pg n/a THREE ESSAYS IN CORPORATE FINANCE AND INSURANCE by QIN LIAN A DISSERTATION
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy
in the Department of Economics, Finance, and Legal Studies
in the Graduate School of The University of Alabama
TUSCALOOSA, ALABAMA
Trang 2UMI Number: 3270497
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Trang 4Submitted by Qin Lian in partial fulfillment of the requirements for the degree of Doctor of Philosophy specializing in Finance
Accepted on behalf of the Faculty of the Graduate School by the dissertation committee: ¿ ⁄2/z Date Ll pared OF- Date Janes Ligon, PH.D Junsoo Lee, Ph.D ID = Mary Stone, Ph.D Kea 8 Anup-Agrawal, Ph.D Co-Chair Ac Stes Harris Schlesinger, ĐẶ.D Co-Chair 6 /2/—— Billy P felms, Ph.D Department Chairman LU rene David A Francko, Ph.D
Dean of the Graduate School
Trang 5I would like to thank my advisors, Professors Anup Agrawal and Harris
Schlesinger, whose advice, guidance, and patience made the completion of this
dissertation possible Their deep insights have been inspiring and their thorough approach
to do research set a model for me I am grateful to other committee members, Professors James Ligon, Junsoo Lee, and Mary Stone for their suggestions I am indebted to
Professor Billy Helms for the financial support through my doctoral program
My sincere thanks also go to the faculty, fellow students, and the staff of the
Economics, Finance, and Legal Studies Department for their help during my graduate
study I offer special thanks to Tommy Cooper, Marsha James, Jones Travis and Christie Parker for their friendship They have edited my dissertation many times and listened to
my frustration I also have been blessed by many friends from TCCF Professor Hanna Chen has been a mother-figure for me; anytime I need advice I would never hesitate to ask her
Last, yet most important, I thank God for leading me through the whole process |
dedicate this dissertation to my parents and my husband Qiming Mom and dad always encourage me for all my endeavors Qiming takes many challenges with me in pursuing my goal Without their love and support, I would quit the program long time ago
Trang 6CONTENTS
Y0) 9À)40)0)9.6)./5))ïbaiiaaaẳẳaắáảả3áảăááiải ili LIST OF TABLES 2000 — vil LIST OF FIGURES 00.0 «4 ix S700 .- ad X
1 INTRODUCTION 002 2 INSURER INFORMATION, FRAUD, AND
INSURANCE CONTRACT DESIƠN ch se se 4
PT PMA(((vVAddadddidđiididđdiiidddd 4
2.2 A model with insurer information about losses 6 2.3 The optimal COnIrACf nQQ HH ng nu ng nà ng ng nà 12 2.3.1 Optimal contract under perfect competition .- 12
2.3.2 Pareto-efficient contracts .cc ccc cccccesesesasuesesecesneenegensneseseenenens 14
OAs Pa OX) 00) 0S (0) 6 (4 16
2D Appendix cece cece cece eee ence eee nee ene e ene tenet eet eee tebe ee eeaetenaeenaa tess 18
3 THE DUAL TRACKING PUZZLE:
WHEN IPO PLANS TURN INTO MERGERS 21
Trang 73.3.2 Multivarlate anaÌyS€S ĐH n HH ng km nà nhện 31
EESVAND on 32
CEO II CC vs o0 nh .‹43 33
3.4 Acquirers’ announcement period returns . . - 35 3.5 Concluding remarks 00 ccc ccc e cece cence nee eee c een eeeeeeetengeeeeeensetanegs 38
4 DOES THE MARKET INCORPORATE
PREVIOUS IPO WITHDRAWALS
WHEN PRICING SECOND-TIME IPOS? HH nhe 52
4.1 — Introduction - cv HH HH ng HH ĐK n nà nh ke nh ve 52
4.2 Data 20810 1 55 4.3 DeSCTIPtiVe StALISLICS 2.0.0.0 ececcececcececeeeeeeeeeateeecseeteeeeeeteueeeenenseaees 58
4.3.1 Firm and issue characf€rISfICS .-c HH nàn nà 58
4.3.2 Market condition comparisons ¬ 60 4.4 Offering valuations of second-tine IPOS .- - 7< 62
4.4.1 Second-time IPOs vs first-time IPOS cọ nhe 62 4.4.2 Second-time IPOs: successful second offering vs
fatled Íirsf OÍfering .- HH HH HH ng kh ng khe 65
4.4.3 Discount at the Immtial fñlíng or after bookbuilding?2 66 4.5 Initial returns: second-time IPOs vs first-time IPOs 68
4.6 Long run performance: second-time IPOs vs
it)00)0001181090.0 70 4.6.1 Long run stock perÍOrmance€ ch nh ky sa 71
Trang 93.1 3.2 3.3 3.4 3.5 3.6 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10
Frequency distribution of [PO filings and subsequent takeovers 4]
Characteristics of acquisitions of dual tracking private targets and matching farĐ€S c con HH ng HH nen ngư nà bên 44 Valuation ratios of dual tracking private targets and matching targets 46
Regressions of valuation ratios for dual tracking private targets and matching targets .cccccccccececenceseeteaeeteseeseueceenecaeeneegensenense 47 Acquirers’ announcement period CARs over days (-l, +1) 49
Regressions for acquirers' CARs for acquisitions of dual tracking private ftargets and matching targefS - con nà 51 Distribution of completed IPOs ,withdrawn IPOs, and second-time IPOs 79
Firm characterIstics for second-time IPOs and ñirst-time [POs 81
[ssue characteristics for second-time IPOs and first-time IPOs 83
Descriptive statistics of market condifions 85
Offering valuations: second-time IPOs vs first-time IPOs - 88
Valuation ratio regressions: second-time IPOs vs first-time IPOs 89
Valuation ratios of second-time IPOs: successful 2nd offerings vs failed Ist Offerings .- 91
Valuation ratio regressions: successful 2nd offerings vs failed 1st Offerings vn vn 92 Expected valuation ratio regressions: second-time IPOs vs first-time IPOS 0.0.0 00 ccc cece eee ee ec cence eee ee nee eu ease 94 Price revision regressions: second-time IPOs vs first-time IPOs 96
Trang 104.11 4.12 4.13 4.14
Initial return regressions: second-time IPOs vs first-tine IPOs 97
Long run stock performance: BHAR cà 99 Long run stock performance: AR 2.0.0 cece cece cence ence nee e ee ence tees ene e eg es 100 Long run operating perÍOrmanC€ .ccc n HH Hs re 102
Vill
Trang 112.1 Optimal contracts with Insurer Information .- 17
3.1 Length of time from IPO to takeover c2 40
4.1 Time gap between first-time IPO withdrawal date
and second-trne [PO dafte - ng SH ng HH nh nen nà 78
Trang 12ABSTRACT
This dissertation contains three essays in the area of insurance and corporate finance
The first essay examines the optimal contract in the presence of an insurer’s Opportunistic behavior when he has private information about loss amounts This optimal contract designates full insurance up to a limit and a constant payment above that limit It
is the mirror image of the optimal contract that Kaplow (1994) and Bond and Crocker
(1997) find when an insured may file a fraudulent claim, The optimal contract with a constant payment for large losses can be generalized to any Pareto-efficient contract
The second essay examines why dual tracking firms — private firms entertaining acquisition offers at the same time as preparing for initial public offerings (IPOs) — withdraw their IPOs after spending considerable time, money, and effort preparing for the
IPO only to be purchased by public acquirers We argue that in dual tracking, filing IPO
registrations reduces the valuation uncertainty between the private targets that withdraw their IPOs and their bidders We document that such private targets sell at a 58 percent acquisition premium relative to comparable private targets that never file IPO registrations, while their acquirers still earn a substantial average abnormal
announcement return of 2.6 percent
In the third essay, we find that second-time IPOs (issuers that return to the IPO market successfully after withdrawing their first IPOs) sell at a significant discount relative to similar contemporaneous first-time IPOs (IPOs that succeed in their first
Trang 13incorporated into offer prices when withdrawn-IPO firms return for second IPO attempts We also find that, 1) the magnitudes of price revision and initial return experienced by
second-time IPOs are similar to those of matched first-time IPOs, 2) in the long run, second-time IPOs do not underperform contemporaneous first-time IPOs in either stock
price or operating performance These findings suggest that the discount is appropriate and that the market fully adjusts the offer price of second-time IPOs to reflect the
negative information conveyed by their previous withdrawals
Trang 14INTRODUCTION
This dissertation contains three essays in the area of insurance and corporate
finance
The first essay examines the optimal contract in the presence of an insurer’s opportunistic behavior when he has private information about loss amounts This study
follows the costly-state-verification literature, where the uninformative party pays to
verify claims We model the optimal insurance contract under perfect competition and
find that it stipulates full insurance up to a limit and a flat indemnity payment for losses exceeding this limit There is no monitoring for losses exceeding this critical level The
insured verifies any loss below this critical level and receives full insurance This
optimal contract can be generalized to any Pareto-efficient contract In particular, we
show how the optimal indemnity in a monopoly market is the same as in a competitive
market except that the monopoly insurer earns a higher premium This optimal contract with full insurance up to a limit and a constant payment above that limit is the mirror
image of the optimal contract that Kaplow (1994) and Bond and Crocker (1997) find when an insured may file a fraudulent claim
The second essay examines why dual tracking firms — private firms entertaining
acquisition offers at the same time as preparing for initial public offerings (IPOs) — withdraw their IPOs after spending considerable time, money, and effort preparing for the IPO only to be purchased by public acquirers To shed light on the dual tracking puzzle, we empirically investigate targets’ acquisition valuations and acquirers’ announcement
Trang 15Specifically, we examine the effects of filing and withdrawing IPO registrations on private targets’ valuations in the M&A market We compare the valuations of dual
tracking private targets and announcement returns of their acquirers to those of three
control samples: 1) pure private targets, 2) newly public targets, and 3) established public targets We document that such private targets sell at a 58 percent acquisition premium relative to comparable private targets that never file IPO registrations, while their
acquirers still earn a substantial average abnormal announcement return of 2.6 percent This finding suggests that, by complying with strict disclosure regulations imposed by the SEC in the IPO filings, the information asymmetry regarding targets’ valuations in takeovers of dual tracking private targets is reduced, and acquirers are willing to value
these “almost public” targets as similar to public targets and to pay more for such dual tracking private targets than for similar pure private targets
The third essay examines how the market perceives successful second-IPO attempts by firms with previously withdrawn JPOs We separate second-time IPOs (issuers that return to the IPO market successfully after withdrawing their first IPOs) from successful first-time IPOs We examine differences between market perceptions of second-time and first-time IPOs by considering three issues: valuations by underwriters, initial returns in the first trading day after offering (i.e., underpricing), and post-IPO long-run performance We find that second-time IPOs (issuers that return to the IPO market successfully after withdrawing their first IPOs) sell at a significant discount relative to similar contemporaneous first-time IPOs (IPOs that succeed in their first attempts) This result
Trang 16prices when withdrawn-IPO firms return for second IPO attempts We also find that, 1)
the magnitudes of price revision and initial return experienced by second-time IPOs are
similar to those of matched first-time IPOs, 2) in the long run, second-time IPOs do not
underperform contemporaneous first-time IPOs in either stock price or operating performance These findings suggest that the discount is appropriate and that the market fully adjusts the offer price of second-time IPOs to reflect the negative information conveyed by their previous withdrawals
Trang 172.1 Introduction
Insurance fraud is typically viewed as an inflated loss claim or a planned criminal scheme via the filing of a false claim by a policyholder However, such opportunistic
behavior is not limited to the insured As stated by Doherty and Muermann (2004),
“Insurers are now more likely to dispute large claims, to offer less than 100 cents on the
dollar, or to try to get away without paying.” As a profit-maximizing entity, the insurer has incentives to minimize the indemnity payment The purpose of this paper is to model settlement curtailment by the insurer and to design an optimal contract that incorporates this opportunistic behavior
The economic analysis of insurance fraud has been developed using two main
approaches: costly state verification and costly state falsification (see Picard (2000) for a review on insurance fraud) In both settings, the research has assumed that the insured
has private information concerning the exact amount of a loss These studies aim at analyzing the strategy of the insurer, given that the insured might submit a fraudulent
claim However, in many instances, the insurer might possess better information about the extent of insurable losses, such as soft-tissue injuries.’ These losses by their nature
can only be assessed imperfectly A claimant and an insurance adjuster typically
negotiate within parameters that are defined by statutes, past practices, and economic
' In a soft-tissue injury, the insured may feel pain but he does not know whether pain is just a minor sprain
Trang 18consideration The insurer possesses better knowledge of such parameters, especially for
the general damage payments for hard-to-verify injuries and sufferings,” while the
insured might have only a vague notion of the true damage amount If the insurer offers a questionable settlement, the insured might have to go through a costly verification
process to determine a more accurate loss amount
Practically, it would be hard for the court to award policyholder punitive damages
for hard-to-verify injuries Hence, the insurer has the incentive to aggressively settle such claims when facing a lower probability of bad-faith penalties This may explain the large discrepancy between claimed losses and settled losses for hard-to-verify injuries, documented by Crocker and Tennyson (2002) Prior studies only analyze the loss-inflated behavior by the insured and assume that the insurer always honors claims with good faith This paper examines an optimal contract in the presence of the insurer’s information about the loss amount
This paper follows the costly-state-verification literature, where the uninformative party pays to verify claims Townsend (1979), Kaplow (1994), Bond and Crocker (1997),
and others, have investigated the impact of the insured’s fraud on the design of insurance
contracts They show that an optimal contract should involve a constant payment for small losses over the non-monitoring region and a full insurance payment for losses
exceeding a critical level Larger losses are always monitored Coinsurance may mitigate the policyholder’s propensity to manipulate audit costs and/or to intentionally falsify
claims
* All settlements consist of a combination of claimed economic loss, called special damages, and a payment for “pain and suffering,” called general damages (see Insurance Research Council, 1999)
Trang 19insured can only observe the occurrence of an accident not the exact loss amount When the settlement proposed by the insurer appears “reasonable”, so that any potential benefits
from verification cannot compensate for the extra verification costs, the insured will accept the insurer’s offer Otherwise, the insured will pay a third party to verify the insurer’s settlement offer For example, the insured may acquire an assessment from a
physician (or a mechanic) about the dollar value of any damages
We model the optimal insurance contract under perfect competition and find that
it stipulates full insurance up to a limit and a flat indemnity payment for losses exceeding
this limit There is no monitoring for losses exceeding this critical level The insured
verifies any loss below this critical level and receives full insurance The optimal
insurance contract obtains a truthful revelation of the damage amount by the insurer (i.e it is a “revelation mechanism’) We extend this analysis to define any Pareto-efficient contract In particular, we show how the optimal indemnity in a monopoly market is the same as in a competitive market except that the monopoly insurer earns a higher premium
The next section begins with a model of the insurer’s information regarding losses Section 2.3 develops an optimal contract under a perfectly competitive setting The optimal contract is generalized to Pareto-efficient contracts with given expected profits
The final section concludes by stressing the main features of our model and suggesting
areas for future research
Trang 20‘Suppose the insurance market comprises two types of economic agents, insureds and insurers An insured will experience a misfortune with probability p, but he is risk
averse so he turns to the insurer to hedge the risk Every insured has the von-Neumann- Morgenstern utility function U(W;), where W; denotes the wealth of the insured in the state i The utility function is continuously differentiable, concave, and strictly increasing The insured maximizes the expected utility of his final wealth EU(Wj) The insurer behaves as if he is risk-neutral because we assume that he holds sufficiently large and diversified portfolios
The losses x have a mixture of discrete and continuous distributions within the interval [0, x] That is, x has a mass of probability f(0) = 1—p at x =0 and a continuous probability density function f(x) over (0,¥] The maximum loss is ¥ <W, where W represents the initial wealth of the insured
The support and distribution of losses are public information When a loss arises, both the insured and insurer know about the occurrence of the loss but only the insurer
knows the exact loss amount, x The insurer will announce his settlement offer x to the
insured Depending on the message, x, from the insurer, the insured has to decide
whether to obtain an independent audit at a cost c The variable, A(%), represents the insured’s decision, which equals one if an audit is performed and zero otherwise, where the insured simply accepts the insurer’s settlement, /(x) The insurance contract is
characterized as a deterministic audit contract; a pre-state agreement that the indemnity payment will depend on whether there is verification.’ The monitoring cost c is assumed
* Our analysis is based on the assumption that only deterministic contracts are feasible Mookherjee and
P’ng (1989) have shown that a stochastic design dominates a deterministic scheme We restrict our attention to deterministic contracts for the reason that our primary concern is the insurer’s opportunistic
Trang 21will reveal the true loss amounts
The insurance indemnity, /, consists of two regions contingent on the insurer’s announcement of the loss size, x, and the insured’s monitoring decision, 8) In the
verification set, M, the insured will always monitor, namely Ø($)=l, while in the
complement MỸ, there is no verification effort Ex ante, the insured will commit to
monitor the loss to enforce the indemnity payment, J = I(x) ifxeM ; otherwise the
insurer will always offer the smallest compensation for losses Further, we assume that the indemnity payment is contingent on the monitoring decision by the insured even in the monitoring region Thus, the optimal contract can provide incentives for the insured to monitor loss amounts even ex post a damaging incident The indemnity payment, J, is assumed to be non-decreasing in the loss amount The contract is characterized by the vector, (z, [(f)), where z is a fixed premium charged by the insurer
In the absence of asymmetric information or costs of verification, there would be no obstacle in achieving the first-best contract with full insurance Because observation
of the true loss is costly, we would not expect the insured to verify the true loss amount at all times The implementation of an optimal contract requires the insurer to reveal the true loss amount to the insured In this paper, we assume that the insurer will announce the loss truthfully if faced with the same payoff whether he is lying or telling the truth
This assumption is similar to an indemnity schedule that has a penalty against large
deviations from the incentive compatible contract In equilibrium, the Revelation Principle applies and announcements are truthful
behavior The deterministic contract is the first step to characterize the optimal contract in the presence of
Trang 22The Revelation Principle allows us to restrict the attention to implementation through a direct mechanism If the insurer falsely announces the loss amount
when B(z)= 1, this misrepresentation will be revealed because the contract requires the
insured to verify the loss amount Thus we need only concern ourselves with the loss amount pronouncements when /Ø{#)= 0 where x is not monitored
Lemma 1: For any optimal contract, there exists a constant indemnity payment, Ib, with /(x) = 1), for the non-monitoring region, 8 (£) =0
The proof is simple and as follows: if J(x) is not constant across the non- observed states, the insurer always has the incentive to announce the non-monitored state with the lowest indemnity payment
Clearly, an indemnity payment in the monitoring region must be lower than that
in the non-monitoring region Otherwise, the insurer would misrepresent the actual loss to
be an element of the non-monitoring set to seek a more favorable settlement Then we
have Lemma 2
Lemma 2: For all settlement announcements, *, such that 6(t)=1, the indemnity payment is lower than the constant payment in the non-monitoring region /(x) < J,
Given the features of indemnity payments over two regions (monitoring and non- monitoring), Lemma 3 characterizes the verification range, where M is the monitoring range, and M* is the non-monitoring range
Trang 23Lemma 3: There exists a critical level of loss amount, m, such that /(x)=/,, if
#eM° =[m,x] and 6($)=0; 1(8) <1, if 3e A4 =[0,m) and Ø(8)=1
Proof: Proof is given by contradiction Let 0 < n; < nạ < m such that
M* =|n,,n, |U[m,x| and M =[0,n,)U(n,,m) From Lemma 1, the indemnity payment, Jo, is constant over the non-monitoring region Let the losses x; and x2 be such that
x, €(n,,n,) Cc M° andx, €(n,,m) <M The non-decreasing property of the indemnity payment gives that /(x,) < I(x2) since x; < x2 However, the indemnity, I(x;) = Ip, which is
supposed to be no less than the indemnity payment over the monitoring region, J(x2), by
Lemma 2 Therefore, /(x;) = I(x2) and n; = nz On the boundary x = m, there is no verification to save the monitoring cost Hence, M° =[m,x] and M =[0,m)
Lemma 3 states that the insured will receive a lower indemnity payment in the monitoring region
The lemmas above assert that the insured’s monitoring decision depends on the
insurer’s announcement offer, x, and the critical level, m If a smal! loss amount is observed, the insurer will announce a small x even if he realizes that this amount will
bring a monitoring action by the insured There is no reason for the insurer to falsely
inflate minor loss amounts On the other hand, if a large loss is observed by the insurer, he has to announce the loss in the non-monitoring set For the insured, if the settlement
Trang 2411 depends on the monitoring decision Thus, the indemnity payment is designed to force
the insured to always audit the loss amounts in the monitoring region
In the extant literature, such as Kaplow (1994), Bond and Crocker (1997), and Picard (1996), the insured is assumed to have private information regarding the actual loss amount, while in this paper it is the insurer who knows the true loss amounts The change in the party possessing the private information symmetrically modifies the structure of the contracts This is because the incentive-compatible contracts are designed
to induce the informed agent to reveal true information, otherwise he always announces payments in his favor The implementable contracts in both settings necessitate a constant
payment in the non-monitoring region and an increasing payment in the monitoring range Indemnity payments are preferred by the uninformed agent in the non-monitoring region rather than in the monitoring region where he has no incentive to verify the actual amount of losses He will assess the true information at some costs only if the benefit from
verification exceeds the monitoring cost The non-monitoring region involves small losses in prior studies but larger losses in this paper, and vice versa for the monitoring
region In our setting, when the settlement offer from the informed agent (insurer) is large, the uninformed agent (insured) will take the offer to save the monitoring cost Otherwise a verification will be obtained
We restrict our analysis to a set of implementable contracts An indemnity
schedule, {(I(x), M)}, is said to be incentive compatible, if the insurer truthfully reveals the magnitude of the loss In the next section, we will consider the optimal contract in a competitive setting and extend it to any Pareto-efficient contract
Trang 252.3 The optimal contract
The implementable contract characterized in the lemmas can be expressed as,
ri for xEM* =[m,x] (1)
I(x)<JI, for xeM =[0,¥)
Given the incentive compatible contract, the insured’s expected utility is given by,
V = (uw —~m—x+I(x)-c)f (x)de
+ [UW -2-x41)f (ade + 1- pUW -7) ”
where f(x) = pgtx), g(x) is the conditional probability density function
The expected profit of the insurer may be written,
Expected Profit = z ~ [" I(x) f(x)dr - [lof (ae (3)
It is rational that the expected profit of the insurer (3) and the expected utility of the insured (2) are non-negative.” In other words, these states guarantee that the insured
accepts the contracts and the insurer stays in the market
2.3.1 Optimal contract under perfect competition
If the insurance market is perfectly competitive, the optimal contract will
maximize the insured’s expected utility subject to the insurer’s participation constraint that requires premiums to cover expected indemnity payments It is clear that this constraint must be binding; otherwise the insurer will not enter into the contract On the
other hand, if the expected utility of the insured is not maximized, another insurer will
offer a more attractive contract to the insured as long as it is profitable The objective is
* In the perfectly competitive setting an individual’s rationality constraint requires that an insurer make zero
profits and the insured maximize his utility, while in the monopoly setting, an individual’s rationality
Trang 2613 to choose the insurance contract, (a, /(B)), so as to maximize the insured’s expected
utility (2) subject to the incentive constraint (1) and the participation constraint of zero
profits for the insurer (3) The optimal contract in a perfect competition is characterized
in Proposition 1
Proposition 1: An optimal insurance contract has the following properties; (a) for xe M =[0,m), I(x) =x+e;
(b) for xe M° =[m,xX],1(x) =1, ,where m+c<I,
An efficient contract is illustrated in Figure 2.1 When the insurer’s assessment is less than a threshold, m, the insured always verifies the loss amount The optimal contract in this region entails verification costs plus full compensation for losses No verification is needed by the insured when the insurer’s offer is large enough to compensate for a loss and there is no financial incentive to incur monitoring costs In the non-monitoring region, a constant payment is offered to save the monitoring cost
The formal proof of Proposition 1 is in the Appendix The optimal contract shares similar characteristics of such contracts found in prior studies: full insurance bundled
together with verification costs provided over the monitoring region, which equates the
marginal utility of wealth when the loss is verified with the marginal utility of wealth
when there is no loss, namely,U'(W —z) =U',, Hence, monitoring costs, borne by the
insured, are passed along to the insurer in an optimal contract In addition, from equations (8) and (9), we can derive U'(W — 7) = EU' we ° The optimal contract also equates the
° EU is the expected marginal utility of wealth when the loss is not verified
Trang 27marginal utility of wealth when the loss is verified (or no loss state) with the expected marginal utility if the loss is not verified In other words, the function of insurance is for
the insurer to bear the risk
The intuitive explanation for a non-binding incentive constraint is that if the insurer was indifferent between being audited and not being audited at the margin of the
verification region, a slight reduction in m would save on monitoring costs In this
competitive-equilibrium setting, the insurer charges a fair premium, which equals the expected loss reimbursement plus the monitoring cost Over the verification region, the insured receives full insurance, while he receives a constant payment in absence of
verification The incomplete information makes complete risk-sharing suboptimal
Since we assume that an indemnity payment depends on whether the insured
audits a loss or not, full insurance plus auditing costs are reimbursed only when the
insured audits the loss In other words, the insurer will reimburse the insured for auditing
costs only if the insured audits his losses Thus, the insured will consistently commit to
auditing losses ex ante and ex post
2.3.2 Pareto-efficient contracts
A more general objective is to analyze the Pareto-efficient allocation, for which
no one can be made better off without someone else being made worse off In this setting, Pareto-efficient contracts mean that the insured is as well off as possible, given the expected profits of the insurer The expected profits are determined by the structure of the
insurance market Now the question becomes whether an industry structure will change
Trang 2815 incentive-compatible regardless of the structure of the market Hence, our analysis is still
based on the incentive compatible contract We can always show that fully compensating
the marginal losses in the monitoring region is optimal in the presence of costly
verification regardless the structure of insurance market Higher (or lower) expected profits make the insurer better off (or worse off) and the insured worse off (better off), but does not affect the marginal conditions for efficiency
Corollary 1: In an insurance market where the insurer makes & expected profits, an optimal insurance contract consists a fixed payment Jp and no verification for loss over a critical value m (< Ip ~c) A full insurance (7 = x + c) is provided and the insured always
monitors for a loss less than m
The proof for this Corollary is straight forward Instead of preserving the zero expected profit for the insurer as in Proposition 1, we maintain that the insurer makes k expected profits All the arguments in the proof of Proposition 1 still apply in the oligopoly market The Corollary simply shows that the optimal contract holds regardless
of the structure of the insurance market One special case is the monopoly market in
which the insurer can extract all consumer surpluses The insurer will set an indemnity schedule which the insured will accept as long as it yields more than the insured’s
reservation level of utility, which is the no-insurance level The insured is indifferent between purchasing an insurance contract and self-insuring the risk The premium in the
monopoly market equals a fair premium plus the premium for the insurer’s monopoly
Trang 29power This additional premium is the only difference in efficient contracts when there is
a monopoly setting versus a competitive market
2.4 Conclusion
We examine an efficient contract in the presence of private information regarding
loss amounts on the part of an insurer The optimal contract consists of two regions: a
non-monitoring region in which losses exceed a critical value and a monitoring region in
which losses are less than this critical level Over the non-monitoring region, a fixed
payment is scheduled to induce the insurer to reveal his true information Over the monitoring region, full insurance is optimal even if verification is costly We extend this optimal contract to the more general Pareto-efficient contracts We show that industry structure does not change the form of the optimal contract
In the real world, the insured may has some information about the loss damages in
those soft-tissue injuries This model generates useful insights about how the possession of information for loss amounts by the insurer changes his payout strategy However, the optimal contract design is still in question and further analysis is needed when the insured
Trang 312.5 Appendix Proof of Proposition 1:
The proof consists of two parts In the first part, we show that the optimal contract
should be where marginal losses are fully compensated in the monitoring region
Additionally, we demonstrate that the monitoring cost would be compensated by the insurer in this region
First step: We first need to find out the optimal indemnity payment, /(x), in the
monitoring region The indemnity maximizes the insured’s utility function (2) subject to
insurer’s incentive constraints (1) and non-negative profit constraint for the insurer (3) >
0 Given m, Io, z, the Hamiltonian for this problem is then written as is: H =UW -2-x+4+1(x)-c)f(x)+6,(4 -1(x) f(x) - (1, f (ade) +, (Ty — 1(x)) Differentiating H with respect to /(x), OH I(x) = U'W —-a-x+I(x)-0)f(x)- 0, f(x)- 4, = 0 (3) If 02 > 0, Ip = I(x), which is not optimal Therefore 62 = 0 Equation (3) becomes: Ư (W ~z—x+1(x)~e)= 6, —> l(x)=x+/
This shows that the optimal indemnity function in the monitoring region involves
full insurance plus a constant
Second step: The indemnity function becomes _ ]|Ïạ for xem.x]
Trang 3219 This equalizes the insured’s wealth in all states over the monitoring region Given
the indemnity function, the problem simplifies to:
max ~ p)UŒ ~z)+ [U0W ~xz~x~—1)ƒ(x)& + | UẬW -z+y+e€)ƒ(x)& subject to m= [If (x)de+ [ (xt) f (ade 20 lạ > m +7 The associated Lagrangian expression is L =(1-p)U(W -z) 710.0,
+ [UỢV =z—x~1,)ƒ(x)& + [UW -a4 740) f (ade
+A(a~ [I f(xde - [xt yf (x)de)+ uy 7 - m)
The first order conditions for the maximum are
5 UW 247 ~e) Fm) ~ AF (m)— =0 (4) = [UW ~~ x4 1y) f(x)de ~ Alp ~ F(m)] + p= 0 (5)
=—=-(~ p)U'0W =z)- [U'@W =xz~x+1,)ƒ(x)& (6)
Trang 33Substitute the equation (8) into (7):
L[ƯỢI -z+y~c)~U0Wể —z~m + J)|ƒ0m)- 8 (9)
= Af (mm + 7 — lạ)
If lạ =m +7, the right hand side of the equation (9) is equal to zero However,
the left hand side of it is negative since 4#>0O and the term in the brackets
U(W -a+y—-—c)—-UW -a-m+1,)<0 Therefore, [,>m+w=> u=0 Using the
fact that incentive constraint is not binding, 4 = 0, and the equations (8) and (4), it yields U (4W -z+y—c)=U (W -z) (10)
Trang 34The Dual Tracking Puzzle: When IPO Plans Turn into Mergers 3.1 Introduction
Investors in a private firm can cash out by either selling shares to investors in the initial public offering (IPO) market or selling the firm to a public firm in the merger and
acquisition (M&A) market By investigating whether an IPO or a takeover is eventually used by a private firm to turn public, Brau, Francis, and Kohers (2003) and Poulsen and Stegemoller (2005) suggest that private firms choose the IPO market instead of the M&A market as the pathway to access the public equity market when they 1) have higher growth opportunities and more capital constraint, 2) are easier to value, and 3) are in a relatively “hot” IPO market
However, by focusing only on which route is chosen eventually by a private firm,
this literature overlooks possible connections between IPO and M&A markets for private firms planning to sell their assets in public equity markets While most private firms do
decide whether an IPO or a takeover is the appropriate pathway to public ownership from the get-go and pursue the strategy from the beginning, a subgroup of private firms,
Trang 35in an attempt to shed additional light on the choice between an IPO and a takeover for a
' private firm planning to go public and possible connections between the IPO and M&A markets
The choice to pursue dual tracking is puzzling The IPO registration process is costly Lee, Ritter, and Zhao (1996) document direct expenses (registration fee and printing; legal and auditing costs) of IPO registration at 3.69 percent of expected proceeds for IPOs from 1990 to 1994 Given that the median dual tracking private firm in our sample plans to raise $44 million in their IPO filings, the IPO registration cost is
estimated to be $1.6 million When a dual tracking private firm withdraws its IPO to be
acquired, it essentially forfeits all the time, money, and effort spent on the registration process So the ultimate question is, if a private firm sells itself to a public firm via a takeover, why does it file for an IPO and endure the additional costs of the IPO
registration process in the first place?
To shed light on the dual tracking puzzle, we empirically investigate targets’
acquisition valuations and acquirers’ announcement returns for a sample of 132 dual tracking private firms between 1984 and 2004
Specifically, we examine the effects of filing and withdrawing IPO registrations
on private targets’ valuations in the M&A market We compare the valuations of dual tracking private targets and announcement returns of their acquirers to those of three control samples: 1) pure private targets, 2) newly public targets, and 3) established public
targets
Finance theories suggest that, on average, prices paid for private targets would be
Trang 3623 is less competition to acquire private targets because they are less known in the takeover
market This reduces the price that an acquirer has to pay for them Second, lack of information about private targets also implies greater information asymmetry between the acquirer and the target that leads to a price discount By comparing the acquisition valuation ratios for similar private and public firms, Koeplin, Sarin, and Shapiro (1996)
document that private targets are acquired at an average 20-30 percent discount relative
to similar public targets Officer (2006) provides additional evidence that stand-alone private targets sell at an average acquisition discount of 15-30 percent relative to a
portfolio of acquisitions of similar public targets, using acquisition valuation multiples
paid by pubic acquirers
We begin by comparing acquisition valuations of dual tracking private targets to
those of comparable pure private targets, newly public targets, and established public targets Our approach builds upon the previous studies by not only considering the impact
of targets’ listing status, private or public, on their acquisition valuations, but also considering that by dual tracking both IPO and M&A markets, private targets that
withdraw their IPOs may command different valuation ratios, compared to other
comparable pure private targets
Consistent with previous studies, we find that private targets sell at an acquisition
discount of 10-20 percent relative to similar public targets However, our results suggest
that the acquisition discount of private targets mainly reflects the discount of pure private targets relative to both newly public and established public targets We find that dual tracking private targets receive valuation multiples comparable to those received by
Trang 37newly public targets They also receive significantly higher valuation multiples than those
received by the matched sample of established public targets
Most importantly, we find that, compared to pure private targets, dual tracking private targets sell at a significant acquisition valuation premium of 58 percent This finding is consistent with the notion that, relative to acquisitions of pure private targets,
there is likely less information asymmetry concerning the targets’ valuation in takeovers
of dual tracking private targets The information environment surrounding these “almost public” private firms (which have to comply with strict disclosure regulations enforced by the SEC in the IPO filing to submit audited financial statements, the business plan of the company, and discussions of the firm’s growth prospects and potential risks) is considerably more transparent than the information environment surrounding pure private
firms Therefore, bidders are willing to value these “almost public” targets as similar to public targets and to pay more for dual tracking private firms than for similar pure private
targets Dual tracking private targets are willing to endure the additional costs of [PO registration because they will likely receive higher acquisition valuations than they otherwise would in takeovers
Trang 3825
CARs when acquiring private targets’ and zero or negative average CARs when acquiring public targets Although this phenomenon of the targets’ listing status determining acquirers’ CARs is not fully understood, Fuller, Netter and Stegemoller (2002) posit and Officer, Pouslen and Stegemoller (2006) show that the lower acquisition prices in acquisitions of private targets explain bidders’ superior CARs when acquiring
private targets Given the significant acquisition premium paid for dual tracking private
targets relative to pure private targets, we would expect that acquirers’ CARs would be significantly lower when acquiring dual tracking private targets than when acquiring pure
private targets
However, we find that acquirers still earn a significant positive average CAR of
2.6 percent around the announcement of acquisitions of dual tracking private targets and
a significant positive average CAR of 2.65 percent around announcement of acquisitions
of pure private targets This difference in CARs is statistically insignificant, even after controlling for the size of the acquirer, acquisition valuation ratios, the method of payment, and the relative size of the target This finding suggests that other market
participants agree with acquirers’ assessment of the valuations of dual tracking private targets and deem the average 58 percent acquisition premium paid for such targets to be
justified
The rest of the paper is organized as follows Section 3.2 describes our data and
presents sample descriptive statistics Section 3.3 examines the acquisition valuation
ratios, and Section 3.4 investigates acquirers’ returns around the announcement period
Section 3.5 concludes
” See Chang (1998), Fuller, Netter, Stegemoller (2002), Faccio, McConnell and Stolin (2006), Officer, Poulsen and Stegemoller (2006), and others
Trang 393.2 Sample selection and data description
3.2.1 Sample and data sources
Our primary data sources are the Global New Issues Database and the Merger and
Acquisition Database from the Securities Data Corporation (SDC) Our sample includes acquisitions over the period January 1, 1984 — July 25, 2004
We first extract a list of IPO withdrawals, firms that file an IPO registration but later withdraw, from the Global New Issues Database From the Merger and Acquisition Database, we construct a takeover sample that contains completed deals involving 100
percent acquisitions of U.S private firms by U.S publicly traded acquirers Using 6-digit CUSIPs to match the withdrawn IPO list and the takeover list, we identify a sample of
dual tracking private targets After verifying the accuracy of matching, using SEC filings
from the SEC Edgar database and the Lexis-Nexis Academic database, we obtain a
sample of 150 dual tracking private targets that announce their acquisitions within three
years of their initial filing dates Following a similar procedure, we also construct a sample of 507 newly public targets that have completed their IPOs and announce their
acquisition within three years of their IPO dates
Panel A of Table 3.1 reports IPO completion and withdrawal activity, as well as subsequent takeover activity for withdrawn IPO and newly public firms by year During the sample period, 1,652 firms (around 20 percent of all IPO filings) withdraw their IPOs
Of those IPO withdrawals, 150 (9 percent) are acquired by public bidders within three
years of their IPO registrations About two-thirds of our sample of acquisitions of dual
tracking firms occurs during the late 1990s Of the successful IPOs, 507 (8 percent) are
Trang 40
27
acquired within three years of their IPO dates Panel B of Table 3.1 summarizes IPO and takeover activity by industry We follow the industry classifications used in Busaba, Benveniste, and Guo’s (2001) study of IPO withdrawal options.’ Dual tracking targets
and newly public targets are widely distributed across industries — the service sector has the most dual tracking targets and newly public targets
Figure 3.1 shows the distribution of the number of days between IPO filing and announcement of acquisition for dual tracking private targets and the number of days between IPO date and announcement of acquisition for newly public targets In the dual tracking private target sample, the average (median) number of days from the IPO
registration filing date to the takeover announcement date is 376 (294) days However, the average (median) number of days for the newly public target sample is 611 (625)
days after its IPO date Sixty-four firms announce their acquisition before they formally file an IPO withdrawal form with the SEC.’° Approximately 25 percent of the targets that
withdraw their [POs are acquired within 115 days of their IPO registrations, but merely 5
percent of newly public firms are acquired within 115 days of their IPO date Dual
tracking firms’ shorter average time from IPO registration to takeover suggests that a potential acquisition drives some of these firms to withdraw their scheduled offerings
Three control samples are constructed as following For each dual tracking private target we find an acquisition of a comparable pure private target, newly public target, and
established public target (a firm that has been public for more than three years)
” We add the financial industry (two-digit primary SIC codes: 60-69), which is excluded in Busaba et al (2001)
'° These 64 firms that are acquired before their formal IPO withdrawals truly dual track [IPO and M&A
markets simultaneously Though not reported, we also separate our sample by whether the acquisition
announcement is before [PO withdrawal Our results do not change qualitatively