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How large are the coinsurance benefits of mergers?

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How large are the coinsurance benefits of mergers?

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Content HOW LARGE ARE THE COINSURANCE BENEFITS OF MERGERS?
by
SAKYA SARKAR

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)
May 2015

Dedication
I am grateful to God for everything.
I thank my father, Santi Ranjan Sarkar, for encouraging me to ask questions, patiently
answering my questions, and for enabling my pursuit of the un-answered questions.
I thank my mother, Dr. Kalyani Sarkar, also my first teacher, for always encouraging and
supporting my education.
I thank my sister, Koushiki Sarkar, for her support and encouragement.
I thank my fiancée, Archana Vinze, for her patient support and generous understanding.

ii

Acknowledgements
A dissertation is often viewed as a solitary accomplishment, a scholarly tour de force that
elevates the apprentice of knowledge to the ranks of its producer; yet, in reality, it is a
collective effort. All those who has made my dissertation possible, without sounding
platitudinous, I would like to thank them. I am grateful to my Dissertation Chair, John
Matsusaka, for his valuable advice and inspiration; without his advice, my scholarship would
have ended years back, and this dissertation would not be possible. I thank Oguzhan Ozbas,
whose research has inspired me. I thank Scott Joslin, who generously shared many hours to
improve my dissertation. I appreciate critical, helpful comments from Kenneth Ahern. I
appreciate how carefully Maria Ogneva, Yongxiang Wang, Arthur Korteweg, and Daniel
Carvalho read many drafts of my paper, and offered useful comments. I am grateful to Lee
Cerling, who taught me that good writing requires not only certitude of grammar and breadth
of diction, but also powerful rhetoric. I also thank Aris Protopapadakis for his advice since the
early years of my PhD. I thank my colleagues Garrett Swanburg, Pramod Kumar, Derek
Horstmeyer, and Zhishan Guo. I also thank my former teachers, who, directly or indirectly,
influenced my scholarship: Satadal Das, Debabrata Das, and Supriyo Ghosh. I appreciate
comments from seminar participants at University of Southern California, Indian School of
Business, and Cornerstone Research. I acknowledge funding from USC Marshall School of
Business.

iii

Table of Contents
Dedication ii
Acknowledgements iii
Table of Contents iv
List of Tables vi
List of Figures vii
Abstract viii
Chapter 1: Introduction 1
Chapter 2: Literature Review on Coinsurance 8
A. The 1970-77 Period 8
B. The Post 2000 Period 11
C. Some Open Questions 13
Chapter 3: A Structural Model for Estimating Coinsurance Benefits 16
A. Valuing the Standalone (Pre-Merger) firms 16
B. Valuing the Merged firm 18
C. Parameter Estimation 21
Chapter 4: Data 23
A. Sample Construction 23
B. Summary Statistics 24
C. Inputs to the Structural Model 26
Chapter 5: Estimated Coinsurance Benefits 28
A. Coinsurance Benefits for the Sample of Mergers 28
iv

B. Counterfactual Coinsurance Benefits for the Randomly Paired Firms 29
C. Coinsurance and Corporate Diversification 32
Chapter 6: Cross Sectional Distribution of Coinsurance 35
A. Coinsurance for Target and Acquirer 35
B. Proxies for Coinsurance 38
Chapter 7: The Stock Price Response to Coinsurance 40
A. Coinsurance Benefits and Announcement Returns 40
B. Stock Price Response to Coinsurance in Diversifying Mergers 41
C. Robustness Tests 43
Chapter 8: Conclusions 45
References 47
Appendices 51
A. Appendix: Variable Description 51
B. Appendix: Monte Carlo Simulations 53
C. Appendix: Out-of-Sample Performance of Structural Model 54
D. Appendix: Accounting Based Measures of Coinsurance Benefits 57
Tables 60
Figures 76

v

List of Tables
Table 1: Summary Statistics: Mergers and Randomly Paired Firms 60
Table 2: Inputs to the Structural Model 61
Table 3: Estimated Coinsurance Benefits for the Sample of Mergers 62
Table 4: Estimated Coinsurance Benefits for the Sample of Randomly Paired Firms 63
Table 5: Coinsurance and Merger Likelihood 64
Table 6: Estimated Coinsurance Benefits for Related and Diversifying Mergers 65
Table 7: Estimated Coinsurance Benefits Compared to Target and Acquirer Size 66
Table 8: Default Probabilities 67
Table 9: Correlation with Estimated Coinsurance Benefit 68
Table 10: Regression of Cumulative Abnormal Return 69
Table 11: Regression of Cumulative Abnormal Return for Related and Diversifying Mergers 70
Table 12: Regression of Cumulative Abnormal Return: Robustness Tests 71
Table 13: Out-of-Sample Performance of Structural Model in Predicting Bankruptcy 72
Table 14: Probability of Default: Ex-ante and Ex-post 73
Table 15: Altman Z” Scores for Target, Acquirer, and Merged Firm 74
Table 16: Default Probabilities a from Altman Z” Scores 75

vi

List of Figures
Figure 1: Distribution of Coinsurance for Mergers and Randomly Paired Firms 76
Figure 2: Distribution of Coinsurance for Related and Diversifying Mergers 77
Figure 3: Coinsurance Index as a Proxy for Coinsurance Benefits 78
Figure 4: Cash Flow Correlation as a Proxy for Coinsurance Benefits 79

vii

Abstract
Using a structural model, I estimate the value gain from coinsurance for a sample of
mergers. For most mergers, estimated gains from coinsurance are small—smaller than
the counterfactual gains if firms were to merge randomly—suggesting that
coinsurance is not the primary motivation for most mergers. Coinsurance from
diversifying mergers is also small, comparable to related mergers. Even though
coinsurance is small compared to combined target and acquirer firm value, it is not
small when compared to deal size, or target size. For a quarter of the sample, targets
are at significant risk of default without the merger; their default risk reduces
significantly due to the merger; coinsurance is thus substantial for mergers involving
highly levered targets. Coinsurance is also substantial for mergers between small
firms, or for mergers between firms of similar size. The cumulative abnormal return
around merger announcement increases 0.89% for every 1% estimated value gain
from coinsurance, suggesting that stockholders benefit from coinsurance.

viii

Chapter 1: Introduction
Coinsurance is the idea that when two firms with imperfectly correlated cash flows merge,
it reduces their probability of default, because when a negative shock hits one division of the
merged firm, the other division rescues it by transferring cash (Lewellen (1971)). In theory this
reduction in probability of default increases firm value—by reducing deadweight costs of
financial distress and increasing tax benefits of debt.
Coinsurance is regarded as an important motive for mergers, particularly for diversifying
mergers, as evidenced by its extensive coverage in academic papers, textbooks (Ross,
Westerfield and Jaffe (2008), Brealey, Myers, and Allen (2006)), and review papers (Khanna and
Yafeh (2007), Maksimovic and Phillips (2013)).
1
Other than diversifying mergers, coinsurance is
advanced as a rationale for business group affiliation (Khanna and Yafeh (2005), Fisman and
Wang (2010), Jia, Shi and Wang (2013) and Luciano and Nicadano (2014)).
Despite the large literature on coinsurance, no study has directly estimated how much
value coinsurance creates in mergers. Previous studies estimated coinsurance indirectly, using
cash flow correlation as a proxy (Duchin (2010), Hann, Ogneva and Ozbas (2013)). But, not only
correlation, coinsurance also depends on factors such as leverage, size, and volatility (Leland
(2007)). Moreover, low correlation can increase value through channels other than avoidance
of default—the primary channel for coinsurance—by avoiding costly external financing of
investment (Froot, Scharfstein and Stein (1993)), or by winner-picking (Stein (1997)).
1
Some recent work includes Penas and Unal (2004), Billet, King and Mauer (2004), Leland (2007), Kuppuswamy
and Vilalonga (2010), Banal-Estañol, Ottaviani and Winton (2013), Hann, Ogneva and Ozbas (2013), Duchin (2010),
Fulghieri and Sevilir (2011), Martos-Vila, Rhodes-Kropf and Harford (2013), Matvos and Seru (2014).
1

In contrast to these previous studies, this paper uses a structural model—that takes into
account not only correlation, but also leverage, size, and volatility—to estimate coinsurance
benefits. The structural model is a trade-off model of capital structure (Leland (1994)),
customized to account for correlated cash flows. The model estimates default probability for
two firms from their stock prices; it also predicts the default probability if these two firms
merge. The model then translates this reduction in default probability through the merger to
coinsurance benefits—the value gained from coinsurance—by discounting gains from lower
cost of distress and higher tax benefits of debt. Because the structural model uses only pre-
merger information to estimate post-merger firm value, the coinsurance benefits are identified:
the estimates are not contaminated with operational synergies, as would be the case if a part of
the actual value change from the merger were to be ascribed to coinsurance.
2
Using the
structural model, coinsurance is estimated for a sample of 1,884 mergers in the United States
between 1981 and 2013.
I find that coinsurance benefits, for both related and diversifying merger, is small. The
mean coinsurance benefit is 0.32% of the target and acquirer’s combined pre-merger value;
only 174 of the 1,884 mergers produced coinsurance exceeding 1%.
3
For comparison, mean
coinsurance benefits are about one-fifth the size of abnormal announcement returns. The
estimated coinsurance benefits remain small, even with high deadweight cost parameters.
2
The value gain (loss) from the merger may come from various other sources: operational synergies, asset
complementarities, agency problems. Without a model, it is difficult to decouple the value gain and attribute a
part to coinsurance. Moreover, if coinsurance reduces default risk, then firms should increase their leverage after
the merger, again increasing the default risk, rendering any comparison of default risks before and after the
merger unfruitful. The structural model bypasses these problems by using only pre-merger information.
3
Coinsurance, though small, is always positive.
2

Although coinsurance from most mergers are small, given that coinsurance has been
traditionally advocated as a rationale for corporate diversification, it could still be the case that
coinsurance from diversifying mergers are large (Lewellen (1971), Hahn, Ogneva and Ozbas
(2013), Duchin (2010)). But, even from diversifying mergers, estimated coinsurance benefits are
small. Comparable to coinsurance from related mergers. In light of this finding, it seems unlikely
that diversifying mergers are pursued for coinsurance.
Though the structural model estimates coinsurance benefits to be small, it does not
immediately follow that coinsurance is unimportant: it is possible that the structural model
underestimates coinsurance benefits.
4
In particular, coinsurance from mergers could still be
large relative to the counterfactual coinsurance if firms were to merge randomly. So, as a
benchmark, I randomly draw firms from COMPUSTAT and pair them, drawing 1,884 random
pairs over the same time period as the mergers sample. The coinsurance benefits from
mergers are smaller than the counterfactual coinsurance benefits from these random pairings.
Reinforcing this result, a logistic regression of merger incidence on coinsurance produces a
negative slope: coinsurance does not make firms more likely to merge.
4
In order to assess whether the structural model used in this paper is reliable, I take the following steps. First, in
Appendix C, I evaluate the performance of the structural model in predicting defaults out-of-sample. The model
predicts default well, even after controlling financial indicators like size, leverage, volatility or correlation; this
demonstrates that the model captures important non-linearities that predict default, but would be missed by a
reduced form approach. Second, as an alternative modeling choice, I estimate coinsurance benefits using Merton’s
(1974) model (unreported). Finally, I adopt an alternative accounting approach, using Altman Z’’-scores to estimate
coinsurance benefits (Altman (1997)), which is presented in Appendix D. To the extent that these approaches differ
in their assumptions, the estimated coinsurance benefits are different. Yet despite their differences, these
approaches agree so far as the main findings about coinsurance are concerned.

3

Even though coinsurance is small compared to the total firm value, it is not small when
compared to the target value (which is comparable to the deal size). Compared to target value
(bond + stock), over a quarter of the sample produces coinsurance in excess of 3%; and for 10%
of mergers, coinsurance exceed 6%. Compared to target size then coinsurance is not small—it is
possible that coinsurance can substantially benefit targets.
To learn whether coinsurance benefits targets and acquirers differentially, I compute their
default probabilities—over a ten year horizon, under the risk neutral measure—under
separation and merger. Whereas few acquirers have any tangible default risk, over a quarter of
targets are at a high risk of default without the merger. These targets benefit significantly from
the merger—through coinsurance—their default risk drops significantly. On the contrary, most
acquirers, at low risk of default even before the merger, experience little risk-reduction through
coinsurance.
To identify high-coinsurance mergers ex-ante—177 of the 1,884 mergers produce
coinsurance benefits exceeding 1% of pre-merger combined value—I construct a coinsurance
index of accounting variables, that can be easily computed without having to estimate a
structural model. Since we don’t have a closed-form solution for coinsurance benefits, this
index is constructed numerically, by selecting the variables using LASSO, a popular statistical
method for parsimonious model selection (Tibshirani (1996)). The coinsurance index loads
positively on relative size: coinsurance is large when small targets are acquired; it also loads
positively on target leverage (squared): coinsurance is large when a highly levered target that
would otherwise head towards distress is acquired; it loads negatively on acquirer size:
coinsurance is high when small firms merge.
4
Notably, the in-sample correlation with estimated coinsurance benefits is 67%. In
comparison, the correlation is 2% for cash flow correlation: the traditional proxy for
coinsurance. This raises questions about using cash flow correlation as a proxy for coinsurance.
The structural model estimates the total value gain from a merger. But, how are
coinsurance benefits apportioned between the bondholders and equity holders? While
significant empirical evidence suggests that bondholders benefit from coinsurance (Billet, King
and Mauer (2004), Penas and Unal (2004)), there is little evidence that equity holders benefit
from coinsurance. Instead, the literature asserts that equity holders suffer from coinsurance,
because the reduction in asset volatility due to coinsurance transfers value from equity holders
to bondholders (Galai and Masulis (1976), Higgins and Schall (1975), Mansi and Reeb (2002)). So
ingrained is the notion that equity holders suffer from coinsurance, that MBA textbooks
routinely label coinsurance as a deleterious reason to merge:
This mutual guarantee, which is called the coinsurance effect, makes the debt
less risky, and more valuable than before. There is no net benefit to the firm as a
whole. The bondholders gain the coinsurance effect, and the stockholders lose
the coinsurance effect. (Ross, Westerfield, and Jaffe (2008), Edition 8, page 825)
In contrast to this view, I find that, using the direct estimates of coinsurance benefits from
the structural model, the equity holders benefit from coinsurance. For every 1% estimated
coinsurance benefit, the cumulative abnormal announcement return increases by 0.89%. These
regression results are statistically significant at the 1% level after including standard controls.
The results are also robust to portioning the sample in various dimensions. This result suggests
that when coinsurance benefits are large it can be a valid motive for mergers.
5
My paper is closely related to Leland (2007), who also models the financial synergies from
mergers, but using a two-period tradeoff cost framework. A theory paper, Leland calibrates the
financial synergies for some typical values of parameters; unlike this paper, he does not
estimate financial synergies for any actual sample of mergers. Leland calibrates financial
synergies to be small for reasonable parameter values. In this, my paper agrees with Leland
(2007), but mine goes a step further: it documents that coinsurance from mergers is smaller
than coinsurance from random pairings. Another conclusion from Leland (2007) is that,
contrary to Lewellen (1971), financial synergies can be negative for some parameter
realizations: this he advances as a rationale for spinoffs. In contrast, I find that, empirically,
coinsurance benefits are always positive—even though there is nothing in my model that
mechanically restricts them to be positive.
How does this paper reconcile with the several previous papers that document the
importance of coinsurance ((Billet, King and Mauer (2004), Penas and Unal (2004) Duchin
(2010), Hann, Ogneva and Ozbas (2013))? In this paper, following the theoretical work on
coinsurance (Lewellen (1971), Leland (2007), and Banal-Estañol, Ottaviani and Winton (2013)),
the benefits from coinsurance stems solely from reduction of default probability—and the
consequent avoidance of deadweight costs and loss of tax shield associated with default. But,
even though avoidance of deadweight costs of distress is perceived as the primary benefit from
coinsurance, as Hann, Ogneva and Ozbas (2013) argue, coinsurance benefits may also stem
from “adverse selection and transaction costs of external finance and resulting investment
distortions, forgone business opportunities due to defections by important stakeholders such as
suppliers, customers, or employees, and so on”. What this paper does then is to show that, if
6
at all coinsurance benefits are substantial, as is argued by the previous literature, then its
benefits stem not from default avoidance, but, perhaps, from those “secondary” channels.

7
Chapter 2: Literature Review on Coinsurance
The literature on coinsurance may be divided into two periods. The first period spans
roughly from 1970-1977. Research in this period was perhaps a reaction to the great
conglomerate merger movement in the 1960’s. There was some lull in this literature during the
1980’s and 1990’s (with the possible exception of Stapleton (1982) and Shastri (1990)). Interest
revived in the mid 1990’s, in part driven by the finding that conglomerates sell at a discount
compared to a benchmark of single-segment firms (Lang and Stulz (1994), Berger and Ofek
(1995)).
A. The 1970-77 Period
The decade of 1960s were a period of intense corporate diversification: about 54% of all
mergers from 1950 to 1980 were diversifying (Aklubut and Matusaka (2010)); conglomeration
peaked between 1968 and 1970, with 70% of mergers in this period diversifying. This
preponderance of diversifying mergers presented a great puzzle to the contemporary
economists—diversification ran counter to the virtues of specialization known since Adam
Smith. Diversifying mergers did not seem to fit into the contemporary theories of merger:
economies of scale, operational synergies, or desire to acquire market power. Reflecting the
contemporary puzzlement, Posner (1972) called this “the puzzle of the conglomerate
corporation.” It is this theoretical gap that led to the search for purely financial theories of
diversifying mergers.
Seeking a purely financial theory to explain diversifying mergers, researchers invoked the
portfolio diversification theory of Markowitz (1952). They argued that conglomerates, like
8
mutual funds, benefit investors by reducing risk through diversification. But this theory did not
hold up to scrutiny: it was pointed out that investors can corner diversification benefits at a
much cheaper price, by building a paper portfolio of stocks, rather than actually combining the
firms (Levy and Sarnat (1970)).
It was in this context that Lewellen wrote his seminal paper on coinsurance. Lewellen
(1971) argued that combining firms with less than perfectly correlated cash flows reduces
default probability; reduced default probability lowers marginal cost of debt, enabling firm to
take more debt, thus increasing their debt capacity; increased debt capacity benefits firms by
reducing their tax bills, increasing firm value.
Lewellen’s (1971) argument that coinsurance increases value was soon challenged. Galai
and Masulis (1976) argued that even if coinsurance benefits bond holders by reducing default
risk, any gain to bondholders came at the expense of stock holders. They came to this
conclusion by using the Black Scholes (1973) framework of option pricing, where equity is
modeled as a call option on firm value. In their framework, which assumed costless bankruptcy
and no taxes, any value gain for bondholders due to reduction in volatility—because of
coinsurance—is at the expense of stockholders. They were supported by Higgins and Schall
(1975), who independently, using a different model, reached at the same conclusion that
coinsurance is a zero-sum game. This argument by Galai and Masulis (1976) and Higgins and
Schall’s (1975)—that coinsurance hurts stockholders—is routinely disseminated through MBA
textbooks, which prescribe managers should eschew coinsurance mergers because they hurt
stockholders, whose interests they are supposed to represent.
9
The notion that coinsurance hurts stockholders rely on the assumption of no frictions:
costless bankruptcy and no taxes. However, in an environment with frictions, coinsurance may
benefit stockholders. Assuming transactions cost of bankruptcy, coinsurance may, theoretically,
increase combined firm value (Galai and Masulis (1976)). In presence of corporate taxes,
coinsurance can increase total firm value, by increasing debt capacity. But, whether it can also
increase shareholder wealth depends upon whether the value of the increased tax subsidy
exceeds the cost of the coinsurance to equity. Even in the case of costly bankruptcy,
conglomerate merger does not consistently increase equity values—and can actually reduce
them (Higgins and Schall (1975)). In general, the effect of conglomerate merger on aggregate
firm value—and also equity value—depends upon the nature of the costs, their probability of
occurrence, and the manner in which investors value risky streams.
One way stockholders can benefit, neutralizing some of the value transfer to bondholders,
is by issuing cheaper debt and taking advantage of the increased debt capacity. Whether this
happens in reality is an empirical question. One early empirical paper, Kim and McConell
(1977), documents that firms do increase leverage following conglomerate mergers.
Even though these papers argue that coinsurance increases debt capacity, they model debt
as exogenous: they do not explicitly solve for the post-merger optimal debt. Scott (1977)
obtains the optimal post-merger debt, using state-dependent preferences. He shows that a
firm's optimal level of debt after merger can either rise or fall—coinsurance does not always
increase debt capacity. Scott further demonstrates theoretically that even though the idea of
coinsurance is about reducing default risk, reducing default risk does not necessarily increase
firm value. A merger between a large stable firm and a small, profitable, but unstable firm may
10
tend to reduce the present value of future bankruptcy costs and thus increase value;
conversely, a merger between a small stable firm and a large volatile one may reduce value by
increasing the present value of future bankruptcy costs. There may be situations where a
merger is unprofitable even though it reduces the joint probability of bankruptcy. The
probability of bankruptcy is thus an inappropriate metric of the profitability of a conglomerate
merger.
The effect of reduction on default risk through mergers is studied by Amihud and Lev
(1981), an influential paper, though not considered as part of the literature on coinsurance.
Amihud and Lev (1981) argue that reducing default risk may be more attractive to managers
than investors: corporate diversification may reduce firm-specific human capital risk of
managers, protecting them from losing their jobs when their firms shut down (Amihud and Lev
(1981)). Coinsurance then can motivate managers to undertake diversifying mergers, even
though such mergers may not benefit stockholders or bondholders.
B. The Post 2000 Period
There was some lull in the coinsurance literature during the 1980’s and the 1990’s, with
the possible exception of Stapleton (1984) and Shastri (1992). Interest revived in the new
millennium, in part driven by the bourgeoning literature on diversification discount: the finding
that conglomerates sell at a discount compared to a benchmark of single-segment firms (Lang
and Stulz (1994), Berger and Ofek (1995)).
The idea that coinsurance can help bondholders found empirical support in the 2000s. In
an empirical work, Billet, King and Mauer (2004) examine the wealth effects of mergers on
target and acquiring firm bondholders in the 1980s and 1990s. They find that below investment
11
grade target bonds earn significantly positive announcement period returns—this they
attribute to coinsurance. In contrast, they find, acquiring firm bonds earn negative
announcement period returns. Additionally, target bonds have significantly larger returns when
the target's rating is below the acquirer's, when the combination is anticipated to decrease
target risk or leverage, and when the target's maturity is shorter than the acquirer's.
Another paper investigating the effect of coinsurance on bond returns is Penas and Unal
(2004). Studying banking industry mergers, they find that the adjusted returns of merging banks
bonds are positive and significant across pre-merger and announcement months. They argue
that the primary determinants of merger-related bondholder gains are diversification gains,
gains associated with achieving too-big-to-fail status, and, to a lesser degree, synergy gains.
Consistent with coinsurance increasing debt capacity, they find that acquirers benefit from the
lower cost of funds on post-merger debt issues.
A recent paper, Furfine and Rosen (2011), investigate empirically if mergers reduce default
risk, by using KMV Moody’s default likelihood data. They find that, on average, mergers
increase the default risk of the acquirer—they conclude that managerial motivations determine
post-merger risk characteristics.
It is commonly thought that diversification reduces unsystematic risk (Ross, Westerfield,
and Jaffe (2008)). Coinsurance can reduce systematic risk through the avoidance of
countercyclical deadweight costs—Hahn, Ogneva and Ozbas (2013) theoretically demonstrate
by using a stylized model. Consistent with a coinsurance reducing systematic risk, empirically,
they find that diversified firms—whose segment cash flows are less correlated—have a lower
cost of capital; this effect is stronger for financially constrained firms. They estimate the
12
magnitude of value gain from coinsurance to be economically significant: an average value gain
of approximately 5%, ceteris paribus, when moving from the highest to the lowest cash flow
correlation quintile.
Most papers since Lewellen (1971), except Scott (1977)), estimate the coinsurance gain
assuming the pre-merger leverage as given and not necessary optimal; such models are often
non-equilibrium models. In an equilibrium framework, Leland (2007) examines the costs and
benefits of financial synergies from mergers. He considers activities with non-synergistic
operational cash flows, and examines the purely financial benefits of separation versus merger,
by using a two period equilibrium tradeoff model. In his model, the magnitude of financial
synergies depends upon tax rates, default costs, relative size, and the riskiness and correlation
of cash flows. He finds that financial synergies from mergers can be negative if firms have quite
different risks or default costs—he interprets this to explain the preponderance of structured
finance techniques such as asset securitization and project finance.
C. Some Open Questions
Our knowledge of coinsurance from the previous literature maybe summarized as follows.
Theoretically, coinsurance, or so-called financial synergies from mergers, may be positive or
negative depending on a host of factors such as tax rates, default costs, relative size, and the
riskiness and correlation of cash flows. Coinsurance can reduce systematic risk through the
avoidance of countercyclical deadweight costs—this can reduce cost of capital and increase
value of the firm (Hahn, Ogneva and Ozbas (2013)). Coinsurance benefits are thought to be
smaller for acquisitions by larger firms, as larger firms are already diversified. Theoretically
coinsurance should benefit bondholders in most situations, but coinsurance benefits can flow
13
to stockholders in some situations. There is strong empirical support to the claim that
coinsurance benefits bondholders, but there is little empirical evidence that coinsurance helps
stockholders; if anything, the dominant discourse of this literature is that coinsurance benefits
bondholders at the expense of stockholders.
Some of the major open questions in this literature may be summarized as below. First,
what is the magnitude of coinsurance benefits from mergers? None of the existing papers
attempt to directly quantify the size of the coinsurance benefits; instead, most papers have
used cash flow correlation as a measure of coinsurance benefits. As early as 1975, Higgins and
Schall cautioned against this approach, noting that a specific valuation equation must be used
in order to predict the consequences of merger on firm values. Moreover, as Shastri (1992) and
Leland (2007) point out, coinsurance benefits are also likely to be influenced by firm specific
characteristics like leverage, size, asset specificity, as well as macroeconomic factors. Thus any
analysis which tells us how big coinsurance benefits are, will be new and important: it will tell
us if coinsurance benefits are large enough to drive mergers.
Estimating coinsurance benefits can also answer a second question: which mergers
produce high coinsurance? Traditionally coinsurance is thought to be important for diversifying
mergers. And there is some evidence that coinsurance depends on size: Demsetz and Strahan
(1994) argue large bank holding companies are better diversified than their small counterparts;
Grass (2010) finds that the degree of risk reduction due to coinsurance depends on firm size—
large acquirers don’t benefit as much from coinsurance as small firms. Estimating coinsurance
in a sample of mergers can tell us in the data which mergers are richer in coinsurance: is it the
diversifying mergers, mergers by small firms, or something else that produces high coinsurance.
14
Thirdly, do stockholders benefit from coinsurance? A large part of the literature suggests
that coinsurance benefits bondholders at the expense of stockholders. This is motivated by
both theory and weight of empirical findings. Theoretical motivation comes from Galai and
Masulis (1976) and Higgins and Schall (1975). The empirical findings that motivate this view are
twofold. Firstly, as Penal and Unal (2004) and Billet et al. (2004) show, bondholders earn
abnormal returns around announcement. Secondly, as Lang and Stulz (1994) and also Berger
and Ofek (1995) document, diversified firms stock trade at a discount compared to a
“comparable” benchmark of single-segment firms. We know from theory that adding debt after
merger at a cheaper price can potentially neutralize some of the transfer to bondholders, and
there is some evidence that coinsurance mergers are accompanied by increase in leverage (Kim
and McConell (1977)). But, I did not find any empirical paper in the prior literature that shows
coinsurance helps stockholders. So this seems to be an open empirical question.
Finally, do firms take advantage of coinsurance and issue cheaper debt? As the long
literature since Lewellen (1971) contends, increasing leverage after merger can transfer some
of the value gain back to stockholders. If coinsurance reduces marginal cost of debt, one should
expect to see firms taking advantage of that. As Kim and McConell (1977) demonstrate, there is
some limited evidence that coinsurance mergers are accompanied by increase in leverage.
Similarly, we should expect to see high coinsurance deals financed less by stock, so as to take
advantage of this reduced marginal cost of debt. To investigate this question rigorously, we
need to determine both the post-merger and pre-merger optimal leverage, which is a non-
trivial task mathematically for the post-merger leverage. If post-merger leverage can be
obtained, we can see what actually happens in the data.
15
Chapter 3: A Structural Model for Estimating Coinsurance Benefits
Consider two firms, labelled 1 and 2, that merge at time 𝑡𝑡 . The value of firm 1, the acquirer,
is 𝑉𝑉 1
( 𝑡𝑡 ); the value of firm 2, the target, is 𝑉𝑉 2
( 𝑡𝑡 ); and the value of the merged firm is 𝑉𝑉 1 2
( 𝑡𝑡 ). The
value gain from the merger is then given by the accounting identity:
∆= 𝑉𝑉 1 2
( 𝑡𝑡 ) − 𝑉𝑉 1
( 𝑡𝑡 ) − 𝑉𝑉 2
( 𝑡𝑡 ). (1)
In order to estimate the value gain, ∆, we need a model that values the pre-merger
standalone firms, 𝑉𝑉 1
( 𝑡𝑡 ), 𝑉𝑉 2
( 𝑡𝑡 ), as well as the merged firm, 𝑉𝑉 1 2
( 𝑡𝑡 ). To value the pre-merger firms,
I use a simple yet widely cited trade-off model: the Leland (1994) model. However, Leland’s
model, in itself, cannot value the merged firm. So I extend Leland’s model—imposing more
structure and customizing it—to value the combined firm when two firms with correlated cash
flows merge.
A. Valuing the Standalone (Pre-Merger) firms
Following Leland (1994), each pre-merger firm 𝑖𝑖 has assets in place, the value of which is
𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ); the asset value is unaffected by any financial decisions. The asset value of each firm
evolves following two correlated geometric Brownian motions:

𝑑𝑑 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )
𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )
= �( 𝜇𝜇 𝑖𝑖 − 𝛿𝛿 ) 𝑑𝑑 𝑡𝑡 + 𝜎𝜎 𝑖𝑖 𝑑𝑑 𝑊𝑊 𝑖𝑖 ( 𝑡𝑡 ) � , 𝑖𝑖 = 1,2 𝜎𝜎 𝑖𝑖 > 0, 𝛿𝛿 ≥ 0. (2)
The correlation coefficient of the two processes is constant: 𝜌𝜌 ∈ [ −1,1]. Also constant are
the expected growth rate of assets, 𝜇𝜇 𝑖𝑖 , its volatility, 𝜎𝜎 𝑖𝑖 , and the payout rate to equity, 𝛿𝛿 .
The firm issues debt: a claim that pays a constant coupon, 𝐶𝐶 𝑖𝑖 , per instant of time, as long as
the firm is solvent. The firm is solvent until the value of its assets falls below a default
boundary, 𝐾𝐾 𝑖𝑖 . Denote the time at which the firm 𝑖𝑖 becomes insolvent by 𝑇𝑇 𝑖𝑖 = 𝑀𝑀 𝑖𝑖 𝑀𝑀 { 𝑡𝑡 ≥
16
0; 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ) = 𝐾𝐾 𝑖𝑖 }. When the firm becomes insolvent, it defaults on the debt: on default, a fixed
proportion, 𝛼𝛼 , of the value of assets is lost due to the deadweight costs; the debt holders get
the remaining (1 − 𝛼𝛼 ) 𝐾𝐾 𝑖𝑖 . This is the cost of debt.
5

The benefit to using debt stems from its tax deductibility. A tax benefit of 𝜏𝜏 𝐶𝐶 𝑖𝑖 accrue to the
firm per instant of time, 𝜏𝜏 being the tax rate. The tax benefit is lost when the firm defaults. The
net benefit to leverage is thus the tax benefit of debt minus the expected cost of distress.
The levered firm’s value, 𝑉𝑉 ( 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )), is the sum of all future expected cash flows, discounted
at the risk free rate, 𝑟𝑟 , with expectations computed under the risk neutral probability measure.
6

𝑉𝑉 ( 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )) = 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ) + 𝔼𝔼 ∗
� ∫ 𝜏𝜏 𝐶𝐶 𝑖𝑖 𝑒𝑒 − 𝑟𝑟𝑟𝑟
𝑇𝑇 𝑖𝑖 𝑟𝑟 = 𝑡𝑡 𝑑𝑑 𝜏𝜏 � − 𝔼𝔼 ∗
[ 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 𝑖𝑖 𝛼𝛼 𝐾𝐾 𝑖𝑖 ] . (3)
Equation (3) demonstrates that the levered firm’s value, 𝑉𝑉 ( 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )), is the sum of three
components: the value of the firm’s assets, 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ), which is unaffected by the leverage; the tax
shield, 𝔼𝔼 ∗
� ∫ 𝜏𝜏 𝐶𝐶 𝑖𝑖 𝑒𝑒 − 𝑟𝑟𝑟𝑟
𝑇𝑇 𝑖𝑖 𝑟𝑟 = 𝑡𝑡 𝑑𝑑 𝜏𝜏 �, which is the expected value of all future tax deductions, discounted
to the current time; and, the cost of debt, − 𝔼𝔼 ∗
[ 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 𝑖𝑖 𝛼𝛼 𝐾𝐾 𝑖𝑖 ], which is the expected deadweight
loss on default, discounted to the current time. Computing these expectations, the value of the
firm simplifies:
𝑉𝑉 ( 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )) = 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ) + 𝜏𝜏 𝐶𝐶 𝑖𝑖 𝑟𝑟 �1 − �
𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )
𝐾𝐾 𝑖𝑖 �
− 𝛾𝛾 𝑖𝑖 � − 𝛼𝛼 𝐾𝐾 𝑖𝑖 �
𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )
𝐾𝐾 𝑖𝑖 �
− 𝛾𝛾 𝑖𝑖 , (4)
where, 𝛾𝛾 𝑖𝑖 = � � 𝑟𝑟 − 𝛿𝛿 −
1
2
𝜎𝜎 𝑖𝑖 2
� + � � 𝑟𝑟 − 𝛿𝛿 −
1
2
𝜎𝜎 𝑖𝑖 2
�
2
+ 2𝜎𝜎 𝑖𝑖 2
𝑟𝑟 �
1
2
� / 𝜎𝜎 𝑖𝑖 2
.
5
The concept of default in Leland (1994) differs somewhat from Lewellen (1971). While Leland’s model is in
continuous time, Lewellen’s is a one period model. In Leland’s model, default occurs if the market value of assets
falls below the default barrier, whereas in Lewellen’s model, default occurs if cash flow falls short of the current
portion of debt payable.
6
Following established notation, the superscript ∗ denotes expectations computed under the risk neutral measure.
17

The default barrier, 𝐾𝐾 𝑖𝑖 , is endogenous: equity holders choose when to default, so that
equity value is maximized. The endogenous default barrier is given by 𝐾𝐾 𝑖𝑖 =
( 1 − 𝑟𝑟 ) 𝐶𝐶 𝑖𝑖 𝑟𝑟 + 0.5 𝜎𝜎 𝑖𝑖 2
. This value
of 𝐾𝐾 𝑖𝑖 can be substituted in equation (4), to obtain the firm value.
Equation (4) presents the value of the firm as a function of the coupon rate 𝐶𝐶 𝑖𝑖 , where 𝐶𝐶 𝑖𝑖
depends on the firms leverage. If we further assume that firms choose their leverage optimally,
then we can choose the value of 𝐶𝐶 𝑖𝑖 that maximizes firm value. Empirically, though, the actual
leverage for most firms depart significantly from the optimal leverage predicted by Leland’s
model, or for that matter the optimal leverage from most tradeoff models (Fama and French
(2002), Leary and Roberts (2005)). As an extreme case, in my sample of 1,884 mergers, as many
as 242 targets and 118 acquirers have zero debt—significantly different from the optimal
leverage predicted by any tradeoff model (Strebulaev and Yang (2013)). Moreover, observed
leverage—that is very different from the optimal leverage predicted by the model—tends to be
stable over time, often stable over twenty years (Lemmon, Roberts and Zender (2006)). Hence,
instead of assuming that the firms are optimally levered, I treat the debt as exogenous,
obtaining the actual debt before the merger from COMPUSTAT.
The product of the observed debt and the risk free rate gives the coupon rate 𝐶𝐶 𝑖𝑖 (Elkamhi,
Ericsson and Parsons (2012)). Now, to value the pre-merger firm using equation (4), we only
need the structural parameters: 𝜎𝜎 1
,𝜎𝜎 2
, and 𝜌𝜌 . These structural parameters are estimated from
the past stock price information, following the procedure described in sub-section D.
B. Valuing the Merged firm
18
To value the merged firm, I extend the Leland (1994) model as follows. First, I assume that,
when the two firms merge, the value of assets of the merged firm, 𝑋𝑋 1 2
( 𝑡𝑡 ), is the sum of the pre-
merger asset values: 𝑋𝑋 1 2
( 𝑡𝑡 ) = 𝑋𝑋 1
( 𝑡𝑡 ) + 𝑋𝑋 2
( 𝑡𝑡 ). This additively abstracts operational synergies
(Lewellen (1971), Leland (2007)).
7
Applying Ito’s Lemma, the dynamics of 𝑋𝑋 1 2
( 𝑡𝑡 ) is given by

𝑑𝑑 𝑋𝑋 12
( 𝑡𝑡 )
𝑋𝑋 12
( 𝑡𝑡 )
= [( 𝜇𝜇 1
− 𝛿𝛿 ) 𝑠𝑠 ( 𝑡𝑡 ) + ( 𝜇𝜇 2
− 𝛿𝛿 ) �1 − 𝑠𝑠 ( 𝑡𝑡 ) �] 𝑑𝑑 𝑡𝑡 + 𝜎𝜎 1
𝑠𝑠 ( 𝑡𝑡 ) 𝑑𝑑 𝑊𝑊 1
( 𝑡𝑡 ) + (1 − 𝑠𝑠 ( 𝑡𝑡 )) 𝜎𝜎 2
𝑑𝑑 𝑊𝑊 2
( 𝑡𝑡 ),(5)
where, 𝑠𝑠 ( 𝑡𝑡 ) =
𝑋𝑋 1
( 𝑡𝑡 )
𝑋𝑋 1
( 𝑡𝑡 )+𝑋𝑋 2
( 𝑡𝑡 )
is itself stochastic. Equation (5) demonstrates that, unlike the pre-
merger standalone firms, the dynamics of asset value is not a geometric Brownian motion;
obtaining a closed form solution for firm value, as in Leland (1994), is no longer feasible.
Second, following Lewellen (1971), I assume that the debt of the merged firm is given by
the sum of the pre-merger debts. In the context of the model, this means that coupon rates are
additive: the merged firm’s coupon rate, 𝐶𝐶 1 2
= 𝐶𝐶 1
+ 𝐶𝐶 2
. An alternative to the additivity
assumption is to compute the optimal leverage for the merged firm: trade off the cost of debt
against its tax benefit (Leland (2007)). But in subsection A, I obtained the pre-merger leverage
from COMPUSTAT, instead of assuming that the pre-merger firm is optimally levered. So,
7
Does coinsurance benefits depend on operational synergies? To answer this question, I relax the additivity
assumption: I simulate firm values assuming 𝑋𝑋 1 2
( 𝑡𝑡 ) = 𝑋𝑋 1
( 𝑡𝑡 ) + 𝑋𝑋 2
( 𝑡𝑡 ) + 𝜀𝜀 ( 𝑡𝑡 ) where 𝜀𝜀 ( 𝑡𝑡 ) is operational synergies. I
consider various configurations of 𝜀𝜀 ( 𝑡𝑡 ) and simulate; the simulations suggest that the effect of operational
synergies on coinsurance is small. Intuitively, when there are synergies default risk is lowered—not because of
cash flow pooling but because combined cash flows are greater—and coinsurance is consequently higher.
Abstracting away operational synergies can thus underestimate coinsurance—however, simulations show that the
effect is second order.
19

assuming the post-merger firm to be optimally levered, while the pre-merger firms are far away
from their optimal leverage, will be comparing apples with oranges.
8

Third, I assume that the post-merger default boundary, 𝐾𝐾 1 2
, to be the sum of the pre-
merger default boundaries: 𝐾𝐾 1 2
= 𝐾𝐾 1
+ 𝐾𝐾 2
. Unlike pre-merger firms, the equity holders are not
choosing the default boundary optimally; instead, they face a default barrier before the merger.
This assumption is because the pre-merger bonds may have positive cash flow covenants,
which may be difficult to supersede after the merger. This assumption, again, makes the
estimated coinsurance benefit a conservative estimate. In robustness checks, I relax this
assumption, and estimate coinsurance benefits for the endogenous default case; those
estimates are not materially different.
Finally, I assume that the proportion of firm value lost in distress, 𝛼𝛼 , the tax rate, 𝜏𝜏 , and the
payout rate, 𝛿𝛿 , are the same for both firms, and they are unchanged by the merger.
9

The value of the merged firm, 𝑉𝑉 ( 𝑋𝑋 1 2
( 𝑡𝑡 )), is then the sum of all expected future cash flows,
discounted to the present at the risk free rate, with expectations computed under the risk
neutral measure.
8
In theory firms should increase their leverage following the merger, taking advantage of the cheaper cost of debt
due to coinsurance. Assuming post-merger leverage to be the sum of pre-merger leverages, in general, makes the
estimated coinsurance benefit a conservative estimate of the true coinsurance. For robustness, I compute
coinsurance using one-year-ahead post- merger debt. The estimates do not change significantly.
9
In general, the cost of distress, the tax rate, or the payout rate is neither homogenous, nor unchanged by the
merger. Glover (2013) demonstrates that there is significant cross-sectional variation in cost of distress. Zhu and
Singhal (2011) document that the proportion of value lost in bankruptcy is higher for diversified firms. In reality,
the tax rates too differ across firms. While it is straight-forward to obtain pre-merger tax rates, it is less clear what
the tax rate will be for the merged firm. Similarly, there is significant cross-sectional variation in payout rates. But,
when two firms with different payout rates merge, it is not clear ex-ante what the post-merger payout rate will be.
20

𝑉𝑉 ( 𝑋𝑋 1 2
( 𝑡𝑡 )) = 𝑋𝑋 1 2
( 𝑡𝑡 ) + 𝔼𝔼 ∗
� ∫ 𝜏𝜏 𝐶𝐶 1 2
𝑒𝑒 − 𝑟𝑟𝑟𝑟
𝑇𝑇 12
𝑟𝑟= 𝑡𝑡 𝑑𝑑 𝜏𝜏 � − 𝔼𝔼 ∗
[ 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 𝑖𝑖 𝛼𝛼 𝐾𝐾 1 2
]. (6)
where 𝑇𝑇 1 2
is time to default for the merged firm.
Once the values of the firms before and after the merger are estimated, I estimate the
coinsurance benefits, ∆, by inserting these values from equations (3) and (6) into equation (1):
∆= (
𝑟𝑟 𝐶𝐶 1
𝑟𝑟 − 𝛼𝛼 𝐾𝐾 1
) 𝔼𝔼 ∗
( 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 1
− 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 12
) + (
𝑟𝑟𝐶𝐶 2
𝑟𝑟 − 𝛼𝛼 𝐾𝐾 2
) 𝔼𝔼 ∗
( 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 1
− 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 12
). (7)
The term, (
𝑟𝑟 𝐶𝐶 1
𝑟𝑟 − 𝛼𝛼 𝐾𝐾 1
) 𝔼𝔼 ∗
( 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 1
− 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 12
), represents the change in net benefit from leverage
due to the merger for the first firm. The second term has a similar interpretation.
C. Parameter Estimation
Estimating the coinsurance benefits through equation (7) requires the structural
parameters: the volatilities, 𝜎𝜎 1
, 𝜎𝜎 2
, and the correlation between the two Brownian motions, 𝜌𝜌 .
To estimate the volatility, I exploit the closed-form expression of equity price for the pre-
merger firms. From Leland (1994), the equity price, 𝐸𝐸 𝑖𝑖 ( 𝑡𝑡 ), for the pre-merger firms is given by
𝐸𝐸 𝑖𝑖 ( 𝑡𝑡 ) = 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ) + (1 − 𝜏𝜏 )
𝐶𝐶 𝑖𝑖 𝑟𝑟 − �(1 − 𝜏𝜏 )
𝐶𝐶 𝑖𝑖 𝑟𝑟 − 𝐾𝐾 𝑖𝑖 � �
𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 )
𝐾𝐾 𝑖𝑖 �
− 𝛾𝛾 𝑖𝑖 , (8)
where 𝛾𝛾 𝑖𝑖 = � � 𝑟𝑟 − 𝛿𝛿 −
1
2
𝜎𝜎 𝑖𝑖 2
� + � � 𝑟𝑟 − 𝛿𝛿 −
1
2
𝜎𝜎 𝑖𝑖 2
�
2
+ 2𝜎𝜎 𝑖𝑖 2
𝑟𝑟 �
1
2
� / 𝜎𝜎 𝑖𝑖 2
, and, 𝐾𝐾 𝑖𝑖 = (1 − 𝜏𝜏 )
𝐶𝐶 𝑖𝑖 𝑟𝑟 +
1
2
𝜎𝜎 𝑖𝑖 2
.
In equation (8), the risk free rate, 𝑟𝑟 , is known; and, the stock price, 𝐸𝐸 𝑖𝑖 ( 𝑡𝑡 ), can be easily
obtained, at least for most firms with traded common stock. Following Leland (1994), the tax
rate, 𝜏𝜏 , is assumed to be 35%, and the payout rate, 𝛿𝛿 , is assumed to be 1% . The only unknowns
in this equation are then the asset value, 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ); and, the volatility of asset returns, 𝜎𝜎 𝑖𝑖 .
As an initial estimate of the volatility, 𝜎𝜎 𝑖𝑖 , I use the standard deviation of the daily stock
returns, computed over a 252 day window that ends 42 days before the merger announcement.
21
The data ends 42 days before the announcement because previous literature has found a run-
up in target prices prior to a merger announcement, starting roughly 42 days before merger
announcement (Schwert (1996)).The initial estimate of volatility, 𝜎𝜎 𝑖𝑖 1
, is inserted into equation
(8), (the superscript 1 denoting that this is the first iteration). Then, equation (8) is solved
numerically to estimate the asset value 𝑋𝑋 𝑖𝑖 1
( 𝑡𝑡 ), for each of these 252 days. From these asset
values, 𝑋𝑋 𝑖𝑖 1
( 𝑡𝑡 ), asset returns are computed: 𝑅𝑅 𝑖𝑖 1
( 𝑡𝑡 ) =
𝑋𝑋 𝑖𝑖 1
( 𝑡𝑡 )
𝑋𝑋 𝑖𝑖 1
( 𝑡𝑡 −1),
− 1. The standard deviation of these
asset returns over the 252 day window gives an updated estimate for the volatility, 𝜎𝜎 𝑖𝑖 2
.
The process is repeated till the absolute difference between the estimates of volatility,
from two successive iterations, is less than 10
−4
. That is | 𝜎𝜎 𝑖𝑖 𝑁𝑁 − 𝜎𝜎 𝑖𝑖 𝑁𝑁 − 1
| < 10
− 4
. Usually,
convergence occurs within five iterations. When convergence occurs, the estimate from the last
iteration, 𝜎𝜎 𝑖𝑖 𝑁𝑁 , is taken as the final estimate for volatility: 𝜎𝜎 𝑖𝑖 = 𝜎𝜎 𝑖𝑖 𝑁𝑁 .
In the course of estimating the volatility, I also estimated a series of asset values, 𝑋𝑋 𝑖𝑖 𝑁𝑁 ( 𝑡𝑡 ).
These estimated asset values, from the final iteration step, are used to generate the asset
returns for each firm. The sample correlation between the asset returns of firm 1 and 2 gives
the estimate of the correlation coefficient between the two Brownian motions, 𝜌𝜌 .
Once the structural parameters are estimated, the coinsurance benefits are estimated
using equation (7), by evaluating the expectations through Monte-Carlo simulations. The
simulation procedure is described in Appendix B.

22
Chapter 4: Data
A. Sample Construction
I compute coinsurance benefits for two different samples. One comprises mergers that
took place in United States between 1981 and 2013. The other is a sample of randomly paired
firms, also corresponding to the same period.
The sample of mergers is collected from the Securities Data Corporation’s (SDC) U.S.
Mergers and Acquisitions Database. The sample consists of mergers in the United States
provided it satisfied the following criteria: (1) the announcement date was between January 1,
1981 and December 31, 2013; (2) deal size was above $10 million (2013 dollars); (3) the
transaction was completed; (4) the acquirer did not have more than a 5% stake in the target
before the merger; (5) after the merger, the acquirer owns more than 99% of the target; (6) at
least one of the target and the acquirer have some debt outstanding—both are not all equity
firms (7) neither the acquirer nor the target is a financial firm, as indicated by their primary SIC
code (8) the target and the acquirer are both public firms, with daily stock market return data
available on Center for Research in Security Prices (CRSP) and fundamentals data available from
Compustat North America.
The sample contains 1,884 mergers. The sample size of 1,884 mergers, between 1981 and
2013, looks reasonable. In comparison, Akbulut and Matsusaka (2010) study 4,764 mergers
from 1950 to 2006, including mergers between financial firms.
The sample of randomly paired firms is constructed as following. First, I take all firm years
in the Fundamentals Annual file from the Compustat North America Database, covering the
same time period as the merger sample: 1981 to 2013. For comparability with the merger
23
sample, I retain only those observations with market capitalization above $10 million (2013
Dollars) and drop all financial firms, as indicated by their primary SIC codes. Let us call this the
population of comparable firms.
From this population, I randomly draw 1,884 firm years without replacement. This
constitutes the sample of hypothetical acquirers. For each observation, the year gives the year
of the hypothetical merger.
Next, for each hypothetical acquirer, I randomly draw—without replacement— one firm
from the population of comparable firms, such that the firm belongs to the same year cohort as
the acquirer. This firm is the hypothetical target. Finally, from that year, I draw a random date—
the hypothetical announcement date. This completes the sample of randomly paired firms.
For both these samples—the mergers as well as the randomly paired firms—I collect data
on stock price, return, and shares outstanding for both target and acquirer from the CRSP Daily
Stock File. Data are collected for each day, over a 252 day window, ending 42 days prior to the
merger announcement. The data ends 42 days before the announcement because the previous
literature has found a run-up in target prices prior to a merger announcement, starting roughly
42 days before the announcement date (Schwert (1996)). When data is not available for all the
252 days, I retain only those firms with at least 90 days of data. I also collect accounting data on
short term and long term debt from the COMPUSTAT North America Database. The fiscal year
for these data corresponds to a year before the merger announcement.
B. Summary Statistics
I compute the size, leverage and cumulative abnormal return on merger announcement,
for both the samples. Detailed variable descriptions are available in Appendix A. For both the
24
target and acquirer, size is computed as the market capitalization 42 days before merger
announcement. Market leverage is computed as the ratio of book value of debt to the sum of
book value of debt and market capitalization.
Cumulative abnormal returns (CAR) are computed using the Fama-French three-factor
model, over a three day (-1, 1) window around the announcement date. The betas for the
model are computed using daily returns, over a 252 day window, which ends 42 days before
merger announcement. Data on Fama-French factors are collected from Kenneth French’s
website.
10
The CARs of the target and acquirer are weighted by the respective pre-merger
market capitalizations, and their average is taken. This gives the combined CAR.
Table 1 presents the summary statistics. Panel A pertains to the mergers; Panel B to the
randomly paired firms. For the sample of mergers, the median size of the acquirers is $ 1.2
billion, whereas the median target size is only $100 million. So the median acquirer is 12 times
larger than the median target. By contrast, for the random pairings, the acquirer and target are
similar in size: the median acquirer is worth $160 million only, while the median target is worth
$140 million.
For the merger sample, the median target leverage is 21.5%, whereas the median acquirer
leverage is 17.2%. The targets thus have slightly more debt than acquirers. For the randomly
paired firms, the median target leverage is 31.5%, and that for acquirers is 32.4%. Thus, both
the target and the acquirer for the random pairings have significantly more debt than merger
participants.
10
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
25

For the merger sample, the cumulative abnormal return ranges from -38.5% to 57.8%; the
mean is 1.5%. There is thus a lot of dispersion. In contrast, the CAR’s are closer to zero for the
random pairings: the mean is -0.0%, and the median is -0.1%. This reflects that the CAR’s are
purely noise for the random pairings, unlike in mergers where the CARs correspond to the
announcement of the merger.
The summary statistics for my sample of mergers are comparable to the previous literature
(for e.g. Akbulut and Matsusaka (2010), Andrade, Mitchell and Stafford (2001)).
C. Inputs to the Structural Model
A crucial input to the structural model is the correlation of asset returns, 𝜌𝜌 . Row 1 of the
table 2 (Panel A) shows that the correlation ranges from -0.18 to 0.99, with mean 0.23, and
median 0.18. A second input is the volatility of asset return, 𝜎𝜎 𝑖𝑖 . As rows 2 and 3 show, the
targets (median 47.0%) are generally more volatile than acquirers (median 34.7%). This is
expected, given that targets are usually smaller. Some targets, and to a lesser extent some
acquirers, are very volatile.
Third, the coupon rates,
𝐶𝐶 𝑖𝑖 𝑋𝑋 𝑖𝑖 ( 0)
, which depend on the leverage, are presented in rows 4 and
5. The median annualized coupon rate is 1.1% of the firm value for the targets, and 0.8% for the
acquirers. Finally, the default barrier,
𝐷𝐷 𝑖𝑖 𝑋𝑋 𝑖𝑖 ( 0)
, is presented in rows 6 and 7. The mean default
barrier is 6.2% of the firm value for the targets, and 5.9% for the acquirers.
In comparison, as panel B demonstrates, the input parameters for the randomly paired
firms are quite different. They are more volatile: the median for acquirers is 35.8%, and that for
26
targets is 35.9%. They also pay higher coupon: the median is 1.6% for acquirers, and 1.5% for
targets. This is because the randomly paired firms are smaller and more levered.

27
Chapter 5: Estimated Coinsurance Benefits
A. Coinsurance Benefits for the Sample of Mergers
This sub-section describes the distribution of the coinsurance benefits for the sample of
mergers. Table 3 presents the results. Row 1 presents the results for the base case, which
assumes the proportion of firm value lost in distress, 𝛼𝛼 , to be 16.5%—the midpoint of Andrade
and Kaplan’s (1998) estimates. The mean gain from coinsurance is 0.32% of the combined pre-
merger firm value. The median is 0.09%. The 90
th
percentile of coinsurance benefits is 0.94%,
while the 99
th
percentile is 2.83%. The 1
st
percentile is 0, and the minimum value is also 0:
coinsurance benefits thus turn out to be always positive.
11

For most mergers, these numbers indicate, the coinsurance benefits are small. To better
understand the economic magnitude of the coinsurance benefits, we can benchmark it against
the announcement period cumulative abnormal return (CAR), which is the markets perception
of the total equity value created through the merger. Row 5 presents the CAR (-1, 1) for the
sample of mergers. The mean CAR is 1.25%; the median is 0.69%; the 75
th
percentile is 4.17%;
and, the 25
th
percentile is -1.87%. In comparison to the CARs, the coinsurance benefits appear
moderate.
For a minority of mergers, particularly those in the top decile, coinsurance benefits appear
substantial: Corresponding to the 90
th
percentile of coinsurance benefits, a merger creates $94
million through coinsurance, assuming a pre-merger combined value of $10 billion. While $94
11
Lewellen (1971) argues coinsurance is always positive. However, Leland (2007) demonstrates theoretically that
financial synergies can be negative, if we relax the assumption that cash flows are always positive. Theoretically, in
this paper, the coinsurance benefits may be negative, depending on parameters values, yet, empirically, the
estimated coinsurance benefits turn out to be positive.
28

million sounds significant economically, compare it to the fees paid to investment banks for
M&A advisory, which is on average 1.22% of the transaction value (Hunter and Jagtiani (2003)).
Even at the 90
th
percentile, the coinsurance benefits are not enough to offset the mean fees.
One reason why the estimated coinsurance benefits are small may be because I did not
consider fixed cost of distress. So, next, I consider a case where there are also fixed costs
incurred at default. Following Elkamhi, Ericsson and Parsons (2012), I assume fixed costs 𝜙𝜙 =
1.32 million, over and above the usual 16.5% proportional costs.
Row 2 presents the results for fixed costs 𝜙𝜙 = 1.32 million. The mean coinsurance benefit is
0.35%, slightly higher than the case with only proportional costs, but still quite small. Likewise,
the median increases to 0.10% from 0.09%. The 90
th
percentile is 1.01%, against 0.94%
previously. Introducing fixed costs increases the estimated coinsurance benefits marginally.
Perhaps, estimated benefits are small because I chose a small proportional cost parameter,
𝛼𝛼 . So instead of the midpoint of Andrade and Kaplan’s (1998) estimates, I choose their upper
limit: 𝛼𝛼 = 23%, while retaining the 1.32 million fixed cost of distress. Row 3 demonstrates that
the mean coinsurance benefit is 0.36% and the median is 0.10%. The coinsurance benefits
increase marginally. Further, following Glover (2013), I choose 𝛼𝛼 = 45%. Row 4 shows that the
mean is 0.40% and the median is 0.11%. Again, the increase in coinsurance benefits is marginal.
To sum up, Table 3 illustrates that even if firms lose almost half their value to deadweight
costs in default, the avoidance of such hefty costs through mergers create but small value
gains—at least for most mergers. The coinsurance benefits for most mergers are small,
regardless of the parameterization of the cost of distress.
B. Counterfactual Coinsurance Benefits for the Randomly Paired Firms
29
Table 3 suggests that coinsurance benefits are economically small. Relatively, though, the
coinsurance benefits from mergers may still be large, relative to the coinsurance that would be
produced if firms were to merge randomly. So as a benchmark, I estimate the counterfactual
coinsurance benefits for the sample of randomly paired firms.
There is another rationale for randomly pairing firms. Lewellen (1971) and the subsequent
literature on coinsurance suggest that firms merge so that they can avoid distress—through
coinsurance. If coinsurance is the main motivation for firms to merge, then we should expect
coinsurance to predict merger incidence. Ceteris paribus, mergers that can produce greater
coinsurance benefits should be more likely to take place than mergers that can produce lesser
coinsurance; conversely, the coinsurance benefits from mergers should be greater than the
counterfactual coinsurance benefits for randomly paired firms. This approach of randomly
pairing firms follows Hoberg and Phillips (2010).
12

Table 4 presents the counterfactual coinsurance benefits for the sample of randomly
paired firms. The first row presents the results for the base case: the proportion of firm value
lost in distress, 𝛼𝛼 = 16.5%, and there are no fixed costs. The mean estimated counterfactual
value gain from coinsurance is 0.70%, more than twice that for the sample of mergers (0.32%).
The median is 0.23%, again more than twice that for the sample of mergers (0.09%). Similarly,
the 90
th
percentile is 1.89%, compared to 0.94% for the mergers; the 99
th
percentile is 6.48%.
12
Hoberg and Phillips (2010) find that when firms are randomly paired asset complementarities from such random
pairings are less than that from mergers. They conclude that high asset complementarities motivate mergers.
30

To determine if the coinsurance benefits from the two samples are significantly different, I
perform a two-sample t-test of means. The t-statistic for the difference in means is -12.20,
rejecting the null hypothesis of equality of means at the 1% level of statistical significance.
The t-test says only compares the means. To instead compare the two distributions, I
perform the Kolmogorov-Smirnov (KS) test. The null hypothesis for the KS Test is that the
coinsurance benefits for both the samples are drawn from the same underlying distribution.
The KS test produces a statistic of 0.077, which corresponds to a P-value of less than 0.001. The
null hypothesis of same distribution is thus rejected at the 1% level of statistical significance.
Next I perform a logistic regression for the combined sample of 1,884 mergers and 1,884
randomly paired firms. The dependent variable is a dummy for merger incidence: the dummy is
one for a merger and zero otherwise. The main explanatory variable is the estimated
coinsurance benefit. I use the following controls: target size, relative size, target leverage,
acquirer leverage, acquirer market-to-book, target market-to-book, correlation of stock return,
and a dummy that is one when the three digit primary SIC codes are different. The results are
presented in Table V.
Column (1) presets the results for the univariate regression. The coefficient is -0.52,
statistically different from zero at the 1% level. Column (2) presets the results for the
multivariate regression. The coefficient is -0.43, different from zero statistically at the 1% level.
Likelihood of two firms merging does not increase, but decreases, with the potential for
coinsurance from that merger. These results suggest that most mergers are not motivated by
the desire to reduce distress risk through coinsurance.
31
But why do randomly pairing firms produce greater coinsurance than mergers? Perhaps,
the answer lies in Table 1, where the characteristics of both populations of firms are compared.
Randomly paired firms are usually smaller than merger participants—smaller than the typical
target, and much smaller than the typical acquirer—even though only firms above $10 million
are sampled; hence they have much higher scope for risk reduction. Moreover, randomly
paired firms have higher debt than merger participants and are thus at higher risk of default,
potentially benefitting more from coinsurance. Finally, most mergers are within the same
industry, whereas randomly paired firms are more likely to be diversifying, with greater scope
of coinsurance.
C. Coinsurance and Corporate Diversification
Although coinsurance benefits for most mergers are small, as many as 174 mergers
produced coinsurance benefits exceeding 1%. Are these mergers that produce (relatively)
higher coinsurance benefits diversifying mergers? It would appear so, given that, the literature
generally associates coinsurance with diversification. Since Lewellen (1971) proposed
coinsurance to explain diversifying mergers, several papers have suggested that coinsurance is
a major benefit from diversification. Hahn, Ogneva and Ozbas (2013) report that coinsurance
reduces cost of capital for diversified firms. Duchin (2010) finds that diversified firms hold less
cash; he argues coinsurance enables diversified firms to manage with less precautionary cash
holdings. In contrast, the literature is largely silent about coinsurance in the context of related
mergers. There is, however, no strong theoretical reason—except that related mergers have
more correlated cash flows—for limiting coinsurance to diversifying mergers. In view of this
literature, I test whether coinsurance benefits are larger for diversifying mergers.
32
I classify a merger as diversifying when none of the divisions of target or acquirer have any
three digit SIC code in common.
13
Otherwise, it is classified as a related merger (Kaplan and
Weisbach (1992), and Akbulut and Matsusaka (2010)). Of the 1,884 mergers, 265 are classified
as diversifying, and the remaining 1,669 as related.
In Table 5, I present the coinsurance benefits for the related and diversifying mergers,
assuming the proportion of firm value lost in distress, 𝛼𝛼 = 16.5%, and no fixed costs. The mean
coinsurance benefit is 0.33% for both diversifying mergers and related mergers. Also similar are
the medians: 0.11% for diversifying and 0.09% for related mergers. The 75
th
percentile is 0.36%
for diversifying mergers, and 0.37% for related mergers. The 90
th
percentile for diversifying
mergers is 1.51%, slightly higher than related mergers, 1.43%. The maximum is 5.23% for
related mergers and 4.82% for diversifying mergers. Of the 174 mergers that produce
coinsurance exceeding 1%, 151 are related—and only 23 are diversifying.
I plot the distribution of coinsurance benefits for the related and diversifying mergers in
Figure 2. The distributions again appear very similar. The t-statistic—for the null hypothesis that
the two means are equal—is 0.16, under the assumption of equal variance. If instead, I assume
different variances, then the t-statistic is 0.15, suggesting that the null hypothesis of equality
cannot be rejected at the 10% level of significance. Next, to determine whether the distribution
of coinsurance benefits is the same for both related and diversifying mergers, I perform the
Kolmogorov-Smirnov (KS) test. The KS test produces a statistic of 0.053, corresponding to a P-
13
The previous literature uses several other approaches for classification: they include defining industries at the
two digit level (Matsusaka (1993); or, using text-based measures instead of SIC codes (Hoberg and Philliips (2010)).
33

value of less than 0.54. The null hypothesis of same distribution thus cannot be rejected, even
at the 10% level of significance.
The finding—that coinsurance benefits are no larger for diversifying than related
mergers—is hard to reconcile with the prevalent notion that pigeon-holes coinsurance into
corporate diversification. Coinsurance is not exclusive to diversifying mergers. Rather, related
mergers are often associated with significant coinsurance benefits, a finding not documented
by the previous literature.
This finding, however, does not contradict the claim that coinsurance is an important
benefit from diversification. It may well be the case that firms undertaking diversifying mergers
are already sufficiently diversified, and the incremental coinsurance benefit from another
diversifying merger is small. Alternatively, coinsurance benefit from diversification may stem
from other channels that Duchin (2010) discusses: avoidance of transaction costs of external
finance, rather than avoidance of deadweight costs of distress.

34
Chapter 6. Cross Sectional Distribution of Coinsurance
A. Coinsurance for Target and Acquirer
Having demonstrated that coinsurance benefits are usually small, compared to pre-merger
combined firm size, it is worth asking whether coinsurance benefits targets or acquirers
differentially. To investigate this question, I express coinsurance benefits as a fraction of pre-
merger target firm value (bond + equity). The results are presented in Table 7, Row 1. The mean
gain from coinsurance is 2.40% of the pre-merger target value. The median is 0.93%; the 90
th

percentile of coinsurance benefits is 6.62%, while the 99
th
percentile is 17.06%. The 1
st

percentile is 0, and the minimum value is also 0.
These numbers indicate that the coinsurance benefits are large when compared to target
size, at least for a substantial part of the sample. Since target size is comparable to deal size (I
don’t have data on deal size for the full sample), compared to deal size, coinsurance is large.
Next, I express coinsurance benefits as a fraction of pre-merger acquirer firm value (bond +
equity). The results are presented in Table 7, Row 2. The mean gain from coinsurance is 0.51%
of the pre-merger acquirer value. The median is 0.11%; the 90
th
percentile of coinsurance
benefits is 1.30%, while the 99
th
percentile is 5.44%. The 1
st
percentile is 0, and the minimum
value is also 0.
These numbers indicate that coinsurance benefits are small when compared to acquirer
size, for most of the sample. The numbers are in fact comparable to the when coinsurance is
expressed as a percentage of pre-merger combined firm value—perhaps because acquirer is
significantly larger than target for the typical merger, and combined firm size is comparable to
35
the acquirer. Overall, Table 7 indicates while coinsurance may be small compared to acquirer
size, it is large compared to targets.
If coinsurance is large compared to targets, does that means coinsurance benefits targets
by reducing their default risk substantially? To answer this question, I compute the default risk
of the target, the acquirer, and the post-merger combined firm after the merger. The results
are presented in Table 8.
Row 1 presents the target’s ten year default probability computed under the risk neutral
measure from the structural model. The median default probability is only 0.004, the 25
th

percentile is 0, and the minimum value is also 0. But the 75
th
percentile is 0.68; the 90
th

percentile is 0.99; the 99
th
percentile is 1. These numbers indicate that more than a quarter of
targets are at substantial risk of default without the merger. Only about half the sample of
targets are safe. Scope of benefit from coinsurance seems large for at least a quarter of targets.
Row 2 presents the acquirer’s ten year default probability computed under the risk neutral
measure from the structural model. The median default probability is 0.000; the 25
th
percentile
is 0, and the minimum value is also 0; even the 75
th
percentile is 0.04. However, the 90
th

percentile is 0.79, and the 99
th
percentile is 0.99. These numbers suggest that most acquirers
are safe; only perhaps 10% or so are at substantial risk of default without the merger. Potential
benefit from coinsurance seems small for most acquirers.
Row 3 presents the combined firm ten year default probability computed under the risk
neutral measure from the structural model. The median default probability is 0.000, the 25
th

percentile is 0, and the minimum value is also 0, even the 75
th
percentile is 0.02. The 90
th

percentile is 0.63; the 99
th
percentile is 0.98. These numbers suggest that most post-merger
36
firms are safe—only 10% or so are at substantial risk of default even after the merger. The
distribution, in fact, looks very similar to the distribution for acquirers.
To evaluate risk-reduction for targets, I compute their reduction in default probability=
probability of default for target-probability of default for merged firm. Row 4 presents the
reduction in default probability for the target. The median reduction is 0; the 75
th
percentile is
0.20; the 90
th
percentile is 0.87, and the 99
th
percentile is 1. On the other hand, the 25
th

percentile is 0; the 10
th
percentile is -0.01; the 5
th
percentile is -0.15; the 1
st
percentile is -0.81.
These numbers indicate that, because of the merger, more than a quarter of targets experience
substantial reduction in risk of default, while less than 5% of targets experience any meaningful
increase in default risk. Coinsurance thus seems to be important for targets.
Similarly for acquirers, to evaluate the risk-reduction I compute the reduction in default
probability= probability of default for acquirer-probability of default for merged firm. Row 5
presents the reduction in default probability for the acquirer. The median reduction in default
probability is 0; the 75
th
percentile is 0; the 90
th
percentile is 0.04; but the 99
th
percentile is
0.86. On the other hand, the 25
th
percentile is 0; the 10
th
percentile is 0; the 5
th
percentile is -
0.04; the 1
st
percentile is -0.46. These numbers indicate that only 10% of acquirer experience
any meaningful reduction in risk of default through the merger, while less than 5% of acquirers
experience some increase in default risk. Coinsurance thus seems to be immaterial for most
acquirers.
To sum up, in this subsection we saw that while coinsurance benefits are small for most
acquirers, it is substantial for targets. And in comparison to the target size, which is comparable
to the deal size, coinsurance benefits from a merger are substantial.
37
B. Proxies for Coinsurance
If I had a closed form solution for coinsurance, I could have identified which mergers are
high in coinsurance. But I don’t—I simulated the coinsurance benefits. So the only way to
identify which mergers are high in coinsurance is to fit an empirical relationship between
coinsurance benefit predicted by the model and merger characteristics.
Table 8 presents the correlation between various merger characteristics and coinsurance
benefit predicted by the model. We can see in Table 8, there are several variables—size,
leverage, correlation—that are correlated with coinsurance. But we don’t know yet which of
these variables are more important. Moreover, since the structural model is nonlinear, the
quadratic as well as interaction terms of these variables are probably correlated with
coinsurance. We can regress coinsurance benefit predicted by the model on these variables;
using so many variables to predict coinsurance will no doubt result in good in-sample fit, but
over fitting will render the predictions susceptible to high standard errors—and out of sample
validity of such a model will be questionable.
To avoid this over-fitting problem, I use LASSO to select the optimal model (Tibshirani
(1996)). LASSO, or least absolute shrinkage and selection operator, is a popular statistical
method for parsimonious model selection. The LASSO produces an optimal five variable index,
given by
0.41 + 0.019 log(
𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑆𝑆 𝑇𝑇 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑆𝑆 𝐴𝐴 ) + 0.37 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝐴𝐴 + 3.02 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝑇𝑇 2
− 0.028log ( 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑒𝑒 𝐴𝐴 ) + 0.31 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝑇𝑇 ×
log(
𝑆𝑆 𝑖𝑖 𝑆𝑆𝑆𝑆
𝑇𝑇 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑆𝑆 𝐴𝐴 ), (10)
where coinsurance benefits are expressed in percentage; leverage in decimals; and, size is
market capitalization in million dollars. The correlation between this coinsurance index and
38
estimated coinsurance benefit is 0.67. Equation (10) demonstrates that coinsurance is large
when (1) target leverage is high; (2) target size is comparable to acquirer size; (3) acquirer size is
small.
Following Lewellen (1971), academics routinely use cash flow correlation, or its variant, as
a proxy for coinsurance. For example, Duchin (2010) uses cross-divisional correlation in cash
flow, and Hann, Ogneva and Ozbas (2013) use correlations of industry level cash flows based on
single-segment firms. This proxy has intuitive appeal, but empirically is cash flow correlation a
good proxy? Since this paper is the first to provide structural estimates of coinsurance—and to
the extent that these estimates of coinsurance are accurate—it enables me to test how good
are the proxies for coinsurance. To test if cash flow correlation—measured as the correlation
between quarterly cash flows of the target and the acquirer, dating back up to eight quarters
prior to the merger announcement—is a good proxy, I compute its correlation with the
estimated coinsurance benefits.
Table 9 reports that the correlation with cash flow correlation to be 0.02, not statistically
different from zero even at the 10% level of significance. This contradicts the popular notion
that cash flow correlation is a good proxy. Even return correlation, which is an input to the
structural model, has a correlation of only -0.05. Instead, target leverage (-0.54), acquirer
leverage (-0.37), and acquirer size (-0.30), predict coinsurance better.
Since cash flow correlation is not a good proxy, it may be worthwhile to use the as
alternative proxy the coinsurance index: a proxy constructed using easy-to-compute accounting
variables, which researchers may easily use, without having to actually estimate a structural
model.
39
Chapter 7. The Stock Price Response to Coinsurance
A. Coinsurance Benefits and Announcement Returns
The paper to this point considers the total gain in firm value from coinsurance. But do the
benefits from coinsurance reach the stockholders? Although several papers document that
coinsurance benefits bond holders, there is little evidence that coinsurance benefits stock
holders ((Billet, King and Mauer (2004), Penas and Unal (2004)). The dominant view in the
literature is that coinsurance benefits bondholders at the expense of stockholders. The
literature argues that coinsurance reduces volatility, consequently reducing the option value of
equity, and transferring value from equity holders to bondholders (Galai and Masulis (1976),
Higgins and Schall (1975), Mansi and Reeb (2002)).
If the stockholders lose from coinsurance, as is argued by the literature, then when a
merger that is high in coinsurance is announced, the stock price should decrease—and not
increase. This argument motivates the following test. The cumulative abnormal return (CAR)
around the merger announcement date is regressed on the estimated coinsurance benefits. I
add standard controls following the literature on announcement returns (Akbulut and
Matsusaka (2010)). The controls are relative size; target size; leverage and Tobin’s Q for both
the target and acquirer; a dummy that is one when the deal is diversifying; a dummy that is one
when the method of payment is all stock; a dummy that is one when the method of payment is
all cash; and year fixed effects.
The results are presented in Table 10. As column (1) shows, the slope of the univariate
regression is 1.94, statistically different from zero at the 1% level of significance. As column (2)
shows, the slope of the multivariate regression is 0.89, also statistically different from zero at
40
the 1% level of significance. The slope coefficient of 0.89 suggests that for every 1% gain in firm
value from coinsurance, the price of equity increases 0.89%. The increase in equity price, at the
announcement of the merger, contradicts the notion that coinsurance hurts equity holders.
14

Few qualifications about the interpretation of the regressions: The estimated coinsurance
benefits are correlated with variables such as size and leverage, because these variables were
inputs to the structural model. But size and leverage, by themselves, also influence
announcement returns (Akbulut and Matsusaka (2010)). I attempt to remedy this by including
these variables as controls in the regression. But, it is not possible to decouple coinsurance
from size and leverage fully, as coinsurance depends on size and leverage nonlinearly.
Moreover, if firms merge in order to benefit from coinsurance, then there are endogeneity
concerns. However, in Sub-section IIIB, we saw that the coinsurance benefits is no greater in
mergers than when firms are paired randomly, suggesting that coinsurance is probably not the
main reason for firms to merge—this mitigates some of the endogeneity concerns. If there is
endegeneity, and despite our best attempts to control for it, if there is an omitted variable bias,
even then as long as the omitted variable—most likely operational synergies—is correlated
negatively with coinsurance, then the slope coefficient on coinsurance is biased downward; the
actual slope is larger: if anything, this makes the results stronger.
B. Stock Price Response to Coinsurance in Diversifying Mergers
14
If, for every 1% gain in firm value from coinsurance equity holders gain 0.89%, then how much do bondholders
gain? The answer depends on the leverage: Consider a hypothetical merger where the combined pre-merger firm
value is $100 million and the leverage is 50%. The pre-merger equity is then worth $50 million. A 1% gain in total
firm value from coinsurance translates to $1 million. The regression slope of 0.89 suggests a gain of 0.89% for the
equity holders, which translates to $0.45 million. The bondholders gain the remaining $0.55 million—a gain of
1.1%. If instead the leverage is 80%, the bondholders gain 1.0%.
41

The literature generally associates coinsurance with diversifying mergers and not related
mergers. In order to determine whether coinsurance benefits stockholders more in diversifying
than related mergers, I regress the announcement abnormal return on the estimated
coinsurance benefits, separately for diversifying and related mergers.
The results are presented in Table 11. Column (1) presents the results for the univariate
regression for diversifying mergers. The coefficient is 1.70, different from zero statistically at
the 5% level of significance. For the multivariate regression, as column (2) shows, the
coefficient on coinsurance is 1.61, not different from zero statistically at the 10% level of
significance.
15
These positive coefficients suggest that coinsurance benefits stockholders in
diversifying mergers, contradicting the claim that coinsurance causes diversification discount.
16

Columns (3) and (4) present the results for related mergers, for the univariate and
multivariate regression respectively. Column (3) shows that the coefficient is 1.97, different
from zero statistically at the 1% level of significance. As column (4) shows, the coefficient is
0.92, also different from zero statistically at the 1% level of significance. These positive slope
coefficients suggest that coinsurance benefits stockholders in related mergers. In contrast to
the dominant thinking that coinsurance is of little consequence in related mergers, coinsurance
can benefit equity holders in related mergers.
The slope coefficient is substantially larger for diversifying mergers (1.70) than for related
mergers (0.92). If markets pay heightened attention to coinsurance when diversifying mergers
15
One reason why the coefficient is not statistically significant may be due to the small sample size: there are only
265 diversifying mergers for which data is available; hence standard errors are large.
16
Mansi and Reeb (2002) argue that coinsurance causes diversification discount. But, recent theory (Matsusaka
(2001)), Maksimovic and Phillips (2002)) and empirical evidence (Chevalier (2004), Villalonga (2009)) suggests that
the decision to diversify is endogenous, and the diversification discount is largely an artifact of sample selection.
42

are announced, then the stock price response to coinsurance should be more pronounced for
diversifying mergers. This may explain why the slope is higher for diversifying mergers.
C. Robustness Tests
While the stock price response to coinsurance seems economically and statistically
significant, is the relationship robust? We saw earlier that the distribution of coinsurance
benefits is highly non-normal. A natural concern is whether the CAR coinsurance relationship is
driven by outliers. To mitigate this concern, I perform a quantile (median) regression of CAR on
coinsurance, retaining all the usual controls. Table 12 presents the results. As column (1) shows,
the coefficient on coinsurance is 1.12, different from zero statistically at the 1% level of
significance. This suggests that the relationship is robust to outliers.
In all these regressions so far, the explanatory variable—coinsurance—is continuous. Now,
I switch to a discrete model. I regress CAR on a high coinsurance dummy that is one when
coinsurance benefits exceed 1%, and zero otherwise. The results are reported in column (2).
The coefficient on the high coinsurance dummy is 1.72, different from zero statistically at the
1% level of significance. The coefficient suggests that that the abnormal returns are higher for
high-coinsurance mergers, consistent with coinsurance benefitting equity holders.
Another potential concern is that the estimated coinsurance benefits may be correlated
with priced risk factors that determine returns. To assuage this concern, I perform a placebo
regression. For the sample of randomly paired firms, I regress the counterfactual CAR on the
counterfactual coinsurance benefits. I include all the usual controls, except the all-stock and all-
cash dummy, as those are not defined for the random pairings. If the estimated coinsurance
benefits are correlated with priced risk factors, then we expect a positive slope—even in the
43
randomly paired sample. As column (3) shows, the coefficient on coinsurance is 0.07. Not
different from zero statistically even at the 10% level of significance. This assuages the concern
that the estimated coinsurance benefits are picking up priced risk factors.

44
Chapter 8: Conclusions
Using a structural model, I estimate the gain in value from coinsurance for a sample of
1,884 mergers in the United States between 1981 and 2013—and for an equal number of
randomly paired firms. The findings may be summarized as follows:
1. Estimated coinsurance benefits from most mergers are small, smaller than what would
be if firms merged randomly, suggesting that coinsurance is not the primary motivation for
most mergers.
2. Coinsurance is small for diversifying mergers—no higher coinsurance than related
mergers.
3. Risk reduction from coinsurance is small for most acquirers because they are at low risk
of default even without the merger; on the contrary, at least for a quarter of the targets are at
high risk of default—and their default risk decreases substantially because of coinsurance.
4. For some 174 mergers, slightly less than 10% of the sample, the value gain is substantial,
ranging from 1% to as much as 5.2%. These are mergers between small firms, mergers between
firms of similar size, or when a distressed target is acquired.
5. For every 1% estimated coinsurance benefit, the cumulative abnormal returns around
the announcement date increases 0.89%, suggesting that coinsurance benefits stockholders.
The findings of this paper suggest several future directions of research, both theoretical
and empirical. Theoretically, I modeled the costs of distress afflicting the firm only at the onset
of bankruptcy. But, long before the onset of bankruptcy, there may be indirect costs of distress;
such as the costs faced by firms producing durable goods, whose customers demand a discount
in view of uncertain enforcement of accompanying warranties (Titman (1984)). To the extent
45
these indirect costs of distress are large, and accrue long before bankruptcy—so that a
proportional model of bankruptcy costs is not suitable (Elkamhi, Ericsson and Parsons (2012))—
then the avoidance of such indirect costs through mergers may produce significant coinsurance
benefits. A model that accommodates such indirect costs accrued before bankruptcy may be an
avenue for future research.
Another theoretical consideration is that, in this paper, I focused only on the benefits from
coinsurance in avoiding distress. But coinsurance may also benefit firms by enabling them to
avoid costly external finance (Matvos and Seru (2014), Duchin (2010)). A model that includes
costly external financing will thus be more general.
In theory, if coinsurance reduces cost of debt and increases debt capacity, firms that are
under levered after the merger should increase their leverage, so as to benefit equity holders.
An open empirical question is whether the extent to which firms increase their leverage can be
explained by coinsurance.

46
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50
Appendices
Appendix A: Variable Description

Variable Definition
1
Estimated
Coinsurance
Benefit ( ∆)
The coinsurance benefit is estimated by simulating a structural model. It
is expressed as a percentage of combined, target plus acquirer, pre-
merger value. For estimating coinsurance benefits, I assume the
following economy-wide parameters: a fixed cost of distress, 𝜙𝜙 ,
expressed in $ millions; a proportional cost, 𝛼𝛼 , which is the percentage of
firm value lost at default; a payout rate, 𝛿𝛿 = 1%; and a tax rate, 𝜏𝜏 = 35%.
2
Cumulative
abnormal return
(CAR)
Abnormal returns are computed over a three day window [-1, 1] around
merger announcement, using the Fama-French three factor model. The
abnormal returns are then weighted by target and acquirer pre-merger
market cap to obtain the Cumulative Abnormal return (CAR). Betas are
computed over a 252 day window before the announcement date for
the merger, after dropping a 42 day window.
3
Cash flow
correlation
Quarterly cash flows for the target and the acquirer are obtained from
COMPUSTAT as
𝑖𝑖 𝑖𝑖 𝑖𝑖 + 𝑑𝑑𝑑𝑑 𝑖𝑖 𝑎𝑎 𝑡𝑡 𝑖𝑖 , for up to eight quarters prior to the merger
announcement, and their correlation is computed.
4
Pre-merger
market Leverage
Pre-merger market Leverage is computed as
𝑑𝑑𝑑𝑑𝑑𝑑 + 𝑑𝑑𝑑𝑑 𝑡𝑡 𝑡𝑡 ( 𝑑𝑑𝑑𝑑𝑑𝑑 + 𝑑𝑑𝑑𝑑 𝑡𝑡 𝑡𝑡 +𝑑𝑑𝑟𝑟 𝑑𝑑𝑑𝑑 ∗ 𝑑𝑑 𝑐𝑐 ℎ 𝑜𝑜 )
. It is
calculated using data from the Compustat Fundamentals Annual file,
corresponding to one year before the announcement of merger.
5
Pre-merger Firm
Size
Size of firm is expressed as the logarithm of market capitalization, 42
days before merger announcement, in million (2013) dollars.
6 Relative Size 𝑅𝑅 𝑒𝑒 𝑅𝑅𝑅𝑅 𝑡𝑡 𝑖𝑖 𝐿𝐿𝑒𝑒 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑒𝑒 = log (
𝑇𝑇 𝑅𝑅𝑟𝑟 𝑇𝑇 𝑒𝑒 𝑡𝑡 𝑠𝑠 𝑖𝑖𝑆𝑆𝑒𝑒
𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴 𝑖𝑖 𝑟𝑟 𝑒𝑒 𝑟𝑟 𝑠𝑠 𝑖𝑖𝑆𝑆𝑒𝑒
)
7
Pre-merger
Tobin’s Q
𝑇𝑇 𝑇𝑇𝑇𝑇 𝑖𝑖𝑀𝑀 ’ 𝑠𝑠 𝑄𝑄
=
𝑚𝑚 𝑅𝑅 𝑟𝑟 𝑚𝑚 𝑒𝑒 𝑡𝑡 𝐿𝐿𝑅𝑅𝑅𝑅𝐴𝐴 𝑒𝑒 𝑇𝑇 𝑜𝑜 𝑅𝑅𝑠𝑠 𝑠𝑠 𝑒𝑒 𝑡𝑡 𝑠𝑠 0.9 ∙ 𝑇𝑇 𝑇𝑇𝑇𝑇𝑚𝑚 𝐿𝐿𝑅𝑅𝑅𝑅𝐴𝐴 𝑒𝑒 𝑇𝑇𝑜𝑜 𝑅𝑅𝑠𝑠 𝑠𝑠 𝑒𝑒 𝑡𝑡 𝑠𝑠 ( 𝑅𝑅𝑡𝑡 ) + 0.1 ∙ 𝑚𝑚 𝑅𝑅 𝑟𝑟 𝑚𝑚 𝑒𝑒 𝑡𝑡 𝐿𝐿𝑅𝑅𝑅𝑅𝐴𝐴 𝑒𝑒 𝑇𝑇𝑜𝑜 𝑅𝑅𝑠𝑠 𝑠𝑠 𝑒𝑒 𝑡𝑡 𝑠𝑠
51
Where 𝑚𝑚 𝑅𝑅 𝑟𝑟 𝑚𝑚 𝑒𝑒 𝑡𝑡 𝐿𝐿𝑅𝑅𝑅𝑅𝐴𝐴 𝑒𝑒 𝑇𝑇 𝑜𝑜 𝑅𝑅𝑠𝑠 𝑠𝑠 𝑒𝑒 𝑡𝑡 𝑠𝑠 = 𝑇𝑇 𝑇𝑇𝑇𝑇𝑚𝑚 𝑅𝑅𝑠𝑠 𝑠𝑠 𝑒𝑒 𝑡𝑡 𝑠𝑠 ( 𝑅𝑅𝑡𝑡 )
+ 𝑚𝑚 𝑅𝑅 𝑟𝑟 𝑚𝑚 𝑒𝑒 𝑡𝑡 𝐿𝐿𝑅𝑅𝑅𝑅𝐴𝐴 𝑒𝑒 𝑇𝑇𝑜𝑜 𝐴𝐴 𝑇𝑇𝑚𝑚𝑚𝑚𝑇𝑇𝑀𝑀 𝑒𝑒 𝐴𝐴 𝐴𝐴 𝑖𝑖 𝑡𝑡 𝑒𝑒 ( 𝐴𝐴 𝑠𝑠 ℎ 𝑇𝑇 ∙ 𝑝𝑝 𝑟𝑟 𝐴𝐴 𝐴𝐴 )
− 𝐴𝐴 𝑇𝑇𝑚𝑚𝑚𝑚𝑇𝑇𝑀𝑀 𝑒𝑒 𝐴𝐴 𝐴𝐴 𝑖𝑖 𝑡𝑡 𝑒𝑒 ( 𝐴𝐴 𝑒𝑒 𝐴𝐴 ) − 𝑑𝑑 𝑒𝑒𝑜𝑜𝑒𝑒𝑟𝑟 𝑟𝑟 𝑒𝑒 𝑑𝑑 𝑡𝑡 𝑅𝑅 𝑡𝑡 𝑒𝑒 𝑠𝑠 ( 𝑡𝑡 𝑡𝑡 𝑑𝑑 𝑇𝑇 )
8
Diversification
dummy
A merger is classified as diversifying if none of the divisions of the target
or the acquirer have any three digit SIC code in common. The
Diversification dummy is 1 if the merger is diversifying and 0 otherwise.
9 All stock dummy
The dummy is 1 if the merger deal is paid fully using stock, according to
information available from SDC.
10 All cash dummy
The dummy is 1 if the merger deal is paid fully using cash, according to
information available from SDC.
11
Coinsurance
Index (Five-
Variable) ∆
�

∆
�
= 0.41 + 0.019 𝑅𝑅 𝑒𝑒 𝑅𝑅𝑅𝑅 𝑡𝑡 𝑖𝑖 𝐿𝐿𝑒𝑒 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑒𝑒 + 0.37 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝐴𝐴 + 3.02 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝑇𝑇 2
− 0.028 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑒𝑒 𝐴𝐴 +
0.31 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝑇𝑇 × 𝑅𝑅 𝑒𝑒 𝑅𝑅𝑅𝑅 𝑡𝑡 𝑖𝑖 𝐿𝐿𝑒𝑒 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑒𝑒
For this index, coinsurance benefits are expressed as a percentage;
leverage 𝐿𝐿 𝑒𝑒 𝐿𝐿 𝐴𝐴 / 𝑇𝑇 in decimals; and, size, 𝑆𝑆 𝑖𝑖 𝑆𝑆 𝑒𝑒 𝐴𝐴 as logarithm of market cap,
where market cap is measured in million (inflation adjusted 2012)
dollars.

52
Appendix B: Monte Carlo Simulations
For each merger, I simulate the asset values for the target and the acquirer recursively:
𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 + ∆ 𝑡𝑡 ) = 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ) 𝑒𝑒 𝑡𝑡 𝑝𝑝 { � 𝑟𝑟 −
1
2
𝜎𝜎 𝑖𝑖 2
� 𝛥𝛥 𝑡𝑡 + 𝜎𝜎 𝑖𝑖 √ ∆ 𝑡𝑡 𝑊𝑊 𝑖𝑖 ( 𝑡𝑡 )} . (B1)
The shocks for the target and acquirer, 𝑊𝑊 𝑖𝑖 ( 𝑡𝑡 ) are drawn from a bivariate normal distribution,
i.i.d. across time:
𝑊𝑊 1
( 𝑡𝑡 )
𝑊𝑊 2
( 𝑡𝑡 ))
~ 𝑁𝑁 �
0
0
, �
1 𝜌𝜌 𝜌𝜌 1
� �. I simulate daily asset values, so that 𝛥𝛥 𝑡𝑡 = 1 . I
simulate up to 𝑡𝑡 = 252,000. The initial asset value for the simulations, 𝑋𝑋 𝑖𝑖 ( 𝑡𝑡 ), is the asset value
42 days before the merger announcement.
I simulate 1,000 paths for each firm. To ensure that the 1,000 paths are enough, I compute
the difference between the standalone firm values computed through simulations and firm
values computed theoretically (using equation (7)). The absolute value of the difference is less
than 0.5%, suggesting that 1,000 paths are enough.
Once these asset values are estimated, the coinsurance, ∆, is computed as
∆= �
𝑟𝑟 𝐶𝐶 1
𝑟𝑟 − 𝛼𝛼 𝐾𝐾 2
�
1
𝑁𝑁 ∑ ( 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 1
𝑗𝑗 − 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 12
𝑗𝑗 )
𝑁𝑁 𝑗𝑗 = 1
+ (
𝑟𝑟𝐶𝐶 2
𝑟𝑟 − 𝛼𝛼 𝐾𝐾 2
)
1
𝑁𝑁 ∑ ( 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 2
𝑗𝑗 − 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 12
𝑗𝑗 )
𝑁𝑁 𝑗𝑗 = 1
, (B2)
where 𝑇𝑇 𝑖𝑖 𝑗𝑗 is the time to default of firm 𝑖𝑖 , for the simulation path 𝑗𝑗 .
The estimated coinsurance benefit depends on the choice of 𝛼𝛼 , the proportion of value lost
on default. In this paper, I choose 𝛼𝛼 = 16.5%, which is the midpoint of the estimates of Andrade
and Kaplan (1998). I check the sensitivity of the coinsurance estimates to this assumption, by
choosing other values of 𝛼𝛼 , and also considering fixed costs of distress.

53
Appendix C: Out-of-Sample Performance of Structural Model
The structural model estimates coinsurance benefits: the gain in value through the merger
due to reduction in likelihood of distress. But does the structural model predict distress
accurately? To answer this question, I investigate if the merged firms filed for bankruptcy,
within ten years from the merger announcement. I collect data on bankruptcies from UCLA-
LoPucki Bankruptcy Research Database (BRD), which lists all large, public company bankruptcies
filed in the United States Bankruptcy Courts since October 1, 1979. Out of the 1,884 mergers,
only 61 firms went bankrupt within ten year from the merger—a bankruptcy rate of 3.23%.
Are these bankrupt firms the ones the structural model predicted to be at high risk of
default? If the structural model is good at predicting default, then the firms that the model
predicted to be at higher risk of default should be the ones that default. This implication is
tested by a logistic regression where the dependent variable is a dummy for bankruptcy
incidence. The dummy is one if the post-merger acquirer files for bankruptcy within ten years
of the merger announcement and zero otherwise. The main explanatory variable is the ten-
year-ahead theoretical probability of default estimated from the structural model. I include as
controls the pre-merger size for both the target and the acquirer; the pre-merger leverage for
both the target and the acquirer; the correlation of returns between the target and acquirer;
and a dummy that is one if the merger is diversifying. Table 13 presents the results.
For Column (1), the explanatory variable is the physical probability of default.
178
The
coefficient is 0.80, statistically different from zero at the 1% level. The positive coefficient
178
The physical probability is computed under the assumption that the assets grow at 𝜇𝜇 𝑖𝑖 − 𝛿𝛿 , whereas the risk
neutral probability assumes a growth rate of 𝑟𝑟 − 𝛿𝛿 .
54

suggests that the firms, which are predicted by the model to be more likely to default, do
indeed default more. The coefficient of 0.80 indicates that the actual default likelihood
increases 0.8%, for every 1% increase in probability of default estimated from the structural
model. For Column (2), the explanatory variable is the risk-neutral probability of default. The
coefficient on probability of default is 3.30, statistically different from zero at the 1% level.
These results suggest that the structural model—that incorporates only information prior to the
merger—is reasonably good at predicting default up to ten years from the merger. The fact that
the structural model predicts bankruptcy well, even after including standard controls, suggests
that the non-linearity of the model is helpful in predicting bankruptcy.
In general, how likely are the merged firms to file bankruptcy? In table 14, I present the
probability of default for the sample of mergers, estimated using the structural model. The
sample of post-merger firms is sorted by the default probability into five quintiles. For each
quintile the median default probability is presented. Row 1, which presents the risk-neutral
probabilities, demonstrates that the three bottom quintiles are at minimal risk of default. In
contrast, the median default probability is 1.7% for the fourth quintile, and 12.5% for the top
quintile. It seems that the default risk is concentrated to a minority of firms. Row 2 presents the
physical probabilities. It echoes the same pattern: the default risk is minimal, except for the top
quintile, which is at substantial risk of default: the median probability is 53.1%.
For comparison, I present the historical default probabilities by Moody’s bond ratings for
the universe of US firms in Panel B row 1. All information presented in Panel B is obtained from
Table 2 of Elkamhi, Ericsson and Parsons (2012). Row 1 demonstrates that for the bottom three
quintiles— Baa, A, and Aa/Aaa—default risk is minimal. In contrast, the median default risk is
55
higher for Ba firms (21.5%), and substantially higher (46.5%) for the B rated firms. This
concentration of default risk in a minority of firms is similar to Panel A. In Row 2, I present the
risk-adjusted probabilities from Almeida and Philippon (2008), which may be compared with
the risk-adjusted probabilities in Panel A. Row 2 indicates that while the top two quintiles are at
significantly higher risk of distress, the firms in the bottom three quintiles are also at some
small risk of distress. Overall, it appears that the firms that merge are at lower risk of default
compared to the universe of US firms. Not surprising then that mergers produce less
coinsurance than randomly paired firms.

56
Appendix D: Accounting Based Measures of Coinsurance Benefits
In this appendix, I present an alternative approach to estimate coinsurance benefits for
mergers, an approach that draws on accounting methods. This approach follows the long
accounting literature on Z-scores (Altman (1967)). Z-scores measure likelihood of distress, as a
linear combination of easy-to-compute accounting variables. A recent version of the Z-score is
the Z’’-score (Altman (1997)). The default likelihood increases with the 𝑍𝑍 ′′
𝑆𝑆 𝐴𝐴𝑇𝑇𝑟𝑟 𝑒𝑒 . Z’’-score is
defined as the reduction in expected cost of distress:
𝑍𝑍 ′′
𝑆𝑆 𝐴𝐴 𝑇𝑇𝑟𝑟 𝑒𝑒 = 3.25 + 6.7
𝐸𝐸𝐸𝐸 𝐸𝐸 𝑇𝑇 𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 + 3.3
𝑅𝑅 𝑆𝑆𝑡𝑡 𝑎𝑎𝑖𝑖 𝑅𝑅 𝑆𝑆𝑑𝑑 𝐸𝐸 𝑎𝑎𝑟𝑟𝑅𝑅 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝑐𝑐 𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 + 1.05
𝐸𝐸 𝑖𝑖 𝐸𝐸 𝑖𝑖𝑡𝑡 𝐸𝐸 𝐿𝐿 𝑖𝑖 𝑎𝑎𝑖𝑖𝑖𝑖 𝑑𝑑 𝑖𝑖 𝑡𝑡 𝐸𝐸 + 6.6
𝑊𝑊 𝑜𝑜𝑟𝑟 𝑊𝑊 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝐶𝐶 𝑎𝑎𝑑𝑑𝑖𝑖 𝑡𝑡 𝑎𝑎𝑑𝑑
𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 . (D1)
I compute Z’’-scores for both the pre-merger target and acquirer, using accounting information
from COMPUSTAT that corresponds to one year prior to merger announcement. For the
merged firm, the projected Z’’-Score is given by
𝑍𝑍 ′′
𝑆𝑆 𝐴𝐴 𝑇𝑇𝑟𝑟 𝑒𝑒 = 3.25 + 6.7
𝐸𝐸𝐸𝐸 𝐸𝐸 𝑇𝑇 1
+ 𝐸𝐸𝐸𝐸 𝐸𝐸 𝑇𝑇 2
𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 1
+ 𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 2
+ 3.3
𝑅𝑅 𝑆𝑆𝑡𝑡 𝑎𝑎𝑖𝑖 𝑅𝑅 𝑆𝑆𝑑𝑑 𝐸𝐸 𝑎𝑎 𝑟𝑟𝑅𝑅 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝑐𝑐 1
+ 𝑅𝑅 𝑆𝑆𝑡𝑡 𝑎𝑎 𝑖𝑖 𝑅𝑅 𝑆𝑆𝑑𝑑 𝐸𝐸 𝑎𝑎𝑟𝑟𝑅𝑅𝑖𝑖 𝑅𝑅 𝐸𝐸 𝑐𝑐 2
𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 1
+𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 2
+
1.05
𝐸𝐸 𝑖𝑖 𝐸𝐸 𝑖𝑖𝑡𝑡 𝐸𝐸 1
+𝐸𝐸 𝑖𝑖 𝐸𝐸 𝑖𝑖 𝑡𝑡 𝐸𝐸 2
𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 1
+ 𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 2
+ 6.6
𝑊𝑊 𝑜𝑜𝑟𝑟 𝑊𝑊 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝐶𝐶 𝑎𝑎𝑑𝑑𝑖𝑖 𝑡𝑡 𝑎𝑎𝑑𝑑
1
+ 𝑊𝑊 𝑜𝑜𝑟𝑟 𝑊𝑊 𝑖𝑖 𝑅𝑅𝐸𝐸 𝐶𝐶 𝑎𝑎𝑑𝑑𝑖𝑖 𝑡𝑡 𝑎𝑎𝑑𝑑
2
𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 1
+𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐 2
(D2)
Table I presents the distribution of Z’’ scores for the pre-merger and post-merger firms. The
table demonstrates that the distribution of Z” score for the merged firm is similar to that of the
acquirer. On the other hand, targets experience significant reduction in Z” scores. The reduction
is more pronounced for the distressed targets.
The Z’’-scores, though correlated to default probability, themselves do not represent
probabilities. To transform Z’’ score into default probabilities, I make two further assumptions:
Table 4 from Altman (1997) presents bond ratings as monotonic functions of Z’’-scores. Using
this table, I convert the Z’’ scores to bond ratings. Table 2 from Altman (1997) presents
57
historical ten year default probabilities by bond ratings. Using this table, I assign default
probabilities to each bond rating. Thus, the Z’’ scores are mapped into default probabilities.
Table 15 presents these ten year default probabilities for the pre-merger firms and the merged
firm. The distribution of default probabilities is similar to the distribution of Z’’-scores. The
targets are at higher risk than acquirers. The default probability of the combined firm is, more
often than not, similar to the acquirer. Most mergers reduce default risk of targets, particularly
benefiting those targets that are otherwise close to distress without the merger.
The next step is computing coinsurance benefits. As discussed in the theory section, default
is costly because of two reasons: first, in default, a portion of the firm’s value is lost; second, in
default, the tax benefit of debt is also lost. Knowing only the probability of default, it is not
possible to estimate the loss of tax benefit, because the tax benefit is a flow. So, instead, I
compute the coinsurance benefit stemming from the reduction in expected cost of distress:
∆= − 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 𝑃𝑃 𝐶𝐶 𝛼𝛼 𝐷𝐷 𝐶𝐶 + 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 𝑃𝑃 1
𝛼𝛼 𝐷𝐷 1
+ 𝑒𝑒 − 𝑟𝑟 𝑇𝑇 𝑃𝑃 2
𝛼𝛼 𝐷𝐷 2
, (D3)
The first term represents the expected cost of distress of the merged firm. The second and third
terms represent the expected cost of distress of the pre-merger firms. Since the ten year
default probabilities do not specify when the firm defaults, I assume 𝑇𝑇 = 5 𝑒𝑒𝑒𝑒𝑅𝑅 𝑟𝑟𝑠𝑠 . Also, I
assume that when the firm defaults its value is the debt level. To be consistent with the
structural model, I assume 𝛼𝛼 to be 16.5%, the mid-point of Andrade and Kaplan’s estimate.
The coinsurance benefits are presented in the last row of Table 16. The mean coinsurance
benefit is 0.03%. The median is 0.00%. The 75
th
percentile is still 0.00%, whereas the 99
th

percentile is 1.86%. The maximum value is 4.05%. These numbers suggest that coinsurance
benefits are material only for a minority of mergers, echoing the result from the structural
58
model. Note that, in general, the coinsurance benefits from this accounting approach are
smaller than those from the structural model. The smaller benefits may be because the
accounting approach does not consider the extinguishment of tax shields on distress. Another
difference is that the accounting approach estimates negative coinsurance benefits for a few
mergers, whereas the coinsurance benefits estimated by the structural model are always
positive.

59
Table 1
Summary Statistics: Mergers and Randomly Paired Firms
Panel A of this table presents the summary statistics on a sample of mergers in the United States for
which data is available from SDC, CRSP, and COMPUSTAT, conditional on satisfying the following criteria:
(1) announcement date was between 1981 and 2013; (2) deal size was above $10 million (2013 dollars);
(3) the transaction was completed; (4) the acquirer did not have more than a 5% stake in the target
before the merger; (5) the acquirer owns more than 99% of the target after the merger; (6) at least one
of the target and the acquirer have some debt outstanding (7) neither the acquirer nor the acquirer is a
financial firm. Panel B presents the summary statistics on a sample of randomly paired US firms. This
sample is created by drawing target and acquirer firms from COMPUSTAT at random. For comparability,
this sample comprises 1,884 randomly paired firms, between 1981 and 2013, and only firms valued
more than $10 million are included. All prices are in 2013 Dollars. The Cumulative abnormal return is
computed over a three day window around merger announcement (-1, 1) using the Fama-French three
factor model. Detailed variable descriptions are available in Appendix A.
Variable
Mean SD Median Min Max
Panel A: Mergers in the US between 1981 and 2013
Size, acquirer ($ billion) 10.0 28.5 1.1 0.002 397.2
Size, target ($ billion) 0.7 3.0 0.1 0.001 56.3
Leverage, acquirer (%) 17.8 17.2 12.6 0.0 82.20
Leverage, target (%) 20.1 21.5 13.2 0.0 96.4
Cumulative abnormal return (%) 1.5 8.0 1.0 -38.5 57.8
Cash flow correlation 0.09 0.46 0.09 -1.00 1.00
Panel B: Randomly Paired Firms in the US between 1981 and 2013
Size, acquirer ($ billion) 1.8 10.0 0.14 0.01 230.0
Size, target ($ billion) 1.5 10.0 0.14 0.01 332.3
Leverage, acquirer (%) 30.6 32.4 15.6 0.0 99.8
Leverage, target (%) 28.6 31.5 14.2 0.0 99.8
Cumulative abnormal return (%) -0.0 4.5 -0.1 -31.3 33.0

60
Table 2
Inputs to the Structural Model
This table presents the inputs to the structural model. Panel A pertains to the mergers and Panel B to
the sample of randomly paired firms. For simulating the model, I assume that the economy wide
parameters are given by: the tax rate, 𝜏𝜏 = 35%; the payout rate, 𝛿𝛿 = 1%; and, the proportion of value
lost in default, 𝛼𝛼 = 16.5%. All the inputs are annualized.
Variable Mean SD Median Min Max
Panel A: Mergers in the US between 1981 and 2013
Asset return correlation, 𝜌𝜌 0.23 0.21 0.18 -0.18 0.99
Volatility of asset return, target (%), 𝜎𝜎 1
53.9 28.4 47.0 11.7 179.4
Volatility of asset return, acquirer (%),
𝜎𝜎 2

41.6 20.7 34.7 11.0 224.4
Coupon rate, target (%),
𝐶𝐶 1
𝑋𝑋 1
( 0)
1.8 2.1 1.1 0.0 15.7
Coupon rate, acquirer (%),
𝐶𝐶 2
𝑋𝑋 2
( 0)
1.4 1.7 0.8 0.0 13.5
Default barrier, target (%),
𝐾𝐾 1
𝑋𝑋 1
( 0)
6.2 7.6 3.1 0.0 44.8
Default barrier, acquirer (%),
𝐾𝐾 2
𝑋𝑋 2
( 0)

5.9 6.7 3.4 0.0 39.5
Panel B: Randomly Paired Firms in the US between 1981 and 2013
Asset return correlation, 𝜌𝜌 0.15 0.18 0.15 -0.39 0.99
Volatility of asset return, target (%), 𝜎𝜎 1
47.4 25.0 40.5 8.7 188.7
Volatility of asset return, acquirer (%),
𝜎𝜎 2

47.5 26.4 40.0 6.7 216.8
Coupon rate, target (%),
𝐶𝐶 1
𝑋𝑋 1
( 0)

2.0 2.5 1.1 0.0 23.3
Coupon rate, acquirer (%),
𝐶𝐶 2
𝑋𝑋 2
( 0)

2.0 2.4 1.1 0.0 17.7
Default barrier, target (%),
𝐾𝐾 1
𝑋𝑋 1
( 0)
7.3 8.7 3.8 0.0 56.7
Default barrier, acquirer (%),
𝐾𝐾 2
𝑋𝑋 2
( 0)

7.3 8.8 3.9 0.0 67.6
61
Table 3
Estimated Coinsurance Benefits for the Sample of Mergers
Rows 1 to 4 of this table presents the distribution of the estimated coinsurance benefits, for a sample of 1,884 mergers in the United States
between 1981 and 2013. Data on the mergers is collected from SDC, security prices from CRSP, and accounting variables from COMPUSTAT.
Coinsurance benefit is the gain in value, when two firms merge, due to reduction in the probability of costly distress. The coinsurance benefit is
estimated by simulating a structural model. It is expressed as a percentage of combined—target plus acquirer—pre-merger value. For estimating
coinsurance benefits, I assume the following economy-wide parameters: a fixed cost of distress, 𝜙𝜙 , expressed in $ millions; a proportional cost,
𝛼𝛼 , which is the percentage of firm value lost at default; a payout rate, 𝛿𝛿 = 1%; and a tax rate, 𝜏𝜏 = 35%. As a benchmark for comparing the
economic magnitude of the coinsurance benefits, Row 5 presents the distribution of cumulative abnormal returns.

Cost of distress
Mean SD
Percentiles
Fixed, 𝜙𝜙 Proportional, 𝛼𝛼 0% 1% 25% 50% 75% 90% 95% 99% 100 %
0.00 16.5 0.32 0.58

0.00 0 .00 0.01 0.09 0.37 0.94 1.44 2.83 5.23

1.32 16.5 0.35 0.61

0.00 0.00 0.01 0.10 0.40 1.01 1.53 3.17 5.25

1.32 23.0 0.36 0.64

0.00 0.00 0.01 0.10 0.41 1.07 1.58 3.26 5.55

1.32 45.0 0.40 0.71

0.00 0.00 0.01 0.11 0.46 1.20 1.76 3.60 6.58

Cumulative Abnormal
Return [-1,1]
1.54 7.99

-38.50 -21.13 -1.97 1.05 4.85 10.24 13.89 26.29 57.80

62

Table 4
Estimated Coinsurance Benefits for the Sample of Randomly Paired Firms
This table presents the estimated coinsurance benefits for a sample of 1,884 randomly paired firms. The sample is constructed by drawing the
target and acquirer from COMPUSTAT, at random. Coinsurance benefit is the gain in value, when two firms merge, due to reduction in the
probability of costly distress. The coinsurance benefit is estimated by simulating a structural model. It is expressed as a percentage of
combined—target and acquirer—pre-merger value. For estimating coinsurance benefits, I assume the following economy-wide parameters: a
fixed cost of distress, 𝜙𝜙 , expressed in $ millions; a proportional cost, 𝛼𝛼 , which is the percentage of firm value lost at default; a payout rate, 𝛿𝛿 =
1%; and a tax rate, 𝜏𝜏 = 35%.
Cost of distress
Mean SD
Percentiles
Fixed, 𝜙𝜙 Proportional, 𝛼𝛼 0% 1% 25% 50% 75% 90% 95% 99% 100 %
0.00 16.5 0.70 1.2 6 0.00 0.00 0.04 0.23 0.76 1.89 3.18 6.48 12.66

1.32 16.5 0.73 1.28 0.00 0.00 0.04 0.25 0.80 2.04 3.32 6.46 12.67

1.32 23.0 0.76 1.33 0.00 0.00 0.05 0.26 0.82 2.09 3.41 6.67 13.49

1.32 45.0 0.84 1.48 0.00 0.00 0.05 0.27 0.90 2.30 3.88 7.52 16.34

63

Table 5
Coinsurance and Merger Likelihood
Each column of this table reports a logistic regression, for a sample containing 1,884 mergers and 1,884
randomly paired firms. The dependent variable is a dummy for merger incidence, which is 1 for a merger
and 0 otherwise. The main explanatory variable is the estimated coinsurance benefit, defined as the
gain in value when two firms merge, due to reduction in the probability of costly distress. Coinsurance is
expressed as a percentage of combined—target plus acquirer—pre-merger value. . The multivariate
regression includes year fixed effect. Standard errors are in parentheses. *** , **, or * represent
statistical significance at the 1%, 5%, or 10% level, respectively.
(1) (2)
Estimated Coinsurance benefit
-0.52
***

(0.05)
-0.43
***

(0.09)
Return correlation

5.87
***

(0.53)
Dummy=1 if same SIC Code
4.11
***

(0.30)
Market-to-Book, acquirer

-0.08
***

(0.02)
Market-to-Book, target

-0.49
***

(0.03)
Size, target

-0.12
***

(0.04)
Size, relative

-0.81
***

(0.04)
Leverage, acquirer
0.30
(0.29)
Leverage, target

0.86
***

(0.27)
Constant
0.23
***

(0.04)
-1.72
***
(0.40)
Pseudo 𝑅𝑅 �
2
0.05 0.57
Observations 3,768 3,768
64

Table 6
Estimated Coinsurance Benefits for Related and Diversifying Mergers
This table presents the distribution of the estimated coinsurance benefit for related and diversifying mergers. A merger is classified as
diversifying when neither of the divisions of target and acquirer have any three digits SIC code in common. Coinsurance benefit is the gain in
value, when two firms merge, due to reduction in the probability of costly distress; it is estimated by simulating a structural model. The
coinsurance benefit is expressed as a percentage of combined pre-merger value of the target and the acquirer. For estimating coinsurance
benefits, I assume fixed cost of distress, 𝜙𝜙 , expressed in $ millions; a proportional cost, 𝛼𝛼 , which is the percentage of firm value lost at default;
the payout rate, 𝛿𝛿 = 3%; and the tax rate, 𝜏𝜏 = 35%.
Type of
Merger N Mean SD
Percentiles
0% 1% 25% 50% 75% 90% 95% 99% 100 %
Diversifying 265 0.33 0.62

0.00 0.00 0.01 0.11 0.36 0.97 1.51 3.70 4.82

Related 1,619 0.32 0.57

0.00 0.00 0.01 0.09 0.37 0.94 1.43 2.74 5.23

65

Table 7
Estimated Coinsurance Benefits Compared to Target and Acquirer Size
Rows 1 to 3 of this table presents the distribution of the estimated coinsurance benefits, for a sample of 1,884 mergers in the United States
between 1981 and 2013. Row 1 expresses coinsurance benefits as a fraction of pre-merger Target firm value, row 2 as a fraction of pre-merger
Acquirer firm value, and row 3 as a fraction of combined pre-merger Target and Acquirer firm value. Coinsurance benefit is the gain in value,
when two firms merge, due to reduction in the probability of costly distress. The coinsurance benefit is estimated by simulating a structural
model. It is expressed as a percentage of combined—target plus acquirer—pre-merger value. For estimating coinsurance benefits, I assume the
following economy-wide parameters: a proportional cost, 𝛼𝛼 = 16.5%, the percentage of firm value lost at default; a payout rate, 𝛿𝛿 = 1%; and a
tax rate, 𝜏𝜏 = 35%.
Coinsurance Benefit Mean SD
Percentiles
0% 1% 25% 50% 75% 90% 95% 99% 100 %
Target 2.40 3.72

0.00 0 .00 0.15 0.93 3.10 6.62 9.49 17.06 49.03

Acquirer 0.51 1.22

0.00 0.00 0.01 0.11 0.50 130 2.32 5.44 26.99

Combined 0.32 0.58

0.00 0 .00 0.01 0.09 0.37 0.94 1.44 2.83 5.23

66

Table 8
Default Probabilities
Rows 1 to 3 present ten year default probabilities computed under the risk neutral measure from the structural model. Rows 4 to 5 present
reduction in default probability for the target and acquirer (Pre-merger default probability- Post-merger default probability).
Variable Mean SD
Percentiles
0% 1% 5% 10% 25% 50% 75% 90% 99% 100%
Probability, Target 0.28 0.40

0.000 0.000 0.000 0.000 0.000 0.004 0.68 0.99 1 1
Probability, Acquirer 0.15 0.31

0.000 0.000 0.000 0.000 0.000 0.000 0.04 0.79 0.99 1
Probability, Merged
firm
0.13 0.29

0.000 0.000 0.000 0.000 0.000 0.000 0.02 0.63 0.98 1
Reduction in Default
Probability, Target
0.15 0.37

-1 -0.81 -0.15 -0.01 0.00 0.00 0.20 0.87 1 1
Reduction in Default
Probability, Acquirer
0.02 0.16

-1 -0.46 -0.04 -0.00 0.00 0.00 0.00 0.04 0.86 1

67

Table 9
Correlation with Estimated Coinsurance Benefit
Each row of this table presents the correlation of the listed variable with the estimated coinsurance
benefits. Coinsurance benefit is the gain in value, when two firms merge, due to reduction in the
probability of costly distress; it is estimated by simulating a structural model. P-values are in
parentheses.
***
,
**
, or
*
represent statistical significance at the 1%, 5%, or 10% level, respectively.

Correlation Observations
Cash flow correlation
0.02
(0.51)
1,179
Return correlation
-0.05
**

(0.02)
1,884
Size, acquirer
-0.30
***

(0.00)
1,884
Size, target
-0.05
**

(0.02)
1,884
Leverage, acquirer
0.37
***

(0.00)
1,872
Leverage, target
0.54
***

(0.00)
1,868
Coinsurance Index
0.67
***

(0.00)
1,858

68

Table 10
Regression of Cumulative Abnormal Return
Each column of this table reports a cross-sectional regression of cumulative abnormal return (CAR). CAR
(target + acquirer) is computed over a three day window around merger announcement [-1, 1], using
the Fama-French three factor model. The main explanatory variable is the estimated coinsurance
benefit. For specifications, (1) and (2), the coinsurance benefits are expressed as a percentage of
combined—target and acquirer—pre-merger firm value. For specifications, (3) and (4), the coinsurance
benefits are expressed as a percentage of combined pre-merger equity value. CAR is also expressed as
percentage. All multivariate regressions include year fixed effect. Heteroskedasticity consistent White
standard errors are in parentheses.
***
,
**
, or
*
represent statistical significance at the 1%, 5%, or 10%
level, respectively.
(1) (2) (3) (4)
Coinsurance benefit (% of firm value)
1.94
***

(0.34)
0.89
**

(0.42)

Coinsurance benefit (% of equity value)
1.26
***

(0.25)
0.43
(0.33)
Dummy=1 if diversifying

0.05
(0.54)
-0.57
(0.46)
Dummy=1 if all stock

-1.96
***

(0.52)
-1.83
***

(0.44)
Dummy=1 if all cash

2.12
***

(0.50)
1.81
***

(0.44)
Tobin’s Q, acquirer

-0.28
(0.27)
-0.38
(0.25)
Tobin’s Q, target

-1.08
***

(0.39)
-0.99
***

(0.37)
Size, target
-0.36
***

(0.12)

-0.32
***

(0.11)
Size, relative

0.89
***

(0.14)
0.75
***

(0.13)
Leverage, acquirer

0.98
(1.39)
-0.55
(1.47)
Leverage, target

-2.44
*

(1.26)
-2.33
**

(1.09)
Constant
1.77
***

(0.34)
6.63
***

(1.44)
1.36
*

(0.82)
6.27
***

(1.27)
Adjusted 𝑅𝑅 �
2
0.03 0.11 0.02 0.10
Observations 1,884 1,676 1,884 1,676

69

Table 11
Regression of Cumulative Abnormal Return for Related and Diversifying Mergers
Each column reports a cross-sectional regression of cumulative abnormal return (%).The main
explanatory variable is the estimated coinsurance benefit (%). Specification (1) and (2) correspond to the
diversifying mergers and specifications (3) and (4) to related mergers. A merger is classified as
diversifying when none of the divisions of target or acquirer have any three-digit SIC code in common.
All regressions include year fixed effect. Heteroskedasticity consistent White standard errors are in
parentheses.
***
,
**
, or
*
represent statistical significance at the 1%, 5%, or 10% level, respectively.
(1) (2) (3) (4)
Estimated Coinsurance benefit
1.70
**

(0.66)
1.61
(1.01)
1.97
***

(0.38)
0.92
**

(0.48)
Dummy=1 if all stock

-2.47
(1.74)
-1.97
***

(0.54)
Dummy=1 if all cash

1.38
(1.60)
2.10
***

(0.54)
Tobin’s Q, acquirer

0.37
(0.76)
-0.38
(0.29)
Tobin’s Q, target

-1.47
(0.93)
-1.03
***

(0.43)
Size, target
-0.68
*

(0.40)

-0.34
***

(0.13)
Size, relative

0.65
*

(0.33)
0.90
***

(0.16)
Leverage, acquirer

3.40
(3.78)
0.54
(1.48)
Leverage, target

-6.86
*

(3.64)
-2.11
(1.33)
Constant
0.20
(0.88)
5.28
**

(2.65)
2.31
*

(1.30)
7.22
***

(1.71)
Adjusted 𝑅𝑅 �
2
0.06 0.08 0.04 0.11
Observations 265 236 1,619 1,440

70

Table 12
Regression of Cumulative Abnormal Return: Robustness Tests
Specifications (1) and (2) pertain to the sample of mergers. Specification (1) reports a quintile (median)
regression of CAR on estimated coinsurance benefits. Specification (2) reports a cross-sectional
regression of CAR on a dummy for high coinsurance: the dummy is 1 when estimated coinsurance
benefits exceed 1%. Specification (3) pertains to the sample of randomly paired firms; it reports a cross-
sectional (placebo) regression of CAR on estimated coinsurance benefits (%). All regressions include year
fixed effect. Standard errors are in parentheses.
***
,
**
, or
*
represent statistical significance at the 1%,
5%, or 10% level, respectively.
(1) (2) (3)
Estimated Coinsurance benefit
1.12
***

(0.49)
0.07
(0.13)
Dummy=1 if coinsurance >1%
1.72
**

(0.13)

Cash flow correlation

Dummy=1 if diversifying
0.58
(0.37)
0.08
*

(0.55)
-1.57
*

(0.92)
Dummy=1 if all stock
-1.17
***

(0.32)
-1.97
***

(0.52)

Dummy=1 if all cash
1.33
***

(0.31)
2.12
***

(0.50)

Tobin’s Q, acquirer
-0.40
*

(0.21)
-0.29
(0.27)
-0.01
(0.11)
Tobin’s Q, target
-0.75
***

(0.19)
-1.07
***

(0.14)
-0.00
(0.13)
Size, target
-0.25
**

(0.10)
-0.37
**

(0.12)
0.02
(0.12)
Size, relative
0.65
***

(0.10)
0.91
***

(0.14)
-0.10
(0.86)
Leverage, acquirer
-1.69
*

(0.90)
-1.14
(1.38)
-0.57
(1.84)
Leverage, target
-2.27
***

(0.86)
-2.27
*

(1.20)
-0.78
(1.10)
Constant
5.09
***

(1.24)
6.75
***

(0.10)
1.27
(1.24)
Adjusted 𝑅𝑅 �
2
NA 0.11 -0.03
Observations 1,676 1,676 693

71

Table 13
Out-of-Sample Performance of Structural Model in Predicting Bankruptcy
Each column of this table reports a logistic regression where the dependent variable is bankruptcy
incidence: a dummy that is one if the post-merger acquirer files for bankruptcy within ten years of the
merger announcement, and zero otherwise. The main explanatory variable is the ten-year-ahead
probability of default, estimated by the structural model, using only pre-merger information. Probability
of default, both under the risk-neutral measure and physical measures, are used. Data on bankruptcy is
obtained from the UCLA-LoPucki Bankruptcy Research Database (BRD), which lists all large, public
company bankruptcies filed in the United States Bankruptcy Courts since October 1, 1979. Standard
errors are in parentheses.
***
,
**
, or
*
represent statistical significance at the 1%, 5%, or 10% level,
respectively.

(1) (2) (3) (4) (5)
Probability of Default (Risk
Neutral)
2.64
***

(0.92)
3.30
***

(0.77)

Probability of Default
(Physical)
0.80
***

(0.38)

1.16
***

(0.35)
1.26
***

(0.25)
Return correlation
-1.25
(1.03)
-1.48
(1.06)

-1.56
*

(0.89)
Dummy=1 if diversifying
0.15
(0.36)
0.17
(0.37)

Size, target
0.32
***

(0.13)
0.34
***

(0.14)

Size, acquirer
-0.51
***

(0.11)
-0.49
***

(0.11)

Leverage, acquirer
1.73
***

(0.73)
1.60
***

(0.73)

Leverage, target
0.48
(0.63)
0.62
(0.65)

Constant
-2.13
***

(0.58)
-2.24
***

(0.58)
-3.56
***

(0.14)
-3.60
***

(0.15)
-3.17
***

(0.17)
Pseudo 𝑅𝑅 �
2
0.12 0.13 0.01 0.01 0.00
Observations 1,858 1,858 1,884 1,884 1,884

72

Table 14
Probability of Default: Ex-ante and Ex-post
Panel A of this table presents the probability that the merged firm defaults within ten years from the
merger, for a sample of 1,884 US Mergers; this probability is computed by a structural model that uses
only pre-merger information. The sample of mergers is sorted on default probability into five quintiles.
Each column corresponds to the median default probability for the firms in the quintile. For comparison,
the first row of Panel B presents the ten-year-ahead historical default probabilities by Moody’s bond
ratings, for all US firms that are rated by Moody’s. The second row of Panel B presents risk adjusted
probabilities from Almeida and Philippon (2007) for the universe of US firms. All information presented
in Panel B is obtained from Table 2 of Elkamhi, Ericsson and Parsons (2012).
Risk Quintiles Highest 2 3 4 Lowest
Panel A: Probability of default for the sample of mergers predicted by the structural model
Risk Neutral 0.125 0.017 0.002 0.000 0.000
Physical 0.531 0.008 0.000 0.000 0.000
Panel B: Probability of default for the universe of firms by bond ratings
Moody’s Bond Rating B Ba Baa A Aa/Aaa
Objective default probabilities
(Moody’s)
0.465 0.215 0.052 0.016 0.009
Risk Adjusted Probabilities
(Almeida and Philippon (2007))
0.625 0.392 0.209 0.127 0.042

73

Table 15
Altman Z” Scores for Target, Acquirer, and Merged Firm
This table summarizes the modified Altman Z” scores (Altman (1997)). Z’’ score is given by
6. 7 𝐸𝐸𝐸𝐸 𝐸𝐸 𝑇𝑇 + 3. 3 𝑅𝑅 𝑆𝑆𝑡𝑡 𝑎𝑎𝑖𝑖 𝑅𝑅 𝑆𝑆𝑑𝑑 𝐸𝐸 𝑎𝑎𝑟𝑟𝑅𝑅 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝑐𝑐 + 6. 6 𝑊𝑊 𝑜𝑜𝑟𝑟 𝑊𝑊 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝐶𝐶 𝑎𝑎𝑑𝑑 𝑖𝑖 𝑡𝑡 𝑎𝑎𝑑𝑑
𝑇𝑇 𝑜𝑜𝑡𝑡 𝑎𝑎𝑑𝑑 𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐
+1.05
𝐸𝐸 𝑖𝑖 𝐸𝐸 𝑖𝑖 𝑡𝑡 𝐸𝐸 𝐿𝐿 𝑖𝑖 𝑎𝑎𝑖𝑖𝑖𝑖 𝑑𝑑 𝑖𝑖 𝑡𝑡 𝐸𝐸 + 3.25. A lower Z”-score is associated with higher likelihood of distress. ΔZ”-Score for target (acquirer) is defined as Z”-Score
merged firm- Z”-Score target (acquirer). A positive ΔZ”-Score suggests that the merger can potentially reduce likelihood of distress. Number of
observations is 1,969.
Z-score Mean SD
Percentiles
0% 1% 5% 10% 25% 50% 75% 95% 99% 100 %
Target 3.2 8.3

-60.7 -23.8 -7.8 -2.2 1.1 3.6 6.4 12.8 22.1 60.7
Acquirer 4.0 6.7

-45.2 -10.4 -1.0 0.5 1.9 3.7 5.8 10.4 15.5 221.7
Merged firm 3.6 4.2

-38.5 -8.8 -1.2 0.5 1.9 3.6 5.4 9.0 12.2 86.6
ΔZ”-Score Acquirer -0.42 4.1

-135 -11.2 -3.2 -1.6 -0.4 -0.0 0.1 1.3 5.5 33.9
ΔZ”-Score Target 0.35 7.4

-58.0 -17.3 -8.3 -5.0 -2.0 0.0 1.8 9.6 23.8 85.6

74

Table 16
Default Probabilities a from Altman Z” Scores
Rows 1 to 3 present ten year default probabilities, mapped from Z” score. Z’’ score is given by
6. 7 𝐸𝐸𝐸𝐸 𝐸𝐸 𝑇𝑇 + 3. 3 𝑅𝑅 𝑆𝑆 𝑡𝑡 𝑎𝑎𝑖𝑖 𝑅𝑅 𝑆𝑆 𝑑𝑑 𝐸𝐸 𝑎𝑎𝑟𝑟𝑅𝑅 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝑐𝑐 + 6. 6 𝑊𝑊 𝑜𝑜𝑟𝑟 𝑊𝑊 𝑖𝑖 𝑅𝑅 𝐸𝐸 𝐶𝐶 𝑎𝑎𝑑𝑑𝑖𝑖 𝑡𝑡 𝑎𝑎𝑑𝑑
𝑇𝑇 𝑜𝑜𝑡𝑡 𝑎𝑎𝑑𝑑 𝐴𝐴 𝑐𝑐𝑐𝑐𝑆𝑆 𝑡𝑡 𝑐𝑐
+1.05
𝐸𝐸 𝑖𝑖 𝐸𝐸 𝑖𝑖 𝑡𝑡 𝐸𝐸 𝐿𝐿 𝑖𝑖 𝑎𝑎𝑖𝑖𝑖𝑖 𝑑𝑑 𝑖𝑖 𝑡𝑡 𝐸𝐸 + 3.25. Row 4 and 5 presents ΔProbability target (acquirer), which is defined as Probability of default of merged firm-Probability
of default of target (acquirer). Row 6 presents the coinsurance benefits. Number of observations is 1,969.
Variable Mean SD
Percentiles
0% 1% 5% 10% 25% 50% 75% 90% 99% 100%
Probability, Target 25.9 40.8

0.01 0.01 0.01 0.01 0.1 4.1 8.4 100 100 100
Probability, Acquirer 16.0 31.6

0.01 0.01 0.01 0.01 0.4 4.1 8.4 100 100 100
Probability, Merged
firm
17.3 32.9

0.01 0.01 0.01 0.10 1.6 4.1 8.4 100 100 100
Δ Probability,
Acquirer
1.3 21.5

-100 -91.7 -4.3 -0.00 0.00 0.00 0.00 4.3 91.7 100
Δ Probability, Target -8.6 40.4

-100 -100 -95.9 -91.7 -2.5 0.0 1.6 8.3 99.6 100
Coinsurance Benefit 0.03 0.44

-2.85 -1.59 -0.01 0.00 0.00 0.00 0.00 0.06 1.86 4.05

75

Figures
Figure 1. Distribution of Coinsurance for Mergers and Randomly Paired Firms
Y-axis represents probability density, and X-axis represents estimated coinsurance benefits (%).
The solid line corresponds to the sample of mergers, while he dotted line corresponds to the
sample of randomly paired firms. A Kernel estimator is used to estimate the density.

76

Figure 2. Distribution of Coinsurance for Related and Diversifying Mergers
Y-axis represents probability density, and X-axis represents estimated coinsurance benefits (%).
The solid line corresponds to sample of related mergers, while the dotted line corresponds to
the diversifying mergers. A Kernel estimator is used to estimate the density.

77

Figure 3. Coinsurance Index as a Proxy for Coinsurance Benefits
The Y-axis of this scatter-plot pertains to coinsurance benefits (%), estimated by simulating the
structural model. The X-axis pertains to the Coinsurance Index (%), estimated by fitting a LASSO
Regression. The red reference line represents the 45 degree line, on which the dots should align
if the fit was perfect.

78

Figure 4. Cash Flow Correlation as a Proxy for Coinsurance Benefits
The Y-axis of this scatter-plot pertains to coinsurance benefits (%), estimated by simulating the
structural model. The X-axis pertains to correlation between the cash flows of the target and
acquirer.

79

Abstract (if available)

Abstract Using a structural model, I estimate the value gain from coinsurance for a sample of mergers. For most mergers, estimated gains from coinsurance are small—smaller than the counterfactual gains if firms were to merge randomly——suggesting that coinsurance is not the primary motivation for most mergers. Coinsurance from diversifying mergers is also small, comparable to related mergers. Even though coinsurance is small compared to combined target and acquirer firm value, it is not small when compared to deal size, or target size. For a quarter of the sample, targets are at significant risk of default without the merger

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Asset Metadata

Creator Sarkar, Sakya (author)

Core Title How large are the coinsurance benefits of mergers?

School Marshall School of Business

Degree Doctor of Philosophy

Degree Program Business Administration

Publication Date 10/20/2016

Defense Date 03/12/2015

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag abnormal return,coinsurance,default risk,diversification,merger,OAI-PMH Harvest,simulations

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Matsusaka, John G. (committee chair), Ahern, Kenneth (committee member), Joslin, Scott (committee member), Ogneva, Maria (committee member), Wang, Yongxiang (committee member)

Creator Email sakya.sarkar@gmail.com,sakyasar@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-554344

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