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YinLuo,CFAViceChairmanQuan.ta.veResearch,Economics,[email protected]@wolferesearch.comJavedJussaJJussa@[email protected],[email protected]:[email protected]
Luo’sQES
Quan?ta?veResearch,Economics,andPorDolioStrategy
October2017
DONOTFORWARD–DONOTDISTRIBUTE–DOCUMENTCANONLYBEPRINTEDTWICEThisreportislimitedsolelyfortheuseofclientsofWolfeResearch.PleaserefertotheDISCLOSURESECTIONlocatedattheendofthisreportforAnalystCer.fica.onsandOtherDisclosures.ForImportantDisclosures,pleasegotowww.WolfeResearch.com/DisclosuresorwritetousatWolfeResearch,LLC,420LexingtonAvenue,Suite648,NewYork,NY10170
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• Targe?ngMergersandAcquisi?ons.Inatypicaltakeoverdeal,theacquireroffersasignificantpremiumoverthetargetfirm’sprevailingshareprice.Theupsidefromaccuratelyiden.fyingtakeovertargetsissignificant.
• Alterna?veBigDataFactors.Inaddi.ontotradi.onalstock-selec.onfactors,weconstructasetofunconven.onalfactors.Inpar.cular,wefindcorporatefilings(e.g.,Forms8-K,4,13D/13G,DFAN14A,etc.)fromtheEDGARdatabasecontainsusefulinforma.onaboutfutureM&Atransac.ons.UsingtheTRSDCdatabase,wealsoconstructpastM&AeventcountsignalsandourproprietarySectorM&AMomentumfactor.
• MachineLearningTakeovers.Todealwiththerareunbalancedsamplenatureoftakeoverpredic.on,weintroducematchedsampleandbaggingtechniques.Weinves.gateawiderangeofmachinelearningalgorithmsintakeoverpredic.on:CART,randomforest,SVM,ANN,andAdaBoost.OurfinalSMAP(Systema.cMergerandAcquisi.onPredic.on)modeltakesadvantageofforecastcombina.on,byblendingsevenmachinelearningmodels.TheSMAPmodeldemonstratesexcellentpredic.vepowerintakeoverpredic.on.
• Applica?onoftheSMAPModel.Discre.onarymanagerscanusetheSAMPmodelasapre-screeningtoolbeforeconduc.ngmorein-depthfundamentalresearch.Buyingthosestockswiththehighesttakeoverprobabili.esachievessuperiorreturn.Long/shortmanagerscanusetheSMAPtoavoidshor.ngpoten.altakeovertargets.TheSMAPoverlayontheshortsidenotonlyboostsreturn,butalsoreducepor8oliovola.lity.
2
10.MachineLearningTakeovers
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3Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
a)OverviewofM&ATarget
EventTypeFrequency ExcessReturnAroundtheEventDate
EventDefini?onNumberofM&AAnnouncements
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4
Characteris?csofM&ATargets
AverageCumula?veExcessReturns MedianCumula?veExcessReturns
FactorExposureBeforeandAXertheM&AAnnouncement AdjustedM&AFrequencybySector
Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
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• WedefineM&AIC(Informa.onCoefficient)astheSpearmanrankcorrela.onbetween:1)therankingofstocksbasedonafactor;and2)avectorofzero’sandone’swhereonesrepresentactualtakeovertargetsinthesubsequentmonth.
• Toadjustforthe.mevaria.onofthepredic.vepower,wealsocomputetherisk-adjustedM&AICas:
5Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
b)ABacktes?ngFrameworkforTakeoverPredic?on
AnExample–M&AIC,theVola?lityFactor
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐼𝐶/𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐼𝐶
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Valuefactors• Moreexpensivebasedon
dividendyield,earningsyield,cashflowyield,tangiblebook-to-market,EBITDA/EV;but
• Cheaperbasedonprice-to-sales,book-to-market,andrevenue/TEV
Qualityfactors• Theyarelessprofitableonmost
metrics,withtheexcep.onofgrossprofitmargin.Aposi.vegrossmarginandanega.venetmarginmeanstargetfirmsarepar.cularlyinefficientingeneralmanagementandadministra.veac.vi.es.
• Theyhavelowercorporategovernancestandardbutpossiblehigherdividendpayingsustainability,asreflectedbythelowerpayoutra.o.
• Theyhaveslightlylowerfinancialleverageandlowerbankruptcyrisk
6
Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
c)Tradi?onalStock-Sec?onFactors
A) Average M&A IC B) Risk Adjusted M&A IC
A) Average M&A IC B) Risk Adjusted M&A IC
Qua
lityFactors
ValueFactors
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Sen.mentfactors• Takeovercompaniesaredislikedbysell-side
analystsbyallcommonmeasures.
Growthfactors• Allthegrowthrelatedfactorsshownega.ve
M&AIC.• Onemajorreasonthatanacquirerwantsto
buyatargetistoturnaroundaslowgrowthcompany,inordertogeneratemergersynergyandoutsizedprofit
TechnicalFactors• Theyhavepoorpricemomentum.• Theyhavelowerliquidity,e.g.,float
turnover,Amihudilliquidity.• Theyaremorevola.le.• Themostinteres.ngaspectisthattarget
companiestendtorallysharplyimmediatelybeforeM&Aannouncements(posi.veone-monthreturn),coupledbyhigherabnormaltradingvolume,andexcessKurtosis.ThisispossibleduetothefactthatM&Atransac.onsaresome.mesan.cipatedbycertainmarketpar.cipants.
7Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
Tradi?onalStock-Sec?onFactors,Cont’dA) Average M&A IC B) Risk Adjusted M&A IC
A) Average M&A IC B) Risk Adjusted M&A IC
TechnicalFactors A) Average M&A IC B) Risk Adjusted M&A IC
GrowthFactors
Sen?
men
tFactors
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• Form3andForm4areSECfilingsthatrelatetoinsidertrading.Everydirector,officerorownerofmorethan10%ofaclassofequitysecurityregisteredmustfilewiththeSECastatementofownershipregardingsuchasecurity.Theini.alfilingisonForm3andchangesarereportedonForm4.
• Forunderperformingcompaniesbutexpec.ngtobeacquired,insidersmayengagemoreac.vetransac.onoftheirownstocks.
• Thereareheavierthannormalinsidertransac.onsfromtwoyearspriortotheannouncementdateun.lsixmonthsrightbeforethedealsareannounced.
• Thenumberofinsidertradesisactuallymuchlighteronequarterbeforetheannouncementdate,possiblybecauseeitherlock-outperiodpreventsinsidersfromtradingrightbeforetransac.ons,orinsiderswanttoavoidbeingperceivedastradingonprivateinforma.on.
8Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
d)EDGARFilingSignals
NumberofFilingsforForm4andForm3
AverageM&AICforFilingMonthsPrior
A) Form 4 B) Form 3
0
500
1000
1500
2000
2500
0
100
200
300
400
500
600
A) Form 4 B) Form 3
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• WeconstructtwosignalsbasedonthenumberofForm3andForm4filingsinthepasttwoandfiveyearlookbackwindows,excludingthemostrecentquarter.
• Wealsoapplyanexponen.allydecaywithahalf-lifeofoneyearand2.5years,respec.vely.
• Inaddi.onal,wedefinetwobinarysignals,whichisoneifthereisafilinginthepasttwo(orfive)yearlookbackwindow,orzerootherwise.
• Signalsbasedonthelongerfive-yearwindowgenerallyhavebeperperformancethantheonesusingashortertwo-yearwindow.
• Thebinarysignalsaresignificantlybeperthantheonesbasedonactualnumberoffilings.
• WeusethenumberofForm3(andForm4)filingsintheimmediatepriormonthasanega.vefactor.
• Themostrecentmonthsignalsaddpoten.aldiversifica.onbenefit
9Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
Form3andForm4,Cont’d
SignalfromlastMonthAddsDiversifica?onBenefit
PerformanceoftheSignalBasedonForm3andForm4
A) Average M&A IC B) Risk Adjusted M&A IC
A) Average M&A IC B) Risk Adjusted M&A IC
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• Form8-Kisaverybroadformusedtono.fyinvestorsofspecifiedeventsthatmaybeimportanttoshareholdersortheSEC.ThisisoneofthemostcommontypesofformsfiledwiththeSEC.Thereportno.fiesthepublicofeventsreportedincludingacquisi.on,bankruptcy,resigna.onofdirectorsandothermaterialevents.Form8-K/Aistheamendmentof8-Kfiling.
• ThenumberofamendmentsinForm8-K/Aismuchlessfrequent
• AmorefrequentfilingofForm8-K(andtoalesserextent,Form8-K/A)inthepasttwoyearsisassociatedwithahigherprobabilityofbeingacquired.
• Alongerfive-yearlookbackwindowproducesslightlybepersignals
• SimilartowhatweseeforForm3/Form4,forthemorepopularForm8-K,binarysignalsperformbeperthanfrequency-basedfactors.
10Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
Form8-KandForm8-K/A
A) Form 8-K B) Form 8-K/A
A) Form 8-K B) Form 8-K/A
A) Average M&A IC B) Risk Adjusted M&A IC
Performan
ce
M&AIC,#ofM
onths
Prior
#ofFilings,Form8-Kand
Form
8-K/A
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• TheSchedule(SC)13Disaformthatmustbefiledwhenapersonorgroupacquiresmorethan5%ofanyclassofacompany'sshares.Thisinforma.onmustbedisclosedwithin10daysofthetransac.on.
• SC13GissimilartoSC13Dusedtoreportaparty'sownershipofstockthatisover5%ofthecompany.SC13Gisshorterandrequireslessinforma.onfromthefilingparty.TobeabletofileSC13GinsteadofSC13D,thepartymustownbetween5and20%inthecompany.Thepartyacquiringthestakeinthecompanymustonlybeapassiveinvestoranddoesnotintendtoexertcontrol.
11Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
SC13DandSC13G
NumberofFilingsforSC13D,SC13D/A,SC13GandSC13G/AA) Form SC 13D B) Form SC 13D/A
C) Form SC 13G B) Form SC 13G/A
RiskAdjustedM&AIC–SC13D,SC13D/A,SC13G,SC13G/A
AverageM&AICforDifferentLagsA) Form SC 13D B) Form SC 13D/A
C) Form SC 13G B) Form SC 13G/A
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• FormDEFA14AisfiledwiththeSEC,whenaddi.onaldefini.veproxymaterialsaregiventoshareholders.
• DFAN14Acoversaddi.onaldefini.veproxysolici.ngmaterialsfiledbynon-management.
• FormPREC14Aisfiledwhenapreliminaryproxystatementisgiventoshareholders.
• FormPRER14Aisfiledwhenarevisedpreliminaryproxystatementisgiventoshareholders.
• Thesefilingsarelessfrequent,strongseasonalpaperns.• Signalfromproxyvo.ngformsseemstodecayfaster.
12Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
ProxyFilings
NumberofFilingsforDEFA14A,DFAN14A,PRER14A,PREC14A
RiskAdjustedM&AIC,ProxyVo?ngBasedSignals
AverageM&AICforDifferentLagsA) Form DEFA14A B) Form DFAN14A
C) Form PRER14A D) Form PREC14A
A) Form DEFA14A B) Form DFAN14A
C) Form PRER14A D) Form PREC14A
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
FormDEFA14A
FormDEFA14ABinary
FormPRER14A
FormPRER14ABinary
FormDFAN14A
FormDFAN14ABinary
FormPREC14A
FormPREC14ABinary
Twoyearwindow Fiveyearwindow
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• Form11-Kistheannualreportoftheemployeestockpurchase,savingsandsimilarplans.
• FormS-8istheini.alregistra.onstatementforsecuri.estobeofferedtotheemployeebenefitplans.
• FormCTORDERstandsforconfiden.altreatmentorder.Itisaformfilledoutinaccordancetocompany’s8-K,10-Qor10-K.Thisformallowsacompanytokeepconfiden.alinforma.onasasecretforacertainperiodof.me;otherwisesuchinforma.onwouldputthefirmatadisadvantage.
• FormCORRESPisanEDGARformtypethatregistrantscanusetosubmitnon-publicinforma.ontoSEC.
• Form424B5mustbefiledwithintwobusinessdaysofthedetermina.ondateofanofferingpriceorthedatefirstusedfollowingtheeffec.vedateofapublicofferingorsaleofsecuri.esbythecompany.
• FormEFFECTistheNo.fica.onsofEffec.veness.Itisapublicdeclara.onbytheSECthatapubliccompany’sregistra.onstatementhasbeenaccepted.Forsharesinthepubliccompaniestotradeontheopenmarket,theymustberegisteredbythecompanyandapprovedbySEC.
• Mostsignalsshowastrongerpredic.vepowerwithalongerfive-yearlookbackwindow
• Theexcep.onliesinthetwoformsrelatedtosecuri.esofferings(Form424B5andFormEFFECT).Becauseoftheshort.melineoftheseforms,aone-quarterlookbackwindowhasthebestperformance.
13Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
OtherEDGARFilings
From424B5andFormEFFECT
AverageM&AIC
A) Average M&A IC B) Risk Adjusted M&A IC
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• Foreachofthemajorfilingform,wepickonefactoramongthefourpossiblechoices(two-yearorfive-yearlookbackwindow,frequency-basedorbinarysignal).
• ForForm3andform4,wechoosetwomoreshort-termsignalsbasedonthenumberoffilingsinthepreviousmonth.
14Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES
e)SummaryofEDGARSignals
PerformanceofEDGARFilingFactors
A) Average M&A IC B) Risk Adjusted M&A IC
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• WeexaminethenumberofpastM&A,buyback,andotherrelatedac.vi.es.
• Foreachofthethreeeventtypes,westudythreelookbackwindows:two-year,five-year,and10-year.
• Longerlookbackwindowfactorsperformbeperthanshorterwindow.
• Binarysignalshavecomparableperformanceasfrequency-basedones.
• Thecorrela.onsamongthesignalsbasedonthethreeeventtypesarefairlylow.
• Totakeadvantageofthediversifica.onbenefit,wefurtherconstructasimpleequallyweightedfactor.Thecombinedfactorbooststheperformanceevenfurther.
15Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
M&AEventCountSignal
A) Average M&A IC B) Risk Adjusted M&A IC
A) Average M&A IC B) Risk Adjusted M&A IC
A) Average M&A IC B) Risk Adjusted M&A IC
Combine
dSignalOffe
rs
Diversifica?o
nBe
nefit
Performan
ceofB
inary
Signals
Performan
ceofM
&A
Even
tCou
ntSigna
ls
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• OursectorM&Amomentumfactor,whichisdefinedas:.
• Hotsectorsaremorelikelytostayhotforawhile.• TheperformanceofoursectorM&Amomentumfactorisstrongand
uncorrelatedtothethreeM&Aeventcountsignals.
16Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
f)SectorM&AMomentumSignal
PercentageofM&ATransac?ons,byGICSSector
PerformanceofDifferentEDGARFilingFactors
A) Percentage of M&A Transactions, by GICS Sector B) Autocorrelation
SectorM&AMomentumSignal,M&AIC
𝑆𝑒𝑐𝑡𝑜𝑟𝑀&𝐴𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚 = . 𝜔𝑡 0# 𝑜𝑓 𝑀&𝐴 𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑆𝑒𝑐𝑡𝑜𝑟
# 𝑜𝑓 𝑆𝑡𝑜𝑐𝑘𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑆𝑒𝑐𝑡𝑜𝑟:
0
𝑡=−120
A) Correlation with M&A Events Signals B) Average M&A IC
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• Wegroupthefactorscoveredintheprevioussec.onsintothreecategories:
– Tradi.onalstock-selec.onfactors– Non-tradi.onalEDGARfilingand
M&Aeventcountfactors– SectorM&Amomentumsignal
• Wenormalizeallfactors,matcheachtargetwitharandomlyselec.oncomparablestock,andstacktogetherallobserva.onsinthepast10yearsasourtrainingsample.
• Thebaggingalgorithmisaddedtoallmachinelearningmodelsinthepaper.Weaveragepredic.onsfromthe10-foldbaggingformsourfinalforecast.
• Weonlysectorneutralizealltradi.onalfactors:
– Sectorneutraliza.onmodestlyimprovestheperformanceoftradi.onalfactors
– Fornon-tradi.onalfactors,sectorneutraliza.onmakesnodifference.
17Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
g)MachineLearningTakeovers
A) Average M&A IC B) Risk Adjusted M&A IC
A) Traditional Quant Factors B) Non-traditional Factors
A) Traditional Quant Factors B) Non-traditional Factors
RiskAdjustedM&AICfo
rtheLogitM
odel
AverageM&AICfo
rthe
LogitM
odel
Performan
ceIm
provem
ent
from
Bagging
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• Logit:modelsthelogoddsoftheeventasalinearfunc.on.
• CART:mostcommonlyusedclassifica.onalgorithms.Prunetotreesusingcross-valida.ontoavoidoverfisng.
• RandomForest:randomlyselectasub-sampleofdatatobuildCARTmodels,taketheaveragefrommul.pleCARTmodel’spredic.ontoderivethefinalforecast
• SVM:findthehyperplanethatmaximizeitsdistancesfromtheclosestdatapointineachofthetwogroups.
• ANN:linearcombina.onofinputvariablestypicallytransformedbyanonlinearfunc.on
• AdaBoost:itadap.velybuildsasequenceofweakclassifierstoconstructthestrongclassifier.Ineachstepofthetraining,itemphasizesonthemisclassifieddatasampletherebygraduallyimprovingtheaccuracyofclassifica.on.
18Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
APrimerofMachineLearningAlgorithms
RandomForestFlowchart
SVMIllustrated
A) The growth of # of papers on SSRN B) Seasonality, # of papers posted by month
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• Aswemovefroma80-factormodeltoa235-factormodel,predic.vepowerimproves.The80factorsareasubsetofthe235signals.• Wecanfurtherboostperformancebyaddingnon-tradi.onalfactors(i.e.,EDGARfilingbased,eventcountsignals,andoursectorM&A
momentumfactor).
19Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
AHorseraceofMachineLearningAlgorithmsinRareEventPredic?on
IncreasingtheNumberofTradi?onalQuantFactor
AddingNon-tradi?onalFactorsfurtherBoostsPerformance
A) Average M&A IC B) Risk adjusted M&A IC
A) Average M&A IC B) Risk Adjusted M&A IC
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• Thereisanicemixofbothtradi.onalandnon-tradi.onalfactorsbeenselectedbythemodel.
• Mostfactorsshowintui.vesigns.• Theweightsofmostfactorsarequitestableover.me.
20Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
FactorsSelectedbytheModel
FactorWeights,LogitModelatMay31,2017
WeightsfortheM&AFrequencyFactor
WeightsforthePrice-to-salesFactor
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• OurfinalSMAP(Systema.cMerger&Acquisi.onPredic.on)model,whichequallyweighsthepredic.onfromsevenoftheeightmodels.WeexcludetheCARTmodel,becauseourrandomforecastmodelencompassestheCARTmodelandofferssignificantlybeperperformance.
21Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
h)AssessingtheModel
SMAPModelPerformsBeierinRecentYears
M&AIC,SMAPModel
A) Average M&A IC B) Risk Adjusted M&A IC
AverageM&AIC
Risk-AdjustedM&AIC
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22Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
Out-of-sampleHitRate
%ofActualTakeoverCaptured#ofActualM&A,byQuin?le
HitRateatVariousCutoffThresholdsHitRatebyForecastedDecileintheSubsequentMonth
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23Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
Forecas?ngAbilityatdifferentHorizon
AverageHitRateatEachDecileforDifferentHorizonSMAPModelAutocorrela?on
Cumula?veM&AICDecayM&AICDecay
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24Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
ACaseStudy–WholeFoodsMarket
MachineLearningModelOutput,October2016
SMAPPercen?leforWFMbeforeitsM&AAnnouncement TopFactorsthatContributetotheChangeofLogitModel,April2017
TopFactorsthatContributetotheChangeofLogitModel,October2016
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• Buyingpoten.alM&Atargetsbeforedealannouncementstocapturethelargeposi.vereturnsontheM&Aannouncementdate
• SMAPisnotatypicalstock-selec.onmodel.Thecorrela.onbetweentheSMAPandourflagshipglobalstock-selec.onmodel–LEAPisactuallynega.vemostofthe.me
• Wecan’tseemtousetheSMAPnaivelyasastockselec.ontool.
• ForM&Atransac.ons,theupsideisallintheoutliers,i.e.,thoseactualtakeovertargetcompanies.
• ThetopdecilebasedontheSMAPmodelclearlyoutperformsthemarketandallotherdeciles
25Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
i)Prac?calApplica?on
Correla?onbetweentheSMAPandLEAP
SMAP,RankICCumula?vePerformance,byDeciles
The top 10% stocks with the highest takeover
probabilities
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26Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
SkewnessDrivenbyM&ATarget
ReturnDistribu?onCAGR,byDecile
MedianSkewness,byDecile
A) Return Distribution for the Top Decile SMAP B) Return Distribution for the Russell 3000 Universe
PerformancefortheSMAPTopDecilewithandwithouttheM&ATarget
A) Wealth Curve B) Sharpe Ratio
The top 10% stocks with the highest takeover
probabilities
The top 10% stocks with the highest takeover probabilities
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• SMAPmodel’shitrateincreasesmonotonicallyaswe.ghtenoutcutoffthreshold.
• Aswelitupourthreshold,ourpor8oliobecomesconcentrated.Theremightbenotakeovertargetatallincertainmonths.
• Asthepor8oliobecomesmoreconcentra.on,thepercentageofmonthswithatleastoneM&Atransac.onalsodecreasesmonotonically
• 5%istheop.malcutoffthreshold,whichgivesus150stockspermonth.
27Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
TheOp?malCutoffThreshold
PerformancefortheSMAPwithDifferentCutoffThreshold
A) Hit Rate by Various Cutoff Threshold B) Percentage of Months with at least One Deal
C) Cumulative Performance D) Sharpe Ratio
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28Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
AvoidShor?ngPoten?alTakeoverTargets
RemovingHigh-Takeover-ProbabilityStocks(basedonSMAP)BoostsPerformance
Cumula?vePerformance,ROEFactorPorDolio ImprovingthePerformanceofLEAPusingtheSMAPModel
A) Annualized Return B) Sharpe Ratio
C) Annualized Volatility D) Max Drawdown
• Weseeacrosstheboardperformanceimprovementbyremovingthehighesttakeoverprobabilitystocksfromtheshortside.
• Bynotshor.ngpoten.alM&Atargetsalsoreducestheriskforallcommonfactors.
• RemovinghightakeoverprobabilitystocksfromtheshortsidealsohelpshighefficacyalphamodelssuchastheLEAP.
A) Sharpe Ratio B) Max Drawdown
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29
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