News and narratives in financial systems: Exploiting big...
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Newsandnarrativesinfinancialsystems:Exploitingbigdataforsystemicriskassessment1
RickardNyman,DavidGregory,SujitKapadia,PaulOrmerod*,DavidTuckett&RobertSmith*
September2016
Abstract:A number of recent contributions have tried to add to the understanding andforecastingofthemacroeconomybyanalysingnewsandnarratives.Thispaperappliesalgorithmicanalysistolargeamountsoffinancialmarkettext‐baseddatatoassesshownarrativesandemotionsplayaroleindrivingdevelopmentsinthefinancial system. We find that changes in the emotional content in marketnarratives are highly correlated across data sources. They show clearly theformation (and subsequent collapse) of very high levels of sentiment – highexcitementrelativetoanxiety–leadinguptotheglobalfinancialcrisis.Andwefindthattheshiftshavepredictivepowerforothercommonlyusedmeasuresofsentimentandvolatility.Wealsoshowthatanewmethodologythatattemptstocapture the emergence of narrative topic consensus gives an intuitiverepresentationoftheincreasinghomogeneityofbeliefsaroundanewparadigmprior to the crisis. With increasing consensus around narratives high inexcitement and lacking anxiety likely to be an important warning sign ofimpending financial system distress, the quantitativemetricswe developmaycomplementotherindicatorsandanalysisinhelpingtogaugesystemicrisk.
1Theviewsexpressedinthispaperaresolelythoseoftheauthor(s)andshouldnotbetakentorepresentthoseoftheBankofEngland,theMonetaryPolicyCommitteeortheFinancialPolicyCommittee.UniversityCollegeLondon,CentrefortheStudyofDecision‐MakingUncertaintyBankofEngland
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1. Introduction
Theyearsprecedingtheglobalfinancialcrisiswerecharacterisedby
widespreadexuberanceinthefinancialsector.Ashasoftenoccurredthroughout
history(ReinhartandRogoff,2009),consensusemergedoveranewparadigm,
underwhichthegreaterefficiencyofmarketsanddistributionofriskaroundthe
systemwasthoughttojustifythestrongpositivesentiment.Whenthecrash
cameduring2007and2008,sentimentreversedrapidlywithfearandanxiety
pervadingthefinancialsystem.
Thispaperappliesalgorithmicanalysistolargeamountsofunstructuredtext‐
baseddatatoidentifyquantitativemetricsthattrytocaptureshiftsinsentiment
alongwiththeextentofconsensusinthemarket.Wefindthatthesemetrics
capturekeydevelopmentsinthefinancialsystemrelativelywellpriortoand
duringtheglobalfinancialcrisis,aswellashavingpredictivepowerforother
commonlyusedmeasuresofsentimentandvolatility.Assuch,thesemetrics
couldpotentiallybeusedforgaugingsystemicriskinfinancialsystemsand
helpingtosignaltheprospectoffuturedistressasacomplementtomore
traditionalindicatorsandanalysis(see,forexample,Drehmannetal,2011,Bank
ofEngland,2014orGieseetal,2014).
Withrapidadvancesinwaystostoreandanalyselargeamountsof
unstructureddata,thereisincreasingawarenessthatthesedatamayprovidea
richsourceofusefulinformationforassessingeconomictrends.Forexample,a
growingliteratureexploitsindividualuser‐generatedsearchenginedata,suchas
GoogleTrends,totrytopredictthecurrentvalueof(‘nowcast’)economic
variablessuchasGDP(seeforexampleChoiandVarian,2012).However,some
recentstudiessuggestsearchenginedatashouldbetreatedwithcare,either
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becauseofalackoftransparencyabouthowthedatahasbeencreated(Lazer,et
al.2014)oruncertaintyaboutthemotivationforsearching–independentlyor
becauseofsocialinfluence(Ormerodetal.,2014).
Bycontrast,themeasuresofsentimentweusearepre‐definedwordlists
representingtwospecificemotionalgroups.Thewordshavebeendeveloped
throughthelensofasocial‐psychologicaltheoryof“convictionnarratives”(CNT)
(TuckettandNikolic,2016),whichisonewayofempiricallyformulatingtheidea
of“animalspirits”asdescribedbyKeynes(1936).CNTisanewtheorywhich
emphasisestheroleofnarrativesandparticulargroupsofaction‐enablingor
disablingemotionsindrivingdecision‐makingunderuncertainty(Chongand
Tuckett,2014;TuckettandNikolic,2016).Ithasbeensuccessfullyusedinother
applications,forexampleasameasureofchangingmacroeconomicconfidence
(Tuckettetal.2015)andispartofagrowingbodyofworkinsideandoutside
economicsdevelopingbroadermodelsofhumancognitionanddecision‐making
behaviour(forexample,AkerlofandShiller,2009;Bruner,1990;Damasio,1999;
LaneandMaxfield,2005;MarandOatley,2008;Beckert,2011andTuckett,
2011)2.
2Narrativecanbeconsideredafundamentalformofmentalorganization(Bruner,1991)thatallowsexperiencetobeorderedinto“chunks”(Miller,1956)withimplicitrelevancetoplans(Pribriametal,1960)andcausalmodels(RottmanandHastie,2013;SlomanandLagnado,2015)andsoexplanationsandpredictionsaboutoutcome.Convictionnarrativesenableactorstodrawonthebeliefs,causalmodelsandrulesofthumbsituatedintheirsocialcontexttoidentifyopportunitiesworthactingon,tosimulatethefutureoutcomeoftheactionsbymeansofwhichtheyplantoachievethoseopportunitiesandtofeelsufficientlyconvincedabouttheanticipatedoutcomestoact(TuckettandNikolic,2016).Theyarefoundedonbiologicallyandsociallyevolvedcopingcapacitiesthatallowindividualstopreparetoexecuteparticularactionseventhoughtheycannotaccuratelyknowwhattheoutcomeswillbe.Convictionnarrativesalsoprovideaneasymeansforactorstocommunicateandgainsupportfromothersfortheirselectedactionsaswellastojustifythemselves.Ideasabouttheroleofsimulationandembodiedcognitionthatarecentraltothesupportiveroleofnarrativesindecision‐makingbuildonexistingworkinaffectiveandcognitiveneuroscience(e.g.Baumeister and Masicampo, 2010; Barsalou, 2008; DamasioandCarvalho,2013;SuddendorfandCorballis,2007.)
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CNTsuggeststhatwithinthecontextofdeep,orKnightian(Knight,1921),
uncertainty(i.e.,uncertaintycharacterisedbyacontextinwhichthespaceof
potentialoutcomesofsomeeventcannotbearticulated)agentsdo(andhaveto)
constructnarrativessupportingtheirexpectationsandthatthesecreateafeeling
ofaccuracythatreadiesthemtoact.Suchconvictionnarrativescombine
cognitionandemotiontointerpretdata,envisionthefutureandsupportaction.
Convictionnarrativescontainafewfundamentalcomponents,notablyafocuson
thespecificemotionalelementsofnarrativesthatevokeattractionorapproach
toanobjectofinvestment(broadlyconceived),versusemotionsthatevoke
repulsionoravoidanceofthatobject.Thisemphasisonapproachandavoidance
inconvictionnarrativetheoryfocusestheideaofsentimentonitsimplications
foractioninuncertaindecision‐making,thusfocusingtheoften‐vaguetopicof
positive/negativesentiment.Inmoreordinarylanguagewefocusonexcitement
aboutthepotentialgainsfromanactionrelativetoanxietyaboutthepotential
losses.Ifexcitementcomestodominaterelativetoanxiety,investmentwillbe
undertaken(TuckettandNikolic,2016).Thus,inthesimplestcase,thekey
variablesofinterestaretheaggregaterelativedifferencebetweenexcitement
andanxietyandshiftsinthisdifferenceovertime.
Atanygivenmoment,therewillbeseveralnarrativesandassociated
emotionscirculatingamongallfinancialagents.Someofthesenarratives,or
piecesofthem,arelikelytobecontainedwithinrelevanttext‐baseddata
sources.Andiftherelativeshiftsintheemotionalcontentcorrelateacross
differentsources,itisplausiblethatatleastsomefinancialagentshadadopteda
subsetofthenarrativesandheldthemastrue,thoughitisimportanttonotethat
onecannotconclude,anditisinsomecaseshighlyunlikely(dependingonthe
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typeofdata),thatthecontentcreatorsthemselveshadadoptedastruethe
narrativesportrayedintheirdocuments–forexample,onecaneasilyimaginea
bigdifferencebetweenfinancialnewsdocumentsandsocialmediadata,inthe
extenttowhichcontentcreatorsfeelwhattheywrite.
Withthisinmind,weanalysethreeunstructuredtext‐baseddatasourcesof
potentialinterest:internalBankofEnglanddailycommentaryonmarketnews
andevents;brokerresearchreports;andReuters’newsarticlesintheUnited
Kingdom.Motivatedbytheconvictionnarrativemethodology,wecapturean
emotionalsummarystatistic(RelativeSentimentShiftorRSS)basedonthese
sources,andexplorechangesinthestatisticovertimetoassesshow
convincinglyandrobustlyitmeasuresshiftsinconfidence.Thismeasureaimsat
capturingtheextenttowhichthecreatorsofthedocumentsportrayemotions
withinthenarrativesand,inparticular,shiftsinthebalancebetweenthe
proportionsofexcitementversusanxietywords.Aswithothertext‐basedand
bigdataapproacheswhichtrytooperationalisetheconceptofsentiment,for
exampleusingdifferentwordlists,akeystrengthofthismethodliesinitstop
downapproach,capturingaggregateshiftslargelyundetectabletothehuman
eye.
Therelativesentimentmetricsthatweextractappear,withthebenefitof
hindsight,togiveearlywarningsignsofsignificantfinancialeventsinrecent
years.Inparticular,overallsentimentwasatveryhighandstablelevelsinthe
mid‐2000s,arguablyindicativeofexuberanceinthefinancialsystemandtherisk
offuturedistress.Frommid‐2007,asurgeinanxietydroverapidfallsin
sentimentthatcontinueduntilsoonafterthecollapseofLehmanBrothers.And
therewerefurtherfallsinsentimentpriortothestartoftheEuroareasovereign
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crisisin2011‐2.Inarelatedexercise,wealsoillustratehowourmethodscanbe
focussedonparticulartopicsorentities,suchas‘property’,thuspotentially
helpingtoshedlightonspecificsectorsoftheeconomy.
Togaugetherobustnessofouraggregatesentimentmetrics,wecompare
themwithbothwithstandardaggregatedmeasuresofconsumerconfidenceand
marketvolatilityandwithsomerelevantbutmoreatheoreticmeasuresof
uncertaintyfromtheliteratureexploitingtext‐basedinformation.Strikingly,we
findthatoursentimentmetricsoftenactasaleadingindicatorofsuchother
measuresandcanpotentiallyhelpustounderstandthem.
Financialbehaviourcanalsooftenbehomogenous.Therefore,inthesecond,
moreexploratory,partofthepaper,weaskwhetherwecanmeasurestructural
changesinthedistributionofnarratives.Specifically,wedevelopamethodology
tomeasure‘narrativeconsensus/disagreement’inthedistributionofnarratives
astheydevelopovertime.Thiscouldbearelevantmeasureoftheextentto
whichsomenarrativeshavebeensubjecttosocial‐psychologicalprocessesand
adoptedastrue(groupfeel(Tuckett,2011)).Forexample,priortotheglobal
financialcrisis,consensusappearedtodevelopacrossinvestorsbothabouta
newparadigminthefinancialsystemandinthebeliefthatitwaspossibleto
achievehigherreturnsthanpreviously–indeed,claimingtodosoarguably
becamenecessaryforfinancialinstitutionstoattractnewinvestment(Aikman
et.al.,2011).Butsuchconsensusinanenvironmentofhighsentimentcouldbe
suggestiveofover‐confidenceorirrationalexuberanceandthetheorypredicts
thatsuchsituationsarelikelytobeunsustainable.Theabilitytomeasurethe
emergenceofconsensusordisagreementwithintextdocumentscouldtherefore
proveusefulinidentifyingfinancialsystemrisks.
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Usingournewlydevelopedmeasureofnarrativedispersal,wefindthat
consensusintheReutersnewsarticlesgrewsignificantlyoveraperiodspanning
severalyearspriortotheglobalfinancialcrisis.Whenviewedtogetherwiththe
sentimentseries,thiscouldbeindicativeofagrowing,predominantlyexcited
consensusaboutanewparadigminthefinancialsystem.Inotherwords,our
top‐downtextanalysismethodologysuggestsevidencethatconsensual,
convictionnarrativesemergedpriortothecrisisinwhichanxietyanddoubt
substantiallydiminished,indicativeofpossibleimpendingdistress.
Otherstudiesthatattempttoquantifysentimenthaveusedtext‐baseddata
sourcessuchascorporatereportsandnewsmediaanalysedwithmuchmore
generalwordliststocaptureemotion.Theyhaveattempted,forexample,to
predictvariousaspectsofassetprices(e.g.,LoughranandMacDonald,2011;
Tetlock2007;Tetlocketal.2008;Tetlock2011;Soo2013)ortocapture
economicpolicyuncertainty(Bakeret.al.,2013).Researchhasalsousedtext
datatoexploreopinionformationincentralbanks(Hansenetal,2014)orhow
thetoneandlanguageofstatementsbycentralbanksmayinfluencevariables
suchasinflationforecastsandinflationexpectations(seeforexampleBlinderet
al.,2008,Sturm&DeHaan,2011,Hubert2012).Morebroadly,thereisalsoa
widerliteratureonhowsentiment,ascapturedviasurveys,marketproxiesor
events,mayaffectfinancialmarketsandrelatedopiniondynamics(e.g.,Baker
andStein2004;BakerandWurgler2006,2007;Bakeretal.,2012;Brownand
Cliff,2005;Edmansetal.2007;Lux,2008;GreenwoodandNagel2009).
Ouremphasisdepartsfromtheaboveliteratureinseveralways.First,by
focusingonarestricteddictionaryofwordswedevelopourmeasuresof
sentimentfromthepointofviewofasocial‐psychologicaltheoryofactionunder
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uncertainty(TuckettandNikolic,2016).Inthiswayweapplyatheoreticalfilter
whichshouldmoreaccuratelydetectfeatureswehypothesisetobeimportant
andavoidsomeofthedifficultiesassociatedwithdatamining.Whenprocessing
‘bigdata’thereisariskofobtainingseeminglysignificantcorrelationsthatdo
notgeneralise.Data‐miningtechniquesmayalsogeneralisepoorlytonewdata
sourcesandtobehighlycontextspecific.Secondourprimaryfocusisspecifically
ongaugingthesystemicrisk,ratherthanonmovementsinparticularasset
pricesorbroadermacroeconomicdevelopments.ConvictionNarrativeTheory
postulatesthatsystemicriskcanbegeneratedwhenmarketagentsgetcaptured
bygroupnarrativesthatopenupagapintheusualbalancebetweenexcitement
andanxietyinnarratives,suggestingthatsomekindof“thistimeisdifferent”
process(ReinhartandRogoff,2009)isgoingon(Tuckett,2011).Ifso,asevere
correctioncanbeexpectedtofollow.Third,muchcurrentresearchthatapplies
someformoftext‐basedsentimentanalysistostudytheeconomyorfinancial
marketstendstoexploitsocialmediagenerateddata.Bycontrast,wefocuson
datasourcesmorespecificallyconnectedtothefinancialsystem,includingone
sourcewrittenwithinacentralbank.
Section2explainsthedatawehaveanalysedandfocusesonourmeasureof
emotion,orsentiment,andexplainsthemethodology.Section3setsoutresults,
includingGrangercausalitytestsbetweentheemotionindicesandvarious
financial/economicindicators.Section4focusesonthemeasureof‘narrative
consensus’,explainingthemethodologyandresults.Section5discusseshow
thesemeasuresmightcomplementmoretraditionalindicatorsandanalysisused
insystemicriskassessment,andSection6concludes.Acertainamountof
technicalmaterialismadeavailableinanAppendix.Fulldetailsofallthe
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statisticaltestsinvolvedinouranalysis,alongwithfurthertechnicalmaterial,is
inaSupplementaryMaterialdocument,availableonrequestfromtheauthors.
2. Data and Methodology
Wemakeuseofavarietyofdatasourceswithamacroeconomicandfinancial
sectorfocus.
2.1 Bank of England internal market commentary
TheMarketsDirectorateoftheBankofEnglandproducesarangeofinternal
reportsonfinancialmarketsandthefinancialsystem,someofwhichprovide
‘high‐frequency’commentaryoneventsandsomeofwhichprovidedeeper,or
morethematic,analysis.Forthisstudy,weanalysedsomedocumentsofthe
formerkind,morespecificallydailyreportsonthecurrentstateofmarkets,
giventhatforthekindofanalysisweemployhere,theidealtypeofdatashould
remainas‘raw’aspossibleinordernotto‘distort’themarketemotionsreflected
within.Thesedocumentsmainlycoverfinancialnewsandhowmarketsappear
torespondtosuchnews.Wethereforeexpectthesedocumentstocorrelatewell
withfinancialsentimentintheUKandpotentiallycontainusefulinformationon
systemicrisk.
Weanalyseonaverage26documentspermonthfromJanuary2000untilJuly
2010.Thedocumentsaretypicallyrelativelyshort,around2‐3pagesofemail
text.Fortherestofthepaper,werefertothesedocumentsas‘Market
CommentaryDaily(MCDAILY)’.
2.2 Broker reports
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Brokerresearchreportsprovidealargesourceofdocumentsofclear
relevancetofinancialmarketsandthemacroeconomy.Weanalyseanarchiveof
14brokersfromJune2010untilJune2013,consistingofapproximately100
documentspermonth.Thedocumentsareverylong(upto50pagesinsome
cases),andsowepickuponalargenumberofwords.Visualinspectionofa
sampleofthesedocumentsrevealsthattheyprimarilyfocusonmacroeconomic
developmentsinthemajoreconomies.Wethereforeexpectthesentimentwithin
thesedatatocorrelatemoststronglywithmacroeconomicvariables.Throughout
therestofthepaper,werefertothisdatabaseas‘Brokerreport(BROKER)’.
2.3 Reuters News Archive
Finally,weusetheThomson‐ReutersNewsarchive,asalsoextensively
studiedbyTuckettetal.(2015)toassessmacroeconomictrends.Atthetimeof
ouranalysis,thearchiveconsistedofover17millionEnglishnewsarticles.For
mostofthispaper,werestrictourattentiontonewspublishedbyReutersin
LondonduringtheperiodbetweenJanuary1996andSeptember2014,inwhich
6,123articleswerepublishedonaverageeachmonth(afterexcludingallarticles
taggedbyReutersas‘Sport’,‘Weather’and/or‘HumanInterest’).Fortherestof
thepaper,werefertothisdatabaseas‘Reuters(RTRS)’.
2.4 Relative Sentiment Shifts
Asummarystatisticoftwoemotionaltraitsisextractedfromourtextdata
sourcesbyawordcountmethodologydescribedinmoredetailelsewhere
(Tuckett,SmithandNyman,2014).Twolistsofpreviouslyappliedand
experimentallyvalidatedwords(Strauss,2013),eachofapproximatelysize150,
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areused,onerepresentingexcitementandonerepresentinganxiety.Random
samplesofthesewordscanbefoundinTable1.
Table1:Emotiondictionarysamples
Anxiety Excitement
Jitter Terrors Excited Excels Threatening Worries Incredible Impressively Distrusted Panics Ideal Encouraging Jeopardized Eroding Attract Impress
ForthesummarystatisticofacollectionoftextsT,wecountthenumberof
occurrencesofexcitementwordsandanxietywordsandthenscalethese
numbersbythetotaltextsizeasmeasuredbythenumberofcharacters.3To
arriveatasinglestatistic,highlyrelevanttothetheoryofconvictionnarratives,
wesubtracttheanxietystatisticfromtheexcitementstatistic,sothatanincrease
inthisrelativeemotionscoreisduetoanincreaseinexcitementand/ora
decreaseinanxiety.
| | | |
Wecomputethisonamonthlybasis.Asevidencedbythedefinitionofthe
measure,wedonotcontrolforpossiblenegationsofthesewords(e.g.‘not
anxious’).Wedidhowevercarryoutatest(ontheRTRSdatabase)ifthe
presenceofnegationwordswouldaffectthesentimentseries(followingthe
procedureoutlinedinLoughranandMcDonald,2011)byexcludingallwords
countedtoproducethesentimentscoreiftheywerepreceded(withinawindow
ofthreewords)byeitherofthewords:“no”,“not”,“none”,“neither”,“never”or
“nobody”.The‘negationaware’seriesthusproducedremainedcorrelatedwith3Insomecasesitcouldbemoresuitabletoscalebythenumberofdocuments.However,inthisparticularcase,somedocumentscontainedtablesandothersdidnot,sothenumberofcharactersisamoreappropriatechoice.
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theoriginalseriesashighlyas0.99,bothinlevelanddifferenceform.Wealso
carriedoutatestoftherobustnessofthemethodologytoanalternative
selectionofsentimentwordlists.Weappliedthesentimentmethodologytothe
‘positive’and‘negative’wordlistsproducedbyLoughranandMcDonaldthatcan
bedownloadedfromtheweb4.TheseriesproducedusingtheRTRSdatabaseis
correlatedwiththeseriesproducedusingourwordlistsby0.84.
Thesimplicityofourmethodisintentionalfortwomainreasons.First,itis
naturaltoconsiderwhethersimpletext‐basedanalysiscanbeinformativebefore
movingtomorecomplexmethods.Second,italsoallowsforaneasier
assessmentoftherobustnessofthemethodology.Inparticular,weapplya
bootstraptechniquetocompute95%confidenceintervalsaroundthesummary
statistic.Wesamplenewweightsforeachwordineachdictionary(sothatthe
sumofweightsequalsthesizeofthedictionary)andre‐computethestatistic.
Repeatingtheproceduregivesadistributionfromwhichtoextractthe
confidenceintervals.5Thistechniquegivesusincreasedconfidencethatthe
meaningofindividualwordsinourtwolistsdoesnotchangeovertime.
3. Results
3.1 The evolution of measures of sentiment WeexploretherelativeemotionseriesextractedfromMCDAILYinFigure1,
annotatingthechartwithkeyeventsrelatingtofinancialstabilityforpurely
illustrativepurposes–inparticular,unlikeeventstudies,wedonottrytoinfer
anythingcausalfromtheeventsthatwedepictonthechartsThegraphmoves
4Thelistsweredownloadedfromthewebsiteastheywereavailablein2011,www3.nd.edu/~McDonald/word_lists.html5Itiseasytoimagineothermethodsofextractingconfidencelevels,e.g.,tosamplewithreplacementfromthecollectionoftexts.
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broadlyasmightbeexpected.Inparticular,itshowsastableincreaseduringthe
mid‐2000s.Thisisfollowedbyalargeandrapiddeclinefrommid2007,muchof
whichoccursbeforethefailureofBearStearnsinMarch2008–strikingly,
althoughthiswasalreadyaperiodofturmoilinthefinancialsystem,theseries
hitsverylowlevelsbeforetheworstpartsofthecrisisataroundthetimeofthe
LehmanBrothersfailure.
Althoughconvictionnarrativetheoryessentiallyreferstotherelativelevelof
sentiment–excitementminusanxiety–itisalsointerestingtoconsiderthetwo
componentpartsseparately.Figure2showsthatthevariationinanxietylevels
ishigherthanthatinexcitementlevels.Thismayreflectthefactthatfear(ora
lackofit)tendstodrivemovementsinthefinancialsystem,consistentwith
heuristic‐basedapproachestoKnightianuncertainty.
Figure1:RelativesentimentofMCDAILY.They‐axisdisplaysthenormalizedvalueswith0meanand
standarddeviation1
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Figure2:EmotionalfactorsofMCDAILY;anxiety(red)andexcitement(green).They‐axisdisplays
theindividualaggregatewordfrequenciesscaledbyvolume
Figure3:RelativesentimentofMCDAILY(black),RTRS(green)andBROKER(red).They‐axis
displaysthenormalizedvalueswith0meanandstandarddeviation1
TheMCDAILYseriesiscomparedwiththoseextractedfromtheothertwo
sources,namelyRTRSandBROKERinFigure3.Eachoftheseriesisnormalised
withmeanzeroandstandarddeviationof1tofacilitatecomparison.Thefigure
suggeststhattheseriesshareacommontrend.MCDAILYandBROKERaremore
volatilethanRTRS(duetoamuchlowernumberofstoriespermonth)and
BROKERwasavailabletousonamuchshorterhorizonthantheothertwo
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archives.TheexactcorrelationsbetweentheseriesarereportedinTable2in
section3.2.
Figures4and5showthetwocomponentpartsofthesentiment,excitement
andanxiety,inRTRSandBROKERrespectively.Againmovementsinanxiety
appeartodrivemuchofthefluctuationinoverallsentiment.Weareprimarily
interestedintheaggregatedifferencebetweentheexcitementandanxiety
components,fortheoreticalreasons.However,theanalysisiscarriedoutusing
thetwocomponentsseparatelyandresultsarepresentedintheappendix.
Figure4:Excitement(green)andAnxiety(red)inRTRS.They‐axisdisplaystheindividualaggregatewordfrequenciesscaledbyvolume
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Figure5:Excitement(green)andAnxiety(red)inBROKER.They‐axisdisplaystheindividualaggregatewordfrequenciesscaledbyvolume
Thusfarwehaveonlydiscussedhowthestatisticcanbeextractedfroma
genericcollectionoftexts,butitisalsoeasytofilterfortextsmatchingagiven
criteria,forexampletextsrelatingtoaparticulartopicorentity.Toexplorethe
potentialofsuchanapproach,wefilteredforthementionof'property'in
Reuters’newsarchive(Figure6)andthenrantherelativesentimentanalysis
onlyonthematchingsentenceswithinallarticles,withthenumberofsentences
reflectedinthebottompanel(recallthesearearticlespublishedinLondon).Itis
particularlyinterestingtonotethesteadyincreaseandlaterdeclineinvolumeof
articlesthatmatchedthepropertycriterianormedbythetotalnumberof
articlespublishedinLondon,theturningpointoccurringaroundthetimeofthe
bankruptcyofLehmanBrothers.Thepeakoftherelativesentimentseries
(whichhasherebeensmoothedwithparameter 0.3)appearstohave
occurredmuchbeforethis,towardstheendof2006,aftertheserieshad
undergoneasteadyincreaseforatleast4years(anxietyrelativetoexcitement
steadilydroppedoutofthediscussion).Therawrelativesentimentseries
correlateswithRTRSat0.57withnostatisticalevidenceofeitheraleadorlag.
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Suchfocusedanalysiscouldpotentiallybeofvalueiftryingtomonitorthe
emergenceofexuberanceinpropertymarkets,orindeedchangesinrisk‐taking
sentimentinanyspecificsectoroftheeconomy.Thisparticularexampleseems
toindicatethatthepropertysectorbecameoverlyexuberantpriortothecrisis.
Figure6:Relativesentimentsurrounding'property'inRTRS.They‐axisdisplaysthenormalized
valueswith0meanandstandarddeviation1
3.2 Structural breaks Importantly,boththeMCDAILYandRTRSshowsharpfallswellinadvanceof
thefinancialcrisis(weonlyhavedataonthethird,BROKER,from2010).For
example,themeanvalueofMCDAILYovertheboomperiodJuly2003through
June2007is0.916,withastandarddeviationof0.567.TheAugust2007value
fellto0.506,andinthesecondhalfof2007,themeanvaluewas0.691.In
January2008,however,therewasasharpfallto‐0.868,3.15standarddeviations
belowthemeanoftheJuly2003toJune2007period,andtheseriescontinuedto
fallwellinadvanceofthefailureofLehmanBrothers.
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ThebreakintrendsintheRTRSserieswasevenearlier.OvertheJuly2003–
June2007period,thisaveraged1.083withastandarddeviationof0.472.As
earlyasJune2007theRTRSfellto‐0.399,3.14standarddeviationsbelowits
2003‐2007mean.ByAugust2007,itwas6.11standarddeviationsbelow.
Weconductasimpleformalstatisticaltestforstructuralbreaksinthe
sentimentseriesusingthemethodofBaiandPerron(2003).Thenumberof
breakpointsmisestimatedusingBayesianInformationCriterion(BIC)andtheir
positionsareestimatedbyminimisingtheresidualsumofsquaresofthem+1
resultinglinesegments.Wesetamaximumnumberofbreakpointstobefound
to5.ThisyieldsfourstructuralbreaksfortheRTRSseriesonAugust2000,May
2003,May2007andApril2010.WefindthreebreaksfortheMCDAILYserieson
April2003,November2004andDecember2007.Wefindtwobreaksforthe
BROKERseriesonMay2011andOctober2011.
3.3 Comparison with other measures
Toillustratehowourmeasurescomparewithsomeothermeasuresof
uncertainty,Figure7showsMCDAILYplottedagainsttheVIX,withtheMCDAILY
variableinvertedforeaseofcomparison.Itisclearthatthemeasurestrackeach
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otherclosely.
Figure7:RelativesentimentofMCDAILY,invertedforconvenience,(black)comparedtotheVIX
(yellow).They‐axisdisplaysthenormalizedvalueswith0meanandstandarddeviation1
Moregenerallywecanlookatcorrelationsbetweenvariables.Toexplorehow
ourmeasurescomparetoarangeofotherfinancialandeconomicindicators,we
conductedasimplecorrelationbasedpairwisecomparisonstudy.The
correlationswiththeMichiganConsumerSentimentindex6(MCI),theVIX7,the
economicpolicyuncertaintyindexofBakeretal.(2013)8(EPU),theBankof
Englandmacroeconomicuncertaintyindex(BoEU–seeHaddowetal.(2013)),
seniorCDSpremia9andPMI10togetherwiththecorrelationsbetweenthe
.6The MCI was created as a means to assess consumers’ ability and willingness to buy. Thesurveyiscarriedoutwithatleast500phoneinterviews,duringaperiodofaround2weeks, inwhich approximately50questionsare asked. Survey results are released twice eachmonthat10.00a.m.EasternTime:preliminary estimates arepublishedusually (variationsoccurduringthewinterseason)onthesecondFridayofeachmonth,andfinalresultsonthefourthFriday.7TheVIX,commonlyknownasthe‘fear’index,isameasureofimpliedvolatilityderivedfromthepriceofS&P500options.WeconsideranaverageofVIX,computedusingclosingpricesofalltradingdaysforagivenmonth.Thusmakingtheseriescomparabletotherelativesentimentseries,whicharealsomonthly‘averages’.8WeusetheUKversionoftheseriesavailableathttp://www.policyuncertainty.com/europe_monthly.html.TheseriesstartsinJanuary1997.9SeniorCDSpremiaaccessiblefromtheBankofEngland’ssetofcoreindicatorsoffinancialstabilityhttp://www.bankofengland.co.uk/financialstability/Pages/fpc/coreindicators.aspx.TheseriesstartsinJanuary2003.
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individualrelativesentimentseriesarepresentedinTable2.11Tofacilitate
comparisonsandsincethesignofallcorrelationsisasexpected,wegivethe
absolutevaluesofthecorrelations.Itisclearthatourthreesentimentmeasures
arefairlyhighlycorrelatedwithalloftheothermeasures.
Table 2
Correlationsbetweenrelativesentimentseriesandcommonmeasuresof
sentiment,ignoringsigns
MCD RTRS BRO VIX MCI EPU BoEU CDS PMI
MCD 1 0.59 ‐ 0.65 0.26 0.43 0.54 0.67 0.38
RTRS ‐ 1 0.71 0.37 0.54 0.61 0.52 0.71 0.51
BRO ‐ ‐ 1 0.57 0.66 0.06 0.60 0.23 0.42
Lead‐lagcorrelationsbetweenthesentimentseriesandtheeconomicvariables
arepresentedinTable3aand3b.
Table 3a
Correlationsbetweenrelativesentimentseriesandcommonmeasuresof
sentiment,ignoringsigns(‐1ist‐1,+1ist+1)
VIX(‐1)VIX(+1)MCI(‐1)MCI(+1)EPU(‐1)EPU(+1)BoEU(‐1)BoEU(+1)
MCD(t)0.560.650.240.27 0.30 0.410.430.61
RTRS(t)0.26 0.370.49 0.58 0.63 0.630.350.67
BRO(t)0.340.650.34 0.87 0.260.010.060.76
Table 3b
10Businessexpectationssurvey(MarkitPMI).Basedonanswerstothequestionifbusinessactivityisexpectedtobehigher,lowerorstaythesamein12months.TheseriesstartsinApril1997.11Correlationsarecomputedonthefullavailablerangeofoverlappingdata..HereMCD=MCDAILYandBRO=BROKER.SincetheBoEUindexisaquarterlyserieswecreatequarterlyseriesofthethreesentimentindicatorsbyaveragingthevalueswithineachquarter.WedonotdothisfortheVIXandtheMCIasthatisoflessrelevancehere.
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Correlationsbetweenrelativesentimentseriesandcommonmeasuresof
sentiment,ignoringsigns(‐1ist‐1,+1ist+1)
CDS(‐1) CDS(+1) PMI(‐1) PMI(+1)
MCD(t) 0.63 0.63 0.38 0.43
RTRS(t) 0.67 0.69 0.43 0.57
BRO(t) 0.05 0.22 0.04 0.42
Toformallytestpotentiallead‐lagrelationshipswereporttheresultsofGranger‐
causality tests between the three sentiment series and the various other
indicators considered above. We use the methodology described in Toda and
Yamamoto (1995), as described in theAppendix (section 2). Here,we simply
reportthefinalstepintheprocesswhichprovidestheevidenceontheexistence
orotherwiseofGrangercausality.
Wecarryouttestsusingtheaggregateversionsofeachofthesentimentseries
(ienetbalancebetweenexcitementandanxiety),aswellasthecomponentparts
of each series, namely excitement and anxiety. All tests are carried out on
unsmoothed relative sentiment series. Table 4 below shows results obtained
testing Granger‐causality from the various versions of the RTRS, BROKER and
MCDAILYvariablestoMCI,VIX,BoEU,EPU,CDSandPMI.Table5belowshows
results obtained testing Granger‐causality between the same variables in the
reversedirection,fromMCI,VIX,BoEU,EPU,CDSandPMItothevariousversions
oftheRTRS,BROKERandMCDAILYvariables.12
Table 4
12ThemissingentriesinbothtablescouldnotbedeterminedbecauseofsomeformofVARmisspecification
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Waldtestp‐valuesofGranger‐causalityfromtherelativesentimentshiftseries
RTRS,BROKERandMCDAILY,andtheircomponentparts,toMCI,VIX,BoEU,
EPU,CDSandPMI
RSSSeries MCI VIX BoEU EPU CDS PMI
RTRSEXC‐ANX 0.005** 0.28 4e‐06** 0.3 0.0002**
RTRSEXC 0.032* 0.044* 0.0013** 0.03* 0.05* 0.05*
RTRSANX 0.003** 0.56 7e‐05** 0.1 0.0004**
MCDAILYEXC‐ANX 0.5 0.09 6e‐05** 0.05* 0.09 0.06
MCDAILYEXC 0.8 0.44 0.13 0.85 0.57 0.57
MCDAILYANX 0.8 0.38 0.001** 0.06 0.12 0.33
BROKEREXC‐ANX 2e‐11** 0.18 0.92 0.6 0.1
BROKEREXC 0.022* 0.84 0.77 0.43 0.82
BROKERANX 3e‐05** 0.12 0.72 0.68 0.03*
Note: *p<0.05;**p<0.01
Table5
Waldtestp‐valuesofGranger‐causalityfromMCI,VIX,BoEU,EPU,CDSandPMI
totherelativesentimentshiftseriesRTRS,BROKERandMCDAILY,andtheir
componentparts
RSSSeries MCI VIX BoEU EPU CDS PMI
RTRSEXC‐ANX 0.29 0.093 0.022* 0.57 0.08
RTRSEXC 0.39 0.22 0.038* 0.62 0.24 0.24
RTRSANX 0.03* 0.0013**0.21 0.85 0.12
MCDAILYEXC‐ANX 0.95 0.39 0.58 0.18 0.89 0.49
MCDAILYEXC 0.94 0.61 0.47 0.76 0.32 0.03*
MCDAILYANX 0.92 0.52 0.69 0.08 0.98 0.95
BROKEREXC‐ANX 0.94 0.16 0.73 0.97 0.41
BROKEREXC 0.22 0.084 0.72 0.001**0.72
BROKERANX 0.72 0.33 0.98 0.78 0.67
Note: *p<0.05;**p<0.01
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There is some evidence of Granger causality fromour text‐based sentiment
measurestothemetricsweconsiderbutlesscausalityintheoppositedirection.
Inparticular,theRTRSmeasureissignificantinmanyofthetests.Aswemight
expect, RTRS and BROKER, sources more reflective of broad macroeconomic
commentary, appear to relate most closely to the MCI, which is the most
macroeconomicmeasureofcomparison.Bycontrast,MCDAILY,asourcewhich
reflects financialmarketcommentary,exhibitsmuch lowerp‐values inrelation
totheVIXandBoEUmeasures.
Aswellasbeingsuggestiveoftherobustnessandusefulnessofthese
measures,theseresultsareindicativeofthepotentialuseoftherelative
sentimentmeasuresasshort‐termforecastingdevices,aswellastheiruseto
gaugefuturefinancialmarketvolatility,consumerconfidenceandvarious
measuresofuncertainty.TheauthorsshowinNymanetal.(2014)howBROKER
canbeusedtopredict,out‐of‐sample,thechangefromthepreviousfinal
estimateoftheMCItothecurrentpreliminaryestimate.TheadjustedRsquared
ofthepredictionswhenregressedontheactualchangesis0.486comparedto
0.114fortheconsensusforecastsmadebyeconomistsandpublishedinReuters.
Theforecastsareunbiasedastheconstanttermisnotsignificantlydifferent
from0andthecoefficientonthepredictionsisnotsignificantlydifferentfrom1.
Figure8illustratesthedifferenceinaccuracyoftheforecastsmadeusing
BROKERandtheconsensusforecasts.
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Figure8:ChangeinMCIcomparedtoforecastsofthechangemadeusingBROKERandconsensuseconomistforecasts
3.4EffectofrelativesentimentontheUKeconomyWefurtherexploretherelationshipbetweenrelativesentimentandeconomic
activityinthecontextofvectorautoregression(VAR).Inthisexerciseweuse
theRTRSseriesextractedfromnewsfortworeasons:1)itisthelongestrelative
sentimentseriesofthethreeand2)emotionsexpressedingeneraleconomic,
financialandbusinessnewsisarguablymorelikelytoberelatedtoeconomic
activitythan,e.g.,financialcommentary.
VARmodelshavebeenusedtoestimatetheeffectofuncertaintyonthe
economy,e.g.Bloom(2009)andHaddow(2013).Itiscommonlyfoundthat
shockstosuch(proxy)measuresofuncertaintyhaveasignificantandnegative
impactoneconomicactivity.
ToestimatetheempiricaleffectofrelativesentimentontheUKeconomywe
usethemodelinHaddowetal.(2013)andreplacetheirmeasureofuncertainty
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bytheUKRSSRTRSseries(aggregatedquarterlybyaveragingthemonthsina
quarter).ThemodelisspecifiedasthefollowingVAR(2):
,
where isthequarterlyrelativesentimentshiftseriesfortheUK, isthe
quarterlylevelofGDP(inlogdeviationsfromastatisticaltrend), isthe
quarterlylevelofemploymentinhoursworked(inlogdeviationsfroma
statisticaltrend), istheseasonallyadjustedleveloftheconsumerprice
index(inlogdeviationsfromastatisticaltrend), isthelevelofBankRateand
isanindicatorofcreditconditions.Ineachcase,thestatisticaltrendis
estimatedusingaHodrick‐Prescottfilterwithsmoothingparametersetto1600.
WeusethesamedataasusedtoestimatethemodelinHaddowetal.13
WefirstreplicatethemodelusingtheHaddowetal.uncertaintyvariableover
theperiod1989Q2–2012Q2andconfirmthataonestandarddeviationshockto
theuncertaintyindexdoeshaveasignificantandnegativeimpactonactivity.
Thisremainstrue(although,onlyjust)whenweshortentheperiodtostartin
1996Q1(thequarterfromwhichwehaverelativesentimentdatafortheUK).
Whenwereplacetheuncertaintyserieswiththerelativesentimentshift
series,wefindaqualitativelysimilarresponseofactivitytoaonestandard
deviationshock,butitisnotstatisticallysignificant.Figure9showsthe
respectiveimpulseresponsefunctionsofGDPtoshockstouncertaintyandRSS.
13The indexpre‐1995istakenfromFernandez‐CorugedoandMuellbauer(2006)andfrom1995onwardsitisaweightedaverageofinterestratesfacinghouseholdsforcreditcardloans,personalloansandmortgages
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Note:MGDPrepresentsquarterlylevelofGDP,aslogdeviationsfromastatistical
trend.
However,theGrangercausalityresultsdescribedabovesuggestthatcausality
flowsfromRSStotheHaddowetal.uncertaintyvariable.Weconfirmthisinthe
contextoftheVARmodelsetoutabovebyextendingitincludingbothRSSand
theHaddowetal.uncertaintyvariableinthemodel.Weobservetheimpactof
eachvariableontheotherintheVARmodel.WefindasignificantimpactofRSS
onuncertaintythatpersistsforabout5‐6quartersbutnosignificantimpactin
thereversedirection.Thisresultremainstrueregardlessoftheorderofthetwo
variablesduringmodelestimation.Therespectiveimpulseresponsefunctions
canbeseeninFigure10.
Figure9:ImpactofonestandarddeviationshocksonGDPfromBoEU(left)andRTRSRSS(right)
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Figure10:ImpactofonestandarddeviationshocksofuncertaintyonRSS(left)andviceversa(right)
Note:HOOrepresentstheuncertaintyvariableofHaddowetalandRSSthe
relativesentimentseries.
Thissuggeststhatitisinfactrelativesentimentthatdrivestheperceptionof
uncertaintyratherthananytrue‘probabilistic’uncertainty,ultimatelyslowing
downtheeconomy.Thismotivatesfurtherresearchintotheeffectsofnarratives
ontheeconomyandfinancialstability.
Wechecktherobustnessoftheabovestatementstotheorderingofthe
variablesintheVAR(placingthevariablesofinterestbothfirstandlastinthe
equation)andtotrending(checkingtheresultsbothwithandwithoutde‐
trendingtherelevantvariablesusingtheHodrick‐Prescottfilter).Therespective
impulseresponsefunctionscanbefoundinthesupplementarymaterial.
4. Measuring consensus
Weturnnowtooursecond,moreexploratory,lineofinvestigation:canwe
measurestructuralchangesinthevariabilityofnarratives–inparticular,ata
givenpointintime,isthereconsensusoverparticularnarrativesorawide
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dispersionofnarratives(disagreement)?Theobjectiveistoinvestigateifwecan
detectwhensomenarrativesgrowtobecomedominant,arguablytothe
detrimentofthesmoothfunctioningoffinancialmarketsandpotentiallyhinting
atimpendingdistressifalsoassociatedwithstronglypositiveaggregate
sentiment.Weintroduceanovelmethodologytoexplorethis.
4.1 Methodology
ForthisinvestigationwefocusedonRTRSasitgenerallyseemstoperform
wellandhasalargersamplethantheothersources,whichishelpfulforthe
techniquesweapply.Tomeasureconsensus,wemakeuseofmodern
informationretrievalmethods.Themainchallengeistofindagoodmethodology
forautomatictopicdetection.Manysuchapproachesexistintheliterature
(Berry,2004),butwerelyonthestraightforwardapproachofclusteringthe
articlesinword‐frequencyspace(afterremovalofcommonplacewords)toform
topicgroups,wherebyeacharticlebelongstoasingledistincttopic.Wethen
measuretheuncertainty(entropy)inthedistributionofthearticlesacrossthe
topicgroups.Weconsideranincreaseintheuncertainty(entropy)ofthetopic
distributionasadecreaseinconsensusandviceversa.Thedetailsand
justificationoftheconstructioncanbefoundintheAppendix(section4).
4.2 Results
WeplotthenarrativeconsensusfoundinRTRSinFigure11.Thegraphshows
aclearincreaseinconsensus(decreaseinentropy)precedingthecrisisperiod
andmuchmoredisagreementsubsequently.14Havingdecomposedthenarrative
discourseintooneindexmeasuringshiftsinemotion(theprevioussection)and14Wealsoinvestigatedtherobustnessofthenarrativeconsensusmetric,aprocesswhichinvolvessometechnicalanalysisessentiallybaseduponthedegreetowhichdocumentsatapointintimearesimilar.WedescribethisintheAppendix.
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anothermeasuringstructuralchangesinconsensus(entropy),itappearsfrom
theseresultsthatapredominantlyexcitedconsensusemergedpriorthecrisis,
drivenbylowlevelsofanxiety.Thisseemsconsistentwiththeconvergenceof
beliefsontheideathatanewparadigmcoulddeliverpermanentlyhigher
returnsinthefinancialsystemthanpreviouslywithoutthreateningstability.
Withtheonsetofthecrisis,thiseventuallyshiftedintopredominantlyanxious
disagreement,asmightbeexpectedinanenvironmentoffearanduncertainty.
Interestingly,however,thenarrativeconsensusseriespeaksinmid‐2007,justas
anxietystartstodominate.Exploringsamplearticlesfromthelargesttopic
clusteratthistimerevealsacommonthemeaboutweakcreditconditionsand
economicuncertainty.
Table6showstheoutcomeofWaldtestsofGrangercausalitybetweenthe
entropyseriesandtheVIX,theBankofEnglandUncertaintyIndex(BoEU),the
EPU,CDSandPMI.Weagainusetheproceduredescribedinsection3.Thep‐
valuesofGranger‐causalityintheconversedirection,inthedirectionofthe
entropyseriesfromtheotherthreevariables,canbeseeninTable7.Theonly
significantdirectionsofGranger‐causalityarefromtheentropyvariabletothe
BoEUindexandfromtheentropyvariabletoEPU.
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Figure11:Relativesentiment(black)andentropy(yellow)inReuters’Londonnews.They‐axis
displaysthenormalizedvalueswith0meanandstandarddeviation1
Table6Waldtestp‐valuesofGranger‐causalityfromEntropytoVIX,BoEU,EPU,CDSand
PMI
RSSSeries VIX BoEU EPU CDS PMI
Entropy 0.12 9.1e‐06** 0.03* 1.0 0.65
Note: *p<0.05;**p<0.01
Table7
Waldtestp‐valuesofGranger‐causalityfromVIX,BoEU,EPU,CDSandPMIto
Entropy
RSSSeries VIX BoEU EPU CDS PMI
Entropy 0.27 0.98 0.52 0.43 0.81
Note: *p<0.05;**p<0.01
Overall,theconsensusseriescapturesboththepresenceofpredominantly
excitedconsensusandpredominantlyanxiousconsensus.Thishighlightshow
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thetwomeasures,ofemotionandnarrativeconsensus,mighttherefore
beneficiallybeinterpretedsidebyside.
5. Discussion
Ourresultshighlighthowourmeasuresofsentimentandnarrativeconsensus
correlatewellwith,andinsomecaseseven‘cause’,certaineconomicand
financialvariables.Dependingonthetextsource,someperformbetterwith
financialvariables,somewithmacroeconomicvariables.Atalowerfrequency,
andwiththebenefitofhindsight,themetricsalsoappeartosignalrising
concernspriortotheglobalfinancialcrisis.Inthissection,wefocusonthe
potentialusesofindicatorsforemergingfinancialsystemstress,butwenotethat
thetextsourceslinkedmorecloselytomacroeconomicvariablescouldbeuseful
inforecastingor‘now‐casting’economicactivity(Tuckettetal,2015).
Therearemanydifferentapproachesforidentifyingandmodellingthreatsto
thefinancialsystem,includingtheuseofstresstests,earlywarningmodels,
compositeindicatorsofsystemicrisk,andMerton‐basedmodelsofsystemicrisk
thatusecontingentclaimsanalysis.15Manyauthoritiesuseindicatordashboards
orcobwebs,includingtheEuropeanSystemicRiskBoard,theOfficeofFinancial
ResearchintheUnitedStates,theWorldBank,theReserveBankofNewZealand
andtheNorgesBank.16IntheUnitedKingdom,theFinancialPolicyCommittee
15SeeAikmanetal.(2009),Burrowsetal.(2012)andKapadiaetal.(2013)foradiscussionoftheBankofEngland’sapproachtostresstestingandits“RAMSI”model.Onearlywarningindicatormodels,seeKaminskyandReinhart(1999),Drehmannetal.(2011),BorioandLowe(2002,2004),Barrelletal.(2010)andGieseetal(2014).Oncompositeindicatormodels,seeIllingandLiu(2006)andHollóetal.(2012).Oncontingentclaimsmodels,seeGrayetal.(2008)andGrayandJobst(2011).16FortheUS,seesection3oftheOFRAnnualReport(2012).TheESRB’sRiskDashboard’ispublishedontheweb(seehttp://www.esrb.europa.eu/pub/rd/html/index.en.html).OnthecobwebapproachusedinNewZealandandNorway,seeBedfordandBloor(2009)andDahletal.(2011),respectively.
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routinelyreviewsasetofcoreindicatorswhichhavebeenhelpfulinidentifying
emergingriskstofinancialstabilityinthepast,andwhichthereforemightbe
usefulindetectingemergingrisks(BankofEngland,2014).17
Recognisingthatnosinglesetofindicatorsormodelscaneverprovidea
perfectguidetosystemicrisk,duetothecomplexityoffinancialinterlinkages
andthetendencyforthefinancialsystemtoevolveovertime,andtimelags
beforerisksbecomeapparent,judgementalsoplaysacrucialroleinspecifying
anypoliciestotacklethreatstothefinancialsystem.Andqualitative
information,includingfrommarketandsupervisoryintelligencetypicallyalso
helpstosupportsuchjudgements.
Aswehaveshowninprevioussections,ourmeasuresofsentimentand
consensus,extractedfromtext‐basedinformation,appeartobeinformativeof,
episodesofemergingsystemicriskandhighmarketvolatility.Assuch,they
offerapotentialmechanismforextractingquantitativemetricsfromqualitative,
text‐basedinformationthatisusedtoinformpolicymakingandmighttherefore
beonecomponentofindicatordashboards,complementingotherapproaches
usedtodetectsystemicrisk.Thesemeasurescouldalsobecalculatedonareal‐
timebasis,offeringthemanimportantadvantageoversomemoreconventional
indicators.Arguably,theyarealsolikelytobemorerobusttotheLucas(1976)
critiquebecausethewritersofindividualdocumentsareveryunlikelyto
respondcollectivelybyadaptingtheirwritingtonesorstylesbecausean
indicatorbasedonvastnumbersofdocumentsisusedasoneguideforhelping
tosetpolicy.
17SeealsoGieseetal.(2014).
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Atthesametime,itisclearlyimportanttotesttheseindicatorsfurther.For
example,whichparticulartext‐basedsourcesshouldbethefocusofattention,
howgoodarethemetricsindistinguishingsignalfromnoise,andhowdothey
comparewithmoreconventionalindicatorsinthisrespect?Weleavethese
questionsforfurtherwork.
6. Conclusion
Inthispaper,wehaveexploredthepotentialofusingalgorithmictext
analysis,appliedthroughthelensofconvictionnarrativetheory,toextract
quantitativesummarystatisticsfromnoveldatasources,whichhavelargelyonly
beenusedqualitativelythusfar.Wehavedemonstratedthattheoutcomeofsuch
procedurescanleadtosomeintuitiveandusefulrepresentationsoffinancial
marketsentiment.Theshiftscorrelatewellwithfinancialmarketeventsand
appeartoleadanumberoffinanciallyorientedeconomicindicators.
Wehavealsodevelopedanovelmethodologytomeasureconsensusinthe
distributionofnarratives.Thismetriccanpotentiallybeusedtomeasure
homogenisationamongmarketparticipants.Greaterconsensus,whenviewed
togetherwithanincreaseintherelativesentimentseries,mayalsobe
interpretedasanincreaseofpredominantlyexcitedconsensusofnarratives
priortotheglobalfinancialcrisis.Thus,weappeartohavefoundnovelempirical
evidenceofgroupfeelandthebuild‐upofsystemicbehaviourleadinguptothe
financialcrisis.
Overall,therelativesentimentandconsensussummarystatisticsdeveloped
maybeusefulingaugingriskstofinancialstabilityarisingfromthecollective
behaviourdiscussed.Whilefurtherworkisneededtorefinethesemetrics,
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includinginrelationtoboththemethodsandthedatainputsused,theyhavethe
potentialtoprovideausefulquantitative,analyticalperspectiveontext‐based
marketinformationwhichcouldhelptocomplementmoretraditionalindicators
ofsystemicrisk.
Acknowledgments
TheauthorswouldliketothankJonRandattheBankofEngland[andothers]
forsupportingtheprojectandprovidingaccesstodata.Theauthorswouldalso
liketothankChrystiaFreeland,RichardBrownandMaciejPomaleckiof
ThomsonReutersforarrangingaccesstotheReutersNewsarchive.
DavidTuckett’sinvolvementinthisworkwasmadepossiblethroughInstitute
ofNewEconomicThinkingGrantnoIN01100025andagrantfromtheEric
SimenhauerTrustofTheInstituteofPsychoanalysis.
ThanksarealsoduetoOliverBurrows,AndyHaldane,PhilipTreleavenand
KimberlyChongforadviceandsupportaswellastocolleaguesinthePhD
FinancialComputingCentre,thePsychoanalysisUnitatUCLandtotheAnna
FreudCentre.Wehavealsobenefittedgreatlyfromdiscussionattheconferences
andothermeetingsatwhichtheworkhasbeenpresented.
Appendix1. Wordlists
Table A1 contains a random sample of 40 anxiety words and 40 excitement words.
Note that when the same word is spelled differently in American and British English
we have included both variants in the list.
Table A1
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Randomly Drawn Selection of Words indicating excitement (about gain) and anxiety
(about loss)
Anxiety Anxiety Excitement Excitement
Jitter Terrors Excited Excels Threatening Worries Incredible Impressively Distrusted Panics Ideal Encouraging Jeopardized Eroding Attract Impress Jitters Terrifying Tremendous Favoured Hurdles Doubt Satisfactorily Enjoy Fears Traumatised Brilliant Pleasures Feared Panic Meritorious Positive Traumatic Imperils Superbly Unique Fail Mistrusts Satisfied Impressed Erodes Failings Perfect Enhances Uneasy Nervousness Win Delighted Distressed Conflicted Amazes Energise Unease Reject Energizing Spectacular Disquieted Doubting Gush Enjoyed Perils Fearing Wonderful Enthusiastic Traumas Dreads Attracts Inspiration Alarm Distrust Enthusiastically Galvanized Distrusting Disquiet Exceptionally Amaze Doubtable Questioned Encouraged Excelling
2. Granger causality procedure
We use the methodology described in Toda and Yamamoto (1996). In outline, in
investigating Granger causality between any two series, this is as follows:
1. Check the order of integration of the two series using Augmented Dickey‐
Fuller (Said and Dickey 1984; p‐values are interpolated from Table 4.2, p. 103
of Banerjee et al. 1993) and the Kwiatowski‐Phillips‐Schmidt‐Shin (1992)
tests. Let m be the maximum order of integration found.
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2. Specify the VAR model using the data in levelled form, regardless of what was
found in step 1, to determine the number of lags to use with standard
method. We use the Akaike Information Criteria
3. Check the stability of the VAR (we use OLS‐CUSUM plots)
4. Test for autocorrelation of residuals. If autocorrelation is found, increase the
number of lags until it goes away. We use the multivariate Portmanteau‐ and
Breusch‐Godfrey tests for serially correlated errors. Let p be the number of
lags then used.
5. Add m extra lags of each variable to the VAR.
6. Perform Wald tests with null being that the first p lags of the independent
variable have coefficients equal to 0. If this is rejected, we have evidence of
Granger‐causality from the independent to dependent variable.
We used the statistical program R to carry out the analysis, and the various packages
used to carry out the above Toda‐Yamamoto procedure are documented in the
Supplement. The details of the specific results obtained, and a description of the
various R packages, using the test procedure are available on request in the form of
a Supplementary Material document.
3. Narrative Consensus
3.1 Constructing the Narrative Consensus series
We proceed as follows, following well‐established methods:
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1. Pre‐process all documents by representing them as ‘bags‐of‐words’ in which
word order is ignored and word‐endings are removed using a standard
English word stemmer, known as the Porter stemmer (Porter, 1980)
2. Compute a word by document frequency matrix, with words as rows and
documents documents as columns (each entry ij is the frequency of word I in
document j)
3. Remove uninformative rows (words at the extremes of the total word
frequency distribution). We remove words at the top of the cumulative
distribution (the smallest number of words accounting for a fixed percentage
of the total word count) and at the bottom (the largest number of words
accounting for a fixed percentage of the total word count) as the most
frequent words rarely help us distinguish between topics and the least
frequent words typically introduce too much noise and fail to show
consistent patterns. Another commonly used technique is to remove all
words in a predefined list, so called ‘stopwords’.
4. Reduce the dimensionality of the document vectors (columns), to d
dimensions, by the use of Singular Value Decomposition (SVD). In the
information retrieval literature, the method we use is referred to as Latent
Semantic Analysis (Deerwester, 1988) and has proved highly successful in a
wide range of applications. LSA is naturally able to model important language
structures, such as the similarity between synonyms.
5. Cluster the document vectors. The clustering algorithm must automatically
determine the number of clusters used to model the data. There are several
such algorithms; we pick an extension of the popular K‐means algorithm
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known as X‐means (Pelleg and Moore, 2000), which iteratively decides
whether or not to split one cluster into two using the Bayesian Information
Criteria (BIC) as a measure of model fit. BIC measures how well a model fits
data by the level of observed noise given the model while penalised linearly
for the number of model parameters (i.e., penalising over‐fitting).
This procedure gives us a distribution of the number of documents in each
cluster, e.g., 1000 articles on sovereign debt, 100 articles on crude oil, etc., and the
total number of clusters found.
Using this distribution, we want our measure of consensus to have two intuitive
properties:
If the number of topics (clusters) is reduced while the size of each cluster is
held fixed and equal ‐ consensus should increase.
If given a fixed number of topics, any particular topic grows in proportion to
the others ‐ consensus should increase.
A measure of the topic distribution, which would give us these properties, is
information entropy (Shannon, 1948).
Definition: Discrete Entropy
For a discrete distribution, such as in our particular case, the entropy is simply a
logarithmically weighted sum of probabilities,
log log log1
log ,
where is the number of articles in cluster , is the total number of articles and
is the number of clusters.
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The entropy is maximised (for a fixed number of clusters ) when documents are
uniformly distributed over the clusters. As the distribution moves away from
uniformity the entropy will decrease. To better understand how entropy changes
with k (the number of found clusters), we can simplify the equation as follows (if we
assume a uniform distribution of documents across the clusters),
log1
log log log log .
It is clear from this that entropy is increasing logarithmically as k increases. In
other words, the entropy is like an inverse consensus measure. Thus, if a narrative
grows to dominate the news, for example narratives such as sovereign debt,
structured finance or housing, the narrative entropy will decrease showing an
increase in consensus. Similarly, if the total number of narratives decrease, all else
fixed, consensus will increase (again signified by a decrease in narrative entropy).
We smooth the result using a method known as double exponential smoothing.
Double exponential smoothing is often chosen as an alternative to the simple single
exponential smoothing when it is believed that the underlying data contains a trend
component.
Definition: Double Exponential Smoothing
Given series … we decompose it into a smoothed series and a trend
component by the procedure
, 2
For 0:
1
1
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for some , ∈ 0,1 . In our case we discard after using it to estimate the
smoothed series .
We run the algorithm across several choices of parameters (the list of model
parameters, and combinations used (e.g., ‘40, 5, 100’ and ’50, 2, 100’), can be found
in Table A1) and smooth (using double exponential smoothing, with 0.3)
and average the results across parameter runs to arrive at the yellow line in Figure 9.
Table A2: Consensus parameters
Table A2 Consensus parameter combinations used to construct the Narrative ENTROPY index
Parameter Values Considered
Upper word bound 40,50,50,60,40,50,50,60 Lower word bound 5,2,10,10,5,2,10,10 Vector Dimensionality 100,100,100,100,200,200,200,200
Note: the considered values were combined in the ordered they are listed, i.e. (40, 5, 100), (50, 2, 100), etc.
3.2 Constructing Narrative Consensus proxy measures
To investigate the robustness of the narrative consensus metric we devise two
further methodologically distinct approaches to capture proxies for narrative
consensus.
1. Average document ‘overlap’
2. Average document ‘similarity’
We compute (1) from the word‐by‐document frequency matrix (after removing the
‘uninformative’ words) by simply dividing the number of non‐zero entries in the
matrix by the total number of entries, giving us a comparable time series (in which
higher document overlap is a proxy for higher narrative consensus). We compute (2)
by repeatedly sampling pairs of document vectors and computing the angle between
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them (a standard similarity metric for document vectors). We repeat the procedure
1000 times and compute the mean angle. In this case, a mean angle closer to zero is
a proxy for higher narrative consensus.
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