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LBO Risk in Credit Spreads

Yael Eisenthal Berkovitz

Submitted in partial fulfillment of the

requirements for the degree

of Doctor of Philosophy

under the Executive Committee

of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2009

UMI Number: 3388444

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ABSTRACT

LBO Risk in Credit Spreads


The buyout wave of the years 2004-2007, unprecedented in both number and value

of transactions, motivates this study of the pricing of LBO risk in credit spreads. This

work studies the effect of LBOs on the cross-sectional variation in corporate spreads,

and, subsequently, discusses and proposes incorporation of this risk in credit pricing

models.

Using a dataset of LBOs, CDS and bonds, I first study the reaction of credit

spreads of target firms to LBO announcements in the US during the years 2001-2007. I

find evidence of credit spread widening by 60-70%, suggesting costs of additional debt

significantly outweigh potential increase in expected cash flows. I document a negative

reaction in prices of unprotected bonds, suggesting wealth transfer from debt-holders

to shareholders. Yet, back-of-the-envelope calculation shows gains to shareholders are

due, in large, to alternate sources, implying value creation in LBOs. I then proceed

to test whether this LBO restructuring risk is priced ex-ante by investors in debt

markets. Using exogenous industry-level variables, I find that firms more likely to

undergo an LBO have spreads that are higher by 30-50 bps. Incorporation of results

into credit pricing models could further our understanding of the credit spread puzzle

and alleviate model spread under-prediction in buyout boom years.

Building on this empirical evidence, I consequently propose a learning-based model

of spreads that explicitly incorporates both default and LBO risk. In this model,

investors revise their beliefs on LBO risk via a mechanism of Bayesian updating

observing industry-level activity. I find increased industry-level clustering in buyout

activity and postulate intra-industry reaction as a driver of the evolution of LBO risk

over time. Estimates of the time series of LBO risk and model spreads suggest the

proposed mechanism is significant in explaining observed market spreads. Estimated

LBO risk is also shown to explain some of the mispricing from a structural credit

model. Interestingly, using this mispricing can improve prediction of LBO likelihood.

Contents

1 Leveraged Buyout Risk and Debt Markets 1

1.1 Introduction 1

1.2 Related literature 6

1.3 Data 7

1.3.1 Credit Default Swaps 7

1.3.2 LBO announcements 10

1.3.3 Bond transaction prices 11

1.3.4 Bond issuance and covenants 12

1.4 Bonds vs Credit Default Swaps in LBOs 13


1.4.2 Improved Market Transparency 13

1.4.3 Bond Indentures 14

1.5 Event Study 16


1.5.2 Corporate Bonds 23

1.5.3 Wealth Transfer and Value Creation 25

2 Pricing of LBO Risk in Credit Spreads 42

2.1 Introduction 42

2.2 LBO risk at the industry level 43

2.2.1 Data 43

2.2.2 Industry clustering in LBO activity over time 43

l

2.2.3 Industry-wide effects of LBO announcements 45

2.3 Methodology 48

2.3.1 Industry-level probability of LBO 48

2.3.2 Test across different markets 49

2.3.3 Characteristics of LBO targets 50

2.3.4 Firm-level instrumental variable: Event-risk covenants . . . . 51

2.4 Empirical Results 53

2.4.1 Results across markets 55

2.4.2 Firm-level results 56

2.5 LBO Monitoring with option implied volatility 57

2.6 Summary 59

3 Modeling LBO Risk in Corporate Spreads: Industry Patterns in

Buyout Activity 71

3.1 Introduction 71

3.2 Related literature 75

3.3 Data 78


3.3.2 LBO announcements 79

3.4 Identifying LBO targets 81

3.4.1 Properties of LBO targets over time 81

3.4.2 Estimation of LBO likelihood 83

3.5 Modeling of LBO risk in credit spreads 88

3.5.1 The Model 90

3.5.2 Modeling of LBO contagion 93

3.5.3 CDS Pricing 96

3.6 Model Estimation 101

3.7 LBO risk and structural model mispricing 107

3.8 Summary 110

ii

Bibliography 124

Appendices 128

List of Figures

1.1 LBO activity worldwide 37

1.2 CDS data coverage 37

1.3 Term structure of average CDS spread 2001-2006 38

1.4 CDS distribution across rating classes 38

1.5 Cumulative abnormal CDS returns 39

1.6 Cumulative abnormal returns by rating class 39

1.7 Changes in level of spreads by rating class 40

1.8 Event-driven change in rating distribution 40

1.9 Cumulative abnormal bond returns 41

1.10 Cumulative abnormal stock returns 41

2.1 LBO restructuring risk in a structural framework 69

2.2 Industry-level clustering in LBO activity 70

2.3 Intra-industry cumulative abnormal change in CDS spreads 70

3.1 CDS distribution across ratings 119

3.2 US LBO announcements 1980-2007 119

3.3 US LBOs and default rates 120

3.4 Change in prior 121

3.5 Average market and model spreads 121

3.6 Average spreads of LBO targets around events 122

3.7 Average spreads of same-industry firms around LBO events 122

3.8 Time series of state probability (example) 123

iv

List of Tables

1.1 Ratings across restructuring clauses 27

1.2 Distribution of CDS spreads 28

1.3 LBO target firms 30

1.4 LBO target issuers 32

1.5 New issues and covenants 33

1.6 Event study in CDS spreads 34

1.7 Event study in bond and equity markets 36

2.1 LBO distribution across industries (%) 61

2.2 Intra-industry changes in CDS spreads 62

2.3 Pricing of LBO risk in CDS spreads - US 2001-2006 64

2.4 Pricing of LBO risk in spreads - across time and firms 65

2.5 Pricing of LBO risk in CDS spreads - Europe 2001-2006 66

2.6 Pricing of LBO risk in CDS spreads - firm-level IV 67

2.7 LBO monitoring using option implied volatility slope 68

3.1 Distribution of CDS spreads by sector 112

3.2 US LBO announcements 1979-2007 112

3.3 Characteristics of LBO targets over time 113

3.4 Estimation of LBO likelihood (US 1980-2007) 114

3.5 Leverage in LBO targets (US 1980-2007) 115

3.6 Model estimates of priced intensities vs. observables 116

3.7 Structural model mispricing (US 2001-2007) 117

3.8 Estimation of LBO likelihood using model mispricing 118

v

Acknowledgements

I would like to thank all the people who have helped and inspired me during my doc

toral studies. I am thankful to Suresh Sundaresan for his guidance and counsel and

for encouraging me to pursue this dissertation topic. His faith and vision helped me in

constructing the foundation of this work. I express special gratitude to Wei Jiang for

her invaluable help and encouragement in all aspects of my doctoral experience. Her

advice on empirics and corporate finance topics proved essential to my work, and her

guidance kept me focused and progressing at every milestone. I am very grateful to

Pierre Collin-Dufresne for guidance and suggestions, which constantly improved the

quality and novelty of my research. His joining the faculty at Columbia was a turning

point in my work. I would like to thank Michael Johannes for fruitful discussions and

helpful advice throughout my degree. Our joint work exposed me to novel methodolo

gies, which broadened my skillset and extended the scope of my work. I am especially

indebted to Mikhail Chernov for his continuing help and support throughout these

years. Through the course of several research collaborations, he introduced me to

credit topics and exposed me to the different facets of research. I am appreciative

of the time and effort invested by my committee members Charles Jones and Rama

Cont. I thank them all for constantly teaching and guiding me through my doctoral

studies and research work.

Many thanks go also to my classmates and friends at Columbia Business School for

the helpful conversations and for making the long hours in Uris hall more enjoyable.

I have made friends for life and I cannot imagine the doctoral experience without

vi

them. I cannot forget to mention my life-long friends back in Israel for their constant

support and confidence in my ability. Their warm encouragement in every visit and

every phone call was invaluable and pushed me on across continents and oceans. My

deep gratitude goes to Sharon and Liat Belenzon for their help in the initial stages

of the project.

I cannot begin to express my gratitude to my family for their unconditional sup

port, encouragement and faith in me throughout this pursuit, despite the hardship of

the long distance from home. The motivation and drive for achievement my parents

instilled in me carried me into the PhD program at Columbia. Their encouragement

has been invaluable throughout my academic and personal development and their

embrace, both from near and afar, has enabled me to surmount all obstacles. I dedi

cate this dissertation to them.

Finally, I would like to thank Tomer, my husband, for his constant support in

every step along this long and winding road. I truly thank him for the extensive

discussions at school and at home, for his motivating suggestions and for keeping me

focused on the big picture when it was so easy to stray. This dissertation would not

be what it is without him by my side.

vn

To my family

vm

1

Chapter 1

Leveraged Buyout Risk and Debt Markets

1.1 Introduction

The years 2004-2007 saw unprecedented leveraged buyout (LBO) activity, both in

number of transactions and in deal size. In an S&P report dated August 2007, lever

aged buyouts are identified as " a primary force behind the global rise in credit risk and

the decline in credit quality"* . This paper is the first to study the effect of leveraged

buyouts on the cross-sectional variation in credit spreads. Using a dataset of LBOs,

CDS and bonds, we first study the reaction of credit spreads of target firms to LBO

announcements in the US during the years 2001-2007. We complement this study

by quantifying the effect in equity markets, to address the fundamental questions of

value creation and wealth transfer in buyouts. After establishing LBOs as a signifi

cant concern for debt investors, we proceed to test the effect of LBO risk on pricing in

debt markets. Using exogenous industry variables and firm-level instruments, we find

that higher LBO risk is, indeed, associated with significantly wider spreads. These

findings help explain the cross-section of credit spreads, furthering our understanding

of the credit spread puzzle. Our study has important implications for pricing of event

risk covenants and for modeling of corporate spreads, specifically suggesting incorpo-

1 "The Leveraging Of America: Recent Leveraged Buyouts Drive Credit Risk Higher As The Market Churns", S&P RatingsDirect, August 6, 2007.

2

ration of a restructuring regime shift to alleviate problems of under-prediction.

In the past few decades, the private equity industry has grown both in terms of

size and geographic reach. Ample liquidity and relatively low spreads have provided

easier access to debt financing, with the growing credit derivatives markets and novel

funding structures allowing easy transfer and trade of credit risk. These factors,

among others, have driven leveraged buyout activity to previously unknown levels.

A recently published report by the World Economic Forum2 finds that more than

40% of all buyouts over the years 1970-2007 have taken place since 1 January 2004.

The total value of firms (equity and debt) acquired in leveraged buyouts over the

years 2001-2007 is estimated at $2.7 trillion. Figure 1.1 shows the volume of LBOs

announced annually more than tripled to approximately 2500 since 2000, and average

deal size reached a new high of over Sl.lbn in the first half of 2007. Size and rating

are no longer protection against a takeover; the last couple of years have seen LBOs

of investment-grade firms of considerable size (e.g. First Data Corp, Alltel Corp).

[Insert Figure 1.1 about here]

A leveraged buyout is an acquisition of a company using a significant amount of

borrowed funds. It involves substitution of equity for debt and, typically, elimina

tion of publicly-held stock. In 2004-2007, equity contribution in LBO deals fell to

as low as 25%. The borrowed funds are issued against the assets of the target firm

and are repaid with cash flows generated by the company or with revenue earned by

selling off the newly acquired company's assets. The post-LBO firm frequently has

extremely high leverage, and the newly issued debt can be senior bank loans and/or

public debt. As a result, LBOs typically cause a dramatic change in the risk profile

of the target firm. Marais, Schiffer & Smith (1990) and Warga & Welch (1993) find

that, on average, the proportion of debt after successful buyout triples and most debt

2 World Economic Forum, Volume 1 of Working Papers on "The Global Economic Impact of Private Equity"

3

is downgraded.

Previous works, mostly post the buyout wave of the 1980s, have studied the effect

of LBOs on stakeholders of the target firm. Shareholders have been found to gain

from high premiums paid by the acquiring firm, with returns ranging from 15% to

40% (Jarrell, Brickley k Netter, 1988, Lehn k Poulsen, 1989, Warga k Welch, 1993).

Yet the source of these gains has been the topic of extensive debate. Do they consti

tute wealth expropriation from bondholders and other non-equity stakeholders or do

LBOs create value?

There has been less consensus as to the effect of LBOs on debtholders. Findings

range from no impact to a loss of 7% over four months, depending on the type of

data used and the time period studied (Lehn k Poison, 1988, Marais, Schiffer k

Smith, 1989, Asquith k Wizman, 1990, Warga k Welch, 1993). Proponents of LBOs

agree leverage is beneficial in tax shields, but also argue LBOs result in added value.

Wealth increases are attributable to improved managerial incentives due to large eq

uity stakes, increased monitoring and disciplining effect of large debt-service payments

on managers (Jensen, 1986). Can these benefits offset increase in default probability,

added bankruptcy costs and possibly reduced effective priority for debtholders?

Using a comprehensive dataset of LBOs, CDS and bonds, we address this question

by studying the reaction of target firm credit spreads to LBO announcements in the

US during the years 2001-2007. We use dealer-quoted, actively traded CDS spreads,

which were found to be the first forum for price discovery (Blanco, Brennan k Marsh,

2005) and a cleaner indicator of default risk. We find evidence of credit spread widen

ing by 60-70%, suggesting costs of additional debt significantly outweigh potential

increase in expected cash flows. Effect is significantly stronger for investment-grade

firms, consistent with the larger change in risk profile relative to high-yield firms.

Price decline for these firms is most likely exacerbated by a sell-off by institutional

investors prohibited from holding below-investment grade securities. To learn about

the effect on bondholders, we study reaction in bond markets, differentiating between

4

bonds protected by event risk covenants and those that are not, to control for takeover

protection. We document a negative reaction of 6% in prices of unprotected bonds,

suggesting LBOs do result in some wealth transfer from debt-holders to shareholders.

We contribute to the question of value creation and wealth expropriation in LBOs by

a similar analysis in the equity market. Back-of-the-envelope calculation shows esti

mated 18% gains to shareholders are due, in large, to alternate sources, supporting

value creation in LBOs.

The large and growing magnitude of buyout activity and its detrimental effect

on debt prices, as established in the event study, suggest LBOs were a considerable

concern for debt investors across markets, industries and rating classes. In the last

buyout wave, a greater number of low-investment-grade and high-speculative-grade

companies across multiple industry sectors went private through LBOs. We hypoth

esize that LBO restructuring risk is priced ex-ante by investors in debt markets. The

variability in LBO risk across firms might help explain the cross-sectional variation

in credit spreads. We proceed to test this in an empirical study of US CDS spreads

from 2001-2006.

To separate the effect of LBO probability from the direct effect of firm character

istics on credit spreads, we use an exogenous industry-level probability. This is based

on previous works that have shown cross-industry variation in event risk (Crabbe,

1991, Lehn, Netter, k, Poulsen, 1990). We find that firms that are more likely to

undergo an LBO in the future have spreads that are higher by 30-50 bps. Results are

robust to exclusion of firms with event-risk protected bonds, implying results are not

driven by differences in covenants. Effect is found to be more significant in the later

half of our sample, in accordance with growth trends in LBO activity. Consistent

with previous findings on LBO determinants (Opler & Titman, 1993) we also find the

effect to be more pronounced in mature firms with high cash levels and high asset

tangibility. We further ensure exogeneity of LBO risk by "exporting" US LBO prob

abilities to European markets, testing the assumption that industry fundamentals

5

determine LBO risk similarly across markets. Non-US buyout activity has grown to

be of a similar magnitude to that of the US in the last few years, mostly in Continen

tal Europe. We find a similar significant effect of LBO risk on pricing in European

spreads.

Incorporation of LBO risk might further our understanding of the cross-sectional

variation in credit spreads. Structural credit risk models, introduced in Merton

(1974), postulate economic drives of changes in corporate spreads, such as asset

volatility, leverage, and interest rates. However, Collin-Dufresne, Goldstein &; Martin

(2001) find that structural model variables explain less than 25% of the total vari

ation in credit spread changes. Structural models have also been found to generate

smaller spreads than those observed in practice, particularly for investment grade

debt (Huang & Huang, 2003, Eom, Helwege, & Huang, 2004)3 . In these models, few,

contemporaneous firm fundamentals affect default probabilities and recovery rates

and thus ultimately drive spreads. Credit spreads are forward-looking, and, as such,

should incorporate all risks perceived by investors. Inclusion of the "Peso problem"

of LBO risk might further our understanding of the credit spread puzzle and alleviate

model spread under-prediction.

The rest of this paper proceeds as follows. Section 1.2 reviews literature related

to our event study. Section 1.3 details our CDS, bond and LBO data. Section 1.4

describes the CDS and cash bond markets in the context of LBOs. Section 1.5 de

scribes the event study of CDS spreads and bond prices around LBO announcements.

The following chapter presents an empirical study of the effect of LBO risk on the

cross-sectional variation in credit spreads.

3 Later extensions of structural models improve prediction - these include Black & Cox, 1976, Longstaff & Schwartz, 1995, Andersen & Sundaresan, 1996, and Collin-Dufresne & Goldstein, 2001.

6

1.2 Related literature

Previous works have studied the effect of LBOs on stakeholders of the target firm

in the buyout wave of the 1980's. LBOs involve elimination of publicly-held equity

and shareholders typically gain from high premiums paid by the acquiring firm. Pre

vious studies consistently document gains to shareholders, reporting returns ranging

from 15% to 40% (Jarrell, Brickley k Netter, 1988, Lehn k Poulsen, 1989, Warga k

Welch, 1993). Yet the source of these gains has been the topic of extensive debate.

Do they constitute wealth expropriation from non-equity stakeholders or do LBOs

create value?

There has been less consensus as to the effect of LBOs on debtholders. Losses to

bondholders would imply transfer of wealth to shareholders, but findings have been

inconsistent. Using exchange-based data, Lehn k Poison (1988) and Marais, Schiffer

k Smith (1989) find none or minimal impact on bondholders. Asquith and Wizman

(1990) document a loss of 3.5% over a window of 4 months around the event. Using

dealer-market quotes, Warga k Welch (1993) find a loss of 7% over a similar time

frame. The latter conclude exchange-based data and matrix prices are not informa

tive, showing these prices do not fully reflect market events.

Higher leverage can reduce the value of outstanding bonds both by increasing the

probability (and deadweight costs) of a future bankruptcy and by reordering the pri

ority of claims in bankruptcy. Even if a bond has priority covenants that prevent the

firm from issuing bonds of equal or higher seniority, these priority rules are not com

pletely upheld in the case of financial distress (Franks k Torous, 1989). Therefore,

an increase in leverage is likely to hurt bondholders unless the benefits of increased

leverage are large enough.

Proponents of LBOs argue that LBO organizations solve the free cash flow prob

lem faced by companies in low-growth industries by providing superior incentives to

managers and their monitors (Jensen, 1986). Wealth increases are attributable to

7

improved managerial incentives due to large equity stakes4 , increased monitoring by

LBO sponsors and forced disgorgement of excess free cash flow that might otherwise

be invested unwisely. Leverage is beneficial both in tax shields and in the disciplining

effect of large debt-service payments on managers. Critics of LBOs argue that most of

the gains to equityholders are due to tax savings and the expropriation of bondhold

ers and other non-equity stakeholders (Lowenstein, 1985, and Shleifer & Summers,

1988).

Some academic studies agree that, while tax savings are a source of large gains,

additional wealth is also created in LBOs (Kaplan, 1989b, and Marais, Schipper &

Smith, 1990). For example, Kaplan (1989a), Muscarella & Vetsuypens (1990), and

Smith (1990) find that cash flows improve after buyout. Can the aforementioned ben

efits of an LBO offset added bankruptcy costs and possibly reduced effective priority

for debt-holders?

In this study we address this question by studying the reaction of target firm

spreads to LBO announcements in the US during the years 2001-2007. To learn

about the effect on bondholders, we study reaction in bond markets, differentiating

between bonds protected by event risk covenants and those that are not, to control

for takeover protection. Finally, we contribute to the question of value creation and

wealth expropriation in LBOs by a similar analysis in the equity market.

1.3 Data

1.3.1 Credit Default Swaps

This dataset includes daily quotes for a broad cross-section of firms actively traded in

the credit derivatives market. Our CDS data are provided by Markit, a comprehen

sive data source that assembles a network of over 30 industry-leading partners who

4 Kaplan, 1989 and Smith, 1989, document median post-buyout equity ownership by management of 22.6% and 16.7%, respectively.

8

contribute information across several thousand credits on a daily basis. Based on the

contributed quotes, Markit creates a daily composite for each CDS contract. Though

the composite CDS spread is based on indicative quotes, rigorous cleaning of the data

helps to ensure that the composite price closely reflects transaction prices. Markit

eliminates stale quotes and outliers, rejecting on average 45% of the data submitted.

Furthermore, Markit constructs composite prices only when at least three dealers

contribute quotes. Once a credit starts being priced, more than 75% of the time it

will continue so going forward on a daily basis. This last feature makes the Markit

database superior to data from other vendors when time-series analysis is required.

Together with the pricing information, the dataset also reports average recovery rates

used by data contributors in pricing each CDS contract. (The quoted recovery rates

reflect market participants' consensus view on expected losses and can thus differ

substantially from realized losses.)

Our dataset consists of 489 US entities and 169 European entities. This dataset is

a random subset of the several thousand firms covered by Markit. The coverage spans

01/2001 to 12/2006 for the entirety of firms and 01/2001 to 09/2007 for firms that

underwent LBOs. (Descriptive statistics are presented for 2001-2006 only.) Figure

1.2 shows the increase in the number of firms covered over the specified time span.


We include all CDS quotes written on U.S. corporate entities and denominated in

U.S. dollars. For consistency, we retain only CDS on senior unsecured debt, which

constitute over 90% of all contracts. We focus on contracts with Modified Restruc

turing (MR) or No Restructuring (XR) clauses as they are the most common in the

US (we use MR contract except if the firm has none traded or if the XR contract is

more common, for more liquid prices; this is the case for 122 of the 489 firms in the

sample).

It is interesting to note the difference in ratings of the firms traded with Modified

Restructuring clause vs. those traded with No Restructuring. It can be seen in Table

9

1.1 that, while 85% of all contracts trading with MR clause are rated investment

grade (IG), only 5% of XR contracts are written on IG firms. Sellers of protection

might oppose inclusion of restructuring as an event triggering payment for firms rated

below IG, which have a higher probability of default.

[Insert Table 1.1 about here]

Our data includes contracts of 1,2,3,5,7 and 10-year maturities. The 5-year con

tract is the most liquid (given for 97% of observations), followed by the 3-year (92% of

observations), the 7-year (90%), the 1-year (89%), the 10-year (86%) and the 2-year

(85%).

Figure 1.3 displays the term structure of CDS spreads over the time period 2001-

2006. Spreads were highest in 2002 (mostly around the Enron crisis in the 3rd and

4th quarters of the year), decreasing afterwards and rising again around the credit

crisis, driven by GM and Ford downgrading, in May 2005. Term structure of spreads

is increasing throughout the sample, more so since 2004, suggesting investors antici

pated higher default risk in the longer term. Table 1.2 presents the distribution of the

CDS spreads. Panel A shows the mean (median) spread in our sample decreased from

2.2% (1.02%) in 2002 to 1.34% (0.46%) in 2006. Panel B of Table 1.2 presents the

increasing term structure of spreads and shows the slope between the 10 and 1-year

spreads to have increased from 1.3 in 2002 to 7.1 in 2006.

Our data provide a solid representation of all sectors. Panel C shows the break

down of firms into sectors and the distribution of CDS spreads in each sector over

the sample years. Technology, telecommunications and consumer services appear to

have the highest spreads, while spreads are lowest for the government and health

care sectors. Our CDS sample also spans all rating classes. Figure 1.4 displays the

distribution of firms across the rating classes (rating as of December 2006). It can be

seen that most of the sample is concentrated in the A-BB categories, but lower and

higher ratings are well represented. Panel D shows the average CDS spread over time

across ratings; spreads are seen to be decreasing in rating, with the most significant

10

jump of almost 300% observed when moving below IG (BBB to BB).


[Insert Figures 1.3 and 1.4 about here]

1.3.2 LBO announcements

Data on LBO announcements are retrieved from Thomson One Banker. A deal is

classified as a Leveraged Buyout if the investor group includes management or the

transaction is identified as such in the financial press and 100% of the company is

acquired. We filter by announced deals of type LBO, where the announcement date

was between 01/2001-09/2007 and the target was a US firm5 . For firms which were

referenced by more than one announcement, we leave only the earliest (or the first

that is not a withdrawal of an offer)6 .

Merging the data on LBO transaction announcements with the CDS spreads leaves

us with 57 firms. Our universe of interest is not a large one, as we are focusing on

firms with public debt and actively traded CDS contracts, which were also targets of

LBO transactions. These would typically be relatively large, public firms (indeed, all

but 2 of our sample firms are public), which are only a small fraction of LBO targets.

A study of all LBOs from the years 1970-2007 by the World Economic Forum7 finds

that public-to-private transactions comprise only 6.7% of all LBOs, and they represent

a smaller fraction of activity compared with that in the LBO wave of the 1980s8 .

5 Based on CapitallQ database and World Economic Forum reports, the coverage of deals in Thomson One Banker seems to be incomplete, but we have no reason to suspect any bias in coverage. Furthermore, since we focus on the intersection of LBOs and CDS quotes, we are, by definition, focusing on the larger, public, highly traded firms, for which the coverage is likely to be high.

6 Some of the deals had been rumored in the press prior to the official announcement.

7 World Economic Forum, Volume 1 of Working Papers on " The Global Economic Impact of Private Equity"

8 Previous event studies around LBOs have all had relatively small samples, due to the lack of data on the post-buyout firms. For example, Warga & Welch (1993) study 16 firms, Asquith & Wizman (1990) use 65 buyouts. Our event study uses 57 firms, while our subsequent cross-sectional study utilizes the entire dataset.

11

For 9 out of the 57 firms, the time series of prices starts only after the event day.

Excluding those with stale or missing prices around the event, we are left with 45

firms. Table 1.3 presents the firms in our sample. Out of the 45 announcements,

23 are of completed deals, 16 pending, 1 intended and 5 withdrawn or discontinued

rumors. Of our sample of events, 18 are announcements made in 2007, 18 were made

in 2006, 7 in 2005, 1 in 2004 and 1 in 2002. The average transaction value in our

sample is $10.4bn and the average firm size (measured by Compustat total assets)

is $12.25bn. The average firm leverage (pre-LBO) is 37%. 36 out of the 45 LBOs

were completed by the end of 2007. In our sample, completed deals went into effect

anywhere between 1 month and 1 year after the announcement.


1.3.3 Bond transaction prices

We use bond transaction price data from the Trade Reporting And Compliance En

gine (TRACE) system assembled by the National Association of Securities Dealers

(NASD). Introduced in the second quarter of 2002, Trace currently represents the

most comprehensive database of all (TRACE-eligible) corporate bonds that are traded

in the U.S. market.

The corporate bond market operates primarily as an over-the-counter one, and a

major obstacle in previous corporate bond studies has been the lack of broad mar

ket price data. As detailed in the introduction, previous research had been limited

to exchange-based or matrix prices. Warga & Welch (1993) discuss the problems

with both these sources. Exchange-based data is an extremely thin market, covering

mostly trade by individuals over a limited number of issues. Institutional data is

more comprehensive, but consists mostly of matrix prices, which are not quotes, but

algorithmic prices that add a fixed spread over a benchmark issue of the same firm

or a firm with similar rating, maturity and coupon. The authors show that these two

sources provide prices that are not informative and fail to pick up reactions observed

12

in dealer-based quotes.

This decentralized nature of the corporate bond market had long meant that price

and volume histories were non-existent for corporate bonds. This changed with the

implementation of TRACE in July 2002, which requires NASD member dealers to

report all over-the-counter corporate bond trades after execution. The TRACE ini

tiative has gradually expanded to cover almost the entire over-the-counter secondary

market in corporate bonds.

The information on TRACE includes time of execution, price, yield, and volume.

Merging our data on LBO announcements with the TRACE bond information yields

an intersection of 355 bonds issued by 129 firms. We drop from the sample all bonds

that do not include the date of announcement in their price history and are left with

179 bonds by 60 issuers. We drop all convertible bonds, as these might be expected

to react differently from non-convertibles. We further exclude bonds that have gaps

or stale prices around the event day and are left with a final sample of 123 bonds

by 32 issuers. Table 1.4 displays the issuers represented in our sample of bonds. We

further merge this data with information on the issue and its covenants, as described

in the following section.


1.3.4 Bond issuance and covenants

We retrieve bond covenant information from The Fixed Income Securities Database

(FISD). FISD contains detailed issue-level information on over 140,000 corporate,

US Agency, US Treasury and supranational debt securities. A unique feature of

FISD is the comprehensive coverage of the bond indenture provisions. The sources

for this information are bond prospectus, issuers' SEC filings including 10-K, 8-K,

Registration forms, etc. For each issue, FISD provides a variable indicating whether

detailed covenant information is collected for that issue. We find covenant information

for 119 of our 123 sample bonds.

13

1.4 Bonds vs Credit Default Swaps in LBOs


In a credit default swap, the party buying protection pays the seller a fixed premium

each period until either default occurs or the swap contract matures. If the underlying

firm defaults on its debt, the protection seller is obligated to buy back from the buyer

the defaulted bond at its par value. Thus, a credit default swap is similar to an

insurance contract that compensates the buyer for losses arising from a default.

Physical delivery is the dominant form of settlement in the market. Deliverable

obligations are typically a broad set consisting of all the outstanding bonds of a specific

seniority of the reference entity (the most common is senior unsecured). Upon default,

the buyer of protection can deliver any of the reference bonds in return for par value.

Consequently, the CDS spread typically reflects the value of the cheapest bond among

all deliverable obligations ("cheapest-to-deliver" feature).

1.4.2 Improved Market Transparency

The emergence of the credit derivative market has provided us with a new instrument

from which to learn about the effect of LBOs on the risk profile of the target firm: the

credit default swap (CDS). Credit default swaps are the most common type of credit

derivative and have been actively traded in financial markets in recent years. The

British Bankers' Association estimated the total notional amount of CDS contracts

at $45 trillion at the end of 2007.

By their nature, the single-name CDS provides researchers with a near-ideal way

of measuring the default component of credit spreads. CDS spreads abstract from

numerous bond characteristics, such as seniority, coupon rates, embedded options,

and guarantees. Also, unlike corporate bond spreads which are believed to contain

a significant portion of liquidity premium (Longstaff, Mithal k, Neis, 2005), CDS

contracts are unfunded and do not face short-sale restrictions. They allow investors

14

to short credit risk over a longer period of time at a known cost by buying protection.

Blanco, Brennan & Marsh (2005) find that CDS prices lead over corporate bond

spreads in the price discovery process. These findings suggest that CDS prices are

useful indicators for measuring credit risk. We, thus, opt to use credit default swap

spreads to study the effect of LBO announcements on credit spreads of target firms.

1.4.3 Bond Indentures

Bond returns around LBOs are largely determined by the protection provided by

their specific covenants (Cook, Easterwood & Martin, 1992, Asquith & Wizman,

1990). The two categories of covenants relevant to LBOs are:

1. "Event risk" covenants: These covenants allow the bond-holder to put the

bond back to the firm at par (or par plus premium) upon a leveraged restructur

ing and subsequent downgrade to below IG. These covenants, which appeared

in 1989 following the buyout boom of the 1980's, are referred to as "super poi

son put". In the case of a leveraged buyout, bonds protected by a super poison

put, selling at a discount prior to the LBO, might not lose or even gain in value

regardless of an increase in default risk.

2. Financing covenants: These covenants restrict the amount or seniority of

additional debt the firm may issue. They include covenants on net worth and

covenants on leverage. Covenants restricting firm leverage place limits on issu

ing funded debt and on leverage levels while net worth covenants restrict the

firm's liabilities. We focus on these covenants as Asquith & Wizman (1990) show

that they are often violated in takeovers and thus provide protection to bond

holders, even in extreme examples of hostile takeovers such as LBOs. In case

of violation of leverage or net worth restrictions, the acquiring firm might buy

out the limiting bonds in a tender offer or make-whole call provision. Asquith

k. Wizman (1990) find that issues protected by these covenants do not lose and

often gain on LBO announcements.

15

In anticipation of triggering any of these covenants, prices of protected bonds might

rise following an LBO announcement, regardless of the implication of the buyout on

firm default risk.

Event risk covenants are currently not found in all bonds; they are quite uncom

mon in bonds of IG rating and large firms, as these were less prone to be buyout

targets in the past. In our sample of CDS, only 25 of the 152 bonds found on Mer-

gent FISD (issued by 9 of the 45 firms) were found to have event risk protection.

Following the last buyout wave, all firms are believed to be susceptible to takeover

risk and event risk covenants are becoming more prevalent. An examination of bond

issuance on Mergent FISD shows that event risk covenants were found in 35% of new

issues in 2000 and in 60% of new issues in 20079 . Table 1.5 shows the percentage

of bonds issued with event risk covenants in recent years and their average rating

compared to overall rating of new issues. Event risk covenants are clearly far more

common in high yield (HY) bonds, but the percentage of IG bonds issued with these

covenants is increasing since 2000 (last column of Table 1.5).

The existence of event risk covenants is highly correlated with that of financing

covenants. In the 152 issues found on FISD for our sample of 45 CDS firms, we find

this correlation to be 0.52. Thus, it is far from the case that all bonds are protected

from buyouts (even if all bonds are protected, it may be enough for the acquirer to buy

just enough of the bonds for a majority vote to change financing covenants)10 . As de

tailed previously, the CDS contract is written on all bonds of a seniority class, and its

spread will track the value of the CTD bond (typically, an unprotected bond). Thus,

CDS spreads and bond prices might move in opposite directions following an LBO

9 We exclude bonds with missing covenant information. Billet, King & Mauer (2004) find missing covenant information to be unrelated to time of issuance, priority, rating, maturity, size of issue or issuer so we expect no bias in selection of bonds examined.

10 CDS holders have also been creative in preventing "orphaned" contracts: following the split of Cendant Corp in February 2006, CDS holders of Avis Budget group payed the company to guarantee subsidiary bonds. CDS holders of Experian bought firm bonds in order to resist a tender offer by buyout firm. Issues of succession are currently under discussion at ISDA. See further discussion at: http://www.fitchratings.com/dtp/pdf4-06/iupdlll7.pdf.

http://www.fitchratings.com/dtp/pdf4-06/iupdlll7.pdf

16

announcement. In a number of recent buyouts CDS spreads have widened while bond

spreads have tightened (e.g. Equity Office Properties, First Data Corp). Moreover,

financing of the LBO deal is often unresolved and/or modified up until completion of

buyout so it is uncertain which covenants will be violated and exactly which bonds

will suffer upon announcement.


1.5 Event Study


We proceed to study the effect of LBO announcements on the credit spreads of our

sample target firms. Our sample consists of 45 firms, with 36 of 45 LBOs completed by

the end of 2007 (presented in Table 1.3.). Completed deals went into effect anywhere

between 1 month and 1 year after the announcement. The average transaction value

in our sample is $10.4bn and the average firm size (measured by Compustat total

assets) is $12.25bn. The average firm leverage (pre-LBO) is 37%.

Event Window

We use an event window of 60 days prior to the event and 60 days following it. The

window is subdivided into seven time intervals: 60 to 31 days before the announce

ment; 30 to 11 days before, 10 days to one day before, the day of the announcement

and the following day and the corresponding time periods after the announcement.

We expect to find a discernible price impact in the [0,-1-1] interval, under the hypoth

esis the announcement has informational value and results in price pressure. The

impact of the announcement is tested over a two day interval because the announce

ment might have been made after markets closed for the day. In the case of less liquid

names, the full impact of a rating announcement might be delayed to the [+2,+20]

17

interval. Furthermore, financing of the transaction is not always finalized upon an

nouncement and is often changed up until deal completion; thus, we look for market

reaction to changes in financing in the windows following the event. If the event is

rumored (as might be the case given the large average deal size in our sample), we

might expect to see a reaction in prices in the windows preceding the announcement,

in particular in the days leading up to it.

Daily Returns

To measure the effect of the LBO announcements on credit spreads, I follow Micu,

Remolona & Wooldridge (2006) and study normalized changes in spreads. The market

value of a CDS contract is dependent on an uncertain stream of premia, and the

calculation of the expected present value of these payments (in particular, the survival

probability of the reference entity up to each payment date) requires a CDS pricing

model. Studying normalized changes in spreads avoids introducing model dependency

into results. For issuer i at time t:

Si,t-1

This ratio might also be considered a proxy for the return of an investor who has

bought protection against default of issuer i (returns to the investor are positive when

CDS spreads widen and negative when CDS spreads tighten). This proxy assumes

the daily return on the contract is largely driven by the change in spread, shown to

be the case in Micu, Remolona & Wooldridge (2006). In comparison to other studies

that focus on absolute changes in CDS spreads (e.g. Hull, Predescu 8z White, 2004),

this proxy adjusts for the differences in levels of spreads and allows comparison across

issuers.

18

Abnormal Returns

We compute abnormal return over the CDX NA IG/HY index. Classification to IG or

HY is determined by the firm's rating prior to the event and stays constant through

out. The firms are typically downgraded post buyout, so an alternative might be to

switch index after the event (for IG firms). However, we believe keeping the rating

type of the firm constant would provide a better measure of the effect of interest: if

the firm is IG prior to the event, we wish to observe its abnormal return as an IG

firm, since the downgrading is a result of the event. We categorize 22 of the firms as

IG and 23 as HY.

Of our sample of events, 18 are announcements made in 2007, 18 were made in

2006, 7 in 2005, 1 in 2004 and 1 in 2002. As the CDX NA 5-year series begins on

November 19, 2004, the latter 2 firms are in fact excluded from the sample. Despite

this, we prefer to use the CDX as it is comprised of the most actively traded con

tracts. (For robustness, we check our results using an index constructed from the

entire CDS dataset and find similar results.) Furthermore, the CDX NA series for

other maturities begins at a later date (first quarter of 2006 for IG and as late as the

beginning of 2007 for HY), therefore, we benchmark all maturities against the 5-year

index.

In computing abnormal returns, we use the market-adjusted model with an esti

mation window of 1 year, i.e. approximately 250 business days. In cases where there

was not a full year of observations before the event, we include in the estimation

window the days up to the beginning of the event window. The shortest estimation

window is 101 business days. One firm did not have a large enough estimation window

and was removed from the sample.

19

Test Statistics

Abnormal returns using the market-adjusted model:

ARiit = Ritt - (an + PiR,DX,t)

where ARijt is the abnormal return for issuer i on day t, Ri:t is the return for issuer

i on day t, RIDX,t is return on index on day t (computed similarly to issuer return),

and Qj and Pi are estimated in a regression of issuer i returns against the index over

the estimation window.

In computing the significance of the abnormal return, we must be careful to ad

dress two issues which may affect the variance of abnormal returns:

• Error in estimation of on and Pi in the estimation window

• Event-induced variance: LBO announcements could potentially lead to a change

in the variance of CDS spreads due to a change in the firm's perceived risk.

Brown & Warner (1980) note that when the variance induced by an event is

underestimated, the test statistic results in the rejection of the null hypothesis

more frequently than it should.

We first correct the variance for estimation error - the corrected standard deviation

of the abnormal return of issuer i in day t of the event window is:

, —r L 1 (RIDX.I - RIDX)2

where T is the number of days in the estimation window, sditt is the estimation-

corrected standard deviation for issuer i on day t, sdi is the standard deviation of

abnormal returns for issuer i from the estimation period, Riox,t is return on the

index on event day t, and RIDX is the average return of the index over the estimation

window.

20

The standardized abnormal return for issuer i in day t of the event window is then:

ARijt SARit sdL

To control for event-induced changes in variance, we employ a standardized cross-

sectional i-test in the event window to test whether the mean of abnormal CDS

returns is significantly different from zero. The cross-sectional standard deviation in

event window day t is computed as:

sdr = \

, l n Y^SARit - SARtf n(n — 1) z—'

where n is the number of observations on day t and SARt is the average standardized

abnormal return over the n observations. Thus, the standardized cross-sectional test

statistic incorporates variance information from both the estimation period and the

event window. The resulting test statistic is:

tcs = SARt

sdfs

The cross-sectional test is extended to a multi-period event window of T0 days:

_ SCARTo

sd£s

where cumulative abnormal return over the time window (CART(j) replaces abnormal

return over a single day (ARt) and variables are defined similarly to the previous

ones:

21

where SCARTg is the average standardized cumulative abnormal return over the n

observations and:

SCAR,, CARit To

sdi,T0

To

CARt,TQ = r ^ i t t=i

sditTo = sdi

\

T I 1 + Z° _|_ T0(RlDX,To - RlDxY

where RIDX,T0 is the average return of the index over the event window.

Empirical Results

Our results indicate that LBO announcements have a statistically significant negative

impact on CDS spreads, i.e. they result in a significant widening in spreads. Table

1.6 provides a detailed description of the empirical results of the event study11 . Panel

A displays the significance results for each of the 5 days preceding and following the

event day. The significance of average abnormal returns in the different time windows

is presented in panel B. For each maturity, the first column in the table is the average

CDS spread level (in percentages), the second column reports the average abnormal

returns and the third reports the respective test statistic. Cumulative abnormal re

turns are displayed in Figure 1.5.

Panel A of Table 1.6 shows abnormal returns of approximately 20% on the an

nouncement day and another 16% on the following day. Panel A of Table 1.6 shows

these changes to be significant at the 1% and 5% level, respectively, for all maturi

ties. We also observe a cumulative abnormal return of approximately 10% in the 10

days leading up to the announcement, and another 15% on the day preceding the an-

11 Results for 1-year spreads are not shown, as we believe the 5-year benchmark is an ill fit; betas seem unreasonable for several firms. However, the 1-year spreads also show a widening in spreads on day 0 and on day 1 that is significant at the 1% level.

22

nouncement, suggesting the announcement is anticipated (as might be expected given

the size and public status of the sample target firms). Most of the anticipation-driven

change occurs in last 5 days before the event day. The average abnormal return is

insignificant for time windows other than that of the event.

Reaction seems to be slightly stronger for shorter-term spreads; initial reaction

is strongest in 3-year spreads (a t-test shows the differences between maturities are

not statistically significantly). This might be explained by the evolution of leverage

in an LBO: leverage is highest after the buyout and over time, debt is repaid and

leverage reverts to lower levels. Thus, the short term following the buyout might

carry the highest risk. Investors in longer maturity contracts might also benefit from

the advantages of an LBO, as described in the introduction.

In short, all maturities display a widening of spreads on the day of the announce

ment and the following day; returns are significant at the 1% level. The cumulative

abnormal return due to the LBO announcement is close to 70% on average (across

firms and maturities), implying a significant increase in firm default risk. This sug

gests the costs of an LBO clearly outweigh its benefits.



IG vs. HY

Given the downgrading of debt post buyout, we expect the reaction of spreads to be

stronger for firms rated IG prior to the event, as these suffer a greater change in risk

profile. Figure 1.6 displays the difference in reaction between the IG and HY firms

in our sample. As seen in the plot, the reaction for IG firms is much larger: 5-year

spreads increase on average by approximately 130% while those of HY firms increase

by 49% on average. A t-test shows the differences between these time series to be

statistically significant. Reaction of IG firms also precedes that of HY.

The larger reaction in IG firms might also be a manifestation of the clientele effect:

23

institutional investors are often prohibited from holding securities rated below IG. An

announcement regarding a pending LBO might result in a rush by these investors to

sell off the reference entity's securities, sending prices even lower. This might also

explain the slight over-reaction observed in the IG spreads.

It is additionally interesting to observe the change in the level of spreads of the

IG firms. Figure 1.7 shows that prior to the announcement, IG 10-year spreads are

(significantly) lower than those of HY firms - 150 bps vs. 260 bps, yet after the

announcement a t-test cannot reject the null of similar means for these series. This

is consistent with the change in distribution of ratings due to the event, as presented

in Figure 1.8. The figure displays the distribution of ratings of our sample firms both

prior to the event and at the latest available rating before September 2007 (end of

sample). The distribution of ratings post buyout is clearly centered lower and is less

dispersed, almost entirely concentrated in the below-IG rating classes. This is in line

with previous studies that have shown that firms are typically severely downgraded

after an LBO (Kaplan, 1989a, Smith, 1990).

[Insert Figures 1.6, 1.7 and 1.8 about here]

1.5.2 Corporate Bonds

In the previous section we studied the reaction of CDS spreads to LBO announcements

to learn about the effect of LBOs on target firm default risk. We now proceed to

learn about the effect on bondholder return. As aforementioned, bond price reaction

is highly correlated with the level of protection. Therefore, we differentiate between

bonds with event risk protection ("super poison put" covenants) and bonds that do

not have explicit LBO protection. We use the sample of 123 bonds, constructed

as explained in the data section, issued by 32 firms (presented in table 1.4). Out

of the 119 bonds for which we have issuance information on FISD, only 22 have

event risk protection; we refer to these as the "protected" bonds, and to the rest

as "unprotected". To measure the effect of LBO announcements we study the daily

24

returns to bondholders:

Abnormal Returns

We compute abnormal return over the Lehman US corporate indices for AA, A,

Baa and HY ratings, both for intermediate maturity (10 years and under) and long

maturity (over 10 years). Bond ratings are as provided on FISD (reports ratings

of all three rating agencies) and the rating class of the bond is determined by its

rating before the event. The rating class of the bond stays constant throughout. In

computing abnormal returns, we use the market-adjusted model with an estimation

window of 1 year, i.e. approximately 250 business days. In cases where there was not

a full year of observations before the event, we include in the estimation window the

days up to the beginning of the event window. The shortest estimation window is 53

business days.

Test statistics are computed similar to the CDS study.

Empirical Results

Our results indicate that LBO announcements have a statistically significant nega

tive impact on bondholder returns as they result in a significant decrease in prices

of unprotected bonds. Cumulative abnormal returns are displayed in Figure 1.9 and

Table 1.7 presents the significance of average abnormal returns.

For unprotected bonds, Figure 1.9 shows a negative return of approximately 1%

on the announcement day and each of the following two days. Panel A of Table 1.7

shows these returns to be significant at the 1% level. We also observe a cumulative

abnormal return of approximately -3% in the 10 days leading up to the announce

ment, suggesting the announcement is anticipated.

For bonds protected by event risk covenants, we observe a positive return. Figure

25

1.9 shows a cumulative abnormal return of approximately 3% leading up to the an

nouncement day, and an additional statistically significant return of 1% on the day

following the announcement. Examining additional covenants on these bonds shows

that 20 out of the 22 also have restrictions on issuance of additional debt. The average

call price for these bonds on announcement day is 103.6, while their average price is

100.3, explaining the observed positive reaction.



1.5.3 Wealth Transfer and Value Creation

Overall, the cumulative return to bondholders of unprotected bonds is on average

approximately -6% in an event window of 60 days around LBO announcements. (We

observe a similar cumulative loss over a window of [-60,+60] around the event.) Bond

prices decrease by 1% on each of the three days following the announcement, a result

that is significant at the 1% level. It seems that for investors in unprotected bonds,

the costs of an LBO clearly outweigh its benefits.

Given the previous literature on the gains to target firm shareholders, these re

sults suggest at least part of this gain is due to wealth transfer from bondholders. To

evaluate whether this wealth transfer is large enough to constitute a buyout incentive

for shareholders, we wish to understand whether the loss to bondholders is a large

fraction of shareholder gains.

We first examine the effect of the LBO announcements on the stock prices of our

LBO target firms. We use stock quotes from CRSP and Bloomberg.

Cumulative abnormal stock returns are shown in Figure 1.10. We observe a signif

icant positive reaction of approximately 7.5% and 4.5% on the day of the announce

ment and the following day. Panel A of Table 1.7 shows this reaction to be significant

at the 1% and 5% level, respectively. We also observe a cumulative abnormal re

turn of approximately 5% in the 10 days leading up to the announcement, suggesting

26

prices incorporate market rumors and the announcement is anticipated. There are

insignificant additional positive returns on day 2 and 3, but almost all information is

incorporated into prices by day 1.

Overall, we observe a cumulative abnormal return of approximately 18% for target

firm shareholders. The event study on unprotected bonds has shown bondholders to

suffer an average loss of approximately 6% in a similar time window. To evaluate

relative magnitudes, we require the average ratio between equity and public debt. As

a proxy, we use book value of leverage. This proxy constitutes an upper bound on the

ratio of public debt to firm value, as leverage includes private (bank) debt, convert

ible debt, lease obligations, mortgages and others. The average leverage ratio in our

sample firms is 37%. Thus, we arrive at a gain to shareholders of 11.3% (=18%*63%)

of firm value, while the upper bound on losses to bondholders is 2.2% (=6%*37%)

of firm value. These results suggest that losses to bondholders are at most 19% of

shareholder gains. While this is not an unsubstantial fraction of gains, this percentage

does represent a very loose upper bound as it assumes all debt is public and that all

debt is unprotected, both clearly an oversimplification of firm capital structure. (In

our entire US database, the average leverage is 28.5%, which would lead to an even

lower fraction.) Therefore, it would not appear that wealth expropriation from bond

holders is enough to constitute shareholder buyout incentive. Our rough calculations

imply buyouts result in other, more substantial sources of gains, suggesting LBOs do,

indeed, create value.


27

Table 1.1: Ratings across restructuring clauses

Rating

AAA

AA

A

BBB

BB

B

CCC

D

Total

Modified

Restructuring

8

21

109

142

28

22

2

0

332

No

Restructuring

0

0

0

8

50

38

10

6

112

Total

8

21

109

150

78

60

12

6

444

Notes: This table reports the distribution of ratings in the CDS contracts with different restructuring

clauses for all rated firms in the sample. The two clauses most common in the US are Modified

Restructuring (MR) and No Restructuring (XR). Ratings are as of December 2006.

Table 1.2: Distribution of CDS spreads

Panel A: Distribution of CDS spreads over time (5-year contract)

year

2001 2002

2003 2004

2005 2006

# firms

235

320

388

429

439 454

mean

1.22

2.20

1.77

1.46

1.56 1.34

std

1.39

3.80 2.84

2.87 4.02

4.47

10%

0.32

0.40

0.25 0.22

0.20 0.16

50%

0.81 1.02

0.70

0.56

0.51 0.46

90%

2.38

4.68

4.30

3.33

3.25 2.96

Panel B: Term structure of median spreads

year

2001

2002

2003

2004

2005

2006

lyr

0.56

0.84

0.49

0.28

0.13

0.10

2yr

0.58

0.84

0.50

0.34

0.22

0.18

3yr

0.70

0.94

0.60

0.42

0.31

0.26

5yr

0.81 1.02 0.70 0.56 0.51 0.46

7yr

0.88

1.03

0.70

0.61

0.62

0.59

lOyr

0.97

1.12

0.75

0.69

0.75

0.73

Panel C: Distribution of CDS spreads across sectors (5-year contract)

sector

Basic Materials Consumer Goods

Consumer Services Financials

Government

Health Care Industrials

Oil & Gas Technology

Telecommunications

Utilities

# firms

31 67 105

70 2

26 57

34

26 28

31

mean

1.46 1.44

2.37

0.78

0.18 0.75

1.27

1.21

2.23

2.61

2.07

std

1.74

3.06

5.56 0.96

0.07

0.99

1.53

2.59

2.87

4.59 5.29

10%

0.22

0.19 0.30 0.21

0.07

0.11 0.20

0.25

0.23

0.26

0.31

50%

0.68

0.69 0.95 0.44

0.19 0.35 0.62

0.56

1.25 0.82

0.69

90%

3.65 3.42

4.77

1.70 0.26

1.96 3.21

2.77

4.78 7.64

3.50

29

Panel D: Average spreads across rat ing classes over t ime (5-year contract)

rating

AAA

AA

A

BBB

BB

B

CCC

CC

D

2001

0.32

0.36

0.61

1.35

3.68

2.09

2002

0.33

0.39

0.63

1.47

3.97

6.53

19.74

28.14

31.46

2003

0.27

0.26

0.43

1.08

3.16

4.86

10.25 14.54

28.12

2004

0.22

0.21

0.33

0.70

2.07

3.68 9.73

9.26

9.17

2005

0.20

0.20 0.29 0.64

2.08

3.30 10.53 13.82

21.16

2006

0.13

0.16

0.27

0.60

1.86 2.97

7.17

2.67 56.24

Notes: This table presents the distribution of CDS spreads (in percentages). Panel A reports the

distribution of 5-year spreads over the sample years. Panel B displays the term structure of spreads.

Panel C reports the distribution of 5-year spreads for the different sectors (sector is as determined

by Markit). Panel D reports the average 5-year spread over time across the different rating classes

(different firms might appear in different rating classes over time).

Tab

le

1.3:

L

BO

tar

get

firm

s

nam

e

Aff

iliat

ed

Com

pute

r Sv

cs I

nc

Alb

erts

ons

Inc

All

tel

Cor

p A

RA

MA

RK

C

orp

Arc

hsto

ne-S

mit

h T

rust

A

vaya

Inc

B

ausc

h &

Lom

b In

c B

ever

ly E

nter

pris

es I

nc

Cab

levi

sion

Sys

tem

s C

orp

Cle

ar C

hann

el C

omm

un I

nc

Dol

e F

ood

Co

Inc

Em

mis

Com

mun

Cor

p E

quit

y O

ffic

e P

rope

rtie

s T

rust

F

irst

Dat

a C

orp

Fre

esca

le S

emic

ondu

ctor

Inc

G

eorg

ia-P

acif

ic

Cor

p H

CA

Inc

H

arm

an I

nter

nati

onal

Ind

s In

c H

ertz

Cor

p H

ilto

n H

otel

s C

orp

Hom

e D

epot

Inc

In

sigh

t C

omm

unic

atio

ns C

o In

c Ja

cuzz

i B

rand

s In

c

date

3/20

/07

1/22

/06

5/20

/07

5/1

/06

5/2

9/0

7 6

/4/0

7 5

/16

/07

11/2

0/05

10

/8/0

6 11

/16/

06

9/23

/02

5/8

/06

11/1

9/06

4

/2/0

7 9/

15/0

6 11

/13/

05

7/24

/06

4/26

/07

9/12

/05

7/3

/07

12/1

/06

3/7

/05

10/1

1/06

rati

ng

at e

vent

BB

B

BB

-A

-B

BB

-B

BB

+

BB

B

BB

B

B-

B+

B

BB

-B

BB

-B

+

BB

B

A

BB

B-

BB

+

BB

+

BB

B+

B

BB

-B

B+

A

A

B-

B+

rati

ng

9/07

BB

B

B

B

B+

B

BB

+

B+

B

+

BB

-B

+

B+

B

- B

BB

B

B+

B

B

B

-B

B-

B

BB

-B

BB

+

CC

C+

B

+

deal

val

ue

($bn

)

8.80

17

.07

25.1

0 8.

26

15.2

5 8.

06

4.45

1.

59

8.64

27

.47

1.46

0.

47

40.6

6 25

.67

17.7

0 12

.63

32.9

2 8.

10

5.60

20

.17

-0.

60

1.24

tota

l as

sets

($

bn)

5.93

18

.35

17.5

8 5.

15

13.4

6 5.

35

3.28

1.

41

9.71

18

.93

3.06

1.

51

25.3

0 34

.46

7.60

22

.33

23.1

2 2.

40

15.7

5 16

.69

52.6

3 3.

77

1.32

leve

rage

(%)

44.0

4 36

.24

15.5

7 39

.01

48.0

2 0.

21

25.4

8 38

.59

-42

.91

39.4

7 52

.70

65.6

1 7.

30

16.2

8 35

.56

50.4

5 6.

65

-45

.30

15.5

0 74

.47

30.4

0

cont

inue

d on

nex

t pa

ge

Kel

lwoo

d C

o K

inde

r M

orga

n In

c M

anor

Car

e In

c M

ayta

g C

orp

Mer

iSta

r H

ospi

tali

ty C

orp

Nei

man

Mar

cus

Gro

up I

nc

Pan

Am

Sat

C

orp

Pen

n N

atio

nal

Gam

ing

Inc

Rea

logy

Cor

p SL

M C

orp

Sab

re H

oldi

ngs

Cor

p S

barr

o In

c S

equa

Cor

p S

ervi

ceM

aste

r C

o S

hopK

o S

tore

s In

c S

tati

on C

asin

os I

nc

TX

U C

orp

Toy

s R

Us

Inc

Tri

ad H

ospi

tals

Inc

U

nite

d R

enta

ls I

nc

Uni

vers

al H

ospi

tal

Serv

ices

U

nivi

sion

Com

mun

icat

ions

Inc

9/1

8/0

7 5/

29/0

6 7

/2/0

7 5/

19/0

5 2/

21/0

6 5

/2/0

5 4/

20/0

4 6

/15

/07

12/1

5/06

4/

16/0

7 12

/12/

06

11/2

2/06

7

/9/0

7 3

/19

/07

10/3

/05

12/4

/06

2/25

/07

3/17

/05

2/4

/07

7/23

/07

4/16

/07

6/27

/06

BB

B

BB

B

BB

-B

B+

B

B

BB

B

B

BB

-B

BB

A

B

BB

C

CC

+

BB

-B

BB

-B

B-

BB

-B

B+

B

B

BB

B

B-

B+

B

BB

-

BB

-B

B-

B

BB

B

B

B

B-

BB

-B

B

BB

B+

B

-C

CC

B

B-

CC

C+

B

B-

B+

C

CC

C

CC

+

B+

B

B-

B+

B

0.54

21

.61

6.16

2.

09

1.85

5.

09

4.28

8.

89

9.37

25

.54

4.99

0.

45

2.01

5.

42

0.91

4.

76

32.1

1 6.

01

6.26

3.

99

0.71

13

.51

1.48

27

.00

2.43

2.

95

2.18

2.

76

5.73

4.

51

7.48

11

6.14

4.

46

0.38

2.

03

3.12

1.

39

3.65

25

.11

9.72

6.

12

5.41

0.

27

8.06

34.2

0 48

.94

39.3

4 32

.80

72.8

4 17

.31

29.6

4 61

.94

36.4

6 93

.07

25.3

5 71

.05

36.9

0 22

.15

20.2

5 93

.49

49.3

1 23

.92

27.8

3 50

.20

-17

.63

Not

es:

Thi

s ta

ble

prov

ides

a

list

of

all

the

LB

O

targ

et

firm

s us

ed

in t

he

even

t st

udy.

T

he t

able

rep

orts

th

e na

me

of t

he f

irm

, th

e da

te o

f th

e

anno

unce

men

t, t

he r

atin

g of

the

fir

m p

rior

to

the

even

t an

d th

e ra

ting

at

the

end

of t

he s

ampl

e (r

atin

gs a

re f

rom

S&

P).

The

las

t th

ree

colu

mns

pro

vide

the

valu

e of

the

dea

l (i

n bi

llio

ns U

SD

), t

he t

otal

ass

ets

of t

he f

irm

(in

bil

lion

s U

SD

) an

d th

e le

vera

ge o

f th

e fi

rm p

rior

to

the

even

t.

CO

32

Table 1.4: LBO target issuers

name

Albertsons Inc Alltel Corp ARAMARK Corp Bausch & Lomb Inc Cablevision Systems Corp Clear Channel Commun Inc Dollar General Corp First Data Corp Freescale Semiconductor Inc Georgia-Pacific Corp HCA Inc Hertz Corp Home Depot Inc nsight Communications Co Inc Jacuzzi Brands Inc Kinder Morgan Inc Marsh Supermarkets Inc Maytag Corp MeriStar Hospitality Corp Neiman Marcus Group Inc Penn National Gaming Inc aders Digest Association Inc Sabre Holdings Corp Sbarro Inc ServiceMaster Co Station Casinos Inc SunGard Data Systems Inc TXU Corp Toys R Us Inc Triad Hospitals Inc Univision Communications Inc VWR International Inc

date

1/22/2006 5/20/2007 5/1/2006

5/16/2007 10/8/2006

11/16/2006 3/11/2007 4/2/2007 9/15/2006 11/13/2005 7/24/2006 9/12/2005 12/1/2006 3/7/2005

10/11/2006 5/29/2006 4/20/2006 5/19/2005 2/21/2006 5/2/2005

6/15/2007 11/16/2006 12/12/2006 11/22/2006 3/19/2007 12/4/2006 3/28/2005 2/25/2007 3/17/2005 2/4/2007

6/27/2006 5/2/2007

number of bonds

8 5 1 1 2 15 1 7 2 12 12 11 4 1 1 4 1 3 1 1 2 1 2 1 1 5 2 4 5 2 3 2

number of bonds with poison put

0 0 0 0 0 0 0 0 2 3 0 0 0 1 1 0 1 0 1 0 2 1 0 1 0 5 0 0 0 2 0 2

Notes: This table provides a list of all the LBO target firms with bond prices around announcement

date. The table reports the name of the firm, the date of the announcement, the number of issues

for which we have prices in our sample and the number of those with event risk protection.

33

Table 1.5: New issues and covenants

year

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

number

of bonds

346

283

379

250

287

519

736

1188

1602

714

916 1204

1667

2032

1499

1236

1385

1141

1319

1306

1107

1072

1339

average

rating

BBB

BB

BB

BBB

BBB

A

A BBB

BBB

BBB

BBB

BBB

BBB

BBB

BBB

BBB

BBB

BBB

BBB

BB

BB

BBB

BBB

% with event

covenants

2.02

11.66

5.28

18.00

49.13

12.52

11.41

18.69

25.47

30.86

23.72

34.30

36.57

41.15

37.01

35.53

32.54

33.80

37.32

50.69

44.43

43.27

61.31

average rating

of protected

B

BBB BB

B

BB

BB

BB

B

B

B

B

B

B

B

B

B

BB

B

B

B

B

B

BB

% of IG

protected

0.58

13.45

3.37

6.50

27.16

7.60

5.24

4.28

4.60

5.99

3.03

5.87

3.09

2.63

3.09

8.06

11.16

5.78

6.67

7.83

4.75

12.43

27.44

Notes: This table reports the percentage of new issues with event risk covenants over the years 1985 to 2007, as retrieved from the Mergent FISD database. Column 1 reports the number of new issues, column 2 reports their average rating, column 3 reports the percentage of new issues that are protected with event risk covenants, column 4 reports the average rating of these protected bonds and column 5 reports the percentage of IG bonds issues with event risk covenants. Year is offering year. Issue is marked as having event risk covenants if issue has either "change control put provision" or "rating decline trigger put" covenant.

34

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co co

co co T-H CO CD CD

T-H O

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b£>

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win

dow

[-60

,-31]

[-

30,-1

1]

[-10

,-1]

[0,1

] [2

,10]

[1

1,30

] [3

1,60

]

avg

n

24.9

24

.8

23.4

31

26

.4

23.7

23

.63

3-ye

ar

avg

CD

S av

g A

R

t-st

at

1.07

5 0.

0008

0.

474

1.18

4 0.

0028

0.

557

1.32

2 0.

0262

1.

404

1.82

1 0.

1911

3.

436*

**

1.74

4 -0

.000

5 0.

178

1.74

9 -0

.000

6 0.

075

1.82

9 0.

0052

1.

690

5-ye

ar

avg

CD

S av

g A

R

t-st

at

1.70

8 0.

0006

0.

477

1.83

0 0.

0023

0.

564

2.01

2 0.

0239

1.

503

2.65

8 0.

1793

3.

178*

**

2.64

8 0.

0031

0.

026

2.69

3 -0

.000

5 0.

128

2.77

1 0.

0023

1.

147

7-ye

ar

avg

CD

S av

g A

R

t-st

at

1.90

9 0.

0007

0.

850

2.07

0 0.

0008

0.

319

2.29

1 0.

0259

1.

276

3.06

0 0.

1839

2.

98**

* 3.

038

0.00

16

0.57

4 3.

093

-0.0

019

-0.7

76

3.08

4 0.

0020

0.

940

10-y

ear

avg

CD

S av

g A

R

t-st

at

2.13

9 0.

0000

0.

360

2.29

5 0.

0011

0.

507

2.53

9 0.

0250

1.

257

3.36

8 0.

1728

3.

061*

**

3.32

2 0.

0007

0.

764

3.41

0 -0

.001

5 -0

.703

3.

401

0.00

12

0.80

1

Pan

el B

: A

ver

age

abn

orm

al r

etu

rns

over

tim

e w

ind

ow

s

Not

es:

Thi

s ta

ble

disp

lays

the

res

ults

of

the

even

t st

udy

of C

DS

retu

rns

arou

nd L

BO

ann

ounc

emen

ts.

Pan

el A

dis

play

s th

e st

atis

tica

l si

gnif

ican

ce

of t

he

abno

rmal

ret

urns

for

3,5

,7 a

nd 1

0-ye

ar s

prea

ds f

or t

he 5

day

s be

fore

and

aft

er

the

even

t da

y.

Pan

el B

pre

sent

s th

e av

erag

e ab

norm

al r

etur

n

and

stat

isti

cal

sign

ific

ance

for

eac

h ti

me

win

dow

in

the

even

t st

udy.

The

fir

st c

olum

n re

port

s th

e nu

mbe

r of

obs

erva

tion

s. F

or e

ach

mat

urit

y, t

he f

irst

colu

mn

is t

he a

vera

ge C

DS

spre

ad l

evel

(in

per

cent

ages

), t

he s

econ

d co

lum

n re

port

s th

e av

erag

e ab

norm

al r

etur

n an

d th

e th

ird

repo

rts

the

resp

ecti

ve

test

sta

tist

ic.

Abn

orm

al r

etur

ns w

ere

com

pute

d ov

er t

he C

DX

NA

IG

/HY

ind

ex.

Co

36

Table 1.7: Event study in bond and equity markets

day

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

unprotected bonds n

59 37 12 38 48 51 51 71 31 17 52 69 59 64 66 44 16 34 57 51 57

avg AR

-0.002 0.006 -0.004 -0.004 -0.002 -0.003 -0.005 0.001 -0.005 -0.001 -0.010 -0.014 -0.009 0.004 0.000 -0.001 0.003 0.000 0.002 0.002 0.000

t-stat

-1.724* 3.005 -1.241 -1.415 -1.415

-2.193** -3.329***

0.038 -2.58** -1.139

-2.885*** -4.736*** -4.286***

3.814 0.159 0.543 0.147 0.476 0.429 1.648 0.063

equity n

26 16 16 29 31 30 32 27 16 15 29 32 29 32 25 16 17 28 31 30 31

avg AR

-0.001 -0.002 0.007 -0.001 0.006 0.003 0.012 0.006 0.003 0.005 0.073 0.046 0.006 0.008 0.003 0.003 -0.003 0.001 0.004 -0.004 -0.001

t-stat

-0.260 -1.196 2.54** -0.329 0.883 0.659 1.652 1.018 0.705 0.787

4.38*** 2.49** 0.596 1.064 0.569 0.942 -1.475 0.751 1.485

-1.584 -1.009

Panel A: Statistical significance of abnormal returns

window

[-60,-31] [-30,-11] [-10,-1]

[0,1] [2,10] [11,30] [31,60]

unprotected bonds avg n avg AR t-stat

40.3 -0.0004 -0.532 41.75 -0.0006 -0.410 41.5 -0.0020 -1.96* 60.5 -0.0122 -3.34***

49.78 0.0001 0.380 44.35 -0.0004 -1.378 38.4 0.0006 1.670

equity avg n avg AR t-stat

25.73 0.0009 0.732 25.45 0.0002 0.077 23.8 0.0040 1.477 30.5 0.0596 4.76***

26.56 0.0018 0.694 25.1 -0.0001 0.077

25.13 0.0001 0.140

Panel B: Average abnormal returns over t ime windows

Notes: This table displays results of the event study of bond and equity prices around LBO an

nouncements. The bond sample includes all bonds without event-risk protection issued by our

sample target firms that had reported prices on Trace both before and after the event. Panel A

displays the statistical significance of the abnormal returns for the 10 days before and after the event

day. Abnormal returns for bonds were computed over the Lehman bond indices, and for stock prices

- over the return on S&P500. Panel B displays the average abnormal return and statistical signifi

cance for each time window in the event study. For each maturity, the first column is the average

number of observations in the time window, the second column reports the average abnormal return

and the third reports the respective test statistic.

37

Figure 1.1: LBO activity worldwide

1,200'

1G0G

500

€00 -

400 -

200 i2o.e

383.0

1,142.4

Wm. mms

2000 2001 2002 2003

Ho.

3,000

2,500

2.000

1.S0O

1.000

500

assess Average Deal Value (Srra!>:

— i — No; of T r a n s a c t s thru June 30

20CE 2006 1H07

Total Number of Transactions

Notes: This figure displays the average LBO deal value (bars, left-hand axis, in SMM) and the total number of LBO transactions (right-hand axis) worldwide in the years 2000-2007. Source: CapitallQ, from S&P RatingsDirect, Aug 6, 2007.

Figure 1.2: CDS data coverage

20-Nov-OO 16-Sep-01 13-Jiil-02 SLH%-03 4-Mar-04 29-Dec04 25-Oct-Q5 21JUig-06

Notes: This figure displays the daily number of firms for which a composite CDS quote is provided in our sample.

38

Figure 1.3: Term structure of average CDS spread 2001-2006

•1-year »2 year 3-year 5-year * 7-year "10-year 1.8

•1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

20-Nw-pO 16-Sep-OI 13Jul-02 9May4B 4-Mar-M 29-Dec04 25-Od-05 21-Aug-06

Notes: This figure displays the time series of average CDS spread of various maturities in our sample over the time period 01/2001-12/2006.

Figure 1.4: CDS distribution across rating classes

125

100

75

50

25

0

^•tBil

«£=7|

^~

k^'

^ ^ iy >"i

AAA AA A BBB BB B CCC D

Notes: This figure displays the distribution of ratings for senior unsecured bonds issued by our sample firms. Ratings used are S&P rating as of December 2006.

39

Figure 1.5: Cumulative abnormal CDS returns

-3-yeir

5-year

7-yeir

-10-yttr

Sk\

s*** 1

Notes: This figure displays the cumulative abnormal CDS return around LBO announcements (as computed from our data sample around LBO events in the years 2004-2007). Abnormal returns were computed over the NA IG/HY CDX.

Figure 1.6: Cumulative abnormal returns by rating class

Notes: This figure displays the cumulative abnormal CDS return around LBO announcements (as computed from our data sample around LBO events in the years 2004-2007) for firms rated investment grade and high yield. Ratings used are S&P ratings for senior unsecured bonds. Abnormal returns were computed over the NA IG/HY CDX.

40

Figure 1.7: Changes in level of spreads by rating class

4 . 5 1

i •

0.5 •

-IG 10-year

-HYlB-jtu

30 -30 -20 10

Notes: This figure displays the change in level of the 10-year CDS spread around LBO announcements (as computed from our data sample in the years 2004-2007) for firms rated investment grade and high yield. Ratings used are S&P ratings for senior unsecured bonds. Spreads are in percentages.

Figure 1.8: Event-driven change in rating distribution

25

20

15

10

/ I Bat event

• la tes t

=P n

i

•

i

• ' 1

• • — ^

• li >

j f i

1 |

--

^

•t t

;

. _ J- dH AA BBB BB B CCC

Notes: This figure displays the distribution of ratings in our sample immediately prior to the buyout announcement (light-colored bars) and after the announcement, at the end of our sample, in September 2007 (dark-colored bars). Ratings used are S&P ratings for senior unsecured bonds.

41

Figure 1.9: Cumulative abnormal bond returns

COS -

0.04-

•0.0A

-•-unprotected -#-protected

•0.06

408 J

1

i 1Q 20 30

Y H ^ * ^ V « ^ ^ H ^ » * * > H ^

Notes: This figure displays the reaction of corporate bonds to LBO announcements (as computed from our data sample around LBO events in the years 2004-2007). The plot presents the return of bonds issued with event risk covenants ("protected") and those issued without ("unprotected"). Abnormal returns were computed over Lehman bond indices.

Figure 1.10: Cumulative abnormal stock returns

0.2

1.15

0.1

10 •30 •20 -10

•0.05

20 30

Notes: This figure displays the reaction of stock prices to LBO announcements (as computed from our data sample around LBO events in the years 2004-2007). Abnormal returns were computed over the return on the S&P500 index.

42

Chapter 2

Pricing of LBO Risk in Credit Spreads

2.1 Introduction

The results of our event study suggest LBOs should be a considerable concern for

investors in debt markets. Event risk covenants are becoming more common in new

issues; the percentage of such covenants in new bonds has risen from 30-35% in 2000

to 60% in 2007. Yet, not all new issues are protected from buyouts, and many of the

older issues do not have event risk covenants. We hypothesize that LBO restructuring

risk is priced ex-ante by investors in debt markets.

Variation in LBO risk might help explain the cross-section of credit spreads. Struc

tural models tend to generate spreads that are lower than those observed in practice,

and model parameters have been found to explain only a small fraction of spreads

(Collin-Dufresne, Goldstein & Martin, 2001, Huang k. Huang, 2003, Eom, Helwege

&: Huang, 2004). Figure 2.1 depicts restructuring risk in the context of a structural

model. These models view equity and debt as options on the firm value. Default oc

curs when the firm value process reaches a default threshold. Variables governing the

firm-value process affect default probabilities and recovery rates and thus ultimately

drive credit spreads. These pricing models incorporate only current firm fundamen

tals, such as leverage and volatility. We believe model prediction and explanatory

power could be improved by incorporating the probability of a switch to a state of

43

higher default risk, i.e. a higher default barrier, as depicted in Figure 2.1. Credit

spreads are forward-looking and should reflect all risks priced in by investors.

Hypothesis: Investors demand premium for restructuring risk ex-ante, therefore we

expect higher spreads in firms more prone to be LBO targets.

In the following section we proceed to test this hypothesis utilizing patterns of buyout

activity both at the firm and industry level.


2.2 LBO risk at the industry level

2.2.1 Data


considered a Leveraged Buyout if the investor group includes management or the



was between 1980-2007 and the target was a US firm. The total is 7416 announce

ments1 . Where there was more than one announcement for a firm on a specific day,

we leave one of status " Completed" (where non is available, we leave that with status

"Pending")2 . This leaves us with 7393 announcements.

2.2.2 Industry clustering in LBO activity over time

In this section we examine industry-level clustering in buyouts in the recent wave of

2004-2007. We also study whether investors now focus on industries different than

1 Based on CapitallQ database and World Economic Forum reports the coverage of deals in Thomson One Banker seems to be incomplete, but we have no reason to suspect any bias in coverage. Furthermore, since we focus on the intersection of LBOs and CDS quotes, we are, by definition, focusing on the larger, public, highly traded firms, for which the coverage is likely to be high.

2 82.5% of the announcements are of status "Completed", 4% are of status "Pending" and 8.5% are of status "Withdrawn". 4% are of unknown status and the rest are a small number of "Intended" or " Rumored" or withdrawn from such.

44

those which experienced a high level of activity in the 1980's.

Table 2.1 presents the percentages of LBOs occuring in each industry out of the

total number of LBOs in the time period. The first column presents numbers for the

years 1980-1989 and the second - for 2004-2007. Industry-level clustering is clearly

evident in both LBO waves. These patterns can be observed more easily in Figure

2.2, which displays percentages for industries that experienced a high level of LBO

activity in either or both waves.

In 1980-1989 most LBOs were concentrated in manufacturing industries. Almost

6% of all LBOs occurred in industrial machinery and computer equipment, and an

additional similar number in electronic and other electrical equipment. Over 8% oc

curred in metal industries and products combined, and over 4.5% in food products.

This is consistent with the finding in Lehn, Netter & Poulsen (1990) that LBOs oc

curred in low growth and low R&D industries.

A different picture emerges when studying the more recent LBO wave. This wave

is heavily concentrated in services, with a clear focus on technology and telecommu

nications. Almost 15% of all LBOs occur in business services, out of which over 9.2%

are in computer software and related services. In the first quarter of 2005 nearly half

of all LBOs targeted technology and telecommunications firms (Billett, Jiang & Lie,

2008). These findings are in line with a recent report by the World Economic Forum3 ,

which points out the increase in buyout activity in high-growth, "high-tech" sectors

in the last decade. Almost 5% occur in engineering and R&D and related services.

An additional 4-5% occur in each of machines & computer equipment and electronic

equipment. Thus, industry concentration appears even stronger in the recent wave;

rank correlation between industries by number of buyouts is 92%-95% in the years

2004-20074.

3 Volume 1 of Working Papers on " The Global Economic Impact of Private Equity"

4 An interesting question in itself is the reason for the documented clustering in these specific industries and the change in focus of LBO sponsors over time. Mitchell and Mulherin (1996) find inter-industry patterns to be directly related to the economic shocks borne by the sample industries, e.g. deregulation, changes in input costs, innovations in financing technology, suggesting a similar

45



2.2.3 Industry-wide effects of LBO announcements

In previous work, we document the detrimental effect of LBO announcements on

credit spreads of target firms. Given industry-level clustering in LBO activity, we

might expect to find an industry-wide reaction to LBO announcements. We proceed

to study the effect of these announcements on the credit spreads of other firms in the

same industry.

Dropping LBO targets from the CDS sample, we are left with 345 firms from 118

different industries, where industry is defined by 3-digit SIC code. From the LBO

data we drop events less than 120 days apart (for a clean time window of 4 months

around the event). We merge the CDS and LBO data by industry and are left with

a final sample of 762 event days in 271 firms in 94 industries5 . For each of these

industries, the sample consists of spreads of non-targets around LBO announcements

in the industry. Since the firms with traded CDS contracts are nearly all public, we

focus on the relevant peer group in events, i.e. on LBO announcements involving

public targets. This leaves us with a sample of 257 event days in 155 firms in 47

industries.

Event Window

We use an event window of 60 days prior to the event and 60 days following it. The

window is subdivided into 7 time intervals: 60 to 31 days before the announcement;

30 to 11 days before, 10 days to one day before, the day of the announcement and

the following day and the corresponding time periods after the announcement. We

shock might have driven recent trends.

5 We further drop firms that have gaps or staleness in time series of prices around event days. This leaves us with a final sample of 703 event days in 252 firms in 90 industries.

46

expect to find a discernible price impact in the [0,+l] interval, under the hypothesis

the announcement has informational value and results in price pressure. The impact

of the announcement is tested over a two day interval because the announcement

might have been made after markets closed for the day. In the case of less liquid

names, the full impact of a rating announcement might be delayed to the [+2,+20]

interval. If the event is rumored, we might expect to see a reaction in prices in the

windows preceding the announcement, in particular in the days leading up to it.

Abnormal Returns

We compute abnormal return over the CDX NA IG/HY index. Classification to IG

or HY is determined by firm rating prior to the event and stays constant throughout.

Reaction to 535 of the events is tested in firms categorized as IG and 163 in those

categorized as HY.

As the CDX NA 5-year series begins on November 19, 2004, we exclude all prior

dates from the sample. Despite the relatively short time series of the index, we prefer

to use the CDX as it is comprised of the most actively traded contracts. Furthermore,

the CDX NA series for other maturities begins at a later date (first quarter of 2006

for IG and as late as the beginning of 2007 for HY), therefore, we benchmark all

maturities against the 5-year index.

In computing abnormal returns, we use the market-adjusted model with an esti

mation window of 1 year, i.e. approximately 250 business days. In cases where there

was not a full year of observations before the event, we include in the estimation

window the days up to the beginning of the event window6 .

Event study methodology, calculation of daily changes in spreads and test statistics

is as detailed in section 1.5.

6 Firms that had an estimation window of less than one month were removed from the sample.

47

Empirical Results

Our results indicate that LBO announcements have a statistically significant negative

impact on CDS spreads of firms in the same industry as the targets, i.e. they result in

a significant widening in the spreads of these firms. Table 2.2 provides a detailed de

scription of the empirical results of the event study. Panel A displays the significance

results for the daily changes in the week around the announcement. The significance

of average abnormal changes in the different time windows is presented in panel B7 .

For each maturity, the first column in the table is the average CDS spread level (in

percentages), the second column reports the average abnormal change in spreads (as

a percentage of the original spread level) and the third reports the respective test

statistic.

Cumulative abnormal changes in spreads are displayed in Figure 2.3. The figure

shows a widening of the spreads on the announcement day and the following day by

approximately 1% and 2% respectively8 ; there is also a widening of approximately

2% on day 2. Panel A of Table 2.2 shows the change on day 0 to be significant at

the 1-5% level for all maturities. The change on day 1 is significant at the 5-10%

level for all maturities. We also observe a cumulative abnormal change in spreads

of approximately 5% in the 10 days leading up to the announcement, suggesting the

announcement is anticipated. Most of the anticipation-driven change occurs in last 3

days before the event day. All maturities show a slight over-reaction that is corrected

in the month following the announcement (we observe an insignificant negative aver

age abnormal return for the time window [31,60] for all maturities).

In short, all maturities display a significant widening of spreads on the day of the

announcement and the following day. The cumulative abnormal change in spreads

7 Results for 1-year spreads are not shown, as we believe the 5-year benchmark is an ill fit; betas seem unreasonable for several firms. However, the 1-year spreads for the remaining firms also show a significant widening in spreads on days 0 and 1.

8 For example, a contract trading at a spread of 100 bps prior to the announcement that experiences a 10% increase in spread will be trading at 110 bps afterwards.

48

due to the LBO announcement is approximately 10% on average (for the 5-year con

tract) in a 2-month interval around the event, displaying a significant within-industry

reaction to LBO announcements. These results are consistent with the hypothesis

proposed by Mitchell &: Mulherin (1996) that buyout inter-industry patterns are di

rectly related to industry economic shocks; an LBO in one firm might provide relevant

information about other industry firms, causing a subsequent change in their pricing.



2.3 Methodology

The main problem in testing our hypothesis is that of identification. Firm-level

characteristics affect both credit spreads and risk of LBO, leading to potential biases

in results. To address this problem we employ a number of different identification

strategies:

2.3.1 Industry-level probability of LBO

To separate the effect of LBO probability from the direct effect of firm characteristics

on credit spreads, we use an exogenous industry-level probability. This is based on

much empirical evidence of cross-industry variation in event risk. Crabbe (1991)

reports LBOs are less common in industries such as financial and utilities due to

regulatory restrictions on leverage, asset sales and dividend payouts; Lehn &: Poulsen

(1991) use industry as a proxy for LBO risk. Mitchell & Mulherin (1996) find inter

industry differences in both the rate and time-series clustering of buyout activity.

They relate inter-industry patterns in the rate of takeovers and restructurings to

economic shocks borne by the sample industries (e.g. deregulation, changes in input

costs, innovations in financing technology). Testing these patterns in the last buyout

wave, we find industry-level clustering in buyout activity to have grown even more

49

pronounced over time, and document statistically significant intra-industry effects in

debt markets to LBO announcements.

These results suggest that LBO risk is, to a significant extent, driven by industry-

level fundamentals. We, thus, opt to use an industry-level probability of LBO, which is

exogenous to the firm. We construct this probability using industry LBO realizations.

We use our sample of US LBO announcements and compute this probability to be

the ratio of: 1. the number of LBO targets in an industry to 2. the number of firms

in the industry9 . We compute these probabilities at the 3-digit sic level, where sic

code is as reported in Compustat. We run the following regression:

CDSjtt = a + j3 • pLBOi<t-i + 7 • characteristicSj>t-i(leveragejj-i,roajj-i, •••)

where j is firm in industry / at year t. Our dependent variable is CDS spread, and our

explanatory variables consist of probability of LBO (pLBO) and firm-level controls.

To avoid any look-ahead bias we use LBO probability of the previous year.

2.3.2 Test across different markets

Non-US private equity activity has grown to be of similar magnitude to that of the

US in the last few years, mostly in Continental Europe. Over the period 2001-2007

US buyout activity constitutes 34.8% of worldwide LBOs in number of deals, while

Continental Europe and the UK account for 46.3%. In terms of deal value, US LBOs

constitute 42.8% and those in Western Europe - 41.6%10 . Therefore, we expect our

hypothesis on pricing of LBO risk in credit spreads to hold also for European firms.

Previous works show industry-level probability of LBO is driven by fundamental

9 Number of industry firms is determined using Compustat. This construction creates a proxy, as Compustat lists only public firms. However, we are not aware of a comprehensive source on private firms. This proxy assumes the ratio of private to public firms is not significantly different across industries, thus leads to no bias in our cross-sectional study.

10 World Economic Forum, Volume 1 of Working Papers on "The Global Economic Impact of Private Equity"

50

industry factors, suggesting probabilities are similar across markets. We make use

of this assumption to ensure additional exogeneity in our industry probability. We

proceed to use probabilities of LBO, as computed in US markets, to explain cross-

sectional variation in spreads in Europe. Here, we use the CDS spreads of European

firms in our dataset.

2.3.3 Characteristics of LBO targets

To verify we are capturing LBO risk and not a different one correlated with industry

probability, we make use of previously documented properties of LBO targets. Stud

ies of the 1980's buyout wave have found LBOs to be associated with certain firm

characteristics. If, indeed, we are capturing the effect of LBO probability on spreads,

we would expect it to be more pronounced for those firms more prone to undergo an

LBO, where risk is more prominent.

Maupin (1987) finds that LBO risk is determined by firm fundamentals, similarly

across markets. Opler & Titman (1993) investigate the determinants of leveraged

buyout activity by comparing firms that implemented LBOs to those that did not.

Consistent with Jensen's (1986) free cash flow theory, they find that firms that ini

tiate LBOs can be characterized as having a combination of unfavorable investment

opportunities (low Tobin's q, low growth) and relatively high cash flow, suggesting

LBOs mitigate agency problems in firms plagued by overinvestment problems.

The firm that may become the target of a leveraged buy-out has to be able to gen

erate large and stable free cash flow from operations to service the large post-buyout

debt payments. A firm with high and steady taxable cash flows would also benefit

more from increased leverage. Thus, high cash flows and lower standard deviation

of cash flows are associated with a higher probability of LBO. High and steady cash

flows are typically a characteristic of more mature and stable firms, thus more mature

firms are more prone than younger ones to be LBO targets.

LBOs have been shown to be associated with low growth firms (within industry),

51

where R&D and capital expenditures and other costs are lower. High asset tangi

bility lowers costs of financial distress, making more tangible firms more likely LBO

candidates. Tangible assets might provide guarantees for the new debt, and would

also allow raising extra cash through asset stripping.

We study the spreads of firms with the aforementioned characteristics to further

support our identification of the effect of LBO risk on CDS prices.

Hypothesis: Pricing of LBO risk should be more significant in credit spreads of ma

ture, low growth firms with high cash and high asset tangibility.

2.3.4 Firm-level instrumental variable: Event-risk covenants

In previous sections we propose identification of LBO risk using an exogenous industry-

level variable. In this section we propose identification at the firm level, using event

risk covenants as a (firm-level) instrumental variable. These covenants affect LBO

risk, as they increase the cost of a takeover, yet they affect credit spreads only via

this channel.

Previous papers have studied the relation between event risk covenants and LBO

probability. Lehn & Poulsen (1991) find a higher percentage of event risk covenants

in unsecured issues of LBO targets compared to unsecured issues of firms not taken

over in an LBO (50.5% vs. 36.1%). They conclude these covenants are used where

risks are greatest. This implies event risk covenants should be positively related to

LBO risk when relation is studied at issuance. Crabbe (1991) finds that event risk

covenants in the late 1980's reduced costs for borrowers by 20-30 bps (effect found to

have declined with decreasing buyout activity), implying relation to LBO risk should

be negative when studied post-issuance. Causality between event risk covenants and

probability df LBO is unclear at issuance, therefore, we use the relation between the

two variables in the post-issuance period (consistent with Wei, 2005, and Bradley,

Brav, Goldstein &; Jiang, 2007). Thus, we expect event risk covenants to have a

negative relation with LBO risk.

52

We use covenant information as reported in Mergent FISD database, filtering out

issues with no available covenant information11 . An issue is marked as having "event

risk" covenants if it has either the " change control put provision" or " rating decline

trigger put" covenant. We construct an event-risk covenant index for each of our US

sample firms in two ways: 1. percentage of bonds issued with event risk covenants

(index jn) and 2. weighted index of bonds issued with event risk covenants, where

weights are offering notional amounts (indexJW). We construct a separate index for

each year (per firm), using the outstanding issues for the year.

In the first stage we run a probit regression of the binary variable of LBO occur

rence in the time period 2001-2007 against the event-risk covenant index and controls

of firm-level characteristics and financing covenants. Our instrument (the covenants)

is correlated with the dependent variable (LBO occurence), as required. We add in

financing covenants, as these have been shown to provide protection to bondholders

in takeovers (Asquith & Wizman, 1990) and thus might affect both probability of

LBO and the level of credit spreads. The variable for each financing covenant is

the percentage of bonds issued by the firm with the specific covenant (since 1980,

according to Mergent FISD database). In the second stage we regress CDS spread

against predicted probability of LBO and similar firm-level controls (note that any

correlation of the instrument with the error term in this regression would imply a

negative relation, inconsistent with our hypothesis).

Setup:

LBOjtt — Oi\ + ot2 • covIdxjtt-i + as • firmCharSj<t-i + on • finlndentureSj

CDSjj — 0\ + 02 • pLBOjj-i + 03 • firmCharSjtt-i + 04 • finlndentureSj

11 We exclude bonds with missing covenant information. Billet, King & Mauer (2004) find missing covenant information to be unrelated to time of issuance, priority, rating, maturity, size of issue or issuer so we expect no bias in selection of bonds examined.

53

where covldx^t is event-risk covenant index for firm j at time t, firmChars is firm-

level controls, finlndentures is firm financing covenants index, LBO is a binary

variable for LBO occurence, CDS is spread and the explanatory variable in the

second-stage regression (pLBO) is the firm-level probability of LBO predicted from

the first stage.

2.4 Empirical Results

To test our hypothesis, we proceed to study the effect of LBO risk on the cross-

sectional variation in credit spreads. For this, we use our entire dataset consisting of

CDS spreads of 489 US corporate reference entities from 2001-2006. We include all

contracts denominated in U.S. dollars and written on senior unsecured debt. This

dataset is described in detail in the data sample section (section 1.3.1). We use only

the 5-year maturity contract, which is the most common (provided for 97% of ob

servations). After merging with Compustat annual data, we are left with 409 firms.

Our dependent variable is CDS spread (for robustness, we test using annual closing

spread, average annual spread and average fourth-quarter spread), and our explana

tory variables consist of probability of LBO (pLBO) and firm-level controls. To avoid

any look-ahead bias we use LBO probability of the previous year. Regression results

are detailed in Table 2.3.

CDS spreads are determined by both default risk and recovery. Along with pricing

information, Markit also reports average recovery rates used by data contributors in

pricing each CDS contract. The quoted recovery rates only reflect market partici

pants' consensus view on expected losses and can thus differ substantially from real

ized losses. To control for cross-sectional variation in recovery, we use Markit-quoted

recovery rates. Year dummies control for any time variation in recovery. Table 2.3

reports results both with and without Markit recovery quotes; this variable does not

have a significant effect on our results.

The first two columns of Table 2.3 report results for the entire dataset. The co-

54

efficient on industry probability of LBO is highly positive and significant (at the 5%

level). To make sure our results are not driven by differences in covenant protection,

we filter out firms that have outstanding issues protected by event risk covenants in

the following way: we extract all available issuance information for our sample firms

from Mergent FISD. We mark a firm as "protected" if it has at least one outstanding

issue with event risk protection. We then proceed to run the same regression using

only "unprotected" firms. Results are reported in the last two columns of Table 2.3.

The significance of LBO risk is unaffected; the coefficient is still highly positive and

significant.

LBO activity has been steadily increasing since 2000. Both the number of deals

and average transaction value have more than tripled from 2000 to 2007. In accor

dance with these statistics, we would expect LBO risk to be a growing concern for

investors over time. Consequently, we divide our sample into the earlier (2001-2003)

and the later years (2004-2006). Regression statistics are presented in the first two

columns of Table 2.4. Results show that LBO risk is, indeed, a significant factor only

in the later half of the sample.

A high probability of LBO is associated with certain firm characteristics. High

cash reserves make a firm a more attractive target. Therefore, we would expect the

effect of LBO risk to be more pronounced in these firms. To test this hypothesis,

we divide our sample into high and low-cash firms, where cutoff is set at the 75"1

percentile of the ratio cash and equivalents to assets. Columns 3 and 4 of Table 2.4

show that industry LBO probability is indeed significant (at the 5% level) for firms

with high cash, but is not significant for low-cash firms. The magnitude of the coeffi

cient for high cash firms is nearly five times that of the entire sample, suggesting the

impact on pricing is stronger for firms with high cash.

Firms with high growth would require more investments in R&D and capex, mak

ing it harder to service a heavy debt load. Therefore, we would expect the effect of

LBO risk to be more pronounced in firms with lower growth. The last columns in

55

Table 2.4 report results when dividing our sample into high and low market-to-book,

where grouping is determined at the 25"1 percentile of the ratio. Results show that

industry LBO probability is indeed significant (at the 1% level) for firms with low

market-to-book ratio, but is not significant for firms with a higher ratio. Again, the

magnitude of the coefficient for low market-to-book firms is significantly higher than

for the entire sample.

Overall, the results of our study on US CDS spreads suggest that an increase of

10% in industry probability of LBO increases credit spreads by 25-30 bps. This effect

is more pronounced for firms with high cash levels and low growth, which are more

prone to undergo an LBO; the effect for these can be as high as 100 bps.

[Insert Tables 2.3 and 2.4 about here]

2.4.1 Results across markets

To further ensure exogeneity of our industry LBO probability, we proceed to "export"

industry probabilities computed in one market - the US, as an explanatory variable

to a second market - Europe. In this study, we use the CDS spreads of 169 European

firms in our dataset. After merging with annual data from Osiris we are left with

130 firms. Regression results are presented in Table 2.5. The first column of the

table reports our results for the entire European dataset. The coefficient on industry

probability of LBO is highly positive and significant at the 1% level. In the study of

European spreads, we find that an increase of 10% in industry probability of LBO

increases credit spreads by as much as 50 bps.

Given previous evidence on LBO firm characteristics, we would expect the effect of

LBO risk to be more pronounced in more mature firms, as these typically have higher

and more stable cash flows than younger firms. In columns 2 and 3, we divide our

sample into "mature" (defined as firms existing for over 100 years) and "younger"

firms (firms under 20 years of age). Results show that industry LBO probability

is indeed significant for more mature firms, and not significant when studying only

56

younger firms.

We then divide our sample by asset tangibility. Firms with few tangible assets

would have high costs of bankruptcy, as well as less potential for cash from asset

stripping, and, thus, are less prone to be LBO targets. We group firms with asset

tangibility lower than the median ratio PPE to assets as "low" tangibility firms, and

the rest as "high" tangibility. The last two columns of Table 2.5 show that, indeed,

LBO risk is not significantly priced in spreads of low tangibility firms, as these are

very unlikely to be the target of a takeover. The coefficient for the other firms is

positive and significant (at the 5% level).


2.4.2 Firm-level results

In this section we use identification of LBO risk at the firm level, using event risk

covenants as a (firm-level) instrumental variable. Results of the two-stage instrumen

tal variable regression are displayed in Table 2.6. The first two columns present results

of the first-stage regression, where the first column displays results using indexjn (in

dex based on percentage of " protected" bonds) and the second column displays results

for indexJW (weighted average using issue notionals). In both cases, the event-risk

covenant coefficient is highly negative and significant at the 1% level. The rela

tion between event-risk protection and LBO occurrence is negative, as expected for

the post-issuance period (in using lagged variables, we study the relation in the post-

issuance period). Few of the financing covenants are also significant in explaining LBO

occurrence; the table shows the covenant restricting payout to shareholders, which is

negatively related with LBO. The predicted probability of LBO as computed from

the first-stage regression is used in the second stage. The last two columns of Table

2.6 show results for the second-stage regression. Predicted value of LBO probability

is significant (at the 5% level) in explaining CDS spreads, with higher probability of

LBO increasing spreads. Our findings support the existence of a positive, significant

57

effect of firm-level LBO risk on credit spreads.


2.5 LBO Monitoring with option implied volatility

The results of our event study in section 1.5 suggest LBO announcements are an

ticipated by the market, as reaction is observed prior to the actual announcement

day. Given the anticipatory reaction of both equity and debt markets, we might also

expect to find a reaction in option prices leading into an announcement. Anticipation

of a pending LBO and a corresponding reaction in stock prices should be reflected

in increased short-term implied volatility (IV). Indeed, implied volatility of short-

maturity options has been found to spike leading up to announcements, rising above

that of longer-term contracts. This "negative slope" of implied volatilities is used in

practice in monitoring firms for identification of those liable to undergo an LBO in the

near future12 . Assuming a negative IV slope is suggestive of a pending LBO, a risk

we have found to be priced in credit markets, we might expect to find a significant

relation between IV slope and CDS spreads. A negative, significant relation would

lend further support to our previous findings.

Table 2.7 presents the results of regressing CDS spreads against IV slope con

trolling for level of IV and firm characteristics. Option implied volatility data was

retrieved from OptionMetrics database. Results are presented at a monthly frequency,

as we are studying a relatively short-term effect, examining IV slope leading up to

LBO announcements. We compute IV slope both as the ratio of the 3-month IV to

the 1-month IV (slopeAmSm) and as the ratio of the 6-month IV to the 1-month IV

(slope_lm_6m). We take the average of call and put implied volatility from ATM op

tions. Table 2.7 shows a negative relation between CDS spreads and implied volatility

slope, for both variables computed. The coefficient of IV slope is highly significant

12 See, for example, "Revisiting Options Around LBOs", Bank of America report, August 28, 2006.

58

at the 1% level. The table also displays a significant, positive relation between CDS

spreads and the level of implied volatility, consistent with findings of previous works

that used IV as a proxy for " riskiness". We conclude that increase in short-term im

plied volatility above that of longer-term contracts, suggestive of an imminent LBO,

is associated with higher credit spreads. These results suggest LBO risk is, indeed,

priced in credit markets, consistent with our previous findings at the industry and

firm level.


59

2.6 Summary

This paper studies the effect of LBO announcements on the cross-sectional variation

in credit spreads. To the best of our knowledge, it is the first to identify and quantify

the effect of LBO risk on spreads.

We first establish LBOs as a significant concern for debt investors by studying the

reaction of credit spreads of target firms to LBO announcements in the US during

the years 2001-2007. We find a cumulative widening of CDS spreads by 60-70% in

a window of two months around the announcement, suggesting default risk increases

as costs of additional debt significantly outweigh potential increase in expected cash

flows. A similar event study in bond and equity markets suggests LBOs result in

wealth expropriation from debt-holders, but that these losses are not a significant

source of shareholder gains, supporting value creation in LBOs. We then proceed to

test the existence and magnitude of ex-ante pricing of LBO restructuring risk in debt

markets. Based on previous works showing cross-industry variation in event risk, we

use exogenous industry-level variables, and further ensure exogeneity of LBO risk

by exporting US probabilities to study European firm spreads. We find evidence of

pricing of LBO risk: firms more likely to undergo an LBO in the future have spreads

that are higher by 30-50 bps. Consistent with previously documented characteristics

of LBO targets, we find the effect to be more pronounced in mature, low-growth,

high-cash firms, with higher asset tangibility. Our results show LBO risk to be a

growing concern, in accordance with buyout trends over the sample years 2001-2007.

A firm-level event-risk instrumental variable study and an options implied volatility

screen both strengthen our findings of pricing of LBO risk in debt markets.

The results of this paper might further our understanding of the variation in credit

spreads, and, consequently, of the credit spread puzzle extensively documented in the

credit risk literature. Structural model variables have been found to explain only 25%

of the total variation in credit spread changes (Collin-Dufresne, Goldstein & Martin,

2001). They have also been found to generate smaller spreads than those observed

60

in practice, particularly for investment grade debt (Huang & Huang, 2003, Eom,

Helwege & Huang, 2004). These models imply drivers of the firm-value process,

such as leverage and volatility, determine default probabilities, recovery rates and,

consequently, credit spreads - and lack additional risks priced in by investors. We

believe LBO restructuring risk to be a significant, omitted risk in the last buyout

wave years. The reference entities of corporate bonds are exposed - more and more

so - to corporate action, such as takeovers, which result in a dramatic change in risk

profile, particularly for investment-grade debt. While 2008 has seen a significant drop

in number and value of LBO deals, buyout activity has been shown to be subject to

recurring boom and bust cycles, and a significant part of the growth in private equity

activity and institutions is believed to be permanent (Kaplan &; Strmberg, 2008).

Future work would incorporate findings of this paper into existing credit pricing

models. A natural extension might be an addition of a potential regime switch, driven

by an LBO restructuring, to a state of higher default risk and higher loss-given-

default. Incorporation of the "Peso problem" of LBO risk into pricing models might

both increase explanatory power and alleviate problems of spread under-prediction.

61

Table 2.1: LBO distribution across industries (%)

sic2

40 29 31 52 53 75 54 17 15 57 45 78 60 79 64 22 61 65 32 24 56 82 23 25 70 13

1980-1989

0.63

0.53

0.58

0.79 1.84

0.63

2.37

0.32

0.58

0.89 1.32

0.79

1.16 0.42

0.26

1.95

0.53

1.00

1.53 0.74

1.63

0.05 2.74

1.32

0.16

0.47

2004-2007

0.09 0.14

0.32

0.36

0.36

0.45

0.45

0.63

0.63

0.63

0.63

0.68 0.72

0.77

0.81

0.81

0.86

0.86

0.86 0.95

0.95

1.08

1.08

1.13 1.22

1.26

sic2

1 63 42 51 39 26 59 33 62 48 49 67 27 20 30 58 37 80 50 38 34 28 36 87 35 73

1980-1989

1.95 1.42

2.11

1.32

1.63

2.68 4.21

1.00 2.53

0.37

1.84

4.05

4.68

2.58

2.58

3.37

1.68 2.84

3.37

4.26

3.89

5.63 1.84

5.68

3.79

2004-2007

1.31

1.35

1.35

1.40

1.53

1.58

1.62

1.94

2.21

'2.25

2.30

2.30 2.52

2.66

2.75

3.15

3.33

3.38

3.42

3.60

3.69

4.10

4.68 5.04

14.58

Notes: This table displays the percentage of LBOs in 2-digit SIC code industries (table shows only industries with at least 10 LBOs). Percentage is out of the total number of LBOs in the specified time frame. The time frames displayed are the two major LBO waves: 1980-1989 and 2004-2007. The total number of LBOs are 1900 and 2222, respectively. Numbers are presented in ascending order for the years 2004-2007.

Ta

ble

2

.2:

Intr

a-i

nd

ust

ry c

han

ges

in

CD

S s

pre

ad

s

day

-3

-2

-1

0 1 2 3

n 169

108

148

250

203

184

191

3-ye

ar

avg

cds

avg A

R

t-st

at

0.71

1

-0.0

04

-1.2

02

0.70

5

0.02

0

1.57

9

0.95

1

0.00

2

0.45

5

0.94

1

0.00

8

2.2

9*

*

1.11

3

0.02

2

1.91

*

1.25

2

0.01

7

3.4*

**

1.14

5

-0.0

03

-1.0

55

5-ye

ar

avg

cds

avg

AR

t-

stat

1.13

6

0.00

0

0.79

3

1.04

5

0.01

9

1.42

2

1.23

7

0.0

03

1.

076

1.34

6

0.01

2

2.7

4***

1.41

7

0.01

9

1.83

*

1.61

9

0.01

0

2.7

9***

1.46

9

0.00

0

0.22

3

7-ye

ar

avg

cds

avg

AR

t-

stat

1.28

3

-0.0

01

-0

.28

9

1.19

6

0.01

5

1.66

*

1.38

0

0.00

2

0.34

6

1.49

3

0.00

9

2.4

5*

*

1.57

5

0.01

9

2.0

5*

*

1.77

1

0.00

6

1.87

*

1.61

5

0.00

1

0.49

1

10-y

ear

avg

cds

avg A

R

t-st

at

1.41

2

0.00

0

-0.3

05

1.33

6

0.00

9

1.41

7

1.52

5

0.00

2

0.58

4

1.63

7

0.01

0

2.94

***

1.72

4

0.01

4

1.72

*

1.92

2

0.00

7

2.1

8*

*

1.75

3

-0.0

02

-1.5

83

Pan

el A

: S

tati

stic

al s

igni

fica

nce

of a

bn

orm

al c

han

ges

in

CD

S s

pre

ads

Not

es:

Thi

s ta

ble

disp

lays

th

e re

sult

s of

th

e ev

ent

stud

y of

wit

hin-

indu

stry

cha

nges

in

CD

S sp

read

s ar

ound

LB

O a

nnou

ncem

ents

. T

his

tabl

e di

spla

ys

the

stat

isti

cal

sign

ific

ance

of

th

e ab

norm

al c

hang

es f

or 3

,5,7

an

d 1

0-ye

ar s

prea

ds.

Th

e fi

rst

colu

mn

repo

rts

the

num

ber

of o

bser

vati

ons.

F

or

each

mat

urit

y, t

he

firs

t co

lum

n is

th

e av

erag

e C

DS

spre

ad l

evel

(in

per

cent

ages

), t

he

seco

nd c

olum

n re

port

s th

e av

erag

e ab

norm

al c

hang

e in

spr

eads

(as

a

perc

enta

ge o

f th

e or

igin

al s

prea

d le

vel)

an

d t

he

thir

d re

port

s th

e re

spec

tive

tes

t st

atis

tic.

A

bnor

mal

cha

nges

wer

e co

mpu

ted

over

th

e C

DX

NA

IG

/HY

inde

x.

05

to

win

dow

[-60

,-31]

[-

30,-1

1]

[-10

,-1]

[0,2

] [3

,10]

[1

1,30

] [3

1,60

]

avg

n

179.

1 17

9.8

168

212.

3 18

0.1

172

166.

1

3-ye

ar

avg

cds

avg

AR

t-

stat

0.86

1 0.

0009

1.

108

0.92

7 -0

.000

1 -0

.252

0.

901

0.00

49

1.94

* 1.

102

0.01

58

2.93

***

1.02

5 0.

0008

0.

757

0.95

9 0.

0009

1.

319

0.91

5 -0

.001

1 -0

.828

5-ye

ar

avg

cds

avg

AR

t-

stat

1.18

7 0.

0009

1.

299

1.24

6 -0

.000

3 -0

.667

1.

228

0.00

41

1.79

* 1.

460

0.01

34

2.63

***

1.35

3 0.

0017

1.

521

1.28

2 0.

0007

0.

925

1.28

1 -0

.000

8 -0

.874

7-ye

ar

avg

cds

avg

AR

t-

stat

1.34

0 0.

0006

1.

180

1.39

7 -0

.000

5 -0

.717

1.

381

0.00

35

1.72

* 1.

613

0.01

17

2.51

**

1.50

2 0.

0014

1.

495

1.44

1 0.

0005

1.

231

1.44

2 -0

.000

4 -0

.701

10-y

ear

avg

cds

avg

AR

t-

stat

1.48

9 0.

0004

0.

971

1.54

5 -0

.000

8 -0

.992

1.

519

0.00

31

1.79

* 1.

761

0.01

03

2.34

**

1.65

0 0.

0013

1.

578

1.59

3 0.

0005

1.

235

1.59

3 -0

.000

9 -1

.432

Pan

el B

: A

ver

age

abn

orm

al c

han

ges

ove

r ti

me

win

do

ws

Not

es:

Thi

s ta

ble

disp

lays

the

ave

rage

abn

orm

al c

hang

e in

spr

eads

and

sta

tist

ical

sig

nifi

canc

e fo

r ea

ch t

ime

win

dow

in

the

even

t st

udy.

T

he

firs

t

colu

mn

repo

rts

the

aver

age

num

ber

of o

bser

vati

ons

in t

he

tim

e w

indo

w.

For

eac

h m

atur

ity,

th

e fi

rst

colu

mn

is t

he a

vera

ge C

DS

spre

ad l

evel

ove

r th

e

tim

e w

indo

w (

in p

erce

ntag

es),

the

sec

ond

colu

mn

repo

rts

the

aver

age

abno

rmal

cha

nge

in s

prea

ds (

as a

per

cent

age

of t

he o

rigi

nal

spre

ad l

evel

) an

d th

e

thir

d re

port

s th

e re

spec

tive

tes

t st

atis

tic.

Abn

orm

al c

hang

es w

ere

com

pute

d ov

er t

he C

DX

NA

IG

/HY

ind

ex.

02

CO

64

Table 2.3: Pricing of LBO risk in CDS spreads - US 2001-2006

dependent variable: CDS spread

industry probability of LBO

leverage

roa

stdev of net income

log sales

recovery

number of observations

R-squared

all

2.489** 2.636**

(1.13) (1.13)

1.439*** 1.410***

(0.29) (0.29)

-3.881*** -3.911***

(0.48) (0.48)

3.093*** 3.022***

(1.07) (1.07)

-0.0695** -0.0694**

(0.034) (0.034)

-0.0025

(0.010)

1703 1723

0.5 0.5

unprotected

2.907** 3.016**

(1.31) (1.31)

0.893** 0.827**

(0.36) (0.37)

-3.804*** -3.818***

(0.57) (0.56)

0.747* 0.681

(0.44) (0.42)

-0.0588 -0.0591

(0.039) (0.039)

-0.0038

(0.012)

1336 1351

0.44 0.45

Notes: This table presents results of regressing credit spreads of US firms from 2001-2006 on lagged

annual industry probability of LBO and firm-level controls. The dependent variable is CDS spread,

using annual closing spread per firm, quoted in percentages (robustness tests show similar results

when using average yearly spread or average spread over fourth quarter). Industry probability of

LBO is computed per year as the ratio of: 1. number of industry firms that were targets of

LBO (according to Thomson Financial LBO announcements) to 2. number of industry firms (as

reported in Compustat). Industry is determined at the 3-digit sic level, where sic is as reported in

Compustat. Leverage is long-term + short-term debt to total assets, roa is EBITDA to total assets,

stdev is standard deviation of net income, avg recovery per year is yearly average of recovery, as

composed by Markit. We use log of sales as a proxy for firm size. The first two columns report

regression results for the enitre sample of CDS contracts. The last two columns report results for

a subsample consisting only of firms with issues that have no event risk protection, as reported

in Mergent FISD database. Regression is run with year, restructuring clause and 2-digit sic fixed

effects, errors are clustered at the firm level. ***i** and * indicate significance at the 1%, 5%, and

10% levels, respectively.

Tab

le 2

.4:

Pri

cing

of

LB

O r

isk

in s

prea

ds -

acr

oss

tim

e an

d fi

rms

dep

end

ent

var

iab

le:

CD

S s

pre

ad

ind

ust

ry p

rob

abil

ity

of L

BO

lev

erag

e

roa

std

ev o

f n

et i

nco

me

log

sale

s

reco

ver

y

nu

mb

er o

f o

bse

rvat

ion

s

R-s

qu

ared

2001

-200

3

2.28

9

(1.9

8)

1.08

5***

(0.4

2)

-5.1

59**

*

(0.7

7)

3.78

5***

(1.2

7)

-0.0

849*

(0.0

5)

767

0.5

2004

-200

6

2.66

8**

(1.1

8)

1.59

0***

(0.3

2)

-2.4

20**

*

(0.5

3)

0.81

5*

(0.4

7)

-0.0

504

(0.0

4)

956

0.55

hig

h ca

sh

9.56

8**

(3.7

7)

4.47

6***

(0.9

8)

-3.6

64**

*

(1.3

4)

4.81

6

(3.7

7)

-0.0

07

7

(0.0

8)

-0.1

85**

*

(0.0

7)

472

0.58

low

cas

h

0.43

1

(1.6

1)

2.35

4***

(0.7

5)

-5.9

03**

*

(1.1

2)

9.92

9**

(4.9

6)

0.01

52

(0.0

8)

-0.1

03**

*

(0.0

32)

1383

0.43

hig

h m

kt/

bo

ok

1.21

4

(0.9

9)

0.98

5***

(0.3

0)

-2.6

98**

*

(0.4

9)

1.65

8*

(0.8

4)

-0.0

623*

(0.0

4)

-0.0

0629

(0.0

1)

1299

0.46

low

mk

t/b

oo

k

10.3

8***

(2.4

2)

2.26

4***

(0.5

5)

-5.7

62**

*

(1.2

0)

0.69

1**

(0.2

7)

-0.0

656

(0.0

6)

0.0

093

(0.0

2)

404

0.65

Not

es:

Thi

s ta

ble

pres

ents

res

ults

of

regr

essi

ng c

redi

t sp

read

s of

US

firm

s fr

om 2

001-

2006

on

lagg

ed a

nnua

l in

dust

ry p

roba

bili

ty o

f L

BO

an

d fi

rm-l

evel

cont

rols

. T

he

depe

nden

t va

riab

le i

s C

DS

spre

ad,

usin

g an

nual

clo

sing

spr

ead

per

fir

m,

quot

ed

in p

erce

ntag

es

(rob

ustn

ess

test

s sh

ow s

imil

ar

resu

lts

whe

n us

ing

aver

age

year

ly s

prea

d or

ave

rage

spr

ead

over

fou

rth

quar

ter)

. In

dust

ry

prob

abil

ity

of L

BO

is

com

pute

d p

er y

ear

as t

he

rati

o of

: 1.

num

ber

of i

ndus

try

firm

s th

at

wer

e ta

rget

s of

LB

O (

acco

rdin

g to

Tho

mso

n F

inan

cial

LB

O a

nnou

ncem

ents

) to

2.

num

ber

of i

ndus

try

firm

s (a

s re

port

ed i

n

Com

pust

at).

In

dust

ry i

s de

term

ined

at

the

3-di

git

sic

leve

l, w

here

sic

is

as r

epor

ted

in C

ompu

stat

. L

ever

age

is l

ong-

term

+ s

hort

-ter

m d

ebt

to t

otal

asse

ts,

roa

is E

BIT

DA

to

tota

l as

sets

, st

dev

is s

tand

ard

devi

atio

n of

net

inc

ome,

rec

over

y is

yea

rly

aver

age

of r

ecov

ery,

as

com

pose

d by

Mar

kit.

W

e u

se

log

of s

ales

as

a pr

oxy

for

firm

siz

e. T

he

firs

t tw

o co

lum

ns r

epor

t re

gres

sion

res

ults

for

div

isio

n of

our

sam

ple

into

ear

lier

an

d la

ter

year

s. C

olum

ns 3

an

d

4 re

port

res

ults

whe

n di

vidi

ng o

ur

sam

ple

into

"hi

gh-c

ash"

an

d "l

ow-c

ash"

fi

rms,

w

here

gro

upin

g is

det

erm

ined

at

the

75"'

per

cent

ile

of t

he

rati

o ca

sh

and

equi

vale

nts

to t

otal

ass

ets.

T

he

last

tw

o co

lum

ns r

epor

t re

sult

s w

hen

divi

ding

ou

r sa

mpl

e in

to "

high

m

arke

t-to

-boo

k"

and

"lo

w

mar

ket-

to-b

ook"

firm

s, w

here

gro

upin

g is

det

erm

ined

at

the

25th

per

cent

ile

of t

he

rati

o m

arke

t-to

-boo

k va

lue

per

sha

re.

Reg

ress

ion

is r

un

wit

h ye

ar,

rest

ruct

urin

g cl

ause

and

2-di

git

sic

fixe

d ef

fect

s, e

rror

s ar

e cl

uste

red

at t

he

firm

lev

el.

***,

** a

nd

* in

dica

te s

igni

fica

nce

at t

he

1%, 5

%, a

nd

10%

lev

els,

res

pect

ivel

y.

02

Tab

le 2

.5:

Pri

cing

of

LB

O r

isk

in C

DS

spre

ads

- E

urop

e 20

01-2

006

CD

S s

pre

ad

ind

ust

ry p

rob

abil

ity

of L

BO

std

ev o

f ro

a

roa

tan

gib

ilit

y

lev

erag

e

rati

ng

nu

mb

er o

f o

bse

rvat

ion

s

R-s

qu

ared

all

4.96

8***

(1.8

0)

6.93

6**

(3.2

1)

-5.5

92**

(2.1

3)

-2.4

24**

*

(0.6

2)

1 71

1***

(0.6

0)

398

0.56

mat

ure

fir

ms

3.05

5**

(1.4

23)

1.46

5

(1.8

00)

-0.0

73

8

(0.5

70)

-0.2

3

(0.2

71)

0.15

5

(0.4

84)

0.08

63**

*

(0.0

19)

125

0.81

yo

un

g fi

rms

1.83

1

(2.3

76)

4.16

9*

(2.3

47)

-0.9

95

(1.3

77)

0.23

9

(0.6

38)

-0.3

18

(0.8

53)

0.21

4***

(0.0

58)

113

0.71

hig

h ta

ng

ibil

ity

5.33

1**

(2.5

60)

2.06

4

(4.1

17)

-0.7

37

(1.1

41)

-0.4

77

(1.4

87)

1.57

6***

(0.4

80)

188

0.60

5

low

tan

gib

ilit

y

1.58

(1.4

60)

9.70

3***

(2.4

11)

-1.4

14

(1.3

35)

0.20

4

(0.6

42)

1.65

8**

(0.7

69)

201

0.79

5

Not

es:

Thi

s ta

ble

pres

ents

res

ults

of

regr

essi

ng c

redi

t sp

read

s of

Eur

opea

n fi

rms

from

20

01-2

006

on

lagg

ed a

nnua

l in

dust

ry p

roba

bili

ty o

f L

BO

an

d

firm

-lev

el c

ontr

ols.

T

he

depe

nden

t va

riab

le i

s C

DS

spre

ad, u

sing

ann

ual

clos

ing

spre

ad p

er f

irm

, qu

oted

in

perc

enta

ges

(rob

ustn

ess

test

s sh

ow s

imil

ar

resu

lts

whe

n us

ing

aver

age

year

ly s

prea

d or

ave

rage

spr

ead

over

fou

rth

quar

ter,

all

con

trac

ts h

ave

MM

res

truc

turi

ng c

laus

e).

Indu

stry

pr

obab

ilit

y o

f

LB

O

is c

ompu

ted

in t

he

US

per

yea

r as

th

e ra

tio

of:

1. n

umbe

r of

ind

ustr

y fi

rms

that

wer

e ta

rget

s of

LB

O (

acco

rdin

g to

Tho

mso

n F

inan

cial

LB

O

anno

unce

men

ts)

to 2

. nu

mbe

r of

ind

ustr

y fi

rms

(as

repo

rted

in

Com

pust

at).

In

dust

ry i

s de

term

ined

at

the

3-di

git

sic

leve

l, w

here

sic

is

as r

epor

ted

in C

ompu

stat

. L

ever

age

is l

ong-

term

+ s

hort

-ter

m d

ebt

to t

otal

ass

ets,

roa

is E

BIT

DA

to

tota

l as

sets

, st

dev

is s

tand

ard

devi

atio

n of

ro

a, r

atin

g is

aver

age

annu

al r

atin

g (f

rom

S&

P, u

sing

a n

umer

ical

sca

le w

here

hig

h-ra

ted

firm

s ha

ve a

low

er n

umer

ical

rat

ing)

. W

e u

se l

og

of

sale

s as

a p

roxy

for

firm

siz

e. T

he

firs

t co

lum

n re

port

s re

gres

sion

res

ults

for

th

e en

tire

sam

ple

of E

urop

ean

CD

S c

ontr

acts

. C

olum

ns 2

an

d 3

repo

rt r

esul

ts f

or s

ubsa

mpl

es

of "

mat

ure"

an

d "y

oung

" fi

rms,

w

here

you

ng i

s de

fine

d as

les

s th

an 2

0 ye

ars

and

mat

ure

is s

et a

t m

ore

than

100

yea

rs.

Th

e la

st t

wo

colu

mns

rep

ort

resu

lts

whe

n di

vidi

ng o

ur

sam

ple

into

"hi

gh-t

angi

bili

ty"

and

"low

-tan

gibi

lity

" fi

rms,

w

here

cut

off

is s

et a

t th

e m

edia

n of

th

e ra

tio

PP

E t

o to

tal

asse

ts.

Reg

ress

ion

is r

un

wit

h ye

ar a

nd

2-di

git

sic

fixe

d ef

fect

s, e

rror

s ar

e cl

uste

red

at t

he

firm

lev

el.

*** i

** a

nd

* in

dica

te s

igni

fica

nce

at t

he

1%,

5%

, an

d 10

%

leve

ls,

resp

ecti

vely

.

Tab

le 2

.6:

Pri

cing

of

LB

O r

isk

in C

DS

spre

ads

- fi

rm-l

evel

IV

dep

var

iab

le:

LB

O

even

t ri

sk i

nd

ex

lev

erag

e

roa

std

ev o

f ro

a

div

rel

ated

cov

nu

m o

f o

bse

rvat

ion

s

pse

ud

o R

-sq

uar

ed

inde

x_n

ind

ex.w

-2.3

13**

* -2

.25

9*

**

(0.5

8)

(0.5

5)

1.08

1 1.

081

(1.4

3)

(1.4

2)

11.2

6***

10

.87*

**

(2.7

0)

(2.6

8)

-18.

28**

-1

8.0

0*

*

(7.3

0)

(7.1

6)

-14.

25**

* -1

3.7

6*

*

(5.5

2)

(5.5

3)

403

403

0.48

0.

48

dep

var

iab

le:

spre

ad

LB

O p

rob

abil

ity

(pre

dic

t)

lev

erag

e

roa

std

ev o

f ro

a

div

rel

ated

cov

nu

m o

f o

bse

rvat

ion

s

R-s

qu

ared

inde

x_n

ind

ex.w

0.70

8**

0.7

03

**

(0.3

5)

(0.3

3)

2.28

7***

2

.29

3*

**

(0.4

9)

(0.4

9)

-5.2

94**

* -5

.323

***

(1.1

8)

(1.1

8)

1.02

9 1.

046

(2.1

0)

(2.0

7)

5.13

1 5.

137

(3.7

1)

(3.7

6)

329

329

0.62

0.

62

Not

es:

Thi

s ta

ble

pres

ents

res

ults

of

two-

stag

e re

gres

sion

of

CD

S sp

read

s of

US

fir

ms

from

20

01-2

006

usin

g ev

ent

risk

cov

enan

ts a

s an

ins

trum

enta

l

vari

able

for

occ

uren

ce o

f L

BO

. T

he

firs

t tw

o co

lum

ns r

epor

t th

e re

sult

s of

th

e fi

rst-

stag

e pr

obit

reg

ress

ion

of t

he

bina

ry v

aria

ble

of L

BO

occ

uren

ce.

Exp

lana

tory

var

iabl

es a

re l

agge

d ev

ent-

risk

cov

enan

t in

dex

and

cont

rols

of

lagg

ed A

rm-l

evel

cha

ract

eris

tics

an

d va

riou

s fi

nanc

ing

cove

nant

s.

Issu

e is

mar

ked

as h

avin

g ev

ent

risk

cov

enan

ts i

f it

has

eit

her

"cha

nge

cont

rol

pu

t pr

ovis

ion"

or

"rat

ing

decl

ine

trig

ger

put"

cov

enan

t. E

vent

-ris

k co

vena

nt i

ndex

is c

ompu

ted

in t

wo

way

s:

1. i

ndex

jn:

the

perc

enta

ge o

f bo

nds

issu

ed w

ith

even

t-ri

sk c

oven

ants

an

d 2

. in

dexj

w:

wei

ghte

d in

dex

of b

onds

iss

ued

wit

h

even

t-ri

sk c

oven

ants

, w

here

th

e w

eigh

ts a

re t

he

issu

e of

feri

ng a

mou

nt.

Eve

nt-r

isk

inde

x is

com

pute

d p

er f

irm

per

yea

r, u

sing

onl

y th

e is

sues

out

stan

ding

that

yea

r.

Fin

anci

ng

cove

nant

co

ntro

ls a

re c

ompu

ted

as t

he

perc

enta

ge o

f bo

nds

wit

h th

e sp

ecif

ic c

oven

ant

issu

ed b

y th

e fi

rm (

sinc

e 19

80,

acco

rdin

g

to F

ISD

Mer

gent

). T

he

cove

nant

sho

wn

is c

oven

ant

rest

rict

ing

payo

ut t

o sh

areh

olde

rs.

Lev

erag

e is

lon

g-te

rm +

sho

rt-t

erm

deb

t to

tot

al a

sset

s, r

oa

is

EB

ITD

A t

o to

tal

asse

ts,

stde

v is

sta

ndar

d de

viat

ion

of r

oa.

We

use

log

of

sale

s as

a p

roxy

for

fir

m s

ize.

Th

e la

st t

wo

colu

mns

rep

ort

the

resu

lts

of t

he

seco

nd-s

tage

reg

ress

ion

whe

re t

he

depe

nden

t va

riab

le i

s C

DS

spre

ad, u

sing

ann

ual

clos

ing

spre

ad p

er f

irm

, qu

oted

in

perc

enta

ges

(rob

ustn

ess

test

s sh

ow

sim

ilar

res

ults

whe

n us

ing

aver

age

spre

ad o

ver

four

th q

uart

er).

Exp

lana

tory

var

iabl

es a

re p

redi

cted

val

ue o

f L

BO

pro

babi

lity

fro

m

firs

t-st

age

regr

essi

on

and

sam

e co

ntro

ls.

Reg

ress

ion

is r

un

wit

h ye

ar a

nd

3-di

git

sic

fixe

d ef

fect

s (s

ic a

s re

port

ed i

n C

ompu

stat

), e

rror

s ar

e cl

uste

red

at t

he

firm

lev

el.

*** ;

**

and

* in

dica

te s

igni

fica

nce

at t

he

1%, 5

%, a

nd

10%

lev

els,

res

pect

ivel

y.

ci

68

Table 2.7: LBO monitoring using option implied volatility slope

CDS spread

IV slope

avg IV

leverage

roa

num of observations

R-squared

slope_lm_3m

-0.703***

(0.15)

5.520***

(0.35)

1.480***

(0.25)

-2.718***

(0.50)

14399

0.65

slope_lm_6m

-0.468***

(0.12)

5.453***

(0.35)

1.501***

(0.25)

-2.626***

(0.49)

14421

0.65

Notes: This table presents results of regressing credit spreads of US firms from 2001-2006 on option

implied volatility slope and lagged implied volatility level and firm-level controls. The dependent

variable is CDS spread, using monthly average spread per firm, quoted in percentages. Implied

volatility (IV) slope is computed as the ratio of the 3-month IV to the 1-month IV (slope-lmSm)

or as the ratio of the 6-month IV to the 1-month IV (slope-lm-6m). Average IV is computed as the

average monthly IV over all ATM contract maturities. Implied volatility is average of call and put

IV. Leverage is long-term + short-term debt to total assets, roa is EBITDA to total assets. We use

log of sales as a proxy for firm size. Regression is run with restructuring clause, year and 3-digit sic

fixed effects (sic as determined on Compustat), errors are clustered at the firm level. ***,** and *

indicate significance at the 1%, 5%, and 10% levels, respectively.

69

Figure 2.1: LBO restructuring risk in a structural framework

0)

m >

CD 0> m <

Horizon

New Default Point

Default Point

Time

Notes: This figure displays LBO restructuring risk in the context of a structural model. The depicted process is the asset value and default occurs when the process hits the default threshold (bottom line). Following an LBO the default barrier of the firm rises (top line). (Source: Moody's KMV 2006)

70

Figure 2.2: Industry-level clustering in LBO activity

Notes: This figure displays the percentage of LBOs in different 2-digit sic industries out of the total number of LBOs in the specified time frame. The light-colored columns are the percentages in the first LBO wave, 1980-1989, and the dark-colored ones are those in the recent wave of 2004-2007. The total number of LBOs are 1900 and 2222, respectively. Industries displayed are those for which the percentage was higher than 3.5% in at least one of the waves, or where the difference in percentages between waves was higher than 1.5%.

Figure 2.3: Intra-industry cumulative abnormal change in CDS spreads

Notes: This figure displays the cumulative abnormal change in CDS spreads around LBO announcements within-industry. Changes in spreads are reported as a percentage of the original spread level.

71

Chapter 3

Modeling LBO Risk in Corporate Spreads:

Industry Patterns in Buyout Activity

3.1 Introduction

The macro environment in the years 2004-2007 was one of ample liquidity, narrow

spreads and consequently, of easier access to debt financing. The growing credit

derivatives market and novel funding structures enabled easy transfer and trade of

credit risk. This environment stimulated explosive growth in private equity, driving

leveraged buyout (LBO) activity to previously unknown levels. In the previous chap

ter, we study the extent to which LBO risk was priced by investors in debt markets.

We document a significant widening in CDS spreads and a loss to holders of unpro

tected outstanding target-firm bonds upon an LBO announcement. Subsequently, it

is shown that LBO risk helps explain the cross-sectional variation in CDS spreads.

In this paper, we study the time-series dynamics of LBO pricing in credit spreads,

focusing on LBO restructuring risk at the industry level. We propose a learning-based

model where investors update their beliefs on LBO risk according to buyouts observed

in the industry. The model disentangles default risk from LBO risk based on empiri

cal evidence on intra-industry LBO restructuring "contagion". Estimates suggest the

proposed mechanism is significant in explaining observed market spreads. Firm-level

72

estimates of LBO risk are related to the likelihood of an LBO as estimated in a probit

model. Finally, estimated LBO risk is shown to explain some of the mispricing from a

structural credit model. Interestingly, using this mispricing helps identify firms more

likely to be LBO targets.

A leveraged buyout, an acquisition of a company using a significant amount of

borrowed funds, significantly alters the capital structure of the target firm and typi

cally eliminates its publicly-held stock. During this buyout wave, equity contribution

in LBO deals has fallen to as low as 25%. The borrowed funds are issued against

the assets of the target firm and are repaid with funds generated from the company

or with revenue earned by divesting the newly acquired company's assets. The post-

LBO firm frequently has extremely high leverage, and the newly issued debt can be

senior bank loans and/or public debt. As a result, LBOs typically cause a dramatic

change in the risk profile of the target firm. Marais, Schiffer 8z Smith (1990) and

Warga & Welch (1993) find that, on average, the proportion of debt after successful

buyout triples and most debt is downgraded.

Studies following the buyout wave of the 1980's have characterized LBO targets

as high and steady cash-flow firms with low growth, susceptible to agency problems

in line with Jensen's (1986) free-cash-flow theory. We extend this question into the

recent wave and, while we still observe similar tendencies, we find characteristics of

LBO targets in the current decade to be more widely dispersed, suggesting LBOs are

now used less as a mechanism for curbing overinvestment problems. We also doc

ument and analyze a positive relationship between leverage and likelihood of LBO,

which breaks down for firms with very low and high levels of leverage. We estimate a

probit model of LBO likelihood utilizing firm, industry and macro-level variables to

identify firms more likely to be LBO targets.

73

In previous work, we establish the detrimental effect of LBO announcements on

the credit spreads of target firms and finds LBO restructuring risk to be priced ex-

ante by investors in debt markets. Based on ample empirical evidence on the effect

of LBOs in debt markets, this paper models and estimates the pricing of LBO risk

in corporate spreads. We propose a learning-based model where CDS spreads are

driven by both default risk and LBO restructuring risk. Since LBO events cause a

significant widening in CDS spreads and a loss to holders of unprotected outstanding

target-firm bonds, in our model an LBO restructuring event affects CDS spreads by

inducing a change in firm default risk.

Previous works have documented cross-industry variation in event risk (e.g. Crabbe,

1991) and have used industry as a proxy for LBO risk (e.g. Lehn & Poulsen, 1991).

In previous work, we find industry-level clustering in buyout activity to have grown

more pronounced over time. In 1980-1989, approximately 5% of all LBOs occurred in

manufacturing, machinery and equipment industries; in 2004-2007, a similar 5% oc

curred in machinery and equipment firms, but almost 15% of all LBOs took place in

business services, specifically in technology and telecommunications. Based on these

findings, we focus on industry-wide aspects of LBO risk and model investor beliefs

on this risk at the industry level. This perspective enables disentanglement of LBO

risk from default risk.

In an empirical study of intra-industry reaction to LBO announcements, we find a

cumulative abnormal change of 10% in spreads of firms in the same industry in a time

window of two months around the event. We label this effect as LBO restructuring

contagion and propose a mechanism by which this effect occurs: Bayesian updating

of investor beliefs on restructuring risk according to LBOs observed in the industry1 .

This means of updating incorporates both trends in the specific industry and general

macro trends in buyouts, as industry buyout levels are highly correlated with the

state of buyout funds and the credit environment. We identify this contagion as a

1 Collin-Dufresne, Goldstein and Helwege (2003) first model contagion in default risk as updating of investor beliefs.

74

third source of risk, in addition to firm-specific default and restructuring risks.

Numerous papers have studied default risk; this paper is novel in its explicit in

corporation of LBO risk, which has been a significant source of risk for debtholders

in the buyout wave years. Moreover, it is the first to explicitly model intra-industry

effects of buyouts in debt markets. We propose industry-wide contagion as a driver

of the time-series of LBO risk in all firms. We utilize these empirical facts to model

and estimate LBO risk in corporate spreads. We use dealer-quoted, actively traded

CDS spreads, a more liquid market than corporate bonds2 and a cleaner indicator of

default risk.

We carry out a joint estimation of all firms in an industry to utilize industry-level

updating of beliefs and to learn about the effect of LBOs on default from industry

targets. Estimates of LBO risk and model spreads suggest the proposed mechanism

is significant in explaining observed market spreads. The model generates the corre

sponding jumps in target and within-industry spreads upon LBOs, and model spreads

have an overall fit of 65%-70% to market spreads. Estimated LBO risk is significantly

correlated with likelihood of LBO as predicted in our probit model, based on firm

characteristics.

Finally, we tie our findings to the literature on mispricing in structural credit

models and show that estimated LBO risk explains some of the documented mispric

ing. Given this link, we also test and find mispricing to be significant in explaining

likelihood of LBO; we find that an increase of 10 bps in model mispricing corresponds

to an additional 2% in LBO probability in the subsequent year. Interestingly, using

this mispricing can improve screening of LBO targets.

The rest of this paper proceeds as follows. Section 3.2 provides a review of the

literature related to properties of LBO targets and the effect of LBOs on target

2 Blanco, Brennan and Marsh (2005) find the CDS market to be the first forum for price discovery.

75

spreads. Section 3.3 details our CDS and LBO data. Section 3.4 studies changes in

properties of LBO targets over time and estimates LBO likelihood in a probit model.

Section 3.5 presents a model of credit spreads incorporating both default and LBO

risk, introducing "LBO restructuring contagion". We derive CDS spreads in this

framework. In Section 3.6 we estimate the model, and section 3.7 ties estimated LBO

risk to mispricing in a structural credit model. Section 3.8 concludes.

3.2 Related literature

LBO risk in credit spreads

The first question to arise in this context is what is the effect of LBO announcements

on credit spreads. Previous works post the buyout wave of the 1980s have studied

the effect of LBOs on stakeholders of the target firm. Shareholders were consistently

found to gain from high premiums paid by the acquiring firm, with returns ranging

from 15% to 40% (Jarrell, Brickley & Netter, 1988, Lehn & Poulsen, 1989, Warga k

Welch, 1993). There has been less consensus as to the effect of LBOs on debthold-

ers. Findings range from no impact to a loss of 7% over four months, depending on

the type of data used and the time period studied (Lehn &; Poison, 1988, Marais,

Schiffer & Smith, 1989, Asquith & Wizman, 1990, Warga & Welch, 1993). Propo

nents of LBOs agree leverage is beneficial in tax shields, but claim LBOs also result

in added value. Wealth increases are attributable to improved managerial incentives

due to large equity stakes, increased monitoring and the disciplining effect of large

debt-service payments on managers (Jensen, 1986). Can the benefits of LBO offset

increase in default probability, added bankruptcy costs and reduced effective priority

for debtholders?

Our previous work addresses this question by studying the reaction of target firm

credit spreads to LBO announcements in the US during the years 2001-2007. This

work utilizes dealer-quoted, actively traded CDS spreads, which were found to be

76

the first forum for price discovery (Blanco, Brennan <fe Marsh, 2005) and a cleaner

indicator of default risk. The event study documents credit spread widening by up

to 70% around LBO announcements, suggesting costs of additional debt significantly

outweigh potential increase in expected cash flows. Effect is significantly stronger

for investment-grade firms, consistent with the larger change in risk profile relative

to high-yield firms. An additional finding of a negative reaction of 6% in prices of

unprotected bonds suggests LBOs do result in some wealth transfer from debt-holders

to shareholders. Yet, a back-of-the-envelope calculation shows estimated 18% gains

to shareholders are due, in large, to alternate sources, supporting value creation in

LBOs.

The large and growing magnitude of buyout activity and its detrimental effect

on debt prices, as established in the event study, suggest LBOs should be a con

siderable concern for investors in debt markets. LBOs are a viable threat across

markets, industries and rating classes. In the recent buyout wave, a greater number

of low-investment-grade and high-speculative-grade companies across multiple indus

try sectors went private through LBOs. In the previous chapter, we use US CDS

spreads from 2001-2006 to test the hypothesis that LBO restructuring risk is ex-ante

priced by investors in debt markets. Using exogenous industry variables and firm-

level instruments, we find that firms more likely to undergo an LBO have spreads that

are higher by 30-50 bps. Effect is found to be more significant in years with larger

buyout activity and in firms more prone to be LBO targets. These results suggest

explicit incorporation of LBO risk into credit pricing models might aid in explaining

and predicting corporate credit spreads.

Properties of LBO targets

Studies of the 1980's buyout wave have found LBOs to be associated with notable

firm characteristics. Jensen (1986) claims desirable leveraged buyout candidates are

frequently firms or divisions of larger firms that have stable business histories and

77

substantial free cash flow, i.e. low growth prospects and high potential for generating

cash flows - as these are situations where agency costs of free cash flow are likely

to be high. The high leverage used in LBO transactions, as well as the use of strip

financing and the allocation of equity, balance the issues of incentives, conflicts of

interest and bankruptcy costs.

Opler & Titman (1993) investigate the determinants of leveraged buyout activity

by comparing firms that implemented LBOs to those that did not. Consistent with

Jensen's (1986) free cash flow theory, they find that LBO targets can be characterized

as having a combination of unfavorable investment opportunities (low Tobin's q) and

relatively high cash flow. Furthermore, firms with high expected costs of financial dis

tress (e.g. those with high R&D expenditures) are less prone to LBOs. Maupin (1987)

finds that LBO risk is determined by firm fundamentals, similarly across markets.

Industry trends in buyout activity

Numerous works following the LBO wave of the 1980's have documented cross-

industry variation in event risk. Crabbe (1991) reports LBOs are less common in

industries such as financial and utilities due to regulatory restrictions on leverage,

asset sales and dividend payouts. Slovin, Sushka & Bendeck (1991) find that going-

private announcements yield statistically significant intra-industry effects in equities.

Lehn, Netter & Poulsen (1990) find that LBOs in the 1980's occured in low growth

and low R&D industries. Lehn & Poulsen (1991) use industry as a proxy for LBO

risk.

Mitchell & Mulherin (1996) find inter-industry differences in both the rate and

time-series clustering of buyout activity. They find inter-industry patterns to be

directly related to the economic shocks borne by the sample industries (e.g. dereg

ulation, changes in input costs, innovations in financing technology). Their results

suggest that much of the takeover activity during the 1980's was driven by broad fun

damental industry factors and have general implications for the stock price spillover

78

effects of takeover announcements, corporate performance following takeovers, and

the timing of takeover waves. This would imply that one firm's takeover announce

ment provides information about other industry firms that may be tied to economic

fundamentals rather than market power, as is often asserted by regulators.

3.3 Data

We carry out this study of credit spreads using credit default swap data. A description

of CDS contracts and an explanation of CDS, bonds and covenants in the context of

leveraged buyouts can be found in section 1.3. Bond returns in buyouts are largely

determined by the protection provided by their specific covenants. The CDS contract

is written on all bonds of a seniority class, and its spread will track the value of the

CTD bond - typically, an unprotected bond. Therefore, CDS appear to be the more

appropriate tool for studying the effect of LBO announcements on credit spreads.

As they have also been found to lead the bond market in price discovery (Blanco,

Brennan & Marsh, 2005), we opt to use CDS spreads in this study.


This dataset includes daily quotes for a broad cross-section of firms actively traded in

the credit derivatives market. Our CDS data are provided by Markit, a comprehen

sive data source that assembles a network of over 30 industry-leading partners who

contribute information across several thousand credits on a daily basis. Based on the

contributed quotes, Markit creates a daily composite for each CDS contract. Though

the composite CDS spread is based on indicative quotes, rigorous cleaning of the data

and elimination of stale quotes and outliers helps to ensure that the composite price

closely reflects transaction prices. Our dataset consists of 489 US entities and 169

European entities. This dataset is a random subset of the several thousand firms

covered by Markit. The coverage spans 01/2001 to 09/2007 (for several firms the

79

time series ends in 12/2006).

We include all CDS quotes written on U.S. corporate entities and denominated in

U.S. dollars. For consistency, we retain only CDS on senior unsecured debt, which

constitute over 90% of all contracts. We focus on contracts with Modified Restruc

turing (MR) or No Restructuring (XR) clauses as they are the most common in the

US (we use MR contract except if the firm has none traded or if the XR contract is

more common, for more liquid prices; this is the case for 122 of the 489 firms in the

sample). Our data includes contracts of 1,2,3,5,7 and 10-year maturities. The 5-year

contract is the most liquid (given for 97% of observations), followed by the 3-year

(92% of observations), the 7-year (90%), the 1-year (89%), the 10-year (86%) and the

2-year (85%).

Table 3.1 shows the breakdown of firms into sectors and the distribution of CDS

spreads in each sector over the sample years. Technology, telecommunications and

consumer services appear to have the highest spreads, while spreads are lowest for the

government and health care sectors. Our CDS sample also spans all rating classes.

Figure 3.1 displays the distribution of firms across the rating classes (rating as of

December 2006). It can be seen that most of the sample is concentrated in the A-BB

categories, but lower and higher ratings are well represented. Merging the CDS with

accounting data (from Compustat) and ratings (from S&P) leaves us with 425 firms.



3.3.2 LBO announcements


considered a Leveraged Buyout if the investor group includes management or the



80

was between 1980-2007 and the target was a US firm. The total is 7416 announce

ments3 . Where there was more than one announcement for a firm on a specific day,

we leave one of status "Completed" (where non is available, we leave that with status

"Pending")4 . This leaves us with 7393 announcements.

Table 3.2 presents the number and total value of LBO announcements on US firms

each year. The trends in buyout activity over time can be seen more clearly in Figure

3.2. The figure shows increased LBO activity in the second half of the 1980's and in

the last 4 years of the sample, 2004-2007, both in number and magnitude of deals.

(An increase is also seen in 2000, yet it is short-lived as 2001 was a recession year.)

The number of LBOs in 2004-2007 is nearly half the total number over all previous

years; and the total value of deals in these years surpasses the total value of all pre

ceding ones. The graph clearly shows the level of LBO activity in 2004-2007 to be

unprecedented in both aspects. (It should be noted that in 2008 a significant drop in

number and value of LBO deals was reported. The numbers updated to the date of

this draft are 390 announcements at a total value of 35 $bn.)



3 Based on CapitallQ database and World Economic Forum reports the coverage of deals in Thomson One Banker seems to be incomplete, but we have no reason to suspect any bias in coverage. Furthermore, since we focus on the intersection of LBOs and CDS quotes, we are, by definition, focusing on the larger, public, highly traded firms, for which the coverage is likely to be high.

4 82.5% of the announcements are of status "Completed", 4% are of status "Pending" and 8.5% are of status "Withdrawn". 4% are of unknown status and the rest are a small number of "Intended" or "Rumored" or withdrawn from such.

81

3.4 Identifying LBO targets

In this section we study the properties of LBO targets in the recent LBO wave of 2004-

2007 and compare characteristics of targets with those found notable in the previous

wave of the 1980's. We then utilize firm-level, industry-level and macro variables to

estimate LBO likelihood for our sample firms. For this study, we download annual

Compustat data from the years 1980-2007. Our sample consists of 26330 firms, with

an average of 21 annual observations per firm. We merge this data with data on LBO

announcements to identify LBO targets in the sample.

3.4.1 Properties of LBO targets over time

We begin by examining how LBO targets differ from non-targets. We study the dis

tributions of firm characteristics in targets vs. non-targets over time, across different

LBO waves. Given the trends in buyout activity, as presented in section 3.3.2, we

divide our sample into three time periods: 1980-1989, 1990-2003 and 2004-2007. In

particular, we are interested in the characteristics of LBO targets in the two LBO

waves. Table 3.3 presents the distribution of properties in LBO targets; the proper

ties shown are those for which the average in targets was found to be significantly

different (at the 1% level) than the average in non-targets.

LBO targets clearly differ in a number of properties. We find that LBO targets

are characterized by high and steady cash flows relative to not-targets (as can be

seen by high ROA and lower standard deviation of ROA). An LBO target has to be

able to generate large and stable free cash flow from operations to service the large

post-buyout debt payments. A firm with high and steady taxable cash flows would

also benefit more from increased leverage. In addition, LBO targets have higher asset

tangibility, which lowers costs of financial distress via asset sales. Tangible assets

might provide guarantees for the new debt and would also allow raising extra cash

through asset stripping. Targets are also characterized by lower market-to-book val

ues relative to their non-target peers. Firms characterized by strong growth may not

82

be suitable for LBOs, as their growth rate would require an excessive increase in net

working capital, as well as absorbing capital for productive capacity enlargement and

increased marketing and R&D expenses. In line with this, LBO targets also appear

to have lower growth in capital expenditures year-over-year.

Interestingly, LBO targets are generally more levered than non-LBO benchmark

firms. Apriori, the relation is unclear; on the one hand, a target firm must have

a large enough capital base on its balance sheet to take on additional heavy debt.

This might suggest low-leverage firms would be more attractive as potential targets.

However, high leverage relative to peers is indicative of high debt capacity, a crucial

requirement for LBO targets. Given possible restructuring of outstanding debt, it

might, therefore, not be very surprising to find the latter to be a stronger argument.

Moreover, assuming firms choose an optimal capital structure, firms with higher lever

age would clearly be those able to benefit from an increased tax shield, shown to be

a substantial source of value in LBOs (Kaplan, 1989). We further study this effect of

leverage on LBO likelihood in the following section.

The results for ROA and growth are consistent with the free cash flow theory

(Jensen, 1986), possibly suggesting LBOs mitigate agency problems in firms plagued

by overinvestment problems. Yet, it is very interesting to note changes in properties

of targets over time. Trends in averages and in medians in Table 3.3 indicate that

LBO targets in the recent wave are, on average, larger than those in the 1980's, have

higher market-to-book ratios and have slightly lower cash flows. This might indicate

LBOs in the recent wave are used less as a tool to curb overinvestment. Moreover, the

higher standard deviations of cash flow, market-to-book ratio and other firm char

acteristics suggest a much greater assortment of firms undergo LBOs in the recent

wave. These results are in line with those of Guo, Hotchkiss & Song (2007), who

find that recent buyouts result in a smaller increase in cash flow than in the 1980's,

suggesting acquirers are no longer necessarily targeting significantly underperforming

companies.

83

These results also fit in the landscape of the general macro environment in which

these recent LBOs occurred, one characterized by a high appetite for risk, high liq

uidity and high willingness to lend and invest in larger deals with higher growth

components. The recent wave of LBOs, spawned by a benign macro environment,

financial innovation and the development of a secondary market for loans, has proved

that size and rating are no longer protection against an LBO; First Data Corp and

Alltel in April and May of 2007, for example, were LBOs of IG firms of over $25bn.

Differences between the two LBO waves are clearly visible in the last rows of Table

3.3: a firm of $0.5bn in market cap was the 90"" percentile in the 1980's, but only the

median in 2004-2007. The average firm size grew by a multiple of 10, from $250M to

$2.5bn. Thus, while buyout funds still target smaller, lower-growth firms, indicating

the motive to reduce agency problems persists into the latest LBO wave, it appears

to be a less prominent one, given trends in averages.


3.4.2 Est imation of LBO likelihood

The aforementioned statistics clearly demonstrate differences in characteristics of

LBO targets vs. non-targets, as well as changes in these characteristics over time. In

this section, we evaluate whether these properties are significant in explaining LBO

occurence and whether they can be used to screen potential LBO candidates. In

particular, we run a regression of the likelihood of becoming an LBO target against

firm characteristics using a probit model, in which the dependent variable equals one

when the firm is an LBO target and zero otherwise. Firm characteristics are lagged.

We also run the regression separately for the two LBO wave periods, i.e., 1980-1989

and 2004-2007 to examine the performance of our predictor variables over time.

Given time and industry trends in LBO activity, we also incorporate variables at

the macro and industry level. At the macro level, we use lagged PE funds, i.e. US

private equity fundraising as a percentage of total US stock market value, taken from

84

Kaplan Sz Stromberg (2008). We find it to be a sufficient statistic for annual buyout

activity; other macro and business cycle variables were not found to be significant in

explaining LBOs when included along with PE funds. At the industry level, we use

a lagged measure of industry probability of LBO, constructed using industry LBO

realizations. We use our sample of US LBO announcements (extracted from Thomson

Financial) and compute this probability to be the ratio of: 1. the number of LBO

targets in an industry to 2. the number of firms in the industry (determined using

Compustat firm listings). We compute these probabilities at the 3-digit sic level,

where sic code is as reported in Compustat.

We run the following probit regression:

LBOij = a + (3 • PEfundst-i + 7 • plndLBOi^-i + 5 • firmVarsitt-\

where LBO^t is a binary variable, which equals 1 if firm i was an LBO target at time

t and 0 otherwise, PE funds is private equity fundraising, plndLBO is our measure

of industry probability of LBO and firmVarsitt-i are the firm-level characteristics

being tested.

Table 3.4 reports the regression results. In line with Jensen's (1986) free cash flow

theory, Opler & Titman (1993) argue that LBOs create value because they reduce

the agency problems in target firms with unfavorable investment opportunities and

relatively high free cash flow. Consistent with their claim, we find LBO targets have

high and steady cash flows and poor investment opportunities, both in the univariate

and multivariate frameworks. In particular, the coefficient on free cash flow (proxied

by ROA) is consistently positive and the coefficients on stability of cash flows (stan

dard deviation of ROA) and growth (market-to-book ratio) are consistently negative.

We find evidence that the motive to reduce agency problems persists into the latest

LBO wave. However, the absolute values of these coefficients are larger (in absolute

value) in the 1980s, consistent with our previous findings of diversion from high cash-

85

flow, low growth firms. This suggests LBOs in the 1980's were more likely used as a

mechanism to curb overinvestment problems than the more recent LBOs.

Thus, high cash flows and lower standard deviation of cash flows, as well as low

market-to-book ratios are associated with a higher probability of LBO. Table 3.4

also shows tangibility (PPE) to be significant at the 1% level in explaining likelihood

of LBO. Given the large debt burden, LBOs are less common in firms with higher

expected bankruptcy costs, as is the case in largely intangible firms. Both industry

and macro-level variables are highly significant at the 1% level throughout, which is

not surprising given the strong evidence on cyclically and industry trends in buyout

activity. Psuedo R2 values range from 4% to 7% (higher for the 1980's wave)5 .

Consistent with our findings in the previous section, leverage has a highly signif

icant positive coefficient throughout the sample (at the 5% level in the 1980's and

1% level afterwards), indicating that higher leverage increases the likelihood of being

acquired in an LBO6 . As discussed above, this finding is consistent with the high

debt capacity required of a target, as well as targeting of firms more likely to enjoy

increased tax benefits. One might hypothesize, though, that this relationship would

change at extreme leverage levels. Firms with extremely high levels of leverage may

have little room for taking on additional debt servicing, making them less than de

sirable targets. At the other extreme, since leverage is highly correlated with rating,

very low levels of leverage might be a proxy for high "quality", i.e. firms with little

room for significant changes in performance. As improvements in operational effi

ciency and better alignment of managerial incentives with those of shareholders are

commonly quoted sources of value in buyouts, we might expect the significant rela

tion with leverage to break down in firms with extremely low debt levels. We test

this hypothesize by re-running the above estimation separately for firms of varying

5 These R2 values are comparable with previous results in the literature, e.g. Cremers, Nair & John (2008)

6 Results are similar both when studying gross debt and net debt, i.e. subtracting cash and equivalents from total debt.

86

leverage levels. Results are shown in Table 3.5. The first column presents results

for firms in top 25th percentile of leverage. As expected, the relation turns negative;

acquirers cannot target firms with extremely high levels of outstanding debt. The

second column presents the results for all firms in the 25"1 to 75"1 percentiles; results

are as discussed before for the entire sample. The third column displays results for

the bottom 25"* percentile in leverage; the relationship is still positive, but not sig

nificant. These results are consistent with our hypothesis and we further corroborate

our findings with the regression in the last column in Table 3.5; we find a positive

relation, significant at the 1% level, with leverage, and a similarly significant negative

relation with leverage squared. In line with our previous reasoning, these findings

imply that controlling for other firm characteristics, firms more highly levered are

more likely to be the target of a leveraged buyout, as long as they do not fall into the

extremes of the leverage distribution. Firms with extremely high leverage levels are

actually less likely to become LBO targets.

[Insert Tables 3.4 and 3.5 about here]

For prediction we utilize a parsimonious model of a subset of consistently significant

variables: ROA and its standard deviation, market-to-book and leverage along with

industry probability of LBO and level of PE funds. The probability of becoming a

target in the current year is estimated from values of the independent variables at

the end of the previous year. Estimation is carried out in a 10-year rolling window

to allow for time variation in coefficients. We evaluate our model by measuring the

extent to which firms fall into either of the extreme groups of lowest and highest

estimated takeover likelihood; we compare the percentage of actual targets falling

into the lowest quartile vs. the percentage falling into the highest. The percentage

of targets in the first and fourth takeover likelihood groups equals 3.7% and 59.95%,

respectively, for 2004, 6.06% and 51.51% for 2005, 6.25% and 54.68% for 2006 and

18% and 38% for 2007. The differences between these percentages and their individual

87

differences from 25% are clearly statistically significant.

Additional variables we tested, not used in our final model, include firm and bond-

level antitakeover provisions. We incorporate the firm-specific defense mechanisms in

place by using the index compiled by Gompers, Ishii & Metrick (2003) from the

Investor Responsibility Research Center (IRRC) publications. The governance index

incorporates 24 takeover protection provisions in 5 categories: tactics for delaying

hostile bidders, voting rights, director/officer protection, other takeover defenses and

state laws. It is viewed as a measure of antitakeover protection7 . We find that fewer

takeover defenses does predict a higher likelihood of a takeover, but the effect was not

found to be significant, possibly due to the relatively small number of firms with IRRC

data. We further incorporate debt-level protection provisions by using bond covenant

information from Mergent Fixed Income Securities Database (FISD)8 . We use as a

protection measure the percentage of bonds outstanding with event risk covenants

per firm-year9 . We find this variable to be significantly, positively associated with

the likelihood of an LBO. These findings are consistent with previous studies that

have found covenants to be more common in LBO targets than in non-targets (Lehn

& Poulsen, 1991), suggesting covenants are found where risks are the greatest. These

antitakeover provision variables, found only for a relatively small subset of our total

sample, were not used in our final prediction to allow a parsimonious model, applicable

for the entire sample of firms.

7 For a more detailed description of the 24 provisions, see Gompers, Ishii & Metrick (2003). The index is formed by adding one point if the firm has a specific defensive provision in place and zero otherwise.

8 FISD contains detailed issue-level information on over 140,000 corporate, US Agency, US Treasury and supranational debt securities. The sources for this information are bond prospectus, issuers' SEC filings including 10-K, 8-K, Registration forms, etc.

9 We exclude bonds with missing covenant information. Billett, King & Mauer (2004) find missing covenant information to be unrelated to time of issuance, priority, rating, maturity, size of issue or issuer so we expect no bias in selection of bonds examined.

88

3.5 Modeling of LBO risk in credit spreads

Previous sections provide ample evidence of heavy industry clustering in LBO ac

tivity. In the wave of the 1980's, industry was, at times, used as a proxy for LBO

risk (Lehn k, Poulsen, 1991), and the industry concentration appears to have become

even stronger in the recent wave. Moreover, we find that LBO announcements yield

statistically significant intra-industry reaction in spreads. Findings tying these pat

terns to industry economic shocks (Mitchell & Mulherin, 1996) suggest LBO risk is,

to a significant extent, driven by industry-level fundamentals and that an LBO of

one firm might imply greater LBO risk for other industry firms. We label this effect

"LBO contagion risk" and utilize it to aid in disentanglement of LBO risk from

fundamental default risk, by modeling updating of LBO risk at the industry level.

It is probably the case that an LBO of one firm also affects other firms of similar

size or rating. For example, recent leveraged buyouts of large, investment-grade firms,

such as Neiman-Marcus, Alltel Corp and others, shattered market perceptions that

some firms were too large to be taken over or that acquirers target only low-rated firms

in distress. However, given the evidence on industry clustering and intra-industry re

actions, we focus on the within-industry effects of buyouts.

The empirical study in the previous chapter introduces LBO restructuring risk as

a driver of the cross-sectional variation in credit spreads. The event study presented

there documents the effect of LBO announcements on the time series of spreads in

LBO targets. This paper completes the picture by introducing LBO risk into the

evolution over time of spreads of non-target firms. We incorporate all the aforemen

tioned empirical evidence into a novel, comprehensive model of credit spreads, in

which spreads are driven by both default and LBO restructuring risk. Utilization

of our findings on " LBO restructuring contagion" allow disentanglement of restruc

turing risk from default risk. We propose a general reduced-form framework where

an unexpected LBO of an individual firm leads to an increase in default risk (and,

thus, in credit spreads) of the individual firm and to an industry-wide increase in

89

restructuring risk (and, thus, in credit spreads).

Empirical study of intra-industry reactions to LBO announcements shows an in

crease in credit spreads of firms within the same industry in the two months following

the event (see Figure 2.3). We find further empirical evidence for this contagion risk:

we study changes in firm spreads in excess of changes in CDX spreads in announce

ment months vs. the excess change in spreads in months in which there were no

announcements of LBOs in the industry. A t-test encompassing all firms and events

in our sample results in a t-stat significant at the 10% level, but when excluding

events in smaller firms (lower 25"* percentile of size) t-stat is 2.6, significant at the

1% level. Interestingly, when narrowing the sample to effect of events in smaller firms

(up to $lbn market cap) on firms of similar size, the resulting t-stat is even higher

(3.46).

A possible mechanism by which a restructuring event of one firm can trigger an

industry-wide response is through Bayesian updating of the "perception" of risk.

An LBO of a firm might signal interest of acquiring firms in that specific industry,

possibly due to its tangibility, cash flows or growth opportunities, or due to a grow

ing interest or expertise in the corresponding products or line of business. A closer

study of intra-industry effects of LBO announcements finds additional evidence that a

Bayesian updating mechanism is particularly suited for explaining the phenomenon.

An examination of the reaction of same-industry firms shows an increase in their

spreads in the month following the announcement, consist with an upwards-updating

of investor beliefs on LBO risk. However, in the subsequent month, given no addi

tional observed LBOs, spreads start trending back downwards (see Table 2.2), which

might be interpreted as a downwards-updating of beliefs.

Our model assumes the LBO target ceases to share in the contagion risk following

the buyout; once a firm undergoes an LBO event, its price is driven solely by default

risk. The post-LBO firm typically has triple the leverage as pre-LBO (Kaplan, 1989,

Smith, 1989), thus the risk of a subsequent LBO in the near future is minimal. We

90

believe this is a plausible assumption in our relatively short time span of 3 years. We

find supporting evidence in the data: we examine the effect of LBO announcements

on spreads of firms in the same industry that have previously been LBO targets. For

these firms, we study the changes in spreads in excess of the change in CDX in an

nouncement months vs. the excess change in spreads in months in which there were

no announcements of LBOs in the industry. Indeed, we find no significant differ

ence between announcement and non-announcement months. This finding reinforces

the assumption that LBO targets are not exposed to significant additional LBO risk

shortly post-LBO.

3.5.1 The Model

In this section, we develop a reduced-form model for pricing debt instruments that

explicitly incorporates both default and restructuring risk, disentangling the two ef

fects. The time series of spreads is driven by changes in the macro environment and

industry-level changes in perceived LBO risk. We derive pricing formulas that are

independent of the number of firms affected by the contagion.

Our model is based on our aforementioned empirical findings:

• LBO risk is priced in the cross-section of CDS spreads, implying spreads are

driven by both fundamental default risk and LBO restructuring risk.

• Credit spreads of LBO targets experience a significant widening upon announce

ment of the LBO.

• An LBO announcement causes an increase in spreads of firms in the same

industry as the LBO target.

• An LBO target is not subject to significant additional LBO risk near-term and,

thus, its credit spreads are not affected by subsequent LBOs in the industry.

Default of a given firm occurs at the first event time rD of a (non-explosive) counting

process N®, relative to a probability space with measure P and an increasing family

91

{Ft}t>o °f information sets10 . LBO restructuring of a given firm occurs at the first

event time TR of a (non-explosive) counting process NR, relative to a probability space

with measure P and an increasing family {Ft}t>0 of information sets. Under mild

technical conditions there exists an equivalent martingale measure Q (risk-neutral

measure), not necessarily unique. The counting processes Nf and NR have respective

risk-neutral intensity processes Af and XR under Q. Intensities are defined by:

Et[dNtD] = Xfdt

Et[dNtR] = XRdt

where Et denotes E conditional on information set Ft.

Default and LBO risk typically evolve over time, thus Af and Af are assumed to be

generally stochastic with conditionally independent paths (doubly stochastic property

applies).

The following describes the modeling of the findings listed above:

• Spreads have been found to widen following an announcement, suggesting an

increase in firm default risk. Mathematically, this can be written as: dNR R •

d\D R y£ 0 for any firm i. Based on this empirical evidence we define

l<Ti

the post-restructuring default intensity: hf = Xf(l + k- 1R) where k is

a random variable and 1R is an indicator variable that equals 1 if t > TR and

0 otherwise. This specification allows for both upward and downward jumps

in the default intensity, yet given empirical evidence, we expect to estimate a

positive k.

Given the increased leverage post-LBO, we also assume a change in

loss-given-default (LGD) following a restructuring event: L = I + lR •

1R G (0,1), where I is LGD prior to LBO and lR is the increment in LGD upon

restructuring. LGD is specified as a fraction of notional.

10 The filtrations we mention are assumed to satisfy the usual conditions. See, for example, Prot-ter(2005) for technical details.

92

• The above specification implies default risk (and LGD) can jump due to an

LBO at most once. Formally, XRt = 0 at any t > rR for any target firm i.

• An unexpected LBO of an individual firm leads to an industry-wide updating

of investor beliefs on LBO risk. This can be written as: dNRrR • dXR

2rR ^ 0 for

any firms i l ^ i2 in the same industry. This mechanism is described in greater

detail in the following sections.

It should be noted that our specification does not explicitly take into account another

source of contagion - that of default risk. Default contagion has been extensively

studied in the credit literature and is not the focus of this paper. Furthermore, in

estimation we limit our sample to the years 12/2004—9/2007, where default rates were

historically low and LBO activity was historically high, as displayed in Figure 3.3.

Therefore, we believe default contagion risk is of a relatively lower magnitude than

LBO contagion in our time sample (and thus, if captured in our LBO risk parameter,

should comprise only a small fraction of the value).


Survival probability

In this setting, intensity of default changes upon LBO. Thus, probability of survival

can be broken down into probability with and without a restructuring event. The

time-i probability of survival until time T is:

SD(t,T) = Pt(rD > T,TR > T) + Pt(r

D >T,TR<T) (3.1)

where:

Pt(rD >T,rR>T)= Et{eM~ f W + XR)ds)} (3.2)

Pt(rD > T,rR <T) = Et{exp(- J Afds)- (3.3)

93

/ [exp(- [V\?ds)-\K-exp(-k f Xfds)]dv} Jt Jt Jv

The above specification assumes that once a firm undergoes an LBO event, its price

is driven solely by default risk. If k > 0, the probability of survival decreases with

restructuring and vice versa. If k = 0, restructuring has no impact on default intensity

and survival probability is just:

S(t,T) = E?{exp(-J Afds)} (3.4)

If intensities are constant, survival probabilities simplify to:

P?{TD >T,TR>T) = exp{-{XD + XR){T - t))

P?(TD >T,TR<T) = exp(-(XD + XR)(T - t))-

XR

kXD - XR [1 - exp((Afi - kXD)(T - *))]

(3.5)

(3.6)

and plugging these back into equation (3.1):

S(t, T) = exp{-(Xu + XH)(T - t)) • 1 + A*

kXD - XR (1 - exp((XH - kXv)(T - t)))

3.5.2 Modeling of LBO contagion

We base our model of LBO contagion via Bayesian updating on the framework utilized

in Collin-Dufresne, Goldstein &, Helwege (2003). The LBO risk of a given firm can

take on one of n discrete values XR, j = 1,..., n, corresponding to n different states of

the world. The values are ordered 0 < Af < ... < A , where n is defined as the state

with highest risk. Investors do not know the true state, but form a prior pj. given all

information available at time t {Ft}, where pi > 0 for each j and X)?=i Pt = 1-Thus,

94

investors perceive LBO risk to be:

n

A? = 5>?-A' (3.7) 3 = 1

where the LBO restructuring intensity is defined through:

Et[dN*} = Xfdt (3.8)

LBO risks across states may be different for each of i = 1,.. . , / firms in an in

dustry, determining Xft for each Nf\ (in future references, we drop either subscript

i or superscript JR to simplify notation); they might, for example, be a function of

firm characteristics. Yet, empirical evidence suggests LBO risk is, to a large extent,

attributable to industry classification. Based on this, investor beliefs on LBO risk are

updated according to LBOs observed in the industry, thus updating is common

across all industry firms. Specifically, {Ft} consists of all LBOs occuring in the

industry up to time t. Investors update their priors on pi based on their observations

on LBOs in the industry during the interval dt. This means of updating incorpo

rates both trends in the specific industry and general macro trends in buyouts, as the

number of buyouts in any industry will be highly correlated with the state of buyout

funds and the credit environment.

Since restructuring is triggered by a point process, investors observe at most one

event per unit time dt. Using Bayes rule, we obtain the updating process for pj. (see

Collin-Dufresne, Goldstein & Helwege, 2003 for the details):

1 XJ

dpi = Pi^Y- - !) • W ' - AM • hr«>t}dt) (3-9)

where j is the state and / is the number of firms sharing in the contagion. If the priors

are 0 or 1, i.e. investors are certain about the correct state, there is no learning and

no updating of priors (Xt = AJ for some j). When no LBO is observed over an interval

95

dt (dNitt = 0 for each i), probabilities of states j with A-7 > (<) At drift downwards

(upwards), whereas upon an event, probabilities of the high (low)-risk states jump up

(down).

This framework remains tractable for stochastic AJ, where the parameters govern

ing the process evolution can take on one of several discrete values corresponding to

different states of the world. The ordering on the intensities is maintained through

ordering on the parameters.

In this framework, the time-i probability of no restructuring until time T can be

computed as:11

n

Et[l{Tn>T}] = £ p ? . e x p { - A J ' ( r - t ) } (3.10)

Similarly, in a setting where investors are uncertain as to the true state of LBO

risk and XR can take on one of several discrete values, survival probability can be

computed as:

n

Et[l{T°>T}] = 52PI • Sj(t,T) (3.11) . 7 = 1

where Sj(t,T) = S(t,T) conditional on being in state j , is as derived in equations

(3.5) and (3.6):

n

S(t,T) = ^ ^ . e x p ( - ( A D + A ^ ) ( r - f ) ) -

11 The probability cannot be computed as:

Etll{T*>T}} = exp{-Af(T-£)}

as the jump in intensity upon a restructuring event violates the "no-jump" condition, see Collin-Dufresne, Goldstein and Helwege (2003) for details.

96

1 + fcA^-Ai ^ ( 1 " 6 X P ( ( A ' " kXD){T ~ t))] (3.12)

An analogous expression can be derived for the stochastic case, using equations (3.2)

and (3.3).

3.5.3 CDS Pricing

Similarly to the derived above, in our framework the CDS premium is a weighted

average of the premiums corresponding to the different levels of restructuring risk,

weighted by the state probabilities. In the following we derive the spread associated

with risk XR - to simplify notation we denote LBO restructuring risk simply as XR.

We assume a continuous payment structure for default swaps where the protection

seller receives a payment flow of c per unit time until maturity T or until a default

occurs. The instantaneous risk-free rate r is assumed to be independent of the default

and restructuring times under Q12 . The CDS premium c is determined such that the

present value of the recovery leg of the swap (PVR) equals that of the payment leg

(PVC).

rT pv

PVC = c exp(- / rsds) • S(0, v)dv Jo Jo

where S(0,v) is as defined in equation (3.1) and:

PVR = PVR(TD <T,TR>T) + PVR(TD <T,TR< T)

12 As the focus of this paper is not interest rate risk, we make this simplifying assumption, based on findings by a number of previous works. Pan & Singleton (2008), for example, find results to be robust to assumption of constant risk-free rate. They explain this finding with a simple arbitrage argument, showing that CDS spreads are approximately equal to the spreads on comparable maturity, par floating rate bonds from the same issuer as the reference bonds underlying the CDS contract (see, e.g., Duffie and Singleton, 2003).

97

where:

PVR(TD <T,TR>T) = 1- [ e x p ( - f r„ds) • EQ[\° • e x p ( - f (Af + XR)ds)}dv Jo Jo Jo

PVR(rD <T,TR<T) = L- [ e x p ( - / r sds)

Jo Jo

•EQ[h° T e x p ( - / (Af + Af)ds )A^exp( - f hfds)du]dv Jo Jo Ju

In the following two subsections, we derive CDS spreads for stochastic intensities and

for the specific case of constant intensities.

S t o c h a s t i c XD, XR

A suitable evolution for the stochastic case is that of a CIR process, to maintain non-

negative intensities. Under the CIR model, CDS spreads are available in closed-form

up to numerical integration.

P a y m e n t Leg We evaluate the present value of payments by the buyer of protec

tion:

PVC = c e x p ( - / rsds) • S(0,t)dt Jo Jo

= c f e x p ( - / rsds) • [PQ(TD >t,rR>t) + PQ(TD > t,TR < t)]dt Jo Jo

For the case of no restructuring and no default, using equation (3.2):

t-T

c f e x p ( - f rsds) • PQ(TD >t:rR> t)dt =

Jo Jo

= c I exp(— / rsds) • £ Q [ e x p ( - / (Af + XR)ds)}dt Jo Jo Jo

98

using independence of the two intensities and assumption of constant r:

= c I exp(-r • t) • EQ[exp(- f \fds)} - EQ[exp(- f \Rds)]dt Jo Jo Jo

f [eX.p(-r-t)-f1(Q,t,eD,l,e^)-fi(O,t,0R,l,ex?)]dt Jo

c

where / i is available in closed-form given the evolution of the intensities under Q

(see formulas for CDS pricing under CIR in the appendix). 0D (9R) stands for the

parameters of the CIR process. For the case of restructuring and no default, using

equation (3.3):

c / exp(- [ rsds) • PQ(TD > t,rR < t)dt = Jo Jo

rT ft rt ru ft

= c exp(-rt)EQ{exp(- / Xfds) / [exp(- / \Rds)\Rexp(-k / \fds)}du}dt Jo Jo Jo Jo Ju

pT ft fu rt

= c / exp(-rt)EQ[exp(- (\° + \R)ds)\Rexp(-(l + k) \°ds)]dudt Jo Jo Jo Ju

interchanging integral and expectation (using Fubini's theorem). Using the law of

iterated expectations:

= cff exp(-r t)£«[exp(- f (A? + X?)ds)\RE%[exp(-(l + k) [ \?ds)]}dudt JO JO Jo Ju

PT /*t ru

= c / exp(-rt)EQ[exp(- (A? + XR)ds)XR • h{u,t,eD,1 + k,ex")]dudt Jo Jo Jo

= c [T f exp(-rt) • EQ[exp(- f \Rds)\R}-Jo Jo Jo

pu EQ[exp(- / A? ds) • f^u, t, 6D, 1 + k, ex°)]dudt

Jo

= c f f exp(-rt) • /2(0, u, 6R, 1, A*, eA*) • /3(0, u, 9D, 1,1 + k, ex°)dudt Jo Jo

where /2 and / 3 are available in closed-form given the evolution of the intensities

under Q (see formulas for CDS pricing under CIR in the appendix).

99

Recovery Leg We evaluate the present value of the recovery payment made by the

seller of protection:

PVR = PVR{rD <T,TR>T) + PVR{TD <T,TR< T)

For the case of no restructuring before default:

PVR{TD <T,TR>T) = ID [ exp(- f rsds) • EQ[X? • exp(- / (A? + Xf)ds)]dt

Jo Jo Jo

using independence of the two intensities and assumption of constant r:

= lD f exp(-r • t) • EQ[exp(- [ Xfds) • A?] • EQ[exp(- f XRds)]dt Jo Jo Jo

i-T

= lD / exp( - r - t ) - / 2 (0 , t , ^ , l ,A? ,e A o D ) - / i (0 ) t ) ^ , l ) eA o R )d t

Jo

For the case of restructuring prior to default:

PVR(TD <T,TR<T) =

pT pt pt pu pt

= LD exp(- / rsds)EQ[hf / exp(- / (Af + Af)ds)XRexp(- / h°ds)du}dt Jo Jo Jo Jo Ju

= LD f f exp{-rt)E^[hf exp{- f (A? +A?)<fe)A?exp(- f hfds)]dudt JO Jo Jo Ju

= LD ( f exp(-r t )£«[exp(- /"(A? + Af )ds)XR • £#[exp(- / h?ds)h?]]dudt Jo Jo Jo Ju

= LD f f e x p ( - r t ) ^ [ e x p ( - /"(Af + Af )ds)XR • f2(u,t,8D, 1 + *, Af ,eA- )}dudt Jo Jo Jo

= LD f f expi-rt) • Efilexpi- f Af^)Af]-Jo Jo Jo

pu EQ[exp(- / Xfds) • f2(u,t,9D,l + k,X°,exS)}dudt

Jo

= LD f f exp(-rt)-f2(0,u,6R,lA§,ex*y Jo Jo

(/3(0, u, 0D, 1,1 + k, exo) + /4(0, u, 9D, 1,1 + k, A?, ex°))dudt

100

interchanging integration and expectation and using iterated expectations, similarly

to the payment leg. Functions /2, fz and f$ are available in closed-form given the

evolution of the intensities under Q (see formulas for CDS pricing under CIR in the

appendix).

Constant XD, XR

If intensities are constant, the CDS premium is known in closed-form:

PVC = c / exp(-Jo

-r • v) • S(0,v)dv

= c exp(-r • v) exp(-(Ar> + \R)v) Jo

1 - e x p ( - r ( r + XD + XR))

1 + A'

kXD - XR • (1 - exp((A* - kXD)v)) dv

r + XD + XR

XR r i - e x p ( - r ( r + AD + Afl)) 1 - e x p ( - r ( r + AD(1 + k))) (kXD - XR) r + XD + XR r + XD(l + k)

for XR 7 kXD. Similarly, the recovery leg is the sum of the following:

PVR(TD <T,TR>T) =

CT < t ,D ^R^^J ,D ,D 1 ~ exp(-T(r + XD + XR)) exp(-v(r + XD + XR))dv = lv • Xu • — v v "

= lD-XD

(r + XD + XR)

PVR(TD <T,TR<T) =

PT PV

= LD(l + k)XDXR [exp(-ru) / exp(-u(Afi + XD)) exp(-(u - n)(l + k)XD)du]dv Jo Jo

D\R LD{l + k)XDX kXD - XR

1 - exp(-T(r + XD + XR)) 1 - exp(-T(r + XD{1 + k))) (r + XD + XR) r + XD(l + k)

Similar expressions can be worked out for the specific case where XR = kXD.

The spread rises with XR, where the difference in spread vs. the base case of no

restructuring risk increases with k. For example, for XD = 0.8%, XR = 1% and k = 1,

101

the difference in the 5-year spread is 1.75% of the level of the base spread without

restructuring risk, for XR = 5%, the difference is 8.5% of the base spread and for

\R = 10% the difference is over 16% of the spread without restructuring risk (setting

\£> = 0.8%, I = 0.53, L = 0.65 and r = 0.0315).

3.6 Model Estimation

We study the case of two states of the world, one defined as a low LBO-risk state

(L), and the other, as a high-risk one (H). In this setting, LBO risk is perceived by

investors to be:

Af = v».\H + {l-p»).\L (3.13)

Using Bayes rule, we can obtain the updating process for pf (see Collin-Dufresne,

Goldstein and Helwege (2003) for the details):

dp? = p ^ . ( i - p f ) ^ [ ^ = ^ L . ( d ^ . ; t _ A . t . i { T « > t } d f ) ] (3.14)

1 \H

Figure 3.4 plots dpH as a function of pH. The top figure displays the downwards drift

when no LBO is observed; the change is always negative, largest (in absolute value) for

highest uncertainty, i.e. at pH = 0.5. The bottom figure displays the upwards jump

upon an industry LBO; the change is positive, lowest for a high probability of being

in the high-risk state, i.e. probability close to 1, and highest at low probabilities, close

to 0.1-0.2. The light and dark-colored plots show changes in probability for different

intensity parameter values; both drift and jump are larger in magnitude when the

difference between the high and low-risk states is greater.


102

The time-i probability of no restructuring until time T can be computed as:

Et[l{rR>T}} = pf •EttH[l{TR>T}) + (l-P?)-EtiL[l{Tn>T}} (3.15)

= pf • exp{-A*(T - t)} + (1 - pf) • exp{-AL(T - t)}

Default Risk

To capture both idiosyncratic default risk and changes in the macro environment, we

postulate the following specification:

A$ = ai + A-Af

where a; and /?» are idiosyncratic default parameters. As a proxy for market-wide

default risk we utilize the spread on the CDX NA index. For simplification, we

adopt the following approximation (exact for assumption of constant default and

recovery rates): Af — fglofen/' w n e r e °dxt is the spread on the CDX and recovery

is set to a constant value used in common practice. In this setting, time variation in

default risk is driven by macro credit events, and c^ captures the average firm-specific

default risk over the sample time period. (We do not explicitly take into account time-

variation in idiosyncratic default risk, as this has been extensively studied and is not

the focus of this paper. Furthermore, focusing on level, rather than on changes, in

default risk seems a plausible simplification for our purposes in the sample period of

12/2004 — 9/2007, where default rates were historically low and LBO activity was

historically high, as displayed in Figure 3.3).

The model

In line with the framework presented above, our model is the following:

cdsitt = cds( Xi>t , Aj ,T ,1 ,L ,k ,rt) + ae^t

103

dp? = P?Y.{>^jr-l)-{dN^-\fyl{Ta>t}dt) t = l Ai't

AS = pf-Af^ + ( l - P f ) -AP

Aj> = ( a i + / 3 l i - £ ^ L _ ) . ( i + fc.i{TH<t}) x recovery' N \ , - //

where cdsitt is market spread at time t for firm i and cds( ) is model spread, as derived

in section 3.5.3. Pricing assumes LBO can occur at most once per firm (following an

LBO, CDS of the target firm are driven solely by default risk). LBO restructuring in

tensity is firm-specific (Af'H, Af'L), yet updating is common across all industry firms.

State variable is pf'", the probability of being in the high-LBO risk state; changes

in pf'H drive changes in the time series of LBO intensity and, consequently, of spreads.

The average level of default risk is idiosyncratic, and time variation in default risk is

driven by macro credit events or upon an LBO event in the firm, which causes a jump

of k% in default risk. Jump in default intensity, k, is common across industry, as is

updating of state variable pf'H. Investors form a prior pQ for each industry. As we are

interested in learning about the risk as priced in spreads, intensities and probabilities

are all under the risk-neutral measure.

We restrict our sample to 12/2004 — 10/2007, where LBO activity was at histor

ically high levels (we are limited to 12/2004 by the CDX time series). We merge

spreads with data on LBO announcements involving targets in the same industry,

where industry is defined by 3-digit SIC code. We further drop firms that have gaps

or staleness in time series of prices and are left with 383 firms from 122 different

industries. We set T = 5 and use spreads for the 5-year CDS contract, which is the

maturity most actively traded. We keep only monthly closing spreads (to capture

cumulative change in spreads in event months).

As LBO targets typically trade at HY levels post-LBO, we set / and L to recovery

rates on senior unsecured bonds for IG and HY firms, respectively, as reported in

104

Altman (2006)13 . For identification purposes, we set all Xt ' to a common minimum

intensity value. We set p0J at industry probability of LBO, computed as the ratio of

the number of targets to the number of firms per industry (as described in section

3.4.2). LBO announcements are used to update industry priors p1^ at monthly inter

vals. We bound pf from above and from below to avoid reaching absorbing states of

0 or 1. We use the 5-year Treasury zero-coupon bond yield for rt.

We perform joint estimation of all industry firms, incorporating updating of LBO

risk at the industry level. For each industry, we optimize over all firm-months. We

maximize the following for an industry with / firms and a sample of T observations,

over the parameters (a,@, XR'H)lxi , k :

P( (cds)TxI ,{N«)TxI\ (XD(a,p))TxI ,(X»,XL)lxI ,k ,Po )

where ( ) A x B denotes a matrix of A x B observations, cdst,i is time-i spread of firm i

and dN*{ = 1 if firm i underwent an LBO at time t and 0 otherwise.

P{ (cds)TxI , (NR)TxI | (XD(a, 0))Txl , (A", XL)1XI ,k,p0)

T

= J J P( (cdst)lxI , {N*)lxI | (A? (a, 0))lxI , (A*, AL) lx, ,k,p0, (NR)T_lxI)

T

= I I E p((cdst)^ > (KUi | St , (A? (a, P))1XI , (X", XL)1XI ,k,Po, (NR)T_1XI) t=l St=L,H

•P(St\ (A? (a, 0))1XI , (X", XL)1XI ,k,p0, (NR)T_1XI )

T

= [ J E p((cdsthxr I St , (Nt«)lxl , (Af (a, (5))1XI , (A", XL)lxI ,k,p0, (NR)T_lxI) t= l St=L,H

• P((NtR)lxI | St , (Af (a, P))1XI , (A», A l)IX, , k ,Po, (iV«)T_lx;)

• P{ St | (Af (a, 0))1XI , (Aw, A£) lx, , k , p 0 , (iVR)r-ix/) T

= 1 1 E ^ ( (cds t ) ix / | cds ( (Af (a ,^ ) ) l x / , (A^) ix / ) A:) )

t= l St=L,H

13 Recovery rates reported are 47% for IG and 35% for HY.

105

•P((iVf) l x / | (A f i A) l x ; , ( iV«) r_1 > < 7)

•P{St\'(\H,\L)lxJ,Po,{NR)T_lxI)

where cds( ) is model spread, as derived in section 3.5.3. This is equivalent to maxi

mizing:

T

J^H E P ( (cdst)lxI\cds((\?(a,p))lxI ,(\R>St)lxI ,k)) t=l St=L,H

• F ( ( ^ f ) l x / | ( A i l A ) 1 x / ) - J P ( 5 t | ( A " , A i - ) l x / , p 0 , ( i V R ) ^ l x ; ) }

The corresponding distributions are:

1. (cdst)lxI ~ MVN{cds(\?(a,0)lxl ,(\R>S<)1XI ,k) ,a2IDIX[), where IDIXI is

the I x I identity matrix

2. P{ {dNtR)lxI | {XRA)lxI ) = ULi exp-^,Stdt if no firm defaults and XR'Stdt oth

erwise

3. Define pf = probability of state 5 at time t, then dpf is updated according to

eq (3.14).

Results

Estimates of LBO risk and model spreads suggest the proposed mechanism is sig

nificant in explaining observed market spreads. The fit of model-implied spreads to

market spreads averages 65.6% across industries (with a standard deviation of 28.8%

and a median of 75.2%). Figure 3.5 plots the average model-implied vs. market

spreads over our sample period, showing our model spreads closely track those ob

served in the market. Moreover, examining model-implied spreads around LBO event

times, it seems the model is successful in generating the observed jumps in spreads

on these days. Average model vs. market spreads for target firms in the sample are

displayed in Figure 3.6. The plot graphs a time window of [-6,6] around event months;

106

the average jump in model spreads is of a magnitude matching that observed in the

market. A similar plot of average intra-industry reaction to LBO announcements

can be seen in Figure 3.7. In industries, for which we have a time series of target

spreads, we estimate an average jump in default risk upon LBO (k) of 65-70% (with

a standard deviation of the same magnitude).

We examine the relation between our model parameter estimates and firm-specific

observables. Studying LBO parameters, estimates of LBO risk in the "high-risk"

state (A ' ) average 20-25% (with a lower median of 4-7%). The first column in

Table 3.6 present the results of the cross-sectional regression of Ai ' against firm

observables, specifically LBO likelihood as estimated in our probit model (denoted

as pLBO, detailed in section 3.4.2). Predicted LBO likelihood is found to be a sig

nificant explanatory variable, implying priced LBO risk is associated with properties

characterizing target firms (as expected, the firm characteristics comprising the probit

model are largely insignificant in addition to pLBO). Figure 3.8 presents an example

of the evolution of pt in one of our sample industries. LBO events are marked on the

plot; the probability is observed to jump upwards upon an event and drift downwards

otherwise. Our estimates of pt average 35-40%, with a standard deviation of similar

magnitude and a significantly lower median (lower than 10%), suggesting investors

might be pricing in high LBO probabilities in high-activity industries (possible indi

cating identification of LBO risk as a "Peso problem" in debt markets).

Studying default-related parameters, estimates of a, capture the average level of

spreads. Time variation in default intensity is introduced through changes in the

index. Overall, median values of \ft are 0.7-0.8%. The last two columns in Table 3.6

present the results of regressing estimates of priced default risk against observable

firm variables. As expected, default risk is explained by firm leverage, profitability

and various proxies for "riskiness"; it is positively related to leverage and standard

deviation of roa and negatively related to roa and firm size. The significance of these

variables decreases when rating is added as an explanatory variable, highly correlated

107

with these proxies for firm riskiness (relation with rating is positive as lower rating

corresponds to a higher numeric value on our scale).

[Insert Figures 3.5, 3.6, 3.7 about here]


3.7 LBO risk and structural model mispricing

Structural credit models based on the Merton (1974) model of the firm are a popular

tool in practice as well as in academic applications of credit risk. Practitioners imple

ment these models to assess bankruptcy risk (e.g. Moody's KMV and CreditGrades),

to price corporate bonds and credit default swaps, and to perform capital structure

arbitrage. In these models unobserved value and volatility of the firm's assets are the

key determinants of credit spreads and bankruptcy probabilities.

Numerous studies have indicated that structural models have difficulty predict

ing corporate bond yield spreads accurately (e.g. Jones, Mason & Rosenfeld, 1984,

Huang & Huang, 2003 and Eom, Helwege, & Huang, 2004). The "credit spread puz

zle" , as cited in the literature, states that short maturity, investment-grade corporate

bonds have credit spreads that are too large to be explained by standard structural

models. Several explanations of the credit spread puzzle rely on jumps in asset val

ues (Delianedis k, Geske, 2001), liquidity effects (Huang & Huang, 2003) and others.

Merton's (1974) model has been extended to allow for more realistic assumptions,

such as the possibility of default before maturity (Black h Cox, 1976), stochastic

interest rates (Longstaff & Schwartz, 1995), mean-reverting leverage ratios (Collin-

Dufresne & Goldstein, 2001) and more. Extensions have been found to improve on

spread predictions, yet Eom, Helwege & Huang (2004) find some of the extended

models severely overstate the credit spreads of bonds issued by very risky firms (firms

with high leverage and high volatility) and underpredict the spreads of safer bonds.

Ericsson, Reneby & Wang (2005) document that mean pricing errors are smaller for

108

CDS spreads, yet pricing inaccuracy (measured by the standard deviation of predic

tion errors) is similar to that of bond credit spreads.

Structural models view equity and debt as options on the firm value. Default

occurs when the firm value process reaches a default threshold. Variables governing

the firm-value process affect default probabilities and default recovery rates and thus

ultimately drive credit spreads. These pricing models incorporate only current firm

fundamentals of leverage and volatility, but, as spreads are forward-looking, they

would incorporate all risks priced in by investors, specifically LBO risk. The ref

erence entities of corporate bonds are exposed - more and more so - to corporate

actions like takeovers, which result in a dramatic change in risk profile, particularly

for investment-grade debt. Our previous work has explicitly shown LBO risk to be

priced in credit spreads. We would, therefore, hypothesize that LBO risk might help

explain some of the mispricing in structural credit models in the buyout boom years.

To test our hypothesize we first generate model spreads using Black and Cox

(1976), an extension of the original Merton (1974) model that allows for bankruptcy

prior to maturity. Default occurs when the firm value process reaches a default thresh

old, commonly interpreted as a corporate barrier, such as covenants specified in debt

contracts. One such covenant is the net worth agreement where creditors have the

right to trigger debt call, default, or bankruptcy whenever the value of assets falls

below that of liabilities. In this first passage time setting the equity can be viewed

as a down-and-out call option on firm assets.

We follow Eom, Helwege k. Huang (2004) and set face value of debt to total firm

liabilities, as equity residual values only begin to accrue after all debt is paid off.

Leverage is then computed as total liabilities (book value of debt proxies for market

value) to market value of equity. We set asset risk premium to 5%, consistent with

an equity premium of 7-8% and a leverage ratio of 30-35%. We set debt maturity to

the sample average of 8 years (calculated from bond issuance for our sample firms on

109

Mergent FISD database). We set the default barrier to face value of debt x recov

ery (as our goal is to match CDS spreads, often priced in practice using a constant

40% recovery rate) and take into account dividends and interest expense in the firm

payout parameter. We use the yield on Treasury zero-coupon bonds of the relevant

maturity as risk-free rate. To find the two unobservables, asset value and volatility,

we calibrate the model to equity prices and rating class default probabilities, as taken

from Moody's14 . We implement the following iterative procedure: for each month,

we initialize asset volatility to equity volatility from the previous 6 months, scaled

by leverage. Asset value is initialized to sum of equity and total liabilities. We solve

for asset value and volatility using equity prices and default probabilities and then

iterate, setting initial values to output from previous iteration. We repeat until con

vergence.

We compute model CDS spreads for the years 2001-2007 (to match our CDS data

sample) given resulting estimates of firm value and volatility. Consistent with previ

ous findings, our model spreads are, for the most part, lower than market spreads.

Our model spreads average 73 bps, compared with 110 bps in market spreads. They

are also more dispersed, with a standard deviation of 194 bps, compared to 148 bps

in market spreads15 . The correlation between market and model-implied spreads is

0.47; mispricing is calculated as the difference between the two.

Table 3.7 presents results of a regression of model mispricing against estimated

LBO risk and other firm-level controls. We control for firm leverage and size, as well

as for variables related to firm capital structure. The structural model assumes a sim

ple capital structure, thus we control for average bond maturity and include a binary

variable indicating whether the firm has convertible debt (as a proxy for complexity of

debt structure). We also control for the level of spreads. Consistent with our hypoth-

14 We use average one-year rates over the years 1970-2007, from Moody's "Corporate Default and Recovery Rates, 1920-2007", February 2008.

15 The median, however, is significantly lower - less than 1 bps, compared with 57 bps in the sample.

110

esis, estimated LBO risk is found to be significant in explaining model mispricing.

The relation is especially significant for firms rated A-BB, more susceptible to being

buyout targets.


These results indicate mispricing is associated with priced LBO risk, thus, we might

expect this link to improve prediction of LBO likelihood. We re-visit our estima

tion model from section 3.4.2, adding in (lagged) structural model mispricing as an

explanatory variable. Table 3.8 presents results of the probit regression. Model mis

pricing is seen to be significant in explaining likelihood of LBO in the subsequent

year (controlling for the level of spreads). An increase of 10 bps in model mispricing

increases the likelihood of LBO in the subsequent year by 2%. We also find structural

model mispricing can improve identification of firms more likely to be LBO targets.

While results for 2006 are roughly comparable to those with our original predictor, the

new predictor with mispricing clearly outperforms in 2007; originally, 18% and 38% of

targets in 2007 fell into the lowest and highest quartiles of predicted LBO probability.

These numbers changed to 9% and 54% when including model mispricing among the

explanatory variables.


3.8 Summary

The benign macro environment in the years 2004-2007 allowed investors relatively

easier access to debt financing and swift transfer and trade of credit risk. In this

environment leveraged buyout activity grew to comprise over 25% and 30% of all

M&A activity in the US in 2006 and 2007, respectively. The time period 1/05-6/07

is reported to have accounted for 43% of total deal value from 1984 and 30% of all

transactions16 . While 2008 has seen a significant drop in LBO activity, this evidence

16 Source: CapitallQ

I l l

is consistent with documented recurring boom and bust cycles in buyout activity.

Kaplan & Stromberg (2008) provide evidence supporting the claim that a significant

part of the recent growth in private equity activity and institutions is permanent,

implying that to some extent, this decrease in LBO risk is only temporary.

In line with the advantageous macro environment, LBO targets in the recent wave

are found to be of larger size and higher market-to-book relative to those in the 1980's.

We find industry-level clustering in buyout activity to have become even more pro

nounced over time, drifting away from manufacturing industries into higher growth

business services, mostly in technology and telecommunications. Subsequently, we

study the time-series dynamics of LBO pricing in credit spreads, focusing on LBO

risk at the industry level. We propose a model where CDS spreads are driven by both

default risk and LBO restructuring risk, disentangling the two risks using evidence on

intra-industry LBO restructuring "contagion". The model generates the correspond

ing jumps in target and within-industry spreads upon LBOs, and model spreads have

an overall fit of 65%-70% to market spreads. Finally, we link estimated LBO risk

to documented mispricing in a structural credit model, and find this mispricing to

improve prediction of LBO likelihood.

Future work can utilize the framework developed in this paper to estimate the

loadings of LBO risk on specific firm characteristics. This would provide interest

ing evidence on the contribution of different properties to the pricing of LBO risk.

Given recent changes in macro conditions, it would also be interesting to extend the

data sample to 2008 and study the extent to which LBO risk now has a role in debt

prices; we would expect to find it significantly decreased. The pricing of LBO risk

should track aforementioned cyclically in buyout activity, rendering it a continuously

relevant question.

Table 3.1: Distribution of CDS spreads by sector

sector

Basic Materials Consumer Goods Consumer Services Financials Government Health Care Industrials Oil & Gas Technology Telecommunications Utilities

#firms

31 67 105 70 2

26 57 34 26 28 31

mean

1.46 1.44 2.37 0.78 0.18 0.75 1.27 1.21 2.23 2.61 2.07

std

1.74 3.06 5.56 0.96 0.07 0.99 1.53 2.59 2.87 4.59 5.29

10%

0.22 0.19 0.30 0.21 0.07 0.11 0.20 0.25 0.23 0.26 0.31

50%

0.68 0.69 0.95 0.44 0.19 0.35 0.62 0.56 1.25 0.82 0.69

90%

3.65 3.42 4.77 1.70 0.26 1.96 3.21 2.77 4.78 7.64 3.50

Notes: Spreads are for 5-year contract, in percentages.

Table 3.2: US LBO announcements 1979-2007

year

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

number of LBOs

4 19 27 82 170 215 276 291 413 418 233 231 257 220

total value LBOs ($bn)

1.65 1.92 3.01 13.97 36.21 51.91 48.83 64.18 183.70 88.93 23.92 6.82 11.52 10.37

year

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

number of LBOs

202 248 223 232 210 240 377 198 216 188 347 497 687 694

total value LBOs ($bn)

12.54 38.61 18.85 22.25 20.29 33.14 48.22 11.33 29.31 24.16 65.77 114.56 378.31 361.21

Source: Thomson Financial

113

Table 3.3: Characteristics of LBO targets over time

roa

stdev roa

leverage

tangibility

market/book

capex change

market cap

years

80-89 90-03 04-07 80-89 90-03 04-07 80-89 90-03 04-07

80-89 90-03 04-07 80-89 90-03 04-07

80-89 90-03 04-07

80-89 90-03 04-07

average

0.142 0.091 0.105 0.087 0.131 0.074 0.280 0.313 0.291 0.328 0.313 0.274 1.468 1.803 2.494

0.649 0.989 0.318 0.251 0.292 2.583

10%

0.047 -0.044 0.011 0.018 0.031 0.027 0.040 0.001 0.000 0.088 0.044 0.034 0.659 0.387 0.901 -0.586 -0.730 -0.430 0.010 0.004 0.030

50%

0.136 0.123 0.108 0.047 0.065 0.049 0.250 0.280 0.274

0.295 0.247 0.211 1.212 1.137 1.918 0.020 0.011 0.118 0.071 0.044 0.574

90%

0.227 0.223 0.216 0.145 0.269 0.104 0.546 0.689 0.627 0.637 0.704 0.687 2.324 3.004 4.136 1.866 1.483 1.248 0.557 0.435 5.539

stdev

0.085 0.217 0.118 0.230 0.324 0.116 0.189 0.260 0.241

0.208 0.245 0.243 1.020 2.621 2.357 3.775 11.874 1.049 0.554 1.662 7.353

number

404 371 195 398 371 201 407 374 209 409 371 196 390 323 187 376 333 185 392 343 197

Notes: This table displays properties of LBO targets over time. The properties shown are those for which the average in targets was found to be significantly different (at the 1% level) than the average in non-targets. Accounting data is from Compustat, LBO data is from Thomson Financial. The timeline is divided into three time periods: 1980-1989, 1990-2003 and 2004-2007. The table presents average and standard deviation, as well as 10"*, 50"* and 90th percentiles. Leverage is long-term + short-term debt to total assets, roa is EBITDA to total assets, stdev is standard deviation of roa to total assets, tangibility is net PPE to total assets, market — to — book is yearly closing price to common equity/common shares outstanding, changeincapex is the change in capital expenditures from the previous year, in millions of dollars, marketcap is in billions of dollars.

Tab

le 3

.4:

Est

imat

ion

of L

BO

lik

elih

ood

(US

1980

-200

7)

depe

nden

t:

LB

O

roa

leve

rage

mar

ket-

to-b

ook

stde

v ro

a

tang

ibil

ity

In (m

arke

t ca

p)

prob

Ind

ustr

y L

BO

PE

fu

nds

num

obs

erva

tion

s R

-squ

ared

1980

-200

7

0.00

125*

* (0

.000

5)

3.08

6***

(0

.291

0)

0.28

3***

(0

.045

8)

2157

65

1.09

%

1980

-200

7

0.10

8***

(0

.036

1)

3.02

9***

(0

.288

0)

0.29

5***

(0

.045

2)

2215

07

1.13

%

1980

-200

7

-0.0

662*

**

(0.0

096)

2.86

3***

(0

.303

0)

0.35

0***

(0

.047

0)

1710

69

2.40

%

1980

-200

7

-0.6

68**

* (0

.189

0)

2.96

5***

(0

.291

0)

0.24

4***

(0

.046

4)

2193

43

2.93

%

1980

-200

7

0.14

8***

(0

.036

3)

3.03

5***

(0

.290

0)

0.29

0***

(0

.045

8)

2178

84

1.13

%

1980

-200

7

0.48

0***

(0

.097

3)

0.29

0***

(0

.066

3)

-0.0

519*

**

(0.0

111)

-0

.963

***

(0.3

070)

0.

109*

* (0

.044

7)

-0.0

160*

* (0

.006

3)

2.58

7***

(0

.309

0)

0.33

3***

(0

.050

2)

1651

34

4.66

%

1980

-199

0

1.19

2***

(0

.179

0)

0.25

5**

(0.1

050)

-0

.106

***

(0.0

246)

-1

.147

* (0

.613

0)

-0.1

98**

* (0

.070

4)

0.02

76**

* (0

.008

7)

2.28

8***

(0

.444

0)

0.51

5***

(0

.101

0)

5229

5 6.

87%

2000

-200

7

0.26

5***

(0

.058

5)

0.41

2***

(0

.109

0)

-0.0

358*

**

(0.0

129)

-0

.514

***

(0.1

950)

0.

257*

**

(0.0

758)

-0

.027

1***

(0

.009

0)

2.12

2***

(0

.477

0)

0.34

8***

(0

.063

4)

5257

3 4.

02%

Not

es:

The

tab

le r

epor

ts r

esul

ts o

f pr

obit

reg

ress

ions

of

the

like

liho

od o

f be

ing

an L

BO

tar

get.

D

ata

sam

ple

is a

ll C

ompu

stat

fir

ms

1980

-200

7.

The

de

pend

ent

vari

able

equ

als

1 fo

r L

BO

tar

gets

at

the

year

of

anno

unce

men

t an

d 0

othe

rwis

e.

Acc

ount

ing

data

is

from

C

ompu

stat

, L

BO

dat

a is

fro

m

Tho

mso

n F

inan

cial

, ro

a is

EB

ITD

A t

o to

tal

asse

ts,

leve

rage

is

long

-ter

m +

sho

rt-t

erm

deb

t to

tot

al a

sset

s, m

arke

t-to

-boo

k is

yea

rly

clos

ing

pric

e to

co

mm

on e

quit

y/co

mm

on s

hare

s ou

tsta

ndin

g, s

tdev

roa

is

stan

dard

dev

iati

on o

f ro

a, t

angi

bili

ty

is n

et P

PE

to

tota

l as

sets

, m

arke

t ca

p is

in

mil

lion

s of

do

llar

s,

prob

Ind

ustr

y L

BO

is

Ind

ustr

y pr

obab

ilit

y of

LB

O,

com

pute

d pe

r ye

ar a

s th

e ra

tio

of:

1. n

umbe

r of

ind

ustr

y fi

rms

that

wer

e ta

rget

s of

LB

O

to 2

. nu

mbe

r of

ind

ustr

y fi

rms.

In

dust

ry i

s de

term

ined

at

the

3-di

git

sic

leve

l, w

here

sic

is

as r

epor

ted

in C

ompu

stat

. P

E f

unds

is

US

priv

ate

equi

ty

fund

rais

ing

as a

per

cent

age

of t

otal

US

stoc

k m

arke

t va

lue,

tak

en f

rom

Kap

lan

and

Str

ombe

rg (

2008

).

Err

ors

are

clus

tere

d at

the

fir

m l

evel

. **

***

and

* in

dica

te s

igni

fica

nce

at t

he 1

%,

5%,

and

10%

lev

els,

res

pect

ivel

y.

Tab

le 3

.5:

Lev

erag

e in

LB

O t

arge

ts (

US

1980

-200

7)

depe

nden

t:

LB

O

leve

rage

leve

rage

2

roa

stde

v ro

a

ln(

mar

ket

cap)

prob

Ind

ustr

y L

BO

PE

fu

nds

num

obs

erva

tion

s R

-squ

ared

top

25%

-0.1

08

(0.1

510)

0.60

1**

(0.2

360)

-0

.775

***

(0.2

680)

-0

.002

03

(0.0

091)

1.

914*

**

(0.5

680)

0.

602*

**

(0.0

911)

3847

7 4.

07%

25%

-75%

0.36

9**

(0.1

500)

1.30

6***

(0

.207

0)

-0.9

57**

(0

.420

0)

-0.0

443*

**

(0.0

072)

2.

674*

**

(0.3

970)

0.

221*

**

(0.0

708)

9311

5 4.

16%

low

25%

1.80

9 (1

.610

0)

0.35

4***

(0

.069

3)

-1.2

00**

* (0

.293

0)

-0.0

562*

**

(0.0

118)

2.

829*

**

(0.8

700)

0.

284*

**

(0.1

020)

4750

3 5.

22%

all

0.72

4***

(0

.152

0)

-0.7

19**

* (0

.197

0)

0.42

4***

(0

.075

8)

-1.0

31**

* (0

.266

0)

-0.0

285*

**

(0.0

053)

2.

604*

**

(0.3

070)

0.

294*

**

(0.0

487)

17

9095

4.

13%

Not

es:

The

tab

le r

epor

ts r

esul

ts o

f pr

obit

re

gres

sion

s of

the

lik

elih

ood

of b

eing

an

LB

O t

arge

t.

The

dep

ende

nt

vari

able

equ

als

1 fo

r L

BO

ta

rget

s at

the

yea

r of

ann

ounc

emen

t an

d 0

othe

rwis

e.

Acc

ount

ing

dat

a is

fro

m C

ompu

stat

, L

BO

dat

a is

fro

m T

hom

son

Fin

anci

al,

leve

rage

is

long

-ter

m

+

shor

t-te

rm d

ebt

to t

otal

ass

ets,

see

Tab

le 3

.4 f

or d

efin

itio

ns o

f ot

her

vari

able

s.

The

fin

al c

olum

n di

spla

ys r

esul

ts f

or t

he e

ntir

e sa

mpl

e, a

ll C

ompu

stat

fi

rms

1980

-200

7.

Col

umns

1-

3 di

vide

the

sam

ple

by l

ever

age

leve

ls:

the

firs

t co

lum

n pr

esen

ts e

stim

atio

n re

sult

s fo

r fi

rms

in t

op q

uart

ile

in t

erm

s of

le

vera

ge,

the

seco

nd p

rese

nts

resu

lts

for

firm

s w

ith

leve

rage

in

the

25"*

— 7

5"*

perc

enti

les

and

the

thir

d co

lum

n pr

esen

ts r

esul

ts f

or t

he b

otto

m q

uart

ile

firm

s.

Err

ors

are

clus

tere

d at

the

fir

m l

evel

. **

*,**

and

* i

ndic

ate

sign

ific

ance

at

the

1%,

5%,

and

10%

lev

els,

res

pect

ivel

y.

oi

116

Table 3.6: Model estimates of priced intensities vs. observables

pLBO

roa

stdev roa

leverage

ln( mktcap )

rating

prob Industry LBO

num observations

R-squared

LBO intensity

15.14**

(7.782)

0.215

(0.383)

-1.989***

(0.633)

-0.091

(0.167)

-0.010

(0.017)

0.0005

(0.006)

-1.467

(1.029)

298

3.75%

default intensity

-0.120***

(0.024)

0.117**

(0.054)

0.0294***

(0.011)

-0.009***

(0.001)

-0.004

(0.033)

9005

30.10%

default intensity

-0.0348**

(0.017)

0.007

(0.055)

0.0261***

(0.009)

0.001

(0.001)

0.006***

(0.001)

0.017-

(0.030)

9005

49.50%

Notes: The table reports results of regressing model estimates of priced LBO and default intensity against firm-specific observables. In the first column the dependent variable is LBO risk, corresponding to Aj ' in the model, i.e. LBO risk in the "high-risk" state. This regression is cross-sectional as this variable is constant per firm (we use averages for all regressors). In the subsequent two columns the dependent variable is default risk, corresponding to \®t(ai,/3i,cdxt) in the model (idiosyncratic component is constant, time variation is introduced through changes in the index). Data sample is over the time period 11/2004 - 9/2007. Regression is run with time fixed-effects, errors are clustered at the firm level. pLBO is predicted LBO likelihood, as estimated in a probit model in section 3.4.2. Accounting data is from Compustat, see Table 3.4 for variable definitions. Ratings are from S&P; we use a numeric scale such that higher ratings correspond to lower numeric values (e.g. AAA is denoted as 1). We include industry probability of LBO as industry-level control. ***;** and * indicate significance at the 1%, 5%, and 10% levels, respectively.

117

Table 3.7: Structural model mispricing (US 2001-2007)

mispricing

LBO intensity

leverage

maturity

convert

lsales

spread

num observations

R-squared

all

3.692**

(1.8740)

-0.0139*

(0.0072)

0.000275*

(0.0001)

-0.00406

(0.0027)

-0.0003

(0.0011)

0.385***

(0.1370)

119974

4.46%

A-BB

3.412***

(1.3400)

-0.0104**

(0.0052)

0.000152

(0.0001)

-0.00283

(0.0020)

-0.0001

(0.0008)

0.578***

(0.1560)

99851

8.99%

Notes: The table reports results of regression of mispricing from a structural model against priced LBO risk, as estimated from our model. The first column reports results for our entire CDS data sample from 2001-2007. The second column limits sample to firms rated A-BB. mispricing is as detailed in section 3.7, in percentage. LBO intensity is priced LBO risk, as estimated in section 3.6, denoted in the model as \ft. convert is a binary variable, indicating whether firm has convertible debt, maturity is average maturity of firm bonds. Bond data is from the Mergent FISD database. leverage is long-term + short-term debt to total assets, and spreads controls for level of market CDS spread (in percentage). Regression is run with time fixed-effects, errors are clustered at the firm level. ***j** and * indicate significance at the 1%, 5%, and 10% levels, respectively.

118

Table 3.8: Estimation of LBO likelihood using model mispricing

dependent: LBO

roa

leverage

market-to-book

stdev roa

tangibility

ln(market cap)

prob Industry LBO

PE funds

mispricing

market spread

num observations R-squared

Compustat 2002-2007

0.204*** (0.0530) 0.417*** (0.1240)

-0.0394*** (0.0141) -0.334* (0.1870) 0.256*** (0.0876) -0.0169 (0.0106) 2.135*** (0.5100) 0.365*** (0.0616)

37113 3.83%

CDS 2002-2007

2.186* (1.3600)

0.406 (0.4900) -0.0234 (0.0262) -6.558** (3.4620) 0.0519

(0.2270) -0.213*** (0.0620) 3.513*

(1.9240) 1.311*** (0.1920)

1863 18.00%

CDS 2002-2007

0.527 (1.5190) 1.093*

(0.6580) 0.0124

(0.0259) -2.614

(3.5610) 0.0338

(0.3820) -0.152

(0.0993) 0.602

(2.2120) 1.225*** (0.2200) 0.154*** (0.0572)

998 17.05%

CDS 2002-2007

0.181 (1.5800) 1.149*

(0.6600) 0.0171

(0.0268) -2.469

(3.5600) 0.0664

(0.3800) -0.195* (0.1120)

0.617 (2.1690) 1.200*** (0.2220) 0.216** (0.0895)

-0.13 (0.1130)

998 17.41%

Notes: The table reports results of probit regressions of the likelihood of being an LBO target firm. In the first column data sample is all Compustat firms from the years matching the CDS data time period, 2002-2007, and second column and onwards is CDS sample. The dependent variable equals 1 for LBO targets at the year of announcement and 0 otherwise. Accounting data is from Compustat, LBO data is from Thomson Financial, mispricing is model mispricing, as detailed in section 3.7, in bps and market spread is market CDS spread in bps. We use average spreads in fourth quarter of previous year, roa is EBITDA to total assets, leverage is long-term + short-term debt to total assets, market-to-book is yearly closing price to common equity/common shares outstanding, stdev roa is standard deviation of roa, tangibility is net PPE to total assets, market cap is in millions of dollars, prob Industry LBO is Industry probability of LBO, computed per year as the ratio of: 1. number of industry firms that were targets of LBO to 2. number of industry firms. Industry is determined at the 3-digit sic level, where sic is as reported in Compustat. PE funds is US private equity fundraising as a percentage of total US stock market value, taken from Kaplan and Stromberg (2008). Errors are clustered at the firm level. ***** and * indicate significance at the 1%, 5%, and 10% levels, respectively.

119

Figure 3.1: CDS distribution across ratings

AAA AA

Notes: This figure displays the distribution of the firms in our sample across the different rating classes. Rating used is S&P rating as of December 2006.

Figure 3.2: US LBO announcements 1980-2007

1980 1983 198S 1989 1992 1995 1998 2001 2004 2007

Notes: This figure displays the number (left axis) and total value (right axis, in billions of dollars) of announcements on US LBO targets over the years 1980-2007.

120

F i g u r e 3 .3 : US LBOs and default rates

800

700 -

600 -

500 -

400 -

300 -

200

100

0

1979

-Number of LBOs

-Corporate default rates (It

1982 1985 1988 1991 1994 1997 2000 2003 2006

Notes: This figure displays the number of LBOs (left axis) and default rates (right axis, in percentage) in the US in the years 1980-2007.

121

Figure 3.4: Change in prior

Drift (no jump)

-0.02 x

-0.06

-0.10

-0.14

-0 .1* -

-0.22 -

-0.26

-0.3 0 ->

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 -0.20 0.10

0.00

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 S 1

Jump

dp{H)

0 —1 1 1 1 1 1 1 1 "!~~ "~ -1 1.1 0.2 0.3 0.4 0.5 0.6 0.7 0.S 0.9 1

Notes: This figure displays the change in state probability, as a function of the prior. The top figure shows the drift downwards when no LBO is observed, and the bottom graph shows the jump upwards when an LBO is observed in the industry. For the dark-colored plot, parameter values are: XR,H = 0 008, \R,L = 0.0008, and for the light-colored plot: A*-" = 0.01, \R'L = 0.0001. Number of firms = 100.

Figure 3.5: Average market and model spreads

2 .5-1

1.5

0.5

- average market spread

-average model spread

11/9/2604 4/18/2005 9/25/2005 3/4/2006 8/11/2006 1/18/2007 6/27/2007

Notes: This figure displays the average market spread in the dataset (11/2004 — 9/2007) and the corresponding average model-generated spread. Spreads are in percentages.

122

Figure 3.6: Average spreads of LBO targets around events

-average market spread

- average model spread

4-1

3.5-

3 -

-6

Notes: This figure displays the average market and model-implied spreads (in percentages) for LBO targets in our sample around announcement months. Time interval is monthly, where month 0 is the event month.

Figure 3.7: Average spreads of same-industry firms around LBO events

- average market, spreads

-average model spreads

•S

Notes: This figure displays the average market and model-implied spreads (in percentages) for firms in the same industry as the LBO targets around announcement months. Time interval is monthly, where month 0 is the event month. Note that these are the average spreads only for industries where only a single LBO occurred in our sample (so that the time window around the event is clean of other industry LBOs).

123

Figure 3.8: Time series of state probability (example)

1.2

OJS -\

10/1/2004 4/19/2005 11/5/2005 12/10/2006 6/28/2007

Notes: This figure displays the time series of estimated pf, the probability of being in the high-risk state, for one of our sample industries (sic code 506, electrical goods). Months in which LBOs occurred in the industry are marked by circles on the graph.

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128

Appendix: Formulas for CDS pricing under CIR

This section lists closed-form expressions used in pricing of CDS contracts. At follows a CIR process:

d\t = K{6 - Xt)dt + o\[xtdWt

• Closed-form bond prices (DufEe et al., 2000):

E®[exp{- / c • Xsds)] = a(T - t, c) • exp(-/?(T - t, , c) • c • Xt

where:

a(T-t,c) = ' V 2 '

(3(T-t,c) =

( 7 + K ) - ( e a ; p ( 7 - ( T - t ) ) - l ) + (2-7)

2 • (exp(7 • (T - t)) - 1) (7 + K ) - ( e x p ( 7 - ( T - i ) ) - l ) + (2-7)

\/K2 + 2 • c • a2

In derivation of CDS spreads we use the definition:

E?[exp(- J f c • Xsds)\ = h (t, T, 9, c, eA<).

• Analytical solution for the following expression (pricing of default digital put, Schonbucher,

2003, Proposition 7.8):

EQ{Xt-exp{- f c-Xsds)] = [K-0- /3(t, c) + ^ c ) • A0] • a(t, c) • exp(-/?(t, c)-c- A0)

where a, ft and 7 are as defined above and:

d/3{t, c) 4 • 72 • exp{-y • t)

dt [(7 + is) • (exp{j . t) - 1) + (2 • 7 ) ] 2

In derivation of CDS spreads we use the definition:

E9\\t • exp( - /„' c • Xsds)\ = /2(0, i, e, c, A0, eA°).

• Analytical solution for the following expression (Lamberton & Lapeyre, 1996, Proposition

129

6.2.5):

E[exp(—d • \t) • exp(— / c-Xsds)] = exp(—K • 9 • <p(d, c, t)) • exp(—Ao • ip(d, c,t)) Jo

where:

4>{d,C,t) = - _ l o g ( — 2

tp(d, c, t) =

d • a2 • (exp(j • t) — 1) + 7 — K + exp(j • t) • (7 + K)

d • (7 + K, + exp(7 • t) • (7 — K)) + 2 • c • (ea;p(7 • i) — 1)) d • a2 • (exp(7 • t) — 1) + 7 — K + exp(7 • £) • (7 + K)

and 7 is as defined above. In derivation of CDS spreads we use the definition:

It-£[exp(-d • At) • exp ( - /* c • Xsds)} = /3(0, t, 9, d, c, eA°).

9 Analytical solution for the following expression, which encompasses all the previous formulas

as specific cases (Hurd & Kuznetsov, 2006, Theorem 3.1):

,E[exp(— / c • \sds) • exp(—d • Xt) • Xt] = Jo

exp( - K .6-t-6)-(0t+d- <5)-("+2» • tf<a+1) • exp ( - A 0 • (6 + e x p ( - 7 • t) • J ^ ^ J ) ) "

a + 1 $ t + d - <5

where:

2 - 7 #t =

5 =

a2 • (1 — exp(—7 • £)) 7 — K

7 = Vie2 + 2 • c • a2

a'2

assuming c > j ^ s , a + 2 > 0 and $ t + d — £ > 0. In derivation of CDS spreads we use the

definition: £'[exp(— J0 c • \sds) • exp(—d • Xt) • Xt] = /4(0, t, 9, d, c, AQ, eA°).

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