Language, news and volatility

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ContentslistsavailableatScienceDirect

Journal

of

International

Financial

Markets,

Institutions

&

Money

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / i n t f i n

Language,

news

and

volatility

夽

Hans

Byström

∗

LundUniversity,DepartmentofEconomics,Box7082,22007Lund,Sweden

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received16June2015

Accepted5March2016

Availableonline14March2016

Keywords: Newsaggregator Language Volatility Stockmarket Chinese

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IuseGoogleNewstostudytherelationbetweennewsvolumesandstockmarketvolatilities. Morethanninemillionstockmarket-relatednewsstoriesinEnglishandChineseare col-lectedandthedynamicsofthenewsvolumeandthestockmarketvolatilityiscompared.I ﬁndthatthestockmarketvolatilityandthenumberofpubliclyavailableglobalnewsstories arestronglylinkedinbothlanguages.Furthermore,thedirectionallinkbetweennewsand volatilityratherisfromnewstovolatilitythanviceversa.Inout-of-sampleevaluationsof volatilityforecastsIﬁndnewsvolumestoimproveforecasts,regardlessoflanguage.

1. Introduction

InthispaperItrytoshedsomenewlightontheoldquestionsofwhethertheamountofpubliclyreportedstock market-relatednewsislinkedtothevolatilityinthestockmarketand,ifso,ifitisnewsthatcausesvolatilityorviceversa.Iuse GoogleNewsTM_,_the_news_aggregator,_to_capture_the_actual_{month-to-month}_dynamics_of_the_global_news_volume._To_make

thestudymoreinclusive,Ilookatnewswritteninthetwomostimportantgloballanguages,i.e.EnglishandChinese. Largemovementsinthestockmarketareoften(expost)explainedasthemarket’sreactiontosuddenimportantnews arrivals.Atothertimes,however,marketsmoveseeminglywithoutanyevidenceofimportantnewsarriving.Acomparison oftworecentso-called“ﬂashcrashes”canbeusedtoexemplifythis.Whilethe“TwitterCrash”ofApril2013,whenS&P500 lost$120billioninmarketvalueinseconds,wascausedbyfaketweets(i.e.news)aboutexplosionsattheWhiteHouse,the “FlashCrash”ofMay2010,when$1trillioninmarketvaluetemporarilywaslost,isnormallynotconsideredtohavebeen causedbythearrivalofnews.Inotherwords,itisnotobviousthatpricemovementsalwaysarereactionstonewinformation (news)arrivinginthemarket.

Themaincontributionofthispaper,comparedtothetypicalstudylinkingnewsandvolatility,isitsuniqueproxyforthe totalamountof(global)stockmarketrelatednewsincirculation.Byusinganautomatedweb-basednewsaggregator,inmy caseGoogleNews,Iamablenotonlytocollectasignificantshareofallgloballyavailablemarket-drivingpublicinformation but,throughthecontinuousdatacollectionprocess,Iamalsoabletocapturetheactualmonth-to-monthdynamicsofthis newsdissemination.Thatis,insteadofmerelylookingatspecificnewsevents,Ilookatthedynamicsoftheoverallflowof publicinformation.Moreover,byfocusingonthebulkoftherelevantinformationflow(eachmonth,Icollectallavailable

夽 ThisworkwassupportedbyTheMarianneandMarcusWallenbergFoundation[MMW2012.0019];andHandelsbankensForskningsstiftelser

[P2013-0209:1].

∗ Tel.:+46462229478;fax:+46462224118.

E-mailaddress:[email protected] http://dx.doi.org/10.1016/j.intﬁn.2016.03.002

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newsstorieswhereagenericphrasesuchas“stockmarket”ismentioned)Iefﬁcientlyavoidanyundueemphasizeonnews storiesthat,expost,turnouttohavehadasigniﬁcanteffectonthevolatilityinourparticularmarkets.

Intotal,Icollectmorethanninemillionstockmarket-relatednewsstoriespublishedbymajornewspapersandother newssourcesworldwideoveraneight-yearlongperiod.Toputthisamountofnewsintoperspectiveitcanbecompared tothe120,000Reuter’sNewsServicenewsreleasescollectedbyBerryandHowe(1994),the752,647WallStreetJournal andBroadtapestoryheadlinescollectedbyMitchellandMulherin(1994)andthe129,737DowJonesandReutersnews announcementscollectedbyJohnsonandMarietta-Westberg(2004).Furthermore,tohighlightthetrulyglobalnatureof bothnewsandstockmarketsIhavecollectednewsinbothofthetwomajorgloballanguages,i.e.EnglishandChinese;eight millionofthenewsstoriesareinEnglishandonemillionareinChinese(Mandarin).Ibelievethistobetheﬁrsttimeanyone looksatnewswritteninChinese,i.e.,arguably,thesecond-mostimportantlanguageintheworld,inconnectiontomarket volatility.TheEnglish-Chineselanguage-pairisalsoparticularlyinterestinginthelightofthetwolanguages’signiﬁcant semanticandlinguisticdifferencesandduetothefactthatfewstockmarketparticipantsactuallyreadnewsbothinEnglish andinChinese.

Stockreturnvolatilityvarieswidelyacrosstime.Italsotendstobepersistentandtoexhibitso-calledvolatilityclustering, whereperiodsofhighvolatilityarefollowedbyhighvolatilityandviceversa.Althoughitissomethingofastylizedfact thatnewinformationreachingthemarket,i.e.news,isthemaincontributorofthisvolatility,andalthoughseveralstudies havelookedintotherelationshipbetweenmarketvolatilityandnewsdissemination,theempiricalevidenceisnotasstrong asonewouldexpect.Infact,evenwhenalinkbetweennewsandmarketmovementsisfound,thestrengthofthelinkis oftenquestioned.OneofthefirststudiesonnewsandvolatilitywasFrenchandRoll(1986)whocomparesthevolatility intheUSstockmarketduringexchangetradinghoursandnon-tradinghoursandconcludesthatthedifferenceintheflow ofinformation,particularlyprivateinformation,explainsthedifferenceinvolatility.Inotherwords,theyconcludethatit isthevariabilityintheflowofprivateinformationthatexplainsmostofthevariabilityinvolatility.MitchellandMulherin (1994),inturn,looksatpublicnewsandfindsapositiveandstatisticallysignificant,albeitweak,relationshipbetweenthe variability(absolutereturn)intheUSstockmarketandthenumberofpublicnewsannouncements,measuredasthedaily numberofstoryheadlinesreportedbyDowJones&Company(WallStreetJournalandBroadtape).BerryandHowe(1994), ontheotherhand,doesnotfindthatpublicinformationisstatisticallyrelatedtostockvolatilityintheUSintradaystock market.BerryandHowe(1994)measurespublicinformationflowasthenumberofnewsreleasesbyReuter’sNewsServices. Theinterestrateandforeignexchangemarketsalsoexhibittime-varyingvolatilityandEderingtonandLee(1993)shows thattheimpactofscheduledmacroeconomicnewsannouncementshasanimmediateeffectonpricesandalonglasting effectonpricevolatility.Inthestockmarket,anevenlongerlastingperiodofelevatedvolatilityafterannouncementsis foundbyPatellandWolfson(1984).Inamorerecentstudy,JohnsonandMarietta-Westberg(2004)findsthatincreasesin idiosyncraticstockreturnvolatilityarepositivelyrelatedtoincreasesintheamountoffirm-specificpublicnews.Andin Byström(2009,2011)IusethesameGoogleNewsmethodologyofcollectingnewsasinthisstudybutacrossamuchshorter sampleandlimitingtheanalysistosimplecross-correlations.1_The_results_in_Byström_(2009,_2011),_even_if_merely_tentative, indicateapositivelinkbetweenstockmarketvolatilityandnewsvolumes.

Arelatedstrandofliteraturefocusesoninvestorattention,ratherthanonnewsdigestion.Here,anotherGoogleproduct, GoogleTrendsTM_,_has_been_employed_recently._In_this_literature,_Google_search_frequencies_(Search_Volume_Index_(SVI))

areusedasaproxyforinvestorattention.DimpflandJank(2015)proxyinvestorattentionwithGooglesearchfrequencies andfindsastrongcorrelation,usingdailydata,betweenthesearchqueryvolumeandUSstockmarketvolatility.Theyalso showthatsearchqueriesimprovevolatilityforecasts.Similarly,Vozlyublennaia(2014)collectsGooglesearchfrequencies onaweeklybasisusingGoogleTrendsforarangeofdifferentfinancialmarkets,includingstockmarkets,butfindsarather weakrelationshipbetweeninvestorattention(Googlesearchfrequency)andvolatility.Finally,Daetal.(2011),although notexplicitlyfocusingonvolatility,findsapositiverelationshipbetweenabnormalabsolutereturnsintheUSstockmarket andtheGoogleSearchVolumeIndex.Likethecurrentpaper,allthesestudiesemploymodernweb-basedtools,butrather thancollectingnewsvolumestheycollectsearchfrequencies.

Moststudiesoninformationﬂowsandstockmarketreactionshavewrestledwithvariousdata-relatedissues.Some studieshavenotbeenabletodifferentiatebetweengood,badandneutralnewsandmanyhavenotbeenabletomeasure theimportanceofaparticularpieceofnews.Otherstudiesisolatespeciﬁceventsandthereforelackacontinuousmeasure ofthenumberofavailablenewsstories.Thisistypicallyanissuewhenmacroeconomicnewsisstudied,andwithouta continuoussamplingoftheamountofnewsincirculation,thedynamicsofnewsvolumescannotbestudied.Ibelievethat severaloftheseissuesareavoidedinourresearchsetup.First,sinceGoogleNewsallowstheusertospecifyexactlywhich wordstringstocrawl,Iam,byconstruction,abletogroupnewsaccordingtosentiment(neutralnewsorbadnews).2_The

1 _Byström_(2009,₂₀₁₁₎_looks_at_the_link_between_news_and_volatility_using_Google_News_but_otherwise_those_studies_differ_{signiﬁcantly}_from_this_study. InthispaperIincludenewsinChinese(Mandarin)aswellasinEnglish,Ilooknotonlyatcontemporaneouscorrelationsbutfocusinsteadprimarily onregressionsbetweencurrentvolatilityandlaggednewsvolumes,Irunlead-lagregressionstotellinwhichdirectioninformationﬂows,Ilookatthe volatilityforecastingperformanceofnews,Ilookatchangesinadditiontolevels,Ilookatatime-periodthatisalmostthreetimesaslong,Iusemonthly non-overlappingnewsvolumeobservationsratherthandailyoverlappingones,IlookattwelvemajorstockindexesinboththeEnglish-languagedominated worldandintheChinese-languagedominatedpartoftheworldandinarobustnesssectionIinvestigatewhetherextremenewsobservations,thecrisis period,missingobservationsortheexactwordingofthenewssearchstringisdrivingtheresults.

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wordstringsinthisparticularpaperarestockmarketandstockmarketcrashintheUSeditionand (stockmarket)and (approximatelytranslatedintoEnglishasstockmarketcollapseandstockmarketcrash)intheChina edition.3_Second,_by_focusing_on_the_amount_of_news_rather_than_the_mere_existence_or_not_of_news_I_am_{automatically}_able totellwhethertheunderlyingactionsoreventsthatshapethenewsareimportantornot.Third,throughthecontinuous datacollectionprocess,Iamabletocapturethetime-seriesdynamicsofthenewsincirculation.Finally,thecomprehensive natureoftheGoogleNewsgeneratednewsdatabaseandthesheernumberofnewsstoriesstrengthenstheresults.

SincethenewsthatIamcollectingusingGoogleNewsisglobalinnature,inthesensethatitiswrittenintwolanguages thattogetherarereadbyamajorityoftheworld’smarketparticipants,Ihavechosentolookatgloballyimportantstock marketsandthevolatilityinthemajorstockindexesinthesemarkets.Duetothedual-languagefocusoftheresearchIhave chosenhalfofthestockindexesfromtheEnglish-speakingworldandhalffromtheChinese-speakingworld.Theformer areMSCIWorld,S&P500,DJIA,Nasdaq,Russell2000andFTSE100andthelatterareShanghaiA,ShanghaiB,ShenzhenA,

ShenzhenB,HangSengChinaEnterprisesandHangSengChina-AfﬁliatedCorporations.Intotal,IlookattheimpactofGoogle News-generatedglobalnewsvolumesontwelvemajorstockindexes.

Tomyknowledge,thisisthefirsttimeanewsaggregatorisemployedintheforecastingoffinancialmarketvolatility and,furthermore,thefirsttimerelativelystrongevidenceisfoundthatnewsvolumescanactuallypredictstockmarket volatility.Inadditiontorobustandsignificantpositive contemporaneouscorrelationsbetweentheamountofnewsin circulationandthevolatilityinvariousmajorstockmarkets,lead-lagregressionstellusthatthedirectionallinkbetween newsandvolatilityratherisfromnewstovolatilitythanviceversa.Ialsofindevidenceofnewsvolumespredicting (one-monthahead)volatility.Thelatterfindingissupportedbysignificantandeconomicallyrelevantnewsvolumeregression parametersandeconomicallymeaningfulout-of-sampleforecastingerrorreductions.Theaverageimpactofaone-standard deviationchangeinnewsvolumeonnextmonth’sstockindexvolatilityis11basispoints(0.11%).Albeitnotlarge,the impactiseconomicallymeaningfulwhencomparedtothemeanofthetwelvestockindexes’unconditionalvolatilityacross thesampleperiod(coveringtheveryvolatilecredit-andeuro-areasovereigncrises)whichis123basispoints.Moreover, whenIlookatchanges,ratherthanlevels,theeconomicsignificanceiseven moresignificant(9basispointsversusa meanof44basis points).Theout-of-samplemeanabsoluteforecastingerror(MAE),inturn,isonaveragereduced by 15%whenlaggednewsvolumesareaddedtopastvolatilitieswhenpredicting(one-monthahead)volatility.Itshouldbe stressedthatnofine-tuningoftheforecastsaremade,theestimationwindowisforexamplesetratherarbitrarily,andfor somemarket/language/searchstringcombinationstheeconomicsignificanceofincludingnewsvolumesinthevolatility predictionismuchlarger.Inotherwords,thereisscopeforamoresubstantialforecastingimprovementwhenallowingfor systematicdatamining.IconcludetheempiricalstudywitharobustnesssectionwhereIfindtheresultstoberobustto theremovalofextremenewsvolumeobservations,creditcrisisobservationsandmissingobservationsaswellastoslight changestothenewsvolumecollectionprocess.

2. Newsvolumecollectionanddatadescription

Newsiswritteninhundredsofdifferentlanguages,somereadandunderstoodbyglobalaudiencesofmillionsofpeople, othersreadsolelybylocalsorcognoscenti.Atanypointintimetherearethousandsofnewspiecesavailabletomarket participantsandanyofthisnewsmayaffectthemarketinonewayoranother.Inthispaper,Ifocusonthevolatilityinthe majorstockmarketsandonhowthisvolatilityisrelatedtotheamountofnewsavailable.Ithereforefocusprimarilyonnews thatislikelytohaveanimpactonstockmarketvolatility.Ihavealsochosentofocusonthetwo,arguably,mostimportant globallanguages,EnglishandChinese.Englishisthelinguafrancaoftodaywithuptoabillionnativeand non-native speakers.Chinese,ontheotherhand,isthemostcommonlyspokennativelanguageintheworldwitharoundonebillion nativespeakers.Comparedtomanyotherﬁnanciallyimportantlanguage-pairs,suchasFrenchandEnglishorSpanishand Portuguese,thereisalsoverylittleoverlapinthereadershipofEnglishandChinesenews.Infact,speakersofonelanguage oftendonotunderstandasinglewordintheotherlanguage.Forus,thisisofimportancesincethismakestheEnglishand ChineseGoogleNews-generatednewsvolumesmoredistinctiveandmorelikelytohavetheirownuniquerelationtomarket volatility.

IcollectwhatIdeemtobestockmarket-relatednewsvolumesusinganEnglishlanguageedition(theUSedition)as wellasaChineselanguageedition(theChinaedition)ofthenewsaggregatorGoogleNews.Thisnewsaggregatormakes itpossibletocollectasigniﬁcantamountofthemanythousandsofavailablenewspiecesaroundtheworldselectedand groupedbytopic.Bycollecting,onamonthlybasis,thenumberofnewsstoriespresentedbyGoogleNewsIgetanestimate ofthedynamicsofthenewsvolume,i.e.thedynamicsoftheoverallﬂowofpublicinformation,ratherthanjustsnapshots ofthevolumearoundcertainchosenevents.ThenewsvolumeiscollectedusingtwodifferenteditionsofGoogleNewsas wellastwodifferentlanguages,EnglishandChinese(Mandarin).4_The_total_number_of_separate_news_stories_collected_in

3_Of_course,_any_search_for_the_phrase_stock_market_also_includes_bad_news_like_stock_market_crash_._On_average,_however,_I_assume_that_good_and_bad_news averageoutwhensearchingforthe“neutral”termstockmarket.

4_A_preliminary_analysis_indicates_that_the_search_results_are_very_similar_when_the_news_data_is_collected_from_the_Hong_Kong_edition_of_Google_News insteadoftheChinaedition.

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Table1

NewsVolume–DescriptiveStatistics.InthisTableIpresentsomedescriptivestatisticsforthe105monthlyEnglishnewsvolumeobservations(numberof

monthlynewsstories)fromSeptember11,2006toSeptember1,2014andforthe51monthlyChinesenewsvolumeobservationsfromNovember1,2010

toSeptember1,2014.

Stockmarket Stockmarket

crash (stockmarket) (stockmarket “collapse”) (stockmarket “crash”) Mean 88,600 1900 19,100 630 890 Standarddeviation 59,100 2500 15,400 390 720 Max 307,000 23,400 54,700 1900 4600 Min 4100 400 1500 190 200

thiswayoverthetime-period2006–2014ismorethanninemillion,ofwhicheightmillionarewritteninEnglishandone millionarewritteninChinese.5

GoogleNewsisanautomatednewsaggregator(computer-generatednewsservice)thatusescomputeralgorithmsto collect,presentandsortwebnewsintocategories.GoogleNewsaggregatesnewsfrommorethan50,000newssources worldwide.Itthengroupssimilarstoriestogether,anddisplaysthemaccordingtoeachreader’sinterests(Google,2014). Newsstoriesarecollectedfromnewspagesonthewebwiththegeographicallocationofthenewssourcesdependenton theedition.GoogleNewsincludesnewsthatappearedonanyoftheselectedwebpagesduringthepast30daysandsince nohumaneditorsareusedthepolitical/ideologicalbiasisminimized(Google,2014).Theactualnewssourcesareprobably notknownoutsidetheGoogleplex(theGoogleHQ)butunveriﬁedrumoursonthewebclaimthatthelargestcontributorsto theGoogleNewsﬂowareNewYorkPost,WashingtonPost,HoustonChronicleandBloombergintheUS,andTheGuardian, BBCNewsandTheTimesintheUK.

InordertomaketheexercisefeasibleIfocussolelyonnewspiecesdeemedrelevanttostockmarketparticipants.I thereforelimitmyGoogleNewssearchtothesearchstringsstockmarketandstockmarketcrashintheUSeditionand

(stockmarket)and (approximatelytranslatedintoEnglishasstockmarketcollapseandstockmarket

crash)intheChineseedition.6_Despite_this_limitation_in_news_coverage_I_still_manage_to_collect_more_than_nine_million separatenewsstoriescontainingthewordstockmarketovertheeight-yearperiod2006–2014.7

Thedata,i.e.theamountofnewspubliclyavailableworldwideoverthelast30days,iscollectedonamonthlybasisfrom September11,2006toSeptember1,2014fortheEnglishnewsandfromNovember1,2010toSeptember1,2014forthe Chinesenews.Moreexactly,thedataiscollectedmanuallybytheauthoreveryfourthMondayatapproximately9:00a.m. CentralEuropeanTimeatthesamelocation(theofficeoftheauthor)andwithoutbeingsignedintoanyGoogleAccount. OnahandfulofoccasionswhentheauthorwastravellingonthescheduledMonday,thedatacollectionwasdoneonthe followingTuesdayorWednesdayoratanotherlocation.8,9_On_any_particular_day,_the_Google_News_aggregator_collects_data fromthelast30-dayperiod.Thismeansthatmydefinitionofamonth(asa4-weekor28-dayperiod)differsslightlyfrom Google’sdefinitionofamonth(a30-dayperiod).Thedifferenceissmall,2days,andsincetheadditional2daysofnews crawlingalwaysconstitutesaweekend(whenmarketsareclosedandthereislessmarket-relatednewsavailable)four weeksagoIbelievethatitbiasestheresultsminimally.Inanycase,thisdiscrepancyshouldprobablybiastheresultsagainst

usﬁndingalinkbetweennewsvolumeandvolatility.10

InTable1,Ipresentsomedescriptivestatisticsonthemonthlysamplednewsvolume.Themonthlynumberofnews storiesvariesnotonlyacrosstimebutamongthesearchstringsusedintheGoogleNewssearch.Whilethemoregeneral searchstringstockmarket(inEnglish)returnsonaverage89,000separatenewsstoriespermonth,themorenarrowsearch stringstockmarketcrash(again,inEnglish)returnsonaverage1900newsstoriespermonth.Thepatternissimilarforthe Chinese-languagenewsvolumeswhere (stockmarket)onaveragereturns19,000newsstoriespermonthand

5 _In_mid-May_2012,_the_number_of_news_stories_reported_by_Google_News_increases_dramatically_(most_likely_by_the_inclusion_of_additional_news_sources)

andIhavethereforechosentoadjustthenumbersfromJune2012onwards.Thenumbersarenormalizedsothattheﬁrstobservationafterthechange

isidenticaltothelastobservationbeforethechange.Reportednumbersarealwaysnormalizedonesandtheyarethereforenotdirectlycomparableto currentGoogleNewsvolumes.Thetotalnumberofnewsstoriesreportedinthispaperisthereforealsounder-reported.Someofthecorrelationanalysis inthispaperhasbeenredonewithdataupuntilthechangewithroughlyunchangedresults.

6 _The_reason_for_choosing_two_different_{“pessimistic”}_search_strings_in_Chinese_is_to_control_for_any_potential_{language-difference}_in_the_{interpretation}_of theword“crash”.

7 _Here,_I_assume_that_no_news_story_is_reprinted_again_at_a_later_stage_and_that_no_two_news_stories_are_exactly_identical.

8 _The_{“December”}_search_result_is_sometimes_missing_due_to_Christmas_and_New_Year’s_Eve._There_are_also_some_missing_observations_on_other_days

randomlyscatteredthroughouttheyearsandtheseaswellasthemissingDecemberobservationsareallreplacedbythelastavailabledatapoint.Inorder toseewhetherthesemissingobservationsaffecttheresults,adummyisaddedtotheregressions,andcorrelationsarerecalculatedwiththedatesofthe

missingobservationsremoved,withalmostunchangedresults.

9 _In_order_to_make_sure_that_the_news_volume_is_not_{platform-dependent}_the_number_of_news_stories_was_occasionally_collected_at_several_locations_the

sameday(atrandomlychosendaysthroughoutthesampleperiod)withveryminordifferences.

10 _According_to_the_home_page_of_Google_News_the_news_aggregator_includes_news_articles_that_have_been_crawled_within_the_last₃₀_days._However,_a

carefulstudyofthesearchresultssometimesrevealsafewnewsstoriesthatare(afewdaysorweeks)olderthan30days.However,ontheseoccasions thenumberofnewsstoriesthatareolderthan30dayshavebeenfoundtobefewcomparedtothetotalnumberofstoriesandthereforelesslikelyto signiﬁcantlybiastheresults.

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Fig.1. English-languagenewsvolumeandMSCIWorldstockmarketvolatility.ThisgraphshowstheEnglish-language(USedition)GoogleNewsvolumes forthesearchstring“stockmarketcrash”togetherwiththeMSCIWorldstockreturnvolatility.Boththenewsvolumeandthestockvolatilityarenormalized

tostartatoneandaresampledonamonthlybasisbutsmoothedusingathree-month(quarterly)smoothingwindow.

Fig.2.Chinese-languagenewsvolumeandMSCIWorldstockmarketvolatility.ThisgraphshowstheChinese-language(Chinaedition)GoogleNews

volumesforthesearchstring“stockmarketcrash”togetherwiththeMSCIWorldstockreturnvolatility.Boththenewsvolumeandthestockvolatilityare

normalizedtostartatoneandaresampledonamonthlybasisbutsmoothedusingathree-month(quarterly)smoothingwindow.

(stockmarketcrash)onaveragereturns890newsstoriespermonth(acrossashortertime-period).Thetime-seriesvariation ofthestockmarketcrashnewsvolumes,inEnglishandinChinese,respectively,isgraphicallypresentedinFigs.1and2.Not surprisingly,theamountofstockmarket-relatednewsincirculationwasatitshighestaroundtheperiodoftheLehmann Brotherscollapse.Thenumberofnewsstoriescontainingthewordsstockmarketwashigherthan200,000(atroughly2½ timesthemonthlyaverage)bothinSeptemberandOctoberin2008.Twoyearsearlier,inSeptemberandOctober2006, thenumberofnewsstorieswasaround70,000permonth.TheChinesenewswasnotcollectedatthetimeoftheLehman Brotherscollapseandthepeakinthenumberofnewsstoriescontainingtheword (stockmarket)wasreachedinthe springof2012whenmorethan30,000newsstorieswerereleasedeachmonthfromMarchtoJune.

Thestockmarketdata,inturn,iscollectedforthesametime-periodasthenewsvolumes,i.e.September11,2006to September1,2014.ThedataisdownloadedfromDatastreamandallthestockindexesaredenominatedintheir home-currency.Iincludetwelvedifferentstockindexesinmyanalysis.Sincemyaimistostudytheeffectoflanguageonthe news-volatilitylink,IhavechosenhalfoftheindexesfromthemainlyEnglish-speakingsphere,i.e.theUS,theUKandthe globalcommunity,andhalffromthemainlyChinese-speakingsphere,whichIdeﬁneasChinaincludingHongKong.As thestockindexrepresentingtheglobalAnglophonecommunityIhavechosentheMSCIWorldstockindexwhichincludes securitiesfrom23developedstockmarketsaroundtheworld.FromtheUSIhaveincludedtheS&P500-,DJIA-, Nasdaq-andRussell2000indexes.Themainmotivationbehindincludingthelasttwoindexesistheirfocusonsmall-capstocks.It ispossiblethatthenews-volatilitylinkisdifferentforsmallstockswherethebalancebetweensmallretail-investorsand largeinstitutionalinvestorsisdifferent.TheUK,ﬁnally,isrepresentedbytheFTSE100indexwhichcoversthe100largest companiesontheLondonStockExchange.

WhilemostreadersarefamiliarwiththeAnglophoneworldanditsstockmarkets,theChinese-speakingsphereandthe stockmarketsdominatedbyChinese-speakersprobablyneedssomeintroduction.SincemyfocusisMandarin,themain languagespokeninChina,IhaveonlychosenstockindexesthatcontainChinesestocks.TheChinesestockmarketishighly segmentedwithdifferentmarketsaimedatdifferentinvestors:

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•A-shares:A-sharesareRMB-denominatedsharesissuedbydomesticcompaniesregisteredinmainlandChinaandlisted ontheShanghaiStockExchangeortheShenzhenStockExchange.A-sharescanonlybepurchasedbydomesticChinese investorsorholdersofQualiﬁedForeignInstitutionalInvestor(QFII)licenses.

•B-shares:B-sharesaredollar-denominatedsharesissuedbydomesticcompaniesregisteredinmainlandChinaandlisted ontheShanghaiStockExchange(US$)ortheShenzhenStockExchange(HK$).B-sharescanonlybepurchasedbyforeign investorsorbydomesticinvestorswithforeigncurrencyholdings,andcapitalcontrolsrestrictChineseresidents’ability topurchaseBshares.

•H-shares:H-sharesareHK$-denominatedsharesissuedbycompaniesincorporatedinmainlandChinabutlistedonthe HongKongStockExchange.H-sharescannotbepurchasedbydomesticChineseinvestors.

•RedChip-shares:RedChip-sharesareHK$-denominatedsharesissuedbyChinesecompaniesincorporatedinHongKong andlistedontheHongKongStockExchange.RedChip-sharescannotbepurchasedbydomesticChineseinvestors.

WhiletheA-share marketisaimedmainlyatdomesticinvestors,theB-sharemarketis aimedpredominantly at for-eigninvestorsandtheH-shareandRedChip-sharemarketsareaimedsolelyatforeigninvestors.Thisgradualincreasein segmentationfacilitatesthestudyoftheeffectofmarketparticipantandlanguageonthenewsvolume—volatilitylink.

IlookatChinesesharestradedonthreedifferentexchanges;A-andB-sharestradedontheShanghaiStockExchange, A-andB-sharestradedontheShenzhenStockExchange,andH-andRedChip-sharestradedontheHongKongStockExchange. TheactualstockindexesaretheShanghaiSEAIndex,theShanghaiSEBIndex,theShenzhenSEAIndex,theShenzhenSEB

Index,theHangSengChinaEnterprisesIndexandtheHangSengChina-AfﬁliatedCorporationsIndex.

Eachmonth,i.e.everyfourthMonday,thepastmonth’sstockmarketvolatilityiscalculatedasthestandarddeviation ofthedailystockindexreturnsoverthelastfourweekssothatthetime-periodforthevolatilityestimatematchesthe time-periodforthenewsvolumecollection(exceptforthe2daysdiscussedabove).Thepossibilityofmatchingthenews collectionperiod(onemonth)withthevolatilitycomputationperiod(onemonth)isonehugeadvantageofusingamonthly frequencyintheanalysis.Anotheradvantageisthattheexacttimestampofthenewsreleaseisnotrequired(aproblem facedbyseveralpreviousstudies,forinstanceDimpﬂandJank(2015))andthatthetimezonedifferencesaroundtheworld, mostnotablybetweenChinaandtheUS,haveaminimaleffectontheresults.

3. Therelationbetweennewsvolumesandstockmarketvolatility

Inefﬁcientﬁnancialmarkets,pricemovementsaretheresultsofmarketparticipantsreactingonmarket-relatednews. Asaresult,themorenewsthatreachesthemarketoveracertaintime-periodthehigherthepricevolatilityinthemarket islikelytobe.InthisstudyofstockmarketsworldwideIthereforeexpectthestockreturnvolatilitytobepositivelylinked totheamountofstockmarket-relatednewsworldwide.Indeed,aninitialvisualinspectionofFigs.1and2,wheremonthly stockmarketvolatilitiesandnewsvolumesarepresentedonamonthlybasisforEnglishandChinesenewsstories(smoothed usingaquarter-of-a-yearlongwindowandnormalizedtostartatone),motivatesustoinvestigatethislinkfurther.

3.1. Correlationanalysisofnewsvolumesandstockmarketvolatility

Tostartwith,Ipresentsimplecontemporaneouscorrelationsbetweennewsaggregatorgeneratednewsvolumesand stockmarketvolatilities.IstudytwelvedifferentstockmarketindexesandcollectnewsinEnglishaswellasinChinese. Allvariablesaresampledonamonthlybasis.InadditiontolevelsIalsolookatchangesinnewsvolumeandvolatility. Forthechanges,IfollowMitchellandMulherin(1994),whotakedifferencesfromamulti-daymovingaverage,bytaking differencesfroma12-monthmovingaverageofthepastnewsvolumeandvolatility,respectively.JustlikeMitchelland Mulherin(1994),Itakemulti-perioddifferencestoavoidthelossofinformationaroundtheoccasionalclusteringofhigh newsvolumesandhighvolatilitylevelsandtoreducetheinﬂuenceofpossiblemonth-of-the-yeareffects.

ThecorrelationresultsarepresentedinTable2,whichisdividedintotwoparts,oneforlevelsandoneforchanges.With veryfewexceptions,thecorrelationcoefficients(basedontheentiresample)amongnewsvolume-andvolatilitylevelsare large,positiveandstatisticallysignificant.Mostcorrelationslieinthe0.3–0.8rangeandtheonlynon-significantcorrelations arethoseinvolvingtheChinese-language (stockmarketcrash)newsstories.Thesecorrelationsaretypicallythe lowest,regardlessofstockmarket,andinthemainlandChinastockmarketsinShanghaiandShenzhenthecorrelationsare occasionallyevenslightlynegative.Evenifthenegativecorrelationsaresmallandnotstatisticallysignificant,itisinteresting thatthelinktoChinesenewsisfoundtobeweakestintheChinesestockmarketregardlessoftheactualGoogleNewssearch string.

WhenIturntochangesratherthanlevels,thecorrelationcoefficientsarestilllargelypositiveandstatisticallysignificant. Thestatisticalsignificance,i.e.thesize,ofthecorrelationsisgenerallysomewhatweakerforchanges,however,andthe correlationsareagainlowestwhenChinesenewsormainlandChinastockmarketsareinvolved.Forchanges,though,the linkbetweenmainlandChinastockmarketsandnewsisweakacrosstheboard,i.e.regardlessofthelanguageofthenews. OnepossibleexplanationfortheratherweakrelationshipbetweennewsandstockvolatilityinmainlandChina(i.e.excluding stockstradedinHongKong)isthatthesemarketsaredominatedbyinvestorsthatdonotread,oratleastdonottradeon, (traditional)news.ThishypothesisisstrengthenedbythefactthatthecorrelationisparticularlyweakintheA-sharemarket

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Table2

CorrelationsbetweenNewsVolumeandVolatilityInthisTableIpresentNewsVolume–Volatilitycorrelationsforthetwelvestockindexeswhennews

volumesarecollectedbyGoogleNewsisinEnglishandChinese,respectively.Resultsarepresentedbothforlevelsandchanges.

NewsinEnglish(Levels)

Stockmarket Stockmarketcrash

MSCI 0.50*** _0.77*** S&P500 0.57*** _0.75*** DJIA 0.57*** _0.77*** NASDAQ 0.55*** _0.74*** Russell2000 0.48*** _0.66*** FTSE100 0.60*** _0.79*** ShanghaiA 0.59*** _0.34*** ShanghaiB 0.47*** _0.33*** ShenzhenA 0.49*** 0.26*** ShenzhenB 0.50*** _0.33*** HongKongH 0.66*** _0.73***

HongKongRedChip 0.66*** _0.69***

NewsinChinese(Levels)

(stockmarket) (stockmarket“collapse”) (stockmarket“crash”)

MSCI 0.47*** _0.75*** _0.22* S&P500 0.34*** _0.71*** _0.26** DJIA 0.32** _0.71*** _0.27*** NASDAQ 0.33*** _0.67*** _0.25** Russell2000 0.35*** _0.67*** _0.19* FTSE100 0.45*** _0.70*** _0.18 ShanghaiA 0.23** _0.40*** _0.00 ShanghaiB 0.33*** _0.42*** ₋_0.02 ShenzhenA 0.23** _0.37*** ₋_0.06 ShenzhenB 0.37*** _0.54*** ₋_0.03 HongKongH 0.29** _0.69*** _0.14

HongKongRedChip 0.33*** _0.67*** _0.17

NewsinEnglish(Changes)

MSCI 0.19** _0.71*** S&P500 0.18** _0.62*** DJIA 0.16* _0.62*** NASDAQ 0.15* _0.61*** Russell2000 0.17** _0.58*** FTSE100 0.23*** _0.62*** ShanghaiA 0.09 0.17** ShanghaiB 0.03 0.18** ShenzhenA 0.02 0.07 ShenzhenB 0.04 0.19** HongKongH 0.21** _0.53***

HongKongRedChip 0.27*** _0.54***

NewsinChinese(Changes)

MSCI 0.09 0.63*** _0.37*** S&P500 0.06 0.61*** _0.35*** DJIA 0.06 0.60*** _0.35*** NASDAQ 0.10 0.59*** 0.35*** Russell2000 0.05 0.58*** _0.27** FTSE100 0.02 0.54*** _0.28** ShanghaiA −0.24** _0.26** _0.04 ShanghaiB 0.01 0.27** _0.10 ShenzhenA −0.08 0.21* ₋_0.01 ShenzhenB −0.05 0.39*** 0.11 HongKongH −0.07 0.54*** _0.23**

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whichisaimedmainlyatdomestic(retail)investors.Infact,thelinkbetweenthevolatilityintheChinesestockmarketand theamountofstockmarket-relatednewsreachingthemarketparticipantsisstrongerinmarketswheretherearefewer mainlandChineseretailinvestors.ThelinkisstrongestintheH-shareandRedChip-sharemarketsinHongKongwhere Chineseretailinvestorsarebannedfromtrading.ThelinkissomewhatweakerinthetwoB-sharemarketswherefew Chineseretailinvestorsinvestanditisweakestintheretail-dominatedA-sharemarkets.

Tosomeextent,thesefindingsonthesegmentationandinformationprocessingintheChinesestockmarketareinline withthelimitedamountofresearchthatexistsonthetopic.InapreviousstudyusingGoogleNews,Byström(2011)finds astronglinkbetweenEnglish-languagenewsvolumesworldwideandtheglobalstockmarketvolatility,andaweakerlink betweentheChinesestockmarketandthesamesetofworldwide(English)news.PoonandFung(2000)looksathow informationflowsamongthevariousChina-andHongKong-basedmarketsandfindsreturnandvolatilityspillovereffects amongsecuritieslistedonthedifferentmarkets.TheRedChipmarketisfoundtoprocessinformationfasterthantheother markets.ThesegmentationandinformationflowsamongChinesestockmarketshasalsobeenstudiedbyYang(2003)who findstheforeigninvestorswhodominatetheB-sharemarkettobebetterinformedthanthedomesticinvestorsinthe A-sharemarket.AndinastudyrelatingnewsandstockmarketvolatilityinthesegmentedChinesestockmarket,Suand Fleisher(1999)findsthevolatilityintheA-sharemarketinthelate90stobesignificantlyhigherthanthatintheB-share marketandtheytrytoexplainthisfactusingargumentsrelatedtonewsflowsanddifferentinvestorbases.

Lookingattheoverallpicture,however,thelinkbetweennewsandvolatilityisstrong.Thosemonthswhenalotofnews isreleasedaretypicallyalsothosemonthswhenthestockmarketvolatilityishigh,andmonthswithsigniﬁcantrelative increases/decreasesinnewsvolumes(relativetothelasttwelvemonths’volume)arealsomonthswithasigniﬁcantrelative increase/decreaseinmarketactivity(i.e.volatility).Additionalevidenceofthestrongassociationbetweennewsandvolatility isgiveninFigs.1and2wheretherelativesizeofthemonthlymovementsinnewsvolumeisquitesimilartotherelative sizeofthemovementsintheMSCIWorldvolatility.Thatis,newsvolumesandstockvolatilitiesarenotonlymovinginthe samedirectionatthesametimebuttheactualsizeofthechangesissimilaraswell.

3.2. Regressionanalysisofnewsvolumesandstockmarketvolatility

Thenextstepistorununivariatelead-lagOLSregressionsbetweennewsvolumeandstockmarketvolatility.The regres-sionsallowustoassessthepredictiveabilityofnewsvolumesandtoevaluatetheeconomicsignificanceoftheinter-temporal news-volatilitylinkinthestockmarket.Iruntwosetsofregressions.Inthefirstset,thedependentvariableisthe(monthly) volatilityandtheexplanatoryvariablesare(one-month)laggednewsvolumeand(one-month)laggedvolatility.Inthe sec-ondset,Ireversetheregressionandthedependentvariableisthe(monthly)newsvolumeandtheexplanatoryvariables are(one-month)laggedvolatilityand(one-month)laggednewsvolume.Toaccountforthepossibleinfluenceofmissing newsvolumevaluesIincludeadummyforthemissingvaluesinallreportedregressions.Theregressionsincludeonlyone lagofvolatilityandnewsvolumeandthereasonisthatIlookatmonthlydata;anyempiricalrelationshipfoundbetween stockmarketvolatilitythismonthandnewsreleasedseveralmonthsagoislikelytobespurious.Indeed,Tetlock(2007), wholooksatdailynewsreleasesandincludeslagsuptofivedays,findsareversalintheinitialreactiontonewsalready fourorfivedaysafterthenewsrelease.AndVozlyublennaia(2014),whocollectsGooglesearchqueriesonaweeklybasis asameasureofinvestorattention,arguesthatherweaklinkbetweenattentionandvolatilitycouldbecausedbyherusing weekly,ratherthandaily,datasincetheeffectofattentiononvolatilitycoulddisappearalreadyindays,ratherthaninweeks. Inotherwords,ifthesameappliestonewsandtoitseffectonvolatility,thereisnoreasontoexpectalinkbetweenstock marketvolatilitythismonthandnewsreleasedseveralmonthsago.

Tables3and4summarizetheregressionresults.Sincemyfocusisprimarilyonthenewsvolume/volatilitycoefficients, theinterceptintheregressionequationandthemissingvaluedummycoefficient(whichisrarelystatisticallysignificant) areleftout.Table3presentstheresultsfortheEnglish-languagenewsandTable4presentstheresultsfornewsinChinese. IntheupperregressionslabelledNews,thenewsvolumeisthedependentvariableandinthelowerregressionslabelled

Volatility,thestockmarketvolatilityisthedependentvariable.Inbothregressions,theﬁrstexplanatoryvariable,ˇ1,is

alwaystheone-monthlaggedvalueoftheothervariable(i.e.volatilityintheupperregressionandnewsvolumeinthe lowerregression)andthesecondexplanatoryvariable,ˇ2,isalwaystheone-monthlaggedvalueofthedependentvariable.

IntheanalysisthatfollowsIthereforefocusonˇ1.Resultsforchangesfollowafterthoseforlevelsand*,**and***represent

signiﬁcanceatthe10%,5%and1%level,respectively.

Overall,ˇ1isoften,butnotalways,positive.ItismuchmoreoftenpositiveintheVolatilityregression(inallbutﬁve

cases),i.e.laggednewsvolumeismorelikelyto(Granger)causevolatilitythanviceversa.FortheVolatilityregression,ˇ1

isstatisticallysigniﬁcantforroughlyhalfthemarket/language/searchstringcombinationsinTables3and4,andthereare 42caseswhennewsvolume(Granger)causesvolatilityandvolatilitydoesnotcausenewsvolume,butonly7caseswhen volatilitycausesnewsvolumeandnewsvolumedoesnotcausevolatility.Interestingly,theselatterseveninstancesareall foundinmainlandChinawhenthenewsisinEnglish.ThisisanindicationofEnglish-languagenewsnotreachingmainland Chinesestockmarketparticipants,oratleastthisnewsdoesnothelpinpredictingvolatility.Meanwhile,Chinesenews (Granger)causesvolatilityinmainlandChinajustasitdoesinmostotherstockmarkets.Asforˇ2,ﬁnally,itiswell-known

thatvolatilityispersistentandthatlaggedvolatilitypredictsfuturevolatilityandthisisevidentinmyregressionsinthe (almost)unanimouslysigniﬁcantˇ2coefﬁcient.Interestingly,newsvolumeseemstobeaspersistentasvolatility.

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Table3

ResultsofEnglish-LanguageNewsVolume–VolatilityRegressionsInthisTableIpresentresultsfromtheEnglish-languageregressions.Intheupper

regressions(News)thenewsvolumeisthedependentvariableandinthelowerregressions(Volatility)thestockmarketvolatilityisthedependent

variable.Inbothregressions,theﬁrstexplanatoryvariable,ˇ1,isalwaystheone-monthlaggedvalueoftheothervariable(i.e.volatilityintheupper

regressionandnewsvolumeinthelowerregression)andthesecondexplanatoryvariable,ˇ2,isalwaystheone-monthlaggedvalueofthedependent

variable.Eachregressionhasanunreportedinterceptandanunreporteddummyformissingvalues(whichisrarelysigniﬁcant).*_,**_and***_represent

signiﬁcanceatthe10%,5%and1%level,respectively.Theregressionsarebasedon105monthlyobservationsfromSeptember11,2006toSeptember1,

2014andtheresultsarepresentedbothforlevelsandforchanges.

Dependentvariable ˇ1 ˇ2 ˇR2 ˇ1 ˇ2 ˇR2 MSCI News −0.11* _0.88*** _0.70 _0.12 _0.42*** _0.25 Volatility 0.058 0.49*** _0.62 _0.11* _0.42*** _0.62 S&P500 News −0.080 0.88*** _0.70 _0.22 _0.35*** _0.26 Volatility 0.083 0.61*** _0.65 _0.14* _0.54*** _0.65 DJIA News −0.090 0.88*** _0.70 _0.17 _0.38*** _0.25 Volatility 0.086* _0.54*** _0.64 _0.14** _0.47*** _0.64 NASDAQ News −0.066 0.87*** _0.70 _0.20 _0.36*** _0.26 Volatility 0.083 0.57*** _0.61 _0.14** _0.49*** _0.62 Russell2000 News −0.080 0.87*** _0.70 _0.092 _0.45*** _0.25 Volatility 0.12** _0.69*** _0.63 _0.22*** _0.58*** _0.64 FTSE100 News −0.080 0.88*** _0.70 _0.186 _0.37*** _0.26 Volatility 0.057 0.49*** _0.55 _0.080 _0.45*** _0.55 ShanghaiA News 0.10* _0.80*** _0.70 _0.22** _0.44*** _0.29 Volatility 0.064 0.50*** _0.56 _0.061 _0.50*** _0.56 ShanghaiB News 0.079 0.82*** _0.70 _0.24*** _0.43*** _0.29 Volatility 0.10 0.56*** _0.39 _0.090 _0.56*** _0.39 ShenzhenA News 0.083 0.82*** _0.70 _0.18** _0.47*** _0.27 Volatility 0.10* _0.40*** _0.41 _0.087 _0.41*** _0.41 ShenzhenB News 0.066 0.83*** _0.70 _0.21** _0.44*** _0.28 Volatility 0.055 0.43*** _0.38 _0.055 _0.42*** _0.38

HongKongH News −0.049 0.87*** _0.70 _0.12 _0.43*** _0.25

Volatility 0.13 0.71*** _0.51 _0.023 _0.76*** _0.50

HongKongRedChip News −0.035 0.86*** _0.70 _0.15 _0.41*** _0.25

Volatility 0.097 0.64*** _0.52 _0.010 _0.67*** _0.51 NewsinEnglish(Changes)

DependentVariable ˇ1 ˇ2 ˇR2 ˇ1 ˇ2 ˇR2 MSCI News −0.038 0.52*** _0.26 _0.049 _0.39*** _0.17 Volatility 0.058 0.34*** _0.39 _0.11* _0.27*** _0.40 S&P500 News −0.014 0.52*** _0.26 _0.071 _0.38*** _0.17 Volatility 0.063 0.33*** _0.35 _0.13** _0.26*** _0.37 DJIA News −0.028 0.52*** _0.26 _0.032 _0.40*** _0.17 Volatility 0.060 0.33*** _0.36 _0.13** _0.26*** _0.38 NASDAQ News −0.019 0.52*** _0.26 _0.056 _0.39*** _0.17 Volatility 0.056 0.27*** _0.31 _0.13** _0.21*** _0.34 Russell2000 News −0.007 0.52*** _0.26 _0.007 _0.42*** _0.16 Volatility 0.08* _0.30*** _0.37 _0.14*** _0.22*** _0.40 FTSE100 News −0.011 0.52*** _0.26 _0.056 _0.39*** _0.17 Volatility 0.044 0.28*** _0.33 _0.079 _0.25*** _0.34 ShanghaiA News 0.13* _0.50*** _0.28 _0.079 _0.41*** _0.17 Volatility 0.026 0.11*** _0.12 _0.029 _0.10*** _0.12 ShanghaiB News 0.11 0.51*** _0.27 _0.14 _0.40*** _0.18 Volatility 0.060 0.12*** _0.09 _0.035 _0.12*** _0.08 ShenzhenA News 0.048 0.52*** _0.26 _0.048 _0.42*** _0.17 Volatility 0.043 0.11*** _0.12 _0.017 _0.10*** _0.11 ShenzhenB News 0.12 0.51*** _0.27 _0.10 _0.41*** _0.18 Volatility 0.025 0.12*** _0.11 _0.022 _0.11*** _0.11

HongKongH News 0.022 0.51*** _0.26 _−0.035 _0.44*** _0.17

Volatility 0.043 0.24*** _0.31 _0.056 _0.22*** _0.31

HongKongRedChip News 0.031 0.51*** _0.26 _0.006 _0.42*** _0.16

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Table4

ResultsofChinese-LanguageNewsVolume-VolatilityRegressionsInthisTableIpresentresultsfromtheChinese-languageregressions.Intheupper regressions(News)thenewsvolumeisthedependentvariableandinthelowerregressions(Volatility)thestockmarketvolatilityisthedependent variable.Inbothregressions,theﬁrstexplanatoryvariable,ˇ1,isalwaystheone-monthlaggedvalueoftheothervariable(i.e.volatilityintheupper

regressionandnewsvolumeinthelowerregression)andthesecondexplanatoryvariable,ˇ2,isalwaystheone-monthlaggedvalueofthedependent

variable.Eachregressionhasanunreportedinterceptandanunreporteddummyformissingvalues(whichisrarelysigniﬁcant).*_,**_and***_represent signiﬁcanceatthe10%,5%and1%level,respectively.Theregressionsarebasedon51monthlyobservationsfromNovember1,2010toSeptember1,2014 andtheresultsarepresentedbothforlevelsandforchanges.

(stockmarket) (stockmarket “collapse”) (stockmarket “crash”) Dependent Variable ˇ1 ˇ2 ˇR2 _ˇ1 _ˇ2 _ˇR2 _ˇ1 _ˇ2 _ˇR2 MSCI News 0.055 0.81*** _0.69 _0.59*** _0.21 _0.56 _0.002 _0.30** _0.09 Volatility 0.092** _0.26*** _0.60 _0.11** _0.22*** _0.60 _0.11** _0.29*** _0.61 S&P500 News 0.005 0.84*** _0.69 _0.52*** _0.29** _0.54 _0.012 _0.30** _0.09 Volatility 0.098* _0.26*** _0.44 _0.22*** _0.14** _0.51 _0.17*** _0.24*** _0.54 DJIA News −0.0002 0.84*** 0.69 0.51*** 0.30** 0.53 0.013 0.30** 0.09 Volatility 0.084* _0.23*** _0.45 _0.17*** _0.14** _0.51 _0.15*** _0.22*** _0.54 NASDAQ News −0.002 0.84*** _0.69 _0.49*** _0.33** _0.54 _0.008 _0.30** _0.09 Volatility 0.12** _0.23*** _0.36 _0.24*** _0.11 _0.45 _0.20** _0.22*** _0.47 Russell2000 News 0.023 0.83*** _0.69 _0.46*** _0.35** _0.52 ₋_0.054 _0.31** _0.09 Volatility 0.14* _0.33*** _0.39 _0.37*** _0.13 _0.55 _0.25*** _0.34*** _0.51 FTSE100 News 0.063 0.81*** _0.69 _0.43*** _0.36** _0.50 ₋_0.001 _0.30** _0.09 Volatility 0.12*** _0.21*** _0.55 _0.14*** _0.16*** _0.53 _0.076* _0.25*** _0.50 ShanghaiA News 0.10 0.82*** _0.70 ₋_0.051 _0.69*** _0.40 ₋_0.12 _0.30** _0.10 Volatility 0.035 0.050 0.02 0.074* _0.030 _0.07 _0.029 _0.060 _0.01 ShanghaiB News 0.10 0.81*** _0.70 _0.14 _0.60*** _0.42 ₋_0.033 _0.30** _0.09 Volatility 0.13* _0.072 _0.09 _0.067 _0.081 _0.04 _0.006 _0.11* _0.02 ShenzhenA News 0.066 0.85*** _0.70 ₋_0.13 _0.71*** _0.42 ₋_0.13 _0.29** _0.11 Volatility 0.052 0.029 0.09 0.067 0.029 0.05 −0.016 0.039 0.00 ShenzhenB News 0.092 0.81*** 0.70 0.19 0.56*** 0.43 −0.065 0.30** 0.09 Volatility 0.11* _0.17*** _0.26 _0.11* _0.14** _0.26 _0.009 _0.21*** _0.20

HongKongH News 0.074 0.82*** _0.70 _0.26* _0.48*** _0.44 ₋_0.075 _0.31** _0.10

Volatility 0.082 0.33*** _0.43 _0.27*** _0.16** _0.57 _0.084 _0.34*** _0.43

HongKongRedChip News 0.071 0.82*** _0.70 _0.24* _0.50*** _0.44 ₋_0.073 _0.31** _0.10

Volatility 0.082 0.30** _0.43 _0.27*** _0.15** _0.57 _0.084 _0.32*** _0.43

(stockmarket) (stockmarket “collapse”) (stockmarket “crash”) Dependent Variable ˇ1 ˇ2 ˇR2 _ˇ1 _ˇ2 _ˇR2 _ˇ1 _ˇ2 _ˇR2 MSCI News 0.025 0.61*** 0.39 0.45*** 0.23 0.36 0.18 0.009 0.00 Volatility 0.039 0.25*** _0.39 _0.16*** _0.16*** _0.48 _0.19*** _0.19*** _0.57 S&P500 News −0.020 0.61*** _0.39 _0.43*** _0.25* _0.35 _0.14 _0.025 _0.00 Volatility 0.051 0.26*** _0.27 _0.29*** _0.09 _0.46 _0.33*** _0.15*** _0.64 DJIA News −0.020 0.61*** _0.39 _0.41*** _0.27* _0.34 _0.14 _0.027 _0.00 Volatility 0.049 0.26*** _0.28 _0.25*** _0.11** _0.44 _0.31*** _0.16*** _0.61 NASDAQ News −0.011 0.61*** _0.39 _0.44*** _0.25* _0.36 _0.13 _0.028 _0.00 Volatility 0.059 0.20*** _0.20 _0.27*** _0.06 _0.40 _0.31*** _0.11** _0.57 Russell2000 News −0.005 0.61*** _0.39 _0.35** _0.31** _0.31 _0.030 _0.067 _0.00 Volatility 0.051 0.24*** _0.22 _0.33*** _0.05 _0.52 _0.31*** _0.15*** _0.61 FTSE100 News 0.026 0.61*** _0.39 _0.34** _0.34** _0.31 _0.12 _0.042 _0.00 Volatility 0.05 0.22*** _0.35 _0.14*** _0.14*** _0.43 _0.17*** _0.16*** _0.52 ShanghaiA News 0.034 0.62*** _0.39 ₋_0.13 _0.55*** _0.24 ₋_0.041 _0.077 _0.00 Volatility −0.074** _0.025 _0.08 _0.049 _0.030 _0.02 _0.049 _0.04 _0.03 ShanghaiB News 0.076 0.61*** _0.39 _0.097 _0.49*** _0.24 _0.10 _0.065 ₋_0.02 Volatility −0.039 0.090* _0.04 _0.019 _0.083 _0.03 _0.048 _0.083* _0.04 ShenzhenA News 0.038 0.62*** _0.39 ₋_0.19 _0.56*** _0.26 ₋_0.081 _0.075 ₋_0.03 Volatility −0.058* 0.03 0.07 0.037 0.026 0.03 0.016 0.04 0.01 ShenzhenB News 0.046 0.62*** _0.39 _0.14 _0.46*** _0.24 _0.065 _0.068 ₋_0.03 Volatility −0.008 0.14*** _0.14 _0.069 _0.11** _0.18 _0.061 _0.13*** _0.17

HongKongH News 0.037 0.62*** _0.39 _0.19 _0.41*** _0.25 _0.019 _0.071 ₋_0.03

Volatility 0.011 0.26*** _0.44 _0.18*** _0.16** _0.59 _0.14*** _0.23*** _0.57

HongKongRedChip News 0.048 0.61*** _0.39 _0.16 _0.43*** _0.24 _0.021 _0.069 ₋_0.03

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Asfortheeconomicsignificanceoftheresults,andhereIfocussolelyontheimpactofnewsonvolatility,thesizeofthe regressionparameterstellsusthat,forlevels,theaverageimpactofaone-standarddeviationchangeinnewsvolumeon nextmonth’sstockindexvolatilityis11basispoints(0.11%).Albeitnotlarge,theimpactiseconomicallymeaningfulwhen comparedtothemeanofthetwelvestockindexes’unconditionalvolatilityacrossthesampleperiod(coveringthevery volatilecredit-andtheeuro-areasovereigncrises)whichis123basispoints.Ofcourse,forsomemarket/language/search stringcombinationstheeconomicsignificanceoftheregressionparametersismuchlarger.FortheRussell2000indexofsmall USfirms,forinstance,theaverageimpactofaone-standarddeviationchangeinthevolumeofnewscontainingtheChinese word“ i.e.stockmarketcollapse”,onnextmonth’sstockindexvolatilityis37basispoints.Forchanges,theaverage impactofaone-standarddeviationchangeinnewsvolume,comparedtothe12-monthaverage,onnextmonth’schangein stockindexvolatilityis9basispoints.11_Although_smaller,_the_impact_for_changes_is_actually_more_economically_meaningful thanthatforlevelswhencomparedtothesmallermeanofthemonthlychangeinthestockindexes’unconditionalvolatility whichis44basispoints.

Theregressionresultsinthissectionrevealthattheamountofnewsthismonthpredictsthevolatilitynextmonth.Thisis possiblyanindicationofnewsdissipatingfromnewssourcetomarketparticipantratherslowlyanditmirrorsresultsfound byEderingtonandLee(1993)intheinterestrateandforeignexchangemarkets.Likethestockmarket,thesetwomarketsalso exhibittime-varyingvolatilityandEderingtonandLee(1993)showsthatscheduledmacroeconomicnewsannouncements haveanimmediateeffectonpricesbutalonglastingeffectonpricevolatility.Inthestockmarket,anevenlonger-lasting periodofelevatedvolatilityafterannouncementsisfoundbyPatellandWolfson(1984).Thisgradualdigestionofnewsby themarketcouldliebehindthepredictabilityofvolatilityusingnews.

Arecurringﬁndinginthispaperisthatpessimistic(negative)newshasasomewhatstrongerconnectiontostockreturn volatilitythanneutralnews.ThecorrelationspresentedinTable2aregenerallylargerfornegativenews,regardlessof language,andthesameholdsfortheregressioncoefﬁcient,ˇ1,inTables3and4.Ihavenoexplanationforthisotherthan

thepossibilitythatrisk-aversemarketparticipantsaremorepronetoreacttonegativethantoneutralnews.

Toconclude,themainfindinginthissectionisthattheinter-temporallinkbetweennewsandvolatilityisstatisticallyand economicallysignificantandthatitseemstobestrongerinthedirectionfromnewstovolatilitythanviceversa.Asecondary findingisthatthepatternisdifferentinmainlandChinawherethereislessofapositiveinter-temporallinkbetweennews andvolatilityandwhere,particularlyEnglish-language,newsdoesnothelpinpredictingvolatility.

3.3. Out-of-sampleforecastingofstockmarketvolatilityusingnewsvolumes

Theregressionsintheprevioussectionshowthatnewscausesvolatilityandthatnewsvolumespossiblycouldbeusedto predictfuturestockmarketvolatility.Whileanyforecasting-assessmentbasedontheregressionsabovearemereindications basedonin-sampleevidence,inthissectionItrytoassesswhethernewshastrueout-of-samplepredictiveabilities.Since Ididnotﬁndvolatilityto(Granger)causenewsvolumeIfocussolelyonvolatilityprediction.Idothisthrougharolling windowestimationoftheOLSregressionparametersintheprevioussectionwhereonlypastinformationisusedtopredict future(one-monthahead)volatility.Thesampleisdividedintotwo(essentially)equallylongperiods,oneestimationperiod andoneevaluationperiod.12_The_volatility_is_then_forecasted_in_two_ways;_(i)_based_solely_on_past_volatility₍_Without_news₎ or(ii)basedonpastvolatilitytogetherwithpastnewsvolume(Withnews).

Theforecastingperformance isassessedusingtwodifferentlossfunctions;themeanabsoluteerror(MAE)andthe quasi-likelihood(QL)lossfunction.Thetwolossfunctionsaredeﬁnedas

MAE=

realized,t+1−forecast,t

(1)

and QL= realized,t+1 forecast,t −log

realized,t+1 forecast,t

−1 (2)

whereforecast,tistheforecastatmonthtofthevolatilityinmontht+1usinginformationavailableuptoandincluding montht.

Forecastingresults,forlevelsaswellasforchanges,arepresentedinTables5and6.13_While_Table₅_present_results_for English-languagenews,Table6presentsthesameresultsfornewsinChinese.Thesmallest(best)forecastingerror/lossis typedinboldandmoreoftenthannot,theinclusionofnews(Withnews)inthepredictionimprovesthevolatilityforecast. ForEnglishnewstheforecastisimprovedforeachandeverystockindexwhentheamountofnewsisacknowledged.This strongresultholdsbothforlevelsandforchanges.Meanwhile,theinclusionofChinese-languagenewsislessusefulfor

11_When_no_lagged_volatility_is_included_in_the_regression,_unreported_regressions_show_that_the_average_impact_of_a_one-standard_deviation_change_in newsvolumeonnextmonth’sstockindexvolatilityis28basispointsandtheaverageimpactforchangesis16basispoints.

12_Somewhat_arbitrarily,_I_have_chosen_to_let_the_evaluation_period_for_the_English_news-based_forecasting_start_at_the_same_date_as_the_Chinese_news collectionstarts,i.e.November1,2010.Thisleaves51monthsintheevaluationperiodand54monthsintheestimationperiod.TheChinesenews-based forecastingstartsonOctober29,2012whichleaves25monthsintheevaluationperiodand26monthsintheestimationperiod.

(12)

Table5

ResultsofVolatilityForecastsusingEnglish-LanguageNewsVolumesInthisTableIpresentstockreturnvolatilityforecastingresultsforEnglish-language newsvolumes.Intotal,51monthlyforecastsaremadeineachcategoryandallforecastsarebasedonregressionparametersestimatedusingarolling

windowof54monthlyobservationsstartingfromNovember1,2010toSeptember1,2014.Thenumbersintherowslabelled(Withoutnews)arethe

oneswhentheone-monthlaggednewsvolumeisnotincludedinthepredictionandthenumbersintherowslabelled(Withnews)aretheoneswhenthe

one-monthlaggednewsvolumeisincludedinadditiontoone-monthlaggedstockreturnvolatility.MAEisthemeanabsoluteerrorandQListheQLloss function.Thesmallest(best)forecastingerror/lossistypedinboldandtheresultsarepresentedbothforlevelsandforchanges.

MAE(×10−5₎ _QL _MAE_(×10−5₎ _QL

MSCI Withoutnews 2.13 0.0244 2.13 0.0244

Withnews 1.80 0.0183 1.56 0.0142

S&P500 Withoutnews 2.91 0.0322 2.91 0.0322

Withnews 2.42 0.0235 1.77 0.0135

DJIA Withoutnews 2.45 0.0249 2.45 0.0249

Withnews 2.13 0.0195 1.40 0.0093

NASDAQ Withoutnews 4.37 0.0646 4.37 0.0646

Withnews 3.74 0.0505 3.51 0.0456

Russell2000 Withoutnews 4.69 0.0470 4.69 0.0470

Withnews 4.00 0.0363 2.88 0.0207

FTSE100 Withoutnews 2.46 0.0247 2.46 0.0247

Withnews 1.88 0.0154 1.66 0.0123

ShanghaiA Withoutnews 3.78 0.0225 3.78 0.0225

Withnews 2.82 0.0134 2.95 0.0146

ShanghaiB Withoutnews 6.37 0.0650 6.37 0.0650

Withnews 4.83 0.0418 5.59 0.0529

ShenzhenA Withoutnews 6.90 0.0483 6.90 0.0483

Withnews 6.09 0.0393 6.39 0.0426

ShenzhenB Withoutnews 6.46 0.1678 6.46 0.1678

Withnews 5.96 0.1505 5.91 0.1487

HongKongH Withoutnews 2.81 0.0111 2.81 0.0111

Withnews 2.39 0.0082 1.38 0.0029

HongKongRedChip Withoutnews 2.96 0.0166 2.96 0.0166

Withnews 2.38 0.0112 2.02 0.0084

NewsinEnglish(changes)

MAE(×10−3₎ _QL _MAE_(×10−3₎ _QL

MSCI Withoutnews 1.24 0.1056 1.24 0.1056

Withnews 1.15 0.0863 0.77 0.0320

S&P500 Withoutnews 1.91 0.2377 1.91 0.2377

Withnews 1.81 0.2019 1.08 0.0507

DJIA Withoutnews 1.95 0.2602 1.95 0.2602

Withnews 1.85 0.2202 1.21 0.0679

NASDAQ Withoutnews 3.92 0.3364 3.92 0.3364

Withnews 3.83 0.3126 3.66 0.2711

Russell2000 Withoutnews 3.25 0.3327 3.25 0.3327

Withnews 3.16 0.3038 2.68 0.1841

FTSE100 Withoutnews 1.08 0.0901 1.08 0.0901

Withnews 1.04 0.0814 0.39 0.0083

ShanghaiA Withoutnews 0.69 0.0865 0.69 0.0865

Withnews 0.66 0.0749 0.35 0.0164

ShanghaiB Withoutnews 1.14 0.1074 1.14 0.1074

Withnews 1.01 0.0774 0.96 0.0676

ShenzhenA Withoutnews 2.78 0.7350 2.78 0.7350

Withnews 2.66 0.6132 2.62 0.5785

ShenzhenB Withoutnews 4.83 0.2569 4.83 0.2569

Withnews 4.78 0.2481 4.70 0.2362

HongKongH Withoutnews 0.96 0.0767 0.96 0.0767

Withnews 0.91 0.0665 0.37 0.0083

HongKongRedChip Withoutnews 1.14 0.1092 1.14 0.1092

(13)

Table6

ResultsofVolatilityForecastsusingChinese-LanguageNewsVolumesInthisTableIpresentstockreturnvolatilityforecastingresultsforChinese-language newsvolumes.Intotal,25monthlyforecastsaremadeineachcategoryandallforecastsarebasedonregressionparametersestimatedusingarolling windowof26monthlyobservationsstartingfromOctober29,2012toSeptember1,2014.Thenumbersintherowslabelled(Withoutnews)aretheones whentheone-monthlaggednewsvolumeisnotincludedinthepredictionandthenumbersintherowslabelled(Withnews)aretheoneswhenthe one-monthlaggednewsvolumeisincludedinadditiontoone-monthlaggedstockreturnvolatility.MAEisthemeanabsoluteerrorandQListheQLloss function.Thesmallest(best)forecastingerror/lossistypedinboldandtheresultsarepresentedbothforlevelsandforchanges.

(stockmarket) (stockmarket “collapse”)

(stockmarket “crash”)

MAE(×10−5₎ _QL _MAE_(×10−5₎ _QL _MAE_(×10−5₎ _QL

MSCI Withoutnews 5.82 0.0400 5.82 0.0400 5.82 0.0400

Withnews 4.91 0.0301 5.47 0.0361 4.71 0.0280

S&P500 Withoutnews 7.49 0.0473 7.49 0.0473 7.49 0.0473

Withnews 6.82 0.0405 6.56 0.0380 7.73 0.0497

DJIA Withoutnews 5.90 0.0329 5.90 0.0329 5.90 0.0329

Withnews 5.31 0.0276 5.30 0.0275 6.35 0.0372

NASDAQ Withoutnews 13.6 0.1210 13.6 0.1210 13.6 0.1210

Withnews 12.7 0.1104 12.7 0.110 14.9 0.1375

Russell2000 Withoutnews 13.7 0.0823 13.7 0.0823 13.7 0.0823

Withnews 14.6 0.0905 13.3 0.0787 13.1 0.0769

FTSE100 Withoutnews 5.90 0.0325 5.90 0.0325 5.90 0.0325

Withnews 4.59 0.0212 6.53 0.0386 3.06 0.0103

ShanghaiA Withoutnews 7.61 0.0221 7.61 0.0221 7.61 0.0221

Withnews 7.70 0.0225 7.04 0.0192 7.87 0.0234

ShanghaiB Withoutnews 7.55 0.0267 7.55 0.0267 7.55 0.0267

Withnews 7.72 0.0277 7.98 0.0294 6.44 0.0203

ShenzhenA Withoutnews 10.9 0.0318 10.9 0.0318 10.9 0.0318

Withnews 11.2 0.0330 11.0 0.0322 8.70 0.0215

ShenzhenB Withoutnews 9.10 0.0998 9.10 0.0998 9.10 0.0998

Withnews 8.42 0.0889 9.17 0.1009 4.79 0.0898

HongKongH Withoutnews 10.6 0.0326 10.6 0.0326 10.6 0.0326

Withnews 9.64 0.0276 10.1 0.0297 10.6 0.0322

HongKongRedChip Withoutnews 7.65 0.0252 7.65 0.0252 7.65 0.0252

Withnews 8.08 0.0276 6.69 0.0199 4.94 0.0116

(stockmarket) (stockmarket “collapse”)

(stockmarket “crash”)

MAE(×10−3₎ _QL _MAE_(×10−3₎ _QL _MAE_(×10−3₎ _QL

MSCI Withoutnews 1.99 0.0571 1.99 0.0571 1.99 0.0571

Withnews 1.86 0.0480 2.52 0.1043 1.77 0.0425

S&P500 Withoutnews 5.19 0.6378 5.19 0.6378 5.19 0.6378

Withnews 5.17 0.6245 5.23 0.6542 6.10 1.3033

DJIA Withoutnews 5.21 0.6697 5.21 0.6697 5.21 0.6697

Withnews 5.15 0.6381 5.35 0.7447 6.02 1.2770

NASDAQ Withoutnews 13.0 3.2917 13.0 3.2917 13.0 3.2917

Withnews 13.0 3.2835 12.9 3.1819 14.0 7.0598

Russell2000 Withoutnews 9.88 1.8001 9.88 1.8001 9.88 1.8001

Withnews 9.93 1.8603 9.50 1.4474 9.21 1.2339

FTSE100 Withoutnews 2.28 0.0983 2.28 0.0983 2.28 0.0983

Withnews 2.22 0.0919 3.06 0.2260 0.50 0.0031

ShanghaiA Withoutnews 3.17 1.2796 3.17 1.2796 3.17 1.2796

Withnews 3.13 1.2003 3.65 2.9913 4.23 19.926

ShanghaiB Withoutnews 0.91 0.0114 0.91 0.0114 0.91 0.0114

Withnews 0.56 0.0040 1.60 0.0416 0.62 0.0050

ShenzhenA Withoutnews 6.06 0.9781 6.06 0.9781 6.06 0.9781

Withnews 6.74 1.6757 6.57 1.4629 5.36 0.5843

ShenzhenB Withoutnews 8.53 0.1660 8.53 0.1660 8.53 0.1660

Withnews 9.25 0.2118 9.01 0.1955 6.46 0.0783

HongKongH Withoutnews 3.06 0.2712 3.06 0.2712 3.06 0.2712

Withnews 3.35 0.3636 3.54 0.4415 3.47 0.4124

HongKongRedChip Withoutnews 2.52 0.1358 2.52 0.1358 2.52 0.1358

(14)

Table7

CorrelationsbetweenNewsVolumeandVolatility:RobustnessAsarobustnesstest,inthisTableIpresentNewsVolume–Volatilitycorrelationsforthe MSCIWorldstockindexwhennewsvolumesarecollectedbyGoogleNewsinEnglishandChinese,respectively.IntheﬁrstpartoftheTableItestthe robustnessoftheresultsinthepapertotheremovalofextremeobservations,crisisobservationsandmissingobservationsandinthesecondpartItest therobustnessoftheresultstoslightchangestothenewsvolumecollectionprocess.Resultsarepresentedbothforlevelsandforchanges.

MSCI

ExcludingExtremes 0.18** _0.35***

ExcludingCrisis −0.04 0.53***

ExcludingMissing 0.47*** _0.79***

NewsinEnglish(Changes)

MSCI

ExcludingExtremes 0.20** 0.50***

ExcludingCrisis 0.11 0.50***

ExcludingMissing 0.18** _0.72***

MSCI ExcludingExtremes 0.44*** 0.57*** 0.07

MSCI ExcludingMissing 0.46*** _0.77*** _0.26**

MSCI ExcludingExtremes 0.05 0.27** _0.24**

MSCI ExcludingMissing 0.06 0.65** _0.40***

Levels

“stockmarketcrash” Globalstockmarketcrash

(stockmarket“slump”)

MSCI 0.67*** _0.71*** _0.54*** S&P500 0.66*** _0.69*** _0.49*** DJIA 0.68*** _0.71*** _0.51*** NASDAQ 0.64*** 0.66*** 0.46*** Russell2000 0.56*** _0.60*** _0.41*** FTSE100 0.71*** _0.75*** _0.55*** ShanghaiA 0.29*** _0.30*** _0.30** ShanghaiB 0.31*** _0.32*** _0.33*** ShenzhenA 0.21** _0.23*** _0.27** ShenzhenB 0.31*** _0.33*** _0.40*** HongKongH 0.69*** _0.72*** _0.38***

HongKongRedChip 0.65*** _0.69*** _0.47***

Changes

“stockmarketcrash” Globalstockmarketcrash

(stockmarket“slump”)

MSCI 0.60*** _0.73*** _0.48*** S&P500 0.51*** _0.65*** _0.41*** DJIA 0.50*** _0.65*** _0.42*** NASDAQ 0.49*** _0.61*** _0.37*** Russell2000 0.48*** _0.59*** _0.34*** FTSE100 0.50*** _0.68*** _0.43*** ShanghaiA 0.11 0.16* _0.26** ShanghaiB 0.18** _0.20** _0.24*** ShenzhenA 0.03 0.07 0.25** ShenzhenB 0.18** _0.25*** _0.33*** HongKongH 0.43*** _0.61*** _0.35***