YinLuo,CFAViceChairmanQuan.ta.veResearch,Economics,andPor8olioStrategyYLuo@wolferesearch.comMiguelAlvarezMAlvarez@wolferesearch.comJavedJussaJJussa@wolferesearch.comShengWangSWang@wolferesearch.comGauravRohal,CFAGRohal@wolferesearch.comQESDeskPhone:1.646.582.9230Luo.QES@wolferesearch.com

Luo’sQES

Quan?ta?veResearch,Economics,andPorDolioStrategy

October2017

DONOTFORWARD–DONOTDISTRIBUTE–DOCUMENTCANONLYBEPRINTEDTWICEThisreportislimitedsolelyfortheuseofclientsofWolfeResearch.PleaserefertotheDISCLOSURESECTIONlocatedattheendofthisreportforAnalystCer.fica.onsandOtherDisclosures.ForImportantDisclosures,pleasegotowww.WolfeResearch.com/DisclosuresorwritetousatWolfeResearch,LLC,420LexingtonAvenue,Suite648,NewYork,NY10170

2

#1RankedQuant&MacroResearchTeam

YinLuo,CFA,CPAViceChairmanQES

JavedJussaDirectorofQuan.ta.veResearch

MiguelAlvarezHeadofInvestmentSolu.ons,RiskandPor8olioConstruc.on

YinLuojoinedWolfeResearchinSeptember2016,asaViceChairmantoleadQESresearch.PriortoWolfeResearch,YinspentsevenyearsasaManagingDirectorandGlobalHeadofQuan.ta.veStrategyatDeutscheBank.BeforeDeutscheBank,hespentover12yearsininvestmentbankingandmanagementconsul.ng.

Javedisresponsibleforalphasignal,BigData,ESG,andsmall-capresearchandmanagingtheday-to-dayopera.onsoftheQESteam.PriortoWolfeResearch,JavedwastheUSHeadofQuan.ta.veStrategyatDeutscheBank.Javedalsohasseveralyearsofexperienceininvestmentbusinessandtechnologyconsul.ng.

Miguelisresponsibleforrisk,afribu.on,por8olioconstruc.on,andinvestmentsolu.ons.PriortoWolfeResearch,hewastheUSHeadofQuan.ta.veStrategyandInvestmentSolu.onsatDeutscheBank.MiguelalsoworkedattheEMinvestmentteamatBGI(nowBlackRock).HebeganhiscareerintheresearchgroupatBarrawherehewasresponsibleforriskmodelandpor8olioconstruc.onresearch.

ShengWangVP

Kar?kArora,PhDHeadofQESInfrastructure

GauravRohal,CFAVPHeadofClientServices

Shengistheteam’sexpertonmachinelearningandglobalstockselec.onmodels.BeforejoiningWolfeResearch,ShengwasinchargeoftheR&DofDeutscheBank’sglobalstockselec.onmodelsusingasuiteofsophis.catedmachinelearningtechniques.

Kar.kistheHeadofQESInfrastructure,responsiblefortheoveralltechnologyinfrastructureandconsul.ngservicestoclients.Kar.khasmanyyearsofexperienceinbothinvestmentresearchandtechnology,includingfiveyearsatDeutscheBank’sglobalquan.ta.veresearchinfrastructureteam.

GauravRohalisinchargeofourclientservices.BeforejoiningWolfeResearch,GauravspentfiveyearsatDeutscheBank’sQuan.ta.veResearchteamintheUSandAsia.Priortothat,GauravworkedataHFTproptradingdeskandataglobalinvestmentbankasaquantforexecu.onalgorithmsanddarkpools.

ZhaoJinAssociate

JasonZhong,PhDAssociate,GlobalMacro

•  MumbaiResearch&TechnologyTeam•  SydneyTechnologyTeam

ZhaoJinispartofthesystema.cequityresearchteam.PriortoWolfeResearch,ZhaoworkedasanAVPatBankofAmericaMerrillLynch,developingtheriskcalcula.onpla8ormforthemortgageteam.Zhaoalsospent.meatSungard,focusingonpor8oliomanagementsonware.

Jasonspecializesinmacroeconomicsandglobalmacroresearch.BeforejoiningWolfeResearch,JasonspentfiveyearsintheDepartmentofQuan.ta.veHealthSciencesintheClevelandClinic,developingtheframeworkofrisk-adjustedoutcomesrepor.ng.

•  #1inQuan.ta.veResearch(II-America,II-Europe,II-Asia)

•  ToprankedinPor8olioStrategyandAccoun.ng&TaxPolicy

•  Theprobabilityoftheleadingdigitbeinga1isnot1/9(11.1%)butrather30%,basedontheBenford’slaw.Surprisingly,wefindalmosteverysinglefinancialstatementlineitemfitstheBenford’slawperfectly.

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

NumericalPaOerns

Salesleadingdigitdistribu?on ConformitytoBenfordlawforstandardaccoun?ngitems

NumberofemployeesinUSpubliccompanies Theore?calorexpecteddistribu?on

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Operations

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4Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

TheMADScien?st

VisuallyconformingtotheBenford’sdistribu?on:Disney Visuallynon-conformingtoBenford’sdistribu?on:Enron

0.0%

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igit

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Disney Theoretical

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Enron Theoretical

Higher numbersaremorefrequentthanthenatural

distribution

•  Therearetwosta.s.cs–theMAD(MeanAbsoluteDevia.on)andtheKolmogorov–Smirnov(KS)sta.s.c.•  MADsimplycomputesthecumula.veabsolutedevia.onbetweenthecompany’sandtheactualdistribu.on

(AD)versusthetheore.calorexpecteddistribu.on(ED):MAD= (∑1↑𝑘▒|𝐴𝐷−𝐸𝐷| )/k 

•  TheKSsta.s.csistypicallyusedtocomparethesimilarityoftwodistribu.ons.Itcomputesthemaximum

absolutedifferencebetweentheactualandexpecteddistribu.on:𝐾𝑆=max{…,…|(𝐴𝐷↓1 + 𝐴𝐷↓2 )−(𝐸𝐷↓1 + 𝐸𝐷↓2 )|,…,|(𝐴𝐷↓1 +…𝐴𝐷↓9 )−(𝐸𝐷↓1 +…𝐸𝐷↓9 )|}

•  WecomputetheMADandKSfactorsforallcompaniesinourinvestmentuniverse,usingabout120accoun.ngitemsfromthebalancesheet,incomestatementandcashflowstatement.Thenon-conformersaredefinedasthebofom5%ofcompanies.

5Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

Irregulari?esUnderperform

Coverageofnon-conformingfirms Sharpera?ocomparison

Cumula?veperformancebasedontheKSmodel Cumula?veperformancebasedontheMADfactor

0

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($)

KSEquallyWeighted Russell3000EquallyWeighted

Non-conformingcompaniessharplyunderperformthe

market

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Non-conformingcompaniessharplyunderperformthe

market

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market

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Sharpe

Ratio

KS Russell

6Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

GlobalPerformance

0.0

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($)

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($)

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0.0

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($)

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0.0

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($)

Index(equalwgt) Shortportfolio(equalweightedKStop5%)

Europe UK Japan

AsiaexJapan Canada Australia/NewZealand

LATAM EMEA

•  PubliccompaniesintheUSfilealmost5000documentseveryday.Thevastmajorityoftheavailableinforma.onisinunstructuredformats,e.g.,text,audio,video,andimage.

•  Theunderwhelmingperformanceoftradi.onalfactorsandtherapiddevelopmentincompu.ngpowerandmachinelearningmeansprocessingunstructuredinforma.ontogenerateusefulnumericalsignalsbecomesincreasinglyimportant.

7Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES

WebScrapingUnstructuredTextualInforma?on

NumberofEDGARfilings(daily) Numberoffilings(formtype)

Numberof10-KfilingsaroundtheyearNumberof10-Qfilingsaroundtheyear

•  Transformingunstructuredinforma.onintonumericdatainreal.merequiresasuiteofintegratedsystem,fromwebscraping,datacollec.on,distributedparallelcompu.ng,advancedNaturalLanguageProcessing(NLP),tomachinelearningtechniques.

•  Cloudcompu.ngproviderssuchasAmazon,Microson,andGooglehavedemocra.zedaccesstothedistributedcompu.ng.•  Weu.lizeHadoopframeworkforperforminglargescaledatamining.EDGARwebsiteprovidesmasterindexfilesthatareusedto

iden.fyrelevantfilingdocumentsandloca.on.Weparse,storeandsinthroughthesefilingsforrelevantqualita.veinforma.on.Thefocusishowtobestquan.fydescrip.vetextualdocumentsintoinvestmentintelligence.

8Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

Map-ReduceframeworkfortextminingtheSECEDGARwebsite

Map-Reduceframeworkdataflowschema

•  Interes.ngly,weseeaplungeintheNettoneofthe“RiskFactors”sec.onduringtheheightofthe2008FinancialCrisis.•  Thiscoincideswiththeintroduc.onofmassivenumberofnewtextualdescrip.onsinthispar.cularsec.onfromOctober2008

toApril2009.Thesenewwordshavepredominantlynega.vetones,expressingtheconcernsabouttherecessionanditsimpactonthecompanyfinancials.

•  Thesharpchangeinthelanguageduringthefinancialcrisisquicklyrevertedbacktobusinessasusualinthesubsequentyears,albeitwithhigherwordcount.

9Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

Sen?mentandtoneanalysis

Wordcountandnetsen?mentforthe“RiskFactors”sec?onofthe10-Qfilings

-0.11

-0.11

-0.10

-0.10

-0.09

-0.09

-0.08

0

50

100

150

200

250

300

350

Netse

ntim

ent(med

ian)

Wordcoun

t(med

ian)

WordCount NetSentiment(rightaxis)

Massivechangeinlanguagewithseverely negativesentiment,duringtheheightoffinancialcrisis.

10Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

Historicalperformanceofthenega?vesen?mentfactorsusing10-Qfilings

Quin?lereturnsofthenega?vesen?mentfactor Sharpera?oofthenega?vesen?mentfactor

Thenega?vesen?mentfactor(Consolidated),rankICThenega?vesen?mentfactor(MarketRisk),rankIC

11Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

Historicalperformanceofthedistancefactorusingthe10-Kfilings

Quin?lereturnsoftheJaccarddistancefactor Sharpera?ooftheJaccarddistancefactor

Sharpera?ooftheCosinedistancefactorQuin?lereturnsoftheCosinedistancefactor

•  Inregulatoryfilings,companiescanpresenttheirperformanceandbusinessusingeithertextualdescrip.onornumericaldata.Arguably,whenthenumbersareweak,firmsmightafempttodistractinvestors’afen.on.

•  Inordertocapturethisphenomenon,wecomputethepercentageofnumericdataembeddedineachsec.onofthefilings.•  Formostsec.ons,firmswithhigherpercentagesofnumericcontentshavehighersubsequentreturns.

12Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

NumericversusTextualPropor?on

Sharpera?oofthenumericpercentagemeasure MD&Asec?on,non-sectorneutralrankIC

MD&Asec?on,sectorneutralrankICMarketRisksec?on,non-sectorneutralrankIC

•  Trafficdetec.onviasatellitesisacomplicatedprocess.Thechartbelowprovidesanoverviewofthevariousstagesinvolvedinanalyzingtrafficdataforretailers.

•  Firstofall,geographicimageryiscapturedfromglobalsatellites,aerial/airplanephotography,anddrones.Imagesareprocessedandessen.allydigi.zedforfeatureextrac.on.Basedontheimagery,variousmodelsaswellasgeoloca.ondatabasesareusedtoisolateroadsandparkinglots.Next,toolsandsonwareprogramsareusedtodetectvehicleswithinparkinglotsandroadways.Lastly,vehiclefeaturesuchassize,color,type(e.g.,car,truck,passengervan)canbeextracted.

13Sources:WolfeResearchLuo’sQES

Satellite101

Satelliteimageprocessing

•  HighEarthorbitorgeosynchronousorbit(GEO)satelliteshoveratanal.tudeabove35,000kilometers.Manyweathersatellitesfallintothiscategory.MediumEarthorbitorMEOtendtostayintherangeof2,000to35,000kilometers.Themostcommonuseforsatellitesinthisregionisfornaviga.onsuchasGPS.LowEarthorbitorLEOsatelliteshoveratarangeof200to2,000kilometers.Thesesatellitesareusefulforobserva.on,scien.ficexplora.onaswellasmilitaryandreconnaissancepurposes.

•  TheheightoftheorbitordistancebetweenthesatelliteandtheEarth’ssurfacedeterminehowquicklythesatelliterotatesaroundtheearth.Asasatellitegetsclosertotheearth,thegravita.onfieldgetsstronger,causingthesatellitetomovequickeraroundtheearth.Therefore,LEOsspinaroundtheearthatamuchgreatervelocitythanGEOs.ALEOcanorbittheearthinanhourwhereasitmaytakeaGEOmorethan24hours.

•  Ahighearthorbitsatellitethatisapproximately36,000kilometersfromtheearth’ssurface(or42,000kilometersfromthecenteroftheearth)matchestherota.onalspeedoftheearth.Thisiswhythesesatellitesarereferredtoasgeosynchronousorgeosta.onary.Thesesatellitesareusefulforcommunica.onbecausegroundsta.onsatelliteslocatedontheearthdonotneedtorotatetotrackthem.Incontrast,LEOsatellitestendtobeusefulforobserva.on.ImagestakenbyLEO’stendtohavehigherresolu.ons.

14Sources:hfps://www.e-educa.on.psu.edu/geog480/node/444

Al?tude&Rota?on

TypesofSatellites

•  ApolarorbitsatellitepassesaboveorneartheNorthandSouthPolesduringitsrota.on.ThesesatellitesaremostlyLEO’sapproximately700kmabovetheearth’ssurface.Ittakesabout90minutesforthesatellitetocompleteoneorbitaroundtheEarth.Polarsatellitestypicallycoveralongandwideregionsincetheyrotatequicklyaroundtheearth.Theyaretypicallyusedforremotesensingandtrafficimagery.Thedisadvantageofapolarorbitalsatelliteisthatthesameregionontheearth’ssurfacecan’tbesensedcon.nuously,becausethesatelliterotatesaroundthepoles,whiletheearthrotatesarounditsaxis

•  Sun-synchronoussatellitestendtopassoveranygivenla.tudeatalmostthesamesolar.meeachday.Simplyput,thesetypesofsatellitestendtopassoveranygivenpointontheplanet’ssurfaceatthesamelocal.me.Suchanorbitplacesasatelliteinconstantsunlight(onthesunnysideoftheearth)andisusefulforimagingandremotesensing.The.meistypicallybetweenmid-morningandmid-anernoononthesunsideoftheorbit.Theycancapturetheearth’scanvasatroughlythesame.meduringeachpass,sothatligh.ngremainsuniform.Thisenablesimagestobecomparableover.me.

•  Thereareseveraladvantagestosun-synchronoussatellitesinanearpolarorbit.Thelowal.tudepermitshighresolu.on,whichispreferredforimagingandremotesensing.Thepolarorbitallowsforalargemosaicswathofdailyimagingcoverage.Mostearthobservingsatellitemissionsusesun-synchronoussatellitesinlownearpolarorbits.Thesetypesofsatellitesareveryusefulforcardetec.on.

15Sources:hfp://tornado.sfsu.edu/geosciences/classes/m415_715/Monteverdi/Satellite/PolarOrbiter/Polar_Orbits.htm

Orbit

Sun-synchronousorbit

•  Spa.alresolu.onreferstothepixelsizeofsatelliteimagerycoveringtheearth’ssurface.Forexample,aspa.alresolu.onof30mmeansthesmallestunitthatmapstoasinglepixelwithinanimageisapproximately30mx30m.Itisapparentthatahigherresolu.onenablesbeferimageprocessingandfeaturedetec.on.

16Sources:hfp://www.eorc.jaxa.jp/ALOS-2/en/img_up/alos2_1st/pal2_1s.mg_20140619-21.htm

Resolu?on

Varyingimageresolu?on

•  Satelliteimagerycanbeobtainedaspanchroma.c(greyscale),naturalcolor(RGB)aswellasmul.spectralbands.Mul.spectralbandscancapturelightbeyondthevisiblelightfrequencies,allowingforextrac.onofaddi.onalinforma.onthathumaneyefailstocapture.Forthepurposesofthisreport,wefocusongreyscaleandnaturalcolors(RGB)wavelengths

•  Forthepurposesofvehicledetec.on,greyscaleisasufficientandthepreferredcolorband.Formanyapplica.onsofimageprocessing,colorinforma.ondoesnothelpusiden.fyimportantedgesorotherfeatures.Byusinggreyscale,wecan,ineffect,reducethesignaltonoisera.o.Conver.ngtogreyscalealsoreducesthedimensionalityofimageprocessing

•  𝐺𝑟𝑒𝑦𝑠𝑐𝑎𝑙𝑒=0.299𝑟+0.58𝑔+0.114𝑏

17Sources:hfps://www.e-educa.on.psu.edu/geog480/node/444

ImagePreprocessing

ColorBands

Sources:hfps://www.e-educa.on.psu.edu/geog480/node/444

RGBtogreyscaleconversion

•  Mul.plethresholdingallowsformul.plebreakpoints.Itreliesoncolortodis.nguishanobjectfromthebackground,whichcanbeproblema.c.Forexample,adarkcoloredvehiclemaybemisclassifiedasthebackground.

•  Bayesianmodelscananalyzethevariousfeaturesofvehiclessuchaschanginggradients,windows,shadows,curvatureetc.Amodelistrainedonwhethertheexistenceofthesefeaturescancorrectlyclassifyavehicle.Sincemachinelearningsmodelsrelyonfeaturesratherthanjustthevehiclecolor,theycanbefarmoreaccuratethantradi.onalmethods.

18Sources:DOI:10.4236/jfs.2014.42015

Mul?pleThresholdingandBayesianNetworks

Thresholding+Cleansed Final

NaturalColor Thresholding

•  Featureextrac.onismoreadvancedandinteres.ngthansimplyrecognizingaparkinglot.Algorithmscanpoten.allyiden.fyqueuesbypixelforma.onshavingsufficientlength,boundedwidthandlowcurvature.Queuesalsoshowarepe..vepafernalongacenterline,bothincontrastandwidth.

•  Differen.a.ngbetweencarsandtruckscanalsobeimplementedsystema.cally.Thebasiclogicistocomputethepixelwidth,length,andareaofeachvehicle.Therearearangeoftechniquesavailabletodetectcertainvehiclefeatures.

•  Insummary,theprocessoftakingsatelliteimageryanddecipheringtrafficpafernsisinteres.ngyetcomplex.Itcanalsobeexpensiveandlaborintensive.Thankfully,datavendorsspecializinginsatelliteimagerycanprovidethetransformedandstructureddata.Inthenextsec.on,wetakeadeepdiveintotheRSMetricsdatasetandseehowitcanbeusedinstockselec.on.

19

Sources:hfp://www.walrusvision.com/wordpress/otsu-thresholder-algorithm-works/

FeatureExtrac?on

Queuefeature Largevehiclefeatureextrac?onusingMeanShifclustering

Sources:hfp://content.iospress.com/ar.cles/journal-of-intelligent-and-fuzzy-systems/ifs2201

•  Fill_Rate=Estimate of number of cars for a company at a point in time/Estimate number of available car spaces for a company at a point in time 

20Sources:RSMetrics

FillRate

Stripcenters Powercentersoroutletmalls

Standaloneretailloca?onwithsquarelot Standaloneretailloca?onwithunconven?onallot

•  Asexpected,Saturdays’havethehighestfillrates,asconsumersventureoutontheweekendshopping.Interes.ngly,Sunday’sfillratesarelowerthanwehadan.cipated,possiblyduetolateopeninghoursonSundays.

•  Thegeographicbreakdownsoffillratescoincidewiththereligiouspar.cipa.onratesorreligiositybystate(seeFigure39).Weseetheweakestreligiouspar.cipa.onratesintheWestcoastwherefillratesarethehighestonSundays.Incontrast,theSouthhashighestreligiouspar.cipa.onrates,resul.nginthelowestfillratesonSundays.

21Sources:Alba-Flores,R.[2005].“Evalua.onoftheUseofHigh-Resolu.onSatelliteImageryinTransporta.onApplica.ons”,FinalReport

FillRatebyDaysofTheWeek

Averagefillratebyregionanddayoftheweek(DOW)

Religiositybystate

•  Theretailbusinessisseasonal.Itisdifficulttogaugethehealthofretailcompaniesbasedondataoverasinglemonth.Rather,performanceshouldbemeasuredonasustained,persistentandconsistentmanner.

22Sources:RSMetrics,BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES

PersistentTrafficGrowth

Quin?lereturnperformance Quan?leSharpeperformance

Coverage Long/ShortPerformance

23

M&ATargets

AverageCumula?veExcessReturns MedianCumula?veExcessReturns

FactorExposureBeforeandAfertheM&AAnnouncement AdjustedM&AFrequencybySector

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

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

24

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

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

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.

25Sources: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

•  Form3andForm4areSECfilingsthatrelatetoinsidertrading.Everydirector,officerorownerofmorethan10%ofaclassofequitysecurityregisteredmustfilewiththeSECastatementofownershipregardingsuchasecurity.Theini.alfilingisonForm3andchangesarereportedonForm4.

•  Forunderperformingcompaniesbutexpec.ngtobeacquired,insidersmayengagemoreac.vetransac.onoftheirownstocks.

•  Thereareheavierthannormalinsidertransac.onsfromtwoyearspriortotheannouncementdateun.lsixmonthsrightbeforethedealsareannounced.

•  Thenumberofinsidertradesisactuallymuchlighteronequarterbeforetheannouncementdate,possiblybecauseeitherlock-outperiodpreventsinsidersfromtradingrightbeforetransac.ons,orinsiderswanttoavoidbeingperceivedastradingonprivateinforma.on.

26Sources:FTSERussell,S&PCapitalIQ,ThomsonReuters,EDGAR,WolfeResearchLuo’sQES

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

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

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

27Sources: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

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

momentumfactor).

28Sources: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

29Sources: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

30

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