Copyright

by

Parth Ramanan Venkat

2017

The Dissertation Committee for Parth Ramanan Venkatcertifies that this is the approved version of the following dissertation:

The Effect of Mergers on Human Capital: Evidence

from Sell-Side Analysts

Committee:

Laura Starks, Supervisor

Jonathan Cohn

Andres Almazan

Cesare Fracassi

Michael Clement

The Effect of Mergers on Human Capital: Evidence

from Sell-Side Analysts

by

Parth Ramanan Venkat, B.S. BIO & BUS ECON & MGT; M.S. Fin.

DISSERTATION

Presented to the Faculty of the Graduate School of

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of

DOCTOR OF PHILOSOPHY

THE UNIVERSITY OF TEXAS AT AUSTIN

May 2017

I dedicate this work to the memory of my grandfather, Rajnikant S. Patel,

for inspiring me, and to the memory of my first finance colleague, Daniel

Strenge, for sharing with me his dream of doing a PhD in finance.

Acknowledgments

My cohort: Many PhD students lack a single close friend. Who could

imagine having six? Adam, Mark, Nathan, Nicole, Sophia, and Zack: not sure

how you put up with me for six years, but you deserve a PhD just for that. To

all the McCombs students: I cannot mention you all, but Gonzalo, your life

optimization has taught me so much; Mitch, your never-ending social energy

will never cease to inspire me; and Billy, not sure how you talk to me every

day, but your therapy and friendship powered me through the job market.

To McCombs Faculty and Staff: How does such a generous and welcoming

group get created? The time and compassion you have shared with me is

illogical. I would be remiss not to single out Katie for your ability to get

everything done, Greg for ushering me to Austin, Cesare for always pushing

me to write finance, and Andres for believing in me from before you admitted

me. Sheridan, people with your accomplishments are not supposed to lack an

ego and have your unbridled curiosity. I will never have your vita, but I strive

for your passion.

Laura and Jonathan, between tenure, teaching, and deaning, how you

were able to invest so much time and energy in me? Thank you for putting

up with my mood swings, crazy ideas, and writing blocks. Your faith in my

ability is the primary motivation behind my work ethic.

v

The Effect of Mergers on Human Capital: Evidence

from Sell-Side Analysts

Publication No.

Parth Ramanan Venkat, Ph.D.

The University of Texas at Austin, 2017

Supervisor: Laura Starks

While mergers often create value, there exist costs that can limit or

offset potential synergies. Literature in a number of different areas of business

suggests these costs can result from issues related to the impairment of firms’

human capital, often when two workforces are being integrated. However,

there exists minimal empirical literature characterizing these costs. In this

dissertation, I use a unique setting in which to examine these integration issues:

sell-side analysts in brokerage house mergers. This setting allows for a better

characterization of the integration issues that leads to a better understanding

of how mergers can impact human capital.

In Chapter 1, I provide an overview of the research questions I address

and how they relate to the current state of the merger literature and the sell-

side analyst literature. I also introduce the conceptual basis for the research

questions - in particular, the role of human capital within a firm.

vi

In Chapter 2, I introduce the data employed in the paper, primarily

I/B/E/S data, along with data on the set of mergers I construct for the sample.

I also introduce two novel measures–quality and redundancy–which are specific

to sell-side analysts. While these measures are critical to understanding how

mergers impact human capital, they may also prove valuable to researchers

addressing other issues.

In Chapter 3, I use the two measures from Chapter 2, as well as the

human capital framework from Chapter 1, to understand and empirically de-

monstrate how mergers impact human capital.

In Chapter 4, I discuss appropriate use of brokerage house mergers as

instruments in past and future literature.

In Chapter 5, I conclude.

vii

Table of Contents

Acknowledgments v

Abstract vi

List of Tables x

List of Figures xii

Chapter 1. Mergers, Human Capital, and Analysts 1

Chapter 2. Data and Measure Development 13

2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Mergers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Analyst Quality . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 Redundancy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Chapter 3. The Impact of Brokerage House Mergers on AnalystOutput: Empirical Results 28

3.1 Hypothesis Development . . . . . . . . . . . . . . . . . . . . . 28

3.2 Results and Empirical Design . . . . . . . . . . . . . . . . . . 31

3.2.1 Overall Estimate Level Changes - Difference in Differences 31

3.2.2 Merger-Related Attrition . . . . . . . . . . . . . . . . . 33

3.2.3 Impact of Mergers on Individual Analyst Accuracy . . . 43

3.2.4 Competition, Crashes, or Merger Integration Issues . . . 46

3.2.5 Merger’s Uncontrolled Impact on Human Capital Output 49

3.3 Further Discussion of Identification Issues . . . . . . . . . . . . 51

Chapter 4. Appropriate Use of Brokerage House Mergers as anInstrument 75

viii

Chapter 5. Conclusion 79

Bibliography 81

ix

List of Tables

2.1 Mergers . . . . . . . . . . . . . . . . . . . . . . . 25

2.2 Summary Statistics For Non-Merger sample. . . . . . . . 26

2.3 Baseline Pr(Separation) Outside of Merger Announcements . 27

2.4 Summary Statistics of Measures. . . . . . . . . . . . . 27

3.1 Estimate Level Operation Changes - Difference-in-Differences57

3.2 Attrition Around Merger Announcements Driven by Target

Redundancy . . . . . . . . . . . . . . . . . . . . . 58

3.3 Attrition Around Merger Announcements - Quality . . . . 59

3.4 Pr(Find Analyst Job)|Separation for Target Analysts. . . . 60

3.5 Redeployment Post Job Transfer . . . . . . . . . . . . 61

3.6 Target Analyst Report Output around Merger Announcements62

3.7 Behavior. . . . . . . . . . . . . . . . . . . . . . . 63

3.8 Analyst Changes in Forecast Error . . . . . . . . . . . 64

3.9 Analyst Changes in Forecast Error - Which Analysts . . . . 65

3.10 Mergers Subsets . . . . . . . . . . . . . . . . . . . 66

3.11 Estimate Level Operation Changes - Difference-in-Differences

- by Merger Type . . . . . . . . . . . . . . . . . . . 67

3.12 Attrition around Merger Announcements - Split by Merger

Type . . . . . . . . . . . . . . . . . . . . . . . . 68

3.13 Analyst Changes in Forecast Error - By Merger Type . . . 69

3.14 Mergers and Output Quality Changes . . . . . . . . . . 70

3.15 Estimate Level Operation Changes - Regressions . . . . . 71

x

3.16 Forecast Error Changes - by Merger Type . . . . . . . . 72

3.17 Estimate Level Operation Changes - Difference-in-Differences - By in Recession . . 73

3.18 Redundant Versus Non-Redundant - Difference-in-Differences74

xi

List of Figures

3.1 Forecast Error Evolution Around Mergers . . . . . . . . . . . 53

3.2 Forecast Error Evolution: Recessions . . . . . . . . . . . . . . 54

3.3 Evolution of Bias and Accuracy by InMerger and Redundancy 55

3.4 Evolution Accuracy by InMerger and Experience . . . . . . . . 56

4.1 Evolution of Forecast Error In and Out of Downturns . . . . . 78

xii

Chapter 1

Mergers, Human Capital, and Analysts

While existing research characterizes the overall value implications of mergers,

understanding how mergers create or destroy value requires further study of

how mergers affect firm assets. Perhaps the least understood implication is

how mergers impact the value of human capital, arguably the most important

class of assets in modern firms. While mergers may unlock value, the process of

integrating two workforces may impose costs that limit synergies. In a recent

survey, a majority of companies that reported their own mergers as “failing”

assigned blame to “people and integration issues.”1 Certain issues can be short

term, such as employee or management distraction, while other issues can be

longer term, such as unresolved cultural mismatch or lost key talent. The

goal of this dissertation is to further our understanding of how mergers impact

firms’ ability to acquire, develop, and retain human capital.

Using a large sample of sell-side analysts, I show that mergers can have

a negative impact on human capital output. Specifically, the forecast error of

estimates produced by analysts from merging houses increases by 10% relative

to forecasts of analysts from non-merging houses for the same set of covered

1The survey of almost 90 Merger and Acquisition professionals from McKinsey & Com-pany (Deutsch and West [2010]).

1

firms. To put this effect in context, 10% is slightly larger than the forecast er-

ror difference between a perfect estimate and the estimate at the 25th forecast

error percentile. The primary contribution of my paper is to decompose this

merger induced forecast error increase into two channels. First, high-quality

analysts often leave target houses if they are likely to cover different firms

within the merged entity.2 This attrition suggests that high-skilled employ-

ees exercise outside options to avoid abandoning specialized human capital.

Second, analysts who work for acquiring houses are less accurate in the four

months after a merger.3 This temporary impairment suggests that distracti-

ons due to the integration of operations or team-related disruptions can impair

employee performance.

Mergers are important for firms. Worldwide in 2015, there were over

44,000 mergers and acquisitions worth over $4.5 trillion.4 Because of the quan-

tity and size of deals, understanding merger motivations, integration proces-

ses, and the value implications of mergers are important to corporate finance.

CEOs discuss several means by which mergers can create value, such as faci-

litating geographic or product diversification, achieving economies of scale or

scope, or obtaining new technology, intellectual property, or human capital.

Existing research shows that mergers create value on average, when

examining the combined effect on the target and the bidder.5 However, in the

2There is no change in acquiring house analyst attrition.3There is no significant change in the accuracy of target house analysts who are retained.4https://imaa-institute.org/mergers-and-acquisitions-statistics/ as of 4/17/20175See Betton et al. [2008] on combined announcement returns and Healy et al. [1992]; see

2

cross-section, some mergers, such as Exxon and Mobil combining, were highly

successful, while others, such as AOL’s merger with Time Warner, were disas-

trous (Weston [2002]). To further our understanding of why this heterogeneity

exists, we need to understand how mergers affect the underlying assets of the

target and bidder. Some papers have shown channels of synergy creation.

Sheen [2014] uses microdata to show that mergers can create value by redu-

cing costs through production consolidation, and Hoberg and Phillips [2010]

use microdata to show that mergers can facilitate product development. Most

research on why mergers destroy value focuses agency issues, such as empire

building desires of the CEO. Consistent with agency frictions, Masulis et al.

[2007] show that poorly governed firms are more likely to destroy shareholder

value via mergers, and Harford et al. [2012] show the value destruction often

comes from poor target choice.

Between the extremes, there exists normative management literature

that argues poor management during post-merger integration (PMI) can cause

well-intentioned mergers to underperform.6 Management research advises

companies to carefully monitor external and internal risks, assure some level of

culture compatibility, and maintain communication and leadership. Similarly,

managers elaborate on “people and integration issues,” to include “cultural

mismatch, loss of key talent, lack of management commitment [and] lack of

employee motivation.”7 PMI also appears in finance theory, such as Huang

Andrade et al. [2001] on operating performance.6Graebner et al. [2016].7Deutsch and West [2010] and referenced in Shermon [2011].

3

et al. [2015], who assume that integration can be lengthy and costly and they

explore the theoretical implications on firm capitalization. In sum, there exists

a management literature that warns against integration issues, management

surveys that cites them as being consequential to merger success, and theory

that assumes they are material. The goal of this paper is to characterize the

”people” issues firms face during post-merger integration.

The theory of the firm literature provides a framework for thinking

about people and integration issues. Williamson [1985] asked why are there so

many firms. If two firms merge, either the firms can operate as separate divi-

sions and common ownership has no value implication. If there are redundant

costs, the merger can increase operating leverage and improve efficiency and

value. What are the diseconomies of scale in equilibrium that limit mergers?

Seminal work from Grossman and Hart [1986] and Hart and Moore [1990]

(GHM) attempts to answer Williamson by incorporating the property rights

view of the firm into an incomplete contracting framework. They argue that

if contracts are incomplete, meaning every contingency cannot be accounted

for, ownership and hence firm boundaries matter. In situations that require

relationship-specific investments, such as investing in firm-specific human ca-

pital, and that allow for ex-post bargaining, a potential hold-up problem can

result in ex-ante underinvestment. GHM suggest that ownership can solve the

underinvestment problem by providing power to the party making the original

investment. Hence, by limiting ownership, merging two firms can be inefficient.

One critique of the GHM framework is that, while ownership can ex-

4

plain why owner-operated businesses exist independently, employee ownership

in large corporations is limited and unlikely to explain larger firm boundaries.

To generalize and build upon the GHM framework, Rajan and Zingales [1998]

argue that ownership is not necessarily the best or most relevant source of

power for employees. They suggest instead “access”, defined as “the ability

to use, or work with, a critical resource.” If this access is privileged, then the

employee is incentivized to invest in specialized human capital - human capital

that is valuable in conjunction with access to the critical resource. This speci-

alized human capital, combined with employee outside options, is a source of

power which can solve hold-up problems.

Human capital, which Goldin [2016] defines as “the stock of skills that

the labor force possesses,” is becoming more important. In Zingales [2000],

Zingales writes

The firm is changing ... Human capital is emerging as the most

crucial asset. The interaction between the nature of the firm and

corporate finance issues has become so intimate that answering the

fundamental questions in theory of the firm has become a precon-

dition for any further advancement in corporate finance.

Supporting the growing importance of human capital, papers such as Acemoglu

[2002] and Abowd et al. [2005] show that there is a widening skill-based wage

gap driven by a widening productivity gap between high and low human capital

employees. Zingales observes that firms are changing from asset-intensive,

5

vertically integrated firms with tight control over employees into human capital

intensive, stand-alone firms with loose forms of collaboration.

Because human capital is becoming more important to firms and pri-

vileged access is a necessary condition for optimal human capital investment,

we are led to a potential merger cost - the people and integration issue. If the

merged firm is unable to provide redundant employees the privileged access

they had in the independent firm, then their previously accrued specialized

human capital may not be valuable in the merged entity. In situations where

employees have specialized human capital, they may choose to exercise outside

options rather than be redeployed if redeployment involves abandoning that

human capital. In addition, if employees are redeployed into new roles, and if

human capital takes time to build, short-term disruptions can result.

In order to empirically test how such a framework can result in human

capital related merger costs, we require data on high human capital employees

who go through mergers. Sell-side analysts employed by merging brokerage

houses provide such a setting. This setting conveys several advantages. First,

analyst groups consist almost solely of human capital (i.e., their expertise

and connections). This allows me to isolate the effect of mergers on human

capital from the effects on other classes of assets. Brown et al. [2015] provide

survey evidence that analysts’ key human capital comes from connections to

management, and Swem [2016] provides empirical evidence that connections to

institutional investors are also valuable. Papers such as Gleason and Lee [2003]

show that analysts’ information is material meaning their human capital does

6

have real value to financial markets. Additionally, analysts’ human capital is

specialized and partially observable. Their expertise and connections exist for

specific firms, which take considerable time and investment to build. Also, the

lead analyst at a brokerage house has privileged access in that he or she is the

primary expert that brokerage house has for a given firm. This maps well into

the Rajan and Zingales [1998] framework. Finally, in the data, each analyst

has a personal identifier that allows me to isolate retention and separation

decisions, as well as to create a slow moving measure of analyst quality based

on an analyst’s entire historical output.

I first corroborate the previous finding from Wu and Zang [2009] that

target analyst monthly attrition increases (to 13% compared to 1% unconditi-

onally), while acquiring house analyst attrition does not, and that this effect is

substantially stronger for target analysts who cover a firm already covered by

the acquiring house. I confirm that this is true even though acquiring house

analysts are not systematically higher in quality, where analyst quality is the

slow moving measure I develop. My primary contribution is developing this

measure of analyst quality and using it to show that the monthly attrition

for the highest quality (i.e., top quintile) target analysts’ increases to 20%,

while the lowest quality (i.e., bottom quintile) target analysts’ monthly attri-

tion only increases to 7%.8 The quality effect is over 50% larger for redundant

target analysts. While this high quality attrition might be partially driven by

cost savings (because wages are likely correlated with quality), higher-quality

8This effect is monotonically increasing across quintiles.

7

analysts are more likely to find another analyst job, which suggests that higher

quality analysts have better outside options. In addition, contingent on finding

a new analyst job, the analysts that leave cover almost all of the firms they

previously covered, implying that their specialized human capital is valuable.

Finally, by using redundancy as a plausibly exogenous shock to an analyst’s

probability of separation, I find that the increased probability of separation

reduces the monthly number of reports an analyst produces. This result sugge-

sts that when target analysts know that job retention likely involves switching

roles, they shift their efforts elsewhere (e.g., towards finding a new job) rather

than increase their efforts to compete within the merged firm.

The result that an increase in the probability of separation reduces

analyst output is not obvious. Termination risk could potentially induce higher

effort if employees fear job loss and believe that increasing effort can reduce

termination risk. Instead, consistent with the Rajan and Zingales framework,

a firm’s commitment to an employee’s continued employment is essential to

drive optimal effort, meaning an increase in termination risk can decrease

employee output. This mechanism is similar to some in the capital structure

literature.9 Specifically, a firm’s optimal capital structure may be lower all

else equal because too much debt can increase the probability of financial

distress and remove a firm’s ability to commit to their stakeholders including

employees.

9Titman [1984] Titman and Wessels [1988]

8

Exploiting variation in merger motivations, I find that the attrition

impacts above are driven almost entirely by mergers in which acquiring the

analysts of the target house is not a stated goal of the merger according to

the merger announcements. Analysts seeing that information may be more

likely to leave because they could interpret this as a signal that their human

capital is not valued by the acquiring house. In addition, the mergers in which

the attrition effects are the largest are the mergers that experience the highest

overall increase in forecast error, confirming that this flight of human capital

has an overall impact.

In summary, high quality target analysts, especially when redundant,

are more likely to separate and to find another analyst job covering the same

firms. Additionally before separating, they are less productive. These results

are consistent with acquiring houses not being able to provide analysts the

privileged access they had in the separate entity. For high quality analysts

who have significant specialized human capital, losing this access and being

redeployed to cover new firms is costly. High quality analysts with better

outside options when it appears the acquiring firm does not value their human

capital may find a new job that will not require abandoning their human

capital. This result is in contrast to that of Tate and Yang [2015], who show

that diversifying mergers can improve output by facilitating human capital

redeployment. My evidence suggests that redeployment may be costly for

employees who have specialized human capital, because redeployment involves

abandoning that human capital. In those situations, employees may choose to

9

exercise outside options instead of waiting to be redeployed.

Second, I document that acquiring house analysts, who remain em-

ployed throughout the merging process, suffer temporary output impairment

using a difference-in-differences framework that includes individual analyst

fixed effects.10 This drop is larger than the difference between the average

star analyst’s accuracy and non-star analyst’s accuracy, where star is defined

by Institutional Investor All-America Research Team designations. The effect

is short lived and is no longer significant after four months. Anecdotal evi-

dence from discussions with sell-side analysts suggests that employees can be

distracted by junior staff shuffling, training, client-base expansion, or moving

offices. Breaking this effect down, the effect is driven by high not low quality

analysts, redundant not unique analysts, and analysts who cover fewer firms

not more after the merger. In addition, rookie analysts (analysts who are

new to the data) who start at merging houses after merger completion have

significantly larger forecast errors than non-rookie merger analysts, while this

rookie / non-rookie split does not exist in non-merging houses.

A related industry-wide increase in forecast error after brokerage house

mergers has been documented by Hong and Kacperczyk [2010], who attri-

bute the merger-related performance decline to industry consolidation and the

accompanying decrease in analyst competition. Hong and Kacperczyk [2010]

argue that forecast error increases because analysts intentionally decide to bias

10Target analysts who keep their jobs improve their accuracy at times, but this effect isnot significant across all mergers.

10

estimates upwards in order to cater to corporate clients. My differential impact

– forecast error increasing more for merging houses than non merging houses

for the same underlying firms – finding cannot be explained by competition

declines, because competition shocks the underlying firms not the analysts

themselves. Therefore competition should not impact analysts within merging

houses more than analysts outside of mergers who cover the same firms. Si-

milarly, because there are not subsequent recoveries in analyst competition,

competition cannot explain the temporary forecast error increase. Finally, I

identify whether an underlying firm is (a) covered by both the target and the

acquiring house or (b) covered by only the target house or only the acquiring

house because the former is much more likely to see analyst separations and

decreases in competition. I use this identification as a shock to the intensity

of the competition decline, dividing estimates into large competition shock

estimates (the firms covered by both the acquirer and the target) and small

competition shock estimates (those covered by only one or the other). There is

no significant difference in merger’s impact on forecast error between these two

groups, which further supports that the merger impact is due to unintended

consequences of the merger process as opposed to intentional bias.

To further differentiate unintended errors stemming from workforce in-

tegration issues from intentional bias resulting from changing priorities, I ex-

ploit a structural shift in analyst incentives, specifically the 2003 Global Ana-

lyst Research Settlements (GARS), which created “brick walls” between the

11

research and investment banking divisions of large investment firms.11 Even

after this plausibly exogenous shock to analysts’ incentives, the impairment

of human capital output persists, implying that results are more likely due to

workforce integration issues as opposed to changing priorities.

My attrition results with respect to redundancy are consistent with

Wu and Zang [2009]. I build on their result by finding the quality attrition

effect and by showing that high quality analysts do post-separation. In ad-

dition, their analysis of how attrition affects forecast error differs from mine.

First, Wu and Zang [2009] find that star or top performer attrition does not

affect forecast error. This may be due to their use of a less comprehensive

measure of quality, which lacks within target variation, potentially resulting

in a lack of power. Second, forecast error increases are not permanent, and

although workforce integration does reduce human capital stock, houses are

able to recover. Finally, their result is hard to disentangle from alternate

explanations. Because analysts are optimistic, crashes can create a spurious

correlation between attrition and forecast error.12 By explicitly controlling

for market downturns, I verify that workforce integration issues drive forecast

error increases.

11See https://www.sec.gov/news/speech/factsheet.htm.12Many of their mergers occurred in 1999 to 2001 before the DotCom Crash.

12

Chapter 2

Data and Measure Development

In this chapter, I introduce the analyst data, my mergers and develop two novel

measures–Quality and Redundancy – that capture characteristics of analysts

and their mergers. Quality is a measure of an analyst’s past performance

based on a large set of performance measures and brokerage house’s revealed

preferences. Like an analyst’s reputation, Quality is slow-moving, in that it

takes time to build and is not volatile, and personal, in that it is tied to the

individual more than the house and therefore providing within-house variation.

Redundancy refers to what percentage of the firms that an analyst covers are

already covered by the other house in the merger. Because analyst human

capital is specialized Redundancy may be particularly relevant to the merger

setting where there may be overlapping expertise in the merging entities.

2.1 Data

Information on analysts comes from the Thomson Reuters Institutio-

nal Brokers Estimate System (I/B/E/S) database spanning the period 1980

through 2013. The I/B/E/S detail history U.S. earnings estimate file provides

individual analyst earnings forecasts, buy-sell-hold recommendations, and re-

13

ported earnings. Unique analyst identifiers allow the tracking of analyst careers

across brokerage houses. All observations with analyst ID number (ANALYS)

equal to 1 or 0 are dropped because they are placeholders. Similarly, estimates

for several indices (DOWI, MID1, RUS2, S4, S5, SAP1, SAP6) are dropped,

as these analysts update their estimates at a very high rate, which makes their

activity measures outliers. While the majority of estimates are annual, 40%

are also quarterly, so unlike previous studies that focus only on annual estima-

tes for convenience of interpretation, I include all estimates, but also conduct

analyses on the annual estimates alone as robustness checks and control for

fiscal periods using fixed effects.

Star analysts are identified using Institutional Investor magazine ran-

kings All-American Research Team poll (e.g., Clement and Tse [2005], Cohn

and Juergens [2014]). Institutional Investor identifies analysts at the extensive

margin as a star or not a star, but also within stars ranks analysts 1-4. Be-

cause the magazine does not contain I/B/E/S identifiers, I hand-match stars

to I/B/E/S using name, brokerage house, and time of employment. Matches

are only included if all three identifiers match.

2.2 Mergers

My sample includes 34 brokerage house mergers, listed in Table 2.1.

Table 2.1 includes the merger announcement and completion dates, the bidder

and targets with their I/B/E/S identifiers and the number of target analysts.

Thirteen are available from Hong and Kacperczyk [2010], which those authors

14

isolate by mapping SDC mergers that belong to SIC code 6211 (Investment

Commodity Firms, Dealers, and Exchanges) to the I/B/E/S database. Four

additional mergers are available from Kelly and Ljungqvist [2012]. I collect an

additional 17 mergers by starting with the set of all brokerage house closures

in the data and then using news articles and company histories to determine

whether the cause of closure was a merger. Matching the target and acquirer

to the I/B/E/S data is difficult because the brokerage house names in I/B/E/S

are shortened nicknames that are often based on historical names as opposed

to current brokerage house names. For instance, Wachovia is represented by

the name WHEAT from one of its predecessors, J.C. Wheat & Co. Thus, mat-

ching requires a careful reading of each brokerage house’s corporate history to

determine whether the I/B/E/S nickname corresponds to any previous histo-

rical names of the brokerage house. I require that at least some target analysts

who leave the target join the acquiring firm around the merger dates, and that

the target house no longer appears in the data after merger completion.

Two financial crisis mergers, Bear Stearns being acquired by JP Morgan

and Merrill Lynch by Bank of America, were omitted. This is because the

federal government was heavily involved in encouraging and subsidizing the

mergers making them very unique and not representative of a usual merger.

Because of the financial crisis, attrition and forecast error are uniquely high.

All results are robust to their inclusion and usually have larger partial effects,

but their exclusion helps attribute effects to expected merger-related issues as

opposed to very unique situations.

15

While most research utilizes the merger completion date, I also compile

and utlize the merger announcement dates. The merger announcement date

is the earliest date that a merger is mentioned in Factiva, a news-aggregation

service. Also from Factiva, I use details from these press releases to confirm

which house is the acquirer and which is the target, and to classify mergers

into two categories: those that appear to highly value the target’s human

capital and those that do not. Mergers in which research expansion, increased

services, or the analysts themselves are mentioned as a primary motivation for

merging are labeled as Labor Valued, while mergers for which increasing assets

under management or access to new clients is the primary driver are labeled

as Labor Not Valued.

The mergers cover a relatively long period, with the earliest merger

occurring in 1988 and the most recent in 2012. These mergers impact 2,594

distinct analysts: 876 from target houses and 1,718 from acquiring houses.

Eight of the merger targets have fewer than seven analysts, while four have over

50 analysts. Justifications for the mergers vary, including (but not limited to)

acquiring an underperforming house, deregulation, industry-wide conditions,

and strategic or geographic expansion. Within four months after the merger

announcement, most mergers are completed and no analysts remain under the

target house name. Mergers occur in both up and down markets which paired

with time period fixed effects mitigates calendar time concerns.

[INSERT TABLE 2.1 HERE]

16

2.3 Analyst Quality

Other analyst research has primarily used one of two proxies for analyst

quality: either the prestige or size of their brokerage house or whether the

analyst is rated as a star by Institutional Investor. While both are useful

proxies, they suffer from the same issue: both have very little within house-

year variation. Stars are often only in the largest and most prestigious house,

so it is very common for target houses to have no stars. Amongst analysts that

are not stars, there is obviously no variation in the star measure. Similarly

house level measures such as size have no within house variation.

In order to create a quality measure with within-house variation, I de-

velop a measure of individual analyst quality. To do so, I fit a logit regression

for analysts outside mergers, with the dependent variable being an indicator

variable for whether an analyst experiences a negative career outcome during

a month against past analyst observables. The negative career outcomes are

non-promotion separations. Separation is a binary variable equal to 1 for any

analyst-period-house observation that is the last period in which an analyst

releases estimates for a particular brokerage house. Using brokerage house

size (both by number of firms covered and by analysts employed) as a proxy

for brokerage house prestige, separations in which an analyst promptly swit-

ches to a more prestigious job are excluded, because these separations are

likely positive career events and would be affected by quality in the opposite

direction.

As independent variables, I use a large set of measures that have been

17

shown to impact analyst career trajectories. Analyst characteristics include

whether an analyst is a star; the analyst’s ranking among other stars; job

tenure; and overall analyst experience. Several analyst measures are associated

with analyst separation. The three most important measures are # Reports,

accuracy and boldness. # Reports is defined as as the number of reports

an analyst releases in a month by counting unique ticker-date pairs for each

analyst month. Accuracy is the difference between each estimate and the

actual earnings and boldness is the deviation from mean consensus. Both

are scaled by stock price. Relative measures are created from the absolute

measures to adjust for any shocks to the underlying covered firms. For each

firm an analyst covers, an analyst’s accuracy and optimism is ranked against

the other analysts who cover the same firm, with rankings normalized from 1

to 100. Accuracy is flipped because it is actually measured as forecast error

so that a score of 100 means an analyst is the most accurate or the most

optimistic analyst for that stock. An analyst’s rankings are averaged for each

month to get a composite score. Absolute measures for boldness (absolute

deviation from consensus) and timing (how many days prior to the earnings

release the estimate is released) are also used. A 3rd set of measures compare

new estimates to previous estimates for the same firm, by the same analyst and

for same fiscal period. Price change is defined as the average deviation from

past estimate and latency as the average amount of time between estimates.

Finally, independent variables include the percentage of estimates for each

analyst-month which are an upgrade, downgrade and a confirmation of the

18

analyst’s past estimate.

If an analyst does not produce an estimate for a given 30 day period,

that 30 day period is counted as a 0 for the number of reports issued and

missing for all other measures.1

The regression specifications used to predict quality take the form:

separationt+1,i = α + β ∗ analystcharacteristicst + αi + θt + ε. (2.1)

Regressions are run both as a linear probability model (LPM) as shown

in Equation 2.1, and as conditional logits to account for the binary dependent

variable. The conditional logit with no fixed effects is used for the quality

measure.

Table 2.2 contains summary statistics for over 13,000 analysts spread

across over 700 brokerage houses over 29 years at monthly frequencies. Ana-

lysts cease releasing estimates for a given house in a given month, or separate,

1.7% of the time. This compounds to an 18% annual turnover. If promotions,

defined as any separation in which an analyst separates but moves to another

larger (by number of analysts or firm covered) house, are removed, then the

separation probability drops to 1.4%, which compounds to 15% annually. Al-

most 12% of firm months are months in which the analyst was labeled as a star

by Institutional Investor, which provides ranks between 1 and 4 (4 being the

highest). Analysts release on average 8 reports (cnt tickdays), each containing

1All tests are run with and without these 0’s

19

on average 4 estimates. Because boldness (absolute deviation from consensus)

and optimism (positive deviation from consensus) are defined as deviation from

consensus, as expected their averages are indistinguishable from zero. Ana-

lysts average 2.5% estimate forecast error with considerable variation (from 0

to 20%). Finally, analysts update their previous estimates on average every 73

days, rarely confirm their previous estimate, and usually change their estimate

by 1%, slightly more often downward than upward. The average analyst has

been working for 6.5 years, 3.9 at the current brokerage house.

The results from these regressions are presented in Table 2.3. While

the R2 for these predictive regressions (without fixed effects) is low (1.4%),

the coefficients are very stable across fixed effect and linear probability model

versus logit specifications. Almost all of them load directionally in their ex-

pected direction. For instance, star analysts and more accurate analysts are

less likely to lose their jobs, while analysts who update their estimates less

frequently are more likely to lose their jobs.

Interpreting some of the coefficients in relation to the 1.4% baseline

probability of separation, being a star decreases the pr(separation) by 80 bp

or over 50% (odds ratio of .4). Also contingent on being a star, more highly

ranked stars are also less likely to separate. A standard deviation change in

the number of reports an analyst produces (7) is associated with a 30 bp drop

in separation probability. All else equal, on a relative basis, the most accurate

analysts are 40% as likely to separate as the least accurate analysts. Bold

and optimistic analysts are also less likely to separate. Age and tenure line

20

up with expectations. As analysts spend more time at the job or more time

as an analyst, their separation probability decreases. But the square term is

positive, implying that the age effect mitigates over time and likely reverses

at some age. This is consistent with the labor literature that turnover is high

for new employees and the oldest.

One alternative to a logit regression is an index created through a prin-

cipal component analysts (PCA). The advantage of the logit is that while the

researchers may have priors on the direction of impact of each variable, a PCA

requires the researcher to assign the direction and weight of each variable.

Instead, running the logit on actual separations relies on brokerage houses’

revealed preferences. The measure captures how brokerage houses actually

value different performance measures. While there is noise in the separation

dependent variable because not all separations are negative career events, se-

parations in which an analyst promptly switches to a more prestigious job, are

excluded. Most coefficients are stable and in the expected direction.

The model with no fixed effects presented in column 4 is used to predict

separation for as many analysts as possible in the merger sample. Quality is

defined as 1 minus the predicted values from this regression for ease of inter-

pretation (a higher value equals higher quality). To some extent, this measure

captures the revealed preferences of brokerage houses for analyst traits.

21

2.4 Redundancy

Past researchers have considered whether employees of merging firms

have duplicative skills. In the analyst literature, overlap has been studied at

the covered firm level. Do the merging houses cover the same firms? Because

analysts are individual people with specialized human capital that takes time

to develop, it makes sense to consider employee level overlap. To this end, I

develop a measure, Redundancy, that captures how duplicative an analyst’s

expertise is within a merging house versus analysts working for non-merging

houses. Redundancy is defined at the analyst-event level as the fraction of firms

an analyst covers that are also covered by the alternate house of the merger.

Specifically, Redundancy is calculated as (Distinct number of companies an

analyst covers 150 days before the merger that are also covered by the alternate

house of the merger) / (Distinct number of companies an analyst covers). This

measure exists for target and acquiring house analysts, as well as control group

analysts, who are not part of the merging entities.2,3

The summary statistics of Redundancy displayed in Table 2.4 help cap-

ture why the measure is important. While 25% of the analysts have no overlap

with the acquiring house coverage, the rest span the range from 0 to 1. The

median is 8%, while the mean is 26%. Only 1% of analysts are completely

2When events overlap in calendar time, the same control group analysts will appearmultiple times in the data with different cov per and different period definitions, all basedon the specific acquiring firm and the specific announcement date.

3For robustness, I can use the total number of companies covered as sum(COVERED),and dummies SOME COV = 1 if cov per > 0, and HALF COV = if cov per > 0.5 or 0otherwise.

22

redundant. While it is true that two houses might have analysts that focus

on specific overlapped industries, it is important to note that their coverage

choice and underlying expertise is rarely a perfect match. The variation in Re-

dundancy of analysts stresses why it is important to think of the measure at

the analyst level (chosen endogenously by the analyst and the house) instead

of a covered-firm level. If an analyst leaves due to overlapping human capi-

tal, this could leave the merged entities with coverage gaps which may require

promotions, hires or book expansion.

Because tests should be agnostic as to which companies an analyst

covers, I create an additional measure, target non-merger redundancy, which

is the fraction of firms a non-merger analyst covers that are also covered by

the merging house. I restrict non-merger analysts to analysts that have a

target coverage overlap of at least 0.5 to only include control-group analysts

with similar expertise to treated (within-merger analysts). With this filter, the

control group analysts and the merger analysts should be affected in a similar

way by idiosyncratic shocks in the firms they cover.

A related measure, competition, which is defined as the average number

of other analysts that cover the stocks an analyst covers in a given month. An

analyst with high competition operates in a highly competitive environment,

covering stocks that many other analysts cover, while an analyst with low

competition operates in a low competition environment, covering stocks that

few analysts cover. While redundancy is specific to the merger (i.e., requires

the alternate house as a reference), competition is independent of the merger,

23

and is used as a control to mitigate concerns that redundant analysts are dif-

ferent from non-redundant analysts by controlling for the level of competition

an analyst faces.

24

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26

Separation Month (No Promotions) LPM LPM Logit (OR)Annual Star Analyst -0.00861*** -0.00794*** 0.479***Annual Star Ranking -0.000625** -0.00245*** 0.845**z(Report #) -0.00226*** -0.00181*** 0.892***z(Estimates / Report) 0.00119*** 0.000365 1.044***Annual Optimism Dummy -0.00309*** -0.00350*** 0.850***Annual Relative Boldness -0.00520*** -0.00775*** 0.756***z(Competition) 0.000621** 0.000569 1.069***Annual Relative Accuracy -0.0211*** -0.0203*** 0.360***Average Forecast Error 0.0510*** 0.0524*** 19.62***Annual Relative Timeliness 0.0560*** 0.0783*** 29.52***z(Avg Estimate Change %) -8.28e-05 -0.000188 0.990% of Estimates Confirmed 0.00767 0.0112 1.518*z(Monthly days elapsed) 0.000640** 0.000916*** 1.053***Years in Data -0.000743*** -0.965*** 0.951***Years in Data Squared 2.49e-05*** -8.11e-05*** 1.002***Years on Job -0.000224** 0.00137*** 0.975***Days Before Close -8.04e-07*** -0.00186 1.000***House Size 7.04e-05* 0.000187 1.005***House Coverage -1.14e-05 -2.26e-05 0.999***Constant 0.00766***Observations 306,627 304,732 316,341R2 0.014 0.201FE Period Anals House#Per None

Table 2.3: Baseline Pr(Separation) Outside of Merger Announcements

Linear probability model estimates and logit odds ratios are reported for analysts not im-pacted by mergers. The binary dependent variable Separation - No Promotion is equal to1 in months in which analysts separate from their brokerage house and do not join a moreprestigious house and 0 otherwise. House prestige is defined by the houses total number ofanalysts. Columns 1-3 are LPMs while Columns 4-6 are logits with odd ratios presented.Spec 4 (logit no FE) is used to to generate a quality proxy.

Variable Obs Mean Std. Dev. Min Max P25 P50 P75Quality (1 - Pr(Sep) 293174 .985 .01 .773 .999 .982 .987 .99Coverage % (Redundancy) 312210 .554 .264 0 1 .429 .571 .727Monthly Forecast Error 330499 .586 1.475 0 39.529 .092 .224 .517

Table 2.4: Summary Statistics of Measures

27

Chapter 3

The Impact of Brokerage House Mergers on

Analyst Output: Empirical Results

3.1 Hypothesis Development

Overall, the merger process can positively or negatively affect the value

of human capital. For instance, mergers can positively impact human capital

by mitigating firm investment issues, such as financing constraints or decli-

ning prospects, that contribute to human capital inefficiencies. In addition,

mergers can positively impact human capital by taking over poorly run firms

and improving monitoring or incentives, or by fostering knowledge spillovers.

Alternatively, mergers can impair human capital if the merged entity fails to

retain high quality employees or if the quality of retained employees’ output

deteriorates due to poor merger integration. I examine overall human capi-

tal output of analysts going through brokerage house mergers to measure the

direction, size, and duration of any merger-related impacts.

Because analyst groups are composed primarily of human capital, once

the overall impact is characterized, the impact can be broken into its human-

capital-related channels. The impact can be driven by changes to the compo-

sition of analysts, i.e., who separates, and who is retained from the acquiring

28

and bidding houses post-merger announcement? Second, among analysts who

are retained, how is their output quality affected?

With regard to the composition of separating and retained analysts, in

a frictionless environment, one would expect that the merging firm would eva-

luate each employee and keep the most valuable ones. Alternatively, frictions

related to institutional investor clients might create a preference for acquiring

house analysts. Institutional investors might simply prefer the analysts they al-

ready have relationships with. In addition, coverage decisions are endogenous,

driven in large part by institutional investor preferences. Acquiring house in-

stitutional clients might prefer their current analysts rather than developing

relationships with new ones.

Analyst human capital is highly specialized, which lends itself well to

the Rajan and Zingales [1998] framework. In their incomplete contracting

framework for an employee to be incentivized to develop specialized human

capital, employees must be given specialized access to a valuable resource and

have outside options. Sell-side analysts have both, in that the lead analyst is

a brokerage house’s primary expert covering a given firm, and analysts switch

brokerage houses frequently. When houses merge, if one analyst from each

house previously had been given access to become the houses’ expert on the

same underlying firm, one of the two analysts may either have to find a new

job or abandon their specialized human capital. This can lead to redundant

analysts being more likely to separate because redeployment may be personally

costly. If an analyst’s outside options are related to his or her quality, high-

29

quality analysts should separate from the target house more frequently than

low-quality analysts. If this is by choice and driven by analysts’ desire to

retain specialized human capital, then analysts should cover the same firms in

their new jobs.

With respect to the second human capital related channel, among ana-

lysts who are retained, target and acquiring house analysts may be impacted

differently. Target house analysts’ performance might improve if they join

more prestigious houses with better access to information, junior staff, or firm

management. It also might temporarily suffer if there are integration issues,

such as moving offices, training, or major cultural differences. Alternatively,

if acquiring house analysts jobs do not change, there may be no integration

issues and their output quality will stay constant. It could improve if there

are spillovers from target house analysts or it could be temporarily impaired

due to integration issues, such as excessive meetings, coverage expansion, or

star junior staff promotions.

To explore potential integration issues further, because target house

separations may lead to coverage gaps, which may limit the merged entities’

ability to on-board targets’ institutional clients, the merged entity has an

incentive to fill those coverage gaps. There are three primary ways a house

can fill a gap: hire a new analyst, promote a junior, or expand the coverage

of an existing analyst. Because the necessary human capital to cover a firm

well takes time to develop, all three of these coverage expansion methods can

result in short term forecast error increases.

30

3.2 Results and Empirical Design

3.2.1 Overall Estimate Level Changes - Difference in Differences

To test the hypothesis on the overall impact of mergers on human

capital output, I run difference-in-differences (DID) specifications on forecast

error, the absolute deviation of the estimate, and the actual earnings scaled by

the firms’ previous stock price. The first difference is between before-merger

announcements and after-merger completions, and the second for estimates

produced by merging houses versus non-merging houses. Evidence is presented

both graphically and as regressions. The regression specification is as follows,

ForecastErrore,t,f,p = β1Postp,Subsumed+

β2Inmergerh + β3Postp ∗ InMergerh + β4Timelinesse,t,f,p + αe,t,f , (3.1)

where timeliness is defined as the days between each estimate’s publication

date and the actual earnings announcement. Timeliness of each estimate lies

on a spectrum from timely (published early in the fiscal period) and unti-

mely (published very close to the announcement). Controlling for timeliness

is important because of previous work showing that as timeliness decreases,

analysts become more accurate (better information) but less optimistic (in-

centive to allow firms to beat their estimates). The regressions include fixed

effect transformations for Event, Period, and Fiscal Period (quarterly or an-

nual estimate) and are clustered at the event level. Event fixed effects soak

up calendar time variation and period fixed effects soak up variation from

the time distance from the merger announcement and closure. Fiscal period

31

fixed effects account for any possible preference issues for merging versus non-

merging houses putting out more or less annual reports which on average have

higher forecast error than quarterly reports. For this analysis I do not run

analyst fixed effects because the goal is to capture overall changes in output

not changes to specific analyst output.

Each merger is treated as an independent event, even if they overlap

in calendar time so that control group analysts can be carefully selected for

each treatment and so that redundancy maps to the relevant merger. Periods

are in event time, extending 30-day periods in each direction from the merger

announcement.

Figure 3.1 panel A shows that even though all estimates experience

some increase in forecast error, the increase is significantly larger, around 10

basis points, in estimates produced by brokerage houses involved in mergers.

This result is confirmed in panel C, where I plot the coefficient estimates of a

difference-in-differences regression with 90% confidence intervals. In Table 3.1,

I present the coefficient estimates for the same regression. Column 1 shows the

overall average impairment due to mergers, which is around 14 and 10 basis

points (90-day and 1-year samples, respectively). The differential impact on

merging houses is significant but also temporary, recovering in the third year.

To put this effect in context, it is around 10% increase with respect to the

mean forecast error of 1% and is slightly larger than the difference between

a perfect estimate (no forecast error) and the estimate at the 25th percentile

(9.6 basis points).

32

For identification purposes it is important that prior to the merger

announcement there is no differential trend in the InMerger and Not InMerger

group. From Table 3.1 Panel A the two lines appear quite parallel. The average

forecast error does appear higher for estimates produced by the merging houses

prior to the merger announcement which may merit further investigation but

it is worth noting that as shown in Panel C of the same figure, the differences

are not statistically significant.

3.2.2 Merger-Related Attrition

In order to test the first channel–how mergers can alter a firm’s col-

lection of human capital–I study monthly analyst attrition using DID regres-

sions with analyst fixed effects. Regressions take the form

Separatione,h,a,t = β1postt ∗ InMergere,h + αe,a,h + ωt, (3.2)

where e denotes event, h house, a analyst, t, event-time. The main

variable of interest is β1, and ω and α denote unobserved heterogeneity. β1

measures within analyst changes in pr(separation), comparing 90 days before

the merger announcement to 90 days after the announcement prior to merger

completion. The dependent variable, Separation Month, equals 1 in analyst-

months before the month an analyst separates from his or her current house

and 0 otherwise, where months of separation are dropped.1 The variables Past

1Unlike before, I do not remove promotion-like separations. Previously, my goals was

33

and InMerger are subsumed by the fixed effects.

Table 3.2 shows these results. Column 1 compares the change in attri-

tion of analysts who are subject to a merger announcement (either as a target

or acquirer) versus analysts who cover similar underlying firms but are not sub-

ject to the merger announcement. Analysts subject to a merger announcement

experience an increased attrition rate of almost 5% (four times the unconditio-

nal average of 1%). The attrition rate drops back to the unconditional average

after merger completion (results not shown).

In Table 3.2 Column 2, analysts impacted by the merger are divided into

acquiring and target house analysts using the dummy Post*InMerger*TargetMerger.

Attrition increases 12% for target analysts in relation to similar control ana-

lysts, while there is no significant increase in attrition for acquiring house

analysts. This result is confirmed in Column 3 by running the regression only

on target house analysts and their comparable control group analysts.

In Table 3.2 Column 4, I subdivide target house analysts to help de-

termine where attrition is the largest using the triple difference specification

of

Separatione,h,a,t = β1postt ∗ InMergere,h+

β2postt∗Redundancye,a,h+β3postt∗InMergere,h∗Redundancye,a,h+αe,a,h+ωt,(3.3)

to measure the stock of human capital, but now I am interested in all separations.

34

where redundancy is defined in the previous subsection. Attrition is much

higher for redundant analysts in the target house versus unique analysts in

the target house. Attrition for unique analysts (i.e., zero firms covered by this

analyst were covered by the acquiring house before the merger announcement)

increases by over 6% as measured by β1. The variable β2 captures the diffe-

rence for control group analysts’ attrition differences between unique versus

redundant analysts, and it demonstrates that redundant analysts who were

unaffected by the merger are slightly more likely to keep their jobs. This is

probably due to the fact that redundant analysts are often high-quality ana-

lysts who cover popular stocks (i.e., stocks covered by more analysts). The

variable of interest is β3, which tells us that attrition for a fully redundant

analyst (i.e., all firms covered by this analyst were covered by the acquiring

house) increases by over 21% when compared to unique target house analysts.2

The takeaways are that (a) merging firms downsize by reducing head count

from the target house and (b) the reduction comes predominately from redun-

dant rather than unique target house analysts. The fact that reduction comes

from the target house could be a result of nepotism but also could be due to

connections with institutional investors that are not easily broken and repla-

ced. The fact that head count reduction does come from redundant analysts is

consistent with the hypothesis developed from the Rajan and Zingales [1998]

framework.

2Most analysts are not either fully redundant or completely unique. I show the re-sults using a standardized redundancy measure and find for a standard deviation change inredundancy attrition increases by almost 7%.

35

Next, analyst quality is used to study target house separations within

mergers. Table 3.3 Column 1 shows that among redundant analysts from the

target and acquiring firm, quality has no differential impact on attrition within

mergers. This tells us that even though target-house analysts separate much

more frequently than acquiring house analysts it is not because acquiring-

house analysts are systematically higher in quality than target-house analysts.

Column 2 shows only target house analysts with no fixed effects, to interpret

each coefficient of the triple difference. Post captures a non-merger selection

effect because there are more 0’s earlier in an analyst’s career because there

are none after separations. InMerger captures the pre-merger announcement

differential in attrition between target house and non-merger analysts. This

coefficient is indistinguishable from zero, suggesting that mergers are not ini-

tiated based on underlying analyst quality. The variable z(Quality) loads

negatively, which provides an out-of-sample test of the quality proxy; high

quality analysts outside of mergers are less likely to separate than low quality

analysts. Post*InMerger captures the increase in attrition after the merger

announcement for low quality target analysts and is small but significantly

greater than zero. The triple difference coefficient, Post*InMerger*z(Quality),

is the variable of interest, and it captures the differential impact in attrition

for high- versus low-quality analysts within merger targets. This coefficient is

economically and statistically significant. A one standard deviation increase

in analyst quality results in a target-house analyst being 4% more likely to

separate from the firm in a given month post-merger announcement.

36

In Table 3.3 Column 3, fixed effects subsume Post, InMerger, z(Quality),

and InMerger*z(Quality). The triple difference coefficient is additional evi-

dence that after merger announcements, high-quality analysts are 4% more

likely to leave their firm than low-quality analysts. In Columns 4 through

8, the data are divided into quality quintiles, with 1 representing analysts of

the lowest quality and 5 representing the highest quality analysts, in order to

confirm the result from Column 2 and 3. The attrition differential for high

quality analysts is 19%, while the differential is only 6% for the lowest quality

quintile. The result monotonically increases across quintiles. Finally, in Co-

lumn 9, I show that the result from Column 3 is 50% larger when the sample

is restricted to analysts with redundancy of over 0.5. To conclude, especially

when redundant, high quality target analysts are significantly more likely to

separate than low quality target analysts which is consistent with analysts’

outside options impacting their separation decisions.

In order to gain an ex ante measure of human capital importance du-

ring the merger, I conduct a textual analysis on the merger announcements,

marking 14 mergers in which human capital, labor, or expanding services are

not mentioned as a motivation for the merger and 20 mergers in which these

motivations are mentioned. I also mark the subset of mergers in which the es-

timates’ forecast error increases the most in order to verify that the attrition

results impact the overall results. The subsets are presented in Table 3.10.

Table 3.12 shows the results from restricting the attrition regressions to the

specific subsets. High-quality target analyst attrition increases from 6 basis

37

points in the full sample to 10 basis points for mergers in which ex ante labor

does not appear to be valued, and attrition doubles for mergers in which suf-

fer the largest drops in forecast error. This former is consistent with analysts

leaving when new management signals their human capital may not be valued

and the later verifies that the attrition results are material to overall output

quality.

Next, I show evidence that the separation is at least in part due to

higher quality target analysts choosing to leave their firms upon the merger

announcement. First, Table 3.4 shows that, contingent on separation, higher

quality analysts are more likely than lower quality analysts to find another

analyst job, and this implies that at least some of the target analysts choose

to leave because they have stronger outside options. Table 3.5 shows how

often analysts drop coverage. For the set of target analysts that find a new

analyst job, either in an alternate brokerage, New Job, or in the merged entity,

Kept Job, the percentage of firms an analyst covered before the merger that

they continue to cover after the merger is very high. It is 91% for the median

analyst who switches to a new house and 97% for the median analyst who

keeps his or her job. This suggests that the human capital of an analyst is not

easily transferable, and that dropping coverage is consistent with abandoning

human capital. Not only are higher quality analysts more likely to find a new

job contingent on separation, they are likely to perform the same job as before

just at a different house.

If redundant target analysts expect that they are unlikely to retain

38

their jobs in their current role because of redundancy, rather than work har-

der to keep their jobs or improve their human capital stock, they may shift

their efforts elsewhere upon the merger announcement (i.e., they may begin

searching for a new job) if their reputations are fairly static and take time

to influence. Empirically testing how termination risk affects output is chal-

lenging due to endogeneity, both from reverse causality and omitted variable

bias. Termination risk can affect effort, but effort can affect termination risk

in that low effort employees may be more likely to get fired. In addition, both

can be affected by omitted variables, such as firm investment opportunities.

Investment opportunities can drive high effort and low turnover and the capa-

city to hire high ability employees. Also, it is important to isolate termination

risk from future incentive-based compensation. Consider an investment ban-

ker or a tenure-track assistant professor. Both face considerable termination

risk and likely work harder than peers with greater job security, but they also

have large potential future benefits, bonuses, or tenure, respectively. Measu-

ring the effects of termination risk on employee effort requires a setting with an

exogenous, heterogeneous shock to the termination risk on a comparable set

of employees with observable but not fully contractible effort, which is exactly

what my empirical strategy delivers.

Because InMerger redundancy is strongly related to attrition, it is a

plausibly exogenous shock to the probability that an analyst separates from

the firm and can be used to test how the probability of separation impacts #Re-

ports, which is defined by counting unique ticker-date pairs for each analyst-

39

month.3 I employ the merger announcement as the treatment rather than the

merger closure to capture the threat of termination as opposed to termination

itself. I focus on the triple difference variation comparing redundant target

analysts to non-redundant target analysts. Assignment of how redundant an

analyst is should have no correlation with changes in analyst behavior separate

from termination risk.

For redundancy to be a viable source of exogenous variation, it must not

only impact termination risk, but also affect changes to analyst output only

via shocks to termination risk. It does not seem plausible that mergers would

be driven by expected future changes in analyst behavior, especially since the

brokerage divisions are usually a small part of the merger targets and so many

analysts are laid off. In addition, for analysts within mergers prior to the

merger announcement, acquiring house analysts after merger announcement,

as well as analysts not impacted by the merger, redundancy has no significant

positive relationship with termination risk, as well as no relationship with

changes to analyst output.4 These facts, along with a lack of any plausible

explanation of why, other than an increase in termination risk, redundant

analysts would change their behavior only within mergers, makes redundancy

a plausibly exogenous shock to termination risk.

Table 3.6 shows within-analyst changes in #Reports around merger an-

3Results are robust to alternate dependent variables, such as total firms covered or dayswith a report. All analyst-periods containing less than 30 days due to analyst separationare removed to avoid biasing the results with partial months.

4If anything, there is a small negative relationship.

40

nouncements. Column 1 contains a triple difference specification that compa-

res unique analysts to redundant analysts who are targets of the same merger.

When comparing #Reports changes within target house analysts, an analyst

who is completely redundant (all firms they cover are covered by the acquiring

house) reduce their #Reports by 0.8 reports in comparison to unique analysts

(no firms they cover are covered by the acquiring house). This is a drop of

about 16% of the mean #Reports for target house analysts prior to the merger

announcement (in relation to the mean #Reports of 5.1). Because redundant

analysts face the highest pr(separation), this is consistent with high-quality

target analysts who are likely to leave, shifting their effort in anticipation of

finding a new job.

In Column 2, instead of using the triple difference specification, I use

redundancy to instrument for the probability that an analyst separates. As

shown in Table 3.2, for target analysts, redundancy is associated with a 20% in-

crease in attrition (relevance) and is arguably not associated with an analyst’s

within-merger change in #Reports for any reason other than the increase in

attrition (exclusion). This makes in-merger redundancy a plausible instrument

for identifying the impact on a change in the pr(separation) on an analyst’s

#Reports. Confirming the finding in Column 1, we observe a large drop-off in

#Reports for analysts who face a 100% increase in pr(separation).

Columns 3-5 contain the specification from Column 1 but on smaller

subsets. Column 3 excludes analysts for which we cannot estimate quality

due to missing data; and Columns 4 and 5 divide that group by above-median

41

and below-median quality. Consistent with the flight of human capital results,

high-quality analysts show a drop in #Reports over 50% more than low-quality

analysts. This result is consistent with analysts looking for a new job when

they expect to be redeployed.

Table 3.7 presents the impact of redundancy on the various behavior

measures that correlated with separation. Redundancy seems to have no sig-

nificant effect on other analyst behaviors. The only significant effect is on

relative timing, meaning redundant analysts publish their reports six days

earlier than they did prior to the merger announcement compared to other

analysts who cover the same firms as they do. I do not know how to interpret

this result and am cautious in doing so due to running so many regressions

with non-results. The take away is that an increase in separation probability

does change the number of reports that analysts put out but seems to have no

clear effect on the quality or content of those reports.

This #Reports regression is robust to several specifications including

but not limited to: 2 months before and after as opposed to 3; Adding back

in Bear Stearns and Lehman; Non-Winsorized #Reports ; Removing the last

month of each target analyst (this is a double remove as this is already done

once); Removing event time = -1 (account for information leakage); Removing

zero activity periods; and As a Poisson regression and as an instrument.

42

3.2.3 Impact of Mergers on Individual Analyst Accuracy

To evaluate the second potential channel, I test how an individual ana-

lyst’s forecast error changes from her pre-merger baseline to her post-merger

estimates. Like the attrition results, these are run at the analyst-month rather

than estimate level. Variables, such as forecast error, are averaged for each

analyst-month. Control groups are limited to analysts who cover at least 50%

of overlapping firms. I run DID regressions with eventXanalystXemployer fixed

effects on analyst accuracy.5 The tight fixed effects specification guarantees

that variation comes from individual analysts who do not change employers

around the merger (with the exception of when target analysts join the mer-

ged firm). The DID specification controls for market downturns and other

underlying stock-related shocks. Because this regression measures variation in

analysts who are selected by the merged entity to stay and select themselves

to stay there may be selection based concerns. Are analysts who are likely

to show improvement more likely to be be retained? Are analysts who know

they will get worse more likely to want to stay? Because these tests measure

changes after the merger completion we can rely on the results from the previ-

ous section which document who stays and leaves prior to merger completion.

From those results, acquiring house analyst attrition does not change so se-

lection is not a major concern for them but there could be a concern with

quality changes for target house analysts. Standard errors are clustered at the

5Employer is defined post-merger to properly account for target analysts who are retai-ned by the merged entity.

43

event level.

Table 3.8 shows the within-analyst changes in forecast error from the

four months before the merger announcement to the four months after merger

completion. Column 1 compares all merger analysts to non-merger analysts,

while Columns 2 and 3 compare target and acquiring house analysts to similar

non-merger analysts, respectively. In the four months after the merger com-

pletion, the average monthly forecast accuracy of analysts who were involved

in the merger drops 6 basis points more than analysts who were not involved

in the merger. Restricting the sample to only acquiring house analysts, the re-

sult increases to 11 basis points. Columns 4 and 5 show that this effect mostly

dissipates within the year. Column 4 shows no pre-trend, and that the results

are only significant for the first 4-month period. Column 5 compares the first

4-month post-period to the next 8-month post-period and shows that the 11

basis point effect is reduced by 8 basis points. To put these changes in per-

spective, a 10 basis-point drop is larger than the difference between the median

accuracy of a star analyst and the median accuracy of a non-star analyst.

Table 3.9 splits the result from Column 3 Table 3.8 by analyst type in

three ways. I split by analyst quality (above and below the median quality),

redundancy (above and below 0.5), and the change in the number of firms

the analyst covers (same or increase and decrease). While the overall effect

was 9 basis points, below median quality, unique analysts, and analysts who

cover the same or more firms experience no short-term quality deterioration.

Meanwhile, the effect is almost double for high quality analysts, redundant

44

analysts and analysts who end up covering fewer firms.

These results suggest that acquiring house analysts suffer a producti-

vity shock that temporarily, but not permanently, harms their human capital

output.6 This could be consistent with several explanations. For one, Schoar

[2002] finds mergers have a positive effect on acquired physical assets, but also

finds an offsetting and larger merger related impairment of the physical assets

already in place. Anecdotal evidence from discussions with sell-side analysts

suggest that mergers are often accompanied by management shakeups, team

related disruptions, such as junior staff layoffs or promotions, training new

staff, catering to new clients, moving offices, or excessive meetings.

While the evidence is not yet definitive, one plausible explanation con-

sistent with these findings is as follows. In cases where target and acquiring

house analysts are redundant but not fully so (perfect redundancy is rare), the

target analyst is likely to leave for another job creating a coverage “gap” in the

merged entity. Because one primary motivation of brokerage house mergers

is to on-board institutional clients from the target, and analyst coverage is

determined in large part by the institutional preferences, the acquiring house

may need to quickly expand coverage. The fact that the temporary decrease

in accuracy is driven by redundant analysts is consistent with the gap filling

hypothesis. The fact the analysts decrease, not increase, the number of firms

they cover is not consistent with existing analysts covering more firms. This

6The target analysts who keep their jobs improve their accuracy at times, but this effectis not significant, on average, across all mergers.

45

leaves either new hires or promotions to fill gaps. Because high quality se-

nior analysts are more likely to have high quality junior analysts and perhaps

upon promotion the senior will let the junior take a company with them, these

results could be consistent with a promotion hypothesis. Figure 3.3 is also

consistent with a promotion hypothesis. While rookie analysts (analysts who

have never been in the data before) outside of mergers do not have elevated

forecast error, rookie analysts within mergers do. This could mean that juni-

ors were promoted early to fill coverage gap and take some time to build the

human capital necessary. The results may also be consistent with redundant

and high-quality acquiring house analysts expending extra effort to on-board

target-house institutions and reducing overall coverage to concentrate on the

new clients.

The impairment is both economically and statistically significant even

without considering physical capital magnifying integration issues, which sug-

gests that operational disruptions from merging firms may be considerable.

3.2.4 Competition, Crashes, or Merger Integration Issues

The findings in the previous sections could be driven by two alternate

channels instead of the merger-related channel that is the subject of this paper.

Hong and Kacperczyk [2010], who attribute the merger-related performance

decline to industry consolidation and the accompanying decrease in analyst

competition. The authors argue that forecast error increases because analysts

intentionally decide to bias estimates upwards in order to cater to corporate

46

clients. Differentiating my results from this alternate channel is important,

because I argue that forecast error increases are due to unintended errors

whereas their hypothesis argues that the increases are due to intended decisions

analysts make. A second alternate explanation is that unexpected market

crashes can cause temporary increases in earnings forecast error across all

analysts.7

The earlier graph, Figure 3.1 Panel A, shows that, even though all

estimates experience some increase in forecast error, the increase is significantly

larger in estimates produced by brokerage houses involved in mergers, and this

difference is temporary, lasting only two years. Because competition and the

market impact underlying firms equally, it is hard to reconcile the differential

impact seen by either explanation when the firms in both groups are the same.

The temporary nature of the effect is not consistent with the competition

story because it is unlikely that there is any off-setting new entry of analysts

systematically two years out. While it is possible that analysts are choosing

short-term catering, Clarke et al. [2007] cast doubt on that channel by studying

star analyst transitions and finding that optimism has no impact on investment

banking deal flow.

To further differentiate between an incentive based explanation and

an unintended consequence based one, I exploit an industry wide incentive

shock. In 2003, U.S. regulatory bodies reached the Global Analyst Research

7See Brav and Lehavy [2003] and Bradshaw et al. [2013] for evidence of analyst optimism.

47

Settlements (GARS), forcing the separation of the research and investment

banking divisions of the largest investment firms.8 This event created a source

of exogenous variation to a brokerage house’s ability to cater to corporate

clients. Table 3.16 presents that there is no significant difference between

pre- and post-global settlement forecast error increases suggesting changing

incentives might not play a large role.

Finally, I divide the estimates into redundant estimates and not-redundant

estimates, where redundant estimates are estimates of firms covered by the

target and the acquirer before the merger announcement, and non-redundant

estimates are covered by just one or the other.9 Because redundant analysts

are more likely to separate, the underlying firms they cover face larger com-

petition shocks (results shown in Hong and Kacperczyk [2010]). As shown

in Figure 3.1 Panel B and Panel D, estimates divided by the intensity of the

competition shock have no significant difference from each other in forecast

error increase. This is true for forecast error and positive bias.

With regard to the recession channel, the difference-in-differences fra-

mework with period fixed effects should mitigate most concerns. Additio-

nally, while the overall increase in forecast error does not increase for the

non-recession mergers, there is still a significant and differential merger rela-

ted impact of non-recession and recession mergers. The recession split results

8See https://www.sec.gov/news/speech/factsheet.htm.9Recall that estimates are only included for firms that are covered by at least one of the

two and that attrition is highest amongst redundant target analysts.

48

are shown in Table 3.17 and Figure 3.2.

Because this differential impact is not caused by a drop in competition,

and not fully explained by external market downturns, that leaves the mergers

themselves as the primary driver of the impairment.

3.2.5 Merger’s Uncontrolled Impact on Human Capital Output

I confirm that the overall DID results are driven by changes in merging

houses and not changes in the control group using single difference regressions

for robustness. The sample for this analysis includes the set of earnings estima-

tes published by the acquirer and the target prior to the merger announcement

(the merging houses) as compared to the set of earnings estimates produced

by the merged entity after the merger completion. The sample is restricted to

estimates for firms that are covered both before and after the merger to miti-

gate any coverage decision selection concerns. Table 3.14 presents the merger

level results. Overall, across the merged firms, average forecast error increases

from 1.03% to 1.41%. This increase can be seen visually in Figure 3.1, Panel

A, represented by the InMerger line. Although there were only 34 merger ob-

servations, this difference in brokerage house aggregate forecast error is both

economically and statistically significant (a change in magnitude of over 35%).

Nonparametric tests (not shown), such as a Wilcoxin sign-rank test, confirm

that these differences are different from zero, which stems from 27 of 34 mer-

gers having at least some negative impact. Further, consistent with output

quality impairment, the combined houses reduce equity coverage by over 1%

49

of the entire universe of covered stocks (5% in relation to the mean), produce

273 fewer overall estimates, and exhibit stronger optimism bias, which I define

as the difference between the estimated and the actual earnings scaled by stock

price.

The increase in forecast error is not permanent Figure 3.1, Panel A,

shows that forecast error continues to increase in the second year (quarters 5-

8), peaks in quarter 8, and then drops sharply over the next four quarters. In

Table 3.15, I confirm the results above using an estimate-level, single-difference

regression with the following specification:

ForecastErrore,t,f,p = β1Postp + β2Timelinesse,t,f,p + αe,t,f , (3.4)

where e denotes event, t ticker, f fiscal period, p is pre (target or acquirer) or

post (merged entity), and β1 is the variable of interest.

In Table 3.15, Post captures the average change in forecast error in

moving from two separate houses to one combined house. Column 1 shows

that the forecast error for estimates produced 90 days before the merger an-

nouncement are 24 basis points lower than estimates produced 90 days after

the merger completion. Column 2 extends the windows to a year on both sides

and the forecast error increase becomes 36 basis points. Column 3 compares

estimates for the base quarter, the one before the merger announcement, to

estimates for the three quarters before that quarter (pre-trend) and to the

estimates a year after, two years after, and three years after the merger com-

pletion. There is no significant difference before the merger announcement

50

(i.e., YN1 is not different from zero), while Y1 and Y2 are both significantly

greater than 0 (.30 and .45, respectively). Confirming the temporary nature

of the result, the Y3 coefficient is not significantly different from zero. Some

might argue that the Y3 coefficient is .20, so not technically zero (even though

it is statistically indistinguishable from zero), so in Column 4, the same re-

gression is broken down into quarters, which show that the effect is in fact

temporary, as the large partial effect is driven only by the first quarter of the

third year. Event*Fiscal Period (FPI) fixed effect transformations control for

unobserved permanent heterogeneity in the events, whether the estimates are

annual or quarterly. Standard errors are clustered at the event level.

3.3 Further Discussion of Identification Issues

The main identifying assumption is that the factor that drives these

mergers (and their announcements) are not correlated with changes to analyst

attrition, forecast error, or # Reports. Reverse causality is unlikely to be an

issue because analysts’ career concerns or future output changes are unlikely

to drive the mergers. Additionally, by using triple difference specifications,

I compare before and after changes of analysts within the same brokerage

house who are affected by the merger. Pre-event falsification tests using a

false merger date two months before the merger announcement shows that no

differential trends in analyst behaviors exist.

Because omitted variables may influence both the outcome and the ex-

planatory variables, I include analyst#event#house and event-time (or someti-

51

mes period, defined as event#event-time) fixed effects. Using within-analyst

variation (especially over the short time window around the merger announce-

ment) controls for variables such as analyst ability. It also mitigates concerns

over selection bias with regard to who gets fired, resigns, or stays at the firm.

10

The period fixed effects mitigate time trend concerns, the largest being

quarterly cyclicality in the earnings season and reports, as well as major market

crashes. Note that there are several overlapping events, thus these are not

month fixed effects but significantly more conservative 30-day period fixed

effects that are independently defined for each merger event.

The results are clustered at the event level. Because explanatory varia-

bles are constant across periods within an event for a given analyst, clustering

time periods is essential. I cluster my main results at the event level to be

conservative. I cluster my falsification tests at the event-analyst levels to work

against falsification.

10All the results hold for analystXevent fixed effects, but I also interact the brokeragehouse to capture only variation from analysts within the target house who have not yetswitched houses.

52

.81

1.2

1.4

1.6

Average Forecast Error

−5

05

1015

Eve

ntT

ime

("Q

uart

ers"

)

InM

erge

r

Not

InM

erge

r

(a)

.6.81

1.2

1.4

Average Forecast Error

−5

05

10

15

Event

Tim

e("

Quart

ers

")

Not R

edundant

Redundant

(b)

−.10.1.2.3

Coefficient Estimate (Forecast Error)

QN

3Q

N2

QN

1Q

1Q

2Q

3Q

4Q

5Q

6Q

7Q

8Q

9Q

10

Q11

Q12

Quart

er

(c)

−.4

−.20.2.4

Coefficient Estimate (Forecast Error)

DupC

ovQ

N3

QN

2Q

N1

Q1

Q2

Q3

Q4

Q5

Q6

Q7

Q8

Q9

Q10

Q11

Q12

Quart

er

(d)

Fig

ure

3.1:

For

ecas

tE

rror

Evo

luti

onA

round

Mer

gers

Lin

ech

art

ssh

ow

fore

cast

erro

rover

even

tti

me

(90-d

ay

per

iod

s).

Pan

els

(a)

&(c

)sp

lit

the

esti

mate

sb

etw

een

those

gen

erate

dIn

Merger

(by

the

targ

et,

acq

uir

er,

or

com

bin

eden

tity

)an

des

tim

ate

sgen

erate

dby

oth

erb

roker

age

hou

ses.

Pan

els

(b)

&(d

)sp

lit

theRedundant

esti

mate

sp

rod

uce

dfo

ru

nd

erly

ing

firm

sco

ver

edby

both

the

targ

etan

dacq

uir

erb

efore

the

mer

ger

,ver

susNotRedundant,

those

of

un

der

lyin

gfi

rms

wit

hou

tany

over

lap

.P

an

els

(a)

&(b

)sh

ow

ssi

mp

leaver

ages

wh

ile

(c)

&(d

)p

lot

the

coeffi

cien

ts(w

ith

90%

con

fid

ence

inte

rvals

)fr

om

diff

eren

ce-i

n-d

iffer

ence

sre

gre

ssio

ns

wit

hE

ven

t,P

erio

d,

an

dF

isca

lP

erio

dfi

xed

effec

ts.

53

.81

1.2

1.4

1.6

mean_acc_id

−5

05

1015

Eve

ntT

ime

("Q

uart

ers"

)

Not

Dow

ntur

n

Dow

ntur

n

(a)

.51

1.52

Average Forecast Error

−5

05

1015

Eve

ntT

ime

("Q

uart

ers"

)

InM

erge

r In

Rec

Not

Mer

ger

InR

ec

InM

erge

r N

oRec

Non

Mer

ger

NoR

ec

(b)

−.10.1.2.3

Coefficient Estimate (Forecast Error)

QN

3Q

N2

QN

1Q

1Q

2Q

3Q

4Q

5Q

6Q

7Q

8Q

9Q

10Q

11Q

12

Qua

rter

Not

in R

eces

sion

(c)

−.10.1.2.3

Coefficient Estimate (Forecast Error)

QN

3Q

N2

QN

1Q

1Q

2Q

3Q

4Q

5Q

6Q

7Q

8Q

9Q

10Q

11Q

12

Qua

rter

In R

eces

sion

(d)

Fig

ure

3.2:

For

ecas

tE

rror

Evo

luti

on:

Rec

essi

ons

Lin

ech

art

ssh

ow

fore

cast

erro

rover

even

tti

me

(90-d

ay

per

iod

s).

Pan

el(a

)sp

lits

the

esti

mate

sb

etw

een

those

gen

erate

dw

ith

inan

NB

ER

rece

ssio

n,

InDownturn

,ver

sus

those

that

wer

en

ot.

Pan

el(c

)fu

rth

ersu

bd

ivid

esth

ees

tim

ate

sb

etw

een

InMerger

an

dn

ot.

Pan

els

(a)

&(b

)sh

ow

sim

ple

aver

ages

.P

an

els

(c)

&(d

)p

lot

the

coeffi

cien

tsfr

om

the

two

lin

esin

(b)

(wit

h90%

con

fid

ence

inte

rvals

)fr

om

diff

eren

ce-i

n-d

iffer

ence

sre

gre

ssio

ns

wit

hE

ven

t,P

erio

d,

an

dF

isca

lP

erio

dfi

xed

effec

ts.

54

−.20.2.4.6

Average Positive Bias

−5

05

1015

Eve

ntT

ime

("Q

uart

ers"

)

InM

erge

r N

R

Not

InM

erge

r N

R

InM

erge

r R

Not

InM

erge

r R

(a)

.51

1.52

Average Forecast Error

−5

05

1015

Eve

ntT

ime

("Q

uart

ers"

)

InM

erge

r N

R

Not

InM

erge

r N

R

InM

erge

r R

Not

InM

erge

r R

(b)

Fig

ure

3.3:

Evo

luti

onof

Bia

san

dA

ccura

cyby

InM

erge

ran

dR

edundan

cy

Lin

ech

arts

show

the

evol

uti

onof

fore

cast

erro

ran

dp

osi

tive

bia

sov

erev

ent

tim

e(9

0-d

ayp

erio

ds)

.T

he

esti

mate

sare

div

ided

bet

wee

nth

ose

gen

erat

edInMerger

by

eith

erth

eta

rget

,acq

uir

er,

or

the

com

bin

eden

tity

vers

us

esti

mate

scr

eate

dby

oth

erb

roke

rage

hou

ses;

then

div

ide

the

esti

mate

sb

etw

een

those

of

und

erly

ing

firm

sco

vere

dby

both

the

targ

etan

dacq

uir

erb

efore

the

mer

ger

vers

us

thos

eof

un

der

lyin

gfi

rms

wit

hou

tany

over

lap

.

55

.81

1.2

1.4

1.6

1.8

mean_acc_oc2

−5

05

10

15

Eve

nt

Tim

e("

Qu

art

ers

")

InM

erg

er

Exp

InM

erg

er

No

Exp

No

tIn

Me

rge

r E

xp

No

tIn

Me

rge

r N

oE

xp

(a)

Fig

ure

3.4:

Evo

luti

onA

ccura

cyby

InM

erge

ran

dE

xp

erie

nce

Lin

ech

arts

show

the

evol

uti

onof

fore

cast

erro

rov

erev

ent

tim

e(9

0-d

ayp

erio

ds)

.T

he

esti

mate

sare

div

ided

bet

wee

nth

ose

gen

erat

edInMerger

by

eith

erth

eta

rget

,acq

uir

er,

or

the

com

bin

eden

tity

vers

us

esti

mate

scr

eate

dby

oth

erb

roke

rage

hou

ses;

then

div

ide

the

esti

mat

esb

etw

een

those

of

exp

erie

nce

dan

aly

sts

vs

those

that

ap

pea

rin

the

data

for

the

firs

tti

me.

56

DepVar: Estimate Forecast Error 90d 1y 3Y 12QPost * InMerger 0.14*** 0.10***

(0.00) (0.00)InMerger 0.11*** 0.10*** 0.11*** 0.11***

(0.00) (0.00) (0.00) (0.00)

YN1 / QN3 * InMerger -0.01 -0.02(0.74) (0.45)

QN2 * InMerger -0.01(0.82)

QN1 * InMerger 0.00(0.86)

Y1 / Q1 * InMerger 0.10** 0.17***(0.01) (0.00)

Q2 * InMerger 0.11**(0.03)

Q3 * InMerger 0.08**(0.05)

Q4 * InMerger 0.07(0.12)

Y2 / Q5 * InMerger 0.10*** 0.10**(0.01) (0.02)

Q6 * InMerger 0.12***(0.01)

Q7 * InMerger 0.14**(0.01)

Q8 * InMerger 0.06(0.25)

Y3 / Q9 * InMerger 0.04 0.04(0.35) (0.35)

Q10 * InMerger 0.01(0.89)

Q11 * InMerger 0.03(0.61)

Q12 * InMerger 0.08(0.18)

z(Timeliness) 0.47*** 0.46*** 0.46*** 0.46***(0.00) (0.00) (0.00) (0.00)

Observations 563,363 3,212,344 6,570,582 6,570,582Adjusted R2 0.069 0.059 0.050 0.050

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.1: Estimate Level Operation Changes - Difference-in-Differences

Difference-in-differences are reported using merger announcements as treatment events from 1988 to 2012. Icompare differences in annual and quarterly forecast error before the merger announcement from the targetand acquiring house and the merged entity after merger completion, to non-merger estimates of the samefirms over the same periods. Results are presented for 90 days, 1 year, 3 years, and 12 quarters. ForecastError is the absolute deviation from actual earnings scaled by current stock price. The binary independentvariable InMerger is equal to 1 for estimates of the merging houses and 0 otherwise. Post*InMerger isthe interaction of InMerger and an indicator equal to 1 for all estimates after merger completion and 0otherwise. z(Timeliness) is the number of days before the earnings announcement an estimate is released.All specifications include event, Period, and FPI fixed effects. Parentheses contain p-values computed fromstandard errors clustered at the event level. 57

DepVar: (1) (2) (3) (4)Separation Month Targ & Acq Targ v Acq Just Targ Redundancy

Post * InMerger 0.04*** 0.01 0.12*** 0.06*(0.00) (0.40) (0.00) (0.05)

Post * InMerger * TargetMerger 0.12***(0.00)

Post * Redundancy -0.02*(0.10)

Post * InMerger * Redundancy 0.21**(0.01)

Observations 148,429 148,429 43,395 43,395Adjusted R2 0.153 0.155 0.157 0.158EventTime Job FE YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.2: Attrition Around Merger Announcements Driven by Target Redun-dancy

Linear probability model estimates are reported for difference-in-difference and triple-difference specifications using merger announcements as treatment events from 1980 to 2012.The binary dependent variable Separation is equal to 1 in months in which analysts sepa-rate from their brokerage house and 0 otherwise. The variable Redundancy is the fractionof coverage overlap that an analyst has with the acquiring house before the announcement.The control group is restricted to analysts with at least a 50% coverage overlap with thetarget house. Specifications in Columns 1 and 2 include both acquirer and target houseanalysts as treated observations, while specifications in Columns 3 and 4 include only tar-get house analysts. All specifications include Event-Time and Event×Analyst×House fixedeffects transformations to control for unobserved heterogeneity and to mitigate selectionbias concerns. Parentheses contain p-values computed from standard errors clustered at theevent level.Specification 4: Separatione,h,a,t = β1postt ∗ InMergere,h + β2postt ∗ Redundancye,a,h +β3postt ∗ InMergere,h ∗ Redundancye,a,h + αe,a,h + ωt where e denotes the event, h thehouse, a the analyst, t represents the event time. β3 is the main variable of interest, and ωand α denotes unobserved heterogeneity.

58

Dep

Var

:C

omb

Tar

gT

arg

Qu

alit

yQ

uin

tile

Tar

gS

epar

atio

nM

onth

Red

No

FE

FE

1(L

ow)

23

45

(Hig

h)

Red

Pos

t*

InM

erge

r0.

10**

*0.

09**

*0.

12**

*0.

06*

0.09

***

0.13

***

0.16

***

0.19

***

0.20

***

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

7)

(0.0

1)

(0.0

0)

(0.0

0)

(0.0

0)

(0.0

0)

Pos

t*

z(Q

ual

)-0

.03*

**-0

.01*

-0.0

3***

-0.0

3***

(0.0

0)

(0.0

8)

(0.0

0)

(0.0

0)

Pos

t*

InM

erge

r*

z(Q

ual

)-0

.00

0.04

**0.

04**

*0.

06**

(0.8

3)(0

.02)

(0.0

0)

(0.0

4)

Pos

t0.

04**

*(0

.00)

InM

erge

r0.

00(0

.96)

z(Q

ual

)-0

.02*

**(0

.00)

InM

erge

r*

z(Q

ual

)-0

.00

(0.8

3)

Con

stan

t0.

04**

*(0

.00)

Ob

serv

atio

ns

36,6

7943

,894

43,3

959,

003

8,74

18,

405

8,37

98,

867

17,9

27A

dju

sted

R2

0.30

40.

022

0.16

10.

166

0.15

50.

152

0.14

70.

160

0.15

9pva

lin

par

enth

eses

,S

tErr

Clu

ster

edat

Eve

nt

Lev

el***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.1

Tab

le3.

3:A

ttri

tion

Aro

und

Mer

ger

Annou

nce

men

ts-

Qual

ity

Tri

ple

-diff

eren

cees

tim

ate

sare

rep

ort

edu

sin

gm

erger

an

nou

nce

men

tsas

trea

tmen

tev

ents

from

1988

to2012.

Th

ista

ble

splits

the

resu

ltfr

om

Tab

le3.2

byz(Quality),

the

conti

nu

ou

sst

an

dard

ized

qu

ality

mea

sure

gen

erate

din

Tab

le2.3

.T

he

bin

ary

dep

end

ent

vari

ab

leSeparation

iseq

ual

to1

inm

onth

sin

wh

ich

an

aly

sts

sep

ara

tefr

om

thei

rb

roker

age

hou

sean

d0

oth

erw

ise.

Th

eco

ntr

ol

gro

up

isre

stri

cted

toin

clu

de

an

aly

sts

wit

hat

least

a50%

over

lap

wit

hth

eta

rget

hou

se.

Colu

mn

1co

mb

ines

Acq

uir

ers

an

dT

arg

ets

as

asi

ngle

hou

sean

din

clu

des

on

lyan

aly

sts

wit

hre

du

nd

an

cym

easu

res

over

1/2.

Colu

mn

2co

nta

ins

no

fixed

effec

ttr

an

sform

ati

on

wh

ile

inC

olu

mn

s3-9

,Post

,In

Merger,

z(Quality),

an

dIn

Merger*

z(Quality)

are

sub

sum

edby

the

even

t-ti

me

an

dan

aly

stfi

xed

effec

ttr

an

sform

ati

on

s.S

pec

ifica

tion

s4

thro

ugh

8are

quality

qu

inti

les

wit

h1

bei

ng

an

aly

sts

of

the

low

est

qu

ality

an

d5

bei

ng

an

aly

sts

of

the

hig

hes

tqu

ality

.C

olu

mn

9is

sim

ilar

toC

olu

mn

3b

ut

itin

clu

des

on

lyan

aly

sts

that

have

are

du

nd

an

cyof

at

least

1/2.

Pare

nth

eses

conta

inp

-valu

esco

mp

ute

dfr

om

stan

dard

erro

rscl

ust

ered

at

the

even

tle

vel

.

59

DepVar: (1) (2) (3)Find New Job LPM LPM CL OR

z(Quality) 0.0762*** 0.0780*** 1.706***(0.00464) (0.000614) (0.00283)

Constant 0.379***(1.07e-08)

Observations 468 468 437Adjusted R-squared 0.020 0.160Event FE No YES YESNumber of event 23

Robust pval in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 3.4: Pr(Find Analyst Job)|Separation for Target Analysts

Linear probability model estimates and conditional logit odds ratios are reported usingmerger announcements from 1980 to 2012 as treatment events. Table 3.4 studies onlytarget house analysts who separate around the merger announcement. The binary depen-dent variable Find New Job is equal to 1 if the analyst finds another analyst job afterseparation and 0 otherwise. Specifications in Columns 2 and 3 include Event-Time andEvent×Analyst×House fixed effects transformations to control for unobserved heteroge-neity and to mitigate selection bias concerns. Parentheses contain p-values computed fromstandard errors clustered at the event level.

60

New Job Kept JobN 266 N 304

Mean 0.83 Mean 0.88

Level Quantile Level Quantile100% Max 1 100% Max 1

99% 1 99% 195% 1 95% 190% 1 90% 1

75% Q3 1 75% Q3 150% Median 0.91 50% Median 0.97

25% Q1 0.73 25% Q1 0.8210% 0.50 10% 0.645% 0.33 5% 0.501% 0.20 1% 0.17

0% Min 0.07 0% Min 0.07

Table 3.5: Redeployment Post Job Transfer

Table 3.5 shows summary statistics for human capital abandonment. For target analyststhat remain in the database, either at a New Job or within the new merged entity, kept job,I calculate the fraction of firms the analyst still covers that they covered previously.

61

DepVar: (1) (2) (3) (4) (5)# Reports Triple Diff Inst Qual 6=. Low Qual High Qual

Post * InMerger 0.193 1.026 0.269 0.178 0.305(0.396) (0.177) (0.248) (0.523) (0.337)

Post * Redundancy -0.003 -0.270 -0.075 -0.308 0.145(0.987) (0.198) (0.708) (0.175) (0.586)

Post * InMerger * Redundancy -0.827* -1.293*** -0.952* -1.557***(0.059) (0.006) (0.084) (0.007)

Separation Month (Inst’d) -6.874**(0.020)

Competition -0.008*(0.086)

Observations 37,790 40,292 31,108 15,201 15,904Adjusted R2 0.508 0.443 0.507 0.478 0.514Job Period FE YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.6: Target Analyst Report Output around Merger Announcements

Triple difference and instrumental-variable estimates are reported using merger announcements as treatmentevents from 1980 to 2012. The dependent variable, # Reports, is measured as the number of reports ananalyst produces in a 30-0day period. The sample compares three months before the merger announcementto the three months after the merger announcement but before the merger closure excluding all periods lessthan 30 days due to analyst separation. Redundancy is the fraction of coverage overlap that an analyst haswith the acquiring house before announcement. The control group is restricted to include analysts with atleast a 50% coverage overlap with the target house. Column 1 reports triple-difference estimates comparingunique to redundant analysts within the target house. In Column 2, redundancy is used as an instrument forpr(separation). The IV specification allows inclusion of time-varying controls, so I add Competition, whichis the average number of other analysts who also cover the stocks the analyst covers. Column 3 includes onlyanalysts for which I can estimate quality. In Columns 4 and 5, I divide this group into high and low-quality.All specifications include Event-Time and Event×Analyst×House fixed effects transformations to controlfor unobserved heterogeneity and mitigate selection bias concerns. Parentheses contain p-values computedfrom standard errors clustered at the event level.Specification 1: #Reportse, h, a, t = β1postt ∗ InMergere,h + β2postt ∗ Redundancye,a,h + β3postt ∗InMergere,h ∗ Redundancye,a,h + ωt + αe,a,h where e denotes event, h house, a analyst, t period. β3is the main variable of interest, and ω and α denotes unobserved heterogeneity.

62

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

A.

Beh

avio

rsre

lop

tare

lbola

rela

cca

relt

ima

mea

nch

an

ge

prc

mea

nla

ten

cyp

erco

nf

per

dow

n

pos

tin

mer

ger

-0.6

612.2

23**

-1.0

98

-3.0

57**

0.0

497

-9.1

94***

0.0

00169

0.0

107

(0.6

15)

(0.0

106)

(0.1

99)

(0.0

446)

(0.4

28)

(0.0

00148)

(0.9

49)

(0.6

15)

pos

tco

vp

er-0

.356

-0.2

64

-0.0

185

-0.3

23

-0.0

186

-0.2

28

0.0

00823

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285**

(0.6

17)

(0.6

94)

(0.9

78)

(0.6

90)

(0.6

79)

(0.7

76)

(0.6

30)

(0.0

168)

pos

tin

mer

ger

cov

per

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27)

(0.6

68)

Ob

serv

atio

ns

52,7

5252,7

52

52,7

32

52,7

52

49,0

80

49,8

46

49,8

46

49,8

46

Rob

ust

pva

lin

par

enth

eses

.S

tan

dard

Err

ors

clu

ster

edby

even

t*h

ou

seR

egre

ssio

ns

incl

ud

eA

nal

yst

*Hou

se*E

vent

and

Per

iod

FE

.***

p<

0.0

1,

**

p<

0.0

5,

*p<

0.1

Tab

le3.

7:B

ehav

ior

Tri

ple

diff

eren

ces

are

run

tote

sth

owb

ehav

iors

chan

ge

wit

hin

mer

ger

s.P

an

elA

show

sth

eeff

ects

of

red

un

dan

cyw

ith

inm

erge

rson

beh

avio

rs.

Dep

end

ant

vari

ab

les

wit

hth

ep

refi

xre

l*are

score

s0-1

00

base

don

aver

age

ran

kin

gw

ithin

cove

rage

com

pan

ies.

Lat

ency

ish

owm

any

day

san

esti

mate

wit

hou

tb

ein

gu

pd

ate

d.

63

DepVar: (1) (2) (3) (4) (5)Analyst Forecast Error Merger Acq Target Acq By Period Acq Post

Post * InMerger 0.06* 0.11** -0.04(0.08) (0.01) (0.42)

MN12-N9 * InMerger 0.01(0.80)

MN8-N5 * InMerger 0.00(0.94)

M1-4 * InMerger 0.09**(0.03)

M5-8 * InMerger 0.04(0.37)

M9-12 * InMerger 0.04(0.37)

M5-12 * InMerger -0.08*(0.08)

Observations 109,598 105,971 103,109 316,893 147,941Adjusted R2 0.171 0.173 0.174 0.156 0.215

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1, Analyst*Event EventTime FE

Table 3.8: Analyst Changes in Forecast Error

Difference-in-difference estimates are reported using merger announcements as treatmentevents from 1988 to 2012. The sample compares analyst quarterly and annual earningsestimates from four months before the merger announcement to up to 2 years after mergercompletion for analysts who retain their job post merger. The dependent variable ForecastError, is measured as the monthly average absolute difference between an analysts esti-mates and the actual earnings per share scaled by the current stock price. The controlgroup is restricted to include analysts with at least a 50% overlap with the target or theacquiring house before the merger. Column 1 compares all merger analysts to non-mergeranalysts. Columns (2), (4) and (5) exclude target house analysts while Column (3) exclu-des acquiring house analysts. Column 4 compares the original post period, four monthsafter merger completion, to the next four months. Column 5 compares every four monthperiod to the original pre-merger period, four months before merger announcement. Allspecifications include Event-Time and Event×Analyst×House fixed effects transformationsto control for unobserved heterogeneity and to mitigate selection bias concerns. Parenthesescontain p-values computed from standard errors clustered at the event level. Specification1: ForecastErrore,h,a,t = β1postt ∗ InMergere,h + ωt + αe,a,h

64

DepVar: Quality Redundancy TickersAnalyst Forecast Error High Low High Low Increase Decrease

MN12-N9 * InMerger 0.07* -0.10 -0.07 0.11 -0.00 0.04(0.07) (0.25) (0.52) (0.11) (0.96) (0.50)

MN8-N5 * InMerger 0.04 -0.08 -0.04 0.01 -0.03 0.06(0.21) (0.24) (0.44) (0.91) (0.47) (0.17)

M1-4 * InMerger 0.15** -0.01 0.17** -0.02 0.01 0.27***(0.03) (0.96) (0.03) (0.82) (0.75) (0.01)

M5-8 * InMerger 0.06 0.09 0.07 0.02 0.01 0.08(0.37) (0.46) (0.44) (0.82) (0.78) (0.47)

M9-12 * InMerger 0.10** 0.04 0.06 -0.11 0.03 0.04(0.03) (0.69) (0.61) (0.32) (0.63) (0.60)

Observations 168,047 63,228 100,712 216,187 215,784 101,115Adjusted R-squared 0.166 0.161 0.162 0.154 0.154 0.160

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1, Analyst*Event EventTime FE

Table 3.9: Analyst Changes in Forecast Error - Which Analysts

Difference-in-difference estimates are reported using merger announcements as treatmentevents from 1988 to 2012. The sample compares analyst quarterly and annual earningsestimates from four months before the merger announcement to up to 2 years after mergercompletion for analysts who retain their job post merger. The dependent variable ForecastError, is measured as the monthly average absolute difference between an analysts estimatesand the actual earnings per share scaled by the current stock price. The control group is re-stricted to include analysts with at least a 50% overlap with the target or the acquiring housebefore the merger. Columns (2) and (3) split analysts by the median quality, (4) and (5)by the median redundancy and (6) and (7) by whether the analyst covers more or less firmsafter the merger. All specifications include Event-Time and Event×Analyst×House fixedeffects transformations to control for unobserved heterogeneity and to mitigate selectionbias concerns. Parentheses contain p-values computed from standard errors clustered at theevent level. Specification 1: ForecastErrore,h,a,t = β1postt ∗ InMergere,h + ωt + αe,a,h

65

Mer

ger

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t

27

5/07

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uri

ties

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itt

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Yes

Yes

30

2/08

6/08

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ris

Baker

Watt

sR

BC

Wea

lth

Man

agem

ent

1.86%

No

Yes

29

11/07

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pen

hei

mer

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C1.30%

No

Yes

28

5/07

9/07

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.E

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82/98

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21

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Park

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34

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18

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ets

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phth

al

Lyon

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oss

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tock

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4/10

7/10

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om

as

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sel

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Tab

le3.

10:

Mer

gers

Subse

ts

Th

ista

ble

ran

ks

mer

gers

by

incr

ease

info

reca

ster

ror.

Mer

ger

sfo

rw

hic

hm

erger

an

nou

nce

men

tsd

onot

men

tion

hu

man

cap

ital

orex

pan

din

gse

rvic

esar

em

ark

edas

No

forLaborValued

.T

he

top

terc

ile

of

mer

ger

sd

ivid

edby

fore

cast

erro

rin

crea

se,

are

mar

ked

wit

ha

bla

ckli

ne.

Th

efi

nal

colu

mn

mark

sm

erger

sth

at

occ

urr

edaft

erth

eG

lob

al

An

aly

stR

esea

rch

Set

tlem

ents

.

66

DepVar: (1) (2) (3) (4) (5) (6)Forecast Error LNV PGS PGS LNV PGS PGS

no ’09 no ’09

Post * InMerger 0.26*** 0.09 0.14**(0.00) (0.20) (0.02)

InMerger 0.07*** 0.12*** 0.09*** 0.05** 0.12** 0.09***(0.00) (0.01) (0.00) (0.05) (0.01) (0.00)

YN1 * InMerger 0.00 -0.04* -0.03(1.00) (0.09) (0.29)

Y1 * InMerger 0.18** 0.06 0.08**(0.01) (0.33) (0.05)

Y2 * InMerger 0.15*** 0.12** 0.12**(0.00) (0.04) (0.03)

Y3* InMerger 0.10* -0.00 0.00(0.08) (0.97) (0.94)

Observations 233,980 334,224 325,629 3,025,456 3,787,328 3,207,414Adjusted R2 0.065 0.062 0.060 0.054 0.046 0.043event period FPI FE YES YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level

*** p<0.01, ** p<0.05, * p<0.1

Table 3.11: Estimate Level Operation Changes - Difference-in-Differences - byMerger Type

Difference-in-difference estimates are reported using merger announcement and completionsas treatment events from 1988 to 2012. I compare the difference in annual and quarterlyestimate forecast error before merger announcement from the target and acquiring houseto estimates of the merged entity after merger completion, to differences in non-mergerestimates of the same firms over the same periods. Results are presented for 90 days andthree years. Columns (1) and (4) include only mergers in which labor is not highly valuedwhile the remaining columns include only mergers after the GARS. In Columns (3) and (6),I remove all observations from 2009. Forecast Error is defined as absolute deviation fromactual earnings scaled by current stock price. The binary independent variable InMergeris equal to 1 for all estimates of the merged entity, target, or acquirer, and 0 otherwise.Post*InMerger is the interaction of InMerger and an indicator equal to 1 for all estimatesafter merger completion and 0 otherwise. z(Timeliness) is defined as the number of daysbefore the earnings announcement the estimate is released. All specifications include event,Period and FPI fixed effects to control for unobserved heterogeneity. Parentheses containp-values computed from standard errors clustered at the event level.

67

(1) (2) (3) (4) (5)DepVar: No Labor Valued? Forecast ErrorSeparation Month Split No Yes Inc Dec

Post * InMerger 0.20*** 0.23*** 0.18** 0.15* 0.25**(0.00) (0.01) (0.04) (0.10) (0.04)

Post * z(Qual) -0.03*** -0.02*** -0.04*** -0.04*** -0.02**(0.00) (0.00) (0.00) (0.00) (0.02)

Post * InMerger * z(Qual) 0.06** 0.10** -0.01 0.12** -0.04(0.04) (0.04) (0.82) (0.03) (0.70)

Observations 17,927 11,238 6,689 4,047 5,464Adjusted R2 0.159 0.156 0.172 0.184 0.151EventTime Job FE YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.12: Attrition around Merger Announcements - Split by Merger Type

Linear probability model estimates are reported for triple-difference specifications usingmerger announcements as treatment events from 1988 to 2012. Table 3.12 divides the resultfrom Table 3.3 by merger type. The binary dependent variable Separation is equal to 1 inmonths in which analysts separate from their brokerage house and 0 otherwise. The controlgroup is restricted to include analysts with at least a 50% overlap with the target house.Columns 2 and 3 split the sample by mergers in which the press release commented onlabor being valued versus mergers focused on acquiring only physical assets. Columns 4and 5 compare the tercile of mergers with the largest increase versus the largest decreasein forecast error. Columns 5 and 6 divide mergers between before and after the globalanalyst settlement. All specifications include Event-Time and Event×Analyst×House fixedeffects transformations to control for unobserved heterogeneity and to mitigate selectionbias concerns. Parentheses contain p-values computed from standard errors clustered at theevent level.

68

DepVar: Merger of Equals Labor ValuedAnalyst Forecast Error Yes No Yes No

MN12-N9 * InMerger 0.02 0.01 0.04 -0.01(0.79) (0.87) (0.54) (0.79)

MN8-N5 * InMerger -0.01 0.01 -0.03 0.02(0.86) (0.80) (0.45) (0.60)

M1-4 * InMerger 0.11* 0.08 0.13* 0.08(0.09) (0.15) (0.06) (0.17)

M5-8 * InMerger -0.02 0.08 -0.00 0.08(0.67) (0.14) (0.97) (0.11)

M9-12 * InMerger 0.08 0.02 0.01 0.09(0.32) (0.66) (0.87) (0.15)

Observations 134,784 182,115 145,714 171,185Adjusted R2 0.156 0.158 0.143 0.169

pval in parentheses, StErr Clustered at Event Level

*** p<0.01, ** p<0.05, * p<0.1, Analyst*Event EventTime FE

Table 3.13: Analyst Changes in Forecast Error - By Merger Type

Difference-in-differences estimates are reported using merger announcements as treatmentevents from 1988 to 2012. The sample compares analyst quarterly earnings estimates from4 months and 12 months prior to the merger announcement to 4 months and 12 monthsafter merger completion for analysts who retain their job post merger. The dependent va-riable Forecast Error is measured as the monthly absolute difference between an analystsestimates and the actual earnings per share scaled by the current stock price. The controlgroup is restricted to include analyst’s with at least a 50% overlap with the target or theacquiring house beore the merger. Columns 1 and 2 are repeated from Table 3.8. Columns3-8 restrict the sample to merger subsets defined in Table 3.14, between mergers in which thepress release commented on labor not being valued, the tercile of mergers with the largestincrease in forecast error, and mergers after the global settlement. All specifications includeEvent-Time and Event×Analyst fixed effects transformations to control for unobserved he-terogeneity and to mitigate selection bias concerns. Parentheses contain p-values computedfrom standard errors clustered at the event level.

69

Mer

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1.54%

210/94

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0.23%

-9.02%

(1,127)

0.31%

32/97

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org

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Sta

nel

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-1.98%

(781)

0.23%

49/97

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mit

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-5.81%

(2,214)

0.40%

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Co

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fel

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an

cial

0.90%

0.15%

262

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27

5/07

Coch

ran

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aro

nia

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ties

Fox-P

itt

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ton

2.87%

0.33%

268

3.10%

28

5/07

A.G

.E

dw

ard

san

dS

on

sW

ach

ovia

0.94%

-5.44%

(978)

0.73%

29

11/07

Op

pen

hei

mer

CIB

C1.30%

-1.64%

505

0.64%

30

2/08

Fer

ris

Baker

Watt

sR

BC

Wea

lth

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agem

ent

1.86%

0.02%

975

0.58%

31

8/09

Fox-P

itt

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ton

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uari

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.70%

3.38%

(705)

-1.20%

32

4/10

Th

om

as

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sel

Part

ner

sS

tife

lF

inan

cial

Corp

-0.48%

-1.52%

(577)

0.45%

33

12/11

Morg

an

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gan

&C

om

pany

Raym

on

d0.19%

-1.75%

(168)

-0.03%

34

11/12

Kee

feB

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ood

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cial

Corp

-0.10%

-8.57%

(1,582)

-0.04%

0.3

7%

-1.1

8%

(273)

0.3

7%

0.0026

0.0413

0.0226

0.0023

Tab

le3.

14:

Mer

gers

and

Outp

ut

Qual

ity

Chan

ges

Forecast

error

(ab

solu

ted

evia

tion

from

act

ualea

rnin

gs,

scale

dby

curr

ent

stock

pri

ce)

isre

port

edfo

rea

chm

erger

from

targ

etan

dacq

uir

eres

tim

ate

son

eyea

rb

efore

the

mer

ger

an

nou

nce

men

tan

dm

erged

enti

tyes

tim

ate

son

eyea

rp

ost

mer

ger

com

ple

tion

.CovBreath

is(#

dis

tin

ctco

mp

an

ies

cover

edby

the

mer

ged

enti

tyL

ES

S#

dis

tin

ctco

mp

anie

sco

ver

edby

targ

etan

dacq

uir

er)

/#

com

pan

ies

cover

edby

at

least

on

ean

aly

st.

Est

Tot

isth

ech

an

ge

into

tal

esti

mate

s.

70

DepVar: (1) (2) (3) (4)Estimate Forecast Error 90d 1yr 3yr 12QPost 0.24** 0.36**

(0.02) (0.01)YN1 / QN3 -0.07 -0.04

(0.68)

QN2 -0.14**(0.02)

QN1 -0.04(0.23)

Y1 / Q1 0.30** 0.27**(0.01) (0.02)

Q2 0.27*(0.06)

Q3 0.30**(0.01)

Q4 0.35***(0.01)

Y2 / Q5 0.45** 0.42**(0.01) (0.01)

Q6 0.40**(0.02)

Q7 0.48**(0.03)

Q8 0.52*(0.05)

Y3 / Q9 0.20 0.39(0.21) (0.11)

Q10 0.21(0.22)

Q11 0.09(0.54)

Q12 0.11(0.41)

z(Timeliness) 0.52*** 0.49*** 0.49*** 0.50***(0.00) (0.00) (0.00) (0.00)

Observations 42,406 228,589 415,344 415,344Adjusted R2 0.066 0.059 0.056 0.056

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.15: Estimate Level Operation Changes - Regressions

OLS estimates of single difference regressions use merger completions as treatment events from 1988 to2012. The sample compares annual and quarterly estimates before merger announcement from the targetand acquiring house to those of the merged entity after merger completion. Forecast Error is the absolutedeviation scaled by current stock price. Post is equal to 1 for all estimates of the merged entity and 0 forestimates of the target and acquiring house before the merger announcement. z(Timeliness), the number ofdays before the actual announcement the estimate is made, helps control for patterns in earnings estimates.Specification: ForecastErrore,t,f,p = β1Postp + β2T imelinesse,t,f,p + αe,t,f

71

(1) (2) (3) (4) (5) (6)DepVar: 90d 1yr

Estimate Forecast Error All LNV PGS All LNV PGS

Post 0.24** 0.50*** 0.34*** 0.36** 0.62*** 0.40***(0.02) (0.00) (0.00) (0.01) (0.00) (0.00)

Post * Labor Valued -0.45** -0.46*(0.01) (0.08)

Post * Post Settlement -0.18 -0.06(0.31) (0.80)

z(Timeliness) 0.52*** 0.53*** 0.52*** 0.49*** 0.49*** 0.49***(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Observations 42,406 42,406 42,406 228,589 228,589 228,589Adjusted R2 0.066 0.068 0.066 0.059 0.061 0.059Event#fpi FE YES YES YES YES YES YES

pval in parentheses, StErr Clustered at Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.16: Forecast Error Changes - by Merger Type

Difference estimates are reported using merger announcements as treatment events from1988 to 2012. Each column compares the full sample (columns presented in earlier tables)to estimates from two restricted samples: 1) mergers in which labor does not appear tobe highly valued in the merger announcement press release and 2) mergers after the GlobalAnalyst Settlement. Regressions (1)-(3) include only 90 days before and after while columns(4)-(6) include one year before and after All specifications include Event#FPI fixed effectstransformations to control for unobserved heterogeneity and to mitigate selection bias con-cerns. Parentheses contain p-values computed from standard errors clustered at the eventlevel..

72

(1) (2) (3) (4)DepVar: Forecast Error In Down turn Not In Down turn

InMerger 0.07*** 0.07*** 0.15*** 0.15***(0.01) (0.01) (0.00) (0.00)

QN3 * InMerger -0.02 -0.02(0.39) (0.48)

QN2 * InMerger 0.01 -0.03(0.61) (0.25)

QN1 / YN1 * InMerger 0.01 0.03* -0.03 -0.03(0.58) (0.09) (0.30) (0.43)

Q1 * InMerger 0.20*** 0.14*(0.00) (0.06)

Q2 * InMerger 0.05 0.15**(0.13) (0.03)

Q3 * InMerger 0.05* 0.08(0.07) (0.15)

Q4 / Y1* InMerger 0.08** 0.05 0.10* 0.06(0.02) (0.43) (0.06) (0.28)

Q5 * InMerger 0.10 0.07(0.11) (0.14)

Q6 * InMerger 0.12** 0.11*(0.04) (0.07)

Q7 * InMerger 0.09* 0.16**(0.05) (0.03)

Q8 / Y2 * InMerger 0.09** 0.05 0.10* 0.05(0.03) (0.15) (0.08) (0.63)

Q9 * InMerger -0.01 0.06(0.88) (0.39)

Q10 * InMerger -0.00 -0.01(0.98) (0.92)

Q11 * InMerger 0.03 0.00(0.74) (0.95)

Q12 / Y3 * InMerger 0.05 0.17* 0.01 -0.03(0.43) (0.06) (0.87) (0.53)

z(Timeliness) 0.44*** 0.44*** 0.48*** 0.48***Observations 3,008,449 3,008,449 3,562,133 3,562,133Adjusted R2 0.053 0.053 0.047 0.047

Robust pval in parentheses clustered at the Event Level*** p<0.01, ** p<0.05, * p<0.1

Table 3.17: Estimate Level Operation Changes - Difference-in-Differences - By in Recession

Difference-in-Difference estimates are reported using merger announcements as treatment events from 1988-2012. The sample compares analyst quarterly earnings estimates from before the merger announcementto after merger completion split by whether the merger occurred just prior or within a recession. Allspecifications include Event, Period and Fiscal Period fixed effects transformations. Parentheses containp-values computed from standard errors clustered at the event level.

73

(1) (2) (3) (4)DepVar: Forecast Error 1y 3y 3y Non-Merger 3y In-Merger

Post * DupCov -0.01(0.91)

DupCov -0.18** -0.15 -0.14 -0.26**(0.02) (0.12) (0.15) (0.02)

YN1 * DupCov -0.01 -0.01 -0.03(0.90) (0.92) (0.65)

Y1 * DupCov -0.03 -0.03 0.05(0.77) (0.74) (0.71)

Y2 * DupCov -0.05 -0.06 0.03(0.70) (0.67) (0.88)

Y3 * DupCov 0.01 0.00 0.10(0.94) (0.98) (0.52)

z(Timeliness) 0.46*** 0.46*** 0.45*** 0.51***(0.00) (0.00) (0.00) (0.00)

Observations 3,212,344 6,570,582 6,163,057 407,525Adjusted R2 0.059 0.050 0.050 0.054event period FPI FE YES YES YES YES

pval in parentheses, StErr Clustered at Event Level

*** p<0.01, ** p<0.05, * p<0.1

Table 3.18: Redundant Versus Non-Redundant - Difference-in-Differences

Difference-in-differences are reported using merger announcement and completions as treat-ment events from 1988 to 2012. I compare the difference in annual and quarterly estimateforecast error prior to merger announcement from estimates for firms covered by both thetarget and acquirer prior to the merger and estimates covered by one or the other. Resultsare presented for one year, three years. Forecast Error is defined as absolute deviation fromactual earnings scaled by current stock price. The binary independent variable DupCov isequal to 1 for all estimates of firms covered by both the target and acquirer prior to themerger announcement and 0 otherwise. Post*Dupcov is the interaction of InMerger and anindicator equal to 1 for all estimates after merger completion and 0 otherwise. z(Timeliness)is defined as # of days prior to the earnings announcement the estimate is released. Allspecifications include Event, Period, and FPI fixed effects to control for unobserved hetero-geneity. Parentheses contain p-values computed from standard errors clustered at the eventlevel.

74

Chapter 4

Appropriate Use of Brokerage House Mergers

as an Instrument

Starting with Hong and Kacperczyk [2010]’s seminal paper, brokerage house

mergers have been used extensively as an instrument or in difference-in-difference

specifications. Their logic is that, because mergers result in analysts leaving

the industry, as long as the reasons analysts leave the industry are not related

to the final outcome variable–in their case, analyst optimism–the relevance

and exclusion restrictions are met.

Since then, the instrument has been used to show that drops in ana-

lyst coverage exogenously impact a large set of dependent variables, including

but not limited to some involving the analysts themselves as in Hong and

Kacperczyk [2010], some involving asset pricing outcomes by changing the in-

formation environment, and others involving corporate finance outcomes by

changing managerial discipline. Through the information channel, the mer-

gers have been shown to lower stock prices and increase uninformed demand

(Kelly and Ljungqvist [2012]), worsen industry-adjusted sales growth (Billett

et al. [2017]), and increase comovement (Israelsen). According to a number of

studies, by changing external discipline, brokerage house mergers cause more

75

corporate tax aggressiveness (Allen et al. [2016]), worse financial reporting

quality (Irani and Oesch [2013]), less internal cash, more CEO excess com-

pensation, and more value-destroying acquisitions (Chen et al. [2015]), more

corporate social responsibility (Adhikari [2016]), more earnings management

and accrual manipulation (Irani and Oesch [2016]), decreased investment and

financing (Derrien and Kecskes [2013]), increased innovation (He and Tian

[2013]), higher takeover premia (Fich et al. [2014]), and more biased credit

ratings (Fong et al. [2014]).

Because of the importance of the instrument, I propose some metho-

dological considerations. First, to quote Roberts and Whited [2012],

“we encourage researchers to discuss the primary endogeneity con-

cern in their study ... What is the endogenous variable(s)? Why

are they endogenous? What are the implications for inferences of

the endogeneity problems? In other words, what are the alterna-

tive hypotheses about which one should be concerned? Only after

answering these questions can researchers put forth a solution to

the endogeneity problem.”

This is essential, especially when utilizing a frequently used instrument. While

it is not impossible, it becomes less and less plausible that the same instrument

can be excluded from an ever-growing set of dependent variables. Each should

be considered on its own merit, not readily accepted because it was used

elsewhere.

76

While I believe the mergers are almost free of reverse causality, there

may be omitted variable bias. First, as shown in Chapter 3, the mergers

themselves disrupt analyst behavior. Hong and Kacperczyk [2010] are care-

ful to show their results for all analysts but also exclude in-merger analysts.

Any studies examining individual analysts should do the same. Another is-

sue is that analysts and brokerage houses endogenously select which firms to

cover. Several variables that impact coverage, and thus the likelihood of redun-

dant coverage, can influence other dependent variables. Hong and Kacperczyk

[2010] filter their control group firms by matching on some observables, but

not all other authors do. It is also important to consider the arbitrary na-

ture of which matching variables to choose. When matching, authors should

show multiple matching specifications and analyze the underlying firms for

non-random assignment that could drive the result.

One source of omitted variable bias of particular concern is the number

of mergers (17/34 in my sample) that happen right before or during a recession,

either the tech crash or the financial crisis. Because firms who struggle in down

times are more likely to be a target of a merger, it is plausible that brokerage

houses that are targets in mergers might simultaneously be more exposed to

stocks that are hit worse by recessions. In Figure 4.1 below, I chart analyst

forecast accuracy in mergers before recessions versus other mergers.

Unsurprisingly, accuracy increases substantially for all analysts around

recessions. This could be an omitted source of variation for dependent variables

such as bias or several of the corporate finance related issues discussed above.

77

.8

1

1.2

1.4

1.6

mea

n_ac

c_id

−5 0 5 10 15

EventTime

("Quarters")

Not Downturn

Downturn

(a)

Figure 4.1: Evolution of Forecast Error In and Out of Downturns

Line charts show the evolution of forecast error over event time (90-day periods). Theestimates are divided between those generated in and out of downturns

My suggestion is to run the tests on both sets of mergers (in and not in

recessions) individually and confirm that the results are not driven by in-

recession mergers. Finally, as shown in chapter 3, the results are temporary

and last only two years. It is important to see whether the effects of the shock

are permanent or similarly last only two years.

78

Chapter 5

Conclusion

This paper provides evidence on how mergers impact the acquisition, perfor-

mance and retention of human capital by analyzing sell-side analyst output

quality and career outcomes around brokerage house mergers. I find evidence

suggesting that analyst output quality is impaired. This impairment is dri-

ven by a failure to retain high-quality analysts from the target house and by

the output quality deterioration of retained analysts from the acquiring house.

These effects are especially large in merger subsets for which human capital

acquisition does not appear to be of first-order importance.

These effects are unlikely unique to brokerage houses. Because analyst

output is observable to the labor markets and managers, one might expect it

would be easier to measure quality resulting in more complete contracts and

thus this is a lower bound for individual employees of acquiring firms. I observe

the opposite because of the mobility and the lack of contract completeness

common to high human capital employees.

Finally, note that I can say little about overall merger efficiency. Suffi-

cient value may be transferred from labor to shareholders through cost savings,

or the brokerage division may be a small portion of a larger firm and merger

79

gains may be earned elsewhere. However, given that sell-side research is con-

sidered a public good due to its positive impact on informational efficiency

(Kelly and Ljungqvist [2012]), impairment of research quality can negatively

impact investors and firms. The FTC and DOJ should more carefully review

the consumer impact of mergers that occur between firms that operate in in-

dustries in which human capital is crucial, but the merging firms do not appear

to value human capital.

80

Bibliography

John M. Abowd, John Haltiwanger, Ron Jarmin, Julia Lane, Paul Lenger-

mann, Kristin McCue, Kevin McKinney, and Kristin Sandusky. The Rela-

tion among Human Capital, Productivity, and Market Value: Building Up

from Micro Evidence. In Measuring Capital in the New Economy, NBER

Chapters, pages 153–204. National Bureau of Economic Research, Inc, Jan-

Jun 2005. URL https://ideas.repec.org/h/nbr/nberch/10621.html.

Daron Acemoglu. Technical change, inequality, and the labor market. Journal

of Economic Literature, 40(1):7–72, 2002. ISSN 00220515. URL http:

//www.jstor.org/stable/2698593.

Binay K. Adhikari. Causal effect of analyst following on corporate social re-

sponsibility. Journal of Corporate Finance, 41:201–216, 2016.

Arthur Allen, Bill B. Francis, Qiang Wu, and Yijiang Zhao. Analyst coverage

and corporate tax aggressiveness. Journal of Banking & Finance, 73:84–98,

DEC 2016. ISSN 0378-4266. doi: {10.1016/j.jbankfin.2016.09.004}.

Gregor Andrade, Mark Mitchell, and Erik Stafford. New evidence and per-

spectives on mergers. Journal of Economic Perspectives, 15(2):103–120,

June 2001. doi: 10.1257/jep.15.2.103. URL http://www.aeaweb.org/

articles?id=10.1257/jep.15.2.103.

81

James Andreoni, William Harbaugh, and Lise Vesterlund. The carrot or the

stick: rewards, punishments, and cooperation. The American Economic

Review, 93(3):893–902, June 2003. ISSN 00028282. doi: 10.2307/3132122.

URL http://www.jstor.org/stable/3132122.

Sandra Betton, B. Espen Eckbo, and Karin S. Thorburn. Corporate takeo-

vers*. In B. Espen Eckbo, editor, Handbook of Empirical Corporate Finance,

Handbooks in Finance, pages 291 – 429. Elsevier, San Diego, 2008. doi: http:

//dx.doi.org/10.1016/B978-0-444-53265-7.50007-X. URL http://www.

sciencedirect.com/science/article/pii/B978044453265750007X.

Matthew T. Billett, Jon A. Garfinkel, and Miaomiao Yu. The effect of

asymmetric information on product market outcomes. Journal of Fi-

nancial Economics, 123(2):357–376, FEB 2017. ISSN 0304-405X. doi:

{10.1016/j.jfineco.2016.11.001}.

Mark T. Bradshaw, Lawrence D. Brown, and Kelly Huang. Do sell-

side analysts exhibit differential target price forecasting ability? Re-

view of Accounting Studies, 18(4):930–955, 2013. ISSN 1573-7136.

doi: 10.1007/s11142-012-9216-5. URL http://dx.doi.org/10.1007/

s11142-012-9216-5.

Alon Brav and Reuven Lehavy. An empirical analysis of analysts’ target pri-

ces: Short-term informativeness and long-term dynamics. The Journal of

Finance, 58(5):1933–1967, 2003. ISSN 1540-6261. doi: 10.1111/1540-6261.

00593. URL http://dx.doi.org/10.1111/1540-6261.00593.

82

Lawrence D. Brown, Andrew C. Call, Michael B. Clement, and Nathan Y.

Sharp. Inside the black box of sell-side financial analysts. Journal of Accoun-

ting Research, 53(1):1–47, 2015. ISSN 1475-679X. doi: 10.1111/1475-679X.

12067. URL http://dx.doi.org/10.1111/1475-679X.12067.

Tao Chen, Jarrad Harford, and Chen Lin. Do analysts matter for governance?

Evidence from natural experiments. Journal of Financial Economics, 115

(2):383–410, February 2015. ISSN 0304-405X. doi: 10.1016/j.jfineco.

2014.10.002. URL http://www.sciencedirect.com/science/article/

pii/S0304405X14002116.

Jonathan Clarke, Ajay Khorana, Ajayl Pate, and P. Raghavendra Rau. The

impact of all-star analyst job changes on their coverage choices and in-

vestment banking deal flow. Journal of Financial Economics, 84(3):713

– 737, 2007. ISSN 0304-405X. doi: http://dx.doi.org/10.1016/j.jfineco.

2005.12.010. URL http://www.sciencedirect.com/science/article/

pii/S0304405X07000177.

Michael B. Clement and Senyo Y. Tse. Financial analyst characteristics and

herding behavior in forecasting. The Journal of Finance, 60(1):307–341,

2005. ISSN 1540-6261. doi: 10.1111/j.1540-6261.2005.00731.x. URL http:

//dx.doi.org/10.1111/j.1540-6261.2005.00731.x.

Jonathan B. Cohn and Jennifer L. Juergens. How much do analysts influence

each others forecasts? Quarterly Journal of Finance, 04(03):1450017, 2014.

83

doi: 10.1142/S2010139214500177. URL http://www.worldscientific.

com/doi/abs/10.1142/S2010139214500177.

Francois Derrien and Ambrus Kecskes. The Real Effects of Financial Shocks:

Evidence from Exogenous Changes in Analyst Coverage. Journal of Finance,

68(4):1407–1440, AUG 2013. ISSN 0022-1082. doi: {10.1111/jofi.12042}.

Clay Deutsch and Andy West. Perspectives on merger integration. Technical

report, 2010.

Eliezer M Fich, Jennifer L Juergens, and Micah S Officer. Analyst coverage

and acquisition returns: Evidence from natural experiments. Unpublished

working paper, Drexel University, 2014.

Kingsley YL Fong, Harrison G Hong, Marcin T Kacperczyk, and Jeffrey D

Kubik. Do security analysts discipline credit rating agencies? 2014.

Cristi A. Gleason and Charles M. C. Lee. Analyst forecast revisions and

market price discovery. The Accounting Review, 78(1):193–225, 2003. ISSN

00014826. URL http://www.jstor.org/stable/3203301.

Claudia Goldin. Human Capital. Springer Verlag, Heidelberg, Germany, 2016.

Melissa Graebner, Koen Heimeriks, Quy Huy, and Eero Vaara. The process of

post-merger integration: a review and agenda for future research. Academy

of Management Annals, pages annals–2014, 2016.

84

Sanford J. Grossman and Oliver D. Hart. The costs and benefits of ownership:

A theory of vertical and lateral integration. Journal of Political Economy,

94(4):691–719, 1986. doi: 10.1086/261404. URL http://dx.doi.org/10.

1086/261404.

Greg Hallman, Jay C. Hartzell, and Christopher A. Parsons. Incentive com-

pensation and the likelihood of termination: Theory and evidence from real

estate organizations. Real Estate Economics, 39(3):507–546, 2011. ISSN

1540-6229. doi: 10.1111/j.1540-6229.2010.00300.x. URL http://dx.doi.

org/10.1111/j.1540-6229.2010.00300.x.

Jarrad Harford, Mark Humphery-Jenner, and Ronan Powell. The sources

of value destruction in acquisitions by entrenched managers. Journal of

Financial Economics, 106(2):247–261, 2012.

Oliver Hart and John Moore. Property rights and the nature of the firm.

Journal of political economy, 98(6):1119–1158, 1990.

Jie Jack He and Xuan Tian. The dark side of analyst coverage: The case of

innovation. Journal of Financial Economics, 109(3):856–878, 2013.

Paul M. Healy, Krishna G. Palepu, and Richard S. Ruback. Does corpo-

rate performance improve after mergers? Journal of Financial Economics,

31(2):135 – 175, 1992. ISSN 0304-405X. doi: http://dx.doi.org/10.1016/

0304-405X(92)90002-F. URL http://www.sciencedirect.com/science/

article/pii/0304405X9290002F.

85

Gerard Hoberg and Gordon Phillips. Product market synergies and compe-

tition in mergers and acquisitions: A text-based analysis. Review of Fi-

nancial Studies, 23(10):3773–3811, 2010. doi: 10.1093/rfs/hhq053. URL

http://rfs.oxfordjournals.org/content/23/10/3773.abstract.

Harrison Hong and Marcin Kacperczyk. Competition and Bias. The Quarterly

Journal of Economics, 125(4):1683–1725, November 2010. doi: 10.1162/

qjec.2010.125.4.1683. URL http://qje.oxfordjournals.org/content/

125/4/1683.abstract.

Harrison Hong and Jeffrey D. Kubik. Analyzing the Analysts: Career Concerns

and Biased Earnings Forecasts. The Journal of Finance, 58(1):313–351,

February 2003. ISSN 00221082. doi: 10.2307/3094489. URL http://www.

jstor.org/stable/3094489.

Jing Huang, Joshua R Pierce, and Sergey Tsyplakov. Post-merger integration

duration and leverage dynamics of mergers: Theory and evidence. 2015.

Rustom M. Irani and David Oesch. Monitoring and corporate disclosure: Evi-

dence from a natural experiment. Journal of Financial Economics, 109(2):

398–418, AUG 2013. ISSN 0304-405X. doi: {10.1016/j.jfineco.2013.02.021}.

Rustom M. Irani and David Oesch. Analyst Coverage and Real Earnings

Management: Quasi-Experimental Evidence. Journal of Financial And

Quantitative Analysis, 51(2):589–627, APR 2016. ISSN 0022-1090. doi:

{10.1017/S0022109016000156}.

86

Ryan D. Israelsen. Does Common Analyst Coverage Explain Excess Como-

vement? Journal of Financial And Quantitative Analysis.

Bryan Kelly and Alexander Ljungqvist. Testing Asymmetric-Information As-

set Pricing Models. Review of Financial Studies, 25(5):1366–1413, May

2012. doi: 10.1093/rfs/hhr134. URL http://rfs.oxfordjournals.org/

content/25/5/1366.abstract.

Ronald W Masulis, Cong Wang, and Fei Xie. Corporate governance and acqui-

rer returns. The Journal of Finance, 62(4):1851–1889, 2007.

Raghuram G Rajan and Luigi Zingales. Power in a theory of the firm. The

Quarterly Journal of Economics, 113(2):387–432, 1998.

Michael R Roberts and Toni M Whited. Endogeneity in empirical corporate

finance. 2012.

Antoinette Schoar. Effects of corporate diversification on productivity. The

Journal of Finance, 57(6):2379–2403, 2002. ISSN 1540-6261. doi: 10.1111/

1540-6261.00500. URL http://dx.doi.org/10.1111/1540-6261.00500.

Albert Sheen. The real product market impact of mergers. The Journal of

Finance, 69(6):2651–2688, 2014. ISSN 1540-6261. doi: 10.1111/jofi.12200.

URL http://dx.doi.org/10.1111/jofi.12200.

Ganesh Shermon. Post Merger People Integration. Technical report, 2011.

87

Nathan Swem. Information in Financial Markets: Who Gets It First? Social

Science Research Network Working Paper Series, August 2016. URL http:

//ssrn.com/abstract=1571891.

Geoffrey Tate and Liu Yang. The bright side of corporate diversifica-

tion: Evidence from internal labor markets. Review of Financial Studies,

2015. doi: 10.1093/rfs/hhv012. URL http://rfs.oxfordjournals.org/

content/early/2015/03/24/rfs.hhv012.abstract.

Sheridan Titman. The effect of capital structure on a firm’s liquidation

decision. Journal of Financial Economics, 13(1):137–151, March 1984.

ISSN 0304-405X. doi: 10.1016/0304-405X(84)90035-7. URL http://www.

sciencedirect.com/science/article/pii/0304405X84900357.

Sheridan Titman and Roberto Wessels. The Determinants of Capital Structure

Choice. The Journal of Finance, 43(1):1–19, March 1988. ISSN 00221082.

doi: 10.2307/2328319. URL http://www.jstor.org/stable/2328319.

Antoinette Weibel, Katja Rost, and Margit Osterloh. Pay for Performance in

the Public SectorBenefits and Hidden Costs. Journal of Public Administra-

tion Research and Theory, 20(2):387–412, April 2010. doi: 10.1093/jopart/

mup009. URL http://jpart.oxfordjournals.org/content/20/2/387.

abstract.

J Fred Weston. The exxon-mobil merger: An archetype. Journal of Applied

Finance, 12(1):69–88, 2002.

88

O.E. Williamson. The Economic Institutions of Capitalism: Firms, Mar-

kets, Relational Contracting. Free Press, 1985. ISBN 9780029348208. URL

https://books.google.com/books?id=lj-6AAAAIAAJ.

Joanna Shuang Wu and Amy Y. Zang. What determine financial analysts

career outcomes during mergers? Journal of Accounting and Economics,

47(12):59 – 86, 2009. ISSN 0165-4101. doi: http://dx.doi.org/10.1016/

j.jacceco.2008.11.002. URL http://www.sciencedirect.com/science/

article/pii/S0165410108000748. Accounting Research on Issues of Con-

temporary Interest.

Luigi Zingales. In search of new foundations. The journal of Finance, 55(4):

1623–1653, 2000.

89