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YinLuo,CFAViceChairmanQuantitativeResearch,Economics,andPortfolioStrategy
QESDeskPhone:1.646.582.9230Luo.QES@wolferesearch.comAlternativeDataIntegration,
AnalysisandInvestmentResearch
L2QConference
DONOTFORWARD– DONOTDISTRIBUTE– DOCUMENTCANONLYBEPRINTEDTWICEThisreportislimitedsolelyfortheuseofclientsofWolfeResearch.PleaserefertotheDISCLOSURESECTIONlocatedattheendofthisreportforAnalystCertificationsandOtherDisclosures.ForImportantDisclosures,pleasegotowww.WolfeResearch.com/DisclosuresorwritetousatWolfeResearch,LLC,420LexingtonAvenue,Suite648,NewYork,NY10170
June20,2017
2
Agenda
Table ofContents
1.IntroducingLuo’sQESResearch
2.CrowdsourcingRevenueandEarningsEstimates
3.TextMiningUnstructuredCorporateFilingData
• TopRankedQuantitativeandMacroResearchTeam.Theteamhasbeenranked#1intheInstitutionalInvestor’sII-AllAmerica,II-Europe,andII-AsiasurveysintheQuantitativeResearchsector,andtoprankedinthePortfolioStrategyandAccounting&TaxPolicycategories.
• BigDataandMachineLearning.WefullyincorporateBigData(e.g.,textmining,newssentiment,satelliteimagery,securitieslending,crowdingsourcing)andmachinelearninginourresearch,asreflectedinourLEAPglobalstockselectionmodel.
• SystematicGlobalMacroResearch.OurresearchonNowcastingeconomicgrowthin>40countries/regionshasreceivedtremendousfeedbackfromclients.Stylerotationandfactortimingisacorecomponentofourglobalstockselectionmodels.Alternativedatasources(e.g.,newssentiment,real-timehiring,satelliteimagery,Googletrends)arealsofullyintegratedinourmacroresearch.
• UsefulToolsforFundamentalManagers.Inadditiontoresearch,wealsoprovideasuiteofusefultools,includingonlinescreeningandfactorperformancetracking,industry-specificmodeling,positionsizingandportfolioconstruction,andportfolioanalyticstodiscretionarymanagers.
• [email protected],ifyouareinterestedinourresearchandservices.
3
1.IntroducingLuo’sQESResearch
• AlternativeBigDataonEarningsandRevenueEstimate.Inthisresearch,westudyanalternativedatasourcebasedontheconceptofcrowdsourcing.Estimizeisanonlineplatformthatallowsindividualswithdifferentbackgroundtocontributetheirfinancialforecast.WefindEstimizeestimatestobenotonlymoreaccurateandtimelierthanthesell-side,butalsohighlycomplementarytotraditionalfactors.
• EstimizeFESModel.DivingintodetailedEstimizeestimates,wefindthataccuracycanbefurtherimprovedalongthreedimensions:thefreshnessoftheestimates,analystexperience,andanalystskill.WethenintroduceasmartEstimizeconsensuscalledFES(Freshness,Experience,andSkill).
• SmartStrategiesaroundEarningsAnnouncement.WeexplorethreedifferenttypeoftradingstrategiesaroundearningsreleasesusingtheEstimizedata.Thepre-earningsannouncementstrategybuysstocksbasedonearningsrevisionsintheweekbeforetheearningsreportingdate.PEAD(PostEarningAnnouncementDrift)strategyattemptstocapturethedriftalphaimmediatelyaftertheearningsannouncement,basedonearningssurprise.Lastly,weproposealowrisklong-onlystrategybyavoidingearningsriskandearningsuncertainty.
• EnhancedValueStrategies.Manyfundamentalandquantitativestrategiesexplicitlyorimplicitlyrelyonearningsandrevenueestimates.Forlong-termvalueinvestors,weshowhowEstimizedataandourFESmodelcanbeusedtoboostperformance.Intheend,wealsooverlayourenhancedvaluestrategywithalowrisktilt(byavoidingearningsuncertainty)tofurtherimprovereturnandreducerisk.
5
2.CrowdsourcingEarningsandRevenueEstimates
6Sources: Estimize,WolfeResearchLuo’sQES
a)TheBasicsofCrowdsourcing
ContributorstotheEstimizedatabase
OtherIndependent ResearchOtherAsset ManagerHedge FundVenture CapitalProprietary Trading FirmMutual FundFund of FundsPrivate EquityPension FundEndowment FundOtherBrokerFinancial AdvisorInvestment BankWealth ManagerInsurance FirmAcademiaStudentHealth CareInformation TechnologyFinancialsIndustrialsMaterialsConsumer StaplesConsumer DiscretionaryEnergyUtilitiesTelecommunication Services
Non Professional
Financial ProfessionalBuy Side
Sell Side
Independent
BreakdownofEstimizedata
Breakdownoffinanceprofessional
30%
25%
45%
Independent BuySide SellSide
46%
54%
FinanceProfessionals Non-professionals
• Earningsandrevenueestimatesareprobablythemostimportantdriversofstockreturnsandrisks.Unlikeconventionalsell-sideconsensus,Estimizecrowdsourcesestimatesfromawiderangeofcontributors.
7Sources: Estimize,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
EstimizeDataDistribution
#ofcompanies(intheRussell3000index)withEstimizeanalystcoverage
Estimizedatasectordistribution
Numberofanalysts
0%
5%
10%
15%
20%
25%
%ofstocksintheEstimizedatabase %ofstocksintheRussell3000index
Numberofdaysbeforeearningsreport
0
400
800
1200
1600>=1anlalyst
>=3anlalysts
>=10anlalysts
>=30anlalysts
8Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
b)TheAccuracyofCrowdsourcedEstimates
EPSestimateaccuracy
EstimizeEPSaccuracybysector
Revenueestimateaccuracy
Estimizeaccuracy,domesticversusmultinationalfirms
0%
50%
100%
Sellsidemoreaccurate Estimizemoreaccurate
40.9% 39.4% 39.2% 38.6%
59.1% 60.6% 60.8% 61.4%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<10%exUS >10%exUS >20%exUS >50%exUS
Sellsidemoreaccurate Estimizemoreaccurate
43% 41% 40% 38% 36%
57% 59% 60% 62% 64%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
>=1analyst >=3analysts >=5analysts >=10analysts >=30analysts
Sell-sidemoreaccurate Estimizemoreaccurate
52% 51% 50% 49% 49%
48% 49% 50% 51% 51%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
>=1analyst >=3analysts >=5analysts >=10analysts >=30analysts
Sell-sidemoreaccurate Estimizemoreaccurate
9Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
c)FactorsDeterminingAnalystAccuracy
Comparingtheaboveandbelowmedianfreshness
Comparingtopandbottomdecile freshness
Comparingaboveandbelowmedianexperience
Comparingtopandbottomdecile experience
Comparingaboveandbelowmedianskill
Comparingtopandbottomdecile skill
39%
61%
Olderestimates,moreaccurate
Newerestimates,moreaccurate
34%
66%
Oldestestimate,moreaccurate
Newestestimates,moreaccurate
38%
62%
Belowmedianexperienceanalysts,moreaccurate
Abovemedianexperienceanalysts,moreaccurate
35%
65%
Bottomdecileexperienceanalysts,moreaccurateTopdecileexperienceanalysts,moreaccurate
41%
59%
Belowmedianskillanalysts,moreaccurate
Abovemedianskillanalysts,moreaccurate
38%
62%
Bottomdecileskillanalysts,moreaccurate
Topdecileskillanalysts,moreaccurate
10Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
EstimizeFES(Freshness,Experience,andSkill)Model
Weightingtheestimatebythefreshness,analystexperience,andanalystskill
EstimizeFEDmodelversussingleweightingscheme
46%54%
Equalweight,moreaccurate
Weightedbyfreshness,moreaccurate
47%53%
Equalweight,moreaccurate
Weightedbyexperience,moreaccurate
47%53%
Equalweight,moreaccurate
Weightedbyskill,moreaccurate
52%48%
FESmodel,moreaccurate
Weightedbyfreshness,moreaccurate
54%46%
FESmodel,moreaccurate
Weightedbyexperience,moreaccurate
55%45%
FESmodel,moreaccurate
Weightedbyskill,moreaccurate
11Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
d)TradingaroundEarningsAnnouncements
EstimizeEPSaccuracyasafunctionofthe#ofdayspriortoearningsannouncement
AnexampleofPandoraMediabeforeearningsannouncement
0%
50%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20#ofdayspriortoearningsrelease
Sell-sidemoreaccurate Estimizemoreaccurate
#ofestimatesforPandoraMediabeforeearningsannouncement
12.0
13.0
14.0
15.0
16.0
17.0
0.01
0.02
0.03
0.04
0.05
0.06
Stockprice
EPS
dates
CIQEPS IBESEPS EstimizeEPS ActualEPS Price
0
10
20
30
40
50
60#ofestim
ates
dates
CIQ IBES Estimize
• Earningsannouncementisamongthemostsignificantmarketmovingcorporateevents
• Asearningsreleasedateapproaches,theEstimatedatabecomesmoreandmoreaccurate.
• Estimizeprovidestimelierestimates,whilesell-sideanalystsarebetteratpredictingearningsoveralongerhorizon.
• Iftheconsensusmovesuprightbeforeearningsannouncement,acompanyismorelikelytobeattheconsensus.
12Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
Earningsrevisionspriortotheannouncementdateleadsannouncementdayreturn
PercentageofEPSchangesintheweekbeforeearningsannouncement
Averageexcessearningannouncementdayreturn
𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑅𝑒𝑣𝑖𝑠𝑖𝑜𝑛,,. =𝐸𝑃𝑆,,. − 𝐸𝑃𝑆,,.34
𝐸𝑃𝑆,,.34𝐸𝑁𝑅𝑃,,. =
𝐸𝑃𝑆,,. − 𝐸𝑃𝑆,,.34𝑃𝑟𝑖𝑐𝑒,,.
A) Most positive EPS revisions B) Most negative EPS revisions
0%
20%
40%
60%
80%
100%
120%
Sell-side EstimizeFES
top1% top2% top5% top10%
-80%-70%-60%-50%-40%-30%-20%-10%0%
Sell-side EstimizeFES
bottom1% bottom2% bottom5% bottom10%
13Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
PostEarningsAnnouncementDrift(PEAD)
Positiveearningssurprise,firstdayPEAD
ExcessreturnwhenEPSbeatsover40% ExcessreturnwhenEPSmissesover40%
Negativeearningssurprise,firstdayPEAD
0.0%
0.1%
0.2%
0.3%
0.4%
0.5%
0.6%
10%surprise 20%surprise 30%surprise 40%surprise
Estimize Sell-side
-0.7%
-0.6%
-0.5%
-0.4%
-0.3%
-0.2%
-0.1%
0.0%
10%surprise 20%surprise 30%surprise 40%surprise
Estimize Sell-side
-0.2%
0.0%
0.2%
0.4%
0.6%
0.8%
day1 day2 day3
Estimize Sell-side
-0.8%
-0.6%
-0.4%
-0.2%
0.0%
0.2%
day1 day2 day3
Estimize Sell-side
• PEADismoresignificant,ifacompanybeats(ormisses)theEstimizeestimates.PEADdecaysawayaftertwodays.
• Benchmarkearningsyield. Forthebenchmarkfactor,weusetheconsensussell-sideEPS,bytakingasimpleaverageofCIQandIBES.
• StandardEstimizeearningsyield. Inthiscase,wereplacethesell-sideconsensuswiththeEstimizeestimateswhenwehaveatleastthreecontributorsintheEstimizedatabase.
• EstimizeFESearningsyield. InsteadofreplacingwiththestandardEstimizeestimates,weusetheFESmodelintroducedintheprevioussection.
14Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
e)Long-termInvestmentStrategies
Cumulative performance, long/short quintile portfolio on S&P500
𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑌𝑖𝑒𝑙𝑑 =𝐹𝑄1𝐸𝑃𝑆𝑃𝑟𝑖𝑐𝑒
Monthlyturnover Sharpe ratio, different transaction cost assumptions
0.9
1.0
1.1
1.2
1.3
Benchmarkearningsyield EstimizeAvgearningsyield
EstimizeFESearningsyield
0%10%20%30%40%50%60%70%80%90%100%
Benchmarkearningsyield
EstimizeAvgearningsyield
EstimizeFESearningsyield
0.0
0.1
0.2
0.3
0.4
0.5
Nocost 2bps 5bps
Benchmarkearningsyield EstimizeAvgearningsyield
EstimizeFESearningsyield
15Sources: Estimize,IBES,S&PCapitalIQ,FTSERussell,WolfeResearchLuo’sQES
EnhancedValueStrategies
Sharperatio,differentuniverse
Longonlyportfolioperformance,Russell3000universe SharpeRatio,LongonlyportfolioRussell3000universe
Sharperatio,monthlyversusdailyrebalance
0.0
0.5
1.0
1.5
2.0
2.5
3.0
EquallyweightedRussell3000 TopquintileEstimizeearningyield0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
EquallyweightedRussell3000 TopquintileEstimizeearningyield
0.00.20.40.60.81.01.21.4
S&P500 Russell3000 Russell3000sectorneutral
Baseline Estimize
0.00.20.40.60.81.01.21.4
Benchmarkearningsyield
EstimizeAvgearningsyield
EstimizeFESearningsyield
Russell3000universe,monthlyrebalance
Russell3000universe,dailyrebalance
• EnhancedearningsyieldusingEstimizedataperformsequallywellinbothlarge- andsmall-capuniverses.• Enhancedvaluestrategiesproducedecentperformanceinalong-onlycontext.
16
4.TextMiningUnstructuredCorporateFilingData
#ofEDGARFilings(Daily)
Average#ofWordsinthe10-KFilings
Sources: EDGAR,WolfeResearchLuo’sQES
• Firmsingeneraluseawell-definedformatfortheirannualandquarterlyfilings.Almostidenticallanguageissimplyrepeatedyearafteryear,untilsomeoneactivelyintervenesandmakeschanges.
• Whenfirmsdobreakawayfromtheirtraditionoftextualdescriptionsintheirfilings,theytypicallyforeseesignificantchangesintheirbusiness,risk,orcorporatestrategy.
• OurNLPalgorithmsareabletoidentifypotentialissuesfromtextualinformationamongUStransportationcompanies.
17Sources: BloombergFinanceLLP,EDGAR,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
LazyPrizes
USTransportationIndustry,CumulativePerformance
38
48
58
68
78
88
98
Price
Substantial and increasingly intense competition for partner relationships; risk surrounding Airline industry.
Ongoing legal proceedings can cause substantial monetary and reputational damages.
USTransportationIndustry,ReturnsbyMerciless
AnExample:AmericanExpress
18Sources: BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
SystematicProfilingEDGARComposite(SPEC)Model
SPECmodelrankIC SPECmodelLong/shortportfolioperformance
PortfolioSharperatioQuintileportfolioreturns
Long/shortmonthlyturnover RankICdecay
• Lastly,itisinterestingtonotethatourfactorhasalmostnoexposuretoclassicriskfactorssuchassize,betaorvolatility.
• OneofthemostimportantreasonsofrelyingonalternativeBigDatasourcesrestsonthediversificationbenefit,inthattheyareexpectedtohaveminimalcorrelationwithtraditionalfactors.
• Asexpected,oursignalsbasedonEDGARtextminingarealmostuncorrelatedtoanyofourtraditionalfactors.
• Finally,asauniquealphasource,theSPECmodelshouldcomplementandaddvaluetotraditionalfactors.
• Theperformanceofalltraditionalfactorsimprovesremarkably,whentheSPECisadded.Growthfactorswitnessthelargestimprovement.
• Improvementinrisk-adjustedperformanceisevenmoresignificant.
• TheSharperatioimprovesby50%forthemajorityoftheconventionalfactors.
19Sources: BloombergFinanceLLP,FTSERussell,S&PCapitalIQ,ThomsonReuters,WolfeResearchLuo’sQES
Interactionwithtraditionalfactors
FactorcorrelationswithEDGARcompositemodels
EarningsYield EarningsRevision Momentum 1MReversal ROE EPSgrowth Marketcap VolatilityComposite10K 11% 6% 4% 1% 10% -3% 11% -14%Composite10Q 8% 3% 3% 1% 7% -2% 8% -10%CompositeAll 11% 5% 5% 2% 11% -2% 11% -13%
0123456789
EarningsRevision
EarningsRevisioncomposite
EarningsYield
EarningsYield
composite
EPSgrowth EPSgrowthcomposite
Momentum Momentumcomposite
ROE ROEcomposite
CAGR
(%)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
EarningsRevision
EarningsRevisioncomposite
EarningsYield
EarningsYield
composite
EPSgrowth EPSgrowthcomposite
Momentum Momentumcomposite
ROE ROEcomposite
Sharpe
ratio
AnnualizedreturnsofStylecompositesvsbasefactors
SharperatioofStylecompositesvsbasefactors
21
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AnalystCertification:TheanalystofWolfeResearchprimarilyresponsibleforthisresearchreportwhosenameappearsfirstonthefrontpageofthisresearchreportherebycertifiesthat(i)therecommendationsandopinionsexpressedinthisresearchreportaccuratelyreflecttheresearchanalysts’personalviewsaboutthesubjectsecuritiesorissuersand(ii)nopartoftheresearchanalysts’compensationwas,isorwillbedirectlyorindirectlyrelatedtothespecificrecommendationsorviewscontainedinthisreport.
OtherDisclosures:WolfeResearch,LLCdoesnotassignratingsofBuy,HoldorSelltothestocksitcovers.Outperform,PeerPerformandUnderperformarenottherespectiveequivalentsofBuy,HoldandSellbutrepresentrelativeweightingsasdefinedabove.Tosatisfyregulatoryrequirements,OutperformhasbeendesignatedtocorrespondwithBuy,PeerPerformhasbeendesignatedtocorrespondwithHoldandUnderperformhasbeendesignatedtocorrespondwithSell.
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