imperfectly predict the behavior of these investment bankersin the financial market (since each investment banker’s welfare and incentives are affected by the investment bank’sreputation). Such a model (which we will not develop here due to space limitations) will imply that the success ofan investment bank’s dealings in the financial market (e.g., in advising acquirers) will not only depend on its owninstitutional reputation, but also on the education, ability, and connections (and therefore the reputations) of theindividual investment bankers involved.

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an acquirer hires an investment bank to advise their firm on an acquisition, he takes into account

not only the institutional strengths of the investment bank, but also the human capital of the

investment bankers employed by it. We further assume that the true value of the synergy between

the acquirer and the target is private information to the investment banker. Finally, investment

bankers build reputation with the financial market through their performance over time, so that

more able and reputable investment bankers are able to convey their private information more

credibly to the financial market.

The human capital and reputation of an investment banker may affect his interactions with the

financial market (and that of the investment bank employing him) in two different ways. First,

investment bankers with greater human capital and reputation may be able to convey their private

information about the synergy between the target and the acquirer (and about other variables that

may affect the value created by the acquisition) more credibly to the financial market. We will

refer to this as the “certification channel” from now on. Second, investment bankers with greater

human capital may be more able to better identify targets with greater synergistic value to the

acquirer, to better negotiate the terms of the deal (so that a larger share of the synergy accrues

to the acquirer), and will be able to complete deals in a shorter time. We will refer to this as the

“ability channel”from now on.

The above has several implications for the relation between investment banker human capital

and acquisition outcome variables. First, the certification channel implies that investment banker

fixed effects will explain a substantial portion of the variation in acquisition CAR, over and above

that explained by investment bank fixed effects. Further, the certification effect also implies that

proxies for investment banker human capital such as education, experience, and connections will be

positively related to acquisition CAR. Second, the ability channel implies that investment banker

fixed effects will explain a substantial portion of the variation in the long-run post-acquisition

operating performance (abnormal ROA) and stock return performance (BHAR). Further, the ability

channel also implies that proxies for investment banker human capital such as education, experience,

and connections will be positively related to the long-run post-acquisition operating and stock return

performance.

If the human capital and reputation of an investment banker affects acquisition outcomes as

discussed above, then this has implications for the investment bank employing an investment banker

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as well. First, if an investment banker with high human capital leaves an investment bank and

joins another, we expect the average performance of deals advised by the former bank (in the area

of specialization of that investment banker) to deteriorate and that of the latter bank to improve.

Second, we would expect acquirers to follow switching high human capital investment bankers from

one investment bank to another. Third, switches by investment bankers with high human capital

will be associated with a negative stock price reaction for the losing bank and a positive stock price

reaction for the gaining bank.

Finally, the ability channel also implies that investment bankers with higher human capital will

be more likely to complete deals and also complete their deals in a shorter time period, on average.

Thus, investment banker fixed effects will be able to explain a substantial portion of the variation

in deal completion time, over and above that explained by investment bank fixed effects. Further,

proxies for investment banker human capital such as education, experience, and connections will

be positively related to deal completion likelihood and negatively related to deal completion time.

4 Empirical Methodology for the Fixed Effects Analysis

Existing literature analyzing individual fixed effects includes Bertrand and Schoar (2003), who use

a fixed effects model to identify senior managements’ impact on firm strategy and performance.

Their identification strategy requires a manager to work for at least two different firms within the

sample period to be able to separately identify manager and firm fixed effects. More recently,

Graham, Li, and Qiu (2011) use the methodology of Abowd, Kramarz, and Margolis (1999) to

isolate manager fixed effects. The idea is that we can keep the set of managers who have worked

at only one firm in the sample as long as another manager has either moved from or to that firm.

This allows the researcher to conduct the analysis using a larger number of individuals and isolate

individual fixed effects from firm fixed effects. We use this latter approach for our tests since it

allows us to include bankers who only have deals associated with them at one investment bank.

First, we hand-collect data on investment bankers primarily from information in the popular

press about investment banker turnover from one bank to another (we describe the data collection

process in detail in the next section). We use the sample of investment banker switchers because it

allows us to identify investment banker fixed effects separately from investment bank fixed effects

12

(since identification requires that a subgroup of bankers have deals associated with them when

they are employed at two or more banks). Further, media reports on banker switches provide

additional information about investment bankers that we need to construct our sample. One piece

of information we obtain from these reports is the industry specialization of the banker, which we use

to attribute investment bankers to acquisition deals. Information from media reports about banker

switches also allows us to exclude bankers that specifically work in debt or equity underwriting and

are not involved with the M&A business of the bank.

Our sample, which consists of investment bankers who switch from one investment bank to

another, is probably more relevant for the purposes of our analysis. In particular, bankers that are

important enough to be listed in popular press when moving from one bank to another are probably

the bankers who are in a position to impact deal performance. A fresh MBA is less likely to be the

ideal laboratory for our analysis because his or her influence on the deal outcome is likely to be

small. Restricting our analysis to prominent investment bankers may also reduce the heterogeneity

of bankers in our sample, thus making it more diffi cult to find a relationship between investment

bankers and deal performance. However, the sample selection process requires us to emphasize that

all our results and inferences are applicable primarily to more prominent investment bankers.

We associate acquisitions with investment bankers if the banker’s employer is advising the

deal and the banker specializes in the acquirer’s industry (additional details of this procedure are

discussed in the next section). Our fixed effects regression framework is the following:

yij = βxij + δt + αj + γbanker + εij (1)

where yij is the performance variable for the ith deal advised by the jth investment bank, xij

are control variables that represent deal, acquirer, target, and time-varying bank characteristics, δt

are time fixed effects, αj are investment bank fixed effects, and γbanker are investment banker fixed

effects. Note that time subscripts are ignored in all the variables other than time fixed effects for

simplicity of notation.

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5 Data and Sample Selection

5.1 Sample Selection

5.1.1 Data on Investment Bankers

We collect data on investment banker turnover from Investment Dealer’s Digest (IDD) articles. We

search IDD publications from January 1, 1996 to December 31, 2006 using “banker”as keyword,

which yields 1,788 articles. IDD reports the name of the banker, the investment bank he/she

switches from and to, his/her title, and often, his/her industry specialization. From these articles,

we identify investment bankers that switch from one investment bank to another (in the U.S.) who

specialize in M&A advising. Out of those, we could identify the industry specialization of 330

bankers.14 Using the information provided in the articles, we classify the investment bankers into

48 different Fama-French (1997) industries. Depending on the banker’s industry specialization,

he/she might be classified into one or more Fama-French (1997) industry groups.

5.1.2 Data on Acquisition Deals

We obtain data on acquisitions from the SDC Platinum database. We select all acquisition deals

from 1994 to 2008 (required to get deals from two years before to two years after the investment

banker switch). We select deals based on the following filters: The acquirer is a public firm, the

deals are between U.S. acquirers and U.S. targets, the deal is not a rumored deal, and there is

at least one advisor to the acquirer on the deal. In addition, we impose the restriction that the

acquirer owns less than 50% of the target prior to the transaction and owns more than 50% of

the target subsequent to the transaction. We also remove exchange offers, repurchases, spinoffs,

recapitalizations, and self-tender offers. Finally, we keep only the deals for which Compustat and

CRSP data is available for the acquirer firm leaving us with 6,126 acquisitions deals.

14 If the article does not provide any specific information about whether the investment banker specializes in M&A,debt underwriting, or equity underwriting, but says that he or she works in a particular industry group, we classifythat banker as working in all three segments since industry groups typically provide all types of investment bankingservices for firms in their industry.

14

5.1.3 Attributing Deals to Investment Bankers

We merge the investment banker data with the data on acquisition deals based on the investment

bank advising the deal and the industry specialization of the investment banker. Over a two year

period prior to the investment banker switch, we select deals that use the investment banker’s

prior investment bank as an advisor and where the acquirer has the same Fama-French (1997)

industry as one of the industry specializations of the banker. This procedure gives us the set of

deals attributed to the investment banker prior to the switch. The implicit assumption behind this

attribution algorithm is that deals advised by the investment bank in the banker’s industry involve

the banker. If this assumption is violated, then we are less likely to find a relationship between

banker fixed effects and deal performance.15 We follow a similar procedure to get the deals that

are attributable to the banker over the two years after the switch.

The above procedure is complicated by merger activity among investment banks over the sample

period. If there is a merger within the two year period prior to the banker switch between the

banker’s investment bank and another bank, we use the deals conducted by the combined entity

after the merger and attribute the deal to the banker in the manner described above.16 We follow

a similar procedure to attribute deals after the switch to the investment banker if the banker’s new

bank merges with another bank after the switch.

The above matching procedure leaves us with deals associated with 215 investment banker

turnovers. To be included in the fixed effects analysis, a banker must satisfy one of the following

conditions. The first condition is that there should be at least one deal in the investment banker’s

industry advised by his previous bank announced within two years before the switch, and at least

one deal in investment banker’s industry advised by his new bank announced within two years

after the switch. The second condition is that the banker only has deals in his industry advised by

one of his employer investment bank (either previous or new bank), and at least one other banker

is associated with the original banker’s investment bank who satisfies the first condition. We

collapse cases where a team of bankers switches together into one observation to avoid duplication

15Using in-sample randomization and out-of-sample simulation exercises, we show that any noise in the attributionof investment bankers to acquisition deals will, if anything, reduce the likelihood of finding a relationship betweeninvestment banker fixed effects and acquisition outcomes. The results for these tests are reported below.16Since we are unable to determine which of the merging entities the banker comes from, we exclude deals prior to

the merger.

15

of observations. The number of bankers included in our fixed effects analysis range from 192 to

195 depending on the deal outcome variable being tested and the satisfaction of one of the two

conditions listed above.

In addition, we obtain biographical data (such as education and education-based connections)

on investment bankers. While we do not need to impose the restrictions of the fixed effects analysis

for the analysis using this data, we lose a significant number of bankers in tests where we use

biographical information because of the lack of availability of such data for many bankers. The

number of bankers in our analysis where we use banker biographical details ranges from 93 to 117

bankers depending on the deal outcome variable we test. We collect education data on bankers

from Bloomberg people search and various online sources such as Zoominfo.com and Forbes lists.

We obtain data on the number of education-based connections between the banker and the senior

managers or directors of the acquiring firm from the BoardEx database. In particular, we define

education-based connections as the number of senior managers or directors of the acquiring firm

that obtained the same degree as the banker from the same educational institution.

5.2 Measures of Acquisition Outcomes

We use various measures of acquisition outcomes. First, we use the short-run announcement period

cumulative abnormal returns (CAR) of the acquirer. This is the announcement period abnormal

stock return of the acquirer calculated as the cumulative returns of the acquirer over a three-

day period around the acquisition announcement (i.e., from one day prior to one day after the

announcement of the deal) minus the predicted returns from a market model. The market model is

estimated starting 30 days prior to the acquisition announcement to 225 days prior to the acquisition

announcement. Data on stock returns are obtained from CRSP.

Our second measure of acquisition performance is based on the long-run operating performance

of the acquirer. Our measure of operating performance is abnormal ROA and is similar to the one

used by Chen, Harford, and Li (2007). This is calculated as the residual from the regression of

the average three year post-acquisition industry-adjusted ROA (operating income to assets) on the

average three year pre-acquisition industry-adjusted ROA. The industry-adjusted ROA for a given

fiscal year is the firm’s ROA in a year minus the median Fama-French (1997) industry ROA of all

16

the firms in the same Fama-French industry as the acquirer in that fiscal year.17

Our third measure of acquisition performance is the long-run stock returns of the acquirer.

This measure is also similar to the measure used by Chen, Harford, and Li (2007). We calculate

the three year buy-and-hold returns of acquirers starting from one month after the acquisition

completion date adjusted by the buy-and-hold returns on a reference portfolio calculated using

a methodology similar to the one proposed by Lyon, Barber, and Tsai (1999). To calculate the

returns for the reference portfolio, we start with the universe of all stocks in the CRSP database.

For each year, we sort the NYSE stocks into ten size-based portfolios, where size is calculated as

the price in December of the prior year multiplied by the shares outstanding in the prior year.

We then allocate the Nasdaq and AMEX stocks to the NYSE size portfolios. Next, we split the

smallest decile portfolio into five portfolios. We split each of the size portfolios into five market-

to-book portfolios. We then allocate each acquirer to one of the 70 size and market-to-book sorted

portfolios. This portfolio serves as the reference portfolio for that acquirer.

Our final measures of deal outcome are deal completion time and deal completion likelihood.

Deal completion time is calculated as the time (in days) between the deal announcement and deal

completion. The results in Hunter and Jagtiani (2003) indicate that completion time might be an

important consideration for acquirers. Acquirers may want to expedite deal completion to reduce

the likelihood of a competing bid or to reduce uncertainty caused by changing market conditions.

We winsorize long-run operating performance, long-run stock returns, and completion time at the

one percent level to account for outliers.

5.3 Investment Bank Reputation

Investment bank reputation is based on the market share of deals advised. This is measured as the

total transaction value of deals advised by the bank relative to the total transaction value of all

deals in the year prior to the sample deal. If more than one bank advises the acquirer, we take the

average market share of the investment banks that advise the deal. Each investment bank advising

the firm gets equal credit for a deal.

We account for merger activity among investment banks in calculating investment bank market

17We also perform our analysis using the difference in the three year average post-acquisition industry-adjustedROA and the three year average pre-acquisition industry-adjusted ROA. Our results with this alternative measureare qualitatively similar to those reported in this paper.

17

share. If a bank was created as a result of a merger in a particular year, we calculate the prior year

market share for the bank as the sum of the market share of all the entities that merge to create

the new bank. Therefore, if Bank 1 in year t+1 was created by the merger of Banks 2 and 3 in year

t, then the market share of Bank 1 in year t is the total market share of Banks 2 and 3 in year t.

5.4 Other Variables

We control for acquirer characteristics such as the log of firm’s assets (Moeller, Schlingemann, and

Stulz (2004)), market-to-book ratio (Lang, Stulz, and Walkling (1991)), operating income to assets

ratio, and total debt to assets ratio. All accounting variables are calculated as of the fiscal year

ending immediately prior to the deal announcement date. All accounting data are obtained from

the COMPUSTAT database. We also control for the prior 12 month compounded stock returns of

the acquirer (Datta, Datta, and Raman (2001)).

We control for whether the target is a private firm, a public firm, or a subsidiary (Fuller, Netter,

and Stegemoller (2002)). This variable is available from the SDC Platinum database. Other deal

specific variables we use are: log of relative size, where relative size is defined as the transaction

value of the deal obtained from SDC Platinum divided by the market capitalization of the acquirer

obtained from COMPUSTAT; a dummy variable for an all-cash deal; a dummy variable for an

all-stock deal; a dummy variable for whether or not the target firm uses an advisor; a dummy

variable for whether or not the deal is friendly; a dummy variable for whether or not the deal is a

tender offer; a dummy variable for whether or not the deal is a two-tier deal; percentage of shares

owned by the acquirer immediately prior to the deal; a dummy variable for whether or not the

deal is a diversifying deal (Morck, Shleifer, and Vishny (1990)), where a deal is diversifying if the

primary Fama-French industry of the acquirer is different from that of the target; and a dummy

variable for whether or not the deal has been challenged. Deal-specific variables are obtained from

the SDC Platinum database.

5.5 Summary Statistics

Panel A of Table 1 reports the yearly distribution of investment banker turnovers in our sample.

The time trend of investment banker turnovers suggests that there was a spike in banker turnover

in our sample during the internet bubble period (1999-2000). However, the number of turnovers

18

are somewhat lower in the post-internet bubble period (i.e., after 2001) than in the bubble period.

The average number of yearly banker turnovers in our sample is 6 before 1999, 27.5 between 1999

to 2000, and 23.7 after 2001. The IPO and M&A activity resulting from the Internet bubble may

have contributed to an increase in the demand for investment bankers during the 1999 to 2000

period.

Panel B of Table 1 reports the title of the investment bankers in our sample. The majority of

bankers are at the senior levels in the hierarchy of the bank. Senior bankers are defined as those that

have titles of managing director, senior managing director, partner, group head, and department

head. Bankers with these titles constitute about 77% of all bankers in our sample. According

to a career website (careers-in-finance.com), bankers with these titles have the highest salaries

and work experience. For instance, based on 2010-2011 survey by this site, managing director or

partner level bankers have 7-10 years of work experience and typical all-in compensation of $1.5

million; and department head level bankers have more than 10 years of experience and typical

all-in compensation of $3.5 million. These figures are consistent with Kaplan and Rauh (2010) who

estimate senior level investment banker compensation to be between $1.1 million to $2.6 million at

the median.18 Thus, given the predominance of senior bankers in our sample, we believe that the

bankers in our sample are important in the hierarchy of investment banks, and that these bankers

are potentially as important to investment banks as top managers are to non-financial firms.

One concern about the data might be that our sample of investment bankers may not be

representative of all the senior investment bankers that work for investment banks. While hand-

collection of investment banker data poses some limitations, our sample of investment bankers is

one of the most comprehensive banker level datasets that has been used in academic research so

far. We also estimate what fraction of all senior M&A bankers we capture in our sample. We

start by obtaining information about the number of senior bankers from the study done by Kaplan

and Rauh (2010). Their study reports that the total number of senior bankers in a sample of top

ten investment banks in the year 2004 is 6,006. However, their list includes investment bankers

that not only provide advice on acquisitions, but also provide other services such as trading and

asset management. Thus, we need to estimate what fraction of their investment bankers are M&A

18Consistent with our definition of senior bankers, Kaplan and Rauh (2010) use the title of “Managing Director”to denote the top echelon of investment bankers in their sample.

19

bankers. We obtain this fraction in three different ways. First, we manually count the total number

of investment bankers that switched banks in 2004 from news articles, and then count how many

of them work in M&A advising. We estimate that 23.29 percent of the bankers that switched jobs

between investment banks in 2004 worked in M&A advising. To check whether this estimate is

appropriate, we obtain data from the financial statements of a major investment bank that was

public in 2004 (Goldman Sachs), and found that investment banking comprised 16.42 percent of its

revenues and 21.7 percent of its operating expenses. Thus, we estimate that senior M&A bankers

in the Kaplan and Rauh (2010) sample to be between 986 to 1,399 investment bankers (based on

multiplying 6,006 to one of the above three fractions). Then, we calculate the number of senior

M&A investment bankers in our sample that worked in one of top ten banks listed in Kaplan and

Rauh (2010). Overall, we find that 119 senior investment bankers in our sample worked for one

of the top ten investment banks in their paper. Thus, we estimate that the fraction of senior

investment bankers working in M&A advisory in our sample as a fraction of those in Kaplan and

Rauh’s (2010) sample is between 8.5% and 12.07%. On average, this fraction is about 10%, which

is considerably higher than the proportions in prior studies.19

Panel C of Table 1 reports the summary statistics of our deal variables. This sample includes

all deals that are attributed to the investment bankers in our sample. At the median, acquisitions

in our sample have a slight negative announcement effect (-0.006 percent). The abnormal ROA and

the BHAR are also negative at the median. Further, the median deal completion time is around 99

days. We report summary statistics of various deal characteristics such as log of assets, log relative

size, market to book ratio of the acquirer; and dummy variables for whether the deal is all cash,

all stock, or diversifying. In addition to sample statistics, we also report comparable statistics for

the average deal in the same industry and year as the sample deal to check the representativeness

of our sample. Overall, we find that the characteristics of our sample of acquisitions are similar to

those of the industry-year peer group.

19For instance, Bertrand and Schoar (2003) use 500 top level managers in their sample from 1969 to 1999. UsingExecucomp, we find that there are 21,572 top level managers during the sample period of 1992 to 1999 (Execucompcoverage starts in 1992). Thus, the estimated fraction of top level managers that are used in Bertrand and Schoar(2003) is around 2.35%.

20

6 Empirical Results

6.1 Fixed Effects Regressions

The results of our first set of regressions are reported in Panel A of Table 2. For each deal

outcome variable, we run one regression without the investment banker fixed effects and one with

the investment banker fixed effects. We cluster the regressions at the deal level since one deal can

appear more than once if multiple banks provide advice on a deal.20

The table reports the results of the joint F-test for the significance of the investment banker fixed

effects. The empirical evidence indicates that investment banker fixed effects are highly significant

for all measures of acquisition outcomes. The table also indicates that there is an increase in

the explanatory power of the regressions when the investment banker fixed effects are included.

For CAR, the explanatory power of the regression (measured by the adjusted R-squared) goes up

from 11.4 percent to 16.9 percent (a 5.5 percentage point increase) once the banker fixed effects

are included. When we use abnormal ROA as the dependent variable, the adjusted R-squared

increases from 18.4 percent without the banker fixed effects to 54.2 percent with the banker fixed

effects. Similarly, the explanatory power for the BHAR regression increases by 4 percentage points

when we include banker fixed effects. We also examine the explanatory power of adding investment

bank fixed effects. Panel B of Table 2 reports the adjusted R-squared for three regression models:

one where we include only the control variables (Column (1)); one where we include the control

variables and bank fixed effects (Column (2)); and one where we include the control variables,

bank fixed effects, and banker fixed effects (Column (3)). Including bank fixed effects increase the

adjusted R-squared by 1.1 percentage points for CAR, 8.9 percentage points for abnormal ROA,

and 9.2 percentage points for BHAR, compared to the regressions with control variables. Other

than BHAR, the adjusted R-squared change on adding investment bank fixed effects are lower than

that on adding investment banker fixed effects.

We can also examine the relative importance of banker fixed effects, bank fixed effects, and other

control variables in explaining the variation in our acquisition outcome variables. In particular, we

follow Graham, Li, and Qui (2011) and note that the model R-squared is calculated as

20We also conduct our fixed effects analysis after dropping duplicate deals. Our results are qualitatively similar tothe ones reported in the paper.

21

R2 =cov(yij , yij)

var(yij)=cov(yij , βxij + δt + αj + γbanker)

var(yij)=cov(yij , βxij + δt)

var(yij)+cov(yij , αj)

var(yij)+cov(yij , γbanker)

var(yij)

Recall from equation (1) that yij is the acquisition outcome variable, xij are the control variables,

δt are the time fixed effects, αj are the investment bank fixed effects, and γbanker are the investment

banker fixed effects. Each normalized covariance term above may be interpreted as a decomposition

of the model’s R-squared, with the covariance values corresponding to the fraction of the model

sum of squares attributable to particular factors. Thus, we can determine the relative importance

of different covariates in explaining the dependent variable for a given regression model using this

method. Panel C of Table 2 reports the relative importance of each group of variables (bank fixed

effects, banker fixed effects, other control variables, and residuals) in explaining the variation of

acquisition outcomes. Our results here mirror those in Panel B of Table 2. We find that investment

banker fixed effects explain 20.2 percent of the variation in cumulative abnormal returns of the

acquirer’s stock on the deal announcement, whereas bank fixed effects explain 2.2 percent of the

variation. In other words, investment banker fixed effects are ten times more important in explaining

the variation in CAR than investment bank fixed effects. We also find that investment banker fixed

effects are roughly five times more important than investment bank fixed effects in explaining the

variation in abnormal ROA, and are roughly twice as important as investment bank fixed effects

in explaining the variation in BHAR.

We also obtain the distribution of investment banker fixed effects. This allows us to understand

whether the relationship between investment banker fixed effects and acquisition performance is

economically meaningful. To account for the fact that the fixed effects are estimated, we weigh the

fixed effects by the inverse of the standard error of the fixed effects from the regression. We then

calculate the 25th, 33rd, 50th, 66th, and 75th percentiles of these fixed effects. The results of this

analysis are reported in Table 3.21

The distributions of investment banker fixed effects for our various acquisition outcomes mea-

sures are reported in Panel D of Table 2. These results indicate that that there is considerable

21 It is important to note that the figures in Panel D of Table 2 are not t-statistics. They are statistics based on theweighted distribution of the fixed effects. Other studies such as Bao and Edmans (2011) and Bertrand and Schoar(2003) have used this methodology to obtain economic magnitudes of fixed effects.

22

dispersion in investment banker fixed effects. The investment banker at the 33rd percentile of the

announcement period CAR fixed effects distribution is associated with a 2.3 percentage point re-

duction in CAR, while the banker at the 66th percentile is associated with a 1.7 percentage point

increase in CAR. Similarly, the investment banker at the 25th percentile is associated with a 3.9

percentage point decrease in CAR, while the banker at the 75th percentile is associated with a 2.7

percentage point increase in CAR. The dispersion in fixed effects is also economically significant for

the abnormal ROA and BHAR measures of long term performance. We find that the investment

banker at the 75th percentile for abnormal ROA fixed effects is associated with a 5.4 percentage

point higher abnormal ROA than the banker at the 25th percentile. Further, the investment banker

at the 25th percentile of the BHAR fixed effects distribution is associated with a 36 percentage point

decrease in BHAR whereas the banker at the 75th percentile is associated with a 13 percentage

point increase in BHAR.22

Our results above indicate that investment bankers have a significant association with acquisi-

tion outcomes, over and above the effect of their banks. While investment banks also are associated

with acquisition outcomes, we find that investment banker effects are able to explain a greater por-

tion of the variation in acquisition outcomes than investment bank fixed effects. Thus, our results

indicate that human capital of investment bankers is indeed important in determining acquisition

outcomes.

6.2 Robustness Checks

6.2.1 Allocating Acquisitions to Investment Bankers

We use our allocation algorithm (based on the industry specialization of the investment banker

and the identity of the investment bank employing him) to link investment bankers to acquisition

deals. In this section, our goal is to show that our allocation methodology, while noisy, does not

bias the results in favor of finding the results that we report in this paper. In fact, we argue that

our results are biased against finding a strong relation between investment banker fixed effects and

acquisition outcomes if there are random errors in allocating investment bankers to deals. This is

22To check whether our fixed effects results are consistent with those found in the prior literature, we also calculatethe distribution of investment bank fixed effects for announcement period CAR for our sample of acquisitions similarto Bao and Edmans (2011). We find that our distribution of CAR investment bank fixed effects is similar to thatreported in Bao and Edmans (2011).

23

consistent with Card (1996), who finds that misspecification in a binary independent variable in

a regression leads to attenuation of the coeffi cient (i.e., biases the coeffi cient towards zero). Using

in-sample randomization and out-of-sample simulations, we show that our investment banker fixed

effects estimates loses significance as the potential misspecification in the allocation of investment

banker fixed effects increases, thus biasing the results against finding a relation between investment

bankers and acquisition outcomes.

Placebo Analysis: Randomly Allocated Investment Banker Fixed Effects We randomly

re-allocate investment bankers in our sample to various acquisition deals and then conduct our

fixed effects analysis using the new (falsified) investment banker fixed effects. We conduct this

exercise 100 times and obtain the median F-statistic for the investment banker fixed effects, the

median change in adjusted R-squareds as a result of adding investment banker fixed effects, median

relative contribution of investment banker fixed effects in explaining acquisition outcomes, and the

distribution of investment banker fixed effects. All specifications include investment bank fixed

effects and all other control variables. We ensure that the base case investment banker fixed effect

remains the same in all the randomizations. Thus, if our allocation methodology is unable to

capture which investment banker is actually associated with a deal (and produces biased results)

and/or our fixed effects model itself produces biased estimates, we should find equally strong results

in the placebo samples as in our main sample.

The results of our placebo fixed effects analysis are reported in Table 3. We report the results

for the randomized samples and also those from our main sample for ease of comparison. In Panel

A of Table 3, we find that, for all measures of deal outcomes, the median change in adjusted

R-squared after adding investment banker fixed effects for the randomized data are substantially

smaller than the changes in adjusted R-squared in our main sample. The median F-statistic for

investment banker fixed effects is substantially smaller for the randomized data than for that for the

main sample. Panel B of Table 3 also shows that randomizing the investment bankers across deals

substantially decreases the explanatory power of investment banker fixed effects. For instance, the

relative explanatory power of investment banker fixed effects for CAR decreases from 20.2 percent

for the main sample to 4.7 percent for the randomized placebo sample. The distribution of the

fixed effects, reported in Panel C of Table 3, is also substantially tighter (i.e., less dispersed) for

24

the randomized sample. We conduct Kolmogorov-Smirnov tests for the equality of distributions

between the randomized and main sample investment banker fixed effects and find that the two

distributions are statistically significantly different for all acquisition outcome measures.

Overall, we find that our fixed effects regression results from the main sample are substantially

more significant, both in statistical and economic sense than the results from the randomized

sample. The results in this section provide additional evidence supporting the validity of our fixed

effects results.

Out-of-Sample Simulation Next, we show, using an out-of-sample simulation, that the weak

results that we obtain above with randomized data is not unique to our sample. That is, we obtain

results similar to those in the previous section even in a randomly generated simulated sample. We

start with a simple model with the assumed parameter values given below:

CARsimi = −0.02 + 0.005Log_Assetsi + γsimbanker + εsimi (2)

Here, CARsimi is cumulative abnormal returns for the ith deal, which is generated from the

equation described above. Log_Assetsi is the log of acquirer book value of assets obtained from

our sample. γsimbanker are randomly generated banker fixed effects from a normal distribution with

mean zero and standard deviation of 0.05. εsimi are random error terms obtained from a normal

distribution with mean zero and standard deviation of 0.01.23 We then run an OLS regression of

equation (2) with no changes to the banker fixed effects, and then by gradually increasing random

misspecification (i.e., misclassification) of investment banker fixed effects.

We report the results of the base model estimation and the estimation of the models with in-

creasing levels of misspecification in the investment banker fixed effects in Table 4. Not surprisingly,

the estimated banker fixed effects are highly significant (based on the F-test) and explain over 95

percent of the variation in the outcome variable. Further, the increase in adjusted R-squared on

adding banker fixed effects is 92.4 percent. Next, we randomly misspecify investment banker fixed

effects for 10 percent, 30 percent, 60 percent, and 90 percent of the observations. Thus, for the 10

percent misspecification sample, we create a random number with a uniform distribution between

23We also conduct our simulations with different parameter values than those described here, and find qualitativelysimilar results.

25

0 and 1 and pick those deals where the value of this random number is less than or equal to 0.1.

Then, to falsify the banker for a deal, we randomly allocate a different investment banker than the

one originally allocated to that deal. We then run the above regression on the misspecified sample,

and document the change in the statistical and economic effect of the investment banker fixed

effects. Consistent with random misspecification biasing our results towards zero, we find that, as

the extent of investment banker misspecification increases, the banker fixed effects F-statistic and

their relative importance in explaining the variation in the dependent variable gradually decreases.

For instance, the relative importance of banker fixed effects drop from 95.6 percent for the no

misspecification sample to 12.5 percent for the 90 percent misspecification sample.

The decrease in the explanatory power of the banker fixed effects in Table 4 is depicted graph-

ically in Figure 1a, which shows a continuous decline in the explanatory power of banker fixed

effects as the extent of banker misspecification increases. For comparison purposes, we also show

the explanatory power of banker fixed effects reported in Table 3 for the base sample and the ran-

domized placebo banker fixed effects sample in Figure 1b. Both Figures 1a and 1b show consistent

declines in the explanatory power of investment banker fixed effects as the extent of misspecification

increases. This evidence provide us with assurance that these different pieces of evidence (i.e., from

Tables 3 and 4) are pointing in the same direction.

We also document how the dispersion of the fixed effects from the simulations changes as we

increase the misspecification of the fixed effects. Figure 2a reports the density of the (demeaned)

fixed effects from the simulation datasets.24 The density is calculated by weighting each fixed

effect by the inverse of its standard error to account for the fact that they are estimated. The

figure shows that there is indeed an attenuation of the fixed effects as the misspecification in the

fixed effects increases. That is, the dispersion in the fixed effects becomes smaller with a greater

extent of misspecification in the bankers allocated to deals. In Figure 2b, we report a comparable

density graph for our (demeaned) sample CAR fixed effects and the CAR fixed effects obtained

from the completely randomized banker data in our placebo analysis (in Table 3). Again, the

similarities between Figure 2a and Figure 2b are comforting. The fixed effects obtained from

the randomized banker sample are substantially attenuated relative to the fixed effects obtained

24We report demeaned fixed effects since we are concerned with the dispersion in the fixed effects rather thanthe actual magnitudes of the fixed effects. Demeaning the fixed effects makes comparison easier across the variousmodels.

26

for the base sample. Thus, the evidence from the in-sample randomization and the out-of-sample

simulation exercises supports our argument that, if anything, our allocation algorithm biases against

finding any relationship between investment bankers and acquisition outcomes.

6.2.2 Persistence of Investment Banker Effects

A skeptical reader may argue that our fixed effects results may not really reflect banker effects on

performance, but rather spurious effects related either to the bank or to the time at which the

banker chooses to switch from one bank to another. For instance, investment bankers may do well

in certain years because they are lucky, and then switch to a better job at another bank. We

address such concerns by analyzing whether our results indicate persistence of banker performance

across various banks. Similar to the methodology in Bertrand and Schoar (2003), we regress

our acquisition outcome variables on all the variables used above including investment bank fixed

effects, but excluding banker fixed effects. We take the residual from this regression and average

them across the deals associated with a banker in a particular investment bank. We then regress

the average residual acquisition outcomes for the post-switch bank of a banker on the average

residual acquisition outcomes for the pre-switch bank and fixed effects for the year in which the

banker switches from the old to the new investment bank. The coeffi cient estimate on the pre-

switch average residual outcomes should be positive and statistically significant if there is indeed

persistence in the outcomes of deals associated with investment bankers.

We can only use the set of bankers for whom we have deals in the pre-switch and the post-switch

bank to measure persistence (similar to Bertrand and Schoar (2003)). One issue with this approach

is that while Bertrand and Schoar (2003) average across multiple years in which a manager was

employed at a firm, acquisitions in a banker’s industry advised by the investment bank may be few

in number, leading to concerns that averaging residual performance may not completely eliminate

noise or idiosyncratic effects. Thus, we conduct our analysis for two groups of bankers. The first

group uses all bankers in the sample that have at least one deal before the switch and at least one

deal after the switch. We call this our full sample. The second group uses all bankers in the first

group who are associated with at least six deals within the sample period in order to increase the

number of deals used to calculate averages and thus reduce idiosyncratic noise effects. We call this

our restricted sample.

27

Our results are reported in Table 5. The number of investment bankers that have deals both

before and after the banker switches is naturally lower than those in the previous tables. Adding

the sample restriction of at least six deals reduces the sample size further, which may lower the

power of the tests. Nevertheless, we do find evidence that acquisition outcomes for bankers show

persistence across the investment banks that a banker works at. The coeffi cient estimate on the

independent variable for CAR is positive in both samples, although it is only statistically significant

in the restricted sample. The persistence coeffi cient estimate is statistically significant in the full

sample for BHAR and abnormal ROA, and in the restricted sample for abnormal ROA.

Overall, we find evidence of persistence in the announcement CAR and long-run performance

variables for investment bankers across investment banks. This result mitigates concerns that

our fixed effects regressions are somehow driven by spurious effects related to investment banks.

Further, our result in this section also alleviate concerns that our empirical evidence in the previous

sections are driven by bankers who are fortunate enough to be associated with better deals in the

pre-switch bank and use this record to move to another bank. Persistence across banks indicates

that our fixed effects results above are not driven by such lucky bankers.

6.3 Investment Banker Characteristics and Deal Outcomes

6.3.1 Education and Experience

In this section, we relate investment banker characteristics such as education and experience with

acquisition outcomes. This analysis allows us to tease out the potential mechanisms through which

the bankers may be associated with deal outcomes. We thus regress acquisition outcomes on a

dummy variable that is one if the investment banker has an Ivy League graduate degree, and zero

otherwise; the banker’s deal experience, which is measured as the log of the deal amount associated

with the investment banker in the two years prior to switching to a new bank; and all the control

variables used in our fixed effects analysis including investment bank and year fixed effects. The

results of this analysis are reported in Table 6 for deal performance variables.25 ,26 In particular,

we find Ivy League graduate education is positively related to CAR, abnormal ROA, and BHAR

25Note that the sample sizes in these regressions are smaller than before since we do not have education data forall the investment bankers in our sample.26The friendly deal dummy drops out of the abnormal ROA and BHAR regression specifications due to lack of

variation in this variable in the more restricted sample used here.

28

measures of performance (reported, respectively, in columns (1), (3), and (5)). Our results indicate

that CAR increases by 1.9 percent, abnormal ROA by 2.6 percent, and BHAR by 14.7 percent

when the deal is associated with an Ivy league school educated banker.

In columns (2), (4), and (6) of Table 6, we split up the Ivy League degree dummy variable into

Ivy League MBA and Ivy League non-MBA degree dummy variables and add a separate MBA

degree dummy variable (which is one if the banker has an MBA degree, and zero otherwise). Our

results indicate that investment bankers with Ivy League MBA degrees are associated with higher

CAR and long-term stock performance, while investment bankers with Ivy League non-MBA de-

grees are associated with higher long-term operating and stock performance after the acquisition.

Further, long-term operating and stock performance measures are higher for the acquirer when the

banker associated with the acquisition has an MBA degree. However, we do not see a statistically

significant relation between our measure of the banker’s deal experience and acquisition perfor-

mance. Broadly, these results suggest that there is a link between investment banker education

and the performance of acquisitions advised by them.

6.3.2 Connections

While higher education may measure skill, certain higher education degrees such as MBA, are

considered particularly valuable in building a network of contacts.27 To further investigate the

channels through which investment bankers may be associated with acquisition outcomes, we ana-

lyze the effect of education-based connections between the banker and the acquiring firm’s senior

management and directors using data from BoardEx. We thus run regressions similar to those in

the previous section and add variables for the number of MBA and non-MBA degree connections

between the investment banker associated with the deal and the acquiring firm’s senior offi cers and

directors. To ensure that our connections measures are not biased by miscoding data for acquiring

firms that are not included in the BoardEx database (for instance, by classifying education-based

network connections as zero if the acquiring firm is not present on BoardEx), we restrict our sample

for this analysis to the subset of acquirers that are present in the BoardEx database in the years

27For instance, one of the main value of getting Harvard MBA according to the Harvard Business School website is“When you graduate with an MBA from Harvard Business School, you earn a place within a community of more than78,000 alumni in 167 countries. . . . More than 40,000 of our alumni have made themselves available to help currentstudents build connections and uncover business opportunities throughout their careers.”The Wharton MBA websitesuggests that one of the three important value drivers of an MBA is “access to a network of MBA students.”

29

that they make their acquisition announcements. As a result, there are fewer observations in these

regressions than in the previous section.

Table 7 reports the results of these regressions. We find that the statistical significance of the

coeffi cient estimates on our MBA degree dummies vanish, and instead the MBA connections variable

is statistically significant and positively related to the long-term (stock and operating) performance

of acquiring firm. This suggests that an important mechanism through which the MBA education

of the banker may affect deal performance is education-based connections. However, we also find

that the non-MBA Ivy League degree variable is still positively and significantly related to the

long-term performance of the acquiring firm. That is, controlling for non-MBA connections does

not take away the significance of the Ivy League non-MBA education variable. This suggests that

high quality education, which may reflect skill, talent, or intelligence of the banker, is still positively

associated with long-term acquisition performance, even after controlling for connections.

Overall, the results in this section indicate that both skill (measured by education) and con-

nections are important channels through which bankers are associated with acquisition outcomes.

The effect of high quality education is diminished but not completely erased by the presence of

education-based connections. Thus, individual investment banker characteristics such as education

and connections have a significant and positive association with acquisition outcomes.

6.4 Investment Bank Deal Performance in the Industry Specialization of the

Investment Banker Before and After the Switch.

In this section, we use the investment banker fixed effects estimates from Table 2 to analyze whether

acquisition outcomes of deals advised by an investment bank in a switching banker’s industry dete-

riorate when a high fixed effects banker leaves the bank. Similarly, we analyze whether acquisition

outcomes of deals advised by an investment bank in a switching banker’s industry improve when

a high fixed effects banker joins the bank. Finding that a move by a high fixed effects banker

adversely affects the performance of deals in the banker’s industry for the bank losing the banker

will further support our conjecture that investment bankers indeed add value to their employer

banks and to client firms. Further, while the prior tests show that investment bankers may be

associated with deal performance, a reader may still have concerns that our fixed effects analysis

may not really reflect a genuine association between investment bankers and acquisition outcomes.

30

Rather there may be other factors, such as industry effects, that may drive our results. We try to

address such concerns by controlling for industry effects in this analysis as well.

We start by obtaining the residuals from the regression of acquisition outcomes on control

variables and bank fixed effects, similar to the analysis in the section on persistence of investment

banker performance. We then calculate, for a pre-switch investment bank, the average residual deal

outcome of all deals associated with the outgoing investment banker (i.e., in the banker’s industry)

advised by the bank in the two years before the banker leaves, and substract it from the average

residual deal performance of all deals in the investment banker’s industry advised by the same bank

in the two years after the banker leaves. This variable represents the change in acquisition outcomes

for the investment bank from before to after an investment banker moves from the bank (for deals

in that banker’s industry). If a high fixed effects banker is indeed responsible for better acquisition

outcomes, then we should see that acquisition outcomes deteriorate in the pre-switch bank after

the banker leaves. Thus, we regress this variable on the banker fixed effect for the corresponding

acquisition outcome. We also control for the average residual deal outcome in the banker’s industry

in the two years after the banker leaves minus the average residual deal outcome in the banker’s

industry in the two years before the banker leaves. We weigh the regressions by the inverse of the

standard errors of the banker fixed effects to account for the fact that the banker fixed effects are

estimated (Bertrand and Schoar (2003)).

The results of this analysis are reported in Panel A of Table 8. Note that the observations in this

analysis are at the bank-banker level. The coeffi cient estimates on banker fixed effects are negative

and statistically significant for all the acquisition outcome variables. Thus, changes in average

residual CAR, average residual abnormal ROA, and average residual BHAR for deals advised by a

bank in the outgoing banker’s industry are negatively associated with the corresponding fixed effect

of the outgoing banker. In other words, our results show that, when a high fixed effects banker

leaves an investment bank, there is a decline in the performance of acquisitions advised by the bank

in the outgoing banker’s industry. This result is consistent with the conjecture that investment

bankers with high fixed effects are indeed related to high outcome acquisitions.

The converse of the result above is that a bank that hires a high fixed effects banker should see

an improvement in acquisition performance for deals in the industry of the incoming banker. Thus,

we conduct an analysis similar to the one described above. We obtain, for a post-switch investment

31

bank, the average residual deal outcome of all deals associated with the incoming investment banker

(i.e., in the banker’s industry) advised by the bank in the two years after the banker joins, and

substract from it the average residual deal performance of all deals in the investment banker’s

industry advised by the same bank in the two years before the banker joins. This variable represents

the change in acquisition outcomes for the investment bank from before to after the investment

banker moves to the bank. If a high fixed effects banker is indeed responsible for better acquisition

outcomes, then we should see that acquisition outcomes improve in the bank after the banker joins.

Thus, we regress this variable on the banker fixed effect for the corresponding acquisition outcome.

We also control for the average residual deal performance in the banker’s industry in the two years

after the banker joins minus the average residual deal performance in the banker’s industry in

the two years before the banker joins. As before, we weigh the regressions by the inverse of the

standard errors of the banker fixed effects to account for the fact that the banker fixed effects

are estimated. The results of this analysis are reported in Panel B of Table 8. The coeffi cient

estimates are positive and statistically significant for all the acquisition outcome variables except

for abnormal ROA. Thus, when a high fixed effects investment banker joins an investment bank,

there is an increase in the performance of acquisitions advised by the bank in the incoming banker’s

industry.

The results in this section indicate that hiring high human capital investment bankers can add

value. One interpretation of the results in this section is that, from the investment bank’s point

of view, hiring a high fixed effects banker may be valuable to the extent that executing deals that

perform well will benefit the bank (potentially through higher reputation with acquirers). Further,

since we control for acquisition outcomes of all deals in the banker’s industry in the regressions

above, our results are not likely to be driven by the classification of deals associated with investment

bankers based on the industry specialization of the banker. The results above also rule out the

potential alternative explanation that investment bankers can somehow “capture” better quality

deals that are already assigned to their investment bank by the acquirer. Such a conjecture is

inconsistent with our result that the acquisition performance of banks that lose high fixed effects

bankers declines and acquisition performance of banks that gain high fixed effects bankers increases.

32

6.5 Acquirer’s Choice of Investment Bank and Investment Banker Fixed Effects

In this section, we investigate acquirers’choice of investment bank. We conduct linear probability

model and probit regressions of whether or not an acquirer switches to a new investment bank for

acquisition advice when a high fixed effects investment banker moves. We start with the sample of

all acquirers that make an acquisition with an investment banker before the banker switches to a

new investment bank. We then keep all acquirers from this group who conduct another acquisition

after their prior banker moves to a new bank. Our dependent variable in this analysis is a dummy

variable that takes the value one if the acquirer uses the new employer of his prior investment

banker to advice on the post-switch acquisition. In other words, the dependent variable measures

whether or not the acquirer moves to the new bank with the investment banker. Our independent

variables for this analysis are investment banker fixed effects and year of switch dummies. We weigh

our observations by the inverse of the standard error of the investment banker fixed effect. Sample

restrictions described above will reduce our sample size, so we expect a decrease in the power of

this test.

We report the results of the linear probability model and probit analyses in Table 9. In spite

of the lower power, we find a statistically significant relation between investment banker fixed

effects (for CAR and abnormal ROA) and the likelihood of an acquiror following an investment

banker to the new investment bank employing him. In particular, a one interquartile increase in

investment banker fixed effect for CAR leads to an 11 percentage point increase in the probability

that the acquirer will move with the investment banker. Similarly, a one interquartile increase in

investment banker fixed effect for abnormal ROA increases the likelihood that the acquirer follows

the investment banker to the new bank by 5 percentage points.

Thus, the results in this section support the conjecture that investment bankers can add value,

particularly to investment banks, by increasing the client base of the bank. From the point of view

of acquirers, our results imply that a move to a new investment bank is valuable if acquirers believe

that the value gained from acquisition advice depends on the individual investment banker and his

human capital.

33

6.6 Investment Bank’s Stock Price Reaction to Investment Banker Switch

In this section, we investigate how stock prices of publicly traded investment banks are affected

when the news of a high fixed effects banker switch is announced. We therefore estimate cumulative

abnormal returns for investment bank stocks around the time of the announcement of the investment

banker’s move in the news media. This is the announcement period abnormal stock return of the

acquirer calculated as the cumulative returns of the acquirer over a (-1,+1) window around the

investment banker move announcement date minus the predicted returns from a market model.

The market model is estimated starting 30 days prior to the acquisition announcement to 225

days prior to the acquisition announcement. Data on stock returns are obtained from CRSP. We

also report the results for the (-5,+5) window around the move announcement to capture any

information leakage or delay in market reaction.

We regress the bank stock announcement returns on the investment banker fixed effects obtained

in Table 2, log of investment bank assets, market to book ratio of assets of the investment bank,

and year of investment banker switch fixed effects. The regressions are weighted by the inverse of

the standard error of the investment banker fixed effects. Our results are reported in Table 10. The

overall statistical significance of our results is weak, which is expected since many of the investment

banks in our sample are not publicly traded and hence drop out of the sample. In Panel A of

Table 10, we nevertheless find that gaining a high fixed effects banker is positively associated with

a higher stock market reaction for the investment bank that the banker joins. This result holds

for investment banker fixed effects for abnormal ROA and BHAR outcomes of the acquisition. In

Panel B of Table 10, we find statistically weaker but negative relation between CAR and BHAR

investment banker fixed effects and the stock returns for the investment bank that loses the banker.

Thus, we find some evidence that investment banks gaining high fixed effects bankers have a

more positive stock market reaction and investment banks losing high fixed effects bankers have a

more negative stock market reaction. In conjunction with the evidence from previous sections, our

results indicate that investment bankers indeed matter from the point of view of investment banks

and acquirers.

34

6.7 Investment Bankers and Deal Completion

Our final analysis relates acquisition deal completion times and rates to investment banker human

capital. The ability to complete deals is important from an acquirer’s point of view if he views

the acquisition as an important part of his long-run strategy. Completion time may also be an

important consideration if changing market conditions can affect the attractiveness of the acquirer’s

offer to the target shareholders, and also more time after announcements increases the likelihood

of competing bids.

We start with conducting our fixed effects analysis, similar to that in Table 2, using completion

time as our independent variable. All regressions include the same control variables as before. The

results of this analysis are reported in Table 11. From the F-test in Panel A of Table 11, we see

that investment banker fixed effects are also significantly associated with completion times. Panel

B of Table 11 shows that adding investment banker fixed effects increases the adjusted R-squared

of the regression by 8.9 percentage points, while adding investment bank fixed effects increases

the adjusted R-squared by 3.4 percentage points. This greater importance of investment banker

fixed effects in explaining the variation in completion times is also clear from Panel C of Table 11,

which shows that investment banker fixed effects explain 21.4 percent of the variation, which is

nearly double the 10.4 percent explained by investment bank fixed effects. Finally, the results in

Panel D of Table 11 indicate that the investment banker at the 25th percentile is associated with a

reduction in completion time of about 52 days, while the investment banker at the 75th percentile

is associated with a reduction in completion time of about 2.77 days.

We then analyze the relationship between our investment banker characteristic variables (i.e.,

banker education, experience, and connections) and deal completion time as well as deal comple-

tion hazard (using a Cox hazard model). The results of this analysis are reported in Table 12.

Column (1) of Table 12 reports the OLS results where the dependent variable is completion time.

We find that investment bankers with Ivy League non-MBA degrees and greater MBA connections

are associated with shorter deal completion times. This result is similar to those for the acqui-

sition performance measures reported earlier. Deal completion hazard ratio is also higher when

the investment banker associated with the deal has an Ivy League non-MBA degree and greater

MBA connections with the acquiring firm. Moreover, deal completion hazard is also statistically

35

significantly and positively related to MBA degree of the investment banker.

Overall, our results in this section are consistent with the human capital of the investment

banker being significantly associated with lower completion times and higher likelihood of deal

completion. To the extent that deal completion time and probability are important to the acquirer,

investment bankers may be able to provide value to their client firms. Moreover, in many cases,

investment banks obtain fees only if acquisitions are completed. Thus, the human capital of the

investment banker is also valuable to the bank employing him, since greater completion hazard

translates into higher advisory fees.

6.8 Potential Alternative Explanations

In this section, we consider various alternative explanations for our empirical results. One alterna-

tive explanation - that bankers may simply be good at joining higher quality acquisitions that has

already been awarded to the bank - is not consistent with our results. If this were the case, then the

bankers’move should have no relation to the performance of acquisitions in the banker’s industry

for the pre-switch bank or the post-switch bank. To the contrary, we find that a loss of high fixed

effects banker is associated with a decline in the performance of acquirers for the pre-switch bank

and the gain of a high fixed-effects banker is associated with an improvement in the performance

of acquirers for the post-switch bank.

Another possibility is that our results are somehow driven by associating bankers with acquisi-

tion deals based on their industry specialization. In particular, suppose acquisitions in a particu-

lar industry perform extremely well in certain years (consistent with merger waves (e.g., Harford

(2005)), then our results may simply be artifacts of performance of mergers in the banker’s industry

in certain years.28 Various results presented above are inconsistent with such explanations. First,

we find evidence that the investment banker’s association with deal outcomes is persistent across

the various banks that the banker joins. It is unlikely that acquisitions in a particular industry

perform consistently well over a four year period (two years before the switch and two years af-

ter the switch).29 Second, we find evidence that, after a high-fixed effects banker leaves a bank,

the performance of the investment bank in the industry of the outgoing banker deteriorates, after

28Harford (2005) finds that the performance of acquisitions in a merger wave may not be worse, and may perhapsbe somewhat better than those acquisitions that are out-of-wave.29For instance, Harford (2005) uses a two year window to define merger waves.

36

controlling for the acquisition outcomes of all deals in the banker’s industry. Similarly, we find

that after a high-fixed effects banker joins a bank, the performance of the investment bank in the

industry of the banker improves, after controlling for the acquisition outcomes of all deals in the

banker’s industry. These results again suggest that our empirical results are not driven by the

performance of acquisitions in the industry of the banker. Finally, our evidence suggesting that

banker education and connections are associated with deal performance is inconsistent with the

conjecture that certain bankers are lucky in their deal outcomes in the pre-switch banks.

Another alternative explanation is related to optimal matching between investment bankers

and firms. Under this argument, firms select investment bankers that are best suited for that

specific deal. In this case, there are no good or bad bankers, but there are bankers that have

the right skills for particular deals. This argument does not preclude value addition by bankers.

To the contrary, it supports the argument that bankers add value because bankers are chosen on

deals where they have the type of skill needed to provide appropriate advice. Therefore, they

are chosen in situations where their value addition is the highest. Further, the optimal matching

argument predicts that, once we control for deal, firm, and target characteristics, banker fixed

effects and banker characteristics such as education or connections should not be statistically or

economically significant in the deal performance regressions since bankers are optimally chosen

by firms. However, our empirical results indicate that, even after controlling for various deal,

target, acquirer, and bank characteristics, banker fixed effects have a significant association with

our acquisition outcome measures and explain a significant extent of the variation in our acquisition

outcome measures. Further, both high-quality education and MBA education-based connections

are associated with acquisition performance after we control for deal, target, acquirer, and bank

characteristics.

Finally, we consider the possibility that while investment banks with many bankers working

in a particular industry may not be significantly affected by the addition of another investment

banker in that industry, investment banks with few bankers in an industry would be affected much

more by the addition of another investment banker in that industry. Thus, it is possible that our

fixed effects regression results may be driven by banks that have a small extent of coverage in an

industry. While we do not have data on the number of investment bankers that specialize in an

industry at the investment bank level, we can proxy for this using the extent of advisory business

37

done by the investment bank in the banker’s industry. Thus, we control for the dollar value of

advisory business done by the investment bank in the banker’s industry over the last two years as

a fraction of the dollar value of all advisory business done by the investment bank over the last

two years. In unreported tests, we find that our fixed effects results do not change as a result of

adding this control variable to our regressions.30 Further, we split our sample by whether or not

the prior advisory business done by the bank in the banker’s industry is greater than the sample

median, and find that our fixed effects results are significant for both the above median and below

median samples. Thus, it is unlikely that our results are driven by investment banks with a small

extent of advisory coverage in the investment bankers’industry.

7 Conclusion

Using a hand-collected dataset of investment banker turnovers between 1996 and 2006, we address

the following question: Does value creation by investment banks in acquisitions arise primarily

from the reputation, culture, and other institutional strengths of a given investment bank or does

it also arise from the human capital accumulated by the individual investment bankers employed

by that bank? We postulate two channels through which investment bankers can create value for

the investment banks employing them and for the acquirers they advise: a “certification channel,”

whereby investment bankers with greater human capital and reputation may be able to convey their

private information about synergy and other value-relevant variables more credibly to the financial

market; and an “ability channel,”where investment bankers with greater human capital are able

to identify targets with greater synergistic value, better negotiate deal terms with target firms, and

have greater deal completion rates and shorter deal completion times. We test these hypotheses

using the announcement effects of acquisitions, and the post-acquisition operating and stock return

performance of acquirers as outcome variables.

Our findings can be summarized as follows. First, investment banker fixed effects explain

a substantial portion of the variation in acquisition outcomes, over and above the fixed effects

associated with the investment bank itself. Further, proxies for investment banker human capital

such as education and connections are positively related to acquisition outcomes. Second, when

30We also run the analysis above using the fraction of the bank’s advisory business which is in the banker’s industryover the prior five year period and find similar results.

38

investment bankers with greater human capital switch from one investment bank to another, the

losing investment bank experiences a decline in acquisition performance and the gaining investment

bank experiences an increase in its acquisition performance. Third, acquirers follow high human

capital investment bankers who switch from one bank to another. Fourth, moves by investment

bankers with high human capital are associated with a negative stock market reaction for the losing

bank and a positive market reaction for the gaining bank. Finally, investment banker human capital

also has a positive impact on deal completion rates and times even after accounting for the fixed

effects associated with the investment banks employing them.

Our results provide some justification for the Wall Street compensation levels observed in re-

cent years. Since the market for investment bankers is competitive (prominent investment banker

switches are regularly listed in the media), and these bankers are the main value drivers for invest-

ment bank clients, compensation levels will reflect demand for their ability. We should caution,

however, that our results do not provide a justification for all levels of investment banker compen-

sation.

39

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Journal of Financial Economics 13, 137-151.

43

Table 1: Summary Statistics This table presents the summary statistics for our sample of 215 investment banker switches from 1996 to 2006 and summary statistics for the deal performance variables. The investment banker switches were determined using keyword searches on Investment Dealers’ Digest (IDD) as described in the text. Panel A reports year distribution of investment banker turnover in our sample. Panel B reports the title of the investment banker at his previous bank. Panel C reports the summary statistics for our deal level variables and for the average values of the same variables for all acquisitions in the same industry and year as our sample acquisitions.

Panel A: Distribution of Investment Banker Turnover by Year of Turnover

Year of turnover

Number of

bankersPercent

1996 7 3.26

1997 7 3.26

1998 4 1.86

1999 20 9.3

2000 35 16.28

2001 14 6.51

2002 22 10.23

2003 26 12.09

2004 20 9.3

2005 27 12.56

2006 33 15.35

Total 215 100

Panel B: Title of Investment Bankers Prior to Departure

Title before turnover Number of

bankers Percent

VP 19 8.84Director/principal 24 11.16Managing director/partner 115 53.49Department head/group head 50 23.26No title 7 3.26

Total 215 100

Panel C: Summary Statistics of Deal Variables

SampleAll acquisitions in

same industry-year

CAR (-1, +1) Mean -0.010 0.000 Median -0.006 -0.004 N 1324 1324

Abnormal ROA Mean 0.006 0.004 Median -0.015 -0.012 N 1165 1165

BHAR Mean -0.156 -0.143 Median -0.196 -0.208 N 1203 1203

Completion Time Mean 113.95 105.18 Median 99.00 103.13 N 1314 1314

Log Relative Size Mean -1.885 -1.877 Median -1.796 -1.924 N 1269 1269

Log of Assets Mean 7.983 7.439 Median 7.873 7.699 N 1393 1393

Market to Book Mean 2.559 2.508 Median 1.475 1.661 N 1363 1363

All Cash Deal (0/1) Mean 0.235 0.231 N 1395 1395

All Stock Deal (0/1) Mean 0.238 0.231 N 1395 1395

Diversifying Deal (0/1) Mean 0.303 0.332 N 1395 1395

Table 2: Investment Banker Fixed Effects Regressions This table presents the results of fixed effects regressions. In Panel A for each measure of performance (as reported in the first column), the first row includes bank and year fixed effects and control variables, the second row includes bank, year, and banker fixed effects and control variables. The control variables are described in the Appendix. Reported F-statistics is for the joint significance of the banker fixed effects. We also report the number of bankers. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively. Panel B presents for each dependent variable the adjusted R2 of four regressions with different set of independent variables. The first column reports the adjusted R2 for the regression that includes only the control variables. The second column reports adjusted R2 for the regression that includes the control variables and bank fixed effects. The third column reports adjusted R2 for the regression that includes the control variables, bank fixed effects, and banker fixed effects. Panel D presents the distribution of the banker fixed effects that are estimated in Table 2. Each fixed effect is weighted by the inverse of its standard error to account for estimation error. All models are estimated with a constant term.

Panel A: Banker Fixed Effects Performance measure Banker FE Number

of bankersF-test for

banker FEAdj. R2 N

CAR (-1, +1) 0.114 1138CAR (-1, +1) Y 194 88.60*** 0.169 1138 Abnormal ROA 0.184 995Abnormal ROA Y 192 263.71*** 0.542 995 BHAR 0.195 1054BHAR Y 195 191.67*** 0.235 1054

Panel B: Adj. R2 After Adding Bank and Banker Fixed Effects

(1) (2) (3)

Performance measure Base case Bank fixed effects Bank fixed effects and banker

fixed effects CAR (-1, +1) 0.103 0.114 0.169 Abnormal ROA 0.095 0.184 0.542 BHAR 0.103 0.195 0.235

Panel C: Relative Importance of Bank and Banker Fixed Effects in Explaining the Variance of Acquisition Outcomes

(1) (2) (3) (4)

Performance measure

Banker fixed

effects )var(/)ˆ,cov( ker yy ban

Bank fixed

effects )var(/)ˆ,cov( yy j

All other control

variables

)var(/)ˆ,cov( yxy

Residuals )var(/)ˆ,cov( yy

CAR (-1, +1) 0.202 0.022 0.141 0.635 Abnormal ROA 0.477 0.098 0.088 0.336 BHAR 0.194 0.118 0.116 0.571

Panel D: Distribution of Investment Banker Fixed Effects

Performance measure

25th percentile

33rd percentile

50th percentile

66th percentile

75th percentile

CAR (-1, +1) -0.039 -0.023 -0.001 0.017 0.027 Abnormal ROA 0.006 0.018 0.035 0.051 0.060 BHAR -0.359 -0.280 -0.119 0.067 0.130

Table 3: Investment Banker Fixed Effects Placebo Analysis: Randomly Allocating Bankers to Banks

This table reports to the results of a placebo analysis of the investment banker fixed effects regressions. The analysis is done by randomly allocating investment bankers to acquisition deals and running fixed effects regressions similar to those in Table 2 for the simulated data. The randomizations are run 100 times. Panel A presents the median values of change in adjusted R2 when banker fixed effects are added and the median values of F-tests for the simulated data. Panel B reports the relative importance of banker fixed effects in explaining the variance of acquisition outcomes for the simulated data. Panel C presents the distribution of banker fixed effects for the simulated data. Each fixed effect is weighted by the inverse of its standard error to account for estimation error. The Kolmogorov-Smirnov test is for the equality of the actual and simulated distribution of investment banker fixed effects.

Panel A: Adj. R-squared Change and F-test for Banker Fixed Effects

Performance measure

Adj R-sq change on adding banker FE

F-test for banker FE

Main

sample

Randomly allocated banker sample

Main

sample

Randomly allocated banker sample

CAR (-1, +1) 0.055 -0.004 88.6 5.82 Abnormal ROA 0.358 -0.007 263.71 16.45 BHAR 0.04 -0.0001 191.67 11.18

Panel B: Relative Importance of Banker Fixed Effects in Explaining the Variance of Acquisition Outcomes: )var(/)ˆ,cov( ker yy ban

Main Sample Randomly allocated

banker sample

Performance measure Banker

fixed effects

Banker fixed

effects CAR (-1, +1) 0.202 0.047 Abnormal ROA 0.477 0.056 BHAR 0.194 0.057

Panel C: Distribution of Banker Fixed Effects

Performance measure

25th percentile

33rd percentile

66th percentile

75th percentile

CAR (-1, +1) Main sample -0.039 -0.023 0.017 0.027

Randomly allocated banker sample -0.006 -0.004 0.003 0.006 Kolmogorov-Smirnov test p-value 0.000***

Abnormal ROA Main sample 0.006 0.018 0.051 0.060

Randomly allocated banker sample -0.018 -0.015 -0.007 -0.004 Kolmogorov-Smirnov test p-value 0.000***

BHAR Main sample -0.359 -0.280 0.067 0.130

Randomly allocated banker sample -0.033 -0.007 0.068 0.098 Kolmogorov-Smirnov test p-value 0.000***

Table 4: Simulation Results: Changes in Banker Fixed Effects’ Explanatory Ability and Banker Misspecification.

This table reports the results of an out-of-sample simulation analysis of the investment banker fixed effects regressions. The analysis is done by simulating the CARs using randomly generated banker fixed effects. The details of the simulation are described in the text. We report the results of the F-tests, the relative importance of banker fixed effects in explaining the variance of acquisition outcomes, and the change in adjusted R2 when banker fixed effects are added for the base model estimation and the estimation of the models with increasing levels of mis-specification in the investment banker fixed effects.

Percentage of misspecified banker fixed

effects

F-test for banker fixed

effects

Relative importance of banker fixed

effects in explaining the variance of

acquisition outcomes )var(/)ˆ,cov( ker yy ban

Ch. in Adj.

R-sq on adding

banker fixed effects

0% 297.86 0.956 0.924 10% 288.51 0.838 0.703 30% 238.57 0.719 0.501 60% 16.74 0.395 0.113 90% 20.56 0.125 0.008

Table 5: Persistence of Investment Banker Effects This table presents the results of persistence tests. For each measure of acquisition performance we regress an investment banker’s average residual in his post-switch firm on his average residual in the pre-switch firm. We control for fixed effects for the year of the banker switch in these regressions. We report the coefficient estimates from these regressions for the full sample, and for the restricted sample, for which the total number of deals attributed to the investment banker before and after his switch is at least six. All models are estimated with a constant term. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

Coefficient estimate N Adj. R2

CAR (-1, +1)

Restricted sample 0.244** 47 0.039 Full sample 0.019 96 -0.043 Abnormal ROA

Restricted sample 0.320** 41 0.195 Full sample 0.559*** 91 0.334 BHAR

Restricted sample 0.109 43 0.107 Full sample 0.149* 92 0.054

Table 6: Investment Banker Education, Deal Experience and Deal Performance This table presents the results of regressions where the dependent variables are deal performance variables listed in the first row. The main independent variables are Ivy League Graduate Education, which is a dummy variable that takes the value of one if the investment banker went to an Ivy League graduate school, and zero otherwise; Ivy League MBA, which is a dummy variable that takes the value of one if the investment banker has an MBA degree from an Ivy League School, and zero otherwise; Ivy League Non-MBA, which is a dummy variable that takes the value of one if the investment banker has a non-MBA graduate degree from an Ivy League School, and zero otherwise; MBA, which is a dummy variable that takes the value of one if the investment banker has an MBA degree, and zero otherwise; Deal Experience, which is the log of the deal amount advised by the investment banker prior to the switch. For each dependent variable, the regression also includes bank and year fixed effects. The control variables are described in the Appendix. All models are estimated with a constant term. We also report the number of bankers. Robust standard errors, clustered at the acquisition level, are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

(1) (2) (3) (4) (5) (6) CAR

(-1, +1) CAR

(-1, +1) Abnormal

ROA Abnormal

ROA BHAR BHAR Ivy League Graduate Education 0.019** 0.026*** 0.147***

(0.008) (0.008) (0.054) Ivy League MBA 0.016* 0.005 0.119*

(0.010) (0.009) (0.065) Ivy League Non-MBA 0.020 0.071*** 0.232**

(0.015) (0.019) (0.100) MBA -0.010 0.039*** 0.149*

(0.013) (0.014) (0.086) Deal Experience 0.001 0.000 0.001 -0.000 -0.001 -0.001

(0.001) (0.001) (0.001) (0.001) (0.009) (0.009) Log Relative Size -0.001 -0.001 -0.014*** -0.014*** 0.009 0.010

(0.004) (0.004) (0.004) (0.004) (0.022) (0.022) All Cash Deal -0.018* -0.018* 0.010 0.009 0.138* 0.135*

(0.010) (0.010) (0.010) (0.010) (0.071) (0.070) All Stock Deal -0.014 -0.015 -0.007 -0.007 -0.159* -0.153*

(0.016) (0.016) (0.015) (0.014) (0.084) (0.084) No Target Advisor 0.018 0.017 -0.004 -0.004 -0.027 -0.027

(0.014) (0.014) (0.012) (0.012) (0.075) (0.076) Friendly Deal 0.056** 0.061**

(0.026) (0.025) Tender Offer 0.055*** 0.056*** 0.008 0.009 -0.066 -0.073

(0.021) (0.021) (0.036) (0.035) (0.199) (0.201) Two Tier Deal -0.042 -0.049 -0.116*** -0.095** -1.140*** -1.051***

(0.033) (0.034) (0.043) (0.044) (0.238) (0.246) Pct. Shares Owned Before Deal 0.001 0.001 -0.003* -0.004** -0.006 -0.006

(0.001) (0.001) (0.002) (0.002) (0.006) (0.006) Diversifying Deal 0.005 0.004 -0.002 -0.005 -0.040 -0.039

(0.010) (0.010) (0.011) (0.011) (0.065) (0.067) Challenged Deal -0.021 -0.022 0.055 0.056 0.089 0.084

(0.029) (0.029) (0.048) (0.047) (0.213) (0.206) Prior Returns -0.009 -0.009 0.000 0.001 -0.116*** -0.110***

(0.010) (0.010) (0.008) (0.008) (0.035) (0.035) Log of Assets -0.002 -0.002 -0.003 -0.002 0.001 0.002

(0.003) (0.003) (0.003) (0.003) (0.019) (0.019)

Market to Book -0.000 -0.000 -0.001 -0.001 -0.000 -0.000 (0.001) (0.001) (0.001) (0.001) (0.003) (0.003)

ROA -0.023 -0.025 0.251*** 0.252*** 0.604** 0.627** (0.045) (0.045) (0.059) (0.057) (0.288) (0.291)

Leverage 0.010 0.010 -0.030** -0.031*** -0.095 -0.094 (0.015) (0.015) (0.012) (0.012) (0.149) (0.150)

Public Target -0.039*** -0.038*** 0.027** 0.026** 0.155** 0.149** (0.013) (0.013) (0.011) (0.011) (0.074) (0.074)

Private Target 0.000 0.001 -0.015 -0.014 0.094 0.089 (0.013) (0.013) (0.012) (0.012) (0.077) (0.078)

Acquirer Advisor Reputation 0.004 0.009 -0.069 -0.006 0.628 0.713 (0.157) (0.157) (0.143) (0.147) (0.742) (0.750)

Observations 619 619 539 539 572 572 Adjusted R2 0.082 0.081 0.243 0.258 0.199 0.201 Number of Bankers 117 117 116 116 117 117

Table 7: Investment Banker Education Connections to the Acquirer Firm and Deal Performance

This table presents the results of regressions where the dependent variables are deal outcome variables listed in the first row. The main independent variables are Ivy League Graduate Education, which is a dummy variable that takes the value of one if the investment banker went to an Ivy League graduate school, and zero otherwise; Ivy League MBA, which is a dummy variable that takes the value of one if the investment banker has an MBA degree from an Ivy League University, and zero otherwise; Ivy League Non-MBA, which is a dummy variable that takes the value of one if the investment banker has a non- MBA graduate degree from an Ivy League University, and zero otherwise; MBA, which is a dummy variable that takes the value of one if the investment banker has an MBA degree, and zero otherwise; MBA Connection is the number of directors and senior executives of the acquirer firm who got an MBA degree from the same university as the investment banker attributed as an advisor to that acquirer. Non-MBA Connection is the number of directors and senior executives of the acquirer firm who got a non-MBA graduate degree from the same university as the investment banker attributed as an advisor to that acquirer; Deal Experience, which is the log of the deal amount advised by the investment banker prior to the switch. For each dependent variable, the regression also includes bank and year fixed effects. The control variables are described in the Appendix. All models are estimated with a constant term. We also report the number of bankers. Robust standard errors, clustered at the acquisition level, are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

(1) (2) (3) (4) (5) (6) CAR

(-1, +1) CAR

(-1, +1) Abnormal

ROA Abnormal

ROA BHAR BHAR Ivy League Graduate Education 0.015* 0.006 0.082

(0.009) (0.009) (0.053) Ivy League MBA 0.009 -0.014 0.064

(0.011) (0.010) (0.062) Ivy League Non-MBA 0.021 0.045** 0.164*

(0.016) (0.020) (0.099) MBA -0.010 0.015 0.131

(0.015) (0.014) (0.083) MBA Connection -0.005 -0.003 0.033*** 0.036*** 0.158*** 0.154***

(0.005) (0.005) (0.011) (0.010) (0.051) (0.050) Non-MBA Connection 0.003 -0.000 0.007 0.002 0.027 0.031

(0.005) (0.005) (0.007) (0.007) (0.052) (0.055) Deal Experience 0.002 0.002 0.001 0.001 -0.007 -0.008

(0.002) (0.002) (0.002) (0.002) (0.008) (0.008) Log Relative Size -0.001 -0.001 -0.015*** -0.015*** -0.026 -0.025

(0.004) (0.004) (0.004) (0.004) (0.022) (0.022) All Cash Deal -0.027** -0.028** 0.009 0.009 0.126** 0.128**

(0.011) (0.011) (0.012) (0.012) (0.063) (0.063) All Stock Deal -0.035** -0.035** -0.003 -0.002 -0.114 -0.113

(0.018) (0.018) (0.017) (0.017) (0.088) (0.087) No Target Advisor 0.009 0.009 -0.002 -0.001 -0.198** -0.193**

(0.015) (0.015) (0.012) (0.012) (0.077) (0.078) Friendly Deal 0.080*** 0.084***

(0.025) (0.026) Tender Offer 0.063** 0.067** 0.022 0.031 -0.300 -0.309*

(0.028) (0.028) (0.055) (0.053) (0.184) (0.185) Pct. Shares Owned Before Deal 0.000 0.000 -0.002** -0.003*** -0.004 -0.005

(0.001) (0.001) (0.001) (0.001) (0.008) (0.008) Diversifying Deal -0.009 -0.010 -0.005 -0.007 -0.009 -0.010

(0.011) (0.011) (0.012) (0.012) (0.064) (0.065) Challenged Deal -0.026 -0.026 0.014 0.015 0.137 0.131 (0.041) (0.040) (0.043) (0.041) (0.204) (0.197) Prior Returns -0.013* -0.015** -0.008 -0.010 -0.120*** -0.111** (0.007) (0.007) (0.007) (0.007) (0.044) (0.043) Log of Assets -0.002 -0.001 -0.002 -0.001 -0.017 -0.017 (0.004) (0.004) (0.003) (0.003) (0.020) (0.020) Market to Book -0.000 -0.000 -0.000 -0.000 -0.016 -0.016 (0.001) (0.001) (0.001) (0.001) (0.010) (0.010) ROA 0.037 0.031 0.261*** 0.257*** 0.281 0.293 (0.068) (0.067) (0.076) (0.073) (0.378) (0.378) Leverage 0.001 0.000 -0.030** -0.032*** 0.305* 0.307* (0.016) (0.016) (0.013) (0.012) (0.171) (0.171) Public Target -0.033** -0.033** 0.029** 0.027** 0.160** 0.157** (0.013) (0.013) (0.012) (0.012) (0.075) (0.075) Private Target 0.001 0.002 -0.009 -0.008 0.184** 0.177** (0.014) (0.014) (0.013) (0.013) (0.073) (0.074) Acquirer Advisor Reputation 0.079 0.086 -0.055 -0.002 0.431 0.532

(0.149) (0.148) (0.135) (0.135) (0.731) (0.738)

Observations 403 403 375 375 381 381 Adjusted R2 0.107 0.109 0.319 0.335 0.274 0.274 Number of Bankers 94 94 94 94 94 94

Table 8: Banker Fixed Effects and Investment Bank Deal Performance in the Industry Specialization of the Banker

Panel A reports the results of the regressions, where the dependent variable is the average residual deal performance in the pre-switch bank after the switch in the banker's industry minus the average residual deal performance in pre-switch bank before the switch in the banker's industry. Panel B reports the results of the regressions, where the dependent variable is the average residual deal performance in post-switch bank after the switch in the banker's industry minus the average residual deal performance in post-switch bank before the switch in the banker's industry. The independent variables are the investment banker fixed effects for deal outcomes estimated in Table 2 and the average residual deal performance in banker’s industry after the switch minus the average residual deal performance in banker’s industry before the switch. Residual deal performance is calculated as the residual from the regression of deal performance variables on control variables for deal, acquirer, and target characteristics, investment bank fixed effects, and year fixed effects. The regressions are weighted by the inverse of the standard error of the investment banker fixed effects. All models are estimated with a constant term. Robust standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

Panel A: Deal Performance in Pre-switch Bank Dependent variable: Change in average residual acquisition performance for the pre-switch bank (in the investment banker’s industry of specialization)

Performance measure

Banker fixed effect

Change in average residual acquisition

performance in banker's industry around the

switch date

N Adj. R2

Change in average residual CAR (-1, +1)

-0.676*** 0.808*** 144 0.362 (0.110) (0.149)

Change in average residual abnormal ROA

-0.218* 1.056*** 127 0.093 (0.130) (0.330)

Change in average residual BHAR

-0.439* 0.910*** 134 0.189 (0.224) (0.226)

Panel B: Deal Performance in Post-switch Bank

Dependent variable: Change in average residual acquisition performance for the post-switch bank (in the investment banker’s industry of specialization)

Performance measure

Banker fixed effect

Change in average residual acquisition

performance in banker's industry around the

switch date

N Adj. R2

Change in average 0.467*** 0.343 98 0.086 residual CAR (-1, +1) (0.137) (0.323)

Change in average -0.035 0.742*** 88 0.100 residual abnormal ROA (0.063) (0.202)

Change in average residual BHAR

0.585*** 0.763** 94 0.183 (0.199) (0.313)

Table 9: Acquirer’s Choice of Investment Bank and Investment Banker Fixed Effects This table reports the results of linear probability and probit models where the dependent variable is one if an acquirer associated with an investment bank moves to the new bank employing the investment banker. The independent variables are the investment banker fixed effects for deal outcomes estimated in Table 2 and year of banker switch dummies. The regressions are weighted by the inverse of the standard error of the investment banker fixed effects. All models are estimated with a constant term. Robust standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

Dependent variable: Acquirer Switches to New Bank Employing the Investment Banker (0/1)

Linear Probability Model Probit Model

Performance Measure

Banker Fixed Effects

N Adj R2

Banker Fixed Effects

N Pseudo

R2 Change in Prob.

CAR (-1, +1) 1.497** 82 0.065 8.167** 62 0.104 0.110 (0.645) (3.547)

Abnormal ROA 1.100** 82 0.076 5.715*** 61 0.121 0.048 (0.452) (2.035)

BHAR 0.063 82 0.017 0.277 62 0.049 0.040 (0.159) (0.639)

Table 10: Investment Bank’s Stock Price Reaction to Investment Banker Switch and Investment Banker Fixed Effects

Panel A reports the results of OLS regression where the dependent variable is the cumulative abnormal return for the publicly-traded investment bank the banker is switching to around the time of the announcement in the media of the investment banker’s move. Panel B reports the results of the regression where the dependent variable is the cumulative abnormal return for the publicly-traded investment bank the banker is switching from around the time of the announcement in the media of the investment banker’s move. The independent variables are investment banker fixed effects obtained in Table 2, log of investment bank assets, market to book ratio of assets of the investment bank, and year of investment banker switch fixed effects. The regressions are weighted by the inverse of the standard error of the investment banker fixed effects. All models are estimated with a constant term. Robust standard errors are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

Panel A: Market reaction to Investment Bankers’ Switch at the Post-switch Bank

Investment Bank Announcement Returns Window

(-1,+1) (-5,+5) (-1,+1) (-5,+5) (-1,+1) (-5,+5)

Banker CAR Fixed Effects 0.004 -0.098(0.016) (0.112)

Banker Abnormal ROA Fixed Effects 0.090* 0.226** (0.047) (0.093)

Banker BHAR Fixed Effects 0.015** 0.023* (0.006) (0.013)

Log of Bank Assets 0.001 -0.010** 0.005*** -0.005 0.001 -0.010* (0.002) (0.005) (0.002) (0.005) (0.002) (0.005)

Market to Book -0.120* -0.411** 0.020*** 0.008 -0.119 -0.421** (0.068) (0.194) (0.004) (0.011) (0.070) (0.183)

Observations 100 100 98 98 101 101 Adjusted R-squared 0.035 0.084 0.149 0.054 0.089 0.086

Panel B: Market reaction to Investment Bankers’ Switch at the Pre-switch Bank Investment Bank Announcement Returns Window

(-1,+1) (-5,+5) (-1,+1) (-5,+5) (-1,+1) (-5,+5) Banker CAR Fixed Effects -0.050* 0.034

(0.026) (0.049) Banker Abnormal ROA Fixed Effects -0.009 -0.017

(0.035) (0.043) Banker BHAR Fixed Effects -0.004 -0.025*

(0.007) (0.012) Log of Bank Assets 0.006*** 0.004 0.005** 0.004 0.005** 0.003

(0.002) (0.003) (0.002) (0.003) (0.002) (0.003) Market to Book 0.067 -0.176* 0.057 -0.09 0.045 -0.11

(0.045) (0.093) (0.047) (0.084) (0.038) (0.086)

Observations 85 85 84 84 87 87 Adjusted R-squared 0.232 0.075 0.308 0.15 0.291 0.147

Table 11: Deal Completion Time and Investment Banker Fixed Effects This table presents the results of fixed effects regressions for the dependent variable, deal completion time. In Panel A the first row includes bank and year fixed effects and control variables, the second row includes bank, year, and banker fixed effects and control variables. The control variables are described in the Appendix. Reported F-statistics is for the joint significance of the banker fixed effects. We also report the number of bankers. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively. Panel B the adjusted R2 of three regressions with different set of independent variables. The first column reports the adjusted R2 for the regression that includes only the control variables. The second column reports adjusted R2 for the regression that includes the control variables and bank fixed effects. The third column reports adjusted R2 for the regression that includes the control variables, bank fixed effects, and banker fixed effects. Panel C reports the relative importance of bank and banker fixed effects in explaining the variance of deal completion time. Panel D presents the distribution of the banker fixed effects that are estimated in Panel A. Each fixed effect is weighted by the inverse of its standard error to account for estimation error. All models are estimated with a constant term.

Panel A: Banker Fixed Effects and Deal Completion Time

Dependent Variable Banker FE Number of

bankers F-test for banker FE

Adj. R2 N

Completion Time 0.360 1077 Completion Time Y 195 208.07*** 0.449 1077

Panel B: Adj. R2 After Adding Bank and Banker Fixed Effects for Deal Completion Time

(1) (2) (3)

Base case Bank fixed effects Bank fixed effects

and banker fixed effects 0.326 0.360 0.449

Panel C: Relative Importance of Bank and Banker Fixed Effects in Explaining the Variance of Deal Completion Time:

(1) (2) (3) (4) Banker fixed

effects

)var(/)ˆ,cov( ker yy ban

Bank fixed

effects

)var(/)ˆ,cov( yy j

All other control

variables

)var(/)ˆ,cov( yxy

Residuals

)var(/)ˆ,cov( yy

0.214 0.104 0.268 0.414

Panel D: Distribution of Investment Banker Fixed Effects for Deal Completion Time 25th

percentile 33rd

percentile 50th

percentile 66th

percentile 75th

percentile -51.745 -46.343 -29.28 -14.44 -2.769

Table 12: Investment Bankers and Deal Completion The first column of this table presents the results of OLS regressions, where the dependent variable is the completion time. The second column presents the results of Cox Hazard model for deal completion (hazard ratios are reported for this column). The main independent variables are Ivy League MBA, which is a dummy variable that takes the value of one if the investment banker has an MBA degree from an Ivy League University, and zero otherwise; Ivy League Non-MBA, which is a dummy variable that takes the value of one if the investment banker has a non-MBA graduate degree from an Ivy League University, and zero otherwise; MBA, which is a dummy variable that takes the value of one if the investment banker has an MBA degree, and zero otherwise; MBA Connection, which is the number of directors and senior executives of the acquirer firm who got an MBA degree from the same university as the investment banker attributed as an advisor to that acquirer; Non-MBA Connection, which is the number of directors and senior executives of the acquirer firm who got a non-MBA graduate degree from the same university as the investment banker attributed as an advisor to that acquirer; Deal Experience, which is the log of the deal amount advised by the investment banker prior to the switch. For each dependent variable, the regression also includes bank and year fixed effects. The control variables are described in the Appendix. All models are estimated with a constant term. We also report the number of bankers. Robust standard errors, clustered at the acquisition level, are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.

(1) (2) Deal Completion

Time: OLS Deal Completion:

Cox Hazard Model Ivy League MBA -4.430 1.013

(9.201) (0.167) Ivy League Non-MBA -34.834* 1.715*

(18.345) (0.477) MBA -23.450 1.465*

(14.611) (0.314) MBA Connection -10.831* 1.309***

(5.889) (0.110) Non-MBA Connection -5.789 1.167

(4.734) (0.191) Deal Experience 1.381 0.982

(1.209) (0.021) Log Relative Size 17.478*** 0.730***

(5.132) (0.043) All Cash Deal -5.727 1.444**

(10.273) (0.269) All Stock Deal -8.897 1.065

(12.189) (0.209) No Target Advisor -1.670 1.231

(11.863) (0.297) Tender Offer -38.505** 1.608

(17.451) (0.509) Pct. Shares Owned Before Deal 2.970*** 0.967***

(0.847) (0.012) Diversifying Deal -1.874 1.375*

(11.381) (0.247) Prior Returns -2.997 1.177

(4.301) (0.141) Challenged Deal 30.134 0.569*

(26.456) (0.188)

Log of Assets 11.912*** 0.796***

(3.117) (0.043) Market to Book 0.022 1.008

(0.384) (0.008) ROA -2.519 2.036

(40.261) (1.689) Leverage -0.634 1.061

(7.704) (0.156) Public Target 32.237** 0.739

(15.147) (0.146) Private Target -26.404** 2.821***

(11.076) (0.740) Acquirer Advisor Reputation -245.093** 5.912

(118.053) (11.742)

Observations 365 378 Wald Chi2 557.6*** Adjusted R-squared 0.392 Number of Bankers 93 93

Figure 1a: Percentage of Variance Explained by Investment Banker Fixed Effects using Simulated Data, by Degree of Investment Banker Fixed Effects Misspecification

Figure 1b: Percentage of Variance Explained by Banker Fixed Effects using Sample Data, by Actual vs. Randomized Banker Fixed Effects.

Figure 2a: Distribution of Simulated Investment Banker Fixed Effects

Figure 2b: Distribution of Sample Investment Banker Fixed Effects for

Cumulative Abnormal Returns (Actual vs. Randomized)

01

02

03

04

0D

ensi

ty

-.3 -.2 -.1 0 .1 .2Banker FE

Sample FE DistributionRandomized Banker FE Distribution

05

10

15

20

Den

sity

-.2 -.1 0 .1 .2Banker FE

No Misspecification

10% Misspecification

30% Misspecification

60% Misspecification

90% Misspecification

Appendix: Variable Definitions

Variable Descriptions Variable Name Description Deal Performance CAR (-1, +1) Cumulative returns of the acquirer over a three day period around the

acquisition announcement (i.e., from one day prior to one day after the announcement of the deal) minus the predicted returns from a market model over the same period.

BHAR The three year buy-and-hold returns of acquirers starting from one month after the acquisition completion date adjusted by the buy-and-hold returns on a reference portfolio calculated using a methodology similar to Lyon, Barber, and Tsai (1999). Details of the methodology are described in the text.

Abnormal ROA The residual from the regression of the average three year post-acquisition industry-adjusted operating income to assets ratio (ROA) on the average three year pre-acquisition industry-adjusted ROA.

Completion Time The time (in days) between the deal announcement and deal completion.

Deal Characteristics Log Relative Size Log of the transaction value of the deal divided by the market capitalization

of the acquirer. All Cash Deal A dummy variable equal to 1 if the acquisition is all cash deal. All Stock Deal A dummy variable equal to 1 if the acquisition is all stock deal. No Target Advisor A dummy variable equal to 1 if the target does not have an advisor. Friendly Deal A dummy variable equal to 1 if the deal is friendly. Tender Offer A dummy variable equal to 1 if the deal is a tender offer. Two Tier Deal A dummy variable equal to 1 if the deal is a two tier deal. Pct. Shares Owned Before Deal The percentage of target’s shares outstanding owned by the acquirer before

the deal. Diversifying Deal A dummy variable equal to 1 if the primary Fama-French (1997) industry of

the acquirer is different from that of the target. Challenged Deal A dummy variable equal to 1 if the deal is challenged. Acquirer Characteristics Prior Returns Compounded monthly stock returns of the acquirer calculated over 12 months

starting from one month before the acquisition announcement. Log of Assets Log of book value of total assets.

Market to Book Market value of acquirer’s total assets divided by book value of total assets.

ROA Operating income divided by book value of total assets.

Leverage Book value of debt divided by book value of total assets.

Target Characteristics Public Target A dummy variable equal to 1 if the target is public.

Private Target A dummy variable equal to 1 if the target is private. Bank Characteristics Acquirer Advisor Reputation The total transaction value of deals advised by the bank divided by the total

transaction value of all deals in the year prior to the sample deal.