luo’s qes miguel alvarez javed jussa quan?ta?ve …com.estimize.public.s3.amazonaws.com/l2q...

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

•  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

3Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

a)OverviewofM&ATarget

EventTypeFrequency ExcessReturnAroundtheEventDate

EventDefini?onNumberofM&AAnnouncements

4

Characteris?csofM&ATargets

AverageCumula?veExcessReturns MedianCumula?veExcessReturns

FactorExposureBeforeandAXertheM&AAnnouncement AdjustedM&AFrequencybySector

Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

•  WedefineM&AIC(Informa.onCoefficient)astheSpearmanrankcorrela.onbetween:1)therankingofstocksbasedonafactor;and2)avectorofzero’sandone’swhereonesrepresentactualtakeovertargetsinthesubsequentmonth.

•  Toadjustforthe.mevaria.onofthepredic.vepower,wealsocomputetherisk-adjustedM&AICas:


b)ABacktes?ngFrameworkforTakeoverPredic?on

AnExample–M&AIC,theVola?lityFactor

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐼𝐶/𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝐼𝐶 

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


Qua

lityFactors

ValueFactors

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.


Tradi?onalStock-Sec?onFactors,Cont’dA) 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

•  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

•  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


Form3andForm4,Cont’d

SignalfromlastMonthAddsDiversifica?onBenefit

PerformanceoftheSignalBasedonForm3andForm4



•  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.


Form8-KandForm8-K/A

A) Form 8-K B) Form 8-K/A

A) Form 8-K B) Form 8-K/A


Performan

ce

M&AIC,#ofM

onths

Prior

#ofFilings,Form8-Kand

Form

8-K/A

•  TheSchedule(SC)13Disaformthatmustbefiledwhenapersonorgroupacquiresmorethan5%ofanyclassofacompany'sshares.Thisinforma.onmustbedisclosedwithin10daysofthetransac.on.

•  SC13GissimilartoSC13Dusedtoreportaparty'sownershipofstockthatisover5%ofthecompany.SC13Gisshorterandrequireslessinforma.onfromthefilingparty.TobeabletofileSC13GinsteadofSC13D,thepartymustownbetween5and20%inthecompany.Thepartyacquiringthestakeinthecompanymustonlybeapassiveinvestoranddoesnotintendtoexertcontrol.


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

•  FormDEFA14AisfiledwiththeSEC,whenaddi.onaldefini.veproxymaterialsaregiventoshareholders.

•  DFAN14Acoversaddi.onaldefini.veproxysolici.ngmaterialsfiledbynon-management.

•  FormPREC14Aisfiledwhenapreliminaryproxystatementisgiventoshareholders.

•  FormPRER14Aisfiledwhenarevisedpreliminaryproxystatementisgiventoshareholders.

•  Thesefilingsarelessfrequent,strongseasonalpaperns.•  Signalfromproxyvo.ngformsseemstodecayfaster.


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

•  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.


OtherEDGARFilings

From424B5andFormEFFECT

AverageM&AIC


•  Foreachofthemajorfilingform,wepickonefactoramongthefourpossiblechoices(two-yearorfive-yearlookbackwindow,frequency-basedorbinarysignal).

•  ForForm3andform4,wechoosetwomoreshort-termsignalsbasedonthenumberoffilingsinthepreviousmonth.


e)SummaryofEDGARSignals

PerformanceofEDGARFilingFactors


•  WeexaminethenumberofpastM&A,buyback,andotherrelatedac.vi.es.

•  Foreachofthethreeeventtypes,westudythreelookbackwindows:two-year,five-year,and10-year.

•  Longerlookbackwindowfactorsperformbeperthanshorterwindow.

•  Binarysignalshavecomparableperformanceasfrequency-basedones.

•  Thecorrela.onsamongthesignalsbasedonthethreeeventtypesarefairlylow.

•  Totakeadvantageofthediversifica.onbenefit,wefurtherconstructasimpleequallyweightedfactor.Thecombinedfactorbooststheperformanceevenfurther.


M&AEventCountSignal




Combine

dSignalOffe

rs

Diversifica?o

nBe

nefit

Performan

ceofB

inary

Signals

Performan

ceofM

&A

Even

tCou

ntSigna

ls

•  OursectorM&Amomentumfactor,whichisdefinedas:.

•  Hotsectorsaremorelikelytostayhotforawhile.•  TheperformanceofoursectorM&Amomentumfactorisstrongand

uncorrelatedtothethreeM&Aeventcountsignals.


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

•  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.


g)MachineLearningTakeovers


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

•  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.


APrimerofMachineLearningAlgorithms

RandomForestFlowchart

SVMIllustrated

A) The growth of # of papers on SSRN B) Seasonality, # of papers posted by month

•  Aswemovefroma80-factormodeltoa235-factormodel,predic.vepowerimproves.The80factorsareasubsetofthe235signals.•  Wecanfurtherboostperformancebyaddingnon-tradi.onalfactors(i.e.,EDGARfilingbased,eventcountsignals,andoursectorM&A

momentumfactor).


AHorseraceofMachineLearningAlgorithmsinRareEventPredic?on

IncreasingtheNumberofTradi?onalQuantFactor

AddingNon-tradi?onalFactorsfurtherBoostsPerformance

A) Average M&A IC B) Risk adjusted M&A IC


•  Thereisanicemixofbothtradi.onalandnon-tradi.onalfactorsbeenselectedbythemodel.

•  Mostfactorsshowintui.vesigns.•  Theweightsofmostfactorsarequitestableover.me.


FactorsSelectedbytheModel

FactorWeights,LogitModelatMay31,2017

WeightsfortheM&AFrequencyFactor

WeightsforthePrice-to-salesFactor

•  OurfinalSMAP(Systema.cMerger&Acquisi.onPredic.on)model,whichequallyweighsthepredic.onfromsevenoftheeightmodels.WeexcludetheCARTmodel,becauseourrandomforecastmodelencompassestheCARTmodelandofferssignificantlybeperperformance.


h)AssessingtheModel

SMAPModelPerformsBeierinRecentYears

M&AIC,SMAPModel


AverageM&AIC

Risk-AdjustedM&AIC


Out-of-sampleHitRate

%ofActualTakeoverCaptured#ofActualM&A,byQuin?le

HitRateatVariousCutoffThresholdsHitRatebyForecastedDecileintheSubsequentMonth


Forecas?ngAbilityatdifferentHorizon

AverageHitRateatEachDecileforDifferentHorizonSMAPModelAutocorrela?on

Cumula?veM&AICDecayM&AICDecay


ACaseStudy–WholeFoodsMarket

MachineLearningModelOutput,October2016

SMAPPercen?leforWFMbeforeitsM&AAnnouncement TopFactorsthatContributetotheChangeofLogitModel,April2017

TopFactorsthatContributetotheChangeofLogitModel,October2016

•  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


i)Prac?calApplica?on

Correla?onbetweentheSMAPandLEAP

SMAP,RankICCumula?vePerformance,byDeciles

The top 10% stocks with the highest takeover

probabilities


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

•  SMAPmodel’shitrateincreasesmonotonicallyaswe.ghtenoutcutoffthreshold.

•  Aswelitupourthreshold,ourpor8oliobecomesconcentrated.Theremightbenotakeovertargetatallincertainmonths.

•  Asthepor8oliobecomesmoreconcentra.on,thepercentageofmonthswithatleastoneM&Atransac.onalsodecreasesmonotonically

•  5%istheop.malcutoffthreshold,whichgivesus150stockspermonth.


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


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

29

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