美联储-OTC中间市场的信息摩擦（英）

FinanceandEconomicsDiscussionSeries

FederalReserveBoard,Washington,D.C.

ISSN1936-2854(Print)

ISSN2767-3898(Online)

InformationFrictioninOTCInterdealerMarkets

BenjaminGardnerandYesolHuh

2024-040

Pleasecitethispaperas:

Gardner,Benjamin,andYesolHuh(2024).“InformationFrictioninOTCInterdealerMar-kets,”FinanceandEconomicsDiscussionSeries2024-040.Washington:BoardofGovernorsoftheFederalReserveSystem,

/10.17016/FEDS.2024.040

.

NOTE:StafworkingpapersintheFinanceandEconomicsDiscussionSeries(FEDS)arepreliminarymaterialscirculatedtostimulatediscussionandcriticalcomment.TheanalysisandconclusionssetfortharethoseoftheauthorsanddonotindicateconcurrencebyothermembersoftheresearchstafortheBoardofGovernors.ReferencesinpublicationstotheFinanceandEconomicsDiscussionSeries(otherthanacknowledgement)shouldbeclearedwiththeauthor(s)toprotectthetentativecharacterofthesepapers.

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InformationFrictioninOTCInterdealerMarkets

BenjaminGardnerandYesolHuh*

November2,2023

Abstract

Inover-the-counter(OTC)securitiesmarkets,interdealermarketsareanimportantvenuethroughwhichdealerscanoffloadpositionsandshareriskamongstthemselves.Contrarytothepopularcon-ceptionthatsearchfrictionsmatterthemostinOTCmarkets,wefindthatintheinterdealermarketforU.S.corporatebonds,informationfrictionsaremostrelevant.Largedealersfacelargeandinformedcustomersandpaymorethansmalldealerstotransactintheinterdealermarket,despiteonaverageprovidingliquiditytootherdealers.Largedealerstendtotradethroughinterdealerbrokers(IDBs)tomitigateinformationleakage,butinterdealermarketsarestillfarfromefficient.

*BenjaminGardner:YaleUniversity,ben.gardner@;YesolHuh:FederalReserveBoard,yesol.huh@.Pleasedirectcommentsandquestionstoyesol.huh@.Theanalysisandconclusionssetfortharethoseoftheauthorsanddonotindicateconcurrencebyothermembersofthestaff,bytheBoardofGovernors,orbytheFederalReserveSystem.

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1Introduction

Inover-the-counter(OTC)securitiesmarkets,interdealermarketsareanimportantvenuethroughwhichdealerscanoffloadpositionsandshareriskwithoneother.Dealersintermediatecustomerorderflowandoffloadsomeofthatorderflowthroughtheinterdealermarket.MuchoftheliteratureonOTCmarketshasfocusedonsearchfrictionsandnetworkformationtoexplainpriceandtradingdynamics(Duffieetal.,2005;LagosandRocheteau,2009;Wang,2016).Moreover,theempiricalliteratureonOTCinterdealermarketshasemphasizedthecore-peripherynetworkinthismarketasanimperfectmechanismtomitigatesearchcosts.

Inthispaper,westudywhichfrictionsaremostrelevantintheOTCinterdealermarkets.WefindthatintheU.S.corporatebondmarket,contrarytopopularconception,informationfrictionsplayalargerole.Largedealersfacelargeandinformedcustomers,sotheyhaveamoredifficulttimeoffloadingcustomerorderintheinterdealermarket—largedealerspaymoretotransactintheinterdealermarketdespitethefactthattheyonaverageprovideliquidityinthismarket.Largedealerstendtotradethroughinterdealerbrokers(IDBs)tomitigateinformationleakage,butinterdealermarketsarestillfarfromefficient.

Wefirstdividedealersintosixcategories.Wegenerallythinkofdealersasengaginginsimilaractivities—intermediatingcustomerorderflowandoffloadingsomeofthoseorderflowintheinterdealermarket—anddifferingmostlyalongthedimensionsofsize,searchcosts,ortheirpositioninthenetwork.However,threecategories—alternativetradingsystems(ATS),interdealerbrokers(IDBs),andclientbrokers—are“special”typesinthatthesetypesofdealersaresomewhatdifferentfromtheusualsetofdealersthataretypicallydiscussedintheliterature.ATSandIDBspredominantlyengageininterdealertradesonly.ATSaredesignatedbyFINRAandaremostlydifferenttypesofelectronicplatforms.1IDBsarebrokersthatmatchbuyerswithsellersintheinterdealermarketandaccountfor25%ofinterdealervolume.Clientbrokersmostlyactasagentsbetweencustomersandotherdealers.Theotherthreedealercategoriesarethe“typical”dealers,whichwedivideintosmall,medium,andlargebycustomervolume.

Existingliteraturehasmostlycomparedcentraldealersandperipheraldealers.Notsurprisingly,largedealersaremorecentral,andsmalldealersareperipheral.Thus,some“centrality”effectsstudiedinthepriorliteraturesuchaswhethercentraldealerschargehigherbid-askspreads(LiandSch¨urhoff,2019;Hollifieldetal.,2016;DiMaggioetal.,2017;Dick-Nielsenetal.,2020)maybedrivenbydealerbalancesheetsizeorcustomervolume.Moreover,ourresultsindicatethatsomeoftheIDBsaccountforalargeshareofinterdealervolumeandarequitecentralintheinterdealernetwork.However,theseIDBsbehavequitedifferentlyfrom

1See

/filing-reporting/otc-transparency/finra-equity-ats-firms-listforthelistofATS

.

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largedealers.Thus,someofthecentralityeffectsmaybeconflatingIDBsandlargedealers.Forinstance,thefindingthatcentraldealerschargehigherbid-askspreadsintheinterdealermarket(DiMaggioetal.,2017;Dick-Nielsenetal.,2020)maybebecauseIDBschargehigherbid-askspreadsandnotbecauselargedealerschargehigherbid-askspreads.

Westudywhoprovidesliquiditytowhomandatwhatpricesdifferentcategoriesofdealerstradeintheinterdealermarket.ConsistentwithLiandSch¨urhoff(2019),wefindthatlargedealerstendtoprovideliquiditytosmallerdealers.However,wealsofindthatdespiteprovidingliquidityonaverage,largedeal-ersactuallypayahighertradingcostintheinterdealermarketcomparedtomediumandsmalldealers.Additionally,interdealertradingcostshaveaU-shapeinwhichthelargestandthesmallestdealerspayhighertradingcoststhanmediumsizeddealers.Weconjecturethatforlargerdealers(top30-40dealers),informationasymmetrymattermore,andaswegotosmallerdealers,searchfrictionsmattermore.Ifso,giventhattop30dealersaccountformuchmoreoftheinterdealervolumethansmalldealers,informationasymmetryisthemoredominantfrictionintheU.S.corporatebondinterdealermarket.

Consistentwiththisconjecture,wefindthatlargedealersabsorbsignificantlymoreinformedcustomerorderflow.Thus,whentheytrytooffloadthoseorderflowintheinterdealermarket,otherswouldbereluctanttotradewiththem.Therefore,largedealersoffloadlessandfacehighertradingcostsintheinterdealermarket.2

Moreover,largedealersaremorelikelytotradethroughIDBsthansmallerdealersare.Ifsearchfrictionsweremoreimportant,wewouldexpectsmalldealerstoutilizeIDBsthemostsincesmalldealershavehighestsearchcosts.Ontheotherhand,informationasymmetrycanleadlargedealerstouseIDBs.Bilateralcontactcanleadtoinformationleakageevenifatradedoesnotultimatelyhappen,becausetradingintentandidentityarerevealedtothecounterparty.Ifthisinformationleakageiscostly,wewouldexpectlargedealerstotradethroughIDBstokeeptheiridentityhiddenandminimizeinformationleakage.Furthermore,GlodeandOpp(2016)arguethatintermediationchainscanhelpmitigateinformationasymmetry.Ifinformationasymmetrybetweenthepotentialbuyerandsellerishigh,trademaynothappendespitethepotentialgainsfromtrade.Tradingthroughamoderately-informedintermediarycanallowthetradetohappen,leadingtobetterallocations.Consistentwiththesechannels,largedealerstendtooffloadlargerpositionsthroughIDBsandsmallerpositionsbilaterally.Moreover,whenlargedealerstradewithIDBs,theirultimatecounterpartyisusuallyotherlargedealersthattheyalreadyhaveatradingrelationshipwith.Therefore,IDBsmostly

2Analternativebutnotmutuallyexclusiveexplanationforwhylargedealersoffloadlessisthatlargerdealersreceivemorecustomerorderflowandpotentiallycanmoreeasilyfindoffloadinginterestfromcustomers,whichwillresultinhigherprofit

thanoffloadingintheinterdealermarket(sl¨u,2019).Whilethischannellikelyplaysarole,itcannotexplainwhylargedealers

payhighertradingcostsintheinterdealermarket.

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servetomitigateinformationfrictionsratherthansearchfrictions.

Lastly,wemeasureinterdealermarketefficiency.Ifinformationfrictionsareimportantenough,potentialgainsfromtradebetweenlargedealersmaybeforgone.Wefocusoncaseswithclearestpotentialgainsfromtrade,whereonedealerhadpositivecustomerorderflowandanotherdealerhadnegativecustomerorderflowinthesamebondonthesameday.Wethentrackwhetherthetwodealerstradewitheachothertooffsettheirpositionsinsubsequentdays,eitherdirectlythroughabilateraltradeorthroughachainoftrades.Wefindthatsuchgainsfromtradesarerealizedonlylessthan5%ofthetimethroughdirectbilateraltradingbetweenthetwodealers,andupto23%ofthetimethroughachainoftrades,usuallyinvolvingIDBs.Therefore,IDBshelpmitigateinformationfriction,butinterdealermarketsarestillrelativelyinefficient.

Overall,ourresultshaveafewimplicationsontheeffectoftransparencyandthesearchliterature.Sinceinformationiscontainedincustomerorderflow,disseminatinginformationaboutcustomertradesimmediatelywouldallowlargedealerstomoreeasilyoffloadininterdealermarketsbutbutmakeitharderforthemtoprofitfromtheinformation.ThisisconsistentwiththeresultsofLewisandSchwert(2021).Moreover,ourresultsindicatethatevenwithpost-tradetransparency,theinterdealermarket,especiallybetweenlargedealers,isinefficient.Thisimpliesthatforlargedealers,theriskofinformationleakageandinformationasymmetryarelargecomparedtotheirinventorycost.

OuranalysesalsohaveimplicationsfortheOTCsearchliterature.First,smalldealersandclientbrokershavetheroleofaggregatingandpassingonsmalluninformedcustomerorderflowtolargerdealers,andthesesmalldealersgetsomewhathigherbutdecentpricebecauseoftwooppositefrictions—highersearchcostsandlowerinformationasymmetry.Theyoffloadalargeshareoftheircustomerorderflowintheinterdealermarketwithinaday,whichismoreconsistentwithactiveoffloadingthanasearchframework.Second,thelengthofintermediationchainsisoftenusedasameasureofthedegreeofsearchfriction(FriewaldandNagler,2019),butourresultsindicatethatlongerintermediationchainslikelyinvolveIDBsandmaybedrivenbyinformationfrictionsratherthansearchfrictions.

LiandSch¨urhoff(2019)andHollifieldetal.(2016)documentthatdealersformtradingnetworkswithacore-peripherystructureinOTCmarketstomitigatesearchfrictions.3Subsequentpapershavefocusedonthecore-peripherystructureoftheinterdealersegmentofOTCmarketsandonwhethercustomerspayahigherbid-askspreadtocentraldealers(“centralitypremium”)ortoperipheraldealers(“centralitydiscount”).Inthemunicipalbondmarket,LiandSch¨urhoff(2019)showthatcoredealersprovideliquidity

andimmediacytobothcustomersandperipheraldealersandthatthereisacentralitypremium.DiMaggio3Hendershottetal.(2020b)documenttheimportanceofclientsestablishingtradingrelationshipswithdealerstomitigate

searchfrictions.

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etal.(2017)andDick-Nielsenetal.(2020)documentacentralitypremiuminthecorporatebondinterdealermarket.Hollifieldetal.(2016)showthatthereisacentralitydiscountinsecuritizationmarkets.

WeaddtothisliteraturebyshowingthatintheinterdealersegmentofOTCmarkets,informationfrictionsmattergreatly,andwithinlargeandmediumdealers,morethansearchfrictions.Giventhattop30dealersaccountforalmost90%ofcustomervolumeandthatinformationfrictionsmattermoreforthesedealers,decreasinginformationfrictionswouldimprovemarketefficiencymorethandecreasinginterdealermarketsearchfrictions.Forthesmallretailtraderthattradeswithasmallperipheraldealer,searchfrictionsmattermore.Thus,overall,thereisaU-shapepatterninthedegreeoffrictions,whichismissedbypreviousliteraturebecausetheyusuallyassumealineareffectoncentrality(Dick-Nielsenetal.,2020).Also,becausemostpapershavefocusedoncompletedintermediationchains,theydonotlookatthedegreetoandthespeedofwhichvariousdealertypesoffloadtheircustomerorderflows,andwefillthatgap.

Wealsoshowthatthereareineffecttwotypesofdealerswithhighcentrality—largetraditionaldealersandIDBs.Thesetwotypesofdealersbehaveverydifferently,andsimplyconsideringacentralitydimensionandputtingtheminthesamecategorymayleadtomisleadingconclusions.IDBsandtheroletheyplayhavenotbeenstudiedmuchdespitethelargeshareofvolumethattheyaccountfor.AnexceptionisDeRoureetal.(2019),whichdocumenttheextensiveuseofIDBsintheGermansovereignbondinterdealermarketmarket.Theirfocusisonvenuechoice(exchange,bilateral,IDB)andarguethatuseofIDBsisdrivenbydealers’desiretopreserveaninformationaladvantageandavoidfrontrunning.WedocumentasimilarextensiveuseofIDBsintheU.S.corporatebondmarketandshowhowthatimpactsnetworkmeasuresandrisksharing.

Anumberofpapersshowthatthereisinformedtradinginthecorporatebondmarketarounddefault(HanandZhou,2014),acquisitions(KediaandZhou,2014),andearningsannouncements(WeiandZhou,2016).Hendershottetal.(2020a)showthatshort-sellersinthecorporatebondmarketareinformed.Thefocusinthesepapersaremostlytoshowtheexistenceofinformedtradingandthatcustomerorderflowcanpredictfuturereturns.Pinteretal.(2022)andCzechandPint´er(2022)showthatinformationasymmetryaffectscustomertradingcostsanddealer-customerconnections.Thesepapersmostlyfocusonthedealer-customermarketanddonotstudytheimpactofinformedtradingintheinterdealermarket.BabusandKondor(2018)modelsinformationpercolationinaninterdealernetwork,wheredealerslearnabouttheircounterparties’privateinformationbytrading.Theyfindthatingeneral,centraldealerspaylowertradingcostsbecausetheircounterpartiestendtobemoreconnected.Weshowthatdealers’informationprimarilycomesfromtheircustomerordersratherthanthroughtheirtradingrelationshipswithotherdealers.

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2Data

WeusetheregulatoryTRACEdataforthesampleperiodofAugust2016throughJuly2019.Weapplystandardcleaningsuchascleaningfortradecancellationsandcorrectionsanddeletetradeswithnon-FINRAaffiliates.Becauseourfocusisoninterdealertrades,wekeepbothsidesofinterdealertradesaswellasaddingtheothersideoftradeforinterdealertradesthatarereportedonlyoncesuchastwo-sidedlocked-intrades.Wedeleteconvertiblebonds,MTNs,and144Abondsaswellastradesthathappeninthefirst30daysofissuance.BondcharacteristicsarefromFISDMergent.SimilartoChoietal.(2023),weaggregatethedealeridentifiers(MPIDs)uptoahighholderlevelbecausesomedealershavemultipleMPIDsorshiftuseofMPIDsovertime.WealsodeletetradesbetweenMPIDsofthesamehighholder.Wekeeptradesthatarereportedasprincipaltradesonly.Ourenddatahas11.8milliondealer-customertradesand18.9millioninterdealertradeobservations,withmostinterdealertradeappearingtwice,spanning11,510cusipsand1,069dealers.

WealsousetheFixedIncomeDataFeedfromICEDataPricing&ReferenceDatatocalculateinformationasymmetryinSection3.2.TheFixedIncomeDataFeedcontainsend-of-daydailypricesformostTRACEbondsoverthesampleperiod.4

3DealerTypesandInformationAsymmetry

3.1Dealerclassification

Weclassifythedealersintosixtypes—ATS,interdealerbrokers(IDBs),clientbrokers,small,medium,andlarge.Foreachdealerwithmorethan2000tradesoverthesampleperiod,wecalculatetheshareofthedealer’strades,separatelyintermsoftradecountandvolume,thatareinterdealertrades.Also,foreachdealer,wecalculatetheshareofprearrangedtradesbyvolumeandcount.5Wethenclassifythedealersinthefollowingway.

•“ATS”:OfthedealersthatareidentifiedasATSbyFINRA,thosethathavemorethan75%oftheirtradesininterdealertradesbybothvolumeandtradecountbasisormorethan90%oftheirtradesbyeithervolumeortradecountbasis

4Pricesare“evaluatedprices”bythedatavendor(IntercontinentalExchange),whichtoourbestofourknowledge,arecalculatedfromdealerquotes,tradedprices,andmatrixpricingmodel.

5Prearrangedtradesareidentifiedastradesthatremaininthedealers’inventoryforlessthan15minutes,andtheconstructionfollowsChoietal.(2023).

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•“Interdealerbrokers”(IDBs):Alldealersthathavemorethan75%oftheirtradesininterdealertradesbybothvolumeandtradecountbasisormorethan90%oftheirtradesbyeithervolumeortradecountbasisthatarenotclassifiedasATS

•“Clientbrokers”(CBs):DealersthatarenotATSandIDBs,andalsohaveeitherprearrangedshareabove75%inbothvolumeandtradecountbasisorabove90%ineithervolumeortradecountbasis

•“Small,”“Medium,”and“Large”:Taketheremainingdealers.Foreachyear(Aug-Julyear),thetop

10bycustomervolumeareclassifiedas“large,”next20areclassifiedas“medium,”restare“small”

Table1providessummarystatisticsondealergroupclassification.Panel(a)reportstheshareofcustomervolumeandtheshareofinterdealervolumethateachdealertypeisinvolvedin.Asshowninpreviouspapers,customertradesareconcentrated,wherethetenlargestdealeraccountforalmost70%ofcustomervolume,andthenext20dealers(mediumdealers)accountforanother20%.Therearealargenumberofsmalldealersthataccountforfairlylittlecustomervolume.Thistablealsoshowsthatthereareanumberofdealersthataccountforverylittlecustomervolumebutafairlylargeamountofinterdealervolume.IDBstogetheraccountformorethan25%ofinterdealervolume,andATSaccountfor8.6%,butbothaccountforlessthan1%ofcustomervolume.Lastly,therearealargenumberofclientbrokers,whichmostlyactasanagentbetweencustomersanddealers.

Panel(b)showstheshareoftradesthatareDC-DC,DC-ID,ID-ID,orinvt>15mintrades.TheseclassificationsarefromChoietal.(2023).DC-DCtradesaredealer-customertradeoffloadedthroughanotherdealer-customertradewithin15minutes,thatis,thedealerprearrangedoffsettingcustomertrades.DC-IDtradesareinstancesinwhichcustomertradesareprearrangedwithoffsettinginterdealertrades.6Similarly,ID-IDtradesareinstancesofprearrangedoffsettinginterdealertrades.Lastly,invt>15mintradesaretradestakenintodealers’inventories.ResultsinPanel(b)indicatethatIDBs,whicharenotrestrictedtohavingahighprearrangedshare,stillprearrangealmost80%oftheirinterdealertrades.Thus,thesedealersmostlyactasbrokersbetweendifferentdealersininterdealertradesratherthanabsorbinginventory,hencewenamedthem“interdealerbrokers.”ATS,bydefinitionareplatformsthatdealerstradeon,andthusaremostlyID-IDtrades,andclientbrokers,byconstruction,containahighshareofDC-IDtrades.Themore“traditional”dealerstakelargershareoftradesintoinventory,butthissharealsovarieswithdealersize.Largedealers,comparedtomediumandsmalldealers,aremorelikelytotakebothcustomertradesandinterdealertradesintoinventoryandtherebyprovideimmediacy.Thisresultondealersizeisconsistentwith

6BoththecustomertradesandtheinterdealertradesinthesepairsarereferredtoasDC-IDtrades.

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Table1:Summarystatisticsbydealertype:Panel(a)presentsforeachdealertype,theaveragenumberofdealersperyear,shareofinterdealertradesinwhichthedealertypeisapartyto,shareofdealer-customertradesinwhichthedealertypeisapartyto,andtheshareofdealertype’stradesthataredealer-customertrades.Panel(b)showsforeachdealertype,theshareofinterdealerordealer-customertradevolumethatareDC-DC,DC-ID,ID-ID,orinvt>15mintrades.TradetypeclassificationsarefromChoietal.(2023).InPanel(c),wepresentthecentralitymeasurescalculatedfrominterdealertrades.deg,ev,andclaredegreecentrality,eignenvectorcentrality,andcloselessmeasures,respectively.degvolsandevvolsaredegreecentralityandeigenvectorcentralityusinginterdealervolumeweights.Wefirstcalculateeachcentralitymeasureatthedealer-yearlevelandpresenttheaveragecentralitymeasures,weightedbyinterdealervolume,foreachdealertype.Panel(d)presentssummarystatisticsonwhotradeswithwhomintheinterdealermarket.Foreachdealertypeineachrow,wepresenttheshareoftheirtradevolumeswitheachcounterpartytypes.

(a)Dealergroupsummarystats

dealertype

#ofdealers

%oftotalIDvolume

%oftotalDCvolume

shareDC

large

10

32.01%

69.57%

81.65%

medium

20

13.72%

19.42%

74.35%

small

243.3

7.02%

4.22%

55.15%

ATS

10

8.57%

0.35%

7.70%

IDB

40

25.58%

0.66%

5.01%

clientbroker

545

13.11%

5.79%

47.50%

(b)Tradetypebydealergroup

dealertype

interdealer

dealer-customer

DC-ID

ID-ID

invt>15min

DC-DC

DC-ID

invt>15min

large

8.40%

0.67%

90.93%

12.03%

1.70%

86.27%

medium

9.05%

1.70%

89.25%

15.51%

2.98%

81.51%

small

20.15%

11.46%

68.39%

18.03%

16.20%

65.77%

ATS

6.63%

91.74%

1.63%

14.61%

81.85%

3.54%

IDB

2.15%

76.80%

21.05%

7.91%

41.44%

50.64%

clientbroker

45.34%

39.16%

15.50%

26.63%

50.21%

23.15%

(c)Dealergroupcentrality

dealertype

deg

degvols

ev

evvols

cl

large

288.563

6.846

0.869

0.469

0.568

medium

225.482

1.848

0.76

0.105

0.542

small

160.911

0.463

0.581

0.026

0.508

ATS

110.383

2.376

0.423

0.114

0.482

IDB

120.198

7.556

0.499

0.503

0.493

clientbroker

135.6

5.655

0.511

0.258

0.492

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(d)Whotradeswithwhom:

dealertype

large

medium

small

ATS

IDB

clientbroker

large

9.18%

6.15%

6.08%

12.89%

49.11%

16.59%

medium

16.68%

7.04%

7.33%

12.49%

32.50%

23.96%

small

29.37%

14.48%

7.81%

8.59%

19.12%

20.63%

ATS

48.95%

22.73%

7.53%

8.31%

12.48%

IDB

61.53%

19.58%

5.70%

2.78%

1.85%

8.57%

clientbroker

33.67%

24.70%

11.17%

7.25%

15.88%

7.32%

LiandSch¨urhoff(2019).

Panel(c)presentstheaveragecentralitymeasuresforeachdealergroups.Manypapers(LiandSch¨urhoff,2019;Hollifieldetal.,2016)havedocumentedacore-peripherystructureinOTCinterdealermarkets.Look-ingatlarge,medium,andsmalldealergroups,dealersthataremorecentralintheinterdealermarketalsohavemorecustomertrades.ItisalsonotablethatIDBshavethehighestcentralitywhenvolume-weightedcentralitymeasuresareused.MostoftheliteraturemissesATSandIDBsthatstandtointermediatebe-tweendealers.BecauseIDBsarecentral,papersthatgroupdealersbycentralitymeasuresmaygroupIDBstogetherwithlargedealers,whichmayconfoundthebehaviorofthesetwoverydifferentgroupsofdealers.

Lastly,Panel(d)looksatwhotradeswithwhomintheinterdealermarket.LargedealerstradealmosthalfoftheirinterdealervolumewithIDBs,whichisquitesurprising.IfIDBs’mainfunctionwastoeasesearchfrictions,smallerdealersshouldutilizeIDBssignificantlymorethanlargedealersdo.However,wefindtheexactopposite—large,medium,andsmalldealerstradeabout49.1%,32.5%,and19.1%oftheirinterdealervolumethroughIDBs,respectively.

3.2InformationAsymmetry

Inthissubsection,weshowthatlargedealersfacethehighestinformationasymmetryfromtheircustomers.Wefirstcalculateinformationasymmetrythateachdealerfacesfromtheircustomersatthedealer,year,andratinggroup(investmentgradeorhighyield)levelinthefollowingway.Ifthedealerreceivedorderflowofvi,tfromcustomersforbondiondayt(positivevi,tmeansthatcustomersboughtfromthedealer,negativevi,tmeansthatcustomerssoldtothedealer):

vi,t|

εvi,t<0ri,[t,t+τ]

vi,t|

(1)

InfoAsym=

εvi,t>0ri,[t,t+τ]

−

εvi,t<0|vi,t

εvi,t>0|vi,t

10

whereri,[t,t+τ]isthemarket-adjustedreturnofbondibetweenendofdaytandt+τwhereτ=5.Dealerandyearsubscriptsareomittedintheequation.Wegetend-of-daybondpricesfromtheFixedIncomeDataFeed.Tocalculatemarket-adjustedreturn,wedividebondsintoportfoliosbyrating(AAAs,AA+throughAA-,A+throughA-,BBB+throughBBB-,BB+throughBB-,B+throughB-,CCCandlower)andtime-to-maturity.Wethensubtracttheportfolioreturnfrombondireturn.Werestrictthesampletobondsinwhichthepricedata(fromFixedIncomeDataFeed)isnotstalebydeletingbondsinwhichpricesremainexactlysameinconsecutivedaysformorethan10%ofthesample.

Thismeasurecaptureshowmuchthepricesmoveagainstthedealerwithinτdaysafterthecustomertrade.Becausethismeasuredoesn’ttakeintoaccounttheactualtradedprice,andthereforethebid-askspreadchargedtothecustomer,apositivemeasuredoesnotimplythatthedealerlosesmoneyonthecustomertrade.Itrathersaysthatifthedealertradedwithacustomerondayt,thepricewillmoveagainstthedealerbetweenendofdaytanddayt+τ.WedonotcalculateInfoAsymforATSandinterdealerbrokersbecausethesedealersdoverylittlecustomertrades.

Table2presentsthesummarystatisticsforInfoAsymbydealergroup.Largedealersfacehighestin-formationasymmetry—onaverage,afteracustomerbuysainvestmentgradebondfromalargedealer,market-adjustedpricesincreaseby10.9bps,comparedwithafteracustomersell.Thisnumbermorethandoubleforhigh-yieldbonds,whichalsosupportstheideathatInfoAsymmeasuresinformationasymme-try.Formediumdealers,averageInfoAsymis6.9bpsand11.3bpsforinvestmentgradeandhighyield,respectively,andInfoAsymismuchlowerforsmalldealersandclientbrokers.

Table2:Informationasymmetrysummarystat:ThetablebelowpresentsthemeanandmedianInfoAsymmeasureforeachde