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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
0
100
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700
1 2 3 4 5 6 7 8 9
Num
bero
fEmployees:Distributionof
FirstD
igit
Thou
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s
FirstDigit
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5%
10%
15%
20%
25%
30%
1 2 3 4 5 6 7 8 9
Theo
reticalDistributionofFirstD
igit
FirstDigit
Benford'sLaw(TheLaw ofFirstDigit)
0
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600
1 2 3 4 5 6 7 8 9
Sales:DistributionofFirstD
igit
Thou
sand
s
FirstDigit
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20%
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100%
SALES COGS TotalAssets CashFlowfrom
Operations
120StandardAccounting
Items
Distrib
utionofFirstD
igit
1
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4
5
6
7
8
9
4Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
TheMADScien?st
VisuallyconformingtotheBenford’sdistribu?on:Disney Visuallynon-conformingtoBenford’sdistribu?on:Enron
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
1 2 3 4 5 6 7 8 9
Freq
uencyDistrib
utionofFirstD
igit
FirstDigit
Disney Theoretical
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
1 2 3 4 5 6 7 8 9
Freq
uencyDistrib
utionofFirstD
igit
FirstDigit
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
5
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15
20
25
Wealth
($)
KSEquallyWeighted Russell3000EquallyWeighted
Non-conformingcompaniessharplyunderperformthe
market
0
5
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15
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25
Wealth
($)
MADEquallyWeighted Russell3000EquallyWeighted
Non-conformingcompaniessharplyunderperformthe
market
Non-conformingcompaniessharplyunderperformthe
market
0
500
1,000
1,500
2,000
2,500
3,000
3,500 Non-Conformers Russell3000
0.00
0.20
0.40
0.60
0.80
EquallyWeighted CapWeighted
Sharpe
Ratio
KS Russell
6Sources:BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
GlobalPerformance
0.0
2.0
4.0
6.0
8.0
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14.0
Wealth
($)
Index(marketcapwgt) Shortportfolio(McapweightedKStop5%)
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($)
Index(equalwgt) Shortportfolio(equalweightedKStop5%)
0.0
0.2
0.4
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0.8
1.0
1.2
1.4
1.6
Wealth
($)
Index(equalwgt) Shortportfolio(equalweightedKStop5%)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Wealth
($)
Index(equalwgt) Shortportfolio(equalweightedKStop5%)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0Wealth
($)
Index(marketcapwgt) Shortportfolio(McapweightedKStop5%)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
Wealth
($)
Index(marketcapwgt) Shortportfolio(McapweightedKStop5%)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
Wealth
($)
Index(marketcapwgt) Shortportfolio(McapweightedKStop5%)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0Wealth
($)
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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