Portfolio123 Virtual Strategy Design Class By Marc Gerstein

Topic4D–UsingQualityFactorsinYourStrategies

ThisisthefinalaspectofQuality,theoneIthinkquiteafewarewaitingfor:EarningsQuality.AndthatwillbethefinalTopicinfundamentalssincewe’llmovenextintoSentimentandthenMomentum(Ihaven’tyetdecidedwhichsequence).ButalthoughTopic4Dmaynotbefocusingonwhatmanymayseeasthegoodstuff,pleasegiveitseriousconsideration.SomethingassimpleasROEcangoalongwaytowardcreatingastrongtailwindthatcansupportanythingelseyou’redoing.Andconsideringhowmuchharderitistoperformoutofsamplethaninsample,weoweittoourselvestounderstandwhatevertailwindswecanfind.Thisisoneofthosetopicsthatprovidesincredible,butoftenunappreciated,opportunitiesforcreativity.InapurelyIvoryTowersense,companieswiththehighestqualitymetricswouldalwaysbetheoneswhosesharesyou’dwanttoown.How,afterall,couldonejustifyowningsharesofotherthanthebestfirms?Intherealworld,though,thesefactorscanoftenproducevariedresults.We’realwayslookingtothefuture,andwhenitcomestofactorsthatcanhelpusdevelopreasonableexpectationsofwhat’stocome,thereoftenareotherfactorsthatdosowithgreatersenseofimmediacy.ThekeytosuccessfuluseofQualityfactorsistorememberwhyweareusingthemandhowtheycanworktoenhanceourprobabilities,GettingStartedLet’sstartinourusualplace,theDDM(DividendDiscountModel)whichwere-jiggertodefineidealPEas:1/(R–G):

• LowerinterestratesdriveRdownandPEup.That’sprettypowerful.Butasidefrommarkettiming(ifyoucandoit),there’snowayforustoworkwiththatsinceinterestratesimpactthemarketasawhole,andonlyasmallernumberofstocksonanindividualbasis.

• AsG(growth)rises,weknowthat’sgood.Butwe’retalkingabouthigherPEsandstrongerstocks.Butit’sfuturegrowthwecareabout.

HoweveryoufeelaboutRandGinfluences,theybothhavethevirtueofbeingprettydirect.Qualityenterstheequationthroughoften-unnoticedbackdoors.

• Ontheonehand,it’spartofRbasedonitsroleintherisk-premium;thehigherthequality,thelowertherisksoallelsebeingequal,thelowertheR.That’sanimportantrelationship.Butitdoesnotlenditselftoa30-secondCNBCsoundbite,especiallysince

manysegmentproducersarelikelycluelessabouttherelationshipbetweenqualityandRandidealPE(assumingtheyevenknowwhatPEis).

• Also,becauseQualityimpactsconsistency(inagoodway),higherlevelsofQualitymakeitmorelikelythatresultswillbemoreconsistent,meaningthatwhateverassumptionsaboutfutureGyoudevelopthroughtheonlythingavailable(thepast),theyaremorelikelytobemoreeffectiveasabasisfordevelopingfutureexpectations.FavorableexpectationsofGarefinebuttheyareworthnothingtousiftheydon’tpanout.Again,however,thisisnotthesortofconversationyou’relikelytoseeinthefinancialmedia.

• Finally,ofcourse,Qualityisindicativeofacompany’sabilitytogenerateGinthefirstplace.

ManagingExpectationsInallthreecases,weseethattheworkingsofQualitytendtobehiddenfromthefor-dummieslevelvantagepoints.Wecouldtrytostudydataandstudysomemoreandstillmoretofindideasthatactuallywork.Orwecouldtakethepathoflessresistance:Usequalityasthemarkethandsittous,asalessconspicuousitemthatcangiveourmainstrategy,sotospeak,anextrabitofoomph.Sometimes,Qualityhelpsusmakeagoodstrategybetter.Othertimes,ithelpsusconvertapotentiallylacklusterstrategyintosomethingbetter.Anddon’tunderestimatethepossibilitythatQualitymaysimplyhelpusnarrowagood350-500stockstrategyintoanumberthat’smoreinvestable,say10-25.Thisiswhy,speakingformyselfandfrommyownexperience,I’veoftenfoundQualityfactorstoworkbestnotnecessarilywhentheyarefront-and-centerinastrategybutwhentheytakeonasupportingrole,aswasdemonstratedbackwhenwecoveredValue.Butfornow,we’llthinkofQualityasaprimarygoal.GiventherelationshipbetweenQualityandRriskcomponentofR,don’tbeatallsurprisedifyoudiscoverthataddinggood-quality-orientedfactorsintoyourmodelsreducessimulatedreturn.Itwon’thappenallthetime,butgiventhenaturalrelationshipbetweenlowerriskandlesserreturn,don’tbesurprisedifyoubumpintoit,often.Test-DrivingReturnonWhateverLets’startwithTable1,whichsummarizesacollectionofscreenbacktests.EachscreenstartedwithaPRussell3000universe,aMAXtestperiod,a4-weekrebalancingassumption,andworkedwithallstocksthatpassedthescreen(asettingof0forMax.No.PassingStocks).Also,eachscreencontainedonerule,assetforthinthetable:

RuleBasicBacktest RollingBacktest–Avg.ExcessReturn

Annl%Ret AnnlStDev Beta AllMkts UpMkts DnMktsFRank("ROE%TTM")>80 10.40 18.24 1.08 0.46 0.62 0.19FRank("ROE%TTM")<20 -0.91 34.68 1.80 -0.05 2.14 -3.46FRank("ROI%TTM")>80 10.82 18.70 1.10 0.49 0.73 0.12FRank("ROI%TTM")<20 -1.52 35.44 1.82 -0.07 2.16 -3.56FRank("ROA%TTM")>80 10.49 18.88 1.10 0.47 0.71 0.08

FRank("ROA%TTM")<20 -1.59 35.68 1.84 -0.07 2.22 -3.65FRank("ROE%5YAvg")>80 10.07 17.66 1.04 0.42 0.44 0.38FRank("ROE%5YAvg")<30* 3.91 25.88 1.36 0.15 1.15 -1.41FRank("ROI%5YAvg")>80 10.49 18.72 1.10 0.45 0.64 0.16FRank("ROI%5YAvg")<30* 1.75 30.06 1.63 0.03 1.71 -2.60FRank("ROA%5YAvg")>80 10.59 18.77 1.10 0.45 0.63 0.18FRank("ROA%5YAvg")<30* 1.40 30.05 1.63 0.01 1.74 -2.69*Thresholdsetat30toproduceareasonablenumberofstockspassingthescreenThetableshowsussomeinterestingthings.

• WhileROE,ROIandROAarecomputeddifferentlyandprovidedifferentinformation,wecanassume,unlessotherwisedictatedbytheuniqueneedsofaparticularstrategy,thattheinvestmentimplicationsofthethreeratiosis,forallpracticalpurposes,thesame.(AndwecanpresumelikewiseregardingthecountlessvariationsthatcanbefoundonInvestopedia,Wikipediaandwho-knows-how-manyothersources.)Weshouldnotbesurprised.Ifwelookattheformulas,weshouldexpectahighcorrelationinrankingsfromoneratiotoanother.

• Inbacktest,thereislittledifferencebetweenTTMand5-yearresultsandthisistobeexpectedgiventheoverallbig-picturepersistenceofthesereturnitems.Butthatdoesn’tmeanwecanflipacoinwhenmodelingforward,aswemustdowhenwethinkaboutrealmoney.Onewhoownsonly10-20stocksneedstobesensitivetotheexceptionsthatgetpaperedoverinlargerstudiessuchasthis.Soregardlessofwhatacademic-typetestsshow,thereshouldstillbeagoodreasonforpickingTTMorafive-yearaverage(thereisneveranacceptablereasonforeventestingmuchlessusingaQnumberinthecontextofQuality;ifanything,itwouldbeaMomentumfactoranditwillbediscussedfurtherwhenwereachthattopic).Generally,amorereturn-orientedmodelcanleantowardthemorehere-and-nowTTMfactorwhileonewhoismoreinterestedinrisk-controlcouldworkwithlonger-termaverages.

• Thegapsbetweenbestandworstraisetheprospectthatwecanaccomplishmuchinourworkevenifwedonothingelsewiththeseitemsotherthantoscreenouttheworst.Thisisoneofcountlessreasonswhyitsvitalthatyounotobsessoverrankingsystems.It’samazinghowmuchyoucanaccomplishevenwithplain-vanillasystemsifyoucanrunthemagainstpre-qualifiedsub-universesthathavealreadyidentifiedandweededoutpotentialtrouble.Onewaytobeatthemarketistoidentifyandoverweightwinners.Anotherequallyeffectivewayistoidentifyandunderweightthedregs.Thatsaid....

• Don’tbeafraidoflowqualityifyouunderstandwhatitmeansandwanttoworkinthatmanner.Notice,thedifferencesinrollingup-anddown-markettestsforthejunkgroups.Thereisalotofdownsiderisk;that’sobvious.Butthereisalsoseriousupsidepotential.Thishappensbecauselowqualityisassociatedwithpoorconsistencyandifyouarebeingaggressive,that’swhatyouwant–poorconsistency.Yourjob,insuchacase,istousescreeningrulestotrytolimityoursub-universetosituationsmorelikely

thannottocapturethegoodpartofinconsistency.Technicalfactors,momentumandsentimentcanhelpalotinthisarea.

• Butthroughitall,ifyoujustwantareasonablypositiveresultsandwithnomorethanreasonablelevelsofrisk,useofROE,ROAorROIcanputaheavytailwindatyourback.This,bytheway,isabigexplanationforhowWarrenBuffettgottobeWarrenBuffett.Hemightnotbeabletocreatea90%-alphap123sim.Butstill,hedidprettywellforhimself.

FromtheBigPicturetoInvestabilityLikeacademicstudies,theoneaboveworkedwithlargeswathsofalargeuniversethatidentifiesaggregatecharacteristics.Westillhavetoworkourwaydowntomanageable-sizeportfolios.Sothenextsetofexperimentswillexaminewhatwecanexpectreturnstoaccomplishforusifwelimitourpositionsto15.InallcasesI’mgoingtoworkwithROE%TTM.Ifyouwanttorepeattheexperimentswith5Yearand/orwithROIorROA,goforit.I’mgoingtostartwithaverysimplescreenagainstthePRussell3000universe(It’sbeenawhilesinceIsaidthissoarefreshercouldn’thurt:PleasedesignyourstrategieswithauniversenobroaderthanthePRussell3000.Yourgoalistocomeupwithastrategythatcanworkwithrealmoney,nottoproduceeye-catchingsims.Soyoudonothelpyourselfifyoudesignusingamarshmallowuniverse.IfyougetsomethingthatworkswiththePRussell3000,youcanalwaysgobacklatertoswapintheAllFundamentalsuniverseandpopinsomeliquidityrules.)ScreeningRule:FRank("ROE%TTM")>80QuickRank:ROE%TTMhigherisbetter,pickTop15Here’stheresult:Figure1

RollingTestAvg.Excess4-weekReturn:0.27%inallperiods,0.00inupperiods,and0.69indownperiodsTheresultispositive,butaheckofalotlessappealingthanwhatwesawinouracademic-styletesting.ThebasicnumbersactuallyseemOK,butthepicturealonesuggestsalotlessappeal.Andtherollingnumbersmakeitclearthatwe’rereallydependentonbadmarkets,whenthismodelmighthavesomedefensiveappeal.Butoverthelongtime,wehavemoregoodperiodsthanbad.SoitseemsthatdespitetheaggregatevirtuesofROE,whenitcomestomanageablesizeportfolios,weneedtodomorethanrankandcountdownfromthetop.Whatwillfollownowisasetofiterations,butthesearen’titerationsinthesensediscussedbystatisticians.We’renotgoingtojustchangethingsarounduntilwefindaresultwelikeandthensay“We’redone.”Eachiterationwillbemotivatedbyaspecificallystatedgoalbasedonfinancialtheory,specificallyasearchforwhatsortofdatapointislikelytopointusinthedirectionofhighROEsthataren’tflukybutreal.Ifrationallyjustified,youcanhaveasmanyiterationsasyouwant,limitedonlybyyourimagination.I’llofferseveniterationshereinordertoillustrateathought-and-feedbackprocess.Onceyouseewhat’sbeingdone,youshouldbeabletocontinueonyourown.Let’sthinkoftheaboveasIteration#1andmoveon.Iteration#2IknowROE%TTMworksintheaggregatebecausefinancialtheorytellsmeitworks,andithelpsthatIsawitinthestudywhoseresultswereshowninTable1.Buttomakeitworkinthecontextofaninvestableportfolio,Ineedtodomorethanpickfromthetop.Knowing,asIdo,thatROEworksbecauseofthewayitimpactsGandRintheDDM,IreasonthatROEcan’t

accomplishwhatIhopeitwillifit’sintheprocessoftrendingdownward.SoI’llexperimentwiththis:ScreeningRules:FRank("ROE%TTM")>80 ROE%TTM>ROE%5YAvgQuickRank:ROE%TTMhigherisbetter,pickTop15Here’stheresult:Figure2

RollingTestAvg.Excess4-weekReturn:0.62%inallperiods,0.99%inupperiods,and0.05%indownperiodsThestandardbacktestisfine.Iexpectedimprovement(throughtheadditionofasecondrulethattriedtoweedoutsituationswhereROE,althoughgood,istrendinglower)andgotit.Butitisateenybitimperfect–lacklusterperformanceintherollingdownperiods(mostlikelynotdifferentfromzerotoasignificantdegree).It’snottheendoftheworld;wedohavemoreupthandownperiods.Butalthoughwecanlivewithwhatwehave,whystopsoquickly.Thegoaliftestingistolearnwhatwecanandcan’tdoaswetranslateideasintop123lingo.Wecanalwayscomebacktothisandsettleinifthat’swhatweultimatelydecidetodo.Iteration#3ScreeningRules:FRank("ROE%TTM")>80 OpMgn%TTM>OpMgn%5YAvgQuickRank:ROE%TTMhigherisbetter,pickTop15

OnewaytoaddresstrendsinROEistodrilldowntoitscomponentparts,oneofwhichismargin.AsweknowNetMarginistheversionwe’dconsiderifwewanttostrictlyreplicatetheDuPontframework.Butforourforward-lookingpurposes,wecanswapinanyothermarginthatwethinkwillbetterilluminatethecompany’spotentialfuture.Let’stryOperatingMargin.It’shighenoughintheincomestatementtoeliminateissuesinvolvingcapitalization,specialitems,etc.butunlikegrossmargin,we’resparedtheburdenofdecidingdifferencesinhowcompaniesmightallocateexpensesasSG&AorCOGS.AswithIteration#2,weseekaTTMfigureinexcessofthefive-yearaverage.Figure3showstheresults.Figure3

RollingTestAvg.Excess4-weekReturn:0.49%inallperiods,0.99%inupperiods,and-0.30%indownperiodsIt’samildstepbackwards.Ifwereallywanttoworkwithmargin,we’dneedtoconsiderwhythismovedusinthewrongdirection.Maybeoperatingmarginwasn’tthebestchoice.MaybeTTM>5Yistoolazyawaytoarticulateatrend;perhapsweneedtogetmoregranularandworkyearbyyear.(WemightthinkofthiswithrespecttoIteration#2aswell.)Maybeweshouldaddsomethingrelatingtoturnover;eitherwedrilldownintoDuPontcomponentsforreal,orwedon’t.I’llleaveittoyoutoworkfurtheralongtheselinesifyouwish.What’simportanttonote,here,ishowyoumovefromoneiterationtothenext.Itisn’tamatterofplugginginonethingoranothertocoverallpossiblebases.Youhavetotalktoyourself,askwhythelastthingwasn’tasgoodasyouexpectedandwhatitmighttaketoaddresstheshortcoming.Iteration#4

ScreeningRules:FRank("ROE%TTM")>80 FRank("DbtTot2CapTTM",#industry)<50QuickRank:ROE%TTMhigherisbetter,pickTop15Weknow,fromtheDuPontframework,thatleverage/debtispartofthepackage.Wealsoknowthatallelsebeingequal,lessdebtisbetter(becarefulthough,allelseisaverybroadconcept).Wealsoknowthatevenifweholdoperatingincomeconstant,wecanboostROEbymakingdebtabiggerpercentofthecapitalstructure.Soperhapswecanmitigatethepotentialbalance-sheetriskposedbyahigh-ROEmodelifweincorporatealeverage-relatedfactor.Sohowfarshouldwegoinlimitingleverage.WehavetostartsomewheresoI’llpickanFRank<50approach.Andthiswouldbeaterrifictimetospotlightanimportantissuethatwehaven’tyetaddressedbutwhichisalwaysonthetable:Shouldwedoacompletesort,orshouldwesospecializedsortsbasedonindustries,sectors,etc.Thereisnoinherentlyrightorwronganswer.Ifwewanttruly“better”companies,weshoulduseanindustry-typesort.Beingabletooperatewithlessleveragecomparedtoothersinthesamebusinesswithsimilarbalancesheetneedstellsussomething.Ifhavingasector/industrydiversifiedportfolioisimportanttous,weshouldworkwithsuchsorts.Ifyou’reaprorunninglargeaccounts,youprobablydoneedtogothisway.Ifyou’reanindividual,however,youcanaffordtorefrainfromthissortofthing.Sectorimbalanceisfine,ifyourmodeltiltsyoutowardbettersectors.Abasicsortagainsttheentiregroupwillworkforyouhere.(Diversificationcanbeaccomplishedthroughmultiplepositionsanduseofmultiplefactors.Sectordiversificationisbasedheavilyonstereotypeandcanincreaseriskifitforcesyoutoincreaseexposuretohigh-riskbusinesses.)Figure4

RollingTestAvg.Excess4-weekReturn:0.49%inallperiods,0.86%inupperiods,and-0.11%indownperiodsThat’snotsohot.It’snothorrible,butwe’veseenbetter.Icheatedandtookoutthe#industryparameter,ranthetestagain,andgotresultsclosertowhatwesawwithIteration#3,whenwefocusedonoperatingmargin.Wedefinitelypaidapriceforhavingtriedtobegoodcitizenswhenitcomestoindustryexposure.Butaswithoperatingmargin,wefindthatifwewanttosupportabasicquestforhighROEwithexaminationofindividualDuPontcomponentswe’llhavetoworkharderatchoosingandarticulatingthemthanI’vesofardone.Iteration#5ScreeningRules:FRank("ROE%TTM")>80 Rating("Basic:Quality")>90QuickRank:ROE%TTMhigherisbetter,pickTop15Timeforachangeofpace:Let’snottrytosupportthebasichighROErulewithpiecemealDuPontconsiderations.Instead,let’sgowholehogwithalotofthematonce.Henceuseofthe“Basic:Quality”rankingsystemaspartofascreeningrule.Thefactorsarevisibletoyousoifyoucheck,you’llseeitcoversalotofterritory.Figure5

RollingTestAvg.Excess4-weekReturn:0.42%inallperiods,0.56%inupperiods,and0.19%indownperiodsWe’vemadeprogress,verysmallprogressbutprogressnonetheless.That’sseenintherollingtests.Thisraisestheprospectthatwecoulddomorealongtheselines,perhapsbytappingintomorefactors,ifnotthroughuseofrankingsystemsthanbyuseofmorescreeningrules.Also,Icouldrelaxthe90threshold.Remember,wecanmakeprogresssimplybyeliminatingdogs.Still,wearegettingabitQualityobsessedhere....Iteration#6ScreeningRules:FRank("ROE%TTM")>80 Rating("Basic:Value")>90QuickRank:ROE%TTMhigherisbetter,pickTop15Here’sachange-up.We’llleavetheoriginalROEfactorsastheyare,acceptthemforwhattheyareproandcon,andbroadenourstrategytogoforQualityatareasonableprice,theGreenbattphilosophy.Greenblattdiditoneway.Thisiterationillustratesanother.Figure6

RollingTestAvg.Excess4-weekReturn:0.96%inallperiods,1.94%inupperiods,and-0.56%indownperiodsOurinterestispiqued.Returnisup.We’renotyethome;volatilityistroublesome.AndthatisconsistentwiththeDDMscript(loweridealvaluationratiosareassociatedwithgreaterrisk),andwithcommonsense(manylowratiosaresuchbecausetheydeservetobelow;i.e.becausethecompaniesarebad).ButValueisoneofthemostwellestablishedreveredapproaches.Soratherthanjustrunawayfromtherisk,let’sseeifwecantameit.Iteration#7ScreeningRules:FRank("ROE%TTM")>80 Rating("Basic:Quality")>90

Rating("Basic:Value")>90QuickRank:ROE%TTMhigherisbetter,pickTop15Wedouble-downonouruseofRatings-basedBuyrules;wewantstockshighlyratedforValueandwecontrolriskbyinsistingthattheybestronginQualitytoo,abroadrangeofQualityandnotjustROETTM.Figure7

RollingTestAvg.Excess4-weekReturn:1.98%inallperiods,3.19%inupperiods,and0.09%indownperiodsWellthat’seye-catching.Wecertainlygotahigherreturn.Butohthatvolatility!Whattheheckishappening?Theanswerisprettyeasytosee.We’reover-screening.Themostrecentrunofthescreenproducedonly7passingstocks.Andweweresimilarlylow,andlower,onmanyotheroccasions.We’regettingtoocrazyandoverlyexposingourselvestotoomanythingsthathavethefunctionalequivalenceofrandomness.That’seasytofix...Iteration#8ScreeningRules:FRank("ROE%TTM")>80 Rating("Basic:Quality")>75

Rating("Basic:Value")>75QuickRank:ROE%TTMhigherisbetter,pickTop15Wejustlowerthescreening-ruleRatingthresholdsfrom90to75(thelatternumberisn’tsacrosanct;Ipulleditoutofthewind).Wenowhave85stocksthatpassthescreen.Maybethat’sfine(weultimatelysortourwaydownto15)ormaybeweneedtolowerthethresholdabitmore.Wecanworkwithit.Butlet’sseeifwe’reatleastontherighttrack.

Figure8

RollingTestAvg.Excess4-weekReturn:0.98%inallperiods,1.45%inupperiods,and0.25%indownperiodsBingo.Thetrainismovingforwardagain.Wedoseeprolongedperiodsofsidewaysmovementandthensomeseriousrallies.That’sOK.Patienceisn’ttheworstthingintheworldiftheideamakessense.Andthefactthatwe’rebringinglowvaluationratiosintothepicturesuggestsitsunavoidable(valueisaninformationarbitragestrategy,theideabeingtolookforsituationsinwhichthemarkethasmisjudgedRandG,andsometimesittakesawhileforthattoplayout).WestillhaveIteration#2inourpockets,sowedon’thavetoforceourselvestosettleforthis.Wecan(theoverallreturnsarefine)butwemaynothaveasmuchpatienceaswewishwedid.It’sstillanopenquestion.Morecanbedone.Buthopefully,younowseehowyoucanpursueanswers.It’sAboutTheProcessOneobviousgoalofthisTopicwastogetyouthinkingthekindsofthoughtsyouneedtobethinkingasyouworkwithQuality.Beyondthat,though,Iwantedtoillustratemorecloselytheprocessofdevelopingandtestingastrategy.Thedifferencebetweencurvefitting,dataminingetc.,practicesthatcanmakeforgreatsimsbutrottenreal-moneyresults,isnottobefoundinstatisticalconcepts(robustness,etc.).Actually,arobustmodelmeansyoudidabetterjobofpredictingthepast,soinsteadofhavingamess,youmayhaverelevateditintoaperfectmess.Butamessisstillmess,andThat’snotwanttobedoing.

Alegitimatelydesignedmodelisonethatspringsfromrationalideas.Ifyou’redatamining,20iterationscanbetoomany.Ifyou’redoingitright,2,000canstillleaveyouwithroomformanymore.Andyouknowyou’redoingitrightifyoucanexplaintoyourselfwhyyouarechoosingtotestsomethingandunderstandingwhythetestmayhavesucceededorfallenshort.Andbytheway,didyounoticethatnoneofthesetestswasdisastrous.Thisisimportant.Ifyourtestsarederivingfromsensibleconcepts,youshouldknow,goingin,thattheresultswon’tbehorrifying.Non-horribleoutcomesshouldbepresumedevenbeforeyouclickto“Run”atest.Whatyoulearniswhetherit’sgoodenough.Also,noticetheDDMmentions.Ididn’tspendalotoftimeonthem.Butit’simportanttorecallhowivory-towertheDDMreallyis:D/(R-G)inaworldwheredividendsmaynotbepaid,whereanon-infiniteGmightmakeforanegativeP,etc.ThisTopicillustrateshow,ifwethinkofDDMasaconceptualanchorratherthanausableformula,wecanbuildsensiblemodelsthattestdecentlyrightfromthefirstpassandwhichstandagoodchanceofsucceedingwithrealmoneyorbeingaspringboardforsomethingwecansuccessfullyuse.Andhopefully,Table1motivatesyoutoexplorethefullrangeofQualityandwhatyoucandowithit.Thenexttopic,thelastinQuality,willbeEarningsQuality.