巴克莱美股基于信用信号动量策略

Equity19OctoberBEAM(BondsinEquityAssetMomentum)EquityMomentumStrategiesBasedonCreditSignals:Scope,Equity19OctoberBEAM(BondsinEquityAssetMomentum)EquityMomentumStrategiesBasedonCreditSignals:Scope,FrequencyandAggregationArikBen+1212526BCI,USSeveralyearsagowestartedtoexaminewhethersystematicequitystrategiescanbenefitfromincorporatingcross-marketinformationfromcreditmarkets.Inparticular,cancorporatebondsreturndynamicsbeusedtoimprovetheconstructionofequitymomentumportfolios?Wecomparedtheperformancedynamicsofthestandardmomentumportfoliobasedonrankingpastequityreturnswithasecondmomentumportfolio(BEAM)thatinsteadrankedstocksbythepastexcessreturnsofbondsissuedbythesamesetoffirms.Hence,bothmomentumportfoliossharedthesameinitialuniverseoffirmsandincludedonlyequitiesbutdifferedinthesourceoftheinformationusedforranking.Employingcorporatebondsreturndynamicsresultedinconsistentlyhigherperformanceandlowervolatility,especiallyinperiodsofmarketreversalsinwhichthestandardmomentumportfoliowasfoundtoperformpoorly.Asaresult,theBEAMportfoliogeneratedaninformationratiothatwasthreetofourtimesthatofthestandardmomentumportfolio.ThisreportprovidesupdatedperformancefiguresfortheBEAMstrategy.BEAMhascontinuedtodeliverstrongpositivereturnsthatareremarkablyconsistentwiththeoriginalstudyin2014.BEAM’sperformancehasbeenstableovertime,withpositivereturnseveryyearsince1999,acrossmarketstates,andallindustries.BEAMsignalisvaluablebothinisolationandwhencombinedwithotherstrategies.UsingBEAMstrategygeneratessignificantalphaaftercontrollingforcommonlyusedassetpricingfactors,andwhencombinedwithmomentumitprovidesconsiderablediversificationbenefits.BEAM’sperformanceremainedstrongafteraccountingfortradingincludingpriceimpact,shortingcosts,andexecutionSince2014,wehavecontinuedtocarryoutresearchontheinformationvalueofbondsignalsforequityinvestors.WefoundthatBEAMisrobustacrossregions(inU.S.andEuropeanmarkets)andfrequencies(dailyandmonthlyfrequency),anddifferentlevelsofsignalaggregation(individualstock-andsector-level).Thesenewresultsprovidecompellingout-of-sampletestsoftheoriginalU.S.BEAMresults,andconfirmthevalueofusingcreditsignalsinequityportfolios.Jingling+1212526BCI,USCarlo+1212526BCI,USBarclaysCapitalInc.and/oroneofitsaffiliatesdoesandseekstodobusinesscompaniescoveredinitsresearchreports.Asaresult,investorsshouldbeawarethatthefirmmayhaveaconflictofinterestthatcouldaffecttheobjectivityofthisreport.Investorsshouldconsiderthisreportasonlyasinglefactorinmakingtheirinvestmentdecision.每日每日免费获取报（增值服务关注回复：研究加入“起点财经”微信群Barclays|BEAM(BondsinEquityBarclays|BEAM(BondsinEquityAssetEquityMomentumStrategiesBasedonCreditAboutfouryearsago,BenDorandXu(2014)introducedtheideaofemployingcreditsignalstoinvestinequities.Theyshowedthataportfolioconstructedbyrankingequitiesbasedontheaggregatereturnsoftheirbonds,inwhichthe‘long’and‘short’legsoftheportfolioareportfolios,henceforthdefinedasBEAM(BondsinEquityAssetMomentum),producesaninformationratiothatisabouttentimesthatofthestandardequitymomentumportfolio(1.16versus0.13),withbothhigheraveragereturnsandlowermonthlyvolatility.Theideaofusingpriceinformationfromothermarkets,inparticularthecorporatebondmarket,isintuitivelyappealing.Forinstance,theMerton(1974)modelpredictstheco-movementofcorporatebondsandequitiesasthevalueofthefirmdrivesboththepriceofequityanddebtoftheissuingentity.Usingcreditsignalsforsystematicequitystrategiesis,however,easiersaidthandone.AlthoughtheU.S.corporatebondmarketisenormous,withoutstandingprincipallargerthan$9trninQ12018(source:SIFMA),corporatebondstradeover-the-counter,tradelessfrequentlyandarelessliquidthanequities.Forallthesereasons,onechallengeinusingcreditsignalsinsystematicequitystrategiesisthelackofahigh-quality,easilyaccessibleandcomprehensivecorporatebondpricingdataset,whichcomprisesbothpricesandanalytics,unlikethosedatasetsavailableforequities.Themostsignificantbottlenecktousingbondpricesforequityinvestingis,however,theavailabilityofareliablelinkingalgorithmbetweenbondsandstocksandcrossassetclassexpertise.Indeed,toincorporatebond-levelinformationintheconstructionofequityportfolios,weneedtobeabletoobserveafirm’scapitalstructure,andspecificallytohaveareliablelinkingtablebetweenthebondidentifier(atthesecuritylevel)anditsparentcompany,asthesedataarenotcommerciallyavailable.Furthermore,toimplementBEAManadditionalkeyinputbeyondthoseusuallyrequiredtobuildsuccessfulstyleportfolios(accordingtoIsrael,JiangandRoss,2017,called“craftsmanship”alpha)isessential−namelycrossclassexpertiseneededforthesignalBuildingontheseminalworkofBenDorandXu(2014),thisreportprovidesupdatedfiguresfortheBEAMstrategy.Theexerciseisinformativeastheout-of-sampleperformanceofamodelisoftenviewedasthe‘goldstandard’ofevaluation.Sincethepublicationofthereport,wehavenowmorethanfouryearsoflivedatatoassessBEAMperformance.Moreover,wecanexamineBEAMperformancerelativetootherequitystrategiesbycomparingBEAMperformancewiththatofthemostcommonlyusedassetpricingportfolios(FamaandFrench,2015).Inthesecondpartofthepaper,welookattheconsistencyofBEAMperformancealongdimensions,includingacrossindustries,overtimeandbymarketstates.Inparticular,weinvestigatewhetherBEAMrepresentsanindustrybet,andtowhatextenttheperformanceofBEAMisdrivenbysectorallocation.WealsoexaminetheconsistencyofBEAMreturns,bothovercalendartime(i.e.,yearsormonths)andacrossdifferenteconomicstates.Acentralinvestingtenetisthatthevalueaddedofasignalshouldnotbeconsideredisolation,butratherinaportfoliosettingwhencombinedwithothersignals.Inthisrespect,understandinghowBEAMreturnsrelatetootherstandardriskfactorsisasimportantasassessingtheBEAMperformanceasastand-alonestrategy.Weusebothparametricandnon-parametricmethodstoshedlightonthediversificationbenefitsfromcombiningBEAMwithotherstrategies.Bydoingso,weaddressanumberofinquirieswereceivedfrominvestorsfollowingthepublicationoftheoriginalstudy,suchaswhatistherelationshipbetweenBEAMandmomentum?IsthereanyvalueofcombiningBEAMandmomentumsignalstogether?AreBEAMreturnsexplainedbyexposurestocommonlyusedassetpricing19October2Barclays|BEAM(BondsinBarclays|BEAM(BondsinEquityAssetfactors?ToassessthepotentialvalueofBEAMtorealinvestorswealsoconsiderincrementalperformancethatcouldbeachievedinanex-postmean-varianceefficientportfoliobyincludingBEAMasaninvestableasset.OuranalysishasmostlyfocusedontheexpectedgrossreturnsoftheBEAMstrategy.investorscanbenefitfromtheBEAMsignalinpractice,however,criticallydependsonthenetoftransactioncostreturns.Hence,weexplorewhethertheBEAMstrategyisimplementableandsizeable,orwhetheritfacessignificantpracticalimpedimentsthatpreventinvestorsfromprofitingfromit.TherobustnessofBEAMperformancetotradingcostsandcapacitylimitsisevaluatedbytakingintoconsiderationthreeimplementationaspects:dayandtimeoftradeexecution,priceimpactandshortingcosts.AkeyfindingisthattheBEAMstrategyhascontinuedtodeliverstrongpositivereturnssincethepublicationoftheoriginalstudyin2014.Intheout-of-sampleperiodaveragereturnsare18.5%anditsinformationratiois0.85comparedwith17.4%and1.16inthein-sampleperiod.Moreover,BEAMperformanceisbetter(i.e.,higheraveragereturnsandlowervolatilityandtailriskmeasures)thanthatofthemostcommonlyusedassetpricingportfolios.Onepotentialreasonwhyinthepost-2014periodBEAMperformancehasremainedsimilartothatofthein-sampleperiodisthatBEAMishardtoreplicate,asitrequiresaccesstoanexhaustivefixedincomedataset,areal-timemappingalgorithmbetweenbondsandstocksandcrossassetexpertise.WedocumentthatBEAMperformancehasbeenconsistentalongseveraldimensions.particular,BEAMperformanceisnotdrivenbyindustryexposures,andhasbeenpositiveacrossallindustries.Anindustry-neutralBEAMportfoliogeneratessimilarreturnscomparedwithaBEAMportfolioinwhichstocksarerankedacrosstheoveralluniverse,butdisplayslowervolatility.Furthermore,BEAM’sperformancehasbeenstableovertime,withpositivereturnseveryyearsince1999,andbymarketstates.Theresultsindicatethattheinformationcontainedinbondpricesisusefulinequitymomentumportfolios.WhetherinvestorscanbenefitfromtheBEAMmethodology,however,isanothermatter.WestudyseveralaspectsrelatedtotheimplementationoftheBEAMstrategyinpractice.WeshowthattheBEAMsignalisvaluablenotonlyinisolation,butalsoinaportfoliosettingwhencombinedwithotherstrategies.Usingparametricmethods,weshowthatBEAMisnotspannedbyexposurestocommonlyusedassetpricingfactors.Furthermore,BEAMgeneratessignificantalpharelativetothosefactors.Hence,aninvestoralreadytradingtheFamaandFrenchfactorscouldrealizesignificantgainsinanex-postmeanvarianceefficientportfoliosettingbystartingtotradeBEAM.Usingnon-parametricmethods,wefindthatthereisconsiderablediversificationbenefitfromcombiningmomentumandBEAMstrategies,despiteBEAMsignalsbeingonlyavailableforasubsetofequitiescomprisingRussell1000universe.WeshowthatBEAMperformanceisrobusttotradingcostsandcapacitylimits.generatesaninformationratioofabout1aftertakingintoaccountpriceimpact,shortingcostsandpotentialexecutionlagsbothintheoriginalsampleandinthepost-2014period.Furthermore,sincecompaniesthatissuecorporatebondsaretoalargeextentlarge-cap,BEAMstrategyiseasiertoimplementcomparedtootherequitystrategies.Inthisrespect,moststockanomalies(i.e.,patternsinaveragestockreturnsthatarenotexplainedbytheCAPM)areconcentratedinmicro-andsmall-caps,whichrepresentonly3%ofthetotalmarketcapitalizationoftheNYSE-Amex-NASDAQuniverse,butaccountfor60%ofthenumberofstocks(see,e.g.,Hou,XueandZhang,2017,fordetailedevidence).Since2014,wehavecontinuedtocarryoutresearchontheinformationvalueofbondforequityinvestors.WehaveextendedBEAMtoEuropeanmarketsandathigherfrequency,i.e.,byusingdailycorporatebondpricesinhigh-frequencyequitymomentum19October3Barclays|BEAM(BondsinEquityAssetBarclays|BEAM(BondsinEquityAssetstrategies(Daily-BEAMorinshortD-BEAM).WehavealsolookedatBEAMperformanceatdifferentlevelofsignalaggregation,andwhethertheBEAMsignalcanbehelpfulforsectortiming.BenDor,GuanandZeng(2018)documentthatsimilartotheU.S.incorporatinginformationfrombondpricesenhancesthetraditionalequitymomentumstrategyalsoinfortheperiodbetween2003and2017,whereasthetraditionalequitymomentumportfoliogeneratesaninformationratioof0.26overthesameperiod.BenDor,GuanandRosa(2018)showthattheuseofdailybondsignalsdeliveredanannualaveragereturnof18%andaninformationratioof1.8startingin2001.Moreover,theycontinuedtogeneratemomentumpatterns,whileequityreturnsexhibitedmean-reversionathigherfrequency.ThesimilarityofperformancedynamicsinthosemarketsdemonstratesthatBEAMisrobustacrossregionsandfrequencies,anddifferentlevelofsignalaggregation.TheresultsofBEAMacrossgeographies,athighfrequencyandatadifferentlevelaggregationcanbeinterpretedasprovidingcompellingout-of-sampletestsoftheU.S.BEAMfinding,andconfirmthevalueofusingcreditsignalsinequityportfolios.Moregenerally,fromaninvestor’sstandpoint,becausetheBEAMsignalworksinternationallyandathigh-frequency,thebreadthofthestrategyisbroaderthanoriginallythought,thusenhancingtheprofitopportunities.Theremainderofthisreportisorganizedasfollows.ThenextsectionprovidesanupdatetheperformanceofBEAMsinceitsdiscoveryin2014.Then,welookattheconsistencyofBEAMperformancealonganumberofdimensions,includingacrossindustries,overtimeandbymarketstates.Weproceedbyexaminingsomepracticalimplementationissues,suchascombiningBEAMandequitymomentumsignals,combiningportfolios,andevaluatingtheimpactoftransactioncostsonBEAMperformance.Since2014,wehaveexpandedtheBEAMcoveragealongthreedimensions:BEAMsignalsforEuropeanmarkets,atdailyfrequency,andforsectortiming.Weconcludewithadiscussionofsomeadditionalapplicationsoftheuniquecross-assetbond-equitydatasetdocumentedinthisanalysis.ThePerformanceofBEAMSinceItsWebeginwithanupdateontheperformanceofBEAMsincethepublicationofthestudyofBenDorandXu(2014),i.e.,fromJanuary2014topresent.Figure1showsvariousperformancemeasuresfortheBEAMandmomentumstrategies.Tofacilitatethecomparison,thetabledisplaysthesamestatisticsfortheperiodJanuary1994toDecember2013intheleftpanels,fortheperiodJanuary2014toAugust2018inthemiddlepanels,andfortheperiodJanuary1994toDecember2013excludingmarketcrashesintheleftpanels.Portfoliosarerebalancedmonthly.Returnsareequallyweightedandignoretransactioncosts.1Allstrategiesareformedonunivariatesorts,andarebasedonportfoliodeciles.AllportfoliosarebasedontheBEAMuniverse,i.e.,theuniverseofequitieswithoutstandingBloomberg-BarclaysindexIntheperiod1994-2013,BEAMhasanaverageannualizedreturnof17%,anvolatilityof15%,andanannualizedinformationratioof1.16.ThenovelaspectoftheFigure1istheBEAMperformanceinthepost-sample(i.e.,2014-2018)period,bothinabsoluteandrelativetoexistingportfolios.ThekeyfindingisthatsincethepublicationofthepapertheBEAMperformancehasremainedsimilartothatofthein-sampleperiod,withanannualizedaveragereturnof18%.BEAMvolatilityhasbeensomewhathigherinthepost-sampleperiodcomparedwiththe1994-2013period,at22%and15%,respectively.Theincreaseinvolatilityinturngeneratesalowerinformationratio(i.e.,0.85)inthepost-sampleperiod.1Weleavethetransactioncostadjustmenttotheimplementationstage,asdifferentsignalsmaybecombinedinportfolioconstruction19October4Barclays|BEAM(BondsinBarclays|BEAM(BondsinEquityAssetTheinformationratiodoesnotdojusticetoBEAMperformanceastheinformationrationotdifferentiatebetweengoodandbadvolatility.Inotherwords,volatilitymeasuresdispersionofreturnsaroundtheiraveragevalue.Assuch,bothreturnsaboveorbelowthemeancontributetoincreasethelevelofvolatility.Fromaninvestor’sstandpoint,returnsabovethemeanrepresent,however,agoodtypeofrisk.TheSortinoratiomodifiestheinformationratiobypenalizingonlythosereturnsfallingbelowacertainthreshold(knownasminimumacceptablereturn).AccordingtotheSortinoratio,BEAMperformancehasremainedremarkablystable,at1.91inthepost-sampleperiodcomparedwith1.97intheoriginalsample.Hence,theincreaseinvolatilityismostlyassociatedtoanincreaseinupsidedeviation,ratherthantoanincreaseintheoverallriskprofileoftheBEAMstrategy.Additionalmeasuresofdownsiderisk,suchasminimummonthlyreturnandmaximumdrawdown,havealsoremainedstableinthetwosampleperiods.ThefindingthatBEAMperformanceremainedstrongisinstarkcontrasttotheresultsreportedinChenandVelikov(2018).Theyshowedthattradingcostsandpost-publicationdecayaccountforthegreatmajorityofstockreturnanomalies.OnepotentialreasonwhyBEAMprofitabilityhasnotbeentradedawayisthatitsconstructionreliesondatathatarenotcommerciallyavailable,suchasanexhaustivefixedincomedataset(whichcomprisesbothpricesandanalytics)andespeciallyarobustreal-timemappingalgorithmbetweeneachfirm’sbondandequitydata.2ToputBEAMperformancefiguresincontext,Figure1reportstheperformanceofequitymomentum,awell-establishedempiricalfactwhere,onaverage,stockswithstrongrecentperformancerelativetootherstocksinthecrosssectionofreturnstendtooutperforminthefuture.Asstandardintheacademicliterature,weconsiderthe12-1rankingwindow,namelywemeasurethetotalreturnofastockoverthepast12months,skippingthemostrecentmonth.Intheperiod1994-2013,theannualizedaveragereturnforthemomentumstrategywasabout4%,whereasforthepost-sampleperiodisabout17%.Thevolatilityhasremainedroughlyconstantat30%.Thus,theinformationratiointhepost-sampleperiodisabout0.6.Aswewilldiscussmoreindetailbelow,theimprovementinthemomentumperformancecanbeascribedtotheabsenceofmarketcrashes,i.e.,infrequent,persistentandlargestringsofnegativereturns,inthepost-2014sampleperiod.Indeed,theleftpanelofFigure1reportstheperformanceofequitymomentumintheoriginalsample,excludingtheyears2002-2003and2008-2009,whichcoincidewithperiodsofrapidmarketreversalsfromseveretroughs.Themostinterestingaspectis,however,thestabilityofBEAMperformanceingoodandbadtimes.Despiteexcludingfouryears,BEAM’sinformationratio,at1.13,isbarelyaffected.ThisfindingindicatesthatBEAMcontinuestogeneratestrongperformancealsointhe2002-03and2008-09years.Duringthoseyears,BEAMinformationratioisactually1.51,henceevenhigherthanitsfullsamplevalue.BEAMoutperformedequitymomentumnotonlyintermsofaveragereturnsandratio,butalsointermsofrealizeddownsiderisk.Furthermore,BEAM’soutperformanceisconsistentbothin-sampleandout-of-sample.Forinstance,inthepost-2014period(i.e.,aparticularlyfavorableperiodformomentumgiventheabsenceofmarketcrashes),BEAMminimumreturnwas-10%anditsmaximumdrawdownwas-23%comparedwith-28%and-39%,respectively,forequitymomentum.2Israel,JiangandRoss(2017)emphasizetheimportanceof“craftmanship”alphathatisrequiredtobuildeffectiveportfolios.ForimplementingBEAMisessentialanadditionalkeyinputbeyondtheknow-howneededtocreatesuccessfulfactorportfolios,namelythecrossassetclassexpertiseneededforthesignalconstruction.19October5Barclays|BEAM(BondsinEquityAssetFIGUREBEAMPerformanceNote:ThesampleperiodsareJanuaryBarclays|BEAM(BondsinEquityAssetFIGUREBEAMPerformanceNote:ThesampleperiodsareJanuary1994toDecember2013(leftpanel),January2014toAugust2018(middlepanel),andJanuary1994toDecember2013,excluding2002-2003and2008-2009(rightpanel),respectively.BEAMandMomentumportfoliosaresortedacrosstheoveralluniverseofequitieswithoutstandingBloomberg-Barclaysindexbonds.TheminimumacceptablereturnfortheSortinoratioissettozero.Source:Compustat,Bloomberg,BarclaysAtthispoint,anaturalquestionarises:howdoesBEAMperformancecomparewiththatofstandardFama-Frenchlong-shortportfolios,usuallyconsideredthestandardstaplesofassetpricing.3Figure2reportstheinformationratiosforthoseportfoliosforthreesampleperiods:1994-2013(originalsample;darkbluebars),2014-2018(out-of-sample;mediumbluebars),and1994-2013butexcludingmarketcrashes(lightbluebars).InlinewithBEAMconstructionmethodology,allFama-Frenchstrategiesareformedonunivariatesorts,andarebasedonportfoliodeciles.Returnsareequally-weighted,andignoretransactioncosts.ThefirsttwosetofbarsarebasedontheBEAMuniverse,i.e.,theuniverseofequitieswithoutstandingBloomberg-Barclaysindexbonds.TheFama-FrenchfactorsaresortedacrosstheCRSPuniverse,whichincludeallstocksquotedinNYSE,NYSEAmerican,NASDAQstockexchanges.ThekeyfindingofFigure2isthatBEAMhasconsistentlydisplayedthehighestratiobothin-sampleandout-of-samplecomparedwithallotherstrategies.Putdifferently,BEAMinformationratioislargeandstableacrossdifferentsampleperiods.TheInvestmentfactoristheonlyfactorthatdisplaysaninformationratiohigherthanthatofBEAM,butonlythe1994-2013period.TheInvestmentfactorwas,however,publishedin2015(FamaandFrench,2015),andwasnotyetwidelyusedbypractitionersin1994.Evenmoreimportantly,theInvestmentfactorperformancehasbecomenegativesincethepublicationofthepaper.Moreover,thewell-establishedSizeeffect,i.e.,thereturnpremiumearnedbysmallcompaniescomparedwithlargecompanies,hasbecomenegativesince2014.AnotherinterestingaspectofFigure2isthesimilarityoftheMomentumperformanceinBEAMuniverseandtheCRSPuniverse,bothonaverageandineachsub-period.AlthoughtheconstructionmethodologyresultedinasamplethatintheBEAMuniverseisskewedtowardlargercapitalizationstocks(ascompanieswithoutstandingbondsincludedintheBloombergBarclaysindextendtobelarger),thefactorperformanceanddynamicsisnotaffectedbythespecificsetofcompaniesusedinthestudy.AnimportantimplicationofthisresultisthatanyoutperformanceweseeinBEAMisnotanartifactoftheparticularsample,butaconsequenceoftheinformationcontentembeddedinthebondsignal.3WethankKenFrenchformakingavailablearichdatalibrarycontainingthetime-seriesdataforvariousportfolios(/pages/faculty/ken.french/data_library.html),includingtheFama-Frenchthreeandfivefactormodelandhistoricalbenchmarkreturns.ThesefactorsaredescribedinFamaandFrench(1993and19October6BenDorandXu(2014;FigureOut-of-Originalsampleexcl.1994-2014-1994-2013excl.2002-3and2008-AvgRet - - - - - -- - - - - -StdDevInfRatioSortinoRatioMinMaxDrawdownBarclays|BEAM(BondsinEquityAssetFIGUREBEAMPerformanceComparedwithStandardRisk10t-BEAMCRSP-1994-1994-20132002-3and2008-2014-Note:ThesampleperiodsBarclays|BEAM(BondsinEquityAssetFIGUREBEAMPerformanceComparedwithStandardRisk10t-BEAMCRSP-1994-1994-20132002-3and2008-2014-Note:ThesampleperiodsareJanuary1994toDecember2013,January1994toDecember2013excluding2002-2003and2008-2009,andJanuary2014toAugust2018.BEAMandMomentumportfoliosaresortedacrosstheoveralluniverseofequitieswithoutstandingBloomberg-Barclaysindexbonds.TheFama-FrenchfactorsaresortedacrosstheCRSPuniverse,arebasedondecileportfolios.Allreturnsarebasedonequally-weightedportfolios.FactorreturnsanddefinitionsareavailableonKenFrench’swebsite.Source:Compustat,Bloomberg,KenFrenchDataLibrary,BarclaysSofarwelookedonlyatsummarystatistics.Figure3providesadditionalinformationthetimeseriespropertiesoftheperformanceofBEAM(darkblueline)andmomentum(lightblueline)byplottingthetrailing12-monthcumulativereturns.ThegraylinedisplaysthecumulativereturnsoftheS&P500,andperiodsofmarketreversalsarehighlightedinred.TheverticallineindicateswhentheBenDorandXu(2014)reportwaspublished.WefindthatBEAMtrailing12mreturnispositivenotonlyonaveragebutmostofthe(e.g.,about90%ofthetimesbothintheoriginalsampleandpost-publicationperiod).Moreover,BEAMoutperformsmomentuminvolatileperiods,suchasin2003whentheU.S.stockmarketreboundedswiftlyfromatrough.TheperformanceofmomentumportfolioisnotonlymorevolatilethanBEAMportfoliointheoverallsample,butalsoitexperiencedafewcrashes.Consistentwithpreviousfindings(see,e.g.,BarrosoandSanta-Clara,2015;DanielandMoskowitz,2016),thesemomentumcrashestendtooccurinpanicstates,namelyfollowingmarketdeclinesandaroundmarketrebounds(asdisplayedbytheredline).Inthepost-publicationperioditisreassuringtofindthatBEAMreturndynamicsduringthemarketreversalof2015-2016remainconsistentwithearlier,in-sample,results.19October7Barclays|BEAM(BondsinEquityAssetFIGUREBEAM12mTrailingCum.Ret.Barclays|BEAM(BondsinEquityAssetFIGUREBEAM12mTrailingCum.Ret.0--Dec-Dec- Dec-Dec-Dec-Dec-Dec-Dec-Dec-Dec-Dec-Dec-S&P500S&P500duringmarketNote:Thisfigureplotsthetrailing12-monthcumulativereturnsforBEAMandMomentum(equallyweighted)portfolios,andtheS&P500index.PeriodsduringS&P500marketreversalsareinred.TheverticaldottedlineindicatesthedatewhentheBEAMpaperwaspublished.Source:Compustat,Bloomberg,BarclaysConsistencyofBEAMPerformance:AcrossIndustries,TimeandbyMarketIndustry-NeutralPreviousresearchhasshownthatindustryeffectscanplayakeyroleindeterminingtheperformanceofequityportfolios.Forinstance,Bali,Demirtas,HovakimianandMerrick(2006)examineindustryeffectsonstockvaluationandportfolioconstruction,anddocumentthatindustry-neutralportfoliosgeneratesignificantlypositivereturns.Similarly,Liu,Pong,Shackleton,andZhang(2014)showthatindustry-neutralportfoliosgeneratebetterperformancecomparedwiththosebasedonthefull-universeforportfoliosformedaccordingtovariousoption-impliedmeasures.TocheckwhetherandtowhatextentBEAMresultsaredrivenbyindustryexposures,weconstructindustry-neutralportfolios,andcompareFigure4reportstheperformanceofBEAMandmomentumportfolios,wherestocksareacrosstheoveralluniverseandwithinindustries,andtheS&P500indexasabenchmark,forthesampleperiodfromJanuary1999toAugust2018.Byconstruction,theportfoliosrankedacrossindustriescontainthesamenumberofstocksastheportfoliosrankedwithinindustries,butmaybesubstantiallyexposedtoindustryeffects.Westartthesamplein1999becauseweneedalargersampleofcompaniestoconstructmeaningfulindustry-neutralBEAMportfolios,andensurethateachindustryiswellrepresented.AsshowninBenDorandXu(2014),thenumberofcompaniesincludedintheBEAMuniversein1994is444comparedwith753inBEAMportfolioperformanceimprovesinrisk-adjustedtermswhenimplementedinitscomparedtorankingacrossindustries,butdisplayslowervolatility,henceresultinginanincreaseinitsinformationratiobyabout20%.Furthermore,theconstructionofindustry-neutralportfoliosconsiderablyimprovesmeasuresofdownsiderisk.Forinstance,themaximumdrawdowndecreasesto17%from27%aftercontrollingforindustryexposures.19October8Barclays|BEAM(BondsinEquityAssetMomentumreturnsandinformationratiosarehigherwhenstocksarerankedacrosstheoveralluniverse,butvolatilityandtailriskmeasuresarebetterforindustry-neutralmomentumstrategies.Thesefindingsindicatethatmomentumstrategyreturnshadasectorallocationcomponent.FIGUREPerformanceofMomentumandBEAMacrossvs.Barclays|BEAM(BondsinEquityAssetMomentumreturnsandinformationratiosarehigherwhenstocksarerankedacrosstheoveralluniverse,butvolatilityandtailriskmeasuresarebetterforindustry-neutralmomentumstrategies.Thesefindingsindicatethatmomentumstrategyreturnshadasectorallocationcomponent.FIGUREPerformanceofMomentumandBEAMacrossvs.withinNote:ThesampleperiodisJanuary1999toAugust2018.Thistabledisplayssummarystatisticsof