Health Economics Series No. 2017-01 - BFI · 2. FDA Binary Options In this section, we consider...
Transcript of Health Economics Series No. 2017-01 - BFI · 2. FDA Binary Options In this section, we consider...
Health Economics Series
No. 2017-01
Sharing R&D Risk in Healthcare via FDA Hedges
Adam Jørring, Andrew W. Lo, Tomas J. Philipson, Manita Singh, and Richard T. Thakor
March 28, 2017
JEL Codes: G11, G12, G13, G22, G23, G31, I18, K23, L65, O32
Keywords: Healthcare Finance, R&D Investments, Drug Development, FDA Approval, Idiosyncratic Risk, Risk Sharing, Hedging
Becker Friedman Institute for Research in Economics
Contact:
773.702.5599 [email protected] bfi.uchicago.edu
SharingR&DRiskinHealthcareviaFDAHedges∗
AdamJørring,1AndrewW.Lo,2TomasJ.Philipson,3
ManitaSingh,4andRichardT.Thakor5
ThisDraft:28March2017
Abstract
Thehighcostofcapitalforfirmsconductingmedicalresearchanddevelopment(R&D)hasbeenpartlyattributedtothegovernmentriskfacinginvestorsinmedicalinnovation.Thisrisk slows downmedical innovation because investors must be compensated for it. Weproposenewandsimplefinancialinstruments,FoodandDrugAdministration(FDA)hedges,to allow medical R&D investors to better share the pipeline risk associated with FDAapprovalwithbroadercapitalmarkets.UsinghistoricalFDAapprovaldata,wediscussthepricingofFDAhedgesandmechanismsunderwhichtheycanbetradedandestimateissuerreturnsfromofferingthem.Usingvariousuniquedatasources,wefindthatFDAapprovalriskhasalowcorrelationacrossdrugclassesaswellaswithotherassetsandtheoverallmarket.Wearguethatthiszero-betapropertyofscientificFDAriskcouldbeamainsourceofgainsfromtradebetweenissuersofFDAhedgeslookingfordiversifiedinvestmentsanddevelopers looking to offload the FDA approval risk. We offer proof of concept of thefeasibilityoftradingthistypeofpipelineriskbyexaminingrelatedsecuritiesissuedaroundmergers and acquisitions activity in the drug industry. Overall, our argument is that, byallowing better risk sharing between those investing in medical innovation and capitalmarketsmoregenerally,FDAhedgescouldultimatelyspurmedicalinnovationandimprovethehealthofpatients.Keywords: Healthcare Finance, R&D Investments, Drug Development, FDA Approval,IdiosyncraticRisk,Risksharing,HedgingJELClassification:G11,G12,G13,G22,G23,G31,I18,K23,L65,O32
∗ We would like to thank Frederico Belo, Mark Egan, Ralph Koijen, Colin Ward, and seminar participants at the Milken Institute for helpful comments and discussions. Any errors are our own. 1UniversityofChicago,BoothSchoolofBusiness2MITSloanSchoolofManagement,CSAIL,andNBER3UniversityofChicago,HarrisSchoolofPublicPolicyandNBER4GoldmanSachsandMITLaboratoryforFinancialEngineering5UniversityofMinnesota,CarlsonSchoolofManagement
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1. Introduction
Biopharmaceuticalandmedicaldevicecompaniestypicallyinvestverylargeamountsof
moneyinordertodevelopatreatment.Forexample,recentestimatessuggestthatthecost
of developing a single new drug in the biopharmaceutical sector is $2.6 billion (DiMasi,
Grabowski,&Hansen,2014).However,thisdevelopmentprocessisalsoquiterisky,notonly
duetotheinherentscientificriskofdevelopingnewcompoundsforhumans,butduetothe
risk involved in regulatory approval processes as well. Companies undertaking drug
developmentintheU.S.aresubjecttotheFoodandDrugAdministration’s(FDA)approval
process.Asaresult,giventhehighcostofdevelopment, failureduetoeitherscientificor
regulatoryriskduringanypartoftheFDAapprovalprocesscanhaveahighlyadverseimpact
on a company undertaking drug research. Significantly, this risk is borne only by those
investingintheparticulartreatmentunderconsiderationbytheFDAandcannoteasilybe
sharedbyotherinvestorsinthegeneralcapitalmarket.Somehavearguedmoregenerally
thatgovernmentuncertaintyultimately slowsdownmedical innovationandhurts future
patients(Koijen,Philipson,&Uhlig,2016).
To overcome this problem, Philipson (2015a,b) suggested financial instruments that
allow those investing in medical innovation to better share scientific and policy-related
developmentriskswithoutsideinvestors.Wecallthesetypesofinstruments“FDAhedges.”
Inthispaper,wediscussthepricingofFDAhedgesandmechanismsunderwhichtheycan
betradedandestimateissuerreturnsfromofferingthem.Inaddition,weexaminetheirrisk
characteristicsandevaluatesomeuniqueevidencesuggestingaproofofconceptthatthese
riskscanbetradedincapitalmarkets.
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If FDA hedges were exchange-traded, there would be direct risk-sharing benefits to
developerswhoareabletolayoffsomedevelopmentriskstootherpartsofcapitalmarkets.
Wethereforeconsiderexchange-tradedFDAbinaryoptions,whichpaya fixedamountof
money in the event of a trigger. Binary options arewell-known and regularly traded on
variousexchanges.6InthecaseofanFDAbinaryoption,thetriggeringeventwouldbethe
failureofaspecificdrug in theFDAapprovalprocess. Weprovidedetailsofhowsucha
binaryoptionwouldbepricedandusehistoricaldataondrugdevelopmentsuccessratesby
phaseanddrugtypetocalculatewhatthetypicalpriceofanFDAbinaryoptionwouldbefor
adrugineachtherapeuticarea.
While the insurance value of FDA hedges is clear to drug developers, the question
remains ofwhat the valuewill be to the issuer. In the absence of exchange-traded FDA
hedges, we consider over-the-counter (OTC) issuers whomight offer a portfolio of FDA
contracts across developers. We simulate the return distribution of such portfolios by
calibratingthedatatohistoricalFDAapprovalratesandestimatetherisk/rewardprofiles
that they imply and how they vary depending on different assumptions related to the
underlyingcontracts.
ApotentialadvantageofFDAhedgestoissuersisthattheyonlydependonpurescientific
riskanddonotaimtoinsurepost-approvalmarketriskofacompound.Thismakesassessing
theriskoftheseoptionseasierandreducestheircorrelationtotraditionalassetclassessuch
asstocksandbonds.ToinvestigatethecorrelationpatternsofFDAoptions,wemakeuseof
a novel dataset of project-level time-series estimates of the likelihood of eventual FDA
6OnedifferencebetweenbondandFDAoptionmarketsinthatoptionsdonotneedtoberated.Thisfacilitatesmarket-makingandtradingrelativetoothertypesofstructures.
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approvalforthousandsofdrugsandbiologics.Weusethesedatatoconstructapaneldataset
of the implied prices and returns of FDA options if they were priced as predicted. We
examinethenatureoftheriskofthesesyntheticFDAoptionsandfindthattheriskislargely
idiosyncratic and unrelated to systematic factors. Since the prices of these hedges are
uncorrelatedwiththebroadermarketorotherfactors,wearguethattheriskassociatedwith
FDAhedgesmayappealtoinvestorsandissuersinterestedindiversification.
Indeed,thiszero-betapropertycouldbethemainsourceofgainsfromtradebetween
issuerslookingfordiversifiedinvestmentsanddeveloperslookingtooffloadtheapproval
risk.Itmaygenerallyholdiftheinherentscientificriskofmoleculesworkinginhumansthat
drive the FDA approval risk were not correlated with other asset classes. A broader
implication of our empirical findings is that the risk of R&D projects is, in general,
idiosyncratic,sincethevalueofFDAoptionsisdirectlytiedtotheunderlyingR&Dprojects.
Toourknowledge,ourpaperisthefirsttoprovideproject-levelevidenceofthispoint,which
hasbeenpositedbyanumberofpapers(e.g.PastorandVeronesi(2009),Fernandez,Stein,
andLo(2012),ThakorandLo(2015)).
Giventheseriskpatterns,weexaminehowwellissuersmaybeabletohedgetheriskof
offeringFDAoptions.Weconsider thehedgeofshorting thestockof theunderlying firm
whosedrug isgoingthroughtheFDAapprovalprocessandexaminethe impliedvalueof
suchhedgesgiventhepricesofthesyntheticFDAhedgesandtheunderlyingstocks.
Finally,weconsidersomeevidencethatFDAriskcanbetraded,andproposeanindirect
measureof thecovarianceof this typeof riskwith thebroadermarket. Inparticular,we
arguethatseveralexchange-tradedContingentValuationRights(CVRs)issuedinconnection
with pharmaceuticalmergers implicitly offer evidence about themarket acceptance and
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covariancepropertiesofFDAhedges.Thefactthatsimilarriskshavebeentradedwithgreat
liquidityisusefulevidencebecauseitnegatespotentialtheoreticalargumentsthattrademay
beinfeasibleduetoasymmetricinformationbetweendevelopersandissuers.Weconsider
thepriceandvolumedata for theseCVRsandexamine their risk.Weshowthat theCVR
contracts have no significant exposure to the overall market and other factors, which
providesfurtherevidencethatFDAhedgeswouldbeattractiveaszero-betaassetstoissuers
interestedindiversification.
Ourpaperrelatesmostcloselytotheemergingliteratureonmeasuringandanalyzingthe
economic implications of policy uncertainty on economic activity (Davis (2015)). It also
relatestoanemergingliteratureontheinteractionbetweenreal-andfinancialhealthcare
marketsandtheimportanceofgovernmentriskinslowingdownmedicalinnovation(Koijen,
Philipson, & Uhlig, 2016; Thakor, Anaya, Zhang, Vilanilam, & Lo, 2016). We extend the
existing literature by suggesting new financial innovations to try to limit the economic
distortions imposedbypolicyuncertainty.Ourwork is also related to the literature that
argues that alternative innovations are needed to mitigate underinvestment in medical
innovation(Fernandez,Stein,&Lo,2012;Thakor&Lo,2017).
WestartinSection2withadiscussionofthepricingofFDAbinaryoptionsandsimulate
theirpricesgivenhistoricalFDAapprovalratesandthetimetheyremainineachFDAphase.
InSection3,weexaminethereturndistributionsofpoolsofFDAhedgesofferedbypotential
over-the-counter issuers. InSection4,weexaminetheriskcharacteristicsofFDAhedges
usingapaneldatasetofFDAapprovalprobabilitiesandexplorehowthisriskmaybehedged
byissuers.InSection5,weprovidetheproofofconceptofmarketacceptanceofFDAhedges
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throughCVRcontractsandanalyzethecorrelationoftheFDAriskwiththebroadermarket.
WeconcludeinSection6withasummaryofourfindingsanddiscussfutureresearch.
2.FDABinaryOptions
In this section, we consider exchange-traded FDA binary options andwe derive and
calibratepricesfortheseoptionsinvarioustherapeuticareas.
2.1BinaryFDAOptions
Binaryoptionsaresimplecontractsthatarecurrentlytradedonseveralexchanges.Using
thisconceptasatemplate,wedefineanFDAbinaryoptionasafinancialcontractthatissold
foracertainprice,entitlingtheholdertobepaidapre-specifiedamountintheeventthata
certaindrugfailsagivenphaseoftheFDAapprovalprocess(ortheentireFDAprocess),and
nothingintheeventthatitsucceeds.AnFDAoptionmaybeissuedatthestartofagiven
phasefortheapprovaloutcomeofthatphase.Withoutlossofgenerality,weassumeitpays
onedollarifthedrugisnotapproved,andzeroifitis.
Throughout, our pricing formulas will use actual probability estimates to compute
expected valueswhich are then discounted at the risk-free rate. Themotivation for this
approachisthefactthattheriskassociatedwithFDAapprovalisunlikelytobecorrelated
withpricedfactorssuchasstockmarketreturnsoraggregateconsumption.Therefore,the
risk inherent in FDA option payoffs should be solely idiosyncratic, in which case the
equilibriumpriceshouldbegivenbytheexpecteddiscountedvalueofthepayoff,discounted
attherisk-freerateofreturn.WeshalltestandconfirmthiskeypropertyexplicitlyinSection
4.
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Assumingthatapprovalriskispurelyidiosyncratic,thepriceofabinaryFDAoptionis
simply thepresentvalueof theprobabilityofnon-approval.The twouncertainties is the
outcomeoftheapprovaldecisionitselfaswellastimewhentheapprovaldecisionismade.
Iftheapprovaltimeisdistributedaccordingthefrequencyf(t),andtheprobabilityofnon-
approvalisp,thepriceatthestartofthephaseisgivenby:7
P= e-rtpf t dt,
where r is the risk-free rate.Clearly, the sooner thedecision ismade, and the larger the
chanceofnon-approval,thehigheristheprice.
2.2CalibratedPricesofFDAOptions
WeestimatethepricesforbinaryFDAoptionsusingrecentevidenceonFDAapproval
rates.Table1belowreportstheaveragehistoricalphasefailureratesfordifferentdisease
groups.8
7Thisassumesthatthereisnocorrelationbetweenthetimeoftheapprovaldecisionandthechanceofnon-approval.Ifthereisadependence,wewouldmodeltheprobabilityasanon-constantfunctionp(t)oftime.8ThesefailureratesarefromThomasetal.(2016),basedupondatafrom2006-2015.
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Table1:ProbabilitiesofPhaseFailurebyDiseaseGroupThetableshowstheaverageprobabilityoffailingeachphaseoftheFDAdrugdevelopmentprocess,brokendownbydiseasegroups.Thesefailureratesarefromdatafrom2006-2015,andaretakenfromThomaset.al.(2016).
ProbabilityofFailingPhaseConditional
onReachingIt
DiseaseGroup Phase1 Phase2 Phase3
NDA/BLAApprovalPhase
OverallProbabilityofFailure
Hematology 27% 43% 25% 16% 74%InfectiousDisease 31% 57% 27% 11% 81%Ophthalmology 15% 55% 42% 23% 83%OtherDiseaseGroups 33% 60% 30% 12% 84%Metabolic 39% 55% 29% 22% 85%Gastroenterology 24% 64% 39% 8% 85%Allergy 32% 68% 29% 6% 85%Endocrine 41% 60% 35% 14% 87%Respiratory 35% 71% 29% 5% 87%Urology 43% 67% 29% 14% 89%Autoimmune/immunology 34% 68% 38% 14% 89%Neurology 41% 70% 43% 17% 92%Cardiovascular 41% 76% 45% 16% 93%Psychiatry 46% 76% 44% 12% 94%Oncology 37% 75% 60% 18% 95%
Giventheseprobabilitiesoffailure,wecalibratethepricesoftheFDAbinaryoptionsthat
payoffonemillionafteragivenphaseifthedrugfailsthatphase.Wecomputetheseprices
forcontractsstructuredassingle-phaseandmultiple-phaseoptions.Forourcalculations,we
assumeanannualrisk-freeinterestrateof1%.
InordertocalibratethetimingofFDAdecisions(f),wereport inTable2 theaverage
duration of each phase of the FDA approval process, taken from DiMasi and Grabowski
(2007).Theestimatesforthephaselengthsaredifferentforbiotechfirmsandpharmafirms.
Wethereforeusetheaveragephaselengthforbiotechandpharmafirmsinourcalculations.
Table2:FDAApprovalProcessPhaseLengths
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ThistableshowstheaveragelengthofeachphaseintheFDAapprovalprocessforthebiotechandpharmasectors.Phaselengthisinmonths(yearsinparentheses).EstimatescomefromDiMasiandGrabowski(2007). AverageLengthoftimeinmonths(years)
Sector Phase1 Phase2 Phase3
NDA/BLAApprovalPhase
TotalLengthofTime
Biotech 19.5(1.6) 29.3(2.4) 32.9(2.7) 16.0(1.3) 97.7(8.1)Pharma 12.3(1.0) 26.0(2.2) 33.8(2.8) 18.2(1.5) 90.3(7.5) Average 15.9(1.3) 27.65(2.3) 33.35(2.8) 17.10(1.4) 94.0(7.8)
CombiningthedataonapprovalratesandthetimingofFDAdecisions,Table3reports
the impliedprices(ifpurchasedat thebeginningof the indicatedphase) forsingle-phase
FDAbinaryoptions—optionsthatpayoff$1millionifthereisfailureintheindicatedphase,
andnothingotherwise.For thepurposeofsimplifyingourcalculationsandmoredirectly
conveying the intuition behind the prices of these FDA options, we do not make
distributional assumptions on f and treat the phase length as deterministic by using the
averagephaselengthsfromTable2directlyindiscountingthepayoffsoftheoptions.Inother
words,thepayoffofasingle-phaseFDAoptioninTable3isgivenbythefollowingformula:
P=e-rTpX,
whereXisthepromisedpayoffoftheoption,pistheprobabilityofnon-approval,andTis
theaveragephaselengthtakenfromTable2.Weusearisk-freeinterestrateof1%inour
calculations.Inoursimulationresultslaterinthepaper,wewillmakeexplicitdistributional
assumptionsonfinourpricing.
Table3:PriceofSingle-PhaseFDABinaryOptionsThetableshowsthepricesofsingle-phaseFDAbinaryoptions,whichareissuedatthestartofeachphaseandpayoffintheeventoffailureinthatphase.Pricesareinthousandsofdollars.
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PriceofFDAOptionthatPays$1minaGivenPhase
($thousands)
DiseaseGroup Phase1 Phase2 Phase3NDA/BLAApproval
Hematology $263 $424 $243 $158InfectiousDisease $301 $560 $266 $111Ophthalmology $150 $541 $406 $222OtherDiseaseGroups $329 $589 $296 $114Metabolic $384 $536 $278 $219Gastroenterology $241 $628 $383 $76Allergy $320 $660 $278 $61Endocrine $406 $585 $340 $138Respiratory $342 $693 $281 $53Urology $423 $658 $278 $141Autoimmune/immunology $338 $667 $368 $138Neurology $404 $687 $414 $166Cardiovascular $406 $742 $433 $156Psychiatry $455 $746 $431 $119Oncology $367 $737 $583 $174
For example, in Hematology it would cost $243,000 to buy insurance for a $1 million insurance
policy. Thepricesofthesingle-phaseoptionscorresponddirectlytothefailureratesineach
phase.Inparticular,thepricetopurchaseanoptionatthebeginningofphase2toinsure
against phase 2 failure is significantly higher than the price to purchase options at the
beginningoftheotherphases.Thisreflectsthefactthatthefailureratesinthedevelopment
processforthevariousdiseasegroupsarethehighestinphase2.Bycontrast,thepricesare
muchlowerinthefinalFDAapprovalphase,wherethefailureratesarethelowest.
Wenextcalculatethepricesofmultiple-phaseFDAbinaryoptions,whichpayoffifthere
isfailureinanysubsequentphaseoftheFDAprocess.Wediscussthepricingoftheseoptions
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intheAppendix.Table4reportsthepricesoftheseoptionsifpurchasedatthebeginningof
agivenphase,therebyprovidinginsuranceagainstfailureinanyoftheremainingphases.9
Table4:ThePriceofMultiple-PhaseFDABinaryOptions,forPayoffineachanySubsequentPhase
Thistableshowsthepricesofmultiple-phaseFDAbinaryoptions,whichareissuedatthestartofeachphaseandpayoff intheeventoffailureinanysubsequentphase.Pricesareinthousandsofdollars.
PriceofFDAOptionthatPays$1mforFailureinSubsequent
Phases($thousands)
DiseaseGroup Phase1 Phase2 Phase3NDA/BLAApproval
Hematology $714 $622 $358 $158InfectiousDisease $784 $704 $344 $111Ophthalmology $797 $773 $531 $222OtherDiseaseGroups $812 $734 $373 $114Metabolic $821 $726 $430 $219Gastroenterology $821 $778 $428 $76Allergy $828 $761 $321 $61Endocrine $843 $753 $428 $138Respiratory $847 $783 $318 $53Urology $862 $778 $376 $141Autoimmune/immunology $862 $807 $451 $138Neurology $890 $834 $507 $166Cardiovascular $907 $863 $517 $156Psychiatry $913 $860 $495 $119Oncology $921 $893 $650 $174
There are a few noteworthy patterns in the table. First, naturally the price to insure
againstany phase riseswith non-approval rates. Second, the price of themultiple-phase
optiongoesdownasoneadvancestosubsequentphases,sincetheconditionalprobability
ofthedrugfailinginthefuturegoesdownovertime.However,thepricethatonewouldpay
9ThedetailsofhowthesepricesarecalculatedareprovidedintheAppendix.
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for themultiple-phaseoptiononlygoesdownslightly fromphase1 tophase2,dropping
muchmoresignificantlyfromphase2tophase3,duetothehighfailureratesinphase2.
Sincethefailurerateismuchhigherinphase2relativetoallotherphases,mostofthecost
oftheoptioninphases1and2willbetoinsureagainstfailureinphase2.Oncefailurein
phase2hasbeenaverted,thepriceoftheoptiondropssignificantly,sincefailureisrelatively
lesslikelygoingforward.
3.Risk/RewardProfileofFDAHedgesviaOTCIssuers
TheinsurancevalueofFDAhedgesiscleartodrugdevelopers,butthequestionremains
ofwhatthevaluewillbetothoseholdingtheothersideofthetrade,i.e.,theissuers.Inthis
section,wethereforeconsiderthevaluetoover-the-counter(OTC)issuersthatofferFDA
contracts to investors. Inorder todoso,wesimulate theriskandreturndistributionsof
poolsofFDAhedgesofferedbyissuers.
3.1Risk/RewardProfileofPoolsofFDAOptions
We first empirically investigate the risk and return tradeoff of a pool of FDA option
contracts.WeexamineaportfolioofNcontracts,eachlinkedtoaparticularFDAapplication.
IftheFDArejectstheapplicationatanytpriortothecontractmaturitydateT,theissuer
paystheinsurancebuyer$1.TheprecisetimingoftheFDA’sapprovaldecisionfisunknown;
wemodelthetimeuntilanFDAdecisionasanexponentialdistributionwithrateparameter
λ. When the FDA reaches a decision before the contract expires, we assume that the
applicationiisrejectedwithprobabilitypi,andinourbasecalculationsweassumethatthere
isnocorrelationbetweentherejectionprobabilitiesoftwodifferentapplications,piandpj.
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Inotherwords,ifeachcontractrepresentsanFDAoptionbasedonthefailure/successofa
different drug, the probabilities of failure of each drug are independent. A priori, this
assumptionofnocorrelationacrosscontractswillholdifalargerprobabilityofonemolecule
working inhumansdoesnot increase the chanceof another.Thiswill likelybe the case,
exceptwhenmoleculesworkwithinthesameindicationormechanismofaction,inwhich
caseacorrelationmayoccur.10InSection5,weprovideevidencethatseemstosuggestthat
theassumptionofnocorrelationbetweenthecontractswouldholdinpractice.
Inourbenchmarksimulationresults,wevarythenumberofcontractswhilefixingother
parameters, inorder toexplore thepotentialdiversificationbenefitsofaddingadditional
contractstotheissuer’sportfolio.Morespecifically,wesimulateportfoliosofN=1,N=10,
N=50,andN=100contracts.WeassumeacontractmaturityofT=5years,andpi=30%.
Wechoosearateparameterofλ=1/3forthetimeuntilanFDAdecisionismade,inorderto
matchameanFDAdecisiontimeofthreeyears.Forrobustness,inTableA1intheappendix,
weprovidetheportfoliopayoutdistributioncharacteristicsforalternativechoicesforthe
sizeoftheportfolioN,therejectionprobabilitypi,thecorrelationacrossdrawsρ,andthe
arrivalrateλ.
Weexaminetherisk-returntradeoffthattheissuerfacesbycalculatingtheSharperatios
of the portfolios. Consider an issuerwho has issuedN contracts priced at price $Pwith
expectedpayoutsX1,…,XN.Heinvests$NPattherisk-freeratewiththereturn:
R=[NP(1+r)-∑Xi]
NP= 1+r -
XP
10AcorrelationwouldalsooccuriftheFDAdecision-makingprocessacrossmoleculesistiedtogetherduetoregulatorybehavior.IntheAppendix,weexplorehowourresultsareaffectedwhenthisassumptionisrelaxedandweallowforcorrelationbetweendrugapplications.
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whereX=( 1N)∑Xi.TheSharperatio iscalculatedbydividing themarkupby thestandard
deviationoftheportfolio:
SR=E R -rσ(R) =
P-E[X]σ(X)
InordertocalculatetheSharperatiosinthissetting,weassumecontractfeesof2%ofthe
expected payout of the portfolio, and a risk-free rate equivalent to the current five-year
Treasury yield. We vary the portfolio markup, up to a maximum markup of 50% over
expectedportfolioreturn.
Figure1belowpresentsthevaluesoftheSharperatioforvariousvaluesofNasafunction
oftheportfoliomarkup.Forexample,foraportfolioofN=10contracts,theexpectedpayout
isestimatedtobe$2.04,andthestandarddeviationoftheportfolioisestimatedtobe0.449.
Withapricegivenbya35%markupovertheexpectedpayout,contractfeesof2%,andrisk-
freerateof1.22%,theSharperatioiscalculatedtobe1.5546.Asthefigureshows,theSharpe
ratio intuitively improves as the markup increases, but an increase in the number of
contracts also consistently improves the Sharpe ratio. Thus, in the case of independent
payoffsamongstthecontracts,a largernumberofcontracts improvethe issuer’sreturns.
Theunderlyingintuitionisthesameasthatofportfoliodiversification.Withanyportfolioof
assets, introducing uncorrelated assetswill reduce the volatility of the portfolio through
diversification.11
Figure1:SharpeRatios
11InSectionA.2oftheAppendix,weprovidetheresultsfortheSharperatiosassumingacorrelationbetweenthecontracts.
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This figure plots the Sharpe ratios of dealer returns as a function of the portfoliomarkup% forvarious values ofN, the number of contracts offered in the pool. These calculations assume nocorrelationbetweenthepayoutsofthecontracts.
3.2Risk-ReturnDistributionsforDiseaseGroups
Theresultsaboveshowtherisk-returntradeofffacedbyissuersforgeneralpoolsofFDA
option contracts. It is informative to examine in more detail how this tradeoff varies
dependingontheparticulardiseasegrouptheFDAOptionsarebasedupon,sincedifferent
diseasegroupscanhaveverydifferentsuccessprobabilities.Table5providestheexpected
payout,variance,andSharperatioforaportfolioofFDAoptionsbasedonadrugprojectin
eachrespectivediseasegroup(assumingN=50contracts inthepool),usingtheaverage
probabilitiesoffailureinPhase3foreachgroupthatwereshowninTable1.
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Table5:ExpectedPayoutsofPortfoliosofN=50ContractsThistableprovidesthesimulationresultsforthemeanportfoliopayoutandvarianceofpayoutfordifferentdiseasegroups,assumingN=50contracts.
DiseaseGroupProbabilityofApprovalinPhaseIII
ExpectedPayout Variance SharpeRatio
Hematology 75% $0.51 0.003 2.39InfectiousDisease 73% 0.50 0.003 2.22Ophthalmology 58% 0.40 0.003 1.91OtherDiseaseGroups 70% 0.48 0.003 2.33Metabolic 71% 0.64 0.004 2.99Gastroenterology 61% 0.42 0.003 2.08Allergy 71% 0.48 0.003 3.16Endocrine 65% 0.44 0.003 2.19Respiratory 71% 0.48 0.003 2.86Urology 71% 0.48 0.003 2.36Autoimmune/immunology 62% 0.42 0.003 2.11Neurology 57% 0.39 0.003 1.98Cardiovascular 55% 0.38 0.003 1.94Psychiatry 56% 0.38 0.003 1.96Oncology 40% 0.27 0.002 1.51
As can be seen from the table, the portfolio payouts vary between disease groups,
dependingontheprobabilityofapproval.Inparticular,theexpectedpayoutsbytheissuer
arehigheriftheprobabilityofapprovalislower(i.e.theprobabilitythattheoptionwillpay
out is higher), with the highest expected payout being in oncology. The variance of the
payouts also increasesas theprobabilityof approvaldecreases.TheSharpe ratio for the
issuerisalsogenerallyhigherfordiseasegroupswithahigherprobabilityofsuccess.For
example,issuerswillfindthatissuingpoolsofFDAoptionsaremoreattractivefordrugsin
HematologythanfordrugsinOncology,sincedrugsinOncologyaremorelikelytofailand
thereforenecessitatepayoutsbytheissuer.Overall,therelativelyhighSharperatiosforall
ofthediseaseclassreinforcethenotionthatFDAoptionsmaybeattractiveforissuers.For
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example,theSharperatiooftheS&P500SPDRETFoverthepastfiveyearswas1.32,which
issubstantiallylowerthantheSharperatiospresentedabove.
Whiletheanalysisaboveprovidesaviewintotherisk-returntradeofffacedbyissuersof
FDAoptions,theriskthattheseissuersareexposedtomaybefurtherreducedifissuersare
abletohedgetheriskoftheseoptions.Weexplorethisissuefurtherinthenextsection.
4.TheRiskofFDAOptions
Inthissection,wereturntotheissueofthenatureoftheriskofFDAhedgesthatissuers
face.FDAhedgesmayhavediversificationappealtoinvestorsandissuersifthereturnsto
thesesecuritiesareuncorrelatedwiththebroadermarketorotherfactors,thatis,iftherisk
ofthehedgeisidiosyncraticandnotsystematic.Weexplorewhetherthisisempiricallythe
caseusinganoveldatasetoftheapprovalprocessofdrugs.Giventhisrisk,wethenexamine
thecircumstancesunderwhichissuersmaybeabletohedgetheriskofFDAoptions.
4.1DatasetDescription
WeuseanoveldatasetonthedrugapprovalprocessfromtheBioMedTrackerPharma
Intelligencedatabase.Thisdatabasecontainsdetaileddrugtrialinformationforpharmaand
biotech companies, including historical approval success rates, development milestone
events,progressupdates,and,mostimportantly,estimatesofthelikelihoodoffutureFDA
approval for individual drugs in development by each company. The database provides
information on 11,587 drugs across 2,893 different companies. Although the dataset
containsinformationonahandfulofdevelopmenteventspriorto2000,ithasfullcoverage
from2000to2016,andwethereforefocusonthisperiodforouranalysis.
15March2017 FDAHedges Page18of41
More specifically,weuse the reported likelihoodof futureFDAapprovalprovidedby
BioMedTrackerinordertoconstructhypotheticalpricesforFDAoptionsonawidevariety
ofdrugs.Foreachdrugand foragivendate,BioMedTrackerprovidesanestimateof the
probability that thedrugwillultimatelybeapprovedby theFDA.Theseprobabilitiesare
updatedeachtimethereisanyannouncementorotherdevelopment-relatedeventrelated
totheparticulardrug.12 Inordertodetermine likelihoodofapproval(LOA)probabilities,
BioMedTracker uses a combination of historical approval rates and analyst adjustments
basedondevelopmentevents.Morespecifically,whenadrugdevelopmentprojectisinitially
started,BioMedTrackerassignsanLOAprobability to itbasedon thehistorical approval
ratesofdrugsintheparticulardiseasegroupthatthedrugbelongsto.BioMedTrackerthen
adjuststheLOAprobabilityforthedrugeachtimeadevelopmenteventoccurs.Iftheevent
conveysnorelevantinformationastotheeventualdevelopmentsuccessofthedrug,then
theLOAisunchanged.However,iftheeventcontainsrelevantinformation(forexample,trial
results), then the LOA is adjusted either up or down by BioMedTracker depending on
whether the information is positive or negative. Themagnitude of the change in LOA is
determined by analysts,who evaluate the information content of the event and assign a
changesizebaseduponpre-specifiedcriteria.
Asanexample,accordingtoBioMedTracker,aneventinPhaseIIIthat“[m]etprimary
endpoint,butwithmarginalefficacyornoquantitativedetails;failedprimaryendpointbut
strongpotentialinsubgroup;someconcernwithefficacyvs.safetybalance”willcausean
increaseintheLOAbetween1%and5%.Incontrast,aneventwhichposted“[m]odestPhase
12 These include a wide variety of events broadly related to the company and drug under development,includingtrialresultsandprogressupdates,regulatorychanges,litigation,andcompanynews.
15March2017 FDAHedges Page19of41
IIIresultsorpositiveresultsinnon-standardsubgroup;metprimaryendpointbutconcerns
over safety profile or study design” causes a decrease in the LOA between 1% and 5%.
BioMedTrackerprovidesevidencethatitsLOAestimateshavepredictiveabilityintermsof
the eventual success/failure of the drug under development. More specifically,
BioMedTrackernotesthat,from2000-2015,87%ofdrugsthatwereeventuallyapproved
hadbeenclassifiedashavinganabove-average(relativetothediseasegroup)LOA.Similarly,
75%ofthedrugsthateventuallymovedfromPhaseIItoPhaseIIIfrom2000-2015hadbeen
assignedanabove-averageLOA.80%ofthedrugsthatwereeventuallysuspendedduring
thesameperiodhadabelow-averageLOA.
4.2RiskExposureofFDAOptions
Weuse this time series data onprobabilities of future approval (LOA) to empirically
verifywhethertheriskofFDAoptionsisidiosyncratic—andthusrelatedonlytoscientific
risk—or systematic and related to the broadermarket or other factors. Specifically, we
construct a time-series of synthetic FDAmulti-phase binary optionprices using the LOA
probabilitiesdescribedintheprevioussection.Atanygiventimet,wesetthepriceFi(t)of
thesyntheticFDAoptiononagivendrugprojectiwhichpaysoff$1iftheprojectfailsas:
Fi(t)=exp(-rt(T-t)) 1-LOAi,t
whereLOAtistheLOAprobabilityattimet,rtistherisk-freeinterestrateattimet,andT-t
is the expected duration of the contract. For simplicity, we use the expected remaining
development time of the drug as a proxy for the expected duration of the contract.We
estimatethisusingtheaveragedevelopmenttimesforeachphasefromTable2.13Asbefore,
13Forexample,foracontractcurrentlyinPhase3,wesetT-t=4.204years.
15March2017 FDAHedges Page20of41
weuseactualprobabilitiestocomputeexpectationsandthendiscounttheexpectedvalueby
therisk-freeratebecausetheriskisassumedtobepurelyidiosyncratic.Welaterprovide
evidence that justifies this assumption. Using this time-series of constructed prices, we
computethereturnsforthesesyntheticoptionsforalldrugsintheBioMedTrackerdatabase.
WeexcludeLOAprobabilitiesthatareeither0(thedrughasbeensuspended)or1(thedrug
hasbeenapproved),sincethereisnofuturedevelopmentuncertaintyforthedrugatthose
timepoints.
Withthesereturns,werunregressionstoestimateCAPMandFamaandFrench(1993)
3-factor betas over the period from 2000-2016, and examine whether these betas are
significant.We run these regressionsat theoption level, aswell as theportfolio levelby
combiningtheoptionsintoanequally-weightedportfolio.Wefirstusedailydatatoestimate
thebetas.Whileusingdailydatahasthepotentialadvantageofincreasingtheprecisionof
thebetapointestimate,onepossibleconcernwithusingdailydatainthissettingisthatthere
istypicallynoinformationoneachdrugbetweeneventdays,andthusthereturnfortheFDA
optionwillbezeroforthosedays.Whilethelackofcorrelationduetofeweventsmayindeed
bevaluabletoanissuer,forrobustnesswealsoprovidethebetaestimatesusingmonthly
data.
Table6belowprovidestheresultsofthesefactorregressions.Ascanbeseenfromthe
table,thecoefficients(betas)areinsignificantlydifferentfromzerofortheCAPMandFama-
Frenchfactors,usingbothdailyandmonthlydataaswellasrunningtheregressionsatboth
the option and portfolio levels. Moreover, the intercept (alpha) estimates are also
insignificant.Thisprovidesevidence that theriskofFDAoptions is idiosyncraticandnot
related to systematic factors, and thus issuing themmay be valuable for diversification
15March2017 FDAHedges Page21of41
purposes.Atabroaderlevel,sincethevalueofFDAoptionsaredirectlytiedtotheunderlying
R&Dprojects,thisprovidesevidencethatisconsistentwiththeriskofR&Dprojectsbeing
idiosyncratic,apointthathasbeenpositedbyanumberofpapers(e.g.PastorandVeronesi
(2009)).
Table6:SystematicRiskofFDAOptions
ThistablegivestheresultsofCAPMandFama-French3-factorregressionsoftheexcessreturnofFDAoptionsonthemarket,size,andvaluefactors.Regressionsarerunattheoptionlevelorportfoliolevelusingeitherdailyormonthlyreturndata from2000 to2016,as indicated.Robuststandarderrors are in parentheses, and are clustered by date when run at the option level. * indicatessignificanceatthe10%level,**indicatessignificanceatthe5%level,and***indicatessignificanceatthe1%level.
DependentVariable:Ri,t–rft
(1) (2) (3) (4) (5) (6) (7) (8) (Mkt–rf)t -0.0003 0.010 -0.0007 0.010 -0.059 -0.0003 -0.061 -0.029 (0.0069) (0.008) (0.008) (0.008) (0.051) (0.059) (0.055) (0.059) SMBt -0.0003 0.015 0.074 0.130 (0.012) (0.025) (0.062) (0.091) HMLt 0.002 -0.026 -0.077 -0.102 (0.019) (0.021) (0.076) (0.111) Constant(α) 0.00003 0.00003 0.00003 0.00003 0.0008 0.0001 0.0007 0.0001 (0.00008) (0.0001) (0.00008) (0.0001) (0.0018) (0.0022) (0.0018) (0.0021) RegressionLevel
Option Portfolio Option Portfolio Option Portfolio Option Portfolio
Data Daily Daily Daily Daily Monthly Monthly Monthly Monthly Obs 20,690,864 3,918 20,690,864 3,918 1,008,291 192 1,008,291 192 R2 0.0000 0.0003 0.000 0.0012 0.0003 0.0000 0.0006 0.0460
4.3ADirectTestofIdiosyncraticRisk
Apotential concernwith our factor regressions is that the lack of significance of the
factorsmaybeduetohowwediscountthepayoffsoftheoptions.Inparticular,iftheriskof
FDAapprovalis,infact,notpurelyidiosyncratic,thenouroptionpricingformulaisincorrect.
Insuchcases,weshouldbeusingthestochasticdiscountfactortocomputeoptionprices,
15March2017 FDAHedges Page22of41
which amounts to discounting option payoffs using risk-neutral probabilities instead of
actual probabilities to compute expectations. It is therefore possible thatwedonot find
significantcorrelationwithpricedfactorsbecausewearenotproperlyaccountingforthe
pricingkernel.
To address this concern, we examinewhether themarket return has any significant
predictivepoweron the successor failureofdrugs.The ideabehind this test is thatany
correlationbetweenFDAoptionreturnsandfactorssuchasthemarketshouldalsomanifest
itselfinwhetherdrugsultimatelysucceedorfail(andthustheFDAoptionexpiresworthless
orpaysoff).Sincethesuccessorfailureissimplyabinaryoutcome,examiningwhetherthe
marketreturnisafactorinpredictingthisoutcomeisthereforeawaytotesttherobustness
ofourresultsabovewithouthavingtodiscountorrelyonestimationofthepricingkernel.
Specifically,weruna logitregressionatthedruglevelwherethedependentvariable isa
binaryvariablewhichequalstooneifthedrugsucceeded(passedU.S.regulatoryapproval)
onthegivendayandequalstozeroifthedrugfailed(developmentsuspension)onthegiven
day.Werunthissuccess/failurevariableonthecontemporaneousmarketreturn,aswellas
thelaggedandforward20-,60-,and90-daycumulativemarketreturns.
TheresultsoftheseregressionsaregivenbelowinTable7.Ascanbeseenfromthetable,
themarketreturnisinsignificantateveryhorizon,indicatingthatthemarketreturndoes
nothavepredictivepoweronthesuccessorfailureoutcomesofdrugs.Thisprovidesfurther
evidencethattheriskofFDAapprovalispurelyidiosyncratic.
Table7:DrugSuccess/FailureOutcomesandtheMarketReturnThistablegivestheresultsoflogitregressionsofdrugsuccessorfailureoutcomesonmarketreturnsoverdifferenttimeperiods.Thedependentvariableisequaltooneifthedrugsucceededonthegivendayandzeroifthedrugfailedonthatday.Themarketreturnsarecumulativereturnsbetweentheindicatedlaggedorforwarddateandthedayt.Regressionsarerunatthedruglevelusingdailydatafrom2000to2016.Robuststandarderrorsareinparentheses,andareclusteredbydate.*indicates
15March2017 FDAHedges Page23of41
significanceatthe10%level,**indicatessignificanceatthe5%level,and***indicatessignificanceatthe1%level.
DependentVariable:DrugSuccess/Failure
MarketReturnWindow:
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Contemporaneous,t -5.201 (4.272) Lagged,t–1tot 1.656 (3.182) Lagged,t–20tot -0.214 (1.066) Lagged,t–60tot -0.314 (0.717) Lagged,t–90tot -0.626 (0.510) Forward,ttot+1 -1.765 (3.002) Forward,ttot+20 0.338 (1.235) Forward,ttot+60 -0.127 (0.770) Forward,ttot+90 -0.464 (0.626) Obs 9,678 9,678 9,678 9,678 9,678 9,676 9,628 9,553 9,474 Pseudo-R2 0.0007 0.0001 0.0000 0.0001 0.0008 0.0002 0.0000 0.0000 0.0003
4.4HedgingtheRiskofFDAOptions
Inthissection,weoutlinetheextenttowhichanissuerofFDAriskcanhedgebytrading
thestockoftheunderlyingdrugdeveloper.Theideaisthatanysignificantmovementsinthe
valueoftheunderlyingprojectthatanFDAoptionisbaseduponwillalsoaffectthestock
priceofthedevelopingfirm.Toillustrateinasimplemanner,considerasingleFDAoption
thattheissuerhedgesbyshortingtheunderlyingfirm.LetthevalueofthefirmbeVbefore
15March2017 FDAHedges Page24of41
theapprovaldecisionismade,andV1ifapprovedandV0ifnotapproved.Theseapproval-
contingentvaluesmaybewrittenas:
V1=X1+A
V0=X0+0
where(X0,X1)arethevalueoftheassetsofthefirmduetootherfactorsthanthedrugunder
consideration,andA isthevalueofthedrugunderconsiderationconditionalonapproval
(andthusequaltozeroafternon-approval).IfX0andX1differ,thereisacorrelationbetween
theapprovaldecisionandthevalueofthefirmsduetootherfactors.Beforetheapproval
decision,thevalueofthefirmis:14
V=pV1+ 1-p V0
Thisequationimpliesthatthepriceincreaseduetoapprovalislargerwhentheprobability
ofnon-approvalislarger.Likewise,pricedropsduetonon-approvalaresmallerwhenthe
probabilityofnon-approvalissmaller.
ConsiderwhentheissueroftheFDAoptionshortstheunderlyingdevelopertohedgethe
FDAoption.Considerthecasewhenthereisindependencebetweentheapprovaldecision
andtheotherfactorsdrivingfirmvalue:,X1=X0.Thepayoffoftheissuerhedgeafternon-
approvalisthen:
V-V0+P-1
The first term is positive because the firm loses value and the second term is negative
because thepayouton theoption is larger than thepricecharged for it.Thepayoffafter
approvalis:
V-V1+P
14Thisignoresthepossibilitythatthestochasticdiscountfactormaydifferacrossthetwoapprovalstates.
15March2017 FDAHedges Page25of41
The first term is negative because the firm gains value, and the second term is positive
becauseoftherevenuefromsellingtheoptioncomeswithoutapayout.
Asanillustrativeexampleofhowthisissuerhedgingmayworkinpractice,considerthe
case of Poniard Pharmaceuticals, a firm with a lead drug known as Picoplatin under
development, which is designed to tackle platinum resistance in chemotherapy. While
Picoplatinwasunderdevelopment for a number of different indications, one of itsmain
indicationswassmallcelllungcancer.AccordingtodrugtrialdatafromtheBioMedTracker,
PicoplatinforsmallcelllungcancerwasinphaseIIIoftheFDAapprovalprocessasoflate
2009,whenithadaprobabilityofeventualFDAapprovalof35%.Supposethatanissuerhad
soldamulti-phaseFDAbinaryoptionasofthispointintime,whichpaysoffintheeventthat
thedrugfailsanysubsequentstageofthedevelopmentprocessorisnotapproved.Ignoring
discounting for simplicity, the price of an FDA option with a $100 face value will be
approximately$100× 1-0.35 =$65.
Now, phase III trial data for Picoplatin for small cell lung cancer was released on
11/16/2009,andtheresultsprecipitatedadropinthelikelihoodofapprovalforthedrugof
20%,from35%to15%.Sincethedrugwasthenlesslikelytobeapproved,thiswouldin
turnimplyanincreaseinthepriceoftheFDAoptionfrom$65to$100× 1-0.15 =$85,or
areturnof-30.7%fromtheperspectiveoftheissuer’sposition.However,supposethatthe
issueralsohadashortpositionintheunderlyingPoniardstock.Inthe10dayssurrounding
thetrialdatareleasedate,Poniard’sstockpostedareturnof-70.8%,thusyieldingareturn
oftheshortpositionof70.8%.15Asaresult,onaone-for-onebasis,theshortpositioninthe
15 One could alternatively examine abnormal returns for the stock, i.e. returns that are attributed to theidiosyncraticmovement of the stock (related to the stock’s fundamentals) and not to themarket or other
15March2017 FDAHedges Page26of41
stockmorethanoffsetstheincreasedliabilityfromtheFDAoptionfromtheperspectiveof
theissuer.Afullhedgeinthiscasewouldthereforeinvolveaportfoliowitharoughly50%
weightintheshortstockanda50%weightinrisk-freeassets.
Moregenerally,wecanusethetime-seriesofapprovalprobabilitydataaswellasstock
returndatainordertoestimatetheoptimalnumberofunderlyingstocksneededfor
issuerstohedgetheriskofFDAoptions.LetF(t)bethepriceoftheFDAoptionatdatet
thatisgivenbyourpreviousformulas.DenotetheunderlyingstockpricereturnbyS(t)and
letnbethenumberofsharesoftheunderlyingstockthatissuersholdinordertohedgethe
FDAoption.Theoptimalnumberofsharesthatminimizestheoverallvarianceoftheissuer
satisfiesthewell-knownformula:
n*=𝜎$σS
ρF,S
whereσFisthestandarddeviationoftheFDAoptionprice,σSisthestandarddeviationof
theunderlyingstockprice,andρF,SisthecorrelationbetweenthepricesoftheFDAoption
andtheunderlyingstock.
Inordertomoreclearlyillustratehowthishedgingmayworkinpractice,weobtainthe
approval probability data for the30 companies in theBioMedTrackerdatabasewith the
lowestmarketcapitalizations,sincethesecompaniesarelikelytohavethefewestnumberof
drugs or indications in development. We then obtain daily stock price data for these
companies.Weeliminatecompaniesforwhichthereareeithernodrugtrialeventsorfor
systematicfactors.Doingsobycalculatingabnormalreturnsrelativetothemarketfactoryieldsanevenlargerdropof74.8%.Thevery largedropmayindicatethat investorsviewedthedisappointingtrialresultsasanindicationthatPicoplatinmayfailsomeofitstrialsforotherindications.Asaresult,inthiscaseitislikelythatthedrugunderconsiderationiscorrelatedwiththeotherassetsofthecompany.
15March2017 FDAHedges Page27of41
whichthereisaninsufficientamountofdrugtrialorstockdata.Thisleaves19companies
forwhichwerunourestimationresultsfor.
Using the time-seriesdataon changes in approvalprobabilities fordifferentdrugs to
estimatethepricesofmultiple-phaseFDAbinaryoptionswrittenonthosedrugs,aswellas
stock price data for the underlying company stocks, belowwe estimate the parameters
neededtodeterminetheoptimalhedgeandtheamountofvariancereductionitimpliesfor
differentdrugs.ThepricesoftheFDAoptionsarecalculatedinthewaydescribedinSection
4.2.Table8belowpresents theoptimalhedge forvariousdrugs.The first threecolumns
correspondtothethreeparametersabove,andthefourthcolumntotheoptimalnumberof
shorted stocks. The fifth column calculates the reduction in variance enabledby optimal
hedging.16
Table8:OptimalIssuerHedgesforFDAOptionsonDifferentDrugs
Thistablegives, forvariousdrugs,thestandarddeviationofthepriceofanFDAbinaryoptionσFbased upon that drug, the standard deviation of the researching company’s stock price σS, the
16Variancesandcorrelationsarecalculatedbasedonthesampleperiodforwhichthereisdataforeachdrug.Forsimplicity,weassumearisk-freeinterestrateof0andweignorethefactthatthetimingoftheFDAapprovaldecisionisuncertain.Accountingforthisuncertaintywillrequireadditionaldistributionalassumptions.
15March2017 FDAHedges Page28of41
correlationbetween thesepricesρF,S, theoptimalnumberof underlying stocks topurchasen* in
ordertohedgetheoptionrisk,andthereductioninvarianceimpliedbythehedge.CompanyName Drug σF σS ρF,S n*
VarianceReduction
AcusphereInc. AI-128forAsthma 14.13 26.78 -0.42 -0.22 17%AcusphereInc. CEP-33222forBreastCancer 15.02 26.78 -0.37 -0.21 13%AdvancedLifeSciencesHoldings ALS-357forMelanoma 2.96 38.63 -0.54 -0.04 10%AdvancedLifeSciencesHoldings RestanzaforRespiratoryTractInfections 4.07 38.63 -0.93 -0.10 82%ARYxTherapeutics ATI-9242forSchizophrenia 4.32 2.27 -0.74 -1.42 53%ARYxTherapeutics NaronaprideforChronicIdiopathicConstipation 12.29 2.27 -0.84 -4.53 70%ARYxTherapeutics NaronaprideforGastroesophagealRefluxDisease 12.23 2.27 -0.84 -4.51 69%BoneMedicalLtd CapsitoninforOsteoporosis/Osteopenia 2.75 84.04 -0.61 -0.02 4%BostonTherapeutics BTI-320forDiabetesMellitus,TypeII 0.63 84.04 -0.24 0.00 0%TaxusCardium GenerxforAngina 0.72 84.04 -0.25 0.00 0%diaDexus AIDSVAXforHIVPrevention 1.70 0.34 0.00 -0.02 1%diaDexus PreviThraxforAnthraxInfection(Antibacterial) 10.01 20.40 -0.81 -0.40 65%EntiaBiosciences ErgoD2forRenalDisease/RenalFailure 5.10 20.40 -0.73 -0.18 53%MultiCellTechnologies MCT-125forMultipleSclerosis(MS) 0.27 108.04 0.57 0.00 8%Neuro-Hitech HuperzineAforAlzheimer'sDisease(AD) 9.48 108.04 -0.51 -0.04 23%NeurobiologicalTechnologies XereceptforCerebralEdema 1.13 0.37 0.24 0.75 0%NuoTherapeutics ALD-201forCoronaryArteryDisease 11.87 0.65 -0.76 -13.86 20%NuoTherapeutics ALD-401forIschemicStroke 10.21 2.74 -0.94 -3.51 88%NuoTherapeutics ALD-451forBrainCancer 4.54 1.03 -0.69 -3.02 41%OrePharmaceuticalHoldings ORE10002forInflammatoryDisorders 1.03 1.10 0.13 0.13 1%OrePharmaceuticalHoldings ORE1001forUlcerativeColitis(UC) 0.31 1.10 0.01 0.00 0%OncoVistaInnovativeTherapies OVI-237forBreastCancer 0.54 1.10 -0.10 -0.05 0%OncoVistaInnovativeTherapies OVI-237forGastricCancer 7.01 0.37 -0.80 -15.10 64%OncoVistaInnovativeTherapies P-AATforAcuteCoronarySyndrome(ACS) 0.54 10.67 0.29 0.01 0%OncoVistaInnovativeTherapies P-AATforDiabetesMellitus,TypeI 2.09 10.67 0.50 0.10 1%PoniardPharmaceuticals PicoplatinforColorectalCancer(CRC) 7.39 0.55 -0.83 -11.07 60%PoniardPharmaceuticals PicoplatinforOvarianCancer 8.50 0.55 -0.78 -11.96 45%PoniardPharmaceuticals PicoplatinforProstateCancer 0.84 0.55 0.20 0.31 1%PoniardPharmaceuticals PicoplatinforSmallCellLungCancer(SCLC) 10.46 1094.51 -0.71 -0.01 8%PoniardPharmaceuticals SkeletalTargetedRadiotherapyforBreastCancer 13.53 1094.51 -0.81 -0.01 11%
PoniardPharmaceuticals SkeletalTargetedRadiotherapyforMultipleMyeloma 13.30 1094.51 -0.35 0.00 5%
Stromacel UMK-121forLiverFailure/Cirrhosis 13.44 1094.51 -0.81 -0.01 11%Proteo ElafinforCoronaryArteryBypassGraft(CABG) 12.66 1094.51 -0.74 -0.01 12%RockCreekPharmaceuticals AnatabinecitrateforAlzheimer'sDisease(AD) 0.98 1094.51 0.78 0.00 12%RockCreekPharmaceuticals AnatabinecitrateforAutoimmuneDisorders 2.66 1094.51 0.52 0.00 6%RockCreekPharmaceuticals AnatabinecitrateforMultipleSclerosis(MS) 0.67 352.99 0.10 0.00 0%RockCreekPharmaceuticals AnatabinecitrateforTraumaticBrainInjury(TBI) 2.19 2.39 -0.21 -0.19 1%VioQuestPharmaceuticals LenoctaforAnti-ParasiticandAnti-Protozoal 0.87 2.39 -0.29 -0.11 2%VioQuestPharmaceuticals LenoctaforSolidTumors 0.77 2.39 -0.29 -0.09 2%VioQuestPharmaceuticals VQD-002forMultipleMyeloma(MM) 14.63 2.39 -0.31 -1.88 3%
15March2017 FDAHedges Page29of41
VioQuestPharmaceuticals VQD-002forSolidTumors 0.59 41.71 0.10 0.00 1%
However, in a number of the other cases, the variance reduction is low—on the
magnitudeof5%orless.Thereareafewreasonsforthis.Onereasonisthat,forsomedrug
indications,thereareonlyafewdateswithanynews,andmoreoverthereisnochangein
theprobabilityofsuccessformanyofthesedates.Becauseofthis,thepriceoftheFDAoption
will remain constant (ignoringdiscounting) formanydates, and thevarianceof theFDA
optionwillbesmallbecausethepriceonlychangeswhenthereisanevent.Thismayleadto
impreciseinputsintotheoptimalhedgecalculation,andthereforealowvariancereduction.
Thesecondreasonisthatcertaindrugsorindicationsmakeuparelativelysmallproportion
ofthevalueofacompany’soveralldrugportfolio.Forexample,acompanymaytestacertain
compoundforefficacyintreatmentareasthataredifferentfromthedrug’sprimarytarget,
and expect a very low likelihood of success. The company’s overall valuewill therefore
relativelyunaffectedbyclinicalnewsaboutthisindication.Asaresult,fortheseparticular
typesofdrugsorindicationsindevelopment,theunderlyingstockofthecompanymaynot
offer an ideal hedge against an FDA option issued on that drug. But as noted, for other
drugs/indications which make up a substantial portion of the company’s portfolio, the
reductioninvariancecanbesubstantialfortheissuer.
6.ProofofConceptviaCVRContracts
15March2017 FDAHedges Page30of41
Thereareseveralpotential theoreticalargumentsagainst the liquidityofFDAhedges.
For example, one may argue that trading FDA hedges is infeasible due to asymmetric
informationbetweensellersandbuyers,preventingthemarketfromfunctioning.
Becauseof thesepotentialobjections, in this sectionwediscussan interesting traded
instrumentthatprovidesa“proofofconcept”oftheliquidityinmarketstradingFDArisks.
It is similar in many respects to FDA hedges, and the instrument is liquid and follows
predicted pricing and volume patterns. This instrument is a particular version of an
exchange traded contingent valuation rights (CVR) issued in mergers and acquisitions
(M&A)deals,whichpaysinvestorspre-specifiedamountswhencertainmilestonesaremet
as part of aM&A deal structure. As thesemilestonesmany times include FDA approval
decisions,thesetradedcontractscontainimplicitFDAoptions.
However,oneprovisoshouldbekeptinmind.AlmostallcurrentbiopharmaCVR’sare
“impure”with respect to FDA approval decisions, as they often include non-FDA related
milestonesinadditiontoFDAapprovals.Forexample,thesemilestonesmayincludesalesor
marketingtargets.Duetotheseadditionalnon-FDAmilestones,thedailypricemovements
oftheCVR’smaybedrivenbyotherfactorsnotrelatedtoFDAapproval.However,thisalso
suggeststhattheCVRitself isnotanadequatehedgeagainstFDAapprovalrisk,andthus
thereisneedformorepureFDAhedges.
6.1ContingentValuationRightswithFDAOptions
The contingentvaluation right (CVR) is a shareholder right, oftengiven to the selling
shareholdersduringamergeroranacquisition,whichgivestheholderacashpaymentif
certainmilestones are achieved. Just as listed companies can be traded on the NYSE or
NASDAQ,CVRscanalsobetradedontheseexchanges.AnexampleofaCVRthatwastraded
15March2017 FDAHedges Page31of41
ontheNASDAQistheCVRissuedbyCelgeneonitsacquisitionofAbraxis.Celgeneissuedthe
Celgene CVR contract, with the holder of the contract entitled to milestone and sales
payments.Forthemilestonepayments,theholderoftheCVRwasentitledtoafixedsumof
money($250milliondividedbythenumberofCVRsoutstanding)uponFDAapprovalofthe
drugAbraxaneforuseinthetreatmentofnon-smallcelllungcancerbyacertaindate.In
addition,theholderoftheCVRwasentitledtoanothersumofmoney($400milliondivided
by the total number of outstanding CVR contracts) if the drug Abraxane achieved FDA
approval foruse inthetreatmentofpancreaticcancer.Thesemilestonepaymentscanbe
viewedasbinaryFDAoptions.
Figure2belowshowsthevolumedataoftheCelgeneCVRcontract,whileFigure3shows
thepricedata.Inbothfigures,thetopgraphsshowthevolumeandpriceoftheCVRcontract,
while the bottom graphs show the volume and price normalized as a percentage of the
underlyingCelgenestockvolume/price.NoticethejumpinpricearoundOctober2012,when
theFDAapprovedAbraxanefornon-smallcelllungcancer,andsimilarlyinNovember,after
atrialthatshowedpromiseforpancreaticcancer.
EventhoughthepriceofthisCVR,byandlarge,hasfollowedtheFDA’sdecisions,itisstill
an“impure”FDAhedge.Forexample,itisanunsecuredobligationofCelgene,andisjunior
toallotherclaims.ItisalsocallablebyCelgene,sothereisoptionalityembeddedintoit.The
CVRalsohassalestargetpaymentsinadditiontomilestonepayments,whichmayinturn
carry with it additional risk correlated to the overall market but not FDA risk. These
additional features generate price movements that are orthogonal to any change in the
15March2017 FDAHedges Page32of41
probabilityofFDAapproval,andthuscounteracttheabilityofthecontracttoactasahedge
againstFDArisk.17
Figure2:CelgeneCVRTradedVolumeThisfigureplotsthedailytradingvolumeoftheCelgeneCVRcontract,CELGZ,innumberofshares(top figure) and as apercentageof thenumberof shares traded in theunderlyingCelgene stock(bottomfigure).
17ForthisparticularCVR,therewerealsomechanicalpricechanges,suchasalargepricedropoccurringinOctober2013duetothepricegoingex-dividend.
0.0%5.0%10.0%15.0%20.0%25.0%30.0%35.0%40.0%
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15March2017 FDAHedges Page33of41
Figure3:CelgeneCVRStockPriceThisfigureplotsthestockpriceoftheCelgeneCVRcontract,CELGZ,pershare(topfigure)andasapercentageofthestockpriceoftheunderlyingCelgenestock(bottomfigure).
AnadditionalexampleistheCVRissuedbyAstraZenecaafteritsacquisitionofOmthera
Pharmaceuticals,Inc.inMay2013.ThisCVRensuredapaymentforshareholdersof$1.18
pershare,providedthatspecificFDAapprovalsforinvestigationalcholesteroldrugEpanova
$0.00$1.00$2.00$3.00$4.00$5.00$6.00$7.00$8.00$9.00$10.00
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CelgeneCorporation(CELGZ)- StockPrice
0.0%2.0%4.0%6.0%8.0%
10.0%12.0%14.0%16.0%18.0%20.0%
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15March2017 FDAHedges Page34of41
werereceivedbyJuly31,2014andanexclusivitydeterminationwasreceivedbySeptember
30,2014.Anadditionalpaymentof$3.52pershareistobepaidifadditionalpre-specified
FDAregulatoryapprovalsarereceivedbyMarch31,2016.
6.2CorrelationsandBetasforContingentValuationRights
InSection5.3,weshowedthattheriskinsyntheticFDAhedgeswasidiosyncratic.We
nowexplorewhetherthisisalsothecaseforCVRcontractsthatareactuallytraded.Below
inTable9,wereport theCAPMandFama-Frenchbetasof threeCVRcontracts—Celgene
(CELGZ), Sanofi (GCVRZ), andWrightMedical Group (WMGIZ).We calculate these betas
usingbothdailyandmonthlydata,inordertoensurethattheresultsarenotduesimplyto
asmalltime-seriessamplesize.Ingeneral,thebetasofthecontractsareinsignificant,even
withfeaturessuchassalestargetsthatmayincludesomesystematicrisk.
For the Celgene CVR contract (Panel A), themarket betas (columns (1) and (3)) are
insignificant using both daily and monthly data. When incorporating the Fama-French
factors, themarketbetabecomesnegativeandsignificantusingdailydata,butnotwhen
using monthly data. Thus there is weak evidence that the Celgene CVR carries some
(negative)marketrisk.ThebetasoftheSanofiCVRcontract(PanelB)areallinsignificant
using both daily andmonthly data. Finally, the betas of theWright Medical Group CVR
contract(PanelC)areallinsignificantwhenusingdailydata;whenusingmonthlydata,the
HMLbetabecomessignificant.However,thereareonly37monthsofdataavailableforthe
WMGIZcontract,andthusthesignificantbetaincolumn(4)maybeanartifactofthesmall
sample size.Overall, the regression results show that thebetas of theCVR contracts are
largelyinsignificant,whichprovidesadditionalevidencethatFDAhedgesarealsolikelyto
15March2017 FDAHedges Page35of41
beuncorrelatedwiththemarketiftradedinthemarket,andthusmayhavediversification
appealtoinvestors.
Table9:CVRFactorRegressionsThis table provides CAPM and Fama-French 3-factor regressions of the excess return of CVRcontractsonthemarket,size,andvaluefactors.Regressionsarerunusingeitherdailyormonthlyreturn data for the Celgene-Abraxane CVR contract (CELGZ) in Panel A, the Sanofi CVR contract(GCVRZ)inPanelB,andtheWrightMedicalGroupCVRcontract(WMGIZ)inPanelC.Standarderrorsareinparentheses.Allregressionsincludeaconstantterm(notreported).*indicatessignificanceatthe10%level,**indicatessignificanceatthe5%level,and***indicatessignificanceatthe1%level.
PanelA:CELGZContractDependentVariable:Ri,t–rft
(1) (2) (3) (4) (Mkt–rf)t -0.209 -0.282* 0.808 0.801
(0.133) (0.145) (0.673) (0.735) SMBt 0.350 -0.092 (0.281) (1.224)
HMLt 0.154 0.388 (0.307) (1.367)
Data Daily Daily Monthly Monthly Obs 1,379 1,379 66 66 R2 0.002 0.003 0.022 0.023
PanelB:GCVRZContractDependentVariable:Ri,t–rft
(1) (2) (3) (4) (Mkt–rf)t -0.330 -0.270 -0.283 -0.568
(0.220) (0.238) (0.820) (0.888) SMBt -0.345 1.068 (0.468) (1.534)
HMLt 0.020 1.582 (0.508) (1.677)
Data Daily Daily Monthly Monthly Obs 1,257 1,257 61 61 R2 0.002 0.002 0.002 0.026
PanelC:WMGIZContract
15March2017 FDAHedges Page36of41
DependentVariable:Ri,t–rft (1) (2) (3) (4)
(Mkt–rf)t 0.757 0.771 0.332 0.386 (0.786) (0.798) (1.720) (1.673)
SMBt -0.205 0.206 (1.400) (2.305)
HMLt -0.186 6.723** (1.591) (2.800)
Data Daily Daily Monthly Monthly Obs 774 774 37 37 R2 0.001 0.001 0.001 0.150
WhilethebetasoftheCVRcontractsgenerallyarenotsignificantlydifferentfromzero,it
ispossiblethatsomeothertypeofriskiscommontoallthesecontracts.Forexample,there
maybe a systematic factorother than themarketorFama-French factors that affect the
pricesandreturnsof thesecontracts.Onepossibility isregulatoryrisk,affectingmultiple
drugs simultaneously (Koijen, Philipson, & Uhlig, 2016). Another possibility is that CVR
contractsmaybebasedoncompaniesworkinginsimilartherapeuticareas,inwhichcase
thesuccessofadrugspecifictoonecompanymaybecorrelatedwiththesuccessofasimilar
drugunderdevelopmentbyanothercompany.
To explore these possibilities, we examine the correlations of the daily andmonthly
returnsfortheCVRcontracts.ThiscorrelationmatrixisshowninTable10below.Thetable
showsthatthecorrelationsbetweenthedifferentcontractsareverylowandinsignificantly
different from zero, suggesting that there is no other common factor is that driving the
returnsoftheCVRs.ThisprovidesfurtherevidencethattheriskembeddedinFDAhedgesis
likelyidiosyncratic,relatedtothesuccessoftheunderlyingdrugs.
15March2017 FDAHedges Page37of41
Table10:CorrelationmatrixofCVRReturnsThistableprovidescorrelationsbetweendaily(PanelA)andmonthly(PanelB)stockreturnsfortheCelgene-AbraxaneCVRcontract(CELGZ),theWrightMedicalGroupCVRcontract(WMGIZ),andtheSanofiCVRcontract(GCVRZ).*indicatessignificanceatthe10%level,**indicatessignificanceatthe5%level,and***indicatessignificanceatthe1%level.
PanelA:DailyReturns CELGZ GCVRZ ObservationsCELGZ 1,379GCVRZ 0.015 1,257WMGIZ 0.009 0.001 774
PanelB:MonthlyReturns CELGZ GCVRZ ObservationsCELGZ 66GCVRZ -0.134 61WMGIZ -0.009 0.107 37
The insignificant betas and low correlation between contracts also underscore an
importantpointrelatedtotheappealofFDAhedgestoOTCissuers.Inparticular,theSharpe
ratiostoOTCissuersofpoolsofFDAhedgesaresubstantiallylowerwhenthepayoffsofthe
contracts are correlated. These results provide evidence that the assumption of no
correlationbetweenthepayoffsofcontractsmaybemoreapplicable,andthatthehigher
SharperatiospresentedinSection3.1withuncorrelatedcontractsapplies.
OnealternateexplanationforthelowbetasandcovariancesoftheseCVRcontractsisthat
theyhavealowtradingvolume,andthereforetheyhavezerocovariancewithanything,since
thecontractsarenottraded.However,evenif lowtradingvolumeisthecauseofthelow
correlation, thecorrelation is still lowwhich isvaluable to issuers.That reasoningaside,
Table11belowgivestheyearlysummarystatisticsforthetradingvolumeofthethreeCVR
contractsdiscussedabove.
15March2017 FDAHedges Page38of41
Table11:CVRDailyTradingVolumeSummaryStatisticsThistableprovidessummarystatisticsforthedailytradingvolumefortheCelgene-AbraxaneCVRcontract (CELGZ), theWrightMedicalGroupCVRcontract (WMGIZ), and theSanofiCVRcontract(GCVRZ).Allnumbersrepresentthenumberofsharestraded.
PanelA:CelgeneCVR(CELGZ) Mean Std.Dev. p25 Median p75
2015 17,012.6 20,114.9 3,875 10,950 20,8502014 21,906.0 44,141.3 5,800 11,600 23,6002013 67,625.4 216,749.9 4,225 18,000 68,0752012 52,040.8 182,213.2 3,050 14,900 38,7502011 35,493.7 140,553.7 2,325 8,950 28,0002010 70,990.2 85,968.6 22,650 49,500 94,500
PanelB:SanofiCVR(GCVRZ) Mean Std.Dev. p25 Median p75
2015 664,032.1 2,055,218.4 110,900 235,250 516,2752014 850,199.2 1,699,940.0 147,225 348,950 777,4502013 1,177,137.3 4,337,753.0 74,150 237,050 624,7002012 609,218.0 1,003,581.5 109,400 207,600 588,1502011 2,321,230.4 4,181,025.7 529,300 1,054,800 2,424,800
PanelC:WrightMedicalGroupCVR(WMGIZ) Mean Std.Dev. p25 Median p75
2015 18,037.3 47,647.4 1,100 4,300 17,7002014 43,925.8 91,923.1 6,900 17,400 47,4502013 108,033.8 335,577.3 10,900 33,800 88,625
Ascanbeseenfromthetable,themeantradingvolumeeachyearissignificantforallof
thecontracts.WhilethevolumeforCELGZandWMGIZaresomewhatsimilar,thetrading
volumeeachyearforGCVRZislarge,andsignificantlyhigherthantheothertwo.Thistable
shows that there is significant trading volume for the CVR contracts, and thus the
correlationsandbetasshownabovearelikelynotduetoilliquidityofthecontracts.
15March2017 FDAHedges Page39of41
7.Conclusion
ThehighcostofcapitalforfirmsconductingmedicalR&Dhasbeenpartlyattributedto
theriskoftheregulatoryapprovalprocessthatinvestorsmustfaceinmedicalinnovation
(Koijen,Philipson,&Uhlig,2016).Weproposednewfinancialinstruments,FDAhedges,to
allow medical R&D investors to better share the pipeline risk associated with the FDA
approval process with broader capital markets. Using FDA approval data, we discussed
pricingofFDAhedgesandmechanismsunderwhichtheycanbetradedandsimulatedtheir
risk and return distributions. We then used a novel panel data set of FDA approval
probabilities toempiricallyexplorethenatureof therisk inherent in thesecontractsand
showed how issuers may effectively hedge this risk. We found evidence that the risk
associatedwithofferingFDAhedgesfacedbyanissuerwaslargelyuncorrelatedwithother
asset classes. Finally,weofferedproof of concept that this typeof risk canbe tradedby
examiningrelatedsecuritiesissuedaroundM&Aactivityinthedrugindustry.
Webelievethetypeofanalysisconductedinthispaperisafirststepindemonstrating
that FDA hedges would enable better risk sharing between those investing in medical
innovationandcapitalmarketsmoregenerally.Byallowingsuchrisksharing,FDAhedges
wouldultimatelyhelpacceleratethedevelopmentofnewmedicalproductsandimprovethe
healthofcountless futurepatients.Therefore, financial innovations like thesemaybetter
spurmedicalinnovation.
15March2017 FDAHedges Page40of41
References
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making."BeckerFriedmanInstituteforResearchinEconomicsWorkingPaper(2015).DiMasi,JosephA.,andGrabowski,HenryG."ThecostofbiopharmaceuticalR&D:isbiotech
different?"ManagerialandDecisionEconomics28,no.4-5(2007):469-479.DiMasi,J.A.,Grabowski,H.G.,andHansen,R.W."Innovationinthepharmaceutical
industry:newestimatesofR&Dcosts."Medford,MA:TuftsCenterfortheStudyofDrugDevelopment(2014).
DiMasi,JosephA.,Hansen,RonaldW.,Grabowski,HenryG.,andLasagna,Louis."Costof
innovationinthepharmaceuticalindustry."JournalofHealthEconomics10,no.2(1991):107-142.
DiMasi,JosephA.,Reichert,JaniceM.,Feldman,Lanna,andMalins,Ashley."Clinical
approvalsuccessratesforinvestigationalcancerdrugs."ClinicalPharmacology&Therapeutics94,no.3(2013):329-335.
Fama,EugeneF.,andFrench,KennethR."Commonriskfactorsinthereturnsonstocksand
bonds."JournalofFinancialEconomics33,no.1(1993):3-56.Fernandez,Jose-Maria,Stein,RogerM.,andLo,AndrewW."Commercializingbiomedical
researchthroughsecuritizationtechniques."NatureBiotechnology30,no.10(2012):964-975.
Koijen,R.,Philipson,T.,andUhlig,H.(2016),“FinancialHealthEconomics”,Econometrica84,no11:195–242
Philipson,Tomas,“HedgingPipelineRiskinPharma:FDASwapsandAnnuities.”Milken
Institute(March2015a).Philipson,Tomas.“SavingLivesthroughFinancialInnovation:FDASwapsandAnnuities”Forbes,(March2015b).
Thakor,RichardT.,Anaya,Nicholas,Zhang,Yuwei,Vilanilam,Christian,andLo,AndrewW.
“FinancialRiskandReturnintheBiotechandPharmaceuticalIndustriesfrom1930to2015”.WorkingPaper,SloanSchoolofManagement,MassachusettsInstituteofTechnology,2016.
Thakor,RichardT.,andAndrewW.Lo.“CompetitionandR&Dfinancingdecisions:Theory
andevidencefromthebiopharmaceuticalindustry.”No.w20903.NationalBureauofEconomicResearch,2015.
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Thakor,RichardT.,andLo,AndrewW.“OptimalFinancingforR&D-intensiveFirms”.
WorkingPaper,2017.Thomas,DavidW.,Burns,Justin,Audette,John,Carroll,Adam,Dow-Hygelund,Corey,and
Hay,Michael.“ClinicalDevelopmentSuccessRates”.BiotechnologyInnovationOrganization(BIO),2016.
15March2017 Appendix:FDAHedges PageA–1of9
AppendixA:AdditionalResults
A.1Multiple-PhaseOptions
AnFDAoptionmaybestructuredtocovermultiplephasesofapproval,sothatitpaysoff
if there is failure inany subsequentphaseof thedrugdevelopmentprocess.Asa simple
example,considerthecasewheretherearefourdiscretedatesintheapprovalprocess:t=1
(phase1), t=2 (phase2), t=3 (phase3), and t=4 (final FDAapproval of aNewDrug
Application orBiologics LicenseApplication). In order to demonstrate the conceptmore
simply,inthefollowingweassumethateachphaseisthesamelengthoftime,thusremoving
theuncertaintyrelatedtothetimewhentheapprovaldecisionismade.Asbefore,weuse
actualprobabilitiestocomputeexpectedvalueswhicharethendiscountedattherisk-free
rateduetotheidiosyncraticnatureofapprovalrisk.IfptistheprobabilitythattheFDAwill
approvethedrugattimet,thenthepriceoftheFDAoptionatt=3willbe:
P3= exp -rt (1-p4)X
Theoptionwillbepricedrecursivelyateachstage.Therefore,theFDAoptionwhichhas
thepayoffindicatedbyFigureA-1below,wouldbepricedatthestartt=0by:
P0= exp -4r p1p2p3(1-p4)X + exp -3r p1p2(1-p3)X + exp -2r p1(1-p2)X
+ exp -r (1-p1)X
Togiveanexample,supposethatabinaryoptionisstructuredsothatitpaysoff$1,000
wheneverthedrugfailstheapprovalprocess.Assumethattherisklessinterestrateis1%
peryear,andthattheprobabilityofsuccessforeachphaseofthedevelopmentprocessisthe
same at 60%. Then purchasing this contract at t = 3 will cost
exp(-0.01) (1-0.60)×1000 =$396.02.Purchasingthiscontractatt=0,however,willcost
15March2017 Appendix:FDAHedges PageA–2of9
$854.10. The high price relative to payoff reflects the fact that the contract offers full
insurance:itwillpayoffifthedrugdevelopmentfailsduringanyphase.Alternatively,one
couldpurchaseacontractofferinginsuranceagainstfailureinaspecificphase,whichwould
thusbevaluedatalowerprice.Thislattercontractmaybevaluableiftherisksoffailurefor
aparticulartypeofdrugareconcentratedinaspecificphase.Forexample,theprobability
ofsuccessforrespiratorydrugsissignificantlylowerinphase2thanitisinanyoftheother
phasesofthedrugdevelopmentprocess(seeThomasetal.(2016)). Asaresult,abinary
optionthatpaysoffintheeventoffailureonlyinphase2maybeparticularlyvaluabletoa
companyoraninvestorthatisfundingsuchadrug.
FigureA1:PayoffDiagramofaFDABinaryOptionattheStartofMultiplePhasesThisfigureshowsthepayoffstructureofamultiple-phaseFDAbinaryoption,whenviewedatthebeginningoftheR&Dprocess.Ineachbranch,ptindicatestheprobabilityofsuccess.
A.2CorrelatedPayoffCalculationsandResults
15March2017 Appendix:FDAHedges PageA–3of9
IntheanalysisinSection3ofthepaper,thepayoffsoftheindividualcontractsinthepool
areassumedtobeindependent.However,asdiscussedpreviously,itispossiblethatthereis
somecorrelationbetween theoutcomesof thevariouscontracts. In thissection,we thus
examinetheresultswhenrelaxingtheassumptionofindependentoutcomes,andintroduce
acorrelationof0.3betweenthepayoutsoftheNcontracts.
Toexplorethis,wesimulatetheX1,…,X50contractsasBernoullirandomvariables,and
weallowforpairwisedependencebetweenallcontractsbyassociatingeachcontractwitha
randomvariableZithatisnormallydistributedwithmean0andvariance1.Ziisassociated
withXiasfollows:
Xi=1ifZi<αi0ifZi≥αi
Here,lettingZ1,…,Z50bedistributedaccordingtoamultivariatestandardnormaldistribution
withcovariancematrix∑allowsthepairwisecorrelationamongX1,…,X50tobecapturedby
thepairwisecorrelationamongtheZi's.
FigureA2presentstheSharperatiosforvariousvaluesofNasafunctionoftheportfolio
markupwiththiscorrelationassumption.Inthiscase,theSharperatiosarelowerthanthe
casewith independent contracts. Moreover, the improvement in the Sharpe ratio is not
monotonic as the number of contracts increase. In particular, while there is a large
improvementintheSharperatiofromN=1toN=10,theSharperatiosareverysimilar
betweenN=50andN=100.ThecorrelationbetweenthecontractsreducestheSharperatio
becausethecorrelationincreasesthestandarddeviationoftheportfolio.Sincethestandard
deviationentersintothedenominatoroftheSharperatio,alargercorrelationwillcausethe
Sharpe ratio to decrease. In this case, introducing correlated assets reduces the
diversificationoftheissuer’sportfolio,thusreducingtheSharperatio.Thisanalysisshows
15March2017 Appendix:FDAHedges PageA–4of9
thatthebenefittotheissuerofholdingcontractscriticallydependsonboththenumberof
contracts, and on the correlation of the payouts between the contracts. However, aswe
previously discussed, a substantial correlation between contracts is not likely to hold in
practice.
FigureA2:SharpeRatios,EquicorrelatedContractsThis figure plots the Sharpe ratios of dealer returns as a function of the portfoliomarkup% forvarious values of N, the number of contracts offered in the pool. These calculations assume acorrelationof30%betweenthepayoutsofthecontracts.
A.3PortfolioPayoffSimulationResultsAcrossVariedParameters
15March2017 Appendix:FDAHedges PageA–5of9
TableA1 belowprovides theportfoliopayoutmean,variance, andstandarddeviation
whenvaryingtheparametersforthenumberofcontractsN,theFDAdecisionarriverateλ,
probabilityofpayoutp,andcorrelationbetweencontractsρ.TableA2providestheportfolio
payoutmean,variance,andstandarddeviationforvariousnumbersofcontractsNacross
thedifferentdiseasegroups.
TableA1:PortfolioDistributionAttributes
This tableprovides the simulation results for themeanportfoliopayout, varianceofpayout, andstandard deviation of payout for various numbers of contractsN, arrival rate parameters λ, forvariousdiseasegroups,andforprobability,andvaryingcorrelationparameters.
NumberofContracts λ Mean Variance StdDev
N=1
0.20 0.15 0.105 0.320.25 0.18 0.117 0.340.33 0.20 0.132 0.360.50 0.24 0.152 0.391.00 0.27 0.176 0.421.50 0.28 0.185 0.432.00 0.29 0.191 0.44
N=10
0.20 0.16 0.011 0.100.25 0.18 0.012 0.110.33 0.20 0.013 0.120.50 0.24 0.015 0.121.00 0.27 0.018 0.131.50 0.28 0.019 0.142.00 0.29 0.019 0.14
N=50
0.20 0.16 0.002 0.050.25 0.18 0.002 0.050.33 0.20 0.003 0.050.50 0.24 0.003 0.061.00 0.27 0.004 0.061.50 0.28 0.004 0.062.00 0.29 0.004 0.06
N=100 0.20 0.16 0.001 0.03
15March2017 Appendix:FDAHedges PageA–6of9
0.25 0.18 0.001 0.030.33 0.20 0.001 0.040.50 0.24 0.002 0.041.00 0.27 0.002 0.041.50 0.28 0.002 0.042.00 0.29 0.002 0.04
NumberofContracts Correlation Mean Variance StdDev
N=1 0.00 0.20 0.133 0.36
NumberofContracts Probability Mean Variance StdDev
N=1
p=0.2 0.14 0.097 0.31p=0.3 0.20 0.133 0.36p=0.4 0.27 0.158 0.40p=0.5 0.34 0.175 0.42p=0.6 0.41 0.182 0.43p=0.7 0.48 0.180 0.42p=0.8 0.54 0.169 0.41
N=10
p=0.2 0.14 0.010 0.10p=0.3 0.21 0.013 0.12p=0.4 0.27 0.016 0.13p=0.5 0.34 0.018 0.13p=0.6 0.41 0.018 0.14p=0.7 0.48 0.018 0.13p=0.8 0.55 0.017 0.13
N=50
p=0.2 0.14 0.002 0.04p=0.3 0.20 0.003 0.05p=0.4 0.27 0.003 0.06p=0.5 0.34 0.003 0.06p=0.6 0.41 0.004 0.06p=0.7 0.48 0.004 0.06p=0.8 0.55 0.003 0.06
N=100
p=0.2 0.14 0.001 0.03p=0.3 0.20 0.001 0.04p=0.4 0.27 0.002 0.04p=0.5 0.34 0.002 0.04p=0.6 0.41 0.002 0.04p=0.7 0.48 0.002 0.04p=0.8 0.55 0.002 0.04
15March2017 Appendix:FDAHedges PageA–7of9
0.05 0.20 0.133 0.360.10 0.21 0.133 0.370.15 0.21 0.133 0.370.20 0.20 0.132 0.36
N=10
0.00 0.20 0.013 0.120.05 0.20 0.019 0.140.10 0.21 0.025 0.160.15 0.20 0.031 0.180.20 0.20 0.037 0.19
N=50
0.00 0.20 0.003 0.050.05 0.20 0.009 0.100.10 0.20 0.016 0.130.15 0.21 0.022 0.150.20 0.20 0.029 0.17
N=100
0.00 0.20 0.001 0.040.05 0.20 0.008 0.090.10 0.20 0.014 0.120.15 0.20 0.021 0.150.20 0.20 0.028 0.17
TableA2:PortfolioDistributionAttributesAcrossDiseaseGroups
This tableprovides the simulation results for themeanportfoliopayout, varianceofpayout, andstandarddeviationofpayoutforvariousnumbersofcontractsN,acrossdifferentdiseasegroups.
NumberofContracts DiseaseGroup Mean Variance StdDev
N=1
Hematology 0.51 0.176 0.42InfectiousDiseases 0.50 0.178 0.42Opthamology 0.40 0.181 0.43OtherDiseaseGroups 0.48 0.180 0.42Metabolic 0.64 0.177 0.42Gastroenterology 0.42 0.182 0.43Allergy 0.48 0.179 0.42Endocrine 0.44 0.182 0.43Respiratory 0.49 0.179 0.42Urology 0.48 0.179 0.42
15March2017 Appendix:FDAHedges PageA–8of9
Autoimmune 0.42 0.182 0.43Neurology 0.39 0.181 0.43Cardiovascular 0.37 0.180 0.42Psychiatry 0.38 0.180 0.43Oncology 0.27 0.158 0.40
N=10
Hematology 0.51 0.018 0.13InfectiousDiseases 0.50 0.018 0.13Opthamology 0.39 0.018 0.14OtherDiseaseGroups 0.48 0.018 0.13Metabolic 0.64 0.018 0.13Gastroenterology 0.42 0.018 0.14Allergy 0.48 0.018 0.13Endocrine 0.44 0.018 0.14Respiratory 0.48 0.018 0.13Urology 0.48 0.018 0.13Autoimmune 0.42 0.018 0.14Neurology 0.39 0.018 0.13Cardiovascular 0.37 0.018 0.13Psychiatry 0.38 0.018 0.13Oncology 0.27 0.016 0.13
N=50
Hematology 0.51 0.003 0.05InfectiousDiseases 0.50 0.003 0.05Opthamology 0.40 0.003 0.05OtherDiseaseGroups 0.48 0.003 0.05Metabolic 0.64 0.004 0.06Gastroenterology 0.42 0.003 0.05Allergy 0.48 0.003 0.05Endocrine 0.44 0.003 0.05Respiratory 0.48 0.003 0.05Urology 0.48 0.003 0.05Autoimmune 0.42 0.003 0.05Neurology 0.39 0.003 0.05Cardiovascular 0.38 0.003 0.05Psychiatry 0.38 0.003 0.05Oncology 0.27 0.002 0.04
N=100
Hematology 0.51 0.002 0.04InfectiousDiseases 0.50 0.002 0.04Opthamology 0.40 0.002 0.04OtherDiseaseGroups 0.48 0.002 0.04
15March2017 Appendix:FDAHedges PageA–9of9
Metabolic 0.64 0.002 0.04Gastroenterology 0.42 0.002 0.04Allergy 0.48 0.002 0.04Endocrine 0.44 0.002 0.04Respiratory 0.48 0.002 0.04Urology 0.48 0.002 0.04Autoimmune 0.42 0.002 0.04Neurology 0.39 0.002 0.04Cardiovascular 0.38 0.002 0.04Psychiatry 0.38 0.002 0.04Oncology 0.27 0.002 0.04