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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
All rights reserved
INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
UMT Dissertation Publishing
UMI 3388444 Copyright 2010 by ProQuest LLC.
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©2009
Yael Eisenthal Berkovitz
All Rights Reserved
ABSTRACT
LBO Risk in Credit Spreads
Yael Eisenthal Berkovitz
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
1.4.1 Credit Default Swaps 13
1.4.2 Improved Market Transparency 13
1.4.3 Bond Indentures 14
1.5 Event Study 16
1.5.1 Credit Default Swaps 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
3.3.1 Credit Default Swaps 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.
[Insert Figure 1.2 about here]
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 Table 1.2 about here]
[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.
[Insert Table 1.3 about here]
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.
[Insert Table 1.4 about here]
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
1.4.1 Credit Default Swaps
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.
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.
[Insert Table 1.5 about here]
1.5 Event Study
1.5.1 Credit Default Swaps
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.
[Insert Table 1.6 about here]
[Insert Figure 1.5 about here]
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.
[Insert Table 1.7 about here]
[Insert Figure 1.9 about here]
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.
[Insert Figure 1.10 about here]
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
xi a5 CD SH Q* CO
in Q O
xi
CO
CD
>
so
XJ
0
3 co
0-y
r—1
U
Q-1
>> ti
5-ye
ar
H o3
-ye
co
03
w -
Pi < bO > 03
WD
Q O bO > o3
03
1/3
+J
< bO
51
co Q U bo > o3
-t >
03
CO 1
tf < bO
S3
CO
Q U bo > 03
-8 1
- 4 ^
Pi < bjO > (A
CO
P o bO > o3
P.
s? X
CO • ^
T — I
T-H
00
o o o
o 00 • ^
CM
o CO
o T-H
CD
o o o
CM CM CM
CM
o LO co o
CD
o o o
o o o CM
CO
t-
o
CD O O o
o CM
co r-H
CD CM
LO
CO 1—1 • « *
1—1
1—1
co o ©
LO
o LO
CM
h LO LO
b -CM O
o
co -* CM
CM
-X-LO oo T—1
OS CM O O
a> ^p CO
r-H
CO i>-CO
T-H
co o o
o CM
co T-H
o CO
"5*
t~ CO CD
T—f
CM O o o
CD CM
l>-CM
* * co CM
co o o o
- 00
-tf CM
00
o LO
o
I>-o o o
t-LO T-H
CM
1.94
7*
co T-H
o o
co o LO
T"H
b -CM
co
CO
oo T—1
T 1
•<# • *
o o
Tf CO CO
CM
t>-CD T-H
T-H
l< h
O
CD
CO CD T-H
CM
oo LO T-H
T-H
LO CO
O o
• *
oo oo T-H
CM T — ;
T-H
T-H
CD
co O O
co CO T [
T [
co T-H
CM
-* CD
co T-H
l>-LO T-H
o
co l-~
oo CM
CI CM CO
T-H
o t>-T-H
o
T-H
o CO
CM
I"-LO CD
T-H
o CD T-H
o
T-H
LO T-H
CM
LO CO co T-H
CO LO T-H
O
T ^
CO • ^
T-H
CD T-H
i—1
* * *
T-H
CO
o o CM
CD
00
free CO
* * * CO
o CO
• *
o CM
CO
LO
fr-o co
-x-* * T-H
LO O co
CD 05 T-H
CD
CM T-H
CO
CM
# * * CD ~* co
T-H
CM CM
O
CD
r-00
T-H
Ol CM
o
* 03
co CM
CM
LO ->tf I — 1
o
05 LO
co CO
# * CO
CM
CM
-* CD i—l
O
CO • < *
o co
# * LO CM CO
CM
CM
co T 1
CO
co o t-<M
* * LO oo CO
CM
T-H
CO T-H
o
CM
t-t-T-H
CO CO
T-H
"* • ^
00
o
fr-T — i
p o
o • ^
co co
* CM r-H
T-H
co CM
O o
LO
oo o co
* CM
T-H
"* CM
p CO
1—1
LO
fr-CM
03
l>-co r-H
CD CM
O o
•<* CO
fr-T-H
02 CM
CM
O C5 T-H
o
CM
o o o
o en CM
CO
T-H
• ^
t-o
t-H
o o o
o LO
o co
00 CO CO
o
oo o o o 1
fr-fr-LO
CM
oo
CM T-H
1
T-H
1—1
o o
CM
o oo T-H
o CO
CO
t- LO O 00 LO O
T-H T-H
CM CO --H O
o o o o
CO L--l>- LO TJH O
co co
co co T-H CO CD CD
T-H O
CM CO T-H O
o o o o
CM CO 00 LO r-H I>-
CO CM
S3 o> CM 0
<=; co
9 <-*
05 05 O CM
o o o o
LO CD LO f~ t- co CM CM
LO O co oo LO OO
T-i CO
CO T-H T-H CM
p o o o
O T-H
co co 00 Tj<
T-H T-H
t- CD CM T-H
•># LO
CO
Q O
o o3
CO CJ
a 03 o
eg '3
b£>
o3
o
CO
a o3
C M
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.
[Insert Figure 2.1 about here]
2.2 LBO risk at the industry level
2.2.1 Data
Data on LBO announcements are retrieved from Thomson One Banker. A deal is
considered 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 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
[Insert Table 2.1 about here]
[Insert Figure 2.2 about here]
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.
[Insert Table 2.2 about here]
[Insert Figure 2.3 about here]
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).
[Insert Table 2.5 about here]
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.
[Insert Table 2.6 about here]
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.
[Insert Table 2.7 about here]
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.
3.3.1 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
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.
[Insert Table 3.1 about here]
[Insert Figure 3.1 about here]
3.3.2 LBO announcements
Data on LBO announcements are retrieved from Thomson One Banker. A deal is
considered 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
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.)
[Insert Table 3.2 about here]
[Insert Figure 3.2 about here]
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.
[Insert Table 3.3 about here]
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).
[Insert Figure 3.3 about here]
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.
[Insert Figure 3.4 about here]
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]
[Insert Table 3.6 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.
[Insert Table 3.7 about here]
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.
[Insert Table 3.8 about here]
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.
Bibliography
Asquith, P., and T. A. Wizman. 1990. Event Risk, Covenants, and Bondholder Returns in Leveraged Buyouts. Journal of Financial Economics 27(1):195-213.
Avramov, D., G. Jostova and A. Philipov. 2006. Corporate Credit Risk Changes: Common Factors and Firm-Level Fundamentals. AFA 2005 Philadelphia Meetings.
Billett, M., Z. Jiang and E. Lie. 2008. The Role of Bondholder Wealth Expropriation in LBO Transactions. Working Paper, University of Iowa.
Billett, M., T.D. King and D. Mauer. 2004. Bondholder wealth effects in mergers and acquisitions: New evidence from the 1980s and 1990s. Journal of Finance 59, 107-135.
Black, F. and J. Cox. 1976. Valuing corporate securities: Some effects of bond indenture provisions. Journal of Finance 31, 351-367.
Blanco, R., S. Brennan, and I. Marsh. 2005. An Empirical Analysis of the Dynamic Relationship between Investment Grade Bonds and Default Swaps. Journal of Finance 60, 2255-2282.
Brown, S. and J. Warner. 1980. Measuring security price performance. Journal of Financial Economics 8, 205-258.
Bradley, M., A. Brav, I. Goldstein and W. Jiang. 2007. Shareholder Activism and Price Dynamics: Evidence from Closed-End Funds. Working Paper.
Brown, S. and J. Warner. 1980. Measuring security price performance. Journal of Financial Economics 8, 205-258.
Collin-Dufresne, P., R. S. Goldstein. 2001. Do credit spreads reflect stationary leverage ratios? Journal of Finance 56, 1929-1957.
Collin-Dufresne, P., R. S. Goldstein, and J. Helwege. 2003. Is credit event risk priced? Modeling contagion via the updating of beliefs. Working Paper, Ohio State University.
Collin-Dufresne, P., R. S. Goldstein, and J. S. Martin. 2001. The Determinants of Credit Spread Changes. Journal of Finance 56, 2177-207.
Cook, D., J. Easterwood, and J. Martin. 1992. Bondholder wealth effects of management buyouts. Financial Management 21, 102-113.
124
125
[14] Crabbe, L. 1991. Event risk; An analysis of losses to bondholders and "super poison put" bond covenants. Journal of Finance 46, 689-706.
[15] Cremers, M., V.B. Nair and K. John. 2008. Takeovers and the cross-section of returns. Review of Financial Studies forthcoming.
[16] Delianedis, G. and R. Geske. 2001. The components of corporate credit spreads: Default, recovery, tax, jumps, liquidity and market forces. Working Paper 22-10, UCLA.
[17] Duffie, D. and K. Singleton. 2003. Credit Risk, Princeton University Press, Princeton.
[18] Eom, Y., J. Helwege, and J.-Z. Huang. 2004. Structural Models of Corporate Bond Pricing: An Empirical Analysis. Review of Financial Studies 17, 499-544.
[19] Ericsson, J., J. Reneby and H. Wang. 2005. Can Structural Models Price Default Risk? New Evidence from Bond and Credit Derivative Markets. EFA 2005 Moscow Meetings Paper.
[20] Franks, J. R., and W. Torous. 1989. An Empirical Investigation of U.S. Firms in Reorganization. Journal of Finance 44, 747-769.
[21] Huang, J.-Z. and M. Huang. 2003. How Much of the Corporate-Treasury Yield Spread is Due to Credit Risk: A New Calibration Approach. Working Paper, Penn State University.
[22] Hull, J., M. Predescu and A. White. 2004. The relationship between credit default swap spreads, bond yields, and credit rating announcements. Journal of Banking and Finance 28, 2789-2811.
[23] Gompers, P., J.L. Ishii and A. Metrick. 2003. Corporate Governance and Equity Prices. Quarterly Journal of Economics 118, 107155.
[24] Guo, S., E.S. Hotchkiss and W. Song. 2008. Do buyouts (still) create value? Working Paper, Boston College.
[25] Jarrell, G. A., Brickley, J. A. and J. M. Netter. 1988. The market for corporate control: The empirical evidence since 1980. Journal of Economic Perspectives 2, 49-68.
[26] Jensen, M. 1986. Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers. American Economic Review, 76, 323-329.
[27] Jones, E. S. Mason and E. Rosenfeld. 1984. Contingent claims anlaysis of corporate capital structures: An empirical analysis. Journal of Finance, 39, 611-625.
[28] Kaplan, S.N. 1989a. The effects of management buyouts on operating performance and value. Journal of Financial Economics 24, 217-254.
[29] Kaplan, S.N. 1989b. Management buyouts: Evidence on taxes as source of value. Journal of Finance 44, 611-632.
[30] Kaplan, S.N. 1991. The staying power of leveraged buyouts. Journal of Financial Economics 29, 287-313.
[31] Kaplan, S.N. 1993. The staying power of leveraged buyouts. Journal of Applied Corporate Finance 15-24.
126
[32] Kaplan, S.N. and P. Stromberg 2008. Leveraged buyouts and private equity. NBER Working Paper No. 14207.
[33] Lehn, K., Netter, J. and A. Poulsen. 1990. Consolidating Corporate Control: The Choice Between Dual-Class Recapitalizations and Going Private Transactions. Journal of Financial Economics, 27, 557-580, October 1990.
[34] Lehn, K. and A. Poulsen. 1988. Leveraged Buyouts: Wealth Created or Wealth Redistributed. Public Policy Toward Corporate Takeovers, 46-62, edited by Murray Weidenbaum and Kenneth Chilton, Transaction Publishers: New Brunswick, NJ.
[35] Lehn, K. and A. Poulsen. 1989. Free cash flow and stockholder gains in going private transactions. Journal of Finance 44, 771-789.
[36] Lehn, K. and A. Poulsen. 1991. Contractual resolution of bondholder-stockholder conflicts in leveraged buyouts. Journal of Law and Economics 34, 645-673.
[37] Longstaff, F., S. Mithal, and E. Neis. 2005. Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Default Swap Market. Journal of Finance 60, 213-2254.
[38] Longstaff, F. and J. Schwartz. 1995. Valuing risky debt: A new approach. Journal of Finance, 789-820.
[39] Lowenstein, L. 1985. Management buyouts. Columbia Law Review 85, 730-784.
[40] Marais, L., Schipper, K. and A. Smith. 1989. Wealth effects of going private for senior securities. Journal of Financial Economics 23, 155-191.
[41] Maupin, R. 1987. Financial and stock market variables as predictors of management buyouts. Strategic Management Journal 8, 319-327.
[42] Merton, R. 1974. On The Pricing of Corporate Debt: The Risk Structure of Interest Rates. Journal of Finance 29, 449-470.
[43] Micu, M., E. Remolona and P. Wooldridge. 2006. The price impact of rating announcements: which announcements matter?. BIS Working Paper 207.
[44] Mitchell, M. and H. Mulherin. 1996. The Impact of Industry Shocks on Takeover and Restructuring Activity. Journal of Financial Economics, 193-229.
[45] Muscarella, C.J., Vetsuypens, M.R. 1990. Efficiency and organizational structure: A study of reverse LBOs. Journal of Finance 45, 1389-1413.
[46] Opler, T. and S. Titman. 1993. The Determinants of Leveraged Buyout Activity: Free Cash Flow vs. Financial Distress Costs. Journal of Finance 48(5), 1985-1999.
[47] Pan, J. and K.J. Singleton. 2008. Default and Recovery Implicit in the Term Structure of Sovereign CDS Spreads. Journal of Finance 63(5), 2345-2384.
[48] Protter P. 2005. Stochastic Integration and Differential Equations, 2nd edition, Springer-Verlag, 2005.
[49] Shleifer, A., Summers, L.H., 1988. Breach of trust in hostile takeovers. In: Auer-bach, A.J. (Ed.), Corporate Takeovers: Causes and Consequences, The University of Chicago Press, Chicago, IL.
127
[50] Slovin, M.B., M.E. Sushka, and Y.M. Bendeck. 1991. The Intra-Industry Effects of Going-Private Transactions. Journal of Finance, September, 1537-1550.
[51] Smith, A.J. 1990. Corporate ownership structure and performance: The case of management buyouts. Journal of Financial Economics 27, 143-164.
[52] Warga, A. and I. Welch. 1993. Bondholder Losses in Leveraged Buyouts. Review of Financial Studies 6:959-82.
[53] Wei, C. 2005. Covenant protection, credit spread dynamics, and managerial incentives. NYU Working Paper.
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°).