THREE ESSAYS IN CORPORATE FINANCE AND FINANCIAL

INSTITUTIONS

by

Chen Liu

A thesis submitted to the Graduate Program in Management

in conformity with the requirements for

the Degree of Doctor of Philosophy

Queen’s University

Kingston, Ontario, Canada

(June, 2014)

Copyright ©Chen Liu, 2014

ii

Abstract

This thesis conducts empirical studies related to financial institutions and corporate

finance. Specifically, I look at banks’ lending behavior, performance of leveraged buyouts

(LBOs), and the cultural impact on cross-border LBOs. Following an introduction in Chapter 1,

in Chapter 2, I study U.S. commercial banks’ herding behavior in their domestic loan decisions,

where herding is defined as the extent to which banks deviate from the industry average lending

decisions and collectively increase or decrease loans to certain categories. I find significant

evidence that herding exists and that banks tend to herd more when the economic condition is

less favorable, regulation is tight, and when banks are struggling . Overall, these findings support

the hypotheses of information asymmetry and regulatory arbitrage as motivations for herding.

Chapter 3 provides a comprehensive study of LBO deal characteristics, participants’

involvement, and their impact on target firms’ performance. I find that better post-buyout

operating performance is associated with larger amounts of leverage added during the LBO

process, tighter LBO loan covenants, and equity contribution by target firms’ incumbent

management. LBOs are more likely to exit through an IPO or a sale if they use more bank debt

with tighter covenants and are sponsored by private equity (PE) firms of high reputation. These

results suggest that the main source of value creation in LBOs is the reduced agency costs

through the disciplining effect of debt, closer monitoring by lenders, and the better aligned

management incentives. PE reputation is also important in ensuring successful deal outcomes.

Chapter 4 (co-authored) examines the impact of cultural differences between PE firms and

target firms on the completion of cross-border LBOs. We find that cultural distance between PE

and target firms reduces the likelihood of buyout completion and increases the time between

buyout announcement and completion. We also find that club deals moderate the negative

(positive) impact of cultural distance on the likelihood (the duration) of LBO completion. This

mitigation effect is through the increased familiarity channel of club formation. Our findings

contribute to the literature that underscores the importance of culture in economic outcomes.

iii

Co-Authorship

Chapter 4 is co-authored with Hui Zhu (University of Ontario Institute of Technology).

iv

Acknowledgements

I am greatly indebted to Professor Lynnette Purda-Heeler, my thesis supervisor, for her

continued guidance, support, encouragement, and patience over the years of my doctoral

study. She has set a life-time example as a “woman in finance” and has greatly stimulated my

passion in research and teaching and my desire to excel in both. I am also very grateful for her

help and encouragement throughout my job search progress. Without her, I wouldn’t become

who I am today.

I wish to express my gratitude to other members of my dissertation committee,

Professors Edwin Neave and Wei Wang, for their constant encouragement, devoted support,

constructive comments, and always inspiring me to think deep. I thank my external examiner

Professor Douglas Cumming for agreeing to help during his extremely busy schedule and his

insightful comments and suggestions that improve my thesis but also inspire future research. I

have learned from Professors Neave, Wang, and Cumming their work ethics and their passion

for research.

I thank Arnold Cowan, Teodora Paligorova, Christian Rauch, and Samir Saadi for their

comments and encouragement along the way.

I thank my co-authors, for inspiring me and allowing me to prioritize my dissertation

and job search over our joint projects.

I am grateful for the opportunity to have received my doctoral training at Queen’s

School of Business, which I will always call home. I thank Professors Lewis Johnson, Frank

Milne, Louis Gagnon, Fatma Sonmez, Fabio Moneta, Alfred Davis, Wulin Suo, Sean Cleary,

Selim Topaloglu, Serena Shuo Wu, and Ning Zhang for their help and support in various

ways through my doctoral study.

I thank a dear friend, who walks with me in the job market and the last year of my

doctoral study.

My deepest gratitude goes to my parents for their unconditional love, endless patience,

and selfless support throughout my life.

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Table of Contents

Abstract ....................................................................................................................................... ii

Co-Authorship ........................................................................................................................... iii

Acknowledgements ................................................................................................................... iv

List of Figures .......................................................................................................................... viii

List of Tables ............................................................................................................................. ix

Chapter 1 Introduction ................................................................................................................ 1

Chapter 2 Herding Behavior in Bank Lending: Evidence from U.S. Commercial Banks ......... 7

2.1 Introduction ....................................................................................................................... 7

2.2 Literature Review and Hypothesis Development ........................................................... 11

2.2.1 Reviewing the Herding Theories and Some Empirical Evidence of Bank Herding 11

2.2.2 Hypotheses Development ......................................................................................... 14

2.3 Data and an Overview of the Banking Industry over Time ............................................ 15

2.3.1 Data .......................................................................................................................... 15

2.3.2 Summary Statistics ................................................................................................... 16

2.4 Measuring Bank Herding: Methodology and Results ..................................................... 21

2.4.1 Methodology: the LSV and FHW Herding Measures .............................................. 21

2.4.2 Validity of the LSV and FHW Herding Measures ................................................... 24

2.4.3 Results: Documenting the Existence of Herding ..................................................... 25

2.5 Herding, Macroeconomic Conditions, and Bank Health ................................................ 29

2.5.1 Economic and Market Conditions, and the Impact of Capital Requirement ........... 29

2.5.2 Bank Health and Herding ......................................................................................... 32

2.6 Herding Measures for Banks of Different Sizes ............................................................. 35

2.7 Robustness Checks ......................................................................................................... 36

2.7.1 Sub-period Regressions ............................................................................................ 36

2.7.2 The Impact of Bank Deregulation ............................................................................ 37

2.8 Conclusion ...................................................................................................................... 38

Chapter 3 Debt Structure, Private Equity Reputation, and Performance in Leveraged Buyouts63

3.1 Introduction ..................................................................................................................... 63

3.2 Literature Review and Hypotheses Development........................................................... 69

vi

3.2.1 Measuring Value Creation in LBOs ......................................................................... 69

3.2.2 How LBOs Create Value: Hypotheses ..................................................................... 72

3.3 Sample, Data, and Post-Buyout Performance Measures ................................................ 78

3.3.1 The Buyout Sample .................................................................................................. 78

3.3.2 Evidence on Post-Buyout Operating Cash Flows .................................................... 81

3.4 LBO Deal Characteristics and Participants Involvement ............................................... 84

3.4.1 Leverage, Debt Structure, and Contractual Features ................................................ 84

3.4.2 Private Equity in LBOs ............................................................................................ 90

3.5 Explanations for Post-Buyout Operating Performance................................................... 95

3.6 Robustness ...................................................................................................................... 99

3.6.1 Subsample Analyses ................................................................................................. 99

3.6.2 Credit Market Conditions ....................................................................................... 101

3.6.3 LBO Deal Outcome ................................................................................................ 103

3.7 Conclusion .................................................................................................................... 106

Chapter 4 Culture Distance and Cross-border Leveraged Buyouts ........................................ 128

4.1 Introduction ................................................................................................................... 128

4.2 Theory and Hypotheses ................................................................................................ 134

4.2.1 Formal and Informal Institutions............................................................................ 134

4.2.2 The Mitigating Effect of Club Deals ...................................................................... 138

4.3 Data and Variables ........................................................................................................ 140

4.3.1 Sample Selection .................................................................................................... 140

4.3.2 Variable Construction ............................................................................................ 142

4.3.3 Summary Statistics ................................................................................................. 149

4.4 Empirical Results .......................................................................................................... 151

4.4.1 The Cultural Impact on Deal Completion and Buyout Duration ........................... 151

4.4.2 The Mitigation Effect of Club Deals ...................................................................... 152

4.4.3 The Familiarity versus Risk-sharing Channel ........................................................ 153

4.5 Robustness Checks ....................................................................................................... 155

4.5.1 Excluding U.S. and U.K. Firms ............................................................................. 155

4.5.2 Alternative Measures for Club ............................................................................... 155

4.5.3 Cultural Distance within PE Consortiums ............................................................. 156

vii

4.5.4 Individual Cultural Dimension ............................................................................... 156

4.6 Conclusion .................................................................................................................... 157

Chapter 5 Conclusion ............................................................................................................. 172

Bibliography ........................................................................................................................... 174

Appendix A : Variable Definitions and Data Sources for Chapter 2 ..................................... 185

Appendix B : Variable Definitions and Data Sources for Chapter 3...................................... 188

Appendix C : Top 25 PE Firms for Chapter 3 ........................................................................ 191

Appendix D : Correlation Table for Chapter 3 ....................................................................... 193

Appendix E : Variable Definitions and Data Sources for Chapter 4 ...................................... 194

Appendix F : Top 20 PE firms for Chapter 4 ......................................................................... 197

viii

List of Figures

Figure 2-1: Five Main Categories of Loans .................................................................................. 56

Figure 2-2: Libor-OIS and Baa-Aaa spreads ................................................................................ 58

Figure 2-3: The LSV and FHW Herding Measures ...................................................................... 59

Figure 2-4: LSV Herding Measure for Different Size Groups ..................................................... 60

Figure 3-1: A typical LBO Transaction and Hypotheses in LBO Value Creation ..................... 126

Figure 3-2: LBO Transactions Each Year .................................................................................. 127

Figure 4-1: LBO Transactions Each Year .................................................................................. 171

ix

List of Tables

Table 2-1: Summaries Statistics.................................................................................................... 41

Table 2-2: LSV and FHW Herding Measures .............................................................................. 44

Table 2-3: Expected Relation between Herding and Explanatory Variables under Different

Hypothesis..................................................................................................................................... 46

Table 2-4: Regression on Macroeconomic and Market variables ................................................ 48

Table 2-5: Regression on Regulatory variables ............................................................................ 49

Table 2-6: Regression on Macroeconomic, Market, and Bank Condition variables .................... 50

Table 2-7: LSV Measures for Different Size Groups ................................................................... 51

Table 2-8: Regression on Economic, Market, and Regulation variables: By Different Bank Size

Group ............................................................................................................................................ 52

Table 2-9: Subsample Analyses .................................................................................................... 53

Table 2-10: Bank Deregulation in the 1980s and Early 1990s ..................................................... 55

Table 3-1: Comparing the 501 LBOs in the Final Sample with 1, 586 Original LBO Sample .. 108

Table 3-2: LBO Year and Industry ............................................................................................. 109

Table 3-3: Median Changes in Operating Performance ............................................................. 110

Table 3-4: Leverage, Debt Structure, and Debt Contractual Features ........................................ 112

Table 3-5: Private Equity Involvement and Reputation ............................................................. 116

Table 3-6: Regression for Post-buyout Performance: Baseline Regression ............................... 118

Table 3-7: Regression for Post-buyout Performance: Subsample Analyses .............................. 121

Table 3-8: Market Timing ........................................................................................................... 123

Table 3-9: LBO Year and Exit Strategy ..................................................................................... 124

Table 3-10: Deal Outcome .......................................................................................................... 125

Table 4-1: Country-Wise Breakdown by PE Countries and Target Countries ........................... 159

Table 4-2: Summary Statistics of Variables ............................................................................... 160

Table 4-3: National Cultural Distance ........................................................................................ 163

Table 4-4: The Impact of Cultural Distance on Deal Completion and Duration ........................ 164

Table 4-5: Buyout Completion and Duration Analysis: The Effect of Club Deals .................... 165

Table 4-6: Familiarity versus Risk-Taking ................................................................................. 166

Table 4-7: Subsample Excluding U.S. and U.K. Firms .............................................................. 167

x

Table 4-8: Alternative Measure for Clubs .................................................................................. 168

Table 4-9: Cultural Distance within a PE Consortium ............................................................... 169

Table 4-10: Four Cultural Dimensions ....................................................................................... 170

1

Chapter 1 Introduction

This thesis examines two important issues in finance: (1) U.S. commercial banks’ herding

behavior in their domestic lending decisions, and (2) how different participants in leveraged

buyouts (LBOs)—banks, private equity (PE) firms, target firms’ management—individually and

interactively affect the performance of U.S. LBOs and the completion of cross-border buyouts.

In Chapter 2, Herding Behavior in Bank Lending: Evidence from U.S. Commercial Banks,

I study whether and how banks herd with each other in their domestic loan decisions, where

herding is defined as the extent to which banks deviate from average industry lending standards

and collectively increase or decrease loans of a certain type. Motivating this paper is the

argument that herding increases systemic risks in the banking system, the industry-specific

characteristics that may lead banks to herd, and the lack of studies on herding behavior in the

setting of banks. While numerous theories exist to explain the motivations behind herding,

empirical studies in herding mainly focus on the capital markets and little work has been done to

document the extent that herding exists in the banking industry. Hence, my study fills the gap by

applying the Lakonishock, Shleifer and Vishny (1992) and Frey, Herst and Walter (2007)

herding measures to U.S. commercial banks during the period from 1976:Q1 to 2010:Q4. Using

data on twelve categories of loans from Federal Reserve’s Call Report, I find that significant

herding exists in that banks extend similar types of loans at the same time.

I next examine the economic and industry-level factors related to bank herding. First, I find

that there tend to be more herding when the economic conditions are less favorable. Second, a

higher level of herding is detected with the implementation of the capital adequacy requirement.

Third, banks tend to herd more when the banking industry is more vulnerable and subject to

higher risks—measured by the extent to which banks are financed by short-term debt rather than

2

insured deposits, the liquidity of banks’ assets, banks’ profitability and loan quality, and the

extent to which banks rely more on the non-interest income rather than interest income from

lending activities. Last, comparing banks of different sizes, I find a higher and more volatile

level of herding among small banks than the large ones.

The third and fourth chapters of this thesis study LBOs—where companies are acquired

using a small portion of equity and a large portion of outside debt financing. Equity investors in

these transactions include PE firms and target firms’ incumbent management while LBO

financiers are usually banks and institutional investors. These two chapters investigate how the

characteristics of different participants have changed over time and how their interaction with

each other and under different market conditions affect the post-buyout performance of U.S.

LBOs and the completion of cross-border buyout transactions.

The third chapter, Debt Structure, Private Equity Reputation, and Performance in

Leveraged Buyouts, provides a comprehensive study of deal characteristics and participants’

involvement in LBOs and their impact on target firms’ performance. Using a hand-collected

sample of 501 U.S. LBOs completed between 1986 and 2011, I find that better operating

performance, as measured by industry-adjusted return on assets and return on sales, is related to

larger amount of leverage added during the LBO process, tighter LBO debt covenants, and

management’s participation in the buyout. PE firms’ reputation, as measured by their market

share based on past LBO transactions, is not related to operational improvement. I also find that

LBOs are more likely to exit through an IPO or a sale if they use more bank debt with tighter

covenants and are sponsored by PE firms of high reputation. These results are robust to credit

market conditions, the aggregated LBO activities, target firms’ cost of borrowing, and buyout

prices. Overall, these results suggest that the main source of LBO value creation is the reduced

3

agency costs through the disciplining effect of debt, closer monitoring by lenders, and better

aligned management incentives.

In Chapter 4 (co-authored), Cultural Distance and Cross-border Leveraged Buyouts, I

extend the LBO study to an international setting by investigating the impact of cultural distance

on the completion of cross-border LBOs. Using a sample of 2,587 cross-border LBOs sponsored

by PE firms from 52 countries and target firms from 41 countries during the 1986–2013 period,

we find that cultural distance between PE firms and target firms reduces the likelihood of LBO

deal completion and increases the time between LBO announcement and completion.

Specifically, we find a one standard deviation increase in cultural distance reduces the likelihood

of buyout completion by 5% and increases the buyout duration by 19%. We also find that club

deals moderate the negative (positive) impact of cultural differences on the likelihood (the

duration) of an LBO deal completion. Further analysis shows such mitigation effect is achieved

through increased familiarity with the target firm through club formation. Our findings are robust

to controlling for LBO transaction characteristics, PE firms’ reputation and experience, target

countries’ economic and LBO market development, corporate governance, and legal

environment, and PE-target country pair variables. Our results underscore the importance of

culture on economic behavior and outcomes, especially in a cross-border setting.

This thesis makes several important contributions to the literature. First, it deepens our

understanding on the interaction between different participants in the financial market—the time-

series patterns and cross-sectional characteristics of these interactions, their causes, and their real

effects. My thesis is the first study that investigates whether U.S. banks make correlated lending

decisions in the credit markets. My finding that herding exists highlights the need for a

macroprudential approach to bank regulation and supervision as current regulation can magnify

4

the effect of herding. My LBO study is among the first to examine the comprehensive impact of

equity investors (PE firms and target firms’ incumbent management) and financiers (banks and

institutional investors) on LBO value creation. The cross-border LBO study addresses the

importance of syndicating PE firms in mitigating the cultural impact on deal completion.

Second, this thesis contributes to the literature of value creation in LBOs. On the one side,

the PE industry has agreed that it needs to refocus on operational improvement since the 2007-09

financial crisis.1 On the other side, there is a gap in the literature in determining target firms’

performance drivers in LBOs using more recent data. My study fills this gap and sheds light on

the issue of when and how an LBO may be successfully employed to improve firm performance.

By doing so, it also facilitates our understanding as to why recent LBOs are less successful than

previous ones.

Third, this thesis also contributes to the literature on bank lending and debt contracting. In

Chapter 2, I examine banks’ correlated lending decisions and how this correlation varies as a

response to the financial health of the banking system. My findings in Chapter 3 stress the

importance of bank loan covenants in monitoring borrowers and therefore improving their

performance. I suggest that bank loans and their contractual terms need to be carefully

constructed in order to reduce the credit risk of these loans and borrowers’ risk shifting

incentives.

Fourth, this thesis furthers our understanding of the private equity industry. My studies

describe the patterns of PE firms’ involvement in the LBO market and their role in the LBO

value creation process. I find PE firms with high reputation do not lead to operating

1 Private Equity International Magazine (2012) states that “There's been a realization, post-finance crisis, that

private equity needs to return to its roots: creating value through operational improvement rather than financial

engineering”.

5

improvement of target firms. This contradicts with the high PE returns that are documented in

the literature. However, I do find that PE firms’ reputation is important in ensuring successful

exit strategies of U.S. LBOs and the completion of cross-border buyouts.

Fifth, my study contributes to the general deal syndication literature by examining whether

and how club deals are related to LBO value creation and cross-border deal completion. Theories

of deal syndication suggest a number of reasons for club deals in LBOs, including capital

constraints, diversification motives, the certification effects of reputable PEs to obtain favorable

terms in debt financing, the synergy from expertise of different PE firms, and the collusion

motivations to depress bidding prices. My study finds no evidence that supports the view of PE

synergy from the perspective of post-buyout operational performance. However, my findings

suggest that club formation mitigates the negative impact of cultural distance on cross-border

deals by increasing the familiarity of culturally distant PE firms to target firms. That is, culturally

distant PE firms strategically team up with other PEs that are culturally closer to the target firms.

I add to the deal syndication literature by proposing the familiarity motives of club formation.

Sixth, my study adds to the emerging research on the economic importance of cultural

difference. Specially, my study sheds light on how culturally distance between countries affects

cross-border investment decisions. My analysis represents the first attempt to examine the link

between culture and the completion of cross-border LBOs.

The rest of the dissertation proceeds as follows. Chapter 2 examines herding behavior in

U.S. banks’ domestic lending decisions and how herding measures are related to macroeconomic

conditions and the overall strength of the banking industry. Chapter 3 first provides a

comprehensive study on the changing characteristics of LBOs in the U.S. and examines factors

related to post-buyout performance improvement and successful exits of these LBOs. Chapter 4

6

investigates the impact of cultural distance on the completion of cross-border LBOs and how

club deals mitigate the cultural impact. Chapter 5 concludes with suggestions for future research.

7

Chapter 2 Herding Behavior in Bank Lending:

Evidence from U.S. Commercial Banks

2.1 Introduction

There has been a growing interest in the literature on herding behavior. Theoretical works

try to explain the motivations behind herding behavior (for example, Banerjee, 1992;

Bikhchandani, Hirshleifer and Welch, 1992, 1998; Scharfstein and Stein, 1990; Borio, Furfine

and Lowe, 2001; Acharya and Yorulmazer, 2008). Most of the empirical studies focus on herding

in the capital markets (Christie and Huang, 1995; Chang, Cheng, and Khorana, 2000), among

mutual fund managers (Wermers, 1999), hedge fund managers (Boyson, 2010), and stock

analysts (Hong and Kubik, 2004). Little empirical work has been done to study herding in banks

or to examine the economic or industry-level factors that are related to actual bank herding.2 This

paper aims to fill the gap by documenting U.S. commercial banks’ herding behavior across the

various categories of loans that banks can choose to extend from 1976 to 2010. It then examines

how herding has changed in response to changes in macroeconomic conditions and bank

financial health over the past 30 year period. It also studies the herding patterns among banks of

different sizes.

In this paper, herding refers to the cases where banks make same or similar lending

decisions. It can be either banks behaving in a similar way when facing similar circumstances

(the spurious herding) or banks intentionally following each other (the intentional herding). It is

very important to study herding in the banking industry for the following reasons. First, herding,

whether it is spurious or intentional, may have a big impact on the safety and soundness of the

2 Exceptions are Jain and Gupta (1987), Chang, Chaudhuri, and Jayaratne (1997), Barron and Valev (2000),

and Stever and Wilcox (2007).

8

banking system and create a number of potential problems, including the deterioration of lending

standards, misallocation of lending resources, asset price bubbles, increased systemic risks, and

the exacerbation of business cycle. The theoretical work of Allen, Babus, and Carletti (2011)

argue that banks’ asset commonality, which is considered as herding in this paper, increases

systemic risks. Also, Boot (2011, p168) states that “in banking, herding is particularly

worrisome”. This is because when banks make similar decisions, risk exposures become highly

correlated and a simultaneous failure may become more likely, creating serious problems to the

real economy. This is illustrated by the 2007-09 financial crisis. Prior to the crisis, financial

institutions had been holding and trading subprime mortgage backed securities, such herding

behavior has been suggested to be associated with banks underestimating the risk of these

securities, leading to overinvestment in mortgage lending, and eventually the crisis.

A second reason as to why it is important to study bank herding is the very industry-

specific characteristics of the banking industry, which make it more likely for herding to occur

(Haiss, 2010). First, there are severe information asymmetry problems between borrowers and

banks and among banks of different types. Uncertainties arising from information concerns may

lead less informed banks to follow the behavior of the more informed ones. Second, the banking

industry is highly regulated and banks may take advantage of the regulatory arbitrage

opportunities. For example, when banks believe that they will be bailed out in case of severe

financial distress, they have the incentives to herd by engaging in collective risk-taking and

management strategies in order to gain more profits without increasing the likelihood of

bankruptcy, due to the explicit or implicit bailout commitment. In addition, certain regulatory

and governance rules such as the capital adequacy requirement that impose boundaries on what

banks can do and limit banks’ decision possibilities may also lead banks to make similar

9

decisions (Vives, 1996).

Using quarterly data from the Federal Reserve’s Report of Condition and Income (hence

after, the Call Report) of all U.S. commercial banks from 1976:Q1 to 2010:Q4, this paper studies

herding behavior in banks’ domestic lending decisions. Specifically, I look at twelve categories

of loans: commercial real estate loans, 1-4 family residential real estate loans, multifamily

residential real estate loans, construction real estate loans, farmland real estate loans, consumer

and industrial (C&I) loans, loans to individuals, loans to agriculture production, loans to

depository institutions, loans to states and political subdivisions, loans categorized as “other

loans” in the Call Report, and the remaining un-categorized loans. For each quarter, I examine

the extent to which banks deviate from the industry average lending decisions and collectively

increase or decrease loans to certain categories. To test the existence of such herding behavior, I

apply the traditional herding measure of Lakonishok, Shleifer and Vishny (1992) (the LSV

measure) and a more recent measure by Frey, Herbst and Walter (2007) (the FHW measure). I

find evidence of significant herding during the entire sample period. The time series of herding

exhibite a “W” shape that presents a decreasing trend in the late 1970s and 1980s, a slight

increase in the early 1990s, followed by a decline in the late 1990s, and a significant increase in

the 2000s.

I next examine economic and industry-level factors that may potentially drive herding.

There are three main findings. First, I find that the LSV and FHW herding measures are

positively related to the inflation rate, the unemployment rate, and interest spreads that measure

credit risks. The results indicate that banks tend to herd more when economic conditions are less

favorable or when there are more uncertainties in the market. Second, herding is positively

related to banks’ equity ratio, a proxy for regulation on capital requirement, and there tend to be

10

more herding with the implementation of the Basel requirements, suggesting a positive relation

between herding and bank capital regulation. Third, banks tend to herd more when the banking

industry is more vulnerable and subject to higher risks—measured the extent to which banks are

financed by short-term debt rather than insured deposits, the liquidity of banks’ assets, banks’

profitability and loan quality, and the extent to which banks rely more on the non-interest income

rather than interest income from lending activities.

I next study herding among banks of different sizes as they may have different information

and therefore behaving differently. I find a higher and more volatile level of herding among

small banks than large ones, consistent with the information asymmetry hypothesis (Banerjee,

1992; Bikhchandani et al., 1992, 1998) that small banks have information disadvantage and tend

to herd more.

Robustness checks confirm that my results hold in each of the three sub-periods: 1976 to

1989, 1990 to 1999, and 2000 to 2010. I also study the effects of bank deregulation—the

removal of the restrictions that banned commercial banks from interstate branch banking (the

McFadden Act), from competing with investment banks and insurance companies (the Glass-

Steagall Act), and from offering high interest rates on deposit accounts (the Federal Reserve

Regulation Q). Using the number of bank mergers as a proxy for deregulation, I find a lower

level of herding associated with the deregulation during the 1980s and 1990s.

Findings of this paper make three important contributions. First, this paper is the first study

that examines herding behavior in domestic lending decisions of U.S. commercial banks.

Second, this paper documents the changing characteristics of the banking industry and

investigates the relationship between herding and macroeconomic and bank-specific variables

and compares herding patterns among banks of different sizes. In doing so, this paper furthers

11

our understanding on the relative importance of the competing explanations of herding behavior

and draws important policy implications.3 Third, the LSV indicator has been extensively used to

study fund manager behavior. This paper is one of the first studies that applies the LSV measure

to banks and is among the few studies that use both the LSV and FHW indicators. In this sense,

this paper serves as an empirical test and comparison of the LSV and FHW herding measures in

the setting of the banking industry.

The remainder of the paper is structured as follows. Section 2 reviews literature and

develops hypotheses. Section 3 provides detailed descriptions of data and an overview of the

changing characteristics of the banking industry. Section 4 describes methodologies and provides

evidence of herding. Section 5 examines the relations between bank herding, the macroeconomic

and market conditions, and bank characteristics. Section 6 studies herding among banks of

different size groups. Section 7 conducts robustness checks. Section 8 concludes.

2.2 Literature Review and Hypothesis Development

2.2.1 Reviewing the Herding Theories and Some Empirical Evidence of Bank Herding

Literature provides three basic explanations for herding: the information cascade

hypothesis, the regulatory arbitrage hypothesis, and the reputation/compensation hypothesis.

First, information cascades arise when there is uncertainty about the accuracy of information and

market participants incorporate information contained in earlier actions into their decisions

(Banerjee, 1992; Birhchandani et al. 1992, 1998; Avery and Zemsky, 1998). Barron and Valev

(2000) develop a model of sequential order for banks’ international lending decisions—bank

3 See Section 2 for a literature review on theoretical and empirical works on bank herding.

12

with less wealth tend to avoid costly information gathering and follow banks with more wealth

that can easily get information.

An alternative explanation is the regulatory arbitrage hypothesis. The model of Acharya

and Yorulmazer (2008) shows that when the number of bank failures is large, regulators find it

optimal to bail out failed banks, whereas when the number of bank failures is small, surviving

banks have to acquire the failed ones, increasing the possibility that the surviving banks could

also fail. Therefore, banks find it optimal to herd so they can survive or fail together since

surviving alone means having to acquire the failed banks and failing together could possibly lead

to banks being bailed out by the central bank. Stever and Wilcox (2007) propose another channel

through which regulations can lead to herding behaviors. They argue that bank regulators grant

banks with more discretion in reporting loan charge-offs and provisions when the whole banking

system is weak. By being similar to each other (as captured by “herding” in this paper), banks

could benefit from the additional reporting discretion when all other banks are in trouble.

A third popular view of herding, although less related to this paper, is the

reputation/compensation hypothesis (Scharfstein and Stein, 1990; Devenov and Welch, 1996;

Borio et al., 2001). It argues that the reward structure of (fund) managers limits blame in the case

of collective as opposed to individual failure and gives managers an incentive to imitate the

benchmark manager. As suggested by Kirkpatrick (2009), as commercial bank managers’

compensation systems become increasingly performance-based, banks are more susceptible to

herding. However, managerial compensation and its impact on herding are beyond the scope of

this paper.

Although theories provide various explanations for herding, several issues make empirical

work on herding particularly difficult. First, detecting herding ideally requires the observation of

13

actions and potentially private information—a challenge for data collection, particularly when

herding is used to hide relevant information. Second, even when one does detect herding, the

statistical measures document correlated behavior without regard to the underlying reasons and

therefore are not able to directly test the suggested theories.

Due to the reasons discussed above, there have been only a few papers that study herding

behavior in bank lending decisions. Jain and Gupta (1987) and Barron and Valev (2000) use the

Granger-causality test and find that small U.S. banks replicate the lending behavior of large U.S.

banks in lending to developing countries during the 1980s and 1990s. Nakagawa (2008) finds

leader-follower relationships between lending behavior of different types of Japanese banks—

local banks follow major banks in urban cities and local banks follow each other in regional

cities. Nakagawa and Uchida (2011) provide some evidence of inefficient herding among

Japanese banks in the 1980s. Uchida and Nakagawa (2007) is the first study to use the LSV

herding measure and is closest to this paper. They apply the LSV measure to Japanese banks’

loans from 1975 to 2003 and find evidence of herding. My paper differs from their study in that I

also examine factors that are related to herding and discuss how my findings relate to different

herding theories.

In a more general study of herding behavior among banks, Stever and Wilcox (2007)

develop herding measures based on stock return data and find evidence of herding among the 30

largest U.S. bank holding companies from 1976 to 2005. Rajan (1994) finds evidence of herding

in banks’ decisions to write down assets and to set aside loan loss reserves. Chang, Chaudhuri,

and Jayaratne (1997) detect herding by U.S. banks in their branch location decisions. My paper

contributes to the literature by providing the first empirical evidence of herding in domestic

lending decisions of U.S. commercial banks and relating herding to economic conditions and

14

bank characteristics.

2.2.2 Hypotheses Development

Based on the herding theories on information asymmetry and regulatory arbitrage and

previous studies that documented herding in other aspects in the banking industry, my hypothesis

on the existence of herding is stated formally as follows:

Hypothesis 1: There is significant level of herding in U.S. commercial banks’ domestic lending

decisions.

According to the information asymmetry hypothesis, there tend to be more herding when

the uncertainty about the information accuracy increases. In addition, the regulatory arbitrage

theory states that when banks face less favorable conditions, they tend to behave more similar to

each other as it increases their chances of getting bailed out by the central bank. Together, they

suggest more herding tend to occur when the economic and market conditions are less favorable

and when the banking industry is more vulnerable and subject to higher risks. This leads to

Hypothesis 2:

Hypothesis 2a: There tend to be more herding when the economic condition is less favorable.

Hypothesis 2b: There tend to be more herding when the credit risk is higher.

Hypothesis 2c: There tend to be more herding when the overall bank condition is less favorable.

To study the impact of regulation on herding, I focus on the capital adequacy requirement,

as it is highly related to banks’ lending decisions and is one of the most important aspects of

regulation in banks. I argue that tighter capital regulation may lead to more herding for two

reasons. First, regulation puts boundaries on banks’ action space and therefore banks tend to

behave in similar ways (Vives, 1996). Second, based on the regulatory arbitrage hypothesis,

banks circumvent capital requirement by engaging more on the off-balance sheet activities,

15

therefore, leading to a collective reduction in traditional loans.

Hypothesis 3: Banks tend to herd more with tighter capital adequacy requirement.

Investigating herding among banks of different size groups, the information asymmetry

theory suggests that there tend to be more herding among small banks as the information

problem they face is more severe than the large ones. However, the regulatory arbitrage

hypothesis would imply more herding among large banks as they have better chances to get

bailed out. Therefore, I leave the relation between herding and bank size as an empirical issue to

be examined in Section 6 of this paper.

2.3 Data and an Overview of the Banking Industry over Time

In this section, I provide a detailed description of the data and an overview of the banking

industry during the sample period. Understanding how the market conditions, regulatory

environment, and bank characteristics have changed over time is important as they are possible

drivers for bank herding.

2.3.1 Data

The bank level data is from the Federal Reserve’s Report of Condition and Income (the

Call Report). The Call Report contains data of all commercial banks that are regulated by the

Federal Reserve System, Federal Deposit Insurance Corporation (FDIC), and the Comptroller of

the Currency and it has been used in previous studies to study U.S. commercial banks’ overall

condition, their balance sheet and incomes statement items, and loan decisions (for example,

Berger and Udell, 2004; Stever and Wilcox, 2007; Huang, 2010; Bassett et al., 2010; Ivashina

and Scharfstein, 2010; Contessi and Francis, 2013).

16

I compile a data set with quarterly information on balance sheet, income statements, and

risk-based capital for all reporting banks over the period from 1976:Q1 to 2010:Q4, covering all

sample periods available in the database at the time of writing and encompassing the 2007-09

financial crisis. I exclude banks in any quarter in which they go through a merger using bank

merger data from the Federal Reserve National Information Center. I also exclude all bank-

quarters with missing information on total assets and total loans. The final data set contains

28,315 banks and 1,534,229 bank-quarters.

Economic and market data used in this paper are collected from the U.S. Bureau of

Economic Analysis, the Federal Reserve Board of Governors Release H.15, and Bloomberg.

2.3.2 Summary Statistics

Table 1 presents the sample mean for key variables used in this paper. All variables are

defined in Appendix A and aggregated for all banks in each quarter.4 Column (1) reports the

sample mean of each variable over the entire sample period. Columns (2)-(4) present the mean

values for each 10-year period. Columns (5)-(9) show the sample mean for each year from 2006

to 2010 to allow for a closer look at the banking industry around the 2007-09 crisis time. Column

(10) shows the time trend from 1976 to 2010 and the nonparametric trend test results.

Panel A shows composition of the banking industry. The number of U.S. commercial banks

filing the Call Report has dropped by more than half—from approximately 14,400 banks that had

remained stable from 1976 to 1985 to around 6,500 in 2010. To further understand the decreasing

number of banks, I use the FDIC data to examine the details of bank mergers, failures, and new

4 This is because the herding measure is at the industry level. Therefore, all bank level variables are calculated

at the industry level. For example, the equity ratio is calculated as the aggregated total equity of all banks in

each quarter divided by the aggregated total assets of all these banks in the quarter. The sample mean presented

in Table 1 is based on time series.

17

charters. I find that the sharp decline in the number of banks in the 1980s and early 1990s is due

to a significant increase in bank mergers and failures and a decrease in new charters, as shown by

Columns (2) and (3). Looking at the total number of mergers, failures, and new charters during

the period from 1976 to 2005, I find that over 11,000 bank charters were merged out of

existence, over 1,900 insolvent banks were shut down, and 5,600 new bank charters were granted

(not tabulated). The average size of a commercial bank, calculated as the aggregated total bank

assets divided by the total number of banks in each quarter, has increased significantly over the

sample period, from $303 million in the late 1970s to $1,646 million in 2010.

Panel B summarizes the composition of banks’ aggregated balance sheet. Part I shows

banks’ total assets, total loans, total deposits, and total equity in 2005 dollars. All variables

showed a significant increasing trend over time. The credit crunch is evidenced by a decline in

total bank loans from $6,353 billion in 2008 to $5,876 billion 2010 (a 7.51% decrease).

Part II of Panel B shows the asset side of banks’ balance sheets. Total loans outstanding as

a proportion of total bank assets were relatively steady, around 55%-60% during the sample

period. The proportion of banks’ holding of securities over total assets was around 18%. Cash

holding decreased significantly from 16% in the late 1970s to around 6% in the 2000s, which can

be related to the implementation of better techniques for cash management in banks. The

proportion of liquid assets was around 18%.

Part III of Panel B presents the liability side of banks’ balance sheets.5 One of the most

important components on banks’ liability side is the equity capital. Banks’ equity ratio has

increased significantly over time, from around 6% in the late 1970s to 8% in 1992, when Basel I

5 All variables on the liability side are calculated as a proportion of total assets. This is because equity ratio is

calculated over total assets in the literature. Therefore, other variables are scaled by total assets as well for

consistency. I also calculate the ratio over total liabilities, and the results are similar.

18

was implemented. It experienced a further jump in 2005 to around 10% with the enforcement of

Basel II. It is also shown in the table that banks rely more on borrowed funds and less on

deposits as a source of funding. The deposits ratio decreased from about 82% in the late 1970s to

its historic low of 65% in 2007 (a 21% decrease). Borrowed funds as a proportion of total assets

increased from 6.8% in 1976 to 17% in 2007 (a 150% increase), as a result of financial

innovations and the recent development of the short-term money market. During the most recent

2007-09 recession, the borrowed fund ratio decreased from 17% to 12% and the deposits ratio

increased from 65% to 70%, indicating banks having to resort to the more traditional funding

method due to the difficulties in the short-term money market.

Panel C summarizes banks’ income and expenses. I use return on assets (ROA), calculated

as the ratio of net income over average assets, to measure banks’ overall profitability. ROA has

increased significantly over the sample period. During the financial crisis period, it dropped

substantially, reaching a historic low of -0.10% in 2009. Net interest margin (NIM) measures

banks’ profitability from traditional banking activities (i.e., holding deposits and lending). NIM

is calculated as net interest income (the difference between total interest income and total interest

expense) over average assets. I do not observe any significant time trend for NIM and it has been

relatively steady around 3.2%. Non-interest income (NII) as a proportion of total assets (or total

income) is used as a proxy for banks’ profitability from the off-balance-sheet activities. NII

include revenue and fee income from service charges on depository accounts, trading, investment

banking, advisory brokerage, securitization, underwriting, insurance, and venture capital. NII has

increased substantially during the sample period, suggesting a growing importance of off-

balance-sheet activities at banks. On average, around 20% of banks’ total income is from NII and

this ratio reaches around 30% in the late 2000s. Non-interest expense (NIE) as a proportion of

19

total assets (or total expense) has also increased significantly over time.

Panel D presents two measures for banks’ loan quality—the proportion of nonperforming

loans in total loans and the ratio of loan loss allowance over total loans, with the former

recording actual bad loans in banks and the latter reflecting banks’ expected loss from bad loans.

The non-performing loan ratio shows a declining trend (not significant) and loan loss allowance

has increased (significant at 10%) over time. During the crisis time, non-performing loan as a

proportion of total loans has increased substantially.

Thus far, statistics have shown the consolidation of the banking industry and an increase in

average bank size. Equity ratio has increased as a result of the capital adequacy requirement.

Banks have relied more on market funding source and less on deposits. Banks’ overall

profitability has increased but the source of such increase is not from the traditional lending

business but the non-interest income sources. In section 5, I will show how these changes are

related to bank herding.

Panel E of Table 1 displays the twelve categories of bank loans that are used to calculate

the herding measure. The largest categories of loans are C&I loans (26.4%), 1-4 family

residential real estate loans (24.5%), individual loans (15.1%), commercial real estate loans

(11.2%), and construction real estate loans (5%). Panel A of Figure 1 plots the time series trend

of these loans. Over time, there has been an increasing trend for all categories of real estate

loans. C&I, individual, and agriculture loans have declined over time. The most noticeable

decline is the C&I loans—its ratio has decreased from around 38.83% in the early 1980s to 17%

in 2010.

Panels B-E of Figure 1 present the holding of commercial real estate, residential real estate

(the sum of 1-4 family and multifamily residential loans), the C&I, and individual loans for

20

banks of different sizes based on their total assets. Overall, large banks hold substantially more

C&I loans in their portfolio and small banks have more individual loans. Before the mid-1990,

large banks extend more commercial real estate loans and small banks hold more residential real

estate loans, but there is no significant difference in the late 1990s and 2000s for the commercial

and residential real estate loans for banks of different sizes.

Panel F of Table 1 shows variables indicating economic conditions. I include three standard

macroeconomic variables—real GDP growth rate, inflation rate, and unemployment—to control

for economic growth and the stages of the business cycle. All three variables have shown a

declining trend over the sample period. I also use the federal funds rate as a proxy for monetary

policy and a measure of banks’ cost of external funding.6 The fed funds rate has declined

significantly over time, suggesting lower costs of external funding for banks.

I also look at the spread between the London interbank offer rate (Libor) and the overnight

index swap (OLS) (the Libor-OIS spread) and the Moody’s spread between corporate bonds with

Baa and Aaa ratings (the Baa-Aaa spread). The Libor-OIS spread reflects what banks believe is

the risk of default associated with lending to other banks and therefore measures the credit risk in

the interbank market.7 The Baa-Aaa spread measures default risk in a more general sense. The

rest of Panel F in Table 1 presents an overall declining trend for both Libor-OIS and Baa-Aaa

spreads. Their time series patterns are shown in Figure 2, with the solid line plotting the 3-month

Libor-OIS spread and the dotted line the Baa-Aaa spread. Both the Libor-OIS and Baa-Aaa

spreads follow a similar pattern, with Baa-Aaa spread being wider than Libor-OIS during most of

6 The fed funds rate is the prevalent measure of monetary policy in empirical work and is advocated by

Bernanke and Blinder (1992) as capturing the stance of monetary policy well because it is sensitive to shocks

to the supply of bank reserves. 7 Libor is the rate at which banks indicate they are willing to lend to other banks for a specified term of the

loan. OIS is an interest rate swap in which the floating rate is tied to an index of overnight rates and the fixed

rate is a proxy for market expectations of future overnight rates, with minimal credit risk (due to the short

maturity of the claim).

21

the sample period, indicating a higher risk in the whole economy than in the banking industry

alone. Both spreads increased dramatically in 2008. The fact that the Libor-OIS spread was

higher than the Baa-Aaa spread in 2008 indicates that the risks were somewhat higher in banking

than in the general economy. The Baa-Aaa spread later became wider than the Libor-OIS spread,

suggesting that the concerns in the real economy became more serious.

Panel G of Table 1 presents correlation coefficients between all key variables to be used in

the regressions in Section 5 and the bolded coefficients indicate significant at 5% level. Most of

the correlations are below the commonly used cut-off threshold of 0.7. However, equity ratio, the

ratio of non-interest income to total assets, and non-interest expense to total assets are highly

correlated. This may be because non-interest income activities, such as securitization and other

off-balance sheet activities, may be used to circumvent capital regulations. And a high non-

interest expense is needed to cover these non-interest income operations. As some of the

variables are highly correlated, I take into account their correlation and the potential

multicollinearity in regressions.

2.4 Measuring Bank Herding: Methodology and Results

2.4.1 Methodology: the LSV and FHW Herding Measures

In this paper, I use the LSV indicator to measure banks’ herding behavior as excessive

concentration on increasing or decreasing loans to certain categories. The LSV indicator is

considered the standard device for empirical investigations of herding behavior and has been

22

applied to investigate herding in different contexts by numerous studies.8

Adapting the LSV measure for bank lending decisions, assume in each quarter 𝑡, bank 𝑖

has loans outstanding to category 𝑗. Let 𝑋𝑗,𝑡 be the number of banks that increased loans to

category 𝑗 as a proportion of total loans9 in quarter 𝑡 and 𝑁𝑗,𝑡 the number of banks that were

active in category 𝑗 at 𝑡. For each of the twelve categories of loans that I study in this paper, the

LSV measure for a particular category 𝑗 at 𝑡 is defined as:

𝐿𝑆𝑉𝑗,𝑡 = |𝑝𝑗,𝑡 − 𝑝𝑡| − 𝐸|𝑝𝑗,𝑡 − 𝑝𝑡|

= |𝑋𝑗,𝑡

𝑁𝑗,𝑡−∑ 𝑋𝑗,𝑡12𝑗=1

∑ 𝑁𝑗,𝑡12𝑗=1

| − 𝐸 [|�̃�𝑗,𝑡

𝑁𝑗,𝑡− 𝑝𝑡| ; �̃�𝑗,𝑡~𝐵(𝑝𝑡, 𝑁𝑗,𝑡)] ; (1)

𝑝𝑗,𝑡 is the proportion of banks that increased loan outstanding to category 𝑗 in quarter 𝑡. 𝑝𝑡

is the ratio of the total number of banks that increased their loans to each category over the total

number of banks that were active in each category. Therefore, it is a proxy for the overall lending

policy of the banking industry in quarter t. If every bank independently increase (or decrease) its

loans outstanding to category j in quarter t with probability 𝑝𝑡 (or 1 − 𝑝𝑡), the observed value of

𝑝𝑗,𝑡 will be close to 𝑝𝑡 and the first term will become zero. If, on the other hand, banks

collectively increase or decrease loans to certain category, the observed value of 𝑝𝑗,𝑡 will deviate

from 𝑝𝑡. The first absolute term in equation (1) thus quantifies the extent to which banks’ lending

policies to loan category 𝑗 deviates from the overall lending policy in quarter 𝑡. Non-independent

increase or decrease leads to a larger value of the absolute term.

The adjustment factor, 𝐸|𝑝𝑗,𝑡 − 𝑝𝑡|, is subtracted to account for the natural dispersion of

8 For example, Grinballatt, Titman and Wermer(1995), Oehler (1998), Choe, Kho and Stulz (1999), Wermers

(1999), Gelos and Wei (2003), Oehler and Chao (2003), Voronkova and Bohl (2005), Wylie (2005), Walter

and Weber (2006), Lobao and Serra (2007), Do, Tan, and Westerholm (2008), Puckett and Yan (2008), Boyed, Buyuksahin, Harris, and Haigh (2009), Arouri, Bellando, Ringuede, and Vanbourg (2010). 9 I also look at the increased proportion to total bank assets or the absolute loan increase, the results are robust.

23

banks’ lending decisions and normalizes the measure to zero under the null hypothesis of no

herding. The adjustment factor is defined as the outcome of a binomial distribution with 𝑋𝑗𝑡 (loan

increase, with probability 𝑝𝑡) and 1 − 𝑋𝑗𝑡 (loan decrease, with probability 1 − 𝑝𝑡) as possible

outcomes in dimension 𝑁𝑗𝑡. Given the distribution of 𝑋𝑗𝑡, the adjustment factor can be written as:

𝐴𝐹𝑗𝑡 = 𝐸 [|�̃�𝑗𝑡

𝑁𝑗𝑡− 𝑝𝑡| ; �̃�𝑗𝑡~𝐵(𝑝𝑡, 𝑁𝑗𝑡)] = ∑(

𝑁𝑗𝑡𝑘) 𝑝𝑡

𝑘(1 − 𝑝𝑡)𝑁𝑗𝑡−𝑘 |

𝑋𝑗𝑡

𝑁𝑗𝑡− 𝑝𝑡|

𝑁𝑗𝑡

𝑘=0

Values of the LSV herding measure thus can be interpreted as the tendency of banks to

increase loans to a given category in a given quarter above the random distribution of lending

decisions. Positive values of the LSV measure that differ significantly from zero provide

evidence bank herding. The higher the LSV measure, the stronger the herding.

As I am interested in examining changes in banks’ overall lending behavior, I calculate a

weighted mean of the LSV measure, 𝐿𝑆𝑉𝑡, across all twelve loan categories for each quarter:

weighted mean LSV: 𝐿𝑆𝑉𝑡 =∑𝑤𝑗𝑡𝐿𝑆𝑉𝑗𝑡

12

𝑗=1

(2)

where 𝑤𝑗𝑡 is the ratio of loans outstanding to category 𝑗 over total loans at the end of quarter 𝑡.

The LSV measure has been criticized as being statistically biased. For example, Frey et al.

(2007) show that the LSV indicator is systematically biased downward. They therefore

introduced the FHW indicator as an alternative herding measure that has better statistical

characteristics. The FHW herding measure is calculated as:

𝐹𝐻𝑊𝑗𝑡 = (𝑝𝑗𝑡 − 𝑝𝑡)2− 𝐸 [(𝑝𝑗𝑡 − 𝑝𝑡)

2]

𝑁𝑗𝑡

(𝑁𝑗𝑡 − 1)=(𝑝𝑗𝑡 − 𝑝𝑡)

2− 𝑝𝑡(1 − 𝑝𝑡) 𝑁𝑗𝑡⁄

(𝑁𝑗𝑡 − 1) 𝑁𝑗𝑡⁄ (3)

where 𝑝𝑗𝑡 =𝑋𝑗𝑡

𝑁𝑗𝑡; 𝑝𝑡 =

∑ 𝑋𝑗𝑡12𝑗=1

∑ 𝑁𝑗𝑡12𝑗=1

24

The numerator in equation (3) is the empirical variance minus the expected variance of a

binomial distribution with parameters 𝑁𝑗𝑡 and 𝑝𝑡. Frey et al. (2007) provides evidence that the

normalization in the denominator and the use of the second moment rather than the first absolute

moment lead to more desirable statistical properties.

The weighted mean FHW herding measure, 𝐹𝐻𝑊, across all twelve loan categories is

calculated as:

weighted mean FHW: 𝐹𝐻𝑊𝑡 =∑𝑤𝑗𝑡𝐹𝐻𝑊𝑗𝑡

12

𝑗=1

(4)

2.4.2 Validity of the LSV and FHW Herding Measures

Bellando (2010) argues that there are statistical biases in both LSV and FHW measures.

However, he finds that herding characteristics affect the biases of the two measures in opposite

ways. Therefore, the real herding values are bounded by both indicators, with LSV as the

minimum value and FHW as the maximum value. I use both measures to test the existence of

herding while acknowledging that neither measure may reflect the true value of herding. The

calculated LSV and FHW measures will provide boundaries of where the true herding value may

lie.

Besides the potential statistical bias, both the LSV and FHW measures have some

limitations in the setting of this paper. First, they do not take into account lending intensity

because they only consider whether banks increase or decrease loan outstanding to certain

categories as a ratio of the total loans regardless of the amount of loan change. Second, the

measures do not consider the initial level of banks’ holding of loans. Third, the Call Report only

allows me to observe the amount of loans at the end of each quarter without details in changes of

loans within a quarter. As a result, the herding measures cannot identify inter-temporal lending

25

patterns.

Regardless of these drawbacks, the LSV indicator is the most frequently used measure to

quantify herding behavior and is “a standard in the literature” (Wylie, 2005 p381). Several

herding measures have been proposed by the literature. However, none of them fits better than

the LSV measure (and the FHW measure which is derived based on LSV) for the purpose of this

study. For example, some measures use the stock return information (Christie and Huang, 1995;

Hofsinger and Sias, 1999; Stever and Wilcox, 2007). However, as the purpose of this paper is to

examine herding in lending decisions, using bank stock return data may capture herding in

general but cannot identify herding in banks’ loan decisions. Therefore, the LSV and FHW

measures are the best to test the existence of herding and to quantify the level of herding for the

purpose of this study.

2.4.3 Results: Documenting the Existence of Herding

The weighted mean LSV measure is presented in Table 2 Panel A and Figure 3 Panel A.

Column (2) shows that the measure is significant during the entire sample period, supporting

Hypothesis 1 for the existence of significant herding in banks’ lending decisions. The calculated

sample means of the herding measure stand for the average fractions of U.S. commercial banks

that increased or decreased loans outstanding due to herding behavior. For example, following

the LSV interpretation, a LSV of 9.80% in the year 2010 implies that since there were 6,529

banks in that year (Column (1)), an increase or decrease in loans outstanding by 640 banks

(9.80% of 6,529 banks) was caused by herding behavior.

Figure 3 Panel A plots the time series pattern of the weighted mean LSV measure. The

vertical lines indicate five NBER dated recessions: January-July 1980, July 1981-November

26

1982, July 1990-March 1991, March-November 2001, December 2007-June 2009. 10

For the

general trend, bank herding presents a “W” shape. It fluctuated in the late 1980s and experienced

a trend of declining in the 1980s. It increased in the early 1990s and decreased in the mid and

late 1990s. It then experienced a significant and continuous increase in the 2000s.

Looking carefully at the sample period, a significant magnitude of herding was detected in

the late 1970s during the second oil crisis. The weighted mean LSV measure was around 10%

during the time, indicating that an increase or decrease in loans outstanding by 1,440 banks (10%

of around 14,400 banks) was caused by herding behavior. This is consistent with the expectation

that banks are inclined to herd in an exogenous shock and during times of increased uncertainties

of the economy. During the oil crisis, banks faced more uncertainties as to the overall state of the

economy and had difficulties evaluating borrowers. This led to both the spurious herding, when

banks in the unfavorable market conditions decreased lending together and the intentional

herding due to information concerns, when banks had asymmetric information as to whether and

to what extent their borrowers were affected by the crisis and therefore following others’ lending

decisions, assuming others had information advantage.

Examining the behavior of herding during the NBER dated recessions,11

I find two distinct

patterns: herding (1) decreased in the early 1980s and the 2007-09 recessions and (2) increased

during the 1991 and 2001 recessions. In the first case, the herding level was already high before

the crisis—LSV measures were around 10% right before the 1980 and 2007 crisis. Herding

decreased during the recession, but then increased when the economy started to recover. In the

second case, the herding indicator was at a relatively low level, around 4%, it then increased

10 From the NBER website of business cycles, http://www.nber.org/cycles.html.

11 I consider the January-July 1980, July 1981-November 1982 recessions as one recession, since the time was

very close, and look at the herding patterns.

27

during the recessions. The increasing trend continued after the end of the recession time. In

section 5, I will conduct more detailed analyses examining the relation between herding and

economic conditions.

Having established that herding is evident using the LSV indicator, I next calculate the

FHW herding measures. Since the FHW measure uses the second (central) moment, I calculate

the square root of the weighted mean FHW measure to make it comparable to the LSV measures.

Comparing the weighted mean LSV and FHW herding measures, Panel B of Figure 3 shows that

both measures follow similar trends. The square roots of the FHW measure are always greater

than the LSV measure. This is consistent with Frey et al. (2007), who show that the LSV

measure underestimates the level of herding. Following Bellando (2010), I consider the real level

of herding to lie in the area between the LSV and FHW measures, with the LSV as the minimal

value and FHW as the maximum.

Table 2 Panel B shows the LSV measures for each loan category.12

Over time, herding

measures for all five categories of real estate loans (commercial, 1-4 family residential,

multifamily residential, construction, and farmland) have declined significantly and herding in

C&I, individual, agriculture, and other loans have increased substantially. Herding in real estate

loans were significant in the 1970s and 1980s, indicating collective actions in real estate loans by

all commercial banks. The measures became insignificant (except for construction loans) in the

1990s. The LSV measure for the 1-4 family residential loans increased in the 2000s, with a

highest level of 9.64% in 2007. Herding in C&I loans was significant during most of the sample

periods. Starting from the 1990s, banks started to herd in individual loans and herding in

12 The LSV measures for (1) loans to depository institute, (2) loans to states and political subdivisions, and (3)

remaining loans are in general insignificant, and therefore not shown in the table.

28

individual loans has increased to 31.80% at the end of the sample periods. Herding in agriculture

production loans and all other loans were also significant in the 1990s and 2000s.

Considering the changing composition of bank loans over time (Figure 1, and Table 1

Panel E) and the herding indicators for each loan category, I find two different cases of herding.

First, herding may capture the concentration in certain loan categories. For example, the

proportion of C&I loans has declined substantially over time, therefore the significant herding

detected in C&I loans suggests banks collectively decrease loan outstanding to the C&I category,

as a result of the increasing popularity of securitization in the banking industry.13

Also, the

significant levels of herding in the 1-4 family residential real estate loans, combined with its

increasing proportion in total bank loans, indicates that banks collectively increase lending to the

family mortgage loan category, consistent with banks’ involving in mortgage lending before the

breakout of the 2007-09 crisis.

Second, herding may not be related to loan concentration or growth. The proportion of

certain loan may be relatively stable but the LSV measure still detects collective increase or

decrease in that loan category, as evidenced by the individual loans in the 1990s and 2000s.

Another case is when we do observe increase in certain loans with no significant herding. An

example is the commercial real estate loans, its ratio in total bank loans increased from 10% in

the late 1990s to 14% in 2010, however herding into such category is insignificant. This may

reflect one of the caveats about the LSV measure mentioned in the last section that it is not able

to capture the intensity of loan change.

13 According to Loutskina (2011), increased securitization is achieved through banks’ issuing more mortgages

and fewer C&I loans.

29

2.5 Herding, Macroeconomic Conditions, and Bank Health

Having documented the existence of bank herding that provides evidence for Hypothesis 1,

I next test Hypotheses 2a-2c and Hypothesis 3 through investigating whether and how the time

series patterns of herding are related to the economic conditions, the capital regulatory

requirement, and the health of the overall banking system. Hypothesis 2a states that banks tend

to herd more when the economic conditions are less favorable, indicated by low a real GDP

growth rate, a high inflation rate, and a high unemployment rate. Base on Hypothesis 2b, banks

tend to herd more when the credit risk is high, suggested by a high Libor-OIS spread and Baa-

Aaa spread. Hypothesis 2c predicts that there tends to be more herding when the banking

industry is more vulnerable and subject to higher risks—measured by the extent to which banks

are financed by short-term debt rather than insured deposits, the proportion of banks’ illiquid

assets as a share of total assets, banks’ profitability and loan quality, and the extent to which

banks rely more on the non-interest income rather than the interest income from lending

activities. Table 3 provides a summary of the expected signs of the relations between herding,

economic and market conditions, capital regulation, and bank characteristics.

2.5.1 Economic and Market Conditions, and the Impact of Capital Requirement

Table 4 presents the results of regressions that use the weighted mean LSV measure

(Columns (1)-(3)) and the square root of the weighted mean FHW measure (Columns (4)-(6)) as

dependent variables and the macroeconomic and market variables as independent variables for

each quarter. To control for endogeneity, all independent variables are lagged one quarter.

Standard errors of all regressions are adjusted to account for serial correlation. Columns (1) and

(4) show that inflation rate and unemployment rate are significantly positively related to herding

measures, suggesting that banks tend to herd more when the economic conditions are less

30

favorable, as indicated by a high inflation and unemployment rate. The significant and positive

coefficients for inflation and unemployment rate are consistent across all columns.

In Columns (2)-(3) and (5)-(6), federal funds rates and interest rate spreads are added to the

regressions. The negative and statistically significant relationship between the herding measure

and the federal funds rate can be explained in two ways. The first explanation is related to the

bank lending channel literature. The model of Mondschean and Pecchenino (1995) suggests that

a decrease in banks’ external cost of funds, measured by the federal funds rates in this paper, will

result in an expansion in banks’ lending. If each bank expects all other banks to do so, it will

increase its lending and the intentional herding occurs. Second, when external financing is less

costly for banks, they tend to ease their loan screening standards and therefore increase loans

outstanding, leading to a higher level of spurious herding. This result is broadly robust across

different interest rate measures, such as the 3-month, 6-month, and 1-year Treasury bill rates, and

the 1-year and 10-year Treasury securities rates.

The Libor-OIS spread and the Baa-Aaa spread are significantly positively related to the

herding measures, suggesting that banks tend to herd more when credit risk in the interbank

market is high or when there are more uncertainties in the economy. The Baa-Aaa spread is

significantly correlated with the unemployment rate and the Libor-OIS spread (Panel G of Table

1), therefore the unemployment rate and Libor-OIS spread are excluded in regressions (4) and (6)

that use the Baa-Aaa spread as an explanatory variable.

Overall, results of Table 4 show that herding is significantly and positively related to the

inflation rate, the unemployment rate, and the risk premiums (indicated by the Libor-OIS spread

and Baa-Aaa spread). These results suggest that banks tend to herd more when economic

conditions are less favorable and when there are more uncertainties in the economy, supporting

31

Hypothesis 2a and Hypothesis 2b. These results also lend some evidence to the information

asymmetry hypothesis of Banerjee (1992) and Birhchandani et al. (1992, 1998) that links more

herding to higher uncertainties. Herding is also significantly and negatively related to banks’

external cost of funding, providing some evidenced of spurious herding.

Table 5 examines the impact of capital requirement on bank herding by including the Basel

dummies and the equity ratio of the banking system. In Columns (1) and (3) of Table 5, Basel1

indicates the implementation of the Basel 1 requirement to the U.S. commercial banks and takes

the value of 1 if it is between 1992:Q1 to 2005:Q2 and 0 otherwise, and Basel2 indicates the

implementation of the Basel 2 requirement and takes the value of 1 if it is between 2005:Q3 to

2010:Q4 and 0 otherwise. The Basel2 dummy is significantly positive at 1 percent level,

indicating that controlling for macroeconomic factors, herding has increased during the time

when the Basel 2 requirement was implemented, supporting Hypothesis 3.

Columns (2) and (4) include the equity ratio of the overall banking system, calculated as

the ratio of total equity (adjusted by capital requirement and the risk-adjusted capital) over the

total assets of the banking industry in each quarter. The equity ratio is used as a proxy for

regulation, as it directly reflects capital requirement at different time over the sample period and

capital requirement is the most important regulation that affect banks’ lending decisions.

Columns (2) and (4) of Table 5 show a positive and statistically very reliable (significant at the 1

percent level) relation between banks’ aggregated equity ratio and herding measures. These

results also support Hypothesis 3 for the positive relation between capital requirement and

herding.

32

2.5.2 Bank Health and Herding

In this section, I examine the relation between herding and bank condition. Table 6 presents

estimates based on regressions that use the weighted mean LSV herding measure as the

dependent variable.14

Bank balance sheet and income statement variables are added to the

regression one at a time as independent variables, since the bank variables are highly correlated

with each other.

I first examine how herding is related to banks’ deposit ratio. Previous studies show that

banks with better access to deposit funding are in a stronger position to provide liquidity during

financial market turmoil (Gatev and Strahan, 2006; Ivashina and Scharfsterin, 2010). I expect a

negative relation between herding and banks’ deposit ratio. This is because when banks have less

(stable) deposit funding and more (unstable) market sources of funding, they are more dependent

on capital markets’ perceptions and therefore are subject to higher funding risk. When the market

source of funding is less available, each bank subject to such funding liquidity shortage will

reduce lending, leading to a spurious herding in collective lending decrease. Column (1) of Table

6 shows the negative and statistically significant relation between herding and deposit ratio,

indicating that there tends to be more herding when banks rely less on deposits and therefore

facing higher funding risk.

Column (2) of Table 6 shows a significant negative relation between herding and banks’

liquidity ratio, calculated as the proportion of banks’ on-balance-sheet liquid assets over total

assets. This negative relation lends some support to the spurious herding explanation and the

information asymmetry hypothesis. First, when banks have less liquid assets, they are less able to

cover their loan losses or losses from other investments and therefore are subject to higher

14 Regressions use the square root of the weighted mean FHW measure as dependent variables show similar

results and therefore are not shown in the table.

33

liquidity risk. As a result, each affected bank will reduce lending and the collective result is an

increase in spurious herding. Second, a low liquidity ratio is usually associated with less

favorable economic conditions where uncertainties are higher and banks herd due to information

concerns.

Columns (3)-(6) present regression results for banks’ income variables. In Column (3), I

find a significant and negative relation between herding and the overall profitability of banks, as

measured by ROA. In Column (4), I look at banks’ profitability from loans and find that the LSV

measure is significantly negatively related to NIM, suggesting more herding occurs at the time

when banks’ profitability from their lending operation is low.

Column (5) shows a significantly positive relation between herding and non-interest

income. There are three possible (but non-mutually-exclusive) explanations. First, banks’

operation associated with non-interest income expose banks to higher market risks. DeYoung and

Roland (2001) argue that the fee incomes from originating and securitizing a mortgage loan are

more sensitive to the volatility of both the housing market and the mortgage interest. Fees

associated with securities brokerage are typically based on the value of assets so that the stream

of fee income generated by these activities contains systematic risks from market fluctuation.

Also, Stiroh (2004) finds no evidence of diversification gains for banks that combine interest and

non-interest income. Therefore, the positive relation between herding and banks’ non-interest

income suggests that more herding tend to occur when banks are subject to market risks.

Second, a high non-interest income ratio suggests that banks are more focused on trading,

brokerage, and other related activities and that the profits from these activities are higher as a

proportion of the total operating income. Meanwhile, banks may be less willing to invest effort

and resources in obtaining information, screening, and monitoring borrowers in their traditional

34

loan business. As a result, they tend to follow each other’s lending decisions to avoid these costs.

Third, a higher level of securitization, and therefore non-interest income, may be a result of

banks’ effort in circumventing the capital regulation. In this sense, the results also lend support to

Hypothesis 3 that argues more herding is related to tighter capital regulation. From Column (6), I

do not find any significant relation between herding and banks’ efficiency.

In Columns (7), herding is significantly positively related to the non-performing loan ratio,

suggesting that more herding occurs at times when the loan quality is low. This relation can be

explained from two perspectives. First, when banks have more bad loans, they have to reduce

new loans due to regulatory requirement, leading to spurious herding. Second, more bad loans in

the banking industry is usually associated with less favorable economic conditions where

uncertainties are higher and banks herd due to information concerns.

Column (8) presents the regression results when the bank variables are put together.15

I still

observe the significance of deposit ratio, liquidity ratio, ROA, and non-performing loans. To

conclude, the regression results in this section show that herding is negatively related to deposit

ratio, liquidity ratio, different profitability measures, and positively related to non-interest

income and non-performing loan ratio. These results suggest that more herding tend to occur

when the banking industry is more vulnerable and subject to higher risks, supporting Hypothesis

2c.

15 I can only have the deposit ratio, liquidity ratio, ROA, and the non-performing loan ratio together in a

regression. Adding other bank variables would cause multicollinearity problems.

35

2.6 Herding Measures for Banks of Different Sizes

To analyze differences in the herding behavior across banks of different sizes, I categorize

banks into different size groups based on their total assets in each quarter: the largest 10% (all

banks with total assets above the 90% percentile in the quarter), the largest 25% (all banks with

total assets above the 75% percentile), the smallest 25% (all banks with total assets below the

25% percentile), and the smallest 10% (all banks with total assets below the 10% percentile).

Table 7 and Figure 4 Panel A show the weighted mean LSV herding measures for banks of

different size groups. Herding among small banks is significant higher and more volatile than

herding among big ones. Columns (1)-(3) of Table 7 compare the herding measure for the largest

and smallest 10% banks. The average LSV measure for the largest 10% banks is 9% while the

LSV of the smallest 10% banks is 22%. Over time, herding among the smallest 10% banks has

decreased significantly and the difference between the largest and smallest 10% has also

decreased. The same trend is observed in Columns (4)-(6) for the largest and the smallest 25%

bank groups. These results support Hypothesis 4 that small banks tend to herd more than the

large ones.

Panel B of Figure 4 shows herding in different loan categories. Herding among small banks

and that of the large ones follow similar time series trend. There tend to be a higher level of

herding for large banks in the C&I and 1-4 family residential real estate loans in the 2000s, while

small banks tend to herd more in all other categories, especially in loans to agriculture

production.

Table 8 presents estimates based on regressions for each size group that use the weighted

mean LSV indicators as dependent variables, and the economic and market variables and the

Basel dummies as independent variables. The regression results for each size group are in

36

general similar to the estimates for the whole sample of commercial banks as shown by Column

(1) of Table 5. The inflation rate and unemployment rate are significantly positive for all size

groups (except for the smallest 5% banks), indicating that banks of most size groups tend to herd

more when economic conditions are less favorable. The herding measure and the federal funds

rate are negative and statistically significant, indicating that more herding tends to occur when

banks’ costs of external financing are lower. The Libor-OIS spread is significantly positively

related to herding measures for all groups but the largest 5% and smallest 10% and 5% banks,

suggesting that banks tend to herd more when the credit risk in the interbank market is high. The

insignificance for the smallest 10% and 5% size groups can be explained by the fact that these

small banks may not participate in the interbank loan market and therefore the credit risk does

not affect their lending decisions.

Comparing the regression results for each size group, the significance of Basel 2 dummy is

only found in Columns (1)-(5) for the large bank groups. For the small bank groups, Basel

dummies are insignificant. The results suggest that while overall banks tend to herd more with

each other during times when Basel 2 is implemented, there are different trends in herding

among banks in each specific size group. Future studies need to look at the between-group

herding in order to understand the time trend.

2.7 Robustness Checks

2.7.1 Sub-period Regressions

I next run sub-sample analyses and examine whether the main results for Hypotheses 2a-2c

and Hypothesis 3 still hold. Specifically, I examine three sub-period based on the time series

pattern of the weighted mean LSV herding measure in Figure 2 Panel A: (1) the period of

37

significant herding decreasing from 1976 to 1989, (2) the slight increase and drop from 1990 to

1999, and (3) the period of dramatic herding increase in the 2000s.

Table 9 presents regression results based on the sub-period analyses. Panel A shows

regression results using economic and market variables and indicators for capital regulations. In

each of the three sub-period, the negative relation between herding and economic condition still

hold, as suggested by a significant and positive coefficient of unemployment rate across all

columns. Federal funds rates are still negatively related to herding (except for Column (2)). More

herding is also related to tighter capital regulation, as evidenced by the significant and positive

coefficient of the Basel dummies and the equity ratio.

Panel B presents regression results with bank variables. Across all three periods, herding is

significantly and negatively related to deposit ratios and there tends to be more herding when

there are more bad loans. Significant coefficients for banks’ profitability and off-balance-sheet

activities are found the 1990s and 2000s.

To summarize, regression results based on sub-period analyses confirm the robustness of

my main results that there tends to be more herding with less favorable economic conditions,

tighter capital regulation, and weaker bank health.

2.7.2 The Impact of Bank Deregulation

My sample period covers the time of deregulation of U.S. commercial banks in the 1980s

and early 1990s with the removal of three main restrictions that affect bank lending: (1) the the

McFadden Act that restricted interstate banking and branching, (2) the Glass-Steagall Act that

banned commercial banks from competing with investment banks, insurance companies, and

brokerage firms, and (3) the Federal Reserve Regulation Q that imposed interest rate ceiling on

deposit accounts to prohibit price competition (Berger, Kashyap, and Scalise, 1995; DeYoung,

38

2010). As a result of the deregulation, U.S. commercial banks have increased their sizes,

expanded through mergers and acquisitions (as shown in Table 1), and consequently increased

their scope and scale of operations. This industry consolidation and deregulation could also be

affect herding behavior. Therefore, I examine whether and how herding has changed as a result

of bank deregulation.

The deregulation process has spanned over a long period of time—the Regulation Q was

removed in 1982 and the Glass-Steagall Act was repealed in 1999. Although the McFadden Act

was removed at the national level in 1994, different states had gradually liberalized geographic

restrictions since 1980. Due to the complex time line for the deregulation, I use the natural log of

the number of bank mergers (LN(merger)) as a proxy for deregulation, as DeYoung (2010, p11)

states that bank mergers and acquisitions are the “most noticeable industry response to

deregulation”.

Table 10 shows regression results that include LN(merger) along with the economic and

market variables. Columns (1) and (2) show estimates based on regressions over the entire

sample period and Columns (3)-(5) present sub-period regression results. Compared with the

baseline regression (in Table 4), the signs and significant levels of the economic and market

variables are unchanged. Controlling for economic and market conditions, LN(merger) is

negatively and significantly related to bank herding, suggesting that deregulation has a negative

impact of herding.

2.8 Conclusion

While the economics and financial literature focuses on the macroeconomic reasons for

systemic risks and failures in the banking system and most of the herding literature concerns

39

itself with capital market herding rather than banking, this paper studies herding in the banking

industry and relates bank herding to macroeconomic and banking industry specific factors.

Applying the LSV measure and the FHW measure of herding to Call Report data on loans

outstanding in twelve categories, I find evidence of significant herding during the period from

1976:Q1 to 2010:Q4. Regression results show that banks tend to herd more when economic

conditions are less favorable, as measured by high inflation and unemployment rates and wide

credit risk spreads. There tend to be more herding with the implementation of the capital

adequacy requirement in the banking industry. Also, Herding is also negatively related to banks’

deposit ratio, liquidity ratio, profitability, and loan quality and positively related to off-balance-

sheet activities, suggesting a higher level of herding when the condition of the overall banking

industry is less favorable and when banks are more engaged in securitization activities. These

results still hold in sub-period analyses. Comparing banks of different size groups, I find small

banks tend to have a higher degree of herding during the sample periods. The deregulation of

U.S. commercial banks is also related to less herding, after controlling for economic and market

variables.

My findings have several policy implications. First, the herding measure calculated in this

paper provides an indicator for the similarity of banks’ lending behavior. Herding, or similarity

among banks, may not be bad from the perspective of each individual bank. However,

considering the whole banking industry, correlated holding of loan portfolio will lead to a higher

systemic risk, as modeled in Allen et al. (2011) and evidenced in the 2007-09 financial crisis.

Therefore, regulators should have a herding measure that captures banks’ correlation in lending.

Second, the positive relation between herding and uncertainties suggest the important role of

information. Considering the industry-specific information asymmetry for banks, regulator may

40

consider making information more available in order to reduce the opaqueness between

borrowers and banks and among banks of different types. Third, this paper also calls for the

reconsideration of regulation, as my findings suggest a higher level of herding with tighter

regulation, especially the implementation of the capital regulation. This paper provides evidence

and highlights the need for macroprudential approach to bank regulation and supervision.

41

Table 2-1: Summaries Statistics

Panels A-F present the sample mean values for key variables used in this paper. All variables are aggregated for all U.S.

commercial banks for each quarter. Column (1) shows the mean values of the variables over the entire sample period

from 1976 to 2010. Columns (2)-(4) present variables for each 10-year period and Columns (5)-(9) shows each year from

2006 to 2010. Column (10) shows the time trend. Panel G presents correlation between the key variables and the bolded

coefficients indicate significant at 5% level. ***, **, and * denote that the nonparametric trend test statistics is

statistically significant at 1%, 5%, and 10% level, respectively. All variables are defined in Appendix A.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

All 76-85 86-95 96-05 2006 2007 2008 2009 2010 Trend

Panel A: Composition of the Banking Industry

Number of Banks 10,958 14,426 12,088 8,325 7,401 7,284 7,088 6,840 6,530 (-)***

Mergers 48 27 111 4 0 1 19 114 130 (-)*

Failures 55 29 109 21 1 15 44 134 127 (+)

New Charters 160 239 122 154 178 175 90 24 5 (-)***

Size (millions 05 dollar) 677 303 426 884 1,320 1,427 1,613 1,563 1,646 (+)***

Panel B: Balance Sheet

Part I: Total Values (billions in 2005 dollars)

Total assets 6,283 4,369 5,078 7,241 9,766 10,392 11,434 10,692 10,751 (+)***

Total loans 3,667 2,460 3,058 4,310 5,789 6,161 6,353 5,874 5,876 (+)***

Total deposits 4,515 3,491 3,856 4,861 6,514 6,797 7,508 7,536 7,586 (+)***

Total equity 518 260 352 654 997 1,063 1,072 1,165 1,192 (+)***

Part II: Asset Side (% total assets)

Total loans 58.4 56.2 60.2 59.6 59.3 59.3 55.6 54.9 54.7 (+)

Total securities 17.9 17.5 19.0 18.0 16.5 14.2 14.2 18.6 19.5 (+)

Total cash 10.0 16.1 9.6 5.9 4.3 4.3 8.5 8.3 7.7 (-)***

Liquid assets 18.1 20.6 15.2 14.7 18.9 17.4 18.1 23.4 25.3 (+)

Part III: Liability Side (% total assets)

Total Equity 7.7 5.9 6.9 8.9 10.2 10.2 9.4 10.9 11.1 (+)***

Borrowed funds 13.0 9.5 12.5 16.1 15.8 16.8 16.9 12.5 12.0 (+)***

Deposits 73.5 80.0 76.0 67.2 66.7 65.4 65.7 70.5 70.6 (-)***

Panel C: Income Statement

ROA (% average assets) 0.8 0.7 0.7 1.2 1.3 0.9 0.1 -0.1 0.6 (+)**

NIM (% average assets) 3.2 3.1 3.5 3.3 2.8 2.7 2.6 3.1 3.3 (+)

NII (% average assets) 1.6 0.8 1.7 2.3 2.2 1.9 1.6 1.6 1.8 (+)***

NII (% total income) 19.9 8.7 17.5 29.0 28.4 25.7 26.8 29.5 31.0 (+)***

NIE (% average assets) 4.1 3.2 4.2 4.4 3.7 3.7 4.1 4.7 4.5 (+)**

NIE (% total expense) 47.9 32.7 44.6 58.5 52.4 50.5 61.1 75.7 80.0 (+)***

42

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

All 76-85 86-95 96-05 2006 2007 2008 2009 2010 Trend

Panel D: Bank Loan Quality (% total loans)

Non-performing 2.2 2.8 2.7 1.1 0.8 1.3 2.9 5.6 5.0 (-)

Allowance 1.8 1.1 2.4 1.7 1.2 1.3 2.3 3.3 3.3 (+)**

Panel E: Banks' Loan Composition ( % total loans):

Real Estate 44.7 28.9 38.4 45.9 53.9 53.5 52.5 55.2 53.5 (+)***

Commercial RE 11.2 6.5 9.9 11.7 12.8 12.4 12.7 14.1 14.1 (+)***

1-4 Family 24.5 15.6 19.7 26.0 29.5 29.1 28.1 30.1 30.2 (+)***

Multifamily 1.8 1.7 1.4 1.8 2.1 1.8 2.1 2.5 2.5 (+)**

Construction 5.0 3.8 4.3 4.3 7.6 7.9 7.6 6.7 4.9 (+)***

Farmland 0.8 0.8 0.7 0.8 0.8 0.8 0.8 0.9 0.9 (+)

C&I 26.4 35.5 30.1 25.7 21 22.2 23.3 21.5 18.7 (-)***

Individual 15.1 16.4 16.2 15.1 13.5 13 13.1 13.2 17.2 (-)**

Agriculture 1.2 2.7 1.4 1.1 0.8 0.8 0.8 0.8 0.8 (-)***

Depository Inst. 0.9 0.5 2.1 1.1 2.2 1.9 2.3 2.3 1.9 (-)

States & Political 0.8 0.8 1.5 0.5 0.6 0.6 0.7 0.8 0.8 (+)

All other loans 2.6 2.7 3.1 2.4 2.1 2.3 2.6 2.3 2.8 (-)

Not Categorized 9.7 13.2 9.7 9.6 9.4 9.1 8.4 7.2 7.1 (-)***

Panel F: Macro and Market Variables

GDP growth (%) 0.71 0.85 0.71 0.82 0.61 0.57 -0.69 0.05 0.69 (-)*

Inflation (%) 0.99 1.71 0.86 0.63 0.63 1.01 0.01 0.68 0.35 (-)**

Unemployment (%) 6.34 7.55 6.25 5.02 4.61 4.62 5.8 9.28 9.63 (-)*

Fed Funds (%) 6.02 9.91 6.06 3.86 4.96 5.02 1.93 0.16 0.18 (-)***

Libor-OIS (%) 0.33 0.57 0.16 0.12 0.22 0.30 1.37 0.88 0.28 (-)***

Baa-Aaa (%) 1.11 1.43 0.95 0.84 0.89 0.93 1.82 1.98 1.10 (-)**

43

Panel G: Correlation Table

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 GDP growth 1

2 Inflation 0.11 1

3 Unemployment -0.04 0.03 1

4 Fed funds 0.04 0.46 0.10 1

5 Libor-OIS -0.2 0.02 0.29 0.12 1

6 Baa-Aaa -0.4 -0.13 0.60 0.27 0.54 1

7 Equity ratio -0.12 -0.4 -0.29 -0.44 -0.05 -0.22 1

8 Deposit ratio 0.18 0.35 0.38 0.45 0.24 0.29 -0.82 1

9 Liquidit ratio 0.02 -0.25 -0.31 -0.40 -0.03 -0.27 0.66 -0.53 1

10 ROA 0.28 -0.07 -0.56 -0.17 -0.39 -0.43 0.31 -0.39 0.44 1

11 NIM 0.35 0.09 0.07 0.29 -0.36 -0.34 -0.58 0.46 -0.16 0.19 1

12 NII -0.21 -0.22 -0.10 -0.87 0.16 -0.01 0.95 -0.30 0.63 0.36 -0.53 1

13 NIE -0.08 -0.58 -0.34 -0.49 -0.27 -0.33 0.78 -0.21 0.47 0.26 -0.31 0.93 1

14 NonPerforming -0.19 0.01 0.61 0.05 0.26 0.45 -0.31 0.47 -0.48 -0.83 0.03 -0.33 -0.16 1

15 LN(merger) -0.08 -0.11 0.46 0.07 0.10 0.32 -0.39 0.20 -0.40 -0.45 0.12 -0.37 -0.01 0.47 1

44

Table 2-2: LSV and FHW Herding Measures Panel A presents the weighted mean LSV and FHW herding measures and the interpretation of LSV

measure using the number of banks in each quarter. Panel B presents the LSV measure for each loan

category. ***, **, * next to the herding measure indicate that the null hypothesis of no herding is rejected

at a 1%, 5%, and 10% significance levels, respectively. ***, **, and * in the last row denote that the

nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively.

Panel A: Weighted Mean LSV Measure and Weighted Mean FHW Measure

year

(1)

NO. Banks

(2)

LSV (%)

(3)

Interpretation from LSV

(4)

FHW (square root, %)

1976 14,410 10.02 *** 1,443 13.54 ***

1977 14,411 10.15 *** 1,462 13.41 ***

1978 14,391 9.96 *** 1,433 12.84 ***

1979 14,364 10.01 *** 1,438 12.29 ***

1980 14,434 8.42 *** 1,216 10.04 ***

1981 14,414 6.82 *** 984 8.73 ***

1982 14,451 6.26 *** 904 9.49 ***

1983 14,469 6.10 *** 883 9.66 ***

1984 14,496 6.12 *** 887 12.82 ***

1985 14,417 4.57 *** 659 10.24 ***

1986 14,210 3.80 ** 540 9.11 ***

1987 13,723 3.38 ** 464 7.93 ***

1988 13,137 3.15 ** 414 7.14 ***

1989 12,715 2.92 ** 371 7.33 ***

1990 12,347 3.26 *** 402 7.68 ***

1991 11,927 4.66 *** 556 8.39 ***

1992 11,467 5.89 *** 675 9.70 ***

1993 10,961 6.34 *** 695 10.21 ***

1994 10,453 5.97 *** 624 9.69 ***

1995 9,943 5.03 *** 500 8.77 ***

1996 9,530 4.35 *** 414 8.08 ***

1997 9,144 4.46 *** 408 8.35 ***

1998 8,777 4.30 *** 377 8.15 ***

1999 8,582 3.81 ** 327 7.84 ***

2000 8,315 3.60 ** 299 7.96 ***

2001 8,082 4.67 *** 378 6.32 ***

2002 7,888 5.50 *** 434 7.86 ***

2003 7,770 6.37 *** 495 9.06 ***

2004 7,631 7.28 *** 556 10.57 ***

2005 7,526 8.17 *** 615 11.66 ***

2006 7,401 9.00 *** 666 12.47 ***

2007 7,283 9.51 *** 692 12.98 ***

2008 7,088 9.44 *** 669 13.27 ***

2009 6,839 9.03 *** 617 14.46 ***

2010 6,529 9.80 *** 640 18.02 ***

Mean 6.35 *** 10.17 ***

Trend (+) (+)

45

Panel B: LSV Measure for Each Loan Category in Percentage

Year

(1)

Commercial

(2)

1-4 Family

(3)

MultiFam

(4)

Construct.

(5)

Farmland

(6)

C&I

(7)

Individual

(8)

Agricult.

(9)

Other

1976 19.27*** 22.49*** 18.36*** 22.69*** 5.58*** 10.26*** 0 0 2.14**

1977 19.28*** 22.08*** 17.96*** 15.89*** 6.50*** 11.64*** 0 0.01 2.46***

1978 19.47*** 18.93*** 18.63*** 10.85*** 8.52*** 13.19*** 0 1.32 2.34***

1979 19.79*** 16.34*** 17.89*** 9.86*** 11.86*** 12.94*** 0 2.76*** 2.01**

1980 17.10*** 12.64*** 17.15*** 14.17*** 14.01*** 10.42*** 0.38 1.53 1.72*

1981 17.45*** 13.48*** 15.82*** 15.36*** 15.92*** 5.56*** 3.56*** 2.32** 0.79

1982 18.49*** 16.43*** 15.06*** 17.02*** 17.39*** 2.25*** 6.00*** 1.25 0.31

1983 18.83*** 17.25*** 13.92*** 15.48*** 17.91*** 1.99* 5.32*** 0.63 0.54

1984 15.00*** 14.46*** 8.63*** 8.44*** 12.74*** 0 1.34* 0 0

1985 11.89*** 12.37*** 6.32*** 6.61*** 10.20*** 0 0 0.43 0

1986 9.34*** 9.61*** 5.55*** 6.67*** 5.53*** 0.28 0 1.42 0.03

1987 5.05*** 4.36*** 3.60*** 6.27*** 2.11** 1.05 1.20* 4.98** 2.79***

1988 4.02*** 1.57** 3.35*** 5.41*** 0.78*** 1.92** 1.65*** 5.99*** 3.90***

1989 3.65*** 0.49* 3.03*** 4.66*** 1.64*** 1.75* 0.85 6.30** 3.91***

1990 2.89*** 0.02 2.43*** 3.88*** 1.29** 3.77** 0.29 6.15** 4.36***

1991 1.13** 0 0.42 5.99*** 0.59* 7.75*** 2.43*** 2.92* 5.12***

1992 0.01 0 0 6.31*** 0.04 11.33*** 5.02*** 2.81* 6.93***

1993 0 0 0 6.24*** 0 12.50*** 7.01*** 2.88 6.42***

1994 0.04 0 0 4.53*** 0.02 12.41*** 6.12*** 1.68 7.09***

1995 0.71 0 0 3.54*** 0.03 10.37*** 4.56*** 2.30* 7.38***

1996 0.79* 0 0 2.22** 0.61 8.20*** 4.37*** 4.31** 7.03***

1997 1.10* 0 0 2.22*** 0.73 8.36*** 4.89*** 5.05** 7.21***

1998 0.63* 0 0.03 1.80** 0.63 7.51*** 5.79*** 4.49** 7.18***

1999 0.01 0 0.26 0.94 0.77 6.42*** 6.22*** 7.26*** 7.06***

2000 0.01* 0 0.21* 0.80** 0.73* 5.68*** 6.51*** 9.92*** 7.70***

2001 0.29 1.27** 2.71*** 0.63 3.88*** 7.54*** 10.08*** 12.89*** 10.56***

2002 0 1.85** 1.03*** 0.21 3.39*** 9.61*** 13.81*** 13.29*** 11.22***

2003 0 1.81** 0.08*** 0.03 1.38** 10.43*** 16.65*** 15.59*** 10.73***

2004 0 2.29** 0.01 0 0.62* 11.71*** 19.98*** 16.57*** 10.73***

2005 0 4.01*** 0.01* 0 0.99* 12.04*** 22.40*** 16.04*** 10.36***

2006 0 7.21*** 0.03 0 0.94** 11.80*** 23.55*** 15.51*** 11.46***

2007 0 9.64*** 0.05 0 0.48 10.61*** 24.71*** 15.26*** 9.08***

2008 0 7.28*** 0 0 0.04 11.56*** 26.56*** 15.55*** 8.36***

2009 0 1.98*** 0 0 0 15.47*** 29.52*** 16.13*** 9.84***

2010 0 0.14** 0 0.42 0 17.20*** 31.80*** 15.00*** 11.89***

Mean 5.59*** 5.86*** 4.67*** 5.40*** 4.00** 9.39*** 7.91*** 6.23*** 6.46*

Trend (-)*** (-)** (-)*** (-)*** (-)** (+)*** (+)*** (+)*** (+)***

46

Table 2-3: Expected Relation between Herding and Explanatory Variables under Different Hypothesis

Variable Expected sign Rationale

GDP Growth rate − More herding when the economic condition is worse, indicated by a low GDP growth rate.

(Hypothesis 2a)

Inflation rate More herding when the economic condition is worse, indicated by a high inflation rate.

(Hypothesis 2a)

Unemployment rate More herding when the economic condition is worse, indicated by a high unemployment rate.

(Hypothesis 2a)

Federal funds rate − Low fed funds rates reduce banks’ cost of external financing. As a result, banks tend to ease their loan

screening standards and increase loan outstandings, leading to a higher level of spurious herding.

Libor-OIS spread More herding when credit risks increase, measured by a wide Libor-OIS spread and/or Baa-Aaa

spread. (Hypothesis 2a) Baa-Aaa spread

Equity ratio Regulation (measured by high equity ratio) puts boundaries on banks’ action space and leads to

herding. It also gives banks more motives to take advantage of the regulatory arbitrage opportunities

by engaging in herding. (Hypothesis 3)

Deposit ratio − When banks have less (stable) deposit funding, they are subject to higher funding risk; they tend to

collectively reduce lending. (Hypothesis 2c)

Liquidity ratio − When banks have less liquid assets, they are subject to higher liquidity risk; they tend to collectively

reduce lending. (Hypothesis 2c)

Return on assets

(ROA)

− When banks’ overall bank profitability is low, bank tend to collectively decrease lending.

(Hypothesis 2c)

Net interest margin

(NIM)

− When banks’ profitability from loans is low, bank tend to collectively decrease lending.

(Hypothesis 2c)

47

Variable Expected sign Rationale

Non-interest income

(NII)

When banks focus more on the non-interest income revenues, they tend to allocate less resource to

lending and therefore tend to herd out of information concerns.

Non-interest expense

(NIE)

− When banks are less efficient, they tend to herd more. (Hypothesis 2c)

Non-performing loan

ratio

Banks tend to have more problem loans at times when the economic condition and banks’ overall

condition are less favorable. (Hypothesis 2c)

48

Table 2-4: Regression on Macroeconomic and Market variables

This table presents multivariate regression results. Dependent variables in Columns (1)-(3) are the

weighted mean LSV herding indicator and dependent variables in Columns (4)-(6) are the square root of

the weighted mean FHW herding indicator. All independent variables are lagged one quarter. Values of p-

values are in brackets. All variables are defined in Appendix A. Standard errors of all regressions are

adjusted to account for serial correlation. ***, **, and * denote significance at the 1%, 5%, and 10%

level, respectively.

(1) (2) (3) (4) (5) (6)

VARIABLES LSV LSV LSV FHW FHW FHW

GDP growth -0.085 -0.081 -0.047 0.300 0.313 0.393

[0.764] [0.668] [0.821] [0.300] [0.147] [0.190]

Inflation 1.237*** 1.601*** 1.640*** 0.811** 1.139*** 1.139***

[0.001] [0.000] [0.000] [0.030] [0.000] [0.000]

Unemployment 0.633*** 0.388** 1.034*** 0.806***

[0.000] [0.013] [0.000] [0.001]

Fed funds

-0.329** -0.429*** -0.297** -0.456***

[0.016] [0.004] [0.025] [0.002]

Libor-OIS

2.244*** 2.120***

[0.000] [0.000]

Baa-Aaa

1.740*** 2.355***

[0.000] [0.000]

Constant 3.227*** 4.159*** 5.127*** 5.420*** 6.395*** 8.763***

[0.061] [0.002] [0.000] [0.013] [0.000] [0.000]

Observations 139 139 139 139 139 139

R-squared 0.215 0.442 0.284 0.327 0.492 0.247

F test model 7.993 14.26 9.396 6.221 20.18 6.043

P-value of F model 0.010 0.000 0.000 0.169 0.000 0.000

49

Table 2-5: Regression on Regulatory variables

This table presents multivariate regression results where the dependent variables in Columns (1)-(2) are

the weighted mean LSV herding indicator and in Columns (3)-(4) are the square root of the weighted

mean FHW herding indicator. All independent variables are lagged one quarter. Values of p-values are in

brackets. All variables are defined in Appendix A. Standard errors of all regressions are adjusted to

account for serial correlation. ***, **, and * denote significance at the 1%, 5%, and 10% level,

respectively.

(1) (2) (3) (4)

VARIABLES LSV LSV FHW FHW

GDP growth -0.067 -0.220 0.185 0.200

[0.651] [0.122] [0.308] [0.250]

Inflation 1.470*** 1.479*** 0.844*** 0.932***

[0.000] [0.000] [0.002] [0.003]

Unemployment 0.352** 0.519*** 0.617*** 0.882***

[0.033] [0.001] [0.000] [0.000]

Fed funds -0.230* -0.109 -0.270*** -0.116

[0.053] [0.373] [0.003] [0.104]

Libor-OIS 1.569*** 2.015*** 1.103*** 1.859***

[0.001] [0.000] [0.003] [0.000]

Basel1 0.079 -0.727

[0.934] [0.319]

Basel2 2.921** 3.485***

[0.016] [0.000]

Equity ratio

84.396*** 93.889***

[0.006] [0.001]

Constant 2.932 -4.511* 6.048*** -3.250

[0.111] [0.094] [0.000] [0.255]

Observations 139 139 139 139

R-squared 0.579 0.554 0.716 0.588

F test model 9.768 14.99 26.42 26.55

P-value of F model 0.000 0.000 0.000 0.000

50

Table 2-6: Regression on Macroeconomic, Market, and Bank Condition variables

This table presents multivariate regression results with the weighted mean LSV measures as dependent

variables. All independent variables are lagged one quarter. Values of p-values are in brackets. All

variables are defined in Appendix A. Standard errors of all regressions are adjusted to account for serial

correlation. ***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES LSV LSV LSV LSV LSV LSV LSV LSV

GDP growth -0.217 -0.048 -0.055 0.272 -0.189 -0.023 -0.083 0.412

[0.226] [0.809] [0.896] [0.243] [0.506] [0.929] [0.835] [0.215]

Inflation 1.446*** 1.656*** 0.491* 0.225 0.426 0.943*** 0.515* 0.214

[0.000] [0.000] [0.072] [0.271] [0.103] [0.000] [0.067] [0.318]

Unemployment 0.252 0.517***

0.345** 0.524*** 0.327

[0.153] [0.006]

[0.022] [0.007] [0.165]

Fed funds -0.413*** -0.274** -0.513*** -0.386***

-0.506*** -0.186**

[0.000] [0.021] [0.000] [0.000]

[0.000] [0.015]

Libor-OIS 2.148*** 2.199*** 1.360** 0.338 1.075** 2.245*** 1.434** 0.311

[0.000] [0.000] [0.038] [0.534] [0.026] [0.000] [0.016] [0.426]

Deposit ratio -4.031***

-4.241***

[0.005]

[0.003]

Liquidity ratio

-1.816**

-2.380***

[0.031]

[0.008]

ROA

-10.577**

-13.220***

[0.026]

[0.003]

NIM

-39.570**

[0.013]

NII

18.701***

[0.001]

NIE

-7.403

[0.245]

NonPerforming

34.841*** 35.976***

[0.006] [0.007]

Constant 2.607 2.690 7.700*** 7.621*** -2.189** 1.836 7.564*** 8.662***

[0.361] [0.122] [0.000] [0.000] [0.042] [0.359] [0.000] [0.001]

Observations 139 139 108 108 108 139 108 108

R-squared 0.472 0.404 0.429 0.463 0.548 0.341 0.537 0.575

F test model 17.11 11.00 21.65 27.09 24.85 10.19 22.08 19.98

P-value of F model 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

51

Table 2-7: LSV Measures for Different Size Groups This table presents the weighted mean LSV herding measure for the largest 10% and 25% bank groups and the

smallest 25% and 10% size group. Column (3) shows the difference between the LSV measures of the smallest

10% group and that of the largest 10%, and column (6) presents the difference in herding measures between

the smallest 25% and the largest 25% group. ***, **, * next to the herding measure indicate that the null

hypothesis of no herding is rejected at a 1%, 5%, and 10% significance levels, respectively. ***, **, and * in

the last row denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10%

level, respectively.

year (1)

largest 10%

(2)

smallest 10%

(3)

=(2)-(1)

(4)

largest 25%

(5)

smallest 25%

(6)

=(5)-(4)

1976 9.70 *** 29.60 *** 19.90 *** 10.23 *** 29.75 *** 19.53 ***

1977 9.79 *** 30.77 *** 20.99 *** 10.76 *** 23.32 *** 12.56 ***

1978 10.59 *** 31.73 *** 21.14 *** 11.12 *** 29.43 *** 18.31 ***

1979 11.23 *** 27.24 *** 16.01 *** 11.74 *** 32.97 *** 21.23 **

1980 10.73 *** 30.53 *** 19.79 *** 10.71 *** 33.91 *** 23.20 **

1981 9.27 *** 29.53 *** 20.26 *** 9.22 *** 29.63 *** 20.40 ***

1982 8.31 *** 32.03 *** 23.72 *** 7.68 *** 22.53 *** 14.85 ***

1983 7.67 *** 25.26 *** 17.58 *** 7.71 *** 19.89 *** 12.18 ***

1984 8.08 *** 22.60 ** 14.52 ** 7.52 *** 16.02 *** 8.50 **

1985 7.88 *** 15.70 *** 7.82 *** 6.79 *** 10.78 *** 3.99 **

1986 7.76 *** 19.82 *** 12.05 *** 7.16 *** 14.06 *** 6.90 ***

1987 8.10 *** 17.37 *** 9.27 *** 7.45 *** 13.96 *** 6.51 ***

1988 7.83 *** 18.79 ** 10.96 ** 7.07 *** 18.87 *** 11.80 **

1989 7.69 *** 17.77 *** 10.08 *** 6.84 *** 15.74 *** 8.90 **

1990 7.81 *** 16.25 *** 8.44 *** 7.05 *** 16.94 *** 9.90 *

1991 8.89 *** 19.77 ** 10.88 8.26 *** 18.52 ** 10.26 *

1992 9.71 *** 22.02 ** 12.31 * 9.09 *** 30.22 ** 21.13 *

1993 9.79 *** 17.16 *** 7.37 ** 9.30 *** 30.88 ** 21.59 **

1994 8.78 *** 17.31 *** 8.53 *** 8.57 *** 28.65 ** 20.08 **

1995 7.56 *** 21.04 ** 13.48 7.70 *** 30.32 ** 22.61 **

1996 7.09 *** 19.27 *** 12.19 *** 7.13 *** 21.85 ** 14.72 *

1997 7.51 *** 27.81 *** 20.30 *** 7.12 *** 14.76 *** 7.64 ***

1998 7.66 *** 25.86 ** 18.20 * 7.27 *** 13.66 *** 6.39 **

1999 7.05 *** 21.32 *** 14.26 *** 6.96 *** 13.81 *** 6.85 ***

2000 6.70 *** 17.21 *** 10.51 ** 6.06 *** 10.97 *** 4.91 ***

2001 7.70 *** 22.99 *** 15.29 ** 7.30 *** 13.99 *** 6.69 ***

2002 8.21 *** 22.47 *** 14.26 *** 7.93 *** 12.71 *** 4.77 ***

2003 9.35 *** 21.19 *** 11.84 *** 9.35 *** 13.62 *** 4.27 ***

2004 9.67 *** 21.53 *** 11.86 *** 10.01 *** 14.12 *** 4.12 **

2005 9.68 *** 22.02 *** 12.34 ** 10.87 *** 15.29 *** 4.42 ***

2006 10.96 *** 20.26 *** 9.29 *** 11.87 *** 14.83 *** 2.96 ***

2007 12.22 *** 27.80 *** 15.58 ** 12.97 *** 15.93 *** 2.96 *

2008 11.83 *** 19.91 *** 8.08 ** 12.14 *** 13.55 *** 1.41 **

2009 11.76 *** 17.57 *** 5.81 ** 11.78 *** 13.05 *** 1.27 *

2010 12.66 *** 15.76 *** 3.10 * 12.70 *** 12.32 *** -0.38 *

mean 9.06 *** 22.44 *** 13.37 *** 8.95 *** 19.45 *** 10.50 ***

trend (+)* (-)*** (-)*** (+)** (-)*** (-)***

52

Table 2-8: Regression on Economic, Market, and Regulation variables: By Different Bank Size Group

This table presents multivariate regression results for each bank size group. Dependent variable for each size group is the weighted mean LSV

indicator that measures herding among banks in that size group. All independent variables are lagged one quarter. Values of p-values are in brackets.

All variables are defined in Appendix A. Standard errors of all regressions are adjusted to account for serial correlation. ***, **, and * denote

significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES Largest 5% Largest 10% largest 25% Largest 50% Smallest 50% Smallest 25% Smallest 10% Smallest 5%

GDP growth -0.033 -0.069 0.011 0.055 0.320 -0.134 0.164 0.108

[0.786] [0.600] [0.945] [0.739] [0.354] [0.863] [0.805] [0.935]

Inflation 0.832*** 0.929*** 1.184*** 1.280*** 1.882*** 4.533*** 1.893*** 1.633

[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.006] [0.247]

Unemployment 0.229** 0.187* 0.155 0.155 0.895*** 1.373** 0.303 -0.022

[0.020] [0.082] [0.215] [0.253] [0.002] [0.043] [0.522] [0.983]

Fed funds -0.138*** -0.179*** -0.222*** -0.229** 0.195 0.233 0.479 0.523

[0.002] [0.001] [0.005] [0.010] [0.205] [0.482] [0.186] [0.422]

Libor-OIS 0.378 0.670** 0.855** 1.175*** 2.309*** 3.684** 1.338 -0.536

[0.159] [0.026] [0.029] [0.006] [0.002] [0.014] [0.302] [0.828]

Basel1 -0.599 -0.288 -0.208 -0.283 0.077 5.270* 2.164 7.229

[0.103] [0.442] [0.695] [0.658] [0.954] [0.097] [0.452] [0.217]

Basel2 1.825*** 2.516*** 3.189*** 2.813*** -1.173 -2.012 1.003 0.959

[0.000] [0.000] [0.000] [0.001] [0.487] [0.483] [0.769] [0.877]

Constant 7.327*** 7.594*** 7.432*** 7.223*** 3.819 1.988 14.137*** 25.051**

[0.000] [0.000] [0.000] [0.000] [0.114] [0.703] [0.008] [0.016]

Observations 139 139 139 139 139 139 139 139

R-squared 0.536 0.642 0.657 0.623 0.491 0.260 0.109 0.056

F test model 17.11 11.00 21.65 27.09 24.85 10.19 22.08 19.98

P-value of F model 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

53

Table 2-9: Subsample Analyses

This table presents multivariate regression results for sub-period analyses: 1976-1989, 1990-1999, and 2000-2010. Panel A shows the impact of

economic and market variables and capital regulations. Panel B presents regression results with bank health variables. Dependent variables of all

columns are weighted mean LSV. All independent variables are lagged one quarter. Values of p-values are in brackets. All variables are defined in

Appendix A. Standard errors of all regressions are adjusted to account for serial correlation. ***, **, and * denote significance at the 1%, 5%, and

10% level, respectively.

Panel A: Economic and Market Variables and Capital Regulation

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES 76-89 76-89 90-99 90-99 90-99 00-10 00-10 00-10

GDP growth -0.097 0.053 -0.040 -0.066 -0.035 -0.179 0.292 -0.393

[0.578] [0.766] [0.792] [0.687] [0.821] [0.772] [0.188] [0.129]

Inflation 2.509*** 1.289*** -0.295* -0.258* -0.249 0.538* -0.067 0.112

[0.000] [0.000] [0.056] [0.081] [0.166] [0.080] [0.579] [0.481]

Unemployment 0.826** 0.729*** 0.510*** 0.577*** 0.646*** 0.633** -0.469*** -0.631*

[0.013] [0.000] [0.000] [0.000] [0.007] [0.013] [0.002] [0.063]

Fed funds -0.466*** -0.013 -0.354*** -0.265** -0.275** 0.118 -0.664*** -0.227

[0.001] [0.901] [0.000] [0.021] [0.037] [0.791] [0.000] [0.286]

Libor-OIS 1.766** 1.644** 0.484 0.567 0.559 1.017 -0.592 0.745***

[0.022] [0.015] [0.344] [0.253] [0.316] [0.253] [0.154] [0.005]

Basel1

0.797**

[0.038]

Basel2

4.400***

[0.000]

Equity Ratio

73.712***

18.346

64.754**

[0.000]

[0.515]

[0.000]

Constant 0.483 -41.638*** 3.878*** 2.703* 1.332 2.905 9.978*** -19.811***

[0.852] [0.000] [0.000] [0.088] [0.733] [0.235] [0.000] [0.000]

Observations 55 55 40 40 40 44 44 44

R-squared 0.608 0.779 0.847 0.848 0.844 0.211 0.864 0.793

F test model 13.21 43.56 94.30 56.20 72.49 4.064 114.7 49.58

P-value of F model 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

54

Panel B: Bank Health

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

VARIABLES 76-89 76-89 76-89 76-89 90-99 90-99 90-99 90-99 00-10 00-10 00-10 00-10

GDP growth -0.125 0.157 0.113 0.053 -0.221 -0.258 -0.057 -0.254 -0.033 -0.200 -0.270 -0.053

[0.218] [0.293] [0.503] [0.711] [0.126] [0.184] [0.732] [0.264] [0.792] [0.133] [0.586] [0.919]

Inflation 0.631** 0.349 0.478 0.258 0.092 0.083 0.42 0.138 0.058 0.220 0.500 0.515

[0.020] [0.198] [0.115] [0.484] [0.693] [0.748] [0.155] [0.727] [0.812] [0.615] [0.214] [0.225]

Unemployment -0.026

0.658**

1.070***

1.041***

1.180***

0.309

[0.805]

[0.014]

[0.000]

[0.000]

[0.000]

[0.204]

Fed funds -0.090* -0.368**

0.353* -0.258*** -0.717***

-0.617*** -0.367* -0.174

-0.159

[0.079] [0.040]

[0.060] [0.003] [0.000]

[0.000] [0.070] [0.624]

[0.690]

Libor-OIS 0.282 0.079 0.081 -0.140 0.889** 0.271 0.519 0.332 0.575*** 0.109 0.482*** 0.352**

[0.385] [0.940] [0.875] [0.892] [0.045] [0.643] [0.268] [0.636] [0.004] [0.593] [0.002] [0.012]

Deposit ratio -3.933***

-3.750***

-5.877***

[0.000]

[0.001]

[0.000]

ROA

-6.328

-13.927**

-19.199**

[0.426]

[0.010]

[0.044]

NII

9.739

16.569***

13.306***

[0.178]

[0.000]

[0.000]

Non-performing

32.367**

24.581*

35.241**

[0.018]

[0.043]

[0.046]

Constant -13.286*** 0.468 0.473 -0.841 13.446*** 10.228*** -4.426*** 7.782*** 19.899*** 9.835*** 0.245 6.654***

[0.000] [0.745] [0.824] [0.754] [0.000] [0.000] [0.001] [0.000] [0.000] [0.000] [0.963] [0.000]

Observations 55 24 24 24 36 36 36 36 44 44 44 44

R-squared 0.825 0.399 0.726 0.380 0.886 0.735 0.846 0.716 0.775 0.296 0.431 0.250

F test model 32.66 45.34 27.33 62.06 53.45 42.76 48.91 38.51 33.00 19.34 34.74 85.71

P-value of F model 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

55

Table 2-10: Bank Deregulation in the 1980s and Early 1990s

This table presents regression results using the natural log of the number of bank mergers as a proxy for

bank deregulations. Dependent variables for Columns (1) and (3)-(5) are the weighted mean LSV

measure and Column (2) uses the square root of the weighted mean FHW measure. Column (1) and (2)

conduct analyses for the whole sample period and Columns (3)-(5) show results of sub-period analyses.

All independent variables are lagged one quarter. Values of p-values are in brackets. All variables are

defined in Appendix A. Standard errors of all regressions are adjusted to account for serial correlation.

***, **, and * denote significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5)

VARIABLES LSV FHW 76-89 90-99 00-10

GDP growth -0.163 0.187 0.029 -0.078 -0.397

[0.364] [0.480] [0.863] [0.648] [0.414]

Inflation 1.443*** 1.027*** 0.407 -0.178 0.790*

[0.000] [0.001] [0.111] [0.451] [0.092]

Unemployment 0.745*** 0.877*** 0.114 0.618*** 1.412**

[0.000] [0.001] [0.356] [0.000] [0.020]

Fed funds -0.331*** -0.389*** -0.299*** -0.327*** 0.249

[0.000] [0.000] [0.000] [0.000] [0.355]

Libor-OIS 2.048*** 2.016*** 0.748** 0.489 1.856**

[0.000] [0.000] [0.046] [0.333] [0.029]

LN(merger) -0.628*** -0.196* -1.628*** -0.095 -0.875*

[0.000] [0.093] [0.000] [0.358] [0.088]

Constant 3.241*** 5.552*** 13.047*** 3.311*** -0.915

[0.002] [0.000] [0.000] [0.000] [0.596]

Observations 139 139 55 40 44

R-squared 0.646 0.535 0.796 0.750 0.771

F test model 30.90 20.15 71.64 80.47 14.498

P-value of F model 0.000 0.000 0.000 0.000 0.000

56

Figure 2-1: Five Main Categories of Loans

This figure plots the quarterly ratio of five main categories of loans over total bank loans outstanding,

including the commercial real estate loans, the 1-4 family residential real estate loans, the construction

real estate loans, the C&I loans, and the individual loans. All variables are defined in Appendix A. The

vertical lines indicate the five NBER dated recessions: January-July 1980, July 1981-November 1982,

July 1990-March 1991, March-November 2001, December 2007-June 2009.

Panel A: Main Categories of Loans for all U.S. Commercial Banks.

0.1

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commercial real estate 1-4 family residential real estate

construction real estate commercial&industry (C&I)

individual

5 Main Categories of Loans

57

Panel B: Commercial Real Estate Loans Panel C: Residential Real Estate Loans

Panel D: Commercial & Industrial Loans Panel E: Individual loans

0

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Commercial RE largest 5% Commercial RE largest 10%

Commercial RE largest 25% Commercial RE smallest 25%

Commercial RE smallest 10% Commercial RE smallest 5%

Commercial Real Estate Loan Ratio

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Residential RE largest 5% Residential RE largest 10%

Residential RE largest 25% Residential RE smallest 25%

Residential RE smallest 10% Residential RE smallest 5%

Residential Real Estate Loan Ratio

.1.2

.3.4

.5

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q119

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C&I largest 5% C&I largest 10%

C&I largest 25% C&I smallest 25%

C&I smallest 10% C&I smallest 5%

C&I Loan Ratio

.1.2

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Individual largest 5% Individual largest 10%

Individual largest 25% Individual smallest 25%

Individual smallest 10% Individual smallest 5%

Individual Loan Ratio

58

Figure 2-2: Libor-OIS and Baa-Aaa spreads

This figure plots the Libor-OIS spread and the Baa-Aaa spread. Variables are defined in Appendix A. The

vertical lines indicate the five NBER dated recessions: January-July 1980, July 1981-November 1982,

July 1990-March 1991, March-November 2001, December 2007-June 2009.

01

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LIBOR_OIS Baa_Aaa

Libor - OIS and Baa - Aaa spreads

59

Figure 2-3: The LSV and FHW Herding Measures

This figure plots herding measures. Panel A shows the weighted mean LSV measure and Panel B presents

the weighted mean LSV measure and the square root of the weighted mean FHW measure. The vertical

lines indicate the five NBER dated recessions: January-July 1980, July 1981-November 1982, July 1990-

March 1991, March-November 2001, December 2007-June 2009.

Panel A: Weighted Mean LSV Measure

Panel B: LSV and FHW Herding Measures

24

68

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hte

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ea

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SV

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Weighted Mean LSV

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weighted mean LSV weighted mean FHW

in percentage

Weighted Mean LSV

60

Figure 2-4: LSV Herding Measure for Different Size Groups

This figure plots the weighted mean LSV measure for banks of the four size groups based on their total

assets: the largest 10% (all banks with total assets above the 90% percentile in the quarter), the largest

25% (total assets above the 75% percentile), the smallest 25% (total assets below the 25% percentile), and

the smallest 10% (total assets below the 10% percentile). Panel A shows the weighted mean LSV

measures and Panel B shows the LSV measures for each loan category. The vertical lines indicate the five

NBER dated recessions: January-July 1980, July 1981-November 1982, July 1990-March 1991, March-

November 2001, December 2007-June 2009.

Panel A: Weighted Mean LSV Measures for Different Size Groups

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LSV Measure by Size

61

Panel B: LSV Measures for Each Category

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LSV Commercial Real Estate Loans

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in percentage

LSV 1 - 4 Family Residential Loans

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in percentage

LSV Multifamily Residential Loans

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LSV Construction Loans

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LSV Farmland Loans

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LSV C & I Loans0

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LSV Loans to Agriculture Product

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LSV Individual Loans

63

Chapter 3 Debt Structure, Private Equity Reputation,

and Performance in Leveraged Buyouts

3.1 Introduction

In a leveraged buyout (LBO), a company is acquired “using a relatively small portion of

equity and a relatively large portion of outside debt financing” (Kaplan and Stromberg, 2009

p121).16

Jensen (1989) argued that the LBO structure of highly leveraged capital structures,

active corporate governance, concentrated ownership stakes, and well-aligned managerial

incentives make the LBO form superior to widely held public corporations. Early empirical work

supported the merits of this structure with papers by Kaplan (1989a) and Smith (1990) finding

improvements in performance for firms undergoing LBOs in the 1980s. However, more recent

studies by Guo, Hotchkiss, Song (2011) and Cohn, Mills, and Towery (2014) find few

improvements to operating performance in LBOs completed in the 1990s and first half of the

2000s. Therefore, I am motivated to examine how recent LBO deals differ from the earlier ones

and whether these differences are responsible for the declined performance improvement in these

later deals.

To do this, I seek to identify factors associated with operational improvement in LBOs

based on finance theories, empirical findings, and anecdotal evidence. I focus on the following

four factors: (1) leverage change and its discipline effect on managers (Jensen, 1986); (2) LBO

debt structure and contractual features that further reduce the free cash flow problems, with a

particular focus on the percentage of bank debt (Diamond 1984, 1993; Park, 2000; Cotter and

16 A management buyout (MBO) is a form of LBO when incumbent management team takes over the firm.

This paper includes MBOs in the sample and uses the general term “LBO”.

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Peck, 2001), LBO debt covenants (Bradley and Roberts, 2004; Chava and Roberts, 2008), and

the maturity structure (Cotter and Peck, 2001); (3) PE firms’ reputation (Axelson, Stromberg,

and Weisbach, 2009; Demiroglu and James, 2010a; and anecdotal evidence), club deals

(anecdotal evidence), bank affiliation (Fang, Ivashina, and Lerner, 2013); and (4) management

participation (Kaplan, 1989a). I also examine whether performance is related to credit market

conditions, the aggregated LBO activities, LBO loan spreads, and the price paid in the LBO

transactions.

Figure 1 presents the structure of a typical LBO transaction. Equity investors are mainly

PE firms and/or incumbent management of target firms and LBO debt is provided by banks and

institutional investors.17

However, LBO participants and their roles in the transactions may have

changed over time. For example, traditionally, banks were heavily involved in financing LBO

deals. However, the role of banks may have changed in LBOs as banks are in general more

involved in securitizing loans that they previously would have held (Bord and Santos, 2012).

This motivates me to begin my examination of LBO performance drivers by investigating how

the structure of these deals and the participants involved have changed over time.

Another motivation for this study is the call for the PE industry to refocus on operational

improvement. Based on the traditional LBO model presented by Figure 1, returns to PE’s

investment are realized through improving target firm’s operational efficiency and later exit the

deal through an IPO or a sale. However, studies have shown that besides operating

improvements, PE firms also generate returns through the expanding price-earnings multiples

and the tax benefits of leverage (Guo et al., 2011; Graf, Kaserer, and Schmidt, 2012). In addition,

17 Bank lenders typically consist of commercial banks, savings and loan institutions, finance companies, and

the investment banks serving as arrangers of the syndicated loans that finance the LBO transaction.

Institutional investors are mutual funds, hedge funds, pension funds, insurance companies, structured vehicles

such a collateralized debt obligation funds (CDOs), and other proprietary investors.

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Ayash, Bartlett III, and Poulsen (2010) find that PE firms’ returns are not generated through

operating performance changes in target firms but stem from PE firms’ market timing ability that

allow them to capitalize on favorable market conditions and to accelerate the liquidation of their

investment. However, since the 2007-09 financial crisis, it has been argued that the PE industry

needs to refocus on operational improvement. The Private Equity Investment (PEI) magazine

(2012) states that “There has been a realization, post-finance crisis, that private equity needs to

return to its roots: creating value through operational improvement rather than financial

engineering”. Guo et al. (2011, p514) also argue that “without consistent operating gains, it is

unlikely that the returns we document can persist under less favorable credit and general market

conditions”. The need for PE firms to “return to its roots” and create value through operational

improvement also motivates me to carefully examine the post-buyout operating performance and

its drivers.

To undertake this examination, I construct a comprehensive dataset of 501 U.S. public-to-

private LBOs completed between January 1, 1986 and December 31, 2011 from Capital IQ and

SDC. I require all transactions to have financing details from LPC’s Dealscan and pre- and post-

buyout financial data from Compustat or Capital IQ, and missing data are filled from the SEC

filings. Using this data, I first measure post-buyout operating performance of target firms.

Following Kaplan (1989a) and Guo et al. (2011), I calculate the percentage changes in EBITDA

and net cash flows scaled by total assets or sales from the last fiscal year before the LBO to the

first three years after the buyout completion, adjusted by industry medians. I find that

performance change is largely positive for LBOs in the 1980s and 1990s but almost insignificant

for the deals in the 2000s. For example, during the period of 1986-1993, the median industry-

adjusted percentage increases in net cash flow to sales are significant at 32.7%, 28.2%, and

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31.5% in the first three years after the buyout. Between 1994 and 2011, these increases are still

significant, but by a lesser extent at 18.5%, 13.7%, and 29.8%. However, from 2002 to 2011,

only the increase in the first year after the buyout is significant at 13.3%, while changes in the

second and third years become insignificant.

I next examine how LBO deal characteristics have changed over time that may be

responsible for the documented decline in operating performance. I find three important changes.

First, LBOs in the 1990s and 2000s do not use as much leverage as the ones in the 1980s. For

deals in the late 1980s, leverage increased by a median of 45% to a post-buyout leverage of 74%.

However, the median leverage increase was only 22% and post-buyout leverage was 57% for

deals in the 2000s. Second, there has been a structural change in the composition of LBO debt.

The proportion of bank debt has decreased from a median of 85% in the late 1980s to a median

of 34% in the 2000s. In the meanwhile, institutional investors have become more important in

the LBO market with institutional loans financing a median of 63% of total LBO debt in the

2000s. In addition, covenants associated with LBO loans have become less restrictive. Third, PE

firms have become more important in LBO transactions. The proportion of deals sponsored by

PE firms increased from 68% in the late 1980s to 96% in the second half of the 2000s. In

addition, there are more club deals in recent years, leading to mega LBOs with large transaction

values between 2005 and 2007.

Having documented a decline in post-buyout operating performance and a shift in deal

structure and participants involvement, the second part of the paper seeks to identify what

aspects of a deal’s structure and the role of participants are associated with its performance.

Possible drivers of performance that I consider are (1) changes in leverage, (2) monitoring by

lenders, (3) involvement of PE firms, and (4) better aligned incentives through management

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participation in the buyout. In examining these performance drivers, I control for pre-buyout

characteristics of target firms, credit market conditions, aggregated LBO activities, LBO loan

spread, and the price paid for the target firm.

Regression results show that higher industry-adjusted improvement in operating

performance occurs when leverage is increased by a larger amount through the buyout process,

when LBO loan covenants are more restrictive, and when incumbent management of target firms

contributes equity and participates in the buyout. However, PE firms’ reputation and their forms

of participation, such as club deals and bank affiliations, are not significantly related to

performance change. These results are robust to sub-period analyses that examine performance

drivers in each LBO wave. In addition, I do not find evidence that links performance to credit

market conditions, LBO loan spreads, or the buyout prices. Overall, these results suggest that the

main source of value creation is the reduced agency costs in the post-buyout firms through the

discipline effect of debt, closer monitoring by lenders, and the better aligned management

incentives. These results also help us to understand the decline in operational performance in

more recent deals as they use less leverage and less restrictive loan covenant, which are

important performance drivers.

Another way to examine LBO success is to look at the outcome of each deal—whether it

goes bankrupt or exits through an IPO or a sale to financial or strategic buyer. Using an IPO or a

sale to indicate LBO success from the perspective of PE investors, I find that LBOs are more

likely to succeed if they use more bank debt and tighter covenants, experience no CEO change,

and are sponsored by highly reputable PE firms. These results are consistent with the lenders’

monitoring and PE firms’ reputation as sources of value creation in LBOs.

Contributions of this paper are as follows. First, this paper contributes to the literature on

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value creation of LBOs by examining the primary drivers of performance improvement and

successful deal outcomes using a large and most up-to-date sample. To the best of my

knowledge, this is the one of the few studies that examines the effects of detailed LBO financing

structure and its contractual features, along with PE reputation on post-buyout operating

performance. Results of this paper will further our understanding of when and how an LBO may

be successfully employed to improve firm performance. By doing so, it facilitates our

understanding as to why recent LBOs seem to be less successful than previous transactions. In

addition, this is one of the first large sample LBO studies with a sample period that covers the

entire cyclicality of the LBO history.

Second, this paper contributes to the literature on debt structure and debt contracting

(Bradley and Roberts, 2004; Chava and Roberts, 2008; Nini, Smith, and Sufi, 2009; Demiroglu

and James, 2010a, 2010b; Ivashina and Kovner, 2011; Achleitner, Braun, Hinterramskogler, and

Tappeiner, 2012).18

This paper finds that loan covenants are important drivers of operational

improvement and are instrumental to ensure successful outcomes. These results have important

implications for practitioners as well as policy makers in that they should focus on covenants to

reduce credit risks. The proportion of bank debt is also important for LBOs to exit through an

IPO or a sale, suggesting that the composition of LBO debt needs to be carefully structured.

Third, this paper contributes to the literature on private equity by being one of the first

studies that investigate how PE reputation is related to portfolio companies’ operating

performance. The finding that, controlling for target and deal characteristics, PE reputation is not

related to operating performance in the first three years after the buyout but is important in

ensuring successful deal outcomes provides some indirect evidence that PE firms may create

18 See Cumming and Johan (2013) for an overview of venture capital and PE contracting.

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value through later stage of LBOs. Findings of this paper will motivate future studies in

investigating when and how PE firms create value in LBOs.

The rest of the paper proceeds as follows. Section 2 reviews related literature and develops

hypotheses. Section 3 describes the sample and provides evidence on post-buyout operating

performance. Section 4 presents the changing characteristics and participants of LBO deals over

time. Section 5 examines the drivers of post-buyout performance. Section 6 conducts robustness

checks. Section 7 concludes.

3.2 Literature Review and Hypotheses Development

3.2.1 Measuring Value Creation in LBOs

Previous studies have examined value creation in LBOs in two ways: returns to LBO

investors and post-buyout performance improvement in LBO target firms (for overviews of LBO

value creation, see Cumming, Siegel, and Wright, 2007; Kaplan and Stromberg, 2009; Eckbo

and Thorburn, 2013). In the first approach, value creation is measured as the returns to invested

debt and equity capital from the time of buyout to a subsequent IPO, sale of the firm, or

bankruptcy. Studies on LBO deal level returns suggest significant value creation through LBOs

as evidenced by positive returns to investors. For example, Kaplan (1989a) estimates a median

market-adjusted return of 28% (mean 42%) for investors in 25 MBOs in the 1980s that went

public after an average of 2.7 years. Also, Guo et al. (2011) finds a median market- and risk-

adjusted return to pre-buyout capital of 68.7% (mean 94.7%) for a sample of 70 LBOs completed

from 1990 to 2006.

On the LBO fund level, literature provides mixed evidence. Kaplan and Schoar (2005)

investigate returns for 160 LBO funds between 1980 and 2001 and find that the median fund

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underperformed stock market index, generating only 80% of the return on the S&P 500. Higson

and Stucke (2012) find that the buyout funds in their sample significantly outperform the S&P

500, with funds liquidated in the period 1980-2000 generating excess returns of on average 4.5%

per year. The different findings of these two studies are mainly due to sample selections as both

studies find large heterogeneity in returns across funds. In addition, both studies show that PE

performance is persistent and suggest that different LBO sponsors may have different skills in

managing their portfolio companies. The heterogeneity in fund returns and performance

persistence motivate me to examine characteristics of PE firms to determine whether and how

they are associated with performance of target firms.

The second way to examine value creation in LBOs is to focus on the post-buyout

operating performance of target firms.19

Kaplan (1989a) studies 48 MBOs from 1980 to 1986

and finds that industry-adjusted ratios of EBITDA to sales increased by 21.3% and cash flow to

sales increased by 28.3% during a three-year period following the buyout. Smith (1990) reports a

significant increase in operating cash flow per employee and per dollar of operating assets for 58

MBOs between 1977 and 1986. Lichtenberg and Siegel (1990) study 131 LBOs between 1981

and 1986 and show that the post-buyout plant total factor productivity increases more than the

industry average. Muscarella and Versuypens (1990) examine 72 reversed LBOs from 1976 to

1987 and find significant operational improvement resulted mainly from the reduced operating

costs after the buyout.

In contrast to the significant performance enhancement documented in the early studies,

the evidence of performance improvement is weaker for more recent U.S. LBOs.20

Guo et al.

19 Some studies, for example Guo et al. (2011), use changes in operating performance as an explanation for

returns to investors. 20

For reviews on operating performance of European buyouts, see Cumming et al. (2007) and Eckbo and

Thorburn (2013).

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(2011) find a median of only 2.25% industry-adjusted increase in operating margins and a

12.54% decrease in cash flow margins for 94 LBOs completed between 1990 and 2006. Cohn et

al. (2014) also find little evidence of performance enhancement using corporate tax return data

for 317 LBOs from 1995 to 2007.

This paper follows the second approach and examines post-buyout operating performance

for the following reasons. First, I am interested in examining whether there is any real effect of

operational improvement in the target firms.21

Therefore using measures of operating

performance serves the purpose, while returns to investors do not directly measure target firms’

operational gains as they are usually calculated upon the exit of the deal, therefore depending on

market conditions and investors’ market timing ability. Second, one of the goals of this paper is

to construct a comprehensive and most up-to-date LBO database that includes deals completed

during and after the 2007-09 credit crunch. Most of these deals have not reached their outcome

yet so no returns to investors are available for these deals. However, I can still examine the value

creation and its drivers by studying changes in operating performance.

According to Cumming et al. (2007), one problem with my performance measures using

cash flow variables from target firms’ financial statements is that they are in general subject to

managerial manipulation. However, as all the performance ratios in the paper are industry-

adjusted and assuming all firms in the same industry are subject to managerial manipulation in

the similar ways, I expect the effect from manipulation to be small although the incentives for

LBOs to show improved performance are probably greater than the average firms.

21 According to KKR founder Henry Kravis, private equity firms create value in LBOs over the long-term as

managers, not merely as financial engineers. Kravis said that “We only make money because we improve the

operations of the newly acquired company”. Source: “Merger Talk - LBO firms rush to exits with quick flips.”

Reuters News, December 30, 2004.

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3.2.2 How LBOs Create Value: Hypotheses

In this subsection, I develop hypotheses on the sources of post-buyout performance

changes of LBO target firms. I hypothesize that operational improvement in target firms is

driven by the disciplining effect of increased leverage, closer monitoring by lenders, active

involvement of PE firms, and better aligned management incentives.

3.2.2.1 The Disciplining and Monitoring Effect of Debt

The first key ingredient in a buyout transaction is leverage. Jensen (1986, p325) states that

“many of the benefits in going-private and leveraged buyout transactions seem to be due to the

control function of debt”. Leverage creates pressure on managers not to waste money, as they

must make interest and principal payments. This pressure therefore reduces the free cash flow

problem described in Jensen (1986) where managers dissipate the free cash flows and overinvest

in negative-NPV projects. Also, the increased risk of financial distress associated with higher

leverage motivates managers to operate the firm efficiently and to increase profit. Therefore, I

expect that target firms that have increased their leverage by a greater amount through the LBO

process perform better.

Hypothesis 1.1(Debt Disciplining Hypothesis): Firms with higher leverage increase during

the LBO process tend to perform better.

In addition to the disciplining effect of debt, I examine whether lenders’ monitoring

associated with the LBO debt leads to additional disciplining effect and therefore generating

better performance of target firms. At the center of this examination is the conflict of interest

between shareholders and debtholders that has negative impact on the value of the firm’s

outstanding debt as well as the total value of the firm (Bradley and Roberts, 2004). Lenders’

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monitoring on managers’ behavior can help to mitigate this conflict and reduce the attendant

agency costs.

To study the monitoring effect, I first examine the proportion of bank debt in the total LBO

debt. This is because banks are generally thought to have more incentives and comparative

advantages in monitoring borrowers (Diamond 1984, 1993; Park, 2000; Cotter and Peck, 2001).

Therefore, I hypothesize that LBOs funded with a larger proportion of bank debt tend to perform

better as these deals are more closely monitored by banks.

Another way to examine the monitoring effect is through the investigation of LBO debt

covenants. Bradley and Roberts (2004) propose the Agency Theory of Covenants (ATC) that

views covenants as contractual solutions to reduce agency costs of debt associated with the

conflict of interest between shareholders and debtholders.22

Chava and Roberts (2008) suggest

that covenants increase firm value in two ways. First, consistent with the ATC, covenants

monitor and control managers’ behavior and therefore mitigating the reduction in firm value

from the agency costs of debt. For example, covenants can restrict borrowers’ use of cash flows

and require them to repay with proceeds of excess cash flow, asset sale, or debt and equity

issuance.23

These requirements mitigate the free cash flow problems described in Jensen (1986).

Second, covenants define the circumstances under which creditors are permitted to intervene in

management. This threat of transfer of control rights from borrowers to creditors serves as

additional discipline mechanism for managers. Therefore, I expect that LBOs with more

restrictive covenants tend to perform better.

22 According to Bradley and Roberts (2004), loans are more likely to include covenants when borrowers are

smaller, have higher growth opportunities, higher leverage, higher cash flow volatilities, and lower asset

tangibility, and during periods of greater financial distress. 23

Based on Miller (2012), in a typical syndicated loan contract, 100% of net proceeds from asset sales and

debt issuance and 50% to 75% of excess cash flow are required to prepay the loans.

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Besides covenants, the maturity structure of LBO debt is also important. When LBOs are

financed with short-term debt, the incentive effects of debt described by Jensen (1986) tend to be

stronger. In particular, a shorter maturity increases required debt service payments, thus

increasing the incentives for mangers to work harder to generate cash and avoid wasting

resources in the earlier stages of the LBOs (Cotter and Peck, 2001). In combination, these LBO

debt characteristics form Hypothesis 1.2.

Hypothesis 1.2 (Lenders’ Monitoring Hypothesis): LBOs financed by a larger proportion

of bank debt with more restrictive covenants and shorter maturity tend to perform better.

3.2.2.2 Private Equity Involvement

Another possible source of performance improvement in LBOs may be the involvement of

PE firms. As equity investors in LBOs, PE firms are incentivized to actively engage in target

firms’ management. Also, general partners (GPs) of PE funds are paid a management fee of 2%

on the fund’s capital and receive a carried interest of 20% of the profits above a certain

benchmark realized by the fund. Therefore, GPs have incentives to closely monitor their

portfolio firms. Masulis and Thomas (2009) discuss the superior corporate governance by PE

firms. Also, as described by KKR’s founder Henry Kravis, PE firms “generally aren’t board

members who show up once a month... Most of us in the industry live with these companies on a

day-to-day basis”. 24

However, it is hard to directly observe PE firms’ involvement in target firms’ management,

as target firms become private after the buyouts and therefore are not required to disclose

24 Source: “Merger Talk - LBO firms rush to exits with quick flips.” Reuters News, December 30, 2004.

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corporate governance information.25

As a result, I use PE firms’ reputation as a proxy for their

experience and skills to manage the target firms, where reputation of each PE firm is measured

by its years of experience or its market share based on past deals. I hypothesize that LBOs

sponsored by highly reputable PE firms perform better.

Hypothesis 2.1 (Private Equity Reputation Hypothesis): LBOs sponsored by PE firms of

higher reputation tend to perform better.

A recent trend in LBOs is club deals, where two or more PE firms pool their assets to

acquire the target firms and manage them collectively. Anecdotal evidence suggests that club

deals can be beneficial as each PE firm may bring different expertise to the target firms. For

example, when KKR teamed up with Bain Capital and Vornado Realty Trust to acquire Toys "R"

Us, the New York Times stated that “it was clear what each firm brought to the table. Kohlberg

Kravis has a good reputation in the retail business, Bain has a good record doing turnarounds,

and Vornado clearly knows real estate”.26

For the venture capital (VC) industry, which is similar

to the PE industry, Wright and Lockett (2003) find that different VCs bring diverse specialist

skills required to restructure and regenerate a particular deal. However, as the number of PE

firms in the club gets larger, it may become harder to make timely operational and management

decisions. For example, Jeffrey Walker, a managing partner of CCMP Capital, argued that it was

25 Some U.K. studies look at corporate governance of target firms using the unique datasets only available for

U.K. firms, such as the FAME and ONESOURCE. For example, Nikoskelainen and Wright (2007) find that

corporate governance mechanisms that include gearing, syndication, and management ownership are critical

for value increase in buyouts. Also, Cornelli and Karakas (2012) examine the board structure of 88 U.K. LBOs

from 1998 to 2003 and find that when a company goes through an LBO, its board size is reduced and outside

directors are replaced by LBO sponsors. They also show that LBO sponsors’ presence on the board increases

operating performance. 26

Source: “Do Too Many Cooks Spoil the Takeover Deal”, the New York Times, April 3, 2005

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difficult to manage an LBO that has more than two or three investors.27

To sum up, anecdotal

evidence suggests that club deals may improve performance through each PE firm offering

valuable management advice. However, as the size of the club gets larger, this benefit may

decrease.

Hypothesis 2.2 (Club Deal Hypothesis): Club Deals perform better than LBOs sponsored

by a single PE firm. However, this advantage tends to decrease as the number of PE firms

participating in a deal gets larger.

Recent studies in LBOs have investigated the relationship between banks and PE firms and

how it affects returns to PE investors at the exit of LBOs. Fang et al. (2013) find that LBOs

sponsored by PE firms that are subsidiaries of banks (the bank-affiliated deals) exhibit worse

equity returns if the deal is completed under a favorable credit market. Ivashina and Kovner

(2011) find that bank relationship formed through repeated interactions between banks and PE

firms lead to more favorable loan terms and higher equity return to the PE firms. Empirical

results on the effect of banking relation on PE returns are mixed, depending on the nature of the

relation and the motivation behind it. I follow Fang et al. (2013) and examine bank-affiliated

deals, as there may be some distinct features of these deals. First, bank-affiliated PEs may have

better access to debt financing provided by their parent banks should an LBO opportunity rise.

Second, bank-affiliated deals provide parent banks with cross-selling opportunities (such as

potential M&A advisory work, cash-management services, etc.) that increase their fee income.

Buyout decisions in such deals may not be based on target firms’ potential in operational

improvement, but to take advantage of these cross-selling opportunities. Based on the two

27 Source: “Buyout Veterans Have Questions about Club Deals”, Dow Jones Newswires, January 24, 2007. The

article also argues that the ideal size of a consortium is two PE firms, based on the experience of the venture

capital industry, which is closely similar to the PE industry.

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reasons above, PE firms may overinvest in unprofitable deals that are less likely to generate

better performance.

Hypothesis 2.3 (Bank Affiliation Hypothesis): Bank-affiliated LBOs tend to perform worse

than stand-alone deals.

3.2.2.3 Management Participation

When incumbent managers of target firms contribute equity and participate in a buyout,

they become equity investors and their incentives are well-aligned with other shareholders. As a

result, agency costs are minimized.

Hypothesis 3 (Management Participation Hypothesis): Management-participated LBOs

tend to perform better.

Some studies look at management turnover and consider it as a way to measure PE firms’

control over target firms. Gong and Wu (2011) find that 51% of incumbent CEOs are replaced

within two years of the LBO announcement. Acharya, Gottschalg, Hahn, and Kehoe (2013) find

that for the LBOs in the U.K., one third of the CEOs are replaced within the first 100 days and

two-thirds are replaced over a four-year period. However, management turnover can be a noisy

measure. First, it may not be clear that the management change is due to PE firms unless it is

explicitly indicated in the proxy statement. Second, even it is confirmed that PE firms replace the

CEO or CFO, management change may not necessarily indicate increased control from the PE

firms. On the one hand, management turnover can be consistent with replacing bad managers by

the good ones. On the other hand, the same managers running the company before and after the

buyout may indicate low pre-buyout agency problem and therefore there is no need to replace

managers. As a result, I expect the effect of management turnover on performance to be

ambiguous and leave it as an empirical question to be examined in Section 5.

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3.3 Sample, Data, and Post-Buyout Performance Measures

3.3.1 The Buyout Sample

My LBO sample is constructed from the Standard and Poor’s Capital IQ and the Securities

Data Company’s (SDC) U.S. Mergers and Acquisitions Database. Compared with SDC, Capital

IQ has some advantage as it allows me to link the LBO transaction details to target firms’

financials and information on LBO buyers (PE firms and/or management teams). Capital IQ has

been used extensively in studies on LBOs and PEs, such as Stromberg (2008), Kaplan and

Stromberg (2009), and Axelson, Jenkinson, Stromberg, and Weisbach (2013). However, one

problem with Capital IQ is its limited coverage of earlier deals. According to Stromberg (2008),

Capital IQ only covers around 70%-85% of LBO transactions in the 1980s. As one of the goals

of this paper is to compare LBO characteristics during the period from 1986 to 2011, I also

collect LBOs from SDC that has better coverage of earlier deals. I manually combine LBO

transactions from the two sources, track for name changes, and eliminate duplicated

observations.

Each LBO transaction in my sample meets the following criteria: (1) the transaction is

flagged as an LBO, MBO, or secondary LBO and completed between January 1, 1986 and

December 31, 2011;28

(2) the target is a publicly traded U.S. company;29

and (3) the transaction

value is $10 million or larger. The minimum deal value of $10 million is lower than that in some

other studies, such as Kaplan (1989a) and Guo et al. (2011). It is chosen to avoid biasing against

28 My sample starts from 1986 because the loan information from Dealscan starts from 1986.

29 I only consider the buyout of an entire publicly-traded firm and exclude all transactions that involve the

acquisition of private firms or divisions of other companies. This may create a sample selection bias, as (1)

LBOs in the early 1990s are mainly the buyouts of private firms (Kaplan and Stromberg, 2009) and (2) MBOs usually involve management buying out subsidiaries or divisions of a large firm (Cumming et al., 2007).

However, including only publicly traded target firms is the only way I can get the detailed information for the

purpose of my study.

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earlier time periods when small deals were more common. This initial screening yields a total of

1,586 LBO transactions.

To reconstruct the financial structure of each deal accurately, I require all transactions to

have financing details available from Reuter’s LPC Dealscan loan database.30

The basic unit of

observation in Dealscan is a loan, often referred to as a facility or tranche. Since most firms in

Dealscan enter into multiple loans at the same time, loans are often grouped into packages. I

match the Dealscan data at package level with the buyout sample by borrower name, deal active

date, and primary loan purpose.31

I then reconstruct the financial structure for each deal using the

tranche level data of LBO debt from Dealscan and the mezzanine debt and high-yield bond from

Capital IQ. This reduces the sample to 885 observations. In addition, I require that all target

firms have pre- and post- buyout financial information from COMPUSTAT or Capital IQ, and

missing data are filled from the SEC filings.32

This drops 384 transactions, the majority of which

are the buyouts in the 1980s, as Capital IQ mainly provides financial statement information for

the 1990s and the 2000s. My final sample consists of 501 LBO transactions. 448 of these deals

have at least one PE firm involved (see section 3.4.2 for detailed description for these deals), 230

30 Restructuring LBO deals across databases requires matching by names of target firms. Target firms mostly

appear under their old names in SDC, Dealscan, and Compustat, while Capital IQ uses only the most recent

names. I keep track of all name changes using a text search in Company Tearsheet of CapitalIQ. I also use the

Wall Street Journal, other news articles, and public filings to identify name changes if the Tearsheet is

ambiguous. 31

If the primary loan purpose is “LBO”, borrower name matches the LBO target firm’s name, and the deal

active date is around LBO announcement or effective date, I confirm Dealscan and LBO sample as matched. If

the primary loan purpose is “takeover”, I match name and date and go through 10K and 8K to confirm the loan

is to fund the buyout. 32

Capital IQ provides financials for private companies that belong to one of the following categories: (1)

Private companies with publicly traded debt, (2) M.A. targets filing financials in 8-K/A SEC forms, (3) D&B

Financials, (4) U.S. bank subsidiaries filing with various regulatory bodies in the U.S., such as FFIEC, CUA,

OTS. My sample contains samples of cases (1) and (2).

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deals have at least one member of the incumbent management team contributing equity and

participating in the buyouts, and 193 LBOs have both PE and management involved.33

Table 1 compares the mean and median inflation-adjusted transaction values (in 2005

dollars) of the 501 LBOs in my final sample with the 1,586 public-to-private LBOs originally

collected from Capital IQ and SDC. Overall, 31% of the initially screened buyouts stay in the

final sample. The median size of the 501 LBOs ($620.97 million) is significantly larger than the

overall sample ($287.38 million). This is because firms with public debt financing and/or

subsequently filing for an IPO, therefore reporting post-buyout financials, are typically larger.

Also, large firms are more likely to use syndicated debt than small ones, therefore are included in

my sample. I am well aware that the requirement for LBOs to have deal financing information

and post-buyout financials available induces a sample selection and survivorship bias. However,

this is the only way to get the detailed information that allows me to test my hypotheses on

performance drivers for LBOs. Nevertheless, I will keep in mind of this problem when

interpreting my results.

Figure 2 presents the total number of LBO deals (left y-axis, solid line) and the inflation-

adjusted total transaction values (right y-axis, bar) by LBO effective year.34

The figure shows

three LBO waves. The first LBO boom occurred in the late 1980s, with total transaction values

increasing from $24 billion in 1987 to a peak of $81 billion in 1989. The largest deal during the

time was KKR’s buyout of RJR Nabisco in 1989 with a transaction value of $39 billion. The first

wave of LBOs ended with the recession in 1990-1991 when the high yield bond market

collapsed. The buyout market started to resume in 1996 but crashed with the bursting of the tech

33 37 deals are pure management buyouts with no PE participation.

34 LBO effective year is the year an LBO is complete. This is shown as the deal closed date or effective date in

Capital IQ and SDC.

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bubble in 2001. In the mid-2000s, LBOs reappeared in a third buyout boom. Total transaction

values increased sharply from $5.4 billion in 2002 to $65 billion in 2005 and reached a historical

high of $273 billion in 2007. The years 2006 and 2007 observed the surge of the mega-buyouts,

including the acquisitions of TXU ($42 billion, later renamed as “Energy Future Holdings

Corp”), HCA Holdings, Inc ($33 billion), Kinder Morgan ($28 billion), and First Data ($27

billion).

Table 2 breaks down the sample by industry grouping based on SIC codes. Target firms are

from eight broad industries but are concentrated in manufacturing with approximately 44.5% of

the sample coming from this industry. Firms in the service industry and the wholesale and retail

industry are the next biggest groupings. In the late 1980s and early 1990s, almost 50% of the

buyout transactions were from the manufacturing industry. Since the year 1997, relatively more

firms come from the service and the wholesale/retail industries. Overall, the sample shows an

increased industry scope for LBOs over time.

3.3.2 Evidence on Post-Buyout Operating Cash Flows

In this subsection, I calculate measures of post-buyout operating performance and examine

how they have changed over time.

3.3.2.1 Methodology

To document the post-buyout operating performance, I use the operating income as

measured by EBITDA and net cash flow (NCF). EBITDA measures the cash generated from

target firms’ operations before depreciation, interest, or taxes, and therefore is not affected by the

level of interest payments—this serves the purpose of this paper that studies the overall income

of the target firms before income being divided between shareholders and debtholders. In

addition, since EBITDA is before tax, it excludes the tax-shield effects of LBOs as in Jensen

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(1989b), allowing me to focus on the operational effect. I use NCF as it is the primary

component of the numerator in a net present value analysis. EBITDA and NCF are scaled by

sales and average assets for each fiscal year. Operating performance is measured as the

percentage changes of cash flow measures in the first three full years after the year of LBO

completion (year +1, +2, and +3) compared to the last fiscal year before the buyout completion

(year -1). To control for pre-buyout firm characteristics, I also look at the percentage changes of

the cash flow measures from two years before LBO (year -2) to the last fiscal year (year -1).

In order to evaluate the economic and statistical significance of pre- to post-buyout changes

in performance, I follow Kaplan (1989a) and Guo et al. (2011) and calculate the industry-

adjusted performance measures. The industry-adjusted change equals the percentage change in

the cash flow variables for the target firms minus the median percentage change over the relevant

period for all Compustat firms in the same industry. Firms in the same industry as the target

firms are those that have the same four-digit SIC code. Comparisons are made at the three-digit

level and then at the two-digit level if fewer than five industry matches are found.

3.3.2.2. Evidence on Changes in Operating Performance

Table 3 summarizes the medians of unadjusted and market-adjusted percentage changes in

operating performance for the last full year prior to completion of the buyout year (year -2 to

year -1) and from year -1 to up to 3 years after the buyout. Panel A shows the median changes

over the entire sample period and Panel B presents the medians in each LBO wave and the time

trend. The table also shows the number of observations for the overall sample and in each wave,

as I exclude firms when they exit through IPOs, sales, or bankruptcies—my results are not

affected by firms that have exited.

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Panel A of Table 3 shows that the industry-adjusted percentage increases in EBITDA to

sales are significant at 7.0%, 6.9%, and 8.9% in years +1, +2, and +3 relative to year -1. Changes

in NCF to sales adjusted by industry medians are also positive and significant, with medians of

18.5%, 13.9%, and 19.6% in years +1, +2, and +3. In contrast to the increased cash flow

variables that are scaled by sales, there are 12.2% and 9.8% significant decreases in EBITDA

over average assets in year +1 and +2 relative to year -1. The median changes become

insignificant in year +3. The industry-adjusted changes in the NCF as a proportion of average

assets decrease by 5.7% in the first year after the buyout (significant at 10%), the median

changes then become insignificant in year +2 and +3. The significant decrease in the first year

after buyout can be explained by Kaplan (1989b) that buyout accounting leads to a change

(usually an increase) in the book value of the assets, representing the difference between the

market value and book value of equity. This may lead to an underestimate of operating

improvement when cash flow measures are scaled by assets.

I next divide the sample period into three sub-periods, 1986-1993, 1994-2001, and 2002-

2011, based on the cyclicality of the LBO market presented by Figure 2. Panel B shows the

median changes of performance in each sub-period and the time trend. The nonparametric trend

test results show that there is less performance enhancement in more recent deals for all four

measures. For example, during the period of 1986-1993, the industry-adjusted percentage

increases in NCF to sales are significant at 32.7%, 28.2%, and 31.5% in the first three years after

the buyout. The increases between 1994 and 2011 are still significant, but by a lesser extent at

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18.5%, 13.7%, and 29.8%. From 2002 to 2011, only the increase in the first year after buyout is

significant at 13.3% and the changes in years +2 and +3 are insignificant.35

To conclude, results in this section show some evidence of post-buyout performance

improvement. However, this is mainly driven by LBOs in the earlier years, as there is a

significant trend of decreased post-buyout performance improvement in more recent deals.

3.4 LBO Deal Characteristics and Participants Involvement

Having documented a decreasing trend of improvements to operating performance, this

section studies the changing characteristics of factors that are expected to be related to

performance based on the debt disciplining, lenders’ monitoring, PE reputation, and management

participation hypotheses developed in section 2.2. Specifically, I focus on the pre- and post-

buyout leverage and its change, the composition of LBO debt and its contractual features, PE

firms’ reputation and their different form of participation in LBOs (i.e., club deals and bank

affiliations).

3.4.1 Leverage, Debt Structure, and Contractual Features

The debt disciplining hypothesis and the lenders’ monitoring hypothesis argue that firms

that have larger amount of leverage added during the LBOs and those that are more closely

monitored by lenders will have better operating performance. Having documented a decreasing

35 Kaplan (1997) argues that public firms seems to have embraced and adopted many of the governance

features of the 1980s buyouts, such as higher incentive pay and learner capital structures, at the same time

institutional investors of the public firms have become more active in governance. Therefore, for recent

buyouts, the performance benefit of going through LBOs may not be as big as those in the earlier deals.

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trend of performance improvement, I now examine whether and how leverage, bank debt

proportion, and the covenants and maturity structure of LBO debt have changed over time.

3.4.1.1 Leverage

Panel A of Table 4 reports target firms’ leverage changes from LBOs. Columns (1)-(3)

show the sample medians of the pre- and post-buyout leverage and leverage changes, calculated

using the financial data from Compustat, Capital IQ, and SEC Filings. Prior to the LBOs, firms

have a median leverage ratio of 31.92%. Leverage increased significantly to a sample median of

64.35% after the buyouts, with a median percentage increase in leverage of 30.55%. Column (4)

shows the median leverage change of all Compustat firms. Comparing with the Compustat

population, I find that leverage increase is unique to LBO firms. The nonparametric trend tests

show that both the post-buyout leverage ratio and the leverage change have decreased

significantly over time. Leverage change in Column (3) also demonstrates some cyclicality

corresponding to the credit market conditions. For example, leverage change is particularly high

during the late-1990s and mid-2000s.

3.4.1.2 LBO Debt Structure

I next study the composition of LBO debt and its contractual features, using tranche (or

facility) level data from Dealscan and Capital IQ. LBO debt is syndicated through different

tranches. According to Miller (2012), revolving credit facilities and term A loans are usually

packaged together and syndicated to banks, and term B, C, and D loans are structured

specifically for institutional investors.36

Therefore, I consider the revolving credit facilities and

36 A revolving credit facility allows borrowers to draw down, repay, and re-borrow capital over time. A term

loan is an installment loan that allows borrowers to draw on the loan during a short commitment period and

repays it based on either a scheduled series of repayments or a one-time lump-sum payment at maturity.

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the term A loans in an LBO debt package as bank debt and the term B, C, and D loans as

institutional debt. I also consider notes that are sold to institutional investors as institutional debt.

The bridge loan tranche from Dealscan and the mezzanine debt and high-yield bond from Capital

IQ that are subordinated to bank and institutional debt are categorized as junior debt.37

Panel B of Table 4 presents the structure of LBO debt. For each category of debt, I

calculate the amount of debt in this category as a proportion in total LBO debt and the

percentage of LBOs that use this type of debt. Columns (1) and (2) demonstrate the use of

revolving credit facilities and term A loans in financing LBOs. The ratio of revolving credit

facilities to total LBO debt has decreased over time, from 60.8% in 1986 to 13.7% in 2011. The

ratio of term A loans to total LBO debt has also declined, from its peak at 76.9% in 1993 to 1.2%

in 2011. Although most LBOs continue to use revolving credit facilities, the proportion of LBOs

that uses term A loans has significantly dropped from 47.1% in 1993 to 5.7% in 2011. As a

result, the ratio of bank debt to total LBO debt and the percentage of LBOs that use bank debt

have decreased significantly (Column (3)), suggesting the declining importance of banks in

financing LBOs. With the decrease of bank debt, there has been an increasing use of institutional

debt since the mid 1990s, as presented in Column (4). The proportion of institutional debt has

increased from 1.2% in 1989 to 58.4% in 2011 and the nonparametric trend test shows a

significant increase in the proportion at 1% level. Since 1998, the proportion of institutional debt

in total LBO debt has exceeded that of bank debt (except in 2009), suggesting that institutional

investors have become more important in financing LBOs.

37 A bridge loan facility is interim, committed financing provided to the borrower to “bridge” to the issuance of

permanent capital and it usually takes the form of an unsecured term loan. Mezzanine debt is a form of

financing that is part debt and part equity and is senior only to common equity. It is usually unsecured and

bears a higher interest rate than secured debt and often gives the lender a stake in the equity of the borrowing

firm. Investors of the mezzanine debt are typically insurance companies and the mezzanine funds.

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Column (5) of Panel B demonstrates the use of junior debt. Junior debt is mainly prevalent

in the late 1980s and the 2000s. The large proportion of junior debt in the total LBO debt in the

late 1980s corresponded to the use of high-yield junk bond. Since 2002, the use of mezzanine

debt in LBO financing has increased dramatically.

In summary, statistics of this subsection show the declining importance of banks in

financing LBOs while institutional investors have become more important. This trend may

provide some explanation for the declining performance in LBOs as bank monitoring has been

considered as instrumental in reducing agency costs of debt and therefore creating operational

gains (Cotter and Peck, 2001). This relation will be tested in the multivariate analysis in section

5.

3.4.1.3 LBO Loan Spread, Maturity, and Covenants

I next examine the spread and maturity of bank debt verses institutional debt and the LBO

debt covenant at package level. Results are presented in Panels C and D of Table 4. Columns (1)-

(3) of Panel C show the medians of all-in-drawn interest spread of bank debt, institutional debt,

and their differences. All-in-drawn spreads (over 6-month London Interbank Offered Rate

(LIBOR)) for each tranche are from Dealscan and include both the interest costs and fees

associated with borrowing. There is a significant increasing trend of bank debt spread, as shown

by Column (1). Institutional debt spreads are higher than bank debt spreads but the institutional-

bank difference reported in Column (3) has narrowed over time with the increased usage of

institutional debt in financing LBOs. This is consistent with Miller’s (2012) argument that the

spread difference between institutional debt and bank debt narrows when the institutional

demand for syndicated loans is high. Columns (4)-(6) of Panel C show the median maturity (in

months). There is no significant change in the maturity structure of bank debt. However, the

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maturity of institutional debt has decreased throughout the sample period, from 120 months in

1992 to 79 months in 2011.

I next examine covenants of LBO debt as they provide specific requirements and

restrictions on management behavior and therefore reducing the agency costs associated with the

conflict of interests between shareholders and debtholders. Covenant information is obtained at

package level from Dealscan. To measure the tightness of covenant restrictions, I modify the

covenant intensity index developed by Bradley and Roberts (2004). For each loan package, the

modified covenant intensity index is the sum of (1) number of financial covenants (up to 6), (2)

number of sweeps (asset sales sweep, debt issue sweep, equity issue sweep, excess cash flow

sweep, insurance proceeds sweep), (3) dividend covenant (0/1 variable), and (4) secured debt

covenant (0/1 variable).38

Financial covenants enforce minimum financial performance measures

that the borrowers must maintain. The sweeps specify the percentage of net proceeds from an

asset sale, debt issuance, equity issuance, excess cash flow, or insurance proceeds that the

borrowers must use to pay down any outstanding debt. Dividend covenant restricts the ability of

borrowers to distribute cash to shareholders and secured debt covenant requires the debt to be

secured. Covenants are unique to packages, so that every tranche in a package is covered by all

of the covenants. If an LBO uses multiple loan packages, I use the index of the most covenant-

heavy package as the covenant index of the deal.

Panel D of Table 4 presents the modified covenant intensity index for the LBO debt in my

sample and the Dealscan population for the period from 1995 to 2011.39

Columns (1) and (4)

38 The covenant intensity measure used in this paper indicates the presence of certain covenants in the loan

contract, not the actual threshold of each covenant. This is because the thresholds for financial covenants and

sweeps are related to many factors, such as the credit market conditions and the borrowers’ specific

characteristics. Therefore, it is hard to compare the threshold directly. 39

As information on covenants is fairly limited prior to 1994 and therefore loans syndicated before 1994 only

having secured debt covenant reported, I only examine the time trend from 1995 to 2011.

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present the median and mean values of covenant intensity based on the LBO loans with non-

missing covenant information shown in Columns (2) and (5). Over time, covenant intensity in

both my sample LBO loans and the Dealscan population has declined. LBOs completed in the

1990s and the early 2000s use more convents while the buyouts in the mid and late 2000s tend to

use less covenants, as a result of the favorable credit market condition during the time.40

The

similar trend is found for all Dealscan loans. For the LBO loans in Column (1), the most

frequently used financial covenants are the maximum debt to EBITDA ratio (in 57% LBOs) and

the minimum interest coverage ratio (48%). Comparing the debt covenants in my LBO sample

and all Dealscan population, I find that LBO loans are associated with tighter covenants—the

median covenant intensity for LBO loans is 7 while that of all Dealscan loans is 2.

Columns (3) and (6) of Panel D show the proportion of LBO loans with no financial

maintenance covenants (the covenant-lite loans).41

For both LBO loans and the Dealscan

population, there is a general trend towards more covenant-lite loans over time, although there is

less covenant-lite loans for LBO purpose.

In summary, results from Panel D show declining tightness of covenant restrictions of LBO

loans, suggesting weaker monitoring by lenders that may lead to worse post-buyout performance.

Overall, the complex structuring of LBO debt motivates me to examine whether and how

different funding sources (banks, institutional investors, and others) and contractual features

(maturity and covenants) are related to performance.

40 For detailed studies on the determinants of debt covenants, see Bradley and Roberts (2004) and Achleitner et

al. (2012). 41

According to Bavaria and Lai (2007), S&P define covenant-lite loans as loans with no maintenance financial

covenants that have to be maintained quarterly through the term of the loan. Instead, covenant-lite loans have only incurrence covenants that do not have to be met on an ongoing basis as maintenance covenants do.

Incurrence covenants only restrict the borrower’s ability to issue new debt, make acquisitions, or take other

action that would breach the covenants.

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3.4.2 Private Equity in LBOs

3.4.2.1 Private Equity Involvement

The private equity reputation hypothesis on LBO operational improvement argues that

LBOs sponsored by PE firms with high reputation tend to perform better as these PEs have better

skills to monitor and advice target firms (Kaplan and Schoar, 2005; Axelson et al., 2009;

Demiroglu and James, 2010a). Also, PE firms’ different forms of participation in LBOs—club

deals versus solo-sponsored deals, or bank-affiliated deals versus stand-alone deals—may also

affect target firms’ performance. In this subsection, I examine the changing characteristics of PE

firms that are expected to be related to LBO performance.

To identify PE firms, I download all private equity funds (PE funds) from Capital IQ. For

each LBO transaction collected from Capital IQ, I merge the buyer Excel Company ID of the

LBO to the PE funds Excel Company ID. For the buyout sample from SDC, I run a text search

for the names of PE firms in the transaction synopses and hand match with the PE funds from

Capital IQ. As this paper is to look at private equity involvement at firm level, I consolidate PE

funds to PE firm level. So if one PE firm has multiple active PE funds, I use the Excel Company

ID at the PE firm level for the analysis. For example, both Lehman Brothers Mezzanine Fund

and Lehman Brothers Capital Partners IV are identified at the PE firm level as the Lehman

Brothers, Private Equity Division. I also track the name changes of the PE firms. Of the 501

LBOs in the sample, 448 deals have at least one PE firm involved. The remaining transactions

are either management buyouts or buyouts by another corporation with no PE firm involved.

There are in total 234 PE firms sponsoring these 448 deals. Appendix C presents the top 25 PE

firms by the number of LBOs and total transaction values of these buyouts they sponsored. The

most frequent PE firms are Kohlberg Kravis Roberts & Co (27 deals), TPG Capital (26 deals),

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The Blackstone Group, (22 deals), Goldman Sachs Private Equity (21 deals), and Bain Capital

Private Equity (20 deals).

Table 5 Panel A presents PE firms’ involvement in LBOs over the sample period. Column

(1) shows the number of buyouts that have PE firms involved and its proportion in all LBOs in

the year (in the brackets). There is an overall trend of increased PE involvement. Following

Officer, Ozbas, and Sensoy (2010), I consider a PE firm as a prominent PE if is ranked by the

Private Equity International (PEI) magazine as the largest 50 PEs based on its fund raising.42

Column (2) shows that 52% of the LBOs in my sample have prominent PE firms and there is a

trend of more prominent PEs involved in LBOs over time.

Column (3) shows the number of bank-affiliated LBOs, defined as buyouts sponsored by

PE firms that are subsidiaries of banks at the LBO announcement day.43

There are in total 103

bank-affiliated deals (21% of 501 LBOs) in the sample and the total transaction values of these

deals is 35% of the total transaction values of all 501 LBOs. This is similar to the findings of

Fang et al. (2013) that bank-affiliated groups account for nearly 30% of the overall PE market,

and the findings reported by Lopez-de-Silanes, Phalippou, and Gottschalg (2011) that roughly

one-third of the investment in the global private equity dataset are done by PE groups that are

subsidiaries of banks and finance companies. Column (3) also shows that bank-affiliated deals

exhibits cyclicality corresponding to the LBO market activities. In the years 1989, 1998, and

42 Starting 2007, the PEI magazine ranks PE firms based on the capital raised over the previous five-year

period. The ranking is conducted every two years and I include rankings from year 2007 to 2013. I also add PE

firms that are listed as the top 25 PEs in my sample from Appendix B to this list of prominent PE firms if they

are not already included in the prominent list. The PE firms added are mainly those that are subsidiaries of

banks (the bank-affiliated PEs), as these firms may not be on the PEI list because they may use internal capital

rather than relying on external fundraising. Following Officer et al. (2010), I also add the HM Capital Partners

(formerly Hicks, Muse, Tate, and Furst) because it was historically prominent PE firms that has been less

active in recent fundraising. 43

Some PE firms started as subsidiaries of major banks but later became independent. I only consider an LBO

as bank-affiliated if it is announced during the time the PE firm is a subsidiary of a major bank.

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2007, when the LBO activities were at the peak of the cycle, the proportions of bank-affiliated

deals in total LBOs was higher. This is consistent with Fang et al. (2013) that PE firms time the

market and that the bank affiliations allow these PE firms to take advantage of favorable credit

market conditions.

Columns (4)-(6) present the time trend for club deals. Specifically, Column (4) shows the

number of club deals and its percentage in total LBOs and Column (5) presents the total

transaction values of these club deals and its ratio over the total transaction values of my sample

LBOs. 30% of the LBOs are club deals and their total transaction values are 50% of all buyout

deals. This is consistent with PE firms pooling their assets to acquire large targets. Looking at the

time trend, club deals were very rare in the late 1980s and early 1990s and they started to

become important in the late 1990s. From 2005 to 2008, club deals reached their peak—almost

50% of LBOs were club deals and the total transaction values of these deals were around 80% of

all LBOs during the time. Column (6) shows the maximum number of PE firms in a club. There

is a general trend of more PE firms participating in a club deal. During the entire sample period,

there are on average 2.6 PE firms in a club (not tabulated). The deal that has the largest number

of PE firms is the buyout of SunGard Data Systems sponsored by seven PE firms and completed

in 2005 with a transaction value of $11.5 billion.44

In summary, analyses of this subsection show that PE firms have become more involved in

LBO deals, as evidenced by the increasing proportion of LBOs sponsored by PE firms and the

increasing importance of club deals. Bank-affiliated LBOs have shown some cyclicality that

corresponds to the credit market condition, suggesting some market timing of these deals.

44 The seven PE firms were Silver Lake, Bain Capital, the Blackstone Group, Goldman Sachs Capital

Partners, KKR, Providence Equity Partners, and the TPG Capital.

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3.4.2.2 Private Equity Reputation

Having documented the time trend of PE firms’ involvement in LBOs, I construct measures

for private equity reputation. There are two explanations as to why PE firms’ reputation based on

past experience may be related to their performance in current deals. First, according to Kaplan

and Schoar (2005), performance of PE funds persists over time and the performance persistence

can be attributed to PE firms’ experience and skills in selecting, restructuring, and monitoring

target firms. Better-performing PE firms gain experience through their experiential learning from

previous deals and PEs with lower returns cannot get funds from investors and fail to exist.

Second, Axelson et al. (2009) show that highly reputable PE firms are less susceptible to risk

shifting as they have incentives to maintain their reputation. Therefore, the concern for

reputation will also help to mitigate the conflict of interests between shareholders and

debtholders.

Previous empirical studies measure PE fund reputation in a number of different ways

including fund size, its market share, the number of recent LBO transactions, and the number of

previous fund raisings.45

As PEs in my sample are at the firm level, I use reputation measures

that can be constructed for each PE firm. Following Demiroglu and James (2010a), I record the

number of LBOs sponsored by a PE firm or the total transaction values of these deals in the past

36 months before an LBO announcement or since 1970.46

Reputation for PE firm j at month t is

measured by PE j’s market share:

e tati n , = m er L n red in ri r 3 m nth r in e 1

t ta n m er L in ri r 3 m nth r in e 1

45 See Demiroglu and James (2010a) for discussions on strengths and weaknesses of each reputation measures.

46 The earliest LBO deal documented by Capital IQ is in 1970.

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r e tati n , = ta V L n red in ri r3 m nth r in e 1

t ta V L in ri r 3 m nth r in e 1

In order to get the LBO transaction history, I use all Capital IQ recorded LBO, MBO, SBO

transactions for U.S. target firms since 1970 plus the buyout sample from SDC—a total of

19,014 deals. For club deals, I consider the buyout as a full deal for each PE firm. When

calculating LBO deal-level PE firm reputation, I use the reputation score of the PE firm with the

highest reputation if the deal has multiple PE firms. The reputation score is set to zero if there is

no PE firm involved in a buyout deal.

Another reputation measure is PE firms’ years of experience, which is calculated as the

number of years based on the first ever LBO deal sponsored by the PE firm and the last LBO

deals in the sample. Overall, the median PE firm in the sample has 24 years (mean: 21 years) of

experience and has invested in 15 LBOs (mean: 22 LBOs) since 1970 (not tabulated).

Panel B of Table 5 presents the relation between PE firms’ reputation, their different forms

of participation in LBOs, and LBO deal characteristics and financing structure. Following

Demiroglu and James (2010a), I run univariate OLS regressions using various measures of LBO

deal and financing characteristics as dependent variable and one reputation or participation

variable as explanatory variable with year dummies. Consistent with Cotter and Peck (2001) and

Demiroglu and James (2010a), I find that highly reputable PE firms tend to sponsor larger LBOs

with lower per-buyout leverage ratio. LBOs sponsored by more reputable PE firms tend to have

larger leverage increase, use less bank debt and more institutional debt with lower spread and

longer maturity. However, there is no significant relation between market share based reputation

measure and covenant intensity.

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3.5 Explanations for Post-Buyout Operating Performance

In this section, I examine the relationship between operating performance and LBO deal

characteristics as the following:

Industry-adjusted performance change

= (leverage change, debt characteristics, PE characteristics, Management participation, pre-

buyout target characteristics)

My goal is to test different hypotheses developed in Section 2.2 to determine factors that

contribute to operational improvement in LBOs. The analysis will also help us to understand

whether the documented changing characteristics of LBOs can be used to explain the reduced

performance improvement observed in the more recent LBO deals.

Appendix D presents correlation coefficients between all variables used in regressions,

including target firms’ pre-buyout characteristics, leverage change, debt structure and contractual

features, PE reputation, and variables that measure market conditions. The boded coefficients

indicate significant at 5% level. All correlations are well below the commonly used cut-of

threshold of 0.7 and an examination of the variance inflation factor (VIF) values after regressions

do not show multicollinearity problems.

Table 6 reports the multivariate regression results for post-buyout operating performance.

The dependent variables are the industry-adjusted percentage changes in EBITDA/sales and

NCF/sales from the last full pre-buyout year (year -1) to the second full year after deal

completion (year +2). This allows me to include LBO deals completed by the end of 2010 to

look at the performance of LBOs during and after the 2007-09 financial crisis. Also, I use

EBITDA scaled by sales, instead of total assets, to avoid the buyout accounting problems related

to total assets as described in Kaplan (1989b). To control for pre-buyout characteristics of target

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firms, all regressions include leverage ratios and asset tangibility at the end of year -1, industry-

adjusted changes in EBITDA/sales or NCF/sales from year -2 to year -1, and target firms’ cash

flow volatiles in the last three years before buyout completion (years -3, -2, and -1). All

regressions include year dummies.

First, I examine the debt disciplining and lender’s monitoring hypotheses in Panel A of

Table 6. Columns (1) and (2) show that the effect of leverage change on performance is

significantly positive. That is, firms with greater amount of leverage added during the LBOs

show more post-buyout performance improvement, supporting the debt disciplining hypothesis.

Looking at the pre-LBO target firms’ characteristics, I find that only pre-buyout cash flow

volatilities are significant at 1% level—better performance is related to more stable cash flows

before the buyout.

I next examine whether and how performance is related to the monitoring by lenders. I

include the proportion of LBO debt that is funded by banks, with the expectations that banks

have more incentives and advantages to monitor the borrowing firms and that the percentage of

bank debt is proportional to banks’ monitoring effort. I also include the modified covenant

intensity index that measures the presence of different covenants in LBO loans. The maturity

structures, calculated as the weighted average maturity (in months) of bank debt and institutional

debt for each LBO loan package, are also considered in the regression, with the expectation that

shorter maturities that require early principal payment and/or refinancing indicate closer

monitoring by lenders therefore leading to better performance. Columns (3) and (4) of Panel A

demonstrate a significantly positive coefficient for covenant intensity, suggesting that controlling

for the leverage effect, tighter covenants are associated with better performance. This positive

relation between covenants tightness and performance can be interpreted in two ways. First,

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based on the agency theory of covenants (Bradley and Roberts, 2004), tighter covenants provide

target firms with more incentives to improve performance, supporting the lenders’ monitoring

hypothesis in this paper. Second, according to Demiroglu and James (2010b)’s study in general

bank loan covenants, borrowers use tighter covenants when they expect their performance to

improve or to signal favorable private information concerning future performance. Therefore, I

should recognize that covenant choice is endogenous and I cannot explicitly infer any causal

connection between covenant choice and future performance. However, as LBO transactions,

especially the PE-sponsored deals, lead to changes of control of the target firm, I expect the

private information and therefore the signalling motivation of selecting tight covenants is quite

weak compared with general bank loans.

Bank debt proportion and maturity are insignificantly related to performance. I also replace

the maturity at package level with bank debt maturity, the coefficient is still insignificant (not

tabulated).

I next examine the effects of PE firms’ reputation and their different forms of participation

in Panel B of Table 6. I use different PE reputation measures constructed in the last section as

independent variables in the regression. However, none of these reputation measures is

significantly related to performance (Columns (1) and (2)).47

Columns (3) and (4) show result for

regressions that include the club deal dummy, as it has been argued that in a club deal, different

PE firms bring different expertise to the target firm’s management, therefore providing another

source of value creation. However, the regression results do not support this argument. I

construct an optimal consortium size dummy variable that takes the value of 1 if there are two or

47 The reported coefficients and p-values in Columns (1) and (2) are for the reputation measure calculated as

the market share of PE firms based on the total transaction values of the LBOs on the past 36 months.

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three PE firms in a club, and 0 other wise. However, regression results presented by Columns (5)

and (6) show no evidence that the optimal consortium size is related to performance. In columns

(7) and (8), I include a dummy variable that indicates whether an LBO is bank-affiliated deal. I

find that bank affiliation has no significant effect on performance. In sum, regression results

presented in Panel B of Table 6 provide no evidence that PE firms’ reputation or different form

of involvement in LBOs is related to the post-buyout operating performance of target firms.

In panel C of Table 6, I test the management participation hypothesis that LBOs tend to

perform better when managers of target firms contribute equity and participate in the buyouts as

their incentives are better aligned with other shareholders. I use a dummy variable that indicates

management participation. Specifically, the dummy variable equals 1 if Capital IQ labels a

transaction as “management buyout”, “management participated”, “individual investor

participated” when the individual investor is confirmed to be board member or management of

the target firm, or the firm is bought out through an employee stock ownership plan (ESOP). If a

transaction is from the SDC database, the dummy is equal to 1 if the SDC synopsis describes the

deal as “management led” or “management participated”. Results in column (1) support the

management participation hypothesis when using industry-adjusted changes in EBITDA/sales as

dependent variable. However, the effect of management participation on changes in NCF/sales is

insignificant.

I next examine whether management turnover is related to performance. I go through

Factiva and manually collect news on CEO and CFO change from the time of buyout

announcement until the deal reaches a final outcome (bankruptcy, IPO, or a sale to another

buyer). I supplement the Factiva results with the Key Development on “Corporate Structure

Related” news from Capital IQ. From the announcement date to the final deal outcome day, 212

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firms (42%) experienced a change in the CEO and 167 firms (33%) experienced CFO change. In

the regression, I use a CEO change dummy that takes the value of 1 if there is CEO change from

the buyout announcement to two full years after the buyout completion.48

This is because the

independent variable is the change in EBITDA/sale in year +2 compared with year -1. Columns

(3) and (4) show insignificant coefficients for CEO change.

When all measures of proposed performance drivers are included in regression analyses

shown by Columns (5) and (6) in Panel C, leverage change, covenant intensity, and management

participation are significant. Test for variance inflation factor does not show any

multicollinearity problem. In general, the baseline regression results of Panels A-C of Table 6

support the debt discipline, lenders’ monitoring, and management participation hypotheses of

value creation in LBOs, but do not support the hypotheses of private equity reputation, club

deals, or bank affiliations.

3.6 Robustness

3.6.1 Subsample Analyses

In this subsection, I examine whether the baseline regression results hold in each LBO

wave and when I divide the sample by management and PE firms’ participation in LBOs. I only

use the industry-adjusted changes in EBITDA/sales from year -1 to year +2 as dependent

variables, as regressions with NCF/sales show similar results.

Columns (1)-(3) of Table 7, Panel A present the regression results for each LBO wave.

Leverage change and covenant intensity are significant and positive across all columns,

48 I also use a dummy for CFO changes, or a dummy for both CEO and CFO changes. The results are the

same.

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suggesting that debt disciplining and lender’s monitoring are important performance drivers in

all LBO waves. Management participation is significantly related to performance in the first and

second wave, however, it becomes insignificant for LBOs in the third wave. PE firm’s reputation

is significantly and negatively related to performance in the third LBO wave at 10% significance

level. One possible explanation is that highly reputable PE firms may focus more on taking

advantage of the favorable credit market conditions in the early- and mid-2000s to generate high

return to their own investment and less on improving operating performance of target firms.

Column (4) of Panel A reports the regression results that exclude LBOs completed in 2006 and

2007, in order to disentangle the impact of the 2007-09 financial crisis on firm performance. The

previous results on leverage change, covenant intensity, and management participation still hold.

Table 7 Panel B divides the sample by management and PE firms’ involvement in LBOs.

Column (1) shows the regression results for all management-participated deals whereas LBOs in

Column (2) have no management involved. Leverage change and lender’s monitoring are still

important performance drivers, whether the monitoring is through a larger proportion of bank

debt or tighter covenants. When management participates in the deal, changing CEO or

management team has negative impact on performance (Column (1)).

Column (3) of Panel B shows LBOs sponsored by PE firms and Column (4) with no PE

(i.e. management-only buyout). For the subsample of PE-involved LBOs, regression results

support the debt disciplining and lender’s monitoring hypotheses. Moreover, the coefficient for

management-participation dummy is significantly positive, indicating that when PE firms

sponsor LBOs, it is important for incumbent management to participate as well. In column (4),

only leverage change is significant. This can be explained by the fact that in management-only

buyout, managers are the only owners of the firm and their incentives are perfectly aligned.

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Therefore, no outside lenders are needed to monitor and discipline managers besides leverage

itself.

3.6.2 Credit Market Conditions

Studies have shown that LBO buyers, whether they are PE firms, managers of target firms,

or other corporations, take advantage of favorable credit market conditions. Kaplan and Stein

(1993) present evidence that the 1980s’ LBO boom is driven by the attractive terms of high-yield

bonds. Shivdasani and Wang (2011) find that the LBO boom from 2004 to 2007 is fueled by

growth in securitization. Axelson et al. (2013) study 1,157 PE deals worldwide from 1980 to

2008 and show that the economy-wide cost of borrowing is the main driver of both the quantity

and the composition of debt in LBOs. These findings suggest that more LBOs will be undertaken

when the credit market is more favorable and leverage is cheaper to acquire and that LBO buyers

may overinvest in unprofitable deals during the time. As a result, LBOs completed during the

favorable credit market conditions may perform worse than other deals. In this section, I test

whether the key results from my hypotheses hold after controlling for credit market conditions.

Following Barry, Mann, Mihov and Rodriguez (2008), I add to the baseline regression the

Baa yield and the difference between the Baa yield in the month of LBO completion and its 60-

month historical average.49

I also include the term structure, calculated as the difference between

10-year T-Bond yield and three-month T-Bill yield. Column (1) of Table 8 presents the

regression results. Leverage change, covenant intensity, and management participation are still

significantly and positively related to post-buyout performance after controlling for market

49 Regressions with the month of LBO announcement or Dealscan’s deal active month generate similar results.

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timing. In addition, there is no evidence of worse performance for deals conducted at time of

favorable credit markets.

Another way to examine market conditions is to look at the hot versus cold LBO market.

Following Colla, Ippolito, and Wagner (2012), I construct a hot market dummy by taking a 12-

month centered moving average of the number of LBOs for each month over the sample period.

Hot months are defined as above the median in the distribution of the monthly moving average

across all months. The hot market dummy takes a value of 1 if a deal is completed in a hot

month, and zero otherwise.50

Column (2) shows that controlling for the LBO market condition,

leverage, covenants, and management participation are still important drivers for performance,

and that deals completed during hot LBO market do not generate worse performance than other

deals.

I also examine whether performance is related to the LBO debt spread. The spread at the

LBO deal level is calculated as the weighted average of all-in-drawn spread across all tranches,

weighted by tranche size. While the Baa yield, its difference from the historical average, term

structure, and the hot market dummy are related to the general credit market and LBO market

conditions, the loan spread measures the actual cost of debt for each LBO deal. If LBO buyers

overinvest in unprofitable deals when leverage is cheaper to acquire, I expect to find less

performance improvement when the LBO loan spread is lower. Column (3) shows that

controlling for loan spread, the effects of leverage, lenders’ monitoring, and management

participation in the LBO transaction are still significant. In the meanwhile, LBO loan spread is

not significantly related to performance.

50 Regressions with the month of LBO announcement generate similar results.

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I also look at buyout prices, calculated as the ratio of EBITDA (in year -1) over the total

transaction value, adjusted by subtracting the S&P 500 market earnings/price ratios for each

month of LBO completion.51

Column (4) shows that leverage change, covenant intensity, and

management participation are still significant and that deal prices are not significantly related to

performance.

To summarize, robustness analyses of this section show that leverage, covenants, and

management participation are still important drivers for post-buyout performance enhancement

after controlling for credit market conditions, LBO market conditions, loan spread, and buyout

prices. These results again support the debt discipline, lenders’ monitoring, and management

participation hypotheses while rejecting the private equity reputation hypothesis. In addition, I

find no evidence that LBOs constructed during favorable market conditions perform worse than

other deals, nor is performance related to prices paid for LBOs.

3.6.3 LBO Deal Outcome

In this section, I conduct additional tests on the effects of LBO deal characteristics on

performance, where performance is now measured by the ultimate outcomes of these deals. I

search Factiva for SEC filings and news to identify deal outcomes that include (1) bankruptcy or

distressed exchange, (2) a subsequent IPO, (3) a sale to a strategic buyer (4) a sale to a financial

buyer (also known as the secondary LBO), (5) still privately held by the same buyer, or (6)

unknown. I supplement Factiva information with the Capital IQ Tearsheet and the company

history from its website. Table 9 shows ultimate deal outcomes as of June 30, 2013 by LBO

effective year. Over the entire sample period, 83 deals (16.6%) file for bankruptcy, initiate a

51 The S&P earnings data are downloaded from the S&P Index Data Platforms.

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financial restructuring, or go through distressed exchange. 35.3% of the LBOs exit through an

IPO, 17% through a sale to a strategic buyer, and 11.8% through a sale to financial buyer.52

The

majority of the deals in the late 2000s are still privately held by the same buyers. For all deals

that have reached outcomes, the median months to exit is 42 months (mean 47 months, not

tabulated).53

Over time, LBOs completed under more favorable credit markets (for example, in

1986, 1987, 1996, and 2006) are more likely to undergo financial distress or bankruptcy. The

ultimate outcomes of club deals are similar to those of the whole sample, while bank-affiliated

deals are more likely to file bankruptcy or to be sold to a financial buyer.

Exit through an IPO or a sale to a financial or strategic buyer is generally considered as a

successful outcome of an LBO, as it is upon an IPO or a sale PE firms’ returns on LBOs are

realized. In order to test whether the key results from my hypotheses still hold when using deal

outcomes to measure LBO success, I run logit regressions. Specifically, for the dependent

variable, I create a success dummy that takes a value of 1 if an LBO exits through an IPO or a

sale to financial or strategic buyer and zero otherwise. As most of the recent deals have not

reached outcomes yet, I only consider LBOs that are completed by December 31st, 2008, taking

into account that the last day of information collection on deal outcome is June 30, 2013 and that

the median months to bankruptcy is 43 months (Table 9, Column (1)).

Column (1) of Table 10 presents results of the baseline regression using the success

dummy as the dependent variable. Consistent with the lenders’ monitoring hypothesis, LBOs are

more likely to reach successful outcomes if they are financed with higher proportion of bank

52 Stromberg (2008, Table 4B) shows that for a sample of 3,839 US and Canadian LBOs (including public to private

transactions, private targets, divisional buyouts) from the Capital IQ database from 1970 to 2002, 9% went

bankruptcy, 15% exited through an IPO, 38% through a sale to strategic buyer, and 26% were sold to financial

buyer. My sample is biased towards LBOs that exited through an IPO due to the sample selection process that

requires the target firms to have post-buyout financial information. 53

Kaplan and Stromberg (2009) find a median holding period of LBOs in their sample to be 6 years.

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debt and have tighter loan covenants. CEO changes during the time target firms are privately

held by PE firms have negative impact on the deal outcome. Leverage change and management

participation are not significant.

Column (1) also shows that LBOs sponsored by PEs with higher reputation score have

better outcomes, supporting the private equity reputation hypothesis. I use different reputation

measures that include PE’s market share based on the number of deals or the total deal values in

the prior 36 months or since 1970, the natural log of the number of deals in the prior 36 month or

since 1970, and natural log of PE’s years of experience. All measures generate significant and

positive estimates on PE reputation. This result provides a clear picture of the roles of PEs in an

LBO—reputation is not directly related to better operating performance in the first two years

after the buyout completion, but is important in ensuring successful outcomes of LBOs.

Regression results also show that bank-affiliated LBOs are less likely to exit through an

IPO or a sale, consistent with Wang (2012) and Fang et al. (2013). Wang (2012) uses accounting

measures and finds that bank-affiliated LBOs in the U.K. underperform standalone deals. She

argues that this is because bank-affiliated PE firms do not select good targets as other PEs do.

Fang et al. (2013) find bank-affiliated deals have worse outcomes if they are consummated

during the peaks of the credit market.

Controlling for credit market conditions and deal prices in Columns (2)-(5), the results of

bank debt proportion, CEO change, PE reputation, and bank affiliation still hold. Moreover,

Column (2) shows that the probability of a successful exit strategy is significantly and positively

related to the Baa spread relative to its historical average over the previous 60 months. This

suggests that LBOs are less likely to succeed if they are completed under favorable credit market

conditions. This result provides some evidence of market timing of LBO buyers that during

106

times of favorable credit market conditions, they tend to overinvest in unprofitable deals that

may not exit successfully, as suggested by Kaplan and Stein (1993) and Axelson et al. (2013).

To summarize, using the exit strategy of IPO or a sale as an indicator for LBO success,

regression analyses show that an LBO is more likely to succeed if it uses more bank debt and

tighter loan covenants and is sponsored by highly reputable PE firms. Bank-affiliated deals and

LBOs that experience CEO change are more likely to fail. These results are in general robust to

credit market conditions and deal prices. Findings of this section lend support to the lenders’

monitoring, private equity reputation, and bank affiliation hypotheses. I also find some evidence

of the market timing of LBO buyers.

3.7 Conclusion

Using a sample of 501 pubic-to-private U.S. LBO transactions completed between 1986

and 2011, I find that better post-buyout operating performance in target firms is related to larger

amount of leverage added during the LBO process, more restrictive covenants of LBO loans, and

management contributing equity and participating in the buyout. These results suggest that the

main source of value creation in LBOs is the reduced agency costs through the discipline effect

of debt, closer monitoring by lenders, and the better aligned management incentives. These

findings are robust after controlling for the credit market and LBO market conditions, target

firms’ costs of borrowing, and buyout prices. Findings of this paper deepens our understanding

on the declined operating performance in recent LBOs that use less leverage and less restrictive

loan covenant, which are important drivers for performance.

Using deal outcome as alternative measures of performance, I find that LBO are more

likely to exit through a successful strategy (an IPO or a sale to financial or strategic buyers) if

107

they use more bank debt and tighter covenants, experience no CEO change, and are sponsored by

PE firms with high reputation. These results are consistent with the lender’s monitoring and the

private equity reputation hypotheses in value creation in LBOs.

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Table 3-1: Comparing the 501 LBOs in the Final Sample with 1, 586 Original LBO Sample

This table compares the median and average transaction value between the 1,586 original LBOs collected from

Capital IQ and SDC and the 501 LBOs with post-buyout data available from Compustat, Capital IQ, and SEC

filings. The percentage in the last column shows the proportion of the deals that have post-LBO financials available

and therefore staying in my sample. Transaction values are inflation-adjusted in 2005 dollars.

Original 1,586 501 LBOs with post-LBO financials

Transaction Value ($Mil) Count Transaction Value ($Mil) Count

Year median mean median mean

1986 300.93 1081.54 52 1548.5 2343.1 12 23%

1987 274.47 902.65 42 990.2 2036.8 12 29%

1988 299.23 588.25 91 430.5 799.0 36 40%

1989 395.95 2231.09 64 858.3 3000.3 27 42%

1990 152.59 449.20 26 226.6 582.6 8 31%

1991 87.23 288.60 16 222.2 222.2 1 6%

1992 157.48 245.43 10 414.5 414.5 2 20%

1993 71.79 202.97 19 154.5 223.8 4 21%

1994 219.53 287.83 18 376.3 299.3 3 17%

1995 78.87 637.44 20 821.2 1485.2 6 30%

1996 178.41 539.66 30 237.8 663.0 15 50%

1997 226.42 350.15 71 386.3 520.3 36 51%

1998 316.93 656.99 77 421.5 741.9 41 53%

1999 250.14 444.11 90 485.5 590.6 30 33%

2000 222.30 482.82 88 485.6 956.5 27 31%

2001 186.08 396.50 52 408.5 493.1 11 21%

2002 139.56 273.42 62 185.4 320.0 15 24%

2003 187.82 499.80 92 727.2 1164.0 28 30%

2004 356.64 661.23 97 697.0 994.3 39 40%

2005 431.81 1541.36 92 738.4 2112.3 31 34%

2006 444.98 1544.31 106 1278.4 3555.8 23 22%

2007 825.79 3144.88 144 1956.7 5353.0 51 35%

2008 232.53 2098.80 50 1863.4 7258.3 8 16%

2009 116.37 437.14 44 531.7 2227.2 3 7%

2010 312.79 641.31 73 822.2 1190.4 19 26%

2011 502.64 1121.56 60 2223.0 2231.0 13 22%

1986-2011 287.38 1034.62 1586 620.97 1795.64 501 32%

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Table 3-2: LBO Year and Industry

The table classifies transactions by LBO effective year and target firm industry. Eight broad industry classifications

are defined according to SIC codes: (1) Agriculture/Fishing/Forestry (SIC 0-999), (2) Mining (SIC 1000-1499), (3)

Construction (SIC 1500-1799), (4) Manufacturing (SIC 2000-3999), (5) Transportation/Communication/Electric/Gas

(SIC 4000-4999), (6) Wholesale/Retail (SIC 5000-5999), (7) Finance/Insurance/Real Estate (SIC 6000-6799), and

(8) Services (SIC 7000-8999). The percentage in the brackets of Column (9) shows the number of deals in each year

as a proportion of total number of deals. The percentage in the brackets of the last row shows the number of deals in

each industry as a proportion of total number of deals.

Year

(1)

Agr

(2)

Mining

(3)

Constr

(4)

Mftr

(5)

Trans

(6)

Wholesale

/Retail

(7)

Fin

(8)

Services

(9)

Total (percentage)

1986

6

5

1 12 (2.4%)

1987

5 1 5

1 12 (2.4%)

1988

1 18 3 8 1 5 36 (7.2%)

1989

14 3 7

3 27 (5.4%)

1990

3

2

3 8 (1.6%)

1991

1

1 (0.2%)

1992

2

2 (0.4%)

1993

2

1

1 4 (0.8%)

1994

1

1

1 3 (0.6%)

1995

3

2

1 6 (1.2%)

1996

12 1 1

1 15 (3.0%)

1997

1

21 1 4

9 36 (7.2%)

1998

1

19 6 6

9 41 (8.2%)

1999

15 2 1 2 10 30 (6.0%)

2000 1

13 3 7

3 27 (5.4%)

2001 1

5

3 1 1 11 (2.2%)

2002 1

6 1 4

3 15 (3.0%)

2003 1 1 1 17 2 1 1 4 28 (5.6%)

2004

4

16 3 7 2 6 39 (7.8%)

2005

11 4 8

8 31 (6.2%)

2006

1 6

5 2 7 23 (4.6%)

2007 1 1 1 12 6 11 6 12 51 (10.2%)

2008

2 2

4 8 (1.6%)

2009

1

1 1

3 (0.6%)

2010 1 1

7 1 5 1 3 19 (3.8%)

2011

6 1 2

4 13 (2.6%)

Total 6 10 4 223 41 97 16 100 501

(1.2%) (2.0%) (0.8%) (44.5%) (8.2%) (19.4%) (3.2%) (20.0%) 100%

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Table 3-3: Median Changes in Operating Performance

This table presents median changes in operating performance. All variables are defined in Appendix B.

Year -1 is the last fiscal year prior to the buyout completion. Years +1, +2, and +3 are the first, second,

and third full fiscal year following the buyout completion. Industry-adjusted changes subtract the medians

for firms in the same industry based on four-digit SIC code. Significance levels of medians are based on a

two-tailed Wilcoxon rank test. ***, **, and * next to the percentage change denote levels that are

significantly different from zero at 1%, 5%, and 10% level, respectively. Significance levels of time trend

are based on the nonparametric trend test, and ***, **, and * next to the time trend bracket denote the

nonparametric trend test statistics is significant at 1%, 5%, and 10% level, respectively.

Panel A: Median Changes in Operating Performance between 1986 and 2011

-2 to -1 -1 to +1 -1 to +2 -1 to +3 -2 to -1 -1 to +1 -1 to +2 -1 to +3

Unadjusted Changes Industry-adjusted Changes

NO. Obs 501 483 419 370 501 483 419 370

EBITDA/sales 1.0%*** 0.8% -0.1% -0.8% 3.7%*** 7.0%** 6.9%** 8.9%**

NCF/sales 0.1% 0.5% -3.8% -2.6% 9.6%*** 18.5%** 13.9%** 19.6%***

EBITDA/assets 9.6%*** -26.3%*** -26.6%*** -24.1%*** 13.0%*** -12.2%** -9.8%** -6.1%

NCF/assets 7.2%*** -29.7%*** -31.0%*** -26.5%*** 12.7%*** -5.7%* 5.3% 3.4%

Panel B: Median Changes in Operating Performance in Sub-Periods

-2 to -1 -1 to +1 -1 to +2 -1 to +3 -2 to -1 -1 to +1 -1 to +2 -1 to +3

Unadjusted Changes Industry-adjusted Changes

NO. Obs

1986-1993 102 94 79 77 102 94 79 77

1994-2001 169 165 151 139 169 165 151 139

2002-2011 230 224 189 154 230 224 189 154

EBITDA/sales

1986-1993 0.5% 7.6%*** 4.7% 2.5% 3.3% 10.6%** 8.1%* 8.4%*

1994-2001 1.5%** -0.3% -2.8% -3.6%* 2.7%** 7.7%** 6.1%* 10.7%**

2002-2011 1.0%** 0.0% 0.1% -0.7% 5.1%*** 4.8% 6.6% 8.5%

time trend (-) (-)** (+) (-) (+) (-)* (-)* (-)

NCF/sales

1986-1993 -12.2% 15.3%** 13.4%** 11.4%** 5.7% 32.7%*** 28.2%** 31.5%**

1994-2001 -0.9% -2.3% -7.0% -3.0% 6.0%** 18.5%** 13.7%** 29.8%***

2002-2011 1.8% -2.8% -4.0% -7.4% 12.9%*** 13.3%** 8.4% 5.7%

time trend (+)** (-)** (-) (-) (+) (-)*** (-)*** (-)**

111

-2 to -1 -1 to +1 -1 to +2 -1 to +3 -2 to -1 -1 to +1 -1 to +2 -1 to +3

Unadjusted Changes Industry-adjusted Changes

EBITDA/assets

1986-1993 4.8%* -18.2%*** -22.4%*** -14.2%*** 7.6%*** -8.9%* 0.0% 0.0%

1994-2001 13.0%*** -17.7%*** -17.8%*** -21.2%*** 18.6%*** 0.9% -4.7% -0.2%

2002-2011 9.6%*** -34.0%*** -34.4%*** -32.1%*** 13.2%*** -27.5%*** -22.5%*** -11.5%**

time trend (+) (-)*** (-)*** (-)* (+) (-)*** (-)*** (-)**

NCF/assets

1986-1993 -9.6% -12.7% -16.3%* -11.4% 7.1% 10.1% 14.9% 13.4%

1994-2001 10.7%** -20.7%** -20.2%* -16.1%* 11.7%*** 7.7%** 20.5%*** 19.2%***

2002-2011 10.6%*** -40.3%*** -40.7%*** -44.9%*** 15.3%*** -24.2%*** -6.7% -8.4%

time trend (+)** (-)*** (-)* (-)** (+) (-)*** (-)** (-)***

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Table 3-4: Leverage, Debt Structure, and Debt Contractual Features

Panel A: Leverage

This table presents the annual medians of leverage. All variables are defined in Appendix B. ***, **, and

* denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level,

respectively.

Year

(1)

Pre-buyout

leverage

(%)

(2)

Post-buyout

leverage

(%)

(3)

Leverage

change

(%)

(4)

Compustat

Leverage

change (%)

1986 17.07 70.37 48.70 -0.05

1987 25.74 75.88 51.37 -0.23

1988 32.03 73.57 37.20 0.20

1989 29.19 63.80 26.34 0.28

1990 23.64 44.33 14.45 0.03

1991 27.72 34.35 6.63 -0.56

1992 28.34 36.93 8.59 -0.73

1993 19.89 34.88 14.75 -0.95

1994 31.90 75.77 39.15 -0.18

1995 37.53 84.84 27.39 -0.22

1996 14.00 58.84 44.84 -0.43

1997 33.66 84.03 52.97 0.09

1998 33.90 83.18 63.95 0.88

1999 21.49 82.24 64.86 0.30

2000 35.77 64.43 28.81 0.22

2001 54.27 70.90 14.26 0.17

2002 37.51 57.63 14.06 0.17

2003 52.40 53.43 11.41 -0.61

2004 34.32 59.64 30.55 -0.68

2005 29.03 61.33 34.09 -0.37

2006 30.54 55.71 25.50 -0.28

2007 26.74 60.83 34.74 0.15

2008 41.81 48.73 22.57 1.08

2009 33.39 26.66 2.50 -1.26

2010 41.83 54.59 15.40 -0.77

2011 30.10 53.59 24.92 -0.21

1986-2011 31.92 64.35 30.55 -0.15

Time Trend (+) (-)*** (-)** (-)

113

Panel B: LBO Debt Structure

This table presents the structure of LBO debt. All variables are defined in Appendix B.. ***, **, and *

denote that the nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level,

respectively.

Year

(1)

Revolvers (%)

(2)

Term Loan A (%)

(3)

Bank Debt=(1)+(2)(%)

(4)

Institutional Debt (%)

(5)

Junior Debt (%)

Revolver

to LBO

debt

%with

Revolver

Term A

to LBO

debt

%with

Term A

Bank debt

to LBO

debt

%with

Bank debt

Inst. debt to

LBO debt

%with

Inst. debt

Junior debt

to LBO

debt

%with

Junior debt

1986 60.8 52.2 36.2 34.8 97.0 87.0

1.3 4.3

1987 17.0 36.8 38.8 31.6 55.8 68.4

38.3 23.7

1988 18.6 36.8 20.4 28.5 38.9 65.3

60.5 31.3

1989 10.3 31.9 41.5 30.2 51.8 62.1 1.2 4.3 46.2 27.6

1990 32.1 36.0 19.5 24.0 51.6 60.0

46.4 32.0

1991 39.4 40.0 45.5 40.0 84.8 80.0

15.2 20.0

1992 53.5 50.0 36.6 25.0 90.1 75.0 9.9 25.0

1993 23.1 42.9 76.9 57.1 100.0 100.0

1994 52.2 41.2 36.1 23.5 88.4 64.7 8.3 29.4 3.3 5.9

1995 18.9 27.3 22.5 22.7 41.4 50.0 41.7 40.9

1996 67.6 38.0 15.0 30.0 82.6 68.0 16.5 30.0 0.9 2.0

1997 38.2 38.9 13.3 17.8 51.5 56.7 41.6 35.6 6.4 5.6

1998 26.6 30.6 16.1 21.7 42.7 52.2 48.4 40.8 8.8 5.7

1999 22.9 30.3 21.2 27.0 44.1 57.4 48.3 38.5 7.5 3.3

2000 23.9 37.3 23.6 29.3 47.5 66.7 52.5 32.0

2001 20.7 32.4 29.5 35.3 50.2 67.6 49.8 32.4

2002 21.4 40.6 6.4 25.0 27.8 65.6 37.2 18.8 35.0 16.3

2003 23.4 37.5 12.2 16.7 35.6 54.2 44.3 36.1 20.1 18.3

2004 31.4 44.7 7.5 11.7 39.0 56.4 52.6 36.2 8.5 13.2

2005 20.3 41.9 6.7 13.5 27.0 55.4 37.8 33.8 35.1 15.4

2006 20.4 43.6 13.5 15.4 33.9 59.0 41.2 25.6 24.9 25.4

2007 12.7 33.0 9.7 17.0 22.4 50.0 64.3 35.1 13.3 19.8

2008 12.5 34.1 11.5 20.5 24.0 54.5 52.3 27.3 23.7 28.2

2009 38.6 50.0 61.4 50.0 100.0 100.0

2010 18.1 44.7 2.0 6.4 20.2 51.1 60.5 34.0 19.3 24.9

2011 13.7 34.3 1.2 5.7 14.9 40.0 58.4 37.1 26.8 22.9

86-11 28.4 38.7 24.0 25.4 52.4 64.1 40.3 31.2 20.5 13.3

Time

Trend (-)* (-) (-)** (-)** (-)*** (-)* (+)*** (+) (-) (-)

114

Panel C: Spread and Maturity of Bank Debt versus Institutional Debt

This table compares the median spread and maturity for bank debt versus institutional debt. All variables

are defined in Appendix B. ***, **, and * denote that the nonparametric trend test statistics is statistically

significant at 1%, 5%, and 10% level, respectively.

Year

(1)

Bank debt

Spread

(2)

Inst. debt

Spread

(3)

Spread Diff

Inst. vs Bank

(4)

Bank debt

Maturity

(months)

(5)

Inst. debt

Maturity

(months)

(6)

Maturity Diff Inst.

vs Bank(months)

1986 237.5 46

1987 255.7 73

1988 278.4 69

1989 268.1 450.0 81 98

1990 335.0 75

1991 300.0 56

1992 275.0 36 120

1993 318.8 60

1994 225.0 362.5 87.5 61 101 32

1995 255.0 334.4 75.0 66 104 31

1996 243.7 320.8 66.7 61 98 19

1997 246.5 275.0 51.8 74 99 16

1998 234.7 276.5 42.1 75 100 16

1999 286.7 353.5 62.9 70 93 18

2000 300.8 353.3 65.5 63 88 21

2001 373.8 356.8 17.5 72 74 12

2002 356.9 333.3 37.5 60 83 18

2003 331.1 373.2 50.0 54 76 7

2004 463.6 272.8 -25.0 74 82 -1

2005 402.5 271.0 -50.0 74 80 1

2006 262.3 251.3 0.0 67 81 4

2007 366.3 307.6 6.3 74 81 3

2008 302.2 390.0 15.0 76 82 5

2009 462.5 48

2010 347.9 441.1 25.0 62 76 9

2011 450.9 473.1 -37.5 75 79 9

1986

-2011 314.6 344.2 28.8 65 88 13

Time

Trend (+)*** (+) (-)** (+) (-)*** (-)**

115

Panel D: Covenant Intensity

This table shows covenants of the LBO sample versus those of the Dealscan population. Columns (1) and

(4) show the median and mean value for LBO loans with non-missing covenants. Columns (2) and (5)

show the proportion of LBO loan package with non-missing covenants. Columns (3) and (6) show the

percentage of covenant-lite loans. All variables are defined in Appendix B. ***, **, and * denote that the

nonparametric trend test statistics is statistically significant at 1%, 5%, and 10% level, respectively.

My sample Dealscan population

year

(1)

Covenant

Intensity

(2)

% Non-missing

Cov

(3)

% Cov-

lite

(4)

Covenant

Intensity

(5)

% Non-missing

Cov

(6)

% Cov-

lite

Median Mean Median Mean

1995 6 6.8 67% 83% 2 2.8 30% 86%

1996 6 6.1 78% 43% 3 3.2 37% 77%

1997 8 7.1 57% 53% 3 3.2 33% 80%

1998 9 8.0 65% 45% 3 3.8 33% 78%

1999 10 8.3 69% 47% 3 3.9 30% 81%

2000 10 9.5 68% 36% 2 3.5 27% 84%

2001 10 8.2 83% 33% 2 3.3 27% 84%

2002 10 8.4 88% 24% 3 3.4 29% 82%

2003 6 6.2 74% 46% 3 3.7 26% 84%

2004 8 7.1 89% 29% 2 3.4 29% 84%

2005 9 7.1 88% 27% 2 3.1 28% 85%

2006 6 5.5 94% 38% 1 2.8 28% 88%

2007 6 5.0 92% 41% 1 2.4 32% 89%

2008 3.5 4.6 77% 38% 1 2.3 28% 89%

2009 0 0.0 0% 100% 1 2.3 29% 89%

2010 1 2.8 86% 67% 1 2.1 31% 89%

2011 2 3.0 86% 57% 1 2.0 30% 89%

1995-

2011 7 6.5 75% 50% 2 2.9 30% 85%

Time

Trend (-)** (+) (+)** (-)*** (+) (+)**

116

Table 3-5: Private Equity Involvement and Reputation

Panel A: Private Equity Involvement

This table presents the involvement of private equity (PE) firms in LBO transactions over time. All

variables are defined in Appendix B. ***, **, and * denote that the nonparametric trend test statistics is

statistically significant at 1%, 5%, and 10% level, respectively.

Year

(1)

# LBOs

with PE

(2)

# LBOs with

Prominent PE

(3)

# LBOs with

Bank-Affiliated

PE

(4)

Number of

Club Deals

(5)

Total Transaction

Value ($million) of

Club Deals

(6)

Max # of

PEs in

the club

1986 10 (83%) 8 (67%) 6 (50%)

1987 9 (75%) 4 (33%) 3 (25%)

1988 21 (58%) 8 (22%) 7 (19%) 1 (3%) 233 (1%) 2

1989 20 (74%) 10 (37%) 10 (37%) 4 (15%) 11,126 (14%) 3

1990 5 (63%) 3 (38%) 1 (13%)

1991 1 (100%)

1992 2 (100%) 1 (50%) 1 (50%) 1 (50%) 723 (87%) 3

1993 1 (25%)

1994 3 (100%) 2 (67%) 1 (33%) 1 (33%) 450 (50%) 2

1995 6 (100%) 5 (83%) 1 (17%)

1996 15 (100%) 6 (40%) 3 (20%) 4 (27%) 3,880 (39%) 3

1997 36 (100%) 16 (44%) 5 (14%) 10 (28%) 5,601 (30%) 3

1998 40 (98%) 21 (51%) 12 (29%) 10 (24%) 7,425 (24%) 4

1999 30 (100%) 16 (53%) 7 (23%) 10 (33%) 5,978 (34%) 3

2000 25 (93%) 9 (33%) 7 (26%) 12 (44%) 8,069 (31%) 4

2001 8 (73%) 3 (27%) 2 (18%) 5 (45%) 2,950 (54%) 3

2002 13 (87%) 6 (40%) 2 (13%) 6 (40%) 2,487 (52%) 4

2003 24 (86%) 18 (64%) 4 (14%) 6 (21%) 5,452 (17%) 3

2004 36 (92%) 24 (62%) 4 (10%) 16 (41%) 17,011 (44%) 4

2005 31 (100%) 20 (65%) 5 (16%) 14 (45%) 47,465 (72%) 7

2006 21 (91%) 15 (65%) 4 (17%) 12 (52%) 72,038 (88%) 4

2007 48 (94%) 35 (69%) 15 (29%) 22 (43%) 179,252 (66%) 5

2008 8 (100%) 7 (88%) 1 (13%) 3 (38%) 49,625 (85%) 3

2009 3 (100%) 1 (33%) 1 (33%) 1 (33%) 6,107 (91%) 4

2010 19 (100%) 12 (63%) 1 (5%) 5 (26%) 5,135 (23%) 3

2011 13 (100%) 11 (85%) 0 (0%) 6 (46%) 18,554 (64%) 3

86-11 448 (89%) 261 (52%) 103 (21%) 149 (30%) 449,562 (50%)

Time

trend (+)*** (+)*** (-) (+)*** (+)*** (+)**

117

Panel B: Relation between PE Reputation, Participation, and LBO Deal Characteristics

This table presents the relation between PE firms’ reputation, their different forms of participation in LBOs, including club deals and bank-

affiliated deals, and LBO deal characteristics and financing structure. I run univariate OLS regressions using various measures of LBO deal and

financing characteristics as dependent variable and reputation and participation as explanatory variable with year dummies. I only report the

coefficient estimates and their significant levels. All variables are defined in Appendix B. All regressions are OLS with heteroskedasticity adjusted

standard errors. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (7) (6) (8) (9) (10)

Size Pre-buyout

leverage

Leverage

change

Revolvers to

LBO Debt

Term A to

LBO Debt

Bank

debt%

Inst.

debt%

Covenant

Intensity

Spread Maturity

PE Reputation 14.931*** -0.824** 1.639*** -0.841** -0.456 -1.297*** 0.800** 1.824 -4.599*** 6.591**

PE Reputation (N) 22.658** -4.366** 5.502* -4.293* 0.029 -4.261* 3.245 0.839 -1.380* 1.557

PE Reputation (N, 70) 29.203*** -3.054* 5.278** -4.137** -0.982 -5.119** 4.033** 11.968 -1.563** 2.699**

Ln(PE Experience (year) 0.119** -0.002 0.028 -0.019 -0.027*** -0.047*** 0.039*** 0.287*** -1.597*** 3.827***

Bank affiliation 0.668*** 0.039 -0.021 -0.069** 0.026 -0.042 0.042 0.249 -1.554 3.424

Club dummy 0.386*** -0.024 0.011 -0.001 -0.024 -0.025 0.005 0.191 -7.527 0.058

# PE in club 0.266*** -0.013 0.002 -0.013 -0.006 -0.019 0.016 0.233** -9.096** 1.200

118

Table 3-6: Regression for Post-buyout Performance: Baseline Regression

This table reports the multivariate regression results for post-buyout performance. Panel A tests the debt discipline

and lenders’ monitoring hypotheses. Panel B examines the impact of PE firms’ reputation and their different form

of participation. Panel C first tests the management participation hypothesis and then include all potential

performance drivers. Dependent variables are the industry-adjusted percentage changes in EBITDA/sales or

NCF/sales from the last full pre-buyout year (year -1) to the second full year after deal completion (year +2). All

variables are defined in Appendix B. P-values are in brackets. All regressions are OLS with heteroskedasticity

adjusted standard errors. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

Panel A: Debt Disciplining and Lenders’ Monitoring Hypotheses

VARIABLES

(1)

EBITDA/sales

(2)

NCF/sales

(3)

EBITDA/sales

(4)

NCF/sales

Size 0.002 0.178* 0.018 0.197*

[0.960] [0.099] [0.629] [0.078]

Leverage change 0.348** 0.780** 0.265** 0.515**

[0.013] [0.012] [0.033] [0.011]

Bank debt % 0.058 0.029

[0.666] [0.952]

Covenant intensity 0.025** 0.081**

[0.019] [0.046]

Maturity 0.001 0.005

[0.468] [0.540]

Pre-LBO Target Characteristics:

EBITDA/sale growth 0.187* 0.179

[0.093] [0.105]

NCF/sale growth 0.033 0.037

[0.662] [0.612]

Leverage 0.228 0.543 0.146 0.228

[0.208] [0.388] [0.427] [0.733]

Asset tangibility -0.075 0.621 -0.086 0.662

[0.674] [0.492] [0.639] [0.471]

Cash flow volatility -4.170*** 2.017 -4.146*** 3.131

[0.093] [0.488] [0.094] [0.313]

Constant 5.506 6.044 4.381 4.153

[0.595] [0.893] [0.273] [0.332]

Year dummies Yes Yes Yes Yes

Observations 419 419 419 419

Adj.R-squared 0.096 0.100 0.100 0.099

119

Panel B: Private Equity Reputation, Club Deal, and Bank-affiliation Hypotheses

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES EBITDA

/Sales

NCF

/Sales

EBITDA

/Sales

NCF

/Sales

EBITDA

/Sales

NCF

/Sales

EBITDA

/Sales

NCF

/Sales

Size 0.010 0.163 0.012 0.222** 0.017 0.205* 0.019 0.213**

[0.801] [0.165] [0.742] [0.048] [0.634] [0.067] [0.608] [0.047]

Leverage change 0.351** 0.780** 0.279** 0.535* 0.281** 0.556* 0.279** 0.555*

[0.013] [0.012] [0.042] [0.089] [0.041] [0.078] [0.043] [0.078]

Bank debt % 0.048 -0.023 0.047 -0.035 0.044 -0.042

[0.732] [0.961] [0.736] [0.941] [0.752] [0.929]

Covenant intensity 0.028*** 0.099** 0.028*** 0.096** 0.028*** 0.095**

[0.003] [0.035] [0.002] [0.050] [0.002] [0.039]

PE Reputation -0.372 -11.561

[0.543] [0.627]

Club dummy 0.037 -0.349

[0.657] [0.254]

Optimal club dummy -0.037 -0.253

[0.636] [0.455]

Bank affiliation -0.025 -0.123

[0.721] [0.712]

Pre-LBO Target Characteristics:

EBITDA/sale growth 0.186* 0.178 0.179 0.181

[0.094] [0.108] [0.106] [0.107]

NCF/sale growth 0.031 0.042 0.039 0.039

[0.690] [0.568] [0.598] [0.588]

Leverage 0.224 0.552 0.154 0.171 0.146 0.213 0.147 0.226

[0.214] [0.380] [0.402] [0.799] [0.427] [0.750] [0.422] [0.736]

Asset tangibility -0.068 0.610 -0.096 0.708 -0.087 0.689 -0.085 0.676

[0.708] [0.501] [0.600] [0.439] [0.632] [0.450] [0.644] [0.462]

Cash flow volatility -4.179*** 2.044 -4.247*** 3.520 -4.158*** 3.345 -4.136*** 3.259

[0.003] [0.485] [0.003] [0.252] [0.004] [0.279] [0.004] [0.292]

Constant 5.927 7.501 8.412 9.037 4.318 6.026 3.892 9.497

[0.570] [0.774] [0.269] [0.435] [0.328] [0.391] [0.262] [0.313]

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes

Observations 419 419 419 419 419 419 419 419

Adj.R-squared 0.096 0.081 0.111 0.096 0.109 0.094 0.109 0.094

120

Panel C: Management Participation Hypothesis and All Variables

(1) (2) (3) (4) (5) (6)

VARIABLES EBITDA

/Sales

NCF

/Sales

EBITDA

/Sales

NCF

/Sales

EBITDA

/Sales

NCF

/Sales

Size 0.023 0.198* 0.029 0.187* 0.035 0.200*

[0.526] [0.077] [0.456] [0.099] [0.427] [0.085]

Leverage change 0.297** 0.563* 0.278** 0.552* 0.293** 0.527*

[0.0258] [0.0772] [0.0403] [0.0810] [0.0279] [0.0904]

Bank debt % 0.056 -0.034 0.025 -0.023 0.035 -0.002

[0.683] [0.943] [0.854] [0.961] [0.797] [0.996]

Covenant intensity 0.025*** 0.093* 0.028*** 0.094* 0.025** 0.098*

[0.009] [0.061] [0.002] [0.078] [0.010] [0.064]

PE Reputation 0.124 -0.340

[0.843] [0.247]

Club dummy 0.044 0.096

[0.607] [0.451]

Bank affiliation -0.052 -0.028

[0.485] [0.933]

Mgmt participation 0.190** 0.064 0.172** 0.119

[0.024] [0.839] [0.046] [0.726]

CEO change -0.156* 0.129 -0.102 0.138

[0.083] [0.667] [0.222] [0.663]

Pre-LBO Target Characteristics:

EBITDA/sale growth 0.180 0.178 0.185*

[0.103] [0.104] [0.092]

NCF/sale growth 0.036 0.034 0.039

[0.618] [0.633] [0.607]

Leverage 0.160 0.230 0.131 0.234 0.162 0.206

[0.375] [0.732] [0.467] [0.727] [0.363] [0.758]

Asset tangibility -0.096 0.658 -0.106 0.680 -0.111 0.701

[0.598] [0.477] [0.565] [0.456] [0.543] [0.446]

Cash flow volatility -4.147*** 3.135 -4.185*** 3.175 -4.279*** 3.496

[0.004] [0.313] [0.005] [0.315] [0.004] [0.245]

Constant 6.695 9.471 9.307 13.267 7.350 8.962

[0.498] [0.333] [0.401] [0.302] [0.521] [0.456]

Year dummies Yes Yes Yes Yes Yes Yes

Observations 419 419 419 419 419 419

Adj.R-squared 0.123 0.093 0.118 0.094 0.130 0.097

121

Table 3-7: Regression for Post-buyout Performance: Subsample Analyses

This table reports the multivariate regression results for post-buyout performance by each LBO wave. Panel A

shows regression results by each LBO wave and the sample period that excludes deals completed in 2006 and

2007. Panel B presents results by management and PE firms’ involvement. All dependent variables are the

industry-adjusted percentage changes in EBITDA/sales from the last full pre-buyout year (year -1) to the second

full year after deal completion (year +2). All variables are defined in Appendix B. P-values are in brackets. All

regressions are OLS with heteroskedasticity adjusted standard errors. ***, **, and * indicate significance at the

1%, 5%, and 10% level, respectively.

Panel A: By LBO Waves

(1) (2) (3) (4)

VARIABLES Wave 1: 86-93 Wave 2: 94-01 Wave 3: 02-11 Exclude 06&07

Size 0.001 0.151** -0.049 0.041

[0.987] [0.050] [0.460] [0.427]

Leverage change 0.187* 0.288* 0.580*** 0.272*

[0.527] [0.065] [0.007] [0.067]

Bank debt % 0.103 0.430* -0.078 0.025

[0.707] [0.094] [0.633] [0.863]

Covenant intensity 0.059** 0.025** 0.032**

[0.022] [0.012] [0.023]

Maturity -0.006 0.008* 0.002 0.004

[0.189] [0.066] [0.636] [0.150]

PE reputation 0.905 0.905 -3.431* 0.641

[0.480] [0.480] [0.060] [0.443]

Club dummy 0.463 0.047 -0.128 0.102

[0.256] [0.587] [0.275] [0.340]

Bank affiliation -0.213 -0.267* 0.044 -0.085

[0.271] [0.084] [0.714] [0.322]

Mgmt participation 0.169** 0.497** 0.129 0.198**

[0.047] [0.015] [0.339] [0.037]

CEO change -0.346 -0.119 -0.069 -0.072

[0.223] [0.397] [0.544] [0.400]

Pre-LBO Target Characteristics:

EBITDA/sale growth 0.587 0.035 0.235 0.226*

[0.268] [0.799] [0.127] [0.061]

Leverage 0.234 0.184 -0.074 0.201

[0.671] [0.617] [0.762] [0.296]

Asset tangibility -0.169 -0.376 0.188 -0.011

[0.719] [0.403] [0.444] [0.959]

Cash flow volatility -5.864 -1.182 -4.515*** -4.731***

[0.556] [0.458] [0.007] [0.002]

Constant 6.812 9.247 -5.894 -7.649

[0.425] [0.284] [0.473] [0.432]

Year dummies Yes Yes Yes Yes

Observations 81 142 196 366

Adj.R-squared 0.213 0.210 0.193 0.164

122

Panel B: By Participants

(1)

EBITDA/sales

(2)

EBITDA/sales

(3)

EBITDA/sales

(4)

EBITDA/sales

VARIABLES Mgmt-participated No Mgmt PE-participated No PE

Size 0.041 0.041 0.021 0.211

[0.610] [0.379] [0.610] [0.202]

Leverage change 0.452* 0.180** 0.292** 0.261*

[0.054] [0.017] [0.037] [0.090]

Bank debt % 0.547** -0.152 0.054 0.359

[0.034] [0.307] [0.690] [0.586]

Covenant intensity 0.023 0.028** 0.020* 0.051

[0.319] [0.042] [0.095] [0.576]

Maturity -0.001 0.002 0.001 0.002

[0.712] [0.392] [0.547] [0.793]

PE reputation 2.545 -1.243 -0.418

[0.428] [0.148] [0.637]

Club dummy -0.022 0.028 0.046

[0.906] [0.766] [0.617]

Bank affiliation -0.057 -0.129 -0.091

[0.730] [0.170] [0.268]

Mgmt participation 0.192** 0.215

[0.023] [0.617]

CEO change -0.385** 0.010 -0.055 -0.668

[0.033] [0.930] [0.518] [0.213]

Pre-LBO Target Characteristics:

EBITDA/sale growth 0.191 0.157 0.178* 0.394

[0.198] [0.280] [0.089] [0.721]

Leverage -0.063 0.245 0.161 -0.395

[0.806] [0.269] [0.380] [0.611]

Asset tangibility -0.607* 0.116 -0.161 0.347

[0.088] [0.594] [0.388] [0.663]

Cash flow volatility -4.485** -4.135** -4.614*** 4.543

[0.035] [0.035] [0.001] [0.224]

Constant -8.791 4.952 7.678 -3.637

[0.355] [0.153] [0.211] [0.945]

Year dummies Yes Yes Yes Yes

Observations 132 235 328 39

Adj.R-squared 0.268 0.134 0.159 0.183

123

Table 3-8: Market Timing

This table reports the multivariate regression results for post-buyout performance. Dependent variables are the

industry-adjusted percentage changes in EBITDA/sales from the last full pre-buyout year (year -1) to the second

full year after deal completion (year +2). All variables are defined in Appendix B. P-values are in brackets. All

regressions are OLS with heteroskedasticity adjusted standard errors. ***, **, and * indicate significance at the

1%, 5%, and 10% level, respectively.

VARIABLES

(1)

EBITDA/sales

(2)

EBITDA/sales

(3)

EBITDA/sales

(4)

EBITDA/sales

Size 0.030 0.025 0.025 0.046

[0.443] [0.527] [0.530] [0.323]

Leverage change 0.316** 0.290** 0.314** 0.301**

[0.029] [0.031] [0.021] [0.025]

Bank debt % 0.076 0.062 0.079 0.031

[0.574] [0.660] [0.564] [0.832]

Covenant intensity 0.030** 0.025** 0.026*** 0.024**

[0.020] [0.011] [0.008] [0.012]

PE reputation 0.058 0.116 0.110 0.032

[0.925] [0.859] [0.862] [0.959]

Mgmt participation 0.188** 0.190** 0.181** 0.196**

[0.031] [0.026] [0.035] [0.023]

Baa 0.092

[0.373]

Baa-HBaa -0.040

[0.600]

Term -0.010

[0.801]

Hot 0.063

[0.524]

Spread 0.483

[0.140]

Deal Pricing 0.204

[0.205]

Pre-LBO Target Characteristics:

EBITDA/sale growth 0.182* 0.179 0.184* 0.179*

[0.095] [0.101] [0.093] [0.095]

Leverage 0.160 0.166 0.176 0.172

[0.392] [0.349] [0.325] [0.340]

Asset tangibility -0.104 -0.084 -0.078 -0.125

[0.572] [0.648] [0.672] [0.480]

Cash flow volatility -4.202*** -4.258*** -4.124*** -4.244***

[0.004] [0.003] [0.004] [0.002]

Constant -5.531 9.037 9.812 7.478

[0.419] [0.475] [0.453] [0.558]

Year dummies No No Yes Yes

Observations 419 419 419 419

Adj.R-squared 0.123 0.124 0.126 0.130

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Table 3-9: LBO Year and Exit Strategy

This table presents the post-buyout outcomes as of June 30, 2013. The number of observations for each exit

strategy is reported, followed in parentheses by the proportion of the outcome in all LBOs each year.

Year

(1)

Bankruptcy

(2)

IPO

(3)

Acquired

by Corp

(4)

Secondary

LBOs

(5)

still private

(6)

Unknown

1986 5 (41.7%) 3 (25.0%) 2 (16.7%) 2 (16.7%) 0 (0.0%) 0 (0.0%)

1987 6 (50.0%) 4 (33.3%) 1 (8.3%) 1 (8.3%) 0 (0.0%) 0 (0.0%)

1988 8 (22.2%) 19 (52.8%) 6 (16.7%) 2 (5.6%) 0 (0.0%) 1 (2.8%)

1989 6 (22.2%) 6 (22.2%) 11 (40.7%) 2 (7.4%) 1 (3.7%) 1 (3.7%)

1990 0 (0.0%) 3 (37.5%) 3 (37.5%) 0 (0.0%) 0 (0.0%) 2 (25.0%)

1991 1 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)

1992 0 (0.0%) 0 (0.0%) 1 (50.0%) 1 (50.0%) 0 (0.0%) 0 (0.0%)

1993 0 (0.0%) 1 (25.0%) 1 (25.0%) 1 (25.0%) 0 (0.0%) 1 (25.0%)

1994 0 (0.0%) 2 (66.7%) 1 (33.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%)

1995 0 (0.0%) 3 (50.0%) 2 (33.3%) 1 (16.7%) 0 (0.0%) 0 (0.0%)

1996 6 (40.0%) 6 (40.0%) 1 (6.7%) 2 (13.3%) 0 (0.0%) 0 (0.0%)

1997 7 (19.4%) 15 (41.7%) 7 (19.4%) 5 (13.9%) 1 (2.8%) 1 (2.8%)

1998 10 (24.4%) 11 (26.8%) 11 (26.8%) 7 (17.1%) 2 (4.9%) 0 (0.0%)

1999 6 (20.0%) 15 (50.0%) 5 (16.7%) 3 (10.0%) 1 (3.3%) 0 (0.0%)

2000 2 (7.4%) 8 (29.6%) 11 (40.7%) 3 (11.1%) 1 (3.7%) 2 (7.4%)

2001 1 (9.1%) 1 (9.1%) 1 (9.1%) 6 (54.5%) 1 (9.1%) 1 (9.1%)

2002 2 (13.3%) 6 (40.0%) 1 (6.7%) 5 (33.3%) 1 (6.7%) 0 (0.0%)

2003 2 (7.1%) 11 (39.3%) 4 (14.3%) 8 (28.6%) 3 (10.7%) 0 (0.0%)

2004 6 (15.4%) 23 (59.0%) 5 (12.8%) 2 (5.1%) 3 (7.7%) 0 (0.0%)

2005 3 (9.7%) 17 (54.8%) 4 (12.9%) 2 (6.5%) 5 (16.1%) 0 (0.0%)

2006 5 (21.7%) 5 (21.7%) 1 (4.3%) 3 (13.0%) 8 (34.8%) 1 (4.4%)

2007 6 (11.8%) 13 (25.5%) 4 (7.8%) 2 (3.9%) 26 (51%) 0 (0.0%)

2008 0 (0.0%) 2 (25.0%) 0 (0.0%) 0 (0.0%) 6 (75.0%) 0 (0.0%)

2009 0 (0.0%) 1 (33.3%) 1 (33.3%) 0 (0.0%) 1 (33.3%) 0 (0.0%)

2010 0 (0.0%) 2 (10.5%) 1 (5.3%) 1 (5.3%) 15 (78.9%) 0 (0.0%)

2011 1 (7.7%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 12 (92.3%) 0 (0.0%)

Total 83 (16.6%) 177 (35.3%) 85 (17.0%) 59 (11.8%) 87 (17.4%) 10 (2.0%)

Months to

Outcome

Median[mean]

43 [49.5] 36 [39.5] 50 [57.9] 44 [48.6]

Club deals 15% 35% 15% 11% 23% 1%

Bank-affiliated 23% 33% 14% 15% 16% 0

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Table 3-10: Deal Outcome

The table presents the results from logit regressions where the dependent variable is equal to 1 if an LBO exits

through an IPO or a sale to financial or strategic buyers and 0 otherwise. Pre-LBO target characteristics include

leverage ratios and asset tangibility at the end of year -1, industry-adjusted changes in EBITDA/sales from year-2

to year -1, and target firms’ cash flow volatilities in the last three years before buyout completion (years -3, -2. -1).

All variables are defined in Appendix B. ***, **, and * indicate significance at the 1%, 5%, and 10% level,

respectively.

(1)

Success

(2)

Success

(3)

Success

(4)

Success

(5)

Success

Leverage change 0.192 -0.004 0.145 0.196 0.200

[0.563] [0.992] [0.697] [0.558] [0.552]

Bank debt % 0.834** 0.667* 0.912** 0.844** 0.862**

[0.029] [0.085] [0.020] [0.029] [0.025]

Covenant intensity 0.079** 0.021 0.071** 0.079** 0.079**

[0.028] [0.613] [0.048] [0.027] [0.028]

Maturity 0.007 0.008 0.008 0.008 0.007

[0.118] [0.116] [0.107] [0.117] [0.127]

PE Reputation 0.288*** 0.250** 0.306*** 0.288*** 0.288***

[0.009] [0.019] [0.005] [0.009] [0.009]

Bank affiliation -0.720*** -0.682** -0.755*** -0.719** -0.720***

[0.009] [0.013] [0.007] [0.010] [0.009]

Club dummy 0.116 0.105 0.141 0.119 0.123

[0.658] [0.702] [0.597] [0.650] [0.640]

Mgmt participation 0.200 0.155 0.160 0.197 0.198

[0.406] [0.518] [0.515] [0.412] [0.412]

CEO change -1.091*** -1.207*** -1.135*** -1.090*** -1.084***

[0.000] [0.000] [0.000] [0.000] [0.000]

Baa -0.574**

[0.029]

Baa-Hbaa 0.437**

[0.029]

Term 0.149

[0.181]

Hot 0.731***

[0.005]

Spread 0.015

[0.872]

Deal pricing -0.395

[0.448]

Constant 12.5*** 15.4*** 14.1*** 13.2*** 13.5***

[0.000] [0.000] [0.000] [0.000] [0.000]

Year dummies Yes No No Yes Yes

Pre-LBO target characteristics Yes Yes Yes Yes Yes

Observations 466 466 466 466 466

Pseudo R-squared 0.170 0.171 0.183 0.170 0.171

126

Figure 3-1: A typical LBO Transaction and Hypotheses in LBO Value Creation

H1.2: Lenders’ Monitoring

(bank loan proportion, covenant, maturity)

H1.1: Debt Disciplining

H2.3: Bank Affiliation Exit

Operational Improvement

H2.1: Private Equity Reputation

H2.2: Club Deal PE’s Return on Equity

H3: Management Participation

Debt

Equity

Equity Investors

Private Equity firms

Management of Targets

Lenders

Banks

Institutional investors

Public debt holders

IPO

Sale

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Figure 3-2: LBO Transactions Each Year

The figure shows the number of LBO deals and total transaction value by LBO effective year. The solid line

that corresponds to the left y-axis plots the number of LBO deals each year. The bar that corresponds to the right

y-axis shows the inflation-adjusted total transaction values in 2005 dollars. LBO transaction sample is

constructed from the Standard and Poor’s Capital IQ and the Securities Data Company’s (SDC) U.S. Mergers

and Acquisitions Database.

128

Chapter 4 Culture Distance and Cross-border Leveraged Buyouts

4.1 Introduction

Cultural distance refers to the degree to which shared norms and beliefs differ from one

country to another. Previous studies have shown that cultural values have profound impacts on

economic outcomes (see Guiso, Sapienza, and Zingales, 2006 for a review). For instance,

cultural differences between countries affect cross-border mergers and acquisitions (M&As)

(Morosini, Shane, and Singh, 1998; Chakrabarti, Gupta-Mukherjee, and Jayaraman, 2009;

Dikova, Sahib, and van Witteloostuijn, 2010; Ahern, Daminelli, and Fracassi, forthcoming),

foreign direct investment (Guiso, Sapienza, and Zingales, 2009; Li, Griffin, Yue and Zhao,

2011), venture capital investment (Bottazzi, Da Rin, and Hellmann, 2012; Nahata, Hazarika, and

Randon, forthcoming), equity investment (Hwang, 2011), and syndicated loans (Giannetti and

Yafeh, 2012) .

Cultural distance is likely to be especially important in cross-border leveraged buyouts

(LBOs) as private equity (PE) firms and target firms from different countries with possibly

conflicting values attempt to make deal negotiations successful. Although literature has studied

the relation between culture and cross-border M&As, no previous work has examined the

cultural impact on cross-border LBOs. Two main differences between cross-border LBOs and

M&As make our study important. First, compared with the buyers in M&As who conduct cross-

border transactions infrequently, PE firms are sophisticated and frequent investors and therefore

may be less subject to cultural impact or have a way to mitigate the cultural impact. Second,

based on the LBO model, PE firms invest a target using large leverage, improve the target’s

operational efficiency for a 3-5 year period, and exit through an IPO or a sale. Access to debt

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financing and the options for exit strategies are essential to LBO success and PE firms’ realized

returns. Therefore, compared with M&As, LBOs tend to be more affected by target countries’

institutional environment and economic development.

In this paper, we fill the gap in the literature by providing some of the first evidence to

show that cultural differences have significant impact on the completion of cross-border LBOs.

We focus on the “intermediary” phase of LBOs, that is, the time between deal announcement and

the completion/cancellation of LBOs. First, we examine the probability that an announced LBO

will be completed or cancelled. Second, in the case of completed buyouts, we study the number

of days between deal announcement and completion (thereafter, the “buyout duration”). We

focus on the intermediary phase for two reasons. First, the complex bargaining process of LBOs

is very likely to be affected by cultural differences. Cultural differences may make LBO

negotiations more cumbersome and therefore increasing the probability of deals getting

cancelled and lengthening the buyout duration for completed LBOs. Also, cultural dissimilarities

may increase the cost of information. Having less precise information, culturally distance PE

firms may consider target firms to be risker, and therefore taking longer time to complete LBOs.

Second, as most of the target firms become private after the buyouts, it is very hard to examine

the post-LBO interactions between PE firms and target firms. Therefore, we argue that the

cultural impact on cross-border LBOs may be best investigated during this intermediary phase.

Our buyout sample is composed of 2,587 cross-border LBOs collected from Capital IQ

from 1986 to 2013, with PE firms from 52 countries and target firms from 41 countries. Our

measure of cultural distance takes into account of Hofstede’s (2001) four dimensions that are

most commonly used in the literature: power distance, individualism, masculinity, and

uncertainty avoidance. We find that cultural distance between PE firms and target firms reduces

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the likelihood of LBO completion and increases the buyout duration. A one standard deviation

increase in cultural distance reduces the likelihood of buyout completion by 5% and increases the

buyout duration by 19%. In addition, we find that LBO deal characteristics also affect the

likelihood of deal completion and the buyout duration. Smaller deals and LBOs with target

firms’ incumbent management participated are more likely to be completed with shorter

duration. PE firms’ reputation increases the likelihood of deal completion but also lengthens the

completion time. We also find that other country-level characteristics influence cross-border

LBOs. For instance, the development of LBO markets in target countries and the quality of

creditor rights and shareholder protection are also related to the likelihood of deal completion. In

addition, when PE countries and target countries are geographically closer, LBOs are more likely

to be completed with shorter duration.

We next examine whether certain LBO deal-level characteristics could mitigate the

documented cultural impact. We propose that club deals, where two or more PE firms pool their

assets to acquire target firms and manage them collectively, could mitigate the cultural impact on

deal completion and buyout duration. We argue that club deals may reduce the cultural distance

between the acquiring PE consortiums and target firms when a culturally distant PE firm teams

up with other PEs that are culturally closer to the target firms. Also, club deals allow risk sharing

among PE firms, which may also lead to a higher completion rate with shorter buyout duration.

We find club deals mitigate the negative impact of culture distance on the likelihood of LBO

completion and moderate the positive cultural effect on buyout duration. Further analyses show

that such mitigating effect is a result of reduced cultural distance between the PE consortiums

and target firms, instead of risk sharing among PE firms. Our findings are robust after controlling

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for LBO transaction characteristics, PE firms’ reputation, target countries’ economic growth,

LBO market development, legal and political environment, and PE-target country-pair variables.

We provide a number of robustness checks on our main results of the cultural impact and

the mitigation effect of club deals. First, since U.S. and U.K. firms account for a large number of

PE and target firms, we exclude these firms from our sample in robustness tests. Our results are

unchanged and in most cases strengthened. Second, we use different measures for club deals. In

particular, we look at (1) the number of unique PE firms in a club and (2) the number of unique

PE countries in a club. Our results still hold. Third, for club deals, we also control the cultural

difference among PE firms in a club. We find that our main results still hold and that cultural

dissimilarities between PE firms do not have any significant impact on deal completion or

buyout duration. Fourth, we examine the impact of each of Hofstede’s cultural dimension and

find that power distance, individualism, and uncertainty avoidance have significant impact on

deal completion and that club deals still mitigate the cultural impact along these three

dimensions.

Our paper contributes to a growing field of research that examines the role of culture in

economics. Specifically, our study sheds light on how cultural distance between countries affects

cross-border investment decisions. Three studies closest to our are Ahern et al. (forthcoming),

Dikova et al. (2010), and Giannetti and Yafeh (2012). Ahern et al. (forthcoming) show that

cultural distance along the dimensions of trust, hierarchy, and individualism reduces cross-border

merger volume and synergy gains. Dikova et al. (2010) use cultural differences as one part of

informal institutions to explain the completion of cross-border acquisitions. Giannetti and Yafeh

(2012) find that cultural distance between banks and borrowers leads to higher interest rates of

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syndicated loans. Our study differs from the above three papers as the first study to examine the

impact of cultural distnace on the completion of cross-border LBOs.

Second, our study also contributes to the LBO literature in the international setting.

International LBOs have become more important over time—during the period from 1986 to

2013, around 60% of LBOs (both domestic and cross-border) occurred in non-U.S. countries.54

Therefore, it is important to study international LBOs and examine the impact of country

characteristics and institutional differences on these deals. Extant literature has largely focused

on how legal environment affect global LBO and PE activities. For LBOs, Cao, Cumming, Qian

(2010a) and Cao, Cumming, Qian, and Wang (2010b) examine buyout activities in countries

with different legal conditions with a focus on creditor rights and shareholder protection. In the

study of global PE activities and returns, Lerner, Sorensen, and Stromberg (2009) study how

institutional environment and economic development affect the international PE transaction

volume and deal outcomes. Cumming and Walz (2010) investigate the impact of accounting

standards and legal framework on the reporting behavior of PE firms. Cumming, Fleming, Johan

and Takeuchi (2010) examine the relation between legal protection, corruption, and private

equity return in Asia. Cumming and Zambelli (2010, 2013) study LBOs and PE returns under the

extreme regulatory environment in Italy. Our paper adds to the international LBO literature by

examining the cultural impact on deal completion, after controlling for legal and other

institutional conditions.

Third, this paper is also related to the strand of studies that examines the deal-making

process in M&As (Officer, 2003; Boone and Mulherin, 2007; Meyer and Altenborg, 2008;

Dikova et al., 2010; Hotchkiss, Qian, and Song, 2013). In the context of LBOs, we are the first

54 Source: Capital IQ.

133

study that investigates the time period between deal announcement and resolution. Previous

studies in LBOs have examined pre-buyout conditions (Halpern, Kieschnick, and Rotenberg,

1999; Bharath and Dittmar, 2010; Mehran and Peristiani, 2010; Fidrmuc et al. 2013) or the post-

buyout target firms’ performance or private equity returns (Kaplan, 1989; Smith, 1990;

Lichtenberg and Siegel, 1990; Kaplan and Schoar, 2005; Guo, Hotchkiss, and Song, 2011). A

closer examination of this intermediary period is important as it could provide valuable insights

into potential deal-breakers as deal cancellation and prolonged deal-making process could

generate substantial costs for both PE firms and target firms. For LBOs, the termination fee is

usually around 3.5% of total transaction value.55

Luo (2005) argues that cancelling an announced

deal can severely impair the cancelling party’s reputation and credibility. In addition, a

prolonged intermediary deal phase creates diversion of managerial attention from other lucrative

investment opportunities (Bainbridge, 1990). Therefore, our study carries potential managerial

implications as avoiding deal abandonment and shortening the deal-making process would

reduce costs for PE firms and targets.

Fourth, we also contribute to the general deal syndication literature, in particular, the

motivations of club formation in LBOs, which include (1) capital constraint, (2) diversification

and risk-sharing, (3) the certification effect by reputable PEs to obtain favorable terms in debt

financing, (4) the synergy from expertise of different participating PE firms, and (5) the collusion

motivation to depress bidding prices. Recent empirical studies on U.S. suggest that collusion and

competition are two major motivations for club formation deals (Officer, Ozbas, and Sonsoy,

2010; Boone and Mulherin, 2011). Our study adds to the literature by proposing the familiarity

motivation. That is, to shorten the cultural distance and build familiarity, culturally distant PE

55 Source: Houlihan Lokey’s 2012 Transaction Termination Fee Study. Available online at

http://www.hl.com/email/pdf/2012-HL-transaction-termination-fee-study.pdf.

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firms may strategically team up with other PEs that are culturally closer to the target firms. In

line with such familiarity motives, we find that consortiums of PE firms from different countries

facilitate the deal completion and shorten the buyout duration.

The rest of the paper is organized as follows. Section 2 reviews the literature and develops

hypotheses. Section 3 describes the data and constructs variables. Section 4 presents empirical

results. Section 5 conducts robustness checks. Section 6 concludes.

4.2 Theory and Hypotheses

4.2.1 Formal and Informal Institutions

We apply the frameworks of North (1990, 1991) and Williamson (2000) to examine the

impact of formal (e.g. legal, political, economic, and financial) and informal (e.g. cultural)

institutions on deal completion and buyout duration for cross-border LBOs. According to North

(1991, p97), institutions are “humanly devised constraints that structure political, economic and

social interaction”. Institutions create order and reduce uncertainties that arise from incomplete

information in exchange. They also enable value-enhancing transactions that would otherwise

not take place.

Williamson (2000) provides an analytical framework that theorizes four levels of

institutions, where the higher level imposes constraints on the level immediately below. The top

level (level 1) considers informal institutions including culture, norms, customs, and religion.

Institutions at this level change very slowly—“on the order of centuries or millennia”

(Williamson 2000, p596). On level 2 are formal rules (constitutions, laws, property rights).

Important features of this level include the definition and enforcement of property rights and

135

contract laws. Governance structures and resource allocation are located at levels 3 and 4,

respectively.

An important feature of Williamson’s institutional theory is that the informal and formal

institutions on levels 1 and 2 are country specific. That is, institutions that constrain behaviors

and therefore reducing uncertainties in economic activities vary across national borders. The

institutional differences across countries lead to complexity, and even conflicting values, in

cross-border transactions. This motivates us to study how cross-border LBOs are affected by

different institutional environment. Specifically, we examine whether and how differences in

informal institutions—mainly, the national culture—between PE countries and target countries

affect the intermediary phase of cross-border LBOs, after controlling for the differences in

formal institutions. In the following subsections, we present separate arguments with regard to

the role of formal and informal institutions in the intermediary phase in LBOs.

4.2.1.1 Formal Institutions

Formal institutional environment refers to the legal and political regulation, economic rules

(e.g. contracts), and more generally, economic and financial development (North, 1990;

Williamson, 2000). Previous studies have shown the important role of formal institutions. For

instance, La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) show that differences in laws,

regulations, and enforcement are related to the development of capital markets, the ownership

structure of firms, and the cost of capital. As LBOs involve PE investors using large leverage to

acquire target firms and exiting through an IPO or a sale, institutional environment such as target

countries’ shareholder protection, creditor rights, credit availability and accessibility, and stock

market development are essential to LBO success.

136

According to La Porta et al. (1998), shareholder protection captures the extent to which

minority shareholders are protected against decisions made by directors and large shareholders

by the legal system. They also show that common-law countries tend to provide better protection

for individual investors than civil-law countries. We argue that it is harder to negotiate an LBO

deal in countries with strong shareholder protection. As a result, announced LBOs are less likely

to complete and for completed deals, the intermediary phase is longer.

As access to credit is crucial for LBOs, we also consider creditor rights and access to credit

in target countries. Previous studies have found that in countries with better creditor rights, bank

loans tend to have lower spread and longer maturity (Qian and Strahan, 2007; Bae and Goyal,

2009) and that the economy-wide cost of borrowing is significantly related to leverage in

buyouts (Axelson, et al., 2013). Cao et al., (2010b) find that LBOs are more active in countries

with strong creditor rights. Therefore, we expect that cross-border LBOs are more likely to be

completed with shorter duration when creditor rights are strong in the target country, as strong

creditor rights facilitate access to credit for LBOs.

We also consider the differences in formal institutions between the PE and target countries

as such differences will increase the complexity of LBO transactions and the dissimilarity to the

target country. Also, such differences will impose pressure for PE firms to comply with target

countries rules and laws that are very different from the one in their home countries. Therefore,

we expect that such formal institutional difference will also play a role in LBO completion.

4.2.1.2 National Culture

Hofstede (1983, 2001) defines culture as the collective mental programming that leads to

patterned ways of thinking, feeling, and acting and that distinguishes one group of people from

another. There are two mechanisms through which culture affects economic activities. First,

137

culture directly influences people’s choices and behaviors by shaping their motivations and

perceptions of the world (Hofstede and Bond, 1988; Licht, Godschmidt, and Schwarts, 2005).

Second, based on Williamson (2000)’s four institutional levels and their top-down relationship—

culture conditions formal institutions, governance structure, and resource allocations, and

therefore indirectly affecting economic outcomes through the other three levels.

Given the theories mentioned above, one can argue that culture has explanatory power over

and above formal institutions such as legal, political, and economic constraints. Recent empirical

research supports the fundamental role played by culture in explaining cross-border investment

decisions. Siegel, Licht, and Schwartz (2012) find negative influence of the cultural dimension in

egalitarianism on cross-border flows of bond and equity issuance, syndicated loans, and M&As,

after controlling for differences in economic performance, legal systems, and financial

development. Giannetti and Yafeh (2012) find that, controlling for legal institutions and

geographic distance, cultural distance between borrowers and lead banks in the syndicated loan

market increases the loan spread. Ahern et al. (forthcoming) document that greater cultural

distance between acquirer and target countries reduces merger volume and leads to lower

combined announcement returns, after considering economic performance, legal systems, and

financial development.

In this paper, we add to the emerging literature on cultural impact on cross-border

investment decisions and examine how cultural distance between PE and target countries affects

deal completion and buyout duration after controlling for deal-level characteristics as well as the

legal, political, financial, and economic environment. We argue that, controlling for formal

institutions, culture may affect deal completion in two (non-mutually exclusive) ways. First,

cultural differences between PE and target countries affect their negotiations during the

138

intermediary phase. For instance, negotiators from individualistic and egalitarian cultures have

the power to accept or reject offers whereas in the more collective and hierarchical cultures

members of the organization with actual decision power may not be present at the negations.

Also, negotiators from cultures where uncertainty is to be avoided may need more detailed

contracts or planning than those from cultures that tolerate uncertainties. Cultural values also

affect corporate policies such as gender equality and diversity; and similar ethnic and gender

stereotypes may affect how comfortable negotiators are with their counterparties (Giannetti and

Yafeh, 2012). All these factors can make cross-cultural negotiations lengthy and ineffective.

Second, it is also possible that cultural distance increases the cost of information gathering.

Having less precise information, culturally distant PE firms may consider the target risker.

Therefore, more time in negotiation or information gathering may be needed before complete the

deal.

Our hypotheses of the cultural impact on LBO deal completion are stated formally as

follows:

Hypothesis 1a: Greater cultural distance between PE firms and target firms reduces the

likelihood that an announced cross-border LBO will be completed.

Hypothesis 1b: Greater cultural distance between PE firms and target firms increases the

number of days between the announcement of a cross-border LBO and its completion.

4.2.2 The Mitigating Effect of Club Deals

As sophisticated investors, PE firms can mitigate the cultural impact through arranging

club deals. Theories of deal syndication suggest a number of motivations for club formation in

LBOs. First, capital constraints may induce PE firms to form clubs in order to bid on large target

139

firms. Second, the diversification and risk-sharing motivation leads PE firms to syndicate large

or risky deals. Third, having multiple PE firms attaching their names and reputations may help to

construct LBO debt in sufficient quantity and under more favorable terms (the “certification”

motive). Fourth, club formation may create greater synergies as each PE firm brings different

expertise to the target firms, thereby improving performance and generating larger returns for PE

investors.56

A fifth motivation for club deals is for PE firms to collude in order to depress bidding

prices by limiting the number of competing bidders.

The capital constraint, risk-sharing, certification, and collusion motivations are all relevant

to our study. They suggest that club deals allow PE investors to use sufficient amount of equity

capital and obtain enough debt financing under favorable terms, therefore leading to higher

probability of deal completion and shorter negotiation time. Club deals also allow risk sharing

among PE firms, especially in cross-border deals when the information asymmetry is more

severe, thereby reducing the uncertainties faced by each PE investor. In addition, if club deals

reduce the competition among potential bidders, it may also facilitate the LBO negotiation

process.

In this paper, we propose another motivation for club formation in cross-border LBOs—to

mitigate the cultural impact. A PE firm that is culturally distant from the target firm can team up

with other PE firms that are culturally closer to the target. This club formation could improve the

PE team’s familiarity to the target and thus reducing the complexity of the deal. We therefore

expect club deals to mitigate the cultural impact on deal completion and buyout duration. This

leads to Hypothesis 2:

56 This motive of club formation has been prevalent among practitioners. For example, when KKR teamed up

with Bain Capital and Vornado Realty Trust to acquire Toys "R" Us, the New York Times stated that “it was clear what each firm brought to the table. Kohlberg Kravis has a good reputation in the retail business, Bain

has a good record doing turnarounds, and Vornado clearly knows real estate”. Source: “Do Too Many Cooks

Spoil the Takeover Deal”, the New York Times, April 3, 2005.

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Hypothesis 2a: Club deals mitigate the negative impact of cultural distance on LBO deal

completion.

Hypothesis 2b: Club deals moderate the positive impact of cultural distance on buyout duration.

4.3 Data and Variables

4.3.1 Sample Selection

Our empirical tests are based on a sample of cross-border LBO transactions that are

obtained from Standard and Poor’s Capital IQ. Each LBO transaction meets the following

criteria: (1) the transaction is announced between January 1st, 1986 and August 31st, 2013; (2)

the transaction has reached an outcome—either completed or cancelled, with a non-missing

completed or cancelled date; (3) transaction value is $10 million or larger; (4) at least one PE

firm is involved in the transaction as the acquirer and at least one PE firm is from a different

country than the target firm.57

This initial screening yields a total of 3,154 LBO transactions.

To construct the measure of cultural distance between PE countries and target countries,

we match our sample countries with Hofstede's (2001) four dimension scores and drop deals

with missing cultural values for PE or target countries.58

We further require that data on all the

country-level characteristics to be available for each target country. Our final sample consists of

2,587 LBO transactions with 1,169 PE firms from 52 countries and target firms from 41

57 To identify PE firms, we download all private equity funds (PE funds) from Capital IQ and merge the buyer

Excel Company ID of each LBO in our sample to the PE funds’ Excel Company ID and consolidate PE funds

to PE firm level. So if one PE firm has multiple active PE funds, we use the Excel Company ID at the PE firm

level for the analysis. For example, both Lehman Brothers Mezzanine Fund and Lehman Brothers Capital

Partners IV are identified at the PE firm level as the Lehman Brothers, Private Equity Division. We also track

the name changes of the PE firms from the Capital IQ Tearsheet and news articles. 58

PE firms’ host country is identified at firm level, not PE’s ultimate parent level. For example, Morgan

Stanley Private Equity Asia Limited is located in Hong Kong, so we take Hong Kong as PE country, rather the

U.S. where the PE’s ultimate parent firm Morgan Stanley is located.

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countries. Our sample LBOs include buyouts of a whole public firm (16%), private firm (75%),

or division/business of a firm (9%).

Our data are subject to two limitations and therefore potential biases. First, we only

observe LBO transactions that have reached the stage of public announcement and do not

observe deals that were cancelled or terminated before the public announcement. Therefore our

cancellation rate may be biased downwards. Second, PE firms self-select into the buyout deals. It

is possible that PE firms that face large institutional distances are less likely to attempt an LBO

and therefore are not included in our sample. As a result, our findings may underestimate the

impact of formal and informal institutional effects.

Figure 1 plots the total number of cross-border LBOs in our sample and their total

transaction values (in 2005 U.S. dollars) by LBO announcement year. Over time, there has been

an increasing trend in the number of buyouts and the total transaction values. LBOs peaked in

2006, with a total number of 270 announced deals and their total transaction values reaching

$336.01 billion. Over the sample period, the proportion of LBOs that are club deals is 34% while

the total transaction values of club deals is 55% of all LBO values. By deal announcement year,

the number of deals increased steadily over time and the total transaction values of these deals

increased significantly—from $0.08 billion in 1986 to $26.08 billion in 2000 until its peak of

$219.95 billion in 2006. LBO activities decline as a result of the 2007-09 financial crisis but

soon recovered.

Table 1 presents a country-wise breakdown of our sample with PE countries listed as the

row variables and target countries on the columns. Only deals from the top 20 countries are listed

but the values in the total column and row include all LBOs. For the 2,587 LBO transactions in

our sample, there are 4,100 PE-target country pairs (due to club deals that have multiple PE

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countries). The U.S. with the most acquiring PE firms and the second most target firms

dominates our cross-border buyout sample. PE firms from the U.K. conduct the second most

LBOs and the U.K. is the largest target country. Appendix F lists the top 20 PE firms by the

number of LBOs they sponsor and the total transaction values of these deals. The majority of the

top 20 PEs are from the U.S. and the U.K.. In both categories of PE and target countries, France

is the third. Germany is the fourth largest LBO target country and Canada is ranked the fourth as

the PE country, followed by the Netherlands. Europe has become very important in the cross-

border LBO market, hosting 65% of the buyouts targets and 53% PE investors (not tabulated).

Country-pair wise, the U.S.-U.K., U.S.-Canada, U.K.-France, and U.K.-Germany combinations

are the most common PE-target relations.

4.3.2 Variable Construction

4.3.2.1 Deal Completion and Buyout Duration

We construct two dependent variables for our main analyses. The first is deal completion

(DEALC), which is an indicator variable that takes the value 1 if an announced LBO is

completed and 0 if it is cancelled. LBOs for which we observe an announcement and a closed or

effective date and with a transaction status labeled as “closed” or “effective” are considered as

completed transactions. Deals labeled as “cancelled” or “withdrawn” take the value of 0. The

second dependent variable is the buyout duration (LEND), calculated as the number of days

between the announcement and completion dates for all completed LBOs. According to Dikova

et al. (2010), the completion date is more than a measure of the close date of an LBO, but a stage

when both parties, i.e. PE and targets, perceive most crucial issues to be solved.

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4.3.2.2 Cultural Distance

Following an extensive literature on international business (Chakrabarti et al., 2009;

Giannetti and Yafeh, 2012; Kwok and Tadesse, 2006; Li, Griffin, Yue, and Zhao, 2013), we use

Hofstede’s (1983, 2001) four cultural dimensions (power distance, individualism, masculinity,

and uncertainty avoidance) to calculate cultural distance.59

According to Hofstede (1983, 2001),

power distance measures the extent to which the less powerful members of organizations and

institutions expect and accept that power is distributed unequally. Individualism captures the

degree to which a society stresses the role of an individual versus that of a group. Masculinity

focuses on the extent to which male assertiveness (e.g., the importance of showing off, of

making money, and of striving for material success) is promoted as dominant values in a society

as opposed to female nurturance (e.g., helping others, not showing off, putting relationships with

people before money). Finally, uncertainty avoidance deals with a society’s tolerance for

uncertainty and ambiguity, and reflects the extent to which people feel uncomfortable about

uncertain, unknown, or unstructured situation.

Following Kogut and Singh (1988) and Morosini et al. (1998), we measure national

cultural distance as the degree to which the cultural norms in one country differ from those in

another country. Specifically, we calculate the multidimensional cultural distance between PE

and target countries using Hofstede's (2001) four dimension scores.

=1

√∑ ( ,𝑗 − ,𝑗)2

𝑗=1 , (1)

where is the cultural distance between each PE-target country pair. ,𝑗 denotes Hofstede's

cultural scores for the PE country along a jth

cultural dimension whereas ,𝑗 denotes

59 See Kirkman, Lowe, and Gibson (2006) for a survey of studies using the Hofstede’s measure.

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Hofstede's cultural scores for the target country along the jth

cultural dimension. For club deals

with PE firms coming from different countries, we use the average CD between each PE-target

country pair as the CD of the deal.

One concern with Hofstede’s cultural measure is that the data may be outdated therefore

not being able to capture the changing nature of culture. However, we argue that Hofstede’s

measure retains its validity over our sample period from 1986 to 2013 for the following reasons.

First, according to Williamson (2000), culture changes very slow “on the order of centuries or

millennia” (p596). Second, based on Hofstede (2001), national culture scores do not provide a

country’s absolute position but rather its position relative to other countries, which rarely shifts

even if cultural changes occur. Third, Leung et al. (2005) indicates that several recently

developed cultural measures, including Schwartz (1994)’s seven-dimension culture measures and

House et al. (2004)’s nine dimensions, are “related conceptually and correlated empirically”

(p366) to Hofstede’s measures, supporting the validity of using Hofstede’s cultural scores in our

paper.

4.3.2.3 Deal-Specific Variables

We control deal characteristics that are related to the completion of buyouts. We first

include deal size (SIZE), as it may take longer to negotiate large deals due to their complexity.

SIZE is calculated as the natural log of transaction value in 2005 U.S. dollars. MGMT is an

indicator variable that takes the value of 1 if incumbent management of the target firm

contributes equity and joins the acquirer team and 0 otherwise. Management participation may

help reduce the information asymmetry between PE firms and the LBO target, thereby

facilitating the completion of the deal and reducing the negotiation time. CLUB is an indicator

variable equals 1 if a deal is composed of more than one PE firm and 0 otherwise. PE_COUNT is

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the number of PE firms in a club and PE_CTR_COUNT is the number of unique PE home

countries in a club. We use these two variables as alternative indicators for club deals.

We also include PE firms’ reputation, as highly reputable PEs may have better experience

or expertise to complete an announced deal. Reputation of PE firms may also have a certification

effect that leads to better access to LBO financing, and subsequently facilitating deal completion.

Previous empirical studies measure PE reputation in a number of different ways including fund

size, its market share, the number of recent LBO transactions, and the number of previous fund

raisings.60

In this paper, we follow Demiroglu and James (2010a) and record the total number of

completed LBOs (both domestic and cross-border) sponsored by a PE firm j in the past 36

months before a new LBO announcement month t.61

Therefore, reputation for PE firm j at month

t is measured by PE j’s market share during the past 36 months:

𝐸 𝐴 𝑁𝑗,𝑡

=𝑁 𝑝 𝑡 𝐿𝐵 𝑝 𝐸𝑗 𝑡𝑤 𝑡 𝑡−1 𝑡 𝑡−

𝑡 𝑡 𝑝 𝑡 𝐿𝐵 𝑤 𝑤𝑖 𝑡𝑤 𝑡 𝑡−1 𝑡 𝑡−

In order to get the LBO transaction history, we use all Capital IQ recorded LBO, MBO,

SBO transactions—a total of 50,304 deals—to calculate reputation. In the case of club deals, we

consider the buyout as a full deal for each PE firm. When calculating LBO deal-level reputation,

we use the reputation score of the PE firm with the highest reputation if the deal has multiple

PEs.

For each LBO, we also look at PE firm’s year of experience (PE_AGE), which is calculated

as the number of years since the first ever LBO (based on the 50,304 deals) conducted by the PE

firm until the announcement year of a new LBO.

60 See Demiroglu and James (2010a) for discussions on strengths and weaknesses of each reputation measures.

61 We also calculate PE reputation based on the total number of LBOs (including both completed and cancelled

deals), the results are similar.

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4.3.2.4 Target Country Characteristics

To better understand the impact of cultural distance on LBO completion and buyout

duration, we control for the economic growth, LBO market development, equity and debt market

conditions, and formal institutions in target countries. We measure target countries’ economic

growth and individual wealth using gross domestic product (GDP) growth one year prior to the

LBO announcement (GPD_G) and GPD per capital in 2005 U.S. dollars (GDPCAP) from the

Penn World Tables 8.1. We also control for each target country’s level of foreign trade,

calculated as the ratio of the country’s total imports and exports to GDP, which we call

OPENNESS. To control for target countries’ LBO market development (LBO_MKT), we record

the total number of LBOs (both domestic and cross-border) in each target country since 1970.62

LBO_MKT at the time of a new LBO announcement is the number of LBOs in each target

country as a proportion of total number of LBOs worldwide since 1970. For robustness, we also

calculate the LBO market development based on the LBO transaction values, the results are

similar.

Since debt financing and future exit options are essential for LBOs, better access to credit

and more developed stock markets in target countries are attractive features to international PE

investors. Therefore, we expect that LBOs are more likely to be completed in these countries.

We measure access to credit (CREDITGDP) as the ratio of target countries’ private credit from

deposit money banks and other financial institutions to GDP and the development of stock

market (STMKCAP) as the proportion of stock market capitalization in target country’s GDP.

Data are from Beck, Demirgüç-Kunt, and Levine (2000, 2009) and Čihák, Demirgüç-Kunt,

Feyen, and Levine (2012).

62 We use 1970 since it is the first year Capital IQ starts to record LBO deals.

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We next control for formal institutions of target countries. We include creditor rights

(CREDITOR_RIGHTS) from La Porta et al. (1998). Previous studies have shown that in

countries with strong creditor rights, LBOs are more active and bank loans tend to have lower

spread and longer maturity (Cao et al., 2010b; Qian and Strahan, 2007; Bae and Goyal, 2009).

We therefore expect that cross-border LBOs are more likely to be completed with shorter

duration in such countries, as strong creditor rights facilitate access to credit for LBOs. We also

consider two measures for investor protection. LEGAL_UK (from La Porta et al., 1998) takes the

value of 1 if a country’s legal origin is English common law, and 0 if the legal origin is French,

German, or Scandinavian civil law. We include this indicator as La Porta et al. (1998) show legal

systems based on common law traditions tend to provide better protection for individual

investors. For shareholder protection, we use the revised anti-director right index (ANTI-

DIRECTOR) from Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2008). We expect that in

countries with strong investor protection, it is difficult to complete an LBO and that the

negotiation with current shareholders of the target firms may be quite lengthy.

We also include three indexes for formal institutional environment in the target countries:

(1) an index for rule of law in the target country (RULELAW) that measures the extent to which

citizens have confidence in and abide by the rules of society, in particular the quality of contract

enforcement and property rights; (2) a regulatory quality index (REGQUALIT) that measures the

ability of the target country’s government to formulate and implement sound policies and

regulations that permit and promote private sector development; (3) a political rights index

(POL_RIGHTS) that measures political institutions in the target country that consider the

electoral process, political pluralism and participation, and the functioning of the government.

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RULELAW and REGQUALITY are from Kaufmann, Kraay, and Mastruzzi (2010) and

POL_RIGHTS is from Freedom House (2008).

4.3.2.5 PE-Target Country Pair Variables

We next consider variables that measure different types of links and distance between PE

countries and target countries. According to Froot and Stein (1991) and Lin, Officer, and Shen

(2013), currency exchange rate and its growth are related to cross-border investment patterns.

Therefore, we calculate the historical exchange rate growth between each PE-target country pair

over the 12 months prior to the LBO announcement (EXCHANGERATE_G). We also record the

fiscal freedom of both PE and target countries, using the data from the Economic Freedom

Index. Fiscal freedom measures the extent to which government permits individuals and

businesses to keep and manage their income and wealth for their own benefit and use; that is, the

tax burden imposed by the government. Following Ahern et al. (forthcoming), we calculate the

absolute difference in fiscal freedom between each PE-target pair (FIS_FREEDOM) to proxy the

difference between financial development of PE and target countries.

We also control for the geographic distance between PE and target countries. First, we

measure the shortest distance (GEO_DIST) between each country’s most important city (in terms

of population) or its capital city, following the great circle formula. We also consider whether the

two countries share a common border. COMBORDER takes the value of 1 if the PE and target

countries share a common border and 0 otherwise. Data on geographic distance are from Centre

D’Etudes Prospective et D’Informations Internationales (CEPII).

Religion and language have also been shown to affect economic outcomes (Guiso,

Sapienza, and Zingales, 2003). Therefore, we record each PE and target country’s primary

spoken language and religion using data from the Central Intelligent Agency (CIA) World

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Factbook. COMLANG takes the value of 1 if PE and target countries share common official

language and COMRELIGION is equal to 1 if they have the same primary religion. In addition,

we also consider whether PE and target countries share a common legal origin. COMLEGAL

takes the value of 1 if they have the same legal system (common law, civil law, versus religious

law), and 0 otherwise.

For club deals, we calculate the above variables for each PE-target country pair and take

the average for the club. Appendix E provides detailed definitions and data sources for all

variables used in our empirical analyses.

4.3.3 Summary Statistics

Table 2 presents summary statistics and correlations of key variables used in our analyses.

To mitigate endogeneity problems, we use all the country and country-pair variables based on

one year prior to the LBO announcement year. Panel A shows the two dependent variables that

measure deal completion and buyout duration, as well as measures for cultural distance. 94.16%

of cross-border LBOs are completed and the rest 5.84% are cancelled. On average, it takes

around 84.18 days (median=63) to complete an announced LBO.63

The mean and median of

national culture differences (CD) are 37.69 and 33.70, respectively.

Panel B presents summary statistics for deal-level characteristics for our sample LBOs. The

average transaction value is $650 million (median=$160 million). The largest deal, the buyout of

Enel Rete Gas SpA—an Italian firm in the natural gas distribution business—by AXA Private

Equity (France) and Fondi Italiani per le Infrastrutture SGR SpA(Italy), has a transaction value

of $87 billion. 25% of the LBOs have management involved and 34% are club deals. For club

63 Hotchkiss et al. (2013) find the average time between deal announcement and solution to be 5.4 months for a

sample of 1,583 U.S. stock mergers from 1994 to 1999. Dikova et al. (2010) find the median time to

completion to be 96 days for a sample of 2,389 cross-border acquisition from 1981 to 2001.

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deals, there are on average 2.63 PEs in a consortium, coming from 1.97 countries. The

transaction with the largest number of PE firms and PE countries in a club is the buyout of

Laureate Education, Inc. (U.S.) in 2007 by 10 PE firms from 5 different countries with a

transaction value of $3.91 million. PE reputation as measured by the PE firms’ market share in

the past 36 months prior to an LBO announcement has a mean of 0.47%. CVC Capital Partners

Limited has the largest reputation score in October 1987, based on its 6.63% share in the global

LBO market. The average age of a PE firm at new LBO announcement is 14 years.

Panel C reports summary statistics for target countries and Panel D presents country-pair

statistics. The mean values of target countries’ private credit to GDP and stock market

capitalization to GDP are 128.26% and 94.75%, respectively, indicating that our sample target

countries tend to have well-developed credit and stock markets. 45% of the target countries have

a common law legal origin. A shared border is found in 27% of our buyout sample, common

language in 46%, common legal system in 53%, and shared religion in 43%.

For the 151 cancelled LBOs, we read through Capital IQ deal synopses and other news

articles to identify reasons for deal cancellation. Panel E lists the primary reasons that include (1)

deals rejected by target firms’ board, shareholders, or creditors, (2) competing bids, (3)

negotiation failed due to differences over valuation and deal details, (4) deal conditions not met

before deadlines or closing conditions not met, (5) deals rejected by regulatory agency, such as

the anti-trust committee, and (6) inadequate or delayed debt financing.

Panel F presents correlation coefficients between all variables used in regressions in the

next section, which do not reveal any multicollinearity problem. All correlations are well below

the commonly used cut-off threshold of 0.7 and an examination of the variance inflation factor

(VIF) values after regressions show that all values range between 1.10 and 5.64, which are well

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below the standard cut-off level of 10. The coefficient table also shows that cultural distance is

weakly correlated with geographic distance and that culturally similar countries tend to share

common language, legal system, and religion.

Table 3 lists PE-target pairs with maximum and minimum cultural distance and shows how

club deals could potentially reduce cultural distance. Panel A of Table 3 shows the ten country

pairs with maximum similarity in culture and ten pairs with the most dissimilar cultures. In our

sample, the US and Australia have the most similar cultures (CD=6.56), followed by Switzerland

and Germany (CD=8.19), and then the U.S. and U.K. (CD=12.88). The U.K. and Portugal have

the most dissimilar cultures (CD=103.02), followed by Belgium and Singapore (CD=102.65).

Panel B lists an example of ten club deals where cultural distance is reduced through club

formation. For example, in case 1, cultural distance between Singapore and Australia (the target

country) is 91. By forming a club deal with PEs from the U.K. and the U.S., the average cultural

distance of the group is reduced to 38.

4.4 Empirical Results

In this section, we test the hypotheses developed in Section 2. We first examine whether

and how cultural distance affects the likelihood of cross-border LBO completion and the buyout

duration and then test whether club deals mitigate the cultural impact.

4.4.1 The Cultural Impact on Deal Completion and Buyout Duration

First, we estimate a binary logistic regression model with deal completion (DEALC) as the

dependent variable and test Hypothesis 1a that greater cultural distance between PE countries

and target countries reduces the likelihood of deal completion. Table 4 Column (1) presents

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regression results. Cultural distance is significantly and negatively related to the probability of

deal completion, supporting Hypothesis 1a. A one standard deviation increase in LN(1+CD)

(sd=0.75) decrease the likelihood of deal completion by 5% (0.75 times 0.066). Larger deals are

less likely to be completed as they tend to be more complicated than smaller ones. Management-

participated LBOs tend to have a higher completion rate. Reputation of PE firms also increases

the likelihood of deal completion. Announced LBOs are more likely to be completed in target

countries with higher GDP per capita, better developed LBO markets, easier access to credit,

stronger creditor rights, and weaker investor protection (as suggested by the negative coefficient

of LEGAL_UK). LBOs are less likely to be completed when PE and target countries are distant

geographically.

Next, we examine the impact of cultural distance on buyout duration. Column (2) of Table

4 reports the regression results. Cultural distance has a significant and positive effect on buyout

duration. A one standard deviation increase in LN(1+CD) increases the buyout duration by 19%

(0.75 times 0.257), providing evidence for Hypothesis 1b that larger cultural distance between

PE and target firms increases buyout duration. Deal size increases the buyout duration and

management participation shortens the time length. In addition, buyout duration is positively

related to the shareholder protection index and the dummy variable that indicates common law

origin. This is because it may take longer time to negotiate with target firms’ shareholders in

countries where they are better protected. We also find that it takes less time to complete a deal

when PE and target firms are closer geographically or share a common language.

4.4.2 The Mitigation Effect of Club Deals

We next examine whether club deals mitigate the impact of cultural distance on deal

completion and buyout duration. We add the interaction term of cultural distance and the club

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dummy to test for such mitigation effect (LN(1+CD) X CLUB). Table 5 presents the regression

results. In Column (1), the interaction term has significant and positive impact on deal

completion, providing evidence for Hypothesis 2a for the mitigation effect of club formation on

the negative impact of cultural differences on LBO completion. Column (2) tests Hypothesis 2b

that argues club deals reduce the effect of cultural distance on buyout duration. We also add a

club dummy to this regression to capture any other impact club deals may have on buyout

duration. We find a significant and negative coefficient on the interaction term that supports the

mitigation effect of club deals on buyout duration.

4.4.3 The Familiarity versus Risk-sharing Channel

The analyses so far show that club deals mitigate the cultural impact on deal completion

and buyout duration. In this subsection, we try to pin down the channel through which such

mitigation effect is achieved. There are several possible mechanisms. First, a PE firm that is

culturally distant from the target firm may team up with other PEs that are culturally closer to the

target firm in order to reduce the cultural impact. We call this the “familiarity channel”. Second,

it is also possible that club deals allow PE firms to share risks, thereby reducing the uncertainties

faced by each PE investor (the “risk-sharing channel”). Under the familiarity channel, we expect

the club mitigation effect is only significant when there is a large variation in the cultural

distance between each PE-target pair in a consortium (the HighCDVar_Club) but not significant

when a consortium is formed by PE firms from the same countries or PEs that have similar

cultural distance from the target firm (the LowCDVar_Club). However, under the risk-sharing

channel, the mitigation effect will be significant for both HighCDVar_Club and

LowCDVar_Club groups.

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Within a club, we have a CD measure that captures the cultural distance between each PE-

target country pair. We measure the variation in cultural distance within a PE consortium in two

ways: (1) the difference between the CD of the PE-target pair that is most culturally distance

from each other and the CD of the pair that is most culturally similar to each other

(CD_MAX_MIN); and (2) the standard deviation of the CDs in each club (CD_SD).

HighCDVar_Club takes the value of 1 if a buyout is a club deal and CD_MAX_MIN is greater

than its medium value (median=20.35), and 0 otherwise. LowCDVar_Club takes the value of 1 if

an LBO is a club deal and CD_MAX_MIN is below its median value and 0 otherwise. For

robustness, we define HighCDVar_Club and LowCDVar_Club using different cut-off points of

CD_MAX_MIN and CD_SD, the results are similiar.

Table 6 presents the regression results with Columns (1)-(2) using club dummy indicating

low CD variation clubs and its interaction with cultural distance and Columns (3)-(4) using the

high CD variation club indicator. The interaction terms of cultural distance and club dummies

are only significant for clubs with large variations of CDs but insignificant for low CD variation

group, supporting the familiarity channel but not the risk-sharing channel.

So far our study shows that PE firms could form clubs to reduce the cultural impact.

However, there are other ways PE firms could go about reducing their cultural distance to the

target firms. For example, they could hire mangers who are more familiar with target firms’

culture or from the same cultural background as the targets. Unfortunately, we do not have data

on the nationality of the PE executives at the time of each deal. Nevertheless, the managerial

level evidence would not affect the significance of cultural impact and the club deals’ mitigation

effect.

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4.5 Robustness Checks

4.5.1 Excluding U.S. and U.K. Firms

As shown in Table 1, the U.S. and U.K. dominate our buyout sample as the countries

hosting the most PEs and target firms. In this subsection, we verify that our results do not change

if we exclude these firms from our analyses by running our baseline regressions using different

subsamples. Table 7 reports the regression results. Columns (1)-(4) show the results that exclude

LBOs sponsored by U.S. PE firms or those with at least one U.S. PE in club deals. Columns (5)-

(8) exclude all U.S. and U.K. target firms. Our results show that cultural distance is still

negatively related to deal completion and positively related to buyout duration and that club

deals still have mitigation effects. In fact, the cultural impact and club mitigation effect are larger

and more significant with the subsamples. Overall, the robustness tests indicate that our results

are not driven by U.S. or U.K. firms.

4.5.2 Alternative Measures for Club

We consider two alternative indicators for club deals—the number of PE firms in a club

(PE_COUNT) and the number of unique PE home countries in a club (PE_CTR_COUNT), in

order to capture any variation inside a club deal. As shown by Table 2, there is some variation

among club deals in terms of the number of PE firms and PE countries involved. For example,

PE_COUNT ranges from 2 to 10 and there can be as high as 5 different PE countries involved in

a club deal. Table 7 presents regression results with the two alternative club measures and their

interaction terms with cultural distance. Under the new measures, the interaction terms are still

significantly positive for the logit regressions for deal completion and negative for the buyout

duration analysis, suggesting the robustness of the mitigation effects of clubs.

156

4.5.3 Cultural Distance within PE Consortiums

If cultural distance affects interactions between market participants, we should also expect

to observe their effects on the interaction between different PE firms within a club deal. In this

subsection, we examine whether the cultural differences among PE firms within a club have any

impact on deal completion and duration and how it affect the mitigation effect of club deals.

We measure the cultural distance within a PE consortium in two ways. First, for each club,

we calculate the cultural distance as defined in equation (1) between each pair of PE countries

and use the largest cultural distance as the within club distance measure (CD_CLUB). We also

use the standard deviation of the cultural distance between each PE-target pair in a club

(CD_SD), as defined in Section 4.3. Table 9 presents results of regressions when CD_CLUB and

CD_SD are added to baseline regressions. Our results of cultural impact and the club mitigation

effects are unchanged. Cultural differences within a PE consortium are in general not significant

during the intermediary phase of cross-border LBOs. This can be explained by the fact that PE

firms are sophisticated investors and therefore cultural differences among PEs will not affect the

collaboration within the consortium.

4.5.4 Individual Cultural Dimension

The analyses so far have focused on the multidimensional cultural distance that aggregate

the four cultural dimensions: power distance (PDI), individualism (IDV), masculinity (MAS), and

uncertainty avoidance (UAI). We next investigate each of the four dimensions individually and

examining its impact on deal completion and duration. Table 10 presents regression results. In

columns (1)-(4) of the logit regressions on deal completion, announced LBOs are less likely to

be completed if the cultural distance between PE and target firm are greater along the dimensions

157

of individualism and uncertainty avoidance and the mitigation effect of club deals is present in

all four dimensions. Columns (5)-(8) report the regression results for buyout duration. It takes

more time to complete an LBO if PE and target firms are more culturally distant along the

dimensions of power distance, individualism, and uncertainty avoidance. These results suggest

that the cultural impact from our baseline models is mainly from the distance along the

dimensions of individualisms and uncertainty avoidance.

4.6 Conclusion

Using a sample of 2,587 cross-border LBOs with PE firms from 52 countries and target

firms from 41 countries during the 1986–2013 period, we find that cultural distance between PE

countries and target countries reduces the likelihood of LBO completion and increases the

buyout duration. We also find that club deals mitigate the negative (positive) impact of cultural

distance on the likelihood (the duration) of deal completion through the familiarity channel.

These findings are robust after controlling for LBO transaction characteristics, PE firms’

reputation and experience, target country’s economic growth, LBO market development, formal

institutional environment, and PE-target country pair variables.

Our findings have important implications for both researchers and practitioners. First, we

provide the first large sample evidence that cultural distance affects the completion of cross-

border LBOs. The significance of cultural impact, after controlling for other institutional

environment, confirms the fundamental role of culture in North (1990) and Williamson (2000).

Our findings therefore contribute to the finance and culture literature by adding new evidence

underscoring the importance of national culture and cultural distance. We also contribute to the

158

international LBO literature and deal syndication literature. Second, our studies show the

potential deal-breakers (cultural distance, geographic distance, strong shareholder protection)

and deal-facilitators (management participation, PE reputation, LBO market development, credit

market conditions, and creditor rights) at informal and formal institutional levels for cross-border

LBOs. Our findings therefore can potentially help PE firms to identify opportunities and threat in

their cross-border LBO investments.

159

Table 4-1: Country-Wise Breakdown by PE Countries and Target Countries

This table presents the country-wide breakdown of the sample. PE countries are listed on the row variables and target countries on the columns.

Countries are ranked by the number of LBOs sponsored by PE countries. Only deals from the top 20 countries are listed but the values in the total

column and row include all LBOs. Data are from Capital IQ.

PE Countries Target Countries

Total USA GBR FRA CAN NLD AUS DEU ESP SWE ITA HKG CHE SGP BEL FIN IRL NOR JPN ISR NZL

United States(USA) 221 310 79 155 54 50 112 28 23 30 15 17 8 13 5 28 9 46 15 4 1331

United Kingdom(GBR) 120 201 200 6 83 27 143 89 51 74 6 33 9 21 24 25 10 12 8 7 1208

France(FRA) 21 57 131 1

1 23 14 1 23

6 1 5 1 2 2

1 295

Canada(CAN) 88 11 2 25 4 4 6

2

1

3 1

1 2 158

Netherlands(NLD) 10 39 9 1 22

14 2 4 6

3

7

2

121

Australia(AUS) 22 15 1 5 2 26 3 2

1

2 1 1 2 1

1

112

Germany(DEU) 12 14 9

6 1 25 3 3

1 14 2 4

2

101

Spain(ESP) 4 8 3

5 51

90

Sweden(SWE) 5 1

1

7 1 13

1

11

24

81

Italy(ITA) 4 2 4

1

1 1

54

2

1

71

Hong Kong(HKG) 4 4

1

5

1

11 1

7

1 52

Switzerland(CHE) 6 5 5 5

1 10 2

10

1

50

Singapore(SGP) 4 6

7 1

1

3

1

34

Belgium(BEL) 2 4 7

2

5

2

1 8

31

Finland(FIN)

1

1

10

1

1 7

1

27

Ireland(IRL) 1 15 4

2 1

1

1

25

Norway(NOR) 3 3

1

4

2 1 5

22

Japan(JPN) 4

15

20

Israel(ISR) 8 2 1

3

1

1

3

19

New Zealand(NZL) 3

9

1

3 16

Total 559 715 463 203 178 138 364 199 109 207 24 93 43 65 52 64 52 86 30 27 4,100

160

Table 4-2: Summary Statistics of Variables

Panels A-D present summary statistics for all variables that are defined in Appendix E. Panel E lists

reasons for deal cancelation. Panel F presents correlation between these variables and the bolded

coefficients indicate significant at 5% level.

N Mean Std.Dev Min p25 p50 p75 Max

Panel A: Dependent and Cultural Variables

DEALC 2587 94.16% 0.23 0 1 1 1 1

LEND (days) 2436 84.18 78.85 1 38 63 109 1461

LN(LEND) 2436 4.05 2.08 0 3.63 4.14 4.69 7.29

CD 2587 37.69 23.84 1.29 15.03 33.70 56.20 103.02

LN(1+CD) 2587 3.41 0.75 0.83 2.77 3.55 4.05 4.64

LN(1+PDI) 2587 2.05 1.07 0.18 1.25 1.79 2.96 4.34

LN(1+IDV) 2587 2.44 1.00 0.18 1.70 2.53 3.14 4.36

LN(1+MAS) 2587 2.29 1.03 0.22 1.61 2.40 3.18 4.32

LN(1+UAI) 2587 2.70 0.97 0.34 2.08 2.89 3.43 4.37

Panel B: Deal-level Variables

TV 2587 $650 2544 $10 $58 $160 $516 $87,025

SIZE(=ln(TV_real)) 2587 5.17 1.53 2.30 4.06 5.07 6.24 11.37

MGMT 2587 0.25 0.43 0 0 0 0 1

CLUB 2587 0.34 0.47 0 0 0 1 1

PE_COUNT 2587 1.55 0.99 1 1 1 2 10

PE_COUNT(club) 875 2.63 1.06 2 2 2 4 10

PE_CTR_COUNT 2587 1.33 0.57 1 1 1 2 5

PE_CTR_COUNT(club) 875 1.97 0.59 1 2 2 2 5

REPUTATION (%) 2587 0.47 0.01 0.01 0.05 0.15 0.46 6.63

PE_AGE (years) 2587 14 8.42 1 7 14 20 44

LN(PE_AGE) 2587 2.31 0.98 0 1.95 2.64 3.00 3.78

Panel C: Country-level Variables

GPD_G (%) 2587 2.30 2.39 -10.89 1.30 2.56 3.53 14.78

LN(GDPCAP) 2587 10.35 0.54 6.34 10.33 10.45 10.56 11.12

LN(1+OPENNESS) 2587 4.09 0.53 2.70 3.91 4.04 4.32 6.11

LBO_MKT (%) 2587 10.10 14.55 0.01 1.24 2.48 17.52 75.92

CREDITGDP(%) 2587 128.26 45.19 22.671 95.13 116.25 168.75 211.27

STMKCAP (%) 2587 94.75 48.32 15.43 56.43 89.44 128.09 271.49

CREDITOR_RIGHTS 2587 2.08 1.32 0 1 2 3 4

ANTI-DIRECTOR 2587 3.85 0.96 2 3 3.5 5 5

LEGAL_UK 2587 0.45 0.50 0 0 0 1 1

161

N Mean Std.Dev Min p25 p50 p75 Max

RULELAW 2587 1.46 0.44 -0.96 1.39 1.60 1.73 1.99

REGQUALITY 2587 1.43 0.42 -0.93 1.22 1.54 1.69 2.20

POL_RIGHTS 2587 1.17 0.76 1 1 1 1 5

Panel D: Country-pair Variables

EXCHANGERATE_G 2587 0.02 0.29 -0.77 -0.04 0.00 0.05 6.00

LN(1+FIS_FREEDOM) 2587 1.84 1.08 0.00 1.47 2.40 2.40 4.11

GEO_DIST 2587 3,746 3972 111 524 2,235 5,770 19,147

LN(GEO_DIST) 2587 7.54 1.28 4.71 6.26 7.71 8.66 9.85

COMBORDER 2587 0.27 0.39 0 0 0 0.5 1

COMLANG 2587 0.46 0.46 0 0 0 1 1

COMLEGAL 2587 0.53 0.47 0 0 0.50 1 1

COMRELIGION 2587 0.43 0.45 0 0 0 1 1

Panel E: Reasons for Deal Cancellation

Reason Frequency %

1.Target board/shareholder/creditors rejects 31 20.5%

2. Competing bid 17 11.3%

3. Negotiation failed; differences over valuation

and deal details 17 11.3%

4. Fail to complete the deal before deadline 10 6.6%

5. Closing conditions not met 8 5.3%

6. Rejected by regulatory agency 7 4.6%

7. Inadequate/delayed debt financing 4 2.6%

8. Other 3 2.0%

9. Undisclosed 54 35.8%

Total 151 100%

162

Panel F: Correlation Table

VARIABLES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

1 LN(1+CD) 1

2 SIZE -0.04 1

3 MGMT -0.03 -0.13 1

4 REPUTATION 0.01 -0.09 0.29 1

5 GDP_G -0.01 0.02 0.08 0.10 1

6 LN(GDPCAP) -0.19 0.14 0.01 -0.07 -0.25 1

7 LBO_MKT -0.30 0.07 0.03 -0.02 0.02 0.24 1

8 CREDITGDP -0.09 0.13 -0.09 -0.22 -0.31 0.36 0.38 1

9 STMKCAP -0.05 0.06 0.01 -0.13 0.18 0.18 0.22 0.27 1

10 OPENNESS 0.24 -0.01 -0.04 -0.01 0.14 0.03 -0.61 -0.30 0.08 1

11 CREDITOR_

RIGHTS -0.08 0.00 0.01 0.01 0.11 -0.15 -0.03 -0.03 0.14 0.30 1

12 ANTI-

DIRECTOR 0.02 -0.02 -0.01 -0.02 0.19 -0.18 -0.14 0.07 0.26 0.12 0.29 1

13 LEGAL_UK -0.29 0.01 -0.03 -0.08 0.23 0.01 0.26 0.24 0.19 -0.20 0.21 0.25 1

14 RULELAW -0.20 0.09 0.03 0.00 -0.08 0.45 0.16 0.28 0.26 0.15 0.03 0.10 0.18 1

15 REGQUALITY -0.23 0.09 0.04 0.01 0.00 0.62 0.29 0.27 0.30 0.18 0.19 0.15 0.27 0.35 1

16 POL_RIGHTS 0.22 -0.07 -0.08 -0.02 0.22 -0.36 -0.16 -0.20 0.23 0.45 0.27 0.17 0.16 -0.29 -0.14 1

17 RULELAW 0.03 -0.01 0.03 -0.01 -0.01 0.03 -0.03 -0.10 -0.06 0.01 0.02 0.01 -0.08 0.02 0.02 -0.03 1

18 FIS_FREEDOM -0.03 0.01 -0.06 -0.07 0.03 -0.15 -0.03 0.06 0.00 -0.08 0.07 0.01 0.09 -0.19 -0.18 0.16 0.01 1

19 LN(GEO_DIST) 0.05 0.14 -0.22 -0.19 0.02 -0.19 0.09 0.13 0.14 -0.14 0.02 0.09 0.16 -0.18 -0.04 0.20 -0.05 0.19 1

20 COMLANG -0.49 -0.02 0.00 -0.06 0.18 -0.03 0.28 0.05 0.25 -0.11 0.00 0.23 0.45 0.08 0.18 0.10 -0.06 -0.04 0.04 1

21 COMLEGAL -0.47 -0.07 -0.02 -0.09 0.12 0.01 0.18 0.01 0.11 -0.07 0.03 0.14 0.39 0.08 0.14 0.05 -0.01 -0.01 0.03 0.52 1

22 COMRELIGION -0.46 0.04 0.02 0.01 -0.02 0.03 0.18 -0.02 -0.05 -0.22 0.01 0.00 0.03 0.02 0.04 -0.14 0.03 0.00 -0.08 0.12 0.18

163

Table 4-3: National Cultural Distance

Panel A shows the ten country pairs with smallest cultural distance and ten country pairs with the largest

cultural distance. Panel B lists ten club deals where the cultural distance is reduced through club

formation. All variables are defined in Appendix E.

Panel A: Country Pairs with Maximum and Minimum Cultural Distance

Country pairs with minimum cultural distance Country pairs with maximum cultural distance

CD

CD

United States Australia 6.56 United Kingdom Portugal 103.02

Switzerland Germany 8.19

Belgium Singapore 102.65

United States United Kingdom 12.88

United Kingdom Russia 101.80

Canada Australia 14.11

Singapore Japan 101.69

France Belgium 14.49

United Kingdom Greece 97.73

United States Canada 15.03

United Kingdom Chile 95.84

China Hong Kong 15.84

Australia Malaysia 95.22

United Kingdom Australia 16.82

United Kingdom Malaysia 94.80

Spain Argentina 16.88

United States Portugal 94.60

South Korea Taiwan 17.23 United Kingdom South Korea 94.31

Panel B: Examples of Club Deals Reducing Cultural Distance

Buyer Country

Target

Country CD

Club

CD

Buyer

Country

Target

Country CD

Club

CD

1 Singapore Australia 91 6 United Kingdom Argentina 69

United Kingdom Australia 17 Spain Argentina 17 43

United States Australia 7 38 7 China Italy 78

2 United States Singapore 89

Luxembourg Italy 28 53

Hong Kong Singapore 24 57 8 United Kingdom Indonesia 90

3 Belgium Singapore 103

Switzerland Indonesia 74

India Singapore 43 73 South Korea Indonesia 42

4 United States Hong Kong 74

Singapore Indonesia 41 62

China Hong Kong 16 45 9 Netherlands Mexico 91

5 Singapore Germany 85

United States Mexico 82

United States Germany 31 58 Spain Mexico 42 72

10 United States Denmark 58

Sweden Denmark 18 38

164

Table 4-4: The Impact of Cultural Distance on Deal Completion and Duration

This table reports the multivariate regression results for buyout completion and duration. Column (1)

presents the logit regression results where dependent variable is equal to 1 if an LBO is completed and 0 if

the deal is cancelled. Column (2) shows OLS regression where the dependent variable is the natural logs

of the number of days from a buyout announcement to buyout completion for all completed LBOs. All

variables are defined in Appendix E. A constant is included in each specification but not reported in the

table. Standard errors of coefficient estimates are clustered at the target country level. P-values are in

brackets. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

(1)

(2)

VARIABLES DEALC

LN(LEND)

Coefficient p-value Coefficient p-value

LN(1+CD) -0.066** [0.041]

0.257** [0.014]

LBO Deal Characteristics:

SIZE -0.150*** [0.001]

0.478*** [0.000]

MGMT 0.280** [0.037]

-0.216** [0.025]

REPUTATION 29.700** [0.046] 14.728* [0.055]

Target Country Characteristics:

GDP_G 0.007 [0.732]

0.016 [0.435]

LN(GDPCAP) 0.270* [0.056]

-0.269 [0.156]

LBO_MKT 1.213** [0.034]

-0.273 [0.792]

LN(1+OPENNESS) 0.186 [0.101]

0.299 [0.107]

CREDITGDP 0.004* [0.061]

-0.002 [0.139]

STMKCAP -0.001 [0.326]

-0.002 [0.148]

CREDITOR_RIGHTS 0.082** [0.030]

-0.037 [0.684]

ANTI-DIRECTOR -0.002 [0.979]

0.150** [0.042]

LEGAL_UK -0.436** [0.015]

0.263 [0.438]

RULELAW -0.183 [0.456]

0.551** [0.011]

REGQUALITY 0.050 [0.877]

-0.148 [0.655]

POL_RIGHTS 0.164 [0.164] 0.112 [0.457]

PE-Target Country-pair Variables:

EXCHANGERATE_G 0.003 [0.997]

-0.297 [0.539]

LN(1+FIS_FREEDOM) -0.011 [0.832]

0.016 [0.791]

LN(GEO_DIST) -0.088** [0.020]

0.047** [0.042]

COMLEGAL 0.136 [0.457]

-0.089 [0.523]

COMLANG 0.196 [0.430]

-0.313* [0.066]

COMRELIGION 0.129 [0.327]

-0.010 [0.931]

Year Dummies Yes

Yes

Observations 2,587

2,436

Pseudo R-squared 0.112

Adj.R-squared

0.137

165

Table 4-5: Buyout Completion and Duration Analysis: The Effect of Club Deals

This table reports the multivariate regression results for buyout completion and duration. Column (1)

presents the logit regression results where dependent variable is equal to 1 if an LBO is completed and 0 if

the deal is cancelled. Column (2) shows OLS regression where the dependent variable is the natural logs

of the number of days from a buyout announcement to buyout completion for all completed LBOs. All

variables are defined in Appendix E. A constant is included in each specification but not reported in the

table. P-values are in brackets. ***, **, and * indicate significance at the 1%, 5%, and 10% level,

respectively.

(3)

(4)

VARIABLES DEALC

LN(LEND)

Coefficient p-value Coefficient p-value

LN(1+CD) -0.078** [0.033]

0.180*** [0.009]

LN(1+CD) X CLUB 0.097** [0.027]

-0.011*** [0.001]

CLUB

0.433*** [0.002]

LBO Deal Characteristics:

SIZE -0.170*** [0.001]

0.085*** [0.000]

MGMT 0.274** [0.046]

-0.213** [0.027]

REPUTATION 32.593** [0.012]

13.62* [0.071]

Target Country Characteristics:

GDP_G 0.008 [0.687]

0.001 [0.970]

LN(GDPCAP) 0.256* [0.070]

-0.266 [0.158]

LBO_MKT 1.172** [0.044]

-0.353 [0.241]

LN(1+OPENNESS) 0.190* [0.100]

0.096 [0.241]

CREDITGDP 0.004* [0.054]

-0.002 [0.145]

STMKCAP -0.001 [0.302]

-0.000 [0.639]

CREDITOR_RIGHTS 0.091** [0.017]

-0.019 [0.578]

ANTI-DIRECTOR -0.003 [0.973]

0.150** [0.044]

LEGAL_UK -0.414** [0.021]

0.091 [0.500]

RULELAW -0.117 [0.641]

0.564*** [0.008]

REGQUALITY 0.053 [0.873]

-0.061 [0.858]

POL_RIGHTS 0.159 [0.180] -0.043 [0.463]

PE-Target Country-pair Variables:

EXCHANGERATE_G 0.044 [0.946]

-0.037 [0.903]

LN(1+FIS_FREEDOM) -0.004 [0.936]

0.007 [0.787]

LN(GEO_DIST) -0.081** [0.047]

0.059*** [0.004]

COMLEGAL 0.146 [0.413]

0.128 [0.234]

COMLANG 0.166 [0.476]

-0.326* [0.061]

COMRELIGION 0.146 [0.267]

-0.006 [0.931]

Year Dummies Yes

Yes

Observations 2,587

2,436

Pseudo R-squared 0.114

Adj.R-squared

0.155

166

Table 4-6: Familiarity versus Risk-Taking

This table reports the multivariate regression results with indicators of high CD variance club and low CD

variance club. Target country variables include GDP_G, LN(GDPCAP), LBO_MKT, LN (1+OPENNESS),

CREDITGDP, STMKCAP, CREDITOR_RIGHTS, ANTI-DIRECTOR, LEGAL_UK, RULELAW,

REGQUALITY and POL_RIGHTS. Target-PE country pair variables include EXCHANGERATE_G, LN

(1+FIS_FREEDOM), LN (GEO_DIST), COMLEGAL, COMLANG, and COMRELIGION. All variables

are defined in Appendix E. A constant is included in each specification but not reported in the table.

Standard errors of coefficient estimates are clustered at the target country level. P-values are in brackets.

***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4)

VARIABLES DEALC LN(LEND) DEALC LN(LEND)

LN(1+CD) -0.066** 0.136** -0.062** 0.152**

[0.041] [0.042] [0.043] [0.030]

LN(1+CD) X LowCDVar_Club 0.077 -0.056

[0.212] [0.153]

LowCDVar_Club

0.271

[0.101]

LN(1+CD) X HighCDVar_Club

0.083** -0.096***

[0.035] [0.007]

HighCDVar_Club

0.333***

[0.005]

SIZE -0.152*** 0.088*** -0.156*** 0.086***

[0.001] [0.000] [0.002] [0.000]

MGMT 0.319** -0.158* 0.285** -0.162*

[0.017] [0.091] [0.041] [0.095]

REPUTATION 28.468** 14.185* 27.713** 13.120*

[0.048] [0.056] [0.044] [0.065]

Target Country Variables Yes Yes Yes Yes

Country-pair Variables Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes

Observations 2,587 2,436 2,587 2,436

Pseudo R-squared 0.105

0.106

Adj.R-squared

0.135

0.139

167

Table 4-7: Subsample Excluding U.S. and U.K. Firms

This table reports the multivariate regression results for buyout completion and duration for subsamples that excludes U.S. and U.K. firms.

Columns (1)-(4) show the results that exclude LBOs that are sponsored by U.S. PE firms or have at least one U.S. PE in club deals. Columns (5)-

(8) exclude all U.S. and UK target firms. Target country variables include GDP_G, LN (GDPCAP), LBO_MKT, LN (1+OPENNESS),

CREDITGDP, STMKCAP, CREDITOR_RIGHTS, ANTI-DIRECTOR, LEGAL_UK, RULELAW, REGQUALITY and POL_RIGHTS. Target-PE

country pair variables include EXCHANGERATE_G, LN (1+FIS_FREEDOM), LN (GEO_DIST), COMLEGAL, COMLANG, and

COMRELIGION. All variables are defined in Appendix E. A constant is included in each specification but not reported in the table. Standard errors

of coefficient estimates are clustered at the target country level. P-values are in brackets. ***, **, and * indicate significance at the 1%, 5%, and

10% level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES DEALC LN(LEND) DEALC LN(LEND) DEALC LN(LEND) DEALC LN(LEND)

Excludes U.S. PEs Excludes U.S. & U.K. Targets

LN(1+CD) -0.089*** 0.263*** -0.084*** 0.222*** -0.085*** 0.244** -0.079** 0.201***

[0.007] [0.009] [0.010] [0.003] [0.008] [0.019] [0.012] [0.005]

LN(1+CD) X CLUB

0.105*** -0.086***

0.103*** -0.084***

[0.003] [0.001]

[0.005] [0.002]

CLUB

0.343***

0.345***

[0.000]

[0.001]

SIZE -0.144*** 0.475*** -0.167*** 0.087*** -0.151*** 0.482*** -0.173*** 0.090***

[0.002] [0.000] [0.001] [0.000] [0.001] [0.000] [0.000] [0.000]

MGMT 0.324** -0.155 0.300** -0.118 0.340*** -0.171* 0.318** -0.128

[0.018] [0.109] [0.033] [0.203] [0.009] [0.077] [0.018] [0.177]

REPUTATION 33.003** 13.462* 27.282* 13.853* 29.959** 14.988** 24.214* 15.252**

[0.031] [0.063] [0.055] [0.065] [0.033] [0.035] [0.059] [0.038]

Target Country Variables Yes Yes Yes Yes Yes Yes Yes Yes

Country-pair Variable Yes Yes Yes Yes Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes Yes Yes Yes Yes

Observations 1,528 1,457 1,528 1,457 1,849 1,740 1,849 1,740

Pseudo R-squared 0.101

0.109

0.0989

0.107

Adj.R-squared

0.139

0.143

0.142

0.155

168

Table 4-8: Alternative Measure for Clubs

This table reports the multivariate regression results for buyout completion and duration using alternative

measures for club deals. Columns (1)-(2) present the logit regression results where dependent variables

are equal to 1 if an LBO is completed and 0 if the deal is cancelled. Columns (3)-(4) show OLS

regressions where dependent variables are the natural logs of the number of days from a buyout

announcement to buyout completion for all completed LBOs. Target country variables include GDP_G,

LN (GDPCAP), LBO_MKT, LN (1+OPENNESS), CREDITGDP, STMKCAP, CREDITOR_RIGHTS,

ANTI-DIRECTOR, LEGAL_UK, RULELAW, REGQUALITY and POL_RIGHTS. Target-PE country pair

variables include EXCHANGERATE_G, LN (1+FIS_FREEDOM), LN (GEO_DIST), COMLEGAL,

COMLANG, and COMRELIGION. All variables are defined in Appendix E. A constant is included in

each specification but not reported in the table. Standard errors of coefficient estimates are clustered at the

target country level. P-values are in brackets. ***, **, and * indicate significance at the 1%, 5%, and 10%

level, respectively.

(1) (2) (3) (4)

VARIABLES DEALC DEALC LN(LEND) LN(LEND)

LN(1+CD) -0.104** -0.136** 0.264*** 0.478***

[0.033] [0.015] [0.006] [0.000]

LN(1+CD) X PE_ COUNT 0.055**

-0.094***

[0.048]

[0.001]

LN(1+CD) X PE_CTR_COUNT

0.069**

-0.269***

[0.038]

[0.000]

PE_COUNT

0.315***

[0.000]

PE_CTR_COUNT

0.962***

[0.000]

SIZE -0.170*** -0.171*** 0.095*** 0.091***

[0.001] [0.001] [0.000] [0.000]

MGMT 0.277** 0.277** -0.140** -0.141**

[0.043] [0.043] [0.026] [0.025]

REPUTATION 26.614* 27.317* 16.538*** 16.859***

[0.089] [0.070] [0.001] [0.001]

Target Country Variables Yes Yes Yes Yes

Country-pair Variables Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes

Observations 2,587 2,587 2,436 2,436

Pseudo R-squared 0.116 0.118

Adj.R-squared

0.155 0.156

169

Table 4-9: Cultural Distance within a PE Consortium

This table reports the multivariate regression results for buyout completion and duration after controlling

for the cultural distance within a PE group. Target country variables include GDP_G, LN (GDPCAP),

LBO_MKT, LN (1+OPENNESS), CREDITGDP, STMKCAP, CREDITOR_RIGHTS, ANTI-DIRECTOR,

LEGAL_UK, RULELAW, REGQUALITY and POL_RIGHTS. Target-PE country pair variables include

EXCHANGERATE_G, LN (1+FIS_FREEDOM), LN (GEO_DIST), COMLEGAL, COMLANG, and

COMRELIGION. All variables are defined in Appendix E. A constant is included in each specification but

not reported in the table. Standard errors of coefficient estimates are clustered at the target country level.

P-values are in brackets. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES DEALC LN(LEND) DEALC LN(LEND) DEALC LN(LEND) DEALC LN(LEND)

LN(1+CD) -0.083** 0.331** -0.080** 0.222*** -0.074** 0.339** -0.071** 0.223***

[0.218] [0.017] [0.257] [0.004] [0.401] [0.017] [0.467] [0.003]

LN(1+CD)XCLUB

0.090* -0.009***

0.088* -0.008***

[0.070] [0.003]

[0.076] [0.004]

CLUB

0.449***

0.454***

[0.000]

[0.000]

CD_CLUB 0.086* -0.001 0.008 -0.003

[0.054] [0.678] [0.843] [0.137]

CD_SD

0.083 -0.009 0.021 -0.035

[0.157] [0.852] [0.751] [0.187]

SIZE -0.165*** 0.474*** -0.177*** 0.088*** -0.163*** 0.474*** -0.175*** 0.087***

[0.001] [0.000] [0.000] [0.000] [0.002] [0.000] [0.001] [0.000]

MGMT 0.264* -0.208** 0.276** -0.212** 0.259* -0.213** 0.269* -0.214**

[0.060] [0.036] [0.048] [0.034] [0.071] [0.034] [0.059] [0.032]

REPUTATION 28.427* 15.219** 25.399* 13.762*** 30.169* 15.288** 26.870* 13.714***

[0.071] [0.031] [0.094] [0.002] [0.063] [0.027] [0.089] [0.002]

Target Country Var Yes Yes Yes Yes Yes Yes Yes Yes

Country-pair Var Yes Yes Yes Yes Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes Yes Yes Yes Yes

Observations 2,587 2,436 2,587 2,436 2,587 2,436 2,587 2,436

Pseudo R-squared 0.117

0.120

0.114

0.118

Adj.R-squared

0.138

0.1381

0.138

0.138

170

Table 4-10: Four Cultural Dimensions

This table reports the multivariate regression results for buyout completion and duration using each individual

cultural dimension. Columns (1)-(4) present the logit regression results where dependent variables are equal to 1

if an LBO is completed and 0 if the deal is cancelled. Columns (5)-(8) show OLS regressions where dependent

variables are the natural logs of the number of days from a buyout announcement to buyout completion for all

completed LBOs. Target country variables include GDP_G, LN (GDPCAP), LBO_MKT, LN (1+OPENNESS),

CREDITGDP, STMKCAP, CREDITOR_RIGHTS, ANTI-DIRECTOR, LEGAL_UK, RULELAW, REGQUALITY

and POL_RIGHTS. Target-PE country pair variables include EXCHANGERATE_G, LN (1+FIS_FREEDOM), LN

(GEO_DIST), COMLEGAL, COMLANG, and COMRELIGION. All variables are defined in Appendix E. A

constant is included in each specification but not reported in the table. Standard errors of coefficient estimates are

clustered at the target country level. P-values are in brackets. ***, **, and * indicate significance at the 1%, 5%,

and 10% level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES DEALC DEALC DEALC DEALC LN(LEND) LN(LEND) LN(LEND) LN(LEND)

LN(1+PDI) -0.021

0.078***

[0.698]

[0.004]

LN(1+IDV)

-0.112***

0.096**

[0.000]

[0.023]

LN(1+MAS)

0.071

0.035

[0.537]

[0.216]

LN(1+UAI)

-0.163**

0.119**

[0.047]

[0.034]

LN(1+PDI)XCLUB 0.119**

-0.171***

[0.039]

[0.002]

LN(1+IDV)XCLUB

0.119**

-0.112**

[0.020]

[0.019]

LN(1+MAS)XCLUB

0.155***

-0.029

[0.009]

[0.253]

LN(1+UAI)XCLUB

0.116**

-0.122***

[0.020]

[0.001]

CLUB

0.218*** 0.218** 0.093 0.289***

[0.001] [0.016] [0.130] [0.000]

SIZE -0.157*** -0.167*** -0.164*** -0.163*** 0.088*** 0.087*** 0.086*** 0.087***

[0.001] [0.001] [0.001] [0.001] [0.000] [0.000] [0.000] [0.000]

MGMT 0.290** 0.290** 0.286** 0.295** -0.096 -0.094 -0.096 -0.164*

[0.038] [0.032] [0.041] [0.033] [0.286] [0.301] [0.290] [0.088]

REPUTATION 27.009* 24.128* 24.898* 24.957* 13.714* 13.944* 13.224* 12.634

[0.053] [0.065] [0.074] [0.067] [0.087] [0.066] [0.086] [0.111]

Target Country Vars Yes Yes Yes Yes Yes Yes Yes Yes

Country-pair Vars Yes Yes Yes Yes Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes Yes Yes Yes Yes

Observations 2,587 2,587 2,587 2,587 2,436 2,436 2,436 2,436

Pseudo R-squared 0.103 0.114 0.107 0.107

Adj.R-squared

0.137 0.139 0.137 0.138

171

Figure 4-1: LBO Transactions Each Year

The figures show the number of deals and total transaction values of all cross-border LBOs and cross-

border club deals. Panel A shows the number of LBOs by LBO announcement year and Panel B shows

the inflation-adjusted total transaction value in 2005 US dollars. LBO transaction sample is constructed

from the Standard and Poor’s Capital IQ Database.

Panel A: Number of LBOs

Panel B: Total Transaction Value

050

10

015

020

025

030

0

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

announcement year

# LBOs all # LBOs club

0

10

020

030

040

0

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

announcement year

Total Trans Value (Bil) all Total Trans Value (Bil) club

172

Chapter 5 Conclusion

This three-essay thesis conducts empirical studies related to financial institutions and

corporate finance. Chapter 2 finds that significant herding exists in banks’ lending decisions in

that banks extend similar types of loans at the same time and finds that banks tend to herd more

when they are struggling. These findings provide some evidence that support the information

asymmetry and regulatory arbitrage motivations for herding.

Chapter 3 finds that better post-LBO performance of target firms are associated with larger

amount of leverage added during the LBO process, tighter LBO loan covenants, and

management participation. LBOs are more likely to exit successfully if they use more bank debt

with tighter covenants and are sponsored by PE firms of high reputation. These results suggest

that the main source of value creation in LBOs is the reduced agency costs through the

disciplining effect of debt, closer monitoring by lenders, and better aligned management

incentives. Private equity firms’ reputation is also important in ensuring successful deal

outcomes.

Chapter 4 finds cultural distance between PE firms and target firms reduces the likelihood

of cross-border LBOs’ completion and increases the buyout duration. Club deals moderate the

negative (positive) impact of cultural differences on the likelihood (the duration) of an LBO deal

completion through the channel of increased familiarity. These results are robust after controlling

target countries’ formal institutional environment, economic growth, LBO market development,

and LBO deal characteristics. These results also provide an explanation for club formation by

introducing the familiarity motivates, implying that familiarity facilitates the completion of

cross-border LBO transactions.

173

My future studies will mainly focus on three areas, extending the work of this dissertation.

First, I will continue to study banks’ lending decisions, with a focus on syndicated loans. Second,

I will continue my research on LBOs and private equity, focusing on the determinants and

impacts of LBO debt structure and the interaction between banks and PE firms. Third, I will

continue to study the cultural impact on various corporate finance issues.

174

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185

Appendix A: Variable Definitions and Data Sources for Chapter 2

This table defines key variables used in Chapter 2. All data are from the Call Report unless otherwise specified.

Variables Descriptions

Panel A: Herding measures

LSVt LSV for each quarter t is calculated as the weighted average of LSV for each loan

category j in quarter t (LSVjt), where LSVjt is calculated as the absolute value of the

proportion of banks that increase their loan ratio to category j at time t minus the

average loan increase across all 12 categories, subtracted by the adjustment factor.

See equations (1) and (2) for more details.

FHWt FHW for each quarter t is calculated as the weighted average of FHW for each loan

category j in quarter t (FHWjt), where FHWjt is the calculated as the square of the

proportion of banks that increase their loan ratio to category j at time t minus the

average loan increase across all 12 categories, subtracted by the adjustment factor.

See equations (3) and (4) for more details.

Panel B: Bank-level Variables

Number of Banks Total number of U.S. commercial banks filling the Call Report.

Merger Total number of bank mergers. (source: FDIC)

Failure Total number of bank failures. (source: FDIC)

New Charters Total number of new charters. (source: FDIC)

Total assets Total assets are calculated as the aggregated assets (item RCFD2170 from Call

Report) of all banks in each quarter.

Total loans Aggregated total loans of all banks in each quarter. For the period prior to 1984:Q1,

total loans are calculated as the sum of total loans (RCFD1400) and lease financing

receivables (RCFD2165); for the period including and after 1984:Q1, total loans are

taken from item RCFD1400.64

Total equity Aggregated total regulatory equity capital (RCFD3210 plus other regulatory capital

according to the capital regulation in each period) of all banks in each quarter.

Equity ratio The ratio of total equity to total assets.

Total deposits Aggregated total deposits (RCFD2200) of all banks in each quarter.

Deposit ratio The ratio of total deposits to total assets.

64 Starting in 1984:Q1, RCFD1400 includes lease-financing receivables. To ensure continuity, total loans must

be computed as the sum of RCFD1400 and RCFD 2165 (lease-financing receivables) before 1984:Q1.

186

Variables Descriptions

Liquid assets Aggregated total on-balance-sheet liquid assets of all banks in each quarter.

Following Loutskina (2011), liquid assets are computed as the sum of book value of

U.S. treasury securities (RCFD0400), book value of U.S. government obligations

(RCFD0600), obligations of states and political subdivisions (RCFD0900), all other

bonds, stocks, and securities (RCFD0380), and Fed funds sold and securities

purchased under agreements to resell (RCFD1350) for the period prior to 1984:Q1.

For the 1984:Q1-1993:Q4 period, liquid assets are the sum of total investment

securities (RCFD0390), RCFD1350, and assets held in trading account

(RCFD2146). Finally, for the 1994:Q1 to 2010:Q4 period, it equals to the sum of

RCFD1350, held-to-maturity securities (RCFD1754), and total available-for-sale

securities (RCFD1773).

Liquidity ratio The ratio of liquid assets over total assets.

Total securities Aggregated book value of total securities of all banks in each quarter. Following

Loutskina (2011), for the period prior to 1984:Q1, it is the sum of RCFD0400,

RCFD0600, RCFD0900, and RCFD0380. For the 1984:Q1-1993:Q4 period, total

securities is the sum of RCFD0390 and RCFD2146. For 1994:Q1 to 2010:Q4 period,

it equals to the sum of RCFD1754 and RCFD1773.

Total cash Aggregated total cash holding (item RCFD0010) of all banks in each quarter.

Borrowed funds Aggregated total borrowed funds (item RCFD2821) of all banks in each quarter.

ROA The ratio of net income to average assets for each quarter. Net income is calculated

as [interest income (RIAD4107) – interest expense (RIAD4073)] + [non-interest

income (RIAD4079) – non-interest expense (RIAD4093)]. Interest expense is the

result of all interest paid to depositors and other creditors of the banks. Non-interest

income includes fiduciary activities income, service charges on depository accounts,

trading revenue, venture capital revenue, securitization income, investment banking,

advisory brokerage, and underwriting fees and commissions, insurance commission

fees and income, and all other income not originated in interest payments. Non-

interest expense includes personal compensations, legal expenses, office occupancy,

equipment expense, and other expenses.

NIM The ratio of net interest income (interest income – interest expense) to average assets

for each quarter.

NII The ratio of non-interest income to average assets for each quarter.

NIE The ratio of non-interest expense to average assets for each quarter.

Non-Performing ratio The ratio of nonperforming loans to total loans. Non-performing loan is the sum of

non-accruing loans (RCFD1403) and loans over 90 days late (RCFD1407).

Allowance The ratio of total loan loss allowance (RCFD3123) to total loans.

187

Variables Descriptions

Real Estate Loans secured by real estate (RCFD1410).

Commercial RE Real estate loans secured by nonfarm nonresidential properties (RCON1480).

1-4 Family Real estate loans secured by 1-4 family residential properties (RCON 1430).

Multifamily Real estate loans secured by multi-family (5 or more) residential properties

(RCON1460).

Construction Construction and land development loans (RCON1415).

Farmland Real estate loans secured by farmland (RCON1420).

C&I Commercial and Industrial Loans. Prior to 1984:Q1, they are the sum of commercial

and industrial loans (RCFD1600) and commercial and industrial loans-all other

(RCFD1766). For the period including and after 1984:Q1, they are taken from item

RCFD1600.

Individual Loans to individuals for household, family, and other personal expenditures

(RCFD1975).

Agriculture Loans to finance agricultural production and other loans to farmers (RCFD1590).

Depository Inst. Loans to depository institutions (RCON1489).

States & Political Loans to states and political subdivisions in the U.S. (RCON2107).

All other loans All other loans (RCON2080).

Not Categorized Loans that are not categorized in any of the loan categories above.

Panel C: Macro and Market Variables

GDP growth Quarterly real GDP growth rate. (Source: Bureau of Economic Analysis)

Inflation rate Quarterly inflation rate. (Source: Bureau of Economic Analysis)

Unemployment rate Quarterly unemployment rate. (Source: Bureau of Economic Analysis)

Fed funds Federal funds rate. (Source: Federal Reserve Board of Governors Release H.15)

Libor-OIS The spread between London interbank offer rate (Libor) and overnight index swap

(OLS). (Source: Bloomberg)

Baa-Aaa Moody’s spread between corporate bonds with Baa and Aaa ratings. (Source:

Federal Reserve Board of Governors Release H.15)

Basel1 An indicator variable that takes a value of one it is between 1992:Q1 to 2005:Q2 and

zero otherwise.

Basel2 An indicator variable that takes a value of one if it is between 2005:Q3 and 2010:Q4

and zero otherwise.

188

Appendix B: Variable Definitions and Data Sources for Chapter 3

Variables Descriptions Sources

Panel A: Target Firm Characteristics

Assets Book assets, in 2005 dollars. Capital IQ,

Compustat, and

SEC Filings

Sales Sales, in 2005 dollars. As above

EBITDA Earnings before interest, tax, depreciation, and amortization, in 2005

dollars.

As above

NCF Net cash flow, calculated as EBITDA minus capital expenditure, in

2005 dollars.

As above

Leverage The ratio of total debt to book assets. Pre-buyout leverage is the

leverage in the last fiscal year prior to the buyout completion and post-

buyout leverage is the leverage in the first full fiscal year following the

buyout completion.

As above

Leverage change Leverage in one year after LBO completion (year +1) minus leverage

one year prior to the LBO completion (year -1).

As above

Asset tangibility The ratio of net PP&E to book assets. As above

Cash flow volatility The standard deviation of EBITDA/Sales in the past 3 years before the

LBO completion.

As above

Panel B: LBO Deal Characteristics

Transaction Value Total LBO transaction value, in 2005 dollars. Capital IQ and

SDC

Size Ln(Transaction Value), in 2005 dollars. As above

Club dummy An indicator variable that takes a value of one if an LBO is sponsored

by more than one PE firms and zero otherwise.

As above

Optimal club dummy An indicator variable that takes a value of one if an LBO is sponsored

by 2 or 3 PE firms and zero otherwise.

As above

Bank affiliation An indicator variable that takes a value of one if an LBO is sponsored

by a PE firm that is a subsidiary of a bank at the LBO announcement

day and zero otherwise.

Capital IQ,

SDC, and

Dealscan

Mgmt participation An indicator variable that takes a value of one if an LBO is labeled by

Capital IQ as “management buyout”, “management participated”,

“individual investor participated” when the individual investor is

confirmed to be board member or management of the target firm, or

the firm is bought out through an employee stock ownership plan

(ESOP); or if the SDC synopsis labels it as “management led” or

“management participated”; and zero otherwise.

Capital IQ and

SDC

189

Variables Descriptions Sources

CEO Change An indicator variable that takes a value of one if there is CEO change

from the buyout announcement to two full years after the buyout

completion.

Capital IQ and

Factiva

Success An indicator variable that takes a value of one if (1) an LBO exits

through an IPO or a sale and (2) the deal is completed by Dec 31st

2008; it takes a value of zero if (1) the target firm goes bankrupt,

remains private, or unknown, and (2) the deal is completed by Dec 31st

2008. It is set to missing if the deal is completed after Dec 31st 2008.

Factiva, SEC

filings, Capital

IQ Tearsheet,

and company

website

Panel C: LBO Financing Details

Bank debt The sum of the revolving credit facility and the term A loan facility in

a debt package.

Dealscan

Bank debt % The ratio of bank debt over total LBO debt amount. As above

Institutional Debt The sum of term B, C, D loan facilities and notes sold to institutional

investors.

As above

Junior Debt The sum of the bridge loan facility from Dealscan and the mezzanine

debt and high-yield bond from Capital IQ.

Dealscan and

Capital IQ

Spread Package-level spread, calculated as the average spread across all

facilities in a package (weighted by facility size). For each facility, the

spread is the difference between the all-in-drawn interest and the

corresponding reference rate defined in the debt contract (e.g., 6-

month LIBOR).

Dealscan

Bank debt spread Same as package-level spread, but using only facilities classified as

bank debt.

As above

Inst. Debt spread Same as package-level spread, but using only facilities classified as

institutional debt.

As above

Maturity Package-level maturity (in months), calculated as the average maturity

across all facilities in a package (weighted by facility size).

As above

Bank debt maturity Same as package-level maturity, but using only facilities classified as

bank debt.

As above

Inst. Debt maturity Same as package-level maturity, but using only facilities classified as

institutional debt.

As above

Covenant Intensity Modified Covenant Intensity. Sum of: (1) number of financial

covenants (up to 6); (2) number of sweep covenants (asset sales

sweep, debt issue sweep, equity issue sweep; excess CF sweep,

insurance proceeds sweeps); (3) dividend covenant (0/1); (4) secured

debt covenant (0/1). Ranges from 0 to 13.

Dealscan

190

Variables Descriptions Sources

Cov-lite loans Covenant-lite loans, defined as syndicated loans that do not have any

financial maintenance covenants.

As above

Deal pricing The ratio of EBITDA (at year -1) over total transaction value, adjusted

by subtracting the S&P 500 market earnings/price ratios for each

month of LBO completion.

Capital IQ,

SDC,

Compustat,

SEC Filings,

S&P Index

Data Platforms.

Panel D: PE Reputation

PE Reputation The market share of a PE firm in the past 36 months, based on the total

transaction value of all LBOs it sponsor.

Capital IQ and

SDC

PE Reputation (N) The market share of a PE firm in the past 36 months, based on the

number of LBOs it sponsor.

As above

PE Reputation (N,

70)

The market share of a PE firm since 1970, based on the number of

LBOs it sponsor.

As above

PE Reputation

(TV,70)

The market share of a PE firm since 1970, based on the total

transaction value of LBOs it sponsor.

As above

PE Experience (year) PE firm's years of experience, calculated as number of years between

the first ever LBO sponsored by a PE to its last LBO.

As above

Panel D: Market Measures

Baa Moody's Seasoned monthly Baa Corporate Bond Yield. FRB's H.15

Release

Baa-HBaa The difference between the Baa yield in the month of LBO completion

and its 60-month historical average.

As above

Term The difference between 10-year T-Bond yield and 3-month T-Bill

yield.

As above

Hot An indicator variable that takes a value of one if an LBO becomes

effective in a hot month. I take a 12-month centered moving average

of the number of LBOs for each month over the sample period. Hot

months are defined as above the median in the distribution of the

monthly moving average.

Capital IQ and

SDC

191

Appendix C: Top 25 PE Firms for Chapter 3

This table ranks private equity (PE) firms by the total dollar amount of transactions and the number of transactions they sponsored in my sample.

Total transaction value is inflation-adjusted with the base year of 2005. Percentage in the brackets of Column (1) shows the sum of transaction

values of all deals sponsored by each PE firm as a proportion of total transaction value of all 501 LBOs. Percentage in brackets of Column (2)

presents the ratio of the number of deals sponsored by each PE firm over the total number of LBOs in the sample. Column (3) shows the number of

club deals each PE firm participates in where club deals are LBOs sponsored by two or more PE firms. If a PE firm is a subsidiary of a bank at the

time of deal announcement and the bank also provides loans for the buyout, the parent bank is listed in Column (4). PEI 2013, 2010, and 2007

indicate the ranking of each PE firm by the Private Equity International (PEI) magazine in the years 2013, 2010, and 2007 based on the capital

raised by each PE firm over the previous five-year period.

Name of Private Equity Firm

(1)

Total Transaction Value

(2)

Number of Transactions

(3)

Club

deals

(4)

Parent Bank

(5)

PEI

2013

(6)

PEI

2010

(7)

PEI

2007 Rank Value % Rank Number %

Kohlberg Kravis Roberts & Co. L.P. 1 $ 228,989 (25.45%) 1 27 (5.4%) 12

4 3 2

TPG Capital, L.P. 2 $ 183,501 (20.40%) 2 26 (5.2%) 20

1 4 5

Goldman Sachs Private Equity 3 $ 142,897 (15.88%) 5 21 (4.2%) 15 Goldman Sachs 6 1 3

Bain Capital Private Equity 4 $ 100,316 (11.15%) 6 20 (4.0%) 13

9 8 8

The Blackstone Group 5 $ 83,372 (9.27%) 3 22 (4.4%) 13

3 7 4

The Carlyle Group LP 6 $ 78,450 (8.72%) 11 15 (3.0%) 7

2 2 1

Thomas H. Lee Partners, L.P. 7 $ 69,421 (7.72%) 7 19 (3.8%) 9

28 30

Lehman Brothers Private Equity 8 $ 66,788 (7.42%) 24 6 (1.2%) 1 Lehman Brothers

25

Merrill Lynch Global PE 9 $ 64,841 (7.21%) 15 8 (1.6%) 5 Merrill Lynch

Citigroup Private Equity LP 10 $ 55,982 (6.22%) 31 5 (1.0%) 4

34 27

Apollo Global Management, LLC 11 $ 49,629 (5.52%) 8 17 (3.4%) 8

8 5 12

Morgan Stanley Private Equity 12 $ 44,419 (4.94%) 88 2 (0.4%) 1 Morgan Stanley

Providence Equity Partners LLC 13 $ 42,050 (4.67%) 20 7 (1.4%) 6

17 9

Madison Dearborn Partners, LLC 14 $ 37,172 (4.13%) 12 14 (2.8%) 7

24 32

Riverstone Holdings LLC 15 $ 30,017 (3.34%) 52 3 (0.6%) 3

11 29

Silver Lake 16 $ 28,807 (3.20%) 33 5 (1.0%) 4

27 33 19

192

Name of Private Equity Firm

(1)

Total Transaction Value

(2)

Number of Transactions

(3)

Club

deals

(4)

Parent Bank

(5)

PEI

2013

(6)

PEI

2010

(7)

PEI

2007 Rank Value % Rank Number %

Clayton, Dubilier & Rice, Inc. 17 $ 25,267 (2.81%) 29 6 (1.2%) 2

33 18 47

DLJ Merchant Banking 18 $ 25,024 (2.78%) 4 22 (4.4%) 11 Credit Suisse

J.P. Morgan Partners, LLC 19 $ 18,848 (2.10%) 10 15 (3.0%) 10 JPMorgan Chase 13

Deutsche Bank AG, Investment Arm 20 $ 13,271 (1.48%) 30 5 (1.0%) 4 Deutsche Bank

Warburg Pincus LLC 21 $ 13,071 (1.45%) 17 8 (1.6%) 2

5 9 14

Credit Suisse Private Equity, LLC 22 $ 12,770 (1.42%) 18 8 (1.6%) 7 Credit Suisse65

Canada Pension Plan Investment

Board 23 $ 11,545 (1.28%) 87 2 (0.4%) 2

20

Court Square Capital Partners 24 $ 9,704 (1.08%) 9 16 (3.2%) 4 Citigroup

Leonard Green & Partners, L.P. 25 $ 9,679 (1.08%) 13 12 (2.4%) 5

39

31

65 Both DLJ Merchant Banking and the Credit Suisse Private Equity, LLC are PE firms listed as subsidiary of Credit Suisse. But I confirmed that they are

different PE firms.

193

Appendix D: Correlation Table for Chapter 3

Appendix D reports correlation coefficients across the key variables used in Chapter 3. All variables are defined in Appendix B. The bolded

coefficients indicate significant at 5% level.

mean sd (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

(1) EBITDA/sale

growth -1 to -2

-0.09 0.81 1

(2) Size 6.49 1.33 -0.01 1

(3) EBITDA/sale

growth -2 to -1

0.22 0.65 0.13 0.03 1

(4) Pre-LBO

Leverage

0.35 0.28 -0.02 -0.01 -0.06 1

(5) Pre-LBO Asset

tangibility

0.31 0.22 -0.01 0.09 -0.10 0.03 1

(6) Pre-LBO Cash

flow vol.

0.03 0.04 -0.15 -0.07 0.13 0.05 0.05 1

(7) Leverage chg 0.37 0.42 0.16 0.01 0.05 -0.49 -0.06 -0.04 1

(8) Bank debt % 0.54 0.35 -0.01 -0.35 -0.10 0.02 0.10 0.13 -0.12 1

(9) Covenant 3.68 3.62 0.13 0.03 0.09 0.12 -0.12 -0.05 0.14 -0.30 1

(10) PE Reputation 0.02 0.03 -0.01 0.36 0.00 -0.11 0.10 -0.07 0.15 -0.10 -0.04 1

(11) Club dummy 0.30 0.46 0.00 0.19 0.02 -0.01 -0.03 0.10 -0.03 -0.12 0.13 0.07 1

(12) Optimal club 0.25 0.44 -0.04 0.11 -0.01 0.00 -0.04 0.09 -0.02 -0.11 0.10 0.03 0.90 1

(13) Bank affiliation 0.21 0.40 -0.01 0.17 0.11 0.05 0.07 0.03 -0.01 -0.01 0.01 0.16 0.23 0.14 1

(14) Mgmt part. 0.34 0.47 0.12 -0.12 0.01 -0.03 0.02 -0.04 0.03 0.02 0.05 -0.12 -0.01 -0.03 0.05 1

(15) CEO change 0.42 0.49 -0.11 0.21 0.01 -0.08 -0.04 -0.04 0.06 -0.13 0.09 0.09 0.03 0.00 0.00 -0.15 1

(16) Baa 7.79 1.52 0.05 -0.17 -0.06 -0.09 0.15 -0.06 0.07 0.31 -0.40 0.04 -0.26 -0.23 0.09 0.16 -0.14 1

(17) Baa-Hbaa -0.84 0.88 0.02 -0.04 0.04 0.06 -0.12 0.09 0.02 -0.15 0.30 -0.11 0.20 0.17 -0.06 -0.06 0.13 -0.38 1

(18) Term 1.45 1.06 -0.01 -0.13 -0.03 0.10 0.04 0.04 -0.20 0.05 -0.10 -0.03 -0.09 -0.09 -0.13 0.07 -0.14 0.02 -0.30 1

(19) Hot 0.76 0.43 0.07 -0.01 0.12 0.02 -0.01 0.04 0.09 -0.08 0.15 -0.12 0.01 0.02 0.02 0.04 0.03 -0.20 0.11 -0.15 1

(20) Spread 2.71 1.18 0.08 0.04 -0.07 0.01 -0.12 -0.05 -0.09 -0.13 -0.01 -0.07 -0.01 0.00 -0.08 0.03 -0.02 -0.04 0.04 0.20 -0.07 1

(21) Deal Pricing 0.12 0.25 0.06 -0.30 0.03 0.05 0.11 0.01 -0.03 0.16 -0.06 -0.01 -0.07 -0.04 -0.03 0.03 -0.04 0.13 -0.07 0.05 -0.08 -0.11

194

Appendix E: Variable Definitions and Data Sources for Chapter 4

Variables Descriptions Sources

Panel A: Dependent and Cultural Variables

DEALC An indicator variable that takes a value of one if an LBO is

completed and zero otherwise.

Capital IQ and authors’

calculation

LEND Number of days between the announcement and the completion

of an LBO.

As above

CD Cultural distance is calculated as a multidimensional measure

that estimates the distance between PE and target countries

along Hofstede's (2001) four dimension scores: power distance,

individualism, masculinity, and uncertainty avoidance. See

equation (1) in Chapter 4 for details.

Hofstede (2001) and

authors’ calculation

PDI The absolute value of the difference between Hofstede’s cultural

score for PE country and target country based on the dimension

of power distance. Power distance measures the extent to which

the less powerful members expect and accept that power is

distributed unequally.

As above

IDV The absolute value of the difference between Hofstede’s cultural

score for PE country and target country based on the dimension

of individualism. Individualism captures the degree to which a

society stresses the role of the individual vs. that of the group.

As above

MAS The absolute value of the difference between Hofstede’s cultural

score for PE country and target country based on the dimension

of masculinity. Masculinity focuses on the extent to which male

assertiveness is promoted as dominant values in a society as

opposed to female nurturance.

As above

UAI The absolute value of the difference between Hofstede’s cultural

score for PE country and target country based on the dimension

of uncertainty avoidance. Uncertainty avoidance reflects the

extent to which people feel uncomfortable about uncertain,

unknown, or unstructured situation.

As above

Panel B: Deal-level Control Variables

SIZE Natural log of LBO transaction value, in 2005 U.S. dollars. Capital IQ

MGMT An indicator variable that takes a value of one if an LBO is

labeled by Capital IQ as “management buyout”, “management

participated”, “individual investor participated” when the

individual investor is confirmed to be board member or

management of the target firm, or the firm is bought out through

an employee stock ownership plan (ESOP); zero otherwise.

As above

195

Variables Descriptions Sources

CLUB An indicator variable that takes a value of one if a deal is

composed of more than one PE firms and zero otherwise.

Capital IO

PE_COUNT The number of different PE firms in a club. Capital IQ and authors’

calculation

PE_CTR_COUNT The number of unique PE home countries in a club. As above

REPUTATION Reputation for each PE firm at the time of a new LBO deal is

calculated as the market share of the PE in the past 36 months

before the buyout announcement based on the number of

completed LBOs the PE sponsors.

As above

PE_AGE

For each LBO, PE firm’s year of experience is calculated as the

number of years since the first ever LBO conducted by the PE

until the announcement year of the current LBO.

As above

Panel C: Country-level Control Variables

GPD_G Target country’s GDP growth one year prior to the LBO

announcement.

Penn World Table 8.1

GDPCAP Target country’s annual GDP per capita one year prior to the

LBO announcement, in 2005 U.S. dollars.

As above

OPENNESS Target country’s exports plus imports divided by GDP one year

prior to the LBO announcement.

As above

LBO_MKT Target country’s LBO market size. Calculated as the number of

completed LBOs from the target country divided by the total

number of completed LBOs since 1970.

Capital IQ and authors’

calculation

CREDITGDP The ratio of target country’s private credit by deposit money

banks and other financial institutions to GDP one year prior to

the LBO announcement.

Beck et al. (2000, 2009),

Čihák et al. (2012)

STMKCAP The ratio of target country’s stock market capitalization to GDP

one year prior to the LBO announcement.

As above

CREDITOR_RIGHTS An index aggregating creditor rights. The index ranges from 0

(weak creditor rights) to 4 (strong creditor rights).

La Porta et al. (1998)

ANTI-DIRECTOR Revised anti-director rights index. The index ranges from 0

(weak shareholder rights) to 6 (strong shareholder rights).

Djankov et al. (2008)

LEGAL_UK An indicator variable that takes a value of one if a country's

legal origin is English common law, and zero if the legal origin

is French, German, or Scandinavian civil law.

La Porta et al. (1998)

196

Variables Descriptions Sources

RULELAW Worldwide Governance Indicator for Rule of law. It reflects

perceptions of the extent to which agents have confidence in

and abide by the rules of society, and in particular the quality of

contract enforcement, property rights, the police, and the courts,

as well as the likelihood of crime and violence. This index is

time-varying and ranges from -2.5 (weak) to 2.5 (strong).

Kaufmann et al. (2010)

REGQUALITY

Worldwide Governance Indicator for Regulatory Quality. It

reflects perceptions of the ability of the government to

formulate and implement sound policies and regulations that

permit and promote private sector development. This index is

time-varying and ranges from -2.5 (weak) to 2.5 (strong).

As above

POL_RIGHTS An index for political rights. The ratings are determined by a

survey including ten political rights questions, which are

grouped into three subgroups regarding the electoral process,

political pluralism and participation, and the functioning of the

government. This index is time-varying and ranges from 1

(most free) to 7 (least free).

Freedom House (2008)

Panel D: Country-pair Control Variables

EXCHANGERATE_G Exchange rate growth 12 months prior to the LBO

announcement between the PE country and target country.

World Development

Indicators and authors’

calculation

FIS_FREEDOM The absolute value of the difference in fiscal freedom between

PE country and target country. Fiscal freedom measures of the

extent to which government permits individuals and businesses

to keep and manage their income and wealth for their own

benefit and use.

Economic Freedom

Index

GEO_DIST Geographical distances between PE and target countries,

calculated following the great circle formula, which uses

latitudes and longitudes of the most important city (in terms of

population) or if its official capital.

CEPII

COMBORDER An indicator variable that takes a value of one if the PE country

and target country share the same border.

As above

COMLEGAL An indicator variable that takes a value of one if the PE country

and target country share the same legal system (common, or

civil, or religious law).

La Porta et al. (1998)

COMLANG An indicator variable that takes a value of one if the PE country

and target country share common official language.

CIA World Factbook and

CEPII

COMRELIGION An indicator variable that takes a value of one if the PE country

and target country share the same primary religion.

CIA World Factbook

197

Appendix F: Top 20 PE firms for Chapter 4

This table ranks private equity (PE) firms by the number of LBOs they sponsored and the total dollar amount of transaction value of these LBOs.

Total transaction value is converted to US dollar and inflation-adjusted (in 2005 dollars).

Number of LBOs Total Transaction value of LBOs

Rank Count PE Name PE Country Rank TV($Bil) PE Name PE Country

1 108 The Carlyle Group LP US 1 131.00 The Carlyle Group LP US

2 107 CVC Capital Partners Limited UK 2 126.68 Goldman Sachs Private Equity Group US

3 80 3i Group plc UK 3 119.60 Ardian (AXA Private Equity) France

4 55 Bridgepoint Advisers Limited UK 4 112.95 CVC Capital Partners Limited UK

5 53 Apax Partners LLP UK 5 98.71 The Blackstone Group, Private Equity Group US

6 52 Advent International Corporation US 6 91.02 Kohlberg Kravis Roberts & Co. US

7 50 Motion Equity Partners S.A.S France 7 83.38 TPG Capital, L.P. US

8 45 Goldman Sachs Private Equity Group US 8 75.74 Apax Partners LLP UK

9 42 J.P. Morgan Partners, LLC US 9 72.38 Fondi Italiani per le Infrastrutture SGR SpA Italy

10 41 Intermediate Capital Group PLC UK 10 64.91 BC Partners UK

11 40 AAC Capital Partners Netherlands 11 59.64 Providence Equity Partners LLC US

12 40 Permira Advisers Ltd. UK 12 56.68 Permira Advisers Ltd. UK

13 39 Equistone Partners Europe UK 13 49.94 Madison Dearborn Partners, LLC US

14 38 Arle Capital Partners Limited UK 14 48.33 Merrill Lynch Capital, Investment Arm US

15 37 The Blackstone Group, PE Group US 15 47.24 Bain Capital Private Equity US

16 37 TPG Capital, L.P. US 16 45.72 Cinven Limited UK

17 34 Kohlberg Kravis Roberts & Co. US 17 32.69 Riverstone Holdings LLC US

18 32 Cinven Limited UK 18 29.14 Macquarie Infrastructure and Real Assets Australia

19 32 BC Partners UK 19 26.40 PAI Partners France

20 31 IK Investment Partners UK 20 25.16 Morgan Stanley Private Equity US